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Title: Ethical and Legal Challenges in Caring for Older Adults with Multimorbidities: Best Practices for Nurses | Body:
1. Introduction
The aging population globally is experiencing an unprecedented increase, leading to a rise in the prevalence of multimorbidity, defined as the coexistence of two or more chronic conditions within an individual [1,2]. This phenomenon is particularly pronounced among older adults, who are often confronted with a complex interplay of health issues that significantly affect their quality of life and healthcare needs [3]. As the demographic shift continues, the ethical challenges associated with providing care to this population have become more prominent, necessitating a comprehensive understanding and proactive management by healthcare professionals, particularly nurses [4,5].
Multimorbidity in older adults is associated with numerous challenges, including polypharmacy, frequent hospitalizations, and increased healthcare costs [6,7]. These individuals often require intricate care plans that involve multiple healthcare providers, making coordination of care crucial [8]. The prevalence of multimorbidity increases with age, with more than 65% of individuals aged 65 and older experiencing two or more chronic conditions. This demographic trend underscores the need for specialized care strategies that address both the medical and psychosocial aspects of aging [9].
One of the primary ethical challenges in caring for older adults with multimorbidities is maintaining patient autonomy [10]. Respect for autonomy is a fundamental ethical principle in healthcare, emphasizing the patient’s right to make informed decisions about their care [11]. However, cognitive impairments, common among older adults, can complicate decision-making processes. So, nurses caring for older adults must balance respecting autonomy with ensuring that patients are not exposed to harm due to their impaired decision-making abilities [12].
The principle of beneficence, which obligates healthcare providers to act in the best interest of the patient, often intersects with ethical dilemmas in this context [13]. Nurses frequently encounter situations where the best medical intervention may not align with the patient’s or their family’s wishes [14]. Aggressive treatments that extend life may not necessarily improve the quality of life for older adults with multiple chronic conditions [15]. There is a growing recognition of the need for palliative care approaches that focus on comfort and quality of life rather than merely extending life [16].
Moreover, the principle of non-maleficence, which mandates that healthcare providers do no harm, is critically important in the management of multimorbid older adults. Polypharmacy, the concurrent use of multiple medications, is a common issue that can lead to adverse drug reactions and interactions, posing significant risks to patients [17]. A study highlights that polypharmacy is associated with increased morbidity and mortality among older adults, necessitating careful medication management and regular review by healthcare professionals [18].
According to the American Nurses Association (2015), documentation serves as a critical component of patient care, providing a detailed account of the patient’s condition, the care provided, and the rationale for clinical decisions [19]. Issues can arise if documentation is incomplete or inaccurate, potentially leading to adverse outcomes for patients and ramifications for healthcare providers [20].
Patient rights, including the right to informed consent and the right to refuse treatment, are legally protected and must be upheld by healthcare providers [21]. The concept of informed consent is legally binding and requires that patients receive comprehensive information about their diagnosis, treatment options, and potential risks and benefits [22]. However, obtaining informed consent from older adults with cognitive impairments presents unique challenges, healthcare providers must use clear communication strategies and, when necessary, involve family members or legal representatives to ensure that the patient’s rights are respected [23].
Furthermore, the issue of advance directives and end-of-life care preferences adds another layer of complexity to the ethical landscape [24]. Advance directives, including living wills and durable powers of attorney for healthcare, allow individuals to articulate their preferences for medical treatment in the event that they become incapacitated [25]. Advance directives are underutilized, and many patients’ end-of-life care preferences are not documented or honored, leading to ethical dilemmas for healthcare providers [26].
Nurses play a pivotal role in advocating for the needs and preferences of older adults with multimorbidities [27]. Advocacy involves not only supporting patients’ rights and autonomy but also addressing systemic issues within the healthcare system that may impede the delivery of high-quality care [28]. As patient advocates, nurses must be knowledgeable about ethical principles and requirements and be prepared to navigate complex situations that arise in the care of this vulnerable population [29].
The increasing prevalence of multimorbidity among older adults presents significant ethical and legal challenges for healthcare providers, particularly nurses [30]. These challenges include maintaining patient autonomy, ensuring beneficence and non-maleficence, managing polypharmacy, and navigating complex legal requirements related to documentation, informed consent, and advance directives [31]. Addressing these issues requires a comprehensive approach that integrates ethical principles with legal standards and best practices. By doing so, nurses can provide compassionate, patient-centered care that respects the rights and dignity of older adults with multimorbidities.
1.1. Aim of the Study
The aim of this study is to explore and understand the ethical and legal challenges faced by nurses in the care of older adults with multimorbidities. It seeks to identify best practices that can be implemented to address these challenges, ensuring that nursing care is both ethically sound and legally compliant. By gaining insights into the experiences and perspectives of nurses, this study aims to contribute to the development of guidelines and strategies that enhance the quality of care for this vulnerable population.
Research Questions
What are the primary ethical challenges encountered by nurses when providing care to older adults with multimorbidities, and how do they navigate these challenges in their daily practice?
What are the legal issues faced by nurses in the care of older adults with multimorbidities, and what best practices can be identified to ensure compliance with legal standards while maintaining high-quality patient care?
1.1. Aim of the Study
The aim of this study is to explore and understand the ethical and legal challenges faced by nurses in the care of older adults with multimorbidities. It seeks to identify best practices that can be implemented to address these challenges, ensuring that nursing care is both ethically sound and legally compliant. By gaining insights into the experiences and perspectives of nurses, this study aims to contribute to the development of guidelines and strategies that enhance the quality of care for this vulnerable population.
Research Questions
What are the primary ethical challenges encountered by nurses when providing care to older adults with multimorbidities, and how do they navigate these challenges in their daily practice?
What are the legal issues faced by nurses in the care of older adults with multimorbidities, and what best practices can be identified to ensure compliance with legal standards while maintaining high-quality patient care?
Research Questions
What are the primary ethical challenges encountered by nurses when providing care to older adults with multimorbidities, and how do they navigate these challenges in their daily practice?
What are the legal issues faced by nurses in the care of older adults with multimorbidities, and what best practices can be identified to ensure compliance with legal standards while maintaining high-quality patient care?
2. Materials and Methods
2.1. Study Design and Participants
This study employed a qualitative descriptive design, deeply rooted in the epistemological frameworks of naturalism and constructivism. Naturalism posits that realities are multiple and subjective, while constructivism emphasizes the interaction between the researcher and the subject in shaping the findings. These frameworks were chosen for their ability to capture the complex, lived experiences and nuanced interactions between healthcare professionals and patients regarding ethical and legal challenges in caring for older adults with Multimorbidities.
To ensure methodological rigor and transparency, our approach adhered closely to the Standards for Reporting Qualitative Research (SRQR) guidelines. This adherence facilitated a systematic and reflective inquiry into the ethical and legal challenges faced by nurses, allowing for detailed exploration and credible documentation of emergent themes that authentically represent participants’ experiences. By integrating these epistemological principles, we aimed to illuminate the subjective and often tacit knowledge that informs nursing practice in this critical area [32].
2.2. Setting
The study was conducted from January 2024 to March 2024 in various healthcare facilities in the Riyadh region of Saudi Arabia. Riyadh, the capital city, is known for its advanced healthcare infrastructure and diverse population, providing a rich environment for exploring the complexities of nursing care for older adults with multiple chronic conditions. The healthcare facilities were selected based on their high volume of older adult patients and their comprehensive nature of services, including chronic condition management, acute care, and palliative care. This selection ensured a diverse and representative sample of nursing practices and patient interactions.
2.3. Recruitment and Sampling
A purposeful sampling technique was utilized to recruit 15 registered nurses actively practicing in the Riyadh region. Participants were selected based on their experience in caring for older adults with multimorbidities, ensuring a comprehensive understanding of the ethical and legal challenges in this context.
The inclusion criteria were as follows:
Registered nurses aged 23 years or older.A minimum of 1 year of experience in nursing, specifically in environments where they regularly care for older adults with multimorbidities.Willingness to participate and provide informed consent.
The exclusion criteria were as follows:
Nurses working in fields other than those involving direct patient care of older adults with Multimorbidities.
The rationale for the sample size of 15 nurses was based on achieving data saturation, a point where no new themes or insights emerge from additional data collection. This sample size was deemed sufficient to capture diverse perspectives and experiences while allowing for in-depth exploration of the research questions. The participants’ ages ranged from 26 to 55 years, with varying levels of educational background from diplomas to master’s degrees in nursing. This diversity in experience and education provided a broad range of insights into the challenges and best practices in nursing care for older adults.
2.4. Development of Interview Guide
The development of the interview guide was a meticulous process influenced by established frameworks and prior studies that explored ethical and legal challenges in nursing [33,34,35]. The guide drew upon foundational works on ethical principles and legal requirements in healthcare. A preliminary literature review identified gaps in existing studies, particularly focusing on the unique challenges faced by nurses in Saudi Arabia. The guide was reviewed by experts in qualitative research and nursing ethics, and piloted with a small group of nurses to ensure clarity and relevance. Questions were formulated to explore themes such as the challenges in obtaining informed consent, managing polypharmacy, and navigating end-of-life care decisions.
2.5. Data Collection
Data collection for this study was conducted from January 2024 to March 2024, using a combination of face-to-face and telephone interviews, observations, and document reviews to gather comprehensive and multifaceted insights into the ethical and legal challenges faced by nurses in caring for older adults with Multimorbidities.
The primary method of data collection was semi-structured interviews. Each interview lasted approximately 45 to 60 min and was conducted in a private room within the healthcare facilities to ensure a conducive environment for open and honest discussion. Some interviews were conducted face-to-face, allowing for richer interactions and the observation of non-verbal cues, which provided additional context to the verbal responses. To accommodate the busy schedules and preferences of some nurses, telephone interviews were also utilized. While these lacked the advantage of observing non-verbal communication, they offered greater flexibility and enabled participation from nurses who might otherwise have been unavailable. Interviews were conducted in both Arabic and English, depending on the participants’ preferences. Bilingual experts were involved in the interviews to ensure accurate translation and to maintain the integrity of the data. The interview guide was designed to explore themes such as challenges in obtaining informed consent, managing polypharmacy, and navigating end-of-life care decisions.
In addition to interviews, observational data were collected to provide a real-time perspective on the ethical and legal challenges in nursing practice. Structured forms were used to systematically record observations of nurse–patient interactions, focusing on non-verbal communication, practical applications of ethical and legal principles, and immediate responses to patient needs. These observations were conducted in various settings within the healthcare facilities, including patient rooms, nurse stations, and common areas, to capture a broad spectrum of interactions and practices.
Document reviews complemented the interviews and observations by providing institutional and procedural context. Relevant documents such as nursing reports, care guidelines, and institutional policies were reviewed to understand the framework within which nurses operate. These documents offered insights into the standard practices, protocols, and policies that guide nursing care for older adults with multimorbidities.
Throughout the data collection process, meticulous attention was given to capturing detailed and nuanced information. Immediate reflections and non-verbal cues were noted during and after the interviews and observations, enriching the verbal data and providing a deeper understanding of the participants’ experiences. All interviews and observations were audio-recorded, with participants’ consent, to ensure accurate and comprehensive data capture. These recordings were later transcribed verbatim for detailed analysis.
2.6. Credibility of the Study
The study incorporated semi-structured interviews, document analysis, and observational data. The semi-structured interviews with 15 registered nurses provided rich qualitative insights into their experiences and strategies in managing ethical and legal challenges while caring for older adults with multimorbidities. These interviews formed the core data source, offering deep and nuanced perspectives directly from the practitioners.
In addition to interviews, document analysis was conducted on relevant materials such as nursing reports, care guidelines, and institutional policies. This analysis provided a broader context and allowed for cross-verification of the qualitative data obtained from the interviews. The documents helped to corroborate participants’ accounts and highlighted institutional support and constraints related to ethical and legal nursing practices.
Observational data further enriched the study by capturing real-time nurse–patient interactions in healthcare settings. Structured forms were used during observations to systematically record behaviors and interactions, ensuring consistency and reliability in data collection. These observations provided an experiential dimension to the study, offering concrete examples of how ethical and legal principles are applied in practice and validating the themes emerging from the interviews and document analysis.
To enhance the validity of the findings, several methodological strategies were employed, which involved systematically cross-verifying data from interviews, document analysis, and observations to ensure consistency and robustness. This process helped to build a comprehensive and reliable understanding of the ethical and legal challenges faced by nurses. Collaborative analysis was another crucial component, where multiple researchers participated in data collection and analysis. This approach minimized potential biases and allowed for a more nuanced interpretation of the data, as diverse perspectives were considered.
Regular peer debriefing sessions were conducted with the research team to review and validate emerging themes and findings. These sessions provided a platform for critical examination and reflection on the data, ensuring that the analysis was thorough and accurate. The iterative process of peer debriefing helped to refine the themes and ensure that they authentically represented the participants’ experiences.
Member checking was also employed to further validate the findings. Summaries of the initial themes were shared with participants to verify the accuracy and completeness of the interpretations. Participants were invited to provide feedback, ensuring that their perspectives were faithfully represented and any discrepancies or misunderstandings were addressed.
2.7. Data Analysis
Data analysis followed a thematic approach as outlined by Braun and Clarke (2006), involving six detailed phases to ensure a rigorous and comprehensive understanding of the data. This approach allowed for the systematic identification, analysis, and reporting of patterns within the data, providing rich and nuanced insights into the ethical and legal challenges faced by nurses caring for older adults with multimorbidities [36].
Familiarization with the Data:
Initial Immersion: Researchers began by reading and re-reading the interview transcripts to immerse themselves thoroughly in the content. This initial immersion helped researchers gain a deep understanding of the context and nuances of each participant’s experiences.Transcription: Verbatim transcriptions of the interviews were generated by two trained investigators to ensure accuracy. Interviews conducted in Arabic were simultaneously translated into English by investigators fluent in both languages. An independent research assistant cross-verified selected English transcripts with their Arabic counterparts for accuracy.Initial Notes: During the familiarization phase, researchers made initial notes and observations about potential patterns, significant statements, and emerging themes.
Generating Initial Codes:
Systematic Coding: Initial codes were generated systematically across the entire dataset. Researchers identified recurring themes and patterns in the data, focusing on meaningful segments of text that captured key aspects of the participants’ experiences and perceptions.Collaborative Effort: The coding process was collaborative, involving discussions among the research team to refine and validate the initial codes. This collaboration ensured that the codes accurately reflected the data and minimized individual biases.
Searching for Themes:
Organizing Codes: The generated codes were then organized into potential themes. This phase involved grouping related codes together to form broader patterns and relationships, which encapsulated significant aspects of the data.Preliminary Themes: The research team identified several preliminary themes that represented the recurring ideas and concepts within the data. These preliminary themes were discussed and refined to ensure they were comprehensive and reflective of the participants’ experiences.
Reviewing Themes:
Refinement Process: The identified themes were reviewed and refined by the research team to ensure they accurately represented the data. This process involved checking if the themes worked in relation to the coded extracts and the entire dataset.Consensus Building: The team engaged in collaborative discussions to resolve any discrepancies in theme identification and refinement, fostering a consensus-driven approach. Themes that did not have enough supporting data were discarded or merged with other themes.
Defining and Naming Themes:
Detailed Definitions: Each theme was defined and named to clearly convey its essence and relevance to the research questions. Detailed definitions and descriptions were developed for each theme, highlighting the core concepts and insights derived from the data.Sub-Themes: Where applicable, sub-themes were identified to capture more specific aspects of the broader themes, providing a nuanced understanding of the data.
Producing the Report:
Integration and Reporting: The final phase involved producing a comprehensive report of the findings. The report integrated the themes into a coherent narrative that addressed the research objectives.Illustrative Quotes: The report included direct quotes from participants to illustrate and support the themes, ensuring that the voices of the nurses were authentically represented.Implications and Conclusions: Key findings and their implications were highlighted, providing insights into the ethical and legal challenges in nursing care for older adults with multimorbidities.
Throughout the thematic analysis, an iterative process was employed to enhance the rigor and validity of the findings. Several researchers participated in theme identification, which helped in triangulating the findings and ensuring a comprehensive and nuanced understanding of the data. Investigator validity was crucial in minimizing potential biases and providing multiple perspectives on the data, thereby strengthening the results.
2.8. Ethical Considerations
The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki, and approved by the ethical committee of King Saud University (KSU_HE-24-002). Informed consent was obtained from all participants prior to their inclusion in the study. Participants were informed about the study’s aims, procedures, potential risks, and benefits. Confidentiality and anonymity were assured, with data stored securely and accessible only to the research team.
2.1. Study Design and Participants
This study employed a qualitative descriptive design, deeply rooted in the epistemological frameworks of naturalism and constructivism. Naturalism posits that realities are multiple and subjective, while constructivism emphasizes the interaction between the researcher and the subject in shaping the findings. These frameworks were chosen for their ability to capture the complex, lived experiences and nuanced interactions between healthcare professionals and patients regarding ethical and legal challenges in caring for older adults with Multimorbidities.
To ensure methodological rigor and transparency, our approach adhered closely to the Standards for Reporting Qualitative Research (SRQR) guidelines. This adherence facilitated a systematic and reflective inquiry into the ethical and legal challenges faced by nurses, allowing for detailed exploration and credible documentation of emergent themes that authentically represent participants’ experiences. By integrating these epistemological principles, we aimed to illuminate the subjective and often tacit knowledge that informs nursing practice in this critical area [32].
2.2. Setting
The study was conducted from January 2024 to March 2024 in various healthcare facilities in the Riyadh region of Saudi Arabia. Riyadh, the capital city, is known for its advanced healthcare infrastructure and diverse population, providing a rich environment for exploring the complexities of nursing care for older adults with multiple chronic conditions. The healthcare facilities were selected based on their high volume of older adult patients and their comprehensive nature of services, including chronic condition management, acute care, and palliative care. This selection ensured a diverse and representative sample of nursing practices and patient interactions.
2.3. Recruitment and Sampling
A purposeful sampling technique was utilized to recruit 15 registered nurses actively practicing in the Riyadh region. Participants were selected based on their experience in caring for older adults with multimorbidities, ensuring a comprehensive understanding of the ethical and legal challenges in this context.
The inclusion criteria were as follows:
Registered nurses aged 23 years or older.A minimum of 1 year of experience in nursing, specifically in environments where they regularly care for older adults with multimorbidities.Willingness to participate and provide informed consent.
The exclusion criteria were as follows:
Nurses working in fields other than those involving direct patient care of older adults with Multimorbidities.
The rationale for the sample size of 15 nurses was based on achieving data saturation, a point where no new themes or insights emerge from additional data collection. This sample size was deemed sufficient to capture diverse perspectives and experiences while allowing for in-depth exploration of the research questions. The participants’ ages ranged from 26 to 55 years, with varying levels of educational background from diplomas to master’s degrees in nursing. This diversity in experience and education provided a broad range of insights into the challenges and best practices in nursing care for older adults.
2.4. Development of Interview Guide
The development of the interview guide was a meticulous process influenced by established frameworks and prior studies that explored ethical and legal challenges in nursing [33,34,35]. The guide drew upon foundational works on ethical principles and legal requirements in healthcare. A preliminary literature review identified gaps in existing studies, particularly focusing on the unique challenges faced by nurses in Saudi Arabia. The guide was reviewed by experts in qualitative research and nursing ethics, and piloted with a small group of nurses to ensure clarity and relevance. Questions were formulated to explore themes such as the challenges in obtaining informed consent, managing polypharmacy, and navigating end-of-life care decisions.
2.5. Data Collection
Data collection for this study was conducted from January 2024 to March 2024, using a combination of face-to-face and telephone interviews, observations, and document reviews to gather comprehensive and multifaceted insights into the ethical and legal challenges faced by nurses in caring for older adults with Multimorbidities.
The primary method of data collection was semi-structured interviews. Each interview lasted approximately 45 to 60 min and was conducted in a private room within the healthcare facilities to ensure a conducive environment for open and honest discussion. Some interviews were conducted face-to-face, allowing for richer interactions and the observation of non-verbal cues, which provided additional context to the verbal responses. To accommodate the busy schedules and preferences of some nurses, telephone interviews were also utilized. While these lacked the advantage of observing non-verbal communication, they offered greater flexibility and enabled participation from nurses who might otherwise have been unavailable. Interviews were conducted in both Arabic and English, depending on the participants’ preferences. Bilingual experts were involved in the interviews to ensure accurate translation and to maintain the integrity of the data. The interview guide was designed to explore themes such as challenges in obtaining informed consent, managing polypharmacy, and navigating end-of-life care decisions.
In addition to interviews, observational data were collected to provide a real-time perspective on the ethical and legal challenges in nursing practice. Structured forms were used to systematically record observations of nurse–patient interactions, focusing on non-verbal communication, practical applications of ethical and legal principles, and immediate responses to patient needs. These observations were conducted in various settings within the healthcare facilities, including patient rooms, nurse stations, and common areas, to capture a broad spectrum of interactions and practices.
Document reviews complemented the interviews and observations by providing institutional and procedural context. Relevant documents such as nursing reports, care guidelines, and institutional policies were reviewed to understand the framework within which nurses operate. These documents offered insights into the standard practices, protocols, and policies that guide nursing care for older adults with multimorbidities.
Throughout the data collection process, meticulous attention was given to capturing detailed and nuanced information. Immediate reflections and non-verbal cues were noted during and after the interviews and observations, enriching the verbal data and providing a deeper understanding of the participants’ experiences. All interviews and observations were audio-recorded, with participants’ consent, to ensure accurate and comprehensive data capture. These recordings were later transcribed verbatim for detailed analysis.
2.6. Credibility of the Study
The study incorporated semi-structured interviews, document analysis, and observational data. The semi-structured interviews with 15 registered nurses provided rich qualitative insights into their experiences and strategies in managing ethical and legal challenges while caring for older adults with multimorbidities. These interviews formed the core data source, offering deep and nuanced perspectives directly from the practitioners.
In addition to interviews, document analysis was conducted on relevant materials such as nursing reports, care guidelines, and institutional policies. This analysis provided a broader context and allowed for cross-verification of the qualitative data obtained from the interviews. The documents helped to corroborate participants’ accounts and highlighted institutional support and constraints related to ethical and legal nursing practices.
Observational data further enriched the study by capturing real-time nurse–patient interactions in healthcare settings. Structured forms were used during observations to systematically record behaviors and interactions, ensuring consistency and reliability in data collection. These observations provided an experiential dimension to the study, offering concrete examples of how ethical and legal principles are applied in practice and validating the themes emerging from the interviews and document analysis.
To enhance the validity of the findings, several methodological strategies were employed, which involved systematically cross-verifying data from interviews, document analysis, and observations to ensure consistency and robustness. This process helped to build a comprehensive and reliable understanding of the ethical and legal challenges faced by nurses. Collaborative analysis was another crucial component, where multiple researchers participated in data collection and analysis. This approach minimized potential biases and allowed for a more nuanced interpretation of the data, as diverse perspectives were considered.
Regular peer debriefing sessions were conducted with the research team to review and validate emerging themes and findings. These sessions provided a platform for critical examination and reflection on the data, ensuring that the analysis was thorough and accurate. The iterative process of peer debriefing helped to refine the themes and ensure that they authentically represented the participants’ experiences.
Member checking was also employed to further validate the findings. Summaries of the initial themes were shared with participants to verify the accuracy and completeness of the interpretations. Participants were invited to provide feedback, ensuring that their perspectives were faithfully represented and any discrepancies or misunderstandings were addressed.
2.7. Data Analysis
Data analysis followed a thematic approach as outlined by Braun and Clarke (2006), involving six detailed phases to ensure a rigorous and comprehensive understanding of the data. This approach allowed for the systematic identification, analysis, and reporting of patterns within the data, providing rich and nuanced insights into the ethical and legal challenges faced by nurses caring for older adults with multimorbidities [36].
Familiarization with the Data:
Initial Immersion: Researchers began by reading and re-reading the interview transcripts to immerse themselves thoroughly in the content. This initial immersion helped researchers gain a deep understanding of the context and nuances of each participant’s experiences.Transcription: Verbatim transcriptions of the interviews were generated by two trained investigators to ensure accuracy. Interviews conducted in Arabic were simultaneously translated into English by investigators fluent in both languages. An independent research assistant cross-verified selected English transcripts with their Arabic counterparts for accuracy.Initial Notes: During the familiarization phase, researchers made initial notes and observations about potential patterns, significant statements, and emerging themes.
Generating Initial Codes:
Systematic Coding: Initial codes were generated systematically across the entire dataset. Researchers identified recurring themes and patterns in the data, focusing on meaningful segments of text that captured key aspects of the participants’ experiences and perceptions.Collaborative Effort: The coding process was collaborative, involving discussions among the research team to refine and validate the initial codes. This collaboration ensured that the codes accurately reflected the data and minimized individual biases.
Searching for Themes:
Organizing Codes: The generated codes were then organized into potential themes. This phase involved grouping related codes together to form broader patterns and relationships, which encapsulated significant aspects of the data.Preliminary Themes: The research team identified several preliminary themes that represented the recurring ideas and concepts within the data. These preliminary themes were discussed and refined to ensure they were comprehensive and reflective of the participants’ experiences.
Reviewing Themes:
Refinement Process: The identified themes were reviewed and refined by the research team to ensure they accurately represented the data. This process involved checking if the themes worked in relation to the coded extracts and the entire dataset.Consensus Building: The team engaged in collaborative discussions to resolve any discrepancies in theme identification and refinement, fostering a consensus-driven approach. Themes that did not have enough supporting data were discarded or merged with other themes.
Defining and Naming Themes:
Detailed Definitions: Each theme was defined and named to clearly convey its essence and relevance to the research questions. Detailed definitions and descriptions were developed for each theme, highlighting the core concepts and insights derived from the data.Sub-Themes: Where applicable, sub-themes were identified to capture more specific aspects of the broader themes, providing a nuanced understanding of the data.
Producing the Report:
Integration and Reporting: The final phase involved producing a comprehensive report of the findings. The report integrated the themes into a coherent narrative that addressed the research objectives.Illustrative Quotes: The report included direct quotes from participants to illustrate and support the themes, ensuring that the voices of the nurses were authentically represented.Implications and Conclusions: Key findings and their implications were highlighted, providing insights into the ethical and legal challenges in nursing care for older adults with multimorbidities.
Throughout the thematic analysis, an iterative process was employed to enhance the rigor and validity of the findings. Several researchers participated in theme identification, which helped in triangulating the findings and ensuring a comprehensive and nuanced understanding of the data. Investigator validity was crucial in minimizing potential biases and providing multiple perspectives on the data, thereby strengthening the results.
2.8. Ethical Considerations
The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki, and approved by the ethical committee of King Saud University (KSU_HE-24-002). Informed consent was obtained from all participants prior to their inclusion in the study. Participants were informed about the study’s aims, procedures, potential risks, and benefits. Confidentiality and anonymity were assured, with data stored securely and accessible only to the research team.
3. Results
3.1. Participant Characteristics
Table 1 provides an overview of the 15 nurses who participated in the study. The participants exhibited a range of nursing experiences, with years of practice varying from 2 to 30 years. This spectrum includes both relatively new graduates and highly experienced practitioners, offering diverse perspectives on the ethical and legal challenges in caring for older adults with Multimorbidities. The participants’ ages range from 26 to 55 years and span different stages of adult life, indicating potential generational differences in their views on nursing practices and care delivery. The majority of the participants are female, consistent with the long-standing prevalence of women in the nursing profession. This gender distribution reflects the broader demographics of the nursing workforce. This spectrum of ages, educational levels, and years of experience (ranging from 2 to 30 years) provided a broad perspective on the ethical and legal challenges faced in caring for older adults with Multimorbidities.
3.2. Thematic Analysis
Table 2 elaborates on the three themes identified through the thematic analysis, each reflecting the ethical and legal challenges faced by nurses in caring for older adults with Multimorbidities.
I.
Ethical Dilemmas in Patient Autonomy and Consent
Challenges in Obtaining Informed Consent and Decision-Making with Cognitive Impairment:
Nurses face significant difficulties in obtaining informed consent from elderly patients with cognitive impairments, often involving family members for clarity. P#3 noted, “It’s often difficult to ensure that patients fully understand the information we provide due to their cognitive limitations”. P#10 added, “We spend extra time explaining and sometimes involve family members, but even then, it’s not always clear if the patient truly grasps the situation”. P#7 highlighted, “We want to honor their wishes, but sometimes it’s a fine line between respecting their autonomy and protecting them from harm”.
Ethical dilemmas arise when patients seem confused despite explanations, as P#5 shared, “In one case, despite multiple attempts to explain the treatment options, the patient still seemed confused. We had to rely on the family to make the final decision”. P#11 added, “There are times when a patient agrees to a procedure just to please their family, without fully understanding the implications”.
Regular challenges occur when patients’ cognitive limitations impede their decision-making. P#1 noted, “When people with dementia refuse therapy, we face potential ethical dilemmas”. P#5 mentioned, “We often navigate ethical gray areas where the patient’s decision-making capacity is compromised, requiring a sensitive and patient-centered approach”.
P#13 shared, “It’s heartbreaking when a patient doesn’t understand their condition and refuses necessary treatment. We have to consider their past wishes, family input, and medical best practices”. P#9 explained, “We work closely with family members to make decisions aligning with the patient’s known preferences and values”. P#7 added, “We sometimes involve the ethics committee to help resolve particularly difficult cases, ensuring all legal and ethical aspects are considered”.
Balancing Autonomy and Safety:
Nurses underlined the significance of striking a balance between respecting patient autonomy and ensuring patient safety. P#6 went on to say, “There are times when we have to make tough calls to protect the patient, even if it means going against their immediate wishes. This is particularly challenging when legal and ethical guidelines are in tension, and the nurse must prioritize patient safety while still honoring the patient’s autonomy as much as possible”. P#14 said, “We strive to involve patients in their care decisions as much as possible, but safety must always be a priority. In situations where patients’ decisions might put them at significant risk, we must navigate the legal implications of overriding their choices, which requires thorough documentation and adherence to institutional policies”.
P#2 highlighted, “Ensuring safety while respecting autonomy is one of the hardest parts of our job, especially with patients who have fluctuating cognitive abilities. The legal responsibility to protect these patients can sometimes necessitate interventions that limit their autonomy, which we handle with great care and clear communication to ensure that both ethical and legal standards are upheld”. P#9 shared, “We had a patient with severe dementia who insisted on leaving the hospital against medical advice. While respecting his autonomy was important, his condition posed a significant risk to his safety. We had to involve his family and legal advisors to make an informed decision that balanced his rights with his well-being”. P#11 added, “There was a case where a diabetic patient refused insulin because of fear of needles. We respected her autonomy but also had to address the imminent risk to her health. We collaborated with her physician to explore alternative administration methods and provided extensive education and support to alleviate her fears while ensuring she received the necessary treatment”.
II.
Managing Polypharmacy and Patient Safety
Risks of Adverse Drug Reactions:
Polypharmacy has arisen as a major ethical and legal problem. Nurses noted difficulty managing several prescriptions for patients with multimorbidities, emphasizing the dangers of harmful drug interactions. P#12 said, “The risk of adverse drug interactions is high, and we have to be very vigilant in monitoring their medication regimens”.
P#8 highlighted, “It’s a constant challenge to balance the benefits and risks of multiple medications, especially when patients are seeing multiple specialists who might not always communicate effectively. As nurses, we are responsible for keeping track of all medications a patient is taking, educating patients about potential interactions, and ensuring adherence to their medication schedules”. P#4 explained, “We often see patients with prescriptions from different doctors, and it’s our job to ensure that these don’t conflict. While doctors prescribe medications based on their specialty and the patient’s specific conditions, it falls upon us as nurses to oversee the comprehensive medication management. This includes coordinating with various doctors to clarify prescriptions, identifying potential drug interactions, and making sure that any changes in medication are communicated and understood by all parties involved”.
Documentation and Regular Reviews:
Participants underlined the need for accurate documentation and regular drug evaluations to guarantee patient safety. According to P#9, “Accurate documentation is crucial to track all medications a patient is on, which helps in identifying potential interactions”. P#14 stated, “Regular medication reviews are essential to adjust treatment plans and minimize risks, but they require time and coordination among the healthcare team”. P#11 stated, “We need to document every medication change meticulously to avoid errors and ensure continuity of care”.
Coordination Among Healthcare Providers:
Effective communication and cooperation among healthcare practitioners were identified as critical to managing polypharmacy safely. P#15 states, “Coordination with other healthcare professionals is critical to managing polypharmacy. We need to guarantee that everyone is on the same page about the patient’s pharmaceutical regimen”.
P#7 added, “Regular team meetings help us discuss and update each patient’s medication regimen, but it’s challenging to keep everyone informed all the time. There are instances where changes made by one specialist are not communicated to others, leading to potential risks for the patient”. P#3 stated, “Having a centralized system for medication records would greatly improve our ability to manage polypharmacy effectively. It would ensure that all healthcare providers have access to the most current and complete medication information, reducing the risk of adverse drug interactions”. P#10 highlighted another aspect, “In our facility, we sometimes use telehealth consultations to bridge the communication gap between different specialists. This helps us coordinate better, especially for patients who are unable to visit multiple doctors frequently”.
P#5 provided an example, “We had a patient who was prescribed conflicting medications by two different specialists. By maintaining open communication channels and conducting a thorough medication review during a team meeting, we were able to identify the conflict and adjust the regimen accordingly”. P#11 emphasized the importance of patient involvement, “We also educate patients and their families to keep an updated list of all medications and to bring this list to every appointment. This empowers patients to be active participants in their care and helps us in maintaining an accurate and safe medication regimen”.
III.
End-of-Life Care and Advance Directives
Honoring Advance Directives:
Nurses highlighted the importance of honoring advance directives to respect patient autonomy and ensure appropriate end-of-life care. P#15 stated, “It’s crucial to have clear communication with patients and their families about their wishes for end-of-life care. We need to ensure that these directives are documented and accessible to all healthcare providers involved”.
P#7 explained, “Regular team meetings help us discuss and update each patient’s care plan, including their advance directives. However, it can be challenging to keep everyone informed all the time”. Effective communication and coordination among healthcare practitioners are essential to ensure that the patient’s wishes are respected.
P#3 emphasized, “Having a centralized system for advance directives would greatly improve our ability to honor patients’ end-of-life wishes. It would ensure that all healthcare providers are aware of and can adhere to the patient’s directives, reducing the likelihood of conflicts or misunderstandings”.
Difficult Conversations:
It was brought to the attention of the participants that discussions regarding advance directives and choices regarding end-of-life care are frequently difficult but essential to delivering adequate care. P#2 stated, “These conversations are tough, but they’re essential to ensure that we respect the patient’s wishes and provide care that’s aligned with their values”. P#6 agreed with this point of view, saying, “It’s our responsibility to initiate these discussions and ensure that patients and their families understand the implications of their choices. Often, legal and ethical guidelines require us to document these conversations meticulously to avoid any misunderstandings later”. P#5 concluded by saying, “We try to approach these topics sensitively, but it’s always a delicate balance. We must ensure the patient’s autonomy is respected while also considering their family’s perspectives and the potential emotional burden”. P#10 added, “One of the biggest challenges is explaining the medical and legal ramifications of their choices in a way that’s understandable and compassionate. Patients and families often have differing opinions, and we need to mediate these discussions to find a common ground”. P#7 shared an example, “I had a patient whose family wanted to pursue aggressive treatment, but the patient had expressed a desire for palliative care only. Navigating these conflicting wishes required careful communication and a thorough understanding of both the ethical implications and legal requirements”. P#3 emphasized the importance of continuous education, “We regularly update our training on how to handle these conversations, including understanding cultural sensitivities and legal aspects. This helps us feel more prepared and confident in these challenging situations”.
Legal Implications of End-of-Life Decisions:
The legal repercussions that may result from decisions about end-of-life care were another major source of anxiety for nurses. P#15 observed, “We need to be very careful. Any deviation from the documented desires of the patient can lead to legal penalties”. This highlights the importance of adhering strictly to the patient’s advance directives to avoid potential legal consequences. P#9 emphasized, “Clear and precise documentation of advance directives helps protect both the patient’s rights and the healthcare providers. Without proper documentation, we risk legal action from family members who might disagree with the decisions made”. Nurses often find themselves navigating complex legal terrain, ensuring that the patient’s wishes are followed while also protecting themselves and their colleagues from legal repercussions. P#11 added, “Understanding the legal framework surrounding end-of-life care is essential to avoid potential legal issues. We frequently consult with legal advisors and ethics committees to ensure that our actions are in line with both the patient’s wishes and legal requirements”. This underscores the necessity for nurses to be well versed in legal protocols and to maintain meticulous records.
P#7 provided an example, “We had a case where the family wanted to continue life-sustaining treatment despite the patient’s advance directive stating otherwise. It was a very challenging situation, but by following the legal documentation and involving the hospital’s legal team, we were able to honor the patient’s wishes without facing legal consequences”. Another example from P#3 illustrated, “In one instance, a patient had not updated their advance directive, and there was confusion about their current wishes. This led to a legal dispute among family members. We had to involve social workers, legal advisors, and even a court ruling to resolve the issue, which highlighted the critical need for clear and current documentation”.
In addition to interviews, observational data were collected to provide real-time perspectives on the ethical and legal challenges in nursing practice. Structured forms were used to systematically record observations of nurse–patient interactions, focusing on non-verbal communication, practical applications of ethical and legal principles, and immediate responses to patient needs. Document reviews provided institutional and procedural context, offering insights into the standard practices, protocols, and policies that guide nursing care for older adults with multimorbidities.
3.1. Participant Characteristics
Table 1 provides an overview of the 15 nurses who participated in the study. The participants exhibited a range of nursing experiences, with years of practice varying from 2 to 30 years. This spectrum includes both relatively new graduates and highly experienced practitioners, offering diverse perspectives on the ethical and legal challenges in caring for older adults with Multimorbidities. The participants’ ages range from 26 to 55 years and span different stages of adult life, indicating potential generational differences in their views on nursing practices and care delivery. The majority of the participants are female, consistent with the long-standing prevalence of women in the nursing profession. This gender distribution reflects the broader demographics of the nursing workforce. This spectrum of ages, educational levels, and years of experience (ranging from 2 to 30 years) provided a broad perspective on the ethical and legal challenges faced in caring for older adults with Multimorbidities.
3.2. Thematic Analysis
Table 2 elaborates on the three themes identified through the thematic analysis, each reflecting the ethical and legal challenges faced by nurses in caring for older adults with Multimorbidities.
I.
Ethical Dilemmas in Patient Autonomy and Consent
Challenges in Obtaining Informed Consent and Decision-Making with Cognitive Impairment:
Nurses face significant difficulties in obtaining informed consent from elderly patients with cognitive impairments, often involving family members for clarity. P#3 noted, “It’s often difficult to ensure that patients fully understand the information we provide due to their cognitive limitations”. P#10 added, “We spend extra time explaining and sometimes involve family members, but even then, it’s not always clear if the patient truly grasps the situation”. P#7 highlighted, “We want to honor their wishes, but sometimes it’s a fine line between respecting their autonomy and protecting them from harm”.
Ethical dilemmas arise when patients seem confused despite explanations, as P#5 shared, “In one case, despite multiple attempts to explain the treatment options, the patient still seemed confused. We had to rely on the family to make the final decision”. P#11 added, “There are times when a patient agrees to a procedure just to please their family, without fully understanding the implications”.
Regular challenges occur when patients’ cognitive limitations impede their decision-making. P#1 noted, “When people with dementia refuse therapy, we face potential ethical dilemmas”. P#5 mentioned, “We often navigate ethical gray areas where the patient’s decision-making capacity is compromised, requiring a sensitive and patient-centered approach”.
P#13 shared, “It’s heartbreaking when a patient doesn’t understand their condition and refuses necessary treatment. We have to consider their past wishes, family input, and medical best practices”. P#9 explained, “We work closely with family members to make decisions aligning with the patient’s known preferences and values”. P#7 added, “We sometimes involve the ethics committee to help resolve particularly difficult cases, ensuring all legal and ethical aspects are considered”.
Balancing Autonomy and Safety:
Nurses underlined the significance of striking a balance between respecting patient autonomy and ensuring patient safety. P#6 went on to say, “There are times when we have to make tough calls to protect the patient, even if it means going against their immediate wishes. This is particularly challenging when legal and ethical guidelines are in tension, and the nurse must prioritize patient safety while still honoring the patient’s autonomy as much as possible”. P#14 said, “We strive to involve patients in their care decisions as much as possible, but safety must always be a priority. In situations where patients’ decisions might put them at significant risk, we must navigate the legal implications of overriding their choices, which requires thorough documentation and adherence to institutional policies”.
P#2 highlighted, “Ensuring safety while respecting autonomy is one of the hardest parts of our job, especially with patients who have fluctuating cognitive abilities. The legal responsibility to protect these patients can sometimes necessitate interventions that limit their autonomy, which we handle with great care and clear communication to ensure that both ethical and legal standards are upheld”. P#9 shared, “We had a patient with severe dementia who insisted on leaving the hospital against medical advice. While respecting his autonomy was important, his condition posed a significant risk to his safety. We had to involve his family and legal advisors to make an informed decision that balanced his rights with his well-being”. P#11 added, “There was a case where a diabetic patient refused insulin because of fear of needles. We respected her autonomy but also had to address the imminent risk to her health. We collaborated with her physician to explore alternative administration methods and provided extensive education and support to alleviate her fears while ensuring she received the necessary treatment”.
II.
Managing Polypharmacy and Patient Safety
Risks of Adverse Drug Reactions:
Polypharmacy has arisen as a major ethical and legal problem. Nurses noted difficulty managing several prescriptions for patients with multimorbidities, emphasizing the dangers of harmful drug interactions. P#12 said, “The risk of adverse drug interactions is high, and we have to be very vigilant in monitoring their medication regimens”.
P#8 highlighted, “It’s a constant challenge to balance the benefits and risks of multiple medications, especially when patients are seeing multiple specialists who might not always communicate effectively. As nurses, we are responsible for keeping track of all medications a patient is taking, educating patients about potential interactions, and ensuring adherence to their medication schedules”. P#4 explained, “We often see patients with prescriptions from different doctors, and it’s our job to ensure that these don’t conflict. While doctors prescribe medications based on their specialty and the patient’s specific conditions, it falls upon us as nurses to oversee the comprehensive medication management. This includes coordinating with various doctors to clarify prescriptions, identifying potential drug interactions, and making sure that any changes in medication are communicated and understood by all parties involved”.
Documentation and Regular Reviews:
Participants underlined the need for accurate documentation and regular drug evaluations to guarantee patient safety. According to P#9, “Accurate documentation is crucial to track all medications a patient is on, which helps in identifying potential interactions”. P#14 stated, “Regular medication reviews are essential to adjust treatment plans and minimize risks, but they require time and coordination among the healthcare team”. P#11 stated, “We need to document every medication change meticulously to avoid errors and ensure continuity of care”.
Coordination Among Healthcare Providers:
Effective communication and cooperation among healthcare practitioners were identified as critical to managing polypharmacy safely. P#15 states, “Coordination with other healthcare professionals is critical to managing polypharmacy. We need to guarantee that everyone is on the same page about the patient’s pharmaceutical regimen”.
P#7 added, “Regular team meetings help us discuss and update each patient’s medication regimen, but it’s challenging to keep everyone informed all the time. There are instances where changes made by one specialist are not communicated to others, leading to potential risks for the patient”. P#3 stated, “Having a centralized system for medication records would greatly improve our ability to manage polypharmacy effectively. It would ensure that all healthcare providers have access to the most current and complete medication information, reducing the risk of adverse drug interactions”. P#10 highlighted another aspect, “In our facility, we sometimes use telehealth consultations to bridge the communication gap between different specialists. This helps us coordinate better, especially for patients who are unable to visit multiple doctors frequently”.
P#5 provided an example, “We had a patient who was prescribed conflicting medications by two different specialists. By maintaining open communication channels and conducting a thorough medication review during a team meeting, we were able to identify the conflict and adjust the regimen accordingly”. P#11 emphasized the importance of patient involvement, “We also educate patients and their families to keep an updated list of all medications and to bring this list to every appointment. This empowers patients to be active participants in their care and helps us in maintaining an accurate and safe medication regimen”.
III.
End-of-Life Care and Advance Directives
Honoring Advance Directives:
Nurses highlighted the importance of honoring advance directives to respect patient autonomy and ensure appropriate end-of-life care. P#15 stated, “It’s crucial to have clear communication with patients and their families about their wishes for end-of-life care. We need to ensure that these directives are documented and accessible to all healthcare providers involved”.
P#7 explained, “Regular team meetings help us discuss and update each patient’s care plan, including their advance directives. However, it can be challenging to keep everyone informed all the time”. Effective communication and coordination among healthcare practitioners are essential to ensure that the patient’s wishes are respected.
P#3 emphasized, “Having a centralized system for advance directives would greatly improve our ability to honor patients’ end-of-life wishes. It would ensure that all healthcare providers are aware of and can adhere to the patient’s directives, reducing the likelihood of conflicts or misunderstandings”.
Difficult Conversations:
It was brought to the attention of the participants that discussions regarding advance directives and choices regarding end-of-life care are frequently difficult but essential to delivering adequate care. P#2 stated, “These conversations are tough, but they’re essential to ensure that we respect the patient’s wishes and provide care that’s aligned with their values”. P#6 agreed with this point of view, saying, “It’s our responsibility to initiate these discussions and ensure that patients and their families understand the implications of their choices. Often, legal and ethical guidelines require us to document these conversations meticulously to avoid any misunderstandings later”. P#5 concluded by saying, “We try to approach these topics sensitively, but it’s always a delicate balance. We must ensure the patient’s autonomy is respected while also considering their family’s perspectives and the potential emotional burden”. P#10 added, “One of the biggest challenges is explaining the medical and legal ramifications of their choices in a way that’s understandable and compassionate. Patients and families often have differing opinions, and we need to mediate these discussions to find a common ground”. P#7 shared an example, “I had a patient whose family wanted to pursue aggressive treatment, but the patient had expressed a desire for palliative care only. Navigating these conflicting wishes required careful communication and a thorough understanding of both the ethical implications and legal requirements”. P#3 emphasized the importance of continuous education, “We regularly update our training on how to handle these conversations, including understanding cultural sensitivities and legal aspects. This helps us feel more prepared and confident in these challenging situations”.
Legal Implications of End-of-Life Decisions:
The legal repercussions that may result from decisions about end-of-life care were another major source of anxiety for nurses. P#15 observed, “We need to be very careful. Any deviation from the documented desires of the patient can lead to legal penalties”. This highlights the importance of adhering strictly to the patient’s advance directives to avoid potential legal consequences. P#9 emphasized, “Clear and precise documentation of advance directives helps protect both the patient’s rights and the healthcare providers. Without proper documentation, we risk legal action from family members who might disagree with the decisions made”. Nurses often find themselves navigating complex legal terrain, ensuring that the patient’s wishes are followed while also protecting themselves and their colleagues from legal repercussions. P#11 added, “Understanding the legal framework surrounding end-of-life care is essential to avoid potential legal issues. We frequently consult with legal advisors and ethics committees to ensure that our actions are in line with both the patient’s wishes and legal requirements”. This underscores the necessity for nurses to be well versed in legal protocols and to maintain meticulous records.
P#7 provided an example, “We had a case where the family wanted to continue life-sustaining treatment despite the patient’s advance directive stating otherwise. It was a very challenging situation, but by following the legal documentation and involving the hospital’s legal team, we were able to honor the patient’s wishes without facing legal consequences”. Another example from P#3 illustrated, “In one instance, a patient had not updated their advance directive, and there was confusion about their current wishes. This led to a legal dispute among family members. We had to involve social workers, legal advisors, and even a court ruling to resolve the issue, which highlighted the critical need for clear and current documentation”.
In addition to interviews, observational data were collected to provide real-time perspectives on the ethical and legal challenges in nursing practice. Structured forms were used to systematically record observations of nurse–patient interactions, focusing on non-verbal communication, practical applications of ethical and legal principles, and immediate responses to patient needs. Document reviews provided institutional and procedural context, offering insights into the standard practices, protocols, and policies that guide nursing care for older adults with multimorbidities.
4. Discussion
The findings of this study highlight the multifaceted ethical and legal challenges that nurses face in the care of older adults with multimorbidities. These challenges revolve around patient autonomy, polypharmacy, and end-of-life care, reflecting the complex nature of nursing in this context.
The ethical dilemmas associated with patient autonomy and informed consent are particularly pronounced. Nurses frequently encounter situations where older adults, especially those with cognitive impairments, are unable to fully comprehend their treatment options. This issue is compounded by the need to respect patient autonomy while ensuring their safety. Previous studies have shown that cognitive impairments can significantly hinder the decision-making process, necessitating a delicate balance between autonomy and beneficence [11,37]. The necessity for family involvement in decision-making processes is crucial, as family members often act as surrogates in the consent process [38]. This dynamic can create tension, especially when the patient’s wishes conflict with those of the family, or when the patient’s ability to understand and make decisions fluctuates [39].
Informed consent is a cornerstone of ethical medical practice, yet it becomes problematic when patients are cognitively impaired [40]. Research suggests that a significant proportion of older adults with dementia are unable to provide fully informed consent, raising ethical concerns about autonomy and decision-making capacity [41,42]. Nurses often have to balance the legal requirements for informed consent with the practical realities of cognitive impairment, which can lead to ethical dilemmas and require sensitive handling [43]. Additionally, the involvement of family members as surrogates can both aid and complicate the decision-making process. Family members may have differing views on the best course of action, and these views may not always align with the patient’s wishes or best interests [44].
Polypharmacy represents another significant challenge. The concurrent use of multiple medications increases the risk of adverse drug reactions, which can exacerbate existing health issues in older adults [45]. Studies have consistently shown that polypharmacy is associated with higher rates of morbidity and mortality in this population [46,47]. The nurses in this study emphasized the importance of thorough documentation and regular medication reviews as critical strategies to mitigate these risks. Effective communication and coordination among healthcare providers are essential to managing polypharmacy, as inconsistent documentation and lack of communication can lead to harmful drug interactions [48].
Polypharmacy is a well-documented issue in geriatric care, with research indicating that nearly 50% of older adults take five or more medications concurrently [49]. This practice increases the likelihood of drug–drug interactions, adverse drug reactions, and medication non-adherence [35,50]. Nurses play a crucial role in monitoring and managing these medication regimens to minimize risks. The emphasis on documentation and regular reviews aligns with best practice recommendations for geriatric care, which advocate for medication reconciliation and periodic reviews to ensure that each medication is necessary and appropriate [27].
The management of end-of-life care and advance directives also presents substantial ethical and legal challenges. Nurses often find themselves navigating complex discussions about advance directives, which are critical for ensuring that the patient’s end-of-life wishes are respected [51]. However, these discussions are fraught with difficulties, particularly when family members disagree with the patient’s documented preferences. This discord can lead to ethical dilemmas and potential legal issues if the healthcare team fails to adhere to the patient’s advance directives [52]. The importance of early and clear communication about end-of-life preferences cannot be overstated, as it helps to align the care provided with the patient’s values and legal rights [53].
Advance directives are intended to guide healthcare decisions when patients are no longer able to express their preferences, but their implementation can be challenging in practice [54]. Research indicates that while advance directives are associated with higher satisfaction with end-of-life care and a higher likelihood of dying in the preferred setting, their use remains inconsistent [55,56]. Nurses often encounter situations where advance directives are not available or are not specific enough to guide decision-making. Furthermore, family members may not be aware of or may disagree with the patient’s wishes, leading to conflicts that require careful mediation by healthcare providers [57,58].
The legal implications of these challenges underscore the need for nurses to be well-versed in relevant healthcare laws and regulations [59]. Proper documentation is not only a legal requirement but also a critical component of quality patient care. Incomplete or inaccurate documentation can lead to legal ramifications and adverse patient outcomes [60]. The nurses in this study highlighted the constant pressure to maintain meticulous records, which is essential for legal compliance and protecting patient rights.
Documentation serves multiple purposes in healthcare, including facilitating communication among providers, supporting clinical decision-making, and ensuring legal and regulatory compliance. In the context of multimorbidity, where patients may see multiple providers and take numerous medications, accurate documentation is particularly important [60]. Studies have shown that poor documentation can lead to errors in treatment, delays in care, and increased liability for healthcare providers. Nurses, therefore, must be diligent in their documentation practices, ensuring that all relevant information is recorded accurately and comprehensively [61]. These illustrate the importance of coordinated efforts among healthcare providers to manage polypharmacy. The roles of nurses and doctors are distinct yet complementary in this context. Doctors primarily focus on diagnosing and prescribing medications tailored to specific conditions, while nurses take on the critical role of overseeing the overall medication management. This includes monitoring for adverse effects, ensuring adherence, and facilitating communication between different healthcare providers to create a cohesive and safe treatment plan [27].
By working together, healthcare providers can effectively manage the complexities of polypharmacy, ensuring that patient safety and care quality are maintained. This collaborative approach is essential to navigate the ethical and legal challenges inherent in managing multiple medications for older adults with multimorbidities [62].
Furthermore, the role of nurses as patient advocates is paramount. Advocacy involves not only supporting the rights and autonomy of patients but also addressing systemic issues within the healthcare system that may impede the delivery of high-quality care [63]. This study’s findings indicate that nurses must be proactive in advocating for ethical practices and legal standards within their institutions. This includes pushing for better communication systems, more comprehensive training on ethical and legal issues, and policies that support patient-centered care [64].
Nurse advocacy extends beyond individual patient interactions to include systemic improvements in healthcare delivery [65]. Nurses are in a unique position to identify gaps and inefficiencies in care processes and to advocate for changes that enhance patient outcomes. This can involve lobbying for policy changes, participating in interdisciplinary care teams, and educating patients and families about their rights and options [66]. Advocacy is a critical component of nursing practice, particularly in the context of multimorbidity, where patients often require complex, coordinated care [8].
5. Limitations of the Study
This study has several limitations that should be acknowledged. First, the sample size was relatively small, comprising only 15 nurses from the Riyadh region, which may limit the generalizability of the findings. The perspectives and experiences of these nurses might not fully represent those of nurses in other regions or healthcare settings. Additionally, the study relied on self-reported data from semi-structured interviews, which can introduce biases such as recall bias or social desirability bias. Participants may have presented their experiences and practices in a more favorable light, potentially skewing the results. Furthermore, the qualitative nature of the study, while providing in-depth insights, does not allow for the quantification of the prevalence or extent of the identified challenges. Future research could benefit from a larger, more diverse sample and the inclusion of quantitative methods to validate and expand upon these findings.
6. Implications of the Study
The findings of this study have significant implications for nursing practice, education, and policy. For nursing practice, the study highlights the need for nurses to receive ongoing training on ethical and legal issues, particularly in the context of caring for older adults with multimorbidities. This training should include strategies for managing informed consent, polypharmacy, and end-of-life care, ensuring that nurses are well equipped to handle these complex situations. For nursing education, incorporating case studies and practical scenarios related to these challenges into curricula can better prepare future nurses for real-world practice. Additionally, healthcare institutions should implement policies that promote effective communication, thorough documentation, and regular medication reviews to enhance patient safety and care quality. On a policy level, there is a need for clearer guidelines and support systems to assist nurses in navigating the ethical and legal complexities of their roles. Advocacy for policies that foster interdisciplinary collaboration and provide resources for ethical reflection and discussion is essential.
7. Conclusions
This study provides valuable insights into the ethical and legal challenges faced by nurses in caring for older adults with multimorbidities. The findings highlight the complexities and nuances of nursing practice in this context, emphasizing the importance of balancing patient autonomy with safety, managing polypharmacy, and ensuring effective coordination among healthcare providers. Despite the relatively small sample size of 15 nurses, the study’s purposive sampling and in-depth qualitative approach allowed for rich, detailed data that contributed significantly to our understanding of these challenges. The diversity in participants’ ages, educational backgrounds, and years of experience added depth to the findings, although it also underscores the need for caution in generalizing the results across different healthcare settings.
One key finding of this study is the critical role nurses play in managing polypharmacy, highlighting the importance of vigilant monitoring, patient education, and coordination with multiple healthcare providers. This study elaborates on previous research by providing concrete examples of how nurses navigate these challenges in practice, emphasizing the need for centralized medication records and regular team meetings to ensure patient safety. The study also confirms the ongoing ethical dilemma of respecting patient autonomy while ensuring safety, particularly for patients with cognitive impairments. The findings reinforce the importance of clear communication and documentation to navigate these legal and ethical boundaries effectively.
Furthermore, the study extends existing knowledge by illustrating the practical strategies nurses employ to overcome barriers in communication and coordination, such as the use of telehealth and patient education. These strategies are crucial for enhancing the quality of care and ensuring that patients’ complex needs are met safely and effectively. However, the methodological limitations, including the small sample size and the reliance on self-reported data, must be acknowledged. Future research should aim to include larger, more diverse samples and explore the use of mixed methods to triangulate findings and provide a more comprehensive understanding of these issues.
In conclusion, this study underscores the vital role of nurses in managing the ethical and legal challenges associated with caring for older adults with multimorbidities. By confirming and elaborating on previous findings, it provides practical insights and recommendations for improving nursing practice and policy. Addressing these challenges requires ongoing education, robust communication systems, and supportive institutional policies to empower nurses. |
Title: Abdominal Hypertrophy Syndrome: Characteristics and Potential Pathophysiology | Body:
Introduction and background
Palumboism, colloquially referred to as steroid gut, represents a distinctive condition characterized by the development of a prominently enlarged and distended abdomen among bodybuilders and athletes who engage in prolonged anabolic steroid usage. Despite its widespread recognition within the bodybuilding and fitness community, Palumboism does not hold official recognition as a medical term or a specific diagnosis in the realm of medical literature. Notably absent are formal classification and diagnostic criteria, resulting in its omission from standard medical textbooks and the authoritative classifications presented by organizations such as the World Health Organization (WHO) and the American Medical Association (AMA).
While Palumboism serves as a well-known concept in certain circles, medical professionals conventionally employ the terms "abdominal enlargement" or "abdominal distension" to depict the visible outcome observed in individuals immersed in extended anabolic steroid utilization. In scientific and medical literature, more encompassing descriptions may emerge, such as "abdominal organomegaly" or "visceral adiposity," focusing on the physiological shifts underlying prolonged steroid exposure rather than relying on the specific label of Palumboism.
The emergence of what is colloquially termed "bubble gut," or Palumboism, among bodybuilders since its introduction in the 1990s has emerged as a notable concern within the fitness community. This issue significantly influences the aesthetic qualities of athletes and bodybuilders alike. Despite its prevalence, a consensus pertaining to medical or surgical management of this condition remains elusive.
While the precise etiology of this phenomenon remains enigmatic, it is hypothesized that various contributing factors synergistically culminate in abdominal distension and enlargement. A conspicuous element influencing bubble gut is the utilization of human growth hormone (HGH) among bodybuilders, frequently in doses that far exceed typical therapeutic ranges. Elevated HGH levels have the potential to induce excessive tissue expansion, including within the intestines, thereby leading to a substantial increase in abdominal size. Studies examining the effects of overexpressing bovine growth hormone (GH) in transgenic mice have revealed marked increases in the weight and length of the small bowel, accompanied by a 50-100% amplification in mucosal mass [1]. This evidence is reinforced by additional investigations demonstrating elevated villus content of IGF-1 mRNA in the bowel of GH transgenic mice compared to their wild-type counterparts, supporting the notion of localized IGF-1 production stimulated by GH. Moreover, in rats subjected to substantial (75-80%) small intestinal resection, both hGH and IGF-1 were found to enhance the postoperative hyperplasia evident in the ileal remnant [2,3].
In the context of humans, GH, or somatotropin, engages its membrane-bound growth hormone receptor (GHR) situated on hepatocytes, subsequently activating the cytoplasmic tyrosine kinase Janus Kinase 2 (JAK-2). This activation leads to tyrosine phosphorylation within both the JAK-2 enzyme and GHR [4,5]. Consequently, the liver secretes insulin-like growth factor-1 (IGF-1), or somatomedin, which binds to its receptor on colonic epithelial cells, orchestrating heightened epithelial proliferation, reduced apoptosis, and intensified angiogenesis [6]. This relationship between extensive epithelial proliferation and a broad proliferation zone with the levels of GH and IGF-1 is evident in the colons of acromegalic patients [7]. Acromegaly, characterized by excessive GH production and resulting in progressive somatic disfigurement and systemic manifestations [8], potentially shares intestinal pathophysiological mechanisms with Palumboism.
While prior research has extensively explored the utilization of anabolic-androgenic steroids (AAS), including testosterone and its derivatives [9,10], limited attention has been dedicated to other frequently used performance-enhancing drugs (PEDs) like GH, insulin-like growth factor-I (IGF-I), and insulin itself. In 1992, a study disclosed that roughly 5% of male high school students acknowledged experimenting with GH during their high school athletic careers, with a notable portion being acquainted with peers similarly involved in GH usage [11]. Moreover, among middle-aged and elderly individuals, the pursuit of GH administration aims to augment muscle mass and recapture youthful physical attributes. The pervasiveness of PED misuse extends to the weightlifting community, as evidenced by a survey of 231 weightlifters where 12% admitted to historical and/or current GH or IGF-I usage, with a substantial 80% of these respondents exhibiting indications of prior or ongoing AAS dependence [12]. Another noteworthy addition to PEDs is insulin, frequently adopted by bodybuilders due to its purported anabolic effects, including the vital stimulation of glycogen synthesis for post-exercise muscle recovery.
Review
To comprehensively investigate Palumboism, an extensive and rigorous literature review was conducted. The primary objective was to gather a broad range of relevant studies pertaining to this condition. To achieve this, a systematic search strategy was employed, utilizing the PubMed database and Google Scholar search engine.
To ensure a comprehensive search, a set of carefully selected keywords was employed. These keywords were chosen based on their relevance to Palumboism and its associated features. The specific terms used in the search included "palumboism," "bodybuilder gut," "steroid gut," "HGH gut," "HGH bloat," "insulin gut," "bubble gut," "muscle gut," "abdominal distension," "abdominal organomegaly," "visceral adiposity," "abdominal obesity," "anabolic steroids," and "growth hormone."
By incorporating these diverse keywords, we aimed to capture a wide array of studies and articles that explored various aspects of Palumboism. The intention was to encompass research related to the pathophysiology, clinical presentation, and management of this condition. Through the utilization of these keywords, we sought to identify studies that specifically addressed the mechanisms underlying Palumboism, the characteristic clinical manifestations observed in affected individuals, and the strategies employed for its management.
The search process involved careful examination and evaluation of each retrieved article to determine its relevance and suitability for inclusion in the literature review. The exclusion criteria consisted of studies that were not peer-reviewed, involved non-adult populations, were duplicates, were not in English, and did not comply with the intended clinical presentation. Only studies that directly addressed the pathophysiology, clinical presentation, and management of Palumboism were selected for further analysis and inclusion in the review. A total of 1,222 studies were identified through the search criteria, of which 451 were screened, 33 were assessed for eligibility, and 30 studies were included in the final review.
During the literature review, it became apparent that there is a scarcity of studies dedicated to investigating Palumboism. The existing body of literature primarily comprises anecdotal evidence, indicating a significant research gap in this field. The limited availability of substantial research underscores the need for further investigation into this condition.
Based on the available evidence, it is suggested that the prolonged and high-dose usage of anabolic steroids, particularly GH and insulin, may play a role in the development of Palumboism. However, the current understanding of the underlying mechanisms is extremely limited.
Several potential mechanisms have been proposed to explain the abdominal distension observed in individuals with Palumboism.
Cushing’s syndrome
Mechanisms of visceral obesity in Cushing's syndrome are related to glucocorticoids’ regulative action on adipose-tissue differentiation, function, and distribution. In excess, glucocorticoids cause central obesity. Adipose stromal cells from omental fat, but not subcutaneous fat, can generate active cortisol from inactive cortisone through the expression of enzyme 11 beta-HSD1, the expression of which is increased further after exposure to cortisol and insulin [13]. Glucocorticoids are regulated by adrenocorticotropic hormone (ACTH). In one controlled study examining the increase of reinforcing value of exercise as opposed to drug-induced euphoria in athletes using anabolic-androgenic steroids (AAS), the AAS-using groups had significantly higher ACTH levels than heavy exercising controls [14]. These mechanisms in part can hypothetically contribute to the development of abdominal distension in athletes and bodybuilders.
GH
Organomegaly, which refers to the abnormal enlargement of organs, has been postulated as another contributing factor to Palumboism. The prolonged use of HGH may lead to hypertrophy or enlargement of abdominal organs, further exacerbating the distension of the abdomen. However, there exists limited comprehensive empirical evidence concerning adverse outcomes associated with the misuse of GH in human subjects. The majority of reported instances detailing side effects are predominantly anecdotal in nature and are often intertwined with the misuse of multiple compounds. These potential consequences are posited to mirror those observed in cases of acromegaly, a condition characterized by excessive GH production, which may manifest as hypertension, carpal tunnel syndrome, diabetes, neuropathy, and a range of other maladies [10,15-18]. However, it is important to acknowledge that the potential for unidentified side effects exists in cases of excessive or inappropriate use, particularly when GH is combined with other agents or administered at elevated doses. In animal studies, the administration of supraphysiological doses of GH has been linked to organ enlargement, most notably cardiomegaly, a phenomenon analogous to that seen in acromegaly [19,20]. Furthermore, instances of edema, orthostatic hypotension, myositis, carpal tunnel syndrome, and gynecomastia have been documented in frail elderly individuals subjected to GH administration [21,22], while occurrences of carpal tunnel syndrome and hyperglycemia have been noted during GH administration in healthy adults [23].
IGF-1
Most attributes associated with the misuse of IGF-I are challenging to differentiate from those arising due to GH misuse, given that heightened GH levels also stimulate IGF-I production. Nonetheless, instances of hypoglycemia, seizures, jaw pain, myalgia, edema, headaches, augmented liver and kidney dimensions, and modified liver function, among other effects, have been documented subsequent to the administration of recombinant human IGF-I (rhIGF-I) [22,24,25,26]. Among the prevalent adverse reactions, erythema and lipohypertrophy at the injection site have been most frequently reported [24].
Insulin
Altered collagen synthesis with the use of insulin has been suggested as a potential mechanism underlying Palumboism. Fibroblasts are the primary cells responsible for the production of collagen. Collagen is a key component of connective tissues, including the fascia and muscles of the abdomen. Insulin increased collagen production by two- to threefold and total protein production by twofold in cultures of lung fibroblasts. In cardiac myocytes, insulin increases DNA and collagen synthesis in fibroblasts in a dose-dependent fashion, and any alterations in collagen synthesis or metabolism could result in structural changes, potentially leading to the characteristic abdominal protrusion seen in Palumboism. The proanabolic capabilities of insulin have been confirmed with the meta-analysis of 25 studies, which showed that it exerts its positive regulation of lean muscle mass principally via an anticatabolic effect in reducing muscle protein breakdown, rather than positively affecting the muscle protein synthesis [27].
Ultimately, pinpointing a specific agent to the development of Palumboism is rather difficult due to polypharmacy among users. The overwhelming majority of individuals who engage in the recreational use of anabolic agents commonly employ a combination of multiple substances. Substances frequently employed as adjuncts to anabolic-androgenic steroids (AAS) encompass a range of compounds including alcohol, amphetamine, caffeine, cannabinoids, clenbuterol, cocaine, codeine, creatine, ephedrine, erythropoietin, gamma hydroxybutyrate, GH, heroin, human chorionic gonadotropin, insulin, insulin-like growth factor-I (IGF-I), tamoxifen, tobacco, and numerous others [28,29,30]. In a United States-based study involving 41 individuals using insulin, it was revealed that 95% of these individuals concurrently utilized AAS and engaged in polypharmacy by incorporating an average of 16.2 ± 5.6 performance-enhancing drugs into their annual regimen [28].
Conclusions
The available evidence on the pathophysiology of Palumboism is very limited. Basic science pathophysiology literature however points to a direct role of insulin and GH in the development of the condition. Furthermore, an anecdotal correlation exists between the prolonged use of high-dose anabolic steroids, particularly GH and IGF-1, and the development of Palumboism. This literature review is limited by the small number of available studies on the target pathology, which restricts the comprehensiveness and generalizability of the findings and underscores the urgent need for further research to build a more robust evidence base. Subsequent research is warranted to deepen our understanding of the pathophysiology of Palumboism and to explore additional contributing factors to this condition. The systematization of the available data has to be conducted to broaden the knowledge of complications, potential prevention, and treatment of Palumboism. |
Title: Abordaje de la obesidad infantil. Comparativa entre comunidades autónomas | Body:
INTRODUCCIÓN
En las últimas cuatro décadas, la prevalencia de la obesidad infantil (OI) mundial ha ido aumentando de forma alarmante, ocupando España la duodécima posición entre los países integrantes de la Organización para la Cooperación y el Desarrollo Económicos (OCDE) para sobrepeso (34,1%) y el vigésimo puesto entre sus miembros para la obesidad (10,8%)
1
. A nivel autonómico, la prevalencia de obesidad y sobrepeso infantil varía de forma significativa. Concretamente, como se puede apreciar en la [Tabla 1], destacan por su alto porcentaje de sobrepeso la Región de Murcia, las Islas Canarias, Melilla y las Islas Baleares; y por su alto porcentaje de OI, Ceuta, la Región de Murcia, Cataluña y Andalucía
2
.
La obesidad en la infancia y la adolescencia representa un grave problema de Salud Pública, dada la relación que mantiene con la aparición de complicaciones graves en la vida adulta de no evitarla a tiempo
3
.
La Ley General de Sanidad describe entre los principios de la sanidad pública el derecho de toda persona a la salud. El Sistema Nacional de Salud (SNS) es la herramienta sanitaria con que cuenta nuestro país para hacer frente a cualquier patología, incluida la OI. A pesar del carácter nacional de dicha institución, sus competencias se encuentran repartidas entre la Administración central, administraciones autonómicas, administraciones locales y, en último lugar, los servicios de salud autonómicos.
El SNS cuenta con infinidad de recursos y herramientas para poder ofrecer una asistencia sanitaria y actividades de prevención de primera calidad. En primer lugar, un personal cualificado es vital para combatir cualquier patología. La prevención del sobrepeso/obesidad infantil se debe realizar desde los centros de Atención Primaria (AP) por parte de pediatría y enfermería pediátrica, dada su cercanía a dicha población
4
5
6
. A pesar de ello, la acción desde AP, en ocasiones, se ve limitada por la falta de tiempo, las consultas sobrecargadas, la falta de personal, la insuficiente comunicación entre distintos ámbitos asistenciales y la falta de profesionales con categoría profesional reconocida, pero no implantada en su totalidad
7
8
. A esto se le puede añadir la escasez de profesionales formados concretamente en Nutrición y Dietética en el SNS, motivando a la Asociación Española de Dietistas-Nutricionistas a reivindicar la presencia de dietistas en el mismo
9
10
.
En segundo lugar, la existencia de programas o planes integrales frente a la OI, atendiendo a los factores de riesgo de obesidad, los hábitos nutricionales y los estilos de vida no saludables, proponen un abordaje adecuado y equitativo, al igual que ayudan a los profesionales sanitarios a alcanzar de una forma protocolizada dichos objetivos. Por último, los recursos económicos, así como conocer el gasto derivado de una enfermedad, sirven para poder resolver aquellos errores que surgen de la gestión sanitaria, haciéndola más eficiente.
A nivel práctico, y dada la descentralización de nuestro sistema sanitario, la hipótesis de nuestro estudio fue que existen diferencias en la atención a la población en función de la comunidad autónoma en la que nos encontremos. El objetivo del presente estudio fue comparar el abordaje de la obesidad infantil en las diecisiete comunidades autónomas y en las ciudades autónomas de Ceuta y Melilla.
MATERIAL Y MÉTODOS
Se realizó un estudio transversal descriptivo a través de la observación y comparación de las variables que a continuación se describen. Los datos poblacionales empleados para la comparativa fueron obtenidos del Instituto Nacional de Estadística (INE).
Tabla 1
Prevalencia de Sobrepeso y OI según sexo y CC. AA. Población de dos a diecisiete años. 2021.
Personal de Pediatría y Enfermería de Atención Primaria del Sistema Nacional de Salud.
Se obtuvo el número de profesionales a través de las últimas cifras publicadas en el portal estadístico del Ministerio de Sanidad en el apartado Sanidad en datos, del año 2020, disponible en su sitio web (11). Para la obtención de la ratio de pediatría se consultó el número de habitantes entre cero y catorce años de cada comunidad autónoma, obteniéndose el número de pediatras de AP por cada mil habitantes entre cero y catorce años. En el caso de enfermería, la ratio se calculó a partir de los datos de población total pues, en su caso, su labor no se circunscribe a dicha edad por no disponer de la especialidad en pediatría plenamente instaurada. Por tanto, la ratio de enfermería se expresó en número de enfermeras de AP por cada mil habitantes de población general
12
.
Reconocimiento legal del personal de Nutrición.
Para conocer el reconocimiento legal autonómico de estos profesionales, se consideró que existía reconocimiento legal cuando existía un decreto de creación de la categoría profesional estatutaria de nutricionistas y técnicos en nutrición en la comunidad autónoma.
Personal de Nutrición en el Sistema Nacional de Salud.
Para conocer el número de profesionales de cada categoría en ejercicio en los servicios de salud, en primer lugar, se analizaron las ofertas de empleo público de cada comunidad autónoma a través de los decretos de ofertas de empleo público entre los años 2016-2021, incluyendo tanto licenciados, diplomados y graduados en Nutrición Humana y Dietética como Técnicos Superiores en Dietética y Nutrición. En segundo lugar, mediante trámite online, se contactó con las consejerías de Sanidad de cada comunidad autónoma a través de una solicitud genérica de acceso a información pública. Se estableció como fecha final de recogida de datos procedentes del trámite electrónico el 30 de abril de 2022.
Planes Integrales contra la Obesidad infantil.
Para conocer la existencia o no de planes accesibles para la población general se realizó una búsqueda en los sitios web oficiales de los servicios de salud de las diferentes comunidades y ciudades autónomas.
Gasto sanitario y partidas presupuestarias.
Se consultó la información de los presupuestos generales para 2022 de cada comunidad autónoma destinados a las consejerías de Salud y/o servicios sanitarios
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
, excepción de Andalucía
29
y Castilla y León
30
, comunidades en las que permanecían vigentes los presupuestos del año 2021. Para la obtención de datos sobre los fondos destinados a combatir la OI y su partida presupuestaria específica, o bien los fondos en concepto de Salud Pública o promoción de la salud, así como el gasto sanitario asociado a la misma, se realizaron solicitudes individualizadas a cada una de las consejerías de Sanidad a través de una petición genérica de acceso a información pública.
De forma adicional, se procedió a calcular los euros invertidos en sanidad por habitante y año usando los datos poblacionales correspondientes a 2021 del INE
31
. Solo se calcularon las ratios de pediatras y enfermeros por 1.000 habitantes y el gasto en salud por habitante pero no se realizó ningún análisis estadístico de los datos.
RESULTADOS
A continuación, se detallan los resultados obtenidos para cada uno de los indicadores analizados.
Personal de Pediatría y Enfermería de Atención Primaria del Sistema Nacional de Salud.
Los datos recopilados en la [Tabla 2] muestran el número de profesionales por categorías y su ratio por cada mil habitantes. La ratio nacional fue de 1,21 para pediatras y 0,65 para enfermería. Entre las comunidades autónomas se apreciaron grandes variaciones para ambas categorías; así, las mayores ratios se encontraron en La Rioja con 1,55 pediatras y 0,89 enfermeros por cada mil habitantes, mientras que en la última posición de la clasificación se situaron las ciudades autónomas de Ceuta y Melilla, con 0,63 pediatras y 0,48 enfermeros por cada mil habitantes.
Reconocimiento legal del personal de Nutrición.
A nivel legislativo, el reconocimiento como personal sanitario de nutricionistas y técnicos en nutrición se encuentra en proceso. Se obtuvieron los decretos o resoluciones correspondientes a cada comunidad autónoma, presentes en la
[Tabla 3]. Se observó que los nutricionistas carecían de reconocimiento como personal sanitario en Andalucía, Asturias, Islas Canarias, Cantabria, Castilla-La Mancha, Extremadura y Galicia. Los técnicos superiores en dietética mostraron una situación similar, existiendo comunidades donde aún no se encontraban reconocidos como personal sanitario de los respectivos servicios de salud. Por otro lado, si bien se hallaron ofertas de empleo público para los técnicos superiores en Dietética y Nutrición
Tabla 2Recuento de Pediatría en AP y ratio por cada 1.000 habitantes de cero-catorce años. Recuento de Enfermería y ratio de enfermeras por 1.000 habitantes en población general. en varias comunidades autónomas, no se pudo localizar los decretos que los reconocían como tal.
Personal de Nutrición en el Sistema Nacional de Salud.
Como se aprecia en la [Tabla 4], la Región de Murcia fue pionera con nueve plazas públicas para nutricionistas ofertadas en los últimos años, seguida de Valencia con siete y Navarra con una. Por otra parte, la oferta de plazas para técnicos superiores en nutrición fue considerablemente mayor, situándose en primer lugar Andalucía, con veintitrés, seguida de Castilla y León con catorce y Navarra con ocho. Respecto al personal contratado en la actualidad por los respectivos servicios de salud, de forma general se observó que la balanza se inclinaba hacia los técnicos superiores en dietética. Destacó la Comunidad Autónoma de Cataluña, con una cantidad de profesionales de ambas
Tabla 3Reconocimiento legal Dietistas-Nutricionistas y Técnicos superiores en dietética y nutrición como personal sanitario.
Tabla 4Número de plazas públicas ofertadas y número de profesionales de Nutrición contratados por las distintas CC. AA. cateorías muy superiores al resto de comunidades, quedando aún pendientes 150 contrataciones de nutricionistas para AP, a realizar durante el transcurso de 2022. Algunas comunidades, como Andalucía, ofrecieron la información sin especificar el ámbito laboral de dichos profesionales, y otras, como las Islas Canarias, Castilla La-Mancha, Galicia, Ceuta y Melilla, no emitieron respuesta alguna a la petición de datos.
Planes Integrales frente a la Obesidad infantil.
En la [Tabla 5] e observa que únicamente nueve comunidades y las dos ciudades autónomas contaban con planes integrales para la OI, si bien se hallaron desactualizados u obsoletos.
Tabla 5Planes Integrales de abordaje de la Obesidad Infantil por comunidades autónomas.
Tabla 6Presupuestos (en euros) destinados a Servicios de Salud, Salud Pública y Promoción de la salud; capital invertido en salud por habitante; partida presupuestaria para OI y gasto sanitario derivado de la OI.
Solo la Región de Murcia poseía este plan relativamente actualizado, con fecha de 2021, seguida del País Vasco (actualizado en 2019), situándose en último lugar Ceuta y Melilla, con un plan que databa de 2007. Por otra parte, Cataluña y Valencia poseían este plan en formato web, estando en constante actualización.
Gasto sanitario y partidas presupuestarias de la Obesidad Infantil.
En la [Tabla 6] se resumen los fondos destinados a los servicios de salud, Salud Pública y Promoción de la Salud en cada comunidad, observando variaciones en el importe por habitante en función del lugar de residencia (Ceuta y Melilla, 1.953,17 euros/habitante, frente a La Rioja, 1.103,52 euros/habitante). Asimismo, se evidenció que solo nueve de las diecisiete comunidades detallaban un importe específico a Salud Pública, y únicamente cuatro de ellas lo hicieron para Promoción de la Salud. En cuanto al gasto sanitario derivado de la Obesidad Infantil, se comprobó que tan solo la Comunidad Autónoma de Cataluña realizaba de forma periódica un análisis económico del gasto sanitario derivado de la obesidad infantil. En relación con una partida presupuestaria específica para luchar contra la OI, no se obtuvieron más respuestas que las dadas por las comunidades autónomas de Castilla y León y La Rioja, que destinaron un importe concreto para programas de alimentación saludable. Islas Canarias, Castilla-La Mancha, Galicia, Ceuta y Melilla no emitieron respuesta alguna.
DISCUSIÓN
Respecto al personal de Pediatría, la ratio media de pediatras se sitúa en 1,21 pediatras por cada mil habitantes de cero-catorce años, lo que equivale aproximadamente a 826,44 habitantes infanto-juveniles por pediatra. Esto coloca a España en una posición positivamente alejada de los valores medios de la Unión Europea, según el artículo de Carrasco Sanz (2011), en el que abordó la situación de los pediatras en Europa y estimó que, de media en Europa, cada pediatra tenía asignado un cupo de 1.250 habitantes
32
. Según el último Informe de Oferta-Necesidad de Especialistas Médicos, de enero de 2022, esta situación positiva se mantendría, e incluso mejoraría, en la Atención Primaria en España entre los años 2021-2035
33
.
En relación con la enfermería de Atención Primaria, la ratio media de enfermeras por cada mil habitantes es de 0,65, lo que equivale a aproximadamente 1.544 habitantes por cada enfermera de Atención Primaria. Una situación similar fue descrita por la Federación de Asociaciones para la Defensa de la Sanidad Pública en 2015, en un análisis de la situación de la enfermería en Atención Primaria en cuanto a profesionales sanitarios en comparación con la Unión Europea. En él, observaron que tanto el número de enfermeras totales respecto al total de sanitarios como el número de enfermeras por cada mil habitantes se encuentra muy por debajo de la media europea, a saber, 57,1% de enfermeras del total de profesionales sanitarios en España frente al 69,2% de enfermeras en Europa y 4,9 enfermeras por cada mil habitantes en España frente a 8,7 en Europa
34
.
Esta situación genera una gran desigualdad en la composición y presencia de la enfermería en los Equipos de Atención Primaria, impidiendo a los profesionales poder abarcar todas las actividades y cuidados que precisa la población y obligándolos a realizar un gran sobresfuerzo para mantener las prestaciones, en concreto, el abordaje de la Obesidad Infantil. Todo ello, podría repercutir negativamente sobre la equidad en el acceso a los cuidados de la población, la calidad de la atención y los derechos laborales del colectivo.
Las diferencias intercomunitarias respecto al personal resultan evidentes en ambas categorías profesionales, encontrando autonomías como Andalucía que, albergando la mayor proporción de población infantil española, en comparación con otras como La Rioja, presenta una ratio de pediatras y enfermeras considerablemente menor: 0,89 frente a 1,55 para pediatría, y 0,58 frente a 0,89 para enfermería, respectivamente. También llama la atención que aquellas comunidades con mayor índice de sobrepeso y obesidad, como Andalucía, Islas Baleares, Ceuta y Melilla o Murcia, tienen las menores ratios, tanto de pediatras como de enfermeros, por debajo de la media nacional.
Por otro lado, la situación de nutricionistas y técnicos superiores en dietética es todavía más desmoralizadora, si se tiene en cuenta que poseen las competencias específicas para el tratamiento nutricional de los pacientes obesos. En España, la contratación y oferta de empleo público realizadas en los últimos años resulta ser anecdótica y no alcanza las exigencias de profesionales marcadas por diferentes organismos. La Unión Europea fijó las necesidades de dietistas-nutricionistas por cama hospitalaria en una por cada cuarenta camas de especialidades, una por cada setenta y cinco camas de pacientes agudos y una por cada cien-ciento cincuenta pacientes de media y larga estancia. Igualmente, la Organización Mundial de la Salud (OMS) aconseja un promedio de un nutricionista por cada cincuenta pacientes
35
, y la Asociación Española de Dietistas y Nutricionistas reclamó, en 2009, la inclusión de nutricionistas en el SNS, solicitando un nutricionista por cada 50.000 tarjetas sanitarias en el ámbito de Atención Primaria, un nutricionista por cada cien camas en Atención Especializada y un nutricionista por cada 500.000 habitantes en el ámbito de la Salud Pública
36
. Además, cabe destacar la variabilidad que existe en la categoría profesional de este colectivo en función del servicio de salud donde ejerza, que se podría traducir en diferentes condiciones laborales, salarios, etc., hecho que pudiera conducir a la no ocupación de dichos puestos en beneficio de otros más atractivos, poniendo en peligro la asistencia sanitaria que precisa la obesidad infantil. Es evidente que España parece no cumplir con estos criterios y, por tanto, dista mucho de las recomendaciones de los organismos oficiales.
Unido a la aparente falta de algunos profesionales considerados cualificados, el presente estudio pone de manifiesto la falta de protocolos estandarizados y actualizados para el abordaje de la OI. En la actualidad, ocho de las comunidades autónomas españolas carecen de planes o programas específicos, o al menos estos no son accesibles, y de las diez que sí los poseen, solo dos cuentan con sus programas en formato web, estando el resto obsoletos o no actualizados. Por su parte, los presupuestos generales para 2022 de las Islas Canarias contemplan, en su disposición adicional trigésimo octava, el compromiso de elaborar el Plan contra la Obesidad Infantil 2022-2026 y dotarlo de los recursos necesarios.
En cuanto a los aspectos económicos de la Obesidad Infantil, reflejan que queda mucho por hacer. Ninguna comunidad autónoma, menos una, cuantifica el gasto derivado de OI. Cataluña es la única comunidad autónoma que, de forma aislada, realiza un análisis periódico del gasto sanitario derivado de la OI, siendo alarmantes los resultados del último informe al que se tuvo acceso. Una persona menor de dieciocho años con OI supone un coste adicional de 153,44 euros/persona y año, es decir, teniendo en cuenta la población que se encuentra en ese rango de edad supone un total de 13.739.080 euros/año. Por ello, resultaría conveniente para el resto de las comunidades realizar un esfuerzo en conocer, de una forma tangible, las consecuencias económicas de esta patología en su territorio concreto.
Por otra parte, casi todas las comunidades autónomas refieren no disponer de una partida presupuestaria específica para la OI. Se encuentra poca información sobre los diversos programas de salud incluidos en las partidas presupuestarias destinadas a Salud Pública y Promoción de la Salud. En los presupuestos generales de cada comunidad autónoma no se desglosan las partidas presupuestarias, quedando la responsabilidad de repartir dichos fondos a los distintos organismos. Además, ese reparto no puede consultarse de forma libre ni está reflejado en los correspondientes sitios web, siendo una limitación del presente trabajo.
Para finalizar, es relevante mencionar otras limitaciones del estudio. Así, parte de la información de carácter público que se publica en este trabajo no está del todo disponible ni es fácilmente accesible para la ciudadanía o profesionales. Fue necesario solicitar un certificado/firma electrónica para poder contactar con las distintas consejerías de Salud y obtener dicha información. Así mismo, algunas comunidades autónomas no dieron respuesta a las solicitudes efectuadas, por lo que faltarían datos para conocer la situación real de nuestro país respecto a los indicadores analizados. A pesar de que España cuenta con la Ley 19/2013, de 9 de diciembre, de transparencia, acceso a la información pública y buen gobierno, todavía queda mucho por avanzar en este ámbito. Por otra parte, los datos de enfermería son de carácter general, no específicos de población pediátrica. Con respecto a las partidas presupuestarias, aquellas destinadas a Promoción de la Salud solo servirían para estimar de forma sucinta la inversión en prevención de la OI, ya que en esos Servicios, además de la OI, se abordan diversos temas como programas de cribados de cáncer, prevención del VIH, ITS, tabaquismo, etc.
Como conclusión general, podemos manifestar que, a nivel intercomunitario, el abordaje de la obesidad infantil parece variar de forma considerable para la mayoría de los indicadores analizados. Por ello, sería recomendable que los responsables de gestión sanitaria a todos los niveles (nacional, autonómico y local) encauzaran sus esfuerzos en homogenizar este abordaje, con el fin de mejorar la calidad asistencial e igualar las oportunidades de prevención y tratamiento en todo el ámbito nacional.
Sirviendo este trabajo como punto de partida, en próximas investigaciones se debería profundizar sobre estos mismos indicadores y analizar otros de igual importancia, como la situación de otros profesionales con competencias sobre la OI (endocrinólogos, psicólogos, técnicos superiores en Nutrición y Control de Alimentos, etc.), analizar el entorno de la población infantil (enfermeros escolares, escuelas promotoras de salud, análisis de instalaciones deportivas disponibles, ciudades adscritas a la Red de Ciudades Saludables), etc.
Personal de Pediatría y Enfermería de Atención Primaria del Sistema Nacional de Salud.
Se obtuvo el número de profesionales a través de las últimas cifras publicadas en el portal estadístico del Ministerio de Sanidad en el apartado Sanidad en datos, del año 2020, disponible en su sitio web (11). Para la obtención de la ratio de pediatría se consultó el número de habitantes entre cero y catorce años de cada comunidad autónoma, obteniéndose el número de pediatras de AP por cada mil habitantes entre cero y catorce años. En el caso de enfermería, la ratio se calculó a partir de los datos de población total pues, en su caso, su labor no se circunscribe a dicha edad por no disponer de la especialidad en pediatría plenamente instaurada. Por tanto, la ratio de enfermería se expresó en número de enfermeras de AP por cada mil habitantes de población general
12
.
Reconocimiento legal del personal de Nutrición.
Para conocer el reconocimiento legal autonómico de estos profesionales, se consideró que existía reconocimiento legal cuando existía un decreto de creación de la categoría profesional estatutaria de nutricionistas y técnicos en nutrición en la comunidad autónoma.
Personal de Nutrición en el Sistema Nacional de Salud.
Para conocer el número de profesionales de cada categoría en ejercicio en los servicios de salud, en primer lugar, se analizaron las ofertas de empleo público de cada comunidad autónoma a través de los decretos de ofertas de empleo público entre los años 2016-2021, incluyendo tanto licenciados, diplomados y graduados en Nutrición Humana y Dietética como Técnicos Superiores en Dietética y Nutrición. En segundo lugar, mediante trámite online, se contactó con las consejerías de Sanidad de cada comunidad autónoma a través de una solicitud genérica de acceso a información pública. Se estableció como fecha final de recogida de datos procedentes del trámite electrónico el 30 de abril de 2022.
Planes Integrales contra la Obesidad infantil.
Para conocer la existencia o no de planes accesibles para la población general se realizó una búsqueda en los sitios web oficiales de los servicios de salud de las diferentes comunidades y ciudades autónomas.
Gasto sanitario y partidas presupuestarias.
Se consultó la información de los presupuestos generales para 2022 de cada comunidad autónoma destinados a las consejerías de Salud y/o servicios sanitarios
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
, excepción de Andalucía
29
y Castilla y León
30
, comunidades en las que permanecían vigentes los presupuestos del año 2021. Para la obtención de datos sobre los fondos destinados a combatir la OI y su partida presupuestaria específica, o bien los fondos en concepto de Salud Pública o promoción de la salud, así como el gasto sanitario asociado a la misma, se realizaron solicitudes individualizadas a cada una de las consejerías de Sanidad a través de una petición genérica de acceso a información pública.
De forma adicional, se procedió a calcular los euros invertidos en sanidad por habitante y año usando los datos poblacionales correspondientes a 2021 del INE
31
. Solo se calcularon las ratios de pediatras y enfermeros por 1.000 habitantes y el gasto en salud por habitante pero no se realizó ningún análisis estadístico de los datos.
RESULTADOS
A continuación, se detallan los resultados obtenidos para cada uno de los indicadores analizados.
Personal de Pediatría y Enfermería de Atención Primaria del Sistema Nacional de Salud.
Los datos recopilados en la [Tabla 2] muestran el número de profesionales por categorías y su ratio por cada mil habitantes. La ratio nacional fue de 1,21 para pediatras y 0,65 para enfermería. Entre las comunidades autónomas se apreciaron grandes variaciones para ambas categorías; así, las mayores ratios se encontraron en La Rioja con 1,55 pediatras y 0,89 enfermeros por cada mil habitantes, mientras que en la última posición de la clasificación se situaron las ciudades autónomas de Ceuta y Melilla, con 0,63 pediatras y 0,48 enfermeros por cada mil habitantes.
Reconocimiento legal del personal de Nutrición.
A nivel legislativo, el reconocimiento como personal sanitario de nutricionistas y técnicos en nutrición se encuentra en proceso. Se obtuvieron los decretos o resoluciones correspondientes a cada comunidad autónoma, presentes en la
[Tabla 3]. Se observó que los nutricionistas carecían de reconocimiento como personal sanitario en Andalucía, Asturias, Islas Canarias, Cantabria, Castilla-La Mancha, Extremadura y Galicia. Los técnicos superiores en dietética mostraron una situación similar, existiendo comunidades donde aún no se encontraban reconocidos como personal sanitario de los respectivos servicios de salud. Por otro lado, si bien se hallaron ofertas de empleo público para los técnicos superiores en Dietética y Nutrición
Tabla 2Recuento de Pediatría en AP y ratio por cada 1.000 habitantes de cero-catorce años. Recuento de Enfermería y ratio de enfermeras por 1.000 habitantes en población general. en varias comunidades autónomas, no se pudo localizar los decretos que los reconocían como tal.
Personal de Nutrición en el Sistema Nacional de Salud.
Como se aprecia en la [Tabla 4], la Región de Murcia fue pionera con nueve plazas públicas para nutricionistas ofertadas en los últimos años, seguida de Valencia con siete y Navarra con una. Por otra parte, la oferta de plazas para técnicos superiores en nutrición fue considerablemente mayor, situándose en primer lugar Andalucía, con veintitrés, seguida de Castilla y León con catorce y Navarra con ocho. Respecto al personal contratado en la actualidad por los respectivos servicios de salud, de forma general se observó que la balanza se inclinaba hacia los técnicos superiores en dietética. Destacó la Comunidad Autónoma de Cataluña, con una cantidad de profesionales de ambas
Tabla 3Reconocimiento legal Dietistas-Nutricionistas y Técnicos superiores en dietética y nutrición como personal sanitario.
Tabla 4Número de plazas públicas ofertadas y número de profesionales de Nutrición contratados por las distintas CC. AA. cateorías muy superiores al resto de comunidades, quedando aún pendientes 150 contrataciones de nutricionistas para AP, a realizar durante el transcurso de 2022. Algunas comunidades, como Andalucía, ofrecieron la información sin especificar el ámbito laboral de dichos profesionales, y otras, como las Islas Canarias, Castilla La-Mancha, Galicia, Ceuta y Melilla, no emitieron respuesta alguna a la petición de datos.
Planes Integrales frente a la Obesidad infantil.
En la [Tabla 5] e observa que únicamente nueve comunidades y las dos ciudades autónomas contaban con planes integrales para la OI, si bien se hallaron desactualizados u obsoletos.
Tabla 5Planes Integrales de abordaje de la Obesidad Infantil por comunidades autónomas.
Tabla 6Presupuestos (en euros) destinados a Servicios de Salud, Salud Pública y Promoción de la salud; capital invertido en salud por habitante; partida presupuestaria para OI y gasto sanitario derivado de la OI.
Solo la Región de Murcia poseía este plan relativamente actualizado, con fecha de 2021, seguida del País Vasco (actualizado en 2019), situándose en último lugar Ceuta y Melilla, con un plan que databa de 2007. Por otra parte, Cataluña y Valencia poseían este plan en formato web, estando en constante actualización.
Gasto sanitario y partidas presupuestarias de la Obesidad Infantil.
En la [Tabla 6] se resumen los fondos destinados a los servicios de salud, Salud Pública y Promoción de la Salud en cada comunidad, observando variaciones en el importe por habitante en función del lugar de residencia (Ceuta y Melilla, 1.953,17 euros/habitante, frente a La Rioja, 1.103,52 euros/habitante). Asimismo, se evidenció que solo nueve de las diecisiete comunidades detallaban un importe específico a Salud Pública, y únicamente cuatro de ellas lo hicieron para Promoción de la Salud. En cuanto al gasto sanitario derivado de la Obesidad Infantil, se comprobó que tan solo la Comunidad Autónoma de Cataluña realizaba de forma periódica un análisis económico del gasto sanitario derivado de la obesidad infantil. En relación con una partida presupuestaria específica para luchar contra la OI, no se obtuvieron más respuestas que las dadas por las comunidades autónomas de Castilla y León y La Rioja, que destinaron un importe concreto para programas de alimentación saludable. Islas Canarias, Castilla-La Mancha, Galicia, Ceuta y Melilla no emitieron respuesta alguna.
Personal de Pediatría y Enfermería de Atención Primaria del Sistema Nacional de Salud.
Los datos recopilados en la [Tabla 2] muestran el número de profesionales por categorías y su ratio por cada mil habitantes. La ratio nacional fue de 1,21 para pediatras y 0,65 para enfermería. Entre las comunidades autónomas se apreciaron grandes variaciones para ambas categorías; así, las mayores ratios se encontraron en La Rioja con 1,55 pediatras y 0,89 enfermeros por cada mil habitantes, mientras que en la última posición de la clasificación se situaron las ciudades autónomas de Ceuta y Melilla, con 0,63 pediatras y 0,48 enfermeros por cada mil habitantes.
Reconocimiento legal del personal de Nutrición.
A nivel legislativo, el reconocimiento como personal sanitario de nutricionistas y técnicos en nutrición se encuentra en proceso. Se obtuvieron los decretos o resoluciones correspondientes a cada comunidad autónoma, presentes en la
[Tabla 3]. Se observó que los nutricionistas carecían de reconocimiento como personal sanitario en Andalucía, Asturias, Islas Canarias, Cantabria, Castilla-La Mancha, Extremadura y Galicia. Los técnicos superiores en dietética mostraron una situación similar, existiendo comunidades donde aún no se encontraban reconocidos como personal sanitario de los respectivos servicios de salud. Por otro lado, si bien se hallaron ofertas de empleo público para los técnicos superiores en Dietética y Nutrición
Tabla 2Recuento de Pediatría en AP y ratio por cada 1.000 habitantes de cero-catorce años. Recuento de Enfermería y ratio de enfermeras por 1.000 habitantes en población general. en varias comunidades autónomas, no se pudo localizar los decretos que los reconocían como tal.
Personal de Nutrición en el Sistema Nacional de Salud.
Como se aprecia en la [Tabla 4], la Región de Murcia fue pionera con nueve plazas públicas para nutricionistas ofertadas en los últimos años, seguida de Valencia con siete y Navarra con una. Por otra parte, la oferta de plazas para técnicos superiores en nutrición fue considerablemente mayor, situándose en primer lugar Andalucía, con veintitrés, seguida de Castilla y León con catorce y Navarra con ocho. Respecto al personal contratado en la actualidad por los respectivos servicios de salud, de forma general se observó que la balanza se inclinaba hacia los técnicos superiores en dietética. Destacó la Comunidad Autónoma de Cataluña, con una cantidad de profesionales de ambas
Tabla 3Reconocimiento legal Dietistas-Nutricionistas y Técnicos superiores en dietética y nutrición como personal sanitario.
Tabla 4Número de plazas públicas ofertadas y número de profesionales de Nutrición contratados por las distintas CC. AA. cateorías muy superiores al resto de comunidades, quedando aún pendientes 150 contrataciones de nutricionistas para AP, a realizar durante el transcurso de 2022. Algunas comunidades, como Andalucía, ofrecieron la información sin especificar el ámbito laboral de dichos profesionales, y otras, como las Islas Canarias, Castilla La-Mancha, Galicia, Ceuta y Melilla, no emitieron respuesta alguna a la petición de datos.
Planes Integrales frente a la Obesidad infantil.
En la [Tabla 5] e observa que únicamente nueve comunidades y las dos ciudades autónomas contaban con planes integrales para la OI, si bien se hallaron desactualizados u obsoletos.
Tabla 5Planes Integrales de abordaje de la Obesidad Infantil por comunidades autónomas.
Tabla 6Presupuestos (en euros) destinados a Servicios de Salud, Salud Pública y Promoción de la salud; capital invertido en salud por habitante; partida presupuestaria para OI y gasto sanitario derivado de la OI.
Solo la Región de Murcia poseía este plan relativamente actualizado, con fecha de 2021, seguida del País Vasco (actualizado en 2019), situándose en último lugar Ceuta y Melilla, con un plan que databa de 2007. Por otra parte, Cataluña y Valencia poseían este plan en formato web, estando en constante actualización.
Gasto sanitario y partidas presupuestarias de la Obesidad Infantil.
En la [Tabla 6] se resumen los fondos destinados a los servicios de salud, Salud Pública y Promoción de la Salud en cada comunidad, observando variaciones en el importe por habitante en función del lugar de residencia (Ceuta y Melilla, 1.953,17 euros/habitante, frente a La Rioja, 1.103,52 euros/habitante). Asimismo, se evidenció que solo nueve de las diecisiete comunidades detallaban un importe específico a Salud Pública, y únicamente cuatro de ellas lo hicieron para Promoción de la Salud. En cuanto al gasto sanitario derivado de la Obesidad Infantil, se comprobó que tan solo la Comunidad Autónoma de Cataluña realizaba de forma periódica un análisis económico del gasto sanitario derivado de la obesidad infantil. En relación con una partida presupuestaria específica para luchar contra la OI, no se obtuvieron más respuestas que las dadas por las comunidades autónomas de Castilla y León y La Rioja, que destinaron un importe concreto para programas de alimentación saludable. Islas Canarias, Castilla-La Mancha, Galicia, Ceuta y Melilla no emitieron respuesta alguna.
DISCUSIÓN
Respecto al personal de Pediatría, la ratio media de pediatras se sitúa en 1,21 pediatras por cada mil habitantes de cero-catorce años, lo que equivale aproximadamente a 826,44 habitantes infanto-juveniles por pediatra. Esto coloca a España en una posición positivamente alejada de los valores medios de la Unión Europea, según el artículo de Carrasco Sanz (2011), en el que abordó la situación de los pediatras en Europa y estimó que, de media en Europa, cada pediatra tenía asignado un cupo de 1.250 habitantes
32
. Según el último Informe de Oferta-Necesidad de Especialistas Médicos, de enero de 2022, esta situación positiva se mantendría, e incluso mejoraría, en la Atención Primaria en España entre los años 2021-2035
33
.
En relación con la enfermería de Atención Primaria, la ratio media de enfermeras por cada mil habitantes es de 0,65, lo que equivale a aproximadamente 1.544 habitantes por cada enfermera de Atención Primaria. Una situación similar fue descrita por la Federación de Asociaciones para la Defensa de la Sanidad Pública en 2015, en un análisis de la situación de la enfermería en Atención Primaria en cuanto a profesionales sanitarios en comparación con la Unión Europea. En él, observaron que tanto el número de enfermeras totales respecto al total de sanitarios como el número de enfermeras por cada mil habitantes se encuentra muy por debajo de la media europea, a saber, 57,1% de enfermeras del total de profesionales sanitarios en España frente al 69,2% de enfermeras en Europa y 4,9 enfermeras por cada mil habitantes en España frente a 8,7 en Europa
34
.
Esta situación genera una gran desigualdad en la composición y presencia de la enfermería en los Equipos de Atención Primaria, impidiendo a los profesionales poder abarcar todas las actividades y cuidados que precisa la población y obligándolos a realizar un gran sobresfuerzo para mantener las prestaciones, en concreto, el abordaje de la Obesidad Infantil. Todo ello, podría repercutir negativamente sobre la equidad en el acceso a los cuidados de la población, la calidad de la atención y los derechos laborales del colectivo.
Las diferencias intercomunitarias respecto al personal resultan evidentes en ambas categorías profesionales, encontrando autonomías como Andalucía que, albergando la mayor proporción de población infantil española, en comparación con otras como La Rioja, presenta una ratio de pediatras y enfermeras considerablemente menor: 0,89 frente a 1,55 para pediatría, y 0,58 frente a 0,89 para enfermería, respectivamente. También llama la atención que aquellas comunidades con mayor índice de sobrepeso y obesidad, como Andalucía, Islas Baleares, Ceuta y Melilla o Murcia, tienen las menores ratios, tanto de pediatras como de enfermeros, por debajo de la media nacional.
Por otro lado, la situación de nutricionistas y técnicos superiores en dietética es todavía más desmoralizadora, si se tiene en cuenta que poseen las competencias específicas para el tratamiento nutricional de los pacientes obesos. En España, la contratación y oferta de empleo público realizadas en los últimos años resulta ser anecdótica y no alcanza las exigencias de profesionales marcadas por diferentes organismos. La Unión Europea fijó las necesidades de dietistas-nutricionistas por cama hospitalaria en una por cada cuarenta camas de especialidades, una por cada setenta y cinco camas de pacientes agudos y una por cada cien-ciento cincuenta pacientes de media y larga estancia. Igualmente, la Organización Mundial de la Salud (OMS) aconseja un promedio de un nutricionista por cada cincuenta pacientes
35
, y la Asociación Española de Dietistas y Nutricionistas reclamó, en 2009, la inclusión de nutricionistas en el SNS, solicitando un nutricionista por cada 50.000 tarjetas sanitarias en el ámbito de Atención Primaria, un nutricionista por cada cien camas en Atención Especializada y un nutricionista por cada 500.000 habitantes en el ámbito de la Salud Pública
36
. Además, cabe destacar la variabilidad que existe en la categoría profesional de este colectivo en función del servicio de salud donde ejerza, que se podría traducir en diferentes condiciones laborales, salarios, etc., hecho que pudiera conducir a la no ocupación de dichos puestos en beneficio de otros más atractivos, poniendo en peligro la asistencia sanitaria que precisa la obesidad infantil. Es evidente que España parece no cumplir con estos criterios y, por tanto, dista mucho de las recomendaciones de los organismos oficiales.
Unido a la aparente falta de algunos profesionales considerados cualificados, el presente estudio pone de manifiesto la falta de protocolos estandarizados y actualizados para el abordaje de la OI. En la actualidad, ocho de las comunidades autónomas españolas carecen de planes o programas específicos, o al menos estos no son accesibles, y de las diez que sí los poseen, solo dos cuentan con sus programas en formato web, estando el resto obsoletos o no actualizados. Por su parte, los presupuestos generales para 2022 de las Islas Canarias contemplan, en su disposición adicional trigésimo octava, el compromiso de elaborar el Plan contra la Obesidad Infantil 2022-2026 y dotarlo de los recursos necesarios.
En cuanto a los aspectos económicos de la Obesidad Infantil, reflejan que queda mucho por hacer. Ninguna comunidad autónoma, menos una, cuantifica el gasto derivado de OI. Cataluña es la única comunidad autónoma que, de forma aislada, realiza un análisis periódico del gasto sanitario derivado de la OI, siendo alarmantes los resultados del último informe al que se tuvo acceso. Una persona menor de dieciocho años con OI supone un coste adicional de 153,44 euros/persona y año, es decir, teniendo en cuenta la población que se encuentra en ese rango de edad supone un total de 13.739.080 euros/año. Por ello, resultaría conveniente para el resto de las comunidades realizar un esfuerzo en conocer, de una forma tangible, las consecuencias económicas de esta patología en su territorio concreto.
Por otra parte, casi todas las comunidades autónomas refieren no disponer de una partida presupuestaria específica para la OI. Se encuentra poca información sobre los diversos programas de salud incluidos en las partidas presupuestarias destinadas a Salud Pública y Promoción de la Salud. En los presupuestos generales de cada comunidad autónoma no se desglosan las partidas presupuestarias, quedando la responsabilidad de repartir dichos fondos a los distintos organismos. Además, ese reparto no puede consultarse de forma libre ni está reflejado en los correspondientes sitios web, siendo una limitación del presente trabajo.
Para finalizar, es relevante mencionar otras limitaciones del estudio. Así, parte de la información de carácter público que se publica en este trabajo no está del todo disponible ni es fácilmente accesible para la ciudadanía o profesionales. Fue necesario solicitar un certificado/firma electrónica para poder contactar con las distintas consejerías de Salud y obtener dicha información. Así mismo, algunas comunidades autónomas no dieron respuesta a las solicitudes efectuadas, por lo que faltarían datos para conocer la situación real de nuestro país respecto a los indicadores analizados. A pesar de que España cuenta con la Ley 19/2013, de 9 de diciembre, de transparencia, acceso a la información pública y buen gobierno, todavía queda mucho por avanzar en este ámbito. Por otra parte, los datos de enfermería son de carácter general, no específicos de población pediátrica. Con respecto a las partidas presupuestarias, aquellas destinadas a Promoción de la Salud solo servirían para estimar de forma sucinta la inversión en prevención de la OI, ya que en esos Servicios, además de la OI, se abordan diversos temas como programas de cribados de cáncer, prevención del VIH, ITS, tabaquismo, etc.
Como conclusión general, podemos manifestar que, a nivel intercomunitario, el abordaje de la obesidad infantil parece variar de forma considerable para la mayoría de los indicadores analizados. Por ello, sería recomendable que los responsables de gestión sanitaria a todos los niveles (nacional, autonómico y local) encauzaran sus esfuerzos en homogenizar este abordaje, con el fin de mejorar la calidad asistencial e igualar las oportunidades de prevención y tratamiento en todo el ámbito nacional.
Sirviendo este trabajo como punto de partida, en próximas investigaciones se debería profundizar sobre estos mismos indicadores y analizar otros de igual importancia, como la situación de otros profesionales con competencias sobre la OI (endocrinólogos, psicólogos, técnicos superiores en Nutrición y Control de Alimentos, etc.), analizar el entorno de la población infantil (enfermeros escolares, escuelas promotoras de salud, análisis de instalaciones deportivas disponibles, ciudades adscritas a la Red de Ciudades Saludables), etc. |
Title: Inclusion of phase III clinical trial costs in health economic evaluations | Body:
Introduction
Phase III clinical trials typically compare a new therapeutic entity (i.e., a new chemical or biological drug) to the standard of care and aim to confirm its efficacy. These trials usually enroll several hundred patients [7, 13, 14]. Phase III clinical trials are the primary drivers of research and development (R&D) costs [4, 12, 17]. However, there is substantial variation in published estimates of the costs of phase III trials. For example, Moore et al. [14] and EvaluatePharma [7] report median costs of US-$19 and US-$127 million, respectively. The costs of phase III trials primarily involve administrative staff (20%), clinical procedures (20%), clinical staff (15%), site monitoring (14%), site retention (11%), and central laboratory (7%) [17, 18].
Protocol-driven costs and activities contribute to the costs of phase III clinical trials and can be allocated to the various cost categories. These costs are necessary to complete the protocol treatment but are typically not incurred outside of the trial, in the real world [8]. Only purely pragmatic trials do not require consideration of protocol-driven costs because they are not incurred.
Protocol-driven activities can be categorized into the following components: (i) activities to enhance patient adherence to medication, (ii) activities for the diagnosis and treatment of conditions that would have remained undetected in clinical practice, (iii) activities for extra testing and data collection, and (iv) activities for quality assurance [9]. Since these protocol-driven activities impact health outcomes and patient utility, they cannot be excluded from a cost-effectiveness analysis (CEA) conducted alongside a clinical trial for the sake of consistency [6, 8]. Despite this, anecdotal evidence suggests that many authors implicitly exclude protocol-driven costs by not addressing them. Authors may also intentionally exclude them, assuming that they are the same in both arms and therefore cancel out, or assuming that they are not incurred in clinical practice. The current practice of excluding protocol-driven costs may also be influenced by the desire to keep the analysis simple or, in cases of conflicts of interest, to demonstrate a favorable incremental cost-effectiveness ratio (ICER). Additionally, current practices may be influenced by the recommendations of international guidelines on economic evaluations. The few guidelines that have explicitly addressed protocol-driven costs either suggest categorical exclusion [3] or exclusion if they do not appear to reflect clinical practice or the target population [2].
Among the various protocol-driven cost components, only the costs to improve patient adherence may be excludable under certain conditions, for example, when it seems appropriate to dichotomize the clinical outcome based on the degree of adherence [8]. As a general rule, all other protocol-driven costs need to be included because the cost and utility impact of the underlying protocol-driven activities cannot be easily separated [9]. The cost and utility impact of protocol-driven activities only cancel out from the ICER under specific conditions (see Methods).
This study aims to determine the change in the ICER when including the costs of protocol-driven expenses. Using a de novo cost-effectiveness model and publicly available data on clinical trial costs and activities, the analysis will be undertaken from both societal and payer perspectives.
Methods
Background
In the following section, I will refer to trials with two arms for simplicity. However, it is important to note that the considerations also apply to trials with more than two arms. Furthermore, the methodological approach is not limited to phase III trials but is also applicable to pivotal phase I and II trials.
While costs and utility from protocol activities may be the same in both arms and cancel out in the incremental cost calculation, this occurs only under restrictive conditions. In reality, several situations exist that violate this condition. One such situation is early treatment termination, where withdrawal from the study intervention occurs due to adverse events, subject request, physician discretion, or death. This can result in a significant difference in the treatment (and observational) period between the arms. Additionally, unanticipated clinical events or unevenly distributed adverse events between treatment arms can lead to differences in disutility and data collection costs.
Moreover, protocol-driven activities can influence the absolute and relative risk reduction of the intervention compared to the control, even when protocol-driven activities are the same in both arms. This can result in the same costs but different patient utilities. For example, higher physician motivation in both arms can amplify the treatment effect, while a lack of motivation in both arms can diminish the treatment effect (in the sense of an interaction effect). Conversely, the same adherence measure may lead to a smaller incremental treatment effect if the control arm shows a larger relative improvement due to adherence measures (e.g., in the case of vitamin K antagonists) or if protocol-driven activities improve treatment of unrelated diseases or concomitant treatments in both arms, thus reducing the relative effectiveness of the investigational compound. Notably, since adherence is generally lower in real-world settings, under certain conditions, a new drug might show a greater additional health benefit than observed in the trial, contrary to conventional expectations [8].
The transferability of incremental costs and utility to the real world is also influenced by other differences between the trial setting and the real world, such as scale economies, capacity utilization, and hospital types [8]. However, these aspects are not the focus of this study.
If protocol-driven costs cannot be assumed to cancel out due to the conditions mentioned earlier, they need to be included in the ICER unless the cost and utility impact of certain protocol-driven activities can be separated [8]. However, the latter condition only applies to resources aimed at improving patient adherence under certain circumstances [8] and still requires the availability of a detailed account of protocol-driven activities. If costs to improve patient adherence cannot be separated, then all protocol-driven costs need to be included. In other words, although phase III trial costs are borne by the manufacturer, the trial resources contribute to patient utility demonstrated in the trial and would thus need to be covered by health insurance to achieve the same utility gain in the real world. Therefore, phase III trial costs are relevant from a payer perspective.
A comprehensive understanding of protocol-driven activities is crucial for accurately calculating protocol-driven costs. If a detailed account of these activities is unavailable, aggregated information on protocol-driven costs can be obtained from phase III trial costs. However, this approach necessitates identifying the portion of phase III trial costs that are protocol-driven. It is challenging to categorically reject any category of phase III trial costs mentioned by Sertkaya et al. [17, 18] as not being protocol-driven because all categories can at least partially have positive direct or indirect impacts on patient utility. For instance, administrative staffing costs may indirectly affect patient utility through better trial site management. Similarly, the need to prepare reports for institutional review boards may provide an incentive to improve patient care.
However, not all phase III trial costs can be deemed protocol-driven. For example, clinical staff time spent on general administrative duties that are not specific to the trial, such as completing routine paperwork, does not directly influence clinical outcomes or patient utility and therefore may not be considered protocol-driven. Another example is the time clinical staff spend administering a drug, which may also be required in the real world. Thus, including all phase III trial costs in the ICER and adding them to costs incurred in the real world could result in overestimation and double-counting. This holds from both societal and payer perspectives. Specifically, from a payer perspective, double-counting can occur in relation to activities that are already covered by health insurance in real-world applications. Therefore, to avoid double-counting from a payer perspective, this study includes all phase III trial costs that positively impact patient utility (because the insurer seeks to obtain and reimburse the utility gain demonstrated in the trial) but excludes phase III protocol-driven costs assumed to be covered by health insurance in a real-world application.
From a societal perspective, valuing resources based on true opportunity costs is an appropriate method [1]. This requires considering capital costs.
Methodology
To capture the direct and indirect impact of phase III trial costs on patient utility, the analysis begins by excluding costs that are unrelated to patient utility from the overall phase III trial costs. Next, the analysis calculates the per-patient phase III trial cost (out-of-pocket) by dividing the total phase III trial cost by the number of patients enrolled. This cost is then divided by the per-patient trial participation period. However, the phase III trial length, which begins before any patients are enrolled and extends beyond the last patient’s follow-up, is not used because the denominator of the ICER refers to the health benefits generated during the phase of trial participation.
In a low-cost scenario, the time from the first visit after initial contact to the start of the trial is included, as protocol-driven activities begin before official trial enrollment and can influence patient utility. For example, the time taken by patients to review trial information, discuss it with their healthcare provider or trial staff, and make an informed decision is considered. Potential participants are also screened to determine if they meet the eligibility criteria specified in the trial protocol. The screening process typically involves medical assessments, laboratory tests, and sometimes genetic testing or imaging studies. Factors such as a washout period for certain medications or certain baseline measurements, can extend the time between obtaining consent and enrollment.
Finally, I multiply the estimated cost per unit of time by the additional trial-based observational period resulting from differential early treatment termination. In cases where a drug significantly affects mortality, additional survival time is used instead, even if the study treatment is not continued during the added survival time. This is because protocol-driven costs are incurred independently of ongoing treatment. For the period of “normal” life-years, it is assumed that the costs of phase III clinical trials cancel out due to the lack of published information on differences in protocol-driven activities. From a societal perspective, I apply the ratio of capitalized to out-of-pocket phase III costs as an additional multiplier.
Data
While company-specific data on the trial and indication in question would be most appropriate for the analysis, they are rarely available for confidentiality reasons. Therefore, this study uses industry averages across indications and companies. There is considerable variation in reported average clinical trial costs and the average number of trial participants in the literature. Median estimates of trial costs range from US-$19.0 million [14] to US-$200 million [4] based on an ad-hoc literature review. Estimates by EvaluatePharma [7] and Sertkaya et al. [18], which are US-$127 million (median) and US-$20 million, respectively, fall in between. It is important to note that these estimates do not include capital costs and costs of failures. Additionally, the estimates pertain to a single phase III trial, whereas regulatory agencies (e.g., the European Medicines Agency) usually require at least two successful phase III trials. However, the costs of failures and confirmatory trials are not considered in this analysis, as they do not directly contribute to patient utility. Nonetheless, confirmatory trials may still reduce uncertainty regarding the added benefit, potentially leading to a utility gain.
Regarding trial size and duration, the study by DiMasi et al. [4] lacks information on mean and average trial size. Therefore, the study by EvaluatePharma [7] was used as an upper bound of clinical trial costs. According to the latter, the median estimates for orphan and non-orphan drugs are $99 million and $150 million, respectively, with an average cost across all trials of $163 million. The study includes “all new drug products entering phase III” from January 1, 2000, until 2015. Trial costs are higher than those reported by Moore et al. [14], which aligns with the smaller share (33%) of orphan-drug trials in EvaluatePharma’s study [7], as orphan-drug trials tend to have lower costs. It is worth noting that EvaluatePharma’s analysis does not specify the type of costs included (e.g., drug manufacturing costs).
The study by Moore et al. [14], which serves as a lower bound, includes pivotal trials for 59 new therapeutic entities approved in 2015/16 by the United States Food and Drug Administration, encompassing orphan drugs (46%) and biological drugs (31%). Notably, there is considerable variation in trial costs based on the therapeutic area, ranging from $9 million for “other” diseases to $157 million for cardiovascular diseases. The authors acknowledge that their estimates do not include costs borne by the sponsor, such as drug manufacturing or supervision of the contract research organization. Consequently, their study may have underestimated true costs.
In phase III clinical trials, a substantial portion of the costs directly impact patient utility due to the extended duration and intensive nature of these trials. These costs encompass direct medical expenses for treatment administration, diagnostic tests, and follow-up visits, as well as patient care costs for managing adverse effects and hospitalizations. In contrast, other costs such as patient recruitment and retention, some Clinical Research Associate (CRA) tasks, site recruitment and retention, administrative staff, site monitoring, data collection/management/analysis, Institutional Review Board (IRB) approvals/amendments, and Source Data Verification (SDV) are essential for the administrative and operational aspects of the trial but do not directly influence patient care or outcomes. While these costs indirectly benefit patient utility by ensuring valid and safe outcomes from the trial, they are not directly related to patient utility.
It is estimated that direct patient-related costs constitute 30–70% of the total trial costs. The lower end of this range (30%) represents trials with limited patient interaction, while the higher end (70%) reflects trials with extensive patient involvement. The higher estimate (70%) is applied in the higher bound scenario, indicating trials with significant patient engagement and interaction, while the lower estimate (30%) is used in the lower bound scenario, indicating trials with limited patient contact.
Regarding trial duration, EvaluatePharma [7] reported a median length of 2.88 years, although it is unclear whether this duration refers to the per-patient participation period or the overall trial duration (which begins before any patients are enrolled and extends beyond the last patient’s follow-up). Moore et al. [14] did not report an average or median length but stated that 64.5% of trials had a duration of 26 weeks or fewer, indicating that the median duration was less than 26 weeks. Most likely, in this case the reported duration refers to the per-patient participation period. In the low-cost scenario, a 4-week period was used to account for protocol-driven activities starting before trial enrollment. A 4-week pre-enrollment period positively can impact patient utility by conducting comprehensive screenings, managing potential health issues, and providing patient education, thereby optimizing patient readiness and engagement in the trial.
Regarding trial size, the analysis by Moore et al. [14] reported a median number of 488 patients. A similar estimate of 347 phase III trial patients was provided by Martin et al. [13]. In contrast, EvaluatePharma [7] reported a considerably higher median number of 921 patients (with an average of 2633).
To determine the costs from a societal perspective, the analysis considered the ratio of capitalized phase III trial cost (66.4 million) to out-of-pocket phase III trial cost (54.0 million) per investigational compound reported by DiMasi et al. [4] (66.4/54 = 1.23). Both costs account for the probability of failure in phase I and II trials.
Background
In the following section, I will refer to trials with two arms for simplicity. However, it is important to note that the considerations also apply to trials with more than two arms. Furthermore, the methodological approach is not limited to phase III trials but is also applicable to pivotal phase I and II trials.
While costs and utility from protocol activities may be the same in both arms and cancel out in the incremental cost calculation, this occurs only under restrictive conditions. In reality, several situations exist that violate this condition. One such situation is early treatment termination, where withdrawal from the study intervention occurs due to adverse events, subject request, physician discretion, or death. This can result in a significant difference in the treatment (and observational) period between the arms. Additionally, unanticipated clinical events or unevenly distributed adverse events between treatment arms can lead to differences in disutility and data collection costs.
Moreover, protocol-driven activities can influence the absolute and relative risk reduction of the intervention compared to the control, even when protocol-driven activities are the same in both arms. This can result in the same costs but different patient utilities. For example, higher physician motivation in both arms can amplify the treatment effect, while a lack of motivation in both arms can diminish the treatment effect (in the sense of an interaction effect). Conversely, the same adherence measure may lead to a smaller incremental treatment effect if the control arm shows a larger relative improvement due to adherence measures (e.g., in the case of vitamin K antagonists) or if protocol-driven activities improve treatment of unrelated diseases or concomitant treatments in both arms, thus reducing the relative effectiveness of the investigational compound. Notably, since adherence is generally lower in real-world settings, under certain conditions, a new drug might show a greater additional health benefit than observed in the trial, contrary to conventional expectations [8].
The transferability of incremental costs and utility to the real world is also influenced by other differences between the trial setting and the real world, such as scale economies, capacity utilization, and hospital types [8]. However, these aspects are not the focus of this study.
If protocol-driven costs cannot be assumed to cancel out due to the conditions mentioned earlier, they need to be included in the ICER unless the cost and utility impact of certain protocol-driven activities can be separated [8]. However, the latter condition only applies to resources aimed at improving patient adherence under certain circumstances [8] and still requires the availability of a detailed account of protocol-driven activities. If costs to improve patient adherence cannot be separated, then all protocol-driven costs need to be included. In other words, although phase III trial costs are borne by the manufacturer, the trial resources contribute to patient utility demonstrated in the trial and would thus need to be covered by health insurance to achieve the same utility gain in the real world. Therefore, phase III trial costs are relevant from a payer perspective.
A comprehensive understanding of protocol-driven activities is crucial for accurately calculating protocol-driven costs. If a detailed account of these activities is unavailable, aggregated information on protocol-driven costs can be obtained from phase III trial costs. However, this approach necessitates identifying the portion of phase III trial costs that are protocol-driven. It is challenging to categorically reject any category of phase III trial costs mentioned by Sertkaya et al. [17, 18] as not being protocol-driven because all categories can at least partially have positive direct or indirect impacts on patient utility. For instance, administrative staffing costs may indirectly affect patient utility through better trial site management. Similarly, the need to prepare reports for institutional review boards may provide an incentive to improve patient care.
However, not all phase III trial costs can be deemed protocol-driven. For example, clinical staff time spent on general administrative duties that are not specific to the trial, such as completing routine paperwork, does not directly influence clinical outcomes or patient utility and therefore may not be considered protocol-driven. Another example is the time clinical staff spend administering a drug, which may also be required in the real world. Thus, including all phase III trial costs in the ICER and adding them to costs incurred in the real world could result in overestimation and double-counting. This holds from both societal and payer perspectives. Specifically, from a payer perspective, double-counting can occur in relation to activities that are already covered by health insurance in real-world applications. Therefore, to avoid double-counting from a payer perspective, this study includes all phase III trial costs that positively impact patient utility (because the insurer seeks to obtain and reimburse the utility gain demonstrated in the trial) but excludes phase III protocol-driven costs assumed to be covered by health insurance in a real-world application.
From a societal perspective, valuing resources based on true opportunity costs is an appropriate method [1]. This requires considering capital costs.
Methodology
To capture the direct and indirect impact of phase III trial costs on patient utility, the analysis begins by excluding costs that are unrelated to patient utility from the overall phase III trial costs. Next, the analysis calculates the per-patient phase III trial cost (out-of-pocket) by dividing the total phase III trial cost by the number of patients enrolled. This cost is then divided by the per-patient trial participation period. However, the phase III trial length, which begins before any patients are enrolled and extends beyond the last patient’s follow-up, is not used because the denominator of the ICER refers to the health benefits generated during the phase of trial participation.
In a low-cost scenario, the time from the first visit after initial contact to the start of the trial is included, as protocol-driven activities begin before official trial enrollment and can influence patient utility. For example, the time taken by patients to review trial information, discuss it with their healthcare provider or trial staff, and make an informed decision is considered. Potential participants are also screened to determine if they meet the eligibility criteria specified in the trial protocol. The screening process typically involves medical assessments, laboratory tests, and sometimes genetic testing or imaging studies. Factors such as a washout period for certain medications or certain baseline measurements, can extend the time between obtaining consent and enrollment.
Finally, I multiply the estimated cost per unit of time by the additional trial-based observational period resulting from differential early treatment termination. In cases where a drug significantly affects mortality, additional survival time is used instead, even if the study treatment is not continued during the added survival time. This is because protocol-driven costs are incurred independently of ongoing treatment. For the period of “normal” life-years, it is assumed that the costs of phase III clinical trials cancel out due to the lack of published information on differences in protocol-driven activities. From a societal perspective, I apply the ratio of capitalized to out-of-pocket phase III costs as an additional multiplier.
Data
While company-specific data on the trial and indication in question would be most appropriate for the analysis, they are rarely available for confidentiality reasons. Therefore, this study uses industry averages across indications and companies. There is considerable variation in reported average clinical trial costs and the average number of trial participants in the literature. Median estimates of trial costs range from US-$19.0 million [14] to US-$200 million [4] based on an ad-hoc literature review. Estimates by EvaluatePharma [7] and Sertkaya et al. [18], which are US-$127 million (median) and US-$20 million, respectively, fall in between. It is important to note that these estimates do not include capital costs and costs of failures. Additionally, the estimates pertain to a single phase III trial, whereas regulatory agencies (e.g., the European Medicines Agency) usually require at least two successful phase III trials. However, the costs of failures and confirmatory trials are not considered in this analysis, as they do not directly contribute to patient utility. Nonetheless, confirmatory trials may still reduce uncertainty regarding the added benefit, potentially leading to a utility gain.
Regarding trial size and duration, the study by DiMasi et al. [4] lacks information on mean and average trial size. Therefore, the study by EvaluatePharma [7] was used as an upper bound of clinical trial costs. According to the latter, the median estimates for orphan and non-orphan drugs are $99 million and $150 million, respectively, with an average cost across all trials of $163 million. The study includes “all new drug products entering phase III” from January 1, 2000, until 2015. Trial costs are higher than those reported by Moore et al. [14], which aligns with the smaller share (33%) of orphan-drug trials in EvaluatePharma’s study [7], as orphan-drug trials tend to have lower costs. It is worth noting that EvaluatePharma’s analysis does not specify the type of costs included (e.g., drug manufacturing costs).
The study by Moore et al. [14], which serves as a lower bound, includes pivotal trials for 59 new therapeutic entities approved in 2015/16 by the United States Food and Drug Administration, encompassing orphan drugs (46%) and biological drugs (31%). Notably, there is considerable variation in trial costs based on the therapeutic area, ranging from $9 million for “other” diseases to $157 million for cardiovascular diseases. The authors acknowledge that their estimates do not include costs borne by the sponsor, such as drug manufacturing or supervision of the contract research organization. Consequently, their study may have underestimated true costs.
In phase III clinical trials, a substantial portion of the costs directly impact patient utility due to the extended duration and intensive nature of these trials. These costs encompass direct medical expenses for treatment administration, diagnostic tests, and follow-up visits, as well as patient care costs for managing adverse effects and hospitalizations. In contrast, other costs such as patient recruitment and retention, some Clinical Research Associate (CRA) tasks, site recruitment and retention, administrative staff, site monitoring, data collection/management/analysis, Institutional Review Board (IRB) approvals/amendments, and Source Data Verification (SDV) are essential for the administrative and operational aspects of the trial but do not directly influence patient care or outcomes. While these costs indirectly benefit patient utility by ensuring valid and safe outcomes from the trial, they are not directly related to patient utility.
It is estimated that direct patient-related costs constitute 30–70% of the total trial costs. The lower end of this range (30%) represents trials with limited patient interaction, while the higher end (70%) reflects trials with extensive patient involvement. The higher estimate (70%) is applied in the higher bound scenario, indicating trials with significant patient engagement and interaction, while the lower estimate (30%) is used in the lower bound scenario, indicating trials with limited patient contact.
Regarding trial duration, EvaluatePharma [7] reported a median length of 2.88 years, although it is unclear whether this duration refers to the per-patient participation period or the overall trial duration (which begins before any patients are enrolled and extends beyond the last patient’s follow-up). Moore et al. [14] did not report an average or median length but stated that 64.5% of trials had a duration of 26 weeks or fewer, indicating that the median duration was less than 26 weeks. Most likely, in this case the reported duration refers to the per-patient participation period. In the low-cost scenario, a 4-week period was used to account for protocol-driven activities starting before trial enrollment. A 4-week pre-enrollment period positively can impact patient utility by conducting comprehensive screenings, managing potential health issues, and providing patient education, thereby optimizing patient readiness and engagement in the trial.
Regarding trial size, the analysis by Moore et al. [14] reported a median number of 488 patients. A similar estimate of 347 phase III trial patients was provided by Martin et al. [13]. In contrast, EvaluatePharma [7] reported a considerably higher median number of 921 patients (with an average of 2633).
To determine the costs from a societal perspective, the analysis considered the ratio of capitalized phase III trial cost (66.4 million) to out-of-pocket phase III trial cost (54.0 million) per investigational compound reported by DiMasi et al. [4] (66.4/54 = 1.23). Both costs account for the probability of failure in phase I and II trials.
Results
The per-patient trial costs, calculated by dividing the phase III trial costs by the number of patients enrolled, range from approximately $17,520 (based on Moore et al. [14]) to $61,907 (based on EvaluatePharma [7]). Hence, the variation in per-patient trial costs is smaller than that of total trial costs. These costs were then multiplied by the percentage of direct patient-related costs, resulting in the values shown in Step 1 of Table 1.
Table 1Calculation of the incremental per-patient phase III trial cost from the perspective of a payer (step 3) and society (step 4). All costs are in US dollarsStepLower boundUpper boundAverage1Calculation of the per-patient phase III trial cost5,25643,33524,2952Normalization to a one-year period9,11186,66947,8903Adjustment to a three-month period2,27821,66711,9724Inclusion of capital costs5,07848,31026,694
To calculate per-patient trial costs over one year, I used an estimate of 0.5 years based on Moore et al. [14], who reported a median per-patient participation period of less than 26 weeks; in the lower bound scenario, a 4-week period was added as per the Methods section. This results in a rather high estimate of approximately $48,000 per patient over a one-year trial period (averaged over the two estimates for per-patient trial costs). Therefore, a treatment that prolongs life, for example, by three months would imply an average phase III cost of $12,000 from a payer perspective that needs to be added to the numerator of the ICER. Refer to Table 1 for estimates in the low-cost and high-cost scenarios. Using the ratio of capitalized to out-of-pocket expected phase III trial cost, which is 1.23, I obtain $27,000 (12,000 + 12,000 × 1.23) per-patient societal costs over a three-month trial period.
Discussion
This study argues for the inclusion of phase III trial costs in the ICER when protocol-driven costs do not cancel out, such as when the investigational compound leads to longer survival. The only exception among the various protocol-driven cost components are the costs to improve patient adherence, which may be excludable under certain conditions but require a simultaneous adjustment of clinical outcomes [8].
This study demonstrates that the impact of phase III trial costs on the ICER is not negligible even when the duration of treatment is extended only within a range of weeks. Phase III trial costs can be of similar magnitude as medication costs. The inclusion of phase III trial costs becomes most significant for decision-makers when the ICER is close to the threshold willingness to pay. However, it is important to note that adding phase III trial costs to the numerator of a conventionally calculated ICER results in double-counting. This is because conventional calculations already consider the portion of phase III trial costs incurred in a standard real-world application. Therefore, when including the estimate of the phase III trial cost of $27,000 (from a societal perspective) in the ICER, the costs of protocol-driven activities covered by health insurance in the real world would need to be excluded (see the Methods section). To account for the latter, a useful approach, at least in some jurisdictions, is to consult the prescribing information of a drug, which specifies the required or recommended drug-related services such as drug application, counseling, monitoring, and testing. Ultimately, for a precise analysis of phase III trial costs specific to a particular drug, company-specific data should be used during the differential treatment and observation period, rather than relying on the averages applied in the example provided.
Importantly, from a societal perspective, when including only the marginal costs of producing and distributing the drug only (according to Garrison et al. [10]), adding phase III trial costs clearly does not result in double-counting of drug costs. Conversely, if the societal perspective also considers dynamic efficiency, which aims to promote innovation, double-counting would occur, as drug prices would then need to reflect the opportunity cost of R&D. From a payer perspective, double-counting does not occur under a value-based pricing (VBP) scheme of drugs, where the “gold standard” for VBP calculations is the ratio of costs to quality-adjusted life-years [15]. This is because, under VBP, the drug price is exogenous to R&D costs and is exclusively based on value by definition. From the payer perspective, value is independent of R&D costs unless the payer (government) invests in later-stage clinical research. In such cases, VBP with an adjustment for R&D costs is justified by subtracting the portion of the value that derives from government-funded R&D costs [15].
A CEA for the duration of a clinical trial can be conducted using primary data collection (referred to as “piggyback” economic evaluation) or decision modeling. Regardless of the approach, protocol-driven costs need to be added in both instances to account for the full impact of the intervention on patient utility.
Our analysis, which focuses on the added treatment period, assumes that during the parallel treatment in both arms, the costs of phase III trials cancel out. However, this may not be true based on the reasons outlined in the Methods section. Additionally, it assumes that the impact of protocol-driven activities on clinical outcomes is the same in both arms during parallel treatment (refer to the Methods section). However, the incremental utility of considering protocol-driven activities may not always be positive; the utility may be larger in the control arm.
Interestingly, this analysis indirectly addresses an ongoing debate among governments, payers, and manufacturers regarding the pricing of new drugs. VBP has become a new paradigm for pricing of new, innovative drugs, making cost-based or cost-plus pricing (which includes pricing based on the profit margin or the profit-to-cost ratio) comparatively less popular, at least in formal pricing exercises. One reason for the lack of interest in cost-based pricing is that it does not incentivize efficient R&D. Nonetheless, decision-makers may still feel that information on the cost of R&D is necessary for transparency and fairer funding decision-making [11]. Moreover, cost considerations remain relevant particularly for the pricing of orphan drugs, as manufacturers often justify high prices by the high R&D expenses incurred to bring these drugs to the market.
However, the cost of bringing a new drug to market is mostly non-transparent [19] and it remains one of the greatest controversies within the pharmaceutical industry. The analysis conducted repeatedly at the Tufts Center for the Study of Drug Development over 25 years, authored by DiMasi, Hansen, and Grabowski, is a well-known and often-cited source. Their most recent estimate (2016) suggests a total cost of $2.6 billion in 2013 dollars to develop a single drug and gain marketing approval. It is important to note that this estimate includes capital costs (i.e., the opportunity cost of developing these drugs) and the costs of failures, which are the major drivers of this estimate. However, the estimate has faced criticism for various reasons, including unrepresentativeness/selection bias (as companies could choose to participate [14], potentially biased data (self-reported by companies), failure to adjust for tax savings for companies, consideration of capital costs, and overestimation of the costs and duration of clinical trials [12]. Light and Warburton [12] concluded that DiMasi et al.’s 2003 estimate overestimated R&D costs by factor 18. Similarly, Prasad and Mailankody [16] suggested a lower estimate of $757 million (including opportunity costs) based on an analysis of cancer drugs produced by companies with no other drugs in the market. However, DiMasi [5] commented on the study by Prasad and Mailankody [16] that the included companies had a higher success rate than the average.
These discrepancies, along with the considerable variation reported in the literature regarding average phase III trial costs and the average number of trial participants discussed earlier, indicate a disincentive for pharmaceutical companies to disclose full information due to the potential competitive disadvantage it may create.
Based on the argument that manufacturers should include costs of protocol-driven activities in cost-effectiveness analyses alongside clinical trials, this study provides a framework for accurate reporting of phase III trial costs. On the one hand, pharmaceutical companies have an incentive to improve the ICER of their products by excluding protocol-driven costs while incorporating the utility gain from protocol-driven activities. On the other hand, there is an incentive for them to overstate the costs of phase III clinical trials under cost-plus pricing. Thus, an inherent conflict arises in the perspective of phase III trial costs between value-based and cost-based pricing. Requiring the inclusion of trial costs can result in a significant increase in the ICER and a corresponding decrease in the value-based drug price. Simultaneously, it raises the cost-plus drug price. By mandating the inclusion of trial-based costs in the ICER, current incentives to overstate R&D/phase III trial costs and understate protocol-driven costs would be mitigated, at least in situations where pricing is based both on value and costs. The drawback of cost-plus pricing, which is the lack of incentive to improve R&D efficiency, would also be alleviated.
In conclusion, this article establishes a connection between two cost components (phase III trial costs and incremental costs) that have historically been considered in isolation to date. This connection arises from the necessity to include the costs of protocol-driven activities in the ICER from both a societal and payer perspective for the sake of consistency. As an unintended consequence, implementing this approach aids in revealing the true R&D cost of pharmaceuticals without imposing a requirement for manufacturers to openly disclose them. |
Title: The vicious cycle of chronic endometriosis and depression—an immunological and physiological perspective | Body:
1
Introduction
Endometriosis stands as one of the commonly encountered benign gynecological conditions in women, where endometrial glands and stroma exhibit extrauterine location, with a prevalence ranging from 6 to 10% among those of reproductive age (1, 2). Aberrant endometrial cells, characterized by genetic polymorphisms and proliferation rather than apoptosis, in response to local signals, lead to disease progression. Additionally, these cells when anomalously displaced into the peritoneal cavity, not only evade peritoneal destruction but also exploit the immediate environment to sustain proliferation in a clonal manner, while normal cells of the individual are systematically removed. Despite its non-malignant character, the inflammatory and erosive nature of the disease contributes to enduring alterations in a woman’s life, manifesting as persistent pelvic pain, dysmenorrhea, dyspareunia, and infertility (3). The disease can lead to additional symptoms such as painful bowel movements or urination, excessive bleeding, fatigue, diarrhea, constipation, bloating and nausea (4, 5). The challenge is exacerbated by the recurrent delay in diagnosis following the manifestation of symptoms and the restricted scope of available intervention strategies. Despite the potential existence of endometriotic lesions in asymptomatic women, a conclusive diagnosis of endometriosis is typically established when the presence of endometrial tissue or lesions is established beyond the confines of the uterus, frequently through surgical means (6). Endometriosis exhibits diverse classifications based on its anatomical location, including superficial peritoneal lesions which is the most common, ovarian endometrioma, deep sub-peritoneal infiltrating endometriosis and adenomyoma, which represents internal endometriosis within the myometrium (7). Endometriotic lesions have been identified in extra-pelvic locations, such as upper abdominal visceral organs, abdominal wall, diaphragm, and pleura, as well as within the nervous system (8). Patients may exhibit various forms concurrently.
The predominant classification method in use is an updated scoring system established by the American Society for Reproductive Medicine. This system is employed to ascertain the stage of endometriosis, denoted by Roman numerals I to IV, which represent the spectrum from ‘minimal’ to ‘severe’. It involves an assessment of type, location, appearance, depth of lesions, and an evaluation of overall extent of disease as well as presence of adhesions (9). However, grading using the ASRM criteria often demonstrates weak correlations of the abundance and location of lesions with the type of lesions, and symptoms of pain reported by patients, when compared to the disease stage. The occurrence of endometriosis in asymptomatic women, along with ambiguous reasons for its manifestation, contributes to varying perspectives on considering endometriosis as a ‘syndrome’ (10). Diagnosis is typically established only when a patient presents with both observable lesions and symptomatic manifestations.
An in-depth understanding of immune imbalance in endometriosis related depression and vice versa may include enhanced immune cell function, altered cytokine and chemokine levels and malfunctioning of regulatory proteins such as growth factors. Major immune cells such as macrophages, neutrophils, dendritic cells (DCs), natural killer cells (NK cells), T cells, and B cells exhibit great importance in the pathogenesis of endometriosis and depression. Increased levels of macrophages were observed in the peritoneal fluid of endometriotic patients (11). Neutrophil to lymphocyte ratio (NLR) was shown to be a clinically relevant indicator of endometriosis and associated outcomes. Increased NLR was also observed in a recent study showing higher numbers of neutrophils in endometriosis subjects (12, 13). Dendritic cells are important antigen presenting cells and in endometriotic patients, peritoneal DCs are found to increase. Furthermore, numbers of immature DCs are found to be greater as compared to mature DCs (14).
Endometriosis is associated with dysfunction in NK cell cytotoxicity and immunomodulation, by tolerating or inhibiting implantation, proliferation, and survival of endometrial cells, impairing their ability to eliminate these cells at ectopic sites (15). This review also sheds light on the role of the adaptive immune response in endometriosis, including helper T and B cells, whose roles remain incompletely understood. Several serum cytokines such as interleukins (IL) IL1β, IL-5, IL-6, IL-7 and IL-12 are involved, and their levels were found to be altered in endometriosis as compared to in healthy women (16). Cytokines IL-6, IL-8, IL-10, TNF-α also have important roles in the development of VEGF, which is involved in the pathogenesis of the disease (17–23).
Chronic stress or chronic depression events can modulate innate and adaptive immune responses with the involvement of enhanced inflammation and lowering the activity of immune protective cells (24). Inflammatory responses can be increased in stress (25). Furthermore, animal studies, showed that administration of proinflammatory cytokines (TNF or IL-1β) affect the central nervous system through decreased motor activity as well as increased social alienation, disturbed sleep patterns, altered appetite, reduced water intake and greater sensitivity to pain (26–28). Immune dysregulation and associated outcomes are the hallmarks of endometriosis. Immune dysregulation has also been shown to cause depression in susceptible individuals and hence may be the primary cause of depression in women with endometriosis.
Women diagnosed with endometriosis exhibit imbalanced immunological states often because of which major lifestyle changes are inevitable (29–31). Endometriosis patients may undergo mental health issues such as depression, physiological stress and anxiety (32). These women may bear day-to-day abdominal pain, painful bowel movements or urination, excessive bleeding, fatigue, diarrhea, constipation, bloating, nausea, fatigue and painful intercourse (4, 5), leading to a stressful life. In chronic cases infertility is very common (3, 33). This review aims to explore the potential links between depression, immunological factors and endometriosis.
1.1
Literature search for the review article
An electronic literature search was meticulously carried out by the authors S.S., M.W.A.K, S.R., K.M., Q.H., and W.A.K., as published by Centini et al. (34). The search team evaluated the existing literature on endometriosis, which included disease identification, symptoms, diagnosis, pathogenesis and immune dysregulations. The search was performed using the online medical MEDLINE database (accessed via PubMed). Terminologies included endometriosis, biomarkers, endometriotic symptoms and diagnosis, gynecological issues in endometriosis, pathogenesis in endometriosis, endometriotic depression. This review includes the most updated published articles as well as original articles which include randomized and non-randomized clinical trials, prospective observational studies, retrospective cohort studies, and case–control studies, review articles, and case reports. The selected articles were further checked for relevance with the aim and objective of the review. The bibliography of the selected articles was thoroughly checked for additional relevant articles. This procedure effectively helped in compiling more relevant, updated and high-quality peer-reviewed articles, providing a nuanced understanding of the specified topics “endometriosis, depression, and their associated immune imbalances.”
1.2
Etiology and incidence
Various physiological factors, including hormonal, metabolic, neurological, and immunological elements, play a role in the processes leading to the manifestation of symptoms. Epidemiological investigations reveal an increased susceptibility to various cancers (ovarian, breast and melanoma), rheumatoid arthritis, asthma and cardiovascular disease among women with endometriosis lesions (10). Endometriosis has familial incidence with heritability of up to 50% (35). It has been reported that having a first degree relative with a severe form of endometriosis raises the risk by up to seven times (36). A study focusing solely on relatives of individuals with endometriosis revealed that 16% of mothers and 22% of sisters of reproductive age had received a surgical diagnosis of endometriosis (36). Genome-wide association studies have found overrepresented single nucleotide polymorphisms (SNPs) in cases of severe disease. Gynecological disorders such as infertility, fibroids, and cancer were found to have overlaps with common SNPs associated with endometriosis, the etiology of which all involve steroid hormones (34, 37–40).
Additionally, five loci significantly associated with endometriosis risk were identified through a meta-analysis of 11 GWAS datasets, which genetically involved sex steroid hormone pathways (41). Irregularities in the role of extracellular matrix protein signaling such as fibronectin (42), laminin (43) and collagen (44) are implicated in abnormal cell migration and adhesion, contributing to fibrosis. Genomic studies have revealed associations between endometriosis and various biological pathways and cellular regulators. Notably, vezatin, a transmembrane adherens junctions’ protein, has been implicated (45), along with vascular endothelial growth factor receptor 2 (VEGFR-2) (46), the mitogen activated protein (MAP) kinase signaling cascade (47), IL1A (48), wingless related integration site (WNT) signaling (49), and steroid metabolism (50). Meta-analysis has highlighted common genetic signatures between migraine and depression in endometriosis, of which depression underscores an association with changes in gut mucosa (51). Additional determinants for endometriosis are low BMI, low birth weight, lower parity, Mullerian abnormalities, early menarche, short menstrual cycles or heavy and prolonged menstrual flow. Scientific evidence indicates variations in prevalence of endometriosis diagnosis across racial and ethnic groups. A systematic review revealed that Asian women exhibited an elevated risk, while Black women demonstrated a reduced risk compared to White women. However, it is plausible that these estimates may be influenced by biases linked to diagnosis and healthcare accessibility (52). The prevalence of endometriosis amongst Asian women of reproductive age is reported to range from 6.8% to as high as 16% (53) (see Table 1).
Table 1
Meta-analysis of genome wide association studies on endometriosis.
No.
Article
Reference
Gene/pathway
1
Gallagher et al., 2019
(37)
WNT4, CDC42, GREB1, ESR1, FSHB
2
Masuda et al., 2020
(38)
GREB 1, LOC730100, PDE1C, TNRC6B
3
Sapkota et al., 2017, 2015
(41, 50)
WNT4, GREB1, ETAA1, ILIA, KDR, ID4, 7p15.2, CDKN2B, VEZT, FN1, CCDC170, SYNE1, 7p12.3, FSHB
1.1
Literature search for the review article
An electronic literature search was meticulously carried out by the authors S.S., M.W.A.K, S.R., K.M., Q.H., and W.A.K., as published by Centini et al. (34). The search team evaluated the existing literature on endometriosis, which included disease identification, symptoms, diagnosis, pathogenesis and immune dysregulations. The search was performed using the online medical MEDLINE database (accessed via PubMed). Terminologies included endometriosis, biomarkers, endometriotic symptoms and diagnosis, gynecological issues in endometriosis, pathogenesis in endometriosis, endometriotic depression. This review includes the most updated published articles as well as original articles which include randomized and non-randomized clinical trials, prospective observational studies, retrospective cohort studies, and case–control studies, review articles, and case reports. The selected articles were further checked for relevance with the aim and objective of the review. The bibliography of the selected articles was thoroughly checked for additional relevant articles. This procedure effectively helped in compiling more relevant, updated and high-quality peer-reviewed articles, providing a nuanced understanding of the specified topics “endometriosis, depression, and their associated immune imbalances.”
1.2
Etiology and incidence
Various physiological factors, including hormonal, metabolic, neurological, and immunological elements, play a role in the processes leading to the manifestation of symptoms. Epidemiological investigations reveal an increased susceptibility to various cancers (ovarian, breast and melanoma), rheumatoid arthritis, asthma and cardiovascular disease among women with endometriosis lesions (10). Endometriosis has familial incidence with heritability of up to 50% (35). It has been reported that having a first degree relative with a severe form of endometriosis raises the risk by up to seven times (36). A study focusing solely on relatives of individuals with endometriosis revealed that 16% of mothers and 22% of sisters of reproductive age had received a surgical diagnosis of endometriosis (36). Genome-wide association studies have found overrepresented single nucleotide polymorphisms (SNPs) in cases of severe disease. Gynecological disorders such as infertility, fibroids, and cancer were found to have overlaps with common SNPs associated with endometriosis, the etiology of which all involve steroid hormones (34, 37–40).
Additionally, five loci significantly associated with endometriosis risk were identified through a meta-analysis of 11 GWAS datasets, which genetically involved sex steroid hormone pathways (41). Irregularities in the role of extracellular matrix protein signaling such as fibronectin (42), laminin (43) and collagen (44) are implicated in abnormal cell migration and adhesion, contributing to fibrosis. Genomic studies have revealed associations between endometriosis and various biological pathways and cellular regulators. Notably, vezatin, a transmembrane adherens junctions’ protein, has been implicated (45), along with vascular endothelial growth factor receptor 2 (VEGFR-2) (46), the mitogen activated protein (MAP) kinase signaling cascade (47), IL1A (48), wingless related integration site (WNT) signaling (49), and steroid metabolism (50). Meta-analysis has highlighted common genetic signatures between migraine and depression in endometriosis, of which depression underscores an association with changes in gut mucosa (51). Additional determinants for endometriosis are low BMI, low birth weight, lower parity, Mullerian abnormalities, early menarche, short menstrual cycles or heavy and prolonged menstrual flow. Scientific evidence indicates variations in prevalence of endometriosis diagnosis across racial and ethnic groups. A systematic review revealed that Asian women exhibited an elevated risk, while Black women demonstrated a reduced risk compared to White women. However, it is plausible that these estimates may be influenced by biases linked to diagnosis and healthcare accessibility (52). The prevalence of endometriosis amongst Asian women of reproductive age is reported to range from 6.8% to as high as 16% (53) (see Table 1).
Table 1
Meta-analysis of genome wide association studies on endometriosis.
No.
Article
Reference
Gene/pathway
1
Gallagher et al., 2019
(37)
WNT4, CDC42, GREB1, ESR1, FSHB
2
Masuda et al., 2020
(38)
GREB 1, LOC730100, PDE1C, TNRC6B
3
Sapkota et al., 2017, 2015
(41, 50)
WNT4, GREB1, ETAA1, ILIA, KDR, ID4, 7p15.2, CDKN2B, VEZT, FN1, CCDC170, SYNE1, 7p12.3, FSHB
2
Clinical symptoms and diagnosis
Medical diagnosis of endometriosis is often difficult and delayed due to a lack of awareness and knowledge of the condition among healthcare professionals and limited understanding of its pathogenesis (35). Further, the complex nature of the disease as well as its manifestations, varying from asymptomatic to its evident phenotypes, add to a complicated diagnosis (3). Pelvic pain stands out as the primary indicator of endometriosis, manifesting in various forms such as dysmenorrhea, dyspareunia, or chronic pelvic pain (54). The intensity of pelvic pain is correlated with type of lesion classification and disease progression (55). Additional symptoms which are commonly found in individuals with the disease include abdominal discomfort, bloating, menometrorrhagia, lower back pain, and fatigue (3). Surgery remains the main method of obtaining a conclusive histopathological diagnosis, with Laparoscopy considered the gold standard diagnostic test. However, prevailing guidelines advocate for a non-surgical diagnostic approach reliant upon symptomatology, physical examination outcomes, and imaging findings. This strategy aims to mitigate delays in commencing treatment. In female patients undergoing surgical interventions, more than 50% will necessitate subsequent surgical interventions within a five-year timeframe (1). Numerous hormonal medical interventions are associated with adverse effects (56).
Research indicates that the greatest prevalence of endometriosis is observed between 25 and 29 years of age (57). However, there is often a significant diagnostic delay, with the average time from the onset of first symptoms to final diagnosis ranging from 4.4 years in the United States to 10.4 years in Germany (58, 59). The primary reasons for this delay may include intermittent use of contraceptives, misdiagnosis, and self-treatment of pain with over-the-counter painkillers. These findings align with the presented study’s results, which report a mean age of 26.9 years at the time of disease recognition and symptom onset ranging from 18.8 years for dysmenorrhea to 24.0 years for dyspareunia. This underscores the importance of early and accurate diagnosis to mitigate prolonged suffering and improve patient outcomes.
Central sensitization (CS) is a type of nociplastic pain characterized by a central nervous system response to peripheral nociceptive or neuropathic triggers, often seen in patients with chronic pains (60). Symptoms of CS include chronic pain, allodynia (pain from stimuli that do not usually provoke pain), hypersensitivity, hyperalgesia (increased sensitivity to painful stimuli), and mood changes (anxiety, panic attacks, and depression) (61–63). A Central Sensitization Inventory (CSI) score of 40 or higher has been effective in identifying CS in women with chronic pelvic pain, including those with endometriosis (62, 64). In a recent study, it has been showed that in endometriosis patients, CS can significantly worsen pain symptoms and is prevalent particularly among those with moderate to severe chronic pelvic pain, involvement of the posterolateral parametrium, high tone pelvic floor (HTF), and comorbid with central sensitivity syndromes like irritable bowel syndrome, anxiety, migraines or severe headaches (65). Therefore, recognizing and addressing CS is crucial for early and accurate diagnosis to mitigate prolonged suffering and improve endometriosis patient outcomes.
The need for reliable noninvasive biomarkers for the diagnosis, potential treatment response and disease prognosis persists as a significant unaddressed requirement. While certain types of endometriosis diagnosis can be expedited through imaging modalities, progress towards validating a dependable noninvasive blood test has been sluggish thus far (66). Other non-surgical diagnostic methods such as transvaginal ultrasonography and magnetic resonance imaging (MRI) have enabled identification of deep endometriosis types (67).
3
Hormones
Female sex hormones, estrogen and progesterone, play critical roles in the pathogenesis of endometriosis. Increased levels of estrogen with decreased progesterone receptor pathway signaling are implicated in disease pathogenesis (Figure 1).
Figure 1
Pathogenesis of endometriosis, immune dysregulation, and mental health dysfunction.
3.1
Enhanced estrogen production
Elevated estrogen production consistently emerges as a dysregulated endocrine characteristic in eutopic endometrium and ectopic endometriotic lesions. The predominant estrogen, estradiol (E2), has a pivotal role in the post-menstrual endometrial regeneration (7). Both proliferation of endothelial cells and the re-establishment of microvasculature in this layer are orchestrated by E2, through interactions with its estrogen receptors (ERs), ERα and ERβ (68). Distinct intracellular localizations of the ERs lead to intricately coordinated and precisely regulated estrogen (E2) signaling pathways, which govern cellular proliferation, differentiation, and apoptosis. Endometrial E2 predominantly originates from the ovaries and, to a lesser extent, from adipocytes and the adrenal gland, transported to tissues through the circulatory system (69). Aromatase P450 (aromP450) is a rate limiting hormone in estrogen biosynthesis that catalyzes the conversion of androgens to estrogen, with subsequent transformation into E2 facilitated by 17β-hydroxysteroid dehydrogenase type 1 (17βHSDT1) (70). Prostaglandin E2 (PGE2) synthesis step is initiated by the rate-limiting cyclooxygenase-2 (COX-2) enzyme, acting on arachidonic acid, inducing dose-dependent aromP450 synthesis in endometriotic lesions (69). In healthy women’s endometrium, aromP450 activity is negligible (71). Cell-specific and menstrual cycle phase-dependent expression of receptors that bind to estrogens (ERα, ERβ and GPER1), androgens, progestins and glucocorticoids are observed in the healthy endometrium (72). However, both the endometrium and ectopic endometriotic lesions in women with endometriosis exhibit significantly elevated levels of aromP450, facilitating local E2 production. The capacity of the lesion to independently generate E2, coupled with the synthesis of the necessary enzymes, may enhance intraperitoneal endometriotic tissue implantation (56). It has been observed that the expression of ERβ is extraordinarily higher in stromal cells of women with endometriosis as compared to ERα. It is suggested that rather than just estrogen dependent, endometriosis should be considered steroid-dependent. Thus, the abnormal functioning of estrogen, its receptors, and estradiol synthesis-related enzymes is closely associated with endometriosis.
3.2
Progesterone resistance
Progesterone is the dominant hormone in the secretory phase of the menstrual cycle, where it counteracts effects of estrogen and prepares the uterus for supporting an embryo. It plays a decisive role in facilitating the differentiation of endometrial epithelial and stromal cells. Suppressed progesterone receptor (PR) expression, a characteristic feature of endometriosis, leads to resistance to progesterone and contributes to the development of severe endometriosis conditions (Figure 1). Endometriotic stromal cells demonstrate resistance to progesterone with reduced responsiveness to hormone (73). This diminished communication between stromal and epithelial cells leads to a subsequent elevation in the expression of ERβ within endometriotic lesions and stromal cells (1). PR-A and PR-B are the two functionally distinct receptor isoforms which interact with progesterone. In mice, the absence of PR-A results in abnormalities in the ovary and uterus, while the lack of PR-B has negligible impact on their function (74). Notably, the transcript for both receptor isoforms originate from the same gene, with PR-A having a shorter transcript than PR-B. This structure allows transrepression of PR-B and other nuclear receptors (75). Lesions in endometriosis exhibit a deficiency in PR-B expression, with minimal expression of the transrepressor PR-A, offering molecular substantiation for progesterone resistance. Subsequently, this leads to elevated local levels of estrogen (E2) as progesterone fails to stimulate 17β-hydroxysteroid dehydrogenase type 2 (17β-HSDT2) (69).
3.1
Enhanced estrogen production
Elevated estrogen production consistently emerges as a dysregulated endocrine characteristic in eutopic endometrium and ectopic endometriotic lesions. The predominant estrogen, estradiol (E2), has a pivotal role in the post-menstrual endometrial regeneration (7). Both proliferation of endothelial cells and the re-establishment of microvasculature in this layer are orchestrated by E2, through interactions with its estrogen receptors (ERs), ERα and ERβ (68). Distinct intracellular localizations of the ERs lead to intricately coordinated and precisely regulated estrogen (E2) signaling pathways, which govern cellular proliferation, differentiation, and apoptosis. Endometrial E2 predominantly originates from the ovaries and, to a lesser extent, from adipocytes and the adrenal gland, transported to tissues through the circulatory system (69). Aromatase P450 (aromP450) is a rate limiting hormone in estrogen biosynthesis that catalyzes the conversion of androgens to estrogen, with subsequent transformation into E2 facilitated by 17β-hydroxysteroid dehydrogenase type 1 (17βHSDT1) (70). Prostaglandin E2 (PGE2) synthesis step is initiated by the rate-limiting cyclooxygenase-2 (COX-2) enzyme, acting on arachidonic acid, inducing dose-dependent aromP450 synthesis in endometriotic lesions (69). In healthy women’s endometrium, aromP450 activity is negligible (71). Cell-specific and menstrual cycle phase-dependent expression of receptors that bind to estrogens (ERα, ERβ and GPER1), androgens, progestins and glucocorticoids are observed in the healthy endometrium (72). However, both the endometrium and ectopic endometriotic lesions in women with endometriosis exhibit significantly elevated levels of aromP450, facilitating local E2 production. The capacity of the lesion to independently generate E2, coupled with the synthesis of the necessary enzymes, may enhance intraperitoneal endometriotic tissue implantation (56). It has been observed that the expression of ERβ is extraordinarily higher in stromal cells of women with endometriosis as compared to ERα. It is suggested that rather than just estrogen dependent, endometriosis should be considered steroid-dependent. Thus, the abnormal functioning of estrogen, its receptors, and estradiol synthesis-related enzymes is closely associated with endometriosis.
3.2
Progesterone resistance
Progesterone is the dominant hormone in the secretory phase of the menstrual cycle, where it counteracts effects of estrogen and prepares the uterus for supporting an embryo. It plays a decisive role in facilitating the differentiation of endometrial epithelial and stromal cells. Suppressed progesterone receptor (PR) expression, a characteristic feature of endometriosis, leads to resistance to progesterone and contributes to the development of severe endometriosis conditions (Figure 1). Endometriotic stromal cells demonstrate resistance to progesterone with reduced responsiveness to hormone (73). This diminished communication between stromal and epithelial cells leads to a subsequent elevation in the expression of ERβ within endometriotic lesions and stromal cells (1). PR-A and PR-B are the two functionally distinct receptor isoforms which interact with progesterone. In mice, the absence of PR-A results in abnormalities in the ovary and uterus, while the lack of PR-B has negligible impact on their function (74). Notably, the transcript for both receptor isoforms originate from the same gene, with PR-A having a shorter transcript than PR-B. This structure allows transrepression of PR-B and other nuclear receptors (75). Lesions in endometriosis exhibit a deficiency in PR-B expression, with minimal expression of the transrepressor PR-A, offering molecular substantiation for progesterone resistance. Subsequently, this leads to elevated local levels of estrogen (E2) as progesterone fails to stimulate 17β-hydroxysteroid dehydrogenase type 2 (17β-HSDT2) (69).
4
Aberrant vascularisation
The normal endometrium constitutes a steroid responsive tissue comprising richly vascularized epithelial and stromal cells as well as a diverse range of immune cells. Cells released from this tissue during menstruation encompass epithelial cells, stromal fibroblasts, vascular cells, and immune cells (neutrophils, monocytes, macrophages and uterine natural killer cells) (76). In retrograde menstruation, these cell types can potentially lead to lesions provided they maintain viability and evade the innate immune response and clearance within the intraperitoneal space. The three most implicated cells in peritoneal lesions are stem/progenitor cells, stromal fibroblasts, and immune cells, particularly stromal and immune cells, which play pivotal roles.
Endometriosis is postulated to originate due to endometrial fragment implantation within the peritoneal space. It potentially employs angiogenesis and vasculogenesis mechanisms to develop vascularization, essential for its sustenance (77, 78). The viability of endometriotic implants within the peritoneal cavity relies on establishing a blood supply to deliver oxygen and nutrients to the developing lesions. Concurrent with endometrial growth, the endometrial vasculature undergoes cyclical proliferation and regeneration orchestrated by ovarian steroids, particularly E2. Vascular endothelial growth factor (VEGF) serves a pivotal function in initiating angiogenesis in endometriosis, particularly in ectopic lesions (69, 79). As a vasoactive agent, it participates in numerous physiological functions, such reestablishment of a vascular network and subsequent healing of the uterus, by modulating proliferation and migration of endothelial cells. Heightened expression of VEGF mRNA in the superficial endometrial layer was reported during both the two phases of the uterine cycle, i.e., proliferative and secretory, suggesting ongoing angiogenesis (80). Furthermore, it was also demonstrated that estradiol was responsible for stimulating expression of VEGF in endometrial cells. Administration of E2 resulted in elevated levels of VEGF mRNA expression compared to endometrial cells not exposed to E2 stimulation. Given the intrinsic angiogenic capacity of healthy endometrium regulated by estradiol, it becomes apparent that dysregulated VEGF expression and E2 levels promote neovascularization in lesions, facilitating their establishment in ectopic sites. Studies indicate that peritoneal fluid (PF) from subjects with advanced endometriosis harbors elevated VEGF concentrations versus those with mild disease or healthy individuals (81). Various immune cells participate in angiogenesis by generating and subsequently increasing levels of proinflammatory and angiogenic cytokines, as well as cellular adhesion factors within the PF, surrounding endometriotic lesions. Secretion of VEGF by neutrophils and macrophages within intraperitoneal lesions facilitates angiogenesis (82). Disruptions in peritoneal homeostasis, coupled with the induction of proinflammatory and proangiogenic cytokine production in endometriosis, collectively contribute to modified innervation and the modulation of pain pathways in affected individuals (54). DCs have also been linked to angiogenesis (83). This was evidenced by a study revealing heightened perivascular localization of VEGFR-2 secreting immature dendritic cells within such lesions. These DCs exhibited the ability to stimulate endothelial cell migration in vitro. Intraperitoneal DCs in the peritoneal cavity led to the development of endometriotic lesions in the murine model (84). An investigation employing a transgenic murine model featuring diphtheria toxin mediated conditional depletion of DCs, scientists observed that endometriotic lesions in DC-depleted mice exhibited notable increased size versus control counterparts, along with reduced CD69 expression, indicative of antigen stimulated T and natural killer cell activation. These results underscore the direct involvement of DCs in regulating the angiogenic process and modulating immune activation subsets during the development of lesions (85). Endometrial cells exhibit enhanced resistance to cell mediated immunity, alongside enhanced proliferation and heightened aromatase expression, culminating in elevated estrogen levels (69, 70, 86).
Comparative studies investigating stromal fibroblast phenotypes in women with endometriosis have revealed behavioral disparities, notably epigenetic alterations leading to aberrant responses to estrogen (87). It is plausible that cell plasticity evolved to expedite endometrial repair post-menstruation, leading to multicellular lesion formation in extrauterine locations. Mechanistic similarities between menstrual regulation and lesion formation encompass transient hypoxia (88), iron release, and platelet activation (89, 90).
5
Immune dysfunction
Endometrial lesions adhere to the peritoneum or are closely associated with the ovaries, exposing them to an altered peritoneal environment comprising immune cells, cytokines, and regulatory proteins such as growth factors, with a high potential for anomalous behavior of these entities. Endometriosis animal model studies are suggestive of the fact that immune cells within lesions consist of a combination of cells from endometrial shedding as well as cells from peritoneal microenvironment (91). Fragments of endometrial tissue elicit intraperitoneal inflammation, which results in activation and recruitment of neutrophils and macrophages to the area. Hence, women with the disease often exhibit elevated concentrations of activated macrophages secreting proinflammatory and chemotactic cytokines in the peritoneal fluid (92). Given that various estrogen receptors are expressed on both macrophages and nerve fibers, estrogen is postulated to modulate macrophage and nerve fibers behavior. Thus, estrogen regulation encompasses macrophage recruitment, atypical neurogenesis atypical inflammation observed in endometriosis (93).
5.1
Cytokines
Several studies were conducted for the involvement of cytokines in the pathogenesis of endometriosis (Figure 1) (16–23). Multan et al., found that serum cytokines IL1β, IL-5, IL-6, IL-7 and IL-12 levels were elevated in serum samples of endometriosis patients compared to normal women (16). Cytokines (IL-6, IL-8, IL-10, TNF-α) and chemokines (CCL-2) as well as growth factor VEGF increased in peritoneal fluid of patients (18–23).
Nerve fibers demonstrate an exceptional capacity to recruit macrophages to the injury site. Numerous mediators identified in this process including leukemia inhibitory factor, IL1α, IL1β (94) and pancreatitis-associated protein 3 (PAP3) (95). Estrogen has also been shown to promote colony-stimulating factor 1 and C-C motif ligand 2 (CCL2) secretions from PNS, thereby amplifying macrophage movement towards lesions (96). Additionally, macrophages contribute to the proliferation of peritoneal implants and act as significant sources of angiogenic factors like TNF-α and IL-8. They also contribute to hypoxia-induced angiogenesis (92).
Endometriosis, like cancer, can be categorized as a metabolic disorder. Under the influence of transforming TGF-β1, tumor cells adopt aerobic glycolytic phenotype, leading to enhanced lactate secretion and accumulation (97). Elevated levels of TGF-β1 and lactate are observed in endometriotic PF. Concurrently, there is a shift from typical mitochondrial phosphorylation to glycolysis in the mesothelial cells lining the peritoneum to support cell survival in a tumor like microenvironment (98). Like in tumorigenesis, endometrial cells also exhibit the Warburg effect, where cells adjacent to tumors exhibit a programmed utilization of aerobic glycolysis induced by TGF-β1, leading to lactate production. This lactate serves as a nutrient source for neighboring tumor cells, thereby establishing a cohesive metabolic microenvironment conducive to tumor progression (99). Lactate induces lactylation or the covalent modification of lysine residues on histones and other proteins. Research findings indicate that elevated levels of lactate and lactate dehydrogenase-A, contribute to enhanced lactylation of histone H3 lysine 18 in ectopic endometrial tissues and ectopic endometrial stromal cells, compared to normal cells (100). Furthermore, lactate promotes cell proliferation, migration, and invasion in endometriosis progression, further linked to immune suppression and possible transformation to a malignant form.
5.2
Macrophages
Macrophages represent the predominant immune cell population in the peritoneum. Alterations in macrophage phenotype, or polarization, are linked to significant metabolic shifts. The peritoneal fluid of patients with endometriosis exhibits increased levels of macrophages (11), as shown in Figure 1. These macrophages do not effectively clear endometrial tissue; instead, they significantly contribute to high levels of cytokines (95). Proinflammatory macrophages primarily rely on glycolysis, whereas anti-inflammatory M2 macrophages exhibit a greater dependence on oxidative phosphorylation (101). Moreover, macrophages produce angiogenic mediators, such as TNF-α and IL-8, thereby promoting the growth of lesions (102). While macrophages appear to play a role in the growth and development of endometriotic tissue, depletion of macrophages does not prevent the implantation of endometrial cells in the peritoneum.
5.3
Neutrophils
Neutrophils are postulated to essay a pivotal role in endometriosis pathogenesis. Neutrophils significantly contribute to the resolution of inflammatory responses. A study found that when neutrophils from healthy women were exposed to endometrial plasma or PF, reduced neutrophil apoptosis was observed versus controls, elucidating the presence of antiapoptotic factors in the plasma and PF (12). Interleukin-8 stood out in the study due to its proinflammatory nature and its involvement in neutrophil chemotaxis during inflammation (12).
5.4
Dendritic cells
Dendritic cells are antigen-presenting cells which initiate and modulate adaptive immune responses. DCs additionally serve a crucial function in the prevention of autoimmunity by functioning as mobile sentinels. They transport self-antigens to naïve T cells residing in lymphoid organs, thereby facilitating the induction of self-tolerance (103). In healthy women, immature dendritic cells are absent from the peritoneal membrane. In endometriosis they are present within endometriotic lesions and adjacent to peritoneum. Additionally, the numbers of mature DCs are significantly reduced in the endometrium throughout the menstrual cycle in women with endometriosis compared to those with healthy endometrium (Figure 1). Endometriotic conditions may impede the maturation of immature DCs and prompt their transition into a macrophage phenotype. Moreover, the progression and vascularization of lesions necessitate the presence of endogenous DCs, which infiltrate these lesions and augment endothelial cell migration through the secretion of proangiogenic factors (104). In murine models, the cell density of peritoneal dendritic cells increased promptly following the injection of endometrial tissues, peaking at 14 days. The proportion of mature DCs within peritoneal DCs initially decreased post-injection, then gradually rose over time, although remaining lower than the control group at 42 days. Conversely, the proportion of immature DCs exhibited contrasting changes (14). The administration of lipopolysaccharide resulted in a significant increase in mature DCs proportion, consequently leading to reduced volume and weight of endometriosis lesions. While DC maturation suppresses the angiogenic response, immature DCs actively promote angiogenesis and lesion growth, thus undergoing a shift in their immunological function from antigen presentation to supporting angiogenesis and the progression of the disease.
5.5
Natural killer
NK cells are cytotoxic effector lymphocytes of the innate immune response characterized by their capacity to induce lysis of target cells independent of prior antigen exposure. Endometriosis is associated with a dysfunction in NK cell cytotoxicity and immunomodulation, by tolerating or inhibiting implantation, proliferation, and survival of endometrial cells, impairing their ability to eliminate these cells at ectopic sites (15). A study identified soluble immunosuppressive factors present in the media of both normal endometrial cells and endometriotic stromal cells. Healthy endometrium possesses immunosuppressive capabilities against NK cell cytotoxicity, potentially facilitating embryo implantation (Figure 1). However, in endometriosis, the immunosuppression is more pronounced, potentially allowing retrogradely displaced endometrial tissue to develop into lesions within the peritoneal environment (105). Functional defects and dysregulation of NK cell cytotoxicity are attributed to various cytokines and inhibitory factors present in both serum and PF. The reduction in NK cytotoxicity appears to result from functional defects. The dysregulated cytotoxicity of peritoneal NK cells in endometriosis can be attributed to various cytokines (IL-6, IL-8, IL-1β, IFN-γ, and TNF-α) and inhibitory factors present in both serum and peritoneal fluid. Also, for such patients, there is a notable reduction in the populations of mature NK cells (CD32CD56+), while immature NK cells are elevated in the PF, leading to apoptosis (106). The observed abnormalities in NK cells among women with endometriosis may indeed be outcomes resulting from the local regulation of microenvironment due to the pathology itself.
Treatment modalities such as inhibition of receptor-ligand interactions involving KIR2DL1, NKG2A, LILRB1/2, and PD-1/PD-L1, TGF-β; stimulation of NK cells via IL-2; and mycobacterial therapy utilizing Bacillus Calmette-Guérin (BCG) (82, 107–109). Moreover, ongoing research is exploring the potential of adoptive NK cell therapy for managing endometriosis. Endometriosis holds promise as a candidate for immunotherapy aimed at blocking negative regulatory checkpoints of NK cells, such as inhibitory NK cell receptors. Attenuating the cellular cytotoxicity of NK cells could potentially mitigate the progression of pelvic pain in individuals affected by the disease. The principal inhibitory receptors on NK cells, which are potential checkpoints for eradication of ectopic endometrial tissue, are leukocyte immunoglobulin-like receptors (LILRs).
5.6
T and B cells
Adaptive immune response entails helper T and B cells, in endometriosis, which remains incompletely understood. A study showed that a higher number of CD8 T cells are present in endometriotic lesions compared to eutopic endometrium (110). However, in blood circulation the CD8 T cell populations show no difference between patients and healthy women. It has been noted that CD8 T cell cytotoxicity is enhanced in menstrual effluent of patients, specifically CD8 T effector memory cells are enriched in eutopic endometrium of patients (Figure 1) (110).
Suppressed CD4 T cells have been reported in endometriosis due to the systemic and local alterations in immune responses (Figure 1). These impaired CD4 T cells potentially contribute to the pathogenesis of endometriosis disease through cytokines, which are important for implantation and proliferation of ectopic endometrial cells, inflammation and angiogenesis (111). In women with this condition, there appears to be a bias towards Th2 cell polarization, as evidenced by robust intracellular IL-4 expression and the absence of IL-2 in ectopic lesion derived lymphocytes (82). The equilibrium of CD4 cells in endometriosis remains contentious, with studies indicating reduced activation of both Th1 and Th2 cells in the peritoneal fluid of affected individuals (110).
Regulatory T (Treg) cells constitute a distinct subset within the T cell population, balancing immunological self-tolerance and homeostasis, thus modulating the immune system’s response to prevent excessive reactions against the host (97). Nevertheless, the precise involvement and significance of Treg cells in the context of endometriosis remain inadequately elucidated. The Forkhead box 3 protein (Foxp3), identified as a pivotal transcriptional factor, serves as a master regulator gene governing the differentiation of CD4+ Treg cells (112). Berbic et al. (113), demonstrated heightened expression levels of Foxp3 within both eutopic and ectopic endometrial tissues during the secretory phase of the menstrual cycle in patients afflicted with endometriosis. Furthermore, elevated Foxp3 expression at the messenger RNA level within ovarian endometrioma tissue (114), along with a relatively higher ratio of CD4 + Foxp3+ cells within the CD4+ cell population (115).
Additionally, recent studies have shown a significant increase in the proportion of CD4 + CD25hiFoxp3+ cells within the PF, but not in peripheral blood, of endometriosis patients, as opposed to those without the disease (Figure 1) (116, 117). These collective findings proved the abundance of Treg cells within localized endometrial lesions, implicating their potential involvement in the pathophysiology of endometriosis.
Additionally, heightened activation of B cells has been observed in both eutopic endometrium and lesions compared to healthy endometrium. Notably, the presence of anti-endometrial antibodies in the serum of endometriosis subjects has led to its occasional classification as an autoimmune disease (118).
5.7
Stem cells
Traditional hypotheses concerning the development of endometriotic lesions have lacked detailed mechanistic explanations for their proliferation and survival until recent studies revealed the involvement of mesenchymal stem cells (MSCs) and myeloid-derived suppressor cells (MDSCs) within a complex network of immune-endocrine signaling. MDSCs typically have strong immunosuppressive and angiogenic characteristics and are found in low numbers in healthy tissue. However, their accumulation is linked to interactions with inflammatory cytokines and has been implicated in several inflammatory diseases. Increased levels of these pro-inflammatory cytokines within the PF of individuals with endometriosis-associated pain may influence the differentiation of monocytes into MDSCs (119).
5.1
Cytokines
Several studies were conducted for the involvement of cytokines in the pathogenesis of endometriosis (Figure 1) (16–23). Multan et al., found that serum cytokines IL1β, IL-5, IL-6, IL-7 and IL-12 levels were elevated in serum samples of endometriosis patients compared to normal women (16). Cytokines (IL-6, IL-8, IL-10, TNF-α) and chemokines (CCL-2) as well as growth factor VEGF increased in peritoneal fluid of patients (18–23).
Nerve fibers demonstrate an exceptional capacity to recruit macrophages to the injury site. Numerous mediators identified in this process including leukemia inhibitory factor, IL1α, IL1β (94) and pancreatitis-associated protein 3 (PAP3) (95). Estrogen has also been shown to promote colony-stimulating factor 1 and C-C motif ligand 2 (CCL2) secretions from PNS, thereby amplifying macrophage movement towards lesions (96). Additionally, macrophages contribute to the proliferation of peritoneal implants and act as significant sources of angiogenic factors like TNF-α and IL-8. They also contribute to hypoxia-induced angiogenesis (92).
Endometriosis, like cancer, can be categorized as a metabolic disorder. Under the influence of transforming TGF-β1, tumor cells adopt aerobic glycolytic phenotype, leading to enhanced lactate secretion and accumulation (97). Elevated levels of TGF-β1 and lactate are observed in endometriotic PF. Concurrently, there is a shift from typical mitochondrial phosphorylation to glycolysis in the mesothelial cells lining the peritoneum to support cell survival in a tumor like microenvironment (98). Like in tumorigenesis, endometrial cells also exhibit the Warburg effect, where cells adjacent to tumors exhibit a programmed utilization of aerobic glycolysis induced by TGF-β1, leading to lactate production. This lactate serves as a nutrient source for neighboring tumor cells, thereby establishing a cohesive metabolic microenvironment conducive to tumor progression (99). Lactate induces lactylation or the covalent modification of lysine residues on histones and other proteins. Research findings indicate that elevated levels of lactate and lactate dehydrogenase-A, contribute to enhanced lactylation of histone H3 lysine 18 in ectopic endometrial tissues and ectopic endometrial stromal cells, compared to normal cells (100). Furthermore, lactate promotes cell proliferation, migration, and invasion in endometriosis progression, further linked to immune suppression and possible transformation to a malignant form.
5.2
Macrophages
Macrophages represent the predominant immune cell population in the peritoneum. Alterations in macrophage phenotype, or polarization, are linked to significant metabolic shifts. The peritoneal fluid of patients with endometriosis exhibits increased levels of macrophages (11), as shown in Figure 1. These macrophages do not effectively clear endometrial tissue; instead, they significantly contribute to high levels of cytokines (95). Proinflammatory macrophages primarily rely on glycolysis, whereas anti-inflammatory M2 macrophages exhibit a greater dependence on oxidative phosphorylation (101). Moreover, macrophages produce angiogenic mediators, such as TNF-α and IL-8, thereby promoting the growth of lesions (102). While macrophages appear to play a role in the growth and development of endometriotic tissue, depletion of macrophages does not prevent the implantation of endometrial cells in the peritoneum.
5.3
Neutrophils
Neutrophils are postulated to essay a pivotal role in endometriosis pathogenesis. Neutrophils significantly contribute to the resolution of inflammatory responses. A study found that when neutrophils from healthy women were exposed to endometrial plasma or PF, reduced neutrophil apoptosis was observed versus controls, elucidating the presence of antiapoptotic factors in the plasma and PF (12). Interleukin-8 stood out in the study due to its proinflammatory nature and its involvement in neutrophil chemotaxis during inflammation (12).
5.4
Dendritic cells
Dendritic cells are antigen-presenting cells which initiate and modulate adaptive immune responses. DCs additionally serve a crucial function in the prevention of autoimmunity by functioning as mobile sentinels. They transport self-antigens to naïve T cells residing in lymphoid organs, thereby facilitating the induction of self-tolerance (103). In healthy women, immature dendritic cells are absent from the peritoneal membrane. In endometriosis they are present within endometriotic lesions and adjacent to peritoneum. Additionally, the numbers of mature DCs are significantly reduced in the endometrium throughout the menstrual cycle in women with endometriosis compared to those with healthy endometrium (Figure 1). Endometriotic conditions may impede the maturation of immature DCs and prompt their transition into a macrophage phenotype. Moreover, the progression and vascularization of lesions necessitate the presence of endogenous DCs, which infiltrate these lesions and augment endothelial cell migration through the secretion of proangiogenic factors (104). In murine models, the cell density of peritoneal dendritic cells increased promptly following the injection of endometrial tissues, peaking at 14 days. The proportion of mature DCs within peritoneal DCs initially decreased post-injection, then gradually rose over time, although remaining lower than the control group at 42 days. Conversely, the proportion of immature DCs exhibited contrasting changes (14). The administration of lipopolysaccharide resulted in a significant increase in mature DCs proportion, consequently leading to reduced volume and weight of endometriosis lesions. While DC maturation suppresses the angiogenic response, immature DCs actively promote angiogenesis and lesion growth, thus undergoing a shift in their immunological function from antigen presentation to supporting angiogenesis and the progression of the disease.
5.5
Natural killer
NK cells are cytotoxic effector lymphocytes of the innate immune response characterized by their capacity to induce lysis of target cells independent of prior antigen exposure. Endometriosis is associated with a dysfunction in NK cell cytotoxicity and immunomodulation, by tolerating or inhibiting implantation, proliferation, and survival of endometrial cells, impairing their ability to eliminate these cells at ectopic sites (15). A study identified soluble immunosuppressive factors present in the media of both normal endometrial cells and endometriotic stromal cells. Healthy endometrium possesses immunosuppressive capabilities against NK cell cytotoxicity, potentially facilitating embryo implantation (Figure 1). However, in endometriosis, the immunosuppression is more pronounced, potentially allowing retrogradely displaced endometrial tissue to develop into lesions within the peritoneal environment (105). Functional defects and dysregulation of NK cell cytotoxicity are attributed to various cytokines and inhibitory factors present in both serum and PF. The reduction in NK cytotoxicity appears to result from functional defects. The dysregulated cytotoxicity of peritoneal NK cells in endometriosis can be attributed to various cytokines (IL-6, IL-8, IL-1β, IFN-γ, and TNF-α) and inhibitory factors present in both serum and peritoneal fluid. Also, for such patients, there is a notable reduction in the populations of mature NK cells (CD32CD56+), while immature NK cells are elevated in the PF, leading to apoptosis (106). The observed abnormalities in NK cells among women with endometriosis may indeed be outcomes resulting from the local regulation of microenvironment due to the pathology itself.
Treatment modalities such as inhibition of receptor-ligand interactions involving KIR2DL1, NKG2A, LILRB1/2, and PD-1/PD-L1, TGF-β; stimulation of NK cells via IL-2; and mycobacterial therapy utilizing Bacillus Calmette-Guérin (BCG) (82, 107–109). Moreover, ongoing research is exploring the potential of adoptive NK cell therapy for managing endometriosis. Endometriosis holds promise as a candidate for immunotherapy aimed at blocking negative regulatory checkpoints of NK cells, such as inhibitory NK cell receptors. Attenuating the cellular cytotoxicity of NK cells could potentially mitigate the progression of pelvic pain in individuals affected by the disease. The principal inhibitory receptors on NK cells, which are potential checkpoints for eradication of ectopic endometrial tissue, are leukocyte immunoglobulin-like receptors (LILRs).
5.6
T and B cells
Adaptive immune response entails helper T and B cells, in endometriosis, which remains incompletely understood. A study showed that a higher number of CD8 T cells are present in endometriotic lesions compared to eutopic endometrium (110). However, in blood circulation the CD8 T cell populations show no difference between patients and healthy women. It has been noted that CD8 T cell cytotoxicity is enhanced in menstrual effluent of patients, specifically CD8 T effector memory cells are enriched in eutopic endometrium of patients (Figure 1) (110).
Suppressed CD4 T cells have been reported in endometriosis due to the systemic and local alterations in immune responses (Figure 1). These impaired CD4 T cells potentially contribute to the pathogenesis of endometriosis disease through cytokines, which are important for implantation and proliferation of ectopic endometrial cells, inflammation and angiogenesis (111). In women with this condition, there appears to be a bias towards Th2 cell polarization, as evidenced by robust intracellular IL-4 expression and the absence of IL-2 in ectopic lesion derived lymphocytes (82). The equilibrium of CD4 cells in endometriosis remains contentious, with studies indicating reduced activation of both Th1 and Th2 cells in the peritoneal fluid of affected individuals (110).
Regulatory T (Treg) cells constitute a distinct subset within the T cell population, balancing immunological self-tolerance and homeostasis, thus modulating the immune system’s response to prevent excessive reactions against the host (97). Nevertheless, the precise involvement and significance of Treg cells in the context of endometriosis remain inadequately elucidated. The Forkhead box 3 protein (Foxp3), identified as a pivotal transcriptional factor, serves as a master regulator gene governing the differentiation of CD4+ Treg cells (112). Berbic et al. (113), demonstrated heightened expression levels of Foxp3 within both eutopic and ectopic endometrial tissues during the secretory phase of the menstrual cycle in patients afflicted with endometriosis. Furthermore, elevated Foxp3 expression at the messenger RNA level within ovarian endometrioma tissue (114), along with a relatively higher ratio of CD4 + Foxp3+ cells within the CD4+ cell population (115).
Additionally, recent studies have shown a significant increase in the proportion of CD4 + CD25hiFoxp3+ cells within the PF, but not in peripheral blood, of endometriosis patients, as opposed to those without the disease (Figure 1) (116, 117). These collective findings proved the abundance of Treg cells within localized endometrial lesions, implicating their potential involvement in the pathophysiology of endometriosis.
Additionally, heightened activation of B cells has been observed in both eutopic endometrium and lesions compared to healthy endometrium. Notably, the presence of anti-endometrial antibodies in the serum of endometriosis subjects has led to its occasional classification as an autoimmune disease (118).
5.7
Stem cells
Traditional hypotheses concerning the development of endometriotic lesions have lacked detailed mechanistic explanations for their proliferation and survival until recent studies revealed the involvement of mesenchymal stem cells (MSCs) and myeloid-derived suppressor cells (MDSCs) within a complex network of immune-endocrine signaling. MDSCs typically have strong immunosuppressive and angiogenic characteristics and are found in low numbers in healthy tissue. However, their accumulation is linked to interactions with inflammatory cytokines and has been implicated in several inflammatory diseases. Increased levels of these pro-inflammatory cytokines within the PF of individuals with endometriosis-associated pain may influence the differentiation of monocytes into MDSCs (119).
6
Immunological pathogenesis of endometriosis
Estrogen dominance fosters immune dysregulation, whereby many features observed in endometriosis mirror immune processes observed in various cancers, including heightened somatic mutations in endometrial epithelial cells. This elevated mutational burden contributes to the development of endometriosis-specific neoantigens, potentially altering the immune microenvironment of the lesions. Additionally, endometriosis often coexists with several chronic inflammatory conditions, characterized by shared dysregulation of the IL-23/IL-17 pathway, as evidenced in inflammatory bowel disease, psoriasis, and rheumatoid arthritis (120).
The crosstalk between immune cells, nerves, and central pain pathways plays a significant role in the pathophysiology of endometriosis. Endometrium is unique among mucosal tissues in the body in that it typically lacks innervation under normal physiological conditions. Nerve fibers are rare within the functional layer of the endometrium in women without any pathology (121). Sensory nerves surrounding endometriotic lesions drive the chronic pain associated with the condition and contribute to a pro-growth phenotype (122). Substantial alterations in nerve activity occur both within endometriotic lesions and the nervous system. Studies indicate that women experiencing pain symptoms associated with endometriosis exhibit notably higher nerve fiber density within the endometrium, myometrium and lesions as compared to those without the condition (123). Nerve fibers within endometriotic lesions consist of a combination of sensory, sympathetic, and parasympathetic fibers, collectively contributing to pain and inflammatory processes (124). The pain associated with endometriosis implies neuronal mechanisms that culminate in CS.
The interplay between macrophages and nerve fibers fosters inflammation and pain manifestations in endometriosis. Given their abundance within endometriotic lesions, macrophages stimulate sensory innervation and sensitization, thereby contributing to lesion proliferation and the prevalent pain experienced in endometriosis (19, 23). Moreover, immune cells release pro-nociceptive and pro-inflammatory mediators that can sensitize nerve fibers, leading to neurogenic inflammation (125). This communication between immune cells and nerves presents promising avenues for therapeutic interventions in endometriosis.
Prostaglandins, particularly prostaglandin E2 (PGE2), also play a significant role in the pathophysiology of endometriosis, contributing to pain and inflammation. Women with endometriosis produce an excess of PGE2, which is responsible for uterine contractions, pain, and inflammation (126). PGE2 is upregulated in the peritoneal cavity in endometriosis and is produced by macrophages and ectopic endometrial cells (127). It is involved in the development and continued growth of endometriosis, as it increases estrogen synthesis, inhibits apoptosis, promotes cell proliferation, affects leukocyte populations, and promotes angiogenesis (127). The presence of endometriosis lesions can trigger inflammation, which further promotes PGE2 activity (128). The release of PGE2 is associated with the development of symptoms and the progression of endometriosis, making it a potential target for therapeutic interventions. These changes in nerve activity contribute to the complex and debilitating pain experienced by individuals with endometriosis. However, the use of painkillers or non-steroidal anti-inflammatory drugs (NSAIDs) alone is not always ideal for managing endometriosis pain, as they may have limited efficacy and potential adverse effects (1). Therefore, understanding the role of PGE2 in endometriosis is important for developing targeted treatment strategies to address the associated pain and inflammation.
7
Clinical consequences of depression in endometriosis
Endometriosis changes the lifestyle of women and may lead to mental health issues such as depression, physiological stress and anxiety as depicted in Figure 1. Endometriosis is linked to psychological disorders in several ways. The disease in chronic stage can cause life impacting abdominal pain during periods, painful bowel movements or urination, chronic pelvic pain, excessive bleeding, fatigue, diarrhea, constipation, bloating, nausea, fatigue and painful intercourse (Figure 2), leading to a compromised quality of life and in several cases infertility. These co-occurring conditions may cause stress, anxiety and psychological disorders (4, 5).
Figure 2
Various health issues in individuals with chronic endometriosis.
A study conducted by Pope et al. (129) highlighted the correlation between endometriosis and a diverse array of psychiatric symptoms, notably depression, anxiety, psychosocial stress, and diminished quality of life. Recent literature further substantiates the prevalence of depression and anxiety as the predominant psychiatric comorbidities in individuals with endometriosis (129–136). In an investigation by Low et al. (137), for the potential role of a distinct psychological profile associated with endometriosis, the author included 81 women participants in the study who were experiencing pelvic pain. Of these, 40 were diagnosed with endometriosis disease and 41 presenting with alternative gynecological issues. All the subjects underwent evaluation through six standardized psychometric assessments, including the Eysenck Personality Questionnaire (EPQ), Beck Depression Inventory (BDI), General Health Questionnaire, State–Trait Anxiety Inventory (STAI), The Golombok Rust Inventory of Marital State, and The Short-Form McGill Pain Questionnaire. In assessments using these criteria, the endometriosis patients exhibited increased level of psychoticism, introversion and anxiety scores than women with other gynecological issues (138).
In a recent study conducted by Warzecha et al. (139), 15.1% of women with endometriosis were diagnosed with depression which aligns with findings by Fried et al., who reported a 14.5% incidence of depressive symptoms. In another study the incidence of symptoms of anxiety were estimated to be 29% among Austrian women with endometriosis (130). A meta-analysis by Gambadauro et al. (140), encompassing 24 studies and 99,614 women, confirmed higher levels of depression in such subjects. Researchers indicate that women with endometriosis accompanied by pelvic pain, the rate of depressive symptoms is significantly higher than in cases of endometriosis without pain. This evidence suggests that endometriosis associated complications such as pain may be a more critical factor in the development of depressive symptoms than the presence of endometriosis alone (140). Furthermore, Warzecha et al. (140), revealed that the mean age at the onset of depressive symptoms among women with endometriosis was 22.2 years, which is closely aligned with the age range for the onset of endometriosis symptoms between 18.8 and 24 years. Additionally, the study found that certain types of pain, specifically chronic pelvic pain and painful defecation, significantly increased the incidence of depressive symptoms (140). These findings underscore the profound impact that specific pain manifestations can have on the mental health of women suffering from endometriosis. In these studies, clinicians caring for women with chronic pelvic pain, particularly when coexisting with endometriosis, should be cognizant of the elevated risk of depressive disorders in this population. Understanding the strong correlation between chronic pain and mental health is essential for providing holistic care. Early recognition and intervention for depressive symptoms in these patients can significantly improve their overall quality of life and treatment outcomes.
Another study based on meta-analysis included 18 relevant quantitative studies (129). Out of the 18 studies, 17 included clinical patients’ samples. Fourteen out of eighteen studies indicated that endometriosis or chronic pelvic pain significantly impaired at least some aspects of psychological functioning, mental health, elevated risk for depression, hypomanic, or anxiety symptoms among affected women.
From the 18 studies, 4 studies (137, 141–143) used clinical diagnostic criteria to assess psychiatric diagnosis. Out of these 4 studies, 3 were used as comparator group (137, 141, 142). From the clinical samples of women (age from late teens to mid-40s), 37% of participants showed endometriosis and 50% exhibited pelvic pain with a reported family history of mood disorders. From the 3 comparator group studies, 2 showed higher risk of psychiatric disorders in women with endometriosis (137, 141). Data from these three studies exhibited that 44 (56%) of the 79 women with endometriosis met the criteria for at least one psychiatric disorder.
Another study was conducted on a Brazilian population including 103 women with an age range of 15 to 49 years (average age 33.4 years) (144). Out of 103 patients, 53 (51.5%) were diagnosed with endometriosis and 50 (48.5%) without endometriosis (control). Subjects were evaluated using a questionnaire (Beck Depression Inventory) providing different levels of depression (mild, moderate, moderate to severe, and severe). Based on the questionnaire, symptoms for depression were observed in 35 (66%) women with endometriosis. Out of these, 20 (37.7%) women showed mild depression, 4 women (7.5%) exhibited mild to moderate, 6 women (11.3%) were found to have moderate to severe depression, and 5 (9.4%) women had severe depression. However, according to the Fisher’s exact test, there was no relationship between endometriosis and depressive symptoms (p = 0.423) (144).
Traumatic stress is very likely in endometriosis diagnosed women compared with the women without endometriosis (145, 146). Harris et al. concluded in a study that children who experienced physical or sexual abuse were likely to develop endometriosis in later stages of life (147). Post-traumatic stress disorder and childhood trauma can impact individuals and may contribute to the development of depression at an early stage (148). Furthermore, a study conducted by Reis et al., showed that depression or stress in the early stages of life may be considered an important factor for the development of endometriosis (149). The persistence of such conditions over a long period of time may lead to hormonal imbalance, neuroendocrine dysfunction, chronic inflammation which are leading factors in the development of depression and endometriosis (146, 150).
8
Immunological aspects of depression in endometriosis
Depression is very much associated with the secretion or formation of proinflammatory molecules such as IL-6, IFN-γ, TNF-α, and IL1β (151–153). Additionally, depression also enhances oxidative stress and increases oxidative molecules such as protein bound carbonyl content and methylglyoxal (151, 152, 154). A study revealed that methylglyoxal, which is a well-known reactive metabolite, plays a vital role in various central nervous system associated cognitive functions and can be linked to stress, depression, anxiety, and neurodegenerative diseases (154, 155). Numerous studies conducted in this area have proven that endometriosis is strongly linked with the increased risk of psychological depression, anxiety, and eating disorders (156).
Studies indicated that endometriosis patients have an increased incidence of autoimmune diseases and cancer (157–159). Women diagnosed with endometriosis are more prone to several autoimmune diseases such as multiple sclerosis, rheumatoid arthritis, inflammatory bowel disease, systemic lupus erythematosus, and Sjogren’s Syndrome (159, 160). Higher estrogen levels in women during endometriosis lead to the modification of macromolecules like insulin, serum albumin etc. (161–163). These modifications not only compromise the functioning of these macromolecules but also lead to the formation of neo-antigens on these molecules that activate a cascade of the reactions causing production of autoantibodies (161–163). Higher levels of autoantibodies were detected in patients with depression (161–163). These elevated levels of autoantibodies, together with several pathological complications in endometriosis as discussed above, further aggravate the disease to extremely severe levels.
T cell involvement in depression has not been investigated in detail. Some studies conducted in this area revealed that T cell responses decrease in depression (164–166). T cell responses were found to decrease against antigens encountered in the skin of depressed individuals (164, 167). In a meta-analysis conducted by Zorrilla et al. (166), depression was associated with a decreased percentage of T cells. CD4+ T cells in depressed individuals exhibited increased expression of Fas (CD95) which is known as death receptor as it triggers apoptosis when it interacts with its ligand (168, 169).
T cell function can be inhibited by glucocorticoid pathways in major depression. Increased levels of glucocorticoids in circulatory blood are hallmark of depression (170). Glucocorticoids mediate cell migration and induce apoptosis of immune cells including T cells (166, 171). Endometriosis is characterized by elevated expression of the HSD11B1 gene, which converts inactive cortisone to cortisol, a biologically potent glucocorticoid in peripheral tissues. Receptor for glucocorticoid expression increases up to 3.5-fold in endometriosis. The interaction of higher levels of glucocorticoids with increased level of receptors in endometriosis may increase the proinflammatory environment surrounding the endometriotic lesion and enhance the activity that supports endometriotic cell survival (172). Higher levels of glucocorticoids also induce infertility in women. Infertility treatment, which are often long and painful processes, as well as the condition itself induce depression and compromises in quality of life.
Finally, it has been suggested that chronic endometriosis arises due to various dysfunctions imbalances and may lead to infertility which may cause women to develop depression and subsequently unleash further physiological, clinical and immune imbalances which further accelerate chronic endometriosis or vice versa (Figure 3). Thus, both endometriosis and depression concomitantly develop a vicious cycle which enhance and exacerbate disease complications.
Figure 3
Interlink between chronic endometriosis and major depression.
9
Links between depression and immunological factors for potential malignant transformation of endometriosis
Although endometriosis is classified as a benign disease, it has the potential to transform into malignancy, which occurs in about 1% of endometriosis patients (173, 174). This malignant transformation most frequently affects the ovaries, with ovarian endometrioid carcinoma and ovarian clear cell carcinoma being the most common types. These two malignancies account for 76% of all endometriosis-related ovarian cancers (174, 175).
Recently, several carcinogenic pathways have been identified for endometriosis-related malignant transformation. Uncontrolled cell division, tissue infiltration, neoangiogenesis, and apoptosis evasion may result from oncogene demethylation and tumor suppressor gene hypermethylation (173, 174). Key events include hypermethylation of the hMLH1 gene promoter, reducing DNA mismatch repair gene expression, and hypomethylation of LINE-1. Tumor suppressor genes RUNX3 and RASSF2 are inactivated by promoter hypermethylation (173). In endometrioid cancer, KRAS oncogene activation and PTEN tumor suppressor gene inactivation is significant (175, 176). Loss of PTEN activity, an early event in malignant transformation, is linked to PTEN gene mutations (177). Additionally, somatic mutations in cancer driver genes ARID1A, PIK3CA, KRAS, and PPP2R1A are found in deep infiltrating endometriosis (178).
A recent meta-analysis study conducted by Centini et al. (34), focusses on atypical endometriosis, which is present in 12–35% of ovarian endometriosis cases and 60–80% of endometriosis associated ovarian cancers. The SWItch/Sucrose Non-Fermentable (SWI/SNF) complex and ARID1A gene alterations offer valuable insights into the pathogenesis of endometriosis and endometriosis-associated ovarian cancer. Also, the use of potential therapeutics based on inhibitors and suggested the use of PARP inhibitors in treating ovarian cancer which may potentially improve outcomes for these conditions.
Retrograde menstruation, where menstrual blood containing erythrocytes, macrophages, and endometrial tissue travels through the fallopian tubes to the peritoneal cavity, is crucial for understanding endometriosis pathogenesis (179, 180). Periodic hemorrhage from ectopic endometriotic lesions causes iron overload, with erythrocyte-derived iron being a well-known inducer of oxidative stress (180). This altered iron metabolism can contribute to endometriosis development and progression (181). At moderate levels, iron-induced reactive oxygen species (ROS) stimulate ectopic endometrial cell proliferation, angiogenesis, and adhesion. Animal models show that iron treatment increases the number and size of endometriotic lesions compared to controls, suggesting that imbalances in iron homeostasis regulate endometriotic cell proliferation (182). These finding suggest that alterations in iron hemostasis may promote endometriotic cell proliferation. Iron overload intensifies intracellular oxidative stress through the Fenton reaction (Fe2+ + H₂O₂ → Fe3+ + OH− + OH), leading to DNA, lipid, and protein damage, and resulting in cytotoxic effects on cells (183). This reaction generates reactive hydroxyl radicals that contribute to cellular injury and dysfunction. Furthermore, excess iron can decrease transferrin concentration in follicular fluid due to increased transferrin saturation. This iron overload and transferrin insufficiency lead to elevated ROS levels, compromising mitotic spindle integrity and promoting chromosome instability (184, 185). Consequently, this may affect the number and maturation of oocytes retrieved from women with endometriosis (184, 185). High content of iron in ovarian endometriomas exert negative effect on granulosa cells via increased level of ROS cause decrease in the number and quality of oocytes leading to impaired fertility (186–188). The increased levels of free radical generation in physiological stress concomitant with impaired fertility in endometriosis may be due to an imbalance in ROS homeostasis.
Furthermore, there is a persistent production of antioxidants, where endometriotic cells adapt to oxidative stress with the support of macrophages. This adaptation enhances antioxidative defenses and influences redox signaling, energy metabolism, and the tumor immune microenvironment, potentially leading to malignant transformation. Moreover, specific molecular alterations, including mutations in ARIDA1/BAF250a, PIK3CA, CTNNB1, and PTEN, as well as microsatellite instability and loss of heterozygosity, have been reported (189–193).
10
Conclusion
Endometriosis is a chronic, estrogen-dependent, proinflammatory disease that can cause various dysfunctions. Hormonal imbalance, inflammation, immune dysregulation, angiogenesis, neurogenic inflammation, epigenetic alterations, and tissue remodeling are common in the pathogenesis of endometriosis. Higher numbers of women diagnosed with endometriosis showed increased levels of depression which can potentially further aggravate the disease. According to published literature strong synergisms were observed in endometriosis patients with depression or vice versa. This review article focuses on the immunological aspects of depression in endometriosis patients by looking at the links between depression and immunological factors responsible for potential malignant transformation of endometriosis. There is a huge gap in the awareness of endometriosis and proper counselling and treatment, especially in underdeveloped and developing countries due to continued reluctance of open discussion of female gynecological issues. Clinicians, academicians, and scientists should reach out to these communities and provide vital information promoting regular screening, early detection of the disease and counselling to prevent further complications. Importantly, increased funding is critical for investigation and identification of factors against which multifactorial drug development is critical to alleviate the pain and suffering of women diagnosed with endometriosis and depression.
Author contributions
SS: Conceptualization, Data curation, Supervision, Writing – original draft, Writing – review & editing. MK: Data curation, Writing – original draft, Writing – review & editing. SR: Data curation, Writing – original draft. KA-M: Data curation, Writing – review & editing. QH: Data curation, Writing – review & editing. WK: Data curation, Writing – review & editing. |
Title: Lanifibranor Reduces Inflammation and Improves Dyslipidemia in Lysosomal Acid Lipase-Deficient Mice | Body:
Introduction
Lysosomal acid lipase deficiency (LAL-D) is a lysosomal storage disorder that affects 1 in 177,452 individuals.1 It may masquerade as metabolic dysfunction-associated steatotic liver disease (MASLD) or present with cryptogenic cirrhosis.1 LAL activity is reduced in adult MASLD2,3 and even more so in metabolic dysfunction-associated steatohepatitis (MASH) and cryptogenic cirrhosis.4,5 Furthermore, a correlation was found between decreased LAL activity and the extent of liver fibrosis in pediatric patients with MASLD.6 MASLD is associated with the accumulation of high levels of fatty acids, triacylglycerols (TG), and ceramides in the liver.7 Depending on the mutations in the LIPA gene and the residual LAL activity, patients develop very severe (early-onset) or less severe (late-onset) LAL-D.8 Since LAL is the sole enzyme known to degrade cholesteryl esters and TG in the acidic lysosomal lumen, a lack of the enzymatic activity causes lysosomal accumulation of the substrates.8 In patients suffering from early-onset LAL-D, the massive ectopic lipid deposition affects multiple organs, leading to death within the first few months of life due to malabsorption, cachexia, and failure to thrive.9 In contrast, patients with late-onset LAL-D survive into adulthood and usually present with hepatosplenomegaly, dyslipidemia, and atherosclerosis.9
Similar to humans, LAL knockout (Lal−/−) mice exhibit lipodystrophy, ectopic lipid accumulation, progressive hepatosplenomegaly, and dyslipidemia.10, 11, 12 The phenotype of Lal−/− mice resembles late-onset LAL-D, despite having no residual LAL activity.10, 11, 12 Lal−/− mice were used to test various treatment approaches for LAL-D, including enzyme replacement therapy (ERT) and gene therapy.13,14 Particularly, ERT with recombinant human LAL showed promising preclinical outcomes by reducing the size of the liver and spleen and decreasing lipid accumulation in multiple tissues of Lal−/− mice.13 Currently, human LAL ERT is the most effective therapeutic strategy for LAL-D patients,8 leading to an improvement of liver injury markers and dyslipidemia.15 However, mild to moderate adverse effects were reported in the majority of treated patients.8 In addition, the impact of ERT on liver histology and inflammation is inconclusive,16 as some patients exhibited improvements, while others experienced a worsening of the liver phenotype.15 Disease severity and its specific stage, LAL-D-dependent cumulative liver injury and age, development of neutralizing antibodies against the drug, or other epigenetic factors may influence the efficacy of ERT treatment in LAL-D patients.
We recently showed that the livers of Lal−/− mice have impaired lysosomal function and decreased expression of proteins related to fatty acid metabolism and peroxisomes.17 In addition, genetic loss of LAL and pharmacological inhibition of lysosomal function18 downregulate peroxisome proliferator-activated receptor (PPAR) α signaling and decrease peroxisomal biogenesis and lipid degradation. PPARs are a family of nuclear receptors that regulate whole-body energy metabolism, inflammation, peroxisomal biogenesis, and fatty acid metabolism.19,20 Due to their distinct functions, PPARα is primarily expressed in the liver, PPARγ in adipocytes, and PPARδ is expressed ubiquitously. Single and dual PPAR agonists have shown promising results in the treatment of diabetes and MASLD-related complications.21 The pan-PPAR agonist lanifibranor combines the benefits of selective PPAR agonists by targeting all 3 PPAR isoforms and successfully reduces hepatic steatosis, inflammation, and fibrosis in MASLD mouse models.22 Treatment with lanifibranor also improved liver inflammation and might lead to regression of fibrosis in MASH patients, as evidenced by the positive impact of lanifibranor on resolution of MASH without worsening of fibrosis and the regression of fibrosis without a deterioration of MASH from the phase 2b trial.23 The lanifibranor-treated group exhibited a dropout rate of less than 5% due to adverse events, which were primarily mild or moderate in intensity. The efficacy and safety of lanifibranor in the treatment of MASH with fibrosis are currently being tested in a large multicenter phase 3 clinical trial (NCT04849728, EudraCT Number: 2020-004986-38).
The aim of the study was to investigate the potential of lanifibranor to ameliorate liver inflammation in Lal−/− mice. The results demonstrate that pan-PPAR agonism, even without ERT, significantly improved dyslipidemia and hepatic inflammation without negatively affecting liver lipid parameters in Lal−/− mice. These findings suggest that targeting PPAR signaling may be an effective approach to alleviating hepatic inflammation in LAL-D and warrant further studies to establish the therapeutic potential of lanifibranor.
Methods
Animals and Pharmacological Treatment
The mice were housed in a clean and temperature-controlled (22 ± 1 °C) environment and had unlimited access to chow diet (Altromin 1324, Lage, Germany) and water on a regular 12-hour light/12-hour dark cycle. The experiments were performed using 10 female Lal−/− mice aged 7–11 weeks on the C57BL/6J background,12 which were randomly selected. Six mice were gavaged a suspension of 1% methylcellulose and 0.1% poloxamer in water (vehicle), while 4 mice were gavaged with the vehicle suspension supplemented with 30 mg/kg of lanifibranor once daily for 21 days, which has already been described to exhibit antifibrotic activity in a liver fibrosis model.24 All treated mice were sacrificed 24 hours after the final treatment and fasted for 4 hours prior to sacrifice.
The animal experiments were conducted in accordance with the European Directive 2010/63/EU, complying with national laws, and approved by the Austrian Federal Ministry of Education, Science, and Research, Vienna, Austria (2022-0.920.281, BMWFW-66.010/0109-WF/V/3b/2015, 2022-0.861.148).
Plasma Lipid and Lipoprotein Quantification
Plasma TG and cholesterol concentrations were determined from ethylenediaminetetraacetic acid blood as previously described.17 Lipoprotein profiles were obtained after the separation of 200 μL pooled plasma by fast protein liquid chromatography (Pharmacia P-500, Uppsala, Sweden) equipped with a Superose 6 column (Amersham Biosciences, Piscataway, NJ).
Tissue Lipid Extraction and Quantification
Liver tissue was lysed in phosphate-buffered saline (PBS) using a Precellys homogenizer (Bertin Instruments, Bretonneux, France) and then centrifuged at 8000 × g for 2 minutes at 4 °C. From the supernatant, an equivalent of 20 mg of tissue was transferred into a new tube, and the volume was filled up to 100 μL with PBS. The samples were treated with 750 μL of methanol and 2.5 mL of methyl tert-butyl ether, vortexed for 10 seconds, and rotated for 1 hour. After the addition of 600 μL ddH2O, the samples were vortexed for 10 seconds and left for 10 minutes. The phases were separated by centrifugation at 2000 × g for 10 minutes. The upper organic phase was collected, to which 200 μL of 2% TritonX-100 in methanol was added before evaporation under a N2 stream. Two hundred microliters of ddH2O were added to the samples, and the precipitates were dissolved by vortexing for 20 seconds, followed by incubation in a sonication bath for 1 hour and with a tip-sonicator for 10 seconds. The lipid levels were determined as described above.
Hematoxylin and Eosin and Masson's Trichrome Staining
Fresh liver tissue was fixed in 4% PBS-buffered paraformaldehyde for 24 hours and embedded in paraffin. Slides with deparaffinized tissue sections were incubated with hematoxylin for 10 minutes and then with eosin for 1 minute. Masson's trichrome staining was performed as previously described.25
Measurement of Liver Injury Marker
Freshly isolated heparinized blood was centrifuged for 7 minutes at 5200 × g and 4 °C, and plasma was collected. Alanine aminotransferase and aspartate aminotransferase (AST) concentrations in plasma were analyzed with Fuji Dri-Chem NX500 (FUJIFILM Holdings Corporation, Tokyo, Japan).
Measurement of Hematological Parameters and Blood Glucose
Blood samples were collected in ethylenediaminetetraacetic acid-coated tubes and analyzed using the V-Sight Vet Hematology Analyzer (A. Menarini Diagnostics, Florence, Italy) according to the manufacturer's protocol.
Glucose was measured from a drop of tail vein blood using a glucometer (AccuCheck, Roche Holding AG, Basel, Switzerland).
Sample Preparation for Mass Spectrometry-Based Proteomics and Measurement
One milliliter of sodium dodecyl sulfate buffer (0.1 M Tris-HCl, pH 8.5%, and 4% sodium dodecyl sulfate) was added to approximately 100 mg liver tissue from Lal−/− mice. Samples were homogenized in a BeatBox tissue homogenizer (PreOmics GmbH; Martinsried, Germany) at 850 rpm and boiled for 10 minutes at 95 °C. Lysates were sonicated using a tip-sonicator and centrifuged at 16,000 × g for 10 minutes. Protein concentrations were determined in the supernatant using the DC Protein Assay (Bio-Rad Laboratories, Hercules, CA). Proteins were reduced and alkylated with final concentrations of 10 mM tris(2-carboxyethyl)phosphine and 50 mM 2-chloroacetamide. Proteins were digested using the protein aggregation capture method26 on a KingFisher Flex robot (Thermo Fisher Scientific, Waltham, MA) in 96-well format. Briefly, magnetic hydroxyl beads (ReSyn Biosciences, Gauteng, South Africa) were mixed with 30 μg protein lysate 1:4 (w:w) in the 96-well plate. A digestion solution containing endoproteinases LysC (1:500, w:w) and trypsin (1:100, w:w) was added to the second 96-well plate. Five additional 96-well plates containing 500 μL 100% acetonitrile (ACN), 700 μL 100% ACN, 1 mL 100% ACN, 1 mL 100% ethanol, and 1 mL 100% ethanol, respectively, were prepared. An in-house program was used to digest the samples. Peptides were purified with 3 × C18 StageTips.27 EvoTips (Evosep, Odense, Denmark) were loaded with 200 ng of purified peptides, which were then separated on a 15-cm column with 150 μM ID packed with C18 beads (1.9 μm) (Pepsep) using Evosep ONE HPLC. The separated peptides were injected into a timsTOF Pro 2 mass spectrometer (Bruker Daltonics, Bremen, Germany) operated in diaPASEF mode, utilizing a CaptiveSpray ionization source with a 20-μm emitter. Mass spectrometry (MS) data were collected between 100 and 1700 m/z. Each MS/MS data acquisition was performed with diaPASEF cycle of 1.8 s and covered ion mobility range between 1.6 and 0.6 1/K0. Ion mobility was calibrated with 622.0289, 922.0097, and 1221.9906 Agilent ESI-L Tuning Mix ions. For diaPASEF, a long gradient with 10 diaPASEF scans with 3 25 Da windows per ramp, a mass range between 400 and 1201 Da, and a mobility range between 1.43 and 0.6 1/K0 was used. The collision energy parameters were set to 59 eV at 1/K0 = 1.3 and linearly decreased to 20 eV at 1/K0 = 0.85 Vs cm−2 during the measurement. Accumulation time and PASEF ramp time were 100 ms.
Proteomics Data Quantification and Bioinformatics
Protein quantification was performed with DIA-NN (1.8.1),28,29 with raw data searched against the Mouse Uniprot reviewed FASTA file downloaded on August 09, 2021 with the following parameters: FASTA digest for library-free search/library generation, deep learning-based spectra, retention time, and ion mobility spectra prediction. Trypsin-digested peptides with up to 2 missed cleavages were accepted, along with 3 variable modifications: N-terminal methionine excision, cysteine carbamidomethylation, and methionine oxidation. The default peptide parameters were 7–30 for the peptide length range, 1–4 for the precursor charge range, 300–1800 for the precursor m/z range, and 200–1800 for the fragment ion m/z range. The precursor false discovery rate (FDR) was set to 1%, mass accuracy to 10 ppm, and MS1 accuracy to 20 ppm. Isotopologues were used in the search, along with match between runs, heuristic protein inference (protein inference was set to genes), and exclusion of shared spectra. Searches were conducted in double-pass mode using a robust, high-precision liquid chromatography quantification strategy, with cross-run normalization as a function of retention time and smart profiling for library generation. Data were analyzed using Perseus (1.6.15.0)30 and Jupyter Notebook with Python 3.9. Protein intensities were log2 transformed before filtering the data for 70% of valid values in at least 1 group. Data were imputed to fill missing abundance values by drawing random numbers from a Gaussian distribution with a standard deviation of 30% and a downshift of 1.8 standard deviations from the mean relative to that of the proteome abundance distribution. Principal component analysis (PCA) was performed on the z-scored data in Python utilizing the packages Pandas, Matplotlib, Numpy, Seaborn, Sklearn, and Bioinfokit. Statistically significant changes were determined by a t-test with S0 = 0.1 and FDR = 0.05. Reactome pathway enrichment with the PANTHER Overrepresentation Test29,30 was performed using 2 clusters representing significantly increased proteins in the livers of lanifibranor- or vehicle-treated Lal−/− mice. KEGG pathway enrichment was performed with z-scored intensities from significantly changed proteins using Pathview.31,32 PPAR signaling was selected from enriched KEGG pathways and changed proteins related to it were counted and visualized. The number of significantly upregulated proteins from livers of vehicle- and lanifibranor-treated mice associated with specific UniProt keywords was determined by counting annotated proteins (Swiss-Prot) from relevant UniProt keywords.
Protein matrix, enriched Reactome pathways, and proteins related to specific UniProt keywords are listed in Table A1.
Nuclear Magnetic Resonance (NMR) Metabolomics
Metabolomics analysis by NMR spectroscopy was performed as previously described.33 Briefly, 20 mg of liver tissue was mixed with a solution of ice-cold methanol and high-purity H2O (2:1) to inhibit enzymatic processes and precipitate proteins. Samples were homogenized 2 times for 20 seconds using a Precellys 24 tissue homogenizer (Bertin Technologies, Montigny-le-Bretonneux, France) in tubes containing Precellys beads (1.4 mm zirconium oxide beads, Bertin Technologies). Next, samples were centrifuged at 9703 × g for 30 minutes at 4 °C, the supernatants were transferred to new tubes and lyophilized at <1 Torr, 824 × g, 25 °C for 10 hours in a vacuum-drying chamber (Savant Speedvac SPD210 vacuum concentrator) with an attached cooling trap (Savant RVT450 refrigerated vapor trap) and vacuum pump (VLP120) (Thermo Scientific, Waltham, MA). Samples were redissolved in 500 μL NMR buffer consisting of D2O, 0.08 M Na2HPO4, 4.6 mM 3-(trimethylsilyl) propionic acid-2,2,3,3-d4 sodium salt (TSP), and 0.04 (w/v)% NaN3 (pH adjusted to 7.4 with HCl and NaOH) and transferred to 5-mm NMR tubes. NMR spectroscopy was carried out using a Bruker Avance Neo 600 MHz spectrometer coupled to a TXI probe head at 310 K. 1H 1D NMR spectra were obtained using the Carr–Purcell–Meiboom–Gill (CPMG) pulse sequence (cpmgpr1d, 128 scans, 73,728 points in F1, 12,019.230 Hz spectral width, recycle delay 4 seconds) with a pre-saturation for water suppression. NMR spectra processing, including Fourier transformation of the free induction decay, automatic phasing, and baseline correction, was done using Bruker Topspin software version 4.0.2. A Matlab script (software version 2014b) was used for importing the spectra. The peaks surrounding the water, TSP, and methanol signals were eliminated, the NMR spectra were aligned,34 and a probabilistic quotient normalization35 was carried out. Chenomx NMR Suite 8.4 (Chenomx Inc, Edmonton, AB, Canada), the human metabolome database,36 and reference chemicals were used to identify metabolites. Metabolites were quantified by signal integration of the normalized spectra. For each metabolite, a representative peak with no overlapping signals was determined. The start and end points of the integration were then selected to revolve around this peak, and the areas of all peaks were integrated for all samples and metabolites using an R script (RStudio 2023 with R version 4.1.3) and provided as arbitrary units (a.u.) proportional to the concentration of the respective analyte. MetaboAnalyst 5.037 was used to calculate univariate statistics (shown as volcano plot). An unpaired 2-tailed Student's t-test with Benjamini-Hochberg correction was used to calculate the significance of metabolite levels between controls and lanifibranor-treated mice. Since none of the metabolites revealed an FDR <0.05, uncorrected P values were plotted. For multivariate statistics, PCA and orthogonal partial least squares discriminant analysis were calculated. The statistical significance of the multivariate model was verified by permutation testing of the quality evaluation statistic Q2, which was barely significant (P = .11).
Statistics
Data analysis and statistics for proteomics and metabolomics data are described in the sections above. For other experiments, comparisons between groups were performed using the unpaired 2-tailed Student's t-test in GraphPad Prism 9.5.1. Data are presented as mean or as mean ± SD. Significance levels were set to: ∗P < .05, ∗∗P ≤ .01, ∗∗∗P ≤ .001, and ∗∗∗∗P ≤ .0001.
Animals and Pharmacological Treatment
The mice were housed in a clean and temperature-controlled (22 ± 1 °C) environment and had unlimited access to chow diet (Altromin 1324, Lage, Germany) and water on a regular 12-hour light/12-hour dark cycle. The experiments were performed using 10 female Lal−/− mice aged 7–11 weeks on the C57BL/6J background,12 which were randomly selected. Six mice were gavaged a suspension of 1% methylcellulose and 0.1% poloxamer in water (vehicle), while 4 mice were gavaged with the vehicle suspension supplemented with 30 mg/kg of lanifibranor once daily for 21 days, which has already been described to exhibit antifibrotic activity in a liver fibrosis model.24 All treated mice were sacrificed 24 hours after the final treatment and fasted for 4 hours prior to sacrifice.
The animal experiments were conducted in accordance with the European Directive 2010/63/EU, complying with national laws, and approved by the Austrian Federal Ministry of Education, Science, and Research, Vienna, Austria (2022-0.920.281, BMWFW-66.010/0109-WF/V/3b/2015, 2022-0.861.148).
Plasma Lipid and Lipoprotein Quantification
Plasma TG and cholesterol concentrations were determined from ethylenediaminetetraacetic acid blood as previously described.17 Lipoprotein profiles were obtained after the separation of 200 μL pooled plasma by fast protein liquid chromatography (Pharmacia P-500, Uppsala, Sweden) equipped with a Superose 6 column (Amersham Biosciences, Piscataway, NJ).
Tissue Lipid Extraction and Quantification
Liver tissue was lysed in phosphate-buffered saline (PBS) using a Precellys homogenizer (Bertin Instruments, Bretonneux, France) and then centrifuged at 8000 × g for 2 minutes at 4 °C. From the supernatant, an equivalent of 20 mg of tissue was transferred into a new tube, and the volume was filled up to 100 μL with PBS. The samples were treated with 750 μL of methanol and 2.5 mL of methyl tert-butyl ether, vortexed for 10 seconds, and rotated for 1 hour. After the addition of 600 μL ddH2O, the samples were vortexed for 10 seconds and left for 10 minutes. The phases were separated by centrifugation at 2000 × g for 10 minutes. The upper organic phase was collected, to which 200 μL of 2% TritonX-100 in methanol was added before evaporation under a N2 stream. Two hundred microliters of ddH2O were added to the samples, and the precipitates were dissolved by vortexing for 20 seconds, followed by incubation in a sonication bath for 1 hour and with a tip-sonicator for 10 seconds. The lipid levels were determined as described above.
Hematoxylin and Eosin and Masson's Trichrome Staining
Fresh liver tissue was fixed in 4% PBS-buffered paraformaldehyde for 24 hours and embedded in paraffin. Slides with deparaffinized tissue sections were incubated with hematoxylin for 10 minutes and then with eosin for 1 minute. Masson's trichrome staining was performed as previously described.25
Measurement of Liver Injury Marker
Freshly isolated heparinized blood was centrifuged for 7 minutes at 5200 × g and 4 °C, and plasma was collected. Alanine aminotransferase and aspartate aminotransferase (AST) concentrations in plasma were analyzed with Fuji Dri-Chem NX500 (FUJIFILM Holdings Corporation, Tokyo, Japan).
Measurement of Hematological Parameters and Blood Glucose
Blood samples were collected in ethylenediaminetetraacetic acid-coated tubes and analyzed using the V-Sight Vet Hematology Analyzer (A. Menarini Diagnostics, Florence, Italy) according to the manufacturer's protocol.
Glucose was measured from a drop of tail vein blood using a glucometer (AccuCheck, Roche Holding AG, Basel, Switzerland).
Sample Preparation for Mass Spectrometry-Based Proteomics and Measurement
One milliliter of sodium dodecyl sulfate buffer (0.1 M Tris-HCl, pH 8.5%, and 4% sodium dodecyl sulfate) was added to approximately 100 mg liver tissue from Lal−/− mice. Samples were homogenized in a BeatBox tissue homogenizer (PreOmics GmbH; Martinsried, Germany) at 850 rpm and boiled for 10 minutes at 95 °C. Lysates were sonicated using a tip-sonicator and centrifuged at 16,000 × g for 10 minutes. Protein concentrations were determined in the supernatant using the DC Protein Assay (Bio-Rad Laboratories, Hercules, CA). Proteins were reduced and alkylated with final concentrations of 10 mM tris(2-carboxyethyl)phosphine and 50 mM 2-chloroacetamide. Proteins were digested using the protein aggregation capture method26 on a KingFisher Flex robot (Thermo Fisher Scientific, Waltham, MA) in 96-well format. Briefly, magnetic hydroxyl beads (ReSyn Biosciences, Gauteng, South Africa) were mixed with 30 μg protein lysate 1:4 (w:w) in the 96-well plate. A digestion solution containing endoproteinases LysC (1:500, w:w) and trypsin (1:100, w:w) was added to the second 96-well plate. Five additional 96-well plates containing 500 μL 100% acetonitrile (ACN), 700 μL 100% ACN, 1 mL 100% ACN, 1 mL 100% ethanol, and 1 mL 100% ethanol, respectively, were prepared. An in-house program was used to digest the samples. Peptides were purified with 3 × C18 StageTips.27 EvoTips (Evosep, Odense, Denmark) were loaded with 200 ng of purified peptides, which were then separated on a 15-cm column with 150 μM ID packed with C18 beads (1.9 μm) (Pepsep) using Evosep ONE HPLC. The separated peptides were injected into a timsTOF Pro 2 mass spectrometer (Bruker Daltonics, Bremen, Germany) operated in diaPASEF mode, utilizing a CaptiveSpray ionization source with a 20-μm emitter. Mass spectrometry (MS) data were collected between 100 and 1700 m/z. Each MS/MS data acquisition was performed with diaPASEF cycle of 1.8 s and covered ion mobility range between 1.6 and 0.6 1/K0. Ion mobility was calibrated with 622.0289, 922.0097, and 1221.9906 Agilent ESI-L Tuning Mix ions. For diaPASEF, a long gradient with 10 diaPASEF scans with 3 25 Da windows per ramp, a mass range between 400 and 1201 Da, and a mobility range between 1.43 and 0.6 1/K0 was used. The collision energy parameters were set to 59 eV at 1/K0 = 1.3 and linearly decreased to 20 eV at 1/K0 = 0.85 Vs cm−2 during the measurement. Accumulation time and PASEF ramp time were 100 ms.
Proteomics Data Quantification and Bioinformatics
Protein quantification was performed with DIA-NN (1.8.1),28,29 with raw data searched against the Mouse Uniprot reviewed FASTA file downloaded on August 09, 2021 with the following parameters: FASTA digest for library-free search/library generation, deep learning-based spectra, retention time, and ion mobility spectra prediction. Trypsin-digested peptides with up to 2 missed cleavages were accepted, along with 3 variable modifications: N-terminal methionine excision, cysteine carbamidomethylation, and methionine oxidation. The default peptide parameters were 7–30 for the peptide length range, 1–4 for the precursor charge range, 300–1800 for the precursor m/z range, and 200–1800 for the fragment ion m/z range. The precursor false discovery rate (FDR) was set to 1%, mass accuracy to 10 ppm, and MS1 accuracy to 20 ppm. Isotopologues were used in the search, along with match between runs, heuristic protein inference (protein inference was set to genes), and exclusion of shared spectra. Searches were conducted in double-pass mode using a robust, high-precision liquid chromatography quantification strategy, with cross-run normalization as a function of retention time and smart profiling for library generation. Data were analyzed using Perseus (1.6.15.0)30 and Jupyter Notebook with Python 3.9. Protein intensities were log2 transformed before filtering the data for 70% of valid values in at least 1 group. Data were imputed to fill missing abundance values by drawing random numbers from a Gaussian distribution with a standard deviation of 30% and a downshift of 1.8 standard deviations from the mean relative to that of the proteome abundance distribution. Principal component analysis (PCA) was performed on the z-scored data in Python utilizing the packages Pandas, Matplotlib, Numpy, Seaborn, Sklearn, and Bioinfokit. Statistically significant changes were determined by a t-test with S0 = 0.1 and FDR = 0.05. Reactome pathway enrichment with the PANTHER Overrepresentation Test29,30 was performed using 2 clusters representing significantly increased proteins in the livers of lanifibranor- or vehicle-treated Lal−/− mice. KEGG pathway enrichment was performed with z-scored intensities from significantly changed proteins using Pathview.31,32 PPAR signaling was selected from enriched KEGG pathways and changed proteins related to it were counted and visualized. The number of significantly upregulated proteins from livers of vehicle- and lanifibranor-treated mice associated with specific UniProt keywords was determined by counting annotated proteins (Swiss-Prot) from relevant UniProt keywords.
Protein matrix, enriched Reactome pathways, and proteins related to specific UniProt keywords are listed in Table A1.
Nuclear Magnetic Resonance (NMR) Metabolomics
Metabolomics analysis by NMR spectroscopy was performed as previously described.33 Briefly, 20 mg of liver tissue was mixed with a solution of ice-cold methanol and high-purity H2O (2:1) to inhibit enzymatic processes and precipitate proteins. Samples were homogenized 2 times for 20 seconds using a Precellys 24 tissue homogenizer (Bertin Technologies, Montigny-le-Bretonneux, France) in tubes containing Precellys beads (1.4 mm zirconium oxide beads, Bertin Technologies). Next, samples were centrifuged at 9703 × g for 30 minutes at 4 °C, the supernatants were transferred to new tubes and lyophilized at <1 Torr, 824 × g, 25 °C for 10 hours in a vacuum-drying chamber (Savant Speedvac SPD210 vacuum concentrator) with an attached cooling trap (Savant RVT450 refrigerated vapor trap) and vacuum pump (VLP120) (Thermo Scientific, Waltham, MA). Samples were redissolved in 500 μL NMR buffer consisting of D2O, 0.08 M Na2HPO4, 4.6 mM 3-(trimethylsilyl) propionic acid-2,2,3,3-d4 sodium salt (TSP), and 0.04 (w/v)% NaN3 (pH adjusted to 7.4 with HCl and NaOH) and transferred to 5-mm NMR tubes. NMR spectroscopy was carried out using a Bruker Avance Neo 600 MHz spectrometer coupled to a TXI probe head at 310 K. 1H 1D NMR spectra were obtained using the Carr–Purcell–Meiboom–Gill (CPMG) pulse sequence (cpmgpr1d, 128 scans, 73,728 points in F1, 12,019.230 Hz spectral width, recycle delay 4 seconds) with a pre-saturation for water suppression. NMR spectra processing, including Fourier transformation of the free induction decay, automatic phasing, and baseline correction, was done using Bruker Topspin software version 4.0.2. A Matlab script (software version 2014b) was used for importing the spectra. The peaks surrounding the water, TSP, and methanol signals were eliminated, the NMR spectra were aligned,34 and a probabilistic quotient normalization35 was carried out. Chenomx NMR Suite 8.4 (Chenomx Inc, Edmonton, AB, Canada), the human metabolome database,36 and reference chemicals were used to identify metabolites. Metabolites were quantified by signal integration of the normalized spectra. For each metabolite, a representative peak with no overlapping signals was determined. The start and end points of the integration were then selected to revolve around this peak, and the areas of all peaks were integrated for all samples and metabolites using an R script (RStudio 2023 with R version 4.1.3) and provided as arbitrary units (a.u.) proportional to the concentration of the respective analyte. MetaboAnalyst 5.037 was used to calculate univariate statistics (shown as volcano plot). An unpaired 2-tailed Student's t-test with Benjamini-Hochberg correction was used to calculate the significance of metabolite levels between controls and lanifibranor-treated mice. Since none of the metabolites revealed an FDR <0.05, uncorrected P values were plotted. For multivariate statistics, PCA and orthogonal partial least squares discriminant analysis were calculated. The statistical significance of the multivariate model was verified by permutation testing of the quality evaluation statistic Q2, which was barely significant (P = .11).
Statistics
Data analysis and statistics for proteomics and metabolomics data are described in the sections above. For other experiments, comparisons between groups were performed using the unpaired 2-tailed Student's t-test in GraphPad Prism 9.5.1. Data are presented as mean or as mean ± SD. Significance levels were set to: ∗P < .05, ∗∗P ≤ .01, ∗∗∗P ≤ .001, and ∗∗∗∗P ≤ .0001.
Results
Lanifibranor Treatment Causes Minor Changes in Organ Weight but Not Body Weight in Lal−/− Mice
We assessed the effects of lanifibranor treatment on the phenotype of female Lal−/− mice after 21 days of daily gavage with vehicle or lanifibranor (Figure 1A). The body weight of both groups was comparable (Figure 1B). The tissue-to-body weight ratio of liver, heart, small intestine, and brown adipose tissue was increased in lanifibranor-treated mice by 1.12, 1.32, 1.21, and 1.49 fold, respectively, whereas the ratio of subcutaneous white adipose tissue and spleen remained unchanged (Figure 1C). We next measured several hematologic parameters to determine the functional consequences of increased liver and heart weight in lanifibranor-treated mice but observed no changes in platelet number (Figure A1A), red blood cell number (Figure A1B), and hemoglobin levels (Figure A1C).Figure 1Lanifibranor treatment causes minor changes in organ weight but not body weight in lysosomal acid lipase (LAL) knockout (Lal−/−) mice. (A) Lal−/− mice were daily gavaged with a suspension of methylcellulose (1%) and poloxamer (0.1%) in water (vehicle) with or without 30 mg/kg lanifibranor for 21 days (created with BioRender.com). (B) Body weight and (C) ratio of tissue weight to body weight of lanifibranor- or vehicle-treated Lal−/− mice. Data represent mean values ± SD (n = 4–6), and statistical significance was determined by 2-tailed Student's t-tests. ∗P < .05, ∗∗P ≤ .01, ∗∗∗∗P ≤ .0001. BAT, brown adipose tissue; sWAT, subcutaneous white adipose tissue.
Lanifibranor Treatment Activated the PPAR Signaling Pathway in the Livers of Lal−/− Mice
To identify potential changes in protein expression following lanifibranor treatment, we performed untargeted label-free quantitative proteomic analysis on livers of lanifibranor- and vehicle-treated Lal−/− mice. The distinct clustering pattern of the data by PCA indicated differences between the 2 groups, with principal component 1 (PC1) explaining 39.3% and PC2 explaining 18.9% of the variance (Figure 2A). The abundance of 6151 quantified proteins covered 4 orders of magnitude, and the detection of multiple PPAR target proteins (Figure 2B) demonstrated the good quality of the proteomics data. Of the 510 significantly changed proteins, 256 were upregulated and 254 were downregulated in the livers of lanifibranor-treated Lal−/− mice (Figure 2C), indicating substantial remodeling of the proteome following treatment. The highest upregulated proteins included acyl-coenzyme A thioesterase 1 (ACOT1), peroxisomal bifunctional enzyme (EHHADH), cytochrome P450 4A14 (CYP4A14), CYP4A10, and ankyrin repeat and SOCS box containing 6 (ASB6), whereas peptidoglycan recognition protein 1 (PGLYRP1), ras-related protein Rab-3C (RAB3C), tubulin beta 4A class IVa (TUBB4A), serine/threonine-protein kinase PLK1 (PLK1), and laminin subunit alpha 1 (LAMA1) were among the 5 most downregulated proteins (Figure 2C). Of the 43 proteins annotated to the KEGG PPAR signaling pathway, 18 were upregulated in the livers of lanifibranor-treated mice, whereas none was upregulated in control livers (Figure 2D). The z-scored intensities of the upregulated proteins involved in PPAR signaling clearly demonstrated that treatment with the pan-PPAR agonist lanifibranor successfully triggered the expression of multiple PPAR targets, with the most pronounced effects on CYP4A10, EHHADH, and peroxisomal acyl-coenzyme A oxidase 1 (ACOX1) (Figure 2E).Figure 2Lanifibranor increased the expression of peroxisome proliferator-activated receptor (PPAR) signaling-associated proteins. (A) Plot of principal component analysis (PCA) and (B) the dynamic range of the quantified liver proteome with some PPAR target proteins upregulated in the livers of lanifibranor-treated Lal−/− mice. (C) The volcano plot displays 254 significantly increased and 246 significantly decreased proteins in the livers of lanifibranor-treated Lal−/− mice. (D) The number of significantly changed proteins associated with the KEGG PPAR signaling pathway and (E) their heatmap representing all significantly altered proteins annotated in the livers of vehicle- or lanifibranor-treated Lal−/− mice (n = 4–6). Statistical significance was determined by 2-tailed Student's t-tests with permutation-based FDR (FDR < 0.05, S0 = 0.1).
Functional Interpretation of Activated PPAR Signaling
To functionally interpret the lanifibranor-induced proteome changes, we performed an overrepresentation analysis on 2 clusters representing significantly upregulated and downregulated proteins in livers of lanifibranor-treated Lal−/− mice (Figure 3A). Numerous Reactome pathways were enriched, including fatty acid beta-oxidation, peroxisomal lipid metabolism, peroxisomal protein import, cholesterol biosynthesis, and lipid metabolism (Figure 3B). In contrast, immune system and signal transduction were among the pathways that were less enriched, indicating decreased inflammation in the livers of lanifibranor-treated mice (Figure 3B). Consistent with this finding, several inflammation-related Reactome pathways, including neutrophil degranulation and (innate) immune system, were enriched in control livers (Figure 3C). Extracellular matrix organization was the most enriched Reactome pathway in control livers, suggesting a profound impact of lanifibranor on extracellular matrix remodeling (Figure 3C). These data indicated that lanifibranor stimulated various processes related to lipid metabolism and peroxisomes and exhibited a favorable impact on liver inflammation in Lal−/− mice.Figure 3Treatment with lanifibranor induces peroxisome and lipid metabolism-related pathways in the livers of Lal−/− mice. (A) Heatmap of z-scored significantly changed proteins in the livers of lanifibranor- and vehicle-treated Lal−/− mice. (B) The highest significantly enriched Reactome pathways of proteins significantly upregulated and (C) downregulated in the livers of lanifibranor-treated Lal−/− mice. Data represent n = 4–6. Statistical significance was determined by (A) 2-tailed Student's t-tests with permutation-based FDR (FDR < 0.05, S0 = 0.1) and (B and C) by Fisher's exact with Benjamini-Hochberg correction (FDR < 0.05).
Lanifibranor Treatment Affects the Liver Proteome of Peroxisomes, Mitochondria, Lipid Metabolism, and Extracellular Matrix Proteins
To further scrutinize the proteomic changes induced by lanifibranor treatment, we quantitated the number of significantly dysregulated proteins annotated to specific UniProt keywords in the livers of lanifibranor- and vehicle-treated Lal−/− mice. Administration of lanifibranor triggered the upregulation of 44 proteins associated with peroxisomes, 74 with mitochondria, 12 with electron transport, and 92 with lipid metabolism (Figure 4A). In contrast, livers of control mice had only 2, 13, 0, and 7 significantly upregulated proteins associated with peroxisomes, mitochondria, electron transport, and lipid metabolism, respectively (Figure 4A). Increased abundance of 3-ketoacyl-CoA thiolase B, peroxisomal (ACAA1B), ACOT4, long-chain fatty acid CoA ligase 6 (ACSL6), peroxisomal membrane protein 11A (PEX11A), and peroxisomal acyl-CoA oxidase 1 (ACOX1) indicated elevated peroxisomal biogenesis and fatty acid oxidation (Figure 4B). Mitochondrion-related upregulated proteins included long-chain fatty acid CoA ligase 1 (ACSL1), optic atrophy 3 protein homolog (OPA3), and mitochondrial pyruvate carrier 1 (MPC1) (Figure 4C). In addition, several proteins important for oxidative phosphorylation, including cytochrome b5 type B (MT-CYB), cytochrome b-c1 complex subunit 6 (UQCRH), and NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 3 (NDUFA3) were upregulated following lanifibranor treatment (Figure 4D), confirming that pan-PPAR agonism enhanced processes related to peroxisomes and mitochondria. Most of the highest upregulated proteins related to lipid metabolism were also peroxisomal proteins (Figure 4E), further substantiating the efficacy of lanifibranor in triggering the expression of peroxisomal proteins. Notably, lanifibranor decreased the expression of multiple fibrosis-related extracellular matrix proteins, such as collagen alpha-2(I) chain (COL1A2) and collagen alpha-1(XIV) chain (COL14A1) (Figure 4F). Thus, lanifibranor upregulated proteins of lipid metabolism and downregulated proteins associated with fibrosis.Figure 4Lanifibranor treatment triggers the expression of proteins associated with peroxisomes, mitochondria, and lipid metabolism in the livers of Lal−/− mice. (A) The number of significantly changed proteins annotated to specific UniProt keywords in the livers of vehicle- or lanifibranor-treated Lal−/− mice. Heatmaps representing the most upregulated proteins in the livers of lanifibranor-treated Lal−/− mice annotated to (B) peroxisome, (C) mitochondrion, (D) electron transport, and (E) lipid metabolism. (F) Heatmap showing the most downregulated proteins annotated to the extracellular matrix. Data represent n = 4–6. Statistical significance was determined by 2-tailed Student's t-tests with permutation-based FDR (FDR < 0.05, S0 = 0.1).
Lanifibranor Treatment Has a Minor Impact on Liver Lipid Levels and Histology
Despite the observed upregulation of 92 proteins related to lipid metabolism (Figure 4A), hepatic TG, total and free cholesterol as well as cholesteryl ester concentrations remained unaltered (Figure 5A–D). Liver histology was unchanged, as evidenced by hematoxylin and eosin staining (Figure 5E) and comparable hepatic collagen content (Figure 5E), suggesting that despite significant proteome changes, lanifibranor treatment had little effect on hepatic steatosis and pathohistology.Figure 5Unaltered liver lipid levels and histology after lanifibranor treatment of Lal−/− mice. Concentrations of hepatic (A) triacylglycerols (TG), (B) total cholesterol (TC), (C) free cholesterol (FC), and (D) cholesteryl esters (CE) in vehicle- and lanifibranor-treated Lal−/− mice. Data represent means ± SD (n = 4). Statistical significance was determined by 2-tailed Student's t-tests. (E) Liver hematoxylin and eosin (H&E) (top row) and Masson's trichrome staining (bottom row).
To gain further insight into the consequences of lanifibranor treatment on hepatic metabolism, we performed a targeted metabolomic analysis, which revealed obvious changes in the liver metabolome upon pan-PPAR agonist treatment (Figure 6A). To determine whether metabolites differed significantly between lanifibranor-treated and control livers and to identify the metabolites primarily contributing to this difference, we applied multivariate orthogonal partial least squares discriminant analysis. This revealed a clear distinction between the groups (Figure 6B). Metabolites with increased hepatic concentrations upon treatment (filtered by their high score in the variable of importance during projection analysis) included glycerophosphocholine, alanine, glutamine, acetic acid, niacinamide, and glutathione (Figure 6C). In contrast, fumaric acid, phosphorylcholine, aspartic acid, dimethylamine, creatine, lysine, glutamic acid, formic acid, and malic acid were among the metabolites that were decreased in lanifibranor-treated Lal−/− livers (Figure 6C). These data suggest that lanifibranor treatment markedly affects the metabolome in the liver of Lal−/− mice.Figure 6Treatment with lanifibranor leads to changes in liver metabolite concentrations in Lal−/− mice. (A) Volcano plot of metabolites with different abundances in the livers of vehicle- and lanifibranor-treated Lal−/− mice. (B) Orthogonal partial least squares discriminant analysis (O-PLS-DA) plot of liver metabolites and (C) ranking of metabolites based on the variable importance during projection (VIP) score. Data represent n = 4–5. (A) Statistical significance was determined by 2-tailed Student's t-tests with Benjamini-Hochberg correction (raw P value was plotted).
Improved Inflammation and Dyslipidemia Following Lanifibranor Treatment
Since the considerable changes in the proteome could not be explained by minor changes in liver lipid levels and histology, we tested whether treatment with the pan-PPAR agonist affected circulating lipid concentrations or systemic inflammation. We observed a trend toward lower plasma alanine aminotransferase (Figure 7A) and significantly decreased plasma AST levels (Figure 7B), indicating a reduction in hepatocellular damage. A marked reduction in circulating white blood cell (Figure 7C) and monocyte (Figure 7D) numbers suggested a beneficial impact of activated PPAR signaling on systemic inflammation. Finally, we examined the response of circulating lipid levels to lanifibranor treatment and observed decreased plasma TG and TC concentrations (Figure 7E) coupled with a reduction in low-density lipoprotein (LDL)-TC and an increase in high-density lipoprotein (HDL)-TC content. In contrast, blood glucose levels remained comparable (Figure A1D). These data indicate that lanifibranor attenuates liver and systemic inflammation while simultaneously improving dyslipidemia in Lal−/− mice.Figure 7Lanifibranor treatment reduced liver inflammation and improved dyslipidemia in Lal−/− mice. Circulating levels of (A) alanine aminotransferase (ALT) and (B) aspartate aminotransferase (AST) in vehicle- and lanifibranor-treated Lal−/− mice. Number of (C) white blood cells (WBC) and (D) monocytes (MON), (E) plasma TG and TC concentrations, and (F) lipoprotein distribution in pooled samples from vehicle- and lanifibranor-treated Lal−/− mice. Data represent means ± SD (n = 4–6). ∗P < .05, ∗∗P ≤ .01. Statistical significance was determined by 2-tailed Student's t-tests.
Lanifibranor Treatment Causes Minor Changes in Organ Weight but Not Body Weight in Lal−/− Mice
We assessed the effects of lanifibranor treatment on the phenotype of female Lal−/− mice after 21 days of daily gavage with vehicle or lanifibranor (Figure 1A). The body weight of both groups was comparable (Figure 1B). The tissue-to-body weight ratio of liver, heart, small intestine, and brown adipose tissue was increased in lanifibranor-treated mice by 1.12, 1.32, 1.21, and 1.49 fold, respectively, whereas the ratio of subcutaneous white adipose tissue and spleen remained unchanged (Figure 1C). We next measured several hematologic parameters to determine the functional consequences of increased liver and heart weight in lanifibranor-treated mice but observed no changes in platelet number (Figure A1A), red blood cell number (Figure A1B), and hemoglobin levels (Figure A1C).Figure 1Lanifibranor treatment causes minor changes in organ weight but not body weight in lysosomal acid lipase (LAL) knockout (Lal−/−) mice. (A) Lal−/− mice were daily gavaged with a suspension of methylcellulose (1%) and poloxamer (0.1%) in water (vehicle) with or without 30 mg/kg lanifibranor for 21 days (created with BioRender.com). (B) Body weight and (C) ratio of tissue weight to body weight of lanifibranor- or vehicle-treated Lal−/− mice. Data represent mean values ± SD (n = 4–6), and statistical significance was determined by 2-tailed Student's t-tests. ∗P < .05, ∗∗P ≤ .01, ∗∗∗∗P ≤ .0001. BAT, brown adipose tissue; sWAT, subcutaneous white adipose tissue.
Lanifibranor Treatment Activated the PPAR Signaling Pathway in the Livers of Lal−/− Mice
To identify potential changes in protein expression following lanifibranor treatment, we performed untargeted label-free quantitative proteomic analysis on livers of lanifibranor- and vehicle-treated Lal−/− mice. The distinct clustering pattern of the data by PCA indicated differences between the 2 groups, with principal component 1 (PC1) explaining 39.3% and PC2 explaining 18.9% of the variance (Figure 2A). The abundance of 6151 quantified proteins covered 4 orders of magnitude, and the detection of multiple PPAR target proteins (Figure 2B) demonstrated the good quality of the proteomics data. Of the 510 significantly changed proteins, 256 were upregulated and 254 were downregulated in the livers of lanifibranor-treated Lal−/− mice (Figure 2C), indicating substantial remodeling of the proteome following treatment. The highest upregulated proteins included acyl-coenzyme A thioesterase 1 (ACOT1), peroxisomal bifunctional enzyme (EHHADH), cytochrome P450 4A14 (CYP4A14), CYP4A10, and ankyrin repeat and SOCS box containing 6 (ASB6), whereas peptidoglycan recognition protein 1 (PGLYRP1), ras-related protein Rab-3C (RAB3C), tubulin beta 4A class IVa (TUBB4A), serine/threonine-protein kinase PLK1 (PLK1), and laminin subunit alpha 1 (LAMA1) were among the 5 most downregulated proteins (Figure 2C). Of the 43 proteins annotated to the KEGG PPAR signaling pathway, 18 were upregulated in the livers of lanifibranor-treated mice, whereas none was upregulated in control livers (Figure 2D). The z-scored intensities of the upregulated proteins involved in PPAR signaling clearly demonstrated that treatment with the pan-PPAR agonist lanifibranor successfully triggered the expression of multiple PPAR targets, with the most pronounced effects on CYP4A10, EHHADH, and peroxisomal acyl-coenzyme A oxidase 1 (ACOX1) (Figure 2E).Figure 2Lanifibranor increased the expression of peroxisome proliferator-activated receptor (PPAR) signaling-associated proteins. (A) Plot of principal component analysis (PCA) and (B) the dynamic range of the quantified liver proteome with some PPAR target proteins upregulated in the livers of lanifibranor-treated Lal−/− mice. (C) The volcano plot displays 254 significantly increased and 246 significantly decreased proteins in the livers of lanifibranor-treated Lal−/− mice. (D) The number of significantly changed proteins associated with the KEGG PPAR signaling pathway and (E) their heatmap representing all significantly altered proteins annotated in the livers of vehicle- or lanifibranor-treated Lal−/− mice (n = 4–6). Statistical significance was determined by 2-tailed Student's t-tests with permutation-based FDR (FDR < 0.05, S0 = 0.1).
Functional Interpretation of Activated PPAR Signaling
To functionally interpret the lanifibranor-induced proteome changes, we performed an overrepresentation analysis on 2 clusters representing significantly upregulated and downregulated proteins in livers of lanifibranor-treated Lal−/− mice (Figure 3A). Numerous Reactome pathways were enriched, including fatty acid beta-oxidation, peroxisomal lipid metabolism, peroxisomal protein import, cholesterol biosynthesis, and lipid metabolism (Figure 3B). In contrast, immune system and signal transduction were among the pathways that were less enriched, indicating decreased inflammation in the livers of lanifibranor-treated mice (Figure 3B). Consistent with this finding, several inflammation-related Reactome pathways, including neutrophil degranulation and (innate) immune system, were enriched in control livers (Figure 3C). Extracellular matrix organization was the most enriched Reactome pathway in control livers, suggesting a profound impact of lanifibranor on extracellular matrix remodeling (Figure 3C). These data indicated that lanifibranor stimulated various processes related to lipid metabolism and peroxisomes and exhibited a favorable impact on liver inflammation in Lal−/− mice.Figure 3Treatment with lanifibranor induces peroxisome and lipid metabolism-related pathways in the livers of Lal−/− mice. (A) Heatmap of z-scored significantly changed proteins in the livers of lanifibranor- and vehicle-treated Lal−/− mice. (B) The highest significantly enriched Reactome pathways of proteins significantly upregulated and (C) downregulated in the livers of lanifibranor-treated Lal−/− mice. Data represent n = 4–6. Statistical significance was determined by (A) 2-tailed Student's t-tests with permutation-based FDR (FDR < 0.05, S0 = 0.1) and (B and C) by Fisher's exact with Benjamini-Hochberg correction (FDR < 0.05).
Lanifibranor Treatment Affects the Liver Proteome of Peroxisomes, Mitochondria, Lipid Metabolism, and Extracellular Matrix Proteins
To further scrutinize the proteomic changes induced by lanifibranor treatment, we quantitated the number of significantly dysregulated proteins annotated to specific UniProt keywords in the livers of lanifibranor- and vehicle-treated Lal−/− mice. Administration of lanifibranor triggered the upregulation of 44 proteins associated with peroxisomes, 74 with mitochondria, 12 with electron transport, and 92 with lipid metabolism (Figure 4A). In contrast, livers of control mice had only 2, 13, 0, and 7 significantly upregulated proteins associated with peroxisomes, mitochondria, electron transport, and lipid metabolism, respectively (Figure 4A). Increased abundance of 3-ketoacyl-CoA thiolase B, peroxisomal (ACAA1B), ACOT4, long-chain fatty acid CoA ligase 6 (ACSL6), peroxisomal membrane protein 11A (PEX11A), and peroxisomal acyl-CoA oxidase 1 (ACOX1) indicated elevated peroxisomal biogenesis and fatty acid oxidation (Figure 4B). Mitochondrion-related upregulated proteins included long-chain fatty acid CoA ligase 1 (ACSL1), optic atrophy 3 protein homolog (OPA3), and mitochondrial pyruvate carrier 1 (MPC1) (Figure 4C). In addition, several proteins important for oxidative phosphorylation, including cytochrome b5 type B (MT-CYB), cytochrome b-c1 complex subunit 6 (UQCRH), and NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 3 (NDUFA3) were upregulated following lanifibranor treatment (Figure 4D), confirming that pan-PPAR agonism enhanced processes related to peroxisomes and mitochondria. Most of the highest upregulated proteins related to lipid metabolism were also peroxisomal proteins (Figure 4E), further substantiating the efficacy of lanifibranor in triggering the expression of peroxisomal proteins. Notably, lanifibranor decreased the expression of multiple fibrosis-related extracellular matrix proteins, such as collagen alpha-2(I) chain (COL1A2) and collagen alpha-1(XIV) chain (COL14A1) (Figure 4F). Thus, lanifibranor upregulated proteins of lipid metabolism and downregulated proteins associated with fibrosis.Figure 4Lanifibranor treatment triggers the expression of proteins associated with peroxisomes, mitochondria, and lipid metabolism in the livers of Lal−/− mice. (A) The number of significantly changed proteins annotated to specific UniProt keywords in the livers of vehicle- or lanifibranor-treated Lal−/− mice. Heatmaps representing the most upregulated proteins in the livers of lanifibranor-treated Lal−/− mice annotated to (B) peroxisome, (C) mitochondrion, (D) electron transport, and (E) lipid metabolism. (F) Heatmap showing the most downregulated proteins annotated to the extracellular matrix. Data represent n = 4–6. Statistical significance was determined by 2-tailed Student's t-tests with permutation-based FDR (FDR < 0.05, S0 = 0.1).
Lanifibranor Treatment Has a Minor Impact on Liver Lipid Levels and Histology
Despite the observed upregulation of 92 proteins related to lipid metabolism (Figure 4A), hepatic TG, total and free cholesterol as well as cholesteryl ester concentrations remained unaltered (Figure 5A–D). Liver histology was unchanged, as evidenced by hematoxylin and eosin staining (Figure 5E) and comparable hepatic collagen content (Figure 5E), suggesting that despite significant proteome changes, lanifibranor treatment had little effect on hepatic steatosis and pathohistology.Figure 5Unaltered liver lipid levels and histology after lanifibranor treatment of Lal−/− mice. Concentrations of hepatic (A) triacylglycerols (TG), (B) total cholesterol (TC), (C) free cholesterol (FC), and (D) cholesteryl esters (CE) in vehicle- and lanifibranor-treated Lal−/− mice. Data represent means ± SD (n = 4). Statistical significance was determined by 2-tailed Student's t-tests. (E) Liver hematoxylin and eosin (H&E) (top row) and Masson's trichrome staining (bottom row).
To gain further insight into the consequences of lanifibranor treatment on hepatic metabolism, we performed a targeted metabolomic analysis, which revealed obvious changes in the liver metabolome upon pan-PPAR agonist treatment (Figure 6A). To determine whether metabolites differed significantly between lanifibranor-treated and control livers and to identify the metabolites primarily contributing to this difference, we applied multivariate orthogonal partial least squares discriminant analysis. This revealed a clear distinction between the groups (Figure 6B). Metabolites with increased hepatic concentrations upon treatment (filtered by their high score in the variable of importance during projection analysis) included glycerophosphocholine, alanine, glutamine, acetic acid, niacinamide, and glutathione (Figure 6C). In contrast, fumaric acid, phosphorylcholine, aspartic acid, dimethylamine, creatine, lysine, glutamic acid, formic acid, and malic acid were among the metabolites that were decreased in lanifibranor-treated Lal−/− livers (Figure 6C). These data suggest that lanifibranor treatment markedly affects the metabolome in the liver of Lal−/− mice.Figure 6Treatment with lanifibranor leads to changes in liver metabolite concentrations in Lal−/− mice. (A) Volcano plot of metabolites with different abundances in the livers of vehicle- and lanifibranor-treated Lal−/− mice. (B) Orthogonal partial least squares discriminant analysis (O-PLS-DA) plot of liver metabolites and (C) ranking of metabolites based on the variable importance during projection (VIP) score. Data represent n = 4–5. (A) Statistical significance was determined by 2-tailed Student's t-tests with Benjamini-Hochberg correction (raw P value was plotted).
Improved Inflammation and Dyslipidemia Following Lanifibranor Treatment
Since the considerable changes in the proteome could not be explained by minor changes in liver lipid levels and histology, we tested whether treatment with the pan-PPAR agonist affected circulating lipid concentrations or systemic inflammation. We observed a trend toward lower plasma alanine aminotransferase (Figure 7A) and significantly decreased plasma AST levels (Figure 7B), indicating a reduction in hepatocellular damage. A marked reduction in circulating white blood cell (Figure 7C) and monocyte (Figure 7D) numbers suggested a beneficial impact of activated PPAR signaling on systemic inflammation. Finally, we examined the response of circulating lipid levels to lanifibranor treatment and observed decreased plasma TG and TC concentrations (Figure 7E) coupled with a reduction in low-density lipoprotein (LDL)-TC and an increase in high-density lipoprotein (HDL)-TC content. In contrast, blood glucose levels remained comparable (Figure A1D). These data indicate that lanifibranor attenuates liver and systemic inflammation while simultaneously improving dyslipidemia in Lal−/− mice.Figure 7Lanifibranor treatment reduced liver inflammation and improved dyslipidemia in Lal−/− mice. Circulating levels of (A) alanine aminotransferase (ALT) and (B) aspartate aminotransferase (AST) in vehicle- and lanifibranor-treated Lal−/− mice. Number of (C) white blood cells (WBC) and (D) monocytes (MON), (E) plasma TG and TC concentrations, and (F) lipoprotein distribution in pooled samples from vehicle- and lanifibranor-treated Lal−/− mice. Data represent means ± SD (n = 4–6). ∗P < .05, ∗∗P ≤ .01. Statistical significance was determined by 2-tailed Student's t-tests.
Discussion
Lanifibranor showed promising results in a phase 2b study in the treatment of patients with active MASH.23 Of the 247 patients enrolled in this study, 166 received either 1200 or 800 mg of lanifibranor once daily for 6 months and 81 patients were given placebo.23 The incidence of nausea, diarrhea, peripheral edema, anemia, and weight gain of approximately 3% occurred more frequently in the lanifibranor groups than in the placebo group. One patient developed mild heart failure, which was not attributable to the treatment. The results of this trial also indicated the potential for benefits with lanifibranor with respect to several secondary end points, including hepatic fibrosis, lipid profile, and glycemic control.23 An ongoing phase 3 clinical trial (NCT04849728) will further elucidate potential adverse effects. While lanifibranor has not yet been studied as a treatment option for LAL-D, it is plausible that it could offer therapeutic benefits, given the observed similarity in liver inflammation between patients with LAL-D and MASLD.8 The proteomic analysis confirmed that treatment with lanifibranor activated the PPAR signaling pathway in Lal−/− mice, with enriched terms related to fatty acid oxidation, peroxisomal biogenesis, and lipid metabolism. PEX11A and PEX7 were among the most upregulated peroxisome-related proteins and are essential for proper peroxisomal function and the prevention of dyslipidemia and obesity.38,39 The upregulated mitochondrial protein OPA3 is a crucial regulator of mitochondrial function,40 whereas the expression levels of ACSL1 and MPC1 are directly correlated with hepatic steatosis.41,42 However, counteracting the upregulation of proteins related to lipid metabolism, mitochondrial function, and fatty acid oxidation may have a superior effect, as these processes are impaired in Lal−/− compared to control mice.17 Following lanifibranor treatment, major components of oxidative phosphorylation were also upregulated, leading to a reduction in fumaric and malic acid, suggesting an increase in mitochondrial respiration. Consistent with several studies showing a favorable effect of pan-PPAR agonism on liver steatosis, inflammation, and fibrosis in mouse and rat models,22,24,43,44 lanifibranor decreased the expression of various fibrosis-related proteins in the livers of Lal−/− mice. In addition, the treatment increased the expression of glutathione, while reducing the concentration of circulating AST. This finding indicates that the reduced expression of proteins related to inflammation and fibrosis resulted in improved liver injury. Thus, despite the distinct pathologies of LAL-D and MASLD, lanifibranor has a beneficial effect on liver inflammation.
Lanifibranor treatment decreases hepatic TG concentrations in mice fed choline-deficient amino acid-defined high-fat diet (CDAA-HFD), high-fat/high-sucrose diet, and methionine-choline-deficient diet.22,44 In Lal−/− mice, lipids accumulate in lysosomes due to the absence of LAL.8 Consequently, these lipids cannot be mobilized, unlike lipids in cytosolic lipid droplets from livers affected by MASLD.45 Therefore, the reason why lanifibranor cannot alter TG and TC levels in the livers of Lal−/− mice may be due to the predominant accumulation of lipids within lysosomes and loss of a functional enzyme to degrade them upstream of the functional consequences of pan-PPAR agonism. Lipids accumulate in the livers of Lal−/− mice during the first weeks of life46 and starting lanifibranor treatment earlier may have a more pronounced impact on lipid metabolism and inflammation. For instance, feeding 5-week-old Lal−/− mice with a fenofibrate-enriched diet for 4 weeks reduced liver TC levels but did not affect TG concentration.12
Compared to the lanifibranor-promoted weight gain in MASH patients,23 the body weight of Lal−/− mice remained unchanged. However, lanifibranor treatment slightly increased the ratios of heart and liver to body weight in Lal−/− mice. In contrast, lanifibranor treatment reduced the liver-to-body weight ratio in WT mice fed a CDAA-HFD.24 However, it had no impact on body or liver weight in a rat model of liver cirrhosis43 or heart weight in rats24 and Alms1 mutant foz/foz mice. Heart weight was not discussed in other diet-induced models of MASLD.44 The phase 2b trial also showed no increase in heart-related complications in patients treated with lanifibranor.23 However, clinical trials have rarely assessed cardiovascular outcomes due to limited power in detecting the potential cardiovascular benefits of PPAR agonist treatment in MASLD patients.47 It would therefore be necessary to determine the influence of lanifibranor on heart weight and pathophysiology in various mouse models of MASLD. Despite an increased liver and heart weight relative to body weight in Lal−/− mice after lanifibranor treatment, spleen weight remained unchanged and no adverse effects on hematological parameters were observed. The slight increase in liver and heart weight in Lal−/− mice, without any changes in liver lipids and histology, might be attributed to the distinct pathophysiology present in LAL-D mice compared to genetic or diet-induced MASLD mouse and rat models. As lipid accumulation in LAL-D mice is already evident in the first few weeks of life,46 it is possible that young mice may exhibit signs of liver inflammation. Consequently, initiating treatment with lanifibranor at an earlier stage and/or a combination of lanifibranor with ERT may result in improved liver histology in LAL-D mice. Studies using lanifibranor in various animal models should also aim to assess organ weights to account for possible side effects of the treatment.
The decreased numbers of white blood cell and monocytes in lanifibranor-treated Lal−/− mice indicate decreased systemic inflammation, consistent with the known ability of PPAR signaling to modulate inflammation and macrophage infiltration.48 In addition, lanifibranor demonstrated a positive effect on plasma lipids by reducing TG and TC concentrations. This effect was also observed in control mice fed high-fat/high-sucrose diet, CDAA-HFD, western-type diet, methionine choline-deficient diet, in a mouse model of CCl4-induced liver fibrosis,22,44 as well as in MASH patients.23 To our knowledge, the consequences of lanifibranor treatment on plasma cholesterol levels in mouse models of MASLD or liver fibrosis have not been investigated. We also found an improved distribution of plasma lipoproteins following lanifibranor treatment, with decreased LDL-cholesterol and increased HDL-cholesterol levels. Similarly, HDL-cholesterol levels improved in lanifibranor-treated MASH patients, whereas LDL-cholesterol remained unchanged.23
In conclusion, the treatment with lanifibranor improved plasma levels of liver injury markers and lipid parameters, as well as lipoprotein distribution. Furthermore, it reduced the expression of various inflammation- and fibrosis-related proteins in the livers of Lal−/− mice. These findings suggest potential additive effects of lanifibranor to ERT that should be explored in future studies, as emphasized above. |
Title: Effects of Risk and Time Preferences on Diet Quality: Empirical Evidence from Rural Madagascar | Body:
1. Introduction
Nutritional improvement has been prioritised in many domestic and international policy agendas, including the Sustainable Development Goals. Since a key risk factor for all forms of malnutrition and diet-related diseases is poor dietary habits [1,2], understanding the underlying motivations for food-choice decisions is critical for the development of effective health policies.
Diet choice is largely related to various competing, interacting, and reinforcing economic, social, and political factors. In the empirical literature, consumer demand for different foods is often explained by price, product characteristics, and income [3] while treating preferences as heterogeneous to an individual. However, there is substantial evidence supporting the notion that behavioural economics is a promising approach to encouraging healthy food choices [4].
Increasing evidence shows that risk and time preferences, which are elements of behavioural economics, play an important role in diet choice [5,6]. An individual’s diet choice is accompanied by delayed or uncertain health consequences [7]. We may assume that risk-averse individuals may not want to adopt new diets or change their dietary behaviour. In contrast, risk-seeking individuals may be willing to divert from their usual routines. Regarding time preference, people who tend to discount the future may fail to invest in nutritious diets ‘now’ to achieve good health ‘in the future’.
In this way, we hypothesise that experimental choices of risk and time preferences are related to healthy eating behaviour measured through the likelihood of adopting a diverse diet. Dietary diversity is a measure of diet quality because it reflects nutrient adequacy [8]. For example, when people diversify their staple diets with more fruits, vegetables, animal-sourced foods, and dairy products, they increase their essential micronutrient intake [9].
Previous research shows the benefits of examining the nexus of behavioural economics and health behaviours. For instance, the relationships between risk and time preferences and healthy or risky behaviour [10,11,12,13,14,15,16] and health outcomes [17,18,19,20,21,22,23,24] have been extensively studied. However, similar evidence on the factors shaping diet choice is relatively limited, as summarised in Supplementary Table S1. Many of these studies are confined to the developed world, yet economic preferences exhibit substantial heterogeneity [25]. While basic nutrition is prioritised in poor economies, promoting healthy nutrition and behaviours to mitigate health risks (i.e., obesity) and sustain economic well-being is the focus in wealthy economies [26]. These observations require new interpretations in the context of developing countries. No prior research has explored the economic determinants underlying dietary choices in the context of this study, and empirical evidence is scarce.
Madagascar, a sub-Saharan African country, has an ‘alarming’ number of nutritional deficiencies. The prevalence of undernourishment in the population increased from 33.7% in 2004–2006 to 51% in 2020–2022, with the latter value being nearly double the average value for low-income countries [27]. Madagascar is in a very early stage of nutritional transition [28,29]. Almost monotonous energy-rich staple-based diets continue to characterise Malagasy cuisine, which features a large portion of rice accompanied by smaller portions of side dishes (laoka). The side dishes are primarily composed of vegetables and occasionally include fish or meat. Households often prioritise the quantity of rice consumed and pay little attention to a diverse diet. Notably, the country faces a substantial need for improved nutrition through healthy dietary choices.
Global evidence suggests that one of the main reasons for malnutrition is food scarcity, which may arise due to geographical or economic inaccessibility [27]. However, in Madagascar, food scarcity cannot be considered the prime reason for malnutrition because a large variety of natural resources, which could potentially be sufficient to feed the entire population, are available [30]. Although nutrient-dense low-cost foods, such as fruits, leafy vegetables, and legumes, are available and affordable in Madagascar, they are not often readily consumed [31,32,33,34], especially by the poorest households [31].
Another factor responsible for the Malagasy diet could be poverty, but poverty alone cannot explain their dietary choices. Rice is the most consumed food by Malagasy people regardless of their economic status, although it is not the most affordable carbohydrate staple [31]. Previous studies have attempted to investigate the diet choices of Malagasy people arising from nutritional and cultural beliefs and food practices [31,32,35], availability of alternative food sources [36,37], and the effect of different awareness approaches [34,38]; however, the understanding of why low-cost nutrient-dense locally available food is not readily consumed by Malagasy people, and what may be the underlying reason for a diet choice that is biased towards rice, remains limited.
This study aims to fill this gap by exploring the relationship between diet choices, as reflected in diverse diets, and risk and time preferences, using the originally collected data from rural Madagascar. We explore the reasons behind food choices and identify the factors that make changing dietary habits in Madagascar difficult from the perspective of consumer behaviour. This study contributes to the growing literature by suggesting the possible association between diet choice and economic preferences and also contributes to policy to provide more focused support for nutritional improvement by identifying behavioural characteristics.
2. Materials and Methods
2.1. Data Collection
This study used a subset of the household data collected for the FertilitY sensing and Variety Amelioration for Rice Yield (Fy Vary) project conducted jointly by the Japan International Research Center for Agricultural Sciences and the Ministry of Agriculture, Livestock, and Fisheries in Madagascar. We used data collected in January and February 2022, which correspond to the period before the main harvest season. The project site was the Vakinakaratra region in the Central Highlands in Madagascar, where 93% of the households are engaged in agriculture and 87% of them are rice farmers [39]. Three of the seven districts in the region were selected for sampling considering the access to the main road. In proportion to the size of each district, we selected 60 villages that ensure wide geographical variation, from which we then randomly selected 10 lowland rice-farming households. The selection yielded an initial sample of 600 households.
The main survey collected detailed information on various topics, including household demographics; socioeconomic information, including agricultural activities, off-farm income generation, household and farm assets, and monthly expenditure on food and non-food items; dietary habits at the household level; and 24 h dietary recall. Economic experiments were incorporated into the main survey. Of the 600 households who participated in the main survey, 539, particularly the head of the household or the person responsible for decision-making in the household, participated in the experiments. Informed consent was obtained from all participants. Ethical approval for this study was obtained from the Malagasy Ethical Committee for Science and Technology (N°014/2020-AM/CMEST/P).
2.2. Economic Experiments
Risk and time preferences were measured using hypothetical economic experiments designed to be sufficiently simple for participants to understand and allow us to estimate the desired preference parameters with theoretical consistency. The hypothetical approach is often used in field surveys to minimise costs and other associated logistic limitations, resulting in the same risk preference profiles as real choices and making no difference in the field [40,41]. A more straightforward task may be preferred when participants exhibit low numeracy as it generates less noisy behaviour but has similar predictive accuracy to a more complex task [42,43]. As our focus was on the relationship between dietary diversity and economic preferences rather than on estimating the average levels of risk aversion and patience in the study population, we measured preferences in terms of row-switching points in both experiments to eliminate any confounding effects that may arise due to parametric assumptions. The values of the choices were determined using several pretests. The choices provided in the experiments are listed in Table 1 and Table 2.
As shown in Table 1, the participants were given a choice between a guaranteed payoff and a 50/50 lottery. In the lottery, the participants were asked to imagine that they were going to pick one ball from a bag containing two differently coloured balls. If the colour of the ball was pink, they could win 8000 MGA. However, if it was green, they would lose and receive only 800 MGA; each option had a 50% probability. If the participants were unwilling to play, they would receive the corresponding payoff, the amount of which increases along the row in Table 1. Following Dohmen et al. [44], the participants were coded as either risk-averse or risk-seeking based on the corresponding row-switching point. As the expected value of the lottery was 4400 MGA, a risk-averse participant would prefer guaranteed payoffs equal to or smaller than 4400 MGA over the lottery (i.e., switching before the seventh row). Similarly, only a risk-seeking participant would prefer the lottery over guaranteed payoffs greater than 4400 MGA (i.e., switching at or after the seventh row). The risk preference measure used in the analysis was a binary variable indicating whether the participant was risk-averse or risk-seeking.
For time preference, the participants were asked to choose between money ‘today’ and a more considerable amount of money ‘one month later’. As Table 2 shows, today’s payoff decreases for each consecutive question. The corresponding row number in which the participants switched from today to later indicated their time preference. (Some participants are more impatient even in the final row of the choice table). We categorised the participants into three groups based on the distribution of the row-switching points. Participants who selected to switch during the earlier stage (≤4th row) were considered patient participants; those who shifted between 5th and 7th rows were categorised as moderately patient; and those who shifted in >8th row and never switched were categorised as impatient.
To account for other individual- and household-level determinants that might be associated with dietary diversity, we included control variables such as individual characteristics (gender, age, and level of formal education of the respondent), household characteristics (i.e., gender of the household head, household size, dependency ratio, land size, involvement in off-farm income generation activities, frequency of market visits, and monthly consumption expenditure), and farm characteristics (e.g., crop diversification and livestock ownership). The variable definitions and their means/proportions are provided in Section 3.
2.3. Empirical Estimation
A variety of methodologies have been used to see the relationship between economic preferences and health/healthy behaviour. For time preferences, proxies included questions on monetary or non-monetary intertemporal choices, which can be either hypothetical or incentivised [42,45], general self-reported measures based on agreement with statements related to patience [46,47], information on savings, financial planning horizons, and so on. Choice tasks, in which the participants choose between a smaller but more immediate award and a more desirable delayed award, represent the most common and typically used approach. These choices are either made in the monetary or non-monetary domain and then compared to actual health-related behaviour [14]. Choosing which method to utilise in eliciting economic preferences, however, is largely dependent on the research question and the characteristics of the sample population [48].
Excessive and unhealthy eating is most commonly assessed using body mass index (BMI) as an indicator [17,18,19,20,22,23,24,47]. Very few studies have estimated the overall diet quality in relation to risk or time preference [7,46,49]. The measurement of diet quality is usually difficult in low-income settings, where a lack of resources and a high level of technical capacity for collecting and analysing detailed dietary data co-exist [50]. In such contexts, dietary diversity (DD), the number of food groups consumed which has long been recognised as a key element of a high-quality diet, can be used in operationalising the diet quality. To either assess the economic access to food or to estimate the food groups a particular household is consuming, the Household Dietary Diversity Score (HDDS), a population-level indicator used as a proxy measure of household food access, is an appropriate tool [8,51].
The 12 food groups used to calculate the HDDS were cereals; roots and tubers; vegetables; fruits; meat, poultry, and offal; eggs; fish and seafood; pulses, legumes, nuts; milk and milk products; oil/fats; sugar/honey; and miscellaneous [8,51]. Each food group was assigned a score of 1 if it was consumed over the previous 24 h or 0 otherwise. The HDDS ranges from 0 to 12 and is equal to the total number of food groups consumed by the household. To consider the dietary patterns of the households at different HDDS levels, the households were categorised into three groups based on HDDS tertiles, which are commonly used for analytical purposes [8,51,52]. Based on the HDDS distribution, the three categories were low (HDDS ≤ 5), middle (HDDS = 6), and high (HDDS ≥ 7).
The basic equation that we estimate is given by Equation (1).
Yh = β1 Risk preference h + β2 Time preference h + β3
Xh + e(1)
Yh represents the dependent variable, which can be the HDDS of the hth household. Risk preference and Time preference, the main explanatory variables of interest, correspond to the risk and time preference measures estimated through the experiments. Xh is the vector of variables of the hth household, and e captures the error.
We conducted ordinary least squares (OLS) analyses for the HDDS. In addition, ordered logistic regression was specified for the categorised HDDS. For the OLS model, the kernel density plot, interquartile range test for normality, and Shapiro–Wilk W test for normal data indicated that the residuals had an approximately normal distribution. According to the multicollinearity test, the mean-variance inflation factor (VIF) value was 1.86, while the individual VIF values were lower than 5, indicating no collinearity among the predictors. For the ordered logistic regression model, we conducted the Brant test. We found that while one variable (frequency of market visits, p < 0.1) violated the underlying proportional odds assumption in ordered logistics, the Akaike information criterion and Bayesian information criterion made strong cases favouring the estimated ordered logit model over the other two alternative models, namely, the multinomial logit and generalised logit models.
2.1. Data Collection
This study used a subset of the household data collected for the FertilitY sensing and Variety Amelioration for Rice Yield (Fy Vary) project conducted jointly by the Japan International Research Center for Agricultural Sciences and the Ministry of Agriculture, Livestock, and Fisheries in Madagascar. We used data collected in January and February 2022, which correspond to the period before the main harvest season. The project site was the Vakinakaratra region in the Central Highlands in Madagascar, where 93% of the households are engaged in agriculture and 87% of them are rice farmers [39]. Three of the seven districts in the region were selected for sampling considering the access to the main road. In proportion to the size of each district, we selected 60 villages that ensure wide geographical variation, from which we then randomly selected 10 lowland rice-farming households. The selection yielded an initial sample of 600 households.
The main survey collected detailed information on various topics, including household demographics; socioeconomic information, including agricultural activities, off-farm income generation, household and farm assets, and monthly expenditure on food and non-food items; dietary habits at the household level; and 24 h dietary recall. Economic experiments were incorporated into the main survey. Of the 600 households who participated in the main survey, 539, particularly the head of the household or the person responsible for decision-making in the household, participated in the experiments. Informed consent was obtained from all participants. Ethical approval for this study was obtained from the Malagasy Ethical Committee for Science and Technology (N°014/2020-AM/CMEST/P).
2.2. Economic Experiments
Risk and time preferences were measured using hypothetical economic experiments designed to be sufficiently simple for participants to understand and allow us to estimate the desired preference parameters with theoretical consistency. The hypothetical approach is often used in field surveys to minimise costs and other associated logistic limitations, resulting in the same risk preference profiles as real choices and making no difference in the field [40,41]. A more straightforward task may be preferred when participants exhibit low numeracy as it generates less noisy behaviour but has similar predictive accuracy to a more complex task [42,43]. As our focus was on the relationship between dietary diversity and economic preferences rather than on estimating the average levels of risk aversion and patience in the study population, we measured preferences in terms of row-switching points in both experiments to eliminate any confounding effects that may arise due to parametric assumptions. The values of the choices were determined using several pretests. The choices provided in the experiments are listed in Table 1 and Table 2.
As shown in Table 1, the participants were given a choice between a guaranteed payoff and a 50/50 lottery. In the lottery, the participants were asked to imagine that they were going to pick one ball from a bag containing two differently coloured balls. If the colour of the ball was pink, they could win 8000 MGA. However, if it was green, they would lose and receive only 800 MGA; each option had a 50% probability. If the participants were unwilling to play, they would receive the corresponding payoff, the amount of which increases along the row in Table 1. Following Dohmen et al. [44], the participants were coded as either risk-averse or risk-seeking based on the corresponding row-switching point. As the expected value of the lottery was 4400 MGA, a risk-averse participant would prefer guaranteed payoffs equal to or smaller than 4400 MGA over the lottery (i.e., switching before the seventh row). Similarly, only a risk-seeking participant would prefer the lottery over guaranteed payoffs greater than 4400 MGA (i.e., switching at or after the seventh row). The risk preference measure used in the analysis was a binary variable indicating whether the participant was risk-averse or risk-seeking.
For time preference, the participants were asked to choose between money ‘today’ and a more considerable amount of money ‘one month later’. As Table 2 shows, today’s payoff decreases for each consecutive question. The corresponding row number in which the participants switched from today to later indicated their time preference. (Some participants are more impatient even in the final row of the choice table). We categorised the participants into three groups based on the distribution of the row-switching points. Participants who selected to switch during the earlier stage (≤4th row) were considered patient participants; those who shifted between 5th and 7th rows were categorised as moderately patient; and those who shifted in >8th row and never switched were categorised as impatient.
To account for other individual- and household-level determinants that might be associated with dietary diversity, we included control variables such as individual characteristics (gender, age, and level of formal education of the respondent), household characteristics (i.e., gender of the household head, household size, dependency ratio, land size, involvement in off-farm income generation activities, frequency of market visits, and monthly consumption expenditure), and farm characteristics (e.g., crop diversification and livestock ownership). The variable definitions and their means/proportions are provided in Section 3.
2.3. Empirical Estimation
A variety of methodologies have been used to see the relationship between economic preferences and health/healthy behaviour. For time preferences, proxies included questions on monetary or non-monetary intertemporal choices, which can be either hypothetical or incentivised [42,45], general self-reported measures based on agreement with statements related to patience [46,47], information on savings, financial planning horizons, and so on. Choice tasks, in which the participants choose between a smaller but more immediate award and a more desirable delayed award, represent the most common and typically used approach. These choices are either made in the monetary or non-monetary domain and then compared to actual health-related behaviour [14]. Choosing which method to utilise in eliciting economic preferences, however, is largely dependent on the research question and the characteristics of the sample population [48].
Excessive and unhealthy eating is most commonly assessed using body mass index (BMI) as an indicator [17,18,19,20,22,23,24,47]. Very few studies have estimated the overall diet quality in relation to risk or time preference [7,46,49]. The measurement of diet quality is usually difficult in low-income settings, where a lack of resources and a high level of technical capacity for collecting and analysing detailed dietary data co-exist [50]. In such contexts, dietary diversity (DD), the number of food groups consumed which has long been recognised as a key element of a high-quality diet, can be used in operationalising the diet quality. To either assess the economic access to food or to estimate the food groups a particular household is consuming, the Household Dietary Diversity Score (HDDS), a population-level indicator used as a proxy measure of household food access, is an appropriate tool [8,51].
The 12 food groups used to calculate the HDDS were cereals; roots and tubers; vegetables; fruits; meat, poultry, and offal; eggs; fish and seafood; pulses, legumes, nuts; milk and milk products; oil/fats; sugar/honey; and miscellaneous [8,51]. Each food group was assigned a score of 1 if it was consumed over the previous 24 h or 0 otherwise. The HDDS ranges from 0 to 12 and is equal to the total number of food groups consumed by the household. To consider the dietary patterns of the households at different HDDS levels, the households were categorised into three groups based on HDDS tertiles, which are commonly used for analytical purposes [8,51,52]. Based on the HDDS distribution, the three categories were low (HDDS ≤ 5), middle (HDDS = 6), and high (HDDS ≥ 7).
The basic equation that we estimate is given by Equation (1).
Yh = β1 Risk preference h + β2 Time preference h + β3
Xh + e(1)
Yh represents the dependent variable, which can be the HDDS of the hth household. Risk preference and Time preference, the main explanatory variables of interest, correspond to the risk and time preference measures estimated through the experiments. Xh is the vector of variables of the hth household, and e captures the error.
We conducted ordinary least squares (OLS) analyses for the HDDS. In addition, ordered logistic regression was specified for the categorised HDDS. For the OLS model, the kernel density plot, interquartile range test for normality, and Shapiro–Wilk W test for normal data indicated that the residuals had an approximately normal distribution. According to the multicollinearity test, the mean-variance inflation factor (VIF) value was 1.86, while the individual VIF values were lower than 5, indicating no collinearity among the predictors. For the ordered logistic regression model, we conducted the Brant test. We found that while one variable (frequency of market visits, p < 0.1) violated the underlying proportional odds assumption in ordered logistics, the Akaike information criterion and Bayesian information criterion made strong cases favouring the estimated ordered logit model over the other two alternative models, namely, the multinomial logit and generalised logit models.
3. Results
3.1. Descriptive Statistics
Table 3 presents the summary statistics of the key-dependent and independent variables. On average, the HDDS for our sample was 5.68, which is similar to that estimated in the previous literature on Malagasy dietary diversity, as presented in Supplementary Table S2. The participants were primarily middle-aged (47.04 ± 14.22 years) men (51%) having at least some formal education (5.14 ± 3.61 years of schooling). Among the participants, 42% were risk-averse. Most of the households were male-headed (88%). The average household size was 4.5. We used total household consumption expenditure as a proxy variable in the absence of accurate monthly household income data. The average monthly household income expenditure was approximately 250,000 MGA, or approximately 63 USD, during the survey period. Most households (89%) had at least one member who had engaged in off-farm income generation activities during the last two months. We also included a binary control variable to identify whether the respondent was primarily responsible for preparing meals to reduce possible measurement errors in the consumption questionnaire. Almost half of the respondents were not primarily responsible for household meal preparation and consumption.
We generated a crop diversification indicator that represented the number of crop species (excluding rice) produced on the farm. On average, the households produced at least one crop other than rice. We determined the livestock ownership of a household by calculating the total livestock unit (TLU), which allowed us to compare different livestock herds. The estimated average TLU per household was 2.03. Most respondents visited local markets at least weekly. In the context of this study, the term ‘local market’ refers to a nearby village or communal market that primarily sells food and daily essentials, such as oil, sugar, and salt.
3.2. Estimation
Table 4 presents the results of the econometric estimations. The ordered logistic regression results of the HDDS categories are reported as adjusted odds ratios (ORs) for moving from one category to the next versus remaining in the same category. Regardless of the model specification, risk aversion was associated with a low HDDS. Figure 1 illustrates the predictive margins with 95% confidence intervals for risk and time preference on HDDS based on Model 1. The results of Model 1 suggest that impatient individuals are more likely to have lower HDDS. Therefore, respondents who were more risk-averse and less patient were likely to be in the lower category of the HDDS. Moreover, the frequency of market visits, involvement in off-farm income generation activities, and the total value of assets owned by households were positively associated with HDDS. These findings are robust across alternative model specifications.
As the ordered logistic regression model identifies the natural ordering of dependent variables, it further allows the estimation of the marginal effect for each independent variable and category of dependent variable pairing. According to Table 5, the marginal effects of Model 2 indicate that the likelihood of being in the low HDDS category increases by 16 percentage points and the likelihood of being in the middle and higher HDDS categories decreases by 5 and 11 percentage points, respectively, if the individual becomes risk-averse instead of risk-seeking, all else remaining constant. Furthermore, on average, impatient individuals are 13 percentage points more likely than patient individuals to be in the low HDDS category and approximately 9 percentage points less likely to be in the high HDDS category.
3.1. Descriptive Statistics
Table 3 presents the summary statistics of the key-dependent and independent variables. On average, the HDDS for our sample was 5.68, which is similar to that estimated in the previous literature on Malagasy dietary diversity, as presented in Supplementary Table S2. The participants were primarily middle-aged (47.04 ± 14.22 years) men (51%) having at least some formal education (5.14 ± 3.61 years of schooling). Among the participants, 42% were risk-averse. Most of the households were male-headed (88%). The average household size was 4.5. We used total household consumption expenditure as a proxy variable in the absence of accurate monthly household income data. The average monthly household income expenditure was approximately 250,000 MGA, or approximately 63 USD, during the survey period. Most households (89%) had at least one member who had engaged in off-farm income generation activities during the last two months. We also included a binary control variable to identify whether the respondent was primarily responsible for preparing meals to reduce possible measurement errors in the consumption questionnaire. Almost half of the respondents were not primarily responsible for household meal preparation and consumption.
We generated a crop diversification indicator that represented the number of crop species (excluding rice) produced on the farm. On average, the households produced at least one crop other than rice. We determined the livestock ownership of a household by calculating the total livestock unit (TLU), which allowed us to compare different livestock herds. The estimated average TLU per household was 2.03. Most respondents visited local markets at least weekly. In the context of this study, the term ‘local market’ refers to a nearby village or communal market that primarily sells food and daily essentials, such as oil, sugar, and salt.
3.2. Estimation
Table 4 presents the results of the econometric estimations. The ordered logistic regression results of the HDDS categories are reported as adjusted odds ratios (ORs) for moving from one category to the next versus remaining in the same category. Regardless of the model specification, risk aversion was associated with a low HDDS. Figure 1 illustrates the predictive margins with 95% confidence intervals for risk and time preference on HDDS based on Model 1. The results of Model 1 suggest that impatient individuals are more likely to have lower HDDS. Therefore, respondents who were more risk-averse and less patient were likely to be in the lower category of the HDDS. Moreover, the frequency of market visits, involvement in off-farm income generation activities, and the total value of assets owned by households were positively associated with HDDS. These findings are robust across alternative model specifications.
As the ordered logistic regression model identifies the natural ordering of dependent variables, it further allows the estimation of the marginal effect for each independent variable and category of dependent variable pairing. According to Table 5, the marginal effects of Model 2 indicate that the likelihood of being in the low HDDS category increases by 16 percentage points and the likelihood of being in the middle and higher HDDS categories decreases by 5 and 11 percentage points, respectively, if the individual becomes risk-averse instead of risk-seeking, all else remaining constant. Furthermore, on average, impatient individuals are 13 percentage points more likely than patient individuals to be in the low HDDS category and approximately 9 percentage points less likely to be in the high HDDS category.
4. Discussion and Policy Implications
4.1. Discussion
Our findings indicate that risk-averse and impatient individuals are likely to have lower HDDS. Previous studies have found that risk aversion and patience are negatively associated with risky health behaviours [14,21,22,54]. Our finding on the time preference measure is consistent with the previous literature, confirming our hypothesis that people who prefer immediate gratification may fail to invest in nutritious diets now to achieve good health in the future.
If we consider monotonous diets as a risky health behaviour that could cause malnutrition, our findings on risk aversion may seem to be contradictory to previous literature. Nevertheless, our findings may still be intuitive, given the context. Malagasy people are accustomed to eating traditional rice-biased food and may thus perceive unfamiliar food as a ‘risk’. In this case, risk-seeking behaviour indicates their willingness to include unfamiliar food in their diet.
These risk and time preferences, elements of behavioural economics, are behind their diet choices in the context of developing countries. From the perspective of consumer behaviour, the preference of risk-averse and impatience could be one of the factors that makes changing dietary habits in Madagascar difficult. A previous study showed that the sub-Saharan population is among the most risk-tolerant countries, and on average the population deviates from the world mean on patience and they are rather impatient [25].
Frequent market visits were found to be associated with increased household diversity, and this finding is in line with other studies [55,56]. The market can provide farmers with not only increased access to diverse food items but also income opportunities [57]. The involvement of household members in off-farm income generation activities also contributed to increased dietary diversity. Household cash income generated from farm production and off-farm labour is an essential driver of food access in smallholder farm households [55]. As the survey was conducted just before the harvesting of the main crop (i.e., rice), the farmers might have had limited consumable and marketable farm produce. Farmers’ risk preference not only affects their diet choices but also has an impact on their inter-temporal food sales. A higher risk perception leads to a greater likelihood of favouring current sales over intertemporal ones [58]. This can indirectly affect their household cash income during lean periods, leading to limited food choices for the household during such times.
The gender of household decision-makers also has a significant association with diverse diets. In our study, male respondents were less likely to contribute to increased dietary diversity in the household. In traditional rural societies, women typically cook and make household food choices. Notably, women tend to prioritise a wider variety of food options available in households [59]. Although this study provides valuable insights, it has several limitations. First, our outcome measure is the HDDS, which is a household-level measurement. Having data on individual-level food consumption would enable us to deepen our findings further because preference measurements are inherent in individual behaviour. Second, the economic experiments used in this study have an established history; however, considering the participants’ very low income and financial literacy, their choices in the economic experiments might have been influenced to a greater extent by immediate financial concerns (as we observed, especially in the time preference experiment) than we might expect in a more well-off population. Third, in consideration of the contextual dependency of experimental findings, future research endeavours must assess the resilience of our present conclusions across diverse contexts, encompassing varying preference measures and geographic locations.
4.2. Policy Implications
We found that risk aversion and impatience observed in this study may be the barriers to household dietary diversification. To the best of our knowledge, our work is one of the few studies to examine whether economic preferences are related to diet choices, particularly in developing countries. The population studied was of particular interest because of minimal or no nutritional transition observed [28] and a higher rate of chronic nutritional deficiencies [27] amid various policy interventions.
Our findings emphasise the importance of integrating behavioural economics tools in analysing household food choice behaviour and the policy for promoting healthier food choices. Unlike many sociodemographic characteristics, economic preferences are flexible; that is, they can be changed through behavioural choices [60,61,62]. Although consumer behaviour perspectives emphasise that dietary choices are rarely considered in nutritional policy interventions, empirical evidence suggests that the use of incentives is effective in promoting changes in dietary behaviour [63]. Combining healthy food subsidies with behavioural interventions (i.e., changes to the choice environment) is effective in modifying health behaviours such as healthy food spending and dietary habits [64].
By identifying the risk-averse and impatient individuals, more focused support for nutritional improvement can be provided. For risk-averse people, a policy to create an environment where people can perceive that adding unfamiliar foods to their diets is safe and rewarding would be necessary. Nutritional education emphasising the health benefits of a diverse diet and recipe introduction through cooking demonstrations, school lunches, or media to get familiar with the new food could contribute to it. Moreover, enhancing access to diverse foods through financial support, such as subsidies for healthy food, or the development of well-functioning local markets that offer diverse foods at affordable prices, would be beneficial, especially for people in poverty. Individuals with limited resources may face higher risks when attempting behavioural changes because they have fewer means to mitigate potential adverse consequences. In this way, price instruments, one of the traditional policy tools, could be effective.
For impatient people, a policy to inform the connection between their current daily consumption and their health in the long run would be needed. Enabling individuals to consider the future could serve as an effective strategy for mitigating unhealthy food consumption among those who are particularly susceptible to this behaviour (i.e., individuals who do not try restricting their caloric intake, and individuals with a higher BMI) [65]. In addition, pre-commitment mechanisms could be applied to adhere to their long-term health benefits. For example, nutritional education can provide pre-planned menus or can include a curriculum for them to plan menus for one week. Pre-planned menus and pre-planned expenditures (budget for nutritious foods) are kinds of commitments. Furthermore, if they have social groups, they can share information and support mutually, which could reinforce commitment. Such mechanisms facilitate more thoughtful consideration of choices for less patient individuals so that they can better weigh decisions that yield long-term health benefits.
In addition to preference measures, our finding thus highlights the importance of empowering women in household decision-making, which can lead to dietary diversification. Markets and non-farm income generation also contribute to household dietary diversity, especially when few or no food crops are produced locally. Therefore, strengthening the inter-regional agro-marketing network, which can facilitate the distribution of surplus produce, is imperative to ensure food availability.
4.1. Discussion
Our findings indicate that risk-averse and impatient individuals are likely to have lower HDDS. Previous studies have found that risk aversion and patience are negatively associated with risky health behaviours [14,21,22,54]. Our finding on the time preference measure is consistent with the previous literature, confirming our hypothesis that people who prefer immediate gratification may fail to invest in nutritious diets now to achieve good health in the future.
If we consider monotonous diets as a risky health behaviour that could cause malnutrition, our findings on risk aversion may seem to be contradictory to previous literature. Nevertheless, our findings may still be intuitive, given the context. Malagasy people are accustomed to eating traditional rice-biased food and may thus perceive unfamiliar food as a ‘risk’. In this case, risk-seeking behaviour indicates their willingness to include unfamiliar food in their diet.
These risk and time preferences, elements of behavioural economics, are behind their diet choices in the context of developing countries. From the perspective of consumer behaviour, the preference of risk-averse and impatience could be one of the factors that makes changing dietary habits in Madagascar difficult. A previous study showed that the sub-Saharan population is among the most risk-tolerant countries, and on average the population deviates from the world mean on patience and they are rather impatient [25].
Frequent market visits were found to be associated with increased household diversity, and this finding is in line with other studies [55,56]. The market can provide farmers with not only increased access to diverse food items but also income opportunities [57]. The involvement of household members in off-farm income generation activities also contributed to increased dietary diversity. Household cash income generated from farm production and off-farm labour is an essential driver of food access in smallholder farm households [55]. As the survey was conducted just before the harvesting of the main crop (i.e., rice), the farmers might have had limited consumable and marketable farm produce. Farmers’ risk preference not only affects their diet choices but also has an impact on their inter-temporal food sales. A higher risk perception leads to a greater likelihood of favouring current sales over intertemporal ones [58]. This can indirectly affect their household cash income during lean periods, leading to limited food choices for the household during such times.
The gender of household decision-makers also has a significant association with diverse diets. In our study, male respondents were less likely to contribute to increased dietary diversity in the household. In traditional rural societies, women typically cook and make household food choices. Notably, women tend to prioritise a wider variety of food options available in households [59]. Although this study provides valuable insights, it has several limitations. First, our outcome measure is the HDDS, which is a household-level measurement. Having data on individual-level food consumption would enable us to deepen our findings further because preference measurements are inherent in individual behaviour. Second, the economic experiments used in this study have an established history; however, considering the participants’ very low income and financial literacy, their choices in the economic experiments might have been influenced to a greater extent by immediate financial concerns (as we observed, especially in the time preference experiment) than we might expect in a more well-off population. Third, in consideration of the contextual dependency of experimental findings, future research endeavours must assess the resilience of our present conclusions across diverse contexts, encompassing varying preference measures and geographic locations.
4.2. Policy Implications
We found that risk aversion and impatience observed in this study may be the barriers to household dietary diversification. To the best of our knowledge, our work is one of the few studies to examine whether economic preferences are related to diet choices, particularly in developing countries. The population studied was of particular interest because of minimal or no nutritional transition observed [28] and a higher rate of chronic nutritional deficiencies [27] amid various policy interventions.
Our findings emphasise the importance of integrating behavioural economics tools in analysing household food choice behaviour and the policy for promoting healthier food choices. Unlike many sociodemographic characteristics, economic preferences are flexible; that is, they can be changed through behavioural choices [60,61,62]. Although consumer behaviour perspectives emphasise that dietary choices are rarely considered in nutritional policy interventions, empirical evidence suggests that the use of incentives is effective in promoting changes in dietary behaviour [63]. Combining healthy food subsidies with behavioural interventions (i.e., changes to the choice environment) is effective in modifying health behaviours such as healthy food spending and dietary habits [64].
By identifying the risk-averse and impatient individuals, more focused support for nutritional improvement can be provided. For risk-averse people, a policy to create an environment where people can perceive that adding unfamiliar foods to their diets is safe and rewarding would be necessary. Nutritional education emphasising the health benefits of a diverse diet and recipe introduction through cooking demonstrations, school lunches, or media to get familiar with the new food could contribute to it. Moreover, enhancing access to diverse foods through financial support, such as subsidies for healthy food, or the development of well-functioning local markets that offer diverse foods at affordable prices, would be beneficial, especially for people in poverty. Individuals with limited resources may face higher risks when attempting behavioural changes because they have fewer means to mitigate potential adverse consequences. In this way, price instruments, one of the traditional policy tools, could be effective.
For impatient people, a policy to inform the connection between their current daily consumption and their health in the long run would be needed. Enabling individuals to consider the future could serve as an effective strategy for mitigating unhealthy food consumption among those who are particularly susceptible to this behaviour (i.e., individuals who do not try restricting their caloric intake, and individuals with a higher BMI) [65]. In addition, pre-commitment mechanisms could be applied to adhere to their long-term health benefits. For example, nutritional education can provide pre-planned menus or can include a curriculum for them to plan menus for one week. Pre-planned menus and pre-planned expenditures (budget for nutritious foods) are kinds of commitments. Furthermore, if they have social groups, they can share information and support mutually, which could reinforce commitment. Such mechanisms facilitate more thoughtful consideration of choices for less patient individuals so that they can better weigh decisions that yield long-term health benefits.
In addition to preference measures, our finding thus highlights the importance of empowering women in household decision-making, which can lead to dietary diversification. Markets and non-farm income generation also contribute to household dietary diversity, especially when few or no food crops are produced locally. Therefore, strengthening the inter-regional agro-marketing network, which can facilitate the distribution of surplus produce, is imperative to ensure food availability.
5. Conclusions
Nutritional deficiencies, which arise partly from less diverse diets, are a significant health concern in Madagascar. Despite global trends, the country is not experiencing a noticeable nutritional transition, and people do not seem willing to divert from their traditional diet, which is biased towards energy-rich staple foods. Using a dataset collected from 539 households in central Madagascar in 2022, this study explored the behavioural factors associated with household dietary diversity, focusing on risk and time preferences. In rural Madagascar, risk aversion and impatience may be the barriers to household dietary diversification. Our insights from behavioural economics contribute to the development of effective intervention strategies to promote dietary diversity and divert the direction of nutritional policies towards nutritional improvement. |
Title: A genome-wide association study of adults with community-acquired pneumonia | Body:
Introduction
Infections are one of the main causes of death globally and nearly one in eight deaths continue to be due to bacterial infections [1]. Community acquired pneumonia (CAP) is considered a major public health problem due to its high morbidity and mortality [2, 3]. Yearly CAP incidence varies widely worldwide, with estimates between 11 and 169 cases per 10,000 persons. The available data for adults in Spain is 46.3 cases per 10,000 inhabitants [4]. The most common CAP complications are sepsis or severe respiratory failure, and mortality by CAP is mostly associated to hospitalized patients, reaching 23% in intensive care units (ICUs) [4, 5].
Host genetics plays a central role in the response to pathogens and contribute to explain the differences in susceptibility and severity among patients [6–8]. Specifically, single nucleotide polymorphism (SNP)-based heritability assessments in pneumonia support that genetic host factors explain a greater proportion of severity than of susceptibility [7]. However, there is a paucity of genetic studies aimed at identifying genetic factors involved in CAP susceptibility or prognosis. In addition, most of them have focused on candidate genes, especially on genes involved in the immune response such as those encoding the MBL, SFTPA1, SFTPA2, SFTPD, IL6, and IL10, among others [9, 10]. A few other studies have relied on genome-wide association studies (GWAS) to reveal pneumonia susceptibility loci in the human leukocyte antigen (HLA), MUC5AC, IL6R, and TNFRSF1A [11, 12], and pneumonia severity loci in CFTR, R3HCC1L, and HBB [7]. However, it must be noted that these studies have not distinguished the source of infection, implying that the patients are a heterogeneous mixture of patients with CAP and with nosocomial infections, i.e., with hospital-acquired pneumonia (HAP).
CAP is typically caused by several bacteria, including Streptococcus pneumoniae and Haemophilus influenzae, or viruses [13, 14]. S. pneumoniae is one of the leading causes of CAP and has been identified in about one-third of hospitalized cases in Europe, although these frequencies may be underestimated [13–16]. The immune response varies widely depending on the causative pathogen. Inborn errors of immunity (IEI, usually referred to as primary immunodeficiencies) strongly support that predisposition to infection by different microorganisms usually relies on different components of the immune system. For example, IEI impairing type I interferon-mediated immunity predispose to susceptibility to severe pneumonia by SARS-CoV-2 or influenza viruses, whereas IEI predisposing to pneumococcal infection are particularly involved in opsonization or phagocytosis of opsonized bacteria by splenic macrophages [6, 17, 18]. Therefore, studies aimed to identify the genetic basis of susceptibility or severity of infections may benefit from a precise homogenization of the source of infection and the causative microorganism.
To identify genetic variants associated with CAP, here we have conducted a GWAS of hospitalized patients with the only diagnosis of CAP, focusing on patients with pneumococcal infection or without identified causal microorganism.
Materials and methods
Study design
We conducted a one stage case-control GWAS of adult subjects of European ancestry from Spain (Fig. 1). No statistical calculation for adequate sample size was performed before the study was conducted. A total of 259 adult hospitalized patients with CAP diagnosis were recruited between March 2001 and 2016 from six Spanish hospitals and constituted the cases. These patients were included in previous candidate gene association studies of CAP [9, 19]. It is assumed that pneumococci cause most CAP cases in which negative test results were found using conventional microbiological methods [14, 16, 20]. Therefore, to keep homogeneous the causative microorganism of CAP, we only included patients with confirmed pneumococcal infection or those in whom no identified causative microorganism was identified. The study inclusion criteria and phenotype descriptions are available in the Supplementary material.
As controls, we used the genetic data available from 3,526 donors from the Spanish DNA Biobank (https://www.bancoadn.org), collected from the National Blood Service and have been used in a previous GWAS of severe COVID-19 [21]. All control participants were unrelated and clinically uncharacterized adults. They self-reported being of Spanish origin and having no personal or familial history of diseases, such as infectious, cancerous, circulatory, endocrine, mental, or behavioral, nervous, visual, auditory, respiratory, and immunological, among others.
Fig. 1Flowchart of the study design. CADD: Combined Annotation Dependent Depletion, CAP: Community-acquired Pneumonia, GWAS: Genome-wide association study, HLA: Human Leukocyte Antigen, HRC: Haplotype Reference Consortium, HWE: Hardy-Weinberg equilibrium, MAF: Minor Allele Frequency, MSC: Mutation Significance Cutoff, QC: Quality Controls, V2G: Variant to Gene, VEP: Variant Effect Predictor. UK Biobank phenotype codes: bacterial pneumonia (480.1), pneumococcal pneumonia (480.11), Pseudomonas pneumonia (480.12), Viral pneumonia (480.2), and (480.3) pneumonia due to fungus (mycoses)
Genotyping, quality control, and variant imputation
Both cases and controls were genotyped with the Axiom Spain Biobank Array (Thermo Fisher Scientific) following the manufacturer’s instructions in the Santiago de Compostela Node of the National Genotyping Center (CeGen-ISCIII; http://www.usc.es/cegen). Genotyping quality control and variant imputation procedures are detailed in the Supplementary material. We conducted a principal component (PC) analysis (PCA) to derive the main PCs for model adjustments and to identify genetic outliers (Figure S1). These procedures left us with a total of 603,603 genetic variants for 257 hospitalized CAP patients and 3,508 controls.
Statistical analysis and the functional assessment of associated loci
Variant association testing
To test the association of genetic variants with CAP, we used additive logistic regression models across all the imputed variants satisfying a good imputation quality (Rsq ≥ 0.3) and a MAF ≥ 1% using EPACTS v.3.2.6 [22]. The association model was adjusted for sex and the first 3 PCs and the results were assessed using the genomic inflation factor (λ) calculated with the gap package for R. Variant associations were considered statistically significant at a threshold p < 5.0 × 10− 8. Independent sentinel variants were identified as those surpassing p < 5.0 × 10− 8 and showing weak linkage disequilibrium (LD; r2 < 0.1) with others in each locus after clumping in PLINK. The pseudo-R2 of Nagelkerke was calculated based on a polygenic risk scores (PRS) model to determine the amount of variance being explained by the sentinel variants. Study power for detecting the statistically associated variants was assessed ad hoc. Detailed information is available in the Supplementary material.
A sensitivity analysis of the sentinel variants was conducted by including other covariates in the logistic regression model (e.g., age), the etiology of CAP, excluding the genetic outliers based on the PCA, and also testing their association with other two recorded severe pneumonia outcomes in the case series by separate and combined: (i) severe sepsis or septic shock according to the criteria available at the moment of the start of the recruitment [23]; and (ii) severe respiratory failure, defined as oxygen saturation < 90% on room air, or a partial pressure of oxygen [PaO2] < 60 mmHg.
Since there is a lack of independent GWAS of CAP which can serve as a formal replication of the findings, we accessed publicly available summary GWAS of pneumonia of infectious, bacterial, fungal, and viral origin from UK Biobank (UKBB) data [24] to validate the association of the independent sentinel variants. We considered that the results were validated if the variant in the UKBB showed significance after Bonferroni correction for the number of variants assessed (p-value < 8.33 × 10− 3) and the same direction of effect as in our study.
Bayesian fine mapping and functional analysis
We performed a fine mapping on the association results in a 2 Megabase pairs (Mb) region around the independent sentinel variants to identify the credible variant set that most likely harbors the causal variant with 95% confidence. For this, we used the corrcoverage package [25] for R assuming that is only one causal variant in each locus. To validate the results, we also inferred the credible set with 95% confidence using the R package SuSiE [26] and assumed 3, 5, or 10 causal variants. Further details of the fine mapping are available in the Supplementary material.
We assessed the biological consequences of the variants included in the credible sets of the associated loci. In addition, we used the Variant-to-Gene (V2G) score to prioritize the genes that were most likely affected by the functional evidence based on data from the Open Targets Genetics portal [27]. To interpret the CADD score and predict the biological impact of the variants on the prioritized genes, we used the Mutation Significance Cutoff (MSC) v.1.6 of genes [28] with a confidence interval of 99%. For those variants deemed to predict a high biological impact (with a CADD > MSC), we performed an in silico analysis to determine their potential regulatory roles. Further details of the functional analysis are available in the Supplementary material.
To assess tissue expression of the genes prioritized in the associated loci and the existence of eQTL in the sentinel variants on artery, esophagus, lung, and whole blood, we used The Genotype-Tissue Expression (GTEx) Release v.8 data [29]. Protein quantitative loci (pQTLs) were evaluated using data available in Open Target Genetics. In parallel, we accessed a public transcriptomic dataset (GSE65682) available to assess expression differences in the genes prioritized in the associated loci among 108 ICU patients with CAP diagnosis and 42 healthy controls. Detailed information of this analysis is available in the Supplementary material.
Association of classic HLA alleles and amino acids
Due to the sequence complexity of the human leukocyte antigen (HLA) complex, its key role in immunity, and the previously reported association with pneumonia susceptibility [12], we also performed a targeted association testing of the variation in genes of the HLA complex with CAP. To this end, we imputed the genetic variation at eight classical HLA genes (-A, -B, -C, -DPA1, -DPB1, -DQA1, -DQB1, and -DRB1) and tested the association of the amino acids and the classical four-digit HLA alleles. The significance was established through the Bonferroni correction at p < 1.93 × 10− 5 for the amino acids and p < 4.35 × 10− 4 for the classical HLA alleles. Further information of this analysis is available in the Supplementary material.
Study design
We conducted a one stage case-control GWAS of adult subjects of European ancestry from Spain (Fig. 1). No statistical calculation for adequate sample size was performed before the study was conducted. A total of 259 adult hospitalized patients with CAP diagnosis were recruited between March 2001 and 2016 from six Spanish hospitals and constituted the cases. These patients were included in previous candidate gene association studies of CAP [9, 19]. It is assumed that pneumococci cause most CAP cases in which negative test results were found using conventional microbiological methods [14, 16, 20]. Therefore, to keep homogeneous the causative microorganism of CAP, we only included patients with confirmed pneumococcal infection or those in whom no identified causative microorganism was identified. The study inclusion criteria and phenotype descriptions are available in the Supplementary material.
As controls, we used the genetic data available from 3,526 donors from the Spanish DNA Biobank (https://www.bancoadn.org), collected from the National Blood Service and have been used in a previous GWAS of severe COVID-19 [21]. All control participants were unrelated and clinically uncharacterized adults. They self-reported being of Spanish origin and having no personal or familial history of diseases, such as infectious, cancerous, circulatory, endocrine, mental, or behavioral, nervous, visual, auditory, respiratory, and immunological, among others.
Fig. 1Flowchart of the study design. CADD: Combined Annotation Dependent Depletion, CAP: Community-acquired Pneumonia, GWAS: Genome-wide association study, HLA: Human Leukocyte Antigen, HRC: Haplotype Reference Consortium, HWE: Hardy-Weinberg equilibrium, MAF: Minor Allele Frequency, MSC: Mutation Significance Cutoff, QC: Quality Controls, V2G: Variant to Gene, VEP: Variant Effect Predictor. UK Biobank phenotype codes: bacterial pneumonia (480.1), pneumococcal pneumonia (480.11), Pseudomonas pneumonia (480.12), Viral pneumonia (480.2), and (480.3) pneumonia due to fungus (mycoses)
Genotyping, quality control, and variant imputation
Both cases and controls were genotyped with the Axiom Spain Biobank Array (Thermo Fisher Scientific) following the manufacturer’s instructions in the Santiago de Compostela Node of the National Genotyping Center (CeGen-ISCIII; http://www.usc.es/cegen). Genotyping quality control and variant imputation procedures are detailed in the Supplementary material. We conducted a principal component (PC) analysis (PCA) to derive the main PCs for model adjustments and to identify genetic outliers (Figure S1). These procedures left us with a total of 603,603 genetic variants for 257 hospitalized CAP patients and 3,508 controls.
Statistical analysis and the functional assessment of associated loci
Variant association testing
To test the association of genetic variants with CAP, we used additive logistic regression models across all the imputed variants satisfying a good imputation quality (Rsq ≥ 0.3) and a MAF ≥ 1% using EPACTS v.3.2.6 [22]. The association model was adjusted for sex and the first 3 PCs and the results were assessed using the genomic inflation factor (λ) calculated with the gap package for R. Variant associations were considered statistically significant at a threshold p < 5.0 × 10− 8. Independent sentinel variants were identified as those surpassing p < 5.0 × 10− 8 and showing weak linkage disequilibrium (LD; r2 < 0.1) with others in each locus after clumping in PLINK. The pseudo-R2 of Nagelkerke was calculated based on a polygenic risk scores (PRS) model to determine the amount of variance being explained by the sentinel variants. Study power for detecting the statistically associated variants was assessed ad hoc. Detailed information is available in the Supplementary material.
A sensitivity analysis of the sentinel variants was conducted by including other covariates in the logistic regression model (e.g., age), the etiology of CAP, excluding the genetic outliers based on the PCA, and also testing their association with other two recorded severe pneumonia outcomes in the case series by separate and combined: (i) severe sepsis or septic shock according to the criteria available at the moment of the start of the recruitment [23]; and (ii) severe respiratory failure, defined as oxygen saturation < 90% on room air, or a partial pressure of oxygen [PaO2] < 60 mmHg.
Since there is a lack of independent GWAS of CAP which can serve as a formal replication of the findings, we accessed publicly available summary GWAS of pneumonia of infectious, bacterial, fungal, and viral origin from UK Biobank (UKBB) data [24] to validate the association of the independent sentinel variants. We considered that the results were validated if the variant in the UKBB showed significance after Bonferroni correction for the number of variants assessed (p-value < 8.33 × 10− 3) and the same direction of effect as in our study.
Bayesian fine mapping and functional analysis
We performed a fine mapping on the association results in a 2 Megabase pairs (Mb) region around the independent sentinel variants to identify the credible variant set that most likely harbors the causal variant with 95% confidence. For this, we used the corrcoverage package [25] for R assuming that is only one causal variant in each locus. To validate the results, we also inferred the credible set with 95% confidence using the R package SuSiE [26] and assumed 3, 5, or 10 causal variants. Further details of the fine mapping are available in the Supplementary material.
We assessed the biological consequences of the variants included in the credible sets of the associated loci. In addition, we used the Variant-to-Gene (V2G) score to prioritize the genes that were most likely affected by the functional evidence based on data from the Open Targets Genetics portal [27]. To interpret the CADD score and predict the biological impact of the variants on the prioritized genes, we used the Mutation Significance Cutoff (MSC) v.1.6 of genes [28] with a confidence interval of 99%. For those variants deemed to predict a high biological impact (with a CADD > MSC), we performed an in silico analysis to determine their potential regulatory roles. Further details of the functional analysis are available in the Supplementary material.
To assess tissue expression of the genes prioritized in the associated loci and the existence of eQTL in the sentinel variants on artery, esophagus, lung, and whole blood, we used The Genotype-Tissue Expression (GTEx) Release v.8 data [29]. Protein quantitative loci (pQTLs) were evaluated using data available in Open Target Genetics. In parallel, we accessed a public transcriptomic dataset (GSE65682) available to assess expression differences in the genes prioritized in the associated loci among 108 ICU patients with CAP diagnosis and 42 healthy controls. Detailed information of this analysis is available in the Supplementary material.
Variant association testing
To test the association of genetic variants with CAP, we used additive logistic regression models across all the imputed variants satisfying a good imputation quality (Rsq ≥ 0.3) and a MAF ≥ 1% using EPACTS v.3.2.6 [22]. The association model was adjusted for sex and the first 3 PCs and the results were assessed using the genomic inflation factor (λ) calculated with the gap package for R. Variant associations were considered statistically significant at a threshold p < 5.0 × 10− 8. Independent sentinel variants were identified as those surpassing p < 5.0 × 10− 8 and showing weak linkage disequilibrium (LD; r2 < 0.1) with others in each locus after clumping in PLINK. The pseudo-R2 of Nagelkerke was calculated based on a polygenic risk scores (PRS) model to determine the amount of variance being explained by the sentinel variants. Study power for detecting the statistically associated variants was assessed ad hoc. Detailed information is available in the Supplementary material.
A sensitivity analysis of the sentinel variants was conducted by including other covariates in the logistic regression model (e.g., age), the etiology of CAP, excluding the genetic outliers based on the PCA, and also testing their association with other two recorded severe pneumonia outcomes in the case series by separate and combined: (i) severe sepsis or septic shock according to the criteria available at the moment of the start of the recruitment [23]; and (ii) severe respiratory failure, defined as oxygen saturation < 90% on room air, or a partial pressure of oxygen [PaO2] < 60 mmHg.
Since there is a lack of independent GWAS of CAP which can serve as a formal replication of the findings, we accessed publicly available summary GWAS of pneumonia of infectious, bacterial, fungal, and viral origin from UK Biobank (UKBB) data [24] to validate the association of the independent sentinel variants. We considered that the results were validated if the variant in the UKBB showed significance after Bonferroni correction for the number of variants assessed (p-value < 8.33 × 10− 3) and the same direction of effect as in our study.
Bayesian fine mapping and functional analysis
We performed a fine mapping on the association results in a 2 Megabase pairs (Mb) region around the independent sentinel variants to identify the credible variant set that most likely harbors the causal variant with 95% confidence. For this, we used the corrcoverage package [25] for R assuming that is only one causal variant in each locus. To validate the results, we also inferred the credible set with 95% confidence using the R package SuSiE [26] and assumed 3, 5, or 10 causal variants. Further details of the fine mapping are available in the Supplementary material.
We assessed the biological consequences of the variants included in the credible sets of the associated loci. In addition, we used the Variant-to-Gene (V2G) score to prioritize the genes that were most likely affected by the functional evidence based on data from the Open Targets Genetics portal [27]. To interpret the CADD score and predict the biological impact of the variants on the prioritized genes, we used the Mutation Significance Cutoff (MSC) v.1.6 of genes [28] with a confidence interval of 99%. For those variants deemed to predict a high biological impact (with a CADD > MSC), we performed an in silico analysis to determine their potential regulatory roles. Further details of the functional analysis are available in the Supplementary material.
To assess tissue expression of the genes prioritized in the associated loci and the existence of eQTL in the sentinel variants on artery, esophagus, lung, and whole blood, we used The Genotype-Tissue Expression (GTEx) Release v.8 data [29]. Protein quantitative loci (pQTLs) were evaluated using data available in Open Target Genetics. In parallel, we accessed a public transcriptomic dataset (GSE65682) available to assess expression differences in the genes prioritized in the associated loci among 108 ICU patients with CAP diagnosis and 42 healthy controls. Detailed information of this analysis is available in the Supplementary material.
Association of classic HLA alleles and amino acids
Due to the sequence complexity of the human leukocyte antigen (HLA) complex, its key role in immunity, and the previously reported association with pneumonia susceptibility [12], we also performed a targeted association testing of the variation in genes of the HLA complex with CAP. To this end, we imputed the genetic variation at eight classical HLA genes (-A, -B, -C, -DPA1, -DPB1, -DQA1, -DQB1, and -DRB1) and tested the association of the amino acids and the classical four-digit HLA alleles. The significance was established through the Bonferroni correction at p < 1.93 × 10− 5 for the amino acids and p < 4.35 × 10− 4 for the classical HLA alleles. Further information of this analysis is available in the Supplementary material.
Results
The study tested the association with CAP in a total of 7,638,472 variants from 3,508 controls and 257 patients (Fig. 2), in which S. pneumoniae was identified in 30% of cases, and the remaining 70% were patients where the causative microorganism was not identified. The clinical and demographic characteristics of the study subjects are shown in Supplementary Table 1. Overall, we did not detect inflation of the association results as the lambda of the study barely deviated from the expected under the null (λ = 1.04). Association testing revealed a total of 67 genome-wide significant variants (Supplementary Table S2) which were located on chromosomes 4q28.2, 6p21.32, 11p12, and 20q11.22. Regional association plots for these results are provided in the Supplementary Figure S2.
Fig. 2Manhattan plot of the genome-wide association study results for adult CAP. The x-axis represents the chromosome positions while the y-axis represents the transformed p-values (–log10 [p-value]) obtained by logistic regression models using EPACTS v.3.2.6. Each point represents the result of a genetic variant analyzed. The variants that were deemed to be significantly associated with CAP exceed the significance threshold set at p-value = 5.0 × 10− 8, represented by the horizontal line. The inset on the top right represents the Quantile-Quantile (QQ) plot comparing the observed -log10 (p-values) from the GWAS analysis on the y-axis with the expected p-values under the null hypothesis on the x-axis. The genomic inflation factor (λ = 1.04) did not suggest inflation of the results obtained
There were six independently associated sentinel variants (Table 1): rs34955650 at 4q28.2 (p = 2.41 × 10− 8) intergenic to C4orf33 and LINC02466; three at 6p21.32, rs456261 and rs2076775 that are both intronic to PFDN6 (p = 4.00 × 10− 28) and SYNGAP1 (p = 7.26 × 10− 10), respectively, and rs213226 that is intergenic to RING1 and HCG25 (p = 8.64 × 10− 9); rs117203606 at 11p12 (p = 2.90 × 10− 8) which is intergenic to LINC02740 and HNRNPKP3; and rs45577437 at 20q11.22 (p = 1.21 × 10− 14) that is exonic to ZNF341. However, the results for rs456261 should be interpreted with caution given its unexpected low MAF (Table 1) compared with the gnomAD data from non-Finnish Europeans and because no other variant in the region was in strong LD with it. A PRS model using the six independent sentinel SNPs explained 11% variance in our study (p-value = 2.09 × 10− 3, OR = 1.67[95%CI: 1.53–1.82]). However, we warn that this must be considered an overestimate and that independent studies should be ascertained to validate the results. An ad hoc statistical power calculation indicated that the study had > 80% power to detect variant associations with an OR > 1.80.
Table 1Results of the sentinel variants independently associated with CAPVariantPositionp-valueMAFOR [95% CI]A1/A2RsqFunc.GeneNearest gene(s)rs349556504:1302542642.41 × 10− 80.052.46 [1.80–3.38]C/T0.99intergenicC4orf33, LINC02466rs2132266:332093108.64 × 10− 90.171.84 [1.49–2.26]A/G0.36intergenicRING1, HCG25rs4562616:332584434.00 × 10− 280.028.23 [5.65–11.99]G/A0.33intronic
PFDN6
rs20767756:333942537.26 × 10− 100.140.50 [0.41–0.63]C/G0.36intronic
SYNGAP1
rs11720360611:423301322.90 × 10− 80.110.21 [0.12–0.36]G/AGenotypedintergenicLINC02740, HNRNPKP3rs4557743720:323410411.21 × 10− 140.440.46 [0.37–0.56]C/TGenotypedexonic
ZNF341
Position: chromosome and base pair according to GRCh37/hg19; MAF: minor allele frequency in the study population; OR: odds ratio; CI: confidence interval; A1: Non-effect allele; A2: Effect allele; Rsq: Imputation R squared; Func.Gene: gene location
Models adjusting for other covariates did not substantially modify these results (Supplementary Table S3). The significance and effect size of the sentinel variants remained unaltered by the exclusion of the genetic outliers (Supplementary Table S4). Sensitivity analyses stratifying by the origin of CAP show that the associations of the six sentinel variants are maintained at least when patients have a non-definite CAP etiology, probably because there was still sufficient sample size to detect the effect of variants (Supplementary Table S5). The results for the association of the two severe pneumonia outcomes considered by separate or combined (i.e., severe sepsis or septic shock and severe respiratory insufficiency) for the six independently sentinel variants are shown in Supplementary Table S6. For these sub-analyses, despite the low sample size reduction, all variants reach significance at the nominal level and the direction of effect is still maintained.
To validate the results, we then evaluated the association of the independent sentinel variants in UKBB GWAS of pneumonia by bacteria, Pseudomonas, pneumococcus, fungi, or virus. Results for three out of the six independent sentinel variants had nominal significance (p < 0.05) and the same direction of effect. However, only rs2076775 was significantly associated with bacterial and pneumococcal pneumonia after Bonferroni correction (Supplementary Table S7).
Bayesian fine mapping by corrcoverage around each of the four chromosome loci to identify the most likely causal variants driving the association was able to delineate a credible set of 52 variants for 4q28.2 and 25 variants for 6p21.32 (Supplementary Table S8 and Figure S3). The variant from each credible set with the highest V2G score was used to assign the most likely gene involved in the association. At 6p21.32, the ranking prioritized the TAPBP gene (V2G max. score = 0.41 and MSC = 4.26), encoding the TAP binding protein. At 4q28.2, the C4orf33 gene was prioritized (V2G max. score = 0.15 and MSC = 4.87). For downstream functional analysis, we selected those genetic variants with the highest probability of biological effect based on the CADD score from the delineated credible sets in the associated loci. For the 6p21.32 locus, we assessed 10 variants (CADD > MSC), and the predictions suggest a relevant biological impact since some of these SNPs predict the affectation of transcription factor binding and regulatory motif and DNase I hypersensitive sites. Furthermore, rs381847, rs2247385, and rs456261 may affect enhancer (H3K4me1 and H3K27ac) and promoter (H3K4me3 and H3K9ac) histone marks in several cell lines, including lung tissue and immune cells. Moreover, these 10 variants are eQTLs of immunity genes, including HLA genes and TAPBP (Supplementary Table S9). For the credible set of 4q28.2, we selected 13 variants with CADD > MSC. Two of these are rs17014611 and rs35004602, both linked to predictions of enhancer histone marks affectation in lung and immune cell lines, and DNase I hypersensitive sites in fetal lung (Supplementary Table S10). Fine mapping with SuSiE assuming different numbers of causal variants increased the number of SNPs within the credible sets and the uncertainty. The credible sets in the chromosomes 4q28.2 and 6p21.32 were also fragmented in two sets (Supplementary Figure S3) although it did not change the prioritized gene on each case. Neither corrcoverage nor SuSiE were able to provide credible sets for 11p12 and 20q11.22.
According to GTEx, the TAPBP gene is highly expressed (i.e., has higher average levels of Transcripts Per Millions) on spleen, lungs, lymphocytes, and whole blood. In fact, the three independent sentinel variants of 6p21.32 were pQTLs on blood plasma for the TAPBP gene and eQTLs on artery, esophagus, lung, and whole blood (Supplementary Table S11). No significant eQTLs were found for any tissue at GTEx for the other three independent sentinel variants at chromosomes 4q28.2, 11p12, and 20q11.22 (rs34955650, rs117203606, and rs45577437). Furthermore, we found a significant upregulation of TAPBP (lowest p = 1.44 × 10− 6 among the four probe sets available) and for C4orf33 (p = 9.67 × 10− 5) among ICU patients with CAP compared to controls (GEO: GSE65682). ZNF341 did not show a significant gene expression difference (Supplementary Figure S4).
Finally, we assessed the association of 155 classical HLA alleles and the 2,584 amino acids for eight classical HLA genes with CAP. Despite the key implication of the HLA genes in infectious diseases, all associations tested were found non-significant (Supplementary Figure S5, and Table S12).
Discussion
Here, we describe the results of a GWAS of CAP conducted in Spanish population. We identified six independently associated variants from four chromosome loci (4q28.2, 6p21.32, 11p12, and 20q11.22) reaching genome-wide significance. Results for one of them rs2076775 (6p21.32) were validated with susceptibility to bacterial and pneumococcal pneumonia in independent studies with cases that were likely a mixture of CAP and HAP patients.
One of the independent variants detected is located on 4q28.2, intergenic to C4orf33 and a long non-coding RNA (LINC02466) which has been involved in cancer [30]. Besides, the sentinel variants on chromosome 6 have been previously associated with platelet and blood cell count, type-I diabetes, or celiac disease, among other traits (GWAS data available at Open Target Genetics). The variant rs2076775, with evidence of validation in UKBB, is intronic to SYNGAP1 which encodes for the synaptic Ras GTPase activating protein 1 which has been associated with neurodevelopmental disorders [31]. This variant has been previously associated (p < 5 × 10− 8) with hematopoietic cell count, rheumatoid arthritis, type 1 diabetes based on Open Targets Genetics data. Finally, another variant was prioritized in 11p12 intergenic to a long non-coding RNA and a pseudogene (HNRNPKP3). To our knowledge, these genes have not been associated with prior susceptibility or severity of infections. However, formal replication of results and further studies of functional characterization are needed to assess the biological effects of the identified genes and variants in pneumonia.
The sentinel variant of the 20q11.22 locus observed in this study is located in exon 5 of ZNF341 and predicts a missense change (p.Pro185Ser). The association at this locus was also supported by two other genome-wide significant intronic variants residing in this gene. ZNF341 acts as a DNA-binding transcription factor, primarily as an activator of Signal Transducer and Activator of Transcription STAT3 gene, and, to a lesser extent, a number of other genes such as STAT1 [32–34]. Mutations at ZNF341, have been reported to cause of hyper-immunoglobulin E syndrome (HIES). HIES is characterized by elevated serum IgE levels, recurrent bacterial and Candida infections, eczema with cold staphylococcal skin abscesses, and other non-immunologic features that affect the skeleton, dentition, and connective tissue. HIES patients have recurrent skin and lung infections, including pneumonia, caused by S. aureus, S. pneumoniae, or H. influenzae, and the pulmonary recovery can involve abnormalities characterized by bronchiectasis and pneumatoceles [33–35]. Based on this evidence, variants affecting the function of ZNF341 could also play an important role in CAP and its severity, although functional studies and replication cohorts are necessary to validate and describe the role of the repotted variants.
The study also prioritized possibly damaging variants in the TAPBP gene that were eQTLs for that gene in different tissues and pQTLs on plasma. It also revealed that TAPBP gene expression was upregulated among ICU patients with CAP compared to controls, although the role of profound inflammatory dysregulation in critical patients cannot be discarded. The TAPBP gene encodes the transporter associated with antigen processing (TAP) binding protein, also called tapasin. Tapasin is part of the peptide loading complex (PLC), which coordinates loading of high affinity peptides onto nascent HLA class I (HLA-I) molecules [36, 37]. Tapasin has a central role in the classical pathway of HLA-I presentation of endogenous peptides and in cross-presentation of exogenous antigens by professional antigen presenting cells [37]. Multiple HLA-I molecules have been reported to be tapasin-dependent, among them the HLA-B*08:01 allele [38]. In spite that a previous GWAS found this class I HLA allele associated with pneumonia susceptibility [12], HLA-B*08:01 was not associated with CAP in our study (p = 0.45). Gene defects in TAPBP or in other PLC components cause an extremely rare IEI known as bare lymphocyte syndrome type I (BLS-I) [39–41]. Despite the clinical and biological heterogeneity, BLS-I patients usually develop symptoms well into late childhood, although some patients remain asymptomatic even in adulthood. They are characterized by recurrent respiratory tract infections, by S. pneumoniae and H. influenzae among others, associated with the development of bronchiectasis and respiratory insufficiency, as well as to cutaneous manifestations [39, 40, 42].
We acknowledge some limitations of the study. First and foremost, the study was based on a small sample of cases. Studies with larger sample sizes would permit providing a more robust estimation of the effects of the reported associations, especially for the low MAF variants, and possibly to reveal additional susceptibility loci beyond those identified here. Secondly, half of the patients had a low Pneumonia Severity Index. However, when the analyses only included the patients with severe sepsis or septic shock, or those with severe respiratory insufficiency, association results of the sentinel variants were maintained. Thirdly, our study lacks a formal assessment of the full spectrum of genetic variants (including other types of variation beyond SNPs and small INDELs, and variants with MAF lower than 1% which accumulate most of the disease-causing variation) for which complementary approaches based on Next-Generation Sequencing, such as whole exome or genome sequencing, would be ideal. Fourth, the precise causative pathogen of CAP was undetermined in 70% of the patients studied. Therefore, besides highlighting the interest in confirming the findings in patients with CAP by other pathogens, we could not draw conclusions regarding the associations with the response to a specific pathogen. Despite this, only one of the variants (rs34955650) did not reach the nominal statistical significance in the analysis of the reduced sample of CAP patients by S. pneumoniae infection. Furthermore, previous studies suggest that pneumococci cause most CAP cases in which negative test results were found using conventional microbiological methods, particularly in Spain [14, 16, 20]. However, infection by other bacteria and viruses is still expected. In addition, a substantial proportion of patients with CAP present coinfections, particularly pneumococcus [14, 20, 43]. Finally, we used controls that could have introduced some confounding in the results since, despite hospitalization for CAP was not recorded for these donors during the recruitment, we cannot discard that they could develop it during their lifetime. Controls not ascertained for the disease risk are widely used in genetic studies for multiple infectious diseases, providing equivalent results as if controls comprise mild or asymptomatic patients [7, 8, 21]. In addition, it was not possible to perform additional sensitivity analyses or consider other variables of interest, such as smoking, due to the limited characterization of the personal history of health of these individuals.
Despite the limitations, this study has been completed for a specific sub-phenotype of pneumonia and is one of the first steps in understanding the genetics of this condition. Besides, mutations at ZNF341 and TAPBP, cause previously known IEIs involved in the susceptibility to bacterial infections, particularly pneumonia. Thus, sequencing studies, together with experimental analyses of function, will be key to evaluate whether deleterious mutations in the novel CAP loci could define new IEIs predisposing to bacterial pneumonia [44]. Although the clinical application of the presented results is limited for the moment, they are valuable for downstream targeted analyses to potentially assist in CAP risk stratification and to inform of potential drug targets.
Conclusion
In summary, we report four novel loci associated with CAP, including two genes that were previously known to cause IEIs predisposing to bacterial pneumonia. Complementary studies are required to replicate the findings in larger studies and to better define the mechanistic links of these variants and genes on predisposition to pneumonia.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary Material 1
Supplementary Material 2 |
Title: Opioid use in thoracic surgery: a retrospective study on postoperative complications | Body:
Key findings
• High in-hospital opioid use was associated with more post-operative complications in thoracic surgery.
What is known and what is new?
• Opioid medications have harmful side effect profiles. In orthopedic patient populations, there is a relationship between post-operative opioid use and post-operative complications.
• The above findings are also applicable to the thoracic surgery population.
What is the implication, and what should change now?
• Further effort(s) should be made to decrease in-hospital opioid use via other non-narcotic modalities to help improve surgical outcomes.
Introduction
Opioid-related overdose deaths in the United States totaled 80,411 in 2021. A total of 16,706 deaths were caused by prescribed opioids (1). From a surgeon’s perspective, it is imperative to minimize the risk of postoperative opioid dependency and to avoid opioid side-effect profiles (i.e., nausea and vomiting, bradycardia, hypotension, respiratory depression, central nervous system depression, delirium, etc.) that can negatively impact patients’ postoperative course. Enhanced recovery after surgery (ERAS) protocols have become prevalent in several surgical subspecialties. A common theme among such protocols is the recommendation for multimodal pain regimens that help reduce the amount of opioid medication required to treat postoperative pain (2). Curtailing the need for opioids can be a unique challenge for patients undergoing thoracic surgery.
Thoracic procedures often require surgery near the intercostal nerves, which patients can expect to contend with incisional pain postoperatively. Opioids play an important role in achieving adequate pain control after thoracic surgery to prevent postoperative pulmonary complications secondary to splinting and respiratory insufficiency (3). In such patient populations, the benefits of opioid analgesia in facilitating adequate pulmonary hygiene must be weighed against the risks of opioid-related complications. At our institution, the Division of Thoracic Surgery has implemented a modified version of the ERAS Society and the European Society of Thoracic Surgeons (ESTS) protocol for postoperative pain control, which includes a non-narcotic analgesic (acetaminophen), gamma-aminobutyric acid (GABA) analog (gabapentin), muscle relaxant (methocarbamol), topical anesthetic (lidocaine patches), and non-steroidal anti-inflammatory drugs (NSAID) (naproxen) in addition to as-needed rescue opioids for breakthrough pain (2). Opioids used on thoracic surgery service included tramadol, oxycodone, morphine, and hydromorphone. Multimodal pain regimens have been shown to decrease the need for outpatient opioid prescriptions after foregut and lung surgeries (4,5). We wanted to determine if there was a relationship between opioid use in the hospital and post-surgical complications. This article is presented in accordance with the STROBE reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-825/rc).
Methods
The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the institutional review board of the Houston Methodist Research Institute (No. Pro00013680), and the requirement for individual consent for this retrospective analysis was waived. We performed a retrospective review of a single-institutional database developed from patients operated on by physicians of the Division of Thoracic Surgery at Houston Methodist Hospital. We performed a nested case-control study to determine if there was a relationship between thoracic surgical interventions and complications after surgery. The inclusion criteria were patients who underwent a thoracic surgical intervention between 11/1/2020 and 11/1/2021. We excluded patients who underwent outpatient procedures, such as endoscopy, flexible bronchoscopy, endobronchial ultrasound, endoscopic ultrasound, and transbronchial lung biopsy. The compiled data included basic demographic information (age, sex, race/ethnicity), body mass index (BMI), medical comorbidities, type of surgery, American Society of Anesthesiologists (ASA) classification, complications, operation duration, and opioid use data. Patient data including complications were identified and recorded in the institutional database at the time of discharge. The type of surgery was based on anatomical structures (foregut, lung, mediastinum, esophagus, etc.). Home pain medication prior to surgery was defined as prescribed or over-the-counter pain medication that the patient was taking at the time of the operation (i.e., acetaminophen, cyclophosphamide, and gabapentin). Opioid use data included history of opioid use at home prior to surgery. We obtained data on opioid use in the operating room, recovery, floor, and intensive care unit (ICU), if applicable. We converted the opioids to morphine milligram equivalent (MME) and calculated the total MME for hospital stay as well as the average daily MME. Complications were identified individually as well as Clavien-Dindo classification (6). The primary outcome of this study was the postoperative complications in patients undergoing thoracic surgical procedures.
Statistical analysis
Demographic and clinical data were reported as frequencies and proportions for categorical variables and as median and interquartile range (IQR) for continuous variables. Differences between groups were compared using the Chi-square or Fisher’s exact test for categorical variables and the Wilcoxon rank-sum test for continuous variables, as appropriate. Receiver operating characteristic (ROC) curve analysis with the Youden index (7) was used to identify the optimal cutoff points for total MME and total MME per day in predicting postoperative complications. Bar charts and box plots were used to visualize the distribution of postoperative complications. Logistic regression modeling was performed to determine the characteristics associated with outcomes (postoperative complications). Variables for the multivariable models were selected based on their clinical importance and Stata’s lasso technique with the cross-validation (CV) selection option (8,9). Model discrimination was determined using area under the ROC curve (AUC). Model calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test, with a non-significant P value indicating good calibration. All analyses were performed using Stata version 18.5 (StataCorp LLC, College Station, TX, USA). Statistical significance was set at P value <0.05.
Statistical analysis
Demographic and clinical data were reported as frequencies and proportions for categorical variables and as median and interquartile range (IQR) for continuous variables. Differences between groups were compared using the Chi-square or Fisher’s exact test for categorical variables and the Wilcoxon rank-sum test for continuous variables, as appropriate. Receiver operating characteristic (ROC) curve analysis with the Youden index (7) was used to identify the optimal cutoff points for total MME and total MME per day in predicting postoperative complications. Bar charts and box plots were used to visualize the distribution of postoperative complications. Logistic regression modeling was performed to determine the characteristics associated with outcomes (postoperative complications). Variables for the multivariable models were selected based on their clinical importance and Stata’s lasso technique with the cross-validation (CV) selection option (8,9). Model discrimination was determined using area under the ROC curve (AUC). Model calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test, with a non-significant P value indicating good calibration. All analyses were performed using Stata version 18.5 (StataCorp LLC, College Station, TX, USA). Statistical significance was set at P value <0.05.
Results
Four hundred nineteen patients met the inclusion criteria. The median age of the patients in the study was 64 (IQR 54, 71) years. Two hundred forty-five patients were female (58.5%). A total of 316 patients were identified as White (75.4%), 48 patients were identified as Black/African American (11.5%), 35 patients were identified as Hispanic (8.4%), 18 patients were identified as Asian (4.3%), and 2 patients were identified as other (0.5%). The four most common comorbidities observed among the patients were hypertension (254 patients, 60.6%), diabetes mellitus (78 patients, 18.6%), coronary artery disease (43 patients, 10.3%), and chronic obstructive pulmonary disease (50 patients, 11.9%). The median ASA score was 3 (IQR 3, 3) (Table 1). The four most common anatomical locations of surgical procedures performed were the foregut (218 cases, 52%), lungs (103 cases, 24.6%), mediastinum (26 cases, 6.2%), and esophagus (18 cases, 4.3%). Except for chest wall procedures, all patients underwent robotic minimally invasive surgery. All patients received general anesthesia, and none of the patients received epidural anesthesia. Patients who had a chest incision received an intercostal nerve block with liposomal bupivacaine, and patients who had an abdominal incision received liposomal bupivacaine in the incisions. Patients were administered multimodal non-opioid pain medication after surgery, including acetaminophen, gabapentin, methocarbamol, a lidocaine patch, and selective use of ketorolac. The patient was administered tramadol for breakthrough pain and intravenous (IV) hydromorphone, if tramadol was insufficient. If the patient required additional opioids despite IV hydromorphone, the patient was placed on IV hydromorphone patient-controlled anesthesia.
Table 1
Characteristics of thoracic surgery patients
Characteristics
Value (N=419)
Age at surgery (years), median (IQR)
64.0 (54.0, 71.0)
Female, n (%)
245 (58.5)
Race/ethnicity, n (%)
White
316 (75.4)
Black
48 (11.5)
Hispanic
35 (8.4)
Asian
18 (4.3)
Other
2 (0.5)
Body mass index (kg/m2), median (IQR)
27.3 (23.8, 31.1)
Comorbidities, n (%)
Hypertension
254 (60.6)
Diabetes
78 (18.6)
Coronary artery disease
43 (10.3)
COPD
50 (11.9)
Surgery type, n (%)
Foregut
218 (52.0)
Lung
103 (24.6)
Mediastinum
26 (6.2)
Esophagus
18 (4.3)
Pleura
17 (4.1)
Diaphragm
15 (3.6)
Chest wall
13 (3.1)
Feeding tube
9 (2.1)
ASA score, median (IQR)
3.0 (3.0, 3.0)
Any home pain medication before surgery, n (%)
160 (38.2)
Any home opioid medication before surgery, n (%)
52 (12.4)
Operation duration (min), median (IQR)
224.0 (186.0, 284.0)
MME (mg)
Total MME, median (IQR)
73.0 (45.0, 118.0)
Total MME per day, median (IQR)
30.0 (18.3, 46.5)
IQR, interquartile range; COPD, chronic obstructive pulmonary disease; ASA, American Society of Anesthesiologists; MME, morphine milligram equivalent.
A history of home pain medication use before surgery was reported in 160 (38.2%) patients. Fifty-two patients (12.4%) reported a history of home opioid pain medication within one month before surgery. There was a trend toward significance in higher postoperative complication rates in patients with preoperative home opioid use than in those without (15.4% vs. 7.9%, respectively, P=0.08). The median total MME during patient hospitalization was 73.0 mg (IQR 45.0, 118.0 mg). The median daily MME during patient hospitalization was 30.0 mg (IQR 18.3, 46.5 mg, Table 1). Median post-operative length of stay was 1.0 day (IQR 1.0, 2.0 days). Thirty-seven (8.8%) patients experienced postoperative complications. The most common postoperative complications were pneumothorax (n=8, 21.6%), pneumonia (n=4, 10.8%), dysphagia (n=3, 8.1%), respiratory insufficiency with new oxygen requirement at discharge (n=3, 8.1%), urinary retention (n=2, 5.4%), pulmonary embolism (n=2, 5.4%), new atrial fibrillation (n=2, 5.4%), postoperative bleeding (n=2, 5.4%), and urinary tract infection (n=2, 5.4%). The Clavien-Dindo classification of patient complications was as follows: class 1, 3 patients (0.7%); class 2, 13 patients (3.1%); class 3, 18 patients (4.3%); and class 4, 3 patients (0.7%, Table 2).
Table 2
Outcome after thoracic surgery procedures
Outcome
Value (N=419)
Any post-operative event, n (%)
37 (8.8)
Post-operative event, n (%)
None
382 (91.2)
Minor
9 (2.1)
Major
28 (6.7)
Clavien-Dindo classification, n (%)
0
382 (91.2)
1
3 (0.7)
2
13 (3.1)
3
18 (4.3)
4
3 (0.7)
Post-operative LOS (days), median (IQR)
1.0 (1.0, 2.0)
30-day mortality, n (%)
0 (0)
LOS, length of stay; IQR, interquartile range.
Patients who suffered complications had significantly higher total MME (median 135.0; IQR 73.0, 743.0) vs. (median 70.0; IQR 45.0, 108.0) mg (P<0.001, Figure 1A). A ROC curve analysis with the Youden index determined that patients who had a total MME ≥241 mg had significantly higher complication rates than patients who had a total MME <241 mg (31.0% vs. 5.3%, respectively; P<0.001, Figure 1B). Similarly, patients who had a daily MME of ≥60 mg had significantly higher complication rates than those who had a daily MME of <60 mg (20.0% vs. 6.8%, respectively; P=0.001, Figure 1C). Multivariable logistic regression analysis demonstrated that the following subgroups were associated with higher odds of having any complications after surgery: Hispanic ethnicity [odds ratio (OR) 4.33, 95% confidence interval (CI) 1.63, 11.51; P=0.003], longer operation duration (OR 1.01; 95% CI: 1.00, 1.01; P=0.01), and total MME (OR 1.001; 95% CI: 1.00, 1.002; P<0.001) (Table 3).
Figure 1
Relationship between MME and complication. (A) Patients with any complication had significantly higher total MME compared to patients who did not have a complication. (B) Total MME ≥241 mg had complication rate of 31% vs. 5.3% (P<0.001). (C) Total MME per day ≥60 had significantly higher complication rate of 20% vs. 6.8% (P=0.001). MME, morphine milligram equivalent; IQR, interquartile range.
Table 3
Multivariable logistic regression analysis for having any complication
Variable
OR (95% CI)
P
Hispanic
4.33 (1.63, 11.51)
0.003
Hypertension
1.85 (0.79, 4.36)
0.16
Operation duration (min)
1.01 (1.00, 1.01)
0.01
Total MME
1.001 (1.00, 1.002)
<0.001
OR, odds ratio; CI, confidence interval; MME, morphine milligram equivalent.
Discussion
There is likely a bidirectional relationship between the observed association between postoperative opioid use and complications after thoracic surgery. The side effect profiles of opioids can cause respiratory depression, altered mental status, dizziness, nausea, and vomiting amongst other side effects, all of which can hinder postoperative mobilization (10). Poor mobilization after surgery subsequently results in complications, such as venous thromboembolism, nosocomial pneumonia, and physical deconditioning (11). Conversely, many of the postoperative complications related to thoracic surgery and the requisite treatment of these complications can cause significant pain. Chest tubes of varying sizes, image-guided drains, nosocomial pneumonia, pulmonary emboli, surgical site infections, and anastomotic leaks are known complications of thoracic surgery and may increase the postoperative opioid requirement.
Multiple studies focusing on ERAS have demonstrated that a multimodal pain regimen decreases the overall need for postoperative opioid pain medication postoperatively (12-14). The ERAS Society and European Society of Thoracic Surgeons in 2019 published guidelines strongly recommend multimodal analgesia to help reduce overall opioid requirements in the postoperative phase. In a related note to our findings, Memtsoudis et al. found that patients managed on a multimodal pain regimen after orthopedic surgery experienced significantly fewer postoperative opioid-related complications than those managed with opioids alone (15). Cozowicz et al. demonstrated that postoperative opioid use was associated with greater postoperative complication rates in patients undergoing orthopedic surgery (16).
To our knowledge, no previous studies have evaluated the relationship between postoperative opioid requirements and postoperative complications in thoracic surgeries. We found that greater opioid use in the immediate postoperative period was associated with higher complication rates after thoracic surgical procedures. The amount of opioid medication received during hospitalization was an independent risk factor for postoperative complications. Efforts to decrease the amount of opioid medication used with multimodal non-opioid medication have the potential to improve surgical outcomes.
No studies have evaluated complication rates in a patient population similar to that being treated at our institution (i.e., comprising majority foregut pathology followed by lung, mediastinal, and esophageal pathology, respectively). For patients undergoing minimally invasive hiatal hernia repair, our complication rates were comparable to those reported in the literature (17,18). For patients undergoing minimally invasive lung resection, our complication rates were lower than those reported in the literature (19-21).
In our patient population, Hispanic ethnicity was found to be an independent risk factor for higher postoperative complication rates. Studies have demonstrated conflicting findings regarding the influence of Hispanic ethnicity on postoperative morbidity and mortality (22). Further studies are needed to identify the etiology of our findings. Longer operative time was also identified as an independent risk factor for postoperative complications. We posit that in more technically challenging cases (i.e., those with abnormal/aberrant anatomy or reoperative cases) requiring longer operative times, there may also be a greater risk of both intraoperative and postoperative complications.
Chronic opioid use has been shown to have a detrimental effect on postoperative outcomes. In the orthopedic population, chronic opioid use is associated with an increased length of stay, greater postoperative opioid requirement, and more frequent emergency department visits, readmissions, and early revision (23,24). In patients undergoing bariatric surgery, chronic opioid use has been associated with greater rates of postoperative complications, increased length of hospital stay, and the need for a reoperation (25,26). We did not observe similar findings in our patient population.
The limitations of this study were that it was a retrospective nested case-control study, and no causative relationship could be proven. There was also the potential for recall bias as the data regarding post-operative complications were gathered based on the inpatient team’s determination at a weekly service meeting of whether a postoperative complication occurred. This is most concerning in patients with long and complex hospital stays, in whom not all postoperative complications may have been identified. We have checked these cases and found that there were no complications that were missed. The generalizability of our findings is limited to patients who underwent thoracic surgery. In conclusion, we have demonstrated that increased postoperative opioid requirements are associated with greater postoperative complications after thoracic surgery, and we strongly support the widespread implementation of the ERAS protocol in the management of postoperative pain, which has been shown to decrease both perioperative and post-discharge opioid requirements. Further studies are needed to understand how postoperative opioid use can be decreased to mitigate its effects on postoperative complications. There is also an additional opportunity to investigate which specific postoperative complications are associated with a higher degree of postoperative opioid use in thoracic surgery.
Conclusions
An increased postoperative opioid requirement was independently associated with an increased risk of postoperative complications in patients undergoing thoracic surgery. These findings, in conjunction with existing knowledge of the adverse side-effect profiles of narcotic medications, provide further support for the need to find effective strategies for moderating post-operative opioid use.
Supplementary
The article’s supplementary files as
10.21037/jtd-24-825
10.21037/jtd-24-825 |
Title: Logistical challenges of CAR T-cell therapy in non-Hodgkin lymphoma: a survey of healthcare professionals | Body:
1.
Introduction
Chimeric antigen receptor T-cell (CAR T) therapy has emerged as a promising treatment option for patients with non-Hodgkin lymphoma (NHL) that is relapsed or refractory (R/R) to other therapies [1]. Four CAR T therapies are currently available for the treatment of various subtypes of R/R NHL [1], including tisagenlecleucel (Kymriah®) for R/R large B-cell lymphoma (LBCL) and R/R follicular lymphoma (FL) [2,3], axicabtagene ciloleucel (Yescarta®) for R/R LBCL and R/R FL [4,5], lisocabtagene maraleucel (Breyanzi®) for R/R LBCL and R/R FL [6], and brexucabtagene autoleucel (Tecartus®) for R/R mantle cell lymphoma [7]. The approval of these CAR T therapies was based on demonstrated efficacy and safety across these different NHL indications in clinical trials [1,4,5,8].
CAR T therapies represent a significant advancement in R/R NHL treatment. However, the fact that all treated patients incur full upfront treatment costs (often exceeding $400,000 per patient), but a fraction experience long-lasting remission, raises concerns regarding the treatment value [9–11]. In addition, the specialized management protocols require treatment to occur at certified centers, typically a large academic institution or cancer center [12]. The patient advocacy group Blood & Marrow Transplant Information Network reports that CAR T for lymphoma is offered at fewer than 150 centers of excellence in the United States (US) [13]. This further reduces the number of patients who may benefit from treatment, as patients without a treatment center near to them may need to travel and lodge near these centers. Patients and their caregivers also may incur out-of-pocket costs and work productivity loss due to these geographical constraints [10].
The CAR T treatment process entails various stages: the referral process, determination of CAR T eligibility, review and approval for CAR T treatment, leukapheresis (often followed by a waiting period as the patient's cells are being manufactured), potential treatments and procedures preceding CAR T infusion (e.g., bridging therapy and conditioning regimens) and subsequent post-infusion monitoring (with decreasing frequency if a patient experiences remission). The complexities of this process have been well documented among healthcare stakeholders [11,14–16]. Still, efforts to characterize specific challenges from the perspective of HCPs have been limited. The aim of this study was to identify and quantify the logistical challenges experienced throughout the CAR T administration process as reported by HCPs in the US and United Kingdom (UK).
2.
Methods
2.1.
Study design & population
This was a cross-sectional, mixed-methods study comprising two phases: an initial set of qualitative interviews and a larger quantitative survey. The participants in both research components included HCPs in the US and UK experienced in the coordination and administration of CAR T for patients with R/R NHL. Eligibility criteria for all participants included a requisite of more than 2 years of clinical experience treating patients with R/R NHL and having provided care to a minimum of three patients eligible for CAR T therapy within the past 2 years. Physicians were deemed eligible if they had experience in the referral of patients to CAR T therapy or if they worked within a CAR T therapy team supporting any part of the treatment process. Nurses, social workers and physician assistants were required to have experience in at least one aspect of the CAR T therapy process. If one reported aspect was a direct CAR T treatment administration process, such as leukapheresis, bridging therapy, or lymphodepletion, experience in a second aspect was then required to ensure respondents were familiar with the patient pathway.
The participants included academic and community-based physicians (hematologist/oncologists), physician assistants, cell therapy coordinators/navigators, social workers, nurses and nurse practitioners working at CAR T therapy centers. Recruitment of study participants was undertaken via panel-based sampling and all participants were required to provide informed consent prior to participation.
Ethics approval for this study was obtained prior to data collection from the Western Institutional Review Board-Copernicus Group (study #1346939).
2.2.
Survey development
2.2.1.
Qualitative interviews
A semi-structured interview guide was created to aid the research team in carrying out the qualitative interviews. Specialists with expertise in CAR T therapy were consulted to inform development of the interview guide. Interviews were conducted via teleconference and audio recorded. At the onset of each interview, participants were briefed on the study and asked to provide verbal consent to participate. Immediately after the interview, brief notes were taken by the interviewer and uploaded into an Excel spreadsheet. All interviews were transcribed verbatim and uploaded into an open-source qualitative analysis software. Themes identified during the qualitative interviews informed the development of the quantitative survey to further investigate the logistical challenges associated with CAR T therapy administration and treatment process.
2.2.2.
Quantitative survey
The quantitative survey was administered using a customized online survey platform and was divided into six main sections. The first five sections corresponded to the chronological stages in the CAR T therapy administration process: referral processes, eligibility determination, pre-infusion procedures (e.g., leukapheresis, bridging therapy, lymphodepletion), infusion and immediate post-infusion monitoring and long-term follow-up. The sixth and final section of the survey addressed other general logistical components of the CAR T therapy process.
Survey participants were asked to identify the logistical challenges encountered at each stage, to select reasons why a patient may not progress to the subsequent treatment stage (e.g., from leukapheresis to infusion), and to provide estimates for relevant measures (e.g., days between leukapheresis and infusion, duration of inpatient stay). After selecting a logistical challenge, respondents were required to indicate the degree of impact of the challenge in determining whether a patient would successfully undergo CAR T therapy using a scale of one to five, ranging from “Not at all impactful” to “Extremely impactful.” Respondents were also asked to assess how frequently the challenge affects patients, caregivers, or HCPs using a scale of one to five, ranging from “Never” to “Very often.” The full survey instrument is available in the Supplementary Material.
2.3.
Statistical analysis
2.3.1.
Qualitative interviews
The qualitative interviews were audio recorded, transcribed verbatim, de-identified and reviewed by interviewers for accuracy. Data were analyzed inductively using reflexive thematic analysis, in accordance with the guidelines described by Braun and Clarke [17]. The researchers first familiarized themselves with the qualitative data by reading interview transcripts and making notes of key observations. An initial codebook was developed, and labels were applied to data excerpts that were germane to the research questions. Codes were iteratively refined and themes were generated, reviewed and finalized by the researchers.
2.3.2.
Quantitative survey
The analysis of quantitative survey data was descriptive. Means, medians, interquartile ranges and maximum/minimum values were calculated for continuous variables and frequencies and percentages were calculated for categorical variables. For the questions on the impact of each logistical challenge on patients successfully receiving CAR T therapy, the mean and median impact (on a scale of one to five) were calculated.
2.1.
Study design & population
This was a cross-sectional, mixed-methods study comprising two phases: an initial set of qualitative interviews and a larger quantitative survey. The participants in both research components included HCPs in the US and UK experienced in the coordination and administration of CAR T for patients with R/R NHL. Eligibility criteria for all participants included a requisite of more than 2 years of clinical experience treating patients with R/R NHL and having provided care to a minimum of three patients eligible for CAR T therapy within the past 2 years. Physicians were deemed eligible if they had experience in the referral of patients to CAR T therapy or if they worked within a CAR T therapy team supporting any part of the treatment process. Nurses, social workers and physician assistants were required to have experience in at least one aspect of the CAR T therapy process. If one reported aspect was a direct CAR T treatment administration process, such as leukapheresis, bridging therapy, or lymphodepletion, experience in a second aspect was then required to ensure respondents were familiar with the patient pathway.
The participants included academic and community-based physicians (hematologist/oncologists), physician assistants, cell therapy coordinators/navigators, social workers, nurses and nurse practitioners working at CAR T therapy centers. Recruitment of study participants was undertaken via panel-based sampling and all participants were required to provide informed consent prior to participation.
Ethics approval for this study was obtained prior to data collection from the Western Institutional Review Board-Copernicus Group (study #1346939).
2.2.
Survey development
2.2.1.
Qualitative interviews
A semi-structured interview guide was created to aid the research team in carrying out the qualitative interviews. Specialists with expertise in CAR T therapy were consulted to inform development of the interview guide. Interviews were conducted via teleconference and audio recorded. At the onset of each interview, participants were briefed on the study and asked to provide verbal consent to participate. Immediately after the interview, brief notes were taken by the interviewer and uploaded into an Excel spreadsheet. All interviews were transcribed verbatim and uploaded into an open-source qualitative analysis software. Themes identified during the qualitative interviews informed the development of the quantitative survey to further investigate the logistical challenges associated with CAR T therapy administration and treatment process.
2.2.2.
Quantitative survey
The quantitative survey was administered using a customized online survey platform and was divided into six main sections. The first five sections corresponded to the chronological stages in the CAR T therapy administration process: referral processes, eligibility determination, pre-infusion procedures (e.g., leukapheresis, bridging therapy, lymphodepletion), infusion and immediate post-infusion monitoring and long-term follow-up. The sixth and final section of the survey addressed other general logistical components of the CAR T therapy process.
Survey participants were asked to identify the logistical challenges encountered at each stage, to select reasons why a patient may not progress to the subsequent treatment stage (e.g., from leukapheresis to infusion), and to provide estimates for relevant measures (e.g., days between leukapheresis and infusion, duration of inpatient stay). After selecting a logistical challenge, respondents were required to indicate the degree of impact of the challenge in determining whether a patient would successfully undergo CAR T therapy using a scale of one to five, ranging from “Not at all impactful” to “Extremely impactful.” Respondents were also asked to assess how frequently the challenge affects patients, caregivers, or HCPs using a scale of one to five, ranging from “Never” to “Very often.” The full survey instrument is available in the Supplementary Material.
2.2.1.
Qualitative interviews
A semi-structured interview guide was created to aid the research team in carrying out the qualitative interviews. Specialists with expertise in CAR T therapy were consulted to inform development of the interview guide. Interviews were conducted via teleconference and audio recorded. At the onset of each interview, participants were briefed on the study and asked to provide verbal consent to participate. Immediately after the interview, brief notes were taken by the interviewer and uploaded into an Excel spreadsheet. All interviews were transcribed verbatim and uploaded into an open-source qualitative analysis software. Themes identified during the qualitative interviews informed the development of the quantitative survey to further investigate the logistical challenges associated with CAR T therapy administration and treatment process.
2.2.2.
Quantitative survey
The quantitative survey was administered using a customized online survey platform and was divided into six main sections. The first five sections corresponded to the chronological stages in the CAR T therapy administration process: referral processes, eligibility determination, pre-infusion procedures (e.g., leukapheresis, bridging therapy, lymphodepletion), infusion and immediate post-infusion monitoring and long-term follow-up. The sixth and final section of the survey addressed other general logistical components of the CAR T therapy process.
Survey participants were asked to identify the logistical challenges encountered at each stage, to select reasons why a patient may not progress to the subsequent treatment stage (e.g., from leukapheresis to infusion), and to provide estimates for relevant measures (e.g., days between leukapheresis and infusion, duration of inpatient stay). After selecting a logistical challenge, respondents were required to indicate the degree of impact of the challenge in determining whether a patient would successfully undergo CAR T therapy using a scale of one to five, ranging from “Not at all impactful” to “Extremely impactful.” Respondents were also asked to assess how frequently the challenge affects patients, caregivers, or HCPs using a scale of one to five, ranging from “Never” to “Very often.” The full survey instrument is available in the Supplementary Material.
2.3.
Statistical analysis
2.3.1.
Qualitative interviews
The qualitative interviews were audio recorded, transcribed verbatim, de-identified and reviewed by interviewers for accuracy. Data were analyzed inductively using reflexive thematic analysis, in accordance with the guidelines described by Braun and Clarke [17]. The researchers first familiarized themselves with the qualitative data by reading interview transcripts and making notes of key observations. An initial codebook was developed, and labels were applied to data excerpts that were germane to the research questions. Codes were iteratively refined and themes were generated, reviewed and finalized by the researchers.
2.3.2.
Quantitative survey
The analysis of quantitative survey data was descriptive. Means, medians, interquartile ranges and maximum/minimum values were calculated for continuous variables and frequencies and percentages were calculated for categorical variables. For the questions on the impact of each logistical challenge on patients successfully receiving CAR T therapy, the mean and median impact (on a scale of one to five) were calculated.
2.3.1.
Qualitative interviews
The qualitative interviews were audio recorded, transcribed verbatim, de-identified and reviewed by interviewers for accuracy. Data were analyzed inductively using reflexive thematic analysis, in accordance with the guidelines described by Braun and Clarke [17]. The researchers first familiarized themselves with the qualitative data by reading interview transcripts and making notes of key observations. An initial codebook was developed, and labels were applied to data excerpts that were germane to the research questions. Codes were iteratively refined and themes were generated, reviewed and finalized by the researchers.
2.3.2.
Quantitative survey
The analysis of quantitative survey data was descriptive. Means, medians, interquartile ranges and maximum/minimum values were calculated for continuous variables and frequencies and percentages were calculated for categorical variables. For the questions on the impact of each logistical challenge on patients successfully receiving CAR T therapy, the mean and median impact (on a scale of one to five) were calculated.
3.
Results
3.1.
Qualitative interviews
Twenty-five HCPs were interviewed (21 US, four UK). Twenty (80%) were physicians and five (20%) were other HCPs. Participants had been treating patients for a median of 18 years and had referred or prescribed CAR T therapy to a median of 12 patients in the past 24 months.
Analysis of interview data revealed a generally positive outlook on the efficacy and safety of CAR T therapy; however, several logistical challenges associated with the treatment process were discussed. Four key themes relating to logistical challenges in the CAR T treatment process were identified: time and administration issues, caregiver support, access to insurance and travel and accommodation challenges. Interview excerpts relating to each key theme are shown below, with further excerpts shown in Table 1.
Table 1.
Qualitative interview excerpts relating to CAR T therapy logistical challenges.
Theme
Relevant excerpts
Caregiver support
“I mean, if I really felt that I had a patient that really had no support whatsoever, I might be hesitant to refer them.” – UK HCP
“We always make sure that somebody has a caregiver before we proceed with CAR T…sometimes, some patients could hire a caregiver, but not everybody has the financial means to do so.” – US HCP
“So, we make sure that the caregivers… we educate them, like, this is a big commitment. You've got to make sure that you're there with the patient for X amount of weeks. You can't really work. You have to be there with the patient present pretty much all the time in the same household monitoring them, interacting with them to make sure that… you know, you're looking for certain types of… the way that the patient is interacting with you. Are they speaking appropriately? Do they have chills? Do they have trouble breathing? Are they feeling hot? Are they shivering? Do they have a fever – things along those lines. Are they nauseous, for example? Are they feeling dizzy? Are they having problems with their memory or something like that, which, again, all these things could point to adverse events from CAR T.” – US HCP
“We kind of monitor patients through the most acute phase of this. I know it's been very emotionally challenging to some of the caregivers who come in to visit and they find their loved one kind of just in the midst of either CRS [cytokine release syndrome] or, even more upsetting, neurotoxicity. Obviously when the patient is in the hospital, they aren't responsible for the care, but it's hard then to see it.” – US HCP
Time and administration
“I think it's more consuming this one [the referral to CAR T] because you have to put all the patient file together, have to put together a letter why the patient needs this therapy and what's the expected result, outcome of this therapy. But also, everything related to the patient from financial or insurance coverages.” – US HCP
“So patient is waiting, waiting, waiting. It is not like bone marrow transplant, you go for transplant and the chances of getting a transplant is north of like 90%. On the other hand, CAR T, it's hard to get. Even if I refer eight patients in a year, very probably at best, two or three will get the CAR T. So it is logistically getting a slot is very important.” – US HCP
“I mean they could be about… I mean at the moment I don't think that any patient has not been able to receive the CAR T because of the long queue. I don't think it has happened. But the infusion time can be… I mean it's just the infusion rather than the queue which is a problem, where the patient's cancer relapse while waiting for the cells.” – US HCP
“In the centre where I work, we have a bottleneck with leukapheresis. If we had infinite leukapheresis capacity, I'm sure we would treat probably 50% more.” – UK HCP
“…in terms of the manufacturing process, it takes sometimes between four to six weeks. That is an issue, and it would be good to sort of maybe shorten that process.” – US HCP
Travel and accommodation
“Yeah, I mean, some patients really don't want to do it because it's not readily available in their surrounding location, in our office, our clinic. They don't want to go the distance.” – US HCP
“Well, it can be work. I know it is an issue. I mean, you have to understand, for some folks, they don't venture out of the state on a general basis. So, travelling to the next state that will do this for them is like, for them, going to Europe in some cases because they just don't travel. That's a big deal for them. So, it can make an issue…[do many people refuse CAR T because of distance]…probably about, again, 15% overall.” – US HCP
“So, if you have to drive a great distance – two, three hours to a CAR T center – that can be somewhat of a burden for patients. So, look, in the United States, you have this concentration of metropolitan cities, and then you have lots of rural areas and lots of driving time and patients that perhaps don't have the support structure to be staying at a CAR T therapy center in that city for a prolonged period of time. So, that can be an issue.” – US HCP
Insurance and cost
“I mean, definitely insurance plays a big role, and we did have a couple of patients who were […] to get it, but despite us trying and case management getting involved, it never worked out because they were… I don't remember what type of insurance they had, or I think one of them was uninsured.” – US HCP
“The insurance approval goes through three steps. One is you first have to get the insurance approval to test the patient for CAR T, and once the testing is complete, then you have to wait for the insurance to approve the CAR T based on a review of the records of the patient. And then once approval is granted, then there is the third step, fairly unique to CAR T in that you have to have a single patient agreement negotiated with the insurance carrier for each and every patient independently. And that adds to delays. And from the time you see the patient to the time you can actually schedule for a CAR T, if the patient has private insurance, it can take up to six weeks or so to then schedule.” – US HCP
“10–15% [of patients wouldn't be financially eligible for CAR T]…For people who are younger who have private insurance, they may have very high deductibles or limited coverage, and then they've exhausted it….It can be $10,000 or $20,000, their overall deductible. Frequently, they've met it, so it's not as big an issue, but for some people, again, every year it comes up, and it depends on the timing. The event could be just more than they can afford.” – US HCP
CAR T: Chimeric antigen receptor T-cell.
3.1.1.
Time & administration
HCPs reported time and administration-related issues as major logistical challenges during the CAR T treatment process. HCPs identified that several elements of the CAR T process required waiting, which could hamper patient outcomes.
“I think it's more consuming this one [the referral to CAR T] because you have to put all the patient file together, have to put together a letter why the patient needs this therapy and what's the expected result, outcome of this therapy. But also, everything related to the patient from financial or insurance coverages.” – US HCP
“So patient is waiting, waiting, waiting. It is not like bone marrow transplant, you go for transplant and the chances of getting a transplant is north of like 90%. On the other hand, CAR T, it's hard to get. Even if I refer eight patients in a year, very probably at best, two or three will get the CAR T. So it is logistically getting a slot is very important.” – US HCP
3.1.2.
Caregiver support
HCPs indicated that having a caregiver to support the patient throughout the CAR T process and post treatment was non-negotiable, and that without this support patients would not be able to proceed with treatment.
“I mean, if I really felt that I had a patient that really had no support whatsoever, I might be hesitant to refer them.” – UK HCP
“We always make sure that somebody has a caregiver before we proceed with CAR T…sometimes, some patients could hire a caregiver, but not everybody has the financial means to do so.” – US HCP
3.1.3.
Access to insurance
HCPs reported access to insurance as a logistical challenge for patients seeking CAR T therapy. Some participants mentioned having patients who could not proceed due to financial barriers, while others noted that the process of applying for coverage can be tedious and time-consuming.
“I mean, definitely insurance plays a big role, and we did have a couple of patients who were […] to get it, but despite us trying and case management getting involved, it never worked out because they were… I don't remember what type of insurance they had, or I think one of them was uninsured.” – US HCP
“The insurance approval goes through three steps. One is you first have to get the insurance approval to test the patient for CAR T, and once the testing is complete, then you have to wait for the insurance to approve the CAR T based on a review of the records of the patient. And then once approval is granted, then there is the third step, fairly unique to CAR T in that you have to have a single patient agreement negotiated with the insurance carrier for each and every patient independently. And that adds to delays. And from the time you see the patient to the time you can actually schedule for a CAR T, if the patient has private insurance, it can take up to six weeks or so to then schedule.” – US HCP
3.1.4.
Travel & accommodations
Patient travel and accommodations were mentioned by many HCPs as logistical challenges in the CAR T therapy process. HCPs stated that they will not recommend a patient for CAR T therapy if the patient lives too far from the treatment center and would not be able to handle the travel. This would be decided together with the patient and their family.
“Yeah, I mean, some patients really don't want to do it because it's not readily available in their surrounding location, in our office, our clinic. They don't want to go the distance.” – US HCP
In summary, four distinct themes characterizing the logistical burden of the CAR T therapy process on several stakeholder groups (HCPs, patients, and caregivers) emerged from the qualitative interviews. These findings underscore the need for a readily available therapeutic option that can potentially alleviate these burdens. These themes and insights guided the creation of a survey which assessed the logistical challenges associated with CAR T therapy in greater detail using a quantitative approach.
3.2.
Quantitative survey
3.2.1.
Respondent characteristics
Of 222 respondents screened for eligibility, 133 were included in the final sample. Of these 133 HCPs, 80 (60%) were from the US and 53 (40%) were from the UK. Of the 80 US HCPs, 67 (84%) were physicians, 7 (9%) were physician assistants and 6 (8%) were nurse practitioners; additionally, 33 (41%) reported affiliation with an academic/teaching certified CAR T center, 22 (28%) with a community-based referring institution, 23 (29%) with a community-based certified CAR T center and 2 (3%) with an academic/teaching referring institution. Of the 53 UK HCPs, 43 (81%) were physicians, 7 (13.2%) were nurses, 2 (4%) were nurse practitioners and 1 (2%) was a cell therapy coordinator/navigator; additionally, 29 (55%) reported affiliation with an academic/teaching certified CAR T center, 8 (15%) with a community-based referring institution, 5 (9%) with a community-based certified CAR T center and 11 (21%) with an academic/teaching referring institution.
In both the US and UK, physicians had referred a median of 15 patients to CAR T therapy in the past 24 months. Nurses had worked with a median of 45 (US) and 30 (UK) patients during the CAR T therapy administration process in the past 24 months. Respondents were also highly experienced across the stages of CAR T therapy administration: 100% (n = 67) of US and 98% (n = 42) of UK physicians had experience with referrals and eligibility assessments; 75% (n = 50) of US and 51% (n = 22) of UK physicians had experience with infusion and post-infusion care; and 69% (n = 9) of US and 70% (n = 7) of UK nurses/coordinators had experience coordinating CAR T therapy logistics (Figure 1).
Figure 1.
Respondent experience with CAR T therapy, by country and profession*.
*Non-physicians included nurses, nurse practitioners, physician assistants and a cell therapy coordinator/navigator.
CAR T: Chimeric antigen receptor T cell; UK: United Kingdom; US: United States.
3.2.2.
Logistical challenges along the patient journey
Two or more logistical challenges were identified by ≥60% of the US and UK respondents across all stages of CAR T administration. Specifically, 86% (US) and 92% (UK) respondents reported ≥2 referral process challenges; 81% (US) and 78% (UK) respondents reported ≥2 CAR T eligibility determination challenges; 75% (US) and 60% (UK) respondents reported ≥2 challenges with procedures before infusion; 72% (US) and 63% (UK) respondents reported ≥2 CAR T infusion and monitoring challenges; and 82% (US) and 64% (UK) respondents reported ≥2 challenges during long-term follow-up processes (Figure 2). Further findings within each stage are described in the subsequent sections.
Figure 2.
Percentage of respondents selecting 2 or more logistical challenges at each stage of CAR T therapy administration.
CAR T: Chimeric antigen receptor T cell; UK: United Kingdom; US: United States.
3.2.3.
Referral process
The most commonly reported challenges impacting the decision to refer patients for CAR T therapy were: manufacturing wait time (reported by 42% of US and 60% of UK respondents), limited capacity at a CAR T therapy center (reported by 40% of US and 58% of UK respondents), lengthy referral process (reported by 45% of US and 37% of UK respondents), travel distance (reported by 36% of US and 38% of UK respondents), slow/complex processes for insurance (US) or National Health Service (NHS; UK) approvals (reported by 36% of US and 31% of UK respondents) and availability of caregiver support (reported by 36% of US and 27% of UK respondents). Additionally, 40% of US respondents reported absence of insurance as impacting the decision to refer a patient for CAR T therapy (Table 2). In terms of impact, in the US, most referral process challenges had a median impact rating of four (very impactful), with the exception of coordinating and providing bridging therapy while waiting for CAR T therapy (median impact rating: five [extremely impactful]) and lack of clarity on the eligibility criteria and/or referral process (median impact rating: three [somewhat impactful]). In the UK, all challenges had a median impact rating of three (somewhat impactful), except for the availability of caregiver support, which had a median impact rating of four (very impactful) (Table 2).
Table 2.
Challenges impacting the CAR T therapy patient referral processa.
Challenge
US (n = 73)
UK (n = 52)
Respondents (%)a
Median impact ratingb
Mean % of PTs not referred
Respondents (%)
Median impact rating
Mean % of PTs not referred
Lengthy referral process
45
4
16
37
3
22
Wait time for manufacturing
42
4
19
60
3
18
Absence of insurance
40
4
25
0
NA
NA
Limited capacity at CAR T therapy center
40
4
21
58
3
19
Availability of caregiver support
36
4
24
27
4
20
Slow/complex process for insurance coverage (US) or NHS approval (UK)
36
4
23
31
3
20
Patient travel distance to treatment center
36
4
20
38
3
17
Patient out-of-pocket costs or loss of productivity/source of income
34
4
21
8
3
26
Coordinating and providing bridging therapy while waiting for CAR T therapy
30
5
15
27
3
20
Coordinating and providing leukapheresis and lymphodepletion
27
4
20
25
3
20
Lack of clarity over eligibility criteria and/or referral process
22
3
25
25
3
23
Patient preference for alternative treatment options
19
4
17
40
3
21
a
Percentage of respondents who selected each factor for the question, “Thinking about your experience referring patients for CAR T, what are the main factors that impact your decision to refer a patient for CAR T?”.
b
For each factor selected, respondents selected the impact of the factor on whether a patient receives CAR T, where 1 = not at all impactful, 2 = slightly impactful, 3 = somewhat impactful, 4 = very impactful, 5 = very impactful.
CAR T: Chimeric antigen receptor T cell; NA: Not applicable; NHS: National Health Service; PT: Patient; UK: United Kingdom; US: United States.
Respondents in the US estimated that a mean of 25% of patients who are deemed eligible for CAR T therapy are not referred due to absence of insurance to cover treatment. Respondents in both the US and UK also reported that more than 20% of their potentially eligible patients were not referred due to lack of clarity over eligibility criteria and/or referral processes (US mean, 25%; UK mean, 23%) and availability of caregiver support (US mean, 24%; UK mean, 20%).
3.2.4.
Eligibility determination
The most commonly reported challenges experienced while determining patient eligibility were: complex processes for applying for insurance approvals (US) or obtaining NHS approval (UK) (reported by 50% of US respondents and 41% of UK respondents), complex work-ups to determine clinical eligibility (reported by 44% of US and 55% of UK respondents), need for transportation/lodging (reported by 41% of US and 35% of UK respondents) and availability of caregiver support (reported by 34% of US and 41% of UK respondents). While 57% of UK respondents indicated that time required for communication between the referring center and CAR T therapy center was a challenge with determining eligibility, only 27% of US respondents selected this challenge (Table 3). In terms of impact, most eligibility determination challenges selected by HCPs had a median impact rating of four (very impactful) in the US. Exceptions were the time required for communication between a referral center and CAR T treatment center, patient loss of productivity/source of income and lack of clarity on eligibility criteria, all of which had a median impact rating of three (somewhat impactful). In the UK, slow or complex processes for obtaining NHS approval had a median impact rating of four (very impactful) while all other challenges had a median impact rating of three (somewhat impactful) (Table 3). In terms of frequency, most selected challenges during the eligibility determination process had a median frequency rating of four (often) in the US. Exceptions were the time required for communication between a referral center and CAR T treatment center, patient loss of productivity/source of income and lack of clarity on eligibility criteria, all of which had a median frequency rating of three (sometimes). In the UK, most challenges had a median frequency rating of three (sometimes), with the exception of patient loss of productivity/source of income, which had a median frequency rating of two (rarely) (Table 3).
Table 3.
Challenges associated with the CAR T therapy eligibility determination process.
Challenge
US (n = 64)
UK (n = 49)
Respondents (%)a
Median impact ratingb
Median frequency ratingc
Respondents (%)
Median impact rating
Median frequency rating
Slow/complex process for insurance coverage (US) or NHS approval (UK)
50
4
4
41
4
3
Patient out-of-pocket costs for medical services to determine eligibility
47
4
4
4
3
3
Complex clinical workup to determine eligibility
44
4
4
55
3
3
Patient transportation/lodging if travel needed to treatment center
41
4
4
35
3
3
Availability of caregiver support
34
4
4
41
3
3
Communication time between referral center and CAR T therapy center
27
3
3
57
3
3
Patient productivity/income loss
25
3
3
10
3
2
Lack of clarity over eligibility criteria
17
3
3
27
3
3
a
Percentage of respondents who selected each factor for the question, “Thinking about your experience assessing patients for CAR T eligibility, what are the main factors you and your patient experience at this stage?”.
b
For each factor selected, respondents selected the impact of the factor on whether a patient receives CAR T, where 1 = not at all impactful, 2 = slightly impactful, 3 = somewhat impactful, 4 = very impactful, 5 = very impactful.
c
For each factor selected, respondents selected the frequency with which patients have had their care impacted as a result of this factor, where 1 = never, 2 = rarely, 3 = sometimes, 4 = often, 5 = very often.
CAR T: Chimeric antigen receptor T cell; NHS: National Health Service; UK: United Kingdom; US: United States.
3.2.5.
Treatments & procedures before infusion
After a patient has been deemed officially eligible for CAR T therapy, logistical challenges may emerge during the procedures (i.e., leukapheresis, bridging therapy, lymphodepletion) leading up to infusion. The most common logistical challenges faced at this stage were: waiting time for healthcare capacity (reported by 57% of US and 71% of UK respondents), communication between the referral center and CAR T therapy center to coordinate treatments (reported by 25% of US and 49% of UK respondents), availability of funding/insurance to cover pre-infusion procedures (reported by 49% of US and 24% of UK respondents) and availability of caregiver support (reported by 34% of US and 29% of UK respondents) (Table 4). In terms of impact, most challenges selected by HCPs had a median impact rating of four (very impactful) in the US. Exceptions were transportation/lodging/meals for patient travel and communications between the referring center and CAR T treatment center, both of which had a median impact rating of three (somewhat impactful). In the UK, HCPs reported that transportation/lodging/meals for patient travel, availability of funding for pre-infusion procedures and patient out-of-pocket costs all had median impact ratings of four (very impactful). Waiting time for healthcare capacity to complete pre-infusion procedures, communication between referral center and CAR T center to coordinate pre-infusion procedures, and availability of caregiver support had median frequency ratings of three (somewhat impactful) (Table 4). In terms of frequency, HCPs in both the US and UK reported that patient out-of-pocket costs or patient loss of productivity/source of income had median frequency ratings of four (often) in the procedures before infusion stage. All other challenges selected by HCPs in both regions had median impact ratings of three (sometimes) (Table 4).
Table 4.
Challenges associated with treatments and procedures before CAR T infusion.
Challenge
US (n = 68)
UK (n = 45)
Respondents (%)a
Median impact ratingb
Median frequency ratingc
Respondents (%)
Median impact rating
Median frequency rating
Waiting time for healthcare capacity to complete pre-infusion treatments
57
4
3
71
3
3
Availability of funding/insurance to cover pre-infusion treatments
49
4
3
24
4
3
Patient out-of-pocket costs or productivity/income loss
37
4
4
9
4
4
Availability of caregiver support
34
4
3
29
3
3
Patient transportation/lodging/meals if travel needed to treatment center
29
3
3
13
4
3
Communication between referring center and CAR T therapy center to coordinate pre-infusion treatments
25
3
3
49
3
3
a
Percentage of respondents who selected each factor for the question, “What are the main logistical factors you and your patients experience during treatment/procedures before CAR T infusion (leukapheresis, bridging therapy and lymphodepletion)?”.
b
For each factor selected, respondents selected the impact of the factor on whether a patient receives CAR T, where 1 = not at all impactful, 2 = slightly impactful, 3 = somewhat impactful, 4 = very impactful, 5 = very impactful.
c
For each factor selected, respondents selected the frequency with which patients have had their care impacted as a result of this factor, where 1 = never, 2 = rarely, 3 = sometimes, 4 = often, 5 = very often.
CAR T: chimeric antigen receptor T cell; UK: United Kingdom; US: United States.
3.2.6.
Infusion & immediate post-infusion monitoring
The most commonly reported logistical challenges experienced in the infusion and immediate post-infusion monitoring stage were: availability of caregiver support (reported by 44% of US and 53% of UK respondents), waiting time for healthcare capacity (e.g., an open inpatient bed) (reported by 40% of US and 50% of UK respondents), transportation, lodging and meals for patients/caregivers (reported by 37% of US and 32% of UK respondents), and patient out-of-pocket costs (reported by 47% of US and 5% of UK respondents) (Table 5). In terms of impact, in the US, patient out-of-pocket costs, requirements for treatment centers to have medicines on-site for managing adverse events and availability of caregiver support had median impact ratings of four (very impactful). Wait time for healthcare capacity, transportation, lodging and meals for patients/caregivers and patient/caregiver productivity or income loss had median impact ratings of three (somewhat impactful). In the UK, all challenges selected by HCPs had median impact ratings of three (somewhat impactful) (Table 5). In terms of frequency, in both the US and UK, most challenges reported during the infusion and immediate post-infusion monitoring stage had median frequency ratings of three (sometimes). Requirements for treatment centers to have medicines on-site for managing adverse events had a median frequency rating of four (often) among US HCPs. Some differences also emerged between regions: patient out-of-pocket costs for treatment had a median frequency rating of four (often) in the US and two (rarely) in the UK (Table 5).
Table 5.
Challenges associated with CAR T infusion and post-infusion monitoring procedures.
Challenge
US (n = 57)
UK (n = 38)
Respondents (%)a
Median impact ratingb
Median frequency ratingc
Respondents (%)
Median impact rating
Median frequency rating
Patient out-of-pocket costs for treatment
47
4
4
5
3
2
Availability of caregiver support
44
4
3
53
3
3
Wait time for healthcare capacity
40
3
3
50
3
3
Patient transportation/lodging/meals if travel needed to treatment center
37
3
3
32
3
3
Requirements for treatment centers to have medicines (eg, tocilizumab) on-site to manage AEs
32
4
4
29
3
3
Patient/caregiver productivity/income loss during infusion
18
3
3
21
3
3
a
Percent of respondents who selected each factor for the question, “What are the main logistical factors you and your patients experience during CAR T infusion?”.
b
For each factor selected, respondents selected the impact of the factor on whether a patient receives CAR T, where 1 = not at all impactful, 2 = slightly impactful, 3 = somewhat impactful, 4 = very impactful, 5 = very impactful.
c
For each factor selected, respondents selected the frequency with which patients have had their care impacted as a result of this factor, where 1 = never, 2 = rarely, 3 = sometimes, 4 = often, 5 = very often.
AE: Adverse event; CAR T: Chimeric antigen receptor T cell; UK: United Kingdom; US: United States.
3.2.7.
Long-term follow-up care
The most common logistical challenges reported during the long-term follow-up stage of the CAR T treatment process were: the availability of caregiver/social support (reported by 55% of US and 72% of UK respondents), transportation/lodging/meals for traveling for follow-up appointments (reported by 39% of US and 38% of UK respondents), availability of funding/insurance for long-term follow-up care (reported by 52% of US and 21% of UK respondents) and patient/caregiver work productivity/income loss (reported by 31% of US and 38% of UK respondents) (Table 6). In terms of impact, in the UK, all challenges reported by HCPs in the long-term follow-up stage had median impact ratings of three (somewhat impactful). In the US, the following challenges had median impact ratings of four (often): patient out-of-pocket costs for long-term follow-up care, availability of funding/insurance for long-term follow-up care and transportation/lodging/meals for traveling for follow-up appointments; the availability of caregiver support and patient or caregiver productivity/income loss both had median impact ratings of three (somewhat impactful) (Table 6). In terms of frequency, the availability of funding/insurance for long-term follow-up care had a median frequency rating of four (often) among US respondents; all other challenges selected by HCPs in both the US and UK had a median frequency rating of three (sometimes) (Table 6).
Table 6.
Challenges associated with long-term follow-up care after CAR T therapy.
Challenge
US (n = 62)
UK (n = 47)
Respondents (%)a
Median impact ratingb
Median frequency ratingc
Respondents (%)
Median impact rating
Median frequency rating
Patient out-of-pocket costs for follow-up care
55
4
3
15
3
3
Availability of caregiver support
55
3
3
72
3
3
Availability of funding/insurance for follow-up care
52
4
4
21
3
3
Patient transportation/lodging/meals if travel needed for follow-up care
39
4
3
38
3
3
Patient/caregiver productivity/income loss when receiving follow-up care
31
3
3
38
3
3
a
Percentage of respondents who selected each factor for the question, “What are the main logistical factors you and your patients experience during long-term follow-up after CAR T administration?”.
b
For each factor selected, respondents selected the impact of the factor on patients' treatment experience, where 1 = not at all impactful, 2 = slightly impactful, 3 = somewhat impactful, 4 = very impactful, 5 = very impactful.
c
For each factor selected, respondents selected the frequency with which patients have had their care impacted as a result of this factor, where 1 = never, 2 = rarely, 3 = sometimes, 4 = often, 5 = very often.
CAR T: Chimeric antigen receptor T cell; UK: United Kingdom; US: United States.
3.2.8.
General CAR T treatment logistics
HCPs in both the US and UK estimated that the mean time between leukapheresis and infusion was 21 days (US mean, 21 days; UK mean, 22 days). They also estimated that patients spend 5 days (US mean, 5 days; UK mean, 6 days) in nearby accommodations prior to infusion, and 12 days during and immediately after infusion (US mean, 13 days; UK mean, 12 days). HCPs estimated that a mean of 21% of patients (US mean, 22%; UK mean, 20%) require intensive care admission during infusion and a mean of 24% (US mean, 25%; UK mean, 24%) were estimated to require re-admission to the hospital during the post-infusion monitoring period (Table 7).
Table 7.
CAR T therapy logistical aspects and caregiver considerations.
Estimated logistical factors
US
UK
n
Mean (Median, IQR)
n
Mean (Median, IQR)
Average number of days between leukapheresis and infusion
50
21 (17, 11–28)
30
22 (23, 13–30)
Nights spent in lodging before infusion
44
5 (3, 2–5)
29
6 (4, 2–5)
Nights spend in lodging during/immediately after infusion
44
13 (7, 6–20)
29
12 (9, 5–14)
Caregiver: days spent with patient for support before infusion
43
6 (3, 2–7)
27
8 (5, 2–6)
Caregiver: days spent with patient for support after infusion
43
14 (10, 4–20)
27
14 (14, 6–20)
n
Mean % (Median %)
n
Mean % (Median %)
Percentage of patients requiring admission to ICU during infusion
51
22 (15)
36
20 (19)
Percentage of patients requiring readmission to the hospital post-infusion
54
25 (20)
35
24 (25)
Percentage of patients with an informal/unpaid caregiver
60
67 (75)
37
67 (75)
For patients without a caregiver:
a
Hired, paid out of pocket
39
23 (20)
25
16 (15)
Hired, funded by insurance (US) or NHS (UK)
37
18 (20)
28
30 (30)
Hired, funded by charity/social services
37
17 (15)
26
19 (16)
Hired, funded by manufacturer
33
13 (10)
22
12 (10)
No caregiver support
31
22 (10)
24
31 (18)
Percentage of caregivers impacted by CAR T therapy in the following ways:
Disruption of daily activities
58
49 (40)
32
45 (40)
Negative impact on quality of life or mental health
54
33 (30)
31
33 (28)
Loss of productivity and source of income
51
38 (30)
30
32 (30)
a
Percentages in this category add up to over 100%, as respondents could have selected more than one source of caregiver support.
CAR T: Chimeric antigen receptor T-cell; ICU: Intensive care unit; IQR: Interquartile range; NHS: National Health Service; UK: United Kingdom; US: United States.
Across the US and UK, the mean estimated percentage of patients having an informal/unpaid caregiver (e.g., family member or friend) to support them during treatment was 67%. When asked to consider patients without an informal/unpaid caregiver, HCPs estimated that about one-quarter (US mean, 22%; UK mean, 31%) remained without any caregiver support while others secured a hired caregiver funded by insurance, the NHS, the CAR T manufacturer, or paid out-of-pocket. Respondents also reported that the CAR T therapy process impacts caregivers by disrupting daily activities, negatively impacting their quality of life/mental health and causing lost productivity and income (Table 7).
3.2.9.
Estimated proportion of patients impacted by CAR T challenges
3.2.9.1.
Patient drop-off between CAR T referral & leukapheresis
On average, HCPs estimated that 32% of patients in the US and 27% of patients in the UK who are initially referred for CAR T therapy do not proceed to leukapheresis. Reasons for not proceeding to leukapheresis included rapid disease progression (reported by 58% of US and 74% of UK respondents), a work-up showing clinical ineligibility (reported by 39% of US and 67% of UK respondents) and insurance (US) or NHS (UK) applications not being approved (reported by 34% of US and 31% of UK respondents) (Table 8).
Table 8.
Percentage of respondents reporting reasons that eligible CAR T therapy patients do not proceed to leukapheresis or infusion.
Reason for not proceeding
Do not proceed to leukapheresis (%)
Do not proceed to infusion (%)
US (n = 59)
UK (n = 42)
US (n = 44)
UK (n = 31)
Rapid disease progression
58
74
57
68
Work-up shows clinical ineligibility
39
67
NA
NA
Insurance coverage/NHS application not approved
34
31
34
NA
High OOP costs for treatment
34
14
32
13
Travel distance to treating center
19
12
20
10
Unavailable caregiver support
15
5
14
16
Patient preference for alternative options
14
26
0
29
Concerns about work productivity/income loss
7
2
7
6
CAR T manufacturing failure
NA
NA
34
48
Long wait time for manufacturing
NA
NA
14
29
CAR T: Chimeric antigen receptor T-cell; NA: Not applicable; NHS: National Health Service; OOP: Out-of-pocket; UK: United Kingdom; US: United States.
3.2.9.2.
Patient drop-off between leukapheresis & infusion
On average, an estimated 19% of US patients and 20% of UK patients who undergo leukapheresis do not proceed to infusion. Reasons for not proceeding to infusion included rapid disease progression (reported by 57% of US and 68% of UK respondents), CAR T manufacturing failure (reported by 34% of US and 48% of UK respondents) and long wait time for manufacturing (reported by 14% of US and 29% of UK respondents) (Table 8).
In summary, based on the estimated proportion of patients not proceeding to leukapheresis or infusion, an estimated 45% of patients referred to CAR T in the US and 42% in the UK do not make it to CAR T infusion due to several logistical challenges including rapid disease progression, manufacturing failure, wait time for manufacturing and access to insurance.
3.1.
Qualitative interviews
Twenty-five HCPs were interviewed (21 US, four UK). Twenty (80%) were physicians and five (20%) were other HCPs. Participants had been treating patients for a median of 18 years and had referred or prescribed CAR T therapy to a median of 12 patients in the past 24 months.
Analysis of interview data revealed a generally positive outlook on the efficacy and safety of CAR T therapy; however, several logistical challenges associated with the treatment process were discussed. Four key themes relating to logistical challenges in the CAR T treatment process were identified: time and administration issues, caregiver support, access to insurance and travel and accommodation challenges. Interview excerpts relating to each key theme are shown below, with further excerpts shown in Table 1.
Table 1.
Qualitative interview excerpts relating to CAR T therapy logistical challenges.
Theme
Relevant excerpts
Caregiver support
“I mean, if I really felt that I had a patient that really had no support whatsoever, I might be hesitant to refer them.” – UK HCP
“We always make sure that somebody has a caregiver before we proceed with CAR T…sometimes, some patients could hire a caregiver, but not everybody has the financial means to do so.” – US HCP
“So, we make sure that the caregivers… we educate them, like, this is a big commitment. You've got to make sure that you're there with the patient for X amount of weeks. You can't really work. You have to be there with the patient present pretty much all the time in the same household monitoring them, interacting with them to make sure that… you know, you're looking for certain types of… the way that the patient is interacting with you. Are they speaking appropriately? Do they have chills? Do they have trouble breathing? Are they feeling hot? Are they shivering? Do they have a fever – things along those lines. Are they nauseous, for example? Are they feeling dizzy? Are they having problems with their memory or something like that, which, again, all these things could point to adverse events from CAR T.” – US HCP
“We kind of monitor patients through the most acute phase of this. I know it's been very emotionally challenging to some of the caregivers who come in to visit and they find their loved one kind of just in the midst of either CRS [cytokine release syndrome] or, even more upsetting, neurotoxicity. Obviously when the patient is in the hospital, they aren't responsible for the care, but it's hard then to see it.” – US HCP
Time and administration
“I think it's more consuming this one [the referral to CAR T] because you have to put all the patient file together, have to put together a letter why the patient needs this therapy and what's the expected result, outcome of this therapy. But also, everything related to the patient from financial or insurance coverages.” – US HCP
“So patient is waiting, waiting, waiting. It is not like bone marrow transplant, you go for transplant and the chances of getting a transplant is north of like 90%. On the other hand, CAR T, it's hard to get. Even if I refer eight patients in a year, very probably at best, two or three will get the CAR T. So it is logistically getting a slot is very important.” – US HCP
“I mean they could be about… I mean at the moment I don't think that any patient has not been able to receive the CAR T because of the long queue. I don't think it has happened. But the infusion time can be… I mean it's just the infusion rather than the queue which is a problem, where the patient's cancer relapse while waiting for the cells.” – US HCP
“In the centre where I work, we have a bottleneck with leukapheresis. If we had infinite leukapheresis capacity, I'm sure we would treat probably 50% more.” – UK HCP
“…in terms of the manufacturing process, it takes sometimes between four to six weeks. That is an issue, and it would be good to sort of maybe shorten that process.” – US HCP
Travel and accommodation
“Yeah, I mean, some patients really don't want to do it because it's not readily available in their surrounding location, in our office, our clinic. They don't want to go the distance.” – US HCP
“Well, it can be work. I know it is an issue. I mean, you have to understand, for some folks, they don't venture out of the state on a general basis. So, travelling to the next state that will do this for them is like, for them, going to Europe in some cases because they just don't travel. That's a big deal for them. So, it can make an issue…[do many people refuse CAR T because of distance]…probably about, again, 15% overall.” – US HCP
“So, if you have to drive a great distance – two, three hours to a CAR T center – that can be somewhat of a burden for patients. So, look, in the United States, you have this concentration of metropolitan cities, and then you have lots of rural areas and lots of driving time and patients that perhaps don't have the support structure to be staying at a CAR T therapy center in that city for a prolonged period of time. So, that can be an issue.” – US HCP
Insurance and cost
“I mean, definitely insurance plays a big role, and we did have a couple of patients who were […] to get it, but despite us trying and case management getting involved, it never worked out because they were… I don't remember what type of insurance they had, or I think one of them was uninsured.” – US HCP
“The insurance approval goes through three steps. One is you first have to get the insurance approval to test the patient for CAR T, and once the testing is complete, then you have to wait for the insurance to approve the CAR T based on a review of the records of the patient. And then once approval is granted, then there is the third step, fairly unique to CAR T in that you have to have a single patient agreement negotiated with the insurance carrier for each and every patient independently. And that adds to delays. And from the time you see the patient to the time you can actually schedule for a CAR T, if the patient has private insurance, it can take up to six weeks or so to then schedule.” – US HCP
“10–15% [of patients wouldn't be financially eligible for CAR T]…For people who are younger who have private insurance, they may have very high deductibles or limited coverage, and then they've exhausted it….It can be $10,000 or $20,000, their overall deductible. Frequently, they've met it, so it's not as big an issue, but for some people, again, every year it comes up, and it depends on the timing. The event could be just more than they can afford.” – US HCP
CAR T: Chimeric antigen receptor T-cell.
3.1.1.
Time & administration
HCPs reported time and administration-related issues as major logistical challenges during the CAR T treatment process. HCPs identified that several elements of the CAR T process required waiting, which could hamper patient outcomes.
“I think it's more consuming this one [the referral to CAR T] because you have to put all the patient file together, have to put together a letter why the patient needs this therapy and what's the expected result, outcome of this therapy. But also, everything related to the patient from financial or insurance coverages.” – US HCP
“So patient is waiting, waiting, waiting. It is not like bone marrow transplant, you go for transplant and the chances of getting a transplant is north of like 90%. On the other hand, CAR T, it's hard to get. Even if I refer eight patients in a year, very probably at best, two or three will get the CAR T. So it is logistically getting a slot is very important.” – US HCP
3.1.2.
Caregiver support
HCPs indicated that having a caregiver to support the patient throughout the CAR T process and post treatment was non-negotiable, and that without this support patients would not be able to proceed with treatment.
“I mean, if I really felt that I had a patient that really had no support whatsoever, I might be hesitant to refer them.” – UK HCP
“We always make sure that somebody has a caregiver before we proceed with CAR T…sometimes, some patients could hire a caregiver, but not everybody has the financial means to do so.” – US HCP
3.1.3.
Access to insurance
HCPs reported access to insurance as a logistical challenge for patients seeking CAR T therapy. Some participants mentioned having patients who could not proceed due to financial barriers, while others noted that the process of applying for coverage can be tedious and time-consuming.
“I mean, definitely insurance plays a big role, and we did have a couple of patients who were […] to get it, but despite us trying and case management getting involved, it never worked out because they were… I don't remember what type of insurance they had, or I think one of them was uninsured.” – US HCP
“The insurance approval goes through three steps. One is you first have to get the insurance approval to test the patient for CAR T, and once the testing is complete, then you have to wait for the insurance to approve the CAR T based on a review of the records of the patient. And then once approval is granted, then there is the third step, fairly unique to CAR T in that you have to have a single patient agreement negotiated with the insurance carrier for each and every patient independently. And that adds to delays. And from the time you see the patient to the time you can actually schedule for a CAR T, if the patient has private insurance, it can take up to six weeks or so to then schedule.” – US HCP
3.1.4.
Travel & accommodations
Patient travel and accommodations were mentioned by many HCPs as logistical challenges in the CAR T therapy process. HCPs stated that they will not recommend a patient for CAR T therapy if the patient lives too far from the treatment center and would not be able to handle the travel. This would be decided together with the patient and their family.
“Yeah, I mean, some patients really don't want to do it because it's not readily available in their surrounding location, in our office, our clinic. They don't want to go the distance.” – US HCP
In summary, four distinct themes characterizing the logistical burden of the CAR T therapy process on several stakeholder groups (HCPs, patients, and caregivers) emerged from the qualitative interviews. These findings underscore the need for a readily available therapeutic option that can potentially alleviate these burdens. These themes and insights guided the creation of a survey which assessed the logistical challenges associated with CAR T therapy in greater detail using a quantitative approach.
3.1.1.
Time & administration
HCPs reported time and administration-related issues as major logistical challenges during the CAR T treatment process. HCPs identified that several elements of the CAR T process required waiting, which could hamper patient outcomes.
“I think it's more consuming this one [the referral to CAR T] because you have to put all the patient file together, have to put together a letter why the patient needs this therapy and what's the expected result, outcome of this therapy. But also, everything related to the patient from financial or insurance coverages.” – US HCP
“So patient is waiting, waiting, waiting. It is not like bone marrow transplant, you go for transplant and the chances of getting a transplant is north of like 90%. On the other hand, CAR T, it's hard to get. Even if I refer eight patients in a year, very probably at best, two or three will get the CAR T. So it is logistically getting a slot is very important.” – US HCP
3.1.2.
Caregiver support
HCPs indicated that having a caregiver to support the patient throughout the CAR T process and post treatment was non-negotiable, and that without this support patients would not be able to proceed with treatment.
“I mean, if I really felt that I had a patient that really had no support whatsoever, I might be hesitant to refer them.” – UK HCP
“We always make sure that somebody has a caregiver before we proceed with CAR T…sometimes, some patients could hire a caregiver, but not everybody has the financial means to do so.” – US HCP
3.1.3.
Access to insurance
HCPs reported access to insurance as a logistical challenge for patients seeking CAR T therapy. Some participants mentioned having patients who could not proceed due to financial barriers, while others noted that the process of applying for coverage can be tedious and time-consuming.
“I mean, definitely insurance plays a big role, and we did have a couple of patients who were […] to get it, but despite us trying and case management getting involved, it never worked out because they were… I don't remember what type of insurance they had, or I think one of them was uninsured.” – US HCP
“The insurance approval goes through three steps. One is you first have to get the insurance approval to test the patient for CAR T, and once the testing is complete, then you have to wait for the insurance to approve the CAR T based on a review of the records of the patient. And then once approval is granted, then there is the third step, fairly unique to CAR T in that you have to have a single patient agreement negotiated with the insurance carrier for each and every patient independently. And that adds to delays. And from the time you see the patient to the time you can actually schedule for a CAR T, if the patient has private insurance, it can take up to six weeks or so to then schedule.” – US HCP
3.1.4.
Travel & accommodations
Patient travel and accommodations were mentioned by many HCPs as logistical challenges in the CAR T therapy process. HCPs stated that they will not recommend a patient for CAR T therapy if the patient lives too far from the treatment center and would not be able to handle the travel. This would be decided together with the patient and their family.
“Yeah, I mean, some patients really don't want to do it because it's not readily available in their surrounding location, in our office, our clinic. They don't want to go the distance.” – US HCP
In summary, four distinct themes characterizing the logistical burden of the CAR T therapy process on several stakeholder groups (HCPs, patients, and caregivers) emerged from the qualitative interviews. These findings underscore the need for a readily available therapeutic option that can potentially alleviate these burdens. These themes and insights guided the creation of a survey which assessed the logistical challenges associated with CAR T therapy in greater detail using a quantitative approach.
3.2.
Quantitative survey
3.2.1.
Respondent characteristics
Of 222 respondents screened for eligibility, 133 were included in the final sample. Of these 133 HCPs, 80 (60%) were from the US and 53 (40%) were from the UK. Of the 80 US HCPs, 67 (84%) were physicians, 7 (9%) were physician assistants and 6 (8%) were nurse practitioners; additionally, 33 (41%) reported affiliation with an academic/teaching certified CAR T center, 22 (28%) with a community-based referring institution, 23 (29%) with a community-based certified CAR T center and 2 (3%) with an academic/teaching referring institution. Of the 53 UK HCPs, 43 (81%) were physicians, 7 (13.2%) were nurses, 2 (4%) were nurse practitioners and 1 (2%) was a cell therapy coordinator/navigator; additionally, 29 (55%) reported affiliation with an academic/teaching certified CAR T center, 8 (15%) with a community-based referring institution, 5 (9%) with a community-based certified CAR T center and 11 (21%) with an academic/teaching referring institution.
In both the US and UK, physicians had referred a median of 15 patients to CAR T therapy in the past 24 months. Nurses had worked with a median of 45 (US) and 30 (UK) patients during the CAR T therapy administration process in the past 24 months. Respondents were also highly experienced across the stages of CAR T therapy administration: 100% (n = 67) of US and 98% (n = 42) of UK physicians had experience with referrals and eligibility assessments; 75% (n = 50) of US and 51% (n = 22) of UK physicians had experience with infusion and post-infusion care; and 69% (n = 9) of US and 70% (n = 7) of UK nurses/coordinators had experience coordinating CAR T therapy logistics (Figure 1).
Figure 1.
Respondent experience with CAR T therapy, by country and profession*.
*Non-physicians included nurses, nurse practitioners, physician assistants and a cell therapy coordinator/navigator.
CAR T: Chimeric antigen receptor T cell; UK: United Kingdom; US: United States.
3.2.2.
Logistical challenges along the patient journey
Two or more logistical challenges were identified by ≥60% of the US and UK respondents across all stages of CAR T administration. Specifically, 86% (US) and 92% (UK) respondents reported ≥2 referral process challenges; 81% (US) and 78% (UK) respondents reported ≥2 CAR T eligibility determination challenges; 75% (US) and 60% (UK) respondents reported ≥2 challenges with procedures before infusion; 72% (US) and 63% (UK) respondents reported ≥2 CAR T infusion and monitoring challenges; and 82% (US) and 64% (UK) respondents reported ≥2 challenges during long-term follow-up processes (Figure 2). Further findings within each stage are described in the subsequent sections.
Figure 2.
Percentage of respondents selecting 2 or more logistical challenges at each stage of CAR T therapy administration.
CAR T: Chimeric antigen receptor T cell; UK: United Kingdom; US: United States.
3.2.3.
Referral process
The most commonly reported challenges impacting the decision to refer patients for CAR T therapy were: manufacturing wait time (reported by 42% of US and 60% of UK respondents), limited capacity at a CAR T therapy center (reported by 40% of US and 58% of UK respondents), lengthy referral process (reported by 45% of US and 37% of UK respondents), travel distance (reported by 36% of US and 38% of UK respondents), slow/complex processes for insurance (US) or National Health Service (NHS; UK) approvals (reported by 36% of US and 31% of UK respondents) and availability of caregiver support (reported by 36% of US and 27% of UK respondents). Additionally, 40% of US respondents reported absence of insurance as impacting the decision to refer a patient for CAR T therapy (Table 2). In terms of impact, in the US, most referral process challenges had a median impact rating of four (very impactful), with the exception of coordinating and providing bridging therapy while waiting for CAR T therapy (median impact rating: five [extremely impactful]) and lack of clarity on the eligibility criteria and/or referral process (median impact rating: three [somewhat impactful]). In the UK, all challenges had a median impact rating of three (somewhat impactful), except for the availability of caregiver support, which had a median impact rating of four (very impactful) (Table 2).
Table 2.
Challenges impacting the CAR T therapy patient referral processa.
Challenge
US (n = 73)
UK (n = 52)
Respondents (%)a
Median impact ratingb
Mean % of PTs not referred
Respondents (%)
Median impact rating
Mean % of PTs not referred
Lengthy referral process
45
4
16
37
3
22
Wait time for manufacturing
42
4
19
60
3
18
Absence of insurance
40
4
25
0
NA
NA
Limited capacity at CAR T therapy center
40
4
21
58
3
19
Availability of caregiver support
36
4
24
27
4
20
Slow/complex process for insurance coverage (US) or NHS approval (UK)
36
4
23
31
3
20
Patient travel distance to treatment center
36
4
20
38
3
17
Patient out-of-pocket costs or loss of productivity/source of income
34
4
21
8
3
26
Coordinating and providing bridging therapy while waiting for CAR T therapy
30
5
15
27
3
20
Coordinating and providing leukapheresis and lymphodepletion
27
4
20
25
3
20
Lack of clarity over eligibility criteria and/or referral process
22
3
25
25
3
23
Patient preference for alternative treatment options
19
4
17
40
3
21
a
Percentage of respondents who selected each factor for the question, “Thinking about your experience referring patients for CAR T, what are the main factors that impact your decision to refer a patient for CAR T?”.
b
For each factor selected, respondents selected the impact of the factor on whether a patient receives CAR T, where 1 = not at all impactful, 2 = slightly impactful, 3 = somewhat impactful, 4 = very impactful, 5 = very impactful.
CAR T: Chimeric antigen receptor T cell; NA: Not applicable; NHS: National Health Service; PT: Patient; UK: United Kingdom; US: United States.
Respondents in the US estimated that a mean of 25% of patients who are deemed eligible for CAR T therapy are not referred due to absence of insurance to cover treatment. Respondents in both the US and UK also reported that more than 20% of their potentially eligible patients were not referred due to lack of clarity over eligibility criteria and/or referral processes (US mean, 25%; UK mean, 23%) and availability of caregiver support (US mean, 24%; UK mean, 20%).
3.2.4.
Eligibility determination
The most commonly reported challenges experienced while determining patient eligibility were: complex processes for applying for insurance approvals (US) or obtaining NHS approval (UK) (reported by 50% of US respondents and 41% of UK respondents), complex work-ups to determine clinical eligibility (reported by 44% of US and 55% of UK respondents), need for transportation/lodging (reported by 41% of US and 35% of UK respondents) and availability of caregiver support (reported by 34% of US and 41% of UK respondents). While 57% of UK respondents indicated that time required for communication between the referring center and CAR T therapy center was a challenge with determining eligibility, only 27% of US respondents selected this challenge (Table 3). In terms of impact, most eligibility determination challenges selected by HCPs had a median impact rating of four (very impactful) in the US. Exceptions were the time required for communication between a referral center and CAR T treatment center, patient loss of productivity/source of income and lack of clarity on eligibility criteria, all of which had a median impact rating of three (somewhat impactful). In the UK, slow or complex processes for obtaining NHS approval had a median impact rating of four (very impactful) while all other challenges had a median impact rating of three (somewhat impactful) (Table 3). In terms of frequency, most selected challenges during the eligibility determination process had a median frequency rating of four (often) in the US. Exceptions were the time required for communication between a referral center and CAR T treatment center, patient loss of productivity/source of income and lack of clarity on eligibility criteria, all of which had a median frequency rating of three (sometimes). In the UK, most challenges had a median frequency rating of three (sometimes), with the exception of patient loss of productivity/source of income, which had a median frequency rating of two (rarely) (Table 3).
Table 3.
Challenges associated with the CAR T therapy eligibility determination process.
Challenge
US (n = 64)
UK (n = 49)
Respondents (%)a
Median impact ratingb
Median frequency ratingc
Respondents (%)
Median impact rating
Median frequency rating
Slow/complex process for insurance coverage (US) or NHS approval (UK)
50
4
4
41
4
3
Patient out-of-pocket costs for medical services to determine eligibility
47
4
4
4
3
3
Complex clinical workup to determine eligibility
44
4
4
55
3
3
Patient transportation/lodging if travel needed to treatment center
41
4
4
35
3
3
Availability of caregiver support
34
4
4
41
3
3
Communication time between referral center and CAR T therapy center
27
3
3
57
3
3
Patient productivity/income loss
25
3
3
10
3
2
Lack of clarity over eligibility criteria
17
3
3
27
3
3
a
Percentage of respondents who selected each factor for the question, “Thinking about your experience assessing patients for CAR T eligibility, what are the main factors you and your patient experience at this stage?”.
b
For each factor selected, respondents selected the impact of the factor on whether a patient receives CAR T, where 1 = not at all impactful, 2 = slightly impactful, 3 = somewhat impactful, 4 = very impactful, 5 = very impactful.
c
For each factor selected, respondents selected the frequency with which patients have had their care impacted as a result of this factor, where 1 = never, 2 = rarely, 3 = sometimes, 4 = often, 5 = very often.
CAR T: Chimeric antigen receptor T cell; NHS: National Health Service; UK: United Kingdom; US: United States.
3.2.5.
Treatments & procedures before infusion
After a patient has been deemed officially eligible for CAR T therapy, logistical challenges may emerge during the procedures (i.e., leukapheresis, bridging therapy, lymphodepletion) leading up to infusion. The most common logistical challenges faced at this stage were: waiting time for healthcare capacity (reported by 57% of US and 71% of UK respondents), communication between the referral center and CAR T therapy center to coordinate treatments (reported by 25% of US and 49% of UK respondents), availability of funding/insurance to cover pre-infusion procedures (reported by 49% of US and 24% of UK respondents) and availability of caregiver support (reported by 34% of US and 29% of UK respondents) (Table 4). In terms of impact, most challenges selected by HCPs had a median impact rating of four (very impactful) in the US. Exceptions were transportation/lodging/meals for patient travel and communications between the referring center and CAR T treatment center, both of which had a median impact rating of three (somewhat impactful). In the UK, HCPs reported that transportation/lodging/meals for patient travel, availability of funding for pre-infusion procedures and patient out-of-pocket costs all had median impact ratings of four (very impactful). Waiting time for healthcare capacity to complete pre-infusion procedures, communication between referral center and CAR T center to coordinate pre-infusion procedures, and availability of caregiver support had median frequency ratings of three (somewhat impactful) (Table 4). In terms of frequency, HCPs in both the US and UK reported that patient out-of-pocket costs or patient loss of productivity/source of income had median frequency ratings of four (often) in the procedures before infusion stage. All other challenges selected by HCPs in both regions had median impact ratings of three (sometimes) (Table 4).
Table 4.
Challenges associated with treatments and procedures before CAR T infusion.
Challenge
US (n = 68)
UK (n = 45)
Respondents (%)a
Median impact ratingb
Median frequency ratingc
Respondents (%)
Median impact rating
Median frequency rating
Waiting time for healthcare capacity to complete pre-infusion treatments
57
4
3
71
3
3
Availability of funding/insurance to cover pre-infusion treatments
49
4
3
24
4
3
Patient out-of-pocket costs or productivity/income loss
37
4
4
9
4
4
Availability of caregiver support
34
4
3
29
3
3
Patient transportation/lodging/meals if travel needed to treatment center
29
3
3
13
4
3
Communication between referring center and CAR T therapy center to coordinate pre-infusion treatments
25
3
3
49
3
3
a
Percentage of respondents who selected each factor for the question, “What are the main logistical factors you and your patients experience during treatment/procedures before CAR T infusion (leukapheresis, bridging therapy and lymphodepletion)?”.
b
For each factor selected, respondents selected the impact of the factor on whether a patient receives CAR T, where 1 = not at all impactful, 2 = slightly impactful, 3 = somewhat impactful, 4 = very impactful, 5 = very impactful.
c
For each factor selected, respondents selected the frequency with which patients have had their care impacted as a result of this factor, where 1 = never, 2 = rarely, 3 = sometimes, 4 = often, 5 = very often.
CAR T: chimeric antigen receptor T cell; UK: United Kingdom; US: United States.
3.2.6.
Infusion & immediate post-infusion monitoring
The most commonly reported logistical challenges experienced in the infusion and immediate post-infusion monitoring stage were: availability of caregiver support (reported by 44% of US and 53% of UK respondents), waiting time for healthcare capacity (e.g., an open inpatient bed) (reported by 40% of US and 50% of UK respondents), transportation, lodging and meals for patients/caregivers (reported by 37% of US and 32% of UK respondents), and patient out-of-pocket costs (reported by 47% of US and 5% of UK respondents) (Table 5). In terms of impact, in the US, patient out-of-pocket costs, requirements for treatment centers to have medicines on-site for managing adverse events and availability of caregiver support had median impact ratings of four (very impactful). Wait time for healthcare capacity, transportation, lodging and meals for patients/caregivers and patient/caregiver productivity or income loss had median impact ratings of three (somewhat impactful). In the UK, all challenges selected by HCPs had median impact ratings of three (somewhat impactful) (Table 5). In terms of frequency, in both the US and UK, most challenges reported during the infusion and immediate post-infusion monitoring stage had median frequency ratings of three (sometimes). Requirements for treatment centers to have medicines on-site for managing adverse events had a median frequency rating of four (often) among US HCPs. Some differences also emerged between regions: patient out-of-pocket costs for treatment had a median frequency rating of four (often) in the US and two (rarely) in the UK (Table 5).
Table 5.
Challenges associated with CAR T infusion and post-infusion monitoring procedures.
Challenge
US (n = 57)
UK (n = 38)
Respondents (%)a
Median impact ratingb
Median frequency ratingc
Respondents (%)
Median impact rating
Median frequency rating
Patient out-of-pocket costs for treatment
47
4
4
5
3
2
Availability of caregiver support
44
4
3
53
3
3
Wait time for healthcare capacity
40
3
3
50
3
3
Patient transportation/lodging/meals if travel needed to treatment center
37
3
3
32
3
3
Requirements for treatment centers to have medicines (eg, tocilizumab) on-site to manage AEs
32
4
4
29
3
3
Patient/caregiver productivity/income loss during infusion
18
3
3
21
3
3
a
Percent of respondents who selected each factor for the question, “What are the main logistical factors you and your patients experience during CAR T infusion?”.
b
For each factor selected, respondents selected the impact of the factor on whether a patient receives CAR T, where 1 = not at all impactful, 2 = slightly impactful, 3 = somewhat impactful, 4 = very impactful, 5 = very impactful.
c
For each factor selected, respondents selected the frequency with which patients have had their care impacted as a result of this factor, where 1 = never, 2 = rarely, 3 = sometimes, 4 = often, 5 = very often.
AE: Adverse event; CAR T: Chimeric antigen receptor T cell; UK: United Kingdom; US: United States.
3.2.7.
Long-term follow-up care
The most common logistical challenges reported during the long-term follow-up stage of the CAR T treatment process were: the availability of caregiver/social support (reported by 55% of US and 72% of UK respondents), transportation/lodging/meals for traveling for follow-up appointments (reported by 39% of US and 38% of UK respondents), availability of funding/insurance for long-term follow-up care (reported by 52% of US and 21% of UK respondents) and patient/caregiver work productivity/income loss (reported by 31% of US and 38% of UK respondents) (Table 6). In terms of impact, in the UK, all challenges reported by HCPs in the long-term follow-up stage had median impact ratings of three (somewhat impactful). In the US, the following challenges had median impact ratings of four (often): patient out-of-pocket costs for long-term follow-up care, availability of funding/insurance for long-term follow-up care and transportation/lodging/meals for traveling for follow-up appointments; the availability of caregiver support and patient or caregiver productivity/income loss both had median impact ratings of three (somewhat impactful) (Table 6). In terms of frequency, the availability of funding/insurance for long-term follow-up care had a median frequency rating of four (often) among US respondents; all other challenges selected by HCPs in both the US and UK had a median frequency rating of three (sometimes) (Table 6).
Table 6.
Challenges associated with long-term follow-up care after CAR T therapy.
Challenge
US (n = 62)
UK (n = 47)
Respondents (%)a
Median impact ratingb
Median frequency ratingc
Respondents (%)
Median impact rating
Median frequency rating
Patient out-of-pocket costs for follow-up care
55
4
3
15
3
3
Availability of caregiver support
55
3
3
72
3
3
Availability of funding/insurance for follow-up care
52
4
4
21
3
3
Patient transportation/lodging/meals if travel needed for follow-up care
39
4
3
38
3
3
Patient/caregiver productivity/income loss when receiving follow-up care
31
3
3
38
3
3
a
Percentage of respondents who selected each factor for the question, “What are the main logistical factors you and your patients experience during long-term follow-up after CAR T administration?”.
b
For each factor selected, respondents selected the impact of the factor on patients' treatment experience, where 1 = not at all impactful, 2 = slightly impactful, 3 = somewhat impactful, 4 = very impactful, 5 = very impactful.
c
For each factor selected, respondents selected the frequency with which patients have had their care impacted as a result of this factor, where 1 = never, 2 = rarely, 3 = sometimes, 4 = often, 5 = very often.
CAR T: Chimeric antigen receptor T cell; UK: United Kingdom; US: United States.
3.2.8.
General CAR T treatment logistics
HCPs in both the US and UK estimated that the mean time between leukapheresis and infusion was 21 days (US mean, 21 days; UK mean, 22 days). They also estimated that patients spend 5 days (US mean, 5 days; UK mean, 6 days) in nearby accommodations prior to infusion, and 12 days during and immediately after infusion (US mean, 13 days; UK mean, 12 days). HCPs estimated that a mean of 21% of patients (US mean, 22%; UK mean, 20%) require intensive care admission during infusion and a mean of 24% (US mean, 25%; UK mean, 24%) were estimated to require re-admission to the hospital during the post-infusion monitoring period (Table 7).
Table 7.
CAR T therapy logistical aspects and caregiver considerations.
Estimated logistical factors
US
UK
n
Mean (Median, IQR)
n
Mean (Median, IQR)
Average number of days between leukapheresis and infusion
50
21 (17, 11–28)
30
22 (23, 13–30)
Nights spent in lodging before infusion
44
5 (3, 2–5)
29
6 (4, 2–5)
Nights spend in lodging during/immediately after infusion
44
13 (7, 6–20)
29
12 (9, 5–14)
Caregiver: days spent with patient for support before infusion
43
6 (3, 2–7)
27
8 (5, 2–6)
Caregiver: days spent with patient for support after infusion
43
14 (10, 4–20)
27
14 (14, 6–20)
n
Mean % (Median %)
n
Mean % (Median %)
Percentage of patients requiring admission to ICU during infusion
51
22 (15)
36
20 (19)
Percentage of patients requiring readmission to the hospital post-infusion
54
25 (20)
35
24 (25)
Percentage of patients with an informal/unpaid caregiver
60
67 (75)
37
67 (75)
For patients without a caregiver:
a
Hired, paid out of pocket
39
23 (20)
25
16 (15)
Hired, funded by insurance (US) or NHS (UK)
37
18 (20)
28
30 (30)
Hired, funded by charity/social services
37
17 (15)
26
19 (16)
Hired, funded by manufacturer
33
13 (10)
22
12 (10)
No caregiver support
31
22 (10)
24
31 (18)
Percentage of caregivers impacted by CAR T therapy in the following ways:
Disruption of daily activities
58
49 (40)
32
45 (40)
Negative impact on quality of life or mental health
54
33 (30)
31
33 (28)
Loss of productivity and source of income
51
38 (30)
30
32 (30)
a
Percentages in this category add up to over 100%, as respondents could have selected more than one source of caregiver support.
CAR T: Chimeric antigen receptor T-cell; ICU: Intensive care unit; IQR: Interquartile range; NHS: National Health Service; UK: United Kingdom; US: United States.
Across the US and UK, the mean estimated percentage of patients having an informal/unpaid caregiver (e.g., family member or friend) to support them during treatment was 67%. When asked to consider patients without an informal/unpaid caregiver, HCPs estimated that about one-quarter (US mean, 22%; UK mean, 31%) remained without any caregiver support while others secured a hired caregiver funded by insurance, the NHS, the CAR T manufacturer, or paid out-of-pocket. Respondents also reported that the CAR T therapy process impacts caregivers by disrupting daily activities, negatively impacting their quality of life/mental health and causing lost productivity and income (Table 7).
3.2.9.
Estimated proportion of patients impacted by CAR T challenges
3.2.9.1.
Patient drop-off between CAR T referral & leukapheresis
On average, HCPs estimated that 32% of patients in the US and 27% of patients in the UK who are initially referred for CAR T therapy do not proceed to leukapheresis. Reasons for not proceeding to leukapheresis included rapid disease progression (reported by 58% of US and 74% of UK respondents), a work-up showing clinical ineligibility (reported by 39% of US and 67% of UK respondents) and insurance (US) or NHS (UK) applications not being approved (reported by 34% of US and 31% of UK respondents) (Table 8).
Table 8.
Percentage of respondents reporting reasons that eligible CAR T therapy patients do not proceed to leukapheresis or infusion.
Reason for not proceeding
Do not proceed to leukapheresis (%)
Do not proceed to infusion (%)
US (n = 59)
UK (n = 42)
US (n = 44)
UK (n = 31)
Rapid disease progression
58
74
57
68
Work-up shows clinical ineligibility
39
67
NA
NA
Insurance coverage/NHS application not approved
34
31
34
NA
High OOP costs for treatment
34
14
32
13
Travel distance to treating center
19
12
20
10
Unavailable caregiver support
15
5
14
16
Patient preference for alternative options
14
26
0
29
Concerns about work productivity/income loss
7
2
7
6
CAR T manufacturing failure
NA
NA
34
48
Long wait time for manufacturing
NA
NA
14
29
CAR T: Chimeric antigen receptor T-cell; NA: Not applicable; NHS: National Health Service; OOP: Out-of-pocket; UK: United Kingdom; US: United States.
3.2.9.2.
Patient drop-off between leukapheresis & infusion
On average, an estimated 19% of US patients and 20% of UK patients who undergo leukapheresis do not proceed to infusion. Reasons for not proceeding to infusion included rapid disease progression (reported by 57% of US and 68% of UK respondents), CAR T manufacturing failure (reported by 34% of US and 48% of UK respondents) and long wait time for manufacturing (reported by 14% of US and 29% of UK respondents) (Table 8).
In summary, based on the estimated proportion of patients not proceeding to leukapheresis or infusion, an estimated 45% of patients referred to CAR T in the US and 42% in the UK do not make it to CAR T infusion due to several logistical challenges including rapid disease progression, manufacturing failure, wait time for manufacturing and access to insurance.
3.2.1.
Respondent characteristics
Of 222 respondents screened for eligibility, 133 were included in the final sample. Of these 133 HCPs, 80 (60%) were from the US and 53 (40%) were from the UK. Of the 80 US HCPs, 67 (84%) were physicians, 7 (9%) were physician assistants and 6 (8%) were nurse practitioners; additionally, 33 (41%) reported affiliation with an academic/teaching certified CAR T center, 22 (28%) with a community-based referring institution, 23 (29%) with a community-based certified CAR T center and 2 (3%) with an academic/teaching referring institution. Of the 53 UK HCPs, 43 (81%) were physicians, 7 (13.2%) were nurses, 2 (4%) were nurse practitioners and 1 (2%) was a cell therapy coordinator/navigator; additionally, 29 (55%) reported affiliation with an academic/teaching certified CAR T center, 8 (15%) with a community-based referring institution, 5 (9%) with a community-based certified CAR T center and 11 (21%) with an academic/teaching referring institution.
In both the US and UK, physicians had referred a median of 15 patients to CAR T therapy in the past 24 months. Nurses had worked with a median of 45 (US) and 30 (UK) patients during the CAR T therapy administration process in the past 24 months. Respondents were also highly experienced across the stages of CAR T therapy administration: 100% (n = 67) of US and 98% (n = 42) of UK physicians had experience with referrals and eligibility assessments; 75% (n = 50) of US and 51% (n = 22) of UK physicians had experience with infusion and post-infusion care; and 69% (n = 9) of US and 70% (n = 7) of UK nurses/coordinators had experience coordinating CAR T therapy logistics (Figure 1).
Figure 1.
Respondent experience with CAR T therapy, by country and profession*.
*Non-physicians included nurses, nurse practitioners, physician assistants and a cell therapy coordinator/navigator.
CAR T: Chimeric antigen receptor T cell; UK: United Kingdom; US: United States.
3.2.2.
Logistical challenges along the patient journey
Two or more logistical challenges were identified by ≥60% of the US and UK respondents across all stages of CAR T administration. Specifically, 86% (US) and 92% (UK) respondents reported ≥2 referral process challenges; 81% (US) and 78% (UK) respondents reported ≥2 CAR T eligibility determination challenges; 75% (US) and 60% (UK) respondents reported ≥2 challenges with procedures before infusion; 72% (US) and 63% (UK) respondents reported ≥2 CAR T infusion and monitoring challenges; and 82% (US) and 64% (UK) respondents reported ≥2 challenges during long-term follow-up processes (Figure 2). Further findings within each stage are described in the subsequent sections.
Figure 2.
Percentage of respondents selecting 2 or more logistical challenges at each stage of CAR T therapy administration.
CAR T: Chimeric antigen receptor T cell; UK: United Kingdom; US: United States.
3.2.3.
Referral process
The most commonly reported challenges impacting the decision to refer patients for CAR T therapy were: manufacturing wait time (reported by 42% of US and 60% of UK respondents), limited capacity at a CAR T therapy center (reported by 40% of US and 58% of UK respondents), lengthy referral process (reported by 45% of US and 37% of UK respondents), travel distance (reported by 36% of US and 38% of UK respondents), slow/complex processes for insurance (US) or National Health Service (NHS; UK) approvals (reported by 36% of US and 31% of UK respondents) and availability of caregiver support (reported by 36% of US and 27% of UK respondents). Additionally, 40% of US respondents reported absence of insurance as impacting the decision to refer a patient for CAR T therapy (Table 2). In terms of impact, in the US, most referral process challenges had a median impact rating of four (very impactful), with the exception of coordinating and providing bridging therapy while waiting for CAR T therapy (median impact rating: five [extremely impactful]) and lack of clarity on the eligibility criteria and/or referral process (median impact rating: three [somewhat impactful]). In the UK, all challenges had a median impact rating of three (somewhat impactful), except for the availability of caregiver support, which had a median impact rating of four (very impactful) (Table 2).
Table 2.
Challenges impacting the CAR T therapy patient referral processa.
Challenge
US (n = 73)
UK (n = 52)
Respondents (%)a
Median impact ratingb
Mean % of PTs not referred
Respondents (%)
Median impact rating
Mean % of PTs not referred
Lengthy referral process
45
4
16
37
3
22
Wait time for manufacturing
42
4
19
60
3
18
Absence of insurance
40
4
25
0
NA
NA
Limited capacity at CAR T therapy center
40
4
21
58
3
19
Availability of caregiver support
36
4
24
27
4
20
Slow/complex process for insurance coverage (US) or NHS approval (UK)
36
4
23
31
3
20
Patient travel distance to treatment center
36
4
20
38
3
17
Patient out-of-pocket costs or loss of productivity/source of income
34
4
21
8
3
26
Coordinating and providing bridging therapy while waiting for CAR T therapy
30
5
15
27
3
20
Coordinating and providing leukapheresis and lymphodepletion
27
4
20
25
3
20
Lack of clarity over eligibility criteria and/or referral process
22
3
25
25
3
23
Patient preference for alternative treatment options
19
4
17
40
3
21
a
Percentage of respondents who selected each factor for the question, “Thinking about your experience referring patients for CAR T, what are the main factors that impact your decision to refer a patient for CAR T?”.
b
For each factor selected, respondents selected the impact of the factor on whether a patient receives CAR T, where 1 = not at all impactful, 2 = slightly impactful, 3 = somewhat impactful, 4 = very impactful, 5 = very impactful.
CAR T: Chimeric antigen receptor T cell; NA: Not applicable; NHS: National Health Service; PT: Patient; UK: United Kingdom; US: United States.
Respondents in the US estimated that a mean of 25% of patients who are deemed eligible for CAR T therapy are not referred due to absence of insurance to cover treatment. Respondents in both the US and UK also reported that more than 20% of their potentially eligible patients were not referred due to lack of clarity over eligibility criteria and/or referral processes (US mean, 25%; UK mean, 23%) and availability of caregiver support (US mean, 24%; UK mean, 20%).
3.2.4.
Eligibility determination
The most commonly reported challenges experienced while determining patient eligibility were: complex processes for applying for insurance approvals (US) or obtaining NHS approval (UK) (reported by 50% of US respondents and 41% of UK respondents), complex work-ups to determine clinical eligibility (reported by 44% of US and 55% of UK respondents), need for transportation/lodging (reported by 41% of US and 35% of UK respondents) and availability of caregiver support (reported by 34% of US and 41% of UK respondents). While 57% of UK respondents indicated that time required for communication between the referring center and CAR T therapy center was a challenge with determining eligibility, only 27% of US respondents selected this challenge (Table 3). In terms of impact, most eligibility determination challenges selected by HCPs had a median impact rating of four (very impactful) in the US. Exceptions were the time required for communication between a referral center and CAR T treatment center, patient loss of productivity/source of income and lack of clarity on eligibility criteria, all of which had a median impact rating of three (somewhat impactful). In the UK, slow or complex processes for obtaining NHS approval had a median impact rating of four (very impactful) while all other challenges had a median impact rating of three (somewhat impactful) (Table 3). In terms of frequency, most selected challenges during the eligibility determination process had a median frequency rating of four (often) in the US. Exceptions were the time required for communication between a referral center and CAR T treatment center, patient loss of productivity/source of income and lack of clarity on eligibility criteria, all of which had a median frequency rating of three (sometimes). In the UK, most challenges had a median frequency rating of three (sometimes), with the exception of patient loss of productivity/source of income, which had a median frequency rating of two (rarely) (Table 3).
Table 3.
Challenges associated with the CAR T therapy eligibility determination process.
Challenge
US (n = 64)
UK (n = 49)
Respondents (%)a
Median impact ratingb
Median frequency ratingc
Respondents (%)
Median impact rating
Median frequency rating
Slow/complex process for insurance coverage (US) or NHS approval (UK)
50
4
4
41
4
3
Patient out-of-pocket costs for medical services to determine eligibility
47
4
4
4
3
3
Complex clinical workup to determine eligibility
44
4
4
55
3
3
Patient transportation/lodging if travel needed to treatment center
41
4
4
35
3
3
Availability of caregiver support
34
4
4
41
3
3
Communication time between referral center and CAR T therapy center
27
3
3
57
3
3
Patient productivity/income loss
25
3
3
10
3
2
Lack of clarity over eligibility criteria
17
3
3
27
3
3
a
Percentage of respondents who selected each factor for the question, “Thinking about your experience assessing patients for CAR T eligibility, what are the main factors you and your patient experience at this stage?”.
b
For each factor selected, respondents selected the impact of the factor on whether a patient receives CAR T, where 1 = not at all impactful, 2 = slightly impactful, 3 = somewhat impactful, 4 = very impactful, 5 = very impactful.
c
For each factor selected, respondents selected the frequency with which patients have had their care impacted as a result of this factor, where 1 = never, 2 = rarely, 3 = sometimes, 4 = often, 5 = very often.
CAR T: Chimeric antigen receptor T cell; NHS: National Health Service; UK: United Kingdom; US: United States.
3.2.5.
Treatments & procedures before infusion
After a patient has been deemed officially eligible for CAR T therapy, logistical challenges may emerge during the procedures (i.e., leukapheresis, bridging therapy, lymphodepletion) leading up to infusion. The most common logistical challenges faced at this stage were: waiting time for healthcare capacity (reported by 57% of US and 71% of UK respondents), communication between the referral center and CAR T therapy center to coordinate treatments (reported by 25% of US and 49% of UK respondents), availability of funding/insurance to cover pre-infusion procedures (reported by 49% of US and 24% of UK respondents) and availability of caregiver support (reported by 34% of US and 29% of UK respondents) (Table 4). In terms of impact, most challenges selected by HCPs had a median impact rating of four (very impactful) in the US. Exceptions were transportation/lodging/meals for patient travel and communications between the referring center and CAR T treatment center, both of which had a median impact rating of three (somewhat impactful). In the UK, HCPs reported that transportation/lodging/meals for patient travel, availability of funding for pre-infusion procedures and patient out-of-pocket costs all had median impact ratings of four (very impactful). Waiting time for healthcare capacity to complete pre-infusion procedures, communication between referral center and CAR T center to coordinate pre-infusion procedures, and availability of caregiver support had median frequency ratings of three (somewhat impactful) (Table 4). In terms of frequency, HCPs in both the US and UK reported that patient out-of-pocket costs or patient loss of productivity/source of income had median frequency ratings of four (often) in the procedures before infusion stage. All other challenges selected by HCPs in both regions had median impact ratings of three (sometimes) (Table 4).
Table 4.
Challenges associated with treatments and procedures before CAR T infusion.
Challenge
US (n = 68)
UK (n = 45)
Respondents (%)a
Median impact ratingb
Median frequency ratingc
Respondents (%)
Median impact rating
Median frequency rating
Waiting time for healthcare capacity to complete pre-infusion treatments
57
4
3
71
3
3
Availability of funding/insurance to cover pre-infusion treatments
49
4
3
24
4
3
Patient out-of-pocket costs or productivity/income loss
37
4
4
9
4
4
Availability of caregiver support
34
4
3
29
3
3
Patient transportation/lodging/meals if travel needed to treatment center
29
3
3
13
4
3
Communication between referring center and CAR T therapy center to coordinate pre-infusion treatments
25
3
3
49
3
3
a
Percentage of respondents who selected each factor for the question, “What are the main logistical factors you and your patients experience during treatment/procedures before CAR T infusion (leukapheresis, bridging therapy and lymphodepletion)?”.
b
For each factor selected, respondents selected the impact of the factor on whether a patient receives CAR T, where 1 = not at all impactful, 2 = slightly impactful, 3 = somewhat impactful, 4 = very impactful, 5 = very impactful.
c
For each factor selected, respondents selected the frequency with which patients have had their care impacted as a result of this factor, where 1 = never, 2 = rarely, 3 = sometimes, 4 = often, 5 = very often.
CAR T: chimeric antigen receptor T cell; UK: United Kingdom; US: United States.
3.2.6.
Infusion & immediate post-infusion monitoring
The most commonly reported logistical challenges experienced in the infusion and immediate post-infusion monitoring stage were: availability of caregiver support (reported by 44% of US and 53% of UK respondents), waiting time for healthcare capacity (e.g., an open inpatient bed) (reported by 40% of US and 50% of UK respondents), transportation, lodging and meals for patients/caregivers (reported by 37% of US and 32% of UK respondents), and patient out-of-pocket costs (reported by 47% of US and 5% of UK respondents) (Table 5). In terms of impact, in the US, patient out-of-pocket costs, requirements for treatment centers to have medicines on-site for managing adverse events and availability of caregiver support had median impact ratings of four (very impactful). Wait time for healthcare capacity, transportation, lodging and meals for patients/caregivers and patient/caregiver productivity or income loss had median impact ratings of three (somewhat impactful). In the UK, all challenges selected by HCPs had median impact ratings of three (somewhat impactful) (Table 5). In terms of frequency, in both the US and UK, most challenges reported during the infusion and immediate post-infusion monitoring stage had median frequency ratings of three (sometimes). Requirements for treatment centers to have medicines on-site for managing adverse events had a median frequency rating of four (often) among US HCPs. Some differences also emerged between regions: patient out-of-pocket costs for treatment had a median frequency rating of four (often) in the US and two (rarely) in the UK (Table 5).
Table 5.
Challenges associated with CAR T infusion and post-infusion monitoring procedures.
Challenge
US (n = 57)
UK (n = 38)
Respondents (%)a
Median impact ratingb
Median frequency ratingc
Respondents (%)
Median impact rating
Median frequency rating
Patient out-of-pocket costs for treatment
47
4
4
5
3
2
Availability of caregiver support
44
4
3
53
3
3
Wait time for healthcare capacity
40
3
3
50
3
3
Patient transportation/lodging/meals if travel needed to treatment center
37
3
3
32
3
3
Requirements for treatment centers to have medicines (eg, tocilizumab) on-site to manage AEs
32
4
4
29
3
3
Patient/caregiver productivity/income loss during infusion
18
3
3
21
3
3
a
Percent of respondents who selected each factor for the question, “What are the main logistical factors you and your patients experience during CAR T infusion?”.
b
For each factor selected, respondents selected the impact of the factor on whether a patient receives CAR T, where 1 = not at all impactful, 2 = slightly impactful, 3 = somewhat impactful, 4 = very impactful, 5 = very impactful.
c
For each factor selected, respondents selected the frequency with which patients have had their care impacted as a result of this factor, where 1 = never, 2 = rarely, 3 = sometimes, 4 = often, 5 = very often.
AE: Adverse event; CAR T: Chimeric antigen receptor T cell; UK: United Kingdom; US: United States.
3.2.7.
Long-term follow-up care
The most common logistical challenges reported during the long-term follow-up stage of the CAR T treatment process were: the availability of caregiver/social support (reported by 55% of US and 72% of UK respondents), transportation/lodging/meals for traveling for follow-up appointments (reported by 39% of US and 38% of UK respondents), availability of funding/insurance for long-term follow-up care (reported by 52% of US and 21% of UK respondents) and patient/caregiver work productivity/income loss (reported by 31% of US and 38% of UK respondents) (Table 6). In terms of impact, in the UK, all challenges reported by HCPs in the long-term follow-up stage had median impact ratings of three (somewhat impactful). In the US, the following challenges had median impact ratings of four (often): patient out-of-pocket costs for long-term follow-up care, availability of funding/insurance for long-term follow-up care and transportation/lodging/meals for traveling for follow-up appointments; the availability of caregiver support and patient or caregiver productivity/income loss both had median impact ratings of three (somewhat impactful) (Table 6). In terms of frequency, the availability of funding/insurance for long-term follow-up care had a median frequency rating of four (often) among US respondents; all other challenges selected by HCPs in both the US and UK had a median frequency rating of three (sometimes) (Table 6).
Table 6.
Challenges associated with long-term follow-up care after CAR T therapy.
Challenge
US (n = 62)
UK (n = 47)
Respondents (%)a
Median impact ratingb
Median frequency ratingc
Respondents (%)
Median impact rating
Median frequency rating
Patient out-of-pocket costs for follow-up care
55
4
3
15
3
3
Availability of caregiver support
55
3
3
72
3
3
Availability of funding/insurance for follow-up care
52
4
4
21
3
3
Patient transportation/lodging/meals if travel needed for follow-up care
39
4
3
38
3
3
Patient/caregiver productivity/income loss when receiving follow-up care
31
3
3
38
3
3
a
Percentage of respondents who selected each factor for the question, “What are the main logistical factors you and your patients experience during long-term follow-up after CAR T administration?”.
b
For each factor selected, respondents selected the impact of the factor on patients' treatment experience, where 1 = not at all impactful, 2 = slightly impactful, 3 = somewhat impactful, 4 = very impactful, 5 = very impactful.
c
For each factor selected, respondents selected the frequency with which patients have had their care impacted as a result of this factor, where 1 = never, 2 = rarely, 3 = sometimes, 4 = often, 5 = very often.
CAR T: Chimeric antigen receptor T cell; UK: United Kingdom; US: United States.
3.2.8.
General CAR T treatment logistics
HCPs in both the US and UK estimated that the mean time between leukapheresis and infusion was 21 days (US mean, 21 days; UK mean, 22 days). They also estimated that patients spend 5 days (US mean, 5 days; UK mean, 6 days) in nearby accommodations prior to infusion, and 12 days during and immediately after infusion (US mean, 13 days; UK mean, 12 days). HCPs estimated that a mean of 21% of patients (US mean, 22%; UK mean, 20%) require intensive care admission during infusion and a mean of 24% (US mean, 25%; UK mean, 24%) were estimated to require re-admission to the hospital during the post-infusion monitoring period (Table 7).
Table 7.
CAR T therapy logistical aspects and caregiver considerations.
Estimated logistical factors
US
UK
n
Mean (Median, IQR)
n
Mean (Median, IQR)
Average number of days between leukapheresis and infusion
50
21 (17, 11–28)
30
22 (23, 13–30)
Nights spent in lodging before infusion
44
5 (3, 2–5)
29
6 (4, 2–5)
Nights spend in lodging during/immediately after infusion
44
13 (7, 6–20)
29
12 (9, 5–14)
Caregiver: days spent with patient for support before infusion
43
6 (3, 2–7)
27
8 (5, 2–6)
Caregiver: days spent with patient for support after infusion
43
14 (10, 4–20)
27
14 (14, 6–20)
n
Mean % (Median %)
n
Mean % (Median %)
Percentage of patients requiring admission to ICU during infusion
51
22 (15)
36
20 (19)
Percentage of patients requiring readmission to the hospital post-infusion
54
25 (20)
35
24 (25)
Percentage of patients with an informal/unpaid caregiver
60
67 (75)
37
67 (75)
For patients without a caregiver:
a
Hired, paid out of pocket
39
23 (20)
25
16 (15)
Hired, funded by insurance (US) or NHS (UK)
37
18 (20)
28
30 (30)
Hired, funded by charity/social services
37
17 (15)
26
19 (16)
Hired, funded by manufacturer
33
13 (10)
22
12 (10)
No caregiver support
31
22 (10)
24
31 (18)
Percentage of caregivers impacted by CAR T therapy in the following ways:
Disruption of daily activities
58
49 (40)
32
45 (40)
Negative impact on quality of life or mental health
54
33 (30)
31
33 (28)
Loss of productivity and source of income
51
38 (30)
30
32 (30)
a
Percentages in this category add up to over 100%, as respondents could have selected more than one source of caregiver support.
CAR T: Chimeric antigen receptor T-cell; ICU: Intensive care unit; IQR: Interquartile range; NHS: National Health Service; UK: United Kingdom; US: United States.
Across the US and UK, the mean estimated percentage of patients having an informal/unpaid caregiver (e.g., family member or friend) to support them during treatment was 67%. When asked to consider patients without an informal/unpaid caregiver, HCPs estimated that about one-quarter (US mean, 22%; UK mean, 31%) remained without any caregiver support while others secured a hired caregiver funded by insurance, the NHS, the CAR T manufacturer, or paid out-of-pocket. Respondents also reported that the CAR T therapy process impacts caregivers by disrupting daily activities, negatively impacting their quality of life/mental health and causing lost productivity and income (Table 7).
3.2.9.
Estimated proportion of patients impacted by CAR T challenges
3.2.9.1.
Patient drop-off between CAR T referral & leukapheresis
On average, HCPs estimated that 32% of patients in the US and 27% of patients in the UK who are initially referred for CAR T therapy do not proceed to leukapheresis. Reasons for not proceeding to leukapheresis included rapid disease progression (reported by 58% of US and 74% of UK respondents), a work-up showing clinical ineligibility (reported by 39% of US and 67% of UK respondents) and insurance (US) or NHS (UK) applications not being approved (reported by 34% of US and 31% of UK respondents) (Table 8).
Table 8.
Percentage of respondents reporting reasons that eligible CAR T therapy patients do not proceed to leukapheresis or infusion.
Reason for not proceeding
Do not proceed to leukapheresis (%)
Do not proceed to infusion (%)
US (n = 59)
UK (n = 42)
US (n = 44)
UK (n = 31)
Rapid disease progression
58
74
57
68
Work-up shows clinical ineligibility
39
67
NA
NA
Insurance coverage/NHS application not approved
34
31
34
NA
High OOP costs for treatment
34
14
32
13
Travel distance to treating center
19
12
20
10
Unavailable caregiver support
15
5
14
16
Patient preference for alternative options
14
26
0
29
Concerns about work productivity/income loss
7
2
7
6
CAR T manufacturing failure
NA
NA
34
48
Long wait time for manufacturing
NA
NA
14
29
CAR T: Chimeric antigen receptor T-cell; NA: Not applicable; NHS: National Health Service; OOP: Out-of-pocket; UK: United Kingdom; US: United States.
3.2.9.2.
Patient drop-off between leukapheresis & infusion
On average, an estimated 19% of US patients and 20% of UK patients who undergo leukapheresis do not proceed to infusion. Reasons for not proceeding to infusion included rapid disease progression (reported by 57% of US and 68% of UK respondents), CAR T manufacturing failure (reported by 34% of US and 48% of UK respondents) and long wait time for manufacturing (reported by 14% of US and 29% of UK respondents) (Table 8).
In summary, based on the estimated proportion of patients not proceeding to leukapheresis or infusion, an estimated 45% of patients referred to CAR T in the US and 42% in the UK do not make it to CAR T infusion due to several logistical challenges including rapid disease progression, manufacturing failure, wait time for manufacturing and access to insurance.
3.2.9.1.
Patient drop-off between CAR T referral & leukapheresis
On average, HCPs estimated that 32% of patients in the US and 27% of patients in the UK who are initially referred for CAR T therapy do not proceed to leukapheresis. Reasons for not proceeding to leukapheresis included rapid disease progression (reported by 58% of US and 74% of UK respondents), a work-up showing clinical ineligibility (reported by 39% of US and 67% of UK respondents) and insurance (US) or NHS (UK) applications not being approved (reported by 34% of US and 31% of UK respondents) (Table 8).
Table 8.
Percentage of respondents reporting reasons that eligible CAR T therapy patients do not proceed to leukapheresis or infusion.
Reason for not proceeding
Do not proceed to leukapheresis (%)
Do not proceed to infusion (%)
US (n = 59)
UK (n = 42)
US (n = 44)
UK (n = 31)
Rapid disease progression
58
74
57
68
Work-up shows clinical ineligibility
39
67
NA
NA
Insurance coverage/NHS application not approved
34
31
34
NA
High OOP costs for treatment
34
14
32
13
Travel distance to treating center
19
12
20
10
Unavailable caregiver support
15
5
14
16
Patient preference for alternative options
14
26
0
29
Concerns about work productivity/income loss
7
2
7
6
CAR T manufacturing failure
NA
NA
34
48
Long wait time for manufacturing
NA
NA
14
29
CAR T: Chimeric antigen receptor T-cell; NA: Not applicable; NHS: National Health Service; OOP: Out-of-pocket; UK: United Kingdom; US: United States.
3.2.9.2.
Patient drop-off between leukapheresis & infusion
On average, an estimated 19% of US patients and 20% of UK patients who undergo leukapheresis do not proceed to infusion. Reasons for not proceeding to infusion included rapid disease progression (reported by 57% of US and 68% of UK respondents), CAR T manufacturing failure (reported by 34% of US and 48% of UK respondents) and long wait time for manufacturing (reported by 14% of US and 29% of UK respondents) (Table 8).
In summary, based on the estimated proportion of patients not proceeding to leukapheresis or infusion, an estimated 45% of patients referred to CAR T in the US and 42% in the UK do not make it to CAR T infusion due to several logistical challenges including rapid disease progression, manufacturing failure, wait time for manufacturing and access to insurance.
4.
Discussion
CAR T therapy has the potential to provide durable response and survival benefits for patients with R/R NHL. However, the treatment administration process is lengthy and resource-intensive, presenting numerous logistical challenges. In this mixed-methods study, we surveyed HCPs in the US and UK to characterize the challenges that arise during the CAR T therapy process – from initial referral to post-infusion care. The most commonly reported barriers centered around prolonged waiting periods (e.g., manufacturing time, awaiting healthcare capacity, slow approvals), travel and accommodations for patients, the availability of caregiver support and – particularly in the US – out-of-pocket costs. Several challenges were reported to be very impactful on patients, caregivers, or HCPs during the CAR T treatment process.
Little previous research has quantified the logistical barriers experienced by patients, caregivers and HCPs during the administration of CAR T therapy. Still, our findings are largely consistent with those of previous studies and suggest that lengthy and complex processes may limit the benefits of CAR T therapy. Gajra et al. (2020) surveyed US community hematologists/oncologists, finding that more than half of respondents felt that the logistics of CAR T therapy administration and follow-up were cumbersome. Further, 27% identified slow approval processes as a challenge [16]. Similarly, 36% of US and 31% of UK participants in this study felt that slow approval processes would impact their decision to refer a patient for CAR T therapy. These treatment delays may be compounded by long manufacturing times – 42% of US and 60% of UK respondents in our study reported that wait time for manufacturing would impact their decision to refer a patient.
After a patient has been successfully referred and deemed eligible, further delays may arise that diminish the potential benefits of CAR T therapy. Respondents in this study estimated that manufacturing time (i.e., time between leukapheresis and infusion) was 21 days and more than half of respondents felt that rapid disease progression while awaiting treatment was a reason why patients do not proceed with treatment. Nearly two-thirds of respondents in Gajra et al. had also encountered the challenge of patients' disease becoming worse before treatment could be administered [16]. Another survey evaluating physician preferences for attributes of different CAR T therapies found that physicians preferred to avoid long wait times and were willing to accept increases in adverse event risks to gain reductions in time spent waiting for an infusion [18]. Further, the required inpatient stay associated with CAR T therapy administration may pose a challenge; we found that waiting time for healthcare capacity was a frequently noted logistical challenge during CAR T therapy pre-infusion and infusion procedures.
Cost and reimbursement challenges have also been at the center of the conversation, as the high cost of CAR T therapy raises affordability concerns for patients, payers and healthcare systems globally. While drug acquisition is the largest component of the cost, other elements of care such as facility and procedure costs can increase healthcare expenditures [10]. Indeed, absence of insurance coverage to cover treatment costs was noted by 40% of US respondents in our study as “very impactful” in the decision to refer a patient and 49% selected availability of funds or insurance to cover pre-infusion procedures such as leukapheresis and bridging therapy as a challenge. The high costs of treatment may be compounded by travel and lodging expenses as well as loss of work productivity or income. Total annual national costs associated with traveling for CAR T therapy have been estimated to be between $14.7 and $21.1 million [12]. Respondents in our study also frequently noted cost, travel and lodging challenges throughout the treatment process, although cost-related barriers were more often experienced and noted as impactful by US than UK participants.
Presence of a caregiver during CAR T therapy is essential, although caregivers of patients undergoing treatment report numerous psychological and logistical burdens associated with CAR T therapy [19]. This topic also emerged as a key theme from our initial qualitative interviews and survey respondents in both the US and UK frequently selected the availability of caregiver support as a logistical challenge throughout the CAR T therapy process. They also reported that caregivers were affected by disruptions to daily activities, loss of work productivity/income and diminished quality of life or mental health. The unique challenges presented by CAR T therapy necessitate further research into the caregiver experience to inform more holistic and patient-centered service models.
Some of the logistical challenges associated with CAR T therapy may be alleviated through enhancing patient services, streamlining operational processes, adopting readily available treatment alternatives and considering outpatient administrations [10,20,21]. Additionally, bridging therapies can help control the disease during the manufacturing period [22,23]. More convenient and readily available treatment alternatives such as T-cell–directed bispecific antibodies (eg, epcoritamab, glofitamab) may also address some of the logistical challenges associated with CAR T therapy.
Our findings should be interpreted considering some limitations. The panel-based sampling nature of recruitment limits the generalizability of these results to the broader HCP populations in the US and UK. We also did not directly solicit the perspectives of patients, caregivers, payers, or other stakeholders and their views on the logistical challenges of CAR T therapy may differ from that of HCPs. Still, we aimed to gain broad healthcare perspectives by surveying physicians, nurses, physician assistants, coordinators and other HCPs in the US and UK. In addition, the potential for recall bias cannot be ruled out as responses were based on HCP recollection of their experiences. Further, although four CAR T products are currently available for the treatment of R/R NHL, this study did not examine differences in the logistical challenges across different CAR T products. Overall, our study adds to the small but growing body of literature addressing the logistical challenges of implementing CAR T therapy in real-world clinical practice.
5.
Conclusion
These study findings characterize the logistical burdens and challenges associated with the stages of the CAR T therapy process from the perspective of HCPs in the US and UK. Reported challenges include lengthy waiting periods, complex administrative hurdles, limited treatment center capacity, travel and lodging for patients, the availability of caregiver support and – particularly in the US – patient out-of-pocket costs. While operational improvements might address some of the logistical barriers, these findings highlight the need for more convenient, readily available and easily administered therapies for patients with NHL.
Supplementary Material
Supplementary Material
Supplementary Infographic
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Title: Features and efficacy of triple-targeted therapy for patients with | Body:
Introduction
Epidermal growth factor receptor (EGFR) mutations are common oncogenic driver alterations in non-small-cell lung cancer (NSCLC).1 EGFR-tyrosine kinase inhibitors (EGFR-TKIs) have revolutionized the management of advanced or metastatic EGFR-mutant (EGFRmut) NSCLC and are routinely administered to such patients.2 Despite these advancements, a large percentage of patients inevitably develop resistance to EGFR-TKIs.
EGFR-dependent mechanisms of resistance have been well studied. The most prevalent mechanism of acquired resistance to first- and second-generation EGFR-TKIs involves the EGFR T790M mutation, which is observed in approximately 40%-55% of patients.3 Other ‘bypass’ mutations, such as those in PIK3CA and KRAS, have also been reported to occur at lower frequencies.4 Approximately 5%-15% of cases of resistance to third-generation EGFR-TKIs can be attributed to EGFR mutations, particularly those affecting exons 18, 20, and 21, such as EGFR C797S and EGFR L718Q.2 Additionally, there are several rare EGFR-independent mechanisms of resistance that involve the activation of signaling pathways, MET amplification, FGF2/FGFR amplification, and BRAF alteration.2 In addition to encoding members of RAS/mitogen-activated protein kinase signaling pathway, when activated, BRAF can induce resistance to EGFR-TKIs in EGFRmut NSCLC.5,6 Acquired BRAF mutations and BRAF fusions are two genomic alterations that cause BRAF activation and play a role in EGFR-independent resistance mechanisms.7 Chen et al.8 reported that among 45 EGFRmut NSCLC patients who were resistant to osimertinib, 2 (4.4%) harbored BRAF V600E mutations, while Vojnic et al.9 reported that BRAF fusion is a mechanism underlying EGFR-TKI resistance in approximately 2% of patients.
Although certain specific subtypes of acquired BRAF mutations and BRAF fusions have been identified, treatment of the affected patients remains challenging due to the rarity of these mutations. The selection of appropriate subsequent therapy that occurs after BRAF alteration is crucial for improving patient outcomes. Previous studies have described several treatment options, such as immune checkpoint inhibitors (ICIs), chemotherapy, prior EGFR-TKIs (i.e. TKIs used before the detection of acquired BRAF alteration) plus MEK inhibition, and dual RAF/MEK inhibition. Several case studies have reported that combinations of prior EGFR-TKIs plus dabrafenib and trametinib (triple-targeted treatment) are effective.9, 10, 11 The TATTON study demonstrated that the combination of osimertinib plus MET inhibitors exhibits encouraging antitumor activity in patients with EGFRmut, MET-amplified, NSCLC after progression on EGFR-TKIs.12 This finding suggests that prior EGFR-TKIs-based therapy may be suitable for EGFRmut NSCLC patients with acquired downstream or bypass pathway genomic alterations. Nevertheless, due to the small number of related studies and small sample sizes, the available data are still rather unclear regarding the optimal treatment strategy for patients with acquired BRAF mutations or BRAF fusions that confer resistance to EGFR-TKIs.
Therefore, we aimed to conduct a retrospective study and carry out an extensive literature search to elucidate the characteristics of and treatment options for patients with EGFRmut NSCLC harboring acquired BRAF alterations who are resistant to EGFR-TKI therapy. We also compared the efficacy of triple-targeted therapy with the efficacy of other treatments.
Patients and methods
Patients in our internal dataset
NSCLC patients with EGFR mutations who underwent tissue and/or liquid-based molecular testing after developing EGFR-TKI resistance between January 2020 and October 2023 at West China Hospital of Sichuan University were included. The inclusion criteria were as follows: (i) pathologically confirmed NSCLC and (ii) acquired BRAF alteration identified after any EGFR-TKI resistance. The exclusion criteria were as follows: (i) the subtype of BRAF alterations was not reported; (ii) the patient underwent surgery and/or perioperative maintenance treatment; and (iii) the patient had other tumors. Data on the following baseline variables were collected from medical records: age, sex, smoking history, tumor histopathology, clinical stage, brain metastasis at baseline, type of EGFR alteration at baseline, treatment history, and treatment outcomes. Efficacy assessments were carried out by the investigator in accordance with the guidelines of RECIST version 1.1. This study was conducted following the provisions of the Declaration of Helsinki (as revised in 2013). The study was approved by the Ethics Committee of West China Hospital of Sichuan University (No. 2024-197), and the need for individual consent for this retrospective analysis was waived.
Identification of BRAF alterations and EGFR mutations
BRAF alterations and EGFR mutations were identified using DNA-based next-generation sequencing (NGS). In detail, the genomic DNA of formalin-fixed, paraffin-embedded (FFPE) NSCLC specimens was purified using the QIAamp DNA FFPE Tissue Kit (Qiagen, Hilden, Germany). DNA concentrations were measured with a Qubit fluorometer and the Qubit dsDNA HS (high sensitivity) Assay Kit (Invitrogen, Carlsbad, CA). DNA profiling was carried out with a panel targeting NSCLC-associated genes (Burning Rock Biotech, Guangzhou, China). Terminal adaptor sequences and low-quality reads were removed from the raw sequencing data, and the reads were aligned to the reference human genome GRCh37 using Burrows–Wheeler Aligner (BWA). SNVs and indels were called using MuTect (version 1.1.4) and GATK (version 3.4-46-gbc02625), respectively.
Search strategy and selection criteria
A systematic literature search via PubMed, Web of Science, and EMBASE was carried out to identify studies from inception to November 2023 that focused on patients with EGFRmut NSCLC and BRAF mutation/fusion after resistance to EGFR-TKI therapy. The terms ‘icotinib’, ‘gefitinib’, ‘erlotinib’, ‘afatinib’, ‘dacomitinib’, ‘osimertinib’, ‘almonertinib’, ‘furmonertinib’, ‘EGFR-TKI(s)’, ‘EGFR inhibitor’, ‘BRAF’, and ‘lung cancer’ were searched in different combinations. Language restriction was applied as long as an English abstract was available to determine eligibility. The studies selected from this search met the following criteria: (i) they involved patients with NSCLC; (ii) acquired BRAF alteration was identified after EGFR-TKI resistance; and (iii) the specific subtype of BRAF alteration was reported. The exclusion criteria were as follows: (i) review articles, comments, meta-analyses, or editorials and (ii) studies not involving human subjects. All original full studies were read in their entirety to assess the appropriateness of their inclusion.
Data extraction from published datasets
Patient characteristics, such as age, sex, smoking status, clinical stage, mutation status at baseline, and brain metastasis status at baseline, were extracted from each eligible study. Additional variables included subtypes of BRAF alterations after acquired resistance to EGFR-TKIs and treatment-related data. The unknown baseline characteristics of patients from publications were recorded as ‘not reported’.
Classification of BRAF alterations
Considering that the activation of BRAF induced by BRAF fusions leads to the loss of the N-terminal autoinhibitory domain of BRAF, which results in the formation of constitutively active BRAF fusion protein dimers, we categorized BRAF alterations into BRAF mutations and BRAF fusions.13 Furthermore, we categorized BRAF mutations into three functional classes (class I: BRAF V600E/K/D/R, class II: BRAF R462I/I463S/G464E/G464V/G464R/G469A/G469V/G469S/E586K/F595L/L597Q/L597R/L597S/L597V/A598V/T599I/K601E/K601N/K601T/A727V, and class III: G466A/G466E/G466V/G466R/S467A/S467E/S467L/G469E/K483M/N581I/N581S/D594A/D594E/D594G/D594H/D594N/D594V/G596A/G596C/G596D/G596R) according to previous studies.13
BRAF mutations were classified as non-class I-III mutations if they could not be characterized as class I-III mutations.
Statistical analysis
Continuous variables are described by the median and interquartile range (IQR). Frequencies and percentages are used to describe categorical variables. Baseline characteristics were compared using the chi-squared test or Fisher’s exact test. Kaplan‒Meier curves were used for calculating progression-free survival (PFS), and differences in variables were analyzed using the log-rank test. The results are presented as hazard ratios (HRs) and 95% confidence intervals (CIs). Statistical analyses were conducted with the statistical software SPSS version 26.0 (SPSS Inc., Chicago, IL) and R software version 4.2.2. Statistical tests were two-tailed, and a P value < 0.05 indicated statistical significance.
Patients in our internal dataset
NSCLC patients with EGFR mutations who underwent tissue and/or liquid-based molecular testing after developing EGFR-TKI resistance between January 2020 and October 2023 at West China Hospital of Sichuan University were included. The inclusion criteria were as follows: (i) pathologically confirmed NSCLC and (ii) acquired BRAF alteration identified after any EGFR-TKI resistance. The exclusion criteria were as follows: (i) the subtype of BRAF alterations was not reported; (ii) the patient underwent surgery and/or perioperative maintenance treatment; and (iii) the patient had other tumors. Data on the following baseline variables were collected from medical records: age, sex, smoking history, tumor histopathology, clinical stage, brain metastasis at baseline, type of EGFR alteration at baseline, treatment history, and treatment outcomes. Efficacy assessments were carried out by the investigator in accordance with the guidelines of RECIST version 1.1. This study was conducted following the provisions of the Declaration of Helsinki (as revised in 2013). The study was approved by the Ethics Committee of West China Hospital of Sichuan University (No. 2024-197), and the need for individual consent for this retrospective analysis was waived.
Identification of BRAF alterations and EGFR mutations
BRAF alterations and EGFR mutations were identified using DNA-based next-generation sequencing (NGS). In detail, the genomic DNA of formalin-fixed, paraffin-embedded (FFPE) NSCLC specimens was purified using the QIAamp DNA FFPE Tissue Kit (Qiagen, Hilden, Germany). DNA concentrations were measured with a Qubit fluorometer and the Qubit dsDNA HS (high sensitivity) Assay Kit (Invitrogen, Carlsbad, CA). DNA profiling was carried out with a panel targeting NSCLC-associated genes (Burning Rock Biotech, Guangzhou, China). Terminal adaptor sequences and low-quality reads were removed from the raw sequencing data, and the reads were aligned to the reference human genome GRCh37 using Burrows–Wheeler Aligner (BWA). SNVs and indels were called using MuTect (version 1.1.4) and GATK (version 3.4-46-gbc02625), respectively.
Search strategy and selection criteria
A systematic literature search via PubMed, Web of Science, and EMBASE was carried out to identify studies from inception to November 2023 that focused on patients with EGFRmut NSCLC and BRAF mutation/fusion after resistance to EGFR-TKI therapy. The terms ‘icotinib’, ‘gefitinib’, ‘erlotinib’, ‘afatinib’, ‘dacomitinib’, ‘osimertinib’, ‘almonertinib’, ‘furmonertinib’, ‘EGFR-TKI(s)’, ‘EGFR inhibitor’, ‘BRAF’, and ‘lung cancer’ were searched in different combinations. Language restriction was applied as long as an English abstract was available to determine eligibility. The studies selected from this search met the following criteria: (i) they involved patients with NSCLC; (ii) acquired BRAF alteration was identified after EGFR-TKI resistance; and (iii) the specific subtype of BRAF alteration was reported. The exclusion criteria were as follows: (i) review articles, comments, meta-analyses, or editorials and (ii) studies not involving human subjects. All original full studies were read in their entirety to assess the appropriateness of their inclusion.
Data extraction from published datasets
Patient characteristics, such as age, sex, smoking status, clinical stage, mutation status at baseline, and brain metastasis status at baseline, were extracted from each eligible study. Additional variables included subtypes of BRAF alterations after acquired resistance to EGFR-TKIs and treatment-related data. The unknown baseline characteristics of patients from publications were recorded as ‘not reported’.
Classification of BRAF alterations
Considering that the activation of BRAF induced by BRAF fusions leads to the loss of the N-terminal autoinhibitory domain of BRAF, which results in the formation of constitutively active BRAF fusion protein dimers, we categorized BRAF alterations into BRAF mutations and BRAF fusions.13 Furthermore, we categorized BRAF mutations into three functional classes (class I: BRAF V600E/K/D/R, class II: BRAF R462I/I463S/G464E/G464V/G464R/G469A/G469V/G469S/E586K/F595L/L597Q/L597R/L597S/L597V/A598V/T599I/K601E/K601N/K601T/A727V, and class III: G466A/G466E/G466V/G466R/S467A/S467E/S467L/G469E/K483M/N581I/N581S/D594A/D594E/D594G/D594H/D594N/D594V/G596A/G596C/G596D/G596R) according to previous studies.13
BRAF mutations were classified as non-class I-III mutations if they could not be characterized as class I-III mutations.
Statistical analysis
Continuous variables are described by the median and interquartile range (IQR). Frequencies and percentages are used to describe categorical variables. Baseline characteristics were compared using the chi-squared test or Fisher’s exact test. Kaplan‒Meier curves were used for calculating progression-free survival (PFS), and differences in variables were analyzed using the log-rank test. The results are presented as hazard ratios (HRs) and 95% confidence intervals (CIs). Statistical analyses were conducted with the statistical software SPSS version 26.0 (SPSS Inc., Chicago, IL) and R software version 4.2.2. Statistical tests were two-tailed, and a P value < 0.05 indicated statistical significance.
Results
Patient demographics
The flow chart of the detailed selection process is presented in Figure 1. In our internal dataset, 184 patients with EGFRmut NSCLC who developed resistance to EGFR-TKIs and underwent molecular testing between January 2020 and October 2023 were identified by screening. Among them, 46.7% (86/184) of the patients exhibited resistance to first- or second-generation EGFR-TKIs (such as erlotinib, gefitinib, and afatinib), while 53.3% (98/184) of the patients demonstrated resistance to third-generation EGFR-TKIs (such as osimertinib, furmonertinib, and almonertinib) (Supplementary Figure S1A, available at https://doi.org/10.1016/j.esmoop.2024.103935). Tissue-based NGS was carried out in 40.2% (74/184) of the patients, whereas liquid-based NGS was conducted in 52.7% (97/184) of the patients (Supplementary Figure S1B, available at https://doi.org/10.1016/j.esmoop.2024.103935). BRAF mutations were detected in two (2 of 184, 1.1%) patients. One patient had previously been treated with gefitinib, whereas the other patient had received furmonertinib. Additionally, 4876 literatures were yielded, and 102 EGFRmut NSCLC patients with acquired BRAF mutations or fusions were identified.2,7, 8, 9, 10, 11,14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52Figure 1Study flow chart. EGFR-TKIs, epidermal growth factor receptor tyrosine kinase inhibitors.
Ultimately, a total of 104 patients were included in the study. The median age was 63.0 years [interquartile range (IQR): 56.0-68.3 years]; 33 patients (31.8%) were females, and 28 patients (26.9%) were males; the sex of the remaining patients was not reported. Fifty-seven patients had lung adenocarcinoma; data for the other patients were unavailable. Fourteen patients (13.5%; 14 of 104) received first-/second-generation EGFR-TKIs, and 68 (65.4%; 68 of 104) received third-generation EGFR-TKIs as the first-line treatment, while the treatment administered for 22 patients was unavailable (Table 1).Table 1Baseline characteristics of patients with EGFR-mutant non-small-cell lung cancer with acquired BRAF alterations resistant to EGFR-TKIs therapyPatient characteristicsOverall (N = 104)Mutations (n = 75)Fusions (n = 29)PAge [median (IQR)]63.0 (56.0-68.3)64.50 (56.0-68.3)62.0 (55.5-68.3)0.672Age group (%) <6022 (21.2)14 (18.7)8 (27.6)0.324 ≥6038 (36.5)26 (34.7)12 (41.4)Not reported44 (42.3)35 (46.6)9 (31.0)Gender (%) Female33 (31.8)19 (25.3)14 (48.3)0.079 Male28 (26.9)22 (29.3)6 (20.7) Not reported43 (41.3)34 (45.4)9 (31.0)Smoking (%) Former/current7 (6.7)5 (6.7)2 (6.9)0.503 Never18 (17.3)15 (20.0)3 (10.3) Not reported79 (76.0)55 (73.3)24 (82.8)Pathological type (%) Adenocarcinoma57 (54.8)44 (58.7)13 (44.8)0.293 Not reported47 (45.2)31 (41.3)16 (55.2)Clinical stages (%) IIIA1 (1.0)1 (1.3)0 (0.0)0.273 IIIB1 (1.0)0 (0.0)1 (3.4) IV25 (24.0)20 (26.7)5 (17.2) Not reported77 (74.0)54 (72.0)23 (79.3)Brain metastases (%) No14 (13.5)12 (16.0)2 (6.9)0.359 Yes10 (9.6)6 (8.0)4 (13.8) Not reported80 (76.9)57 (76.0)23 (79.3)Mutation at baseline (%) EGFR 19del37 (35.6)27 (36.0)10 (34.5)0.617 EGFR 19del, C797S, C797G1 (1.0)1 (1.3)0 (0.0) EGFR C797G1 (1.0)1 (1.3)0 (0.0) EGFR G719X, S781I1 (1.0)1 (1.3)0 (0.0) EGFR G719C1 (1.0)1 (1.3)0 (0.0) EGFR L858R19 (18.3)10 (13.3)9 (31.0) EGFR L858R, C797S1 (1.0)1 (1.3)0 (0.0) EGFR L861Q1 (1.0)1 (1.3)0 (0.0) EGFR+42 (40.4)32 (42.7)10 (34.5)Prior EGFR-TKIs (%) First-/second-generation TKIs14 (13.5)8 (10.7)6 (20.7)0.306 Third-generation TKIs68 (65.4)52 (69.3)16 (55.2) Not reported22 (21.2)15 (20.0)7 (24.1)Treatment post-resistance (%) Not reported60 (57.7)41 (54.7)19 (65.5)0.005 RAF and MEK inhibitors6 (5.8)5 (6.7)1 (3.4) RAF and MEK inhibitors plus prior EGFR-TKIs19 (18.3)16 (21.3)3 (10.3) RAF inhibitors plus prior EGFR-TKIs8 (7.7)8 (10.7)0 (0.0) Chemo with or without Bev2 (1.9)1 (1.3)1 (3.4) EGFR-TKIs + Chemo/Bev4 (3.8)4 (5.3)0 (0.0) ICI2 (1.9)0 (0.0)2 (6.9) MEK inhibitors plus prior EGFR-TKIs3 (2.9)0 (0.0)3 (10.3)RAF inhibitors (%) No11 (10.6)5 (6.7)6 (20.7)0.015 Yes33 (31.7)29 (38.7)4 (13.8) Not reported60 (57.7)41 (54.7)19 (65.5)MEK inhibitors (%)0.547 No16 (15.4)13 (17.3)3 (10.3) Yes28 (26.9)21 (28.0)7 (24.1) Not reported60 (57.7)41 (54.7)19 (65.5)Combined prior EGFR-TKIs (%) No11 (10.6)6 (8.0)4 (13.8)0.221 Yes33 (31.7)28 (37.3)6 (20.7) Not reported60 (57.7)41 (54.7)19 (65.5)RAF and MEK inhibitors plus prior EGFR-TKIs (%) No26 (25.0)18 (24.0)7 (24.1)0.405 Yes18 (17.3)16 (21.3)3 (10.3) Not reported60 (57.7)41 (54.7)19 (65.5)Bev, bevacizumab; Chemo, chemotherapy; EGFR-TKIs, epidermal growth factor receptor tyrosine kinase inhibitors; ICI, immune checkpoint inhibitor; IQR, interquartile range.
Characteristics of EGFRmut NSCLC patients with acquired BRAF alterations
Among the 104 patients included in the study, BRAF mutations were reported in 75 patients (72.1%; 75/104), of whom 57 patients (54.8%; 57/104) had class I mutations, 7 patients (6.7%; 7/104) harbored class II mutations, and 9 patients (8.7%; 9/104) had class III mutations. Non-class I-III mutations were detected in two patients (1.9%; 2/104) (Figure 2A and B). The characteristics of patients in various BRAF mutation subgroups are summarized in Supplementary Table S1, available at https://doi.org/10.1016/j.esmoop.2024.103935. Twenty-nine patients (27.9%; 29/104) were identified to have BRAF fusions (Figure 2A). AGK-BRAF fusion was predominant and accounted for 7.7% (8 of 104); other fusion partners included AGAP3, ARMC10, ESYT2, MKRN, BTN2A1, DHHC20, DLG1, DOCK4, EPS515, ERC1, GHR, KIAA1549, PJA2, SALL2, SPTBN1, and ZC3HAV1 (Figure 2C and Supplementary Table S2, available at https://doi.org/10.1016/j.esmoop.2024.103935). The basic characteristics of patients with acquired BRAF mutations and BRAF fusions are summarized in Table 1. There was no statistically significant difference in age (P = 0.680), sex (P = 0.079), or smoking history (P = 0.503) between patients with BRAF mutations and those with BRAF fusions (Supplementary Figure S2, available at https://doi.org/10.1016/j.esmoop.2024.103935).Figure 2Distribution of the acquired BRAF alterations. (A) Pie chart illustrating the proportion of acquired BRAF alterations in the whole cohort. The distribution and proportion of acquired BRAF mutations (B) and acquired BRAF fusions (C). (D) The proportion of acquired BRAF mutations and BRAF fusions in patients who received first-/second-generation EGFR-TKIs or third-generation EGFR-TKIs. EGFR-TKIs, epidermal growth factor receptor tyrosine kinase inhibitors.
In BRAF-altered patients who were treated with first-generation or second-generation EGFR-TKIs, acquired BRAF mutations and BRAF fusions were detected in 57.1% (8/14) and 42.9% (6/14) of patients, respectively. Among BRAF-altered patients who received third-generation EGFR-TKIs, 76.5% (52/68) and 23.5% (16/68) had acquired BRAF mutations and BRAF fusions, respectively (P = 0.185) (Figure 2D). Supplementary Table S3, available at https://doi.org/10.1016/j.esmoop.2024.103935, presents the subtypes and proportions of patients with BRAF alterations after acquired resistance to EGFR-TKIs. The most common subtype was the BRAF V600E mutation after resistance to both first-/second-generation EGFR-TKIs and third-generation EGFR-TKIs, which was detected in 21.4% (3 of 14) and 63.2% (43 of 68) of patients, respectively. AGK-BRAF fusion was the most common subtype of acquired BRAF fusion and was identified in 14.3% (2 of 14) and 7.4% (5 of 68) of patients who were treated with first-/second-generation EGFR-TKIs and third-generation EGFR-TKIs, respectively.
Additionally, we analyzed the variation in the proportions of BRAF mutations and BRAF fusions according to the presence or absence of brain metastases. Among patients with brain metastases, BRAF mutations were reported in six patients (60.0%; 6/10), and four patients (40%; 4/10) were identified to have BRAF fusions. In contrast, for patients without brain metastases, these proportions were 85.7% (12/14) and 14.3% (2/14), respectively. However, no significant differences in the proportions were observed (P = 0.192, Supplementary Figure S3, available at https://doi.org/10.1016/j.esmoop.2024.103935).
Efficacy of triple-targeted therapy with prior EGFR-TKIs plus RAF and MEK inhibitors in the overall population
Among the cohort, 44 patients received subsequent treatment after developing resistance to EGFR-TKIs; 3 patients were excluded due to inadequate data quality with respect to PFS estimates (Figure 1). A total of 41 patients had available data for evaluating treatment efficacy; 31 patients (75.6%) had BRAF mutations, and 10 patients (24.4%) harbored BRAF fusions (Supplementary Figure S4, available at https://doi.org/10.1016/j.esmoop.2024.103935). The characteristics of the patients in the BRAF mutation and BRAF fusion groups were similar, except for gender (Supplementary Table S4, available at https://doi.org/10.1016/j.esmoop.2024.103935). The PFS of each patient is shown in a swimmer plot (Figure 3).Figure 3The swimmer plot illustrates the PFS of each of the patients. The x-axis denotes the PFS of the patient in each line of treatment received. The black arrow denotes the continuation of treatment. Bev, bevacizumab; Chemo, chemotherapy; EGFR-TKIs, epidermal growth factor receptor tyrosine kinase inhibitors; ICI, immune checkpoint inhibitors; PFS, progression-free survival.
Eighteen of the 41 patients received triple-targeted therapy, including prior EGFR-TKIs in combination with RAF and MEK inhibitors, and 23 patients received other agents (RAF and MEK inhibitors, n = 5; MEK inhibitors plus prior EGFR-TKIs, n = 3; RAF inhibitors plus prior EGFR-TKIs, n = 7; chemotherapy with or without bevacizumab, n = 2; ICIs, n = 2; prior EGFR-TKIs plus chemotherapy or bevacizumab, n = 4) (Supplementary Table S4, available at https://doi.org/10.1016/j.esmoop.2024.103935). Baseline characteristics were generally well balanced across the treatment arms, except that patients in the other therapies arm had significantly more brain metastases (Supplementary Table S5, available at https://doi.org/10.1016/j.esmoop.2024.103935). Patients who received triple-targeted therapy demonstrated significantly better median PFS (mPFS) than those who received other treatments [8.0 months (95% CI 6.0 months-not reached [NR]) versus 2.5 months (95% CI 2.0-5.0 months), P < 0.001] (Figure 4A).Figure 4Progression-free survival (PFS) of patients according to the BRAF alterations class. PFS in patients with BRAF alterations (A), BRAF mutations (B), BRAF class I mutations (C), and BRAF fusions (D) who were and were not treated with triplet-targeted therapy with prior EGFR-TKIs plus RAF and MEK inhibitors. CI, confidence interval; EGFR-TKIs, epidermal growth factor receptor tyrosine kinase inhibitors; HR, hazard ratio; NR, not reached.
Efficacy of triple-targeted therapy with prior EGFR-TKIs plus RAF and MEK inhibitors in patients with acquired BRAF mutations
Among the 41 patients with evaluable efficacy, 31 patients harbored BRAF mutations. Fifteen of these 31 patients received triple-targeted therapy, and 16 patients received other treatments (Supplementary Table S4, available at https://doi.org/10.1016/j.esmoop.2024.103935). A similar improvement in the mPFS was found in BRAFmut patients [mPFS: 9.0 months (95% CI 8.0 months-NR) versus 2.8 months (95% CI 2.0-8.0 months), P = 0.004] (Figure 4B).
Twenty-six of the 31 patients harbored BRAF class I mutations (Supplementary Figure S4, available at https://doi.org/10.1016/j.esmoop.2024.103935). Significantly improved mPFS was found in patients who received triple-targeted therapy compared with those who received other treatments [9.0 months (95% CI 8.0 months-NR) versus 2.5 months (95% CI 2.0 months-NR), P < 0.01] (Figure 4C). However, among the five patients with BRAF non-class I mutations, there was no significant difference in the mPFS among those treated with different regimens (Supplementary Figure S5, available at https://doi.org/10.1016/j.esmoop.2024.103935).
Efficacy of triple-targeted therapy with prior EGFR-TKIs plus RAF and MEK inhibitors in patients with acquired BRAF fusions
Among the 10 patients with acquired BRAF fusions, 30% (3 of 10) received triple-targeted therapy, and 70% (7 of 10) were treated with other regimens (RAF and MEK inhibitors, n = 1; MEK inhibitors plus prior EGFR-TKIs, n = 3; chemotherapy, n = 1; ICIs, n = 2) (Supplementary Table S4, available at https://doi.org/10.1016/j.esmoop.2024.103935). Although no significant differences in mPFS were observed, the mPFS tended to be longer in patients who received triple-targeted therapy than in those who did not [5.0 months (95% CI NR-NR) versus 2.0 months (95% CI 1.7 months-NR), P = 0.230] (Figure 4D).
Efficacy of triple-targeted therapy with prior EGFR-TKIs plus RAF and MEK inhibitors compared with RAF and MEK inhibitors without prior EGFR-TKIs
To further investigate whether EGFRmut patients harboring acquired BRAF alterations should discontinue their prior EGFR-TKI treatment, we compared the efficacy of triple-targeted therapy with dual RAF/MEK inhibition. Twenty-three patients with BRAF alterations (19 with BRAF mutations and 4 with BRAF fusions) were included in the analysis. Baseline characteristics were well balanced across the treatment arms (Supplementary Table S6, available at https://doi.org/10.1016/j.esmoop.2024.103935). The median PFS was longer in patients with BRAF alterations who received triple-targeted therapy than in those who received RAF and MEK inhibitor combination therapy without EGFR-TKIs [mPFS: 8.0 months (95% CI 6.0 months-NR) versus 2.0 months (95% CI 1.5 months-NR), P < 0.001; Supplementary Figure S6A, available at https://doi.org/10.1016/j.esmoop.2024.103935]. A similar improvement in the mPFS was found in patients with BRAFmut [mPFS: 9.0 months (95% CI 8.0 months-NR) versus 2.0 months (95% CI 1.3 months-NR), P < 0.001; Supplementary Figure S6B, available at https://doi.org/10.1016/j.esmoop.2024.103935].
Efficacy of triple-targeted therapy with prior EGFR-TKIs plus RAF and MEK inhibitors compared with prior EGFR-TKIs plus either RAF or MEK inhibitor
We also conducted an analysis of the therapeutic effectiveness in patients who underwent triple-targeted therapy versus those who were administered prior EGFR-TKIs in combination with either RAF or MEK inhibitor. Of the 28 patients who received prior EGFR-TKI-based treatment, 18 patients underwent triple-targeted therapy, and 10 patients received prior EGFR-TKIs plus either RAF or MEK inhibitor. Baseline characteristics were generally well balanced across the treatment arms, except that the triple-targeted therapy arm had significantly older patients and more male patients (Supplementary Table S7, available at https://doi.org/10.1016/j.esmoop.2024.103935). Compared with patients with BRAF alterations who received prior EGFR-TKIs plus either RAF or MEK inhibitor, patients who received triple-targeted therapy had significantly improved mPFS [mPFS: 8.0 months (95% CI 6.0 months-NR) versus 4.8 months (95% CI 2.0 months-NR), P = 0.010; Supplementary Figure S6C, available at https://doi.org/10.1016/j.esmoop.2024.103935]. A similar improvement in the mPFS was found in BRAFmut patients [mPFS: 9.0 months (95% CI 8.0 months-NR) versus 4.5 months (95% CI 2.0 months-NR), P = 0.029; Supplementary Figure S6D, available at https://doi.org/10.1016/j.esmoop.2024.103935].
Treatment-related adverse events
Treatment-related adverse events occurred in 20 patients (48.8%, 20 of 41). Ten patients were treated with dabrafenib and trametinib plus osimertinib, six patients received vemurafenib plus osimertinib, three patients were administered dabrafenib and trametinib, and the remaining one patient was treated with osimertinib plus trametinib. The most common adverse events of any grade were fever (19.5%) and fatigue (19.5%), followed by rash (12.2%) and diarrhea (9.8%). A dose reduction of the RAF or MEK inhibitors was required in five patients (12.2%, 5 of 41), and permanent discontinuation of treatment occurred in five patients (12.2%, 5 of 41). Specifically, dabrafenib and trametinib plus prior EGFR-TKI therapy was discontinued in two patients owing to pneumonitis and grade 1 interstitial lung disease; prior EGFR-TKI plus vemurafenib was discontinued in one patient due to grade IV vomiting; and prior EGFR-TKI plus trametinib was discontinued in one patient because of gastrointestinal bleed. In another case, RAF inhibitor treatment was discontinued while maintaining prior EGFR-TKI therapy plus MEK inhibitor owing to fatigue, diarrhea, and weight loss (Supplementary Table S8, available at https://doi.org/10.1016/j.esmoop.2024.103935).
Patient demographics
The flow chart of the detailed selection process is presented in Figure 1. In our internal dataset, 184 patients with EGFRmut NSCLC who developed resistance to EGFR-TKIs and underwent molecular testing between January 2020 and October 2023 were identified by screening. Among them, 46.7% (86/184) of the patients exhibited resistance to first- or second-generation EGFR-TKIs (such as erlotinib, gefitinib, and afatinib), while 53.3% (98/184) of the patients demonstrated resistance to third-generation EGFR-TKIs (such as osimertinib, furmonertinib, and almonertinib) (Supplementary Figure S1A, available at https://doi.org/10.1016/j.esmoop.2024.103935). Tissue-based NGS was carried out in 40.2% (74/184) of the patients, whereas liquid-based NGS was conducted in 52.7% (97/184) of the patients (Supplementary Figure S1B, available at https://doi.org/10.1016/j.esmoop.2024.103935). BRAF mutations were detected in two (2 of 184, 1.1%) patients. One patient had previously been treated with gefitinib, whereas the other patient had received furmonertinib. Additionally, 4876 literatures were yielded, and 102 EGFRmut NSCLC patients with acquired BRAF mutations or fusions were identified.2,7, 8, 9, 10, 11,14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52Figure 1Study flow chart. EGFR-TKIs, epidermal growth factor receptor tyrosine kinase inhibitors.
Ultimately, a total of 104 patients were included in the study. The median age was 63.0 years [interquartile range (IQR): 56.0-68.3 years]; 33 patients (31.8%) were females, and 28 patients (26.9%) were males; the sex of the remaining patients was not reported. Fifty-seven patients had lung adenocarcinoma; data for the other patients were unavailable. Fourteen patients (13.5%; 14 of 104) received first-/second-generation EGFR-TKIs, and 68 (65.4%; 68 of 104) received third-generation EGFR-TKIs as the first-line treatment, while the treatment administered for 22 patients was unavailable (Table 1).Table 1Baseline characteristics of patients with EGFR-mutant non-small-cell lung cancer with acquired BRAF alterations resistant to EGFR-TKIs therapyPatient characteristicsOverall (N = 104)Mutations (n = 75)Fusions (n = 29)PAge [median (IQR)]63.0 (56.0-68.3)64.50 (56.0-68.3)62.0 (55.5-68.3)0.672Age group (%) <6022 (21.2)14 (18.7)8 (27.6)0.324 ≥6038 (36.5)26 (34.7)12 (41.4)Not reported44 (42.3)35 (46.6)9 (31.0)Gender (%) Female33 (31.8)19 (25.3)14 (48.3)0.079 Male28 (26.9)22 (29.3)6 (20.7) Not reported43 (41.3)34 (45.4)9 (31.0)Smoking (%) Former/current7 (6.7)5 (6.7)2 (6.9)0.503 Never18 (17.3)15 (20.0)3 (10.3) Not reported79 (76.0)55 (73.3)24 (82.8)Pathological type (%) Adenocarcinoma57 (54.8)44 (58.7)13 (44.8)0.293 Not reported47 (45.2)31 (41.3)16 (55.2)Clinical stages (%) IIIA1 (1.0)1 (1.3)0 (0.0)0.273 IIIB1 (1.0)0 (0.0)1 (3.4) IV25 (24.0)20 (26.7)5 (17.2) Not reported77 (74.0)54 (72.0)23 (79.3)Brain metastases (%) No14 (13.5)12 (16.0)2 (6.9)0.359 Yes10 (9.6)6 (8.0)4 (13.8) Not reported80 (76.9)57 (76.0)23 (79.3)Mutation at baseline (%) EGFR 19del37 (35.6)27 (36.0)10 (34.5)0.617 EGFR 19del, C797S, C797G1 (1.0)1 (1.3)0 (0.0) EGFR C797G1 (1.0)1 (1.3)0 (0.0) EGFR G719X, S781I1 (1.0)1 (1.3)0 (0.0) EGFR G719C1 (1.0)1 (1.3)0 (0.0) EGFR L858R19 (18.3)10 (13.3)9 (31.0) EGFR L858R, C797S1 (1.0)1 (1.3)0 (0.0) EGFR L861Q1 (1.0)1 (1.3)0 (0.0) EGFR+42 (40.4)32 (42.7)10 (34.5)Prior EGFR-TKIs (%) First-/second-generation TKIs14 (13.5)8 (10.7)6 (20.7)0.306 Third-generation TKIs68 (65.4)52 (69.3)16 (55.2) Not reported22 (21.2)15 (20.0)7 (24.1)Treatment post-resistance (%) Not reported60 (57.7)41 (54.7)19 (65.5)0.005 RAF and MEK inhibitors6 (5.8)5 (6.7)1 (3.4) RAF and MEK inhibitors plus prior EGFR-TKIs19 (18.3)16 (21.3)3 (10.3) RAF inhibitors plus prior EGFR-TKIs8 (7.7)8 (10.7)0 (0.0) Chemo with or without Bev2 (1.9)1 (1.3)1 (3.4) EGFR-TKIs + Chemo/Bev4 (3.8)4 (5.3)0 (0.0) ICI2 (1.9)0 (0.0)2 (6.9) MEK inhibitors plus prior EGFR-TKIs3 (2.9)0 (0.0)3 (10.3)RAF inhibitors (%) No11 (10.6)5 (6.7)6 (20.7)0.015 Yes33 (31.7)29 (38.7)4 (13.8) Not reported60 (57.7)41 (54.7)19 (65.5)MEK inhibitors (%)0.547 No16 (15.4)13 (17.3)3 (10.3) Yes28 (26.9)21 (28.0)7 (24.1) Not reported60 (57.7)41 (54.7)19 (65.5)Combined prior EGFR-TKIs (%) No11 (10.6)6 (8.0)4 (13.8)0.221 Yes33 (31.7)28 (37.3)6 (20.7) Not reported60 (57.7)41 (54.7)19 (65.5)RAF and MEK inhibitors plus prior EGFR-TKIs (%) No26 (25.0)18 (24.0)7 (24.1)0.405 Yes18 (17.3)16 (21.3)3 (10.3) Not reported60 (57.7)41 (54.7)19 (65.5)Bev, bevacizumab; Chemo, chemotherapy; EGFR-TKIs, epidermal growth factor receptor tyrosine kinase inhibitors; ICI, immune checkpoint inhibitor; IQR, interquartile range.
Characteristics of EGFRmut NSCLC patients with acquired BRAF alterations
Among the 104 patients included in the study, BRAF mutations were reported in 75 patients (72.1%; 75/104), of whom 57 patients (54.8%; 57/104) had class I mutations, 7 patients (6.7%; 7/104) harbored class II mutations, and 9 patients (8.7%; 9/104) had class III mutations. Non-class I-III mutations were detected in two patients (1.9%; 2/104) (Figure 2A and B). The characteristics of patients in various BRAF mutation subgroups are summarized in Supplementary Table S1, available at https://doi.org/10.1016/j.esmoop.2024.103935. Twenty-nine patients (27.9%; 29/104) were identified to have BRAF fusions (Figure 2A). AGK-BRAF fusion was predominant and accounted for 7.7% (8 of 104); other fusion partners included AGAP3, ARMC10, ESYT2, MKRN, BTN2A1, DHHC20, DLG1, DOCK4, EPS515, ERC1, GHR, KIAA1549, PJA2, SALL2, SPTBN1, and ZC3HAV1 (Figure 2C and Supplementary Table S2, available at https://doi.org/10.1016/j.esmoop.2024.103935). The basic characteristics of patients with acquired BRAF mutations and BRAF fusions are summarized in Table 1. There was no statistically significant difference in age (P = 0.680), sex (P = 0.079), or smoking history (P = 0.503) between patients with BRAF mutations and those with BRAF fusions (Supplementary Figure S2, available at https://doi.org/10.1016/j.esmoop.2024.103935).Figure 2Distribution of the acquired BRAF alterations. (A) Pie chart illustrating the proportion of acquired BRAF alterations in the whole cohort. The distribution and proportion of acquired BRAF mutations (B) and acquired BRAF fusions (C). (D) The proportion of acquired BRAF mutations and BRAF fusions in patients who received first-/second-generation EGFR-TKIs or third-generation EGFR-TKIs. EGFR-TKIs, epidermal growth factor receptor tyrosine kinase inhibitors.
In BRAF-altered patients who were treated with first-generation or second-generation EGFR-TKIs, acquired BRAF mutations and BRAF fusions were detected in 57.1% (8/14) and 42.9% (6/14) of patients, respectively. Among BRAF-altered patients who received third-generation EGFR-TKIs, 76.5% (52/68) and 23.5% (16/68) had acquired BRAF mutations and BRAF fusions, respectively (P = 0.185) (Figure 2D). Supplementary Table S3, available at https://doi.org/10.1016/j.esmoop.2024.103935, presents the subtypes and proportions of patients with BRAF alterations after acquired resistance to EGFR-TKIs. The most common subtype was the BRAF V600E mutation after resistance to both first-/second-generation EGFR-TKIs and third-generation EGFR-TKIs, which was detected in 21.4% (3 of 14) and 63.2% (43 of 68) of patients, respectively. AGK-BRAF fusion was the most common subtype of acquired BRAF fusion and was identified in 14.3% (2 of 14) and 7.4% (5 of 68) of patients who were treated with first-/second-generation EGFR-TKIs and third-generation EGFR-TKIs, respectively.
Additionally, we analyzed the variation in the proportions of BRAF mutations and BRAF fusions according to the presence or absence of brain metastases. Among patients with brain metastases, BRAF mutations were reported in six patients (60.0%; 6/10), and four patients (40%; 4/10) were identified to have BRAF fusions. In contrast, for patients without brain metastases, these proportions were 85.7% (12/14) and 14.3% (2/14), respectively. However, no significant differences in the proportions were observed (P = 0.192, Supplementary Figure S3, available at https://doi.org/10.1016/j.esmoop.2024.103935).
Efficacy of triple-targeted therapy with prior EGFR-TKIs plus RAF and MEK inhibitors in the overall population
Among the cohort, 44 patients received subsequent treatment after developing resistance to EGFR-TKIs; 3 patients were excluded due to inadequate data quality with respect to PFS estimates (Figure 1). A total of 41 patients had available data for evaluating treatment efficacy; 31 patients (75.6%) had BRAF mutations, and 10 patients (24.4%) harbored BRAF fusions (Supplementary Figure S4, available at https://doi.org/10.1016/j.esmoop.2024.103935). The characteristics of the patients in the BRAF mutation and BRAF fusion groups were similar, except for gender (Supplementary Table S4, available at https://doi.org/10.1016/j.esmoop.2024.103935). The PFS of each patient is shown in a swimmer plot (Figure 3).Figure 3The swimmer plot illustrates the PFS of each of the patients. The x-axis denotes the PFS of the patient in each line of treatment received. The black arrow denotes the continuation of treatment. Bev, bevacizumab; Chemo, chemotherapy; EGFR-TKIs, epidermal growth factor receptor tyrosine kinase inhibitors; ICI, immune checkpoint inhibitors; PFS, progression-free survival.
Eighteen of the 41 patients received triple-targeted therapy, including prior EGFR-TKIs in combination with RAF and MEK inhibitors, and 23 patients received other agents (RAF and MEK inhibitors, n = 5; MEK inhibitors plus prior EGFR-TKIs, n = 3; RAF inhibitors plus prior EGFR-TKIs, n = 7; chemotherapy with or without bevacizumab, n = 2; ICIs, n = 2; prior EGFR-TKIs plus chemotherapy or bevacizumab, n = 4) (Supplementary Table S4, available at https://doi.org/10.1016/j.esmoop.2024.103935). Baseline characteristics were generally well balanced across the treatment arms, except that patients in the other therapies arm had significantly more brain metastases (Supplementary Table S5, available at https://doi.org/10.1016/j.esmoop.2024.103935). Patients who received triple-targeted therapy demonstrated significantly better median PFS (mPFS) than those who received other treatments [8.0 months (95% CI 6.0 months-not reached [NR]) versus 2.5 months (95% CI 2.0-5.0 months), P < 0.001] (Figure 4A).Figure 4Progression-free survival (PFS) of patients according to the BRAF alterations class. PFS in patients with BRAF alterations (A), BRAF mutations (B), BRAF class I mutations (C), and BRAF fusions (D) who were and were not treated with triplet-targeted therapy with prior EGFR-TKIs plus RAF and MEK inhibitors. CI, confidence interval; EGFR-TKIs, epidermal growth factor receptor tyrosine kinase inhibitors; HR, hazard ratio; NR, not reached.
Efficacy of triple-targeted therapy with prior EGFR-TKIs plus RAF and MEK inhibitors in patients with acquired BRAF mutations
Among the 41 patients with evaluable efficacy, 31 patients harbored BRAF mutations. Fifteen of these 31 patients received triple-targeted therapy, and 16 patients received other treatments (Supplementary Table S4, available at https://doi.org/10.1016/j.esmoop.2024.103935). A similar improvement in the mPFS was found in BRAFmut patients [mPFS: 9.0 months (95% CI 8.0 months-NR) versus 2.8 months (95% CI 2.0-8.0 months), P = 0.004] (Figure 4B).
Twenty-six of the 31 patients harbored BRAF class I mutations (Supplementary Figure S4, available at https://doi.org/10.1016/j.esmoop.2024.103935). Significantly improved mPFS was found in patients who received triple-targeted therapy compared with those who received other treatments [9.0 months (95% CI 8.0 months-NR) versus 2.5 months (95% CI 2.0 months-NR), P < 0.01] (Figure 4C). However, among the five patients with BRAF non-class I mutations, there was no significant difference in the mPFS among those treated with different regimens (Supplementary Figure S5, available at https://doi.org/10.1016/j.esmoop.2024.103935).
Efficacy of triple-targeted therapy with prior EGFR-TKIs plus RAF and MEK inhibitors in patients with acquired BRAF fusions
Among the 10 patients with acquired BRAF fusions, 30% (3 of 10) received triple-targeted therapy, and 70% (7 of 10) were treated with other regimens (RAF and MEK inhibitors, n = 1; MEK inhibitors plus prior EGFR-TKIs, n = 3; chemotherapy, n = 1; ICIs, n = 2) (Supplementary Table S4, available at https://doi.org/10.1016/j.esmoop.2024.103935). Although no significant differences in mPFS were observed, the mPFS tended to be longer in patients who received triple-targeted therapy than in those who did not [5.0 months (95% CI NR-NR) versus 2.0 months (95% CI 1.7 months-NR), P = 0.230] (Figure 4D).
Efficacy of triple-targeted therapy with prior EGFR-TKIs plus RAF and MEK inhibitors compared with RAF and MEK inhibitors without prior EGFR-TKIs
To further investigate whether EGFRmut patients harboring acquired BRAF alterations should discontinue their prior EGFR-TKI treatment, we compared the efficacy of triple-targeted therapy with dual RAF/MEK inhibition. Twenty-three patients with BRAF alterations (19 with BRAF mutations and 4 with BRAF fusions) were included in the analysis. Baseline characteristics were well balanced across the treatment arms (Supplementary Table S6, available at https://doi.org/10.1016/j.esmoop.2024.103935). The median PFS was longer in patients with BRAF alterations who received triple-targeted therapy than in those who received RAF and MEK inhibitor combination therapy without EGFR-TKIs [mPFS: 8.0 months (95% CI 6.0 months-NR) versus 2.0 months (95% CI 1.5 months-NR), P < 0.001; Supplementary Figure S6A, available at https://doi.org/10.1016/j.esmoop.2024.103935]. A similar improvement in the mPFS was found in patients with BRAFmut [mPFS: 9.0 months (95% CI 8.0 months-NR) versus 2.0 months (95% CI 1.3 months-NR), P < 0.001; Supplementary Figure S6B, available at https://doi.org/10.1016/j.esmoop.2024.103935].
Efficacy of triple-targeted therapy with prior EGFR-TKIs plus RAF and MEK inhibitors compared with prior EGFR-TKIs plus either RAF or MEK inhibitor
We also conducted an analysis of the therapeutic effectiveness in patients who underwent triple-targeted therapy versus those who were administered prior EGFR-TKIs in combination with either RAF or MEK inhibitor. Of the 28 patients who received prior EGFR-TKI-based treatment, 18 patients underwent triple-targeted therapy, and 10 patients received prior EGFR-TKIs plus either RAF or MEK inhibitor. Baseline characteristics were generally well balanced across the treatment arms, except that the triple-targeted therapy arm had significantly older patients and more male patients (Supplementary Table S7, available at https://doi.org/10.1016/j.esmoop.2024.103935). Compared with patients with BRAF alterations who received prior EGFR-TKIs plus either RAF or MEK inhibitor, patients who received triple-targeted therapy had significantly improved mPFS [mPFS: 8.0 months (95% CI 6.0 months-NR) versus 4.8 months (95% CI 2.0 months-NR), P = 0.010; Supplementary Figure S6C, available at https://doi.org/10.1016/j.esmoop.2024.103935]. A similar improvement in the mPFS was found in BRAFmut patients [mPFS: 9.0 months (95% CI 8.0 months-NR) versus 4.5 months (95% CI 2.0 months-NR), P = 0.029; Supplementary Figure S6D, available at https://doi.org/10.1016/j.esmoop.2024.103935].
Treatment-related adverse events
Treatment-related adverse events occurred in 20 patients (48.8%, 20 of 41). Ten patients were treated with dabrafenib and trametinib plus osimertinib, six patients received vemurafenib plus osimertinib, three patients were administered dabrafenib and trametinib, and the remaining one patient was treated with osimertinib plus trametinib. The most common adverse events of any grade were fever (19.5%) and fatigue (19.5%), followed by rash (12.2%) and diarrhea (9.8%). A dose reduction of the RAF or MEK inhibitors was required in five patients (12.2%, 5 of 41), and permanent discontinuation of treatment occurred in five patients (12.2%, 5 of 41). Specifically, dabrafenib and trametinib plus prior EGFR-TKI therapy was discontinued in two patients owing to pneumonitis and grade 1 interstitial lung disease; prior EGFR-TKI plus vemurafenib was discontinued in one patient due to grade IV vomiting; and prior EGFR-TKI plus trametinib was discontinued in one patient because of gastrointestinal bleed. In another case, RAF inhibitor treatment was discontinued while maintaining prior EGFR-TKI therapy plus MEK inhibitor owing to fatigue, diarrhea, and weight loss (Supplementary Table S8, available at https://doi.org/10.1016/j.esmoop.2024.103935).
Discussion
In this study, we described the characteristics of patients with EGFRmut NSCLC harboring acquired BRAF alterations who presented resistance to EGFR-TKI therapy in the largest cohort to date and highlighted that acquired BRAF alterations are predominantly characterized by BRAF mutations and BRAF fusions. Additionally, this is the first report demonstrating that triple-targeted therapy is effective and safe for patients with EGFR-mutant NSCLC with acquired BRAF alterations, mainly in patients with BRAF class I mutations and potentially in patients with BRAF fusions.
Several studies have indicated that BRAF alterations can confer resistance to EGFR-TKIs as oncogenic drivers that activate bypass signaling pathways, and BRAF mutations and BRAF fusions are among the common driver alteration types.29,34 In our EGFRmut NSCLC cohort, acquired BRAF mutations were detected in 1.1% of patients. This frequency was similar to that reported in a previous study. Sui et al.7 revealed that the prevalence of acquired BRAF alterations was 1.4%. Herein, it was also demonstrated that the BRAF V600E mutation and AGK-BRAF fusion were the most common alternation subtypes conferring resistance to EGFR-TKIs. This finding was consistent with those reported by Wei et al.,2 who showed that BRAF V600E was the most common acquired BRAF variant in patients with EGFRmut NSCLC and also introduced an AGK-BRAF fusion after progression on afatinib. The present study revealed a complex EGFR-independent resistance mediated by various subtypes of BRAF mutations and BRAF fusions, thus emphasizing the importance of investigating the characteristics of different subtypes.
It is worth mentioning that there are no studies evaluating the optimal treatment for patients who acquire a BRAF alteration as a resistance mechanism to EGFR-TKIs. Our study provides clinical evidence of the benefits conferred by triple-targeted therapy. These findings suggested that prior EGFR-TKIs should not be discontinued in EGFRmut NSCLC patients with acquired BRAF alterations, mainly in BRAF class I mutations. Mauclet et al.48 demonstrated that a partial response was observed with osimertinib restarted along with dabrafenib and trametinib in a lung adenocarcinoma patient who developed a BRAF V600R mutation after resistance to osimertinib. One possible reason is that distinct molecular driver alterations associated with EGFR-TKI resistance, including BRAF mutations, usually coexist with the initial EGFR mutation. Synergistic treatment based on prior EGFR-TKIs and inhibitors targeting two driver alterations helped to restore cell sensitivity to EGFR-TKIs, overcoming resistance in corresponding patient-derived models.8 Nevertheless, among the patients with BRAF non-class I mutations, patients who had received triple-targeted therapy showed comparable mPFS to the patients who did not receive this therapy. Novel therapies for patients with acquired BRAF non-class I mutations should be developed in the future.
Although Vojnic et al.9 highlighted that acquired BRAF fusions are rare yet potentially targetable mechanisms of EGFR-TKI resistance, clinical experience in treating NSCLCs with acquired BRAF fusions resistant to EGFR-TKI therapy has been limited. Preclinical data have shown that tumors with BRAF fusions are RAS-independent, as BRAF and RAS signal as a constitutive dimer, and are resistant to vemurafenib (a RAF inhibitor).5 Additionally, although BRAF fusions seem to be therapeutic targets, the Food and Drug Administration (FDA)-approved RAF inhibitors (vemurafenib or dabrafenib) have shown limited efficacy against BRAF fusions.9 Similarly, our study demonstrated that the mPFS was longer in patients who received triple-targeted therapy than in those who did not, but this difference was not significant. A concern arising from our study is that despite the same triple-targeted treatment, patients with BRAF fusions exhibited shorter PFS than those harboring BRAF mutations (5.0 versus 9.0 months). Therefore, further preclinical and clinical studies are necessary to evaluate acquired BRAF fusions as potential therapeutic targets.
Understanding the toxicity profiles of combination therapy with prior EGFR-TKIs plus RAF and/or MEK inhibitors is essential for guiding clinicians to better evaluate therapy risks and manage potential adverse effects. Sui et al.7 reported that the most common treatment-related adverse events were rash, diarrhea, and mucositis. Eight patients (53%) required dose reduction, four patients (26%) required permanent treatment discontinuation, and this discontinuation rate is much higher than that of our cohort. Other potential factors contributing to this inconsistency might be the small sample size of the referenced study or reporting bias in cases of the previous study. Notably, as two driver alterations, acquired BRAF alteration and initial EGFR mutation, exist within a single cell, combination strategies of triple-targeted treatment should be considered over a sequential treatment approach as facing potential toxicity profiles.8
There are some limitations to our study. Firstly, the majority of the included publications consisted of small patient cohorts, and prospective studies on this specific patient population are lacking. Nevertheless, the identification of an effective treatment strategy involving dabrafenib and trametinib plus prior EGFR-TKIs to overcome resistance mediated by BRAF alterations has great clinical relevance. Secondly, PFS was evaluable in only 41 patients who received subsequent therapy, so there were nonsignificant differences in certain subset analyses; additionally, most data utilized in this study were derived directly from the author’s literature on the PFS which is significantly affected by the frequency and modality of scans. These results call for prospective validation in larger cohorts by central review. Thirdly, it should be noted that efficacy data for the recently FDA-approved combination of encorafenib plus binimetinib for patients with BRAFV600-mutant metastatic NSCLC were not available in the present study.53 Finally, the effect of medication doses on the safety outcomes remains unclear. It is important to acknowledge that the adverse event rates may have been underestimated due to the potential omission of minor toxicities in some cases, while the resolution of grade 1-2 adverse events usually occurred with systemic steroids and did not appear to negatively affect PFS.2,21
Taken together, our study demonstrated that BRAF alterations that confer resistance mechanisms to EGFR-TKI therapy are generally BRAF mutations and BRAF fusions. The triple-targeted therapy of dabrafenib and trametinib plus prior EGFR-TKIs could be a potential treatment option in EGFRmut NSCLC patients with acquired BRAF alterations, mainly in patients with BRAF class I mutations and potentially in patients with BRAF fusions. The optimal dose, safety, and efficacy of this strategy should be investigated in a clinical trial. |
Title: Whole-Exome Sequencing Improves Understanding of Inherited Retinal Dystrophies in Korean Patients | Body:
1. Introduction
Approximately one in every four thousand individuals worldwide is afflicted with RP, the most common form of inherited retinal dystrophies (IRDs). RP (MIM: 268,000) is defined by the primary degeneration of rods, often presenting as progressive night blindness and subsequent visual field constriction. The condition begins with cones malfunctioning, leading to decreased visual acuity and central vision loss [1,2]. Moreover, in most patients, a symptom known as “bone spicule” pigmentation is observed in the retina. It is marked by the gradual degeneration of rod and cone photoreceptors, resulting in significant vision impairment in both eyes [3]. Cmmon symptoms encompass advancing nyctalopia, visual field constriction, and visual acuity decrease.
Usually, RP patients share similar genetic backgrounds, with phenotypic heterogeneity among people. Furthermore, RP is a genetically diverse illness with autosomal-dominant, autosomal-recessive, or X-linked inheritance. Genetic, allelic, and phenotypic variation make the diagnosis of RP patients a difficult process. About 93 genes are linked to nonsyndromic RP variants, and the RetNet database (https://web.sph.uth.edu/RetNet/ (accessed on 20 May 2023)) provides up-to-date information [4,5].
This research aimed to discover the putative pathogenic gene associated with Korean families exhibiting retinitis pigmentosa via whole-exome sequencing. Furthermore, we aimed to assess the diagnostic efficacy of this approach and to determine the relationship between candidate genes and clinical characteristics.
2. Materials and Methods
2.1. Clinical Examination
A group of 21 families who were diagnosed with RP but did not have any previous molecular test results, and who had a well-documented family history, were chosen from an elite eye clinic in Seoul, Korea, between January 2021 and October 2022.
All patients visiting the elite eye clinic received comprehensive ophthalmic examinations, including best-corrected visual acuity (BCVA) and intraocular pressure (IOP) measurements, slit lamp biomicroscopy, color fundus photography (Topcon Inc., Tokyo, Japan), ocular biometry applying optical low-coherence reflectometry (Lenstar 900 Optical Biometer, Haag-Streit, Koeniz, Switzerland), OCT and OCT angiography (Heidelberg Engineering, Max-Jarecki-Straße 8, 69115 Heidelberg, Germany), and stationary perimetry tests (Humphery field analyzer; Carl Zeiss Meditec, Inc., Dublin, CA, USA).
Written informed consent was obtained from all participants or their guardians, and the study received approval from a Bioethics Committee authorized by the Ministry of Health and Welfare (MOHW) of and Hanyang University and OneOmics. All study protocols followed the principles of the Declaration of Helsinki.
2.2. Whole-Exome Sequencing
Using Exgene Blood SV mini (GeneAll, Seoul, Republic of Korea), DNA was extracted from the blood or saliva of patients and their families according to the manufacturer’s protocol [6]. Exomes were captured and amplified via PCR by using a xGen Exome Research Panel V2 (Integrated DNA Technologies, Coralville, IA, USA) exome kit [7]. Paired-end sequencing was performed using Novaseq 6000 (Illumina, San Diego, CA, USA) [8]. Variant discoveries were performed using TAK’s best practice. Discovered variants were functionally annotated by utilizing the software tool snpEff (version 5.0) in conjunction with the genome annotation release GRCh38.102 [9]. To assess in silico variant effects, dbNSFP v4.5 (Gencode release 29/Ensembl version 94) was annotated using SnpSift v5.0 [10,11]. dbSNP build 155 and the CLINVAR database were annotated using bcftools v1.3 [12,13,14]. Variants have a frequency of more than 1% in GnomAD and the KRGDB [15,16,17].
To avoid missing disease-related variations with an allele frequency of 1% or higher, the detected variants were filtered based on an allele frequency of below 1% in the KRGDB [18]. Each variant that successfully passed the screening process was examined against 93 genes associated with RP as listed in the RetNet database [19].
The pathogenicity of the chosen variants was assessed based on the norms and recommendations set out by the American College of Medical Genetics and Genomics (ACMG) guidelines using Intervar [20,21]. GATK-gCNV analysis allows for a germline copy number variation (CNV) pipeline [22].
2.3. Sanger Confirmation
The novel candidate variations were confirmed via capillary sequencing. Sanger sequencing was conducted on the relevant gene segments to conduct a segregation analysis and determine the inheritance pattern of the modified alleles in specific families using the DNA samples of available relatives.
2.1. Clinical Examination
A group of 21 families who were diagnosed with RP but did not have any previous molecular test results, and who had a well-documented family history, were chosen from an elite eye clinic in Seoul, Korea, between January 2021 and October 2022.
All patients visiting the elite eye clinic received comprehensive ophthalmic examinations, including best-corrected visual acuity (BCVA) and intraocular pressure (IOP) measurements, slit lamp biomicroscopy, color fundus photography (Topcon Inc., Tokyo, Japan), ocular biometry applying optical low-coherence reflectometry (Lenstar 900 Optical Biometer, Haag-Streit, Koeniz, Switzerland), OCT and OCT angiography (Heidelberg Engineering, Max-Jarecki-Straße 8, 69115 Heidelberg, Germany), and stationary perimetry tests (Humphery field analyzer; Carl Zeiss Meditec, Inc., Dublin, CA, USA).
Written informed consent was obtained from all participants or their guardians, and the study received approval from a Bioethics Committee authorized by the Ministry of Health and Welfare (MOHW) of and Hanyang University and OneOmics. All study protocols followed the principles of the Declaration of Helsinki.
2.2. Whole-Exome Sequencing
Using Exgene Blood SV mini (GeneAll, Seoul, Republic of Korea), DNA was extracted from the blood or saliva of patients and their families according to the manufacturer’s protocol [6]. Exomes were captured and amplified via PCR by using a xGen Exome Research Panel V2 (Integrated DNA Technologies, Coralville, IA, USA) exome kit [7]. Paired-end sequencing was performed using Novaseq 6000 (Illumina, San Diego, CA, USA) [8]. Variant discoveries were performed using TAK’s best practice. Discovered variants were functionally annotated by utilizing the software tool snpEff (version 5.0) in conjunction with the genome annotation release GRCh38.102 [9]. To assess in silico variant effects, dbNSFP v4.5 (Gencode release 29/Ensembl version 94) was annotated using SnpSift v5.0 [10,11]. dbSNP build 155 and the CLINVAR database were annotated using bcftools v1.3 [12,13,14]. Variants have a frequency of more than 1% in GnomAD and the KRGDB [15,16,17].
To avoid missing disease-related variations with an allele frequency of 1% or higher, the detected variants were filtered based on an allele frequency of below 1% in the KRGDB [18]. Each variant that successfully passed the screening process was examined against 93 genes associated with RP as listed in the RetNet database [19].
The pathogenicity of the chosen variants was assessed based on the norms and recommendations set out by the American College of Medical Genetics and Genomics (ACMG) guidelines using Intervar [20,21]. GATK-gCNV analysis allows for a germline copy number variation (CNV) pipeline [22].
2.3. Sanger Confirmation
The novel candidate variations were confirmed via capillary sequencing. Sanger sequencing was conducted on the relevant gene segments to conduct a segregation analysis and determine the inheritance pattern of the modified alleles in specific families using the DNA samples of available relatives.
3. Results
The present study includes 21 patients who have been diagnosed with nonsyndromic RP. The average age at which RP was diagnosed was 31.72 ± 12.76 years (mean ± SD), whereas the mean age of examination was assessed as being 37.78 ± 12.39 years (Table 1). All participants had typical characteristics of RP, such as the first manifestation of night blindness being the primary symptom, followed by a gradual reduction in the visual field over subsequent years.
Cataracts were detected in patients FRP_0196, FRP_0170, FRP_0043, FRP_0207, FRP_0355, FRP_0157, FRP_0221, and FRP_0243. Detailed ophthalmological observations of the patients are presented in Table 1, and the clinical characteristics of family with novel variants of PDE6B gene are documented in Figure 1.
Thirteen families had cases that were not resolved even after WES was performed. WES identified potentially causal variations in genes associated with RP in 8 out of the 21 families (Table 2). These variants include one new variant and eight variants that have been previously described. The genes involved include PDE6B, CRB1, PRE65, RHO, KLHL7, RP1, and USH2A. The in silico investigation of CNVs utilizing GATK’s gCNV did not detect any noteworthy structural changes. In addition, variants from the remaining 13 families did not meet the criteria outlined by the ACMG standard.
A novel variant was identified from gene PDE6B (c.869G>A, p.Trp290*). On PDE6B, c.869G>A(p.Trp290*) is a putative pathogenic novel variant in the KRP16 family. The proband was diagnosed with retinitis pigmentosa (Figure 2). The patient did not exhibit any indications or symptoms that were present in his mother and brother. The variant tester agreed on the potential impact of stop-gain (automatically causing disease).
4. Discussion
Inherited RP impacts people of all age groups and covers a wide range of diseases with significant genetic and phenotypic diversity. The pathogenesis of these disorders involves pathogenic variations that may vary from single-nucleotide changes to chromosomal rearrangements [23].
These variants impact genes that encode various signaling and structural components. Several genetic investigations have been carried out to comprehend the hereditary foundations of RP, encompassing molecular genetic research and studies on the aggregation of families. These investigations have revealed a genetic predisposition causing RP, revealing many pathogenic genes or susceptibility regions that are significantly linked to its development in Korean population specifically.
Trio-based WES is a more comprehensive method compared to WES focused on a single individual (proband-centered WES). This approach allows for the discovery of gene variations and a thorough evaluation of their pathogenicity. Additionally, it permits the investigation of gene variants using the principles of cogenetic segregation. This methodology can create an improved strong network that maps the association between genes and phenotypes, enabling a more comprehensive exploration of the inheritance patterns of mutant genes. In addition, because of the scarcity of genetic studies focusing on IRDs and the urgent need for RP research, specifically within the unique population of Korea, we recruited a group of 21 families who have been diagnosed with RP. Using a trio-based WES technique, our goal is to obtain a more comprehensive understanding of the genetic factors involved in RP. Specifically, we seek to uncover how genetic variations contribute to the development of this disorder within families.
The variant p.Trp290* is believed to cause the denaturation of PDE6B by interfering with the structure of the GAF2 region of the protein [24]. Phosphodiesterase 6B (PDE6B) variants often cause autosomal recessive retinitis pigmentosa, also known as rod–cone dystrophy. The PDE6B protein plays a crucial role in phototransduction. It is crucial to comprehend the pathogenicity and functional significance of identified PDE6B polymorphisms to provide genetic information to families and facilitate participation in therapeutic trials for autosomal-recessive PDE6B-related retinitis pigmentosa. PDE6A and PDE6B combine to create a heterodimer that is blocked by a homodimer consisting of two γ subunits, which are produced by PDE6G. PDE6A and PDE6B each contain two potential noncatalytic domains (GAF1 and GAF2) in the N-terminus, which interact with cGMP and the polycationic region of two γ subunits in the inhibitory state of the complex [25].
Given that the one cause of RP materializes through genetic influence, our study includes an examination of documented variants in RP risk genes: PDE6B, CRB1, RPE65, RHO, KLHL7, RP1, and USH2A. These genes hold pivotal roles in bone spicule formation, modulating scleral thickness, influencing choroidal blood flow, and orchestrating other factors germane to the onset of RP. The identification of pathogenic variants in these genes promises to enrich our understanding of the pathogenesis of RP.
The objective of this work was to conduct a multimodal study that could characterize a unique form of IRD in Korean population, broadening understanding of how a novel pathogenic variant affects the phenotype differently from a similar one [26].
To gain a more comprehensive understanding of the genetic components that contribute to RP, it is essential to have a broader sequencing coverage that encompasses noncoding areas. This may be achieved by utilizing advanced technologies like WGS, along with a larger collection of well-organized clinical data. Moreover, it is crucial to carry out additional functional investigations and ex vivo experimental validations to verify the several risk genes for RP that have been found in our research.
To summarize, our study’s thorough investigation of RP families from Korea tackles a gap in genetic research in this area. Through the consolidation and integration of data, our goal is to create a helpful resource for genetic counselling and personalized treatment strategies. |
Title: Overcoming Cancer Persister Cells by Stabilizing the | Body:
1
Introduction
Cancer cells enter a reversible persister state to evade death from chemotherapy or targeted agents.[
1
] Persister cells (PS) are a type of cells that exhibit limited or infrequent division following cancer chemotherapy.[
2
] These cells are distinguished by their slow proliferation rate, ability to adapt to their surrounding microenvironment, and capacity for phenotypic plasticity.[
3
] PS is omnipresent in all types of clinical responses and can eventually and unpredictably give rise to metastatic relapses, known as invisible adversaries.[
3
] Since the 1950s, glutamine (Q) has been widely identified as a crucial nutrient for cancer cells.[
4
] Glutamine often observed as “depleted” in primary solid tumors.[
5
] Glutamine depletion in response to the amino acid response pathway modulates DNA‐to‐RNA‐to‐protein cascades, coordinating genetic expression control to remodel metabolic reprogramming.[
6
] Over ten distinct types of carcinomas have demonstrated sensitivity to glutamine deprivation, including lung cancer, pancreatic cancer, and breast cancer.[
7
] Targeting the unique glutamine metabolic dependence of PS may reveal vulnerabilities that can be exploited therapeutically. Depriving cells of glutamine sensitizes prostate cancer cells to radiotherapy and kelch like ECH associated protein 1 (KEAP1)‐mutant lung adenocarcinoma.[
8
] Furthermore, targeting glutamine metabolism at the transporter level[
9
] and enzymes involved in glutamine synthesis and catabolism[
10
] offers a promising approach for refining cancer medicine. Presently, 14 clinical trials targeting glutamine metabolism have been registered in ClinicalTrials (https://clinicaltrials.gov/). Notably, four ongoing clinical investigations have focused on non‐small‐cell lung cancer (NSCLC), specifically the CB‐839 trials (phase 1/2, NCT02071862, NCT02771626, NCT03965845, and NCT04265534).
Targeting the glutamine metabolic dependencies of PS is a viable approach. An example involves the potential of the timed inhibition of glutamine metabolism to eliminate persistent acute myeloid leukemia cells.[
11
] This research underscores the potential of dietary interventions in treating various metabolic disorders, including cancer. Restricting dietary glutamine intake affects cerebellar glutamine levels and enhances survival rates in mouse brain tumor models.[
12
] Nonetheless, a comprehensive understanding of the mechanisms by which persister cancer cells adapt to glutamine deprivation remains elusive, creating an urgent need for effective therapeutic strategies.
Glutaminase (GLS) has been implicated in glutamine addiction within tumors and exhibits oncogenic properties.[
13
] Under glutamine restriction conditions, GLS transitions from a low‐activity dimer to a highly active polymer, thereby maximizing intracellular glutamine utilization at minimal concentrations. GLS filament formation is a physiological occurrence in solid tumors, suggesting the universality of glutamine depletion in solid tumors.[
14
] The integrated stress response (ISR) is a complex signaling pathway observed in eukaryotic cells.[
15
] Ryan et al. proposed an activating transcription factor 4 (ATF4)‐mediated ISR strategy to enhance metabolic adaptation during mitochondrial dysfunction.[
16
] ATF4 plays a pivotal role in signaling cascades that promote both survival and apoptosis.[
17
] Currently, multiple approaches are available for targeting ATF4 in cancer treatment.[
18
] Initial strategies have focused on upstream eukaryotic translation initiation factor 2 (eIF2α) kinases, such as PKR‐like endoplasmic reticulum kinase (PERK), double‐stranded RNA‐dependent protein kinase (PKR), general control nonderepressible 2 (GCN2), or heme‐regulated eIF2α kinase (HRI), or on augmenting the activity of eIF2α phosphatase to inhibit eIF2α phosphorylation,[
19
] thereby reducing overall ATF4 translation. Alternatively, inhibition of pathways regulated by ATF4 transcription offers an effective approach for targeting ATF4‐expressing cells.[
15b
] ATF4 is a pivotal determinant of cellular fate during ISR activation, and stress‐induced translational reprogramming of ATF4 promotes PS survival. Consequently, inhibition of ATF4 has the potential to obstruct the persister phenotype in the context of nutritional restriction therapy.
Bioinformatics and experimental studies have revealed the accumulation of G‐quadruplex (G4)‐forming sequences in specific regions of the human genome, such as oncogene promoters, telomeres, and 5′‐UTR, influencing gene expression and genome stability.[
20
] Small‐molecule drugs designed to stabilize G4 structures inhibit gene replication and transcription, thereby inducing cancer cell death.[
21
] With the promising outcomes of G4‐targeting drugs in clinical trials,[
22
] G4 structures have emerged as new therapeutic targets.[
23
] The recent emphasis on ATF4 underscores its pivotal role in tumor progression.[
7f
] The ATF4 protein is considered a poor and challenging target.[
18
] Fortunately, we (and other researchers at the time of manuscript preparation) discovered that the ATF4 promoter region can form a DNA G4 structure (ATF4‐G4).[
24
] It is crucial to determine the function of this particular secondary structure and its role in pathological processes to ascertain its potential utility as a therapeutic target. Additionally, detailed structural information on ATF4‐G4 remains largely unknown, and the development of small molecules targeting ATF4‐G4 has not yet been reported. Natural products (NPs) are valuable sources of diverse structures that exhibit several biological attributes and are widely used in clinical diseases, especially cancer.[
25
] Anti‐tumor alkaloids have demonstrated strong binding affinities for diverse DNA G4s, offering a novel strategy for anticancer drug development.[
26
]
In this study, we found that over 50% of persister cancer cells exhibit resilience to glutamine restriction therapy, leading to tolerance toward chemotherapeutic agents. We identified ATF4 as the key driver behind this persister phenotype. In the clinical cohort, 23% of cancer patients had high levels of ATF4 expression during glutamine depletion. Given the “undruggble” properity of ATF4 protein, we focused on the role of G4 structure within ATF4 promoter region. We determined the utility of COP, serving as an ATF4‐G4 stabilizer, inhibiting ATF4 expression both in vivo and in vitro. Using nuclear magnetic resonance (NMR), we determined the solution structure of ATF4‐G4 and its complex with coptisine (COP). Furthermore, we observed a unique metabolic feature of COP in glutamine restriction therapy, characterized by the limitation of glutamine transporter activity and the decrease of glutamine uptake that correlated with the severity of the disease. Moreover, we conducted a comprehensive screening and investigation of the transcriptional activity of the transcription factor AP‐2 alpha (TFAP2A) in relation to ATF4, both in the presence and absence of COP. Collectively, these findings shed light on tumor adaptive survival induced by glutamine deprivation.
2
Results
2.1
Glutamine‐Restrictive Therapy Fails to Enhance the Sensitivity of the Persister Cancer Cells
We subjected seven distinct lung cancer cell lines to a glutamine‐deficient environment (0.25 mM glutamine) to assess their cell viability. Evidently, different cells exhibit distinct responses under similar conditions. Among these, NCI‐H460, A549, and NCI‐H1975 cells displayed heightened sensitivity to low‐glutamine conditions, whereas NCI‐H1299 cells demonstrated moderate sensitivity. In contrast, PC‐9, HCC827, and NCI‐H661 cells were insensitive to the glutamine‐restrictive milieu (Figure
1A). Our research focuses on the effectiveness of glutamine deprivation therapies and overcoming the problem of cellular resistance that arises during the course of glutamine deprivation therapies. Therefore, the H460 and H1299 cell lines, which are highly and moderately sensitive to glutamine deprivation, respectively, were selected for subsequent experiments. The impacts of glutamine deficiency on cell survival and proliferation were concentration‐dependent, as depicted in Figure S1A–C (Supporting Information for survival) and Figure 1B along with Figure S1D (Supporting Information for proliferation). These outcomes underscore the unique metabolic reliance of cancer cells on glutamine to facilitate synthetic metabolism pathways.[
27
] Glutamine, an amino acid conditionally essential, and abundant in both plasma and the intracellular amino acid pool, plays a pivotal role in bolstering the synthesis of glutathione—an ROS scavenger. Consequently, our findings demonstrated an increase in ROS levels due to the absence of glutamine (Figure S1E, Supporting Information). Glutamine is catabolized into α‐ketoglutaric acid, which subsequently enters the tricarboxylic acid (TCA) cycle to generate NADH and FADH2. Under conditions involving glutamine restriction, the presence of glutamine as a carbon and non‐essential amino acid nitrogen donor partially influences the dependence of cancer cells on other nutrients to fulfill the demands of the TCA cycle. This disruption of “glutamine addiction” impairs mitochondrial respiration, resulting in a reduced oxygen consumption rate (OCR), as depicted in Figure 1C. Furthermore, under low‐glutamine conditions, a significant increase in the uptake rate of glutamine by cancer cells was observed (Figure 1D). Energy metabolism analysis (Figure 1E) also confirmed that the intracellular glutamine content did not significantly differ between cells subjected to glutamine restriction and those treated with 4 mM glutamine, indicating a stress response aimed at maintaining the cancer cell state. Notably, the TCA cycle significantly reduced argininosuccinic acid, ornithine, L‐aspartate, L‐glutamic acid, and trehalose‐6‐phosphate levels. Likewise, the uracil content in the pentose phosphate pathway declined, whereas the levels of fructose 1,6‐bisphosphate in the glycolysis pathway increased.
Figure 1
Glutamine‐restrictive therapy fails to enhance the sensitivity of the persister cancer cells. A) Sensitivity test to glutamine restriction in NCI‐H460, NCI‐H1299, NCI‐H1975, A549, PC‐9, NCI‐H661, and HCC827 cells. B) Clonogenic survival assay of NCI‐H460 and NCI‐H1299 cells under 4 mM to 0 mM glutamine conditions. Four thousand cells were cultured in six‐well plates for 10 days, followed by crystal violet staining and imaging. C) Seahorse XF assay measuring the OCR in NCI‐H460 and NCI‐H1299 cells under 4 mM or 0.25 mM glutamine conditions (n = 3 independent experiments). D) Glutamine uptake rate determination in NCI‐H460 and NCI‐H1299 cells cultured for 36 h in 4 mM or 0.25 mM glutamine using a glutamine assay kit (ab197011, Abcam) (n = 3 independent experiments). E) Analysis of differential metabolites between 4 mM and 0.25 mM glutamine conditions on H460 cells. The x‐axis represents groups, and the y‐axis represents expression (n = 3 independent experiments). F) CCK‐8 assay‐based analysis on the effect of glutamine (4 mM or 0.25 mM) on cisplatin treatment sensitivity in NCI‐H460 and NCI‐H1299 persister cells (n = 3 independent experiments). G) Generation of persister cells: NCI‐H460 and NCI‐H1299 cells treated with 4 mM or 0.25 mM glutamine for 1–5 days were switched to complete RPMI‐1640 medium for 2 h to create persister cells. Live cells were sorted, reseeded, and tested for cisplatin IC50. Then, live cells were harvested using the Dead Cell Removal kit (Miltenyi Biotec) (n = 3 independent experiments). Glutamine concentrations of 4 mM to 0 mM are represented by 4Q to 0Q, respectively. Data are shown as mean ± SD. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. Data analyzed using two‐tailed Student's t‐tests (D, E, G) in GraphPad Prism 9.5.0.
Although glutamine restriction effectively promoted cancer cell death, notably the survival rate of these cells still exceeded 50% (Figure 1A). To further validate the anti‐cancer effects of glutamine constraint, we sorted PS of lung cancer to evaluate the tolerance to cisplatin under two different conditions involving 4 mM and 0.25 mM glutamine (Figure 1F). The results revealed that cells treated with 0.25 mM glutamine either exhibited heightened resistance to cisplatin (Figure 1G) or did not exhibit increased sensitivity (Figure S1F, Supporting Information). This led us to hypothesize that glutamine restriction induces a stress response, thereby triggering a protective adaptive mechanism.
2.2
Glutamine Deficiency Induces Integrated Stress Response to Activate the GCN2‐ATF4‐ASCT2 Axis
The ISR pathway in eukaryotic cells is a multifaceted signaling network activated in response to a range of physiological alterations and diverse pathological conditions (Figure
2A). When amino acids are depleted, the ISR, activated by GCN2, curtails the requirement for amino acids in protein synthesis, thereby mitigating stress levels.[
28
] In glutamine deprivation conditions, our observations revealed that GCN2 (Figure 2B; Figure S2A,B, Supporting Information) triggered the phosphorylation of eIF2α at serine 51, which was distinct from that observed in HRI (Figure S2C, Supporting Information), PERK, and PKR. This eIF2α phosphorylation extends the ribosome scanning process at the open reading frame uORF1, located upstream of ATF4 transcript, consequently reinstating the transcriptional translation of ATF4[
18
,
29
] (Figure 2A). RNA‐Seq analysis demonstrated an increase in ATF4 expression when exposed to glutamine restriction (Figure 2C). The RNA (Figure 2D) and protein levels of ATF4 (Figure 2E; Figure S2D,E,G, Supporting Information), as well as the transcriptional activity of ATF4 (Figure 2F) increased in the highly and moderately glutamine‐deprivation‐susceptible NCI‐H460 and NCI‐H1299 cell lines under glutamine‐restricted conditions. Conversely, the expression levels of ATF4 in cell lines insensitive to glutamine restriction remained unaltered (Figure S2D,F, Supporting Information). Subsequent validation confirmed that the urgently induced ATF4 expression by ISR was suppressed by compound 968, a glutaminase inhibitor (Figure S2H, Supporting Information). This stress‐activated ATF4 acts as a chief transcription factor for genes responsive to stress, thus facilitating cellular recovery.[
28
] Our hypothesis revolves around the potential of disrupting ATF4, the primary regulatory factor in ISR, to inhibit the adaptive survival reaction initiated by cancer cells via ISR. In PS induced with either 4 mM or 0.25 mM glutamine, the knockdown of ATF4 led to a reduction in cisplatin resistance linked to glutamine deprivation (Figure 2G). Our experiments demonstrated that the proliferation of NCI‐H460 and NCI‐H1299 cells under glutamine deficiency was significantly curtailed upon ATF4 knockdown (Figure 2H,I; Figure S3A–F, Supporting Information). Conversely, the introduction of exogenous ATF4 (Figure S2I, Supporting Information) undermined this inhibitory effect (Figure 2H,I; Figure S3A,F, Supporting Information). Furthermore, the knockdown of ATF4 disrupted mitochondrial respiration in NCI‐H460 and NCI‐H1299 cells (Figure 2J).
Figure 2
Glutamine deficiency induces integrated stress response to activate the GCN2‐ATF4‐ASCT2 axis. A) Schematic representation of GCN2‐eIF2α‐ATF4 axis activation in response to glutamine nutritional restriction. B) Immunoblotting analysis of indicated proteins in NCI‐H460 cells 48 h after treatment with GCN2iB, an ATP‐competitive inhibitor of the serine/threonine‐protein kinase general control nonderepressible 2 (GCN2). C) RNA‐Seq analysis compared differentially expressed genes in the H460 cell line (4Q versus 0.25Q). The ATF4 gene showed high expression in the 4Q versus 0.25Q fraction (n = 3 independent experiments). D) qRT‐PCR analysis of ATF4 mRNA levels in NCI‐H460 and NCI‐1299 cells after 24 h of treatment with varying glutamine concentrations (4 mM, 0.5 mM, 0.25 mM, and 0 mM) and 48 h of treatment with 0.25 mM glutamine (n = 3 independent experiments, average of three technical replicates). The colors of the heatmap represent values of 2−ΔΔCt. E) Immunoblotting of ATF4 in NCI‐H460 and NCI‐H1299 cells after treatment with 0.25 mM glutamine for 12, 24, and 48 h. F) Luciferase activity of ATF4 in NCI‐H1299 cells treated with 4 mM or 0.25 mM glutamine for 24 h (n = 3 independent experiments). G) NCI‐H460 and NCI‐H1299 cells treated with 4 mM or 0.25 mM glutamine for 3 days were switched to complete RPMI‐1640 medium for 2 h to induce persister cells. These persister cells were then transfected with NC and siATF4, and the IC50 of cisplatin was assayed in live cell plates post‐transfection (n = 3 independent experiments). H) Clonogenic survival assay of NCI‐H460 and NCI‐H1299 cells transfected with shScramble, shATF4, Vector, and ATF4 OE plasmids under 4 mM or 0.25 mM glutamine. I) Survival rates of NCI‐H460 and NCI‐H1299 cells transfected with NC, siATF4, Vector, and ATF4 OE plasmids under 4 mM or 0.25 mM glutamine for 48 h. OE: Overexpression ATF4 (n = 3 independent experiments). J) Seahorse XF assay measuring OCR in NCI‐H460 and NCI‐H1299 cells after treatment with 4 mM or 0.25 mM glutamine following siATF4 transfection (n = 3 independent experiments). Immunoblots represent three similar results. Glutamine concentrations of 4 mM to 0 mM are represented by 4Q to 0Q, respectively. Data shown as mean ± SD. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. Data analyzed using two‐tailed Student's t‐tests (F) and One‐way ANOVA (G, I) in GraphPad Prism 9.5.0.
2.3
Glutamine‐Restrictive Therapy Benefits from the Low Levels of ATF4
Glutamine is a critical nutrient for various solid tumors, particularly rapidly dividing tumor cells, which have an increased demand for glutamine. As a result, tumors growth is often associated with increased glutamine consumption, particularly in the central regions of solid tumors.[
30
] As the tumor grows, elevated local glutamine utilization may reduce the glutamine supply to other areas, resulting in a heterogeneous distribution of glutamine throughout the tumor tissue.[
31
] When cancer cells encounter a limited supply of glutamine, including regional glutamine deficiency or pharmacological blockade of glutamine metabolism, alternative intracellular adaptive mechanisms are employed to survive and continue to proliferate.[
31
,
32
] It is necessary to assess the potential mechanisms of tumor development under the inhibition of glutamine metabolism. Glutamine undergoes glutaminolysis by GLS prior to its contribution to bioenergetic processes and macromolecular synthesis.[
33
] The induction of filament formation due to glutamine scarcity amplifies GLS activity and augments substrate‐binding affinity, thereby facilitating the efficient utilization of intracellular glutamine even at exceedingly low concentrations.[
14
] Through the analysis of clinical tissue microarrays of NSCLC patients, we identified elevated expression of GLS in tumor tissues, accompanied by conspicuous GLS filament structures (Figure
3A), suggesting a prevalent scarcity of glutamine in NSCLC.
Figure 3
Glutamine‐restrictive therapy benefits from the low levels of ATF4. A) Tissue microarray of NSCLC patients with tissue immunofluorescence assay. Representative tissue immunofluorescence images with ATF4 (Red), GS (Green), and GLS (Pink). White arrows indicate GLS filaments. Scale bar: 10 µm. B) Correlation analysis between the number of relative GLS filament structures and positive cell density of ATF4 in the tissue microarray. C) Correlation analysis between positive cell density of GS and ATF4 in the tissue microarray. D) Schematic of tumors after intratumoral injection of shATF4 lentivirus with glutamine‐deficient diet and 2% glutamine diet in the established xenograft model (n = 6 per group). E) Tumor images (n = 6 per group). F) Tumor volume, body weight, and tumor weight of nude mice with intratumoral injection of shATF4 lentivirus and different glutamine diets (n = 6 per group). G) Protein expression levels in xenograft tumors after intratumoral injection of shATF4 lentivirus and different glutamine diets (n = 3 independent experiments). H) Relative glutamine content in different glutamine diet groups (n = 6 independent experiments). Glutamine concentrations of 2% and 0% glutamine diet are represented by 2%Q and ‐Q, respectively. Data are shown as mean ± SD. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. Data were analyzed using two‐tailed Student's t‐tests (F, G, H) in GraphPad Prism 9.5.0. Source unprocessed blots are provided.
Glutamine synthetase (GS), also known as GLUL, is a pivotal metabolic enzyme in cancer cells,[
34
] facilitating the synthesis of glutamine from glutamate,[
35
] which enables cells to persevere under glutamine‐depleted conditions, thereby influencing cancer circuitry and cellular outcomes.[
36
] Our investigation revealed a positive association between GLS filament structure and ATF4 levels (Figure 3B). Similarly, a positive correlation was observed between the GS levels and ATF4 expression in human NSCLC tissues (Figure 3C). These insights garnered from tissue microarray analysis affirmed the link between glutamine depletion and heightened ATF4 levels in 23% of patients with NSCLC (18/78).
To assess the influence of glutamine restriction and ATF4 activity on NSCLC progression in vivo, we evaluated the growth of subcutaneous H460 xenograft tumors in mice fed a glutamine‐deficient diet (Figure 3D). This dietary regimen significantly impeded tumor growth, as evidenced by a reduction in tumor volume and endpoint tumor weight (Figure 3E,F). Glutamine restriction alone significantly inhibited tumor volume compared with the normal diet (Figure 3F). However, ATF4 knockdown effectively inhibited tumor progression (Figure 3E,F). Immunohistochemistry (Figure S4A, Supporting Information) and western blotting (Figure 3G) analyses demonstrated that ATF4 knockdown suppressed the expression of the glutamine transporter alanine, serine, and cysteine‐preferring transporter 2 (ASCT2), thereby affecting glutamine uptake and utilization in tumor cells during glutamine restriction (Figure 3H). ATF4 knockdown curbed tumor cell proliferation in mice fed either a normal or glutamine‐deficient diet compared to that observed in the corresponding controls. Additionally, it reduced Ki67 levels, a widely used marker for assessing the proportion of proliferating cells in tumors.[
37
] (Figure S4A, Supporting Information). Additionally, Kaplan‐Meier survival analysis indicated that lower ATF4 levels correlated with improved prognosis for patients with NSCLC (Figure S4B, Supporting Information). These findings suggest that dietary glutamine restriction activates the GCN2 signaling pathway and downstream ATF4 and ASCT2 expression, leading to an elevated glutamine uptake rate that sustains cancer cells. Inhibition of ATF4 counteracts the pro‐survival effect induced by stress and augments the responsiveness to glutamine‐based dietary therapy.
2.4
G‐Rich Tracts in the ATF4 Proximal Promoter Region form a Specific G‐Quadruplex
These data provide compelling evidence that targeting the ATF4 signaling pathway is an appealing strategy for combination cancer therapy involving glutamine deprivation. Regrettably, ATF4 protein lacks a binding pocket for small molecules, which renders it challenging to target directly.[
18
] Notably, targeting oncogene promoter G4s have been proven to be effective strategies in addressing the “undruggable” proteins, including MYC and KRAS.[
26
,
38
] This perspective prompted us to focus on ATF4 gene transcriptional regulation by targeting the ATF4 promoter G4s. The ATF4 promoter sequence contained multiple G‐rich stretches, suggesting its potential to form various types of DNA G4 structures (Figure
4A). To guide our investigation, we selected the Pu45 sequence, based on published G4‐specific ChIP‐seq data[
39
] for further validation of the desired G4 structure formation using a dimethyl sulfate (DMS) footprinting assay, a method recognized for determining G4 formation under solution conditions. This assay is based on the fact that guanine N7 within a G‐tetrad involved in Hoogsteen hydrogen bonding remains protected from methylation and subsequent cleavage.[
40
] The pivotal role of cations, particularly K+, in facilitating G4 formation is undisputed due to their superior coordination interactions with guanine O6s and lower dehydration energy. In contrast to the darker bands observed in Li+‐containing solutions, the lighter protective bands evident in K+‐containing solutions unequivocally indicate the involvement of the 1st, 2nd, 3rd, and 4th G‐tracts for the major ATF4‐G4 formation (Figure 4B). Furthermore, a minor G4 species may also be present, which is involved in the 5th and 6th G‐tracts (Figure 4B). We then employed the truncated wild‐type Pu21 sequence for further investigation, as it included all necessary G‐tracts for major ATF4‐G4 formation.
Figure 4
G‐rich tracts presented in ATF4 promoter form a specific G‐quadruplex. A) Schematic of the human ATF4 gene promoter and the G4‐forming region. The DNA sequence and its modifications are shown. The G‐runs with two or three continuous guanines are underlined and numbered. The guanines involved in the major G4 formation are colored in red and the mutations are colored in blue. The CD melting temperatures of 20 µM DNA in 5 mM K+‐containing solution are shown. B) DMS foot printing of the wild‐type Pu45 DNA showing guanines implicated in ATF4‐G4 formation. Pu45 DNA Sequence located in the ATF4 proximal promoter region, spanning from ‐85 to ‐129 bp. In comparison to the darker blots observed in Li+‐containing solutions, the lighter protective blots evident in K+‐containing solutions undeniably signify the specific involvement of the 1st, 2nd, 3rd, and 4th G‐tracts in G‐tetrad formation, with a minor species possibly implicated in the 5th and 6th G‐tracts. C) 1D 1H‐NMR spectra of wild‐type and mutant ATF4 gene promoter sequences. The G‐tetrad imino proton signals at the 5′‐end, middle, and 3′‐end are labeled in blue, black, and red respectively. Conditions: 150 µM DNA, pH 7, 50 mM K+. D) Folding topology of ATF4‐G4 with the Pu22T DNA sequence. E, F) H1′‐H6/H8 region, H1‐H1 and H1‐H8 regions from 2D‐NOESY spectra of Pu22T DNA in H2O with sequential assignment pathway. Missing connectivity is marked with red asterisks. G) Schematic diagram of the assigned three G‐tetrad planes of ATF4‐G4 by NMR experiments. H) Superposition of the 10 lowest energy NMR structures of ATF4‐G4. I) Surface view of a representative refined ATF4‐G4 structure. J) Cartoon representation of a refined ATF4‐G4 with partial numbering (PDB ID: 8WA4). Red, adenine; gray, guanine; blue, thymine; and orange, cytosine. K, L) 5′‐end and 3′‐end top views of ATF4‐G4.
Through the 1D NMR 1H spectra (Figure 4C), we distinguished twelve distinct imino proton peaks in Pu21 positioned at chemical shifts of 10–12 ppm, which are characteristic of G4 structure formation. Elevated NMR baselines and miscellaneous peaks were observed, indicating the presence of minor structural species. Furthermore, the characteristic NMR peaks of the longer wild‐type Pu30 DNA containing the 5th and 6th G‐tracts were similar to those of the Pu21 sequence (Figure 4A,C). This further confirmed that major ATF4‐G4 was formed using the 1st, 2nd, 3rd, and 4th G‐tracts. Recognizing the importance of flanking residues in G4 formation and specific ligand recognition,[
41
] we introduced thymine at the 5′‐end of Pu21, resulting in Pu22T. This variant exhibited twelve well‐defined imino proton resonances, indicating a stable three‐G‐tetrad‐stacked G4 structure suitable for subsequent structural determination (hereafter referred to as ATF4‐G4 for convenience). The consistency of the peak shape across different temperatures indicated the relative stability of ATF4‐G4 (Figure S5A, Supporting Information). Circular dichroism (CD) spectra displayed apparent parallel‐strand G4 characteristics in both Pu21 and Pu22T DNA, as evidently by the positive band at 264 nm and the negative band at 242 nm.[
42
] Moreover, the native EMSA gel analysis unveiled the monomeric topology of free ATF4‐G4 (Figure S5D, Supporting Information). Additionally, the G to A mutation in each of the middle Gs within four consecutive G‐tracts leads to an ATF4 G4‐mut sequence (Table S3, Supporting Information), which has been confirmed to be unable to form G4 structures through 1H‐NMR and CD experiments (Figure 4C; Figure S5B,C, Supporting Information).
For a more comprehensive analysis, we collected 2D NMR spectra, including 1H‐1H NOESY, 1H‐1H DQF‐COSY, and 1H‐13C HSQC, at different temperatures and mixing times in K+‐containing solutions (Figure 4E,F; Figure S5E, Supporting Information). Assignments were made for all resonances in the spectrum. The coherent core of G4 consisted of three G‐tetrads: G4‐G8‐G12‐G17, G5‐G9‐G13‐G18, and G6‐G10‐G14‐G19 (Figure 4G). Medium intensities of H1′‐H6/H8 NOE cross‐peaks and corresponding downfield C6/C8 chemical shifts indicated that all DNA residues adopted anti‐glycosidic torsion angles (Tables S5–S8 and Figure S5E, Supporting Information), in line with the characteristics of a parallel‐stranded G4.
To obtain a high‐resolution NMR solution structure of ATF4‐G4, we employed restrained molecular dynamics (MD) simulations based on the distance information derived from the NOESY spectra (Table 1; Table S6–S8, Supporting Information). Guided by a total of 522 NOE‐derived distances, 48 H‐bond restraints, and 22 torsion‐angle restraints, the resulting ten lowest energy structures displayed good convergence, with a heavy atom root‐mean‐square deviation (RMSD) of 0.56 ± 0.18 Å for the G‐tetrad core and 0.74 ± 0.24 Å for all residues (Table 1).
Table 1
NMR Restraints and Structural Statistics for the free ATF4‐G4 and its complex with coptisine.
ATF4‐G4
Coptisine‐ATF4‐G4
NOE‐Based Distance Restraints
Total
522
519
Intra‐residue
284
292
Inter‐residue
Sequential
182
156
Long‐range
56
32
Ligand‐G4
–
39
Other Restraints
Hydrogen bonds restraints
48
48
Torsion angles restraints
22
22
G‐tetrad planarity restraints
48
48
Structural Statistics
Pairwise heavy atom RMSD [Å]
G‐tetrad core
0.56 ± 0.18
0.53 ± 0.13
All residues
0.74 ± 0.24
0.59 ± 0.16
Restraint violations [Å]
Max. NOE restraint violation
0.16
0.19
Mean NOE restraint violation
0.002 ± 0.012
0.002 ± 0.011
John Wiley & Sons, Ltd.
The high‐resolution solution unequivocally confirmed the well‐defined parallel‐stranded G4 structure (Figure 4H,I). At the 5′‐end site, three extended flanking residues formed a cohesive capping structure, with A2 and A3 stacking at the outer G‐tetrad (Figure 4J,K). This capping structure was supported by NOE cross‐peaks from A2H8 and A3H8 to the H1 of the 5′‐end tetrad‐forming Gs (Figure 4K; Table S6 and Figure S5F, Supporting Information). In contrast, the 3′‐end AGC residues adopted a distinct stacking arrangement from the 5′‐end segments (Figure 4J,L). The sequential residues G19, A20, and G21 formed a stacking structure, supported by the key NOE cross‐peaks of A20H8 to G19H8, A20H2 to G19H1, G14H1, G6H1, and A20H8 to G21H8 (Figure 4L; Table S7 and Figure S5G, Supporting Information). Two classic propeller loops, A7 and A11 (Figure 4H–J), conformed to the well‐documented arrangement.[
37
,
39
,
43
] A unique feature of this structure was the double residue A15‐A16 loop, which has not been reported previously. Non‐parallel stacking primarily resulted from NOEs involving A15H8‐G17H8 and A15H8‐G18H8 (Figure 4H–J; Table S8 and Figure S5H, Supporting Information). In conclusion, ATF4‐G4 comprised a well‐defined three‐G‐tetrad stacked parallel G4 structure with a distinctive non‐parallel stacking 2nt‐loop and organized capping structures. CD experiments verified that the core mutant sequence does not possess G4 formation capability.
2.5
Coptisine Strongly Binds and Stabilizes the ATF4‐G4 to Hinder the Interaction Between TFAP2A and ATF4 Promoter
COP has demonstrated substantial potential as an anticancer drug in our recent study, which was attributed to its remarkable binding activity to parallel G4s[
38
] (Figure
5A). Given that ATF4‐G4 also adopts a parallel G4 structure, we investigated its binding to COP. Notably, we observed a significant enhancement in ATF4‐G4 thermal stability, with a notable 25 °C increase in a CD melting experiment (Figure 5B). To further assess the binding affinity, we determined their Kd
value to be ≈3.9 µM using a fluorescence‐based assay (Figure S6A, Supporting Information).
Figure 5
Coptisine strongly binds and stabilizes the ATF4‐G4 to hinder the interaction between TFAP2A and ATF4 promoter. A) Chemical structure of Coptisine with numbering. (CAS NO. 6020‐18‐4) B) CD thermal melting curves and CD spectra of Pu22T DNA with coptisine. Conditions: 20 µM DNA, pH 7, 5 mM K+ solution. The ΔTm values of COP to the Pu22T DNA were determined. The melting temperature (Tm) was obtained at the intersection between the median of the fitted baselines and the melting curve. C) 1D 1H NMR titration of Pu22T DNA with COP. The G‐tetrad imino proton signals at the 5′‐end, middle, and 3′‐end are labeled in blue, black, and red respectively. Conditions: 150 µM DNA, pH 7, 50 mM K+ solution, 25 °C. D) 2D NMR spectra of ATF4‐G4 in complex with COP. Select regions of the 2D‐NOESY spectra of 2.4:1 COP‐ATF4‐G4 complexes in H2O showing intermolecular cross‐peaks between compound and DNA imino protons. Conditions: 2.11 mM Pu22T DNA, pH 7, 10 mM K+ solution, 25 °C. E) Superposition of the 10 lowest energy NMR structures of the COP‐ATF4‐G4. F) Cartoon representation of a refined COP‐ATF4‐G4 with partial numbering (PDB ID: 8Y2R). Red, adenine; gray, guanine; blue, thymine; orange, cytosine and yellow, COP. G, H) 5′‐end and 3′‐end top views of COP‐ATF4‐G4. I) Construct a small interfering library of 22 transcriptional regulatory ATF4 with the highest predicted score on Jasper and EPD websites, and screen out potential transcriptional factor TFAP2A (n = 3 independent experiments, average of three technical replicates). J) Schematic diagram of TFAP2A binding site in ATF4 promoter region. K) Fold enrichment of G4 on ATF4 promoter using ChIP‐qPCR analysis under 4Q or 0.25 mM glutamine conditions with or without COP treatment (20 µM) on NCI‐H1299 cells (12 h) (n = 3 independent experiments). L) Fold enrichment of TFAP2A on ATF4 promoter using ChIP‐qPCR analysis under 4Q or 0.25 mM glutamine conditions with or without COP treatment (20 µM) on NCI‐H1299 cells (12 h) (n = 3 independent experiments). 4Q: 4 mM glutamine, 0.25Q: 0.25 mM glutamine, COP: coptisine chloride. The data are shown as mean values ± SD from triplicated samples. *p < 0.05, **p < 0.01, ***p < 0.001. Data were analyzed by One‐way ANOVA (K, L) in GraphPad Prism 9.5.0.
In a K+‐containing solution, we conducted 1H‐NMR titration experiments to probe the binding interactions between COP and ATF4‐G4 DNA. Free ATF4‐G4 exhibited 12 imino proton peaks, corresponding to three stacked G‐tetrads (Figure S5A, Supporting Information). Gradual addition of COP induced upfield shifts in nearly all imino proton resonances of free ATF4‐G4. At the lower drug ratio of 1:1, the imino proton peaks broadened, whereas at higher ratios of 2:1 and 3:1, the peaks sharpened, indicating the binding of one compound to each outer G‐tetrad via end‐stacking interactions (Figure 5C). Significantly, a new set of 12 distinct peaks emerged after COP addition, implying the formation of a dominant conformation within the COP‐ATF4‐G4 complexes.
The binding of COP to ATF4‐G4 was substantiated using NMR spectroscopy (Figure 5D; Figure S6B–D, Supporting Information), which generated well‐resolved NMR spectra suitable for high‐resolution structural analysis (Table S9, Supporting Information). A total of 519 NOE‐derived distance restraints were used to determine the COP‐ATF4‐G4 structure (Table 1). The final ten lowest energy structures demonstrated good convergence, with an RMSD of 0.59 ± 0.16 Å for all residues (Figure 5E and summarized in Table 1). COP adopted a 2:1 binding mode, optimizing its interaction with the outer G‐tetrads (Figure 5E–H). COP's positioning was supported by numerous NOE cross‐peaks, such as COPH6‐G4H1, COPH6‐G8H1, COPH6‐G12H1, COPH6‐G17H1, COPH8‐G4H1, COPH8‐G8H1, COPH8‐G12H1, COPH8‐G17H1, COPH6‐G6H1, COPH6‐G10H1, COPH6‐G14H1, COPH6‐G19H1, COPH8‐G6H1, COPH8‐G10H1, COPH8‐G14H1, and COPH8‐G19H1 (Figure 5D; Table S10, Supporting Information). Electrostatic interactions may arise between the positively charged COPN7 and the negatively polarized carbonyl groups of the tetrad guanine. Moreover, the resonances of COPHA‐G6H8, COPHB‐G17H8, COPHA‐G6H8, and COPHB‐G14H8 played a pivotal role in determining the stacking direction (Table S10, Supporting Information). Moreover, the higher affinity of COP for the 5′ end capping structure can be attributed to a plausible hydrogen bond between COP and A3H2 (Figure 5G; Figure S6E, Supporting Information). Considering their positions above the outermost tetrads, designing coptisine derivatives with longer alkyl chains could be a rational strategy to offer molecular guidance for enhancing the affinity and selectivity with drug‐like properties through hydrogen bonding and electrostatic interactions.
The overall binding mode of COP‐ATF4‐G4 closely resembles the reported COP‐KRAS‐G4 binding mode[
38
] (Figure 5E–F). Each COP molecule recruited an adjacent flanking residue, forming a plane that was stacked over two external G‐tetrads (Tables S11 and S12, Supporting Information). The 2nt‐loop structure did not contribute to binding pocket formation, consistent with the fact that almost the same conformation was observed in both the ATF4‐G4 free structure and the COP‐ATF4‐G4 complex (Tables S8 and S13, Supporting Information). Moreover, clear assignments of intermolecular NOE cross‐peaks between the COP and A3 residues were observed at high threshold levels, suggesting multiple orientations of A3. For instance, intermolecular NOE cross peaks from COPH8 to A3H8 were observed, implying a potential flip of the A3 orientation by ≈180° from the determined conformations. However, limited NOE cross‐peaks hindered the precise structural determination of this minor species.
Numerous G4 sites and transcription factors that recognize G4 structures play pivotal roles in human chromatin regulation.[
44
] Therefore, it is crucial to identify transcription factors that can bind to ATF4‐G4 to gain a comprehensive understanding of the biological functions of ATF4‐G4. Using JASPAR (http://jaspar.genereg.net/) and EPD (https://epd.epfl.ch//index.php), we identified twenty‐two transcription factors with predicted scores exceeding eight for potential regulation of ATF4 gene expression (Figure 5I). A siRNA library was then constructed, and qRT‐PCR was used to validate the transcriptional effect of these factors on ATF4, resulting in the identification of the transcription factor TFAP2A (Figure 5J). Presently, in vitro analysis has suggested that TFAP2A binds to the palindrome motif GCCN3GGC, as well as some variants such as GCCN4GGC and GCCN3/4GGG.[
45
] ChIP‐seq experiments have demonstrated that SCCYSRGGS (S = G or C, R = A or G, and Y = C or T) are the consensus sites for human TFAP2A.[
46
] Moreover, TFAP2A has also been shown to bind to G4‐forming sequences.[
47
] Therefore, we hypothesized that TFAP2A could potentially bind to the ATF4‐G4‐forming sequence. To validate our hypothesis, primer sequences were specifically designed for the ATF4‐G4‐forming region, and chromatin immunoprecipitation followed by quantitative polymerase chain reaction (ChIP‐qPCR) experiments were conducted. Firstly, ChIP‐qPCR was performed using anti‐G4 antibodies to confirm the presence of the G4 structure within the ATF4 promoter region in the cells (Figure 5K). Subsequently, ChIP‐qPCR was conducted with TFAP2A antibodies to investigate TFAP2A binding to ATF4‐G4. The results showed that the DNA fragment for the ATF4‐G4 forming region was enriched by both the G4 antibody and TFAP2A antibody under 0.25 mM glutamine conditions, and COP stabilized the ATF4‐G4 and inhibited its interaction with TFAP2A (Figure 5K,L). These findings indicate that the binding of TFAP2A to the ATF4‐G4‐forming sequence is involved in the ATF4 transcription regulation and small molecules can disrupt their interaction by stabilizing the ATF4‐G4 structure. Kaplan‐Meier analysis revealed that high TFAP2A expression might predict poor clinical prognosis in patients with lung cancer (Figure S6F, Supporting Information). Notably, TFAP2A and ATF4 levels significantly increased under 0.25 mM glutamine stimulation (Figure S6G, Supporting Information). Inhibition of TFAP2A reduced ATF4 expression and suppressed cancer cell proliferation in the presence of 0.25 mM glutamine (Figure S6H,I, Supporting Information). These findings collectively suggest that glutamine restriction upregulates TFAP2A expression levels, and TFAP2A directly binds to the ATF4 promoter sequence to positively regulate ATF4 transcription.
2.6
COP Suppresses the GCN2‐ATF4‐ASCT2 Axis and the Downstream mTOR Signaling Pathway
The intriguing affinity of COP for G4 molecular structures piqued our interest in the further investigation of its biological activities. RNA‐Seq analysis revealed that the presence of COP decreased ATF4 expression under conditions of glutamine restriction (Figure S7A, Supporting Information). We demonstrated that COP significantly reduced the expression of ATF4 induced by glutamine restriction (Figure
6A–C; Figure S7B,C, Supporting Information), leading to substantial inhibition of cancer cell proliferation (Figure S7D–F, Supporting Information). In PS induced with either 4 mM or 0.25 mM glutamine, the addition of COP resulted in a reduction of cisplatin resistance linked to glutamine deficiency (Figure 6D). Moreover, the OCR assay revealed that COP notably decreased mitochondrial respiration in NCI‐H460 and NCI‐H1299 cells (Figure 6E). Notably, the enhanced glutamine uptake and utilization in the context of glutamine deficiency was substantially hindered by COP treatment or ATF4 knockdown (Figure 6F).
Figure 6
COP suppresses the GCN2‐ATF4‐ASCT2 axis and the downstream mTOR signaling pathway. A) qRT‐PCR analysis of ATF4 mRNA level in NCI‐H460 or NCI‐H1299 cells after 24 h treatment with 0.25 mM glutamine or 0.25 mM glutamine plus COP (10 µM or 20 µM) (n = 3 independent experiments, average of three technical replicates). Heatmap colors represent 2−ΔΔCt values. B) Effects of COP (10 µM) on NCI‐H460 stimulated with 0.25 mM glutamine analyzed by immunofluorescence (n = 3 independent experiments). Scale bar: 20 µm. C) Immunoblotting of GCN2 signaling analyses: phospho‐GCN2 (p‐GCN2, Thr899), total GCN2, phospho‐eIF2α (p‐eIF2α, Ser51), total eIF2α, and ATF4 in NCI‐H460 and NCI‐H1299 cells cultured in 4 mM or 0.25 mM glutamine conditions (12 h). D) NCI‐H460 and NCI‐H1299 cells treated with 4 mM or 0.25 mM glutamine for 3 days were switched to complete RPMI‐1640 medium for 2 h to induce persister cells. These persister cells were subsequently treated with COP (20 µM), and the IC50 of cisplatin was assessed in live cell plates following drug treatment. (n = 3 independent experiments). E) Seahorse XF assay measuring OCR of NCI‐H460 or NCI‐H1299 cells in 0.25 mM glutamine or 0.25 mM glutamine combined with COP (10 µM) (n = 3 independent experiments). F) Glutamine uptake rate of NCI‐H460 or NCI‐H1299 cells cultured in 4 mM or 0.25 mM glutamine in the absence or presence of COP (20 µM) for 36 h or ATF4 knockdown (n = 3 independent experiments). G) Immunoblotting of ASCT2, LAT1, phospho‐mTOR (p‐mTOR, S2448), total mTOR, phospho‐P70S6K (p‐P70S6K, Thr389), total P70S6K, phospho‐4EBP1 (p‐4EBP1, Thr37/46), total 4EBP1 protein levels in NCI‐H460 and NCI‐H1299 cells cultured in 4 mM or 0.25 mM glutamine conditions with or without COP treatment (20 µM) (48 h). H) Mass spectrometry analysis of differential metabolites in 0.25 mM glutamine and 0.25 mM glutamine plus 20 µM COP on H460 cells (n = 3 independent experiments). I) Correlation analysis of differential metabolites in 0.25 mM glutamine and 0.25 mM glutamine combined with COP (20 µM). The size of the point in the figure represents the Log2FC value, and the larger the point is, the larger the corresponding Log2FC value is, the color of the point represents the source classification of the differential metabolites in this group, and the connection represents the correlation coefficient value of the metabolite in the corresponding position. J) qRT‐PCR analysis of ATF4‐downstream transcripts in NCI‐H460 cells after COP (20 µM) treatment in 0.25 mM glutamine for 24 h (n = 3 independent experiments, average of three technical replicates). 4Q: 4 mM glutamine, 0.25Q: 0.25 mM glutamine, COP: coptisine chloride. All immunoblots are representative of three biological replicates that showed similar results. Data are shown as mean ± SD. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. Data were analyzed by two‐tailed Student's t‐tests (H) and One‐way ANOVA (D, F) in GraphPad Prism 9.5.0.
Activation of GCN2 is observed under conditions involving amino acid stress, subsequently leading to the phosphorylation of eIF2α, a process utilized to suppress general mRNA translation while promoting ATF4 expression. Our investigation elucidated that the phosphorylation of GCN2 (Thr899) and eIF2α (Ser51) was triggered by glutamine‐nutritional restriction, subsequently driving the protein expression of ATF4. Importantly, this stress‐induced activation was attenuated by COP treatment (Figure 6C; Figure S7C, Supporting Information). Glutamine is transported into cells through the glutamine transporter (SLC1A5), which controls intracellular glutamine levels. Subsequently, SLC7A5 utilizes intracellular glutamine as an efflux substrate to modulate the uptake of extracellular leucine into cells.[
48
] Previous studies have established that leucine regulates the mTORC1 signaling pathway in a variety of cellular processes to promote proliferation.[
49
] Activation of GCN2 under glutamine deficiency triggers increased expression of the SLC1A5 transporter, facilitating the exchange of extracellular leucine and ultimately leading to mTORC1 signaling activation. mTOR is a conserved serine/threonine kinase that responds to changes in nutrient levels and growth signals.[
50
] It acts as a catalytic subunit for two primary protein complexes: mTORC1, which is a key regulator of cell metabolism and growth, and mTORC2, which is crucial for controlling cell proliferation and survival. mTORC1 is a central signaling hub that integrates signals from nutrients, metabolic intermediates, and growth factors to regulate cellular metabolism in response to the environment.[
51
] Our experiments demonstrated that the phosphorylation of mTORC1 and its downstream effectors P70S6K and 4EBP1 was activated by glutamine restriction, and this activation was inhibited in the presence of COP (Figure 6G; Figure S7G, Supporting Information). Collectively, these findings suggest that COP reduces ATF4 levels, thereby diminishing stress‐induced cancer cell survival.
To further validate the potential impact of COP, we conducted a metabolomic analysis on NCI‐H460 cells treated with different conditions: 4 mM glutamine, 4 mM glutamine plus COP, 0.25 mM glutamine, or 0.25 mM glutamine plus COP. Using the Energy Metabolism Database v2.0, we identified 68 metabolites and enriched metabolic pathways by analyzing the differential metabolites upon COP stimulation (Figure S8A, Supporting Information). Principal component analysis of metabolism revealed the COP‐induced downregulation of Glutamine, L‐Leucine, L‐Glutamic acid, and L‐Asparagine (Figure 6H). These findings suggest that COP not only further restrains amino acid breakdown and utilization in cancer cells but also heightens sensitivity to glutamine deprivation. Using Pearson's correlation analysis, we assessed the correlation between metabolites with significant differences (Figure 6I). Additionally, to observe changes in metabolite trends across various samples, we standardized and centralized the relative contents of distinct metabolites and subjected them to K‐means clustering analysis. Remarkably, the inclusion of COP significantly affected the TCA cycle and the pentose phosphate pathway (Figure S8B, Supporting Information).
Quantitative real‐time polymerase chain reaction (qRT‐PCR) was employed to gauge changes in downstream target genes of ATF4 in NCI‐H460 cells (Figure 6J). These results indicated that the expression of genes downstream of ATF4 was upregulated under glutamine deficiency and subsequently downregulated upon COP treatment. In addition, COP exerted a notable inhibitory effect on the amino acid transporter ASCT2. Findings from colony formation and EdU assays showed that ASCT2 knockdown markedly intensified cancer cell proliferation (Figure S9A–E, Supporting Information). The efficacy of ASCT2 inhibition in curbing glutamine uptake has been demonstrated across various cancer types, including melanoma,[
52
] non‐small cell lung cancer,[
53
] prostate cancer,[
54
] and acute myeloid leukemia.[
55
] Kaplan‐Meier plots, coupled with the log‐rank (Mantel‐Cox) test highlighted the prognostic benefits associated with low ASCT2 expression in NSCLC (Figure S9F, Supporting Information).
2.7
COP Decreases Glutamine Deficiency‐Induced ATF4 Levels and Improves the Efficacy of Glutamine‐Restrictive Therapy
To assess the effects of COP in vivo, we conducted xenograft experiments under both normal (with adequate glutamine) and glutamine‐deficient conditions (Figure
7A). Compared to normal conditions, xenograft growth was delayed owing to glutamine restriction (Figure 7B,C). Remarkably, COP significantly enhanced the inhibitory effect of glutamine‐restrictive therapy on tumor growth (Figure 7D; Figure S10A,B, Supporting Information). Western blot analysis demonstrated that COP effectively disrupted the GCN2‐ATF4‐ASCT2 axis (Figure 7E). Furthermore, IHC analysis revealed that COP diminished the expression of ATF4 and ASCT2 during glutamine restriction (Figure S10C, Supporting Information), thereby influencing the uptake and utilization of glutamine by ASCT2 within tumor tissues during glutamine deficiency (Figure 7F). Collectively, these findings highlight the ability of COP to augment tumor responsiveness to dietary glutamine restriction, offering a novel therapeutic avenue for cancer treatment.
Figure 7
COP decreases glutamine deficiency‐induced ATF4 levels and improves the efficacy of glutamine‐restrictive therapy. A) Protocol of COP (100 mg·kg−1) administration in glutamine restriction therapy and establishment of H460 xenograft tumor model. B) Image of tumor removal after intraperitoneal injection of COP (100 mg·kg−1) (n = 6 per group). C) Growth curves of tumor volume (n = 6 per group). D) Tumor volume of nude mice with COP treatment and glutamine restriction diet (n = 6 per group). E) Immunoblotting of indicated proteins in xenograft tumors after COP and glutamine restriction diet therapy in vivo (n = 3 independent experiments). F) Determination of glutamine content in xenograft tumors with glutamine restriction (n = 6 independent experiments). G) Proposed model of COP targeting ATF4‐G4 enhancing sensitivity to glutamine restriction therapy. All immunoblots are representative of three biological replicates that showed similar results. Glutamine concentrations of 2% and 0% glutamine diet are represented by 2%Q and ‐Q, respectively. COP: coptisine chloride. Data are shown as mean ± SD. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. Data were analyzed by two‐tailed Student's t‐tests (D,F) and One‐way ANOVA (E) in GraphPad Prism 9.5.0.
As illustrated in Figure 7G, glutamine restriction triggered ATF4 translational reprogramming, which accounted for over 50% of persister cancer cell survival and weakened the efficacy of glutamine‐deficient therapy. Our study demonstrates that the natural compound COP targets the ATF4‐G4 structure, disrupts the interaction between TFAP2A and the ATF4 promoter, and curtails glutamine uptake and utilization by inhibiting the ATF4 downstream gene SLC1A5. This cascade of events subsequently suppresses the mTOR signaling pathway, ultimately enhancing the effectiveness of glutamine‐restrictive therapy.
2.1
Glutamine‐Restrictive Therapy Fails to Enhance the Sensitivity of the Persister Cancer Cells
We subjected seven distinct lung cancer cell lines to a glutamine‐deficient environment (0.25 mM glutamine) to assess their cell viability. Evidently, different cells exhibit distinct responses under similar conditions. Among these, NCI‐H460, A549, and NCI‐H1975 cells displayed heightened sensitivity to low‐glutamine conditions, whereas NCI‐H1299 cells demonstrated moderate sensitivity. In contrast, PC‐9, HCC827, and NCI‐H661 cells were insensitive to the glutamine‐restrictive milieu (Figure
1A). Our research focuses on the effectiveness of glutamine deprivation therapies and overcoming the problem of cellular resistance that arises during the course of glutamine deprivation therapies. Therefore, the H460 and H1299 cell lines, which are highly and moderately sensitive to glutamine deprivation, respectively, were selected for subsequent experiments. The impacts of glutamine deficiency on cell survival and proliferation were concentration‐dependent, as depicted in Figure S1A–C (Supporting Information for survival) and Figure 1B along with Figure S1D (Supporting Information for proliferation). These outcomes underscore the unique metabolic reliance of cancer cells on glutamine to facilitate synthetic metabolism pathways.[
27
] Glutamine, an amino acid conditionally essential, and abundant in both plasma and the intracellular amino acid pool, plays a pivotal role in bolstering the synthesis of glutathione—an ROS scavenger. Consequently, our findings demonstrated an increase in ROS levels due to the absence of glutamine (Figure S1E, Supporting Information). Glutamine is catabolized into α‐ketoglutaric acid, which subsequently enters the tricarboxylic acid (TCA) cycle to generate NADH and FADH2. Under conditions involving glutamine restriction, the presence of glutamine as a carbon and non‐essential amino acid nitrogen donor partially influences the dependence of cancer cells on other nutrients to fulfill the demands of the TCA cycle. This disruption of “glutamine addiction” impairs mitochondrial respiration, resulting in a reduced oxygen consumption rate (OCR), as depicted in Figure 1C. Furthermore, under low‐glutamine conditions, a significant increase in the uptake rate of glutamine by cancer cells was observed (Figure 1D). Energy metabolism analysis (Figure 1E) also confirmed that the intracellular glutamine content did not significantly differ between cells subjected to glutamine restriction and those treated with 4 mM glutamine, indicating a stress response aimed at maintaining the cancer cell state. Notably, the TCA cycle significantly reduced argininosuccinic acid, ornithine, L‐aspartate, L‐glutamic acid, and trehalose‐6‐phosphate levels. Likewise, the uracil content in the pentose phosphate pathway declined, whereas the levels of fructose 1,6‐bisphosphate in the glycolysis pathway increased.
Figure 1
Glutamine‐restrictive therapy fails to enhance the sensitivity of the persister cancer cells. A) Sensitivity test to glutamine restriction in NCI‐H460, NCI‐H1299, NCI‐H1975, A549, PC‐9, NCI‐H661, and HCC827 cells. B) Clonogenic survival assay of NCI‐H460 and NCI‐H1299 cells under 4 mM to 0 mM glutamine conditions. Four thousand cells were cultured in six‐well plates for 10 days, followed by crystal violet staining and imaging. C) Seahorse XF assay measuring the OCR in NCI‐H460 and NCI‐H1299 cells under 4 mM or 0.25 mM glutamine conditions (n = 3 independent experiments). D) Glutamine uptake rate determination in NCI‐H460 and NCI‐H1299 cells cultured for 36 h in 4 mM or 0.25 mM glutamine using a glutamine assay kit (ab197011, Abcam) (n = 3 independent experiments). E) Analysis of differential metabolites between 4 mM and 0.25 mM glutamine conditions on H460 cells. The x‐axis represents groups, and the y‐axis represents expression (n = 3 independent experiments). F) CCK‐8 assay‐based analysis on the effect of glutamine (4 mM or 0.25 mM) on cisplatin treatment sensitivity in NCI‐H460 and NCI‐H1299 persister cells (n = 3 independent experiments). G) Generation of persister cells: NCI‐H460 and NCI‐H1299 cells treated with 4 mM or 0.25 mM glutamine for 1–5 days were switched to complete RPMI‐1640 medium for 2 h to create persister cells. Live cells were sorted, reseeded, and tested for cisplatin IC50. Then, live cells were harvested using the Dead Cell Removal kit (Miltenyi Biotec) (n = 3 independent experiments). Glutamine concentrations of 4 mM to 0 mM are represented by 4Q to 0Q, respectively. Data are shown as mean ± SD. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. Data analyzed using two‐tailed Student's t‐tests (D, E, G) in GraphPad Prism 9.5.0.
Although glutamine restriction effectively promoted cancer cell death, notably the survival rate of these cells still exceeded 50% (Figure 1A). To further validate the anti‐cancer effects of glutamine constraint, we sorted PS of lung cancer to evaluate the tolerance to cisplatin under two different conditions involving 4 mM and 0.25 mM glutamine (Figure 1F). The results revealed that cells treated with 0.25 mM glutamine either exhibited heightened resistance to cisplatin (Figure 1G) or did not exhibit increased sensitivity (Figure S1F, Supporting Information). This led us to hypothesize that glutamine restriction induces a stress response, thereby triggering a protective adaptive mechanism.
2.2
Glutamine Deficiency Induces Integrated Stress Response to Activate the GCN2‐ATF4‐ASCT2 Axis
The ISR pathway in eukaryotic cells is a multifaceted signaling network activated in response to a range of physiological alterations and diverse pathological conditions (Figure
2A). When amino acids are depleted, the ISR, activated by GCN2, curtails the requirement for amino acids in protein synthesis, thereby mitigating stress levels.[
28
] In glutamine deprivation conditions, our observations revealed that GCN2 (Figure 2B; Figure S2A,B, Supporting Information) triggered the phosphorylation of eIF2α at serine 51, which was distinct from that observed in HRI (Figure S2C, Supporting Information), PERK, and PKR. This eIF2α phosphorylation extends the ribosome scanning process at the open reading frame uORF1, located upstream of ATF4 transcript, consequently reinstating the transcriptional translation of ATF4[
18
,
29
] (Figure 2A). RNA‐Seq analysis demonstrated an increase in ATF4 expression when exposed to glutamine restriction (Figure 2C). The RNA (Figure 2D) and protein levels of ATF4 (Figure 2E; Figure S2D,E,G, Supporting Information), as well as the transcriptional activity of ATF4 (Figure 2F) increased in the highly and moderately glutamine‐deprivation‐susceptible NCI‐H460 and NCI‐H1299 cell lines under glutamine‐restricted conditions. Conversely, the expression levels of ATF4 in cell lines insensitive to glutamine restriction remained unaltered (Figure S2D,F, Supporting Information). Subsequent validation confirmed that the urgently induced ATF4 expression by ISR was suppressed by compound 968, a glutaminase inhibitor (Figure S2H, Supporting Information). This stress‐activated ATF4 acts as a chief transcription factor for genes responsive to stress, thus facilitating cellular recovery.[
28
] Our hypothesis revolves around the potential of disrupting ATF4, the primary regulatory factor in ISR, to inhibit the adaptive survival reaction initiated by cancer cells via ISR. In PS induced with either 4 mM or 0.25 mM glutamine, the knockdown of ATF4 led to a reduction in cisplatin resistance linked to glutamine deprivation (Figure 2G). Our experiments demonstrated that the proliferation of NCI‐H460 and NCI‐H1299 cells under glutamine deficiency was significantly curtailed upon ATF4 knockdown (Figure 2H,I; Figure S3A–F, Supporting Information). Conversely, the introduction of exogenous ATF4 (Figure S2I, Supporting Information) undermined this inhibitory effect (Figure 2H,I; Figure S3A,F, Supporting Information). Furthermore, the knockdown of ATF4 disrupted mitochondrial respiration in NCI‐H460 and NCI‐H1299 cells (Figure 2J).
Figure 2
Glutamine deficiency induces integrated stress response to activate the GCN2‐ATF4‐ASCT2 axis. A) Schematic representation of GCN2‐eIF2α‐ATF4 axis activation in response to glutamine nutritional restriction. B) Immunoblotting analysis of indicated proteins in NCI‐H460 cells 48 h after treatment with GCN2iB, an ATP‐competitive inhibitor of the serine/threonine‐protein kinase general control nonderepressible 2 (GCN2). C) RNA‐Seq analysis compared differentially expressed genes in the H460 cell line (4Q versus 0.25Q). The ATF4 gene showed high expression in the 4Q versus 0.25Q fraction (n = 3 independent experiments). D) qRT‐PCR analysis of ATF4 mRNA levels in NCI‐H460 and NCI‐1299 cells after 24 h of treatment with varying glutamine concentrations (4 mM, 0.5 mM, 0.25 mM, and 0 mM) and 48 h of treatment with 0.25 mM glutamine (n = 3 independent experiments, average of three technical replicates). The colors of the heatmap represent values of 2−ΔΔCt. E) Immunoblotting of ATF4 in NCI‐H460 and NCI‐H1299 cells after treatment with 0.25 mM glutamine for 12, 24, and 48 h. F) Luciferase activity of ATF4 in NCI‐H1299 cells treated with 4 mM or 0.25 mM glutamine for 24 h (n = 3 independent experiments). G) NCI‐H460 and NCI‐H1299 cells treated with 4 mM or 0.25 mM glutamine for 3 days were switched to complete RPMI‐1640 medium for 2 h to induce persister cells. These persister cells were then transfected with NC and siATF4, and the IC50 of cisplatin was assayed in live cell plates post‐transfection (n = 3 independent experiments). H) Clonogenic survival assay of NCI‐H460 and NCI‐H1299 cells transfected with shScramble, shATF4, Vector, and ATF4 OE plasmids under 4 mM or 0.25 mM glutamine. I) Survival rates of NCI‐H460 and NCI‐H1299 cells transfected with NC, siATF4, Vector, and ATF4 OE plasmids under 4 mM or 0.25 mM glutamine for 48 h. OE: Overexpression ATF4 (n = 3 independent experiments). J) Seahorse XF assay measuring OCR in NCI‐H460 and NCI‐H1299 cells after treatment with 4 mM or 0.25 mM glutamine following siATF4 transfection (n = 3 independent experiments). Immunoblots represent three similar results. Glutamine concentrations of 4 mM to 0 mM are represented by 4Q to 0Q, respectively. Data shown as mean ± SD. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. Data analyzed using two‐tailed Student's t‐tests (F) and One‐way ANOVA (G, I) in GraphPad Prism 9.5.0.
2.3
Glutamine‐Restrictive Therapy Benefits from the Low Levels of ATF4
Glutamine is a critical nutrient for various solid tumors, particularly rapidly dividing tumor cells, which have an increased demand for glutamine. As a result, tumors growth is often associated with increased glutamine consumption, particularly in the central regions of solid tumors.[
30
] As the tumor grows, elevated local glutamine utilization may reduce the glutamine supply to other areas, resulting in a heterogeneous distribution of glutamine throughout the tumor tissue.[
31
] When cancer cells encounter a limited supply of glutamine, including regional glutamine deficiency or pharmacological blockade of glutamine metabolism, alternative intracellular adaptive mechanisms are employed to survive and continue to proliferate.[
31
,
32
] It is necessary to assess the potential mechanisms of tumor development under the inhibition of glutamine metabolism. Glutamine undergoes glutaminolysis by GLS prior to its contribution to bioenergetic processes and macromolecular synthesis.[
33
] The induction of filament formation due to glutamine scarcity amplifies GLS activity and augments substrate‐binding affinity, thereby facilitating the efficient utilization of intracellular glutamine even at exceedingly low concentrations.[
14
] Through the analysis of clinical tissue microarrays of NSCLC patients, we identified elevated expression of GLS in tumor tissues, accompanied by conspicuous GLS filament structures (Figure
3A), suggesting a prevalent scarcity of glutamine in NSCLC.
Figure 3
Glutamine‐restrictive therapy benefits from the low levels of ATF4. A) Tissue microarray of NSCLC patients with tissue immunofluorescence assay. Representative tissue immunofluorescence images with ATF4 (Red), GS (Green), and GLS (Pink). White arrows indicate GLS filaments. Scale bar: 10 µm. B) Correlation analysis between the number of relative GLS filament structures and positive cell density of ATF4 in the tissue microarray. C) Correlation analysis between positive cell density of GS and ATF4 in the tissue microarray. D) Schematic of tumors after intratumoral injection of shATF4 lentivirus with glutamine‐deficient diet and 2% glutamine diet in the established xenograft model (n = 6 per group). E) Tumor images (n = 6 per group). F) Tumor volume, body weight, and tumor weight of nude mice with intratumoral injection of shATF4 lentivirus and different glutamine diets (n = 6 per group). G) Protein expression levels in xenograft tumors after intratumoral injection of shATF4 lentivirus and different glutamine diets (n = 3 independent experiments). H) Relative glutamine content in different glutamine diet groups (n = 6 independent experiments). Glutamine concentrations of 2% and 0% glutamine diet are represented by 2%Q and ‐Q, respectively. Data are shown as mean ± SD. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. Data were analyzed using two‐tailed Student's t‐tests (F, G, H) in GraphPad Prism 9.5.0. Source unprocessed blots are provided.
Glutamine synthetase (GS), also known as GLUL, is a pivotal metabolic enzyme in cancer cells,[
34
] facilitating the synthesis of glutamine from glutamate,[
35
] which enables cells to persevere under glutamine‐depleted conditions, thereby influencing cancer circuitry and cellular outcomes.[
36
] Our investigation revealed a positive association between GLS filament structure and ATF4 levels (Figure 3B). Similarly, a positive correlation was observed between the GS levels and ATF4 expression in human NSCLC tissues (Figure 3C). These insights garnered from tissue microarray analysis affirmed the link between glutamine depletion and heightened ATF4 levels in 23% of patients with NSCLC (18/78).
To assess the influence of glutamine restriction and ATF4 activity on NSCLC progression in vivo, we evaluated the growth of subcutaneous H460 xenograft tumors in mice fed a glutamine‐deficient diet (Figure 3D). This dietary regimen significantly impeded tumor growth, as evidenced by a reduction in tumor volume and endpoint tumor weight (Figure 3E,F). Glutamine restriction alone significantly inhibited tumor volume compared with the normal diet (Figure 3F). However, ATF4 knockdown effectively inhibited tumor progression (Figure 3E,F). Immunohistochemistry (Figure S4A, Supporting Information) and western blotting (Figure 3G) analyses demonstrated that ATF4 knockdown suppressed the expression of the glutamine transporter alanine, serine, and cysteine‐preferring transporter 2 (ASCT2), thereby affecting glutamine uptake and utilization in tumor cells during glutamine restriction (Figure 3H). ATF4 knockdown curbed tumor cell proliferation in mice fed either a normal or glutamine‐deficient diet compared to that observed in the corresponding controls. Additionally, it reduced Ki67 levels, a widely used marker for assessing the proportion of proliferating cells in tumors.[
37
] (Figure S4A, Supporting Information). Additionally, Kaplan‐Meier survival analysis indicated that lower ATF4 levels correlated with improved prognosis for patients with NSCLC (Figure S4B, Supporting Information). These findings suggest that dietary glutamine restriction activates the GCN2 signaling pathway and downstream ATF4 and ASCT2 expression, leading to an elevated glutamine uptake rate that sustains cancer cells. Inhibition of ATF4 counteracts the pro‐survival effect induced by stress and augments the responsiveness to glutamine‐based dietary therapy.
2.4
G‐Rich Tracts in the ATF4 Proximal Promoter Region form a Specific G‐Quadruplex
These data provide compelling evidence that targeting the ATF4 signaling pathway is an appealing strategy for combination cancer therapy involving glutamine deprivation. Regrettably, ATF4 protein lacks a binding pocket for small molecules, which renders it challenging to target directly.[
18
] Notably, targeting oncogene promoter G4s have been proven to be effective strategies in addressing the “undruggable” proteins, including MYC and KRAS.[
26
,
38
] This perspective prompted us to focus on ATF4 gene transcriptional regulation by targeting the ATF4 promoter G4s. The ATF4 promoter sequence contained multiple G‐rich stretches, suggesting its potential to form various types of DNA G4 structures (Figure
4A). To guide our investigation, we selected the Pu45 sequence, based on published G4‐specific ChIP‐seq data[
39
] for further validation of the desired G4 structure formation using a dimethyl sulfate (DMS) footprinting assay, a method recognized for determining G4 formation under solution conditions. This assay is based on the fact that guanine N7 within a G‐tetrad involved in Hoogsteen hydrogen bonding remains protected from methylation and subsequent cleavage.[
40
] The pivotal role of cations, particularly K+, in facilitating G4 formation is undisputed due to their superior coordination interactions with guanine O6s and lower dehydration energy. In contrast to the darker bands observed in Li+‐containing solutions, the lighter protective bands evident in K+‐containing solutions unequivocally indicate the involvement of the 1st, 2nd, 3rd, and 4th G‐tracts for the major ATF4‐G4 formation (Figure 4B). Furthermore, a minor G4 species may also be present, which is involved in the 5th and 6th G‐tracts (Figure 4B). We then employed the truncated wild‐type Pu21 sequence for further investigation, as it included all necessary G‐tracts for major ATF4‐G4 formation.
Figure 4
G‐rich tracts presented in ATF4 promoter form a specific G‐quadruplex. A) Schematic of the human ATF4 gene promoter and the G4‐forming region. The DNA sequence and its modifications are shown. The G‐runs with two or three continuous guanines are underlined and numbered. The guanines involved in the major G4 formation are colored in red and the mutations are colored in blue. The CD melting temperatures of 20 µM DNA in 5 mM K+‐containing solution are shown. B) DMS foot printing of the wild‐type Pu45 DNA showing guanines implicated in ATF4‐G4 formation. Pu45 DNA Sequence located in the ATF4 proximal promoter region, spanning from ‐85 to ‐129 bp. In comparison to the darker blots observed in Li+‐containing solutions, the lighter protective blots evident in K+‐containing solutions undeniably signify the specific involvement of the 1st, 2nd, 3rd, and 4th G‐tracts in G‐tetrad formation, with a minor species possibly implicated in the 5th and 6th G‐tracts. C) 1D 1H‐NMR spectra of wild‐type and mutant ATF4 gene promoter sequences. The G‐tetrad imino proton signals at the 5′‐end, middle, and 3′‐end are labeled in blue, black, and red respectively. Conditions: 150 µM DNA, pH 7, 50 mM K+. D) Folding topology of ATF4‐G4 with the Pu22T DNA sequence. E, F) H1′‐H6/H8 region, H1‐H1 and H1‐H8 regions from 2D‐NOESY spectra of Pu22T DNA in H2O with sequential assignment pathway. Missing connectivity is marked with red asterisks. G) Schematic diagram of the assigned three G‐tetrad planes of ATF4‐G4 by NMR experiments. H) Superposition of the 10 lowest energy NMR structures of ATF4‐G4. I) Surface view of a representative refined ATF4‐G4 structure. J) Cartoon representation of a refined ATF4‐G4 with partial numbering (PDB ID: 8WA4). Red, adenine; gray, guanine; blue, thymine; and orange, cytosine. K, L) 5′‐end and 3′‐end top views of ATF4‐G4.
Through the 1D NMR 1H spectra (Figure 4C), we distinguished twelve distinct imino proton peaks in Pu21 positioned at chemical shifts of 10–12 ppm, which are characteristic of G4 structure formation. Elevated NMR baselines and miscellaneous peaks were observed, indicating the presence of minor structural species. Furthermore, the characteristic NMR peaks of the longer wild‐type Pu30 DNA containing the 5th and 6th G‐tracts were similar to those of the Pu21 sequence (Figure 4A,C). This further confirmed that major ATF4‐G4 was formed using the 1st, 2nd, 3rd, and 4th G‐tracts. Recognizing the importance of flanking residues in G4 formation and specific ligand recognition,[
41
] we introduced thymine at the 5′‐end of Pu21, resulting in Pu22T. This variant exhibited twelve well‐defined imino proton resonances, indicating a stable three‐G‐tetrad‐stacked G4 structure suitable for subsequent structural determination (hereafter referred to as ATF4‐G4 for convenience). The consistency of the peak shape across different temperatures indicated the relative stability of ATF4‐G4 (Figure S5A, Supporting Information). Circular dichroism (CD) spectra displayed apparent parallel‐strand G4 characteristics in both Pu21 and Pu22T DNA, as evidently by the positive band at 264 nm and the negative band at 242 nm.[
42
] Moreover, the native EMSA gel analysis unveiled the monomeric topology of free ATF4‐G4 (Figure S5D, Supporting Information). Additionally, the G to A mutation in each of the middle Gs within four consecutive G‐tracts leads to an ATF4 G4‐mut sequence (Table S3, Supporting Information), which has been confirmed to be unable to form G4 structures through 1H‐NMR and CD experiments (Figure 4C; Figure S5B,C, Supporting Information).
For a more comprehensive analysis, we collected 2D NMR spectra, including 1H‐1H NOESY, 1H‐1H DQF‐COSY, and 1H‐13C HSQC, at different temperatures and mixing times in K+‐containing solutions (Figure 4E,F; Figure S5E, Supporting Information). Assignments were made for all resonances in the spectrum. The coherent core of G4 consisted of three G‐tetrads: G4‐G8‐G12‐G17, G5‐G9‐G13‐G18, and G6‐G10‐G14‐G19 (Figure 4G). Medium intensities of H1′‐H6/H8 NOE cross‐peaks and corresponding downfield C6/C8 chemical shifts indicated that all DNA residues adopted anti‐glycosidic torsion angles (Tables S5–S8 and Figure S5E, Supporting Information), in line with the characteristics of a parallel‐stranded G4.
To obtain a high‐resolution NMR solution structure of ATF4‐G4, we employed restrained molecular dynamics (MD) simulations based on the distance information derived from the NOESY spectra (Table 1; Table S6–S8, Supporting Information). Guided by a total of 522 NOE‐derived distances, 48 H‐bond restraints, and 22 torsion‐angle restraints, the resulting ten lowest energy structures displayed good convergence, with a heavy atom root‐mean‐square deviation (RMSD) of 0.56 ± 0.18 Å for the G‐tetrad core and 0.74 ± 0.24 Å for all residues (Table 1).
Table 1
NMR Restraints and Structural Statistics for the free ATF4‐G4 and its complex with coptisine.
ATF4‐G4
Coptisine‐ATF4‐G4
NOE‐Based Distance Restraints
Total
522
519
Intra‐residue
284
292
Inter‐residue
Sequential
182
156
Long‐range
56
32
Ligand‐G4
–
39
Other Restraints
Hydrogen bonds restraints
48
48
Torsion angles restraints
22
22
G‐tetrad planarity restraints
48
48
Structural Statistics
Pairwise heavy atom RMSD [Å]
G‐tetrad core
0.56 ± 0.18
0.53 ± 0.13
All residues
0.74 ± 0.24
0.59 ± 0.16
Restraint violations [Å]
Max. NOE restraint violation
0.16
0.19
Mean NOE restraint violation
0.002 ± 0.012
0.002 ± 0.011
John Wiley & Sons, Ltd.
The high‐resolution solution unequivocally confirmed the well‐defined parallel‐stranded G4 structure (Figure 4H,I). At the 5′‐end site, three extended flanking residues formed a cohesive capping structure, with A2 and A3 stacking at the outer G‐tetrad (Figure 4J,K). This capping structure was supported by NOE cross‐peaks from A2H8 and A3H8 to the H1 of the 5′‐end tetrad‐forming Gs (Figure 4K; Table S6 and Figure S5F, Supporting Information). In contrast, the 3′‐end AGC residues adopted a distinct stacking arrangement from the 5′‐end segments (Figure 4J,L). The sequential residues G19, A20, and G21 formed a stacking structure, supported by the key NOE cross‐peaks of A20H8 to G19H8, A20H2 to G19H1, G14H1, G6H1, and A20H8 to G21H8 (Figure 4L; Table S7 and Figure S5G, Supporting Information). Two classic propeller loops, A7 and A11 (Figure 4H–J), conformed to the well‐documented arrangement.[
37
,
39
,
43
] A unique feature of this structure was the double residue A15‐A16 loop, which has not been reported previously. Non‐parallel stacking primarily resulted from NOEs involving A15H8‐G17H8 and A15H8‐G18H8 (Figure 4H–J; Table S8 and Figure S5H, Supporting Information). In conclusion, ATF4‐G4 comprised a well‐defined three‐G‐tetrad stacked parallel G4 structure with a distinctive non‐parallel stacking 2nt‐loop and organized capping structures. CD experiments verified that the core mutant sequence does not possess G4 formation capability.
2.5
Coptisine Strongly Binds and Stabilizes the ATF4‐G4 to Hinder the Interaction Between TFAP2A and ATF4 Promoter
COP has demonstrated substantial potential as an anticancer drug in our recent study, which was attributed to its remarkable binding activity to parallel G4s[
38
] (Figure
5A). Given that ATF4‐G4 also adopts a parallel G4 structure, we investigated its binding to COP. Notably, we observed a significant enhancement in ATF4‐G4 thermal stability, with a notable 25 °C increase in a CD melting experiment (Figure 5B). To further assess the binding affinity, we determined their Kd
value to be ≈3.9 µM using a fluorescence‐based assay (Figure S6A, Supporting Information).
Figure 5
Coptisine strongly binds and stabilizes the ATF4‐G4 to hinder the interaction between TFAP2A and ATF4 promoter. A) Chemical structure of Coptisine with numbering. (CAS NO. 6020‐18‐4) B) CD thermal melting curves and CD spectra of Pu22T DNA with coptisine. Conditions: 20 µM DNA, pH 7, 5 mM K+ solution. The ΔTm values of COP to the Pu22T DNA were determined. The melting temperature (Tm) was obtained at the intersection between the median of the fitted baselines and the melting curve. C) 1D 1H NMR titration of Pu22T DNA with COP. The G‐tetrad imino proton signals at the 5′‐end, middle, and 3′‐end are labeled in blue, black, and red respectively. Conditions: 150 µM DNA, pH 7, 50 mM K+ solution, 25 °C. D) 2D NMR spectra of ATF4‐G4 in complex with COP. Select regions of the 2D‐NOESY spectra of 2.4:1 COP‐ATF4‐G4 complexes in H2O showing intermolecular cross‐peaks between compound and DNA imino protons. Conditions: 2.11 mM Pu22T DNA, pH 7, 10 mM K+ solution, 25 °C. E) Superposition of the 10 lowest energy NMR structures of the COP‐ATF4‐G4. F) Cartoon representation of a refined COP‐ATF4‐G4 with partial numbering (PDB ID: 8Y2R). Red, adenine; gray, guanine; blue, thymine; orange, cytosine and yellow, COP. G, H) 5′‐end and 3′‐end top views of COP‐ATF4‐G4. I) Construct a small interfering library of 22 transcriptional regulatory ATF4 with the highest predicted score on Jasper and EPD websites, and screen out potential transcriptional factor TFAP2A (n = 3 independent experiments, average of three technical replicates). J) Schematic diagram of TFAP2A binding site in ATF4 promoter region. K) Fold enrichment of G4 on ATF4 promoter using ChIP‐qPCR analysis under 4Q or 0.25 mM glutamine conditions with or without COP treatment (20 µM) on NCI‐H1299 cells (12 h) (n = 3 independent experiments). L) Fold enrichment of TFAP2A on ATF4 promoter using ChIP‐qPCR analysis under 4Q or 0.25 mM glutamine conditions with or without COP treatment (20 µM) on NCI‐H1299 cells (12 h) (n = 3 independent experiments). 4Q: 4 mM glutamine, 0.25Q: 0.25 mM glutamine, COP: coptisine chloride. The data are shown as mean values ± SD from triplicated samples. *p < 0.05, **p < 0.01, ***p < 0.001. Data were analyzed by One‐way ANOVA (K, L) in GraphPad Prism 9.5.0.
In a K+‐containing solution, we conducted 1H‐NMR titration experiments to probe the binding interactions between COP and ATF4‐G4 DNA. Free ATF4‐G4 exhibited 12 imino proton peaks, corresponding to three stacked G‐tetrads (Figure S5A, Supporting Information). Gradual addition of COP induced upfield shifts in nearly all imino proton resonances of free ATF4‐G4. At the lower drug ratio of 1:1, the imino proton peaks broadened, whereas at higher ratios of 2:1 and 3:1, the peaks sharpened, indicating the binding of one compound to each outer G‐tetrad via end‐stacking interactions (Figure 5C). Significantly, a new set of 12 distinct peaks emerged after COP addition, implying the formation of a dominant conformation within the COP‐ATF4‐G4 complexes.
The binding of COP to ATF4‐G4 was substantiated using NMR spectroscopy (Figure 5D; Figure S6B–D, Supporting Information), which generated well‐resolved NMR spectra suitable for high‐resolution structural analysis (Table S9, Supporting Information). A total of 519 NOE‐derived distance restraints were used to determine the COP‐ATF4‐G4 structure (Table 1). The final ten lowest energy structures demonstrated good convergence, with an RMSD of 0.59 ± 0.16 Å for all residues (Figure 5E and summarized in Table 1). COP adopted a 2:1 binding mode, optimizing its interaction with the outer G‐tetrads (Figure 5E–H). COP's positioning was supported by numerous NOE cross‐peaks, such as COPH6‐G4H1, COPH6‐G8H1, COPH6‐G12H1, COPH6‐G17H1, COPH8‐G4H1, COPH8‐G8H1, COPH8‐G12H1, COPH8‐G17H1, COPH6‐G6H1, COPH6‐G10H1, COPH6‐G14H1, COPH6‐G19H1, COPH8‐G6H1, COPH8‐G10H1, COPH8‐G14H1, and COPH8‐G19H1 (Figure 5D; Table S10, Supporting Information). Electrostatic interactions may arise between the positively charged COPN7 and the negatively polarized carbonyl groups of the tetrad guanine. Moreover, the resonances of COPHA‐G6H8, COPHB‐G17H8, COPHA‐G6H8, and COPHB‐G14H8 played a pivotal role in determining the stacking direction (Table S10, Supporting Information). Moreover, the higher affinity of COP for the 5′ end capping structure can be attributed to a plausible hydrogen bond between COP and A3H2 (Figure 5G; Figure S6E, Supporting Information). Considering their positions above the outermost tetrads, designing coptisine derivatives with longer alkyl chains could be a rational strategy to offer molecular guidance for enhancing the affinity and selectivity with drug‐like properties through hydrogen bonding and electrostatic interactions.
The overall binding mode of COP‐ATF4‐G4 closely resembles the reported COP‐KRAS‐G4 binding mode[
38
] (Figure 5E–F). Each COP molecule recruited an adjacent flanking residue, forming a plane that was stacked over two external G‐tetrads (Tables S11 and S12, Supporting Information). The 2nt‐loop structure did not contribute to binding pocket formation, consistent with the fact that almost the same conformation was observed in both the ATF4‐G4 free structure and the COP‐ATF4‐G4 complex (Tables S8 and S13, Supporting Information). Moreover, clear assignments of intermolecular NOE cross‐peaks between the COP and A3 residues were observed at high threshold levels, suggesting multiple orientations of A3. For instance, intermolecular NOE cross peaks from COPH8 to A3H8 were observed, implying a potential flip of the A3 orientation by ≈180° from the determined conformations. However, limited NOE cross‐peaks hindered the precise structural determination of this minor species.
Numerous G4 sites and transcription factors that recognize G4 structures play pivotal roles in human chromatin regulation.[
44
] Therefore, it is crucial to identify transcription factors that can bind to ATF4‐G4 to gain a comprehensive understanding of the biological functions of ATF4‐G4. Using JASPAR (http://jaspar.genereg.net/) and EPD (https://epd.epfl.ch//index.php), we identified twenty‐two transcription factors with predicted scores exceeding eight for potential regulation of ATF4 gene expression (Figure 5I). A siRNA library was then constructed, and qRT‐PCR was used to validate the transcriptional effect of these factors on ATF4, resulting in the identification of the transcription factor TFAP2A (Figure 5J). Presently, in vitro analysis has suggested that TFAP2A binds to the palindrome motif GCCN3GGC, as well as some variants such as GCCN4GGC and GCCN3/4GGG.[
45
] ChIP‐seq experiments have demonstrated that SCCYSRGGS (S = G or C, R = A or G, and Y = C or T) are the consensus sites for human TFAP2A.[
46
] Moreover, TFAP2A has also been shown to bind to G4‐forming sequences.[
47
] Therefore, we hypothesized that TFAP2A could potentially bind to the ATF4‐G4‐forming sequence. To validate our hypothesis, primer sequences were specifically designed for the ATF4‐G4‐forming region, and chromatin immunoprecipitation followed by quantitative polymerase chain reaction (ChIP‐qPCR) experiments were conducted. Firstly, ChIP‐qPCR was performed using anti‐G4 antibodies to confirm the presence of the G4 structure within the ATF4 promoter region in the cells (Figure 5K). Subsequently, ChIP‐qPCR was conducted with TFAP2A antibodies to investigate TFAP2A binding to ATF4‐G4. The results showed that the DNA fragment for the ATF4‐G4 forming region was enriched by both the G4 antibody and TFAP2A antibody under 0.25 mM glutamine conditions, and COP stabilized the ATF4‐G4 and inhibited its interaction with TFAP2A (Figure 5K,L). These findings indicate that the binding of TFAP2A to the ATF4‐G4‐forming sequence is involved in the ATF4 transcription regulation and small molecules can disrupt their interaction by stabilizing the ATF4‐G4 structure. Kaplan‐Meier analysis revealed that high TFAP2A expression might predict poor clinical prognosis in patients with lung cancer (Figure S6F, Supporting Information). Notably, TFAP2A and ATF4 levels significantly increased under 0.25 mM glutamine stimulation (Figure S6G, Supporting Information). Inhibition of TFAP2A reduced ATF4 expression and suppressed cancer cell proliferation in the presence of 0.25 mM glutamine (Figure S6H,I, Supporting Information). These findings collectively suggest that glutamine restriction upregulates TFAP2A expression levels, and TFAP2A directly binds to the ATF4 promoter sequence to positively regulate ATF4 transcription.
2.6
COP Suppresses the GCN2‐ATF4‐ASCT2 Axis and the Downstream mTOR Signaling Pathway
The intriguing affinity of COP for G4 molecular structures piqued our interest in the further investigation of its biological activities. RNA‐Seq analysis revealed that the presence of COP decreased ATF4 expression under conditions of glutamine restriction (Figure S7A, Supporting Information). We demonstrated that COP significantly reduced the expression of ATF4 induced by glutamine restriction (Figure
6A–C; Figure S7B,C, Supporting Information), leading to substantial inhibition of cancer cell proliferation (Figure S7D–F, Supporting Information). In PS induced with either 4 mM or 0.25 mM glutamine, the addition of COP resulted in a reduction of cisplatin resistance linked to glutamine deficiency (Figure 6D). Moreover, the OCR assay revealed that COP notably decreased mitochondrial respiration in NCI‐H460 and NCI‐H1299 cells (Figure 6E). Notably, the enhanced glutamine uptake and utilization in the context of glutamine deficiency was substantially hindered by COP treatment or ATF4 knockdown (Figure 6F).
Figure 6
COP suppresses the GCN2‐ATF4‐ASCT2 axis and the downstream mTOR signaling pathway. A) qRT‐PCR analysis of ATF4 mRNA level in NCI‐H460 or NCI‐H1299 cells after 24 h treatment with 0.25 mM glutamine or 0.25 mM glutamine plus COP (10 µM or 20 µM) (n = 3 independent experiments, average of three technical replicates). Heatmap colors represent 2−ΔΔCt values. B) Effects of COP (10 µM) on NCI‐H460 stimulated with 0.25 mM glutamine analyzed by immunofluorescence (n = 3 independent experiments). Scale bar: 20 µm. C) Immunoblotting of GCN2 signaling analyses: phospho‐GCN2 (p‐GCN2, Thr899), total GCN2, phospho‐eIF2α (p‐eIF2α, Ser51), total eIF2α, and ATF4 in NCI‐H460 and NCI‐H1299 cells cultured in 4 mM or 0.25 mM glutamine conditions (12 h). D) NCI‐H460 and NCI‐H1299 cells treated with 4 mM or 0.25 mM glutamine for 3 days were switched to complete RPMI‐1640 medium for 2 h to induce persister cells. These persister cells were subsequently treated with COP (20 µM), and the IC50 of cisplatin was assessed in live cell plates following drug treatment. (n = 3 independent experiments). E) Seahorse XF assay measuring OCR of NCI‐H460 or NCI‐H1299 cells in 0.25 mM glutamine or 0.25 mM glutamine combined with COP (10 µM) (n = 3 independent experiments). F) Glutamine uptake rate of NCI‐H460 or NCI‐H1299 cells cultured in 4 mM or 0.25 mM glutamine in the absence or presence of COP (20 µM) for 36 h or ATF4 knockdown (n = 3 independent experiments). G) Immunoblotting of ASCT2, LAT1, phospho‐mTOR (p‐mTOR, S2448), total mTOR, phospho‐P70S6K (p‐P70S6K, Thr389), total P70S6K, phospho‐4EBP1 (p‐4EBP1, Thr37/46), total 4EBP1 protein levels in NCI‐H460 and NCI‐H1299 cells cultured in 4 mM or 0.25 mM glutamine conditions with or without COP treatment (20 µM) (48 h). H) Mass spectrometry analysis of differential metabolites in 0.25 mM glutamine and 0.25 mM glutamine plus 20 µM COP on H460 cells (n = 3 independent experiments). I) Correlation analysis of differential metabolites in 0.25 mM glutamine and 0.25 mM glutamine combined with COP (20 µM). The size of the point in the figure represents the Log2FC value, and the larger the point is, the larger the corresponding Log2FC value is, the color of the point represents the source classification of the differential metabolites in this group, and the connection represents the correlation coefficient value of the metabolite in the corresponding position. J) qRT‐PCR analysis of ATF4‐downstream transcripts in NCI‐H460 cells after COP (20 µM) treatment in 0.25 mM glutamine for 24 h (n = 3 independent experiments, average of three technical replicates). 4Q: 4 mM glutamine, 0.25Q: 0.25 mM glutamine, COP: coptisine chloride. All immunoblots are representative of three biological replicates that showed similar results. Data are shown as mean ± SD. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. Data were analyzed by two‐tailed Student's t‐tests (H) and One‐way ANOVA (D, F) in GraphPad Prism 9.5.0.
Activation of GCN2 is observed under conditions involving amino acid stress, subsequently leading to the phosphorylation of eIF2α, a process utilized to suppress general mRNA translation while promoting ATF4 expression. Our investigation elucidated that the phosphorylation of GCN2 (Thr899) and eIF2α (Ser51) was triggered by glutamine‐nutritional restriction, subsequently driving the protein expression of ATF4. Importantly, this stress‐induced activation was attenuated by COP treatment (Figure 6C; Figure S7C, Supporting Information). Glutamine is transported into cells through the glutamine transporter (SLC1A5), which controls intracellular glutamine levels. Subsequently, SLC7A5 utilizes intracellular glutamine as an efflux substrate to modulate the uptake of extracellular leucine into cells.[
48
] Previous studies have established that leucine regulates the mTORC1 signaling pathway in a variety of cellular processes to promote proliferation.[
49
] Activation of GCN2 under glutamine deficiency triggers increased expression of the SLC1A5 transporter, facilitating the exchange of extracellular leucine and ultimately leading to mTORC1 signaling activation. mTOR is a conserved serine/threonine kinase that responds to changes in nutrient levels and growth signals.[
50
] It acts as a catalytic subunit for two primary protein complexes: mTORC1, which is a key regulator of cell metabolism and growth, and mTORC2, which is crucial for controlling cell proliferation and survival. mTORC1 is a central signaling hub that integrates signals from nutrients, metabolic intermediates, and growth factors to regulate cellular metabolism in response to the environment.[
51
] Our experiments demonstrated that the phosphorylation of mTORC1 and its downstream effectors P70S6K and 4EBP1 was activated by glutamine restriction, and this activation was inhibited in the presence of COP (Figure 6G; Figure S7G, Supporting Information). Collectively, these findings suggest that COP reduces ATF4 levels, thereby diminishing stress‐induced cancer cell survival.
To further validate the potential impact of COP, we conducted a metabolomic analysis on NCI‐H460 cells treated with different conditions: 4 mM glutamine, 4 mM glutamine plus COP, 0.25 mM glutamine, or 0.25 mM glutamine plus COP. Using the Energy Metabolism Database v2.0, we identified 68 metabolites and enriched metabolic pathways by analyzing the differential metabolites upon COP stimulation (Figure S8A, Supporting Information). Principal component analysis of metabolism revealed the COP‐induced downregulation of Glutamine, L‐Leucine, L‐Glutamic acid, and L‐Asparagine (Figure 6H). These findings suggest that COP not only further restrains amino acid breakdown and utilization in cancer cells but also heightens sensitivity to glutamine deprivation. Using Pearson's correlation analysis, we assessed the correlation between metabolites with significant differences (Figure 6I). Additionally, to observe changes in metabolite trends across various samples, we standardized and centralized the relative contents of distinct metabolites and subjected them to K‐means clustering analysis. Remarkably, the inclusion of COP significantly affected the TCA cycle and the pentose phosphate pathway (Figure S8B, Supporting Information).
Quantitative real‐time polymerase chain reaction (qRT‐PCR) was employed to gauge changes in downstream target genes of ATF4 in NCI‐H460 cells (Figure 6J). These results indicated that the expression of genes downstream of ATF4 was upregulated under glutamine deficiency and subsequently downregulated upon COP treatment. In addition, COP exerted a notable inhibitory effect on the amino acid transporter ASCT2. Findings from colony formation and EdU assays showed that ASCT2 knockdown markedly intensified cancer cell proliferation (Figure S9A–E, Supporting Information). The efficacy of ASCT2 inhibition in curbing glutamine uptake has been demonstrated across various cancer types, including melanoma,[
52
] non‐small cell lung cancer,[
53
] prostate cancer,[
54
] and acute myeloid leukemia.[
55
] Kaplan‐Meier plots, coupled with the log‐rank (Mantel‐Cox) test highlighted the prognostic benefits associated with low ASCT2 expression in NSCLC (Figure S9F, Supporting Information).
2.7
COP Decreases Glutamine Deficiency‐Induced ATF4 Levels and Improves the Efficacy of Glutamine‐Restrictive Therapy
To assess the effects of COP in vivo, we conducted xenograft experiments under both normal (with adequate glutamine) and glutamine‐deficient conditions (Figure
7A). Compared to normal conditions, xenograft growth was delayed owing to glutamine restriction (Figure 7B,C). Remarkably, COP significantly enhanced the inhibitory effect of glutamine‐restrictive therapy on tumor growth (Figure 7D; Figure S10A,B, Supporting Information). Western blot analysis demonstrated that COP effectively disrupted the GCN2‐ATF4‐ASCT2 axis (Figure 7E). Furthermore, IHC analysis revealed that COP diminished the expression of ATF4 and ASCT2 during glutamine restriction (Figure S10C, Supporting Information), thereby influencing the uptake and utilization of glutamine by ASCT2 within tumor tissues during glutamine deficiency (Figure 7F). Collectively, these findings highlight the ability of COP to augment tumor responsiveness to dietary glutamine restriction, offering a novel therapeutic avenue for cancer treatment.
Figure 7
COP decreases glutamine deficiency‐induced ATF4 levels and improves the efficacy of glutamine‐restrictive therapy. A) Protocol of COP (100 mg·kg−1) administration in glutamine restriction therapy and establishment of H460 xenograft tumor model. B) Image of tumor removal after intraperitoneal injection of COP (100 mg·kg−1) (n = 6 per group). C) Growth curves of tumor volume (n = 6 per group). D) Tumor volume of nude mice with COP treatment and glutamine restriction diet (n = 6 per group). E) Immunoblotting of indicated proteins in xenograft tumors after COP and glutamine restriction diet therapy in vivo (n = 3 independent experiments). F) Determination of glutamine content in xenograft tumors with glutamine restriction (n = 6 independent experiments). G) Proposed model of COP targeting ATF4‐G4 enhancing sensitivity to glutamine restriction therapy. All immunoblots are representative of three biological replicates that showed similar results. Glutamine concentrations of 2% and 0% glutamine diet are represented by 2%Q and ‐Q, respectively. COP: coptisine chloride. Data are shown as mean ± SD. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. Data were analyzed by two‐tailed Student's t‐tests (D,F) and One‐way ANOVA (E) in GraphPad Prism 9.5.0.
As illustrated in Figure 7G, glutamine restriction triggered ATF4 translational reprogramming, which accounted for over 50% of persister cancer cell survival and weakened the efficacy of glutamine‐deficient therapy. Our study demonstrates that the natural compound COP targets the ATF4‐G4 structure, disrupts the interaction between TFAP2A and the ATF4 promoter, and curtails glutamine uptake and utilization by inhibiting the ATF4 downstream gene SLC1A5. This cascade of events subsequently suppresses the mTOR signaling pathway, ultimately enhancing the effectiveness of glutamine‐restrictive therapy.
3
Discussion
PS represents a nongenetically reversible state and exhibits a slow‐cycling phenotype in cancer.[
1
] Cancer relapse begins when malignant cells experience the extreme metabolic stress caused by chemotherapy.[
11
] Metabolic networks provide cells with stress protection systems. Exploiting limited amino acid metabolism to selectively target highly proliferative cancer cells has gained substantial attention in recent years.[
56
] Tumor growth and survival are intricately linked to the nutrients obtained from the host. Modifications in the host diet can potentially alter nutritional availability within the tumor microenvironment, offering a promising approach to impede tumor growth. Amino acid restriction orchestrates various genetic expression steps including chromatin configuration, transcription initiation sites, transcription rates, mRNA splicing, RNA export, RNA turnover, and translation, all of which ultimately govern the DNA‐to‐RNA‐to‐protein cascade.[
6b
] These promising results suggest that amino acid restriction strategies are potential rational therapeutic interventions for nutrient‐dependent persister cancer cells. However, dietary protein restriction triggers the activation of metabolic reprogramming pathways aimed at restoring cellular amino acid equilibrium, which poses a significant challenge for the effective clinical translation of amino acid restriction methodologies.[
57
] Our research revealed that under glutamine nutritional restriction, the ISR component, GCN2, was activated. This activation led to the phosphorylation of translation initiation factor eIF2α,[
58
] subsequently triggering the activation of ATF4. Therefore, targeting ATF4 is a promising strategy for enhancing the persistent cellular sensitivity to glutamine restriction therapy.
The ATF4 promoter contains a unique nucleic acid secondary structure known as G4, which functions as a negative transcriptional modulator susceptible to intervention by small molecules. However, the lack of a well‐established complex structure involving ATF4‐G4 and its ligand has impeded progress in targeting key factors in glutamine restriction therapy for NSCLC. In this context, we present the NMR solution structures of ATF4‐G4 bound to COP. The resulting complex structure revealed a 2:1 binding stoichiometry, with each compound engaging the flanking adenine residue to create a “quasi‐triad plane” that intercalated between the two external G‐tetrads. This binding mechanism involved both π‐stacking and electrostatic interactions. Furthermore, COP significantly reduced ATF4 mRNA levels in cancer cells. Mechanistically, COP targets ATF4 to curtail glutamine uptake and utilization in NSCLC, concurrently inhibiting the mTOR signaling pathway. The revelation of COP interaction with the ATF4‐G4 structure advances research on glutamine restriction therapy for NSCLC. Our study provides valuable insights into ligand interactions with ATF4‐G4 and paves the way for targeting the ATF4‐G4 structure to impede tumor growth, which is a critical aspect in the development of drugs that interact with ATF4‐G4.
The concept of “starving cancer to death” is based on the notion that numerous metabolic changes within cancer cells offer potential vulnerabilities that can be targeted.[
59
] Our metabolic analysis of NCI‐H460 cancer cells revealed significant changes in the amino acid profile of NSCLC cells in response to glutamine nutritional restriction, which reduced the reliance of NSCLC cells on glutamine addiction. The multifaceted role of glutamine in tumor progression, coupled with its efficacy in inhibiting tumor growth across diverse cancer types in vitro and in vivo,[
60
] has been extensively demonstrated. Presently, dietary amino acid manipulation involving either amino acid deprivation or supplementation is a promising avenue for overcoming chemotherapy resistance and halting cancer progression.[
61
] In various melanoma xenograft models, dietary glutamine supplementation led to heightened α‐ketoglutarate‐dependent hypomethylation, desensitizing BRAF inhibitor‐resistant cells.[
62
] Cisplatin‐resistant NSCLC cells exhibit increased extracellular glutamine uptake and enhanced glutaminase activity, rendering them susceptible to glutamine deprivation.[
63
] Modulation of glutamine metabolism targets transcription activating factors (STAT) 1/3, reducing IDO gene expression within tumor cells and enhancing the effectiveness of anti‐cancer T cells,[
64
] thereby rejuvenating checkpoint blockade therapy in drug‐resistant tumors. The growing recognition of nutrient‐based strategies that exploit tumor metabolic vulnerabilities underscores their potential efficacy in curbing cancer nutrition. However, universal dietary composition recommendations are unlikely to emerge for cancer prevention and treatment. Instead, combining chemotherapeutic agents with targeted amino acid therapies has the potential to combat drug‐resistant cancers driven by amino acid metabolism. Similar to novel drug combinations, a personalized approach involving medication and dietary adjustments is crucial for each cancer type, location, and grade. Our research demonstrates that the natural compound COP enhances cancer sensitivity to glutamine restriction by specifically targeting ATF4‐G4, a pivotal component of the ISR signaling pathway that is activated in response to glutamine deficiency. We explored the distinct metabolic attributes associated with elevated ATF4 levels under glutamine restriction and COP treatment. Our findings also revealed that TFAP2A bound to the ATF4 promoter region. This interaction was observed at the G4 formation site within the ATF4 promoter, ultimately promoting the positive modulation of ATF4 expression.
In conclusion, our study provides insights into the therapeutic potential of selectively targeting ATF4‐G4 using small molecules to enhance the glutamine restriction strategy. The findings offer a new avenue for the elimination of PS in glutamine restriction therapy.
4
Experimental Section
Reagents and Cell Culture
The cell lines utilized in this study were obtained from the National Collection of Authenticated Cell Cultures (Shanghai), and included NCI‐H460, NCI‐H1299, NCI‐H1975, NCI‐HCC827, NCI‐H661, A549, and PC‐9. These cell lines were cultured in RPMI 1640 medium (Gibco, 11875‐093), supplemented with 10% fetal bovine serum (ExCell Bio, FCS500) and 100 U/ml penicillin‐streptomycin (New Cell and Molecular Biotech, China; C100C5). The cells were maintained at 37 °C in a humidified atmosphere composed of 95% air and 5% CO2. Regular testing was conducted to ensure the absence of Mycoplasma contamination. Following attachment, the cells were treated with conditioned medium containing varying concentrations of glutamine (4 mM, 0.5 mM, 0.25 mM, and 0 mM glutamine).
DNA oligonucleotides were obtained from Sangon Biotech (Shanghai) Co., Ltd. and were available in two different purity grades: HPLC and PAGE. The DNA was solubilized in a buffer containing 37.5 mM KCl and 12.5 mM K2HPO4/KH2PO4, pH 7, with a D2O/H2O ratio of 10/90. Final concentrations were determined using a UV spectrometer. The sequences of the oligonucleotides are listed in Table S3 (Supporting Information). The compound coptisine chloride (COP) (CAS NO. 6020‐18‐4) was purchased from Shanghai Standard Technology Co., Ltd. The GCN2 inhibitor GCN2iB (CAS NO. 2183470‐12‐2) was obtained from Med Chem Express (America).
Generation of Persister Cells
NCI‐H460 and NCI‐H1299 cells treated with 4 mM or 0.25 mM glutamine for 1–5 days were switched to complete RPMI‐1640 medium for 2 h to create persister cells.[
65
] Live cells were sorted using the Dead Cell Removal Kit (Miltenyi Biotec, Germany). The Dead Cell Removal MicroBeads target a moiety in the plasma membrane of both apoptotic and dead cells. To deplete dead cells, cells were magnetically labeled with Dead Cell Removal MicroBeads and passed through a separation column. The magnetically labeled dead cells were captured within the column, while the unlabeled living cells passed through, resulting in a cell fraction free of dead cells. NCI‐H460 and NCI‐H1299 cells were counted after treatment with 4 mM or 0.25 mM glutamine. The cell suspension was centrifuged at 300×g for 10 minutes, followed by complete aspiration of the supernatant. The cell pellet was then resuspended in 100 µL of Dead Cell Removal MicroBeads per 10⁷ total cells, mixed well, and incubated for 15 min at room temperature (20 to 25 °C). 1× Binding Buffer was added to the cell suspension to achieve a minimum volume of 500 µL for separation. Magnetic separation was then carried out by placing the column in the magnetic field of a suitable MACS Separator, rinsing the column with the appropriate amount of 1× Binding Buffer, applying the cell suspension onto the column, and collecting the flow‐through containing unlabeled cells (live cells). The column was washed with the appropriate amount of 1× Binding Buffer and mixed thoroughly. The screened live cells were then centrifuged, resuspended in a medium containing 4 mM and 0.25 mM glutamine for counting, and finally seeded into plates.
Cell Viability Assay
Cell viabilities were assessed using the Cell Counting Kit‐8 (Med Chem Express, America). In brief, cells were seeded onto 96‐well plates at a density of 2 × 103 per well and exposed to varying concentrations of COP or conditioned medium. The plates were then maintained at 37°C for 24, 48, and 72 h. Subsequently, 10 µL of CCK‐8 reagent (diluted in 100 µL medium per well) was added to the cells, and they were incubated for 2 h at 37 °C in a 5% CO2 incubator. The absorbance at a wavelength of 450 nm was measured using a microplate reader. This assay was repeated at least three times, with triplicate measurements each time. For the trypan blue exclusion assay, 2 × 105 cells were cultured in complete medium in 60 mm3 dishes and incubated at 37 °C. The cells were treated with conditioned medium (4 mM glutamine, 0.25 mM glutamine) and the appropriate culture medium for 1 to 5 days, starting from the second day. After preparing a cell suspension, the suspension was mixed with 0.4% trypan blue at a 9:1 ratio (final concentration 0.04%). The mixture was then stained for 3 min, and the viable cells were counted. In the ethynyldeoxyuridine (EdU) analysis, 1.2 × 104 cells were seeded in 96‐well plates and exposed to EdU for a duration of 4 h. The subsequent steps were performed according to the manufacturer's protocol. The results were captured using the Molecular Devices ImageXpress High Content Confocal Imaging System and quantified using Image J. For the cell apoptosis experiment, cells were treated with conditioned medium (4 mM glutamine, 0.25 mM glutamine) for 24 h. After fixation, the cells were stained with Hoechst 33 342 (1000×) and visualized using a fluorescent microscope.
Colony Formation Assay
A range of 2000 to 4000 cells per well were seeded into 6‐well plates with complete growth medium and allowed to settle overnight. On the following day, the cells were treated with conditioned medium for a duration of 7 to 12 days, during which visible colonies formed. For NCI‐H460 and NCI‐H1299 cells, transfection was carried out using shScramble, shATF4, pHBLV‐CMV‐MCS‐3Flag‐ZsGreen‐T2A‐PURO vector, and pHBLV‐CMV‐ATF4‐3Flag‐ZsGreen‐T2A‐PURO plasmids (HANBIO, China) in combination with Lipofectamine 3000 (Thermo Fisher Scientific). Post‐transfection, cells were plated into 6‐well plates with 2000 cells per well, following the previously described protocol. The resultant colonies were washed with phosphate‐buffered saline (PBS) and fixed using 4% Paraformaldehyde Fix Solution (Servicebio, G1101‐500ML) for 10 min at room temperature. Subsequently, they were stained with Crystal Violet Staining Solution (Beyotime, C0121‐100ML) for 10 min. After a PBS wash, colony counting was performed for statistical analysis.
RNA Extraction and Quantitative RT‐PCR
Total RNA was extracted using the RNA extraction kit (Shanghaiyishan, China) following the manufacturer's instructions. Subsequently, cDNA was synthesized using the HiScript Q RT SuperMix kit (Vazyme, China). Quantitative RT‐PCR analyses were conducted on the LightCycler 480 real‐time fluorescent quantitative PCR system (Roche, Germany). The threshold cycles (Ct values) of the target genes were normalized to GAPDH, which was used as the endogenous control. All qPCR amplifications were carried out in triplicate and repeated in three independent experimental runs. The primer sequences utilized for qPCR can be found in Table S1 (Supporting Information).
Measurement of ROS Production
Cells were exposed to conditioned medium, followed by trypsinization and collection into 1.5 mL tubes. After collection, the cells were resuspended in a diluted solution of DCFH‐DA and incubated at 37°C for 20 min. Subsequently, the cells underwent three washes using serum‐free cell culture medium to remove any residual DCFH‐DA that had not entered the cells. The detection procedure was conducted using a BD Caliber flow cytometer (BD Biosciences), and the results were analyzed using FlowJo software.
Western Blot Analysis
Total proteins were extracted from cells or tumor tissue homogenates using NP‐40 lysis buffer. The protein samples were then separated by SDS‐PAGE gel and transferred onto polyvinylidene fluoride (PVDF) membranes (Bio‐Rad, CA, USA). Subsequent to transfer, these membranes were incubated with specific antibodies, including the following primary antibodies: ASCT2 (Proteintech, 20350‐1‐AP, 1:2000), HRI (Proteintech, 20499‐1‐AP, 1:1000), TFAP2A (Proteintech, 13019‐3‐AP, 1:2000), p‐P70(S6K) (Thr389) (Proteintech, 28735‐1‐AP, 1:2000), P70(S6K) (Proteintech, 14485‐1‐AP, 1:2000), SLC7A5 (Proteintech, 28670‐1‐AP, 1:2000), ATF4 (Abcam, ab184909, 1:500), mTOR (Abcam, ab13a4903, 1:10000), p‐mTOR (S2448) (Abcam, ab109268, 1:1000), GCN2 (Abcam, ab134053, 1:1000), eIF4EBP1 (Abcam, ab32024, 1:2000), p‐GCN2 (Thr899) (Affinity, AF8154, 1:1000), p‐4EBP1 (Thr37/46) (Cell Signaling Technology, 2855, 1:1000), eIF2α (Cell Signaling Technology, 5324, 1:1000), p‐eIF2α (Ser51) (Cell Signaling Technology, 3398, 1:1000), GAPDH (Cell Signaling Technology, 97 166, 1:1000). Following incubation with primary antibodies, immunocomplexes were visualized through a chemiluminescence reaction using ECL reagents (Vazyme, China) and quantified using Image J software. The antibodies employed for western blotting are listed in Table S4 (Supporting Information).
Glutamine Uptake Assay
Cells were initially seeded in 100 mm3 tissue culture plates with complete growth medium and incubated overnight. The following day, cells were harvested after 36 h of treatment with conditioned medium. For tissue samples, cells were lysed using established protocols, and each group was then normalized based on protein quantification. To quantify glutamine uptake, a glutamine assay kit (ab197011, Abcam) was used, following the manufacturer's instructions. This experiment was repeated three times and conducted in three independent iterations.
Seahorse Analysis
The cellular oxygen consumption rate (OCR) was measured using the Seahorse XFe96 Analyzer (Agilent Technologies). Cells treated with COP and knock‐down ATF4 were resuspended in Seahorse XF RPMI 1640 medium (supplemented with 10 mM glucose, 1 mM pyruvate, and 2 mM glutamine), and seeded in Cell‐Tak (Corning) precoated Seahorse 96‐well plates. The OCR was assessed under basal conditions and in response to 1.5 µM oligomycin, 1.5 µM fluorocarbonyl cyanide phenylhydrazone (FCCP), 0.5 µM rotenone, and antimycin A.
Immunofluorescence
After the cellular pre‐treatment, cells were fixed in a 4% paraformaldehyde solution for 20 min. Subsequently, cells were permeabilized with 0.1% Triton X‐100 for 10 min, followed by blocking with a 0.5% bovine serum albumin solution for 2 h at room temperature. In the next step, samples were subjected to an overnight incubation with primary antibodies, followed by an additional incubation with fluorescently conjugated secondary antibodies and DAPI. Captured images were acquired using the ImageXpress Micro system, and quantification was performed using Image J software. Immunofluorescence antibodies are listed in Table S4.
NMR Spectroscopy Experiments
NMR spectra were acquired using a Bruker AV‐600 spectrometer equipped with a QCI Cryoprobe. The w5 water peak suppression was configured for the entire spectrum collection process. NMR spectra processing was performed using Topspin 3.5 (Bruker) and Sparky (UCSF) software. 2D NOESY experiments were conducted with varying mixing times of 80, 300, 350, and 400 ms, at temperatures spanning 5, 10, 15, 25, and 35 °C in a solvent mixture of 90% H2O/10% D2O. The DQF‐COSY spectrum was acquired at a temperature of 25 °C. 1H‐13C HSQC spectra were obtained using the hsqcetgpsi pulse sequence with a 1J (C, H) coupling constant of 145 Hz.
Structure Calculation
NOE‐based distance restraints were categorized into different strengths: strong (2.9 ± 1.1 Å), medium (4.0 ± 1.5 Å), weak (5.5 ± 1.5 Å), and very weak (6.0 ± 1.5 Å), based on the NOESY spectra collected with mixing times of 80 and 350 ms at temperatures of 25 and 5 °C, respectively. Exchangeable protons were assigned corresponding to medium (4.0 ± 1.2 Å), weak (5.0 ± 1.2 Å), and very weak (6.0 ± 1.2 Å) distances. Intermolecular cross‐peaks between COP and DNA were defined by very weak (6.0 ± 1.5 Å), weak (5.0 ± 1.5 Å), and medium (4.0 ± 1.5 Å) distances. Overlapping and ambiguous resonances were categorized as having a distance of 5.0 ± 2.0 Å.
For the 12 tetrad guanines, 48 hydrogen bond restraints were applied. Dihedral restraints were employed for glycosidic torsion angles with values of 170°−310° and 200°−280°, corresponding to anti‐conformations in loop regions and within the G‐tetrad, respectively. The structure calculation incorporating NOE restraints was carried out using Xplor‐NIH74 in conjunction with the Amber 20 package. An ensemble of 100 starting structures was generated using Xplor‐NIH. COP parameter files were initially obtained from ChemDraw18.2 and were further optimized and computed using the Gaussian 09 program. Subsequently, the 100 initial structures underwent simulated annealing using the sander module of the Amber 20 package. The 20 structures with the lowest energy were selected for molecular dynamic simulations with the pmemd module of the Amber 20 package, in the presence of K+ cations and TIP3P water. Finally, the last 500 ps of trajectories were averaged, followed by energy minimization for 500 steps in a vacuum, after removal of water molecules and cations. The final ensemble consisted of the 10 lowest‐energy structures, which were chosen for deposition and analysis. Visualization and analysis were performed using VMD and PyMOL software.
CD Experiments
CD data were acquired using a Jasco‐1500 spectropolarimeter (Jasco Inc., Japan). DNA samples were prepared in a buffer containing 5 mM K2HPO4/KH2PO4 at pH 7, with a final concentration of 20 µM. The DNA samples were annealed at 95 °C for 5 min and then gradually cooled to room temperature using a heating block. Subsequently, the desired concentration of COP was added, and the mixture was incubated for 2 h. CD experiments were performed in a 1 mm path length quartz cuvette at a temperature of 25 °C. A baseline measurement was obtained using the buffer spectrum. To account for any blank, the baseline was subtracted, and the Savitzky‐Golay smoothing algorithm was applied to enhance the spectral smoothness. For CD melting analysis, the sample was heated from 25 to 95 °C at a heating rate of 2 °C·min−1, while monitoring the CD ellipticity at 264 nm. The melting temperature was determined at the point where the median of the fitted baselines intersected with the melting curve.
Fluorescence Measurements
Fluorescence measurements were performed using a Jasco‐FP8300 spectrofluorometer (Jasco Inc., Japan). The fluorescence spectra were recorded using a 1 cm path length quartz cell. Emission spectra were captured within the range of 520 to 600 nm, with an excitation wavelength set at 377 nm. COP concentrations were held at 0.2 µM in a 50 mM K+‐containing solution, with a total volume of 3 mL. DNA was incrementally introduced at the desired concentration. Following a 2‐min incubation period at each step, fluorescence spectra were collected.
Native Electrophoretic Mobility Shift Assay (EMSA)
EMSA gels were prepared using a 10 × 7 cm native gel with a thickness of 1 mm, containing 12.5 mM KCl and 16% acrylamide (acrylamide/bisacrylamide ratio of 29:1), pH 8.0. DNA samples were dissolved in a 5 mM KCl, 12.5 mM phosphate buffer at pH 7.0. COP was added at the desired concentration and incubated for 2 h. Electrophoresis was carried out at a voltage of 45 V in 1 × TBE running buffer. DNA bands were visualized at 260 nm using UV light.
DMS Footprinting
DMS footprinting was conducted following the described protocol with certain modifications.[
39b
] DNA samples were annealed in the presence of 50 mM KCl or LiCl and subsequently treated with 0.4% DMS (final concentration) for 2 min at room temperature in a total volume of 200 µL. The methylation reaction was promptly stopped by adding 150 µL of freshly prepared stop buffer containing 2.5 M NH4OAc, 0.1 M β‐mercaptoethanol, and 1 mg·mL−1 sperm DNA. Following phenol/chloroform extraction and ethanol precipitation, the DNA was reconstituted in water with the addition of 100 µL of 10% (vol/vol) piperidine in water and then heated at 90 °C for 30 min to cleave the methylated DNA. Subsequently, chloroform extraction and ethanol precipitation were performed. The precipitated DNA was dissolved in 80% (vol/vol) deionized formamide in water, denatured at 95 °C for 5 min, and separated on a 20% denaturing polyacrylamide gel. DNA fragments were visualized using a FAM dye covalently labeled at the 5′‐end of the DNA on a GelGo Imager (BIO‐RAD, USA) and digitized using the ImageQuant 5.2 software.
ChIP
NCI‐H1299 cells in the logarithmic growth phase were seeded into sterile cell culture dishes at a density of 1 × 106 cells per dish. Once adhered, the medium was exchanged with either 4 mM glutamine, 0.25 mM glutamine, or 0.25 mM glutamine supplemented with 20 µM COP. After a 12‐h treatment period, the cells were cross‐linked using 1% formaldehyde at room temperature for 10–15 min. Following centrifugation, the cells were lysed using Lysis Buffer (1 × 106 cells / 200 µL) on ice for 10 min. Subsequently, the cells were resuspended in ChIP buffer (1 × 106 cells / 100 µL) and subjected to ultrasonic treatment at 25% power for 3–4 pulses to facilitate chromatin extraction. DNA shearing was verified via agarose gel electrophoresis. The ChIP reaction system was prepared, followed by reverse cross‐linking and DNA release. DNA purification and qRT‐PCR reactions were performed. The enrichment rate was calculated using the formula FE% = 2−ΔΔCt × 100, where ΔΔCt = (Ct target gene – Ct IgG) in the treatment group – (Ct target gene – Ct IgG) in the untreated group. The Chroma Flash High‐Sensitivity ChIP Kit (EpigenTek) was employed following the manufacturer's instructions. The experiment was carried out in triplicate and replicated three times. Primers used for ChIP are listed in Table S2 (Supporting Information).
RNA‐Seq
Three distinct sample groups (4Q, 0.25Q, 0.25Q + COP) underwent total RNA extraction, rRNA removal, and mRNA enrichment using conventional kits. The enriched mRNA was reverse transcribed into double‐stranded cDNA, followed by repair of the cDNA ends, addition of junctions, and PCR amplification to generate on‐board libraries. Subsequently, RNA library sequencing was performed on the Illumina HiseqTM 2500/4000 by Gene Denovo Biotechnology Co., Ltd (Guangzhou, China). Bioinformatic analysis was performed using Omicsmart, a real‐time interactive online platform for data analysis available at http://www.omicsmart.com.
Energy Metabolism Analysis
Metabolic profiling was performed on four distinct sample groups using liquid chromatography tandem mass spectrometry (LC‐MS/MS). Sample pre‐treatment was followed by machine analysis, with the liquid phase conditions as follows: The chromatographic column employed was ACQUITY UPLC BEH Amide (1.7 µm, 100 mm × 2.1 mm i.d.), maintained at a constant temperature of 40 °C. Phase A consisted of ultrapure water containing 10 mM ammonium acetate and 0.3% ammonia water, while phase B comprised a mixture of 90% acetonitrile and water. The flow rate was set at 0.40 mL·min−1, with an injection volume of 2 µL. The linear gradient elution profile was as follows: 0–1.2 min A/B at a ratio of 5:95 (V/V), 8 min A/B at 30:70 (V/V), 9–11 min A/B at 50:50 (V/V), and 11.1‐15 min A/B at 5:95 (V/V). The mass spectrometry conditions were set as follows: The spray ion source (ESI) temperature was adjusted to 550 °C. The mass spectrometry voltage in positive ion mode was set to 5500 V, while in negative ion mode, it was set to ‐4500 V. The Curtain Gas (CUR) pressure was maintained at 35 psi. Within the Q‐Trap 6500+, each ion pair was scanned and detected based on optimized settings for declustering potential (DP) and collision energy (CE). Quantitative analysis was carried out using multiple reaction monitoring (MRM) mode on the triple quadrupole mass spectrometry system. The mass spectrometry data were processed using Analyst 1.6.3 software and MultiQuant 3.0.3 software.
Xenograft Tumor Assay
The animal experimental protocol underwent examination and approval by the Institutional Animal Care and Use Committee (IACUC) of the China Pharmaceutical University Experimental Animal Center (2023‐02‐015). Female BALB/c nude mice aged four to five weeks were sourced from Shanghai Sippr‐BK Laboratory Animal Co, Ltd. The mice were provided with both normal and glutamine‐deficient diets. A total of 3 × 106 NCI‐H460 cells, suspended in 200 µL of PBS, were subcutaneously injected into the right flank of the mice. Once the xenograft volumes reached approximately 100 mm3, the mice were randomly assigned to their respective groups: Control (1% DMSO in cyclodextrin solution, normal diet), COP group (50 mg·kg−1 and 100 mg·kg−1 in cyclodextrin solution, normal diet), glutamine‐deficient feed group (1% DMSO in cyclodextrin solution, glutamine‐deficient diet), and the combination of glutamine‐deficient feed and COP group (50 mg·kg−1 in cyclodextrin solution, glutamine‐deficient diet) (n = 6 mice per group). Intraperitoneal (i.p.) injections of the compound were administered every other day for a span of two weeks. The tumor volume (mm3) of the mice was measured daily using calipers and calculated using the formula (length × width
2
)/2. Mice were euthanized via intraperitoneal injection of pentobarbital sodium. The xenografts were dissected, photographed, weighed, and fixed in 4% paraformaldehyde. For the intratumoral injection of shATF4 lentivirus, the mice were also randomly assigned to specific groups: Control (intratumoral injection of scramble control shRNA lentivirus, normal diet), shATF4 group (intratumoral injection of shATF4 lentivirus, normal diet), glutamine‐deficient feed group (intratumoral injection of scramble control shRNA lentivirus, glutamine‐deficient diet), and the combination of glutamine‐deficient feed and shATF4 group (intratumoral injection of shATF4 lentivirus, glutamine‐deficient diet). The lentivirus titer used was 1.0 × 109 TU·mL−1 (shATF4 lentivirus: 5′‐GTTGGATGACACTTGTGAT‐3′, scramble control shRNA lentivirus: 5′‐TTCTCCGAACGTGTCACGTAA‐3′). Each tumor was injected at three points with 10 µL per point. Injections were administered every four days, totaling three injections. Following the injections, the normal feed group collected tumors on the 12th day, while the glutamine‐deficient group collected tumors on the 18th day. GraphPad Prism software was utilized to assess tumor volume, weight, and the weight of the mice. All analyses were conducted by individuals who were blinded to the experimental groups.
Immunohistochemistry (IHC)
Immunohistochemistry (IHC) was performed following a standardized protocol. NSCLC tumor tissues were retrieved from mice, embedded in paraffin, and then sectioned into 3 µm slices on glass slides. The tissue sections underwent deparaffinization and rehydration. Primary antibodies were incubated at 4 °C overnight following antigen retrieval. The primary antibodies employed in this study were as follows: ATF4 (Abcam, ab184909, 1:100), ASCT2 (Proteintech, 20350‐1‐AP, 1:400), and Ki67 (Cell Signaling Technology, 9449, 1:800). Subsequently, the sections were exposed to fluorescence‐labeled secondary antibodies. Images were captured using a customized Nikon Eclipse C1 fluorescence microscope. Immunohistochemistry antibody details are listed in Table S4 (Supporting Information).
Tissue Microarray Analysis
The NSCLC tissue microarray (AF‐LucSur2201) was collected and processed by AiFang Biological, and approved by the Institutional Review Board of Shanghai Pulmonary Hospital (K23‐334). In this study, tissue immunofluorescence was employed to evaluate the expression and colocalization of GS, GLS, and ATF4. Initially, the tissue microarray underwent rehydration, antigen retrieval, and blocking with peroxidase (3% hydrogen peroxide solution) and serum. Subsequently, the tissue microarray was incubated with anti‐ATF4 (Abcam, ab184909, 1:200), GS (Affinity, DF7341, 1:200), and GLS (Proteintech, 23549‐1‐AP, 1:200) antibodies overnight at 4 °C. Following this, horseradish peroxidase (HRP)‐labeled secondary antibodies were applied, and DAPI was used to restain the nuclei. Images were captured using a Nikon Eclipse C1 fluorescence microscope. Fluorescence quantification was performed by Wuhan Servicebio Biotechnology Co., Ltd using Aipathwell software.
Statistical Analysis
Statistical analysis and graphical representation were performed using GraphPad Prism 9.5.0. The results are presented as means ± standard deviation (SD). All experiments were conducted independently at least three times. Data preprocessing for western blot and immunofluorescence quantification involved normalized quantification. A two‐tailed Student's t‐test was utilized to compare two groups, whereas one‐way analysis of variance (ANOVA) was employed for comparisons among multiple groups. Correlations were calculated using Pearson's correlation coefficient test. Survival curves were evaluated with Kaplan‐Meier plots and analyzed using logarithmic rank (Mantel‐Cox) tests. Statistical significance was defined as p values below 0.05, with “ns” representing non‐significant results.
Reagents and Cell Culture
The cell lines utilized in this study were obtained from the National Collection of Authenticated Cell Cultures (Shanghai), and included NCI‐H460, NCI‐H1299, NCI‐H1975, NCI‐HCC827, NCI‐H661, A549, and PC‐9. These cell lines were cultured in RPMI 1640 medium (Gibco, 11875‐093), supplemented with 10% fetal bovine serum (ExCell Bio, FCS500) and 100 U/ml penicillin‐streptomycin (New Cell and Molecular Biotech, China; C100C5). The cells were maintained at 37 °C in a humidified atmosphere composed of 95% air and 5% CO2. Regular testing was conducted to ensure the absence of Mycoplasma contamination. Following attachment, the cells were treated with conditioned medium containing varying concentrations of glutamine (4 mM, 0.5 mM, 0.25 mM, and 0 mM glutamine).
DNA oligonucleotides were obtained from Sangon Biotech (Shanghai) Co., Ltd. and were available in two different purity grades: HPLC and PAGE. The DNA was solubilized in a buffer containing 37.5 mM KCl and 12.5 mM K2HPO4/KH2PO4, pH 7, with a D2O/H2O ratio of 10/90. Final concentrations were determined using a UV spectrometer. The sequences of the oligonucleotides are listed in Table S3 (Supporting Information). The compound coptisine chloride (COP) (CAS NO. 6020‐18‐4) was purchased from Shanghai Standard Technology Co., Ltd. The GCN2 inhibitor GCN2iB (CAS NO. 2183470‐12‐2) was obtained from Med Chem Express (America).
Generation of Persister Cells
NCI‐H460 and NCI‐H1299 cells treated with 4 mM or 0.25 mM glutamine for 1–5 days were switched to complete RPMI‐1640 medium for 2 h to create persister cells.[
65
] Live cells were sorted using the Dead Cell Removal Kit (Miltenyi Biotec, Germany). The Dead Cell Removal MicroBeads target a moiety in the plasma membrane of both apoptotic and dead cells. To deplete dead cells, cells were magnetically labeled with Dead Cell Removal MicroBeads and passed through a separation column. The magnetically labeled dead cells were captured within the column, while the unlabeled living cells passed through, resulting in a cell fraction free of dead cells. NCI‐H460 and NCI‐H1299 cells were counted after treatment with 4 mM or 0.25 mM glutamine. The cell suspension was centrifuged at 300×g for 10 minutes, followed by complete aspiration of the supernatant. The cell pellet was then resuspended in 100 µL of Dead Cell Removal MicroBeads per 10⁷ total cells, mixed well, and incubated for 15 min at room temperature (20 to 25 °C). 1× Binding Buffer was added to the cell suspension to achieve a minimum volume of 500 µL for separation. Magnetic separation was then carried out by placing the column in the magnetic field of a suitable MACS Separator, rinsing the column with the appropriate amount of 1× Binding Buffer, applying the cell suspension onto the column, and collecting the flow‐through containing unlabeled cells (live cells). The column was washed with the appropriate amount of 1× Binding Buffer and mixed thoroughly. The screened live cells were then centrifuged, resuspended in a medium containing 4 mM and 0.25 mM glutamine for counting, and finally seeded into plates.
Cell Viability Assay
Cell viabilities were assessed using the Cell Counting Kit‐8 (Med Chem Express, America). In brief, cells were seeded onto 96‐well plates at a density of 2 × 103 per well and exposed to varying concentrations of COP or conditioned medium. The plates were then maintained at 37°C for 24, 48, and 72 h. Subsequently, 10 µL of CCK‐8 reagent (diluted in 100 µL medium per well) was added to the cells, and they were incubated for 2 h at 37 °C in a 5% CO2 incubator. The absorbance at a wavelength of 450 nm was measured using a microplate reader. This assay was repeated at least three times, with triplicate measurements each time. For the trypan blue exclusion assay, 2 × 105 cells were cultured in complete medium in 60 mm3 dishes and incubated at 37 °C. The cells were treated with conditioned medium (4 mM glutamine, 0.25 mM glutamine) and the appropriate culture medium for 1 to 5 days, starting from the second day. After preparing a cell suspension, the suspension was mixed with 0.4% trypan blue at a 9:1 ratio (final concentration 0.04%). The mixture was then stained for 3 min, and the viable cells were counted. In the ethynyldeoxyuridine (EdU) analysis, 1.2 × 104 cells were seeded in 96‐well plates and exposed to EdU for a duration of 4 h. The subsequent steps were performed according to the manufacturer's protocol. The results were captured using the Molecular Devices ImageXpress High Content Confocal Imaging System and quantified using Image J. For the cell apoptosis experiment, cells were treated with conditioned medium (4 mM glutamine, 0.25 mM glutamine) for 24 h. After fixation, the cells were stained with Hoechst 33 342 (1000×) and visualized using a fluorescent microscope.
Colony Formation Assay
A range of 2000 to 4000 cells per well were seeded into 6‐well plates with complete growth medium and allowed to settle overnight. On the following day, the cells were treated with conditioned medium for a duration of 7 to 12 days, during which visible colonies formed. For NCI‐H460 and NCI‐H1299 cells, transfection was carried out using shScramble, shATF4, pHBLV‐CMV‐MCS‐3Flag‐ZsGreen‐T2A‐PURO vector, and pHBLV‐CMV‐ATF4‐3Flag‐ZsGreen‐T2A‐PURO plasmids (HANBIO, China) in combination with Lipofectamine 3000 (Thermo Fisher Scientific). Post‐transfection, cells were plated into 6‐well plates with 2000 cells per well, following the previously described protocol. The resultant colonies were washed with phosphate‐buffered saline (PBS) and fixed using 4% Paraformaldehyde Fix Solution (Servicebio, G1101‐500ML) for 10 min at room temperature. Subsequently, they were stained with Crystal Violet Staining Solution (Beyotime, C0121‐100ML) for 10 min. After a PBS wash, colony counting was performed for statistical analysis.
RNA Extraction and Quantitative RT‐PCR
Total RNA was extracted using the RNA extraction kit (Shanghaiyishan, China) following the manufacturer's instructions. Subsequently, cDNA was synthesized using the HiScript Q RT SuperMix kit (Vazyme, China). Quantitative RT‐PCR analyses were conducted on the LightCycler 480 real‐time fluorescent quantitative PCR system (Roche, Germany). The threshold cycles (Ct values) of the target genes were normalized to GAPDH, which was used as the endogenous control. All qPCR amplifications were carried out in triplicate and repeated in three independent experimental runs. The primer sequences utilized for qPCR can be found in Table S1 (Supporting Information).
Measurement of ROS Production
Cells were exposed to conditioned medium, followed by trypsinization and collection into 1.5 mL tubes. After collection, the cells were resuspended in a diluted solution of DCFH‐DA and incubated at 37°C for 20 min. Subsequently, the cells underwent three washes using serum‐free cell culture medium to remove any residual DCFH‐DA that had not entered the cells. The detection procedure was conducted using a BD Caliber flow cytometer (BD Biosciences), and the results were analyzed using FlowJo software.
Western Blot Analysis
Total proteins were extracted from cells or tumor tissue homogenates using NP‐40 lysis buffer. The protein samples were then separated by SDS‐PAGE gel and transferred onto polyvinylidene fluoride (PVDF) membranes (Bio‐Rad, CA, USA). Subsequent to transfer, these membranes were incubated with specific antibodies, including the following primary antibodies: ASCT2 (Proteintech, 20350‐1‐AP, 1:2000), HRI (Proteintech, 20499‐1‐AP, 1:1000), TFAP2A (Proteintech, 13019‐3‐AP, 1:2000), p‐P70(S6K) (Thr389) (Proteintech, 28735‐1‐AP, 1:2000), P70(S6K) (Proteintech, 14485‐1‐AP, 1:2000), SLC7A5 (Proteintech, 28670‐1‐AP, 1:2000), ATF4 (Abcam, ab184909, 1:500), mTOR (Abcam, ab13a4903, 1:10000), p‐mTOR (S2448) (Abcam, ab109268, 1:1000), GCN2 (Abcam, ab134053, 1:1000), eIF4EBP1 (Abcam, ab32024, 1:2000), p‐GCN2 (Thr899) (Affinity, AF8154, 1:1000), p‐4EBP1 (Thr37/46) (Cell Signaling Technology, 2855, 1:1000), eIF2α (Cell Signaling Technology, 5324, 1:1000), p‐eIF2α (Ser51) (Cell Signaling Technology, 3398, 1:1000), GAPDH (Cell Signaling Technology, 97 166, 1:1000). Following incubation with primary antibodies, immunocomplexes were visualized through a chemiluminescence reaction using ECL reagents (Vazyme, China) and quantified using Image J software. The antibodies employed for western blotting are listed in Table S4 (Supporting Information).
Glutamine Uptake Assay
Cells were initially seeded in 100 mm3 tissue culture plates with complete growth medium and incubated overnight. The following day, cells were harvested after 36 h of treatment with conditioned medium. For tissue samples, cells were lysed using established protocols, and each group was then normalized based on protein quantification. To quantify glutamine uptake, a glutamine assay kit (ab197011, Abcam) was used, following the manufacturer's instructions. This experiment was repeated three times and conducted in three independent iterations.
Seahorse Analysis
The cellular oxygen consumption rate (OCR) was measured using the Seahorse XFe96 Analyzer (Agilent Technologies). Cells treated with COP and knock‐down ATF4 were resuspended in Seahorse XF RPMI 1640 medium (supplemented with 10 mM glucose, 1 mM pyruvate, and 2 mM glutamine), and seeded in Cell‐Tak (Corning) precoated Seahorse 96‐well plates. The OCR was assessed under basal conditions and in response to 1.5 µM oligomycin, 1.5 µM fluorocarbonyl cyanide phenylhydrazone (FCCP), 0.5 µM rotenone, and antimycin A.
Immunofluorescence
After the cellular pre‐treatment, cells were fixed in a 4% paraformaldehyde solution for 20 min. Subsequently, cells were permeabilized with 0.1% Triton X‐100 for 10 min, followed by blocking with a 0.5% bovine serum albumin solution for 2 h at room temperature. In the next step, samples were subjected to an overnight incubation with primary antibodies, followed by an additional incubation with fluorescently conjugated secondary antibodies and DAPI. Captured images were acquired using the ImageXpress Micro system, and quantification was performed using Image J software. Immunofluorescence antibodies are listed in Table S4.
NMR Spectroscopy Experiments
NMR spectra were acquired using a Bruker AV‐600 spectrometer equipped with a QCI Cryoprobe. The w5 water peak suppression was configured for the entire spectrum collection process. NMR spectra processing was performed using Topspin 3.5 (Bruker) and Sparky (UCSF) software. 2D NOESY experiments were conducted with varying mixing times of 80, 300, 350, and 400 ms, at temperatures spanning 5, 10, 15, 25, and 35 °C in a solvent mixture of 90% H2O/10% D2O. The DQF‐COSY spectrum was acquired at a temperature of 25 °C. 1H‐13C HSQC spectra were obtained using the hsqcetgpsi pulse sequence with a 1J (C, H) coupling constant of 145 Hz.
Structure Calculation
NOE‐based distance restraints were categorized into different strengths: strong (2.9 ± 1.1 Å), medium (4.0 ± 1.5 Å), weak (5.5 ± 1.5 Å), and very weak (6.0 ± 1.5 Å), based on the NOESY spectra collected with mixing times of 80 and 350 ms at temperatures of 25 and 5 °C, respectively. Exchangeable protons were assigned corresponding to medium (4.0 ± 1.2 Å), weak (5.0 ± 1.2 Å), and very weak (6.0 ± 1.2 Å) distances. Intermolecular cross‐peaks between COP and DNA were defined by very weak (6.0 ± 1.5 Å), weak (5.0 ± 1.5 Å), and medium (4.0 ± 1.5 Å) distances. Overlapping and ambiguous resonances were categorized as having a distance of 5.0 ± 2.0 Å.
For the 12 tetrad guanines, 48 hydrogen bond restraints were applied. Dihedral restraints were employed for glycosidic torsion angles with values of 170°−310° and 200°−280°, corresponding to anti‐conformations in loop regions and within the G‐tetrad, respectively. The structure calculation incorporating NOE restraints was carried out using Xplor‐NIH74 in conjunction with the Amber 20 package. An ensemble of 100 starting structures was generated using Xplor‐NIH. COP parameter files were initially obtained from ChemDraw18.2 and were further optimized and computed using the Gaussian 09 program. Subsequently, the 100 initial structures underwent simulated annealing using the sander module of the Amber 20 package. The 20 structures with the lowest energy were selected for molecular dynamic simulations with the pmemd module of the Amber 20 package, in the presence of K+ cations and TIP3P water. Finally, the last 500 ps of trajectories were averaged, followed by energy minimization for 500 steps in a vacuum, after removal of water molecules and cations. The final ensemble consisted of the 10 lowest‐energy structures, which were chosen for deposition and analysis. Visualization and analysis were performed using VMD and PyMOL software.
CD Experiments
CD data were acquired using a Jasco‐1500 spectropolarimeter (Jasco Inc., Japan). DNA samples were prepared in a buffer containing 5 mM K2HPO4/KH2PO4 at pH 7, with a final concentration of 20 µM. The DNA samples were annealed at 95 °C for 5 min and then gradually cooled to room temperature using a heating block. Subsequently, the desired concentration of COP was added, and the mixture was incubated for 2 h. CD experiments were performed in a 1 mm path length quartz cuvette at a temperature of 25 °C. A baseline measurement was obtained using the buffer spectrum. To account for any blank, the baseline was subtracted, and the Savitzky‐Golay smoothing algorithm was applied to enhance the spectral smoothness. For CD melting analysis, the sample was heated from 25 to 95 °C at a heating rate of 2 °C·min−1, while monitoring the CD ellipticity at 264 nm. The melting temperature was determined at the point where the median of the fitted baselines intersected with the melting curve.
Fluorescence Measurements
Fluorescence measurements were performed using a Jasco‐FP8300 spectrofluorometer (Jasco Inc., Japan). The fluorescence spectra were recorded using a 1 cm path length quartz cell. Emission spectra were captured within the range of 520 to 600 nm, with an excitation wavelength set at 377 nm. COP concentrations were held at 0.2 µM in a 50 mM K+‐containing solution, with a total volume of 3 mL. DNA was incrementally introduced at the desired concentration. Following a 2‐min incubation period at each step, fluorescence spectra were collected.
Native Electrophoretic Mobility Shift Assay (EMSA)
EMSA gels were prepared using a 10 × 7 cm native gel with a thickness of 1 mm, containing 12.5 mM KCl and 16% acrylamide (acrylamide/bisacrylamide ratio of 29:1), pH 8.0. DNA samples were dissolved in a 5 mM KCl, 12.5 mM phosphate buffer at pH 7.0. COP was added at the desired concentration and incubated for 2 h. Electrophoresis was carried out at a voltage of 45 V in 1 × TBE running buffer. DNA bands were visualized at 260 nm using UV light.
DMS Footprinting
DMS footprinting was conducted following the described protocol with certain modifications.[
39b
] DNA samples were annealed in the presence of 50 mM KCl or LiCl and subsequently treated with 0.4% DMS (final concentration) for 2 min at room temperature in a total volume of 200 µL. The methylation reaction was promptly stopped by adding 150 µL of freshly prepared stop buffer containing 2.5 M NH4OAc, 0.1 M β‐mercaptoethanol, and 1 mg·mL−1 sperm DNA. Following phenol/chloroform extraction and ethanol precipitation, the DNA was reconstituted in water with the addition of 100 µL of 10% (vol/vol) piperidine in water and then heated at 90 °C for 30 min to cleave the methylated DNA. Subsequently, chloroform extraction and ethanol precipitation were performed. The precipitated DNA was dissolved in 80% (vol/vol) deionized formamide in water, denatured at 95 °C for 5 min, and separated on a 20% denaturing polyacrylamide gel. DNA fragments were visualized using a FAM dye covalently labeled at the 5′‐end of the DNA on a GelGo Imager (BIO‐RAD, USA) and digitized using the ImageQuant 5.2 software.
ChIP
NCI‐H1299 cells in the logarithmic growth phase were seeded into sterile cell culture dishes at a density of 1 × 106 cells per dish. Once adhered, the medium was exchanged with either 4 mM glutamine, 0.25 mM glutamine, or 0.25 mM glutamine supplemented with 20 µM COP. After a 12‐h treatment period, the cells were cross‐linked using 1% formaldehyde at room temperature for 10–15 min. Following centrifugation, the cells were lysed using Lysis Buffer (1 × 106 cells / 200 µL) on ice for 10 min. Subsequently, the cells were resuspended in ChIP buffer (1 × 106 cells / 100 µL) and subjected to ultrasonic treatment at 25% power for 3–4 pulses to facilitate chromatin extraction. DNA shearing was verified via agarose gel electrophoresis. The ChIP reaction system was prepared, followed by reverse cross‐linking and DNA release. DNA purification and qRT‐PCR reactions were performed. The enrichment rate was calculated using the formula FE% = 2−ΔΔCt × 100, where ΔΔCt = (Ct target gene – Ct IgG) in the treatment group – (Ct target gene – Ct IgG) in the untreated group. The Chroma Flash High‐Sensitivity ChIP Kit (EpigenTek) was employed following the manufacturer's instructions. The experiment was carried out in triplicate and replicated three times. Primers used for ChIP are listed in Table S2 (Supporting Information).
RNA‐Seq
Three distinct sample groups (4Q, 0.25Q, 0.25Q + COP) underwent total RNA extraction, rRNA removal, and mRNA enrichment using conventional kits. The enriched mRNA was reverse transcribed into double‐stranded cDNA, followed by repair of the cDNA ends, addition of junctions, and PCR amplification to generate on‐board libraries. Subsequently, RNA library sequencing was performed on the Illumina HiseqTM 2500/4000 by Gene Denovo Biotechnology Co., Ltd (Guangzhou, China). Bioinformatic analysis was performed using Omicsmart, a real‐time interactive online platform for data analysis available at http://www.omicsmart.com.
Energy Metabolism Analysis
Metabolic profiling was performed on four distinct sample groups using liquid chromatography tandem mass spectrometry (LC‐MS/MS). Sample pre‐treatment was followed by machine analysis, with the liquid phase conditions as follows: The chromatographic column employed was ACQUITY UPLC BEH Amide (1.7 µm, 100 mm × 2.1 mm i.d.), maintained at a constant temperature of 40 °C. Phase A consisted of ultrapure water containing 10 mM ammonium acetate and 0.3% ammonia water, while phase B comprised a mixture of 90% acetonitrile and water. The flow rate was set at 0.40 mL·min−1, with an injection volume of 2 µL. The linear gradient elution profile was as follows: 0–1.2 min A/B at a ratio of 5:95 (V/V), 8 min A/B at 30:70 (V/V), 9–11 min A/B at 50:50 (V/V), and 11.1‐15 min A/B at 5:95 (V/V). The mass spectrometry conditions were set as follows: The spray ion source (ESI) temperature was adjusted to 550 °C. The mass spectrometry voltage in positive ion mode was set to 5500 V, while in negative ion mode, it was set to ‐4500 V. The Curtain Gas (CUR) pressure was maintained at 35 psi. Within the Q‐Trap 6500+, each ion pair was scanned and detected based on optimized settings for declustering potential (DP) and collision energy (CE). Quantitative analysis was carried out using multiple reaction monitoring (MRM) mode on the triple quadrupole mass spectrometry system. The mass spectrometry data were processed using Analyst 1.6.3 software and MultiQuant 3.0.3 software.
Xenograft Tumor Assay
The animal experimental protocol underwent examination and approval by the Institutional Animal Care and Use Committee (IACUC) of the China Pharmaceutical University Experimental Animal Center (2023‐02‐015). Female BALB/c nude mice aged four to five weeks were sourced from Shanghai Sippr‐BK Laboratory Animal Co, Ltd. The mice were provided with both normal and glutamine‐deficient diets. A total of 3 × 106 NCI‐H460 cells, suspended in 200 µL of PBS, were subcutaneously injected into the right flank of the mice. Once the xenograft volumes reached approximately 100 mm3, the mice were randomly assigned to their respective groups: Control (1% DMSO in cyclodextrin solution, normal diet), COP group (50 mg·kg−1 and 100 mg·kg−1 in cyclodextrin solution, normal diet), glutamine‐deficient feed group (1% DMSO in cyclodextrin solution, glutamine‐deficient diet), and the combination of glutamine‐deficient feed and COP group (50 mg·kg−1 in cyclodextrin solution, glutamine‐deficient diet) (n = 6 mice per group). Intraperitoneal (i.p.) injections of the compound were administered every other day for a span of two weeks. The tumor volume (mm3) of the mice was measured daily using calipers and calculated using the formula (length × width
2
)/2. Mice were euthanized via intraperitoneal injection of pentobarbital sodium. The xenografts were dissected, photographed, weighed, and fixed in 4% paraformaldehyde. For the intratumoral injection of shATF4 lentivirus, the mice were also randomly assigned to specific groups: Control (intratumoral injection of scramble control shRNA lentivirus, normal diet), shATF4 group (intratumoral injection of shATF4 lentivirus, normal diet), glutamine‐deficient feed group (intratumoral injection of scramble control shRNA lentivirus, glutamine‐deficient diet), and the combination of glutamine‐deficient feed and shATF4 group (intratumoral injection of shATF4 lentivirus, glutamine‐deficient diet). The lentivirus titer used was 1.0 × 109 TU·mL−1 (shATF4 lentivirus: 5′‐GTTGGATGACACTTGTGAT‐3′, scramble control shRNA lentivirus: 5′‐TTCTCCGAACGTGTCACGTAA‐3′). Each tumor was injected at three points with 10 µL per point. Injections were administered every four days, totaling three injections. Following the injections, the normal feed group collected tumors on the 12th day, while the glutamine‐deficient group collected tumors on the 18th day. GraphPad Prism software was utilized to assess tumor volume, weight, and the weight of the mice. All analyses were conducted by individuals who were blinded to the experimental groups.
Immunohistochemistry (IHC)
Immunohistochemistry (IHC) was performed following a standardized protocol. NSCLC tumor tissues were retrieved from mice, embedded in paraffin, and then sectioned into 3 µm slices on glass slides. The tissue sections underwent deparaffinization and rehydration. Primary antibodies were incubated at 4 °C overnight following antigen retrieval. The primary antibodies employed in this study were as follows: ATF4 (Abcam, ab184909, 1:100), ASCT2 (Proteintech, 20350‐1‐AP, 1:400), and Ki67 (Cell Signaling Technology, 9449, 1:800). Subsequently, the sections were exposed to fluorescence‐labeled secondary antibodies. Images were captured using a customized Nikon Eclipse C1 fluorescence microscope. Immunohistochemistry antibody details are listed in Table S4 (Supporting Information).
Tissue Microarray Analysis
The NSCLC tissue microarray (AF‐LucSur2201) was collected and processed by AiFang Biological, and approved by the Institutional Review Board of Shanghai Pulmonary Hospital (K23‐334). In this study, tissue immunofluorescence was employed to evaluate the expression and colocalization of GS, GLS, and ATF4. Initially, the tissue microarray underwent rehydration, antigen retrieval, and blocking with peroxidase (3% hydrogen peroxide solution) and serum. Subsequently, the tissue microarray was incubated with anti‐ATF4 (Abcam, ab184909, 1:200), GS (Affinity, DF7341, 1:200), and GLS (Proteintech, 23549‐1‐AP, 1:200) antibodies overnight at 4 °C. Following this, horseradish peroxidase (HRP)‐labeled secondary antibodies were applied, and DAPI was used to restain the nuclei. Images were captured using a Nikon Eclipse C1 fluorescence microscope. Fluorescence quantification was performed by Wuhan Servicebio Biotechnology Co., Ltd using Aipathwell software.
Statistical Analysis
Statistical analysis and graphical representation were performed using GraphPad Prism 9.5.0. The results are presented as means ± standard deviation (SD). All experiments were conducted independently at least three times. Data preprocessing for western blot and immunofluorescence quantification involved normalized quantification. A two‐tailed Student's t‐test was utilized to compare two groups, whereas one‐way analysis of variance (ANOVA) was employed for comparisons among multiple groups. Correlations were calculated using Pearson's correlation coefficient test. Survival curves were evaluated with Kaplan‐Meier plots and analyzed using logarithmic rank (Mantel‐Cox) tests. Statistical significance was defined as p values below 0.05, with “ns” representing non‐significant results.
Conflict of Interest
The authors declare no conflict of interest.
Author Contributions
C.X., Y.L., and Y.L. contributed equally to this work. C.X. performed most of the pharmacological experiments. Y.L. and Y.L. performed the G‐quadruplex related studies, particularly the NMR solution structure studies. C.X., Y.L., and Y.L. jointly analyzed the data, and wrote the original draft of the manuscript. K.W., Y.X., and L.K. initiated the project and made significant revisions to the manuscript. R.D. and X.H. participated in experimental method design and animal experiments. Q.L. and X.Z. performed the data statistics. All authors contributed to data analysis, drafting, and revising the paper and agreed to be accountable for all aspects of the work. All authors read and approved the final manuscript.
Supporting information
Supporting Information
Supporting Information |
Title: Optimizing selenium-enriched yeast supplementation in laying hens: Enhancing egg quality, selenium concentration in eggs, antioxidant defense, and liver health | Body:
Introduction
Selenium (Se) is an essential trace element that vital for maintaining health and physiological functions in humans and animals. As an integral component of selenoproteins, selenium participates in biochemical processes, including antioxidant defense, immune modulation, and metabolic regulation (Wang et al., 2020; Razaghi et al., 2021). Its incorporation into enzymes such as glutathione peroxidase (GSH-Px) and thioredoxin reductase, improve cellular protection against oxidative damage by neutralizing reactive oxygen species (ROS) and maintaining redox homeostasis (Zhang et al., 2023). The European Food Safety Authority (EFSA) recommends selenium levels of 0.15 to 0.3 mg/kg feed for poultry nutrition (EFSA, 2024), highlighting its importance in animal nutrition.
Despite its natural presence in soil and water, selenium distribution varies significantly across regions, leading to deficiencies in areas such as China, parts of Europe, and the United States: this poses a risk to human and animal health (Rayman, 2020; Kieliszek and Serrano Sandoval, 2023). In selenium-deficient animals, including poultry, there is an increased risk of oxidative stress, metabolic disorders, and immune dysfunction, which can result in severe health issues such as erythrocyte hemolysis (Zheng et al., 2019). Given that the recommended daily intake for adults ranges from 30 to 40 µg, with an upper limit of 40 µg to prevent toxicity (Lee and Jeong, 2012; Marchetti et al., 2014), optimizing selenium levels in poultry diets becomes crucial for enhancing health, productivity, and the production of functional foods.
Selenium supplementation in poultry has gained prominence for its dual benefits: enhancing animal health and producing selenium-enriched eggs, valued as functional foods with human health benefits. Selenium is commonly supplemented as inorganic (e.g., sodium selenite) or organic forms, with organic selenium gaining preference due to its superior bioavailability, efficacy, and safety (Surai et al., 2018). Additionally, organic selenium sources have exhibited rapid and efficient deposition of Selenium into eggs at higher concentrations (Lu et al., 2018; Lu et al., 2020; Liu et al., 2020), attributable to its absorption via specific amino acid transport pathways in the small intestine (Xin and Gao, 2022). Our previous research work demonstrated the higher efficacy of organic selenium in enhancing antioxidant function, immune response, and production of selenium-enriched eggs in hens (Qiu et al., 2021a, 2021b). Furthermore, organic selenium also supports animal health and product quality by improving gut morphology and microbial composition (Muhammad et al., 2021), mitigating the effects of heat stress (Wang et al., 2022), and extending the shelf life of eggs (Li et al., 2024).
Selenium-enriched yeast (SY), an organic selenium supplement primarily composed of selenomethionine, provides high bioavailability and low toxicity, efficiently mimicking methionine in metabolic pathways to enhance absorption and deposition in tissues and eggs (Suhajda et al., 2000; Hachemi et al., 2023). Previous studies have shown that SY supplementation at various dosage levels exert varying effects on animal performance and health (Meng et al., 2019; Muhammad et al., 2021; Li et al., 2024; Chen et al., 2024). The study by Meng et al. (2019) and Muhammad et al. (2021), reported that SY supplementation at 0.3 mg/kg was found to improve laying rate, egg weight, and feed efficiency. Conversely, research suggests that a lower dose of 0.15 mg/kg is more effective than 0.3 mg/kg when substituting for inorganic selenium, particularly in enhancing performance in laying hens (Li et al., 2024). Meanwhile, a dosage of 2.0 mg/kg, enhanced laying performance but not antioxidant function, without negative health consequences (Chen et al., 2024). However, determining the optimal SY dosage remains a critical challenge due to potential toxicity risk at excessive levels.
Furthermore, despite the established benefits, the effects of SY on liver health and antioxidant gene expression remain underexplored. This study seeks to address this gap by investigating the impact of dietary SY supplementation on hepatic gene expression (antioxidant genes: Nrf2, HO-1, CAT, Keap1 and NQO1), which play crucial roles in oxidative stress response and liver health. Therefore, the study investigated the impact of dietary SY supplementation at dosages of 0.3, 1.5, and 6.0 mg/kg on egg quality, selenium deposition, serum antioxidant enzyme activities, liver histology, and hepatic gene expression in Hy-Line Brown laying hens. We hypothesize that SY supplementation will enhance selenium deposition, upregulate hepatic antioxidant genes, and bolster antioxidant defenses, thereby improving overall health and performance. Identifying the optimal dosage of SY will provide valuable insights into producing functional foods while ensuring the welfare of poultry.
Materials and methods
Animal ethics statement
All experimental protocols were approved by the Animal Care and Use Committee of the Institute of Feed Research, Chinese Academy of Agricultural Sciences (ACE-CAAS-20230628), and all animal experiments were conducted following the ARRIVE guidelines (Kilkenny et al., 2010).
Birds, diets and study design
A total of 360 healthy Hy-Line Brown laying hens, aged 28 weeks, were procured from a commercial poultry farm (Hebei Shengxuan Agricultural Technology Development Co., Ltd). The selection of hens for the experiment was based on similar body weight and laying rate. The hens were randomly assigned to four experimental groups, each containing 90 hens (six replicates of 15 hens each). The groups were designated as follows: Control (0 mg/kg Se), basal diets supplemented with SY with each diet containing Se at: 0.3 mg/kg (SY03), 1.5 mg/kg (SY15), and 6.0 mg/kg (SY60). The basal diets were formulated devoid of selenium, according to the Chicken Feeding Standards (NY/T 33-2004). The nutrient composition of the basal diet is presented in Table 1, Table 2. The experiment period lasted for 12 weeks (age of birds: 28 weeks old to 39-week-old). The birds were kept in battery cages (3 tiers: 40 cm × 40 cm × 35 cm), fed ad libitum, and the environmental conditions (temperature range of 22-24°C and a relative humidity of 60-70%) were maintained throughout the feeding trial. The animals were healthy throughout the feeding trial.Table 1Composition and nutrient levels of the basal diet (as-fed basis, %).Table 1IngredientContent (%)Nutrient level2Content (%)Corn64.67Metabolizable energy (MJ/kg)11.33Soybean meal (44.8% CP)23.50Crude protein16.07 (16.45)Soybean oil0.60Calcium3.50 (4.35)Limestone9.00Total phosphorus0.53 (0.45)Dicalcium phosphorus0.84Non-phytate phosphorus0.32Sodium chloride0.15Lysine0.75 (0.751)Sodium bicarbonate0.65Methionine0.39 (0.405)DL-Methionine (98%)0.17Methionine + cysteine0.65 (0.685)L-Lysine-HCl (78%)0.02Threonine0.55 (0.613)L-Threonine (98%)0.04Selenium0 (0.040)Choline chloride (50%)0.20Premix10.13Phytase0.03Total1001Premix supplied per kilogram of diet: vitamin A, 12,500 IU; vitamin D3, 4,125 IU; vitamin E, 15 IU; vitamin K3, 2 mg; thiamine, 1 mg; riboflavin, 8.5 mg; pyridoxine 8 mg vitamin B12, 0.04 mg; biotin, 0.1 mg; folic acid, 1.25 mg; Ca-pantothenate, 50 mg; niacin, 32.5 mg; Cu, 8 mg; Zn, 65 mg; Fe, 60 mg; Mn, 65 mg; I, 1 mg.2The values in parenthesis indicate analyzed values. Others are calculated values.CP (GB/T6432-2018), Ca (GB/T6436-2018) and TP (GB/T6437-2018) were measured values, while the other nutrient levels were calculated values referred to NY/T33-2004.Table 2The Se level of experimental diets.Table 2ItemExperimental treatment1CONSY03SY15SY60Measured value, mg/kg0.0400.2041.8705.7031Abbreviations: CON, control; SY03, 0.3 mg/kg; SY15, 1.5 mg/kg; SY60, 6.0 mg/kg.
Sample collection and analytical determination
Laying performance
Daily egg production and egg weight were monitored and recorded on a replicate basis. The laying rate is expressed as the average hen-day production, calculated from the total number of eggs divided by the total number of days multiplied by 100. Whereas, egg weight was expressed as average egg weight (AEW), calculated from total egg weight in grams per number of eggs produced. Feed intake (FI) was recorded on a replicate basis at weekly intervals and expressed as average daily feed intake (ADFI). Feed to egg ratio was calculated as grams of feed consumed per grams of eggs produced.
Egg quality assessment
A total of 144 eggs (six eggs per replicate = 36 eggs per group) were collected at the end of weeks 4 (31 weeks-old), 8 (35 weeks-old), and 12 (39 weeks-old), for egg quality assessment. The collected eggs were kept at room temperature, and all egg quality indicators were assessed with various instruments, within 24 h of collection.
The egg shape index was measured using an egg-shaped index apparatus (Egg Index Reader, Fujihira Industry Co., Tokyo, Japan). The eggshell strength was obtained using an eggshell strength analyzer (Egg Force Reader™, Model EFR-01, Orka Food Technology Ltd., Ramat Hasharon, Israel). The eggshell thickness was determined at three specific points (the air cell, equator, and sharp end) with an Egg Shell Thickness Gauge (Orka Technology Ltd., Ramat Hasharon, Israel). Furthermore, the albumen height, Haugh Unit (HU), and yolk color were precisely measured using an egg quality auto-analyzer (Egg Analyzer™, Orka Technology Ltd., Ramat Hasharon, Israel).
Se assay
A total of 288 eggs (12 eggs per replicate = 72 eggs per group) were collected at the end of feeding trial (Week 12). The eggs were utilized for analyzing selenium deposition or content in the whole egg, as well as in the albumen and yolk, respectively. The eggs were divided into two sets; In the first set, the eggs (n = 144 eggs, six eggs per replicate = 36 eggs per group) were broken, and an egg separator was used to separate the egg yolk and albumen. Whereas, in the second set, eggs (n = 144 eggs, six eggs per replicate = 36 eggs per group) were broken, and albumen and yolk were homogenized to obtain whole egg sample. The respective sample: whole egg, albumen and yolk, were dried, ground, and digested with concentrated HNO3. The selenium content in both egg yolks and albumen was precisely determined using a method specifically designed for inductively coupled plasma mass spectrometry (ICP-MS), a highly sensitive and accurate analytical technique. We adopted the procedure described by Mohammadsadeghi et al. (2023).
It is worth mentioning that based on our findings for egg quality and selenium deposition in eggs, both the 1.5 mg/kg and 6.0 mg/kg groups showed comparable effects on egg quality and selenium concentration, thus, we excluded the SY03 group during the biochemical analysis using blood parameters and histological examination of the liver. This ensures focus on dosage-dependent effects, allowing for optimal dosage investigation.
Clinical blood parameters
At the end of the 12th week (age: 39 weeks old), 24 birds (1 per replicate) were selected and deprived of feed for about eight hours before slaughter. About 5 ml of blood was collected from the jugular vein into a collection tube, kept in a slant position to stand for about 2 h. Then, centrifuged at 2500 rpm for 10 min, the harvested serum samples were transferred into Eppendorf tubes and stored at -20°C for serum biochemical analysis. Serum levels of alanine aminotransferase (ALT), alkaline phosphatase (ALP), aspartate aminotransferase (AST), total protein (TP), albumin (ALB), globulin (GLB), and uric acid (UA) were measured using an automatic biochemical analyzer (Zhuoyue 300, Kehua Bio. Co., Ltd. Shanghai, China). The GLB content was mathematically derived by subtracting the albumin content from the TP content. The enzymatic activities of total antioxidant capacity (T-AOC), catalase (CAT), glutathione peroxidase, and superoxide dismutase (SOD) in serum were measured using commercial assay kits provided by the (Nanjing Jiancheng Bioengineering Institute, Nanjing, China). Serum immunoglobulin (Ig) indices, specifically IgA, IgM, and IgG, were determined using assay kits specifically for birds (Shanghai Meilian Bio. Co., Ltd. Shanghai, China). All the protocols used were strictly based on the manufacturer's instructions.
Organ index determination
After blood collection, the hens were euthanized and dissected to obtain the following organs: liver, heart, spleen, lung, and kidney, which were then weighed and measured using an electrical scale (quantitative analysis at 0.01 g level). The organ index was calculated by the formula as follows: Organindex=organweight/bodyweight×100%
Histological examination of the liver tissue
A portion of liver tissue from each bird was cut and fixed in 4% paraformaldehyde for 24 h, for histological examination, according to that described by Peng et al. (2019). The essence of the fixation is to preserve tissue morphology and cellular integrity, while enhancing the penetration of staining reagent in subsequent processing steps, improving the visibility and staining quality. Following fixation, the tissue was processed for paraffin embedding, and serial sections of 5-7 μm thickness were subsequently cut using a microtome. These sections were then de-paraffinized through solvents, stained with hematoxylin and eosin (H & E) for histological analysis, and finally mounted on glass slides. The stained slides were examined under an optical microscope (Nikon Eclipse E600, Japan), with magnification (40 x), for detailed histological assessment. Procedures described by Bancroft et al. (1990) was used.
Owing to the fact that SY supplementation at 1.5 mg/kg have shown optimal performance for egg quality and physiological responses, with almost a zero score for histopathology of the liver. Also, there was a marked significance between the control and the dietary groups for most parameters evaluated, suggesting distinct dietary influence. We therefore, selected only the control and SY15 group for analysis of hepatic antioxidant gene expression, to further highlight the molecular mechanisms underlying the dietary influence.
Hepatic gene expression analysis
A portion of liver tissue from each bird was cut and placed into a freezing tube, then stored at -80°C for RNA extraction and quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis (gene expression analysis). Briefly, the frozen liver sample was grounded under liquid nitrogen condition, and the total RNA was extracted the samples using the TransZol Up Plus RNA kit (Alltech Jinsheng Biotech Co., Ltd. Beijing, China). The concentration and purity of the extracted RNA were determined using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA), and stored at -80°C until further processing. The RNA was reverse transcribed into cDNA using the FastQuant RT Kit (Tiangen Biochemical Technology Co., Ltd. Beijing, China), ensuring accurate and efficient conversion. The resulting cDNA was then carefully stored at -20°C to preserve its integrity for subsequent experiments. The RT-PCR amplification was performed on a Bio-Rad C1000 Thermal Cycler (Bio-Rad Laboratories, Inc., California, USA), equipped with a CFX96 Touch Real-Time PCR Detection System using SuperReal PreMix Plus (SYBR Green, FP205, Tengen Biotech, Beijing, China), to quantify the mRNA expression levels of Nrf2, Keap1, HO-1, NQO1, and CAT genes. The procedures described in Wang et al. (2019) was adopted. The primer sequences used for amplification are shown in Table 3. The PCR reaction conditions were set as follows: initial denaturation at 95°C for 15 min, denaturation at 95°C for 10 s, annealing at 60°C for 30 s, and a final extension for 40 cycles (as required by the protocol). Amplification was stopped at the end of the cycle, and measurements were repeated for each sample to ensure reproducibility. The relative gene expression was calculated using the 2−ΔΔCt method (Livak and Schmittgen, 2001), and β-actin was used as the reference gene. This allows comparative analysis of gene expression across samples.Table 3Gene-specific primers for real-time quantitative reverse transcription PCR.Table 3GenesPrimers (5′-3′)Gene numberNrf2Forward: GGTGACACAGGAACAACANM_205117.2Reverse: AAGTCTTATCTCCACAGGTAGKeap1Forward: ATCACCTCTTCTGCACCGAAXM_015274015.1Reverse: GGTTCGGTTACCGTCCTGCHO-1Forward: CTGAAGGAAGCCACCAAGNM_205344.2Reverse: CCAGAGCAGAGTAGATGAAGNQO1Forward: CACCATCTCTGACCTCTACNM_001277620.2Reverse: CCGCTTCAATCTTCTTCTGCATForward: CACTGTTGCTGGAGAATCTNM_001031215.2Reverse: GGCTATGGATGAAGGATGGβ-actinForward: TATGTGCAAGGCCGGTTTCNM_205518.2Reverse: TGTCTTTCTGGCCCATACCAAAbbreviations: Nrf2, Nuclear factor erythroid 2-related factor 2; Keap1, Kelch-like ECH-associated protein 1; HO-1, Heme oxygenase-1; NQO1, NAD(P)H: quinone oxidoreductase 1; CAT, Catalase; β-actin, Beta-actin.
Statistical analysis
All data were analyzed using SPSS statistical software (version 27.0). A one-way analysis of variance (ANOVA) was followed by Duncan's multiple comparison test, which was performed to compare the means among different treatments. A t-test was used to analyze the hepatic expression of antioxidant genes between the control and SY15 group. Differences were considered statistically significant at P < 0.05. Data are presented as means ± pooled SEM.
Animal ethics statement
All experimental protocols were approved by the Animal Care and Use Committee of the Institute of Feed Research, Chinese Academy of Agricultural Sciences (ACE-CAAS-20230628), and all animal experiments were conducted following the ARRIVE guidelines (Kilkenny et al., 2010).
Birds, diets and study design
A total of 360 healthy Hy-Line Brown laying hens, aged 28 weeks, were procured from a commercial poultry farm (Hebei Shengxuan Agricultural Technology Development Co., Ltd). The selection of hens for the experiment was based on similar body weight and laying rate. The hens were randomly assigned to four experimental groups, each containing 90 hens (six replicates of 15 hens each). The groups were designated as follows: Control (0 mg/kg Se), basal diets supplemented with SY with each diet containing Se at: 0.3 mg/kg (SY03), 1.5 mg/kg (SY15), and 6.0 mg/kg (SY60). The basal diets were formulated devoid of selenium, according to the Chicken Feeding Standards (NY/T 33-2004). The nutrient composition of the basal diet is presented in Table 1, Table 2. The experiment period lasted for 12 weeks (age of birds: 28 weeks old to 39-week-old). The birds were kept in battery cages (3 tiers: 40 cm × 40 cm × 35 cm), fed ad libitum, and the environmental conditions (temperature range of 22-24°C and a relative humidity of 60-70%) were maintained throughout the feeding trial. The animals were healthy throughout the feeding trial.Table 1Composition and nutrient levels of the basal diet (as-fed basis, %).Table 1IngredientContent (%)Nutrient level2Content (%)Corn64.67Metabolizable energy (MJ/kg)11.33Soybean meal (44.8% CP)23.50Crude protein16.07 (16.45)Soybean oil0.60Calcium3.50 (4.35)Limestone9.00Total phosphorus0.53 (0.45)Dicalcium phosphorus0.84Non-phytate phosphorus0.32Sodium chloride0.15Lysine0.75 (0.751)Sodium bicarbonate0.65Methionine0.39 (0.405)DL-Methionine (98%)0.17Methionine + cysteine0.65 (0.685)L-Lysine-HCl (78%)0.02Threonine0.55 (0.613)L-Threonine (98%)0.04Selenium0 (0.040)Choline chloride (50%)0.20Premix10.13Phytase0.03Total1001Premix supplied per kilogram of diet: vitamin A, 12,500 IU; vitamin D3, 4,125 IU; vitamin E, 15 IU; vitamin K3, 2 mg; thiamine, 1 mg; riboflavin, 8.5 mg; pyridoxine 8 mg vitamin B12, 0.04 mg; biotin, 0.1 mg; folic acid, 1.25 mg; Ca-pantothenate, 50 mg; niacin, 32.5 mg; Cu, 8 mg; Zn, 65 mg; Fe, 60 mg; Mn, 65 mg; I, 1 mg.2The values in parenthesis indicate analyzed values. Others are calculated values.CP (GB/T6432-2018), Ca (GB/T6436-2018) and TP (GB/T6437-2018) were measured values, while the other nutrient levels were calculated values referred to NY/T33-2004.Table 2The Se level of experimental diets.Table 2ItemExperimental treatment1CONSY03SY15SY60Measured value, mg/kg0.0400.2041.8705.7031Abbreviations: CON, control; SY03, 0.3 mg/kg; SY15, 1.5 mg/kg; SY60, 6.0 mg/kg.
Sample collection and analytical determination
Laying performance
Daily egg production and egg weight were monitored and recorded on a replicate basis. The laying rate is expressed as the average hen-day production, calculated from the total number of eggs divided by the total number of days multiplied by 100. Whereas, egg weight was expressed as average egg weight (AEW), calculated from total egg weight in grams per number of eggs produced. Feed intake (FI) was recorded on a replicate basis at weekly intervals and expressed as average daily feed intake (ADFI). Feed to egg ratio was calculated as grams of feed consumed per grams of eggs produced.
Egg quality assessment
A total of 144 eggs (six eggs per replicate = 36 eggs per group) were collected at the end of weeks 4 (31 weeks-old), 8 (35 weeks-old), and 12 (39 weeks-old), for egg quality assessment. The collected eggs were kept at room temperature, and all egg quality indicators were assessed with various instruments, within 24 h of collection.
The egg shape index was measured using an egg-shaped index apparatus (Egg Index Reader, Fujihira Industry Co., Tokyo, Japan). The eggshell strength was obtained using an eggshell strength analyzer (Egg Force Reader™, Model EFR-01, Orka Food Technology Ltd., Ramat Hasharon, Israel). The eggshell thickness was determined at three specific points (the air cell, equator, and sharp end) with an Egg Shell Thickness Gauge (Orka Technology Ltd., Ramat Hasharon, Israel). Furthermore, the albumen height, Haugh Unit (HU), and yolk color were precisely measured using an egg quality auto-analyzer (Egg Analyzer™, Orka Technology Ltd., Ramat Hasharon, Israel).
Se assay
A total of 288 eggs (12 eggs per replicate = 72 eggs per group) were collected at the end of feeding trial (Week 12). The eggs were utilized for analyzing selenium deposition or content in the whole egg, as well as in the albumen and yolk, respectively. The eggs were divided into two sets; In the first set, the eggs (n = 144 eggs, six eggs per replicate = 36 eggs per group) were broken, and an egg separator was used to separate the egg yolk and albumen. Whereas, in the second set, eggs (n = 144 eggs, six eggs per replicate = 36 eggs per group) were broken, and albumen and yolk were homogenized to obtain whole egg sample. The respective sample: whole egg, albumen and yolk, were dried, ground, and digested with concentrated HNO3. The selenium content in both egg yolks and albumen was precisely determined using a method specifically designed for inductively coupled plasma mass spectrometry (ICP-MS), a highly sensitive and accurate analytical technique. We adopted the procedure described by Mohammadsadeghi et al. (2023).
It is worth mentioning that based on our findings for egg quality and selenium deposition in eggs, both the 1.5 mg/kg and 6.0 mg/kg groups showed comparable effects on egg quality and selenium concentration, thus, we excluded the SY03 group during the biochemical analysis using blood parameters and histological examination of the liver. This ensures focus on dosage-dependent effects, allowing for optimal dosage investigation.
Clinical blood parameters
At the end of the 12th week (age: 39 weeks old), 24 birds (1 per replicate) were selected and deprived of feed for about eight hours before slaughter. About 5 ml of blood was collected from the jugular vein into a collection tube, kept in a slant position to stand for about 2 h. Then, centrifuged at 2500 rpm for 10 min, the harvested serum samples were transferred into Eppendorf tubes and stored at -20°C for serum biochemical analysis. Serum levels of alanine aminotransferase (ALT), alkaline phosphatase (ALP), aspartate aminotransferase (AST), total protein (TP), albumin (ALB), globulin (GLB), and uric acid (UA) were measured using an automatic biochemical analyzer (Zhuoyue 300, Kehua Bio. Co., Ltd. Shanghai, China). The GLB content was mathematically derived by subtracting the albumin content from the TP content. The enzymatic activities of total antioxidant capacity (T-AOC), catalase (CAT), glutathione peroxidase, and superoxide dismutase (SOD) in serum were measured using commercial assay kits provided by the (Nanjing Jiancheng Bioengineering Institute, Nanjing, China). Serum immunoglobulin (Ig) indices, specifically IgA, IgM, and IgG, were determined using assay kits specifically for birds (Shanghai Meilian Bio. Co., Ltd. Shanghai, China). All the protocols used were strictly based on the manufacturer's instructions.
Organ index determination
After blood collection, the hens were euthanized and dissected to obtain the following organs: liver, heart, spleen, lung, and kidney, which were then weighed and measured using an electrical scale (quantitative analysis at 0.01 g level). The organ index was calculated by the formula as follows: Organindex=organweight/bodyweight×100%
Histological examination of the liver tissue
A portion of liver tissue from each bird was cut and fixed in 4% paraformaldehyde for 24 h, for histological examination, according to that described by Peng et al. (2019). The essence of the fixation is to preserve tissue morphology and cellular integrity, while enhancing the penetration of staining reagent in subsequent processing steps, improving the visibility and staining quality. Following fixation, the tissue was processed for paraffin embedding, and serial sections of 5-7 μm thickness were subsequently cut using a microtome. These sections were then de-paraffinized through solvents, stained with hematoxylin and eosin (H & E) for histological analysis, and finally mounted on glass slides. The stained slides were examined under an optical microscope (Nikon Eclipse E600, Japan), with magnification (40 x), for detailed histological assessment. Procedures described by Bancroft et al. (1990) was used.
Owing to the fact that SY supplementation at 1.5 mg/kg have shown optimal performance for egg quality and physiological responses, with almost a zero score for histopathology of the liver. Also, there was a marked significance between the control and the dietary groups for most parameters evaluated, suggesting distinct dietary influence. We therefore, selected only the control and SY15 group for analysis of hepatic antioxidant gene expression, to further highlight the molecular mechanisms underlying the dietary influence.
Hepatic gene expression analysis
A portion of liver tissue from each bird was cut and placed into a freezing tube, then stored at -80°C for RNA extraction and quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis (gene expression analysis). Briefly, the frozen liver sample was grounded under liquid nitrogen condition, and the total RNA was extracted the samples using the TransZol Up Plus RNA kit (Alltech Jinsheng Biotech Co., Ltd. Beijing, China). The concentration and purity of the extracted RNA were determined using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA), and stored at -80°C until further processing. The RNA was reverse transcribed into cDNA using the FastQuant RT Kit (Tiangen Biochemical Technology Co., Ltd. Beijing, China), ensuring accurate and efficient conversion. The resulting cDNA was then carefully stored at -20°C to preserve its integrity for subsequent experiments. The RT-PCR amplification was performed on a Bio-Rad C1000 Thermal Cycler (Bio-Rad Laboratories, Inc., California, USA), equipped with a CFX96 Touch Real-Time PCR Detection System using SuperReal PreMix Plus (SYBR Green, FP205, Tengen Biotech, Beijing, China), to quantify the mRNA expression levels of Nrf2, Keap1, HO-1, NQO1, and CAT genes. The procedures described in Wang et al. (2019) was adopted. The primer sequences used for amplification are shown in Table 3. The PCR reaction conditions were set as follows: initial denaturation at 95°C for 15 min, denaturation at 95°C for 10 s, annealing at 60°C for 30 s, and a final extension for 40 cycles (as required by the protocol). Amplification was stopped at the end of the cycle, and measurements were repeated for each sample to ensure reproducibility. The relative gene expression was calculated using the 2−ΔΔCt method (Livak and Schmittgen, 2001), and β-actin was used as the reference gene. This allows comparative analysis of gene expression across samples.Table 3Gene-specific primers for real-time quantitative reverse transcription PCR.Table 3GenesPrimers (5′-3′)Gene numberNrf2Forward: GGTGACACAGGAACAACANM_205117.2Reverse: AAGTCTTATCTCCACAGGTAGKeap1Forward: ATCACCTCTTCTGCACCGAAXM_015274015.1Reverse: GGTTCGGTTACCGTCCTGCHO-1Forward: CTGAAGGAAGCCACCAAGNM_205344.2Reverse: CCAGAGCAGAGTAGATGAAGNQO1Forward: CACCATCTCTGACCTCTACNM_001277620.2Reverse: CCGCTTCAATCTTCTTCTGCATForward: CACTGTTGCTGGAGAATCTNM_001031215.2Reverse: GGCTATGGATGAAGGATGGβ-actinForward: TATGTGCAAGGCCGGTTTCNM_205518.2Reverse: TGTCTTTCTGGCCCATACCAAAbbreviations: Nrf2, Nuclear factor erythroid 2-related factor 2; Keap1, Kelch-like ECH-associated protein 1; HO-1, Heme oxygenase-1; NQO1, NAD(P)H: quinone oxidoreductase 1; CAT, Catalase; β-actin, Beta-actin.
Laying performance
Daily egg production and egg weight were monitored and recorded on a replicate basis. The laying rate is expressed as the average hen-day production, calculated from the total number of eggs divided by the total number of days multiplied by 100. Whereas, egg weight was expressed as average egg weight (AEW), calculated from total egg weight in grams per number of eggs produced. Feed intake (FI) was recorded on a replicate basis at weekly intervals and expressed as average daily feed intake (ADFI). Feed to egg ratio was calculated as grams of feed consumed per grams of eggs produced.
Egg quality assessment
A total of 144 eggs (six eggs per replicate = 36 eggs per group) were collected at the end of weeks 4 (31 weeks-old), 8 (35 weeks-old), and 12 (39 weeks-old), for egg quality assessment. The collected eggs were kept at room temperature, and all egg quality indicators were assessed with various instruments, within 24 h of collection.
The egg shape index was measured using an egg-shaped index apparatus (Egg Index Reader, Fujihira Industry Co., Tokyo, Japan). The eggshell strength was obtained using an eggshell strength analyzer (Egg Force Reader™, Model EFR-01, Orka Food Technology Ltd., Ramat Hasharon, Israel). The eggshell thickness was determined at three specific points (the air cell, equator, and sharp end) with an Egg Shell Thickness Gauge (Orka Technology Ltd., Ramat Hasharon, Israel). Furthermore, the albumen height, Haugh Unit (HU), and yolk color were precisely measured using an egg quality auto-analyzer (Egg Analyzer™, Orka Technology Ltd., Ramat Hasharon, Israel).
Se assay
A total of 288 eggs (12 eggs per replicate = 72 eggs per group) were collected at the end of feeding trial (Week 12). The eggs were utilized for analyzing selenium deposition or content in the whole egg, as well as in the albumen and yolk, respectively. The eggs were divided into two sets; In the first set, the eggs (n = 144 eggs, six eggs per replicate = 36 eggs per group) were broken, and an egg separator was used to separate the egg yolk and albumen. Whereas, in the second set, eggs (n = 144 eggs, six eggs per replicate = 36 eggs per group) were broken, and albumen and yolk were homogenized to obtain whole egg sample. The respective sample: whole egg, albumen and yolk, were dried, ground, and digested with concentrated HNO3. The selenium content in both egg yolks and albumen was precisely determined using a method specifically designed for inductively coupled plasma mass spectrometry (ICP-MS), a highly sensitive and accurate analytical technique. We adopted the procedure described by Mohammadsadeghi et al. (2023).
It is worth mentioning that based on our findings for egg quality and selenium deposition in eggs, both the 1.5 mg/kg and 6.0 mg/kg groups showed comparable effects on egg quality and selenium concentration, thus, we excluded the SY03 group during the biochemical analysis using blood parameters and histological examination of the liver. This ensures focus on dosage-dependent effects, allowing for optimal dosage investigation.
Clinical blood parameters
At the end of the 12th week (age: 39 weeks old), 24 birds (1 per replicate) were selected and deprived of feed for about eight hours before slaughter. About 5 ml of blood was collected from the jugular vein into a collection tube, kept in a slant position to stand for about 2 h. Then, centrifuged at 2500 rpm for 10 min, the harvested serum samples were transferred into Eppendorf tubes and stored at -20°C for serum biochemical analysis. Serum levels of alanine aminotransferase (ALT), alkaline phosphatase (ALP), aspartate aminotransferase (AST), total protein (TP), albumin (ALB), globulin (GLB), and uric acid (UA) were measured using an automatic biochemical analyzer (Zhuoyue 300, Kehua Bio. Co., Ltd. Shanghai, China). The GLB content was mathematically derived by subtracting the albumin content from the TP content. The enzymatic activities of total antioxidant capacity (T-AOC), catalase (CAT), glutathione peroxidase, and superoxide dismutase (SOD) in serum were measured using commercial assay kits provided by the (Nanjing Jiancheng Bioengineering Institute, Nanjing, China). Serum immunoglobulin (Ig) indices, specifically IgA, IgM, and IgG, were determined using assay kits specifically for birds (Shanghai Meilian Bio. Co., Ltd. Shanghai, China). All the protocols used were strictly based on the manufacturer's instructions.
Organ index determination
After blood collection, the hens were euthanized and dissected to obtain the following organs: liver, heart, spleen, lung, and kidney, which were then weighed and measured using an electrical scale (quantitative analysis at 0.01 g level). The organ index was calculated by the formula as follows: Organindex=organweight/bodyweight×100%
Histological examination of the liver tissue
A portion of liver tissue from each bird was cut and fixed in 4% paraformaldehyde for 24 h, for histological examination, according to that described by Peng et al. (2019). The essence of the fixation is to preserve tissue morphology and cellular integrity, while enhancing the penetration of staining reagent in subsequent processing steps, improving the visibility and staining quality. Following fixation, the tissue was processed for paraffin embedding, and serial sections of 5-7 μm thickness were subsequently cut using a microtome. These sections were then de-paraffinized through solvents, stained with hematoxylin and eosin (H & E) for histological analysis, and finally mounted on glass slides. The stained slides were examined under an optical microscope (Nikon Eclipse E600, Japan), with magnification (40 x), for detailed histological assessment. Procedures described by Bancroft et al. (1990) was used.
Owing to the fact that SY supplementation at 1.5 mg/kg have shown optimal performance for egg quality and physiological responses, with almost a zero score for histopathology of the liver. Also, there was a marked significance between the control and the dietary groups for most parameters evaluated, suggesting distinct dietary influence. We therefore, selected only the control and SY15 group for analysis of hepatic antioxidant gene expression, to further highlight the molecular mechanisms underlying the dietary influence.
Hepatic gene expression analysis
A portion of liver tissue from each bird was cut and placed into a freezing tube, then stored at -80°C for RNA extraction and quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis (gene expression analysis). Briefly, the frozen liver sample was grounded under liquid nitrogen condition, and the total RNA was extracted the samples using the TransZol Up Plus RNA kit (Alltech Jinsheng Biotech Co., Ltd. Beijing, China). The concentration and purity of the extracted RNA were determined using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA), and stored at -80°C until further processing. The RNA was reverse transcribed into cDNA using the FastQuant RT Kit (Tiangen Biochemical Technology Co., Ltd. Beijing, China), ensuring accurate and efficient conversion. The resulting cDNA was then carefully stored at -20°C to preserve its integrity for subsequent experiments. The RT-PCR amplification was performed on a Bio-Rad C1000 Thermal Cycler (Bio-Rad Laboratories, Inc., California, USA), equipped with a CFX96 Touch Real-Time PCR Detection System using SuperReal PreMix Plus (SYBR Green, FP205, Tengen Biotech, Beijing, China), to quantify the mRNA expression levels of Nrf2, Keap1, HO-1, NQO1, and CAT genes. The procedures described in Wang et al. (2019) was adopted. The primer sequences used for amplification are shown in Table 3. The PCR reaction conditions were set as follows: initial denaturation at 95°C for 15 min, denaturation at 95°C for 10 s, annealing at 60°C for 30 s, and a final extension for 40 cycles (as required by the protocol). Amplification was stopped at the end of the cycle, and measurements were repeated for each sample to ensure reproducibility. The relative gene expression was calculated using the 2−ΔΔCt method (Livak and Schmittgen, 2001), and β-actin was used as the reference gene. This allows comparative analysis of gene expression across samples.Table 3Gene-specific primers for real-time quantitative reverse transcription PCR.Table 3GenesPrimers (5′-3′)Gene numberNrf2Forward: GGTGACACAGGAACAACANM_205117.2Reverse: AAGTCTTATCTCCACAGGTAGKeap1Forward: ATCACCTCTTCTGCACCGAAXM_015274015.1Reverse: GGTTCGGTTACCGTCCTGCHO-1Forward: CTGAAGGAAGCCACCAAGNM_205344.2Reverse: CCAGAGCAGAGTAGATGAAGNQO1Forward: CACCATCTCTGACCTCTACNM_001277620.2Reverse: CCGCTTCAATCTTCTTCTGCATForward: CACTGTTGCTGGAGAATCTNM_001031215.2Reverse: GGCTATGGATGAAGGATGGβ-actinForward: TATGTGCAAGGCCGGTTTCNM_205518.2Reverse: TGTCTTTCTGGCCCATACCAAAbbreviations: Nrf2, Nuclear factor erythroid 2-related factor 2; Keap1, Kelch-like ECH-associated protein 1; HO-1, Heme oxygenase-1; NQO1, NAD(P)H: quinone oxidoreductase 1; CAT, Catalase; β-actin, Beta-actin.
Statistical analysis
All data were analyzed using SPSS statistical software (version 27.0). A one-way analysis of variance (ANOVA) was followed by Duncan's multiple comparison test, which was performed to compare the means among different treatments. A t-test was used to analyze the hepatic expression of antioxidant genes between the control and SY15 group. Differences were considered statistically significant at P < 0.05. Data are presented as means ± pooled SEM.
Results
Effect of dietary SY on laying performance
The effects of dietary SY supplementation on laying performance are shown in Table 4. There were no significant effects of dietary SY on egg production (EP), average egg weight, average daily feed intake, or feed conversion ratio (FCR) between the control and treatment groups (P > 0.05) throughout the 12-week experimental period.Table 4Effect of dietary selenium-enriched yeast on performance of laying hens.Table 4ItemExperimental treatmentSEMP valueCONSY03SY15SY60ANOVALinearQuadratic28 to 31 wkEP, %87.2489.7489.0587.690.7560.6480.6780.737AEW, g56.0656.4257.3657.310.2810.2700.1560.134ADFI, g102.88103.68104.39104.690.6870.8170.4340.637FCR2.112.052.052.090.0230.7980.8400.73632 to 35 wkEP, %80.2280.2281.0478.650.6390.6380.2840.424AEW, g58.7158.7757.2558.620.4820.6650.9710.473ADFI, g107.00104.24105.98107.950.5980.1490.1260.278F/E2.282.212.292.350.0240.2470.0810.22536 to 39 wkEP, %81.0180.9582.3477.780.7140.1310.0450.058AEW, g56.5958.0058.5658.260.3460.1910.2950.161ADFI, g111.84109.21111.05112.050.8980.6990.5340.817F/E2.292.192.172.320.0250.0880.1480.06928 to39 wkEP, %83.2484.1384.5182.150.4560.2740.1450.154AEW, g57.1257.7357.7258.060.2770.7090.3530.607ADFI, g106.26105.04106.39107.440.4600.3470.1300.326F/E2.212.142.162.230.0160.1820.1380.196Abbreviations: CON, control; SY03, 0.3 mg/kg; SY15, 1.5 mg/kg, SY60, 6.0 mg/kg; EP, Egg production; AEW, Average egg weight; ADFI, Average daily feed intake; F/E, feed-to-egg mass ratio. n = 6 replicates per treatment.
Effect of dietary SY on egg quality
As presented in Table 5, no significant differences in egg quality parameters, including egg shape index, eggshell thickness, eggshell strength, albumen height, Haugh unit and yolk colour, were observed across the groups during weeks 4 and 8, stages of the experiment (P > 0.05). However, by the end of the week 12, the SY15 group showed a significant improvement in albumen height, Haugh unit and yolk color compared to the control (P < 0.05), while egg shape index, eggshell thickness, and eggshell strength were not significantly influenced by diets (P > 0.05).Table 5Effect of dietary selenium-enriched yeast on egg quality of laying hens.Table 5ItemExperimental treatmentSEMP valueCONSY03SY15SY60ANOVALinearQuadratic27 wkEgg shape index1.401.391.381.380.0060.4280.2740.266Eggshell thickness, × 0.01 mm41.4241.9440.3740.330.9390.9210.6060.817Eggshell strength, N41.2540.7940.6839.360.8870.9050.4500.757Albumen height, mm8.357.817.977.620.1570.4200.2170.455Haugh unit91.5588.3788.3286.540.9550.3260.1420.285Yolk color4.925.395.065.390.0890.1420.2210.46333 wkEgg shape index1.391.381.391.380.0050.8400.9680.844Eggshell thickness, × 0.01 mm37.3738.4337.6737.390.5850.9230.7570.952Eggshell strength, N36.7437.6237.8035.590.8010.7790.4040.595Albumen height, mm8.198.608.758.480.1030.2740.8130.240Haugh unit89.7092.2093.0692.180.5330.1300.4020.132Yolk color5.145.194.755.140.1170.5360.9310.37339 wkEgg shape index1.331.351.351.330.0040.2120.5070.266Eggshell thickness, × 0.01 mm32.8932.5532.4032.420.1230.4830.3470.373Eggshell strength, N42.1340.4643.7942.090.7270.4780.8010.526Albumen height, mm7.65c8.04b8.50a8.21b0.1180.0450.2870.031Haugh unit86.21c88.91b91.68a89.07b0.6860.0330.4890.017Yolk color4.86c5.07b5.65a5.22b0.0900.0040.3890.002Abbreviations: CON, control; SY03, 0.3 mg/kg; SY15, 1.5 mg/kg; SY60, 6.0 mg/kg.a-cWithin a row, means with no common superscript differ significantly (P < 0.05). n = 6 replicates per treatment.
Effect of dietary SY on Se concentration in whole eggs, albumen and yolk
The impact of dietary SY supplementation on selenium concentrations in egg, albumen and yolk were presented in Table 6. There was a significant increase in the concentrations of selenium in whole egg, albumen and yolk in a dose-dependent manner (P < 0.001). The highest selenium concentrations were found in the eggs of SY60 group, followed by that in the SY15 and SY03 groups, with significant differences across all groups (P < 0.001). Notably, within each SY supplementation group, the selenium concentration in the yolk was consistently higher than that in the albumen (P < 0.001).Table 6Effect of dietary selenium-enriched yeast on the concentration of selenium in egg, albumen and yolk.Table 6ItemExperimental treatmentSEMP valueCONSY03SY15SY60ANOVALinearQuadraticEgg Se, μg/100g12.00d37.10c96.12b151.30a11.397<0.001<0.001<0.001Albumen Se, μg/100g5.04d21.20c72.34b104.71a8.453<0.001<0.001<0.001Yolk Se, μg/100g30.00d77.57c158.49b273.90a19.532<0.001<0.001<0.001Abbreviations: CON, control; SY03, 0.3 mg/kg; SY15, 1.5 mg/kg; SY60, 6.0 mg/kg.a-dWithin a row, means with no common superscript differ significantly (P < 0.05). n = 6 replicates per treatment.
Effect of dietary SY on serum biochemical parameters
The effect of dietary SY supplementation on serum biochemical indices in are presented in Fig. 1. There were significant changes in serum biochemical indices of laying hens due to dietary supplementation of SY (P < 0.05). The SY60 group exhibited significantly higher activities of liver enzymes such as ALT, ALP, AST, and level of protein metabolic indicator like UA, while the control group showed the lowest values (P < 0.05). There was no significant influence of dietary SY on other protein metabolic indicators such as total protein, albumin, or globulin levels (P > 0.05), as they were comparable to the control group.Fig. 1Effect of dietary selenium-enriched yeast on the serum biochemical of laying hens. CON, control; SY15, 1.5 mg/kg; SY60, 6.0 mg/kg. (A) ALT, alanine transaminase. (B) ALP, alkaline phosphatase. (C) AST, aspartate transaminase. (D) TP, total protein. (E) ALB, albumin. (F) GLB, globulin. (G) UA, uric acid. a-c Bars with no common superscript differ significantly (P < 0.05). n = 6 replicates per treatment.Fig 1
The effects of SY supplementation on the immunoglobulin levels and activities of antioxidant enzymes are presented in Fig. 2.Fig. 2Effect of dietary selenium-enriched yeast on the serum immunological and antioxidant parameters of laying hens. CON, control; SY15, 1.5 mg/kg; SY60, 6.0 mg/kg. (A) IgA, immunoglobulin A. (B) IgG, immunoglobulin G. (C) IgM, immunoglobulin M. (D) T-AOC, total antioxidant capacity. (E) CAT, catalase. (F) GSH-Px, glutathione peroxidase. (G) SOD, superoxide dismutase. a-c Bars with no common superscript differ significantly (P < 0.05). n = 6 replicates per treatment.Fig 2
Dietary SY significantly influenced IgM levels (P < 0.05). The SY60 group was significantly higher (P < 0.05) than the SY15 group. However, no significant dietary influences were observed for IgA and IgG levels (P > 0.05).
There was a marked significant effect of SY on the activities of antioxidant enzymes CAT, GSH-Px, and SOD as well as levels of T-AOC (P < 0.05). The activities of T-AOC, and CAT were comparable between SY15 and SY60 (P > 0.05), while the activity of SOD was significantly higher in the SY60 group compared to the S15 group (P < 0.05). Moreover, the activity of GSH-Px was higher in SY15 group (P < 0.05), compared to that in SY60 group.
Effect of dietary SY on organ index
The effects of dietary SY on the organ index of the heart, lung, kidney, liver and spleen of laying hens are presented in Fig. 3. The dietary addition of SY60 significantly increased the liver index compared to the control group (P < 0.001), with SY group recording the highest value (P < 0.05), while the values for SY15 and control group were comparable (P > 0.05). However, dietary SY had no significant effect on the heart, spleen, lung, and kidney index (P > 0.05), and was comparable to the control group.Fig. 3Effect of dietary selenium-enriched yeast on the organs index of laying hens. CON, control; SY15, 1.5 mg/kg; SY60, 6.0 mg/kg. a,b Bars with no common superscript differ significantly (P < 0.05). n = 6 replicates per treatment.Fig 3
Effect of dietary SY on histomorphology of the liver
Effect of dietary selenium-enriched yeast on the histomorphology and histopathology scores of the liver in laying hens, in control and experimental groups were illustrated in Fig. 4 A and B, respectively. There were significant effects of dietary SY on histopathology scores of the liver (P < 0.05). The effect of SY on histopathology scores of the liver was highly significant for SY60 group (P < 0.001), compared to SY15 and control groups which recorded much lower values nearest to zero level. There were significant effects of dietary SY on histomorphology of the liver (P < 0.05). The hepatocytes in the SY60 group, displayed signs of eosinophilic degeneration in hepatocytes, manifested by abundant eosinophilic cytoplasm and rounded nuclei, indicative of oxidative cellular stress or damage. In contrast, the hepatocytes in control group, exhibited normal morphology characterized by distinct cellular boundaries, clear cytoplasmic detail, and moderately stained cytoplasm.Fig. 4Effect of dietary selenium-enriched yeast on the histomorphology and histopathology scores of the liver in laying hens. (H&E, 40 ×) staining of liver sections, scale bar: 100 μm. CV, central vein; HC, hepatic cell; HS, hepatic sinusoid; CON, control; SY15, 1.5 mg/kg; SY60, 6.0 mg/kg. a,b Bars with no common superscript differ significantly (P < 0.05). n = 6 replicates per treatment.Fig 4
Effect of dietary SY on expression of antioxidant genes in the liver
The influence of SY supplementation on the hepatic expression of key antioxidant genes and pathways are shown in Fig. 5. There was significant influence of SY supplementation on the relative expression of antioxidant genes in the liver (P < 0.05). The relative expression levels of Nrf2, HO-1, and NQO1 were significantly upregulated, while the Keap1 was downregulated in the SY15 group compared to the control (P < 0.05). Contrarily, no significant effect of dietary treatment was observable in the expression of CAT genes (P > 0.05).Fig. 5Effect of dietary selenium-enriched yeast on hepatic gene expression (relative to β-actin) in laying hens. CON, control; SY15, 1.5 mg/kg. (A) Nrf2 mRNA expression. (B) Keap1 mRNA expression. (C) HO-1 mRNA expression. (D) NQO1 mRNA expression. (E) CAT mRNA expression. Primer pairs used for these analyses are listed in Table 2. a,b Bars with no common superscript differ significantly (P < 0.05). n = 6 replicates per treatment.Fig 5
Effect of dietary SY on laying performance
The effects of dietary SY supplementation on laying performance are shown in Table 4. There were no significant effects of dietary SY on egg production (EP), average egg weight, average daily feed intake, or feed conversion ratio (FCR) between the control and treatment groups (P > 0.05) throughout the 12-week experimental period.Table 4Effect of dietary selenium-enriched yeast on performance of laying hens.Table 4ItemExperimental treatmentSEMP valueCONSY03SY15SY60ANOVALinearQuadratic28 to 31 wkEP, %87.2489.7489.0587.690.7560.6480.6780.737AEW, g56.0656.4257.3657.310.2810.2700.1560.134ADFI, g102.88103.68104.39104.690.6870.8170.4340.637FCR2.112.052.052.090.0230.7980.8400.73632 to 35 wkEP, %80.2280.2281.0478.650.6390.6380.2840.424AEW, g58.7158.7757.2558.620.4820.6650.9710.473ADFI, g107.00104.24105.98107.950.5980.1490.1260.278F/E2.282.212.292.350.0240.2470.0810.22536 to 39 wkEP, %81.0180.9582.3477.780.7140.1310.0450.058AEW, g56.5958.0058.5658.260.3460.1910.2950.161ADFI, g111.84109.21111.05112.050.8980.6990.5340.817F/E2.292.192.172.320.0250.0880.1480.06928 to39 wkEP, %83.2484.1384.5182.150.4560.2740.1450.154AEW, g57.1257.7357.7258.060.2770.7090.3530.607ADFI, g106.26105.04106.39107.440.4600.3470.1300.326F/E2.212.142.162.230.0160.1820.1380.196Abbreviations: CON, control; SY03, 0.3 mg/kg; SY15, 1.5 mg/kg, SY60, 6.0 mg/kg; EP, Egg production; AEW, Average egg weight; ADFI, Average daily feed intake; F/E, feed-to-egg mass ratio. n = 6 replicates per treatment.
Effect of dietary SY on egg quality
As presented in Table 5, no significant differences in egg quality parameters, including egg shape index, eggshell thickness, eggshell strength, albumen height, Haugh unit and yolk colour, were observed across the groups during weeks 4 and 8, stages of the experiment (P > 0.05). However, by the end of the week 12, the SY15 group showed a significant improvement in albumen height, Haugh unit and yolk color compared to the control (P < 0.05), while egg shape index, eggshell thickness, and eggshell strength were not significantly influenced by diets (P > 0.05).Table 5Effect of dietary selenium-enriched yeast on egg quality of laying hens.Table 5ItemExperimental treatmentSEMP valueCONSY03SY15SY60ANOVALinearQuadratic27 wkEgg shape index1.401.391.381.380.0060.4280.2740.266Eggshell thickness, × 0.01 mm41.4241.9440.3740.330.9390.9210.6060.817Eggshell strength, N41.2540.7940.6839.360.8870.9050.4500.757Albumen height, mm8.357.817.977.620.1570.4200.2170.455Haugh unit91.5588.3788.3286.540.9550.3260.1420.285Yolk color4.925.395.065.390.0890.1420.2210.46333 wkEgg shape index1.391.381.391.380.0050.8400.9680.844Eggshell thickness, × 0.01 mm37.3738.4337.6737.390.5850.9230.7570.952Eggshell strength, N36.7437.6237.8035.590.8010.7790.4040.595Albumen height, mm8.198.608.758.480.1030.2740.8130.240Haugh unit89.7092.2093.0692.180.5330.1300.4020.132Yolk color5.145.194.755.140.1170.5360.9310.37339 wkEgg shape index1.331.351.351.330.0040.2120.5070.266Eggshell thickness, × 0.01 mm32.8932.5532.4032.420.1230.4830.3470.373Eggshell strength, N42.1340.4643.7942.090.7270.4780.8010.526Albumen height, mm7.65c8.04b8.50a8.21b0.1180.0450.2870.031Haugh unit86.21c88.91b91.68a89.07b0.6860.0330.4890.017Yolk color4.86c5.07b5.65a5.22b0.0900.0040.3890.002Abbreviations: CON, control; SY03, 0.3 mg/kg; SY15, 1.5 mg/kg; SY60, 6.0 mg/kg.a-cWithin a row, means with no common superscript differ significantly (P < 0.05). n = 6 replicates per treatment.
Effect of dietary SY on Se concentration in whole eggs, albumen and yolk
The impact of dietary SY supplementation on selenium concentrations in egg, albumen and yolk were presented in Table 6. There was a significant increase in the concentrations of selenium in whole egg, albumen and yolk in a dose-dependent manner (P < 0.001). The highest selenium concentrations were found in the eggs of SY60 group, followed by that in the SY15 and SY03 groups, with significant differences across all groups (P < 0.001). Notably, within each SY supplementation group, the selenium concentration in the yolk was consistently higher than that in the albumen (P < 0.001).Table 6Effect of dietary selenium-enriched yeast on the concentration of selenium in egg, albumen and yolk.Table 6ItemExperimental treatmentSEMP valueCONSY03SY15SY60ANOVALinearQuadraticEgg Se, μg/100g12.00d37.10c96.12b151.30a11.397<0.001<0.001<0.001Albumen Se, μg/100g5.04d21.20c72.34b104.71a8.453<0.001<0.001<0.001Yolk Se, μg/100g30.00d77.57c158.49b273.90a19.532<0.001<0.001<0.001Abbreviations: CON, control; SY03, 0.3 mg/kg; SY15, 1.5 mg/kg; SY60, 6.0 mg/kg.a-dWithin a row, means with no common superscript differ significantly (P < 0.05). n = 6 replicates per treatment.
Effect of dietary SY on serum biochemical parameters
The effect of dietary SY supplementation on serum biochemical indices in are presented in Fig. 1. There were significant changes in serum biochemical indices of laying hens due to dietary supplementation of SY (P < 0.05). The SY60 group exhibited significantly higher activities of liver enzymes such as ALT, ALP, AST, and level of protein metabolic indicator like UA, while the control group showed the lowest values (P < 0.05). There was no significant influence of dietary SY on other protein metabolic indicators such as total protein, albumin, or globulin levels (P > 0.05), as they were comparable to the control group.Fig. 1Effect of dietary selenium-enriched yeast on the serum biochemical of laying hens. CON, control; SY15, 1.5 mg/kg; SY60, 6.0 mg/kg. (A) ALT, alanine transaminase. (B) ALP, alkaline phosphatase. (C) AST, aspartate transaminase. (D) TP, total protein. (E) ALB, albumin. (F) GLB, globulin. (G) UA, uric acid. a-c Bars with no common superscript differ significantly (P < 0.05). n = 6 replicates per treatment.Fig 1
The effects of SY supplementation on the immunoglobulin levels and activities of antioxidant enzymes are presented in Fig. 2.Fig. 2Effect of dietary selenium-enriched yeast on the serum immunological and antioxidant parameters of laying hens. CON, control; SY15, 1.5 mg/kg; SY60, 6.0 mg/kg. (A) IgA, immunoglobulin A. (B) IgG, immunoglobulin G. (C) IgM, immunoglobulin M. (D) T-AOC, total antioxidant capacity. (E) CAT, catalase. (F) GSH-Px, glutathione peroxidase. (G) SOD, superoxide dismutase. a-c Bars with no common superscript differ significantly (P < 0.05). n = 6 replicates per treatment.Fig 2
Dietary SY significantly influenced IgM levels (P < 0.05). The SY60 group was significantly higher (P < 0.05) than the SY15 group. However, no significant dietary influences were observed for IgA and IgG levels (P > 0.05).
There was a marked significant effect of SY on the activities of antioxidant enzymes CAT, GSH-Px, and SOD as well as levels of T-AOC (P < 0.05). The activities of T-AOC, and CAT were comparable between SY15 and SY60 (P > 0.05), while the activity of SOD was significantly higher in the SY60 group compared to the S15 group (P < 0.05). Moreover, the activity of GSH-Px was higher in SY15 group (P < 0.05), compared to that in SY60 group.
Effect of dietary SY on organ index
The effects of dietary SY on the organ index of the heart, lung, kidney, liver and spleen of laying hens are presented in Fig. 3. The dietary addition of SY60 significantly increased the liver index compared to the control group (P < 0.001), with SY group recording the highest value (P < 0.05), while the values for SY15 and control group were comparable (P > 0.05). However, dietary SY had no significant effect on the heart, spleen, lung, and kidney index (P > 0.05), and was comparable to the control group.Fig. 3Effect of dietary selenium-enriched yeast on the organs index of laying hens. CON, control; SY15, 1.5 mg/kg; SY60, 6.0 mg/kg. a,b Bars with no common superscript differ significantly (P < 0.05). n = 6 replicates per treatment.Fig 3
Effect of dietary SY on histomorphology of the liver
Effect of dietary selenium-enriched yeast on the histomorphology and histopathology scores of the liver in laying hens, in control and experimental groups were illustrated in Fig. 4 A and B, respectively. There were significant effects of dietary SY on histopathology scores of the liver (P < 0.05). The effect of SY on histopathology scores of the liver was highly significant for SY60 group (P < 0.001), compared to SY15 and control groups which recorded much lower values nearest to zero level. There were significant effects of dietary SY on histomorphology of the liver (P < 0.05). The hepatocytes in the SY60 group, displayed signs of eosinophilic degeneration in hepatocytes, manifested by abundant eosinophilic cytoplasm and rounded nuclei, indicative of oxidative cellular stress or damage. In contrast, the hepatocytes in control group, exhibited normal morphology characterized by distinct cellular boundaries, clear cytoplasmic detail, and moderately stained cytoplasm.Fig. 4Effect of dietary selenium-enriched yeast on the histomorphology and histopathology scores of the liver in laying hens. (H&E, 40 ×) staining of liver sections, scale bar: 100 μm. CV, central vein; HC, hepatic cell; HS, hepatic sinusoid; CON, control; SY15, 1.5 mg/kg; SY60, 6.0 mg/kg. a,b Bars with no common superscript differ significantly (P < 0.05). n = 6 replicates per treatment.Fig 4
Effect of dietary SY on expression of antioxidant genes in the liver
The influence of SY supplementation on the hepatic expression of key antioxidant genes and pathways are shown in Fig. 5. There was significant influence of SY supplementation on the relative expression of antioxidant genes in the liver (P < 0.05). The relative expression levels of Nrf2, HO-1, and NQO1 were significantly upregulated, while the Keap1 was downregulated in the SY15 group compared to the control (P < 0.05). Contrarily, no significant effect of dietary treatment was observable in the expression of CAT genes (P > 0.05).Fig. 5Effect of dietary selenium-enriched yeast on hepatic gene expression (relative to β-actin) in laying hens. CON, control; SY15, 1.5 mg/kg. (A) Nrf2 mRNA expression. (B) Keap1 mRNA expression. (C) HO-1 mRNA expression. (D) NQO1 mRNA expression. (E) CAT mRNA expression. Primer pairs used for these analyses are listed in Table 2. a,b Bars with no common superscript differ significantly (P < 0.05). n = 6 replicates per treatment.Fig 5
Discussion
As per nutritional guidelines, the Se requirement for laying hens ranges from 0.05 to 0.08 mg/kg, with toxic effects observed at doses of tenfold higher (Surai, 2002). In this study, all dietary treatments exceeded this range, ensuring sufficient Se intake. The observed toxicity at 6.0 mg/kg is consistent with selenium's narrow safety margin, highlighting the need for precise dosage management. Our results confirm that SY effectively accumulates selenium in eggs due to its high bioavailability and efficiency (Utterback et al., 2005; Pavlovic et al., 2009). These findings support our hypothesis that varying SY dosages affect laying hens differently, possibly due to their impact on liver health and antioxidant function.
Laying performance
The study revealed no significant effects of SY supplementation on laying performance parameters such as EP, AEW, ADFI, or F/E. These findings align with previous studies reporting no substantial impact of SY on production performance metrics (Lu et al., 2018; Lu et al., 2019; Lu et al., 2020). This suggests that the inclusion of selenium in its organic form at the tested doses does not directly enhance the fundamental production efficiency of laying hens, particularly during shorter feeding periods. Selenium's primary role as an antioxidant likely supports physiological processes that maintain overall health but may not immediately translate to measurable improvements in production metrics. Interestingly, studies involving long-term feeding of SY at 0.4–0.8 mg/kg (Pavlovic et al., 2009) and 2 mg/kg (Chen et al., 2024) reported significant improvements in laying performance and feed efficiency over a 16 weeks feeding trial. This implies that prolonged selenium exposure promotes tissue selenium accumulation, enhancing its utilization efficiency and potentially contributing to improved production outcomes. However, discrepancies in findings across studies, where SY has been reported to enhance both laying rate and feed efficiency (Zia et al., 2016; Meng et al., 2019; Muhammad et al., 2021), improve laying rate without affecting feed efficiency (Liu et al., 2020), or increase feed efficiency without impacting laying rate (Meng et al., 2021), may stem from variations in factors such as bird age, feeding trial duration, and SY dosages.
Our findings on the slight reduction in laying rates in the 6.0 mg/kg SY group during weeks 37–40 suggest that higher doses of SY may contribute to long-term physiological stress, highlighting the need for dosage specificity. This finding underscores the need for additional research to investigate the long-term impacts of high-dose SY supplementation on both performance metrics and oxidative stress levels. Understanding the optimal dosage and duration for selenium supplementation is critical for maximizing performance while safeguarding the health of laying hens. Additionally, exploring combined selenium products may offer insights for enhancing laying performance. For example, Han et al. (2017) demonstrated that combining sodium selenite with SY significantly improved laying rates compared to using either component alone, likely due to enhanced absorption efficiency. Such synergistic interactions between selenium compounds merit further investigation to elucidate their mechanisms and contributions to both short-term and long-term production outcomes. Although SY supplementation did not markedly improve laying performance in this study, examining its effects on egg quality may provide a clearer understanding of the relationship between selenium absorption efficiency and its benefits in poultry production
Egg quality
Egg quality parameters, such as albumen height, HU, yolk color, and eggshell strength, serve as critical economic indicators in the poultry industry, reflecting internal freshness and consumer preferences. In our study, supplementation with 1.5 mg/kg of SY resulted in significant improvements in albumen height, HU, and yolk color, all of which are essential for both consumer appeal and functional properties in food processing. The results align with previous reports which demonstrated that SY supplementation at 0.3 mg/kg (Muhammad et al., 2021) and 2 mg/kg (Chen et al., 2024), respectively, enhanced HU and eggshell strength, and albumen height over a 16-weeks feeding trial. These enhancements can be attributed to selenium's antioxidant properties, which help preserve the integrity of egg proteins and support efficient protein metabolism. Improved albumen quality is particularly advantageous for the food industry due to its desirable technological properties, including gelling, foaming, and emulsification. Contrarily, supplementation of SY at various dosages of 0.2 mg/kg (Li et al., 2024), 0.3-3.0 mg/kg (Lu et al., 2019), and 0.3-0.5 mg/kg (Liu et al., 2020), 0.1-0.4 mg/kg (Lu et al., 2020) had no significant effect on egg quality traits. Additionally, the study by Han et al. (2017) demonstrated that supplementation of SY in the diet of laying hens for 11 weeks had no significant impact on egg quality traits. Discrepancies among studies may not be solely attributed to dosage levels and the duration of feeding trials; factors such as the age of the animals and environmental conditions may also contribute to the variability in results. Notably, supplementation at 6.0 mg/kg led to increased selenium deposition in eggs but did not yield additional improvements in egg quality metrics when compared to the 1.5 mg/kg level. This suggests a plateau effect and potential metabolic strain at higher doses. These findings highlight the importance of optimizing selenium supplementation levels to achieve desired egg quality outcomes while avoiding undue physiological burdens on the hens. Future research should focus on elucidating the molecular mechanisms underlying selenium's role in protein preservation and its broader implications for egg quality.
Selenium deposition in eggs
Selenium-enriched eggs are increasingly becoming consumers utmost preference due to their considerable health benefits, due to their antioxidant and immune boosting properties (Davil-Vega et al., 2023). As a mineral absorption and retention model, avian eggs are particularly valuable for assessing selenium's bioavailability.
Our findings demonstrated the dose-dependent increase in selenium deposition in whole eggs, albumen, and yolk, affirming the high bioavailability and efficiency of SY as a selenium source. Previous studies have shown similar trends, with SY outperforming other selenium sources (Lu et al., 2018; Słupczyńska et al., 2018; Lu et al., 2020; Li et al., 2024), and non-selenium supplemented diets (Lu et al., 2019), in bioavailability and deposition efficiency. The primary component of SY, selenomethionine, mimics methionine, enabling rapid absorption through specific amino acid transport pathways in the small intestine. This facilitates its efficient incorporation into proteins, resulting in superior selenium retention compared to inorganic sources (Sunde et al., 2016; Hariharan and Dharmaraj, 2020). Also, there were reports that SY supplementation increased Se deposition in yolk compared to albumen (Li et al., 2024). The elevated concentrations of selenium in the yolk relative to the albumen correspond with the yolk's function as the primary nutrient reservoir, reinforcing its critical role as the primary nutrient reservoir (Chen et al., 2024). The enhanced retention of selenium in whole eggs and individual components, further substantiates the superior bioavailability of organic selenium in terms of its incorporation into egg matrices. Future research should focus on understanding selenium deposition dynamics across egg components, particularly the yolk, to refine supplementation strategies for producing selenium-enriched functional foods. The efficiency of selenium deposition in eggs is closely tied to liver function, as the liver plays a central role in selenium metabolism and the synthesis of selenoproteins, which are critical for antioxidant defenses and the transport of selenium to target tissues like the yolk.
Laying performance
The study revealed no significant effects of SY supplementation on laying performance parameters such as EP, AEW, ADFI, or F/E. These findings align with previous studies reporting no substantial impact of SY on production performance metrics (Lu et al., 2018; Lu et al., 2019; Lu et al., 2020). This suggests that the inclusion of selenium in its organic form at the tested doses does not directly enhance the fundamental production efficiency of laying hens, particularly during shorter feeding periods. Selenium's primary role as an antioxidant likely supports physiological processes that maintain overall health but may not immediately translate to measurable improvements in production metrics. Interestingly, studies involving long-term feeding of SY at 0.4–0.8 mg/kg (Pavlovic et al., 2009) and 2 mg/kg (Chen et al., 2024) reported significant improvements in laying performance and feed efficiency over a 16 weeks feeding trial. This implies that prolonged selenium exposure promotes tissue selenium accumulation, enhancing its utilization efficiency and potentially contributing to improved production outcomes. However, discrepancies in findings across studies, where SY has been reported to enhance both laying rate and feed efficiency (Zia et al., 2016; Meng et al., 2019; Muhammad et al., 2021), improve laying rate without affecting feed efficiency (Liu et al., 2020), or increase feed efficiency without impacting laying rate (Meng et al., 2021), may stem from variations in factors such as bird age, feeding trial duration, and SY dosages.
Our findings on the slight reduction in laying rates in the 6.0 mg/kg SY group during weeks 37–40 suggest that higher doses of SY may contribute to long-term physiological stress, highlighting the need for dosage specificity. This finding underscores the need for additional research to investigate the long-term impacts of high-dose SY supplementation on both performance metrics and oxidative stress levels. Understanding the optimal dosage and duration for selenium supplementation is critical for maximizing performance while safeguarding the health of laying hens. Additionally, exploring combined selenium products may offer insights for enhancing laying performance. For example, Han et al. (2017) demonstrated that combining sodium selenite with SY significantly improved laying rates compared to using either component alone, likely due to enhanced absorption efficiency. Such synergistic interactions between selenium compounds merit further investigation to elucidate their mechanisms and contributions to both short-term and long-term production outcomes. Although SY supplementation did not markedly improve laying performance in this study, examining its effects on egg quality may provide a clearer understanding of the relationship between selenium absorption efficiency and its benefits in poultry production
Egg quality
Egg quality parameters, such as albumen height, HU, yolk color, and eggshell strength, serve as critical economic indicators in the poultry industry, reflecting internal freshness and consumer preferences. In our study, supplementation with 1.5 mg/kg of SY resulted in significant improvements in albumen height, HU, and yolk color, all of which are essential for both consumer appeal and functional properties in food processing. The results align with previous reports which demonstrated that SY supplementation at 0.3 mg/kg (Muhammad et al., 2021) and 2 mg/kg (Chen et al., 2024), respectively, enhanced HU and eggshell strength, and albumen height over a 16-weeks feeding trial. These enhancements can be attributed to selenium's antioxidant properties, which help preserve the integrity of egg proteins and support efficient protein metabolism. Improved albumen quality is particularly advantageous for the food industry due to its desirable technological properties, including gelling, foaming, and emulsification. Contrarily, supplementation of SY at various dosages of 0.2 mg/kg (Li et al., 2024), 0.3-3.0 mg/kg (Lu et al., 2019), and 0.3-0.5 mg/kg (Liu et al., 2020), 0.1-0.4 mg/kg (Lu et al., 2020) had no significant effect on egg quality traits. Additionally, the study by Han et al. (2017) demonstrated that supplementation of SY in the diet of laying hens for 11 weeks had no significant impact on egg quality traits. Discrepancies among studies may not be solely attributed to dosage levels and the duration of feeding trials; factors such as the age of the animals and environmental conditions may also contribute to the variability in results. Notably, supplementation at 6.0 mg/kg led to increased selenium deposition in eggs but did not yield additional improvements in egg quality metrics when compared to the 1.5 mg/kg level. This suggests a plateau effect and potential metabolic strain at higher doses. These findings highlight the importance of optimizing selenium supplementation levels to achieve desired egg quality outcomes while avoiding undue physiological burdens on the hens. Future research should focus on elucidating the molecular mechanisms underlying selenium's role in protein preservation and its broader implications for egg quality.
Selenium deposition in eggs
Selenium-enriched eggs are increasingly becoming consumers utmost preference due to their considerable health benefits, due to their antioxidant and immune boosting properties (Davil-Vega et al., 2023). As a mineral absorption and retention model, avian eggs are particularly valuable for assessing selenium's bioavailability.
Our findings demonstrated the dose-dependent increase in selenium deposition in whole eggs, albumen, and yolk, affirming the high bioavailability and efficiency of SY as a selenium source. Previous studies have shown similar trends, with SY outperforming other selenium sources (Lu et al., 2018; Słupczyńska et al., 2018; Lu et al., 2020; Li et al., 2024), and non-selenium supplemented diets (Lu et al., 2019), in bioavailability and deposition efficiency. The primary component of SY, selenomethionine, mimics methionine, enabling rapid absorption through specific amino acid transport pathways in the small intestine. This facilitates its efficient incorporation into proteins, resulting in superior selenium retention compared to inorganic sources (Sunde et al., 2016; Hariharan and Dharmaraj, 2020). Also, there were reports that SY supplementation increased Se deposition in yolk compared to albumen (Li et al., 2024). The elevated concentrations of selenium in the yolk relative to the albumen correspond with the yolk's function as the primary nutrient reservoir, reinforcing its critical role as the primary nutrient reservoir (Chen et al., 2024). The enhanced retention of selenium in whole eggs and individual components, further substantiates the superior bioavailability of organic selenium in terms of its incorporation into egg matrices. Future research should focus on understanding selenium deposition dynamics across egg components, particularly the yolk, to refine supplementation strategies for producing selenium-enriched functional foods. The efficiency of selenium deposition in eggs is closely tied to liver function, as the liver plays a central role in selenium metabolism and the synthesis of selenoproteins, which are critical for antioxidant defenses and the transport of selenium to target tissues like the yolk.
Liver health and toxicity at high Se levels
Liver health and function are critical indicators of systemic wellbeing and are often assessed by measuring enzymatic activities of ALT, ALP, and AST (Guerrini et al., 2022). Elevated levels of these enzymes indicate hepatocellular damage or dysfunction, commonly associated with oxidative stress or selenium toxicity at high doses (Zhang et al., 2023). In this study, the SY60 group exhibited significantly elevated enzyme activities alongside a higher liver index, suggesting liver damage, increased intrahepatic fat, or liver dysfunction, likely linked to selenium metabolism in the liver. These findings underscore the need for further investigation into the dose-dependent effects of selenium and the factors contributing to its toxicity.
Histopathological analysis provided additional insight into the specific structural effects of SY on the liver cell morphology, assessing any signs of inflammation, fibrosis, or other pathological changes using microscopy (Malyar et al., 2021; Li et al., 2023). The liver, a vital organ located in the upper right quadrant of the abdomen, is organized into hepatic lobules composed of hepatocyte cords and sinusoids surrounding the central vein of each lobule (Michalczuk et al., 2021). Examination of liver samples revealed eosinophilic degeneration of hepatocytes in the SY60 group, a hallmark of hepatocyte injury associated with diminished cellular function and impaired liver metabolism (Hora and Wuestefeld, 2023). This degeneration is indicative of hepatocellular stress and early signs of liver damage caused by excessive selenium intake. Selenium toxicity is linked to the generation of free radicals, which induce oxidative stress, cause DNA damage, and disrupt protein functions due to selenium's high affinity for thiol groups (Letavayova et al., 2008).
In contrast, the SY15 group exhibited relatively healthy hepatocytes, suggesting that moderate SY supplementation (1.5 mg/kg) supports liver health and avoids hepatocellular damage. Similarly, lower selenium dosages, such as 0.4 mg/kg, have been shown to protect hepatocytes against oxidative stress, highlighting the importance of appropriate dosage management to prevent liver damage and metabolic disturbances (Abbas et al., 2022).
The findings suggest that while selenium plays a crucial role in antioxidant defense and overall health, excessive doses can overwhelm the liver's metabolic capacity, leading to toxicity and structural damage. It is essential to monitor the progression of hepatic degeneration in high-dose groups like SY60 to evaluate potential long-term health risks. Further research is needed to elucidate the molecular mechanisms underlying selenium-induced liver damage, particularly at higher dosages, to optimize supplementation strategies and ensure safety in poultry production.
Effect of SY on various organ indices
In addition to the observed effects on liver health, SY supplementation had no significant impact on the indices of non-hepatic organs such as the heart, spleen, lung, and kidney. This indicates that SY supplementation within the tested range does not induce systemic toxicity or abnormal organ development in tissues not directly involved in selenium metabolism. These findings support the overall safety of SY supplementation in poultry, emphasizing that observed hepatotoxicity at higher doses is likely confined to selenium's metabolism in the liver. This reinforces the importance of dosage optimization to maximize health benefits while minimizing adverse effects.
Serum biochemical indices
Serum biochemical indicators can provide insights into animals' metabolic and health status; Total protein, albumin, and uric acid are key indicators of protein metabolism, while globulin is closely tied to immune function (Geng et al., 2021). Activities or levels of protein metabolism indices, immunoglobulins, and antioxidant enzymes often encompass the serum biochemical indices used for birds' metabolic assessment in response to dietary treatments. This stability in organ indices aligns with the unchanged serum biochemical markers observed in this study, further supporting the systemic safety of SY supplementation at the tested dosages.
Protein metabolism indices
Serum biochemical markers, including TP, ALB, and GLB, remained unchanged at both 1.5 mg/kg and 6.0 mg/kg SY supplementation, indicating no adverse effects on protein metabolism. Previous studies reported similar trends at lower dosages, such as 0.3-3 mg/kg (Lu et al., 2019) and 0.3-0.5 mg/kg (Lu et al., 2020). These findings suggest that SY supplementation, within the tested range, supports protein balance through regulatory mechanisms. Although, supplementation of SY at 6.0 mg/kg, had no effect on protein metabolism indicators, it caused a negative impact on liver heath and function particularly affecting fat metabolism and energy balance, indicating that SY supports protein balance through regulatory mechanisms. This suggests that protein metabolism markers are less sensitive to selenium-induced liver dysfunction than liver-specific enzymes, and excessive selenium may primarily affect liver health without altering serum protein levels.
Protein metabolism indices
Serum biochemical markers, including TP, ALB, and GLB, remained unchanged at both 1.5 mg/kg and 6.0 mg/kg SY supplementation, indicating no adverse effects on protein metabolism. Previous studies reported similar trends at lower dosages, such as 0.3-3 mg/kg (Lu et al., 2019) and 0.3-0.5 mg/kg (Lu et al., 2020). These findings suggest that SY supplementation, within the tested range, supports protein balance through regulatory mechanisms. Although, supplementation of SY at 6.0 mg/kg, had no effect on protein metabolism indicators, it caused a negative impact on liver heath and function particularly affecting fat metabolism and energy balance, indicating that SY supports protein balance through regulatory mechanisms. This suggests that protein metabolism markers are less sensitive to selenium-induced liver dysfunction than liver-specific enzymes, and excessive selenium may primarily affect liver health without altering serum protein levels.
Immune function and antioxidant activity
Immune function
Immunoglobulins, such as IgA, IgG, and IgM, are critical markers of humoral immunity in avian species, playing essential roles in infection defense (Schroeder and Cavacini, 2010). In this study, SY supplementation did not significantly affect IgA or IgG levels but did influence IgM concentrations, suggesting that the 84-day feeding regimen supported immune function without compromising immunity. The IgM, a key marker of humoral immunity (Liu et al., 2019) was significantly influenced, aligning with previous findings that selenium-enriched diets benefit immune function in poultry (Li et al., 2024). However, the inconsistent effects of SY on IgA and IgG highlight the need for further research to clarify its specific impact on these immunoglobulins and optimize supplementation strategies for immune enhancement in laying hens.
Antioxidant activity
The antioxidant system, comprising key enzymes such as SOD, GSH-Px, CAT, and T-AOC, plays a critical role in neutralizing oxidative stress and maintaining cellular health and productivity in animals (Ozgocmen et al., 2007). The GSH-Px, a selenium-dependent enzyme, reduces harmful peroxides to protect cells, while SOD and CAT work to detoxify reactive oxygen species (Delwing-Dal et al., 2016; Muhammad et al., 2022). T-AOC reflects the combined antioxidant activity of enzymatic and non-enzymatic compounds. These components work synergistically to protect cells from oxidative stress, ensuring animals' overall health and well-being by enhancing their resilience against environmental and metabolic challenges.
In this study, SY supplementation significantly increased SOD and CAT activities in the SY60 group, with both SY15 and SY60 groups showing elevated T-AOC levels, while GSH-Px activity were notably higher in the SY15 group, highlighting selenium's critical role in redox homeostasis (Liu et al., 2023). These findings align with previous studies demonstrating SY's positive impact on antioxidant enzymes, such as increased activities of GSH-Px (Han et al., 2017), GSH-Px, SOD, and T-AOC levels (Li et al., 2024). Another study reported that Se supplementation enhanced the T-AOC levels in the serum, while SY specifically enhanced activities of CAT and SOD, although both are not Se-dependent enzymes (Meng et al., 2021). The positive effect of selenium (Se) on the antioxidant defense system is attributed to its role in forming selenocysteine, a crucial component of glutathione peroxidase (GSH-Px), which reduces harmful peroxides and mitigates oxidative damage (Yang et al., 2016). This underscores selenium's importance as a vital nutrient for enhancing the body's resilience against oxidative stress. However, conflicting results have been reported, with some studies finding no significant effect (Delezie et al., 2014) or lower effect (Meng et al., 2019) of SY on GSH-Px compared to other selenium sources such as sodium selenite, probably due to absorption efficiency. In another study, supplementation of SY at 2 mg/kg had no significant effect on T-AOC and GSH-Px but increased SOD (Chen et al., 2024). Variations may stem from differences in selenium sources, bioavailability, and dosages.
Interestingly, the SY60 group did not provide additional antioxidant benefits, indicating that 1.5 mg/kg is optimal for enhancing antioxidant capacity without toxicity risks. While higher doses (6 mg/kg) increased antioxidant enzyme activity, they also posed liver damage risks, as evidenced by elevated liver enzymes and histopathological changes. These findings emphasize the importance of optimal dosage selection/utilization to make the most of selenium's benefits without compromising health or productivity.
To further understand selenium's antioxidant effects, mRNA expression of key antioxidant genes (Nrf2, Keap1, HO-1, NQO1, and CAT) in the liver was analyzed. These insights could clarify the molecular mechanisms by which selenium modulates antioxidant defenses, contributing to optimized supplementation strategies in poultry.
Immune function
Immunoglobulins, such as IgA, IgG, and IgM, are critical markers of humoral immunity in avian species, playing essential roles in infection defense (Schroeder and Cavacini, 2010). In this study, SY supplementation did not significantly affect IgA or IgG levels but did influence IgM concentrations, suggesting that the 84-day feeding regimen supported immune function without compromising immunity. The IgM, a key marker of humoral immunity (Liu et al., 2019) was significantly influenced, aligning with previous findings that selenium-enriched diets benefit immune function in poultry (Li et al., 2024). However, the inconsistent effects of SY on IgA and IgG highlight the need for further research to clarify its specific impact on these immunoglobulins and optimize supplementation strategies for immune enhancement in laying hens.
Antioxidant activity
The antioxidant system, comprising key enzymes such as SOD, GSH-Px, CAT, and T-AOC, plays a critical role in neutralizing oxidative stress and maintaining cellular health and productivity in animals (Ozgocmen et al., 2007). The GSH-Px, a selenium-dependent enzyme, reduces harmful peroxides to protect cells, while SOD and CAT work to detoxify reactive oxygen species (Delwing-Dal et al., 2016; Muhammad et al., 2022). T-AOC reflects the combined antioxidant activity of enzymatic and non-enzymatic compounds. These components work synergistically to protect cells from oxidative stress, ensuring animals' overall health and well-being by enhancing their resilience against environmental and metabolic challenges.
In this study, SY supplementation significantly increased SOD and CAT activities in the SY60 group, with both SY15 and SY60 groups showing elevated T-AOC levels, while GSH-Px activity were notably higher in the SY15 group, highlighting selenium's critical role in redox homeostasis (Liu et al., 2023). These findings align with previous studies demonstrating SY's positive impact on antioxidant enzymes, such as increased activities of GSH-Px (Han et al., 2017), GSH-Px, SOD, and T-AOC levels (Li et al., 2024). Another study reported that Se supplementation enhanced the T-AOC levels in the serum, while SY specifically enhanced activities of CAT and SOD, although both are not Se-dependent enzymes (Meng et al., 2021). The positive effect of selenium (Se) on the antioxidant defense system is attributed to its role in forming selenocysteine, a crucial component of glutathione peroxidase (GSH-Px), which reduces harmful peroxides and mitigates oxidative damage (Yang et al., 2016). This underscores selenium's importance as a vital nutrient for enhancing the body's resilience against oxidative stress. However, conflicting results have been reported, with some studies finding no significant effect (Delezie et al., 2014) or lower effect (Meng et al., 2019) of SY on GSH-Px compared to other selenium sources such as sodium selenite, probably due to absorption efficiency. In another study, supplementation of SY at 2 mg/kg had no significant effect on T-AOC and GSH-Px but increased SOD (Chen et al., 2024). Variations may stem from differences in selenium sources, bioavailability, and dosages.
Interestingly, the SY60 group did not provide additional antioxidant benefits, indicating that 1.5 mg/kg is optimal for enhancing antioxidant capacity without toxicity risks. While higher doses (6 mg/kg) increased antioxidant enzyme activity, they also posed liver damage risks, as evidenced by elevated liver enzymes and histopathological changes. These findings emphasize the importance of optimal dosage selection/utilization to make the most of selenium's benefits without compromising health or productivity.
To further understand selenium's antioxidant effects, mRNA expression of key antioxidant genes (Nrf2, Keap1, HO-1, NQO1, and CAT) in the liver was analyzed. These insights could clarify the molecular mechanisms by which selenium modulates antioxidant defenses, contributing to optimized supplementation strategies in poultry.
Hepatic gene expression
This study utilized molecular techniques, such as qPCR to analyze liver gene expression in hens treated with selenium yeast (SY) at varying dosages. The focus was on key antioxidant pathway genes: Nrf2, Keap1, HO-1, NQO1, and CAT, to elucidate SY's regulatory effects on liver function at the transcriptional level (Seehofer et al., 2008). Selenium supplementation has been shown to enhance the expression of antioxidant genes, likely through direct regulation of enzyme activity (Meng et al., 2019; Chen et al., 2016).
Our findings demonstrated that SY15 supplementation significantly upregulated Nrf2, HO-1, and NQO1 expression while downregulating Keap1, suggesting enhanced hepatic antioxidant capacity via Nrf2 pathway activation. The Nrf2, a transcription factor critical for cellular protection against oxidative stress, is regulated by Keap1, whose reduced expression may promote Nrf2 activation and downstream antioxidant responses (Ngo and Duennwald, 2022). These results support the hypothesis that SY modulates liver antioxidant defenses by influencing Nrf2 signaling pathways. Particularly, CAT mRNA expression was unaffected, likely due to its unique regulatory mechanisms and role within the antioxidant system.
The observed transcriptional changes align with selenium's known role in bolstering antioxidant defenses, particularly through selenoprotein functions. A study by Lin et al. (2020) found that dietary SY increased the activity of GPX1 compared to Nano selenium. However, Meng et al. (2021) reported no significant differences in GPX1 activity or the expression of other antioxidant genes in the liver when comparing different selenium sources. These findings align with the broader understanding of selenium's role in modulating gene expression to bolster the body's defense against oxidative damage (Alshammari et al., 2022).
We could deduce that supplementation of SY 1.5 mg/kg is optimal for meeting physiological needs and improving performance without inducing toxicity. These findings clarify the specific effects of SY on antioxidant gene expression and offer valuable insights for optimizing selenium supplementation strategies in poultry.
SY enhances selenium retention in eggs by boosting antioxidant defenses and supporting liver health, with optimal dosing (1.5 mg/kg) ensuring efficient deposition in the yolk. Excessive doses (6.0 mg/kg), however, cause oxidative stress and reduce retention efficiency. Balanced dosing is key to producing selenium-enriched functional foods while maintaining hen health.
Conclusion
This study demonstrated that selenium-enriched yeast (SY) supplementation at 1.5 mg/kg optimally enhances egg quality, selenium deposition, antioxidant capacity, and hepatic gene expression while maintaining protein metabolism and immune function without adverse effects on liver health. Supplementation of SY significantly upregulated key antioxidant genes (Nrf2, HO-1, NQO1) while downregulating Keap1, and improved activities serum antioxidant enzyme (GSH-Px, SOD) and levels of T-AOC, supporting enhanced oxidative stress defense. Conversely, 6.0 mg/kg SY induced hepatocellular damage, as indicated by elevated liver enzymes and histopathological changes, despite increased selenium deposition. These findings emphasize the importance of balanced SY dosing to optimize functional benefits while safeguarding hen health and productivity. Future research should explore the long-term effects of selenium supplementation on physiological and molecular responses to refine dietary strategies for poultry.
Disclosures
All authors approve the submission of this manuscript and declare no conflict of interest.
Declaration of competing interest
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Shugeng Wu reports financial support was provided by Beijing Municipal Poultry Innovation Team (BAIC06-2024), the National Natural Science Foundation of China (32272907), the Agricultural Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences (ASTIP). The Authors they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
Title: 11-Hydroxyeicosatetraenoics induces cellular hypertrophy in an enantioselective manner | Body:
1 Introduction
Cardiac hypertrophy (CH) is a reversible response of the heart to various stress conditions, such as hypertension and chronic heart valve diseases. If left untreated, CH develops into more serious and irreversible heart failure (Nakamura and Sadoshima, 2018). It is characterized by the enlargement of the heart muscle cells and the thickening of the walls of the ventricles. CH can be categorized into two types: physiological and pathological. While CH typically begins as an adaptive response to the stimuli, it has a significant propensity to progress to heart failure when it becomes chronic and persistent (Shimizu and Minamino, 2016). Several studies have demonstrated the role of arachidonic acid (AA) metabolites in the pathogenesis of many cardiovascular diseases (CVD) including CH especially midchain hydroxyeicosatetraenoic acids (HETEs) (mainly 5-, 12-, and 15 HETEs) (Maayah and El-Kadi, 2016; Zhou et al., 2021; Hidayat et al., 2023a; Isse et al., 2023b; Helal et al., 2023).
It is well known that HETEs existing in biological systems are typically a combination of R and S configurations (Isse et al., 2023a). Interestingly 11-HETE, one of the midchain HETEs, can be generated either enzymatically or via non-enzymatic oxidation of AA. It can be produced by an enzymatic process, exclusively in the R-configuration, as a by-product of prostaglandin biosynthesis via the cyclooxygenase-1 (COX-1) and COX-2 enzymes (Xiao et al., 1997). Additionally, incubating rat liver microsome with AA produced 11(R)- and 11(S)-HETE via cytochrome P450 in NADPH-dependent metabolism with the R-enantiomer being more predominant than the S-enantiomer (Capdevila et al., 1986). Furthermore, cultured rat aorta smooth muscle cells synthesize both prostacyclin and significant quantities of 11- and 15-HETE via the COX pathway in response to AA or physiological stimuli such as thrombin (Bailey et al., 1983). The non-enzymatic synthesis of 11-HETE, as well as 8 and 9-HETE, occurs due to the free radical oxidation of AA (Feugray et al., 2022). The elevated plasma level of 11-HETE has been identified as a marker of lipid peroxidation and may suggest heightened oxidative stress and an increase in reactive oxygen species (Austin Pickens et al., 2019).
Each HETE enantiomer can exhibit distinct biological or pathophysiological effects. As shown previously in our laboratory, some HETE enantiomers may exert more pronounced effects on the induction of cardiac hypertrophy compared to the other enantiomers (Shoieb and El-Kadi, 2018; Hidayat et al., 2023b; Isse et al., 2023b). Since COX and cytochrome P450 (CYP) enzymes produce mainly 11(R) HETE and the levels of 11(S)-HETE are higher than those of 11(R)-HETE in isolated human plasma and serum, it was necessary to study the potential involvement of 11-HETE enantiomers in the process of CH.
2 Materials and methods
2.1 Materials
11-HETE (R and S) enantiomers were purchased from Cayman Chemical (Ann Arbor, MI, United States). Dulbecco’s Modified Eagle’s Medium/F-12 (DMEM/F-12) and trypsin were obtained from Gibco, Life Technologies (Grand Island, NY, United States). TRIzol reagent was an Invitrogen brand (Thermo Fisher Scientific, Carlsbad, CA). High-Capacity cDNA Reverse Transcription kit and SYBR® Green PCR Master Mix were both obtained from Applied Biosystems (Foster City, CA, United States). According to the previously published sequences, Integrated DNA Technologies (Coralville, IA, United States) formulated the real-time polymerase chain reaction (RT-PCR) primers. Trans-Blot Turbo RTA Transfer Kit and 2X Laemmli Sample Buffer were purchased from Bio-Rad Laboratories (Hercules, CA, United States). Recombinant monoclonal CYP1B1 antibody (ab185954), CYP4A11 (ab3573), CYP4F2 (ab230709), and glyceraldehyde-3-phosphate dehydrogenase (ab8245) mouse monoclonal antibody was purchased from Abcam (Toronto, ON). CYP2J antibody (ABS1605) was obtained from Millipore Sigma (Burlington, MA, United States). Both the anti-mouse and the anti-rabbit IgG HRP-linked secondary antibodies were from Cell Signaling (Massachusetts, United States). Chemiluminescence Western blotting detection reagent (ECL) was purchased from GE Healthcare Life Sciences (Pittsburgh, PA, United States). Resorufin, 7-ethoxy resorufin (7-ER), nicotinamide adenine dinucleotide phosphate (NADPH) tetrasodium salt, and fetal bovine serum were purchased from Sigma Chemical Co (St. Louis, MO, United States). ProLong Gold Antifade, 4′,6-diamidino-2-phenylindole (DAPI), Alexa Fluor 488 Conjugate, and wheat germ agglutinin were obtained from Thermo Fisher Scientific (Edmonton, Canada). Human recombinant CYP1B1 supersomes supplemented with NADPH–cytochrome P450-oxidoreductase were obtained from (Gen test, MA, United States). Human liver microsomes (InVitroCYP™) were purchased from BioIVT (Hicksville, NY, United States). All other chemicals and reagents used in the experiments were purchased from Fisher Scientific Company (Toronto, ON).
2.2 Cell culture
Human fetal ventricular cardiomyocyte (RL-14) cells (Patent Deposit Designation No. PTA-1499) were purchased from the American Type Culture Collection (ATCC) (Manassas, VA, United States). RL-14 cells were grown in DMEM/F-12 with phenol red that is supplemented with 12.5% fetal bovine serum, 20 μM l-glutamine, 100 IU/ml penicillin G, and 100 μg/ml streptomycin. Cells were grown in 75 cm2 tissue culture flasks at 37°C under a 5% CO2 humidified environment. Each 75-cm2 flask had an average of 7 × 106 cells. For seeding, each well contained an average of 9.8 × 105 cells in the 6-well plate, an average of 3.5 × 105 cells in the 12-well plate, and an average of 1.8 × 105 cells in the 48-well plate. Cells were cultured in the complete media until they achieved a confluency state suitable for platting.
2.3 Chemical treatments
The cells in the control group were treated with the vehicle [serum-free DMEM/F-12 containing 0.5% dimethylsulfoxide (DMSO)]. The other groups were treated by adding 20 μM (R) or (S) 11-HETE to the serum free media (SFM) for 24 h. Both (R) and (S) 11-HETE were supplied as a stock solution in DMSO and were stored at −20°C until use. DMSO concentration didn’t exceed 0.5% in the treated groups during all the performed experiments.
2.4 Measurement of cell viability
Cell viability test was determined by using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay which measures the ability of the living cells to reduce the yellow tetrazolium salt to its water-insoluble purple formazan crystals. The optical density of the formazan crystals reflects the population of living cells. Cells were plated in a 48-well plate under 37°C temperature and 5% CO2 humidified condition until sufficient confluency was achieved. Then the cells were treated with 2.5, 5, 10, and 20 μM of either (R) or (S) 11-HETE for 24 h. The media containing the treatment was discarded and 100 μL of MTT reagent (1.2 mM) dissolved in SFM was added and incubated with the cells at 37°C. After incubation for 2 h, the medium was discarded and 200 μL of DMSO were added to solubilize the formed formazan crystals. Synergy™ H1 Hybrid Multi-Mode Reader (BioTek Instruments; Winooski, VT, United States) was used to measure the color intensity at a wavelength of 570 nm.
2.5 Measurement of cell surface area
RL-14 cells were plated in a 6-well plate and treated with 20 μM of either (R) or (S) 11-HETE for 24 h. After that, cells were washed with 1x PBS (pH 7.4) 3 times and fixed with 4% paraformaldehyde for 15 min at 4°C. Then 10 μg/ml of wheat germ agglutinin, Alexa Fluor 488 conjugate was added, and the plates were incubated for 2 h in a dark place. The plates were washed again with 1x PBS (pH 7.4) 3 times each for 5 min using a shaker. Thereafter, the coverslips that have the stained cells were put on a glass slide with ProLong antifade reagent with DAPI. The slides were then imaged by an inverted microscope using the ×20 objective lens as described previously (Alammari et al., 2023) and the surface area was measured using Zeiss AxioVision Software (Carl Zeiss Imaging Solutions, version 4.8). Sixty-five individual cells from each group were included in the analysis.
2.6 RNA extraction and cDNA synthesis
RNA extraction and cDNA synthesis were performed on the (R) and (S) 11-HETE-treated RL-14 cells according to the method described previously (Shoieb and El-Kadi, 2020). In brief, cells were plated in 12-well plates and treated with 20 μM (R) and (S) 11-HETE for 24 h. Thereafter, the total RNA was isolated with TRIzol reagent, and the concentration was determined by measuring the absorbance at 260 nm. The RNA purity was determined by measuring the 260/280 ratio (>1.8). The first strand of cDNA was performed according to the manufacturer’s instructions by mixing 1.25 µg of the total RNA isolated from each sample with high-capacity cDNA reverse transcription reagents (Applied Biosystems). Finally, the reaction mixture was inserted in a thermocycler and underwent the following conditions: 25°C for 10 min, 37°C for 120 min, 85°C for 5 min, and at last it was cooled to 4°C.
2.7 Real-time polymerase chain reaction (RT-PCR) for quantification of mRNA gene expression
The mRNA gene expression was quantified in a 384-well optical reaction plate using Applied Biosystems Quant Studio 5 RT-PCR System. Each 20 µL of the reaction mixture contains equal volumes of forward and reverse primers (0.04 µL) with 20 nM final concentration of each, 8.92 µL of nuclease-free water, 10 µL SYBR Green Universal Master Mix, and 1 µL of the cDNA sample. Thermocycling conditions were as described previously (Shoieb et al., 2022) [initiation at 95°C for 10 min followed by 40 cycles of denaturation (95°C, 15 s) and combined annealing/extension (60°C, 60 s)]. The sequences of the human primers used in this study are listed in Table 1. Analysis of the RT-PCR data for the genes of interest and the reference gene (β-actin) was carried out using the relative gene expression (i.e., ΔΔ CT) method, as previously reported (Livak and Schmittgen, 2001).
TABLE 1
Primer sequences used for RT- PCR reactions.
Genes
Forward primer
Reverse primer
ANP
CAACGCAGACCTGATGGATTT
AGCCCCCGCTTCTTCATTC
α-MHC
GCCCTTTGACATTCGCACTG
GGTTTCAGCAATGACCTTGCC
β-MHC
TCACCAACAACCCCTACGATT
CTCCTCAGCGTCATCAATGGA
ACTA-1
AGGTCATCACCATCGGCAACGA
GCTGTTGTAGGTGGTCTCGTGA
BNP
CAGAAGCTGCTGGAGCTGATAAG
TGTAGGGCCTTGGTCCTTTG
1B1
TTCGGCCACTACTCGGAGC
AAGAAGTTGCGCATCATGCTG
1A1
CTATCTGGGCTGTGGGCA
CTGGCTCAAGCACAACTTGG
4A11
CCATCCCCATTGCACGACTT
CAGGTACAGAAGCAGGTAGGG
4F11
CATCTCCCGATGTTGCACG
TCTCTTGGTCGAAACGGAAGG
4F2
GAGGGTAGTGCCTGTTTGGAT
CAGGAGGATCTCATGGTGTCTT
2J2
GAGCTTAGAGGAACGCATTCAG
GAAATGAGGGTCAAAAGGCTGT
2E1
ATGTCTGCCCTCGGAGTCA
CGATGATGGGAAGCGGGAAA
2C8
CATTACTGACTTCCGTGCTACAT
CTCCTGCACAAATTCGTTTTC
β-actin
CTGGCACCCAGCACAATG
GCCGATCCACACGGAGTACT
2.8 Protein extraction from RL-14 cells and western blot analysis
Protein extraction from the cells and Western blot analysis were performed as previously described by Shoieb and El-Kadi (2020). In brief, Rl-14 cells were grown in 6-well plates and incubated with 20 μM (R) or (S) 11-HETE for 24 h. Thereafter, the cell lysates were collected using 100 μL from the lysis buffer containing 50 mM HEPES, 1.5 mM magnesium chloride, 0.5 M sodium chloride, 10% (v/v) glycerol, 1 mM EDTA, 1% Triton X-100, and 5 μL/ml protease inhibitor cocktail. Subsequently, the Lowry assay was done to determine the concentration of the protein using bovine serum albumin as a reference standard (Lowry et al., 1951).
Western blot analysis was performed by separating 100 μg of the total cell lysate by 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS–PAGE). The separated proteins were transferred to polyvinylidene difluoride membranes and were incubated with the specific primary antibody for the desired protein overnight at 4°C. The membranes were then incubated with secondary antibodies (anti-rabbit IgG HRP-linked secondary antibodies or anti-mouse IgG HRP-linked secondary antibodies) in a blocking solution for 45 min at room temperature. The protein bands were finally visualized after the addition of ECL prime Western blot detection reagent using the enhanced chemiluminescence method and the ChemiDoc Imaging System (Bio-Rad Laboratories; CA, United States). The band’s signals were quantified relative to the signals obtained for the Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) protein (loading control), using the Image Laboratory Software, version 6.1 (Bio-Rad Laboratories, Hercules, CA).
2.9 Effect of 11-HETE enantiomers on human recombinant CYP1B1 enzymatic activities
The rates of the O-dealkylation of 7-ethoxyresorufin catalyzed by recombinant human CYP1B1 were measured using the ethoxyresorufin-O-deethylase (EROD) assay. The measurements were conducted in the absence or presence of either (R) or (S) 11-HETE. The assay was done using a white 96-well microplate. Different concentrations of 7-ER (7-ethoxyresorufin; final concentration of 0, 5, 10, 20, 40, and 100 nM) were subjected to incubation with a reaction mixture containing 100 mM potassium phosphate (pH 7.4) buffer supplemented with 5 mM magnesium chloride hexahydrate and 1 pmol of human recombinant CYP1B1 enzyme. After that, various concentrations (final concentration of 0, 0.5, 2.5, 10, and 40 nM) of either (R) or (S) 11-HETE were added to the reaction mixture. Then 100 μL of this reaction mixture was added to each well of the 96-well plate followed by 100 μL of NADPH (2 mM) to start the reaction. The fluorescent reading of the plate related to the resorufin formation was measured by BioTek Synergy H1 Hybrid Reader (BioTek Instruments, Inc.) every min for 30 min. The signal was recorded with 550/585 nm excitation/emission wavelengths, respectively. A resorufin standard curve was prepared and used to calculate the amount of the resorufin formed. The rate of resorufin formation was plotted versus the concentration of 7-ER for each concentration of 11-HETE.
2.10 Effect of 11-HETE enantiomers on cytochrome P450 enzymatic activities in human liver microsomes
Incubation with human liver microsomes was performed to test the effect of either (R) or (S) 11-HETE on modulating the enzymatic activities of the CYP1B1 enzyme. Human liver microsomes pooled from 25 different individuals (0.1 mg/mL) were incubated with different concentrations of (R) or (S) 11-HETE (0, 10, 20, 40, and 100 nM). 2 μM of EROD was used as the substrate in the reaction that also contains 100 mM potassium phosphate buffer (pH 7.4) supplemented with 5 mM magnesium chloride hexahydrate. 100 μL of the reaction mixture was added to the wells of 96-well polystyrene microplates. To start the reaction, 100 μL of 1 mM NADPH was added to the reaction mixture in each well. A BioTek Synergy H1 Hybrid Reader (BioTek Instruments, Inc) was used to measure the fluorescent signal generated due to the formation of resorufin every minute for 30 min under 37°C at 550/585 nm excitation/emission wavelengths, respectively.
2.11 Statistical analysis
The results were represented as mean ± standard error of the mean (SEM). Data were analyzed using one-way analysis of variance (ANOVA) followed by a Tukey post hoc test for all experiments except the microsomal and supersome incubation experiments where we used Dunnett test. The result was considered significantly different when the p-value was less than 0.05. The rate of resorufin formation was plotted against 7-ER concentration and was fitted considering each replicate as an individual point to the Michalis-Menten model. All the Statistical analysis, the graph plotting, and the enzymology module were executed using GraphPad Prism software for Windows, version 8.4.3. (GraphPad Software, Inc. La Jolla, CA).
2.1 Materials
11-HETE (R and S) enantiomers were purchased from Cayman Chemical (Ann Arbor, MI, United States). Dulbecco’s Modified Eagle’s Medium/F-12 (DMEM/F-12) and trypsin were obtained from Gibco, Life Technologies (Grand Island, NY, United States). TRIzol reagent was an Invitrogen brand (Thermo Fisher Scientific, Carlsbad, CA). High-Capacity cDNA Reverse Transcription kit and SYBR® Green PCR Master Mix were both obtained from Applied Biosystems (Foster City, CA, United States). According to the previously published sequences, Integrated DNA Technologies (Coralville, IA, United States) formulated the real-time polymerase chain reaction (RT-PCR) primers. Trans-Blot Turbo RTA Transfer Kit and 2X Laemmli Sample Buffer were purchased from Bio-Rad Laboratories (Hercules, CA, United States). Recombinant monoclonal CYP1B1 antibody (ab185954), CYP4A11 (ab3573), CYP4F2 (ab230709), and glyceraldehyde-3-phosphate dehydrogenase (ab8245) mouse monoclonal antibody was purchased from Abcam (Toronto, ON). CYP2J antibody (ABS1605) was obtained from Millipore Sigma (Burlington, MA, United States). Both the anti-mouse and the anti-rabbit IgG HRP-linked secondary antibodies were from Cell Signaling (Massachusetts, United States). Chemiluminescence Western blotting detection reagent (ECL) was purchased from GE Healthcare Life Sciences (Pittsburgh, PA, United States). Resorufin, 7-ethoxy resorufin (7-ER), nicotinamide adenine dinucleotide phosphate (NADPH) tetrasodium salt, and fetal bovine serum were purchased from Sigma Chemical Co (St. Louis, MO, United States). ProLong Gold Antifade, 4′,6-diamidino-2-phenylindole (DAPI), Alexa Fluor 488 Conjugate, and wheat germ agglutinin were obtained from Thermo Fisher Scientific (Edmonton, Canada). Human recombinant CYP1B1 supersomes supplemented with NADPH–cytochrome P450-oxidoreductase were obtained from (Gen test, MA, United States). Human liver microsomes (InVitroCYP™) were purchased from BioIVT (Hicksville, NY, United States). All other chemicals and reagents used in the experiments were purchased from Fisher Scientific Company (Toronto, ON).
2.2 Cell culture
Human fetal ventricular cardiomyocyte (RL-14) cells (Patent Deposit Designation No. PTA-1499) were purchased from the American Type Culture Collection (ATCC) (Manassas, VA, United States). RL-14 cells were grown in DMEM/F-12 with phenol red that is supplemented with 12.5% fetal bovine serum, 20 μM l-glutamine, 100 IU/ml penicillin G, and 100 μg/ml streptomycin. Cells were grown in 75 cm2 tissue culture flasks at 37°C under a 5% CO2 humidified environment. Each 75-cm2 flask had an average of 7 × 106 cells. For seeding, each well contained an average of 9.8 × 105 cells in the 6-well plate, an average of 3.5 × 105 cells in the 12-well plate, and an average of 1.8 × 105 cells in the 48-well plate. Cells were cultured in the complete media until they achieved a confluency state suitable for platting.
2.3 Chemical treatments
The cells in the control group were treated with the vehicle [serum-free DMEM/F-12 containing 0.5% dimethylsulfoxide (DMSO)]. The other groups were treated by adding 20 μM (R) or (S) 11-HETE to the serum free media (SFM) for 24 h. Both (R) and (S) 11-HETE were supplied as a stock solution in DMSO and were stored at −20°C until use. DMSO concentration didn’t exceed 0.5% in the treated groups during all the performed experiments.
2.4 Measurement of cell viability
Cell viability test was determined by using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay which measures the ability of the living cells to reduce the yellow tetrazolium salt to its water-insoluble purple formazan crystals. The optical density of the formazan crystals reflects the population of living cells. Cells were plated in a 48-well plate under 37°C temperature and 5% CO2 humidified condition until sufficient confluency was achieved. Then the cells were treated with 2.5, 5, 10, and 20 μM of either (R) or (S) 11-HETE for 24 h. The media containing the treatment was discarded and 100 μL of MTT reagent (1.2 mM) dissolved in SFM was added and incubated with the cells at 37°C. After incubation for 2 h, the medium was discarded and 200 μL of DMSO were added to solubilize the formed formazan crystals. Synergy™ H1 Hybrid Multi-Mode Reader (BioTek Instruments; Winooski, VT, United States) was used to measure the color intensity at a wavelength of 570 nm.
2.5 Measurement of cell surface area
RL-14 cells were plated in a 6-well plate and treated with 20 μM of either (R) or (S) 11-HETE for 24 h. After that, cells were washed with 1x PBS (pH 7.4) 3 times and fixed with 4% paraformaldehyde for 15 min at 4°C. Then 10 μg/ml of wheat germ agglutinin, Alexa Fluor 488 conjugate was added, and the plates were incubated for 2 h in a dark place. The plates were washed again with 1x PBS (pH 7.4) 3 times each for 5 min using a shaker. Thereafter, the coverslips that have the stained cells were put on a glass slide with ProLong antifade reagent with DAPI. The slides were then imaged by an inverted microscope using the ×20 objective lens as described previously (Alammari et al., 2023) and the surface area was measured using Zeiss AxioVision Software (Carl Zeiss Imaging Solutions, version 4.8). Sixty-five individual cells from each group were included in the analysis.
2.6 RNA extraction and cDNA synthesis
RNA extraction and cDNA synthesis were performed on the (R) and (S) 11-HETE-treated RL-14 cells according to the method described previously (Shoieb and El-Kadi, 2020). In brief, cells were plated in 12-well plates and treated with 20 μM (R) and (S) 11-HETE for 24 h. Thereafter, the total RNA was isolated with TRIzol reagent, and the concentration was determined by measuring the absorbance at 260 nm. The RNA purity was determined by measuring the 260/280 ratio (>1.8). The first strand of cDNA was performed according to the manufacturer’s instructions by mixing 1.25 µg of the total RNA isolated from each sample with high-capacity cDNA reverse transcription reagents (Applied Biosystems). Finally, the reaction mixture was inserted in a thermocycler and underwent the following conditions: 25°C for 10 min, 37°C for 120 min, 85°C for 5 min, and at last it was cooled to 4°C.
2.7 Real-time polymerase chain reaction (RT-PCR) for quantification of mRNA gene expression
The mRNA gene expression was quantified in a 384-well optical reaction plate using Applied Biosystems Quant Studio 5 RT-PCR System. Each 20 µL of the reaction mixture contains equal volumes of forward and reverse primers (0.04 µL) with 20 nM final concentration of each, 8.92 µL of nuclease-free water, 10 µL SYBR Green Universal Master Mix, and 1 µL of the cDNA sample. Thermocycling conditions were as described previously (Shoieb et al., 2022) [initiation at 95°C for 10 min followed by 40 cycles of denaturation (95°C, 15 s) and combined annealing/extension (60°C, 60 s)]. The sequences of the human primers used in this study are listed in Table 1. Analysis of the RT-PCR data for the genes of interest and the reference gene (β-actin) was carried out using the relative gene expression (i.e., ΔΔ CT) method, as previously reported (Livak and Schmittgen, 2001).
TABLE 1
Primer sequences used for RT- PCR reactions.
Genes
Forward primer
Reverse primer
ANP
CAACGCAGACCTGATGGATTT
AGCCCCCGCTTCTTCATTC
α-MHC
GCCCTTTGACATTCGCACTG
GGTTTCAGCAATGACCTTGCC
β-MHC
TCACCAACAACCCCTACGATT
CTCCTCAGCGTCATCAATGGA
ACTA-1
AGGTCATCACCATCGGCAACGA
GCTGTTGTAGGTGGTCTCGTGA
BNP
CAGAAGCTGCTGGAGCTGATAAG
TGTAGGGCCTTGGTCCTTTG
1B1
TTCGGCCACTACTCGGAGC
AAGAAGTTGCGCATCATGCTG
1A1
CTATCTGGGCTGTGGGCA
CTGGCTCAAGCACAACTTGG
4A11
CCATCCCCATTGCACGACTT
CAGGTACAGAAGCAGGTAGGG
4F11
CATCTCCCGATGTTGCACG
TCTCTTGGTCGAAACGGAAGG
4F2
GAGGGTAGTGCCTGTTTGGAT
CAGGAGGATCTCATGGTGTCTT
2J2
GAGCTTAGAGGAACGCATTCAG
GAAATGAGGGTCAAAAGGCTGT
2E1
ATGTCTGCCCTCGGAGTCA
CGATGATGGGAAGCGGGAAA
2C8
CATTACTGACTTCCGTGCTACAT
CTCCTGCACAAATTCGTTTTC
β-actin
CTGGCACCCAGCACAATG
GCCGATCCACACGGAGTACT
2.8 Protein extraction from RL-14 cells and western blot analysis
Protein extraction from the cells and Western blot analysis were performed as previously described by Shoieb and El-Kadi (2020). In brief, Rl-14 cells were grown in 6-well plates and incubated with 20 μM (R) or (S) 11-HETE for 24 h. Thereafter, the cell lysates were collected using 100 μL from the lysis buffer containing 50 mM HEPES, 1.5 mM magnesium chloride, 0.5 M sodium chloride, 10% (v/v) glycerol, 1 mM EDTA, 1% Triton X-100, and 5 μL/ml protease inhibitor cocktail. Subsequently, the Lowry assay was done to determine the concentration of the protein using bovine serum albumin as a reference standard (Lowry et al., 1951).
Western blot analysis was performed by separating 100 μg of the total cell lysate by 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS–PAGE). The separated proteins were transferred to polyvinylidene difluoride membranes and were incubated with the specific primary antibody for the desired protein overnight at 4°C. The membranes were then incubated with secondary antibodies (anti-rabbit IgG HRP-linked secondary antibodies or anti-mouse IgG HRP-linked secondary antibodies) in a blocking solution for 45 min at room temperature. The protein bands were finally visualized after the addition of ECL prime Western blot detection reagent using the enhanced chemiluminescence method and the ChemiDoc Imaging System (Bio-Rad Laboratories; CA, United States). The band’s signals were quantified relative to the signals obtained for the Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) protein (loading control), using the Image Laboratory Software, version 6.1 (Bio-Rad Laboratories, Hercules, CA).
2.9 Effect of 11-HETE enantiomers on human recombinant CYP1B1 enzymatic activities
The rates of the O-dealkylation of 7-ethoxyresorufin catalyzed by recombinant human CYP1B1 were measured using the ethoxyresorufin-O-deethylase (EROD) assay. The measurements were conducted in the absence or presence of either (R) or (S) 11-HETE. The assay was done using a white 96-well microplate. Different concentrations of 7-ER (7-ethoxyresorufin; final concentration of 0, 5, 10, 20, 40, and 100 nM) were subjected to incubation with a reaction mixture containing 100 mM potassium phosphate (pH 7.4) buffer supplemented with 5 mM magnesium chloride hexahydrate and 1 pmol of human recombinant CYP1B1 enzyme. After that, various concentrations (final concentration of 0, 0.5, 2.5, 10, and 40 nM) of either (R) or (S) 11-HETE were added to the reaction mixture. Then 100 μL of this reaction mixture was added to each well of the 96-well plate followed by 100 μL of NADPH (2 mM) to start the reaction. The fluorescent reading of the plate related to the resorufin formation was measured by BioTek Synergy H1 Hybrid Reader (BioTek Instruments, Inc.) every min for 30 min. The signal was recorded with 550/585 nm excitation/emission wavelengths, respectively. A resorufin standard curve was prepared and used to calculate the amount of the resorufin formed. The rate of resorufin formation was plotted versus the concentration of 7-ER for each concentration of 11-HETE.
2.10 Effect of 11-HETE enantiomers on cytochrome P450 enzymatic activities in human liver microsomes
Incubation with human liver microsomes was performed to test the effect of either (R) or (S) 11-HETE on modulating the enzymatic activities of the CYP1B1 enzyme. Human liver microsomes pooled from 25 different individuals (0.1 mg/mL) were incubated with different concentrations of (R) or (S) 11-HETE (0, 10, 20, 40, and 100 nM). 2 μM of EROD was used as the substrate in the reaction that also contains 100 mM potassium phosphate buffer (pH 7.4) supplemented with 5 mM magnesium chloride hexahydrate. 100 μL of the reaction mixture was added to the wells of 96-well polystyrene microplates. To start the reaction, 100 μL of 1 mM NADPH was added to the reaction mixture in each well. A BioTek Synergy H1 Hybrid Reader (BioTek Instruments, Inc) was used to measure the fluorescent signal generated due to the formation of resorufin every minute for 30 min under 37°C at 550/585 nm excitation/emission wavelengths, respectively.
2.11 Statistical analysis
The results were represented as mean ± standard error of the mean (SEM). Data were analyzed using one-way analysis of variance (ANOVA) followed by a Tukey post hoc test for all experiments except the microsomal and supersome incubation experiments where we used Dunnett test. The result was considered significantly different when the p-value was less than 0.05. The rate of resorufin formation was plotted against 7-ER concentration and was fitted considering each replicate as an individual point to the Michalis-Menten model. All the Statistical analysis, the graph plotting, and the enzymology module were executed using GraphPad Prism software for Windows, version 8.4.3. (GraphPad Software, Inc. La Jolla, CA).
3 Results
3.1 Effect of (R) and (S) 11-HETE on cell viability
MTT assay was used to assess the cytotoxicity of the 11-HETE concentrations used. RL-14 cells were treated with 2.5, 5, 10, and 20 μM of (R) and (S) 11-HETE for 24 h. All the concentrations tested did not significantly alter the cell viability (depicted by the viability above 90%) when compared to the control (data not shown). As a result, we used the 20 μM concentration in all the subsequent experiments.
3.2 Effect of 11-HETE enantiomers on cellular hypertrophic markers in RL-14 cells
To evaluate the potential effect of 11-HETE enantiomers in inducing cellular hypertrophy, RL-14 cells were treated with 20 μM of either 11(R)-HETE or 11(S)-HETE for 24 h. Thereafter, the expressions of the cardiac hypertrophic markers such as atrial natriuretic peptide (ANP), α-myosin heavy chain (α-MHC), β-MHC, skeletal α-actin (ACTA-1), and brain natriuretic peptide (BNP) were measured using RT-PCR. Figure 1 shows that 11(S)-HETE significantly increased the cardiac hypertrophic markers: ANP, β-MHC, and β/α-MHC by 231%, 499%, and 107%, respectively. 11(R)-HETE significantly increased the cardiac hypertrophic marker; β/α-MHC by 132%. Furthermore, ACTA-1 gene expression was increased by 46% in the 11(R)-HETE-treated group and was significantly increased by 282% in the 11(S)-HETE-treated group compared to the control (Figure 1). Both β-MHC and ACTA-1 gene expression were significantly increased in the 11(S)- compared to the 11(R) HETE-treated group.
FIGURE 1
Effect of (R) and (S) 11-HETE on cellular hypertrophy in RL-14 cells. RL-14 cells were treated for 24 h with 20 μM of 11-HETE enantiomers; then, the mRNA levels of ANP, α-MHC, β-MHC, ACTA-1, and BNP were quantified using real-time PCR. The values represent mean ± SEM (n = 4–5). Data were analyzed using one-way ANOVA. *p < 0.05 significant compared to the control group, #p < 0.05 significant compared to the 11(R)-HETE treated group.
It was established that the increase in the hypertrophic markers is associated with the increase in the cell surface area. As shown in Figure 2, our results showed that treating the RL-14 cells with 20 μM of either (R) or (S)- 11 HETE significantly increased the cell surface area by 29% and 34% compared to the control, respectively.
FIGURE 2
Effect of 11-HETE enantiomers on RL-14 cells surface area. RL-14 cells were treated with 20 µM of 11-HETE enantiomers for 24 h. Cell surface area was then determined by phase-contrast imaging using Zeiss Axio Observer Z1 inverted microscope using a ×20 objective lens. The values represent mean ± SEM (n = 65). Data were analyzed using one-way ANOVA. *p < 0.05 significant compared to the control group.
3.3 Effect of 11-HETE enantiomers on CYP mRNA gene expression in RL-14 cells
To examine the effect of 11-HETE enantiomers on CYP enzymes, RL-14 cells were treated with 20 μM (R) or (S) 11-HETE for 24 h. Thereafter, CYP1B1, CYP1A1, CYP4A11, CYP4F11, CYP4F2, CYP2J2, CYP2E1 and CYP2C8 mRNA were determined using RT-PCR. The CYP1B1, CYP1A1, CYP4A11, CYP4F11 and CYP4F2 mRNA were significantly increased in the cells treated with 11(R)-HETE by 116%, 112%, 70%, 238% and 167%, respectively, compared to the control group. Similarly, the 11(S)-HETE-treated group showed a significant increase in the gene expression of the same enzymes by 142%, 109%, 90%, 416% and 257% respectively, compared to the control (Figure 3). Albeit both (R) and (S) enantiomers have significantly increased the CYP2E1 mRNA gene expression by 146% and 163% respectively compared to the control group, only 11(S)-HETE increased the CYP2J2 mRNA gene expression by 47%.
FIGURE 3
Effect of 11-HETE enantiomers on the CYPs gene expression. RL-14 cells were treated with 20 µM of 11-HETE enantiomers for 24 h. After that, CYP1B1, CYP1A1, CYP4A11, CYP4F11, CYP4F2, CYP2J2, CYP2E1, and CYP2C8 gene expression were quantified using real-time PCR and normalized to ß-actin. The values represent mean ± SEM (n = 5–6). Data were analyzed using one-way ANOVA. *p < 0.05 significant compared to the control group, #p < 0.05 significant compared to the 11(R)-HETE treated group.
3.4 Effect of 11-HETE enantiomers on the protein level of CYP enzymes in RL-14 cells
It was essential to assess the protein levels of the CYP enzymes of interest since the mRNA expression may not consistently align with the levels of these enzymes in terms of protein expression. The protein level of CYP1B1, CYP4F2, and CYP4A11 in the cells treated with 11(S-) HETE showed a significant increase by 186%, 153%, and 152%, respectively, compared to the control. While the 11(R)-HETE-treated cells did not affect the protein level to the same degree, it significantly increased the protein level of CYP1B1, CYP4F2, and CYP4A11 by 156%, 126%, and 141%, respectively, compared to the control (Figure 4).
FIGURE 4
Effect of 11-HETE enantiomers on CYPs protein level. RL-14 cells were treated with 20 µM of 11-HETE enantiomers for 24 h. After that, cell lysates were harvested, and CYP1B1, CYP1A1, CYP4F2, CYP4A11, CYP2J, and CYP2C8 protein levels were determined using Western blot analysis. The values represent mean ± SEM (n = 4–5). Data were analyzed using one-way ANOVA. *p < 0.05 significant compared to the control group, #p < 0.05 significant compared to the 11(R)-HETE treated group.
Interestingly, the CYP2J protein level was significantly increased in the cells treated with 11(S-HETE) enantiomers by 135%, compared to the control group. Regarding CYP2C8, the increase in the protein level was not significant for both enantiomers (Figure 4). There was an increase in the gene expression of CYP4F11 for both enantiomers, however, CYP4F11 protein level was below the detection limit.
3.5 Effect of 11-HETE enantiomers on recombinant human CYP1B1 enzyme activity
The direct effect of R and S enantiomers of 11-HETE on rhCYP1B1 catalytic activity was assessed using rhCYP1B1-mediated EROD. The rate of resorufin formation (V) by rhCYP1B1 with various concentrations of 7-ER co-incubated with either S or R enantiomers of 11-HETE is shown in Figures 5A,C. 11(S)-HETE led to allosteric activation of CYP1B1 activity, causing a concentration-dependent increase in Vmax value, compared with control, by 1.03, 1.1, 1.5 and 1.4-fold for 0.5, 2.5, 10 and 40 nM of 11(S)-HETE, respectively (Table 2); whereas, 11(R)-HETE did not affect Vmax (Table 2). Km values of 7-ER hydrolysis of rhCYP1B1 did not change by either R or S enantiomers of 11-HETE; therefore, shared Km value was assumed in Michaelis-Menten model fitting, estimated to be 131.3 nM. The double reciprocal (Lineweaver-Burk) plots show intercepting lines for S and R enantiomers of 11-HETE, which in terms of allosteric interactions means changes in Vmax with no substantial effect on Km (Figures 5B,D).
FIGURE 5
Effect of 11-HETE enantiomers on recombinant human CYP1B1 enzyme activity. (A) 11(S)-HETE allosterically activated CYP1B1 activity; (C) 11(R)-HETE did not affect CYP1B1 activity; (B,D) Lineweaver-Burk plots show intercepting lines for S and R enantiomers of 11-HETE. Using a white 96-well microplate, the reaction mixture containing both buffer and 1 pmol of recombinant human CYP1B1 was incubated with 0–100 nM of 7-ER. After that, 0, 0.5, 2.5, 10, and 40 nM of 11 (R) or (S)-HETE were added. Then, 100 μL of 2 mM NADPH was added to each well to start the reaction. The fluorescent signal related to resorufin formation was measured every minute for 30 min at 550/585 nm excitation/emission wavelengths using BioTek Synergy H1Hybrid reader. The quantity of formed resorufin was calculated by forming a standard curve of 0–200 nM resorufin dissolved in the same incubation buffer. Data displays mean ± SEM (n = 3).
TABLE 2
Best fit values for resorufin formation rate kinetics mediated by recombinant human CYP1B1.
0 nM
0.5 nM
2.5 nM
10 nM
40 nM
11(R)-HETE
Vmax
15.4 ± 2.2
16.4 ± 2.7
16.6 ± 2.8
16.6 ± 2.7
17.2 ± 2.8
Km
131.3 ± 27.7
11(S)-HETE
Vmax
15.4 ± 2.2
16.0 ± 2.7
16.9 ± 2.8
23.0 ± 3.3*
21.8 ± 3.1*
Km
131.3 ± 27.7
The mean ± SD for Vmax and Km parameters for CYP1B1 activity in the absence and presence of 11-HETE enantiomers (n > 3). The enzyme kinetics were determined from best fit using the Enzyme kinetic module in GraphPad Prism. Vmax, maximum velocity; Km, the substrate concentration that provides the enzyme to achieve half Vmax. *p < 0.05 significant compared to the control group.
3.6 Effect of 11-HETE enantiomers on CYP1B1 activity in the human liver microsomes
To further confirm the results obtained from rhCYP1B1, we have tested the possible effect of both 11-HETE enantiomers on the catalytic activity of CYP1B1 using EROD assay in the human liver microsomes. We used fixed concentrations of the substrate and varying concentrations of either 11(R) or 11(S) HETE (0, 10, 20, 40, and 100 nM). As shown in Figure 6, the results showed that incubation of human liver microsomes with increasing concentrations of 11(S)-HETE was associated with a concentration-dependent increase in the EROD formation rate when compared to the control group. 11(S)-HETE showed a stronger effect than 11(R)-HETE. A significant increase in the catalyzed EROD activity to 107%, 119%, 136%, and 183% was observed for the 10, 20, 40, and 100 nM of the 11(S)HETE compared to the control, respectively. Similarly, the concentrations of 40 and 100 nM 11(R)-HETE showed a significant increase to 87% and 145%, respectively (Figure 6).
FIGURE 6
Effect of 11-HETE enantiomers on CYP1B1 activity in the human liver microsomes. (A) 11(R)-HETE effect on CYP1B1 activity in the human liver microsomes; (B) 11(S)-HETE effect on CYP1B1 activity in the human liver microsomes. 11-HETE enantiomers increase EROD activity in human liver microsomes. Human liver microsomes were used in a concentration of 0.1 mg/mL in the absence and presence of varying concentrations of 11-HETE enantiomers. In addition, 2 μM of 7-ER was used as a substrate. The reaction was initiated by adding 100 μL of 1 mM NADPH, and the fluorescent signal related to the formation of resorufin was determined. The values represent mean ± SEM (n = 4). Data were analyzed using one-way ANOVA. *p < 0.05 significant compared to the control group.
3.1 Effect of (R) and (S) 11-HETE on cell viability
MTT assay was used to assess the cytotoxicity of the 11-HETE concentrations used. RL-14 cells were treated with 2.5, 5, 10, and 20 μM of (R) and (S) 11-HETE for 24 h. All the concentrations tested did not significantly alter the cell viability (depicted by the viability above 90%) when compared to the control (data not shown). As a result, we used the 20 μM concentration in all the subsequent experiments.
3.2 Effect of 11-HETE enantiomers on cellular hypertrophic markers in RL-14 cells
To evaluate the potential effect of 11-HETE enantiomers in inducing cellular hypertrophy, RL-14 cells were treated with 20 μM of either 11(R)-HETE or 11(S)-HETE for 24 h. Thereafter, the expressions of the cardiac hypertrophic markers such as atrial natriuretic peptide (ANP), α-myosin heavy chain (α-MHC), β-MHC, skeletal α-actin (ACTA-1), and brain natriuretic peptide (BNP) were measured using RT-PCR. Figure 1 shows that 11(S)-HETE significantly increased the cardiac hypertrophic markers: ANP, β-MHC, and β/α-MHC by 231%, 499%, and 107%, respectively. 11(R)-HETE significantly increased the cardiac hypertrophic marker; β/α-MHC by 132%. Furthermore, ACTA-1 gene expression was increased by 46% in the 11(R)-HETE-treated group and was significantly increased by 282% in the 11(S)-HETE-treated group compared to the control (Figure 1). Both β-MHC and ACTA-1 gene expression were significantly increased in the 11(S)- compared to the 11(R) HETE-treated group.
FIGURE 1
Effect of (R) and (S) 11-HETE on cellular hypertrophy in RL-14 cells. RL-14 cells were treated for 24 h with 20 μM of 11-HETE enantiomers; then, the mRNA levels of ANP, α-MHC, β-MHC, ACTA-1, and BNP were quantified using real-time PCR. The values represent mean ± SEM (n = 4–5). Data were analyzed using one-way ANOVA. *p < 0.05 significant compared to the control group, #p < 0.05 significant compared to the 11(R)-HETE treated group.
It was established that the increase in the hypertrophic markers is associated with the increase in the cell surface area. As shown in Figure 2, our results showed that treating the RL-14 cells with 20 μM of either (R) or (S)- 11 HETE significantly increased the cell surface area by 29% and 34% compared to the control, respectively.
FIGURE 2
Effect of 11-HETE enantiomers on RL-14 cells surface area. RL-14 cells were treated with 20 µM of 11-HETE enantiomers for 24 h. Cell surface area was then determined by phase-contrast imaging using Zeiss Axio Observer Z1 inverted microscope using a ×20 objective lens. The values represent mean ± SEM (n = 65). Data were analyzed using one-way ANOVA. *p < 0.05 significant compared to the control group.
3.3 Effect of 11-HETE enantiomers on CYP mRNA gene expression in RL-14 cells
To examine the effect of 11-HETE enantiomers on CYP enzymes, RL-14 cells were treated with 20 μM (R) or (S) 11-HETE for 24 h. Thereafter, CYP1B1, CYP1A1, CYP4A11, CYP4F11, CYP4F2, CYP2J2, CYP2E1 and CYP2C8 mRNA were determined using RT-PCR. The CYP1B1, CYP1A1, CYP4A11, CYP4F11 and CYP4F2 mRNA were significantly increased in the cells treated with 11(R)-HETE by 116%, 112%, 70%, 238% and 167%, respectively, compared to the control group. Similarly, the 11(S)-HETE-treated group showed a significant increase in the gene expression of the same enzymes by 142%, 109%, 90%, 416% and 257% respectively, compared to the control (Figure 3). Albeit both (R) and (S) enantiomers have significantly increased the CYP2E1 mRNA gene expression by 146% and 163% respectively compared to the control group, only 11(S)-HETE increased the CYP2J2 mRNA gene expression by 47%.
FIGURE 3
Effect of 11-HETE enantiomers on the CYPs gene expression. RL-14 cells were treated with 20 µM of 11-HETE enantiomers for 24 h. After that, CYP1B1, CYP1A1, CYP4A11, CYP4F11, CYP4F2, CYP2J2, CYP2E1, and CYP2C8 gene expression were quantified using real-time PCR and normalized to ß-actin. The values represent mean ± SEM (n = 5–6). Data were analyzed using one-way ANOVA. *p < 0.05 significant compared to the control group, #p < 0.05 significant compared to the 11(R)-HETE treated group.
3.4 Effect of 11-HETE enantiomers on the protein level of CYP enzymes in RL-14 cells
It was essential to assess the protein levels of the CYP enzymes of interest since the mRNA expression may not consistently align with the levels of these enzymes in terms of protein expression. The protein level of CYP1B1, CYP4F2, and CYP4A11 in the cells treated with 11(S-) HETE showed a significant increase by 186%, 153%, and 152%, respectively, compared to the control. While the 11(R)-HETE-treated cells did not affect the protein level to the same degree, it significantly increased the protein level of CYP1B1, CYP4F2, and CYP4A11 by 156%, 126%, and 141%, respectively, compared to the control (Figure 4).
FIGURE 4
Effect of 11-HETE enantiomers on CYPs protein level. RL-14 cells were treated with 20 µM of 11-HETE enantiomers for 24 h. After that, cell lysates were harvested, and CYP1B1, CYP1A1, CYP4F2, CYP4A11, CYP2J, and CYP2C8 protein levels were determined using Western blot analysis. The values represent mean ± SEM (n = 4–5). Data were analyzed using one-way ANOVA. *p < 0.05 significant compared to the control group, #p < 0.05 significant compared to the 11(R)-HETE treated group.
Interestingly, the CYP2J protein level was significantly increased in the cells treated with 11(S-HETE) enantiomers by 135%, compared to the control group. Regarding CYP2C8, the increase in the protein level was not significant for both enantiomers (Figure 4). There was an increase in the gene expression of CYP4F11 for both enantiomers, however, CYP4F11 protein level was below the detection limit.
3.5 Effect of 11-HETE enantiomers on recombinant human CYP1B1 enzyme activity
The direct effect of R and S enantiomers of 11-HETE on rhCYP1B1 catalytic activity was assessed using rhCYP1B1-mediated EROD. The rate of resorufin formation (V) by rhCYP1B1 with various concentrations of 7-ER co-incubated with either S or R enantiomers of 11-HETE is shown in Figures 5A,C. 11(S)-HETE led to allosteric activation of CYP1B1 activity, causing a concentration-dependent increase in Vmax value, compared with control, by 1.03, 1.1, 1.5 and 1.4-fold for 0.5, 2.5, 10 and 40 nM of 11(S)-HETE, respectively (Table 2); whereas, 11(R)-HETE did not affect Vmax (Table 2). Km values of 7-ER hydrolysis of rhCYP1B1 did not change by either R or S enantiomers of 11-HETE; therefore, shared Km value was assumed in Michaelis-Menten model fitting, estimated to be 131.3 nM. The double reciprocal (Lineweaver-Burk) plots show intercepting lines for S and R enantiomers of 11-HETE, which in terms of allosteric interactions means changes in Vmax with no substantial effect on Km (Figures 5B,D).
FIGURE 5
Effect of 11-HETE enantiomers on recombinant human CYP1B1 enzyme activity. (A) 11(S)-HETE allosterically activated CYP1B1 activity; (C) 11(R)-HETE did not affect CYP1B1 activity; (B,D) Lineweaver-Burk plots show intercepting lines for S and R enantiomers of 11-HETE. Using a white 96-well microplate, the reaction mixture containing both buffer and 1 pmol of recombinant human CYP1B1 was incubated with 0–100 nM of 7-ER. After that, 0, 0.5, 2.5, 10, and 40 nM of 11 (R) or (S)-HETE were added. Then, 100 μL of 2 mM NADPH was added to each well to start the reaction. The fluorescent signal related to resorufin formation was measured every minute for 30 min at 550/585 nm excitation/emission wavelengths using BioTek Synergy H1Hybrid reader. The quantity of formed resorufin was calculated by forming a standard curve of 0–200 nM resorufin dissolved in the same incubation buffer. Data displays mean ± SEM (n = 3).
TABLE 2
Best fit values for resorufin formation rate kinetics mediated by recombinant human CYP1B1.
0 nM
0.5 nM
2.5 nM
10 nM
40 nM
11(R)-HETE
Vmax
15.4 ± 2.2
16.4 ± 2.7
16.6 ± 2.8
16.6 ± 2.7
17.2 ± 2.8
Km
131.3 ± 27.7
11(S)-HETE
Vmax
15.4 ± 2.2
16.0 ± 2.7
16.9 ± 2.8
23.0 ± 3.3*
21.8 ± 3.1*
Km
131.3 ± 27.7
The mean ± SD for Vmax and Km parameters for CYP1B1 activity in the absence and presence of 11-HETE enantiomers (n > 3). The enzyme kinetics were determined from best fit using the Enzyme kinetic module in GraphPad Prism. Vmax, maximum velocity; Km, the substrate concentration that provides the enzyme to achieve half Vmax. *p < 0.05 significant compared to the control group.
3.6 Effect of 11-HETE enantiomers on CYP1B1 activity in the human liver microsomes
To further confirm the results obtained from rhCYP1B1, we have tested the possible effect of both 11-HETE enantiomers on the catalytic activity of CYP1B1 using EROD assay in the human liver microsomes. We used fixed concentrations of the substrate and varying concentrations of either 11(R) or 11(S) HETE (0, 10, 20, 40, and 100 nM). As shown in Figure 6, the results showed that incubation of human liver microsomes with increasing concentrations of 11(S)-HETE was associated with a concentration-dependent increase in the EROD formation rate when compared to the control group. 11(S)-HETE showed a stronger effect than 11(R)-HETE. A significant increase in the catalyzed EROD activity to 107%, 119%, 136%, and 183% was observed for the 10, 20, 40, and 100 nM of the 11(S)HETE compared to the control, respectively. Similarly, the concentrations of 40 and 100 nM 11(R)-HETE showed a significant increase to 87% and 145%, respectively (Figure 6).
FIGURE 6
Effect of 11-HETE enantiomers on CYP1B1 activity in the human liver microsomes. (A) 11(R)-HETE effect on CYP1B1 activity in the human liver microsomes; (B) 11(S)-HETE effect on CYP1B1 activity in the human liver microsomes. 11-HETE enantiomers increase EROD activity in human liver microsomes. Human liver microsomes were used in a concentration of 0.1 mg/mL in the absence and presence of varying concentrations of 11-HETE enantiomers. In addition, 2 μM of 7-ER was used as a substrate. The reaction was initiated by adding 100 μL of 1 mM NADPH, and the fluorescent signal related to the formation of resorufin was determined. The values represent mean ± SEM (n = 4). Data were analyzed using one-way ANOVA. *p < 0.05 significant compared to the control group.
4 Discussion
In the current study, the effect of (R) and (S) enantiomers of 11-HETE was found to induce hypertrophy in human cardiomyocytes. This was associated with an increase in CYP1B1 activity through direct activation of CYP1B1 and upregulation of CYP1B1 levels, which is known to mediate the formation of cardiotoxic metabolites, midchain HETEs. 11(S)-HETE was found to be more potent, compared with 11(R)-HETE, concerning the induction of hypertrophy, as well as the increase in CYP1B1. The catalytic activity of CYP1B1 in recombinant human CYP1B1 was also significantly increased by the (S) enantiomer, indicating allosteric activation. Since 11(S)-HETE is mainly produced via the interaction of AA with reactive oxygen species, these results link oxidative stress with the induction of CYP1B1 in the heart and the development of cellular hypertrophy.
Arachidonic acid is hydroxylated, in vivo, to 11 different hydroxy-arachidonic acids; HETEs. 5-, 8-, 9-, 11-, 12-, 15-, 16-, 17-, 18-, 19-HETEs (except for 20-HETE) exist in two configurations (R and S enantiomers) (Helal et al., 2024). Previous studies in our laboratory have shown the enantioselective differences in the 19-, 16- and 17- HETEs. The S-enantiomer of 19-HETE exhibited a selective inhibition of the catalytic activity of the CYP1B1 enzyme (Shoieb et al., 2019) and protect against cellular hypertrophy induced by angiotensin II in RL-14 and H9c2 cell lines (Shoieb and El-Kadi, 2018). In adult human cardiomyocytes AC-16 cell lines, cellular hypertrophy was significantly induced by 17(S)-HETE and exhibited greater allosteric activation of human recombinant CYP1B1 enzyme compared to 17(R)-HETE (Isse et al., 2023b). In addition, the 16(R)-HETE enantiomer upregulated CYP1B1 gene and protein expression and significantly increased the cellular hypertrophy in RL-14 cells greater than its enantiomer, 16(S)-HETE (Hidayat et al., 2023a).
The contribution of 11-HETE in several pathological conditions has been previously reported. Elevated plasma levels of 11-HETE, reaching up to six times the normal level, were observed in patients with hyperplastic colon polyps and adenomas and this could be an early indicator of the progression of malignant tumors (Austin Pickens et al., 2019). In COVID-19 patients, lower AA levels and remarkably elevated levels of both 5- and 11-HETE were detected in the bronchoalveolar lavage fluid. This finding provides strong evidence that 11-HETE can mediate the innate immune response to COVID-19 and could play a crucial role in determining the outcome of the infection (Pérez et al., 2021). An increased plasma level of 11-HETE is indicative of elevated oxidative stress and enhanced levels of reactive oxygen species production (Guido et al., 1993). Another study showed that 11-HETE exhibited a positive correlation with body mass index, waist circumference, and elevated serum leptin levels in individuals with obesity (Pickens et al., 2017). The content of 11-HETE in the rats brain also showed a significant increase 72 h after middle cerebral artery occlusion in comparison to the normal brain (Usui et al., 1985). Previous reports also indicated an association between elevated baseline levels of 11-HETE and the subsequent occurrence of acute myocardial infarction. Furthermore, a positive correlation exists between the serum levels of this metabolite and the levels of certain inflammatory and cardiac biomarkers, including tumor necrosis factor-α and pro-BNP (Huang et al., 2020).
In the current study we determined the gene expression and the protein level of important CYP enzymes that are responsible for metabolizing AA; hydroxylases (CYP1B1, CYP1A1, CYP2E1, CYP4A11, and CYP4F) and epoxygenases (CYP2C8, CYP2J) after treating RL-14 cells with either (R) or (S) 11-HETE. These enzymes produce metabolic products with varying effects, which can either be protective or pathogenic (Alsaad et al., 2013). The epoxygenases create a range of regiospecific and stereospecific epoxides (5,6-, 8,9-, 11,12-, and 14,15-epoxyeicosatrienoic acids (EETs)), while the ω-hydroxylases generate subterminal and ω-terminal HETEs (Kroetz and Zeldin, 2002).
CYP1B1 enzyme is one of the hydroxylases that are constitutively expressed in numerous tissues, most importantly in the heart, and mainly contributes to the formation of midchain HETEs (Carrera et al., 2020). The upregulation of CYP1B1 and its cardiotoxic metabolites, midchain HETEs, is a prevalent feature in various diseases and conditions, including inflammation, cancer, and cardiac hypertrophy (Li et al., 2017). The relation between the level of CYP1B1 enzyme and the induction of cardiac hypertrophy due to the increase in the production of midchain HETEs has been well-established (Maayah et al., 2017). In the current study, the treatment of RL-14 cells with the S-enantiomer of 11-HETE significantly increased the gene expression and the protein level of CYP1B1. The effect of the R-enantiomer was not as strong as the S-enantiomer in the upregulation of the mRNA and protein level of the CYP1B1. Furthermore, there was a concentration-dependent CYP1B1 activation in the concentrations between 0–40 nm of (S) 11-HETE when performing the EROD assay using rhCYP1B1enzyme which suggests an allosteric activation of the enzyme. To further confirm the results, both enantiomers were incubated with human liver microsomes, and the EROD formation rate was determined in the presence of different concentrations of the 11-HETE. There was a significant gradual increase in the EROD formation rate, and the S-enantiomer effect was more pronounced than the R-enantiomer. These effects on the level and the activity of CYP1B1 were consistent with the previous studies in our laboratory which showed that both 16-HETE (Hidayat et al., 2023b) and 17-HETE (Isse et al., 2023b) allosterically activated the CYP1B1 enzyme in an enantioselective manner.
In addition to CYP1B1, the increase in other hydroxylases such as CYP1A1, CYP4A11, CYP4F11, CYP4F2 and CYP2E1 at the gene expression and protein level has been reported previously. Both CYP1B1 and CYP1A1 were reported to be upregulated in the hearts of rats treated with isoprenaline-induced cardiac hypertrophy (Zordoky et al., 2008). The presence of CYP1A1 mRNA has also been observed in the right ventricle and left atrium among patients with dilated cardiomyopathy (Thum and Borlak, 2000). CYP4A11 was reported to be upregulated by 2- to 3-fold in patients with hypertrophic left ventricles in comparison to control (Thum and Borlak, 2002). In addition, CYP4F showed high expression in patients with cardiovascular diseases (Chaudhary et al., 2009). Moreover, the increased expression of CYP1A1, CYP1B1, CYP4A11, and CYP4F leads to an elevation in the 20-HETE formation rate. Enhanced formation of 20-HETE was reported to be involved in hypertrophic cardiac remodeling and cardiac failure (Rocic and Schwartzman, 2018) and contributes to the exaggeration of ischemia-reperfusion injury (Chaudhary et al., 2009). Moreover, increased expression of CYP2E1 in the heart has various pathophysiological implications, encompassing heightened oxidative stress and apoptosis (Guan et al., 2019; Ren et al., 2019). Elevated levels of CYP2E1 have been observed in the ischemic and dilated human heart, as well as in the left ventricular tissue of spontaneously hypertensive rats (Thum and Borlak, 2002). There was also an upregulation in the expression of CYP2E1 in the hearts affected by dilated cardiomyopathy in cTnTR141W transgenic mice (Zhang et al., 2011).
In addition to the increase in the CYP hydroxylases, there was also an increase in the mRNA expression of some epoxygenases mainly CYP2J2 and CYP2C8. Although the CYP2J protein level of the cells treated with 11(S)-HETE increased significantly compared to the control group, there was no significant increase in the protein level of CYP2C8. The mRNA expression of CYP2J2 in humans is abundant in the cardiovascular system and liver, with a predominant presence in the right ventricle of the heart (Solanki et al., 2018). CYP2J2 and CYP2C8 have been identified as the primary CYP isoforms expressed in healthy human hearts, with CYP2J2 mRNA levels significantly surpassing those of CYP2C8. While numerous studies have documented the biologically protective role of these CYP enzymes and their products, EETs (Wang et al., 2016; Xu et al., 2016), some research findings have demonstrated the contrary. In individuals with hypertrophic hearts, there was an observed elevation in the expression of CYP2J, impacting ventricular force and leading to an increase in the heart’s muscle mass (Tan et al., 2002). A fivefold increase in CYP2J has been documented in human hypertrophic hearts compared to assist device-supported hearts (Thum and Borlak, 2002). While CYP2J2 has been shown to contribute to enhanced postischemic functional recovery in mice, Wang et al. proposed the involvement of CYP2J in cocaine-induced cardiac toxicity (Wang et al., 2002). Some research justified this increase as a protective mechanism from the heart cells to overcome the deleterious effects caused due to hypertrophy.
5 Conclusion
To our knowledge this is the first study to investigate the cellular hypertrophic effect of 11-HETE enantiomers in RL-14 cells and the mechanisms involved. Both R and S enantiomers of the midchain 11-HETE could induce cellular hypertrophy in RL-14 cells. They significantly increased several hypertrophic markers as well as the protein level and the gene expression of various CYP enzymes. The S- enantiomer allosterically activates human recombinant CYP1B1 and both enantiomers significantly increased the EROD activity in human liver microsomes. The investigation of the role of high 11-HETE concentration in various cardiovascular diseases should be expanded, and strategies to inhibit its effect could be tested as potential therapeutic strategies. |
Title: The Mediating Role of Social Support on Unmet Healthcare Needs in Vietnamese Older People During the COVID-19 | Body:
Introduction
The COVID-19 pandemic posed unprecedented challenges worldwide. Beyond causing millions of deaths, it has severely strained healthcare systems.
1
To curb the rapid spread of the COVID-19 within and across countries, governments imposed nationwide knockdown or curfews and called for reduction in hospital admissions except for urgent cases in the provision of health. These measures resulted in restricted access to both inpatient and outpatient services for people with regular healthcare needs.1,2 The pandemic presented a considerable barrier for diagnosis, follow-up, and treatment of chronic diseases among non-COVID-19 cases due to service disruptions, closures, and appointment cancellations in all healthcare providers.3,4 Moreover, factors such as fear of infection, travel restrictions, and income losses further limited individuals’ ability to seek healthcare.5,6 These restrictions likely increased subjective unmet needs (SUN), which potentially resulted in poorer health outcomes, economic losses, and increased health inequality.7,8 Sustainable healthcare access is vital for health and well-being of older people, particularly in a crisis like the COVID-19, because they need greater healthcare demands than other groups due to higher prevalence of chronic conditions, disabilities, and mental problems that may exacerbate their vulnerability to the pandemic.9,10 and these health conditions might increase risk of mortality if older persons were infected.11,12 Similarly, older persons might potentially lack of social support and experience misinformation due to measures such as social distancing.
13
Access to healthcare is considered as one of the most important achievements of health systems. SUN, defined as individuals’ subjective assessments of whether they could receive necessary care, is an important measure of barriers to healthcare access.
14
Understanding the factors influencing SUN during the COVID-19 pandemic is crucial for future policy planning, given the significantly negative impacts of unmet healthcare needs on health outcomes. The existing studies on the determinants of SUN during the COVID-19 had 2 key research gaps. First, a limited number of studies specifically addressed older population, with much of the existing research conducted in high-income settings.15
-19 This highlights a lack of knowledge regarding the relationship between experiencing SUN and its determinants in low- and middle-income countries. Secondly, despite the vital role of social support in improving health and well-being among older persons20
-22 and decreasing unmet healthcare needs,
23
to the best of our knowledge, there have been no studies investigating the mediating effect of social support on the association between experiencing SUN and health in older persons during the pandemic.
As a middle-income country, a rapidly aging population has posed significant healthcare challenges for Vietnam.
24
The proportion of older population (those aged 60 and over) is projected to reach about 25% of the total population by 2050.
25
This demographic shift signals an increased demand for healthcare services because chronic disease risks and disability prevalence tend to rise along with age-related health deterioration. The national survey on Vietnamese older people in 2019 highlighted this concern: 52.6% reported poor or very poor health status.
24
Moreover, the prevalence of multiple diagnosed chronic diseases was high, peaking at 53.4% for the middle-old (those aged 70-79) and 42.1% for the oldest old (those aged 80 and over).
24
Heart disease, cancers, and chronic obstructive pulmonary diseases (COPD) were among the most common, collectively accounting for approximately 89% of lost disability-adjusted life years among the Vietnamese older people.
24
Recognizing the challenges of an aging population, the Government of Vietnam (GOV) has implemented a series of healthcare and social protection policies specifically designed for its older citizens. The Law on the Elderly outlines the rights of older persons to access healthcare and prioritizes healthcare for the oldest old.
26
Moreover, GOV provides financial support for vulnerable groups, offering free health insurance to low-income individuals and social assistance programs for those living alone or in poor households without family support.27,28
Vietnam was significantly impacted by the COVID-19 pandemic, in which older persons were particularly affected. The first COVID-19 case was reported on January 23rd, 2020. During the early phases of the COVID-19 pandemic, GOV implemented preventive measures, including social distancing, lockdown, and curfews. These measures were gradually lifted in 2022,
29
and over 11 million cases of infection and 43 000 deaths were reported by early 2024.
30
Notably, on average, 50.4% of fatalities occurred among those aged 65 and older, which was substantially higher than 13.8% of those aged 18 to 49.
31
Despite the government’s proactive efforts to safeguard older people, challenges in meeting their healthcare needs persisted from both demand side (such as fears of infection and reluctance to receive vaccinations) and supply side (such as lower capacity in meeting demand for check-ups, medications, assistive devices, and rehabilitation services).
32
To date, however, there have been no studies to examine the factors influencing unmet healthcare needs in Vietnamese older people during the pandemic. Our study therefore aimed to provide the first empirical evidence on these factors and whether social support could mitigate the relationship between SUN and chronic diseases among Vietnamese older people. More specifically, using the most recent national survey on Vietnamese older people and a standard measure of SUN, we pioneered the examination of various factors affecting SUN and investigated the extent to which social support mediated the relationship between chronic diseases and experiencing SUN.
Literature Review on Social Support and SUN
Social support is a multifaceted concept that can be defined as the perceived availability of assistance from family, friends, and loved ones. Social support plays a crucial role in accessing resources because individuals’ interpretations of life stressors and their perceived availability of coping resources influence the protective benefits of social support.
20
Social support can be categorized into three forms among others,
33
as follows: (i) emotional support (providing a sense of belonging and connection through close relationships and feelings of being cared for); (ii) tangible/instrumental support (offering practical assistance in the form of financial aid, help with daily tasks, and support during crises); and (iii) informational support (providing access to valuable information, such as healthcare recommendations, through advice and discussions).
Social support could affect SUN through buffering and main-effect pathways.
20
For the former, social support acted as a buffer against the negative effects of stress by enhancing self-esteem, promoting healthy coping mechanisms, reducing feelings of loneliness and helplessness,34,35 and reducing loneliness and enhancing physical and mental health for older persons.
36
Stronger social support networks resulted in positive health behaviors (eg, medication adherence, regular exercise, and a healthy diet), and thus increasing confidence and lowering anxiety.
37
For the latter, social support improved access to social resources, particularly tangible/instrumental and informational support, which in turn provided protection to individuals via healthcare services and resources. Strong connections with family, friends, and relatives could offer various support forms, which helped not only to alleviate the burdens associated with stressful situations but also fulfill the psychological need of feeling valued by others.38,39 Significant and positive association between stronger social support and better healthcare access was found.23,40,41 Given these two pathways, we hypothesized in this study that social support, particularly family and community support, mediated the association between experiencing SUN and chronic diseases in Vietnamese older persons.
Data and Analytical Methods
Data
We used the most recent nationally representative survey on the Vietnamese older people (those aged 60 and over), namely the Vietnam Aging Survey (VNAS) in 2022, which was reviewed and approved by the Institute of Social and Medical Studies (ISMS)’s Institutional Review Board in Biomedical Research under the Decision 04/HDDD-ISMS dated July 18th, 2022. The survey aimed to provide timely information on the impact of COVID-19 on Vietnamese older persons’ socioeconomic status, access and utilization of healthcare services and social participation. Face-to-face interviews using structured questionnaires were used to collect data. The survey employed a multiple-stage sampling and population proportionate to size (PPS) methods were used to select provinces, districts, and communes. More specifically, 12 provinces were selected across 6 economic regions in Vietnam. Out of these provinces, 3 districts were chosen in each province and then 4 communes were drawn per 1 selected district. At cluster (village) and individual levels, a systematic random sampling method was used to select 3 villages per commune and 10 people per village. In total, 3168 older people were successfully interviewed.
Since we were interested in unmet needs for healthcare access during the COVID-19, we excluded those who responded that they did not have needs for healthcare services during this time. Finally, the analytic sample consisted of 2140 observations with non-missing values on variables of interest.
Measures
Dependent variable
Subjective unmet needs (SUN) was the outcome of interest, which was measured using the following 3 yes/no questions since the COVID-19 pandemic:
Question 1 measured the forgone healthcare services due to COVID-19 infection: “Have you ever forgone a medical treatment because you were afraid to become infected by the COVID-19?”
Question 2 measured the postponed health treatment: “Have you ever delayed/postponed a medical treatment because you were afraid to be infected by the COVID-19?”
Question 3 measured the unavailable healthcare services: “Did you have a scheduled medical appointment that the doctor or medical facility decided to postpone due to the COVID-19?”
These questions have been used widely to measure SUN across developed and developing countries during the COVID-19 pandemic.18,24 In order to examine different aspects of limits in healthcare access during the COVID-19, we created 3 binary outcomes: (i) forgone SUN if the respondent answered “yes” to the first question; (ii) postponed SUN if the respondent answered “yes” to the second question; and (iii) global SUN if the respondent answered “yes” to each of the 3 questions. The measures of each type of SUN were depicted in Figure 1. Due to the small proportion of the third question (2.4%), it was not feasible to run a regression with this.
Figure 1.
Determination of each type of subjective unmet need (SUN).
Source. Own illustration.
Independent variables
We followed previous studies to select 4 main groups of independent variables.18,24 The first group included demographic variables: age groups (60-64 as reference group; 65-69; 70-74; 75-79; and 80+), gender (1 if females; 0 otherwise), marital status (1 if currently married; 0 otherwise), ethnicity (1 if Kinh [majority] people; 0 otherwise), place of residence (1 if residing in rural areas; 0 if urban areas), and living arrangements (1 if living alone; 0 otherwise).
The second group, presenting socioeconomic status (SES), consisted of the highest educational attainment (no schooling or incomplete primary school [hereafter no schooling] as reference group; primary school; secondary school; and high school and above). Household wealth scores were derived using principal component analysis (PCA) based on a set of durable household assets.42,43 This method has been used in previous studies in Vietnam.
44
The wealth scores were then divided into 5 quintiles, among which the first quintile denoted the poorest (reference group) and the fifth indicated the wealthiest. Health insurance status took on the value of 1 if an older person had a health insurance card, and 0 otherwise.
The third group refers to heath conditions of the respondents. Activities of daily living (ADLs) measured levels of difficulty in 5 activities (eg, eating, getting dressed and undressed, bathing, getting up when lying down, and toileting). Each activity was given 4 responses: no difficulty; mild difficulty; severe difficulty; and could not do at all. Summation of these responses across the 5 activities resulted in 15 points, which were then divided in 4 categories: no ADL difficulty (as reference group); 1-2 ADLs; 3-4 ADLs; and 5+ ADLs. Chronic diseases (such as arthritis and blood pressure problem) consisted of a list of 17 yes/no questions on diagnosed diseases by doctors. If a respondent responded “yes” to a specific disease, he/she was subsequently asked whether he/she received treatment or took medications for that disease in the last 12 months. To align with the above definitions of SUN and following previous literature, we measured the presence of a chronic disease as those who had a chronic disease and were under treatment or took medications for that disease.
24
We measured chronic diseases in categories: no disease (reference group); 1 disease; 2 diseases; 3-4 diseases; and 5+ diseases.
Finally, we controlled for social support variables, which have found to be important determinants of unmet needs for healthcare utilization.
45
The first variable was children’s support, which was measured by whether the respondents received either cash or in-kinds from their children in the last 12 months. The second variable was community support, which was derived from the following question: “Since the COVID-19 pandemic, what types of support that you received from community and individuals (except supports receiving when you/your family gets COVID-19)?” This binary variable took a value of 1 if the respondents received any of the following support (medical supplies such as oxygen or monitor machine; health counseling; health checks; medicines; medicated sanitizers; foods; or money), and 0 otherwise.
Analytical Strategy
We first provided descriptive statistics of independent variables disaggregated by SUN outcomes. Chi-squared tests were used to assess differences in proportion between the dependent and independent variables.
The existing literature has demonstrated the importance of model specification to avoid biased and misleading estimates.46,47 Since our dependent variables were measured as binary, we employed logistic regressions to examine the factors associated with the likelihood of experiencing each type of SUN, controlling for all above-mentioned independents. Following previous literature, we employed Pregibon’s link test to assess the specification form of independent variables.
47
The link test results indicate that the squared predicted values had no explanatory power (ie, P-value > .1) when we regressed our dependent variables on the predicted values and its square, suggesting that all independent variables controlled in our models were well specified. Similarly, to examine the presence of multicollinearity issue, we performed variance inflation factor (VIF) tests, and the results show that the VIF value of each dependent variable was smaller than 2, implying there was no evidence of perfect multicollinearity.
48
In order to further examine whether social support mediated the association between chronic diseases and SUN, we added an interaction term between social support and chronic diseases to logistic regression for each type of SUN. Results were presented by odds ratios (ORs).
All statistical analyses were weighted using the survey weights and clustered at the cluster level (villages) to account for the multistage sample design. Also, statistical significance level was set to P < .05.
Data
We used the most recent nationally representative survey on the Vietnamese older people (those aged 60 and over), namely the Vietnam Aging Survey (VNAS) in 2022, which was reviewed and approved by the Institute of Social and Medical Studies (ISMS)’s Institutional Review Board in Biomedical Research under the Decision 04/HDDD-ISMS dated July 18th, 2022. The survey aimed to provide timely information on the impact of COVID-19 on Vietnamese older persons’ socioeconomic status, access and utilization of healthcare services and social participation. Face-to-face interviews using structured questionnaires were used to collect data. The survey employed a multiple-stage sampling and population proportionate to size (PPS) methods were used to select provinces, districts, and communes. More specifically, 12 provinces were selected across 6 economic regions in Vietnam. Out of these provinces, 3 districts were chosen in each province and then 4 communes were drawn per 1 selected district. At cluster (village) and individual levels, a systematic random sampling method was used to select 3 villages per commune and 10 people per village. In total, 3168 older people were successfully interviewed.
Since we were interested in unmet needs for healthcare access during the COVID-19, we excluded those who responded that they did not have needs for healthcare services during this time. Finally, the analytic sample consisted of 2140 observations with non-missing values on variables of interest.
Measures
Dependent variable
Subjective unmet needs (SUN) was the outcome of interest, which was measured using the following 3 yes/no questions since the COVID-19 pandemic:
Question 1 measured the forgone healthcare services due to COVID-19 infection: “Have you ever forgone a medical treatment because you were afraid to become infected by the COVID-19?”
Question 2 measured the postponed health treatment: “Have you ever delayed/postponed a medical treatment because you were afraid to be infected by the COVID-19?”
Question 3 measured the unavailable healthcare services: “Did you have a scheduled medical appointment that the doctor or medical facility decided to postpone due to the COVID-19?”
These questions have been used widely to measure SUN across developed and developing countries during the COVID-19 pandemic.18,24 In order to examine different aspects of limits in healthcare access during the COVID-19, we created 3 binary outcomes: (i) forgone SUN if the respondent answered “yes” to the first question; (ii) postponed SUN if the respondent answered “yes” to the second question; and (iii) global SUN if the respondent answered “yes” to each of the 3 questions. The measures of each type of SUN were depicted in Figure 1. Due to the small proportion of the third question (2.4%), it was not feasible to run a regression with this.
Figure 1.
Determination of each type of subjective unmet need (SUN).
Source. Own illustration.
Independent variables
We followed previous studies to select 4 main groups of independent variables.18,24 The first group included demographic variables: age groups (60-64 as reference group; 65-69; 70-74; 75-79; and 80+), gender (1 if females; 0 otherwise), marital status (1 if currently married; 0 otherwise), ethnicity (1 if Kinh [majority] people; 0 otherwise), place of residence (1 if residing in rural areas; 0 if urban areas), and living arrangements (1 if living alone; 0 otherwise).
The second group, presenting socioeconomic status (SES), consisted of the highest educational attainment (no schooling or incomplete primary school [hereafter no schooling] as reference group; primary school; secondary school; and high school and above). Household wealth scores were derived using principal component analysis (PCA) based on a set of durable household assets.42,43 This method has been used in previous studies in Vietnam.
44
The wealth scores were then divided into 5 quintiles, among which the first quintile denoted the poorest (reference group) and the fifth indicated the wealthiest. Health insurance status took on the value of 1 if an older person had a health insurance card, and 0 otherwise.
The third group refers to heath conditions of the respondents. Activities of daily living (ADLs) measured levels of difficulty in 5 activities (eg, eating, getting dressed and undressed, bathing, getting up when lying down, and toileting). Each activity was given 4 responses: no difficulty; mild difficulty; severe difficulty; and could not do at all. Summation of these responses across the 5 activities resulted in 15 points, which were then divided in 4 categories: no ADL difficulty (as reference group); 1-2 ADLs; 3-4 ADLs; and 5+ ADLs. Chronic diseases (such as arthritis and blood pressure problem) consisted of a list of 17 yes/no questions on diagnosed diseases by doctors. If a respondent responded “yes” to a specific disease, he/she was subsequently asked whether he/she received treatment or took medications for that disease in the last 12 months. To align with the above definitions of SUN and following previous literature, we measured the presence of a chronic disease as those who had a chronic disease and were under treatment or took medications for that disease.
24
We measured chronic diseases in categories: no disease (reference group); 1 disease; 2 diseases; 3-4 diseases; and 5+ diseases.
Finally, we controlled for social support variables, which have found to be important determinants of unmet needs for healthcare utilization.
45
The first variable was children’s support, which was measured by whether the respondents received either cash or in-kinds from their children in the last 12 months. The second variable was community support, which was derived from the following question: “Since the COVID-19 pandemic, what types of support that you received from community and individuals (except supports receiving when you/your family gets COVID-19)?” This binary variable took a value of 1 if the respondents received any of the following support (medical supplies such as oxygen or monitor machine; health counseling; health checks; medicines; medicated sanitizers; foods; or money), and 0 otherwise.
Dependent variable
Subjective unmet needs (SUN) was the outcome of interest, which was measured using the following 3 yes/no questions since the COVID-19 pandemic:
Question 1 measured the forgone healthcare services due to COVID-19 infection: “Have you ever forgone a medical treatment because you were afraid to become infected by the COVID-19?”
Question 2 measured the postponed health treatment: “Have you ever delayed/postponed a medical treatment because you were afraid to be infected by the COVID-19?”
Question 3 measured the unavailable healthcare services: “Did you have a scheduled medical appointment that the doctor or medical facility decided to postpone due to the COVID-19?”
These questions have been used widely to measure SUN across developed and developing countries during the COVID-19 pandemic.18,24 In order to examine different aspects of limits in healthcare access during the COVID-19, we created 3 binary outcomes: (i) forgone SUN if the respondent answered “yes” to the first question; (ii) postponed SUN if the respondent answered “yes” to the second question; and (iii) global SUN if the respondent answered “yes” to each of the 3 questions. The measures of each type of SUN were depicted in Figure 1. Due to the small proportion of the third question (2.4%), it was not feasible to run a regression with this.
Figure 1.
Determination of each type of subjective unmet need (SUN).
Source. Own illustration.
Independent variables
We followed previous studies to select 4 main groups of independent variables.18,24 The first group included demographic variables: age groups (60-64 as reference group; 65-69; 70-74; 75-79; and 80+), gender (1 if females; 0 otherwise), marital status (1 if currently married; 0 otherwise), ethnicity (1 if Kinh [majority] people; 0 otherwise), place of residence (1 if residing in rural areas; 0 if urban areas), and living arrangements (1 if living alone; 0 otherwise).
The second group, presenting socioeconomic status (SES), consisted of the highest educational attainment (no schooling or incomplete primary school [hereafter no schooling] as reference group; primary school; secondary school; and high school and above). Household wealth scores were derived using principal component analysis (PCA) based on a set of durable household assets.42,43 This method has been used in previous studies in Vietnam.
44
The wealth scores were then divided into 5 quintiles, among which the first quintile denoted the poorest (reference group) and the fifth indicated the wealthiest. Health insurance status took on the value of 1 if an older person had a health insurance card, and 0 otherwise.
The third group refers to heath conditions of the respondents. Activities of daily living (ADLs) measured levels of difficulty in 5 activities (eg, eating, getting dressed and undressed, bathing, getting up when lying down, and toileting). Each activity was given 4 responses: no difficulty; mild difficulty; severe difficulty; and could not do at all. Summation of these responses across the 5 activities resulted in 15 points, which were then divided in 4 categories: no ADL difficulty (as reference group); 1-2 ADLs; 3-4 ADLs; and 5+ ADLs. Chronic diseases (such as arthritis and blood pressure problem) consisted of a list of 17 yes/no questions on diagnosed diseases by doctors. If a respondent responded “yes” to a specific disease, he/she was subsequently asked whether he/she received treatment or took medications for that disease in the last 12 months. To align with the above definitions of SUN and following previous literature, we measured the presence of a chronic disease as those who had a chronic disease and were under treatment or took medications for that disease.
24
We measured chronic diseases in categories: no disease (reference group); 1 disease; 2 diseases; 3-4 diseases; and 5+ diseases.
Finally, we controlled for social support variables, which have found to be important determinants of unmet needs for healthcare utilization.
45
The first variable was children’s support, which was measured by whether the respondents received either cash or in-kinds from their children in the last 12 months. The second variable was community support, which was derived from the following question: “Since the COVID-19 pandemic, what types of support that you received from community and individuals (except supports receiving when you/your family gets COVID-19)?” This binary variable took a value of 1 if the respondents received any of the following support (medical supplies such as oxygen or monitor machine; health counseling; health checks; medicines; medicated sanitizers; foods; or money), and 0 otherwise.
Analytical Strategy
We first provided descriptive statistics of independent variables disaggregated by SUN outcomes. Chi-squared tests were used to assess differences in proportion between the dependent and independent variables.
The existing literature has demonstrated the importance of model specification to avoid biased and misleading estimates.46,47 Since our dependent variables were measured as binary, we employed logistic regressions to examine the factors associated with the likelihood of experiencing each type of SUN, controlling for all above-mentioned independents. Following previous literature, we employed Pregibon’s link test to assess the specification form of independent variables.
47
The link test results indicate that the squared predicted values had no explanatory power (ie, P-value > .1) when we regressed our dependent variables on the predicted values and its square, suggesting that all independent variables controlled in our models were well specified. Similarly, to examine the presence of multicollinearity issue, we performed variance inflation factor (VIF) tests, and the results show that the VIF value of each dependent variable was smaller than 2, implying there was no evidence of perfect multicollinearity.
48
In order to further examine whether social support mediated the association between chronic diseases and SUN, we added an interaction term between social support and chronic diseases to logistic regression for each type of SUN. Results were presented by odds ratios (ORs).
All statistical analyses were weighted using the survey weights and clustered at the cluster level (villages) to account for the multistage sample design. Also, statistical significance level was set to P < .05.
Results
Descriptive Statistics
Table 1 presents descriptive statistics of the respondents’ characteristics by types of SUN. In general, 25.1% of respondents experienced SUN due to either forgone, postponed, or unavailable services during the COVID-19. Among them, 21.2% were forwent medical treatments; 11.8% were postponed with their pre-scheduled services, and 2.4% were denied healthcare. Analysis of global SUN revealed significant differences in proportions of those experiencing SUN across age groups, ethnicity, living arrangements, place of residence, the highest educational level, health insurance status, ADL categories, chronic disease categories, and community support. Similar results were found for forgone and postponed SUN, with the following exceptions: living arrangements and health insurance status were statistically insignificant, while gender showed statistical significance.
Table 1.
Descriptive Statistics by Types of Subjective Unmet Need (SUN).
Characteristics
Global SUN
P-value
Forgone SUN
P-value
Postponed SUN
P-value
Age groups
60-64
19.6
<.001
19.6
<.05
17.9
<.001
65-69
20.7
20.2
21.0
70-74
24.2
23.6
29.8
75-79
16.9
17.0
17.9
80+
18.6
19.6
13.4
Gender
Male
40.0
39.9
34.5
<.05
Female
60.0
60.1
65.5
Marital status
Married
61.8
59.7
61.1
Other
38.2
40.3
38.9
Ethnicity
Kinh
94.6
<.001
94.3
<.001
94.8
<.001
Other
5.4
5.7
5.2
Living arrangements
Living alone
6.3
<.05
7.0
8.7
Other
93.7
93.0
91.3
Place of residence
Urban
76.9
<.001
24.9
<.001
18.7
Rural
23.1
75.1
81.3
Education
No school
46.0
<.05
48.2
<.01
42.9
Primary
18.2
18.9
19.4
Secondary
21.8
18.4
27.4
High school+
14.0
14.5
10.3
Household wealth
First quintile
22.5
24.0
25.0
Second quintile
18.6
19.2
15.1
Third quintile
19.4
19.6
17.1
Fourth quintile
18.3
17.2
19.4
Fifth quintile
21.2
20.0
23.4
Health insurance
No
99.1
<.05
98.9
100
<.05
Yes
0.9
1.1
0.0
ADLs
No ADL
17.9
<.05
18.9
<.01
11.5
<.001
1-2 ADLs
20.5
18.1
24.6
3-4 ADLs
22.1
23.6
21.4
5+ ADLs
39.5
39.4
42.5
Chronic diseases
No disease
5.6
<.001
5.1
<.001
5.6
<.001
1 disease
14.3
14.3
14.7
2 diseases
20.1
19.6
19.8
3-4 diseases
34.1
33.7
32.1
5+ diseases
25.9
27.3
27.8
Community support
No
38
<.001
39.6
<.001
35.3
<.05
Yes
62
60.4
64.7
Children support
No
11.9
12.6
9.9
Yes
88.1
87.4
90.1
Source. Own calculations, using data from VNAS 2022.
Multiple Regression Results
Figure 2 presents odds ratios (ORs) with 95% confidence intervals for the 3 types of SUN, highlighting factors associated with the likelihood of experiencing each type of SUN. The detailed numerical estimates are provided in Appendix Table A1.
Figure 2.
Determinants of experiencing subjective unmet need (SUN), presented as odds ratios and associated 95% confidence intervals (CI).
Source. Own calculations, using data from VNAS 2022.
Note. 95% CI are represented by 2 vertical bars and ORs are represented as rectangular, circle, square, and diamond markers within these bars.
In order to facilitate interpretation, results are reported by groups of independent variables. Among demographic characteristics, the oldest old had significantly lower odds of experiencing global and forgone SUN (54% and 52% reduction, respectively) compared to the youngest old (those aged 60-64 years), while those aged 70 to 74 were 2.1 times more likely to experience postponed SUN, ceteris paribus. Individuals living alone or residing in rural areas had lower odds of experiencing global and forgone SUN (63% and 32% reduction, respectively) compared to their counterparts.
Regarding socioeconomic status (SES), a consistent pattern emerged: having higher educational attainment, living in wealthier households, and having health insurance were associated with lower probabilities of experiencing all types of SUN.
In terms of health conditions, individuals with 1-2, 3-4, or 5+ chronic diseases had 2.3 to 2.7 times higher odds of experiencing global and forgone SUN compared to those without chronic diseases, ceteris paribus. Interestingly, ADLs were only significantly associated with postponed SUN: individuals with 3 or more ADLs had 1.9 to 2.1 times higher odds compared to those with no ADLs.
Finally, social support played a significantly protective role, that is, receiving community support was associated with a 30% decrease in the odds of experiencing any type of SUN.
Mediating Role of Social Support on the Association Between Experiencing SUN and Chronic Diseases
Given that only community support was consistently associated with all types of SUN, we explored whether it moderated the relationship between SUN and chronic disease categories. We introduced an interaction term between community support and chronic disease categories into the multiple logistic regression models. As the focus was on the interaction effect, and since the magnitudes and significance of other variables remained relatively stable compared to models without interaction, only the interaction term results are reported in Table 2.
Table 2.
Mediating Effects of Community Support, Reported as Odds Ratios and 95% Confidence Intervals (CI).
Global SUN
Forgone SUN
Postponed SUN
Variables
OR [95% CI]
OR [95% CI]
OR [95% CI]
Community support* 1 disease
0.145* [0.023, 0.905]
0.167 [0.023, 1.224]
0.138 [0.015, 1.251]
Community support* 2 diseases
0.118* [0.020, 0.683]
0.141* [0.022, 0.909]
0.137 [0.015, 1.278]
Community support* 3-4 diseases
0.279 [0.054, 1.436]
0.345 [0.059, 2.137]
0.850 [0.113, 2.393]
Community support* 5+ diseases
0.241 [0.043, 1.347]
0.429 [0.068, 2.713]
0.307 [0.040, 2.346]
Observations
2.140
2.140
2.094
a
Source. Own calculations, using data from VNAS 2022.
Note. 95% confidence intervals are reported in square brackets.
a
Forty-six observations were dropped due to zero observation in having health insurance.
*
indicates P < .05.
In general, the results indicate that community support was consistently associated with lower odds of experiencing any type of SUN across chronic disease categories. However, a significant interaction effect was only observed for those with 1 or 2 chronic diseases experiencing global SUN, and for those with 2 chronic diseases experiencing forgone SUN. More specifically, among those with 1 chronic disease, the community support receivers had 0.145 lower odds of experiencing global SUN than the non-receivers. Regarding individuals with 2 diseases, the community support receivers had 0.118 and 0.141 lower odds of reporting global SUN and forgone SUN than the non-receivers, respectively.
Descriptive Statistics
Table 1 presents descriptive statistics of the respondents’ characteristics by types of SUN. In general, 25.1% of respondents experienced SUN due to either forgone, postponed, or unavailable services during the COVID-19. Among them, 21.2% were forwent medical treatments; 11.8% were postponed with their pre-scheduled services, and 2.4% were denied healthcare. Analysis of global SUN revealed significant differences in proportions of those experiencing SUN across age groups, ethnicity, living arrangements, place of residence, the highest educational level, health insurance status, ADL categories, chronic disease categories, and community support. Similar results were found for forgone and postponed SUN, with the following exceptions: living arrangements and health insurance status were statistically insignificant, while gender showed statistical significance.
Table 1.
Descriptive Statistics by Types of Subjective Unmet Need (SUN).
Characteristics
Global SUN
P-value
Forgone SUN
P-value
Postponed SUN
P-value
Age groups
60-64
19.6
<.001
19.6
<.05
17.9
<.001
65-69
20.7
20.2
21.0
70-74
24.2
23.6
29.8
75-79
16.9
17.0
17.9
80+
18.6
19.6
13.4
Gender
Male
40.0
39.9
34.5
<.05
Female
60.0
60.1
65.5
Marital status
Married
61.8
59.7
61.1
Other
38.2
40.3
38.9
Ethnicity
Kinh
94.6
<.001
94.3
<.001
94.8
<.001
Other
5.4
5.7
5.2
Living arrangements
Living alone
6.3
<.05
7.0
8.7
Other
93.7
93.0
91.3
Place of residence
Urban
76.9
<.001
24.9
<.001
18.7
Rural
23.1
75.1
81.3
Education
No school
46.0
<.05
48.2
<.01
42.9
Primary
18.2
18.9
19.4
Secondary
21.8
18.4
27.4
High school+
14.0
14.5
10.3
Household wealth
First quintile
22.5
24.0
25.0
Second quintile
18.6
19.2
15.1
Third quintile
19.4
19.6
17.1
Fourth quintile
18.3
17.2
19.4
Fifth quintile
21.2
20.0
23.4
Health insurance
No
99.1
<.05
98.9
100
<.05
Yes
0.9
1.1
0.0
ADLs
No ADL
17.9
<.05
18.9
<.01
11.5
<.001
1-2 ADLs
20.5
18.1
24.6
3-4 ADLs
22.1
23.6
21.4
5+ ADLs
39.5
39.4
42.5
Chronic diseases
No disease
5.6
<.001
5.1
<.001
5.6
<.001
1 disease
14.3
14.3
14.7
2 diseases
20.1
19.6
19.8
3-4 diseases
34.1
33.7
32.1
5+ diseases
25.9
27.3
27.8
Community support
No
38
<.001
39.6
<.001
35.3
<.05
Yes
62
60.4
64.7
Children support
No
11.9
12.6
9.9
Yes
88.1
87.4
90.1
Source. Own calculations, using data from VNAS 2022.
Multiple Regression Results
Figure 2 presents odds ratios (ORs) with 95% confidence intervals for the 3 types of SUN, highlighting factors associated with the likelihood of experiencing each type of SUN. The detailed numerical estimates are provided in Appendix Table A1.
Figure 2.
Determinants of experiencing subjective unmet need (SUN), presented as odds ratios and associated 95% confidence intervals (CI).
Source. Own calculations, using data from VNAS 2022.
Note. 95% CI are represented by 2 vertical bars and ORs are represented as rectangular, circle, square, and diamond markers within these bars.
In order to facilitate interpretation, results are reported by groups of independent variables. Among demographic characteristics, the oldest old had significantly lower odds of experiencing global and forgone SUN (54% and 52% reduction, respectively) compared to the youngest old (those aged 60-64 years), while those aged 70 to 74 were 2.1 times more likely to experience postponed SUN, ceteris paribus. Individuals living alone or residing in rural areas had lower odds of experiencing global and forgone SUN (63% and 32% reduction, respectively) compared to their counterparts.
Regarding socioeconomic status (SES), a consistent pattern emerged: having higher educational attainment, living in wealthier households, and having health insurance were associated with lower probabilities of experiencing all types of SUN.
In terms of health conditions, individuals with 1-2, 3-4, or 5+ chronic diseases had 2.3 to 2.7 times higher odds of experiencing global and forgone SUN compared to those without chronic diseases, ceteris paribus. Interestingly, ADLs were only significantly associated with postponed SUN: individuals with 3 or more ADLs had 1.9 to 2.1 times higher odds compared to those with no ADLs.
Finally, social support played a significantly protective role, that is, receiving community support was associated with a 30% decrease in the odds of experiencing any type of SUN.
Mediating Role of Social Support on the Association Between Experiencing SUN and Chronic Diseases
Given that only community support was consistently associated with all types of SUN, we explored whether it moderated the relationship between SUN and chronic disease categories. We introduced an interaction term between community support and chronic disease categories into the multiple logistic regression models. As the focus was on the interaction effect, and since the magnitudes and significance of other variables remained relatively stable compared to models without interaction, only the interaction term results are reported in Table 2.
Table 2.
Mediating Effects of Community Support, Reported as Odds Ratios and 95% Confidence Intervals (CI).
Global SUN
Forgone SUN
Postponed SUN
Variables
OR [95% CI]
OR [95% CI]
OR [95% CI]
Community support* 1 disease
0.145* [0.023, 0.905]
0.167 [0.023, 1.224]
0.138 [0.015, 1.251]
Community support* 2 diseases
0.118* [0.020, 0.683]
0.141* [0.022, 0.909]
0.137 [0.015, 1.278]
Community support* 3-4 diseases
0.279 [0.054, 1.436]
0.345 [0.059, 2.137]
0.850 [0.113, 2.393]
Community support* 5+ diseases
0.241 [0.043, 1.347]
0.429 [0.068, 2.713]
0.307 [0.040, 2.346]
Observations
2.140
2.140
2.094
a
Source. Own calculations, using data from VNAS 2022.
Note. 95% confidence intervals are reported in square brackets.
a
Forty-six observations were dropped due to zero observation in having health insurance.
*
indicates P < .05.
In general, the results indicate that community support was consistently associated with lower odds of experiencing any type of SUN across chronic disease categories. However, a significant interaction effect was only observed for those with 1 or 2 chronic diseases experiencing global SUN, and for those with 2 chronic diseases experiencing forgone SUN. More specifically, among those with 1 chronic disease, the community support receivers had 0.145 lower odds of experiencing global SUN than the non-receivers. Regarding individuals with 2 diseases, the community support receivers had 0.118 and 0.141 lower odds of reporting global SUN and forgone SUN than the non-receivers, respectively.
Discussion
Utilizing the latest nationally representative survey on older people, we contributed to the limited empirical evidence on factors influencing SUN during the COVID-19 in Vietnam as a middle-income country. We further highlighted the vital role of social support in mediating the relationship between experiencing SUN and chronic diseases in older persons. This topic has been largely unexplored in the literature. Our findings indicated that about one-fourth of Vietnamese older persons experienced SUN during the COVID-19. More particularly, 21.2% were forwent medical treatments, 11.8% experienced postponed services, and 2.4% were denied care. These figures were slightly lower than those reported in Bangladesh and some European countries.17,18,24
Concerning demographic determinants, we found that higher age was significantly associated with the likelihood of experiencing SUN, although this result was found only for the oldest old. The association between age and SUN during the COVID-19 was not consensus, for example, studies in Canada and Europe found that higher age had a protective effect against SUN,16,18,19 while null effect of such a relationship was found in Korea.
49
Older people living alone were associated with a higher likelihood of SUN than their counterparts living with others, and this finding could be explained by the following 3 combined factors: (i) living alone at old age presents socioeconomic disadvantages and increases risks for physical and mental health across countries,50
-52 and the case of Vietnam showed older people living alone had a higher probability of experiencing chronic diseases, loneliness, and depression53,54; (ii) living alone is significantly associated with increased healthcare utilization15,55,56; and (iii) living alone during the COVID-19 could be exacerbated older persons’ situations since governments applied various preventive measures such as social distancing, knockdown, or curfews.
Regarding SES determinants of SUN, higher educated and wealthier older persons were found to be less likely to experience SUN. This finding was consistent with studies in other countries,57
-59 and Vietnam as well.44,60 Individuals with higher education tend to have better knowledge, resources, and ability in accessing and processing information, which in turn might lower the likelihood of experiencing SUN during COVID-19. The results on these relationships in developed countries were inconsistent, possibly due to variation in study designs in terms of sampling selections and social contexts (ie, education systems and policies during COVID-19).18,19,49
Financial protection for healthcare is generally an important scheme to achieving universal health coverage, especially for older people whose healthcare demand is high. Vietnam is no exception. GOV has made significant efforts in establishing several health insurance schemes to improve financial protection for its older citizens.
28
A national survey on the Vietnamese older persons in 2019 showed that approximately 94% of older people having health insurance cards.
24
During a stressful time like COVID-19, having a health insurance card might provide a sense of security and reduce anxiety related to potential medical expenses, which is in line with the buffering pathway in mitigating SUN as discussed above. Another possible explanation is that medical expenses have been a major reason for delayed medical care among older people whose income tends to decrease at older ages, and as such Vietnamese older persons with health insurance might be less likely to postpone seeking treatments because health insurance could protect them against unanticipated and out-of-pocket expenses. To support this, previous studies showed a significant relationship between health insurance and healthcare utilization and emphasized the role of health insurance in mitigating financial burden among Vietnamese older people.61,62
Since presence of diseases are strongly associated with healthcare utilization among Vietnamese older people,63,64 it was not surprising in this study that chronic diseases were significant predictors of SUN. This result was similar to those found in several studies on European countries.18,19
Finally, we found that social support, particularly community support, significantly mediated the relationship between chronic diseases and experiencing SUN among older persons. Social support is known to be a crucial protective factor for healthy lifestyle, health and wellbeing among older people.22,65,66 This in turn benefits older people with chronic diseases.67
-70 Previous studies found that older people with better social support were less likely to experience chronic diseases and have less challenges in managing their chronic conditions.66,71,72 This evidence supports the buffering and main-effect mechanisms explaining how social support mitigates experiencing SUN.
Based on the above findings, we would suggest the following policies to mitigate unmet healthcare needs among older persons in Vietnam.
First, as more vulnerable older persons (such as those at more advanced age, with lower educational level, living alone, living in less wealthy households, and lack of healthcare insurance) were more likely to experience any type of SUN, they should be prioritized in any healthcare policies and programs so as to be accessible to affordable and adequate healthcare services.
Second, development of grassroot-level healthcare facilities, particularly at commune health centers (CHCs), along with a responsive family doctor system would help provide timely services to local people in general, and older persons in particular. This would help to reduce heavily overloaded services at higher technical level facilities (such as provincial and central hospitals). Various studies have shown that out-of-pocket (OOP) payments, particularly those for transportation, accommodation, and potential income losses for providing care assistance, would have been significantly reduced for those who get treatments at CHCs where some chronic diseases (such as diabetes and hypertension) can be managed.73,74 This would be also important to mitigate forgone, delayed or denied healthcare services for older persons in COVID-19-like situations.
Third, as community support played an important role in reducing unmet healthcare needs of older persons, further promoting socio-political organizations (such as Vietnam Association of the Elderly—VAE; Vietnam’s Women Union—VWU) at community level and encouraging older persons to actively participate in their social activities would be beneficial to older persons to understand their rights to healthcare. Also, community-based activities can supplement or complement family support systems to address the healthcare needs of older Vietnamese, especially during possible crises like COVID-19. A well-known community-based care model in Vietnam, namely Intergenerational Self-Help Club (ISHC), is an example of multi-tasking community support to boost older persons’ health and well-being.
75
These clubs across provinces played important roles in helping older persons in needs for healthcare, social care and income security during the COVID-19.
76
Last, but not least, along with a rapidly aging population, raising awareness and implementing educational programs on chronic diseases (such as changing bad health-related behaviors and practicing good lifestyles) for younger generations would bring a long-term benefit to Vietnam with a healthier population in the coming decades.
Conclusion
This study could provide novel evidence on the mechanisms through which social support potentially mitigated the relationship between experiencing SUN and chronic diseases during COVID-19. We found that the risk factors of SUN included more advanced age, living alone, lack of healthcare insurance, and chronic diseases, while the protective factors included higher education, better wealth, and stronger social support. We also found that social support, particularly from the community, significantly moderated the link between SUN and chronic diseases.
We acknowledge some limitations of this study. Firstly, as the study’s design focused on identifying determinants of SUN and the mediating role of social support, it could not establish causal relationships. Secondly, the cross-sectional nature of the study limits our ability to control for macro-level factors that could have influenced SUN during the COVID-19 pandemic, such as the number of hospitals with geriatric departments, the availability of physicians, public healthcare spending, specific preventive measures, and mortality rates. Future research would benefit from incorporating these macro variables, as existing literature suggests their potential impact on SUN.18,19 Finally, we acknowledge the possibility of reporting bias during the pandemic. |
Title: 5110 Development Of Exosome-Mediated Suicide Gene Therapy For Enhanced Cancer Treatment | Body: |
Title: Combining metabolomics and machine learning to discover biomarkers for early-stage breast cancer diagnosis | Body:
Introduction
Breast cancer (BC) ranks among the most prevalent malignant neoplasms in women. The World Health Organization (WHO) reported alarming estimates of over 2.3 million BC diagnoses and 685,000 BC fatalities in 2020 [1]. Particularly concerning fact is that BC is the foremost cause of cancer-related deaths in women under the age of 45 years [2]. The elevated mortality rate associated with BC in numerous countries can be attributed to inadequacies in screening, early detection, and diagnosis [1]. The importance of screening and detecting BC at an initial stage cannot be overstated, as this is pivotal in enhancing treatment efficacy and decreasing mortality. Approaches ranging from imaging techniques to molecular biomarkers [3, 4], not only strive for precise diagnosis but also aim to classify BC subtypes, thereby guiding oncological decision-making [5]. For example, BC can be classified as preinvasive (ductal carcinoma in situ and lobular carcinoma in situ) and invasive (ductal carcinoma and lobular carcinoma) based on histological information, or molecular subtypes such as luminal A, luminal B and triple-negative BC based on immunohistochemistry information [6].
In the past several decades, mammography has emerged as a principal tool for BC screening and has contributed to a decline in mortality rates [7]. Furthermore, the incorporation of artificial intelligence and machine learning is garnering attention because of their potential to improve the accuracy of BC diagnosis based on mammography images [8]. According to the Breast Imaging Reporting and Data System (BI-RADS) lexicon, mammography results can be divided into 7 categories. Category 0 stands for incomplete information. Categories 1 to 3 are related to cancer negative, benign or probably benign. Category 4 represents cases with a likelihood of BC. Category 5 and 6 indicated highly suggestive and biopsy-proven of malignancy, respectively [9]. Despite proven clinical merits, this technique is hampered by a high false-positive rate, which can contribute to misdiagnosis [7]. Moreover, conventional classification methods do not adequately address the diverse clinical trajectories of individual cancer cases [5]. Consequently, molecular classifications employing cutting-edge, high-throughput technologies, such as multi-omics, are under investigation for their potential to enhance BC diagnosis [5].
Blood-based biomarkers have shown promise in the early detection and diagnosis of BC. Cancer antigen 15–3 (MUC-1 antigen) and carcinoembryonic antigen are two serum biomarkers that have been applied in clinical settings [10, 11]. However, the limited sensitivity and selectivity of these markers can result in misdiagnosis [10]. This underscores the need to identify novel biomarkers with high sensitivity and selectivity to improve BC diagnosis.
Metabolic rewiring is a hallmark of cancer and is closely associated with tumor initiation, progression, metastasis, and resistance to antineoplastic drugs [12]. Untargeted metabolomics and lipidomics utilizing liquid chromatography—tandem mass spectrometry (LC-MS/MS) have demonstrated immense potential in the discovery of novel biomarkers and the generation of hypotheses concerning metabolic alterations [13]. Accordingly, several studies have explored the metabolic and lipid profiles of BC patients using high-throughput LC-MS/MS [14–16]. For example, L-octanoylcarnitine, 5-oxoproline, hypoxanthine, and docosahexaenoic acid have been identified as potential plasma biomarkers for BC diagnosis [16]. Moreover, L-arginine and arachidonic acid could be used for both detecting BC and predicting the efficacy of trastuzumab [17]. In addition, lipids play a significant role in cell signaling processes, which are linked to membrane properties, metabolism, and the invasive and metastatic behavior of tumor cells [14, 18]. Therefore, metabolomics and lipidomics research is indispensable for the early detection, accurate diagnosis, prognosis, and treatment of BC [16, 19].
This study aimed to employ a multimodal omics approach in conjunction with machine learning models to identify and validate potential endogenous biomarkers that can differentiate the metabolic and lipid profiles of BC versus benign patients. Our findings highlighted several metabolites and lipids, particularly ether-linked phosphatidylcholine (PC(O-)), which exhibited significant alterations in BC compared with benign tumors. Additionally, we identified a metabolism-centric biosignature that exhibited good performance in cases where mammography yielded suboptimal results. The insights of this study will potentially serve as a foundation for the development of supplementary tools enhancing the effectiveness of mammography in the screening and early diagnosis of BC.
Materials and methods
Clinical samples and ethical approval
Patients recruited between January 1st, 2019, and July 30th, 2022, who had available clinical data along with plasma samples stored in the Inje University College of Medicine Biobank were included in this study. All participants provided written informed consent for the use of their clinical information and plasma samples for research purpose of the biobank. The study was conducted with the approval of the Institutional Review Board of Inje University College of Medicine Busan Paik Hospital (IRB No. 2022-08-052). The specimens were acquired from the biobank after the IRB approval on September 16th, 2022. The study used information distributed from the biobank and identifiable information was not obtained.
Clinical characteristics of the patients and study design
The design and workflow of the study are illustrated in Fig 1. For the training set, aimed at the discovery of potential biomarkers, patients who had concordant preoperative mammography (Mammography BI-RADS Assessment Categories) findings and immunohistochemistry results obtained from biopsy procedures were included [20]. Specifically, the cancer group comprised 13 patients diagnosed with invasive ductal carcinoma who had category 6 mammography findings. The benign group included 18 patients with benign tumors and mammography findings categorized as 1 or 2. For the validation set, patients with discordant findings between the mammography category and immunohistochemistry results were included. This set consisted of five patients with invasive ductal carcinoma and seven cases with benign tumors. The available clinical characteristics provided by the biobank are presented in Table 1.
10.1371/journal.pone.0311810.g001
Fig 1
Schematic diagram representing the study design and computational workflow.
10.1371/journal.pone.0311810.t001
Table 1
The clinical characteristics of the patients.
Training set
Cancer group (N = 13)
Benign group (N = 18)
Age, year, mean (range)
47.5 ± 5.8 (37–55)
42.4 ± 12.2 (20–68)
BMI, kg/m2, mean (range)
22.16 ± 1.37 (19.37–24.31)
22.82 ± 3.41 (18.86–31.75)
Mammography, n (%)
Category 0
-
-
Category 1
-
12 (66.7%)
Category 2
-
6 (33.3%)
Category 3
-
-
Category 4A
-
-
Category 4B
-
-
Category 4C
-
-
Category 5
-
-
Category 6
13 (100.0%)
-
IHC, n (%)
M9020/0
-
7 (38.9%)
M8500/3
13 (100.0%)
-
Unknown
-
11 (61.1%)
Molecular subtype
Luminal A
2 (15.4%)
-
Luminal B
4 (30.7%)
-
Luminal B & HER2 positive
3 (23.1%)
-
HER2 positive
1 (7.7%)
-
Triple negative
3 (23.1%)
-
Final diagnosis, n (%)
Invasive ductal carcinoma
13 (100.0%)
-
Benign
Fibroadenoma
-
2 (11.1%)
Fibrocystic change
-
3 (16.7%)
Fibroepithelial tumor
-
9 (50.0%)
Intraductal papilloma with epithelial hyperplasia
-
1 (5.6%)
Mature adipose tissue
-
1 (5.6%)
Paraffinoma
-
2 (11.1%)
Stage (TNM categories), n (%)
T
T0
1 (7.7%)
-
T1a
1 (7.7%)
-
T1b
1 (7.7%)
-
T1c
5 (38.5%)
-
T2
4 (30.8%)
-
Tis
1 (7.7%)
-
Unclassified
-
18 (100%)
N
N0
7 (53.9%)
-
N1a
5 (38.5%)
-
Nx
1 (7.7%)
-
Unclassified
-
18 (100%)
Chemotherapy received, n (%)
10 (76.9%)
-
Test set
Cancer group (N = 5)
Benign group (N = 7)
Age, year, mean (range)
51.4 ± 7.2 (46–63)
46.1 ± 7.4 (37–55)
BMI, kg/m2, mean (range)
24.59 ± 5.14 (18.68–31.89)
24.03 ± 3.58 (18.59–29.52)
Mammography, n (%)
Category 0
1 (20.0%)
2 (28.6%)
Category 1
-
-
Category 2
3 (60.0%)
-
Category 3
-
3 (42.8%)
Category 4A
-
1 (14.3%)
Category 4B
1 (20.0%)
-
Category 4C
-
-
Category 5
-
-
Category 6
-
1 (14.3%)
IHC, n (%)
M9020/0
-
2 (28.6%)
M8500/3
5 (100.0%)
-
Unknown
-
5 (71.4%)
Molecular subtype
Luminal A
2 (40.0%)
-
Luminal B
1 (20.0%)
-
Triple negative
2 (40.0%)
-
Final diagnosis, n (%)
Invasive ductal carcinoma
5 (100.0%)
-
Benign
Fibroadenoma
-
2 (28.6%)
Fibrocystic change
-
2 (28.6%)
Follicular lymphoma
-
1 (14.3%)
Phyllodes tumor
-
2 (28.6%)
Stage (TNM categories), n (%)
T
T0
-
-
T1a
1 (20.0%)
-
T1b
1 (20.0%)
-
T1c
2 (40.0%)
-
T2
1 (20.0%)
-
Tis
-
-
Unclassified
-
7 (100%)
N
N0
5 (100%)
-
N1a
-
-
Nx
-
-
Unclassified
-
7 (100%)
Chemotherapy received, n (%)
-
-
Abbreviations: BMI, Body Mass Index; IHC, immunohistochemistry; T, Tumor; N, Node; M, Metastasis.
Reagents and chemicals
LC-MS-grade solvents including water, acetonitrile, methanol (MeOH), and isopropanol were sourced from Merck KGaA (Darmstadt, Germany). Formic acid, ammonium formate, ammonium acetate, methyl tert-butyl ether (MTBE), and toluene were procured from Sigma-Aldrich (St. Louis, Missouri, USA). Internal standards (IS) for metabolomics, such as acetyl-L-carnitine-(N-methyl-d3), L-phenyl-d5-alanine, L-tryptophan-(indole-d5), leucine enkephalin, SM(d18:1/15:0)-d9, and cholic acid-2,2,3,4,4-d5, were also obtained from Sigma-Aldrich (St. Louis, Missouri, USA). For lipidomics analysis, the LIPIDOMIX® Mass Spec Standard and Deuterated Ceramide Mass Spec Standard were acquired from Avanti Polar Lipids (Alabama, USA). The UPLC ACQUITY ethylene bridged hybrid (BEH) C18 column (100 mm × 2.1 mm, 1.7 μm) was used for metabolomics, while the UPLC ACQUITY BEH C18 column (50 mm × 2.1 mm; 1.7 μm), paired with a UPLC BEH C18 VanGuard pre-column (5 mm × 2.1 mm; 1.7 μm) (Waters, Milford, MA, USA), was employed for lipidomics analysis.
Sample preparation
For untargeted metabolomics, we employed the extraction protocol outlined in our previous study [21]. Samples were stored at -80°C prior to any experiment. Initially, 50 μL of the plasma samples were thawed on ice for 30 min and then briefly vortexed for 10 s. Next, 150 μL MeOH at −20°C, containing premixed IS was added to the plasma. The mixtures were vortexed for 30 s and centrifuged at 14,000 rcf and 4°C for 2 min. Thereafter, 150 μL of the supernatant was transferred to a new tube and evaporated completely under a stream of nitrogen gas at room temperature. For LC-MS/MS analysis, the dried extracts were reconstituted in 200 μL 50% MeOH and centrifuged for 2 min at 14,000 rcf and 4°C. Subsequently, 100 μL of the supernatant from each sample was allocated for analysis, while the remaining 50 μL was used to create pooled quality control (QC) samples.
For lipidomics, we utilized an MTBE-based biphasic extraction method, which was based on previously established protocols with minor modifications [21, 22]. Briefly, 5 μL of each lipid IS mix was spiked into every sample (50 μL) after thawing for approximately 30 min, followed by brief vortexing. The mixtures were then incubated on ice for 20 min, with intermittent vortexing. Subsequently, 300 μL of MeOH and 1,000 μL MTBE, both at −20°C were added to the samples. The mixtures were vortexed for 10 s and then shaken for 20 min at 1,200 rpm and 4°C. Following this, 250 μL water was added to the samples, and the samples were vortexed for 20 s. The samples were then centrifuged at 14,000 rcf and 4°C for 2 min, after which 500 μL of the upper phase was collected and transferred to a new tube. The lipid extracts were dried under a stream of nitrogen gas at room temperature and stored at −80°C until analysis. For reconstitution, the dried extracts were dissolved in 200 μL of a MeOH/toluene mixture (9:1, v/v). QC samples were generated by pooling 50 μL of each sample, and the rest of the sample was used for untargeted lipidomics analysis.
Data acquisition using LC-MS/MS
Data acquisition for both metabolomics and lipidomics analyses was performed using the Shimadzu Nexera LC (Kyoto, Japan) system coupled with the X500R Quadrupole Time-of-Flight mass spectrometer (SCIEX, MA, USA). The autosampler temperature was maintained at 4°C. For untargeted metabolomics, the gradient method, injection volumes, data acquisition, and MS/MS settings were selected in accordance with our previously established protocol [21]. For lipidomics, we utilized the rapid LC method for untargeted lipidomics as described by Cajka et al. [23]. The injection volume was set at 1 μL for the positive ion (ESI+) mode and 3 μL for the negative ion (ESI-) mode. Data Dependent Acquisition was employed for data acquisition, using the same MS/MS parameters as in our prior study [21]. Mass calibration was executed after every four injections for metabolomics and after eight injections for lipidomics via the X500R’s calibrant delivery system to ensure the quality of analysis.
Data processing and treatment
The raw mass spectrometry data files (.wiff) were processed using MS-DIAL version 4.9.0. The parameters for MS-DIAL were based on our previous study [21]. For metabolomics, the retention time was corrected using internal standards. Statistically significant hydrophilic metabolites were identified based on the established in-house library [21], the public MS-DIAL libraries, and IRCCS Istituto Giannina Gaslini-Mass Spectra Library [24]. The data were subsequently normalized using a locally weighted scatterplot smoothing (LOWESS) algorithm and the IS-normalization method. For lipidomics, lipids were annotated using MS-DIAL built-in library and Fiehn’s lab lipidomics library [25, 26]. The lipidomics data were also LOWESS-normalized.
Exploratory data analysis
The aligned data exported from MS-DIAL were analyzed using the MetaboAnalyst 5.0 platform and the MetaboAnalystR package version 3.2.0 to retain only the features with a missing rate of 50% or less [27, 28]. Missing values in these features were then imputed using the k-nearest neighbors algorithm. Features displaying a relative standard deviation exceeding 25% in the QC samples were excluded. Both metabolomic and lipidomic profiles were visualized employing principal component analysis (PCA), wherein the data were log-transformed and Pareto-scaled. To explore distinctions between the cancer and benign groups, a partial least squares—discriminant analysis (PLS-DA) was conducted. The performance of the PLS-DA model was evaluated via a five-fold cross-validation method, with Q2 used to determine the optimal model. Unless specified otherwise, data visualization was performed using the ggplot2 package (version 3.4.1) in R version 4.2.2.
Statistical analysis
In this study, the training set was used to identify differential molecules (DMs) in BC and potential biomarkers differentiating between the cancer and benign groups. Linear models incorporating age and BMI adjustments (for DMs) and classical univariate receiver operating characteristic (ROC) curve analysis were conducted using MetaboAnalyst 5.0. The thresholds for statistical significance in the linear models were a p-value of 0.05 and a false discovery rate (FDR) of 0.25. Additionally, features with an area under the ROC curve (AUC) ≥ 0.7 and p-value < 0.05 were chosen as potential biomarker candidates. All potential biomarker candidates identified in the training set underwent validation using the validation set. Biomarkers that showed consistency between the training and validation sets were subjected to classical univariate ROC analysis. The AUC and p-value obtained from the models were used to assess the ability of the biomarkers to differentiate cancer from benign patients. Candidates demonstrating robust performance and consistent expression between the training and validation set were selected for univariate ROC analysis using the validation data. Subsequently, the best performers were chosen to create a single biosignature. The diagnostic potential of this biosignature was then assessed by a multivariate machine learning-based ROC model using the validation set. Linear support vector machine (SVM) and random forest algorithms, in conjunction with age and BMI as covariates, were implemented.
Clinical samples and ethical approval
Patients recruited between January 1st, 2019, and July 30th, 2022, who had available clinical data along with plasma samples stored in the Inje University College of Medicine Biobank were included in this study. All participants provided written informed consent for the use of their clinical information and plasma samples for research purpose of the biobank. The study was conducted with the approval of the Institutional Review Board of Inje University College of Medicine Busan Paik Hospital (IRB No. 2022-08-052). The specimens were acquired from the biobank after the IRB approval on September 16th, 2022. The study used information distributed from the biobank and identifiable information was not obtained.
Clinical characteristics of the patients and study design
The design and workflow of the study are illustrated in Fig 1. For the training set, aimed at the discovery of potential biomarkers, patients who had concordant preoperative mammography (Mammography BI-RADS Assessment Categories) findings and immunohistochemistry results obtained from biopsy procedures were included [20]. Specifically, the cancer group comprised 13 patients diagnosed with invasive ductal carcinoma who had category 6 mammography findings. The benign group included 18 patients with benign tumors and mammography findings categorized as 1 or 2. For the validation set, patients with discordant findings between the mammography category and immunohistochemistry results were included. This set consisted of five patients with invasive ductal carcinoma and seven cases with benign tumors. The available clinical characteristics provided by the biobank are presented in Table 1.
10.1371/journal.pone.0311810.g001
Fig 1
Schematic diagram representing the study design and computational workflow.
10.1371/journal.pone.0311810.t001
Table 1
The clinical characteristics of the patients.
Training set
Cancer group (N = 13)
Benign group (N = 18)
Age, year, mean (range)
47.5 ± 5.8 (37–55)
42.4 ± 12.2 (20–68)
BMI, kg/m2, mean (range)
22.16 ± 1.37 (19.37–24.31)
22.82 ± 3.41 (18.86–31.75)
Mammography, n (%)
Category 0
-
-
Category 1
-
12 (66.7%)
Category 2
-
6 (33.3%)
Category 3
-
-
Category 4A
-
-
Category 4B
-
-
Category 4C
-
-
Category 5
-
-
Category 6
13 (100.0%)
-
IHC, n (%)
M9020/0
-
7 (38.9%)
M8500/3
13 (100.0%)
-
Unknown
-
11 (61.1%)
Molecular subtype
Luminal A
2 (15.4%)
-
Luminal B
4 (30.7%)
-
Luminal B & HER2 positive
3 (23.1%)
-
HER2 positive
1 (7.7%)
-
Triple negative
3 (23.1%)
-
Final diagnosis, n (%)
Invasive ductal carcinoma
13 (100.0%)
-
Benign
Fibroadenoma
-
2 (11.1%)
Fibrocystic change
-
3 (16.7%)
Fibroepithelial tumor
-
9 (50.0%)
Intraductal papilloma with epithelial hyperplasia
-
1 (5.6%)
Mature adipose tissue
-
1 (5.6%)
Paraffinoma
-
2 (11.1%)
Stage (TNM categories), n (%)
T
T0
1 (7.7%)
-
T1a
1 (7.7%)
-
T1b
1 (7.7%)
-
T1c
5 (38.5%)
-
T2
4 (30.8%)
-
Tis
1 (7.7%)
-
Unclassified
-
18 (100%)
N
N0
7 (53.9%)
-
N1a
5 (38.5%)
-
Nx
1 (7.7%)
-
Unclassified
-
18 (100%)
Chemotherapy received, n (%)
10 (76.9%)
-
Test set
Cancer group (N = 5)
Benign group (N = 7)
Age, year, mean (range)
51.4 ± 7.2 (46–63)
46.1 ± 7.4 (37–55)
BMI, kg/m2, mean (range)
24.59 ± 5.14 (18.68–31.89)
24.03 ± 3.58 (18.59–29.52)
Mammography, n (%)
Category 0
1 (20.0%)
2 (28.6%)
Category 1
-
-
Category 2
3 (60.0%)
-
Category 3
-
3 (42.8%)
Category 4A
-
1 (14.3%)
Category 4B
1 (20.0%)
-
Category 4C
-
-
Category 5
-
-
Category 6
-
1 (14.3%)
IHC, n (%)
M9020/0
-
2 (28.6%)
M8500/3
5 (100.0%)
-
Unknown
-
5 (71.4%)
Molecular subtype
Luminal A
2 (40.0%)
-
Luminal B
1 (20.0%)
-
Triple negative
2 (40.0%)
-
Final diagnosis, n (%)
Invasive ductal carcinoma
5 (100.0%)
-
Benign
Fibroadenoma
-
2 (28.6%)
Fibrocystic change
-
2 (28.6%)
Follicular lymphoma
-
1 (14.3%)
Phyllodes tumor
-
2 (28.6%)
Stage (TNM categories), n (%)
T
T0
-
-
T1a
1 (20.0%)
-
T1b
1 (20.0%)
-
T1c
2 (40.0%)
-
T2
1 (20.0%)
-
Tis
-
-
Unclassified
-
7 (100%)
N
N0
5 (100%)
-
N1a
-
-
Nx
-
-
Unclassified
-
7 (100%)
Chemotherapy received, n (%)
-
-
Abbreviations: BMI, Body Mass Index; IHC, immunohistochemistry; T, Tumor; N, Node; M, Metastasis.
Reagents and chemicals
LC-MS-grade solvents including water, acetonitrile, methanol (MeOH), and isopropanol were sourced from Merck KGaA (Darmstadt, Germany). Formic acid, ammonium formate, ammonium acetate, methyl tert-butyl ether (MTBE), and toluene were procured from Sigma-Aldrich (St. Louis, Missouri, USA). Internal standards (IS) for metabolomics, such as acetyl-L-carnitine-(N-methyl-d3), L-phenyl-d5-alanine, L-tryptophan-(indole-d5), leucine enkephalin, SM(d18:1/15:0)-d9, and cholic acid-2,2,3,4,4-d5, were also obtained from Sigma-Aldrich (St. Louis, Missouri, USA). For lipidomics analysis, the LIPIDOMIX® Mass Spec Standard and Deuterated Ceramide Mass Spec Standard were acquired from Avanti Polar Lipids (Alabama, USA). The UPLC ACQUITY ethylene bridged hybrid (BEH) C18 column (100 mm × 2.1 mm, 1.7 μm) was used for metabolomics, while the UPLC ACQUITY BEH C18 column (50 mm × 2.1 mm; 1.7 μm), paired with a UPLC BEH C18 VanGuard pre-column (5 mm × 2.1 mm; 1.7 μm) (Waters, Milford, MA, USA), was employed for lipidomics analysis.
Sample preparation
For untargeted metabolomics, we employed the extraction protocol outlined in our previous study [21]. Samples were stored at -80°C prior to any experiment. Initially, 50 μL of the plasma samples were thawed on ice for 30 min and then briefly vortexed for 10 s. Next, 150 μL MeOH at −20°C, containing premixed IS was added to the plasma. The mixtures were vortexed for 30 s and centrifuged at 14,000 rcf and 4°C for 2 min. Thereafter, 150 μL of the supernatant was transferred to a new tube and evaporated completely under a stream of nitrogen gas at room temperature. For LC-MS/MS analysis, the dried extracts were reconstituted in 200 μL 50% MeOH and centrifuged for 2 min at 14,000 rcf and 4°C. Subsequently, 100 μL of the supernatant from each sample was allocated for analysis, while the remaining 50 μL was used to create pooled quality control (QC) samples.
For lipidomics, we utilized an MTBE-based biphasic extraction method, which was based on previously established protocols with minor modifications [21, 22]. Briefly, 5 μL of each lipid IS mix was spiked into every sample (50 μL) after thawing for approximately 30 min, followed by brief vortexing. The mixtures were then incubated on ice for 20 min, with intermittent vortexing. Subsequently, 300 μL of MeOH and 1,000 μL MTBE, both at −20°C were added to the samples. The mixtures were vortexed for 10 s and then shaken for 20 min at 1,200 rpm and 4°C. Following this, 250 μL water was added to the samples, and the samples were vortexed for 20 s. The samples were then centrifuged at 14,000 rcf and 4°C for 2 min, after which 500 μL of the upper phase was collected and transferred to a new tube. The lipid extracts were dried under a stream of nitrogen gas at room temperature and stored at −80°C until analysis. For reconstitution, the dried extracts were dissolved in 200 μL of a MeOH/toluene mixture (9:1, v/v). QC samples were generated by pooling 50 μL of each sample, and the rest of the sample was used for untargeted lipidomics analysis.
Data acquisition using LC-MS/MS
Data acquisition for both metabolomics and lipidomics analyses was performed using the Shimadzu Nexera LC (Kyoto, Japan) system coupled with the X500R Quadrupole Time-of-Flight mass spectrometer (SCIEX, MA, USA). The autosampler temperature was maintained at 4°C. For untargeted metabolomics, the gradient method, injection volumes, data acquisition, and MS/MS settings were selected in accordance with our previously established protocol [21]. For lipidomics, we utilized the rapid LC method for untargeted lipidomics as described by Cajka et al. [23]. The injection volume was set at 1 μL for the positive ion (ESI+) mode and 3 μL for the negative ion (ESI-) mode. Data Dependent Acquisition was employed for data acquisition, using the same MS/MS parameters as in our prior study [21]. Mass calibration was executed after every four injections for metabolomics and after eight injections for lipidomics via the X500R’s calibrant delivery system to ensure the quality of analysis.
Data processing and treatment
The raw mass spectrometry data files (.wiff) were processed using MS-DIAL version 4.9.0. The parameters for MS-DIAL were based on our previous study [21]. For metabolomics, the retention time was corrected using internal standards. Statistically significant hydrophilic metabolites were identified based on the established in-house library [21], the public MS-DIAL libraries, and IRCCS Istituto Giannina Gaslini-Mass Spectra Library [24]. The data were subsequently normalized using a locally weighted scatterplot smoothing (LOWESS) algorithm and the IS-normalization method. For lipidomics, lipids were annotated using MS-DIAL built-in library and Fiehn’s lab lipidomics library [25, 26]. The lipidomics data were also LOWESS-normalized.
Exploratory data analysis
The aligned data exported from MS-DIAL were analyzed using the MetaboAnalyst 5.0 platform and the MetaboAnalystR package version 3.2.0 to retain only the features with a missing rate of 50% or less [27, 28]. Missing values in these features were then imputed using the k-nearest neighbors algorithm. Features displaying a relative standard deviation exceeding 25% in the QC samples were excluded. Both metabolomic and lipidomic profiles were visualized employing principal component analysis (PCA), wherein the data were log-transformed and Pareto-scaled. To explore distinctions between the cancer and benign groups, a partial least squares—discriminant analysis (PLS-DA) was conducted. The performance of the PLS-DA model was evaluated via a five-fold cross-validation method, with Q2 used to determine the optimal model. Unless specified otherwise, data visualization was performed using the ggplot2 package (version 3.4.1) in R version 4.2.2.
Statistical analysis
In this study, the training set was used to identify differential molecules (DMs) in BC and potential biomarkers differentiating between the cancer and benign groups. Linear models incorporating age and BMI adjustments (for DMs) and classical univariate receiver operating characteristic (ROC) curve analysis were conducted using MetaboAnalyst 5.0. The thresholds for statistical significance in the linear models were a p-value of 0.05 and a false discovery rate (FDR) of 0.25. Additionally, features with an area under the ROC curve (AUC) ≥ 0.7 and p-value < 0.05 were chosen as potential biomarker candidates. All potential biomarker candidates identified in the training set underwent validation using the validation set. Biomarkers that showed consistency between the training and validation sets were subjected to classical univariate ROC analysis. The AUC and p-value obtained from the models were used to assess the ability of the biomarkers to differentiate cancer from benign patients. Candidates demonstrating robust performance and consistent expression between the training and validation set were selected for univariate ROC analysis using the validation data. Subsequently, the best performers were chosen to create a single biosignature. The diagnostic potential of this biosignature was then assessed by a multivariate machine learning-based ROC model using the validation set. Linear support vector machine (SVM) and random forest algorithms, in conjunction with age and BMI as covariates, were implemented.
Results
Data exploration revealed subtle differences in plasma metabolic profiles between cancer and benign groups
PCA was performed on the metabolomics data to examine sample trends without considering the origins of the samples. In the PCA scores plots between patient’s and QC samples, the QC samples clustered indicating repeatability and satisfactory data acquisition process (S1A, S1B Fig in S1 File). The PCA scores plot without QC samples of the metabolomics data in ESI+ mode hinted at a slight distinction between the cancer and benign groups, whereas the PCA scores plot of ESI- mode did not display any apparent separation (Fig 2A and 2B). PCA was also applied to the lipidomics data in both the ESI+ and ESI- modes. The PCA scores plots with QC samples showed clustering of QC samples similar to metabolomics analysis (S1C, S1D Fig in S1 File). The PCA scores plot of clinical samples in ESI+ mode exhibited a general overlap between the cancer and benign groups (Fig 2C). In line with the results from ESI+ mode, no significant separation was evident between the cancer and benign groups in the PCA scores plot of ESI- mode (Fig 2D). Notably, the variance explained by PC1 and PC2 was considerably below 50%, indicating that the relationship among features was complex, and a linear model utilizing the complete profile might not effectively capture the biological variance between the two groups.
10.1371/journal.pone.0311810.g002
Fig 2
PCA scores plot visualizing metabolomic and lipidomic profiles in the breast cancer and benign tumor control groups.
A. Metabolomics positive ion mode. B. Metabolomics negative ion mode. C. Lipidomics positive ion mode. D. Lipidomics negative ion mode.
Subsequently, PLS-DA was conducted using the metabolic profiles to discern differences between the cancer and benign groups. Analysis using the ESI+ mode metabolomics data showed that PLS-DA could not efficiently distinguish the two groups (accuracy = 0.757, R2 = 0.944, Q2 < 0, Fig 3A). In accordance with the ESI+ mode, PLS-DA of ESI- mode data failed to reliably differentiate the two groups (accuracy = 0.600, R2 = 0.997, Q2 < 0, Fig 3B). The models demonstrated unsatisfactory predictive performance, as indicated by the negative Q2 values. When analyzing lipidomics data, PLS-DA scores plot in ESI+ mode revealed some separation in lipid profiles between the cancer and benign groups but with limited predictive accuracy (accuracy = 0.803, R2 = 0.982, Q2 = 0.246, Fig 3C), as did PLS-DA in ESI- mode (accuracy = 0.672, R2 = 0.573, Q2 = 0.111, Fig 3D). The results suggest that, similar to metabolomics analysis, PLS-DA in the lipidomics analysis also possessed limited predictive ability (Q2 < 0.4).
10.1371/journal.pone.0311810.g003
Fig 3
PLS-DA scores plot visualizing metabolomic and lipidomic profiles in the breast cancer and benign tumor control groups.
A. Metabolomics positive ion mode. B. Metabolomics negative ion mode. C. Lipidomics positive ion mode. D. Lipidomics negative ion mode.
In summary, the data exploration implied subtle differences in plasma metabolic profiles between the cancer and benign groups. However, both PCA and PLS-DA revealed that the distinctions were not pronounced, and the predictive performance was limited.
Univariate biomarker analysis identified potential biomarker candidates
A linear model, adjusted for age and BMI, was employed to identify molecules with significant differences between the cancer and benign groups. The analysis utilizing the linear model with metabolomics data revealed 86 significant features in ESI+ mode (9 upregulated and 77 downregulated in cancer) and 52 significant features in ESI- mode (30 upregulated and 22 downregulated in cancer), based on p < 0.05. Notably, only the ether-linked lysophosphatidylcholine (LPC) (O-22:2) remained significant after applying an adjusted p-value (FDR < 0.25). Using lipidomics data, the linear model analysis yielded 141 significant features in ESI+ mode (25 upregulated and 116 downregulated in cancer) and 76 significant features in ESI- mode (28 upregulated and 48 downregulated in cancer), with p < 0.05. Among these, 30 lipids in ESI+ mode and 11 in ESI- mode retained significance after adjusting the p-value (FDR < 0.25). The significant lipids were classified into subclasses including LPC(O-), PC(O-), sphingomyelin (SM), and free fatty acids (FA).
The results from the linear model, adjusted for age and BMI, were in harmony with the observations made in the exploratory data analysis, wherein only a handful of metabolites and lipids fulfilled the criteria for statistical significance. Consequently, univariate ROC analysis was employed to further assess the potential of plasma polar metabolites and lipids to distinguish between cancer and benign cases. In the metabolomics analysis, two annotated features in ESI+ mode and six in ESI- mode exhibited an AUC ≥ 0.7 for differentiating between the cancer and benign groups. Among these metabolites, deoxycholic acid glycine conjugate/glycoursodeoxycholic acid and LPC(O-22:2) demonstrated good performance (AUC > 0.8). In the lipidomics data analysis, a total of 53 lipids in ESI+ mode and 17 in ESI- mode achieved an AUC ≥ 0.7 (p < 0.05). Remarkably, PC(O-48:8) and PC(O-42:2) exhibited exceptional performance in differentiating between the cancer and benign groups (AUC > 0.9). Moreover, 14 lipids in ESI+ mode and 5 lipids in ESI- mode achieved an AUC > 0.8.
In summary, the univariate ROC analysis identified 73 potential biomarker candidates in BC (5 of which were detected in both ion modes), while the linear model detected 38 DMs in BC (4 of which were detected in both ion modes). The statistical attributes of these DMs and biomarker candidates are provided in S1 Table in S1 File.
External validation highlighted the potential of metabolism-centric biomarkers in aiding BC diagnosis
External validation was conducted to evaluate the potential diagnostic abilities of biomarkers identified in the training set, utilizing a distinct dataset. Initially, biomarker candidates with an AUC ≥ 0.7 and p < 0.05 in the univariate ROC analysis were chosen. In instances where candidates were detected in both ion modes, data from the ESI+ mode was selected. Subsequently, the expression direction of the biomarker candidates was compared between the training and validation datasets. A total of 61 candidates that demonstrated consistency in expression trends between both datasets were selected for further validation (S2 Fig in S1 File).
As part of the exploratory data analysis, PCA and PLS-DA were performed using the biomarker candidates to distinguish between cancer and benign cases within the validation set. Importantly, the PLS-DA scores plot displayed a distinct separation between the two groups along with strong predictive ability (accuracy = 0.933, R2 = 0.996, Q2 = 0.697, Fig 4A and 4B). This highlights the potential of the chosen biomarker candidates in differentiating between cancer and benign cases.
10.1371/journal.pone.0311810.g004
Fig 4
External validation of the metabolism-centric biosignature.
A. PCA scores plot. B. PLS-DA scores plot. C. Linear SVM ROC curve. D. Random Forest ROC curve. Abbreviations: CI, Confidence interval.
Additionally, univariate ROC analysis was employed in the validation set to assess the classification performance of the biomarker candidates. Specifically, seven biomarker candidates demonstrated robust performance in differentiating between the cancer and benign groups and were thus chosen to create a composite signature (Table 2). Interestingly, though consistent as a biomarker, PC(O-) did not exhibit as of strong performance in differentiating between cancer and benign groups in the validation set as it did in the training set. This implies that individual biomarkers may not consistently deliver reliable results for precise BC diagnostics.
10.1371/journal.pone.0311810.t002
Table 2
The classic univariate ROC analysis on the validation set of significant biomarker candidates.
ID
Analyte
AUC
p-value
1
Dimethyluric acid
1.000
5.75E-05
2
Paraxanthine
1.000
1.39E-04
3
PC(O-33:2)
0.943
4.61E-03
4
Deoxycholic acid glycine conjugate/Glycoursodeoxycholic acid
0.886
2.49E-02
5
PC(32:2)
0.886
1.45E-02
6
SM(40:2;2O)
0.829
3.49E-02
7
SM(41:2;2O)
0.829
3.90E-02
Abbreviations: AUC, Area Under the Curve; PC, Phosphatidylcholine; PC (O-), Ether-linked Phosphatidylcholine; SM, Sphingomyelin.
Consequently, multivariate machine learning models, namely linear SVM and random forest, incorporating age, BMI, and the refined signature, were employed to classify BC cases in the validation set. Notably, the machine learning model exhibited exceptional performance in distinguishing between cancer and benign cases (Fig 4C and 4D). The multivariate ROC analysis employing linear SVM yielded an AUC of 0.996 (95% CI, 1.000–1.000), while the random forest model yielded an AUC of 0.985 (95% CI, 0.875–1.000).
These outcomes of the multivariate ROC analysis using machine learning models suggest that metabolism-centric biomarkers hold promise for enhancing the accuracy of BC screening and diagnosis.
Data exploration revealed subtle differences in plasma metabolic profiles between cancer and benign groups
PCA was performed on the metabolomics data to examine sample trends without considering the origins of the samples. In the PCA scores plots between patient’s and QC samples, the QC samples clustered indicating repeatability and satisfactory data acquisition process (S1A, S1B Fig in S1 File). The PCA scores plot without QC samples of the metabolomics data in ESI+ mode hinted at a slight distinction between the cancer and benign groups, whereas the PCA scores plot of ESI- mode did not display any apparent separation (Fig 2A and 2B). PCA was also applied to the lipidomics data in both the ESI+ and ESI- modes. The PCA scores plots with QC samples showed clustering of QC samples similar to metabolomics analysis (S1C, S1D Fig in S1 File). The PCA scores plot of clinical samples in ESI+ mode exhibited a general overlap between the cancer and benign groups (Fig 2C). In line with the results from ESI+ mode, no significant separation was evident between the cancer and benign groups in the PCA scores plot of ESI- mode (Fig 2D). Notably, the variance explained by PC1 and PC2 was considerably below 50%, indicating that the relationship among features was complex, and a linear model utilizing the complete profile might not effectively capture the biological variance between the two groups.
10.1371/journal.pone.0311810.g002
Fig 2
PCA scores plot visualizing metabolomic and lipidomic profiles in the breast cancer and benign tumor control groups.
A. Metabolomics positive ion mode. B. Metabolomics negative ion mode. C. Lipidomics positive ion mode. D. Lipidomics negative ion mode.
Subsequently, PLS-DA was conducted using the metabolic profiles to discern differences between the cancer and benign groups. Analysis using the ESI+ mode metabolomics data showed that PLS-DA could not efficiently distinguish the two groups (accuracy = 0.757, R2 = 0.944, Q2 < 0, Fig 3A). In accordance with the ESI+ mode, PLS-DA of ESI- mode data failed to reliably differentiate the two groups (accuracy = 0.600, R2 = 0.997, Q2 < 0, Fig 3B). The models demonstrated unsatisfactory predictive performance, as indicated by the negative Q2 values. When analyzing lipidomics data, PLS-DA scores plot in ESI+ mode revealed some separation in lipid profiles between the cancer and benign groups but with limited predictive accuracy (accuracy = 0.803, R2 = 0.982, Q2 = 0.246, Fig 3C), as did PLS-DA in ESI- mode (accuracy = 0.672, R2 = 0.573, Q2 = 0.111, Fig 3D). The results suggest that, similar to metabolomics analysis, PLS-DA in the lipidomics analysis also possessed limited predictive ability (Q2 < 0.4).
10.1371/journal.pone.0311810.g003
Fig 3
PLS-DA scores plot visualizing metabolomic and lipidomic profiles in the breast cancer and benign tumor control groups.
A. Metabolomics positive ion mode. B. Metabolomics negative ion mode. C. Lipidomics positive ion mode. D. Lipidomics negative ion mode.
In summary, the data exploration implied subtle differences in plasma metabolic profiles between the cancer and benign groups. However, both PCA and PLS-DA revealed that the distinctions were not pronounced, and the predictive performance was limited.
Univariate biomarker analysis identified potential biomarker candidates
A linear model, adjusted for age and BMI, was employed to identify molecules with significant differences between the cancer and benign groups. The analysis utilizing the linear model with metabolomics data revealed 86 significant features in ESI+ mode (9 upregulated and 77 downregulated in cancer) and 52 significant features in ESI- mode (30 upregulated and 22 downregulated in cancer), based on p < 0.05. Notably, only the ether-linked lysophosphatidylcholine (LPC) (O-22:2) remained significant after applying an adjusted p-value (FDR < 0.25). Using lipidomics data, the linear model analysis yielded 141 significant features in ESI+ mode (25 upregulated and 116 downregulated in cancer) and 76 significant features in ESI- mode (28 upregulated and 48 downregulated in cancer), with p < 0.05. Among these, 30 lipids in ESI+ mode and 11 in ESI- mode retained significance after adjusting the p-value (FDR < 0.25). The significant lipids were classified into subclasses including LPC(O-), PC(O-), sphingomyelin (SM), and free fatty acids (FA).
The results from the linear model, adjusted for age and BMI, were in harmony with the observations made in the exploratory data analysis, wherein only a handful of metabolites and lipids fulfilled the criteria for statistical significance. Consequently, univariate ROC analysis was employed to further assess the potential of plasma polar metabolites and lipids to distinguish between cancer and benign cases. In the metabolomics analysis, two annotated features in ESI+ mode and six in ESI- mode exhibited an AUC ≥ 0.7 for differentiating between the cancer and benign groups. Among these metabolites, deoxycholic acid glycine conjugate/glycoursodeoxycholic acid and LPC(O-22:2) demonstrated good performance (AUC > 0.8). In the lipidomics data analysis, a total of 53 lipids in ESI+ mode and 17 in ESI- mode achieved an AUC ≥ 0.7 (p < 0.05). Remarkably, PC(O-48:8) and PC(O-42:2) exhibited exceptional performance in differentiating between the cancer and benign groups (AUC > 0.9). Moreover, 14 lipids in ESI+ mode and 5 lipids in ESI- mode achieved an AUC > 0.8.
In summary, the univariate ROC analysis identified 73 potential biomarker candidates in BC (5 of which were detected in both ion modes), while the linear model detected 38 DMs in BC (4 of which were detected in both ion modes). The statistical attributes of these DMs and biomarker candidates are provided in S1 Table in S1 File.
External validation highlighted the potential of metabolism-centric biomarkers in aiding BC diagnosis
External validation was conducted to evaluate the potential diagnostic abilities of biomarkers identified in the training set, utilizing a distinct dataset. Initially, biomarker candidates with an AUC ≥ 0.7 and p < 0.05 in the univariate ROC analysis were chosen. In instances where candidates were detected in both ion modes, data from the ESI+ mode was selected. Subsequently, the expression direction of the biomarker candidates was compared between the training and validation datasets. A total of 61 candidates that demonstrated consistency in expression trends between both datasets were selected for further validation (S2 Fig in S1 File).
As part of the exploratory data analysis, PCA and PLS-DA were performed using the biomarker candidates to distinguish between cancer and benign cases within the validation set. Importantly, the PLS-DA scores plot displayed a distinct separation between the two groups along with strong predictive ability (accuracy = 0.933, R2 = 0.996, Q2 = 0.697, Fig 4A and 4B). This highlights the potential of the chosen biomarker candidates in differentiating between cancer and benign cases.
10.1371/journal.pone.0311810.g004
Fig 4
External validation of the metabolism-centric biosignature.
A. PCA scores plot. B. PLS-DA scores plot. C. Linear SVM ROC curve. D. Random Forest ROC curve. Abbreviations: CI, Confidence interval.
Additionally, univariate ROC analysis was employed in the validation set to assess the classification performance of the biomarker candidates. Specifically, seven biomarker candidates demonstrated robust performance in differentiating between the cancer and benign groups and were thus chosen to create a composite signature (Table 2). Interestingly, though consistent as a biomarker, PC(O-) did not exhibit as of strong performance in differentiating between cancer and benign groups in the validation set as it did in the training set. This implies that individual biomarkers may not consistently deliver reliable results for precise BC diagnostics.
10.1371/journal.pone.0311810.t002
Table 2
The classic univariate ROC analysis on the validation set of significant biomarker candidates.
ID
Analyte
AUC
p-value
1
Dimethyluric acid
1.000
5.75E-05
2
Paraxanthine
1.000
1.39E-04
3
PC(O-33:2)
0.943
4.61E-03
4
Deoxycholic acid glycine conjugate/Glycoursodeoxycholic acid
0.886
2.49E-02
5
PC(32:2)
0.886
1.45E-02
6
SM(40:2;2O)
0.829
3.49E-02
7
SM(41:2;2O)
0.829
3.90E-02
Abbreviations: AUC, Area Under the Curve; PC, Phosphatidylcholine; PC (O-), Ether-linked Phosphatidylcholine; SM, Sphingomyelin.
Consequently, multivariate machine learning models, namely linear SVM and random forest, incorporating age, BMI, and the refined signature, were employed to classify BC cases in the validation set. Notably, the machine learning model exhibited exceptional performance in distinguishing between cancer and benign cases (Fig 4C and 4D). The multivariate ROC analysis employing linear SVM yielded an AUC of 0.996 (95% CI, 1.000–1.000), while the random forest model yielded an AUC of 0.985 (95% CI, 0.875–1.000).
These outcomes of the multivariate ROC analysis using machine learning models suggest that metabolism-centric biomarkers hold promise for enhancing the accuracy of BC screening and diagnosis.
Discussion
Early detection of BC is crucial for reducing patient mortality. However, mammography may not be sufficiently effective for the screening and accurate diagnosis of BC due to the inherent molecular heterogeneity of the disease [5]. Moreover, high-throughput technologies have provided multiple opportunities for biomarker discovery and validation, which can significantly enhance BC diagnosis and subtyping [5, 29]. Numerous studies have documented alterations in the metabolome and lipidome of BC patients, and these perturbations in small endogenous molecules are associated with the progression and metastatic potential of BC [30, 31]. Additionally, there is growing evidence supporting that multiple-marker biosignatures are inherently more robust and reliable in diverse clinical settings compared with single biomarkers [32]. Machine learning has also emerged as a pivotal tool in the research pipeline for biomarker discovery and validation [33]. Conventional statistical methods adequately characterize population interferences from a sample. On the other hand, machine learning can recognize potential predictive patterns [34]. Therefore, machine learning can empower exploratory omics-based biomarker studies for human diseases. In this study, we employed a multimodal omics data mining approach coupled with machine learning modeling to identify potential markers for early-stage BC detection.
Seven biomarkers for differentiating cancer and benign were confirmed by the validation process. These biomarkers exhibited outstanding performance in both the linear SVM and random forest models, with AUC values exceeding 0.9. This suggests significant promise of plasma metabolites in aiding the early-stage screening and diagnosis of BC. Among these, certain hydrophilic metabolites such as glutamate and glycochenodeoxycholate were altered in BC patients. For instance, glutamate levels were elevated in the cancer group compared with the benign group. Past research has indicated that accumulation of glutamate plays a pivotal role in energy provision, promotion of signaling pathways, and progression of tumors [35]. The elevated glutamate levels observed in our study may be partly attributed to the dysregulation of glutamine metabolism in cancer cells, particularly enhancement of glutaminolysis, which converts glutamine to glutamate [36]. Furthermore, we observed upregulation of glycochenodeoxycholate levels in BC patients relative to benign patients. This finding aligns with a previous study that reported increased bile acid concentrations in the serum of BC patients compared with healthy controls [37]. Collectively, our findings cohere with earlier reports on metabolic biomarkers in BC, supporting their potential role in BC screening and diagnosis.
The association between lipidomic alterations and BC invasiveness was evident in our study. Notably, PC(O-) exhibited significant differences between the invasive ductal carcinoma and benign tumor groups. The roles of PC(O-) in cancer have been well-documented [38]. It has been reported that ether lipids are involved in membrane trafficking and cell signalling, and are enriched in cancer cells [39]. For instance, some PC(O-) species have been linked to metabolic pathways that provide energy for cancer progression and activate oncogenic signaling pathways, promoting tumor growth [40]. Furthermore, interventional studies on human have suggested that increased circulated ether-linked phosphatidylcholine level could be a predictive biomarker for the progression of prostate cancer or colorectal cancer [41, 42]. Circulating lipid profiles also associated with treatment resistance in prostate cancer, further implicate the important role of lipids in cancer pathophysiology [43]. However, the univariate ROC analysis using the validation set showed subpar performance of PC(O-) compared with SM, which demonstrated better predictive ability. The type of mammography employed in the validation set could be related to this discrepancy. SM has been reported to participate in several intrinsic and extrinsic pathways that mediated cell proliferation and apoptosis via regulation of SM and ceramide balance [44, 45]. Of note, imbalance of SM and Ceramide could cause abnormal apoptotic activity that led to BC cell proliferation [46]. Additionally, alterations in SM metabolism have been correlated with tumor growth and drug resistance [47]. PC and SM lipid subclasses were altered in the plasma of BC patients compared with healthy controls [39]. It is important to note that the exact lipid species were not readily matched between the previous report and our study, possibly due to the difference of the study subjects, i.e., benign tumors rather than healthy controls. Moreover, we detected alterations in the plasma levels of other lipid subclasses, including triglycerides and free fatty acids. Changes in plasma TG levels have been detected in BC [48] and different stages of colorectal cancer [49].
This study has several limitations. First, the sample size was small, and various molecular subtypes were included within each group of interest. However, our focus was on early-stage BC profiles, which are critical for enhancing patient outcomes. The generalizability of our findings may be limited due to the small sample size and high heterogeneity of the molecular subtypes of BC patients. We tried to minimize the over-optimistic validation results by further filtering biomarker candidates (derived from the training set) using the concordance examination and the results of the univariate ROC analysis in the validation set. Then, only several of the most promising biomarker candidates, ranked based on the AUC of the ROC curve, were used for the machine learning model training and cross-validation in the validation set. However, the feature selection procedure was not conducted as part of the machine learning model training process, and the models were established and evaluated on the validation set; it is potentially subjected to selection bias [50, 51]. Second, the study was limited to one BC subtype, specifically invasive ductal carcinoma. This limits the generalizability of our findings to other BC subtypes. Third, the study only assessed relative changes in biomarker levels between cancer and benign patients, without providing quantitative measurements. The implementation of quantitative bioassays is necessary for a more comprehensive validation of the candidate biomarkers. Fourth, there was heterogeneity regarding metastatic status and neoadjuvant chemotherapy between the training and the validation cohort, which may affect the metabolome and lipidome of included patients. Subsequent studies are needed to validate our findings. Fifth, our study compared the early-stage BC patients to individuals with non-malignant tumors. The two groups had highly overlapping clinical manifestations, resulting in subtle differences in plasma metabolic profiles. Only one biomarker detected in the metabolomics analysis remained statistically significant after p-value adjustment. Therefore, pathway analysis for further biological interpretation was not conducted.
Conclusion
In conclusion, this study employed untargeted metabolomics and lipidomics analyses, coupled with robust feature selection and machine learning modeling, to identify and partially validate potential biomarkers aiding the screening and diagnosis of BC. While many studies on BC typically compared patients of all stages to healthy controls, our study differentiated patient samples to analyze differences between invasive BC and benign tumors. Furthermore, this study offers insights into alterations in hydrophilic and hydrophobic metabolites associated with BC, with a particular focus on the roles of PC(O-) and SM. Future research is imperative to thoroughly validate these biomarkers and to develop robust assay methods. Such biomarkers could serve as valuable tools to enhance screening and diagnosis of BC.
Supporting information
S1 File
Supplementary information of the study.
(DOCX)
S2 File
(DOCX)
S3 File
(DOCX)
S1 Data
(XLSX)
S1 Checklist
Human participants research checklist.
(DOCX) |
Title: C‐reactive protein/albumin ratio as a novel predictor for nutritional status of geriatric patients | Body:
1
INTRODUCTION
Malnutrition in geriatric individuals has been linked to various adverse outcomes (Dong et al., 2021; Kramer et al., 2022; Lim et al., 2012; Sharma et al., 2017). For instance, hospitalized patients experiencing malnutrition tend to face increased costs, prolonged lengths of stay, and elevated rates of readmission and mortality (Lim et al., 2012; Sharma et al., 2017). Poor nutritional status is associated with diminished physical performance and serves as a predictor of all‐cause mortality (Dong et al., 2021; Kramer et al., 2022). In light of these detrimental consequences, it is imperative to assess the nutritional status of individuals accurately. The literature suggests several methods for ensuring precise nutritional screening of geriatric individuals (Bellanti et al., 2022; Cederholm et al., 2017). Multiple validated screening tools are available and commonly employed (Bellanti et al., 2020; Rubenstein et al., 2001; Stratton et al., 2004; Vellas et al., 1999). However, a universally accepted gold standard method for nutritional assessment is lacking, leading to varied recommendations in the existing literature (Cederholm et al., 2017; Cederholm et al., 2019).
The serum albumin level is frequently utilized as a biomarker to assess the nutritional status of patients (Bellanti et al., 2022; Cederholm et al., 2017; Cederholm et al., 2019). It is a negative acute phase reactant, and its level decreases mainly in chronic inflammation and malnutrition. In this context, C‐reactive protein (CRP) serves as a sensitive marker indicative of body inflammation and also is a well‐known acute phase reactant and has been associated with various diseases (Eckart et al., 2020; Hizli et al., 2021). The CRP‐to‐albumin ratio (CAR), a novel parameter that has garnered attention in recent years, has been subject to investigations (Liao et al., 2021; Luan et al., 2021; Yu et al., 2021). Studies have reported the CAR alone as a significant prognostic biomarker for poor outcomes and overall survival in specific diseases (Liao et al., 2021; Luan et al., 2021; Yu et al., 2021). CAR has been suggested to be a more reliable risk indicator for inflammatory conditions than CRP or albumin alone. It also appears as a combination of systemic inflammation and nutritional status and has been associated with clinical outcome in many diseases (Haider Kazmi et al., 2022; Kunutsor & Laukkanen, 2022).
Malnutrition in the geriatric population is a serious health problem, and it is important to assess nutritional status in this population as it can lead to various diseases. Dysphagia also has a major impact on nutritional status and malnutrition, and the geriatric population is also at risk in this respect. Therefore, the detection of malnutrition in the geriatric population is of great importance (Bayram et al., 2021; Epçaçan et al., 2023). Given these findings, we hypothesize that the CAR could serve as a valuable indicator for nutritional screening. Hence, this research aims to explore the potential of the CAR in predicting the nutritional status of geriatric patients.
2
MATERIALS AND METHODS
This cross‐sectional study encompassed geriatric patients (aged ≥65 years) consecutively admitted to the internal medicine outpatient clinic of a training and research hospital between May 2022 and August 2022. Exclusion criteria comprised individuals with acute/chronic infection, malignancy, rheumatic diseases, steroid use, chronic hematological diseases, hospitalization within the last month, liver cirrhosis, protein‐losing enteropathy, nephrotic syndrome, chronic kidney failure, and CRP values exceeding 10 mg/L. Recorded data included the patients' demographic and clinical characteristics, nutritional status, and laboratory information. Approval for conducting the study was obtained from the local university's Ethics Committee of the Faculty of Medicine (approval No. E‐71522473‐050.01.04‐128291). Participants were adequately informed about the study, and their participation was contingent upon providing signed informed consent.
The Mini Nutritional Assessment (MNA) served as the tool for assessing the nutritional status of the patients (Vellas et al., 1999). Comprising 18 items related to anamnesis, dietary characteristics, and anthropometric measurements, including mid‐arm circumference, body mass index (BMI), calf circumference, triceps skin fold (TSF), and the MNA covered various aspects, including number of meals, daily menu content, fluid intake, fruit and vegetable consumption, presence of neuropsychological conditions and other diseases, mobility, and ability to eat independently (Vellas et al., 1999). The validity and reliability of MNA have been demonstrated in previous studies (Ekici et al., 2021). Additionally, there are studies evaluating its validity in the geriatric population (Sarikaya et al., 2015). Total MNA scores falling within the range of 24−30 indicated normal nutritional status, scores between 17−23.5 signaled the risk of malnutrition, and scores below 17 signified malnutrition (Vellas et al., 1999). Two trained researchers administered the MNA questionnaire and conducted measurements for height, weight, BMI, mid‐arm circumference, TSF, and calf circumference. BMI was calculated by dividing weight (kilograms) by the square of the height (meters). The International Biological Program was analyzed for the evaluation of anthropometric measurements (Weiner & Lourie, 1969). Subsequently, patients were categorized into two groups: those with normal nutrition (MNA scores of 24−30) and those classified as malnourished or at risk of malnutrition (MNA scores ≤23.5). The CAR and other parameters were then compared between the two groups.
The results of patients' laboratory examinations were obtained from the hospital automation system, with those lacking measurements for serum albumin and CRP values being excluded from the study. Serum albumin, creatinine, and total cholesterol were assessed using a spectrophotometric technique. The CRP level was determined using an immune‐turbidimetric method on an Olympus AU5800 auto‐analyzer. Accepted reference ranges were 3.2−4.6 g/dL for albumin and 0−5 mg/L for CRP. The CAR was introduced as a novel parameter, calculated by dividing the CRP value by the serum albumin value. The study focused on exploring the relationship between the CAR and nutritional parameters, as well as evaluating the CAR's efficacy in predicting the nutritional status of the included patients.
Continuous variables were presented as the mean ± standard deviation or median (25th−75th percentile), while categorical variables were expressed as frequency and percentage. The normality of variable distributions was assessed using the Kolmogorov–Smirnov test. Chi‐square tests were employed for categorical variables, and the Mann–Whitney U test or Student's t‐test was utilized for continuous variables. Spearman's correlation test was applied to evaluate the association between the CAR and MNA scores. The receiver operating characteristic (ROC) curve was employed to identify the optimal cut‐off value for the CAR in predicting nutritional status. To assess the CAR's performance regarding the nutritional status of patients, ROC curves, sensitivity, and specificity values were calculated. Binary logistic regression analysis was conducted to determine the association between the CAR and patients' nutritional status. A significance level of p < .05 was applied to all statistical analyses, which were carried out using the Statistical Package for Social Science (SPSS version 22).
3
RESULTS
The study consisted of 154 patients who met all criteria. The patients’ mean age was 73.2 ± 5.9 years, and 99 (64.3%) were female. The clinical features of the participants are shown in Table 1. Among the participants, 40.2% (n = 62) were malnourished or at risk of malnutrition. The mean age of the malnourished or at‐risk‐of‐malnutrition patients was higher than that of patients with normal nutrition (75.2 ± 6.3 vs. 71.9 ± 5.3 years, p = .001) (Table 1). No intergroup differences were found in terms of patient sex or other demographic characteristics. The CAR and nutritional parameters of the patients based on the MNA results are shown in Table 2. The median CAR value of the participants who were malnourished or at risk of malnutrition was significantly higher than that of participants with normal nutritional status (0.84 [0.74−2.22] vs. 0.77 [0.70−0.90], p = .012) (Table 2).
TABLE 1
Clinical characteristics of patients according to nutritional status.
Characteristic
Total (n = 154)
Well nourished
(MNA score 24–30) (n = 92)
Malnourished or
at risk of malnutrition
(MNA score ≤23.5)
(n = 62)
p‐value
Gender (male/female)
55/99
35/57
20/42
.573
Age (years)
73.2 ± 5.9
71.9 ± 5.3
75.2 ± 6.3
.001
Hypertension n (%)
100 (64.9)
58 (63)
42 (67.7)
.669
Diabetes n (%)
57 (37)
33 (35.9)
24 (38.7)
.851
CVD n (%)
17 (11)
7 (7.6)
10 (16.1)
.164
CVA n (%)
11 (7.1)
6 (6.5)
5 (8.1)
.439
White blood cells (K/μL)
6.79 ± 1.77
6.82 ± 1.71
6.75 ± 1.88
.596
Hemoglobin (g/dL)
13 ± 1.3
13.2 ± 1.1
12.8 ± 1.5
.054
Platelets (K/μL)
250 ± 59
250 ± 59
250 ± 60
.938
AST (U/L)
20.2 ± 9.1
20.1 ± 9.2
20.2 ± 9
.889
ALT (U/L)
14 (11–21)
14.5 (12–22)
13 (10–21)
.197
Creatinine (mg/dL)
0.8 ± 0.2
0.8 ± 0.2
0.9 ± 0.2
.068
Note: Data were shown as mean ± SD, number (percentage), or median (percentiles 25–75).
Abbreviations: ALT, alanine amino‐transferase; AST, aspartate aminotransferase; CVA, cerebrovascular accident; CVD, cardiovascular disease; MNA, Mini Nutritional Assessment.
John Wiley & Sons, Ltd.
TABLE 2
C‐reactive protein to albumin ratio and nutritional parameters of 154 geriatric patients according to nutritional status.
Parameters
Well nourished (MNA score 24–30) (n = 92)
Malnourished or at risk of malnutrition (MNA score ≤ 23.5)
(n = 62)
p‐value
MNA score
26 ± 1.4
21.1 ± 2
<.001
CAR
0.77 (0.7–0.9)
0.84 (0.74–2.22)
.012
CRP (mg/L)
3.2 (3.2–3.6)
3.2 (3.2–3.5)
.818
Albumin (g/dL)
4.2 ± 0.3
4 ± 0.4
.037
Neutrophil count (K/uL)
3.85 ± 1.48
3.93 ± 1.36
.715
Lymphocyte count (K/uL)
2.22 (1.73–2.59)
1.86 (1.26–2.58)
.051
Total cholesterol (mg/dL)
218 ± 39
211 ± 48
.264
LDL‐C (mg/dL)
134 ± 33
131 ± 35
.401
Triglyceride (mg/dL)
133 (98–179)
122 (88–171)
.306
Weight (kg)
76.6 ± 11.7
65.2 ± 13.1
<.001
Height (m)
158 ± 9
155 ± 8.7
.014
BMI (kg/m2)
30.5 ± 4.6
27.2 ± 5.3
<.001
MAC (cm)
30.5 ± 3
27.8 ± 3.7
<.001
Calf circumference (cm)
36.8 ± 3.3
34.4 ± 4.3
<.001
TSF (mm)
18 (11.2–23.8)
12 (8.5–19.5)
.005
Note: Data were shown as mean ± SD or median (percentiles 25–75).
Abbreviations: BMI, body mass index; CAR, C‐reactive protein to albumin ratio; CRP, C‐reactive protein; LDL, low‐density lipoprotein; MAC, mid‐arm circumference; MNA, Mini Nutritional Assessment; TSF, triceps skin fold.
John Wiley & Sons, Ltd.
There was a significant correlation between the MNA results and the CAR (r = −0.196, p = .015) (Figure 1). The ROC curve analysis revealed that the CAR could be considered a significant predictor of malnourishment or the risk of undernutrition (area under the curve = 0.619; p = .012; 95% confidence interval, 0.526−0.713) (Figure 2). A CAR optimum cut‐off value of ≥0.86 predicted nutritional status with a sensitivity of 48.4% and specificity of 71.7%. Moreover, binary logistic regression analysis suggested that the CAR alone was an independent predictor of undernutrition or the risk of malnutrition (odds ratio, 0.714; 95% confidence interval, 0.532−0.958; p = .025).
FIGURE 1
Correlation graph of C‐reactive protein to albumin ratio and Mini Nutritional Assessment score (r = −0.196, p = .015).
FIGURE 2
Receiver operating characteristics curve of C‐reactive protein to albumin ratio for predicting nutritional status in geriatric patients (AUC = 0.619, p = .012). ROC, receiver operating characteristic.
4
DISCUSSION
The current study uncovered a significant elevation in the CAR among geriatric patients who were either malnourished or at risk of malnutrition compared to those with a normal nutritional status. The CAR emerged as a biomarker capable of independently predicting malnourishment or the risk of malnutrition in these patients. Moreover, the CAR was identified as an independent predictor of malnourishment or the risk of malnutrition in the geriatric population. Consequently, the CAR may offer a novel parameter for stand‐alone use in the nutritional screening of the geriatric population.
The well‐established association between malnutrition in geriatric individuals and various adverse clinical outcomes underscores the importance of nutritional screening and appropriate support for this population (Dong et al., 2021; Kramer et al., 2022; Lim et al., 2012; Sharma et al., 2017). Currently, specific validated tools are utilized to assess the nutritional status of geriatric individuals (Bellanti et al., 2020; Rubenstein et al., 2001; Stratton et al., 2004; Vellas et al., 1999). However, a universally accepted method for this purpose is lacking. Malnutrition and inflammation represent distinct clinical conditions that can mutually influence each other. Certain nutritional deficiencies can compromise mechanisms involving neutrophils, lymphocytes, the complement system, and mucosal immunity—critical components of the body's defense system. This compromise may expose the body to microbial factors, triggering an increase in inflammation indicators. Conversely, symptoms such as loss of appetite, low oral intake, nausea, vomiting, fever, and fatigue, often accompanying inflammation, can lead to malnutrition in patients (Nájera et al., 2004; Scrimshaw & SanGiovanni, 1997).
Recent studies have indicated that the neutrophil‐to‐lymphocyte ratio, an inflammation parameter, serves as a significant marker of patients' nutritional status (Kaya et al., 2019; Wang et al., 2022). This aligns with the findings of the present study, which demonstrated that the CRP‐derived CAR is indicative of the nutritional status of geriatric individuals. The observed outcomes strongly suggest a relationship between inflammation and nutritional status.
Albumin has long been used as a significant indicator of the nutritional status of the individuals (Cabrerizo et al., 2015; Cederholm et al., 2017). The CAR, representing the ratio of CRP to serum albumin, is a novel parameter with the added benefit of being cost‐effective and easily accessible. It incurs no additional costs, is readily calculable, and has demonstrated significant associations with prognosis and specific clinical conditions in various diseases (Liao et al., 2021; Luan et al., 2021; Wu et al., 2022; Yu et al., 2021). Multiple meta‐analyses involving participants with head and neck, colorectal, and urological malignancies have emphasized the CAR as a noteworthy prognostic marker (Liao et al., 2021; Luan et al., 2021; Wu et al., 2022). While numerous studies have explored the relationship between CRP, albumin, and nutritional status, with some mentioned previously (Bellanti et al., 2020; Cederholm et al., 2017; Eckart et al., 2020; Scrimshaw & SanGiovanni, 1997), research specifically focused on the connection between the CAR and nutritional status is limited. A recent study investigating this relationship in 393 inpatients with chronic obstructive pulmonary disease (COPD) identified a CAR value of >3.26 as a significant marker of nutritional status (Baldemir et al., 2022).
To the best of our knowledge, this current study is the first to evaluate the role of the CAR in the nutritional screening of geriatric patients. It determined that an optimal CAR cut‐off value of ≥0.86 predicted nutritional status with a sensitivity of 48.4% and a specificity of 71.7% in geriatric patients. Notably, this CAR cut‐off value is lower than that reported in patients with COPD (Baldemir et al., 2022). It is important to highlight that the present study excluded patients with active inflammatory diseases and CRP values exceeding 10 mg/L, which differs from the other study where 21.9% of patients had pneumonia (Baldemir et al., 2022). This discrepancy may contribute to the variations in CAR cut‐off values between the studies.
The present research is subject to certain limitations. First, the data were collected from a single center, and the study population exclusively consisted of geriatric patients visiting the internal medicine outpatient clinic. Future studies should aim to include a more diverse sample from multiple centers, encompassing individuals of different age groups, including inpatients and residents of nursing homes. This broader approach would enhance the generalizability of the findings. Second, the current study was designed as a cross‐sectional investigation. Future prospective studies, specifically those comparing changes in the CAR with nutritional support interventions, could provide valuable insights. Although CAR was found to be a significant marker of nutritional status, the r: −0.196 and area under the curve (AUC): 0.619 on the ROC curve indicate that the results are not very strong.
In conclusion, our findings indicate a significant elevation in the CAR among geriatric patients attending the internal medicine outpatient clinic who were either malnourished or at risk of malnutrition, in comparison to those with normal nutritional status. The CAR exhibited a notable correlation with the nutritional status of these patients and demonstrated predictive capability for identifying geriatric individuals at risk of malnutrition. As a cost‐effective and easily calculable marker, the CAR emerges as a potential independent predictor of malnourishment or the risk of malnutrition in the aging population. This study suggests that the CAR may serve as a valuable tool for nutritional screening in geriatric patients, emphasizing its potential application in clinical practice.
AUTHOR CONTRIBUTIONS
Tezcan Kaya: Conceptualization; methodology; writing—original draft; formal analysis. Sena Boncuk Ulaş: Software; writing—review and editing; data curation. Ahmet Nalbant: Visualization; project administration. İlhan Yıldırım: Software; investigation; data curation. Kubilay İşsever: Supervision; validation; project administration. Cengiz Karacaer: Visualization; formal analysis. Cahit Bilgin: Data curation; writing—original draft. Asli Vatan: Validation; methodology. Türkan Acar: Project administration; writing—review and editing. Bilgehan Atılgan Acar: Writing—review and editing; project administration; supervision. Yeşim Güzey Aras: Conceptualization; methodology; visualization. Mehmet Köroğlu: Data curation; investigation.
CONFLICT OF INTEREST STATEMENT
The authors declare no conflicts of interest.
PEER REVIEW
The peer review history for this article is available at https://publons.com/publon/10.1002/brb3.70017.
PEER REVIEW
The peer review history for this article is available at https://publons.com/publon/10.1002/brb3.70017. |
Title: Performance comparison of high throughput single-cell RNA-Seq platforms in complex tissues | Body:
1
Introduction
Single-cell RNA sequencing (scRNAseq) enables simultaneous profiling of gene expression of individual cells and is the tool of preference to define cell phenotypes and discover cell subsets and states [1]. Single-cell transcriptomics provides an unprecedented resolution of the composition and functionality of cellular niches in homeostatic tissues and during disease [2,3]. For example, scRNAseq has been used to unravel multicellular dynamic processes during embryogenesis and cell differentiation, tissue regeneration and morphogenesis, disease initiation and progression, and response to stimuli including drug treatments [[4], [5], [6], [7], [8], [9], [10], [11], [12]].
The ability to sequence genomic material combined with barcoding strategies to label each cell and RNA molecule extended the possibility of simultaneous analysis of barcoded cells in the same reaction, dramatically reducing cost and labour [13,14]. The subsequent application of microfluidic devices to encapsulate individual cells in nano-droplet-sized bioreactors, like Drop-seq [6,7], or the development of high-density microwell plates to partition and capture individual cells, as Microwell-Seq [15,16] considerably increased the scRNAseq scale, enabling the analysis of tens-of-thousands of cells in a single reaction. These high throughput scRNAseq methods can be technically challenging and difficult to control to deliver consistent outputs [17]; however, commercial solutions, like 10× Chromium and BD Rhapsody, have streamlined these processes, easing the technical demand and standardising procedures while ensuring consistent reagent quality, [18].
At a first glance, all high throughput scRNAseq methods offer similar data outputs and they can interchangeably be used to interrogate any biological system. However, each method is technically different, with its own intrinsic capabilities, biases and limitations; thus, platform performance could vary with particular cell types and tissues, and specific platforms may be better suited to answer particular biological questions. There is limited information on a systematic comparison of the performance of different high throughput scRNAseq platforms and the available scRNAseq platform comparisons are based on relatively homogenous cell cultures [19,20], or artificial mixed pools [[21], [22], [23]]. Thus, these comparisons are unable to assess each platform's ability to distinguish cellular heterogeneity in a complex tissue scenario, failing to compare performance between cells of different lineages. For example, a recent study comparing five single cells/nucleus RNAseq high-throughput methods concluded that the commercial platform 10× Chromium was the top performing technology compared with the home brew methods [24]. This study, however, did not included other commercial high-throughput platforms, such as the BD Rhapsody; and for the single-cell comparison, it only used cultured cells or PBMC samples which do not require tissue dissociation.
Here, we present a technical and biological comparison of two well-established and widely used 3′-scRNAseq commercial platforms, - 10× Chromium (10× Genomics) and BD Rhapsody (Becton Dickinson); using mammary gland tumours from the MMTV-PyMT mouse model, biologically complex but reproducible samples, [[25], [26], [27], [28]]. 10× Chromium is a droplet-based microfluidic platforms while BD Rhapsody uses microwell-based technology where cells are randomly deposited by gravity into an array of picoliter-size wells. All systems track the cell of origin with a cell barcode and count individual molecules using unique molecular identifiers (UMIs). Although these two platforms are designed to produce similar readouts, essentially a digital count of gene expression in each cell, the device design (microfluidic or microwell), the nature of the capture beads, the molecular design of the barcodes and UMIs, and the essential nature of the molecular workflow for RNA reverse transcription and amplification strongly differs.
In this study, we evaluate how 10× Chromium and BD Rhapsody 3′-scRNAseq platforms manage the challenges of complex tissues to produce meaningful data to discover the strengths and weaknesses of each platform in this context.
2
Material and methods
2.1
Mouse model
The MMTV-Polyoma Middle T antigen (PyMT) was a gift from Dr. William J. Muller (McGill University) and its generation has been previously described [26,27]. At ethical endpoint, (10 % ± 3 % tumour/body weight, which approximately corresponds to 14-week-old animals) the mice were euthanized, and size-matched tumours were harvested and processed for single cell digestions. The tumour location nomenclature used is as follows: Tumour A - right cervical and/or thoracic mammary gland; tumour B - left cervical and/or thoracic mammary gland; tumour C - right abdominal and/or inguinal mammary glands; tumour D - left abdominal and/or inguinal mammary glands. Genotyping was performed at the Garvan Molecular Genetics facility (NATA accredited, ISO 17025) by PCR of DNA extracted from the mouse tail tip using the following primers: CGGCGGAGCGAGGAACTGAGGAGAG and TCAGAA GACTCGGCAGTCTTAGGCG. The touchdown PCR conditions were 94 °C for 10 s of initial denaturation, followed by 10 cycles of 94 °C 10 s, 65-55 °C for 30 s and 72 °C for 1 min and 10 s and then 31 cycles of 94 °C 10 s, 55 °C for 30 s and 72 °C for 1 min and 10 s; the final extension is 72 °C for 3 min. All animals used in this study are heterozygous for the PyMT gene.
All animal experiments were carried out according to guidelines contained within the NSW (Australia) Animal Research Act 1985, the NSW (Australia) Animal Research Regulation 2010 and the Australian code of practice for the care and use of animals for scientific purposes, (8th Edition 2013, National Health and Medical Research Council (Australia)). All experiments involving mice have been approved by the St. Vincent's Campus Animal Research Committee AEC #19/02.
2.2
Tissue digestion and single-cell isolation
PyMT tumours were digested as described in Refs. [29,30]. We aimed to have a minimum of two mice per condition and platform and a minimum of 2 mixed tumours per mice from different locations (Table S1 and S2). Briefly, tumours were manually dissected into 3–5 mm pieces using a surgical scalpel blade and further chopped to 100 μm using a tissue chopper (McIIwain). Samples were enzymatically digested with 15,000 U of collagenase (Sigma Aldrich Cat# C9891) and 5,000 U of hyaluronidase (Sigma Aldrich Cat# H3506) for 30 min at 37 °C. The samples were then further digested by pipetting up and down with 0.25 % trypsin (Gibco Cat# 15090-046), in 1 mM EGTA and 0.1 mg/mL of Polyvinyl alcohol dissolved in Dulbecco's phosphate-buffered saline (DPBS, Gibco Cat# 14190-250) for 1 min at 37 °C in a waterbath. Red blood cells were then lysed with 0.8 % ammonium chloride (Sigma Aldrich Cat# A9434) dissolved in water for 5 min at 37 °C. Single cell suspensions were washed with DPBS containing 2 % of Foetal Bovine Serum (FBS, GE Healthcare Cat# SH30406.02) and spun at 200×g for 5 min at 4 °C between each step. The supernatant was aspirated and 1 mg/mL DNase I (Roche Cat# 10104159001) was mixed with the sample before incubation with each step. Finally, cells were filtered through a 40 μm cell sterile strainer (Corning® Cat# 431750) and resuspended in DPBS with 2 % FBS. For the generation of low-quality-like samples, digested tumours were left overnight for 24 h at 4 °C, and check that viability was reduced by flow cytometry by at least 20 % before processing the sample in autoMACS® Pro (Miltenyi).
All tumours were labelled with Annexin specific MACS beads using the Dead Cell Removal Kit (Miltenyi Biotec Cat# 130-090-101) following the manufacturers’ instructions and dead cells were removed by passing the labelled cells through the autoMACS® Pro (Miltenyi) (see more details at [17]). All samples showed high percentage of viable cells (≥80 % viability assessed by DAPI in flow cytometry, Table S3). Samples for BD Rhapsody underwent additional steps prior to the cell capture for LMO multiplexing (see below and Fig. S1B). When all samples from both methods were ready for cell capture, high percentage of cell viability was again verified by microscopy with 0.4 % of Trypan blue solution (Sigma-Aldrich, Cat# T8154), to ensure that comparable cell viability was achieved from both single-cell capture methods (≥85 %).
2.3
Flow cytometry
The viability and cellular content of the main cell compartments of the tumours was assessed by flow cytometry. Digested tissue samples were washed with DPBS (Gibco Cat#14190136) supplemented with 2 % FBS (GE Healthcare Cat# SH30406.02) and centrifuged at 200×g for 5 min at 4°C. The pellet was then resuspended with 2 % FBS in PBS for use in flow cytometry analysis. For flow cytometry analysis, single cell suspensions were incubated with antibodies for anti-mouse EpCAM (BioLegend Cat#118205, RRID: AB_1134176) and anti-mouse CD45 (Clone 30-F11, BioLegend, Cat# 103114 RRID: AB_312979) on ice in the dark for 30 min before they were washed, centrifuged, and resuspended with 2 % FBS in DPBS for analysis using the BD FACSymphony™ Cell Analyser. To check viability, cells were stained with DAPI in a 0.5 μg/mL concentration (Invitrogen, D1306) at 4 °C for 3 min immediately before running the samples in the flow cytometer. Flow cytometry data were analysed using the software package FlowJo (version 10.4.2).
2.4
10× Chromium
We aimed to capture 8,000 cells with >85 % cell viability from each condition. Libraries were prepared using the Chromium Next GEM Single Cell 3ʹ Kit from 10× Genomics (Cat# PN-1000269) following the manufacturer's instructions. Sequencing was performed on an Illumina NovaSeq™ 6000 System (Illumina, Cat# 20012850) using the NovaSeq 6000 S4 Reagent Kit (200 cycles) (Illumina, Cat # 20028313) using the following configuration: 28bp for Read 1, 91bp for Read 2 and 8bp for Index, to an estimated depth of 20,000–30,000 reads per cell. Cell Ranger pipeline v3.0.1 was used for Fastq file generation, alignment to the mm10 (Release M19 (GRCm38.p6) transcriptome reference and UMI counting. Barcodes corresponding to empty droplets were excluded using cell calling algorithm from Cell Ranger based on EmptyDrops [31]. We used “subset” function from Seurat v5.0.1., for random cell subsampling.
2.5
BD Rhapsody
We adopted the MULTI-seq protocol [32] based on lipid-modified oligos (LMOs) for multiplexing different samples in the same BD Rhapsody cartridge (Fig. S1B). Single-cell suspensions from four different tumours were washed twice with DPBS (Gibco Cat#14190136) and incubated in a 200 nM solution containing equal amounts of anchor LMO (generously gifted by Prof. Gartner's laboratory) and sample barcode oligonucleotides (©Integrated DNA Technologies, IDT, Inc) on ice for 5min. After LMO-barcode labelling, we incubated the co-anchor LMO (gifted by Prof. Gartner's laboratory, final concentration of 200 nM) in each sample for 5min on ice. 1 mL of 1 % BSA (Sigma-Aldrich Cat#A9418) was added in each sample to quench the LMO binding and washed once with 1 % BSA. The tumour samples were pooled in equal proportions, washed twice with 1 % BSA and resuspended in the cold Sample Buffer (BD Rhapsody Cat. No. 650000062). Altogether, the multiplexing step added 30 min extra prior to the cell capture, which represents 11 % of the total time for sample processing (typically 4 h for 4 single-plex samples [29]). Two extra QC steps were performed to ensure that the multiplexing workflow did not compromise cell viability: 1) 0.4 % Trypan blue cell staining (Sigma-Aldrich, T8154) to make sure cell viability was comparable to the 10× samples (see above in Tissue digestion and single-cell isolation section), and then, 2) immediately before cells were loaded into a BD Rhapsody cartridge, a high percentage of cell viability was corroborated in the BD Rhapsody™ Scanner using Calcein AM (Thermo Fisher Scientific, Cat# C1430) and Draq7TM (Cat# 564904) in the pooled multiplexed samples (Table S3). Cell capture, cDNA synthesis and exonuclease I treatment were performed using the standard protocols from BD Rhapsody Express Single-Cell Analysis System Instrument User guide (Doc ID 214062).cDNA and LMO libraries were prepared using a custom protocol combining BD Rhapsody's mRNA whole transcriptome analysis (WTA) library preparation protocol (Doc ID 23-21711-00 Rev 7/2019) and the MULTI-seq protocol [32]. First, we performed 2 sequential random priming and extension (RPE) reactions of the Exonuclease I-treated beads containing the whole cellular transcriptome and LMO cDNA to increase assay sensitivity using half of the reagents for each round from the BD Rhapsody™ WTA Amplification Kit (Cat#633801). After this step we performed two parallel workflows, one for the WTA libraries, using the RPE product from the supernatant and the other one for the LMO libraries from the post-RPE beads. The RPE supernatant underwent WTA library preparation following the manufacturers' instructions from the BD Rhapsody™ System mRNA Whole Transcriptome Analysis (WTA) Library Preparation Protocol (Doc ID 23-21711-00 Rev 7/2019) that included the steps of purifying RPE product, performing RPE PCR, purifying the RPE PCR amplification product, performing WTA Index PCR and purifying the WTA index PCR product. The LMO libraries were obtained from the post-RPE beads, which were resuspended and washed in the cold Bead Resuspension Buffer (BD Rhapsody Cat. No. 650000062)and after three washes, the beads were resuspended in 80 μL of the MULTI-seq cDNA amplification mix containing 1 μL of 2.5 μM Multi-seq primer 5′-CTTGGCACCCGAGAATTCC-3′, 69 μL PCR Master Mix (Part# 91–118 from BD Rhapsody™ WTA Amplification Kit, Cat# 633801), 10 μL Universal Oligo (Part# 650000074 from BD Rhapsody™ WTA Amplification Kit, Cat# 633801) and the PCR was performed as follows: 95 °C, 3 min; 95 °C, 30 s, 60 °C, 1 min and 72 °C, 1 min for 14 cycles; 72 °C, 2 min; 4 °C hold. Next, we purified the PCR products of the LMOs using a 0.6× ratio of the SPRI beads (Beckman Coulter Cat# B23317) and keeping the supernatant, followed by a left side selection using 1.8× ratio. The LMO library was eluted in 69 μL of Elution Buffer (Part# 91–1084 from BD Rhapsody™ WTA Amplification Kit, Cat# 633801) and was quantified using Qubit (dsDNA HS Assay, Invitrogen™ Cat# Q32851) and the expected DNA size (∼95–115 bp) was confirmed with the Agilent 4200 TapeStation system (Agilent High Sensitivity D5000 ScreenTape Assay, Agilent Cat# 5067–5593 and 5067–5592). Next, we used 3.5 ng of LMO cDNA purified product to perform a LMO Index PCR, the PCR mix had 26.25 μl of 2× KAPA HiFi HotStart Ready mix (KAPABiosystems Cat# KK2601), 2.5 μl of 10 μM Library Forward Primer (Part# 91–1085 from BD Rhapsody™ WTA Amplification Kit, Cat# 633801), 2.5 μl of 10 μM Library Reverse Primer (reagent part from BD Rhapsody™ WTA Amplification Kit, Cat# 633801, a different primer was chosen for each sample for sequencing multiplexing); and the PCR conditions were 95 °C, 5 min; 98 °C, 15 min; 60 °C, 30 min; 72 °C, 30 min; 8 cycles; 72 °C, 1 min; 4 °C hold A final cleanup of the LMO libraries using a 1.6× SPRI bead ratio was performed before sequencing.
WTA libraries and LMO libraries were pooled and sequenced on the NovaSeq™ 6000 System (Illumina, Cat# 20012850) using the NovaSeq 6000 S1 Reagent Kit (100 cycles) (Illumina, Cat # 20028319) using the following configuration: 100bp for Read 1, 100bp for Read 2 and 8bp for Index to an estimated depth of 25,000 reads per cell for the WTA library and 2,500 reads per cell for the LMOs.
Fastq files from BD Rhapsody samples were processed using BD Rhapsody™ WTA Analysis Pipeline, v1.9.1 in Seven Bridges Genomics cloud platform. WTA reads were aligned to the mm10 reference genome (Release M19 (GRCm38.p6) and LMO sequences (8bp) were included as sample tags, allowing a maximum of 1 mismatch. The sample multiplexing option of the WTA Analysis pipeline was used to determine the sample of origin of each cell and singlets were used for downstream analysis. The downsampling to reduce the number of reads was performed using the “Donwsampling tool” in the Seven Bridges Genomics pipeline.
2.6
Single-cell data integration, clustering, and annotation
Single-cell clustering and annotation were performed using Seurat v5.0.1. A Seurat object of each sample was created using CreateSeuratObject() function without a minimum gene or counts filtering. First, samples from the same platform were merged and normalized and variable genes were detected using the SCTransform method [33]. Cell clustering was determined by shared nearest neighbour (SNN) modularity using 15 dimensions and a resolution of 0.3 and dimensional reduction was performed using RunUMAP() using 15 dimensions in all three platforms. Secondly, data from both platforms were integrated utilizing either reciprocal PCA (RPCA), canonical correlation analysis (CCA), Harmony [34] and Join PCS to identify anchors. RPCA integrated object with 5 anchors and normalized using SCTransform were used for further analysis. Cell identities were annotated using SingleR signatures based on the mouse cell type reference generated by the Immunologic Genome Project [35] and based on single cell reference mapping using the TransferData() function and the previously characterized PyMT tumour dataset as reference [30]. Silhouette score per cell at each resolution was calculated using silhouette() function from the cluster R package and represented as a boxplot per platform using ggplot2. Bar plots for comparing cluster abundance across platforms were generated using dittoSeq package and dotplots and boxplots using ggplot2. Gene markers of specific cell types or clusters were identified using FindMarkers() or FindAllMarkers() functions from Seurat using logfc.threshold = 1, min.pct = 0.3 and logfc.threshold = 0.25, min.pct = 0.25, respectively. To determine gene sets enriched in the upregulated genes of a specific cluster we used the enrichGO() function from the clusterProfiler package, p-value was adjusted using Benjamini-Hochberg formula and the q value cutoff set at 0.2. Gene expression correlation between different samples was calculated using the DESeq2 package. Cell communication analysis was performed using the CellChat R package [36].
2.7
Public data processing and analysis
10× Chromium and BD Rhapsody from bone marrow and whole blood gene expression and antibody matrices (BioProject ID: PRJNA734283) were downloaded and re-processed using Seurat v4.2.1. Filtered 10× cells based on the default settings of Cell Ranger or based on a custom threshold of Protein UMI counts per cell (>10) as Qi et al. [37] were first demultiplexed using the Hashtag oligo (HTO) libraries and the HTODemux() function from Seurat. Barcodes with more than one hashtag were discarded. Cells from the bone marrow and whole blood in each platform were merged, clustered, and visualised as explained above.
2.8
Ambient noise detection
The single-cell Ambient Remover (scAR) model was used to determine the gene expression profile of the empty droplets or wells representing the ambient noise, the ratio of ambient noise in each cell and to create a denoised matrix [38]. For the mouse datasets, for the setup_anndata() function, the whole list of barcodes selected were used as the unfiltered matrix and the putative cells based on either Cell Ranger of the BD Rhapsody pipeline for the filtered matrix, using a probability threshold of 0.995. Empty barcodes were used to calculate the ambient profile and train the model. For the human dataset from BD Rhapsody the noise ratio was calculated the same way than the mouse dataset for both the Gene expression and protein matrices. For the human datasets generated in 10× Chromium, the putative cells used in the setup_anndata() function were based on the manually filtering the UMI Protein counts (>10). DecontX was also used to detect ambient noise, named contamination in this package [39] and decontX function was run per sample without using the empty barcodes as background.
2.9
Statistical analysis
scRNAseq data was generated from 16 different tumours across 4 different mice. Specific statistical details for sample size and the threshold for statistical significance can be found either in the figure legend or in the method section. The data in bar graphs are presented as mean ± SEM, and the statistical analysis employed an unpaired t-test. Box plots display data by depicting the minimum and maximum values as whiskers, the median of the lower half (quartile 1), and the median of the upper half (quartile 3) as a box. The graphs and statistical analyses for scRNAseq were performed using R and for flow cytometry using GraphPad Prism.
2.1
Mouse model
The MMTV-Polyoma Middle T antigen (PyMT) was a gift from Dr. William J. Muller (McGill University) and its generation has been previously described [26,27]. At ethical endpoint, (10 % ± 3 % tumour/body weight, which approximately corresponds to 14-week-old animals) the mice were euthanized, and size-matched tumours were harvested and processed for single cell digestions. The tumour location nomenclature used is as follows: Tumour A - right cervical and/or thoracic mammary gland; tumour B - left cervical and/or thoracic mammary gland; tumour C - right abdominal and/or inguinal mammary glands; tumour D - left abdominal and/or inguinal mammary glands. Genotyping was performed at the Garvan Molecular Genetics facility (NATA accredited, ISO 17025) by PCR of DNA extracted from the mouse tail tip using the following primers: CGGCGGAGCGAGGAACTGAGGAGAG and TCAGAA GACTCGGCAGTCTTAGGCG. The touchdown PCR conditions were 94 °C for 10 s of initial denaturation, followed by 10 cycles of 94 °C 10 s, 65-55 °C for 30 s and 72 °C for 1 min and 10 s and then 31 cycles of 94 °C 10 s, 55 °C for 30 s and 72 °C for 1 min and 10 s; the final extension is 72 °C for 3 min. All animals used in this study are heterozygous for the PyMT gene.
All animal experiments were carried out according to guidelines contained within the NSW (Australia) Animal Research Act 1985, the NSW (Australia) Animal Research Regulation 2010 and the Australian code of practice for the care and use of animals for scientific purposes, (8th Edition 2013, National Health and Medical Research Council (Australia)). All experiments involving mice have been approved by the St. Vincent's Campus Animal Research Committee AEC #19/02.
2.2
Tissue digestion and single-cell isolation
PyMT tumours were digested as described in Refs. [29,30]. We aimed to have a minimum of two mice per condition and platform and a minimum of 2 mixed tumours per mice from different locations (Table S1 and S2). Briefly, tumours were manually dissected into 3–5 mm pieces using a surgical scalpel blade and further chopped to 100 μm using a tissue chopper (McIIwain). Samples were enzymatically digested with 15,000 U of collagenase (Sigma Aldrich Cat# C9891) and 5,000 U of hyaluronidase (Sigma Aldrich Cat# H3506) for 30 min at 37 °C. The samples were then further digested by pipetting up and down with 0.25 % trypsin (Gibco Cat# 15090-046), in 1 mM EGTA and 0.1 mg/mL of Polyvinyl alcohol dissolved in Dulbecco's phosphate-buffered saline (DPBS, Gibco Cat# 14190-250) for 1 min at 37 °C in a waterbath. Red blood cells were then lysed with 0.8 % ammonium chloride (Sigma Aldrich Cat# A9434) dissolved in water for 5 min at 37 °C. Single cell suspensions were washed with DPBS containing 2 % of Foetal Bovine Serum (FBS, GE Healthcare Cat# SH30406.02) and spun at 200×g for 5 min at 4 °C between each step. The supernatant was aspirated and 1 mg/mL DNase I (Roche Cat# 10104159001) was mixed with the sample before incubation with each step. Finally, cells were filtered through a 40 μm cell sterile strainer (Corning® Cat# 431750) and resuspended in DPBS with 2 % FBS. For the generation of low-quality-like samples, digested tumours were left overnight for 24 h at 4 °C, and check that viability was reduced by flow cytometry by at least 20 % before processing the sample in autoMACS® Pro (Miltenyi).
All tumours were labelled with Annexin specific MACS beads using the Dead Cell Removal Kit (Miltenyi Biotec Cat# 130-090-101) following the manufacturers’ instructions and dead cells were removed by passing the labelled cells through the autoMACS® Pro (Miltenyi) (see more details at [17]). All samples showed high percentage of viable cells (≥80 % viability assessed by DAPI in flow cytometry, Table S3). Samples for BD Rhapsody underwent additional steps prior to the cell capture for LMO multiplexing (see below and Fig. S1B). When all samples from both methods were ready for cell capture, high percentage of cell viability was again verified by microscopy with 0.4 % of Trypan blue solution (Sigma-Aldrich, Cat# T8154), to ensure that comparable cell viability was achieved from both single-cell capture methods (≥85 %).
2.3
Flow cytometry
The viability and cellular content of the main cell compartments of the tumours was assessed by flow cytometry. Digested tissue samples were washed with DPBS (Gibco Cat#14190136) supplemented with 2 % FBS (GE Healthcare Cat# SH30406.02) and centrifuged at 200×g for 5 min at 4°C. The pellet was then resuspended with 2 % FBS in PBS for use in flow cytometry analysis. For flow cytometry analysis, single cell suspensions were incubated with antibodies for anti-mouse EpCAM (BioLegend Cat#118205, RRID: AB_1134176) and anti-mouse CD45 (Clone 30-F11, BioLegend, Cat# 103114 RRID: AB_312979) on ice in the dark for 30 min before they were washed, centrifuged, and resuspended with 2 % FBS in DPBS for analysis using the BD FACSymphony™ Cell Analyser. To check viability, cells were stained with DAPI in a 0.5 μg/mL concentration (Invitrogen, D1306) at 4 °C for 3 min immediately before running the samples in the flow cytometer. Flow cytometry data were analysed using the software package FlowJo (version 10.4.2).
2.4
10× Chromium
We aimed to capture 8,000 cells with >85 % cell viability from each condition. Libraries were prepared using the Chromium Next GEM Single Cell 3ʹ Kit from 10× Genomics (Cat# PN-1000269) following the manufacturer's instructions. Sequencing was performed on an Illumina NovaSeq™ 6000 System (Illumina, Cat# 20012850) using the NovaSeq 6000 S4 Reagent Kit (200 cycles) (Illumina, Cat # 20028313) using the following configuration: 28bp for Read 1, 91bp for Read 2 and 8bp for Index, to an estimated depth of 20,000–30,000 reads per cell. Cell Ranger pipeline v3.0.1 was used for Fastq file generation, alignment to the mm10 (Release M19 (GRCm38.p6) transcriptome reference and UMI counting. Barcodes corresponding to empty droplets were excluded using cell calling algorithm from Cell Ranger based on EmptyDrops [31]. We used “subset” function from Seurat v5.0.1., for random cell subsampling.
2.5
BD Rhapsody
We adopted the MULTI-seq protocol [32] based on lipid-modified oligos (LMOs) for multiplexing different samples in the same BD Rhapsody cartridge (Fig. S1B). Single-cell suspensions from four different tumours were washed twice with DPBS (Gibco Cat#14190136) and incubated in a 200 nM solution containing equal amounts of anchor LMO (generously gifted by Prof. Gartner's laboratory) and sample barcode oligonucleotides (©Integrated DNA Technologies, IDT, Inc) on ice for 5min. After LMO-barcode labelling, we incubated the co-anchor LMO (gifted by Prof. Gartner's laboratory, final concentration of 200 nM) in each sample for 5min on ice. 1 mL of 1 % BSA (Sigma-Aldrich Cat#A9418) was added in each sample to quench the LMO binding and washed once with 1 % BSA. The tumour samples were pooled in equal proportions, washed twice with 1 % BSA and resuspended in the cold Sample Buffer (BD Rhapsody Cat. No. 650000062). Altogether, the multiplexing step added 30 min extra prior to the cell capture, which represents 11 % of the total time for sample processing (typically 4 h for 4 single-plex samples [29]). Two extra QC steps were performed to ensure that the multiplexing workflow did not compromise cell viability: 1) 0.4 % Trypan blue cell staining (Sigma-Aldrich, T8154) to make sure cell viability was comparable to the 10× samples (see above in Tissue digestion and single-cell isolation section), and then, 2) immediately before cells were loaded into a BD Rhapsody cartridge, a high percentage of cell viability was corroborated in the BD Rhapsody™ Scanner using Calcein AM (Thermo Fisher Scientific, Cat# C1430) and Draq7TM (Cat# 564904) in the pooled multiplexed samples (Table S3). Cell capture, cDNA synthesis and exonuclease I treatment were performed using the standard protocols from BD Rhapsody Express Single-Cell Analysis System Instrument User guide (Doc ID 214062).cDNA and LMO libraries were prepared using a custom protocol combining BD Rhapsody's mRNA whole transcriptome analysis (WTA) library preparation protocol (Doc ID 23-21711-00 Rev 7/2019) and the MULTI-seq protocol [32]. First, we performed 2 sequential random priming and extension (RPE) reactions of the Exonuclease I-treated beads containing the whole cellular transcriptome and LMO cDNA to increase assay sensitivity using half of the reagents for each round from the BD Rhapsody™ WTA Amplification Kit (Cat#633801). After this step we performed two parallel workflows, one for the WTA libraries, using the RPE product from the supernatant and the other one for the LMO libraries from the post-RPE beads. The RPE supernatant underwent WTA library preparation following the manufacturers' instructions from the BD Rhapsody™ System mRNA Whole Transcriptome Analysis (WTA) Library Preparation Protocol (Doc ID 23-21711-00 Rev 7/2019) that included the steps of purifying RPE product, performing RPE PCR, purifying the RPE PCR amplification product, performing WTA Index PCR and purifying the WTA index PCR product. The LMO libraries were obtained from the post-RPE beads, which were resuspended and washed in the cold Bead Resuspension Buffer (BD Rhapsody Cat. No. 650000062)and after three washes, the beads were resuspended in 80 μL of the MULTI-seq cDNA amplification mix containing 1 μL of 2.5 μM Multi-seq primer 5′-CTTGGCACCCGAGAATTCC-3′, 69 μL PCR Master Mix (Part# 91–118 from BD Rhapsody™ WTA Amplification Kit, Cat# 633801), 10 μL Universal Oligo (Part# 650000074 from BD Rhapsody™ WTA Amplification Kit, Cat# 633801) and the PCR was performed as follows: 95 °C, 3 min; 95 °C, 30 s, 60 °C, 1 min and 72 °C, 1 min for 14 cycles; 72 °C, 2 min; 4 °C hold. Next, we purified the PCR products of the LMOs using a 0.6× ratio of the SPRI beads (Beckman Coulter Cat# B23317) and keeping the supernatant, followed by a left side selection using 1.8× ratio. The LMO library was eluted in 69 μL of Elution Buffer (Part# 91–1084 from BD Rhapsody™ WTA Amplification Kit, Cat# 633801) and was quantified using Qubit (dsDNA HS Assay, Invitrogen™ Cat# Q32851) and the expected DNA size (∼95–115 bp) was confirmed with the Agilent 4200 TapeStation system (Agilent High Sensitivity D5000 ScreenTape Assay, Agilent Cat# 5067–5593 and 5067–5592). Next, we used 3.5 ng of LMO cDNA purified product to perform a LMO Index PCR, the PCR mix had 26.25 μl of 2× KAPA HiFi HotStart Ready mix (KAPABiosystems Cat# KK2601), 2.5 μl of 10 μM Library Forward Primer (Part# 91–1085 from BD Rhapsody™ WTA Amplification Kit, Cat# 633801), 2.5 μl of 10 μM Library Reverse Primer (reagent part from BD Rhapsody™ WTA Amplification Kit, Cat# 633801, a different primer was chosen for each sample for sequencing multiplexing); and the PCR conditions were 95 °C, 5 min; 98 °C, 15 min; 60 °C, 30 min; 72 °C, 30 min; 8 cycles; 72 °C, 1 min; 4 °C hold A final cleanup of the LMO libraries using a 1.6× SPRI bead ratio was performed before sequencing.
WTA libraries and LMO libraries were pooled and sequenced on the NovaSeq™ 6000 System (Illumina, Cat# 20012850) using the NovaSeq 6000 S1 Reagent Kit (100 cycles) (Illumina, Cat # 20028319) using the following configuration: 100bp for Read 1, 100bp for Read 2 and 8bp for Index to an estimated depth of 25,000 reads per cell for the WTA library and 2,500 reads per cell for the LMOs.
Fastq files from BD Rhapsody samples were processed using BD Rhapsody™ WTA Analysis Pipeline, v1.9.1 in Seven Bridges Genomics cloud platform. WTA reads were aligned to the mm10 reference genome (Release M19 (GRCm38.p6) and LMO sequences (8bp) were included as sample tags, allowing a maximum of 1 mismatch. The sample multiplexing option of the WTA Analysis pipeline was used to determine the sample of origin of each cell and singlets were used for downstream analysis. The downsampling to reduce the number of reads was performed using the “Donwsampling tool” in the Seven Bridges Genomics pipeline.
2.6
Single-cell data integration, clustering, and annotation
Single-cell clustering and annotation were performed using Seurat v5.0.1. A Seurat object of each sample was created using CreateSeuratObject() function without a minimum gene or counts filtering. First, samples from the same platform were merged and normalized and variable genes were detected using the SCTransform method [33]. Cell clustering was determined by shared nearest neighbour (SNN) modularity using 15 dimensions and a resolution of 0.3 and dimensional reduction was performed using RunUMAP() using 15 dimensions in all three platforms. Secondly, data from both platforms were integrated utilizing either reciprocal PCA (RPCA), canonical correlation analysis (CCA), Harmony [34] and Join PCS to identify anchors. RPCA integrated object with 5 anchors and normalized using SCTransform were used for further analysis. Cell identities were annotated using SingleR signatures based on the mouse cell type reference generated by the Immunologic Genome Project [35] and based on single cell reference mapping using the TransferData() function and the previously characterized PyMT tumour dataset as reference [30]. Silhouette score per cell at each resolution was calculated using silhouette() function from the cluster R package and represented as a boxplot per platform using ggplot2. Bar plots for comparing cluster abundance across platforms were generated using dittoSeq package and dotplots and boxplots using ggplot2. Gene markers of specific cell types or clusters were identified using FindMarkers() or FindAllMarkers() functions from Seurat using logfc.threshold = 1, min.pct = 0.3 and logfc.threshold = 0.25, min.pct = 0.25, respectively. To determine gene sets enriched in the upregulated genes of a specific cluster we used the enrichGO() function from the clusterProfiler package, p-value was adjusted using Benjamini-Hochberg formula and the q value cutoff set at 0.2. Gene expression correlation between different samples was calculated using the DESeq2 package. Cell communication analysis was performed using the CellChat R package [36].
2.7
Public data processing and analysis
10× Chromium and BD Rhapsody from bone marrow and whole blood gene expression and antibody matrices (BioProject ID: PRJNA734283) were downloaded and re-processed using Seurat v4.2.1. Filtered 10× cells based on the default settings of Cell Ranger or based on a custom threshold of Protein UMI counts per cell (>10) as Qi et al. [37] were first demultiplexed using the Hashtag oligo (HTO) libraries and the HTODemux() function from Seurat. Barcodes with more than one hashtag were discarded. Cells from the bone marrow and whole blood in each platform were merged, clustered, and visualised as explained above.
2.8
Ambient noise detection
The single-cell Ambient Remover (scAR) model was used to determine the gene expression profile of the empty droplets or wells representing the ambient noise, the ratio of ambient noise in each cell and to create a denoised matrix [38]. For the mouse datasets, for the setup_anndata() function, the whole list of barcodes selected were used as the unfiltered matrix and the putative cells based on either Cell Ranger of the BD Rhapsody pipeline for the filtered matrix, using a probability threshold of 0.995. Empty barcodes were used to calculate the ambient profile and train the model. For the human dataset from BD Rhapsody the noise ratio was calculated the same way than the mouse dataset for both the Gene expression and protein matrices. For the human datasets generated in 10× Chromium, the putative cells used in the setup_anndata() function were based on the manually filtering the UMI Protein counts (>10). DecontX was also used to detect ambient noise, named contamination in this package [39] and decontX function was run per sample without using the empty barcodes as background.
2.9
Statistical analysis
scRNAseq data was generated from 16 different tumours across 4 different mice. Specific statistical details for sample size and the threshold for statistical significance can be found either in the figure legend or in the method section. The data in bar graphs are presented as mean ± SEM, and the statistical analysis employed an unpaired t-test. Box plots display data by depicting the minimum and maximum values as whiskers, the median of the lower half (quartile 1), and the median of the upper half (quartile 3) as a box. The graphs and statistical analyses for scRNAseq were performed using R and for flow cytometry using GraphPad Prism.
3
Results
3.1
Experimental design and data processing
Our comparison is achieved by using mammary gland tumours from the widely used Polyoma Middle T antigen (PyMT) transgenic mouse model [26]. These tumours present multiple lineages and high cell diversity while retaining reproducibility due to the limited variability from a genetically controlled congenic mouse strain (FVBn) [30,40,41]. In addition, tumours are biologically challenging samples, as they are not homeostatic tissues, and present tissue damage, hypoxia or cell death; features that will put to the test any scRNAseq method. Tumour fragments from the 4 different mice (4 tumours per mouse, Table S1) were randomized, digested and enriched for viable tumour cells using Magnetic Associated Cell Separation (MACS) as previously described [29]. Single cell suspensions containing viable cells (≥85 %, Table S3) were loaded into a 10× Chromium, or multiplexed using lipid-modified oligos (LMOs) and then loaded into BD Rhapsody chip (Tables S1 and S3 and Figs. S1A and B) and run following the corresponding molecular workflows (see Methods section, Figs. S1A and S1B).
Each platform has a different read structure for barcodes and UMIs and each system has a different processing bioinformatic pipeline. Therefore, to replicate the standard user experience, we have used the intended pipeline for each platform to generate a Digital Gene Expression (DGE) matrix per cell: cellranger for 10× Chromium and the whole transcriptome analysis (WTA) pipeline in Seven Bridges Genomics cloud platform for BD Rhapsody. In all cases, data was subsequently integrated and visualised using the Seurat R package v5.0.1 [42]. A critical step for single-cell data processing is to determine how many real cell barcodes were detected. 10× Chromium's pipeline, Cell Ranger (>v3.0) and BD's pipeline in the Seven Bridges Genomics platform, now include a step that detects putative cells. Therefore, we filtered out empty barcodes based on the default parameters of each platform. Doublets were excluded using DoubletFinder [43] in the case of 10× Chromium, while for BD Rhapsody we used the information from the sample tags used to demultiplex tumour samples. After removing the empty droplets and doublets, we obtained 10,713 cells in 10× Chromium, and 6,363 for BD Rhapsody (Table S1). For BD Rhapsody we had 4 sample tags, but unfortunately one of the LMOs was not detected and thus that sample had to be excluded from the study (Fresh 1 BD Rhapsody, see details in Table S1) as we could not distinguish the cells from this sample from the untagged cells from the other samples. From the 3 remaining BD Rhapsody LMO-tagged samples and after demultiplexing the number of singlets detected was 1,434 and 1,611 cells, while the third one had 713 cells. A total of 5,315 cells and 4,941 singlets were identified in 10× Chromium from the two replicates. To keep a consistent sequencing depth per cell in our downstream technical and cell type comparisons (Fig. 1, Fig. 2, Fig. 3, Fig. 4), we selected the two replicates with the highest number of cells from BD Rhapsody (Fresh 2, 1,434 cells and Fresh 3, 1,611 cells) and the two existing replicates from 10× Chromium (Fresh 1, 5,315 and Fresh 2, 4,941 cells) and subsampled the later to obtain around 4,000 cells as well as downsampled the sequencing depth of BD Rhapsody to reach around similar sequencing depth per cell (∼24,000 reads). This resulted in 2,093 and 1,907 cells from 10× Chromium that were compared with 1,434 and 1,611 cells from BD Rhapsody at similar sequencing depth per cell (Table S1).Fig. 1Measurements of the technical quality, sensitivity and reproducibility among scRNAseq platforms. A. Violin plots showing the number of UMIs (nCounts), and genes (nGenes) detected per cell, as well as the percentage of mitochondrial content (%Mito) per cell in 10× Chromium (red) and BD Rhapsody (blue) each using two biological replicates (r1, r2). B. Overlap between the top 3000 most variable genes in each platform. C. Principal component (PC) plot showing the total variability identified in each system measured as the standard deviation at increasing PCs. D. Cluster tree showing the phylogenetic relationship of clusters in each platform at increasing resolutions. E. Box plot of the Silhouette scores (y axis) between the 10× Chromium (red) and BD Rhapsody (blue) samples at different resolutions (x axis). The silhouette ranges from −1 to +1, where a high value indicates that the object is well matched to its own cluster and poorly matched to neighbouring clusters. F. UMAP plots showing the overlap between two replicates in each platform, the “r” corresponds to the Pearson correlation coefficient of gene expression between replicates.Fig. 1Fig. 2Identification of the main cell lineages in tumours by each scRNAseq platform. A UMAP plot of all integrated samples using the Reciprocal Principal Component Analysis (RPCA) method with 5 anchors. B. UMAP plot showing the three main cell compartments: epithelial, immune and stroma cells identified in all cells from all replicates and platforms after integration. C. Proportion of cells from each main cell lineage. The box plot represents the relative proportion of each cell lineage identified by flow cytometry, EpCAM+ (epithelial cells), CD45+ (leukocytes), and EpCAM−/CD45− (stroma), and the colour dots represent each of our samples from the single cell RNAseq dataset. D. UMAP plots showing the cell types assigned using label transfer analysis of the PyMT reference scRNAseq previously published [30], and split based on the platform. E. Bar plot of the proportion of cells from each cell type per sample (left) and magnified and excluding the epithelial cells (right). F. Cell type relative proportions of each cell type per sample. The box plot represents the proportions found in the PyMT reference scRNAseq [30] and the colour dots represent each of our samples from our single cell dataset. G. Dot plot showing the gene expression comparison of the top cell lineage marker genes between platforms. H. Heatmaps of the Pearson Correlation coefficient between 10× Chromium and BD Rhapsody samples (r1 and r2) in the different annotated cell types.Fig. 2Fig. 3Comparison of cell-cell communication prediction in each platform. A. Bar plot showing the overall number of putative interactions (left) and predicted interaction strength in each platform (right). B. Circle plot visualizing the differential cell–cell interaction networks predicted by each platform. In the left panel, the thickness of the line represents the number of differential interactions predicted between the connecting cell types, while in the right panel, the width of the line corresponds to the differential strength of the interaction by assessing the level of expression of the ligand-receptor pairs after correcting the differences in cell type abundance. Blue lines represent higher number or stronger predicted interactions in BD Rhapsody while red lines correspond to predicted interactions enriched in 10×. C. Heatmaps representing the relative signalling strength of the signalling pathways across platforms. The top grey bar plots show the total signalling strength of each signalling pathway combining all cell types, and the coloured bar plots on the left show the total signalling strength of each cell type combining all pathways.Fig. 3Fig. 4Cancer-epithelial and fibroblast subtype detection across platforms. A. UMAP projections of epithelial cells showing the unsupervised KNN clusters split by platform. B UMAP plot showing the epithelial cell types based on the annotated PyMT reference [30]. (LP: luminal progenitor; HS: Hormone sensing) C. Bar plot of the proportion of cells from each epithelial cell type per sample. D. Gene Set Enrichment Analysis of the marker genes from cluster 3 (A) versus the rest of the luminal progenitor cells (GO: Gene Ontology. BP: Biological Processes). E. UMAP visualization of the number of genes (n_Genes) detected and the percentages of mitochondrial genes (% Mito) per cell in each platform. F. UMAP projections showing the fibroblast subtypes divided by platform. (iCAF: immune cancer-associated fibroblasts; ECM_CAFs: extracellular-matrix synthesis cancer-associated fibroblasts). G. Bar plot representation of the proportion of each fibroblast subtype per sample. H. Violin plots for the expression of marker genes for ECM CAFs (top), iCAFs (middle) and myofibroblasts (bottom) comparing each fibroblast subtypes between 10× (red) and BD Rhapsody (blue) platforms.Fig. 4
3.2
Technical comparison: assessing sensitivity and reproducibility
An important quality metric for scRNA-seq is the sensitivity of gene and UMI detection per cell. This sensitivity metric was different between the platforms (Fig. 1A); we detected a median of 2,995.5 and 2,791 genes, and 10,513.5 and 9,880 UMIs per cell in 10× Chromium and BD Rhapsody, respectively. Based on this analysis 10× Chromium and BD Rhapsody had a similar UMI and genes counts per cell at an equivalent number of reads per cell (Table S1). The proportion of mitochondrial genes is another metric commonly used to assess cell quality. Here we found that cells processed with 10× Chromium have significantly less mitochondrial content than BD Rhapsody samples, where 10× had a median of 6.5 % of mitochondrial transcripts and BD Rhapsody 15.3 % (Fig. 1A, right plot).
An essential application for scRNAseq is to define single-cell phenotypes by their gene expression signature through dimensional reduction, principal component analysis (PCA), and clustering algorithms [44]. We used the top 3,000 variable genes for dimensional reduction analysis in both platforms, noting that the variable genes detected in each platform were slightly different, with 2,140 (71.3 %) common genes (Fig. 1B). The total variability identified in the system, measured as the standard deviation at increasing principal components (PCs) had similar standard deviations per PC between platforms (Fig. 1C). When we examined the clustering capabilities at increasing resolutions, we found that at the lowest resolution, only 10× Chromium was able to split the cells into two clusters suggesting that their detected transcriptome was more distinct; moreover, at increasing resolutions (>0.1), we found that 10× had a greater number of clusters and more consistent cluster composition than BD Rhapsody (Fig. 1D). Statistical analysis using Silhouette confirmed that at resolutions higher than 0.3, Chromium 10× data had slightly higher silhouette score, suggesting that cells were more cohesive with their own cluster and more different to the cells from other clusters in 10× (Fig. 1E). In conclusion, BD Rhapsody and 10× Chromium had similar gene variability but 10× Chromium outperformed BD Rhapsody at consistently assigning cells to cluster at increasing resolutions.
Finally, to assess the reproducibility of each platform, we looked at the correlation of gene expression between replicates (Fig. 1F). We found that both 10× Chromium and BD Rhapsody had over 0.99 correlation values confirming high reproducibility for both platforms.
3.3
Cell type identification and lineage biases between platforms
To directly compare the performance of each platform to resolve tumour heterogeneity, we merged (Fig. S1C) and integrated (Fig. 2A and S1C) the data from the two platforms into one Seurat object. For the integration, we tested canonical Reciprocal PCA integration (RPCA) (Fig. 2A), correlation analysis (CCA), Join Principal Components Space (PCS) and Harmony Integration [34] (Fig. S1D). We found a similar level of integration using all four methods. RPCA integration has been reported to be less prone to overcorrection [45], therefore, we used this approach for the downstream analyses.
For cell type identification, first, we annotated the main cell lineage compartments in PyMT tumours (epithelial, immune and stroma) in the integrated object (Fig. 2B) and we compared their relative proportion in each platform to the measured proportion of these main cell types for PYMT tumours by flow cytometry, as it is the current gold standard method for cell type annotation and validation (Fig. 2C). Overall, we found similar proportions between scRNAseq and flow cytometry, but the samples analysed through 10× Chromium had a slightly higher number, but consistent between replicates, of stroma cells and fewer epithelial cells. Next, we assigned each cell cluster to the main cell types based on SingleR [35] (Figs. S2A and B) and further refined based on label transfer of the cell types from our previously published PyMT tumour reference [30] (Figs. S2C–S2E). We found that all major cell types: B cells, T cells, myeloid cells, endothelial cells, epithelial cells, fibroblasts and myofibroblasts were detected in both platforms (Fig. 2D), with similar proportions of cell types between replicates and platforms, except for endothelial cells and myofibroblasts (Fig. 2E). BD Rhapsody identified a lower percentage of endothelial cells, from 1 % of the total cells in BD Rhapsody compared to 9-6% in 10× Chromium, and myofibroblasts, 10 times less detected in BD Rhapsody compared to 10× Chromium. Consistently, the relative proportion of endothelial cells and myofibroblasts in BD Rhapsody deviated from the expected inter-tumour variability in the PyMT tumour scRNAseq reference atlas across eight tumours [30], suggesting that these differences were not a consequence of tumour heterogeneity (Fig. 2F). Of note, the proportion of fibroblasts was very variable between 10×/BD Rhapsody and the PyMT tumour reference atlas this could be due to a technical bias of the Drop-seq platform used in this atlas [30]. We further confirmed the consistency of cell annotation across platforms by looking at the top markers of each cell type and confirmed that these markers were similarly expressed in all cells captured in both platforms (Fig. 2G) We also confirmed a high correlation between platforms when we compared the gene expression differences of the same cell types (Pearson correlation coefficient >0.9, Fig. 2H).
To determine how the different cell population ratios, gene marker dropout or ambient noise can affect downstream analysis, we performed cell-to-cell communication predictions using the CellChat software on these two platforms independently. We found that overall BD Rhapsody detected a larger number of predicted interactions and higher predicted interaction strength (Fig. 3A). In line with the number of cells detected, BD Rhapsody predicted fewer and weaker interactions between myofibroblasts with epithelial cells or fibroblasts and between endothelial and epithelial cells (Fig. 3B, red lines), however, it detected more predicted interactions between most of the cell line pairs and it had stronger putative interactions within the epithelial compartment, between the fibroblast and the epithelial cells and between myeloid cells and epithelial or fibroblast cells (Fig. 3B, blue lines). CellChat analyses also revealed that some signalling genes were missing in BD Rhapsody such as PTN and OCLN in the epithelial cells, NT and SMA7 in fibroblasts and APELIN in endothelial cells, while 10× did not detect 16 genes related to cell-to cell communication pathways including ITGAL, CD45 and BAFF from myeloid cells, CLEC, CD48 and CD137 from B cells or ICOS and CD86 from B cells (Fig. 3C). Overall BD Rhapsody seems to predict more cell-cell interactions and as expected, changes in cell type proportions had an impact on the predicted interaction strength detected with those cell types.
In summary, 10× Chromium detected more endothelial cells and myofibroblasts, which resulted in different levels of cell-cell interactions predicted with those cell types in each platform, however overall, the genes identify in BD Rhapsody per cell subtype were better on predicting cell to cell interactions, as a higher number and stronger putative interactions were detected.
3.4
Cell subtype proportions across platforms
To further evaluate how each platform can distinguish among cellular subtypes within the main cell types in tumours, we performed subclustering of some of these main cell types. First, we selected the most abundant cell type in PyMT tumours, the epithelial compartment; unsupervised clustering revealed visual differential distribution between platforms in some cell clusters including clusters 1 and 3 out of the 7 clusters (Fig. 4A and S3A). To infer the identity of each cell subcluster, we again used the scRNAseq PyMT tumour atlas [30] of the epithelial cell subtypes in the integrated epithelial cluster (Figs. S3B and S3C), which resulted in the annotation of 8 epithelial subtypes manually (Fig. 4B). As expected in this transgenic mouse model [[26], [27], [28]], most cells were classified as luminal progenitors (LP), including a subset of hormone-sensitive luminal progenitor (LP-HS) cells where no major differences were observed between platforms (Fig. 4C). Interestingly, the cell clusters where we visually observed differences (Fig. 4A) were classified as basal (cluster 1) and luminal progenitor/luminal hormone-sensing (LP/LP-HS) (cluster 3) (Fig. 4B and C). Cluster 3, or LP/LP-HS cells, was missing from the PyMT reference (previously done using Drop-seq) and was heavily comprised of cells from the 10× Chromium platform. Gene set enrichment analysis identified that this 10x-specific luminal progenitor cluster was enriched with genes involved in immune responses, including antiviral response and interferon (Fig. 4D). This suggests that the 10× Chromium platform detects more epithelial cells that are actively interacting with the immune system. Interestingly, basal cells (cluster 1) were almost undetected in BD Rhapsody (3 cells detected), while in the 10× platform comprised around 2 % of the epithelial compartment. We also identify differential proportion of cells between platforms in the Multi/Stem cluster (Fig. 4C) with the highest fraction found in the BD Rhapsody replicates. Even though, the gene expression profile of the cluster 7 correlated with the multipotent/stem cell type (Figs. S3B and C), we found that these clusters also had the highest percentage of mitochondrial gene content, and the lowest number of genes detected in both platforms (Fig. 4E), potentially suggesting damaged cells. Considering cells with high mitochondrial content but low number of genes detected as the definition of damaged cells, the 10× Chromium platform had the lowest ratio of damaged cells (Fig. 1A), which may explain the absence of these cells and suggest that these are damaged cells.
Next, we did the same analysis in a less abundant population, fibroblasts. We again assigned cell types based on the fibroblast subsets in scRNAseq PyMT tumour reference [30] (Fig. 4F) and found that within the fibroblast partitions, there were three clusters that correlated with either myofibroblasts or secretory cancer-associated fibroblasts (CAFs) which were subdivided into extracellular-matrix synthesis CAFs (ECM-CAFs) and inflammatory CAFs (iCAFs) (Fig. 4F). The 10× Chromium platform detected a lower percentage of inflammatory CAFs, while the BD Rhapsody platform detected fewer ECM-CAFs and myofibroblasts (Fig. 4G). In this context, the expression of key markers of the inflammatory CAFs, like Ly6c1 and C4b, was higher in BD Rhapsody, while the myoepithelial markers Acta2, Mylk and Myh11 were higher in the 10× data (Fig. 4H).
Together these data suggest that, even though all major cell types were represented in both platforms, there are still cell type detection biases intrinsic to each platform which should be considered when analysing the tissue heterogeneity.
3.5
Ambient noise comparison between 10× chromium and BD Rhapsody in high and low-quality samples
One of the limitations of single-cell data is the technical noise caused by the ambient RNA contamination [39], amplification bias during library preparation and index swapping during sequencing [46]. The nature of the cell capture method (microwell or oil droplet) and the differences in the molecular workflows for RNA amplification in each platform (Fig. S1A) could result in a differential origin of the technical and/or ambient noise. Ambient RNA is defined as the mRNA molecules that have been released from dead or stressed cells and are part of the cell suspension. The ambient noise is especially present in samples that require tissue digestion and in low-quality samples, such as tissues stored for an extended period before processing, or tissues with active cell dead or hypoxia regions like tumour tissues.
Our data suggest that noise might be handled differently by the commercial platforms (Fig. 1, Fig. 4E). Thus, we studied how these two commercial platforms, performed with the technical noise from challenging samples. To recreate this “low quality sample”, we incubated PyMT digested tumours for 24 h at 4 °C. After 24 h, cell viability dropped 20 % compared with the freshly digested sample as measured by DAPI positive cells in flow cytometry, indicating a substantial cell decay (Fig. 5A, S4 and Table S3). As done before with fresh PyMT tumour samples, we removed dead cells using autoMACS® Pro and processed the samples for scRNAseq using the 10× Chromium or BD Rhapsody platforms. Cell viability was checked before and after autoMACS® Pro, and even damaged cells had a viability of ≥85 % before running the single cell RNAseq experiments, reflecting a real-life scenario.Fig. 5Analysis of the ambient noise in challenging samples in 10× Chromium and BD Rhapsody. A. Bar plot showing the percentage of cell viability measured by flow cytometry as the percentage of DAPI negative cells in digested fresh tumours (Fresh) and simulated low-quality tumour samples (24 h). B. Line plot showing the changes in percentage of cells for each cell type between fresh tumours and low-quality tumours (24 h) measured from the scRNAseq data. C. Violin plots showing noise ratio by scAR (left) and contamination ratio by DecontX per cell in each condition. Statistical analysis of the comparisons between fresh and low-quality (24 h) samples (black) or between fresh 10× and fresh BD (brown) was performed using paired t-test adjusted with the Bonferroni method, ****adjusted p-value<0.001; ns = not significant. D. Violin plots split by cell type of the noise ratio by scAR (left) and the contamination ratio by DecontX (right) detected in each cell. Statistical analysis of the noise ratio comparisons per cell type between fresh and low quality (24 h) samples in each platform (black) or between fresh 10× and fresh BD (brown) was performed using paired t-test adjusted with the Bonferroni method, **** adjusted p-value <0.001; ns = not significant. One-way ANOVA was also performed to compare the noise ratio distribution across all cell types in the fresh samples in 10× Chromium (p-value = 0.4903), or BD Rhapsody (p-value = 2.2e-16). E. Dot plot showing average expression of the top gene markers of each cell type using the integrated raw data or denoised (scAR) or decontaminated (DecontX) gene expression matrix.Fig. 5
First, we compared the tissue heterogeneity of the damaged samples to the fresh cells. As for this analysis, we increased the number of cells to ∼4000 cells per condition (Table S2), which allowed us to further split the myeloid cell compartment into neutrophils and macrophages. We found that endothelial cells, fibroblasts and myofibroblasts were consistently lost on the damaged sample regardless of the platform (Fig. 5B).
Next, to assess the technical noise found in each platform, we used the single-cell Ambient Remover (scAR) method, a universal model to detect noise across single-cell platforms based on probabilistic deep learning of the ambient signal [38]. Interestingly, around 5 % of cells in both platforms and in any condition (fresh or damaged) were considered empty cells as their transcriptome was indistinguishable from the ambient signal of the empty barcodes (Fig. S5A). Those assigned “empty cells” also had a high percentage of mitochondrial content and low gene content (Fig. S5B) and therefore confirming that these assigned “cells” were instead damaged cells that needed to be removed from downstream analysis. When we compared the noise ratio of the remaining cells, we found that BD Rhapsody had significantly more ambient noise per cell than 10× Chromium (Fig. 5C, left plot Fresh samples), however, the ambient contamination (noise ratio) was significantly increased in the damaged samples (24 h) in the 10× Chromium but not in BD Rhapsody (Fig. 5C, left plot). To further explore the origin of ambient RNA, we used an additional decontamination tool, DecontX, a Bayesian method to estimate and remove contamination in individual cells [39] without modelling the noise from the empty droplets, but from each putative cell. Fig. 5C right plot, shows that the differences in the contamination ratio were less pronounced between platforms than with scAR. Altogether the ambient RNA has different origins, empty droplets and from ambient RNA from putative cells and each platform handles this noise differently, with BD Rhapsody having more noise in empty droplets (noise) while the ambient RNA in the putative cells (contamination) is comparable between platforms.
We also confirmed our previous analyses of a higher percentage of mitochondrial content in Rhapsody (Fig. 1A), and as expected, in the damaged samples the mitochondrial content increased similarly in both platforms (Fig. S5C, left plot). The number of genes found per cell decreased in the damaged samples compared to their fresh counterpart; however, the decrease on the number of genes was more pronounced with BD Rhapsody (Fig. S5C, right plot). We explored how a standard filtering based on mitochondrial content and number of genes would perform to denoise the data. As the overall mitochondria content per cell was different per platform (Fig. 1A and S5C), we used the threshold based on the percentiles of each platform, cells with more than the 25 percentile of mitochondria content and less than the 10 % percentile of number of genes per cells were labelled as low-quality control (lowQC) (Figs. S5D and S5E). Even though, this stringent threshold removed the majority of “empty cells” (Fig. S5F) this type of filtering does not clean the ambient RNA contamination per cell type (Fig. S5G vs Fig. 5G). In fact, the noise ratio does not correlate well with either the percentage of mitochondria genes (r = 0.33), or the number of genes (r = −0.12) (Fig. S5H).
Next, to evaluate which cell lineages are most affected by the technical noise in each platform, we compared the noise ratio per cell type and platform in each sample condition, using both scAR and DecontX (Fig. 5D and E). The analysis with scAR, showed that in BD Rhapsody all cell types had a similar level of noise, regardless of their damaged condition, while 10× Chromium's noise was notably different between cell types and there were significant differences between fresh and damaged conditions (Fig. 5D, left plot). Particularly, in 10× Chromium, neutrophils had the highest percentage of noise which increased to an even higher ratio in the damaged samples. As expected, neutrophils also had the lowest number of genes detected especially in Chromium 10× (Fig. S6A), independently of sequencing depth (Fig. S6B). To understand which cell types and genes are driving the ambient signal, we analysed the expression across cell types of the top genes contributing to the ambient pool based on scAR analysis (Fig. S6C). Remarkably, in 10× Chromium, the ambient expression of the S100a9 neutrophil marker was >25-fold times higher than any other cell type marker and was even higher in the damaged samples, despite the low number of neutrophils identified in the sample; while in BD Rhapsody the ambient gene expression was more uniformly distributed and did not show major differences between fresh and damaged sample (Fig. S6C). To confirm the neutrophil biases from 10× Chromium in the ambient profile, we correlated the average transcriptome of the single cells with the ambient gene expression and observed a higher presence of neutrophil marker genes in ambient RNA than in the single cells in 10× Chromium, but not in BD Rhapsody (Fig. S6D). Interestingly, the same cell type analysis for the distribution of the ambient RNA calculated using only putative cells with DecontX did not show major cell type biases either in 10× Chromium or BD Rhapsody, and less pronounced differences between platforms with myofibroblasts showing the highest differences in contamination between platforms, where BD Rhapsody had higher contamination (Fig. 5D, right plot).
The scAR and DecountX algorithms can denoise the count matrix based on the ambient composition. When we used the denoised data using scAR to resolve cell type clusters, we found that clusters are more distinct using denoised data and there is less background expression of non-specific markers compared to the raw data (Fig. 5E) or the data processed with standard QC filtering (Fig. S5G). As expected, the expression of the neutrophil markers was completely lost in the 10× Chromium data in this cell subtype (Fig. 5E) The decontaminated data using DecontX on the other hand maintained the neutrophil marker however had a comparable non-specific background than the raw data or QC filtering (Fig. 5E and S5G). This dichotomy may be explained by the different approaches used by the two ambient RNA removal tools. For scAR, we modelled ambient RNA based on the empty barcodes and putative cells, while for DecontX we measured ambient RNA using only putative cells (“in cell”) and based on the expression of key gene markers in other cells populations. Therefore, this suggests that the signature of neutrophils mainly comes from droplets that have captured neutrophils but due to its in-drop degradation the amount of RNA detected in the sequencing is very low and thus considered an empty droplet. This explanation also supports the fact that when the empty droplets are not considered for ambient RNA calculations, DecontX, all cell types have a similar contribution to the noise between platforms (Fig. 5C right plot).
In conclusion, the differential molecular design, the microfluidic versus microwell format and the sample quality are sources of the ambient noise. In BD Rhapsody there is a generalised level of noise that is independent of the cell of origin and sample quality, however there is some bias towards myofibroblast specially in low-quality samples; while in 10× Chromium, ambient noise is cell type-specific and overrepresented by the neutrophil population specially in empty droplets suggesting in droplet neutrophil-specific RNA degradation.
3.6
Cell type bias noise validation on public datasets
To determine if the cell type biases and technical noise differences between 10× Chromium and BD Rhapsody were universal, we assessed another two public data sets, human whole blood and bone marrow, where more neutrophils are expected, and both platforms were used [37]. This data was processed using CITEseq (10× Chromium) and Ab-Seq + whole transcriptome analysis (BD Rhapsody) and therefore it also included 30 antibody-derived barcodes in both datasets. Additionally, hashtag oligos (HTO) were used in 10× Chromium platform to demultiplex samples.
We first compared the abundance of each cell type detected based on the antibody markers in both platforms using the default parameters for filtering established by Cell Ranger and the Rhapsody pipelines (Fig. 6A). Interestingly, the whole blood and the bone marrow samples processed with 10× had virtually no granulocytes (neutrophils, basophils, or eosinophils) (Fig. 6B). This is in line with previous reports of a low number of neutrophils in human datasets detected using this platform [47,48]. Interestingly, if we filtered the 10× Chromium cell barcodes using the custom threshold based on the Protein UMI counts per cell as Qi et al. [37], we recovered 5,320 and 7,702 cells, in the bone marrow and whole blood samples, respectively, from which the majority were neutrophils (Fig. 6C). In addition, using this custom filtering for all samples, resulted in very similar relative proportions of each cell type in the bone marrow and whole blood between the 10× and BD Rhapsody platforms (Fig. 6D). However, even though the UMAP using the protein matrix was able to distinguish all major cell types in 10× Chromium (Fig. 6E), the low gene sensitivity in the granulocyte population, with a median of fewer than 100 counts per cell (Fig. 6F), did not manage to further resolve the granulocyte clusters at the transcriptome level (Fig. 6F and 6E right panel). In fact, when we run the scAR algorithm using the threshold for filtering cells based on Protein UMI counts, we found that in 10× Chromium some granulocytes, especially eosinophils, still had a higher RNA noise ratio, although not as pronounced as in our mouse dataset in comparison with other cell types (Fig. 6, Fig. 5D). Overall, the noise ratio from both the RNA and the protein data was more similar across cell types in BD Rhapsody than in 10× Chromium, confirming that in BD Rhapsody the ambient noise is not cell type specific (Fig. 6G). In summary, 10× Chromium was unable to detect granulocytes in human samples based exclusively on their RNA content, and their low gene detection hinders the denoising algorithms to distinguish empty droplets from real cells with low UMI counts.Fig. 6Performance evaluation for the detection of human granulocytes between 10× Chromium and BD Rhapsody. A. UMAPs of cell types found in human whole blood and bone marrow samples processed with 10× Chromium or BD Rhapsody using the transcriptome for dimension reduction. Cell barcodes were filtered using either Cell Ranger or BD Rhapsody pipelines. B. Bar plot showing the number of cells found in each cell type per condition. C. Bar plot showing the number of cells found in each cell type in 10× Chromium after custom filtering based on a minimum of 10 Protein UMI counts. D. Bar plot of the relative proportion of cells from each cell type per condition using the same custom threshold than panel C for 10× Chromium. E. UMAP plots of the human datasets processed with 10× Chromium and filtered based on custom threshold where the dimensional reduction was done using either the protein data (left) or the RNA data (right) F. Violin plots of the number of RNA and Protein UMI counts in each cell split by cell type. The red dotted line highlights a 100 counts threshold. The y axis is shown in logarithmic scale. G. Noise ratio in each cell using either from the RNA (top) or the protein (bottom) data matrix and split by cell type. (BM: bone marrow; WB: whole blood).Fig. 6
3.1
Experimental design and data processing
Our comparison is achieved by using mammary gland tumours from the widely used Polyoma Middle T antigen (PyMT) transgenic mouse model [26]. These tumours present multiple lineages and high cell diversity while retaining reproducibility due to the limited variability from a genetically controlled congenic mouse strain (FVBn) [30,40,41]. In addition, tumours are biologically challenging samples, as they are not homeostatic tissues, and present tissue damage, hypoxia or cell death; features that will put to the test any scRNAseq method. Tumour fragments from the 4 different mice (4 tumours per mouse, Table S1) were randomized, digested and enriched for viable tumour cells using Magnetic Associated Cell Separation (MACS) as previously described [29]. Single cell suspensions containing viable cells (≥85 %, Table S3) were loaded into a 10× Chromium, or multiplexed using lipid-modified oligos (LMOs) and then loaded into BD Rhapsody chip (Tables S1 and S3 and Figs. S1A and B) and run following the corresponding molecular workflows (see Methods section, Figs. S1A and S1B).
Each platform has a different read structure for barcodes and UMIs and each system has a different processing bioinformatic pipeline. Therefore, to replicate the standard user experience, we have used the intended pipeline for each platform to generate a Digital Gene Expression (DGE) matrix per cell: cellranger for 10× Chromium and the whole transcriptome analysis (WTA) pipeline in Seven Bridges Genomics cloud platform for BD Rhapsody. In all cases, data was subsequently integrated and visualised using the Seurat R package v5.0.1 [42]. A critical step for single-cell data processing is to determine how many real cell barcodes were detected. 10× Chromium's pipeline, Cell Ranger (>v3.0) and BD's pipeline in the Seven Bridges Genomics platform, now include a step that detects putative cells. Therefore, we filtered out empty barcodes based on the default parameters of each platform. Doublets were excluded using DoubletFinder [43] in the case of 10× Chromium, while for BD Rhapsody we used the information from the sample tags used to demultiplex tumour samples. After removing the empty droplets and doublets, we obtained 10,713 cells in 10× Chromium, and 6,363 for BD Rhapsody (Table S1). For BD Rhapsody we had 4 sample tags, but unfortunately one of the LMOs was not detected and thus that sample had to be excluded from the study (Fresh 1 BD Rhapsody, see details in Table S1) as we could not distinguish the cells from this sample from the untagged cells from the other samples. From the 3 remaining BD Rhapsody LMO-tagged samples and after demultiplexing the number of singlets detected was 1,434 and 1,611 cells, while the third one had 713 cells. A total of 5,315 cells and 4,941 singlets were identified in 10× Chromium from the two replicates. To keep a consistent sequencing depth per cell in our downstream technical and cell type comparisons (Fig. 1, Fig. 2, Fig. 3, Fig. 4), we selected the two replicates with the highest number of cells from BD Rhapsody (Fresh 2, 1,434 cells and Fresh 3, 1,611 cells) and the two existing replicates from 10× Chromium (Fresh 1, 5,315 and Fresh 2, 4,941 cells) and subsampled the later to obtain around 4,000 cells as well as downsampled the sequencing depth of BD Rhapsody to reach around similar sequencing depth per cell (∼24,000 reads). This resulted in 2,093 and 1,907 cells from 10× Chromium that were compared with 1,434 and 1,611 cells from BD Rhapsody at similar sequencing depth per cell (Table S1).Fig. 1Measurements of the technical quality, sensitivity and reproducibility among scRNAseq platforms. A. Violin plots showing the number of UMIs (nCounts), and genes (nGenes) detected per cell, as well as the percentage of mitochondrial content (%Mito) per cell in 10× Chromium (red) and BD Rhapsody (blue) each using two biological replicates (r1, r2). B. Overlap between the top 3000 most variable genes in each platform. C. Principal component (PC) plot showing the total variability identified in each system measured as the standard deviation at increasing PCs. D. Cluster tree showing the phylogenetic relationship of clusters in each platform at increasing resolutions. E. Box plot of the Silhouette scores (y axis) between the 10× Chromium (red) and BD Rhapsody (blue) samples at different resolutions (x axis). The silhouette ranges from −1 to +1, where a high value indicates that the object is well matched to its own cluster and poorly matched to neighbouring clusters. F. UMAP plots showing the overlap between two replicates in each platform, the “r” corresponds to the Pearson correlation coefficient of gene expression between replicates.Fig. 1Fig. 2Identification of the main cell lineages in tumours by each scRNAseq platform. A UMAP plot of all integrated samples using the Reciprocal Principal Component Analysis (RPCA) method with 5 anchors. B. UMAP plot showing the three main cell compartments: epithelial, immune and stroma cells identified in all cells from all replicates and platforms after integration. C. Proportion of cells from each main cell lineage. The box plot represents the relative proportion of each cell lineage identified by flow cytometry, EpCAM+ (epithelial cells), CD45+ (leukocytes), and EpCAM−/CD45− (stroma), and the colour dots represent each of our samples from the single cell RNAseq dataset. D. UMAP plots showing the cell types assigned using label transfer analysis of the PyMT reference scRNAseq previously published [30], and split based on the platform. E. Bar plot of the proportion of cells from each cell type per sample (left) and magnified and excluding the epithelial cells (right). F. Cell type relative proportions of each cell type per sample. The box plot represents the proportions found in the PyMT reference scRNAseq [30] and the colour dots represent each of our samples from our single cell dataset. G. Dot plot showing the gene expression comparison of the top cell lineage marker genes between platforms. H. Heatmaps of the Pearson Correlation coefficient between 10× Chromium and BD Rhapsody samples (r1 and r2) in the different annotated cell types.Fig. 2Fig. 3Comparison of cell-cell communication prediction in each platform. A. Bar plot showing the overall number of putative interactions (left) and predicted interaction strength in each platform (right). B. Circle plot visualizing the differential cell–cell interaction networks predicted by each platform. In the left panel, the thickness of the line represents the number of differential interactions predicted between the connecting cell types, while in the right panel, the width of the line corresponds to the differential strength of the interaction by assessing the level of expression of the ligand-receptor pairs after correcting the differences in cell type abundance. Blue lines represent higher number or stronger predicted interactions in BD Rhapsody while red lines correspond to predicted interactions enriched in 10×. C. Heatmaps representing the relative signalling strength of the signalling pathways across platforms. The top grey bar plots show the total signalling strength of each signalling pathway combining all cell types, and the coloured bar plots on the left show the total signalling strength of each cell type combining all pathways.Fig. 3Fig. 4Cancer-epithelial and fibroblast subtype detection across platforms. A. UMAP projections of epithelial cells showing the unsupervised KNN clusters split by platform. B UMAP plot showing the epithelial cell types based on the annotated PyMT reference [30]. (LP: luminal progenitor; HS: Hormone sensing) C. Bar plot of the proportion of cells from each epithelial cell type per sample. D. Gene Set Enrichment Analysis of the marker genes from cluster 3 (A) versus the rest of the luminal progenitor cells (GO: Gene Ontology. BP: Biological Processes). E. UMAP visualization of the number of genes (n_Genes) detected and the percentages of mitochondrial genes (% Mito) per cell in each platform. F. UMAP projections showing the fibroblast subtypes divided by platform. (iCAF: immune cancer-associated fibroblasts; ECM_CAFs: extracellular-matrix synthesis cancer-associated fibroblasts). G. Bar plot representation of the proportion of each fibroblast subtype per sample. H. Violin plots for the expression of marker genes for ECM CAFs (top), iCAFs (middle) and myofibroblasts (bottom) comparing each fibroblast subtypes between 10× (red) and BD Rhapsody (blue) platforms.Fig. 4
3.2
Technical comparison: assessing sensitivity and reproducibility
An important quality metric for scRNA-seq is the sensitivity of gene and UMI detection per cell. This sensitivity metric was different between the platforms (Fig. 1A); we detected a median of 2,995.5 and 2,791 genes, and 10,513.5 and 9,880 UMIs per cell in 10× Chromium and BD Rhapsody, respectively. Based on this analysis 10× Chromium and BD Rhapsody had a similar UMI and genes counts per cell at an equivalent number of reads per cell (Table S1). The proportion of mitochondrial genes is another metric commonly used to assess cell quality. Here we found that cells processed with 10× Chromium have significantly less mitochondrial content than BD Rhapsody samples, where 10× had a median of 6.5 % of mitochondrial transcripts and BD Rhapsody 15.3 % (Fig. 1A, right plot).
An essential application for scRNAseq is to define single-cell phenotypes by their gene expression signature through dimensional reduction, principal component analysis (PCA), and clustering algorithms [44]. We used the top 3,000 variable genes for dimensional reduction analysis in both platforms, noting that the variable genes detected in each platform were slightly different, with 2,140 (71.3 %) common genes (Fig. 1B). The total variability identified in the system, measured as the standard deviation at increasing principal components (PCs) had similar standard deviations per PC between platforms (Fig. 1C). When we examined the clustering capabilities at increasing resolutions, we found that at the lowest resolution, only 10× Chromium was able to split the cells into two clusters suggesting that their detected transcriptome was more distinct; moreover, at increasing resolutions (>0.1), we found that 10× had a greater number of clusters and more consistent cluster composition than BD Rhapsody (Fig. 1D). Statistical analysis using Silhouette confirmed that at resolutions higher than 0.3, Chromium 10× data had slightly higher silhouette score, suggesting that cells were more cohesive with their own cluster and more different to the cells from other clusters in 10× (Fig. 1E). In conclusion, BD Rhapsody and 10× Chromium had similar gene variability but 10× Chromium outperformed BD Rhapsody at consistently assigning cells to cluster at increasing resolutions.
Finally, to assess the reproducibility of each platform, we looked at the correlation of gene expression between replicates (Fig. 1F). We found that both 10× Chromium and BD Rhapsody had over 0.99 correlation values confirming high reproducibility for both platforms.
3.3
Cell type identification and lineage biases between platforms
To directly compare the performance of each platform to resolve tumour heterogeneity, we merged (Fig. S1C) and integrated (Fig. 2A and S1C) the data from the two platforms into one Seurat object. For the integration, we tested canonical Reciprocal PCA integration (RPCA) (Fig. 2A), correlation analysis (CCA), Join Principal Components Space (PCS) and Harmony Integration [34] (Fig. S1D). We found a similar level of integration using all four methods. RPCA integration has been reported to be less prone to overcorrection [45], therefore, we used this approach for the downstream analyses.
For cell type identification, first, we annotated the main cell lineage compartments in PyMT tumours (epithelial, immune and stroma) in the integrated object (Fig. 2B) and we compared their relative proportion in each platform to the measured proportion of these main cell types for PYMT tumours by flow cytometry, as it is the current gold standard method for cell type annotation and validation (Fig. 2C). Overall, we found similar proportions between scRNAseq and flow cytometry, but the samples analysed through 10× Chromium had a slightly higher number, but consistent between replicates, of stroma cells and fewer epithelial cells. Next, we assigned each cell cluster to the main cell types based on SingleR [35] (Figs. S2A and B) and further refined based on label transfer of the cell types from our previously published PyMT tumour reference [30] (Figs. S2C–S2E). We found that all major cell types: B cells, T cells, myeloid cells, endothelial cells, epithelial cells, fibroblasts and myofibroblasts were detected in both platforms (Fig. 2D), with similar proportions of cell types between replicates and platforms, except for endothelial cells and myofibroblasts (Fig. 2E). BD Rhapsody identified a lower percentage of endothelial cells, from 1 % of the total cells in BD Rhapsody compared to 9-6% in 10× Chromium, and myofibroblasts, 10 times less detected in BD Rhapsody compared to 10× Chromium. Consistently, the relative proportion of endothelial cells and myofibroblasts in BD Rhapsody deviated from the expected inter-tumour variability in the PyMT tumour scRNAseq reference atlas across eight tumours [30], suggesting that these differences were not a consequence of tumour heterogeneity (Fig. 2F). Of note, the proportion of fibroblasts was very variable between 10×/BD Rhapsody and the PyMT tumour reference atlas this could be due to a technical bias of the Drop-seq platform used in this atlas [30]. We further confirmed the consistency of cell annotation across platforms by looking at the top markers of each cell type and confirmed that these markers were similarly expressed in all cells captured in both platforms (Fig. 2G) We also confirmed a high correlation between platforms when we compared the gene expression differences of the same cell types (Pearson correlation coefficient >0.9, Fig. 2H).
To determine how the different cell population ratios, gene marker dropout or ambient noise can affect downstream analysis, we performed cell-to-cell communication predictions using the CellChat software on these two platforms independently. We found that overall BD Rhapsody detected a larger number of predicted interactions and higher predicted interaction strength (Fig. 3A). In line with the number of cells detected, BD Rhapsody predicted fewer and weaker interactions between myofibroblasts with epithelial cells or fibroblasts and between endothelial and epithelial cells (Fig. 3B, red lines), however, it detected more predicted interactions between most of the cell line pairs and it had stronger putative interactions within the epithelial compartment, between the fibroblast and the epithelial cells and between myeloid cells and epithelial or fibroblast cells (Fig. 3B, blue lines). CellChat analyses also revealed that some signalling genes were missing in BD Rhapsody such as PTN and OCLN in the epithelial cells, NT and SMA7 in fibroblasts and APELIN in endothelial cells, while 10× did not detect 16 genes related to cell-to cell communication pathways including ITGAL, CD45 and BAFF from myeloid cells, CLEC, CD48 and CD137 from B cells or ICOS and CD86 from B cells (Fig. 3C). Overall BD Rhapsody seems to predict more cell-cell interactions and as expected, changes in cell type proportions had an impact on the predicted interaction strength detected with those cell types.
In summary, 10× Chromium detected more endothelial cells and myofibroblasts, which resulted in different levels of cell-cell interactions predicted with those cell types in each platform, however overall, the genes identify in BD Rhapsody per cell subtype were better on predicting cell to cell interactions, as a higher number and stronger putative interactions were detected.
3.4
Cell subtype proportions across platforms
To further evaluate how each platform can distinguish among cellular subtypes within the main cell types in tumours, we performed subclustering of some of these main cell types. First, we selected the most abundant cell type in PyMT tumours, the epithelial compartment; unsupervised clustering revealed visual differential distribution between platforms in some cell clusters including clusters 1 and 3 out of the 7 clusters (Fig. 4A and S3A). To infer the identity of each cell subcluster, we again used the scRNAseq PyMT tumour atlas [30] of the epithelial cell subtypes in the integrated epithelial cluster (Figs. S3B and S3C), which resulted in the annotation of 8 epithelial subtypes manually (Fig. 4B). As expected in this transgenic mouse model [[26], [27], [28]], most cells were classified as luminal progenitors (LP), including a subset of hormone-sensitive luminal progenitor (LP-HS) cells where no major differences were observed between platforms (Fig. 4C). Interestingly, the cell clusters where we visually observed differences (Fig. 4A) were classified as basal (cluster 1) and luminal progenitor/luminal hormone-sensing (LP/LP-HS) (cluster 3) (Fig. 4B and C). Cluster 3, or LP/LP-HS cells, was missing from the PyMT reference (previously done using Drop-seq) and was heavily comprised of cells from the 10× Chromium platform. Gene set enrichment analysis identified that this 10x-specific luminal progenitor cluster was enriched with genes involved in immune responses, including antiviral response and interferon (Fig. 4D). This suggests that the 10× Chromium platform detects more epithelial cells that are actively interacting with the immune system. Interestingly, basal cells (cluster 1) were almost undetected in BD Rhapsody (3 cells detected), while in the 10× platform comprised around 2 % of the epithelial compartment. We also identify differential proportion of cells between platforms in the Multi/Stem cluster (Fig. 4C) with the highest fraction found in the BD Rhapsody replicates. Even though, the gene expression profile of the cluster 7 correlated with the multipotent/stem cell type (Figs. S3B and C), we found that these clusters also had the highest percentage of mitochondrial gene content, and the lowest number of genes detected in both platforms (Fig. 4E), potentially suggesting damaged cells. Considering cells with high mitochondrial content but low number of genes detected as the definition of damaged cells, the 10× Chromium platform had the lowest ratio of damaged cells (Fig. 1A), which may explain the absence of these cells and suggest that these are damaged cells.
Next, we did the same analysis in a less abundant population, fibroblasts. We again assigned cell types based on the fibroblast subsets in scRNAseq PyMT tumour reference [30] (Fig. 4F) and found that within the fibroblast partitions, there were three clusters that correlated with either myofibroblasts or secretory cancer-associated fibroblasts (CAFs) which were subdivided into extracellular-matrix synthesis CAFs (ECM-CAFs) and inflammatory CAFs (iCAFs) (Fig. 4F). The 10× Chromium platform detected a lower percentage of inflammatory CAFs, while the BD Rhapsody platform detected fewer ECM-CAFs and myofibroblasts (Fig. 4G). In this context, the expression of key markers of the inflammatory CAFs, like Ly6c1 and C4b, was higher in BD Rhapsody, while the myoepithelial markers Acta2, Mylk and Myh11 were higher in the 10× data (Fig. 4H).
Together these data suggest that, even though all major cell types were represented in both platforms, there are still cell type detection biases intrinsic to each platform which should be considered when analysing the tissue heterogeneity.
3.5
Ambient noise comparison between 10× chromium and BD Rhapsody in high and low-quality samples
One of the limitations of single-cell data is the technical noise caused by the ambient RNA contamination [39], amplification bias during library preparation and index swapping during sequencing [46]. The nature of the cell capture method (microwell or oil droplet) and the differences in the molecular workflows for RNA amplification in each platform (Fig. S1A) could result in a differential origin of the technical and/or ambient noise. Ambient RNA is defined as the mRNA molecules that have been released from dead or stressed cells and are part of the cell suspension. The ambient noise is especially present in samples that require tissue digestion and in low-quality samples, such as tissues stored for an extended period before processing, or tissues with active cell dead or hypoxia regions like tumour tissues.
Our data suggest that noise might be handled differently by the commercial platforms (Fig. 1, Fig. 4E). Thus, we studied how these two commercial platforms, performed with the technical noise from challenging samples. To recreate this “low quality sample”, we incubated PyMT digested tumours for 24 h at 4 °C. After 24 h, cell viability dropped 20 % compared with the freshly digested sample as measured by DAPI positive cells in flow cytometry, indicating a substantial cell decay (Fig. 5A, S4 and Table S3). As done before with fresh PyMT tumour samples, we removed dead cells using autoMACS® Pro and processed the samples for scRNAseq using the 10× Chromium or BD Rhapsody platforms. Cell viability was checked before and after autoMACS® Pro, and even damaged cells had a viability of ≥85 % before running the single cell RNAseq experiments, reflecting a real-life scenario.Fig. 5Analysis of the ambient noise in challenging samples in 10× Chromium and BD Rhapsody. A. Bar plot showing the percentage of cell viability measured by flow cytometry as the percentage of DAPI negative cells in digested fresh tumours (Fresh) and simulated low-quality tumour samples (24 h). B. Line plot showing the changes in percentage of cells for each cell type between fresh tumours and low-quality tumours (24 h) measured from the scRNAseq data. C. Violin plots showing noise ratio by scAR (left) and contamination ratio by DecontX per cell in each condition. Statistical analysis of the comparisons between fresh and low-quality (24 h) samples (black) or between fresh 10× and fresh BD (brown) was performed using paired t-test adjusted with the Bonferroni method, ****adjusted p-value<0.001; ns = not significant. D. Violin plots split by cell type of the noise ratio by scAR (left) and the contamination ratio by DecontX (right) detected in each cell. Statistical analysis of the noise ratio comparisons per cell type between fresh and low quality (24 h) samples in each platform (black) or between fresh 10× and fresh BD (brown) was performed using paired t-test adjusted with the Bonferroni method, **** adjusted p-value <0.001; ns = not significant. One-way ANOVA was also performed to compare the noise ratio distribution across all cell types in the fresh samples in 10× Chromium (p-value = 0.4903), or BD Rhapsody (p-value = 2.2e-16). E. Dot plot showing average expression of the top gene markers of each cell type using the integrated raw data or denoised (scAR) or decontaminated (DecontX) gene expression matrix.Fig. 5
First, we compared the tissue heterogeneity of the damaged samples to the fresh cells. As for this analysis, we increased the number of cells to ∼4000 cells per condition (Table S2), which allowed us to further split the myeloid cell compartment into neutrophils and macrophages. We found that endothelial cells, fibroblasts and myofibroblasts were consistently lost on the damaged sample regardless of the platform (Fig. 5B).
Next, to assess the technical noise found in each platform, we used the single-cell Ambient Remover (scAR) method, a universal model to detect noise across single-cell platforms based on probabilistic deep learning of the ambient signal [38]. Interestingly, around 5 % of cells in both platforms and in any condition (fresh or damaged) were considered empty cells as their transcriptome was indistinguishable from the ambient signal of the empty barcodes (Fig. S5A). Those assigned “empty cells” also had a high percentage of mitochondrial content and low gene content (Fig. S5B) and therefore confirming that these assigned “cells” were instead damaged cells that needed to be removed from downstream analysis. When we compared the noise ratio of the remaining cells, we found that BD Rhapsody had significantly more ambient noise per cell than 10× Chromium (Fig. 5C, left plot Fresh samples), however, the ambient contamination (noise ratio) was significantly increased in the damaged samples (24 h) in the 10× Chromium but not in BD Rhapsody (Fig. 5C, left plot). To further explore the origin of ambient RNA, we used an additional decontamination tool, DecontX, a Bayesian method to estimate and remove contamination in individual cells [39] without modelling the noise from the empty droplets, but from each putative cell. Fig. 5C right plot, shows that the differences in the contamination ratio were less pronounced between platforms than with scAR. Altogether the ambient RNA has different origins, empty droplets and from ambient RNA from putative cells and each platform handles this noise differently, with BD Rhapsody having more noise in empty droplets (noise) while the ambient RNA in the putative cells (contamination) is comparable between platforms.
We also confirmed our previous analyses of a higher percentage of mitochondrial content in Rhapsody (Fig. 1A), and as expected, in the damaged samples the mitochondrial content increased similarly in both platforms (Fig. S5C, left plot). The number of genes found per cell decreased in the damaged samples compared to their fresh counterpart; however, the decrease on the number of genes was more pronounced with BD Rhapsody (Fig. S5C, right plot). We explored how a standard filtering based on mitochondrial content and number of genes would perform to denoise the data. As the overall mitochondria content per cell was different per platform (Fig. 1A and S5C), we used the threshold based on the percentiles of each platform, cells with more than the 25 percentile of mitochondria content and less than the 10 % percentile of number of genes per cells were labelled as low-quality control (lowQC) (Figs. S5D and S5E). Even though, this stringent threshold removed the majority of “empty cells” (Fig. S5F) this type of filtering does not clean the ambient RNA contamination per cell type (Fig. S5G vs Fig. 5G). In fact, the noise ratio does not correlate well with either the percentage of mitochondria genes (r = 0.33), or the number of genes (r = −0.12) (Fig. S5H).
Next, to evaluate which cell lineages are most affected by the technical noise in each platform, we compared the noise ratio per cell type and platform in each sample condition, using both scAR and DecontX (Fig. 5D and E). The analysis with scAR, showed that in BD Rhapsody all cell types had a similar level of noise, regardless of their damaged condition, while 10× Chromium's noise was notably different between cell types and there were significant differences between fresh and damaged conditions (Fig. 5D, left plot). Particularly, in 10× Chromium, neutrophils had the highest percentage of noise which increased to an even higher ratio in the damaged samples. As expected, neutrophils also had the lowest number of genes detected especially in Chromium 10× (Fig. S6A), independently of sequencing depth (Fig. S6B). To understand which cell types and genes are driving the ambient signal, we analysed the expression across cell types of the top genes contributing to the ambient pool based on scAR analysis (Fig. S6C). Remarkably, in 10× Chromium, the ambient expression of the S100a9 neutrophil marker was >25-fold times higher than any other cell type marker and was even higher in the damaged samples, despite the low number of neutrophils identified in the sample; while in BD Rhapsody the ambient gene expression was more uniformly distributed and did not show major differences between fresh and damaged sample (Fig. S6C). To confirm the neutrophil biases from 10× Chromium in the ambient profile, we correlated the average transcriptome of the single cells with the ambient gene expression and observed a higher presence of neutrophil marker genes in ambient RNA than in the single cells in 10× Chromium, but not in BD Rhapsody (Fig. S6D). Interestingly, the same cell type analysis for the distribution of the ambient RNA calculated using only putative cells with DecontX did not show major cell type biases either in 10× Chromium or BD Rhapsody, and less pronounced differences between platforms with myofibroblasts showing the highest differences in contamination between platforms, where BD Rhapsody had higher contamination (Fig. 5D, right plot).
The scAR and DecountX algorithms can denoise the count matrix based on the ambient composition. When we used the denoised data using scAR to resolve cell type clusters, we found that clusters are more distinct using denoised data and there is less background expression of non-specific markers compared to the raw data (Fig. 5E) or the data processed with standard QC filtering (Fig. S5G). As expected, the expression of the neutrophil markers was completely lost in the 10× Chromium data in this cell subtype (Fig. 5E) The decontaminated data using DecontX on the other hand maintained the neutrophil marker however had a comparable non-specific background than the raw data or QC filtering (Fig. 5E and S5G). This dichotomy may be explained by the different approaches used by the two ambient RNA removal tools. For scAR, we modelled ambient RNA based on the empty barcodes and putative cells, while for DecontX we measured ambient RNA using only putative cells (“in cell”) and based on the expression of key gene markers in other cells populations. Therefore, this suggests that the signature of neutrophils mainly comes from droplets that have captured neutrophils but due to its in-drop degradation the amount of RNA detected in the sequencing is very low and thus considered an empty droplet. This explanation also supports the fact that when the empty droplets are not considered for ambient RNA calculations, DecontX, all cell types have a similar contribution to the noise between platforms (Fig. 5C right plot).
In conclusion, the differential molecular design, the microfluidic versus microwell format and the sample quality are sources of the ambient noise. In BD Rhapsody there is a generalised level of noise that is independent of the cell of origin and sample quality, however there is some bias towards myofibroblast specially in low-quality samples; while in 10× Chromium, ambient noise is cell type-specific and overrepresented by the neutrophil population specially in empty droplets suggesting in droplet neutrophil-specific RNA degradation.
3.6
Cell type bias noise validation on public datasets
To determine if the cell type biases and technical noise differences between 10× Chromium and BD Rhapsody were universal, we assessed another two public data sets, human whole blood and bone marrow, where more neutrophils are expected, and both platforms were used [37]. This data was processed using CITEseq (10× Chromium) and Ab-Seq + whole transcriptome analysis (BD Rhapsody) and therefore it also included 30 antibody-derived barcodes in both datasets. Additionally, hashtag oligos (HTO) were used in 10× Chromium platform to demultiplex samples.
We first compared the abundance of each cell type detected based on the antibody markers in both platforms using the default parameters for filtering established by Cell Ranger and the Rhapsody pipelines (Fig. 6A). Interestingly, the whole blood and the bone marrow samples processed with 10× had virtually no granulocytes (neutrophils, basophils, or eosinophils) (Fig. 6B). This is in line with previous reports of a low number of neutrophils in human datasets detected using this platform [47,48]. Interestingly, if we filtered the 10× Chromium cell barcodes using the custom threshold based on the Protein UMI counts per cell as Qi et al. [37], we recovered 5,320 and 7,702 cells, in the bone marrow and whole blood samples, respectively, from which the majority were neutrophils (Fig. 6C). In addition, using this custom filtering for all samples, resulted in very similar relative proportions of each cell type in the bone marrow and whole blood between the 10× and BD Rhapsody platforms (Fig. 6D). However, even though the UMAP using the protein matrix was able to distinguish all major cell types in 10× Chromium (Fig. 6E), the low gene sensitivity in the granulocyte population, with a median of fewer than 100 counts per cell (Fig. 6F), did not manage to further resolve the granulocyte clusters at the transcriptome level (Fig. 6F and 6E right panel). In fact, when we run the scAR algorithm using the threshold for filtering cells based on Protein UMI counts, we found that in 10× Chromium some granulocytes, especially eosinophils, still had a higher RNA noise ratio, although not as pronounced as in our mouse dataset in comparison with other cell types (Fig. 6, Fig. 5D). Overall, the noise ratio from both the RNA and the protein data was more similar across cell types in BD Rhapsody than in 10× Chromium, confirming that in BD Rhapsody the ambient noise is not cell type specific (Fig. 6G). In summary, 10× Chromium was unable to detect granulocytes in human samples based exclusively on their RNA content, and their low gene detection hinders the denoising algorithms to distinguish empty droplets from real cells with low UMI counts.Fig. 6Performance evaluation for the detection of human granulocytes between 10× Chromium and BD Rhapsody. A. UMAPs of cell types found in human whole blood and bone marrow samples processed with 10× Chromium or BD Rhapsody using the transcriptome for dimension reduction. Cell barcodes were filtered using either Cell Ranger or BD Rhapsody pipelines. B. Bar plot showing the number of cells found in each cell type per condition. C. Bar plot showing the number of cells found in each cell type in 10× Chromium after custom filtering based on a minimum of 10 Protein UMI counts. D. Bar plot of the relative proportion of cells from each cell type per condition using the same custom threshold than panel C for 10× Chromium. E. UMAP plots of the human datasets processed with 10× Chromium and filtered based on custom threshold where the dimensional reduction was done using either the protein data (left) or the RNA data (right) F. Violin plots of the number of RNA and Protein UMI counts in each cell split by cell type. The red dotted line highlights a 100 counts threshold. The y axis is shown in logarithmic scale. G. Noise ratio in each cell using either from the RNA (top) or the protein (bottom) data matrix and split by cell type. (BM: bone marrow; WB: whole blood).Fig. 6
4
Discussion
Single-cell RNA-seq allows the classification of different cell types based on the gene expression profile of individual cells. The rise of commercial instruments and kits has enabled the use of this methodology by the broad scientific community. However, there are still technical challenges during sample preparation, cell capture and library preparation [49], that can affect the bioinformatic readout and consequently, the biological interpretation driven from the data. Therefore, it is crucial to understand the advantages and limitations of each platform to best tailor the selection of a platform to the sample type and the biological question that wants to be answered, in addition to the costs and expertise level of the user. Here, we have compared the most popular commercial platforms, the microfluidic-based system, 10× Chromium, and the microwell plate-based system, BD Rhapsody, using mammary gland tumours from the PyMT breast cancer mouse model [[25], [26], [27], [28]]. Analysing samples that require tissue digestion prior to the single-cell experiments is more challenging as the damaged cells can introduce additional ambient noise and different cell-type vulnerabilities can change the observed tissue heterogeneity. An advantage of our design is the comparison of tumour samples from a transgenic mouse model digested using the same protocol and in the same laboratory, which allows us to discard sample preparation as the source of variability. In addition, we have created low-quality samples using tumours from the same mouse model with reduced viability to assess how the quality of the sample affects the readout from 10× Chromium and BD Rhapsody. Frequently, the samples processed for scRNAseq have high levels of damaged cells, for example, tumour samples have necrotic regions or increased cell death due to their high cell turnover or exposure to treatments, such as radiotherapy or chemotherapy. Transportation of the biological material to the laboratory where cell capture is performed may also increase the cellular stress of the sample. In this study, we have used a biologically relevant tissue and replicated the standard user experience to perform a realistic assessment of the different technologies in challenging samples.
Our comprehensive data analysis identified, first, at the technical level, that 10× Chromium and BD Rhapsody had comparable overall gene sensitivity and reproducibility at similar sequencing depths. Next, we measured tissue heterogeneity and found a lower percentage of endothelial and myofibroblast cells in BD Rhapsody compared to 10× Chromium. However, comparing their ratios with the flow cytometry data, we found that 10× Chromium tends to detect more cells from the stroma than BD Rhapsody and flow cytometry. This suggests that 10× Chromium may enrich stroma cells which could be beneficial if you are interested in those populations. Interestingly, endothelial, and myofibroblast cells are reduced in low-quality samples in both platforms. We also found differences within the epithelial compartment, including an immune-responsive cluster that were enriched in the 10× Chromium samples. Orthogonal validation of this cell population is needed to understand its origin.
In the immune compartment, the most notable differences were found within the myeloid cells, especially in granulocytes, which 10× Chromium was not able to detect granulocytes neither in the PyMT mammary tumours nor in the human whole blood or bone marrow datasets, due to the low gene detection, which misclassifies them as empty droplets. It is known that granules in neutrophils, eosinophils and basophils have high levels of nucleases including RNases [50]. This has been suggested to be the reason why granulocytes have lower genes and counts detected than other cell types [37,51]. We have also confirmed in our PyMT dataset and human blood and bone marrow public datasets that granulocytes, including neutrophils, have the lowest number of genes detected. Interestingly, BD Rhapsody's neutrophils had an average of 1,240 or 602 genes while in 10× Chromium neutrophils only had 551 or 10 in the mouse and human datasets, respectively. We hypothesise that there are two factors that may contribute to this phenomenon; firstly, neutrophils may be more sensitive to the pressure from the microfluidic devices which could cause their breakage and loss of RNA to the ambient pool in the 10× Chromium system; secondly, we hypothesise that the 10× platform is more sensitive to the presence of RNases released by neutrophils than BD Rhapsody. Supporting this theory, Qi et al. found that cell doublets of neutrophils and T cells from 10× Chromium had less number of UMIs detected than T cell singlets, suggesting that the RNases from the neutrophils degraded the RNA of the T cells [37]. The exact recipes of the lysis buffers used in 10× Chromium and BD Rhapsody are not disclosed, but it is possible that BD Rhapsody has stronger detergents or RNase inhibitors that degrade RNases. BD Rhapsody workflow also includes washes of the beads before cDNA generation which could remove any remaining RNases or strong detergent from the lysis buffer to not interfere with the reverse transcription reaction. Moreover, a recent report describing TAS-seq, a BD Rhapsody-based technique with improved cDNA amplification step, resulted in better cellular composition fidelity, especially in neutrophils, using their system compared to 10× Chromium and Smart-seq [52]. Together, this demonstrates that BD Rhapsody is better suited for granulocytes research, and new strategies for cDNA amplification that increase the number of genes detected per cell will further improve the transcriptional resolution of this challenging cell population.
Mitochondrial content has been commonly used as a sign of damaged cells, where their cytoplasmatic RNA has been lost but the RNA contained in the mitochondria remains [53]. In fact, we see an increase in the mitochondrial fraction in the damaged samples. Surprisingly, all cells processed with the BD Rhapsody have a higher percentage of mitochondrial genes, even though they are not considered damaged. This phenomenon has also been recently reported by others [54,55]. [56]Our experimental design included additional steps for multiplexing only in the BD Rhapsody samples prior to the cell capture (Figs. S1A and B) that may have contributed to a higher mitochondrial content. However, this is highly unlikely as the additional time required for the overall 4-h process was 30 min and cell viability did not substantially change after this step (Table S3). Nevertheless, to further explore this, we have analysed the mitochondrial content from publicly available whole blood and bone marrow samples (Fig. 6), where multiplexing was performed in 10× Chromium but not in BD Rhapsody samples. We found that mitochondrial content was consistently higher in BD Rhapsody when we compared two cell types that have equivalent RNA quantity, T cells and monocytes (Fig. S7) [37]. Furthermore, Gao et al. showed that the mitochondrial content in BD Rhapsody was higher comparing the demo PBMCs dataset from both platforms where multiplexing was not performed [55]. A possible explanation for this mitochondrial disparity could be that the lysis buffer of BD Rhapsody is more effective at digesting the organelles including mitochondria and therefore releasing the mitochondrial RNA into the cytosol to bind to the beads. For this reason, different thresholds of mitochondrial content should be used to filter out damaged cells in BD Rhapsody or 10× Chromium.
Ambient RNA released during the sample preparation and cell capture introduces noise to the data affecting the downstream analysis. Here, we have compared the level of ambient noise using two different computational frameworks, scAR [38] and DecontX [39] to identify and define vulnerabilities across platforms. We run these two methods using different strategies to calculate the ambient RNA. For scAR, the noise was measured for each barcode corresponding to putative cells and the gene expression of the ambient RNA from empty barcoded droplets; while for DecontX, we analysed the data without using the empty barcodes as background, and thus we only calculated the ambient RNA coming from droplets containing putative cells. We found that using scAR, 10× Chromium ambient pool was biased towards neutrophils, however this was not seen when the ambient RNA was only measured in putative cells (DecontX). This confirms that the RNA from captured neutrophils is likely degraded by the RNases from these cells reducing the number of UMIs making it hard to distinguish between real or empty barcodes, as previously discussed. This confounding factor explains why scAR detected less ambient RNA per cell overall in 10× Chromium compared to BD Rhapsody, as the ambient expression in 10× was mostly made of neutrophil marker genes that are not likely detected in putative cells from other cell types while BD ambient RNA pool was comprise by RNA from all cell types. In fact, DecontX found similar levels of ambient RNA per cell across platforms. This analysis highlights the importance of understanding the origin of ambient RNA calculated by denoising tools before using them to clean up the data. This issue has been in fact considered by 10× Genomics and they recommend including additional steps on their workflows to optimise single cell assays for neutrophils/granulocytes, these include: immediate sample processing within 2 h after sample collection, supplementing with RNAse inhibitors in the wash and resuspension buffers, avoid long incubations on ice, increasing the PCR cycles during cDNA amplification, sorting neutrophils to enrich this population and to change the filtering parameters in Cell Ranger using the “force cell” parameter to recover cells with little RNA (neutrophils).
In this study, we also adapted the LMO sample multiplexing approach from MULTI-seq [32] for our BD Rhapsody samples (Fig. S1B, see Material and Methods) using 4 LMO sample tags with a lower-than-expected capture efficiency of 59 % likely due to a drop out of one LMO (Table S1). The MULTI-seq method uses a highly efficient approach for DNA sample tagging in live cells based on the affinity of lipids to the cell membranes which ensures a full capture of these LMO-oligos in the cells as well as in the bead capture [32]. The unexpected lower capture efficiency of our adapted MULTI-seq method could be due to the addition of an incorrect amount of one of the sample barcode oligonucleotides that resulted in a non-efficient hybridization to the anchor LMO. This is further supported by the fact that the rest of the LMO-tagged samples had an average capture efficiency of 79 %, over the expected amount of captured cells/sample (1,591 singlets/sample, Table S1). These LMOs for sample tagging are now commercially available through Sigma-Aldrich (Cat# LMO001) and could be used for sample multiplexing in both platforms, as previously described in the case of 10× Chromium [32] or following the method described in this study for BD Rhapsody. Alternatively, BD offers a multiplexing kit (BD® Single-Cell Multiplexing Kit) using polyadenylated DNA barcodes conjugated to a universal antibody.
A limitation of our comparison analysis is the fact that we have used mouse mammary gland tumours, human whole blood and bone marrow only. Other tissues where different cell types are found may show other cell type biases, especially for lowly represented cell types. However, this study highlights the importance of using different technologies when assessing the population ratios of a sample and avoiding comparing cell type ratios across biological conditions performed in different platforms. Based on our analysis, we also found that the expected quality of the sample and origin of the tissue should also be considered when choosing a platform to perform single-cell RNAseq, as damaged tissues or tissues with high levels of RNases, such as bone marrow or spleen [51], would be more susceptible to RNA loss in 10× Chromium.
Complementary strategies, such as performing multi-omic studies where two or more molecular layers of information are investigated per cell [57], or spatial transcriptomics [58], as well as alternative methods for tissue preservation such as the ALTEN system [11] or parafilm fixation in combination with fixed RNA profiling [51,59], may also help dissect the true heterogeneity of complex tissues and overcome the current limitations of single-cell RNAseq.
Ethics declarations
This study was reviewed and approved by the St. Vincent's Campus Animal Research Committee with the approval number: 19/02, dated March 01, 2019.
Data availability
Data generated in this paper is available through Gene Expression Omnibus (GEO): GSE229765. PyMT mouse data processed with Dropseq is available in GSE158677. Human data sets were downloaded from PRJNA73428.
CRediT authorship contribution statement
Yolanda Colino-Sanguino: Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Laura Rodriguez de la Fuente: Validation, Resources, Methodology, Data curation. Brian Gloss: Methodology, Data curation. Andrew M.K. Law: Resources, Methodology. Kristina Handler: Writing – review & editing, Methodology. Marina Pajic: Resources. Robert Salomon: Resources. David Gallego-Ortega: Writing – review & editing, Writing – original draft, Supervision, Resources, Project administration, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Fatima Valdes-Mora: Writing – review & editing, Writing – original draft, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
Title: Secreted protein TNA: a promising biomarker for understanding the adipose-bone axis and its impact on bone metabolism | Body:
Introduction
Osteoporosis (OP) is the most common bone disease, affecting an estimated 200 million people worldwide [1]. It is a systemic metabolic bone disease characterized by reduced bone mass and microstructural deterioration, which increase bone fragility and fracture risk, potentially leading to serious complications [2]. Bone mineral density (BMD) is the primary diagnostic indicator for OP. However, this diagnostic method is typically employed only after an individual with OP exhibits symptoms, which may delay preventive and therapeutic interventions. Therefore, identifying new hematological biomarkers is of great importance in OP.
Blood and bone cells coexist in close proximity within the bone marrow microenvironment. Platelets, the second most common type of blood cell, are small and short-lived (7–10 days) cell fragments derived from mature megakaryocytes [3]. In addition, platelets play critical roles in various pathophysiological processes, including inflammation, tissue repair, tumor growth, and metastasis [4]. Studies have shown that high platelet counts are associated with low BMD [5, 6]. In terms of clinical relevance, a cohort study of 1,010 men from the MrOS study in Gothenburg, Sweden, showed that high platelet counts were associated with lower BMD at all sites, including total hip BMD (r = − 0.11, p = 0.003) [6]. Additionally, Song found that the platelet/lymphocyte ratio (PLR) is significantly elevated in patients with OP exhibiting fragility fractures, and the ROC diagnostic curve suggests a high diagnostic value (AUC = 0.835) [7]. Regarding mechanistic relevance, firstly, platelet count is associated with inflammation, and previous studies have shown that inflammation is associated with OP [8]. Platelets store serotonin [9], whose counts are positively correlated with serotonin levels. Although the role of serotonin in bone metabolism is markedly complex, studies have shown that high serum serotonin levels are associated with decreased bone mass [10]. Therefore, the platelet count clear correlation with lower BMD values may, in part, be explained by high levels of serotonin and inflammation, although other hormones or cytokines are likely involved. Secondly, data show that the in vitro activation of platelets by prostaglandins and nuclear factor kappa B ligand (RANKL) predominantly induces osteoclast formation [11]. This process may also involve the provision of TGF-β as a source to activate osteoclast formation signaling pathways [12]. Other studies have shown that megakaryocytes can enhance osteoblast proliferation while inhibiting osteoclast formation [13]. Additionally, platelets perform many of their emerging pathophysiological functions by directly interacting with other cells or by storing and releasing cytokines [14]. Adipose tissue can also influence platelet function through the secretion of adipokines, ultimately leading to platelet activation [15].
Tetranectin (TNA) is a calcium-binding protein encoded by the C-Type Lectin Domain Family 3 Member B (CLEC3B) gene. The C-terminal end of the TNA protein monomer contains a glycan recognition structural domain, known as the carbohydrate recognition domain (CRD), which is approximately 130 AA in length. This domain is characteristic of the C-type lectin superfamily and can recognize plasminogen (Plg) to promote the production of plasmin [16]. TNA has been studied in various diseases. In hepatocellular carcinoma, TNA expression has been significantly correlated with tumor purity and immune cell infiltration levels, suggesting a complex interaction between TNA expression and the immune microenvironment in cancer [17]. The specific role of TNA in the regulation of bone remodeling has also been previously reported. Wewer’s study identified the potential role of TNA as a bone matrix protein. The expression of TNA is temporally and spatially consistent with mineralization both in vivo and in vitro. TNA is highly expressed in the newly formed reticular bone of newborn mice, and its overexpression leads to increased tumorigenic osteoid formation in pheochromocytoma cells in vivo, suggesting that TNA may be involved in the process of osteogenesis and mineralization [18]. In addition, TNA is an adipogenic serum protein. Seulgi Go’s study found that the adipogenic function of TNA is mediated by enhancing mitotic clonal expansion via ERK signaling [19].
Figure 1 illustrated the flowchart of our study, which investigated the role of platelet-associated plasma proteins in the development of OP. We firstly identified the key plasma protein TNA and analyzed its expression across various human tissues, discovering that it may mediate bone metabolism through the adipose-bone axis. In conjunction with RNA-seq data from different stem cell sources, our findings suggest that TNA may promote osteogenic differentiation. However, this finding was based on bioinformatics data and needs further experimental verification. Furthermore, in vitro cell experiments showed that TNA inhibits osteoclast differentiation and resorption.
Fig. 1The flowchart of this study
Materials and methods
Data acquisition and processing
We recruited 18 patients with primary OP and 18 individuals with normal bone mass between March 2018 and February 2019 at Honghui Hospital, Xi’an Jiaotong University, People’s Republic of China. Plasma samples from all 36 participants were subjected to proteomic sequencing. Each participant provided informed consent, and the study was approved by the Institutional Review Board of Honghui Hospital, Xi’an Jiaotong University (project number: 2018-22). Detailed descriptions of the proteomic data and the inclusion and exclusion criteria are available in our previously published study [20]. Additionally, plasma proteomic data from 43 patients with obesity, both before and after weight loss, were obtained from Geyer’s study [21]. In addition, we downloaded 300 platelet-related genes from Li’s study (Supplementary Table 1) [22].
RNA-seq data from wild-type (WT) MSCs derived from induced pluripotent stem cells (iPSCs) differentiated into osteoblasts at five time points (0, 7, 14, and 17 days) were obtained from GSE102732 in the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/). Using the Nature Genetics data [23] from GSE113253, we obtained RNA-seq data of bone marrow mesenchymal stromal cells (BMSCs) and adipose-derived stem cells (ADSCs) for osteogenic and adipogenic differentiation at five time points: 4 h, 1 day, 3 days, 7 days, and 14 days. On the basis of Robert’s study [24], we acquired ADSCs and performed RNA-seq following osteogenic and adipogenic differentiation induction for 24 h. The RNA-seq raw files in fastq format were obtained from the functional genomics data collection (ArrayExpress repository, https://www.ebi.ac.uk/biostudies/arrayexpress) under the number E-MTAB-6298, running FastQC (version 0.10) to assess read quality, generating Indexes, aligning to genome with STAR-aligner (version 2.7), summarizing gene counts with featureCounts (version 2.0.1), importing gene counts into R/RStudio, and using DESeq2 (version 1.10) to find significant genes.
Analysis of differentially expressed proteins (DEPs)
We performed a differential analysis of plasma proteomic data between patients with OP and healthy individuals. The mean protein values were log2-transformed and visualized using a volcano plot (|Log2FC| > 0.5, p-value < 0.05). We used the “Retrieve/IDmapping” function to perform the id transformation based on Uniprot database (https://www.uniprot.org/id-mapping), which is the most extensive and informative protein database and is the first choice for querying protein functions. Moreover, 66 DEPs, identified after gene ID mapping, were intersected with 300 PLTs and displayed in a Venn diagram.
Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis
After performing differential analysis, we identified six platelet-related OP genes. Then, the “clusterProfile” package in R was used to perform GO and KEGG enrichment analyses to elucidate the potential pathogenic mechanisms and biological pathways associated with these genes. The results of the GO and KEGG analyses are presented using bar graphs. In addition, the pairwise similarity of enriched terms was calculated using the Jaccard similarity index (JC). The “ggplot2,” “igraph,” and “ggraph” packages were employed for EMAP visualization of the similarity results.
Protein-protein interaction (PPI) and friends analysis
The online STRING database (http://string-db.org) is widely used for constructing PPI networks and scoring interactions between target proteins. We conducted a PPI network analysis of six proteins to explore their interactions using a confidence cutoff of > 0.4. The hub proteins within the PPI network were identified and scored using the “Cytohubba” plugin in Cytoscape. In addition, we performed Friends analysis using the “GOSemSim” package to identify key genes by constructing a gene interaction network and assessing the importance of each gene through network topology analysis.
Human protein atlas (HPA) database analysis
Immunohistochemical (IHC) data for A2M, COL1A1, and TNA in adipose tissue were obtained from the HPA database (http://www.proteinatlas.org) [25]. In addition, we analyzed the expression levels (nTPM) in human tissues using the Genotype-Tissue Expression (GTEx) database.
The DeepTMHMM tool
DeepTMHMM (https://dtu.biolib.com/DeepTMHMM) is a deep learning model for transmembrane topology prediction and classification and is currently the most complete and best-performing method for the prediction of the topology of both alpha-helical and beta-barrel transmembrane proteins [26]. We used DeepTMHMM to analyze the TNA protein to determine whether it was a classical secretory protein and whether it had transmembrane domains.
Single-cell RNA-seq (scRNA-seq) data
On the basis of Zhong’s study [27], we obtained scRNA-seq data for BMSCs from the Single Cell Portal database (https://singlecell.broadinstitute.org/single_cell). We analyzed the expression of TNA in each cell cluster, including early mesenchymal progenitors (EMPs, cluster 1), intermediate mesenchymal progenitors (IMPs, cluster 2), late mesenchymal progenitors (LMPs, cluster 3), osteoblasts (cluster 4), osteocytes (cluster 5), lineage-committed progenitors (LCPs, cluster 6), adipocytes (cluster 7), and chondrocytes (clusters 8 and 9).
Cell cultures and transfection
HEK293 cell lines, obtained from the American Type Culture Collection (ATCC), were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM; Invitrogen), supplemented with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin. For the isolation and culture of bone marrow–derived monocyte-macrophages (BMMs), mouse bone marrow cells were harvested from the femurs and tibias by flushing the bones with sterile phosphate-buffered saline (PBS). The harvested bone marrow cells were then passed through a 70 μm cell strainer to remove debris, followed by centrifugation at 300 x g for 5 min. After discarding the supernatant, the cell pellet was resuspended in red blood cell lysis buffer and incubated for 2 min to lyse erythrocytes, followed by another round of centrifugation. The resulting cells were then resuspended in α-modified Minimal Essential Medium (α-MEM; Invitrogen), supplemented with 10% FBS and 10 ng/mL macrophage colony-stimulating factor (M-CSF; PeproTech), and cultured for 48 h. After incubation, nonadherent cells, which represent the BMM population, were carefully collected and prepared for subsequent experiments.
Lentiviral packaging and infection
For the lentivirus-mediated knockdown assay, sense and anti-sense short hairpin DNA (shDNA) oligonucleotides targeting the mouse TNA gene (5ʹ-gccttacagactgtgtgcctg-3ʹ) were annealed and ligated into the pLKO.1 vector (Addgene 10878). Next, 293T cells at approximately 70% confluency were transfected with 1 μg of recombinant pLKO.1 vector, 750 ng of psPAX2 (Addgene 12260), and 250 ng of pMD2.G (Addgene 12259) using Lipofectamine 2000 (Invitrogen). Twelve hours post-transfection, the medium was replaced. After an additional 36 h, the culture medium containing the virus was harvested and used to infect BMMs at 50% confluency. Following 48 h of infection, the BMMs were selected with 1 μg/mL puromycin for at least 3 days before proceeding with subsequent experiments.
In vitro osteoclastogenesis
Bone marrow–derived monocyte-macrophages (BMMs) were plated at 1 × 105 cells/cm2 and cultured in a medium containing 10 ng/mL M-CSF and 50 ng/mL sRANKL (PeproTech). Osteoclast cultures on plastic were terminated at 5 days and on bone at 6 days. The medium was refreshed on days 2 and 4, with the start of induction designated as day 0.
Tartrate-resistant acid phosphatase (TRAP) staining assay
Cells were fixed and stained for TRAP activity after 5 days in culture, using a commercial kit (Sigma 387-A) according to the product instructions. ImageJ software (National Institutes of Health, Bethesda, MD, USA) was used to quantify the proportion of TRAP-positive cells.
Pit-formation assay
6 days after the initiation of induction, bone slices were incubated in 0.5 N NaOH for 30 s and cells scraped off using a cotton swab, then incubated with 20 mg/mL peroxidase-conjugated wheat germ agglutinin (Sigma) in PBS for 30 min, washed with PBS three times, and exposed to 3,30-Diaminobenzidine tablets (Sigma; D4168) for 15 min before washing. BioQuant OSTEO 2010 (BioQuant Image Analysis Corporation, Nashville, TN, USA) was used to quantify pit area. ImageJ software (National Institutes of Health, Bethesda, MD, USA) was used to quantify the area of bone resorption.
Real-time polymerase chain reaction (rt-qPCR)
Total RNA was extracted using TRIzol reagent (Invitrogen), and cDNA was synthesized from 2 μg of total RNA using the SuperScript II First-Strand Synthesis System (Invitrogen). Quantitative real-time PCR was conducted in a 25 μl mixture containing 12.5 μl SYBR Green PCR Master Mix (Takara), 200 nM of each primer, and 5 μL of cDNA. A CFX96 Sequence Detection System (Bio-Rad) was used, employing the comparative threshold cycle method for relative quantification. GAPDH was used as an internal control. Primer sequences are listed in Table 1.
Table 1Primer sequences for rt-qPCRGenePrimer sequenceClec3bforward5’- CGAGGAACTCAAGAACAGGATGG − 3’reverse5’-GCCTCATGGAAGGTCTTCGGTT-3’Itgb3forward5’-GGCGTTGTTGTTGGAGAGTC-3’reverse5’-CTTCAGGTTACATCGGGGTGA-3’Acp5forward5’-CACTCCCACCCTGAGATTTGT-3’reverse5’-CCCCAGAGACATGATGAAGTCA-3’Dcstampforward5’-CGGCGGCCAATCTAAGGTC-3’reverse5’-CCCACCATGCCCTTGAACA-3’Ctskforward5’-CTCGGCGTTTAATTTGGGAGA-3’reverse5’-TCGAGAGGGAGGTATTCTGAGT-3’
Statistics analysis
All statistical analyses were conducted using R software (version 4.2.2) and GraphPad Prism 9. A p-value of < 0.05, determined by an unpaired Student’s t-test, was considered statistically significant between the two groups. Data are presented as means ± standard errors of the mean (SEM) unless otherwise specified. The results are representative of more than three independent experiments. Pearson correlation analysis was conducted. All statistical tests were two-sided, and the level of statistical significance was set at a p-value of < 0.05.
Data acquisition and processing
We recruited 18 patients with primary OP and 18 individuals with normal bone mass between March 2018 and February 2019 at Honghui Hospital, Xi’an Jiaotong University, People’s Republic of China. Plasma samples from all 36 participants were subjected to proteomic sequencing. Each participant provided informed consent, and the study was approved by the Institutional Review Board of Honghui Hospital, Xi’an Jiaotong University (project number: 2018-22). Detailed descriptions of the proteomic data and the inclusion and exclusion criteria are available in our previously published study [20]. Additionally, plasma proteomic data from 43 patients with obesity, both before and after weight loss, were obtained from Geyer’s study [21]. In addition, we downloaded 300 platelet-related genes from Li’s study (Supplementary Table 1) [22].
RNA-seq data from wild-type (WT) MSCs derived from induced pluripotent stem cells (iPSCs) differentiated into osteoblasts at five time points (0, 7, 14, and 17 days) were obtained from GSE102732 in the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/). Using the Nature Genetics data [23] from GSE113253, we obtained RNA-seq data of bone marrow mesenchymal stromal cells (BMSCs) and adipose-derived stem cells (ADSCs) for osteogenic and adipogenic differentiation at five time points: 4 h, 1 day, 3 days, 7 days, and 14 days. On the basis of Robert’s study [24], we acquired ADSCs and performed RNA-seq following osteogenic and adipogenic differentiation induction for 24 h. The RNA-seq raw files in fastq format were obtained from the functional genomics data collection (ArrayExpress repository, https://www.ebi.ac.uk/biostudies/arrayexpress) under the number E-MTAB-6298, running FastQC (version 0.10) to assess read quality, generating Indexes, aligning to genome with STAR-aligner (version 2.7), summarizing gene counts with featureCounts (version 2.0.1), importing gene counts into R/RStudio, and using DESeq2 (version 1.10) to find significant genes.
Analysis of differentially expressed proteins (DEPs)
We performed a differential analysis of plasma proteomic data between patients with OP and healthy individuals. The mean protein values were log2-transformed and visualized using a volcano plot (|Log2FC| > 0.5, p-value < 0.05). We used the “Retrieve/IDmapping” function to perform the id transformation based on Uniprot database (https://www.uniprot.org/id-mapping), which is the most extensive and informative protein database and is the first choice for querying protein functions. Moreover, 66 DEPs, identified after gene ID mapping, were intersected with 300 PLTs and displayed in a Venn diagram.
Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis
After performing differential analysis, we identified six platelet-related OP genes. Then, the “clusterProfile” package in R was used to perform GO and KEGG enrichment analyses to elucidate the potential pathogenic mechanisms and biological pathways associated with these genes. The results of the GO and KEGG analyses are presented using bar graphs. In addition, the pairwise similarity of enriched terms was calculated using the Jaccard similarity index (JC). The “ggplot2,” “igraph,” and “ggraph” packages were employed for EMAP visualization of the similarity results.
Protein-protein interaction (PPI) and friends analysis
The online STRING database (http://string-db.org) is widely used for constructing PPI networks and scoring interactions between target proteins. We conducted a PPI network analysis of six proteins to explore their interactions using a confidence cutoff of > 0.4. The hub proteins within the PPI network were identified and scored using the “Cytohubba” plugin in Cytoscape. In addition, we performed Friends analysis using the “GOSemSim” package to identify key genes by constructing a gene interaction network and assessing the importance of each gene through network topology analysis.
Human protein atlas (HPA) database analysis
Immunohistochemical (IHC) data for A2M, COL1A1, and TNA in adipose tissue were obtained from the HPA database (http://www.proteinatlas.org) [25]. In addition, we analyzed the expression levels (nTPM) in human tissues using the Genotype-Tissue Expression (GTEx) database.
The DeepTMHMM tool
DeepTMHMM (https://dtu.biolib.com/DeepTMHMM) is a deep learning model for transmembrane topology prediction and classification and is currently the most complete and best-performing method for the prediction of the topology of both alpha-helical and beta-barrel transmembrane proteins [26]. We used DeepTMHMM to analyze the TNA protein to determine whether it was a classical secretory protein and whether it had transmembrane domains.
Single-cell RNA-seq (scRNA-seq) data
On the basis of Zhong’s study [27], we obtained scRNA-seq data for BMSCs from the Single Cell Portal database (https://singlecell.broadinstitute.org/single_cell). We analyzed the expression of TNA in each cell cluster, including early mesenchymal progenitors (EMPs, cluster 1), intermediate mesenchymal progenitors (IMPs, cluster 2), late mesenchymal progenitors (LMPs, cluster 3), osteoblasts (cluster 4), osteocytes (cluster 5), lineage-committed progenitors (LCPs, cluster 6), adipocytes (cluster 7), and chondrocytes (clusters 8 and 9).
Cell cultures and transfection
HEK293 cell lines, obtained from the American Type Culture Collection (ATCC), were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM; Invitrogen), supplemented with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin. For the isolation and culture of bone marrow–derived monocyte-macrophages (BMMs), mouse bone marrow cells were harvested from the femurs and tibias by flushing the bones with sterile phosphate-buffered saline (PBS). The harvested bone marrow cells were then passed through a 70 μm cell strainer to remove debris, followed by centrifugation at 300 x g for 5 min. After discarding the supernatant, the cell pellet was resuspended in red blood cell lysis buffer and incubated for 2 min to lyse erythrocytes, followed by another round of centrifugation. The resulting cells were then resuspended in α-modified Minimal Essential Medium (α-MEM; Invitrogen), supplemented with 10% FBS and 10 ng/mL macrophage colony-stimulating factor (M-CSF; PeproTech), and cultured for 48 h. After incubation, nonadherent cells, which represent the BMM population, were carefully collected and prepared for subsequent experiments.
Lentiviral packaging and infection
For the lentivirus-mediated knockdown assay, sense and anti-sense short hairpin DNA (shDNA) oligonucleotides targeting the mouse TNA gene (5ʹ-gccttacagactgtgtgcctg-3ʹ) were annealed and ligated into the pLKO.1 vector (Addgene 10878). Next, 293T cells at approximately 70% confluency were transfected with 1 μg of recombinant pLKO.1 vector, 750 ng of psPAX2 (Addgene 12260), and 250 ng of pMD2.G (Addgene 12259) using Lipofectamine 2000 (Invitrogen). Twelve hours post-transfection, the medium was replaced. After an additional 36 h, the culture medium containing the virus was harvested and used to infect BMMs at 50% confluency. Following 48 h of infection, the BMMs were selected with 1 μg/mL puromycin for at least 3 days before proceeding with subsequent experiments.
In vitro osteoclastogenesis
Bone marrow–derived monocyte-macrophages (BMMs) were plated at 1 × 105 cells/cm2 and cultured in a medium containing 10 ng/mL M-CSF and 50 ng/mL sRANKL (PeproTech). Osteoclast cultures on plastic were terminated at 5 days and on bone at 6 days. The medium was refreshed on days 2 and 4, with the start of induction designated as day 0.
Tartrate-resistant acid phosphatase (TRAP) staining assay
Cells were fixed and stained for TRAP activity after 5 days in culture, using a commercial kit (Sigma 387-A) according to the product instructions. ImageJ software (National Institutes of Health, Bethesda, MD, USA) was used to quantify the proportion of TRAP-positive cells.
Pit-formation assay
6 days after the initiation of induction, bone slices were incubated in 0.5 N NaOH for 30 s and cells scraped off using a cotton swab, then incubated with 20 mg/mL peroxidase-conjugated wheat germ agglutinin (Sigma) in PBS for 30 min, washed with PBS three times, and exposed to 3,30-Diaminobenzidine tablets (Sigma; D4168) for 15 min before washing. BioQuant OSTEO 2010 (BioQuant Image Analysis Corporation, Nashville, TN, USA) was used to quantify pit area. ImageJ software (National Institutes of Health, Bethesda, MD, USA) was used to quantify the area of bone resorption.
Real-time polymerase chain reaction (rt-qPCR)
Total RNA was extracted using TRIzol reagent (Invitrogen), and cDNA was synthesized from 2 μg of total RNA using the SuperScript II First-Strand Synthesis System (Invitrogen). Quantitative real-time PCR was conducted in a 25 μl mixture containing 12.5 μl SYBR Green PCR Master Mix (Takara), 200 nM of each primer, and 5 μL of cDNA. A CFX96 Sequence Detection System (Bio-Rad) was used, employing the comparative threshold cycle method for relative quantification. GAPDH was used as an internal control. Primer sequences are listed in Table 1.
Table 1Primer sequences for rt-qPCRGenePrimer sequenceClec3bforward5’- CGAGGAACTCAAGAACAGGATGG − 3’reverse5’-GCCTCATGGAAGGTCTTCGGTT-3’Itgb3forward5’-GGCGTTGTTGTTGGAGAGTC-3’reverse5’-CTTCAGGTTACATCGGGGTGA-3’Acp5forward5’-CACTCCCACCCTGAGATTTGT-3’reverse5’-CCCCAGAGACATGATGAAGTCA-3’Dcstampforward5’-CGGCGGCCAATCTAAGGTC-3’reverse5’-CCCACCATGCCCTTGAACA-3’Ctskforward5’-CTCGGCGTTTAATTTGGGAGA-3’reverse5’-TCGAGAGGGAGGTATTCTGAGT-3’
Statistics analysis
All statistical analyses were conducted using R software (version 4.2.2) and GraphPad Prism 9. A p-value of < 0.05, determined by an unpaired Student’s t-test, was considered statistically significant between the two groups. Data are presented as means ± standard errors of the mean (SEM) unless otherwise specified. The results are representative of more than three independent experiments. Pearson correlation analysis was conducted. All statistical tests were two-sided, and the level of statistical significance was set at a p-value of < 0.05.
Results
Identification of differentially expressed platelet-related proteins in OP
Figure 2A illustrates our collection of 36 samples, comprising 18 patients with primary OP and 18 individuals with normal bone mass. We identified platelet-related OP genes through differential and intersection analyses. Differential analysis of plasma proteome data was conducted using volcano plots (Fig. 2B). The results indicated that 19 plasma proteins were highly upregulated in the OP group, whereas 112 proteins were downregulated (|Log2FC| > 0.5, p < 0.05). Following protein and gene ID conversion, a Venn plot indicated that six genes—A2M, TF, PF4, SPP2, TNA, and COL1A1—were associated with platelet-related OP proteins (Fig. 2C). Furthermore, a map was generated to illustrate the differential expression of these six key proteins between the two groups. Except for A2M, the expression levels of the other five proteins were significantly higher in the OP group (Fig. 2D).
Fig. 2Differentially expressed platelet-related genes in osteoporosis. (A) OP plasma proteome data and platelet-related gene list acquisition and flow chart. (B) Differential analysis of plasma proteome between OP and normal group by volcano plot (|Log2FC| > 0.5, p-value < 0.05) (OP = 18 samples, Normal = 18 samples). (C) Venn diagram of the differential plasma proteins and the 300 platelet related genes. (D) Differential expression level of six key genes (FC = OP/Normal)
Functional enrichment of six key proteins
Functional enrichment analysis was conducted for these six proteins. The results of the GO (Fig. 3A) and KEGG (Fig. 3B) enrichment analyses are presented using bar graphs, with detailed information provided in Table 2. The significantly enriched biological process (BP) terms identified included the following: humoral immune response, bone remodeling, response to nutrients, response to corticosteroids, biomineralization, positive regulation of bone resorption, regulation of macrophage differentiation, regulation of bone remodeling, and positive regulation of the canonical Wnt signaling pathway. The significantly enriched cellular component (CC) terms identified were secretory granule lumen and endoplasmic reticulum lumen. The significantly enriched molecular function (MF) terms identified were protease binding and chemokine activity. In addition, the significantly enriched KEGG terms identified were ferroptosis, mineral absorption, complement and coagulation cascades, extracellular matrix (ECM)-receptor interaction, AGE-RAGE signaling pathway in diabetic complications, viral protein interaction with cytokines and cytokine receptors, and protein digestion and absorption.
Table 2GO and KEGG terms of six platelet related osteoporosis genesONTOLOGYIDDescriptionP valueBPGO:0006959humoral immune response0.0001BPGO:0046849bone remodeling0.0003BPGO:0007584response to nutrient0.0009BPGO:0031960response to corticosteroid0.0010BPGO:0110148biomineralization0.0012BPGO:0045780positive regulation of bone resorption0.0051BPGO:0045649regulation of macrophage differentiation0.0076BPGO:0046850regulation of bone remodeling0.0155BPGO:0090263positive regulation of canonical Wnt signaling pathway0.0343CCGO:0034774secretory granule lumen0.0001CCGO:0005788endoplasmic reticulum lumen0.0001MFGO:0002020protease binding0.0008MFGO:0008009chemokine activity0.0159KEGGhsa04216Ferroptosis0.0199KEGGhsa04978Mineral absorption0.0291KEGGhsa04610Complement and coagulation cascades0.0410KEGGhsa04512ECM-receptor interaction0.0424KEGGhsa04061Viral protein interaction with cytokine and cytokine receptor0.0481KEGGhsa04933AGE-RAGE signaling pathway in diabetic complications0.0481KEGGhsa04974Protein digestion and absorption0.0495
Fig. 3Functional enrichment analysis of six key genes. (A) GO enrichment analysis, including BP, CC, and MF. (B) KEGG enrichment analysis. (C) Similarity analysis between related terms in GO enrichment analysis. (D) Similarity analysis between related passways in KEGG enrichment analysis. (p < 0.05)
Moreover, EMAP plots showed the similarity between individual functional terms, with line thickness representing the degree of pairwise similarity between the terms. Thicker lines indicate greater similarity (Fig. 3C, D). We observed a high similarity between the terms “positive regulation of bone resorption” and “regulation of bone remodeling,” as well as with “response to corticosteroids,” " response to nutrients,” and “protease binding.”
PPI network and identification of hub proteins
The PPI network of the six proteins was analyzed using the STRING database (scores > 0.4) (Fig. 4A). Subsequently, the “CytoHubba” plug-in in Cytoscape software was used to rank the importance analysis, revealing that A2M and CLEC3B exhibited the highest scores and rankings (Fig. 4B). Additionally, Friends analysis identified A2M, COL1A1, and CLEC3B as having the highest importance scores among the six genes (Fig. 4C). The genes were ranked in descending order based on their average similarity to other genes, with the top-ranked genes showing the greatest similarity to other genes and being identified as key genes.
Fig. 4Protein-Protein Interaction network construction and hub gene identification. (A) The PPI network by STRING database (scores > 0.4). (B) Protein scores were calculated using the Cytohubba plug-in. (C) Importance scores between 6 genes are calculated using Friends analysis
Expression levels of Hub Proteins and identification of secretion type
The expression levels (nTPM) of A2M, COL1A1, and TNA in human tissues were analyzed using GTEx data from the HPA database. The results indicated that the expression of TNA was highest in adipose tissue. Additionally, immunostaining analysis of the three proteins in adipose tissue revealed that A2M and TNA were moderately expressed, whereas the COL1A1 protein was expressed at low levels (Fig. 5A) based on HPA database.
Fig. 5Identification of hub gene expression in adipose tissue and analysis of secretion type. (A) Expression of A2M, COL1A1, CLEC3B in tissues from the GTEx database and immunohistochemistry in adipose tissue from HPA database. (B) Flow chart of screening obesity and osteoporosis co-related key gene. (C) Venn plot between plasma proteome in 43 obese patients before and after weight loss, and 3 hub genes. (D) Clec3b expression level in brain, muscle, subcutaneous adipose and visceral adipose of WT mice (2 samples). (E) Identification the secretion mode of TNA protein by DeepTMHMM database
The flow chart for determining the intersection between the DEPs in the plasma proteome of 43 patients with obesity and the three hub genes is presented in Fig. 5B. Unexpectedly, only TNA was identified as a key protein (Fig. 5C). We performed RT-qPCR analysis of CLEC3B in four mouse tissues: brain, muscle, subcutaneous adipose, and visceral adipose tissue (two samples from WT mice). The results showed that CLEC3B expression was high in adipose tissue, but statistical analysis could not be made due to the lack of samples. (Fig. 5D).
In addition, the type of TNA protein was identified using the DeepTMHMM tool, revealing a signal peptide sequence at its N-terminus: MELWGAYLLLCLFSLLTOVTT. No transmembrane domains were predicted, suggesting that TNA is a classically secreted protein (Fig. 5E).
CLEC3B expression in MSC subsets
Through scRNA-seq data of the bone marrow microenvironment (BMM), we found that CLEC3B expression was significantly higher in the cluster 1 subset, which corresponds to EMPs. Additionally, we observed an increase in CLEC3B expression in a small number of IMP cells (Fig. 6A–C).
Fig. 6CLEC3B expression in mesenchymal stem cell subsets, and its possible roles. (A) Cell subsets map of Bone Marrow Microenvironment. (B) Expression of CLEC3B in different cell subsets. (C) Expression of CLEC3B in different cell subsets by violin plot. (D) Expression of CLEC3B in osteogenic differentiation of BMSCs (GSE102732, each group = 2 duplicate samples). (E) Expression of CLEC3B in osteogenic differentiation of BMSCs from NG dataset (GSE113253, each group = 3 duplicate samples). (F) Expression of CLEC3B in osteogenic differentiation of ADSCs from NG dataset (GSE113253, each group = 3 duplicate samples). (G) Expression of CLEC3B in 24 h osteogenic differentiation of ADSCs (E-MTAB-6298, each group = 3 duplicate samples). (H) Expression of CLEC3B in adipogenic differentiation of BMSCs from NG dataset (GSE113253, each group = 3 duplicate samples). (I) Expression of CLEC3B in adipogenic differentiation of ADSCs from NG dataset (GSE113253, each group = 3 duplicate samples). (J) Expression of CLEC3B in 24 h adipogenic differentiation of ADSCs (E-MTAB-6298, each group = 3 duplicate samples)
Osteoblast differentiation was assessed at multiple time points using BMSCs. The expression of CLEC3B was significantly upregulated over time (Fig. 6D). However, in another data set, CLEC3B expression decreased abruptly on day 14 (Fig. 6E; Supplementary Table 2). During the osteogenic induction of ADSCs (adipose tissue [AT]), CLEC3B expression was significantly upregulated on days 3 and 7 but downregulated on day 14 (Fig. 6F; Supplementary Table 3). Additionally, in another data set, CLEC3B expression was significantly elevated within 24 h of osteogenic differentiation induction (Fig. 6G). No significant change was observed in the expression of CLEC3B during the adipogenic differentiation of BMSCs (Fig. 6H; Supplementary Table 2). In addition, no significant difference was observed in the adipogenic differentiation of ADSCs (Fig. 6I, J; Supplementary Table 3).
TNA regulates osteoclast differentiation and function in vitro
We specifically examined osteoclast (OC) genesis by inducing differentiation in primary BMMs, assessing the impact of TNA expression modulation. To evaluate TNA’s role in this process, we transduced primary BMMs with lentivirus expressing short hairpin RNA (shRNA) against TNA or a control shRNA. Our findings revealed that TNA deficiency in BMMs led to the upregulation of mRNAs encoding osteoclastic markers, including Itgb3 (which encodes Integrin β3, a crucial receptor on the osteoclast surface), Acp5 (encoding tartrate-resistant acid phosphatase, an enzyme abundantly secreted during osteoclast activation), Dcstamp (encoding dendritic cell-specific transmembrane protein, essential for osteoclast fusion) [28–30], and Ctsk (encoding cathepsin K, a lysosomal protease highly expressed in osteoclasts) (Fig. 7A).
Fig. 7TNA knockdown promotes osteoclast differentiation and activity in BMMs. (A) Knockdown of TNA leads to an increase in the mRNA levels of osteoclastic markers in BMMs. BMMs were induced with 10 ng/mL M-CSF and 50 ng/mL RANKL for 5 days before being harvested for mRNA analysis. (B) TNA deficiency promotes the formation of mature osteoclasts in BMMs. BMMs were either non-transduced (control) or transduced with TNA-inhibiting shRNA and subsequently induced to differentiate with 10 ng/mL M-CSF and 50 ng/mL RANKL for 5 days. The cells were then fixed and stained with TRAP solution (Sigma 387-A). The black arrows point to the osteoclasts. Scale bar = 100 μm. TRAP-positive multinucleated cells were counted. (C) TNA deficiency increases osteoclast-mediated bone resorption in BMMs. Control and shTNA BMMs were cultured on bone slices with 10 ng/mL M-CSF and 50 ng/mL RANKL for 6 days. The cells were removed, and resorption pits were stained with peroxidase-conjugated wheat germ agglutinin. Scale bar = 100 μm. The area of the pits was measured
Further analysis of TNA’s effect on osteoclast differentiation and function showed a significant increase in TRAP-positive cells and enhanced bone resorption in TNA-inhibited BMMs (Fig. 7B). The pit formation assay results demonstrated that TNA-inhibited cells produced a greater total area of bone resorption pits, indicating that the inhibition of TNA expression enhances overall bone resorption. (Fig. 7C). These results are representative of more than three independent experiments.
Identification of differentially expressed platelet-related proteins in OP
Figure 2A illustrates our collection of 36 samples, comprising 18 patients with primary OP and 18 individuals with normal bone mass. We identified platelet-related OP genes through differential and intersection analyses. Differential analysis of plasma proteome data was conducted using volcano plots (Fig. 2B). The results indicated that 19 plasma proteins were highly upregulated in the OP group, whereas 112 proteins were downregulated (|Log2FC| > 0.5, p < 0.05). Following protein and gene ID conversion, a Venn plot indicated that six genes—A2M, TF, PF4, SPP2, TNA, and COL1A1—were associated with platelet-related OP proteins (Fig. 2C). Furthermore, a map was generated to illustrate the differential expression of these six key proteins between the two groups. Except for A2M, the expression levels of the other five proteins were significantly higher in the OP group (Fig. 2D).
Fig. 2Differentially expressed platelet-related genes in osteoporosis. (A) OP plasma proteome data and platelet-related gene list acquisition and flow chart. (B) Differential analysis of plasma proteome between OP and normal group by volcano plot (|Log2FC| > 0.5, p-value < 0.05) (OP = 18 samples, Normal = 18 samples). (C) Venn diagram of the differential plasma proteins and the 300 platelet related genes. (D) Differential expression level of six key genes (FC = OP/Normal)
Functional enrichment of six key proteins
Functional enrichment analysis was conducted for these six proteins. The results of the GO (Fig. 3A) and KEGG (Fig. 3B) enrichment analyses are presented using bar graphs, with detailed information provided in Table 2. The significantly enriched biological process (BP) terms identified included the following: humoral immune response, bone remodeling, response to nutrients, response to corticosteroids, biomineralization, positive regulation of bone resorption, regulation of macrophage differentiation, regulation of bone remodeling, and positive regulation of the canonical Wnt signaling pathway. The significantly enriched cellular component (CC) terms identified were secretory granule lumen and endoplasmic reticulum lumen. The significantly enriched molecular function (MF) terms identified were protease binding and chemokine activity. In addition, the significantly enriched KEGG terms identified were ferroptosis, mineral absorption, complement and coagulation cascades, extracellular matrix (ECM)-receptor interaction, AGE-RAGE signaling pathway in diabetic complications, viral protein interaction with cytokines and cytokine receptors, and protein digestion and absorption.
Table 2GO and KEGG terms of six platelet related osteoporosis genesONTOLOGYIDDescriptionP valueBPGO:0006959humoral immune response0.0001BPGO:0046849bone remodeling0.0003BPGO:0007584response to nutrient0.0009BPGO:0031960response to corticosteroid0.0010BPGO:0110148biomineralization0.0012BPGO:0045780positive regulation of bone resorption0.0051BPGO:0045649regulation of macrophage differentiation0.0076BPGO:0046850regulation of bone remodeling0.0155BPGO:0090263positive regulation of canonical Wnt signaling pathway0.0343CCGO:0034774secretory granule lumen0.0001CCGO:0005788endoplasmic reticulum lumen0.0001MFGO:0002020protease binding0.0008MFGO:0008009chemokine activity0.0159KEGGhsa04216Ferroptosis0.0199KEGGhsa04978Mineral absorption0.0291KEGGhsa04610Complement and coagulation cascades0.0410KEGGhsa04512ECM-receptor interaction0.0424KEGGhsa04061Viral protein interaction with cytokine and cytokine receptor0.0481KEGGhsa04933AGE-RAGE signaling pathway in diabetic complications0.0481KEGGhsa04974Protein digestion and absorption0.0495
Fig. 3Functional enrichment analysis of six key genes. (A) GO enrichment analysis, including BP, CC, and MF. (B) KEGG enrichment analysis. (C) Similarity analysis between related terms in GO enrichment analysis. (D) Similarity analysis between related passways in KEGG enrichment analysis. (p < 0.05)
Moreover, EMAP plots showed the similarity between individual functional terms, with line thickness representing the degree of pairwise similarity between the terms. Thicker lines indicate greater similarity (Fig. 3C, D). We observed a high similarity between the terms “positive regulation of bone resorption” and “regulation of bone remodeling,” as well as with “response to corticosteroids,” " response to nutrients,” and “protease binding.”
PPI network and identification of hub proteins
The PPI network of the six proteins was analyzed using the STRING database (scores > 0.4) (Fig. 4A). Subsequently, the “CytoHubba” plug-in in Cytoscape software was used to rank the importance analysis, revealing that A2M and CLEC3B exhibited the highest scores and rankings (Fig. 4B). Additionally, Friends analysis identified A2M, COL1A1, and CLEC3B as having the highest importance scores among the six genes (Fig. 4C). The genes were ranked in descending order based on their average similarity to other genes, with the top-ranked genes showing the greatest similarity to other genes and being identified as key genes.
Fig. 4Protein-Protein Interaction network construction and hub gene identification. (A) The PPI network by STRING database (scores > 0.4). (B) Protein scores were calculated using the Cytohubba plug-in. (C) Importance scores between 6 genes are calculated using Friends analysis
Expression levels of Hub Proteins and identification of secretion type
The expression levels (nTPM) of A2M, COL1A1, and TNA in human tissues were analyzed using GTEx data from the HPA database. The results indicated that the expression of TNA was highest in adipose tissue. Additionally, immunostaining analysis of the three proteins in adipose tissue revealed that A2M and TNA were moderately expressed, whereas the COL1A1 protein was expressed at low levels (Fig. 5A) based on HPA database.
Fig. 5Identification of hub gene expression in adipose tissue and analysis of secretion type. (A) Expression of A2M, COL1A1, CLEC3B in tissues from the GTEx database and immunohistochemistry in adipose tissue from HPA database. (B) Flow chart of screening obesity and osteoporosis co-related key gene. (C) Venn plot between plasma proteome in 43 obese patients before and after weight loss, and 3 hub genes. (D) Clec3b expression level in brain, muscle, subcutaneous adipose and visceral adipose of WT mice (2 samples). (E) Identification the secretion mode of TNA protein by DeepTMHMM database
The flow chart for determining the intersection between the DEPs in the plasma proteome of 43 patients with obesity and the three hub genes is presented in Fig. 5B. Unexpectedly, only TNA was identified as a key protein (Fig. 5C). We performed RT-qPCR analysis of CLEC3B in four mouse tissues: brain, muscle, subcutaneous adipose, and visceral adipose tissue (two samples from WT mice). The results showed that CLEC3B expression was high in adipose tissue, but statistical analysis could not be made due to the lack of samples. (Fig. 5D).
In addition, the type of TNA protein was identified using the DeepTMHMM tool, revealing a signal peptide sequence at its N-terminus: MELWGAYLLLCLFSLLTOVTT. No transmembrane domains were predicted, suggesting that TNA is a classically secreted protein (Fig. 5E).
CLEC3B expression in MSC subsets
Through scRNA-seq data of the bone marrow microenvironment (BMM), we found that CLEC3B expression was significantly higher in the cluster 1 subset, which corresponds to EMPs. Additionally, we observed an increase in CLEC3B expression in a small number of IMP cells (Fig. 6A–C).
Fig. 6CLEC3B expression in mesenchymal stem cell subsets, and its possible roles. (A) Cell subsets map of Bone Marrow Microenvironment. (B) Expression of CLEC3B in different cell subsets. (C) Expression of CLEC3B in different cell subsets by violin plot. (D) Expression of CLEC3B in osteogenic differentiation of BMSCs (GSE102732, each group = 2 duplicate samples). (E) Expression of CLEC3B in osteogenic differentiation of BMSCs from NG dataset (GSE113253, each group = 3 duplicate samples). (F) Expression of CLEC3B in osteogenic differentiation of ADSCs from NG dataset (GSE113253, each group = 3 duplicate samples). (G) Expression of CLEC3B in 24 h osteogenic differentiation of ADSCs (E-MTAB-6298, each group = 3 duplicate samples). (H) Expression of CLEC3B in adipogenic differentiation of BMSCs from NG dataset (GSE113253, each group = 3 duplicate samples). (I) Expression of CLEC3B in adipogenic differentiation of ADSCs from NG dataset (GSE113253, each group = 3 duplicate samples). (J) Expression of CLEC3B in 24 h adipogenic differentiation of ADSCs (E-MTAB-6298, each group = 3 duplicate samples)
Osteoblast differentiation was assessed at multiple time points using BMSCs. The expression of CLEC3B was significantly upregulated over time (Fig. 6D). However, in another data set, CLEC3B expression decreased abruptly on day 14 (Fig. 6E; Supplementary Table 2). During the osteogenic induction of ADSCs (adipose tissue [AT]), CLEC3B expression was significantly upregulated on days 3 and 7 but downregulated on day 14 (Fig. 6F; Supplementary Table 3). Additionally, in another data set, CLEC3B expression was significantly elevated within 24 h of osteogenic differentiation induction (Fig. 6G). No significant change was observed in the expression of CLEC3B during the adipogenic differentiation of BMSCs (Fig. 6H; Supplementary Table 2). In addition, no significant difference was observed in the adipogenic differentiation of ADSCs (Fig. 6I, J; Supplementary Table 3).
TNA regulates osteoclast differentiation and function in vitro
We specifically examined osteoclast (OC) genesis by inducing differentiation in primary BMMs, assessing the impact of TNA expression modulation. To evaluate TNA’s role in this process, we transduced primary BMMs with lentivirus expressing short hairpin RNA (shRNA) against TNA or a control shRNA. Our findings revealed that TNA deficiency in BMMs led to the upregulation of mRNAs encoding osteoclastic markers, including Itgb3 (which encodes Integrin β3, a crucial receptor on the osteoclast surface), Acp5 (encoding tartrate-resistant acid phosphatase, an enzyme abundantly secreted during osteoclast activation), Dcstamp (encoding dendritic cell-specific transmembrane protein, essential for osteoclast fusion) [28–30], and Ctsk (encoding cathepsin K, a lysosomal protease highly expressed in osteoclasts) (Fig. 7A).
Fig. 7TNA knockdown promotes osteoclast differentiation and activity in BMMs. (A) Knockdown of TNA leads to an increase in the mRNA levels of osteoclastic markers in BMMs. BMMs were induced with 10 ng/mL M-CSF and 50 ng/mL RANKL for 5 days before being harvested for mRNA analysis. (B) TNA deficiency promotes the formation of mature osteoclasts in BMMs. BMMs were either non-transduced (control) or transduced with TNA-inhibiting shRNA and subsequently induced to differentiate with 10 ng/mL M-CSF and 50 ng/mL RANKL for 5 days. The cells were then fixed and stained with TRAP solution (Sigma 387-A). The black arrows point to the osteoclasts. Scale bar = 100 μm. TRAP-positive multinucleated cells were counted. (C) TNA deficiency increases osteoclast-mediated bone resorption in BMMs. Control and shTNA BMMs were cultured on bone slices with 10 ng/mL M-CSF and 50 ng/mL RANKL for 6 days. The cells were removed, and resorption pits were stained with peroxidase-conjugated wheat germ agglutinin. Scale bar = 100 μm. The area of the pits was measured
Further analysis of TNA’s effect on osteoclast differentiation and function showed a significant increase in TRAP-positive cells and enhanced bone resorption in TNA-inhibited BMMs (Fig. 7B). The pit formation assay results demonstrated that TNA-inhibited cells produced a greater total area of bone resorption pits, indicating that the inhibition of TNA expression enhances overall bone resorption. (Fig. 7C). These results are representative of more than three independent experiments.
Discussion
In this study, we first identified TNA as a key osteoporotic platelet-associated plasma protein. Notably, we found that TNA is significantly overexpressed in adipose tissue and is a secreted protein. Employing multi-RNA-seq data. In vitro experiments showed that knockdown of TNA expression significantly increases the expression of osteoclast markers and enhances resorption functions.
The MeOS cohort had demonstrated an association between high platelet count and low BMD [6]; however, the underlying mechanism remained unclear. Platelets can mediate the uptake and release of cytokines. In our research, we utilized plasma proteomics to explore the link between OP related to platelet related proteins and identified TNA as a key plasma protein, which may provide a potential explanation. TNA is a secreted protein released from the platelets of patients with stable angina pectoris (SAP) [31]. This finding is consistent with our hypothesis regarding the secretion pattern of TNA, but further investigation is required to determine the source of TNA secretion. Certain cancer cells or cells associated with cancerous tissue have been demonstrated to be capable of expressing TNA and secreting it into the ECM. From structural analysis, we also found TNA to be an exocrine protein. However, the level of TNA present in the serum of patients with cancer is markedly reduced, and the relationship between these two is not always straightforward [17, 32–39]. We observed a similar pattern where TNA is highly expressed in adipose tissue but relatively low in the plasma of patients with obesity. This suggests a discrepancy between the tissue expression of TNA and its expression in circulating blood.
The bone-adipose axis hypothesis has been extensively reviewed elsewhere [40]. Obesity, a major global health problem, is strongly associated with various chronic bone metabolic disorders, such as OP. When adipose tissue reaches its maximum energy storage capacity, adipokines such as Endolipin, Omentin-1, Chemerin, Lipocalin 2, vaspin, retinol-binding protein-4 (RBP-4), Nesfatin-1, Apelin, and apolipoproteins (APNs) are released, which can influence the progression of bone metabolic diseases [41]. Adipose tissue can also affect platelet function through the secretion of adipokines, ultimately leading to platelet activation [16]. TNA has also been identified as an adipogenic serum protein that enhances adipogenesis [42, 43]. We found that TNA was highly expressed in adipose tissue, and functional enrichment analysis revealed a possible involvement in processes such as ECM receptor interaction. However, the specific role of TNA in bone metabolism has been poorly studied.
The root cause of OP is the imbalance between osteoclast-mediated bone resorption and osteoblast-mediated osteogenic homeostasis. The expression of TNA has been shown to significantly increase during the mineralization phase of bone development [18, 44]. This finding aligns with our observation that TNA expression levels gradually increase during the osteogenic differentiation of stem cells. Notably, we also observed a decrease in TNA expression at later stages of osteogenic differentiation, suggesting that TNA may inhibit mineralized nodules at these advanced stages. While impaired osteogenic mineralization can contribute to OP, the high expression of TNA in the plasma of patients with OP may be a potential contributing factor.
Osteoclast differentiation is regulated by Ca2+ shock and its downstream signals [40]. Specifically, intracellular Ca2+ regulates osteoclast bone resorption through the CaMKIV-CREB and Cn-NFATc1 pathways [45]. TNA can also bind to Ca2+ and polysaccharide sulfate. The three Ca2+ binding sites of TNA are located in the CRD, and TNA binds to Plg K4 only in the absence of Ca2+. This suggests a competitive relationship between Ca2+ and Plg K4, which may serve as a switch for TNA binding to Plg K4 [46]. The binding of Ca²⁺ to regulate downstream responses may be an important function of TNA. Furthermore, we showed that knockdown of TNA expression significantly increased the expression of osteoclast markers and enhanced the resorption function of osteoclasts by in vitro experiments.
We explored the potential role of TNA and found that it may influence the differentiation and function of osteoblasts and osteoclasts through the adipose-bone axis. However, our study has some limitations. First, the sample size of the OP plasma proteome we analyzed was limited to 18 samples, necessitating the collection of additional samples for validation. Second, the induction of osteoblast differentiation by TNA was based solely on bioinformatic data, without corresponding experimental validation. Finally, while our study offers a new perspective, the specific mechanism of TNA remains unclear and requires further investigation. In particular, how to mediate the bone resorption function of osteoclasts and need to construct gene knockout mice.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary Material 1 |
Title: The triad in current neuroblastoma challenges: Targeting antigens, enhancing effective cytotoxicity and accurate 3D | Body:
Introduction
Neuroblastoma is an embryonic tumour originating from neural crest cells in the developing foetus or during the early years of life. The aggressiveness of neuroblastoma is driven by many molecular features that contribute to its heterogeneity, including age at diagnosis, tumour localisation and size, histopathologic classification, DNA content, chromosome imbalances and oncogene amplification [[1], [2], [3]]. The addition of multi-modal treatment regimens has boosted the survival probabilities for high-risk neuroblastoma from 15 % to almost 50 % [4]. However, nearly half of all high-risk patients do not respond to first-line therapy, progressing to the maintenance phase of treatment with more novel treatments required to tackle minimal residual disease [5]. Immunotherapy represents a key player in these novel therapeutic interventions though progress in their development, testing and approval remains tiresomely slow (Fig. 1). Partially, this is due to its unique tumour-immune microenvironment with low immunogenicity and a large number of immunomodulatory mechanisms contributing both locally and systemically, which, in turn, leads to low numbers and questionable tumour reactivity of tumour infiltrating lymphocytes impeding successful implementation of immunotherapy [6]. Major challenges to speed up this process lie in the identification of actionable tumour-associated antigens (TAA), overcoming the immunologically “cold” neuroblastoma tumour microenvironment and using novel techniques to test immunotherapeutics in vitro limiting the use animal models. This review collates the current and emerging insights into this three-pronged approach to tackle the challenge of neuroblastoma immunotherapy.Fig. 1The timeline of immunotherapy development in neuroblastoma. Created with BioRender.com.Fig. 1
Emerging neuroblastoma immunotherapies
Most active clinical trials for neuroblastoma immunotherapies focus on anti-GD2 monoclonal antibodies directed against the disialoganglioside GD2 (Fig. 2). This is the first TAA with FDA/EMA approved immunotherapy. Other emerging novel options, such as CAR-T cells, vaccines, and adoptive cell therapies, are also under evaluation.Fig. 2Active trails in neuroblastoma. Created with BioRender.com.Fig. 2
Currently, immunotherapy is given during the maintenance phase of neuroblastoma treatment plans to patients who have already undergone an extremely intensive cytotoxic regimen. Lymphopenia is a common side effect of many chemotherapeutic agents, reducing total T cell counts in patients [7]. This can lead to compromised tumour-specific T-cell responses [8]. Of the survivors, chemotherapy-exposed T cells have been described as having reduced metabolism and lower potential to convert to effector or memory states [9,10]. A study by Das et al. described a mere 36 % ex vivo processing “pass rate” of neuroblastoma patient-derived T cells (n = 11) pre-treatment. This percentage dropped dramatically to 0 % after only one cycle of chemotherapy. The expansion capacity of CD3+ cells recovered to ∼25 % in the third cycle. However, populations of naïve T cells plummeted to <15 %, replaced by a majority proportion of terminal effector T cells (45 %).
Interestingly, effector and central memory T cell subsets almost doubled over 3 courses of chemotherapy, stabilising at such levels throughout the final 2 rounds [9], therefore highlighting significant phenotypic differences between pre-and post-chemotherapy T cells of neuroblastoma patients. Almost 70 % of cells derived from neuroblastoma patients at diagnosis could not pass the ex vivo expansion required for treatments such as CAR-T cell therapy [9]. This difficulty can be potentially overcome by recent advances in iPSC-derived T and NK or NK96 cell technologies tested in pre-clinical and early-phase clinical studies in adult cancers and neuroblastoma [[11], [12], [13]]. Additionally, if chemotherapy spares central and effector memory T cells, other types of immunotherapy, such as vaccination, may represent a more logical route to target the specificities of neuroblastoma.
CAR cells
First-generation CAR-T cells targeting GD2 have shown some clinical responses in neuroblastoma patients with relatively low toxicity levels. In one study (NCT00085930), 3 of 11 patients treated achieved a complete response; one with metastatic disease regressed entirely within 6 weeks but ultimately relapsed. The other two patients remained disease-free 20 and 16 months post-infusion [14]. Another study (NCT02761915) demonstrated mild neurotoxicity (headache and fever-associated hallucination) in only 2 out of 11 patients treated with GD2 CAR-T cells. However, no clinical responses were observed in any patients [15]. Various rounds of CAR-T cell improvements by different groups are ongoing, as are clinical trials which may bring to light more significant patient responses.
Chimeric-antigen receptor natural killer (CAR-NK) cells are derived from primary NK cells or cell lines modified with an antigen-directed chimeric receptor. Although no neuroblastoma clinical trial is currently assessing this type of immunotherapy, pre-clinical work has shown some significance. CAR-NK cells exhibited cytotoxicity against GD2-expressing neuroblastoma tumour cells and the T-cell inhibitory myeloid-derived suppressor cells (MDSCs) [16,17]. A pre-clinical study developed modified NK cells with the activating receptor NKG2D fused to the cytotoxic ζ-chain of the T-cell receptor (NKG2D.ζ) [16]. In mice, these cells eliminated ∼90 % of intra-tumoural myeloid-derived suppressor cells (MDSCs) and resulted in a 400 % reduction in tumour volume compared to unmodified NK cells, which prolonged survival by a median of 44 days. The same study investigated if pre-infusion with these cells could recruit GD2 CAR-T cells to the tumour site, with a 10.9 ± 0.2-fold increase in recruitment [16]. This data highlights the potential of combination immunotherapies.
Vaccines
There are currently 8 active clinical trials for neuroblastoma that are investigating a vaccine-related therapeutic (Fig 2); 3 immunostimulatory vaccines (NCT04853017, NCT05726864, NCT04936529), 2 modified neuroblastoma cell vaccines (NCT04239040, NCT01192555), 2 vaccines directed against GD2 (NCT00911560, NCT06057948) and 1 DNA vaccine against personalised tumour antigens (NCT04049864). Critical to any vaccine's design, particularly a therapeutic cancer vaccine, is the antigen on which the vaccine is designed to target. It is no surprise that the active clinical trials and the majority of the completed clinical trials for neuroblastoma are either modified neuroblastoma cells reintroduced to patients or peptide vaccines against GD2, the most researched TAA on neuroblastomas.
A phase I trial investigating a dendritic cell vaccine (NCT01241162) showed disappointing results with associated adverse toxic effects in almost half of all patients, mainly myelosuppression due to side-arm decitabine treatment. This vaccine was prepared by isolating a sample of each patient's DCs from peripheral blood mononuclear cells and subsequently stimulating these cells with neuroblastoma TAA peptide pools. These antigens, MAGE-A1, MAGE- A3 and NY-ESO-1, are known to be over-expressed on neuroblastoma and other cancers [18]. The stimulated DCs are then reintroduced to patients once weekly for two weeks. Only two out of ten patients experienced favourable outcomes; one had a complete response, and one remained disease-free 2 years post-treatment [19]. Of note, these patients experienced only minimal disease at the start of the trial. So, while this trial highlights the feasibility of this vaccine for some patients, a more potent and specific vaccine is required to address the majority of high-risk patients who present not with minimal disease but with highly aggressive tumours.
Another phase I/II trial (NCT00923351) was completed investigating a tumour lysate-pulsed DC vaccine. Of the 30 patients treated, all patients experienced an immune response to vaccination, 12 remain on follow-up and are stable or without evidence of disease, 7 are receiving additional external therapy, and 11 died from progressive disease. To date, no publication with a detailed breakdown of patient responses exists. However, the results above, again, show highly variable responses from patients.
Adoptive cell therapy with cytokine-producing cells
Based on NK cells' role in the success of anti-GD2 therapy and their potential for recognition of MHC-lacking neuroblastoma cells, these innate immune cells have attracted interest for use as “off-the-shelf” allogeneic cell therapy. This involves extraction of a patientʼs NK cells, stimulation of the cells ex vivo using cytokines and re-infusion to the patient. Currently, there are 20 phase I/II clinical trials investigating NK cell infusions with mixed results from those completed [[20], [21], [22]] or active. A clinical trial examined the longitudinal effects of NK cell infusions. Post-chemo-immunotherapy infusion with NK cells stimulated with IL-2/IL-15 ex vivo improved NK cell cytotoxicity with a range of 4.32–21.56 % increase across 6 patients [21]. While this therapeutic seems well tolerated in neuroblastoma patients, the direct contribution of NK cell infusions to survival outcomes remains to be seen.
CAR cells
First-generation CAR-T cells targeting GD2 have shown some clinical responses in neuroblastoma patients with relatively low toxicity levels. In one study (NCT00085930), 3 of 11 patients treated achieved a complete response; one with metastatic disease regressed entirely within 6 weeks but ultimately relapsed. The other two patients remained disease-free 20 and 16 months post-infusion [14]. Another study (NCT02761915) demonstrated mild neurotoxicity (headache and fever-associated hallucination) in only 2 out of 11 patients treated with GD2 CAR-T cells. However, no clinical responses were observed in any patients [15]. Various rounds of CAR-T cell improvements by different groups are ongoing, as are clinical trials which may bring to light more significant patient responses.
Chimeric-antigen receptor natural killer (CAR-NK) cells are derived from primary NK cells or cell lines modified with an antigen-directed chimeric receptor. Although no neuroblastoma clinical trial is currently assessing this type of immunotherapy, pre-clinical work has shown some significance. CAR-NK cells exhibited cytotoxicity against GD2-expressing neuroblastoma tumour cells and the T-cell inhibitory myeloid-derived suppressor cells (MDSCs) [16,17]. A pre-clinical study developed modified NK cells with the activating receptor NKG2D fused to the cytotoxic ζ-chain of the T-cell receptor (NKG2D.ζ) [16]. In mice, these cells eliminated ∼90 % of intra-tumoural myeloid-derived suppressor cells (MDSCs) and resulted in a 400 % reduction in tumour volume compared to unmodified NK cells, which prolonged survival by a median of 44 days. The same study investigated if pre-infusion with these cells could recruit GD2 CAR-T cells to the tumour site, with a 10.9 ± 0.2-fold increase in recruitment [16]. This data highlights the potential of combination immunotherapies.
Vaccines
There are currently 8 active clinical trials for neuroblastoma that are investigating a vaccine-related therapeutic (Fig 2); 3 immunostimulatory vaccines (NCT04853017, NCT05726864, NCT04936529), 2 modified neuroblastoma cell vaccines (NCT04239040, NCT01192555), 2 vaccines directed against GD2 (NCT00911560, NCT06057948) and 1 DNA vaccine against personalised tumour antigens (NCT04049864). Critical to any vaccine's design, particularly a therapeutic cancer vaccine, is the antigen on which the vaccine is designed to target. It is no surprise that the active clinical trials and the majority of the completed clinical trials for neuroblastoma are either modified neuroblastoma cells reintroduced to patients or peptide vaccines against GD2, the most researched TAA on neuroblastomas.
A phase I trial investigating a dendritic cell vaccine (NCT01241162) showed disappointing results with associated adverse toxic effects in almost half of all patients, mainly myelosuppression due to side-arm decitabine treatment. This vaccine was prepared by isolating a sample of each patient's DCs from peripheral blood mononuclear cells and subsequently stimulating these cells with neuroblastoma TAA peptide pools. These antigens, MAGE-A1, MAGE- A3 and NY-ESO-1, are known to be over-expressed on neuroblastoma and other cancers [18]. The stimulated DCs are then reintroduced to patients once weekly for two weeks. Only two out of ten patients experienced favourable outcomes; one had a complete response, and one remained disease-free 2 years post-treatment [19]. Of note, these patients experienced only minimal disease at the start of the trial. So, while this trial highlights the feasibility of this vaccine for some patients, a more potent and specific vaccine is required to address the majority of high-risk patients who present not with minimal disease but with highly aggressive tumours.
Another phase I/II trial (NCT00923351) was completed investigating a tumour lysate-pulsed DC vaccine. Of the 30 patients treated, all patients experienced an immune response to vaccination, 12 remain on follow-up and are stable or without evidence of disease, 7 are receiving additional external therapy, and 11 died from progressive disease. To date, no publication with a detailed breakdown of patient responses exists. However, the results above, again, show highly variable responses from patients.
Adoptive cell therapy with cytokine-producing cells
Based on NK cells' role in the success of anti-GD2 therapy and their potential for recognition of MHC-lacking neuroblastoma cells, these innate immune cells have attracted interest for use as “off-the-shelf” allogeneic cell therapy. This involves extraction of a patientʼs NK cells, stimulation of the cells ex vivo using cytokines and re-infusion to the patient. Currently, there are 20 phase I/II clinical trials investigating NK cell infusions with mixed results from those completed [[20], [21], [22]] or active. A clinical trial examined the longitudinal effects of NK cell infusions. Post-chemo-immunotherapy infusion with NK cells stimulated with IL-2/IL-15 ex vivo improved NK cell cytotoxicity with a range of 4.32–21.56 % increase across 6 patients [21]. While this therapeutic seems well tolerated in neuroblastoma patients, the direct contribution of NK cell infusions to survival outcomes remains to be seen.
Emerging neuroblastoma-associated antigens
Most immunotherapies for neuroblastoma have focused on the disialoganglioside GD2, based on the high density of over-expression on tumour cells and low levels of expression on healthy cells. However, significant neural toxicities have been associated with this therapy due to the expression of GD2 on nerve cells [23]. To combat this, many groups have expanded on novel immunotherapeutic target discovery with antigens such as glypican-2 (GPC2), New York oesophageal squamous cell carcinoma 1 (NY-ESO-1), B7-H3 (also known as CD276), neural cell adhesion molecule (NCAM)(also known as CD56), L1 cell adhesion molecule (L1CAM) (also known as CD171), preferentially expressed in melanoma antigen (PRAME) and programmed death-ligand 1 (PD-L1) (also known as CD274) showing more promise in the neuroblastoma field (reviewed extensively in [4,6]).
GPC2
GPC2 is membrane-expressed proteoglycan and part of a family of 6 proteins with involvement in wingless (Wnt), hedgehog (Hh), fibroblast growth factor (FGF), and bone morphogenetic protein (BMP) signalling pathways [24]. GPC2 is over-expressed on neuroblastoma, as well as a variety of other cancers such as small-cell lung cancer, osteosarcoma and Ewing sarcoma [[25], [26], [27]]. Health tissue expression of this antigen is highly restricted, with low levels of GPC2 found mainly in the testes [25]. This poses GPC2 as a promising immunotherapeutic target, with multiple groups showing anti-neuroblastoma activity in mice treated with CAR-T cells with no significant associated toxicities [25,28,29] and clinical trial underway [NCT05650749]. Approximately 40 % of neuroblastoma patients experience chromosome 7q gain. Given that GPC2 is located on chromosome 7q22.1, therapeutics designed to target this antigen should not be limited by any loss of expression due to genetic abnormalities. A subset of patients with this genetic background could benefit greatly from GPC2-directed therapy.
NY-ESO-1
NY-ESO-1, located on chromosome Xq28, is a cancer-testis antigen with restricted expression in germ cells [30]. Involvement of NY-ESO-1 in cell cycle progression, cell differentiation, apoptosis and germ cell self-renewal has been suggested [31,32]. This antigen is over-expressed to varying degrees in several cancers, and the restricted expression presents another viable option for immunotherapeutic targeting [33,34]. CAR-T cells directed against NY-ESO-1 significantly delayed localised and disseminated neuroblastoma progression in mice and improved survival. Compared to negative controls, which saw significant tumour progression at day 10, tumours treated with CAR-T cells did not experience considerable progression until between days 25 and 30 (p < 0.001). A median survival of 42 days in CAR-T cell-treated mice contrasted sharply to just 17 days for PBS-treated mice [35]. Additionally, a peptide vaccine against this antigen could stimulate both humoral and cellular immune responses in three neuroblastoma patients; however, no survival or anti-tumour activity data has been reported for this small trial. The frequency of NY-ESO-1-specific CD8+ T cells rose from undetectable levels pre-vaccination to 0.35 % - 0.82 % after 3 immunisations [33].
B7-H3
B7-H3 (CD276), located on chromosome 15, is an immune checkpoint protein over-expressed on multiple malignancies including neuroblastoma [36]. B7-H3 works in a pro-tumorigenic fashion from two angles. Firstly, B7-H3 expressed on cancer cells and healthy normal and immune cell types can bind lymphocytes (via a currently unknown receptor), inducing a more regulatory, tumour-promoting phenotype. Secondly, B7-H3 has been implicated in neuroblastoma proliferation, cell cycle arrest and drug resistance [37]. Targeting this antigen in the clinic has progressed to 5 trials investigating B7-H3-directed monoclonal antibodies and 6 focused on CAR-T cells with no published results. The pre-clinical work behind this has shown anti-neuroblastoma activity with both immunotherapeutics (reviewed extensively by[37]).
NCAM
NCAM (CD56), located on chromosome 11q23, is a glycoprotein expressed on neurons, glia, skeletal muscle, and neuroblastoma tumours [38]. Expression of this antigen correlates with metastatic capacity and stemness in cancer cells. A pre-clinical study investigating two antibody-drug conjugates against NCAM has described potent cytotoxicity in vitro. Two of the four neuroblastoma cell lines tested were responsive to ADC treatment after four days, while the others responded by day 6. They found ADC IC50 values of 0.16 and 0.19 pM for SK-N-FI and SK-N-AS cells, respectively. In comparison, an isotype control ADC had detectable cytotoxicity in the same cell lines at >1000 times these concentrations [39]. However, the location of this gene on chromosome 11 could prevent treatment efficacy in the subset of neuroblastoma tumours with 11q loss, approximately 35–45 % of the neuroblastoma patient population[40].
L1CAM
L1CAM (CD171), a glycoprotein encoded by a gene located on the X chromosome, is part of the family of neural adhesion molecules and has been studied in relation to novel cancer antigens. This protein is involved in tumour cell differentiation, growth, migration and invasion [[41], [42], [43]]. CAR-T cells against L1CAM developed from cells directly isolated from 4 neuroblastoma patients have shown anti-tumour reactivity in vitro and in immunocompromised mice. All four CD8+ CAR-T cell products exhibited lysis of CD171+ cells in vitro with between 10 % and 20 % increase in lytic activity compared to that against CD171- cells. In vivo, these 4 patient-derived CAR-T cell products induced tumour regression on day 5, with a reduction in baseline levels on day 15 [44]. The location of CD171 on chromosome X does not limit a subsection of the neuroblastoma population with no known genetic aberrations on this chromosome associated with neuroblastoma.
PRAME
PRAME, another member of the cancer testis antigen family, is expressed by multiple cancers and is considered a potential immunotherapeutic target. While this antigen is yet to be clinically evaluated, pre-clinical analysis has identified PRAME expression in 93 % of primary and 100 % of advanced neuroblastoma patient tumour samples. Increased expression of this protein is associated with tumour stage (high expression found in 80 % of stage 4 neuroblastoma tumours) and age of the patient at diagnosis (higher expression associated with higher age at diagnosis, p < 0.01) [45]. PRAME is located at chromosome 22q11.22, with no known aberrations in neuroblastoma and, therefore, a potentially significant site to target with immunotherapeutics.
PD-L1
PD-L1, located on chromosome 9p24.1, is a transmembrane protein that expressed on the surface of some antigen presenting cells and some tumour cells, including neuroblastoma. Its notable therapeutic efficacy lies in blockade of PD-1/PD- L1 interaction by restoring T cell reactivity [46]. PD-L1 expression in neuroblastoma samples returns conflicting results on its detection with IHC and prognostic significance (extensively reviewed in[6]). Nevertheless, clinical trials targeting the PD1-PD-L1 axe are ongoing with Pembrolizumab (NCT02332668), anti-PD1 Nivolumab and/or anti-CTLA4 Ipilimumab (NCT04500548, NCT02304458, NCT03838042, NCT02914405, NCT04412408, NCT01445379) that will provide a robust evidence of their potential efficacy.
GPC2
GPC2 is membrane-expressed proteoglycan and part of a family of 6 proteins with involvement in wingless (Wnt), hedgehog (Hh), fibroblast growth factor (FGF), and bone morphogenetic protein (BMP) signalling pathways [24]. GPC2 is over-expressed on neuroblastoma, as well as a variety of other cancers such as small-cell lung cancer, osteosarcoma and Ewing sarcoma [[25], [26], [27]]. Health tissue expression of this antigen is highly restricted, with low levels of GPC2 found mainly in the testes [25]. This poses GPC2 as a promising immunotherapeutic target, with multiple groups showing anti-neuroblastoma activity in mice treated with CAR-T cells with no significant associated toxicities [25,28,29] and clinical trial underway [NCT05650749]. Approximately 40 % of neuroblastoma patients experience chromosome 7q gain. Given that GPC2 is located on chromosome 7q22.1, therapeutics designed to target this antigen should not be limited by any loss of expression due to genetic abnormalities. A subset of patients with this genetic background could benefit greatly from GPC2-directed therapy.
NY-ESO-1
NY-ESO-1, located on chromosome Xq28, is a cancer-testis antigen with restricted expression in germ cells [30]. Involvement of NY-ESO-1 in cell cycle progression, cell differentiation, apoptosis and germ cell self-renewal has been suggested [31,32]. This antigen is over-expressed to varying degrees in several cancers, and the restricted expression presents another viable option for immunotherapeutic targeting [33,34]. CAR-T cells directed against NY-ESO-1 significantly delayed localised and disseminated neuroblastoma progression in mice and improved survival. Compared to negative controls, which saw significant tumour progression at day 10, tumours treated with CAR-T cells did not experience considerable progression until between days 25 and 30 (p < 0.001). A median survival of 42 days in CAR-T cell-treated mice contrasted sharply to just 17 days for PBS-treated mice [35]. Additionally, a peptide vaccine against this antigen could stimulate both humoral and cellular immune responses in three neuroblastoma patients; however, no survival or anti-tumour activity data has been reported for this small trial. The frequency of NY-ESO-1-specific CD8+ T cells rose from undetectable levels pre-vaccination to 0.35 % - 0.82 % after 3 immunisations [33].
B7-H3
B7-H3 (CD276), located on chromosome 15, is an immune checkpoint protein over-expressed on multiple malignancies including neuroblastoma [36]. B7-H3 works in a pro-tumorigenic fashion from two angles. Firstly, B7-H3 expressed on cancer cells and healthy normal and immune cell types can bind lymphocytes (via a currently unknown receptor), inducing a more regulatory, tumour-promoting phenotype. Secondly, B7-H3 has been implicated in neuroblastoma proliferation, cell cycle arrest and drug resistance [37]. Targeting this antigen in the clinic has progressed to 5 trials investigating B7-H3-directed monoclonal antibodies and 6 focused on CAR-T cells with no published results. The pre-clinical work behind this has shown anti-neuroblastoma activity with both immunotherapeutics (reviewed extensively by[37]).
NCAM
NCAM (CD56), located on chromosome 11q23, is a glycoprotein expressed on neurons, glia, skeletal muscle, and neuroblastoma tumours [38]. Expression of this antigen correlates with metastatic capacity and stemness in cancer cells. A pre-clinical study investigating two antibody-drug conjugates against NCAM has described potent cytotoxicity in vitro. Two of the four neuroblastoma cell lines tested were responsive to ADC treatment after four days, while the others responded by day 6. They found ADC IC50 values of 0.16 and 0.19 pM for SK-N-FI and SK-N-AS cells, respectively. In comparison, an isotype control ADC had detectable cytotoxicity in the same cell lines at >1000 times these concentrations [39]. However, the location of this gene on chromosome 11 could prevent treatment efficacy in the subset of neuroblastoma tumours with 11q loss, approximately 35–45 % of the neuroblastoma patient population[40].
L1CAM
L1CAM (CD171), a glycoprotein encoded by a gene located on the X chromosome, is part of the family of neural adhesion molecules and has been studied in relation to novel cancer antigens. This protein is involved in tumour cell differentiation, growth, migration and invasion [[41], [42], [43]]. CAR-T cells against L1CAM developed from cells directly isolated from 4 neuroblastoma patients have shown anti-tumour reactivity in vitro and in immunocompromised mice. All four CD8+ CAR-T cell products exhibited lysis of CD171+ cells in vitro with between 10 % and 20 % increase in lytic activity compared to that against CD171- cells. In vivo, these 4 patient-derived CAR-T cell products induced tumour regression on day 5, with a reduction in baseline levels on day 15 [44]. The location of CD171 on chromosome X does not limit a subsection of the neuroblastoma population with no known genetic aberrations on this chromosome associated with neuroblastoma.
PRAME
PRAME, another member of the cancer testis antigen family, is expressed by multiple cancers and is considered a potential immunotherapeutic target. While this antigen is yet to be clinically evaluated, pre-clinical analysis has identified PRAME expression in 93 % of primary and 100 % of advanced neuroblastoma patient tumour samples. Increased expression of this protein is associated with tumour stage (high expression found in 80 % of stage 4 neuroblastoma tumours) and age of the patient at diagnosis (higher expression associated with higher age at diagnosis, p < 0.01) [45]. PRAME is located at chromosome 22q11.22, with no known aberrations in neuroblastoma and, therefore, a potentially significant site to target with immunotherapeutics.
PD-L1
PD-L1, located on chromosome 9p24.1, is a transmembrane protein that expressed on the surface of some antigen presenting cells and some tumour cells, including neuroblastoma. Its notable therapeutic efficacy lies in blockade of PD-1/PD- L1 interaction by restoring T cell reactivity [46]. PD-L1 expression in neuroblastoma samples returns conflicting results on its detection with IHC and prognostic significance (extensively reviewed in[6]). Nevertheless, clinical trials targeting the PD1-PD-L1 axe are ongoing with Pembrolizumab (NCT02332668), anti-PD1 Nivolumab and/or anti-CTLA4 Ipilimumab (NCT04500548, NCT02304458, NCT03838042, NCT02914405, NCT04412408, NCT01445379) that will provide a robust evidence of their potential efficacy.
Neuroblastoma immunomodulation
Growing evidence supports the notion that non-specific biological or chemical immunomodulating agents can influence the anti-tumour immune response. Through various downstream mechanisms such as immunogenic tumour cell death, tumour antigen release and subsequent T cell activation, host recognition of tumour cells can be boosted above baseline.
Biological immunomodulating agents
In neuroblastoma, biological immunomodulating agents are represented by cytokines. Cytokine therapy, mainly interleukin-2 (IL-2) and granulocyte-macrophage colony-stimulating factor (GM-CSF), is used in combination with anti-GD2 antibodies (extensively reviewed by[47]). Using both immunotherapies simultaneously has improved the efficacy of anti-GD2 therapies through increased antibody-dependent cell-mediated cytotoxicity. Almost 20 clinical trials investigating this combination have been completed over the past decade with substantial anti-tumour activity while highlighting the need for close monitoring for adverse effects when treating via cytokines [47]. Cytokine therapy is known for stimulating toxicities in patients across various cancers [48]. Notably, recent results of a neuroblastoma clinical trial (NCT00026312) have shown that 71 % of patients treated with this combination plus isotretinoin were still alive 5 years later [49,50]. While 61 % of patients had no evidence of tumour growth, ∼40 % of patients experienced progression in this trial, and ∼30 % still died from this disease. These results highlight potential benefits of a personalised approach to immunotherapies but further advances are required.
Chemical immunomodulating agents
On the other hand, traditional chemotherapy can decrease populations of immunosuppressive regulatory T-cells (Tregs) or modulate their function [45,46]. Several chemical drugs are in clinical trials for neuroblastoma with immunomodulating potential (Busulfan, Gemcitabine [[51], [52], [53], [54]], Lenalidomide [55,56], Paclitaxel [[57], [58], [59]], Vincristine, Vorinostat [60,61], Etinostat [62]. Regardless of their original mechanism of action, two of them demonstrated immune cell modulation in vivo: vorinostat, an epigenetic drug, and lenalidomide, an immunomodulatory drug with potent antineoplastic, anti-angiogenic, and anti-inflammatory properties.
Epigenetic drugs alter gene transcription by upregulating, downregulating, or silencing genes completely through DNA- and histone-methylation and acetylation. In tumour cells, such aberrant regulation can upregulate TAA expression on the cell surface, enabling the host immune system to attack tumour cells. Studies have demonstrated that vorinostat increased the expression of GD2, the primary target for immunotherapeutic monoclonal antibodies, and shifted a suppressive tumour microenvironment (TME) to a permissive TME for tumour-directed antibody therapy [60,61]. Vorinostat increased the number of macrophage effector cells expressing high Fc-receptors (FcR) levels and decreased the number and function of myeloid-derived suppressor cells (MDSC).
Another immune-modulating drug, lenalidomide, increased GD2 expression in NLF tumours, which had relatively low ganglioside expression compared to most primary NB [56] and decreased NK cell infiltration. Co-treatment with lenalidomide and ch14.18 suppressed neuroblastoma tumour growth in NOD/SCID mice through activation of NK cells and prevented their suppression by IL-6 and TGFβ1 in the microenvironment. The authors suggested that lenalidomide inhibited STAT3 for IL-6 and SMAD2/3 for TGFβ1.
We do not know how and what doses of chemical agents have a modulating effect on the immune system of patients with neuroblastoma rather than entirely vanishing immune cells together with cancer cells. Preclinical studies similar to [60,61] combined with a systematic evaluation and meta-analysis of the drug immunomodulation could have filled that gap and helped to review current treatment protocols. However, other emerging novel immune-modulating agents could also be considered, such as a small-molecule agonist (ADH-503) [63].
Biological immunomodulating agents
In neuroblastoma, biological immunomodulating agents are represented by cytokines. Cytokine therapy, mainly interleukin-2 (IL-2) and granulocyte-macrophage colony-stimulating factor (GM-CSF), is used in combination with anti-GD2 antibodies (extensively reviewed by[47]). Using both immunotherapies simultaneously has improved the efficacy of anti-GD2 therapies through increased antibody-dependent cell-mediated cytotoxicity. Almost 20 clinical trials investigating this combination have been completed over the past decade with substantial anti-tumour activity while highlighting the need for close monitoring for adverse effects when treating via cytokines [47]. Cytokine therapy is known for stimulating toxicities in patients across various cancers [48]. Notably, recent results of a neuroblastoma clinical trial (NCT00026312) have shown that 71 % of patients treated with this combination plus isotretinoin were still alive 5 years later [49,50]. While 61 % of patients had no evidence of tumour growth, ∼40 % of patients experienced progression in this trial, and ∼30 % still died from this disease. These results highlight potential benefits of a personalised approach to immunotherapies but further advances are required.
Chemical immunomodulating agents
On the other hand, traditional chemotherapy can decrease populations of immunosuppressive regulatory T-cells (Tregs) or modulate their function [45,46]. Several chemical drugs are in clinical trials for neuroblastoma with immunomodulating potential (Busulfan, Gemcitabine [[51], [52], [53], [54]], Lenalidomide [55,56], Paclitaxel [[57], [58], [59]], Vincristine, Vorinostat [60,61], Etinostat [62]. Regardless of their original mechanism of action, two of them demonstrated immune cell modulation in vivo: vorinostat, an epigenetic drug, and lenalidomide, an immunomodulatory drug with potent antineoplastic, anti-angiogenic, and anti-inflammatory properties.
Epigenetic drugs alter gene transcription by upregulating, downregulating, or silencing genes completely through DNA- and histone-methylation and acetylation. In tumour cells, such aberrant regulation can upregulate TAA expression on the cell surface, enabling the host immune system to attack tumour cells. Studies have demonstrated that vorinostat increased the expression of GD2, the primary target for immunotherapeutic monoclonal antibodies, and shifted a suppressive tumour microenvironment (TME) to a permissive TME for tumour-directed antibody therapy [60,61]. Vorinostat increased the number of macrophage effector cells expressing high Fc-receptors (FcR) levels and decreased the number and function of myeloid-derived suppressor cells (MDSC).
Another immune-modulating drug, lenalidomide, increased GD2 expression in NLF tumours, which had relatively low ganglioside expression compared to most primary NB [56] and decreased NK cell infiltration. Co-treatment with lenalidomide and ch14.18 suppressed neuroblastoma tumour growth in NOD/SCID mice through activation of NK cells and prevented their suppression by IL-6 and TGFβ1 in the microenvironment. The authors suggested that lenalidomide inhibited STAT3 for IL-6 and SMAD2/3 for TGFβ1.
We do not know how and what doses of chemical agents have a modulating effect on the immune system of patients with neuroblastoma rather than entirely vanishing immune cells together with cancer cells. Preclinical studies similar to [60,61] combined with a systematic evaluation and meta-analysis of the drug immunomodulation could have filled that gap and helped to review current treatment protocols. However, other emerging novel immune-modulating agents could also be considered, such as a small-molecule agonist (ADH-503) [63].
Modelling neuroblastoma-immune interactions in 3D in vitro
Replication of the tumour-immune landscape in vitro is crucial with the recent rise of immunotherapy development. The 3D in vitro cancer concepts and tools have been amply reviewed for neuroblastoma (e.g. [64].). We conducted a comprehensive PubMed search for the 3D in vitro models of neuroblastoma investigating a tumour-immune interaction or immune evasion mechanisms. The search returned a handful of papers published in the past 5 years, highlighting the novelty of this field (Fig 3).Fig. 3Current 3D in vitro models of investigating tumour immune-interactions in co-cultures of neuroblastoma (underlined) and immune cells (cursive). The immune cells can be derived from immortalised cell lines or Peripheral Blood Mononuclear Cells (PBMCs) isolated from donor blood. The grey area indicates that no data published at the time of review submission. Created with BioRender.com.Fig. 3
Co-culture of cancer cells with immune cells represents a natural increase in 3D model complexity. While possible to include immortalised cell lines, all models discussed here, were created using immune cells isolated from donor blood, either PBMCs [65,66] or, more specifically, T or NK cells only [65,[67], [68], [69]] (Fig 3). To improve cytotoxicity, they were primed with cytokines [65,67] or modified to express a specific γ/δ-T cell receptor [68] or a chimeric antigen receptor [69].
Immunotherapeutics and antibody-drug conjugates
Immunotherapeutics are the most common investigation using the 3D in vitro models and focusing on modified T cells [[67], [68], [69]]. Grunewald et al. concluded their CAR T cells demonstrated physiological relevance better in 3D than 2D, where cytotoxicity was less pronounced. Almost 100 % cell lysis was reported in 2D co-cultures, with only around 40 % killing in 3D (p < 0.05) [69]. Interestingly, as measured by CD25 and CD137 expression, T cell activation showed an opposite trend, with expression increasing about 30 % from 2D to 3D.
Strijker et al. found their γδ-T cells, engineered from αβ-T cells, produced IFN-γ in co-culture with half of the tested patient-derived organoids of up to 200 pg/mL as opposed to untransduced αβ-T cells which did not trigger IFN-γ production[68]. While this T cell activation was found to be independent of MHC I expression, Pamidronate treatment increased lysis by up to 6-fold.
Cornel et al. found that (PRAME)-reactive tumour-specific T cells increase patient-derived neuroblastoma organoid killing by about 100 %; 40 % and 70 % after pre-treatment with IFN-γ (p < 0.0001), IFN-α (p < 0.0001) and immunomodulator entinostat, respectively (p < 0.0001)[67]. On the other hand, healthy-donor natural killer (NK) cells increased their killing only in response to IFN-α and entinostat by about 10 % and 30 %, respectively.
Instead of T cells, Heinze et al. [65]. and Kholosy et al. [66]. focussed on NK and Cytokine-induced killer (CIK) cells or collective peripheral blood mononuclear cells (PBMCs), respectively. The former found both cell types to be effective in tumour cell eradication at various effector:target (E:T) ratios, with NK cells outperforming CIKs in the short-term killing. Kholosy et al. used PBMCs to validate their model with the FDA-approved anti-GD2 immunotherapeutic [66].
Finally, using the antibody-drug conjugate vobramitamab duocarmazine rather than an immunotherapy, Brignole et al. found a dose-dependent viability reduction in neuroblastoma spheroids[70].
Effector: Target ratios
Co-culture experiments are particularly relevant for our understanding of molecular mechanisms triggering tumour cell recognition by immune cells. Different effector: target cell ratios (E:T) may help to shed light on the required numbers of effector cells, as different ratios can result in very different degrees of cancer cell eradication. Neuroblastoma cell lysis of NK cells via europium release was tested in 4 different E:Ts ranging from 10:1 to 0.5:1 in different types of medium [65]. Degree of lysis inversely correlated to E:T with up to 94.92 % lysis at 10:1 and less than 20 % at an E:T of 0.5:1. This highlights the crucial effect a chosen E:T has on the apparent effectiveness of a drug, begging the question of the most physiologically relevant E:T. Other researchers used only single E:Ts in their immunotherapeutic studies. Strijker et al. did not find a clear trend with E:Ts of 0.3:1, 1:1, and 3:1 using engineered γδ- T cells [68]. They instead observed 10 µM Pamidronate to increase cytotoxicity up to 6-fold. Cornel et al. chose 1:1 [67], and Grunewald et al. picked 5:1, which was physiologically reduced to 1:10 through limited T-cell infiltration [69]. Kholosy et al. used 20:1 [66], the only ones using healthy donor PBMCs as effector cells. Thus, more immune cell types, including ones with lower or no efficacy, interacted within a single 3D cancer model, resulting in more variables to assess. However, the question remains: what E:T is physiologically relevant? As not otherwise stated, the above studies used immune cells from healthy donors, presumably adults. Using healthy donor effector cells does not reflect the actual scenario in modelling immunotherapeutics in vitro and in vivo, taking into consideration marked differences in the expression profiles of PBMCs from healthy donors and cancer patients of different stages [71], with expression profiles even potentially predicting relapse [72]. Notably, PBMCs are typically depleted or exhausted in cancer patients[9]. Similarly, adult and paediatric PBMCs or neonatal cord blood differ in cell type abundance [73] and function [74]. While these facts highlight current limitations in 3D cancer models, the published sc-RNS profiling of various immune cell population can bridge the gap by selecting clinically relevant immune cells and their sources [75]. This in turn, will facilitate the evidence-based move from the lab to the clinic for immunotherapies.
Receptor expression
Alongside cytotoxic investigations, receptor expression was a commonly investigated immune interaction feature [65,67,68]. Specifically, expression levels of major histocompatibility I (MHC I) are a protein expressed on the membrane of all cells in an organism and used to present fragments of proteins within the cell to the immune system. Low MHC I expression of tumour cells is a well-known mechanism of immune evasion [76]. Cornel et al. found MHC I expression increased by exposure to cytokines IFN-α and IFN-γ and even more by exposure to cytokines and entinostat (up to 10-fold chance at p < 0.01 to p < 0.0001)[67]. Both Heinze et al. and Strijker et al. related the cell inherent MHC I expression profiles to their investigations [65,68]. While Strijker et al. reported their cell-based immunotherapeutic works in an MHC I independent manner [68], Heinze et al. found slightly greater killing efficacy of NK cells against the MHC I low SK-N-SH spheroids than the MHC I high SKN-AS spheroids [65].
2D vs 3D in vitro co-culture
From the early days of 3D in vitro modelling, some marked differences in cell behaviour between 2D and 3D and compared to in vivo, such as drug tolerances, have been reported [77,78]. Two independent studies compared their 3D data to 2D cell culture conditions [69,70]. Brignole et al. found the dose-dependent viability reductions in response to the antibody-drug conjugate, vobramitamab duocarmazine confirmed in 2D [70]. However, higher drug concentrations were required in 3D to reach a 50 % reduction in cell viability. Similarly, Grunewald et al. found CAR-T cell-mediated killing more effective in 2D but more strongly activated in 3D as measured by CD25 (depending on CAR T-cell type up to p ≤ 0.01) and CD137 (p ≤ 0.5) expression [69].
Out of all papers discussed in this section, Brignole et al. compared their 3D in vitro model to in vivo data. They found the reduced cancer cell viability found in 3D in vitro in response to vobramitamab duocarmazine translated to extended survival times in 5 out of 6 mouse models of 7 to 49 days [70].
In comparison with in vivo models, 3D in vitro models provide a system to experiment with the extracellular matrix and how different stiffnesses affect immune and tumour cell behaviour (Table 1). In a hydrogel model of human adenocarcinoma for example it has been found that stiffness and macrophage phenotype jointly cause a more invasive tumour phenotype, highlighting the importance of 3D models over in vivo models in investigating cellular cross talk [79].Table 1Comparison of 2D, 3D cell culture and in vivo models of neuroblastoma.Table 12D cell cultureAttachment independent 3D models (spheroids and organoids)Attachment dependent 3D models (those including ECM)In vivo animal modelsCost€€€€ - €€€€€€Ease of AcquisitionEasyEasy to very difficultEasy to IntermediateDifficult, considering the ethical approvals and facilities requiredSpecialised equipment requiredNoneNoneScaffolds: freeze dryer or electrospinning apparatusTumour-on-a-chip: Photolithography Setup or Etching Equipment or Soft Lithography Tools or a 3D Printer for the fabrication of the chip and a rotary culture system to maintain a dynamic cultureAnimal housing facilities, surgical and injection equipment, imaging, monitoring and data collection equipmentBiological ComplexityLowIntermediateIntermediateHighTechnical complexityLowIntermediateIntermediateHighHuman RelevanceLowIntermediateIntermediate to HighHighImmune system interactionsSimple immune cell – cancer cell interactionsBoth simple and complex immune cell – cancer cell interactions and invasion through 3D cultureBoth simple and complex immune cells – cancer cell interactions and invasion through 3D culture and extracellular matrix. Perfusion is possibleTypical, immunodeficient models otherwise murine immune systemHuman immune system targetsYesYesYesOnly in immunodeficient xenograftsModelling matrix interactionsNoNoYesYesLabour intensivenessLowLow-IntermediateIntermediateHigh
Immunotherapeutics and antibody-drug conjugates
Immunotherapeutics are the most common investigation using the 3D in vitro models and focusing on modified T cells [[67], [68], [69]]. Grunewald et al. concluded their CAR T cells demonstrated physiological relevance better in 3D than 2D, where cytotoxicity was less pronounced. Almost 100 % cell lysis was reported in 2D co-cultures, with only around 40 % killing in 3D (p < 0.05) [69]. Interestingly, as measured by CD25 and CD137 expression, T cell activation showed an opposite trend, with expression increasing about 30 % from 2D to 3D.
Strijker et al. found their γδ-T cells, engineered from αβ-T cells, produced IFN-γ in co-culture with half of the tested patient-derived organoids of up to 200 pg/mL as opposed to untransduced αβ-T cells which did not trigger IFN-γ production[68]. While this T cell activation was found to be independent of MHC I expression, Pamidronate treatment increased lysis by up to 6-fold.
Cornel et al. found that (PRAME)-reactive tumour-specific T cells increase patient-derived neuroblastoma organoid killing by about 100 %; 40 % and 70 % after pre-treatment with IFN-γ (p < 0.0001), IFN-α (p < 0.0001) and immunomodulator entinostat, respectively (p < 0.0001)[67]. On the other hand, healthy-donor natural killer (NK) cells increased their killing only in response to IFN-α and entinostat by about 10 % and 30 %, respectively.
Instead of T cells, Heinze et al. [65]. and Kholosy et al. [66]. focussed on NK and Cytokine-induced killer (CIK) cells or collective peripheral blood mononuclear cells (PBMCs), respectively. The former found both cell types to be effective in tumour cell eradication at various effector:target (E:T) ratios, with NK cells outperforming CIKs in the short-term killing. Kholosy et al. used PBMCs to validate their model with the FDA-approved anti-GD2 immunotherapeutic [66].
Finally, using the antibody-drug conjugate vobramitamab duocarmazine rather than an immunotherapy, Brignole et al. found a dose-dependent viability reduction in neuroblastoma spheroids[70].
Effector: Target ratios
Co-culture experiments are particularly relevant for our understanding of molecular mechanisms triggering tumour cell recognition by immune cells. Different effector: target cell ratios (E:T) may help to shed light on the required numbers of effector cells, as different ratios can result in very different degrees of cancer cell eradication. Neuroblastoma cell lysis of NK cells via europium release was tested in 4 different E:Ts ranging from 10:1 to 0.5:1 in different types of medium [65]. Degree of lysis inversely correlated to E:T with up to 94.92 % lysis at 10:1 and less than 20 % at an E:T of 0.5:1. This highlights the crucial effect a chosen E:T has on the apparent effectiveness of a drug, begging the question of the most physiologically relevant E:T. Other researchers used only single E:Ts in their immunotherapeutic studies. Strijker et al. did not find a clear trend with E:Ts of 0.3:1, 1:1, and 3:1 using engineered γδ- T cells [68]. They instead observed 10 µM Pamidronate to increase cytotoxicity up to 6-fold. Cornel et al. chose 1:1 [67], and Grunewald et al. picked 5:1, which was physiologically reduced to 1:10 through limited T-cell infiltration [69]. Kholosy et al. used 20:1 [66], the only ones using healthy donor PBMCs as effector cells. Thus, more immune cell types, including ones with lower or no efficacy, interacted within a single 3D cancer model, resulting in more variables to assess. However, the question remains: what E:T is physiologically relevant? As not otherwise stated, the above studies used immune cells from healthy donors, presumably adults. Using healthy donor effector cells does not reflect the actual scenario in modelling immunotherapeutics in vitro and in vivo, taking into consideration marked differences in the expression profiles of PBMCs from healthy donors and cancer patients of different stages [71], with expression profiles even potentially predicting relapse [72]. Notably, PBMCs are typically depleted or exhausted in cancer patients[9]. Similarly, adult and paediatric PBMCs or neonatal cord blood differ in cell type abundance [73] and function [74]. While these facts highlight current limitations in 3D cancer models, the published sc-RNS profiling of various immune cell population can bridge the gap by selecting clinically relevant immune cells and their sources [75]. This in turn, will facilitate the evidence-based move from the lab to the clinic for immunotherapies.
Receptor expression
Alongside cytotoxic investigations, receptor expression was a commonly investigated immune interaction feature [65,67,68]. Specifically, expression levels of major histocompatibility I (MHC I) are a protein expressed on the membrane of all cells in an organism and used to present fragments of proteins within the cell to the immune system. Low MHC I expression of tumour cells is a well-known mechanism of immune evasion [76]. Cornel et al. found MHC I expression increased by exposure to cytokines IFN-α and IFN-γ and even more by exposure to cytokines and entinostat (up to 10-fold chance at p < 0.01 to p < 0.0001)[67]. Both Heinze et al. and Strijker et al. related the cell inherent MHC I expression profiles to their investigations [65,68]. While Strijker et al. reported their cell-based immunotherapeutic works in an MHC I independent manner [68], Heinze et al. found slightly greater killing efficacy of NK cells against the MHC I low SK-N-SH spheroids than the MHC I high SKN-AS spheroids [65].
2D vs 3D in vitro co-culture
From the early days of 3D in vitro modelling, some marked differences in cell behaviour between 2D and 3D and compared to in vivo, such as drug tolerances, have been reported [77,78]. Two independent studies compared their 3D data to 2D cell culture conditions [69,70]. Brignole et al. found the dose-dependent viability reductions in response to the antibody-drug conjugate, vobramitamab duocarmazine confirmed in 2D [70]. However, higher drug concentrations were required in 3D to reach a 50 % reduction in cell viability. Similarly, Grunewald et al. found CAR-T cell-mediated killing more effective in 2D but more strongly activated in 3D as measured by CD25 (depending on CAR T-cell type up to p ≤ 0.01) and CD137 (p ≤ 0.5) expression [69].
Out of all papers discussed in this section, Brignole et al. compared their 3D in vitro model to in vivo data. They found the reduced cancer cell viability found in 3D in vitro in response to vobramitamab duocarmazine translated to extended survival times in 5 out of 6 mouse models of 7 to 49 days [70].
In comparison with in vivo models, 3D in vitro models provide a system to experiment with the extracellular matrix and how different stiffnesses affect immune and tumour cell behaviour (Table 1). In a hydrogel model of human adenocarcinoma for example it has been found that stiffness and macrophage phenotype jointly cause a more invasive tumour phenotype, highlighting the importance of 3D models over in vivo models in investigating cellular cross talk [79].Table 1Comparison of 2D, 3D cell culture and in vivo models of neuroblastoma.Table 12D cell cultureAttachment independent 3D models (spheroids and organoids)Attachment dependent 3D models (those including ECM)In vivo animal modelsCost€€€€ - €€€€€€Ease of AcquisitionEasyEasy to very difficultEasy to IntermediateDifficult, considering the ethical approvals and facilities requiredSpecialised equipment requiredNoneNoneScaffolds: freeze dryer or electrospinning apparatusTumour-on-a-chip: Photolithography Setup or Etching Equipment or Soft Lithography Tools or a 3D Printer for the fabrication of the chip and a rotary culture system to maintain a dynamic cultureAnimal housing facilities, surgical and injection equipment, imaging, monitoring and data collection equipmentBiological ComplexityLowIntermediateIntermediateHighTechnical complexityLowIntermediateIntermediateHighHuman RelevanceLowIntermediateIntermediate to HighHighImmune system interactionsSimple immune cell – cancer cell interactionsBoth simple and complex immune cell – cancer cell interactions and invasion through 3D cultureBoth simple and complex immune cells – cancer cell interactions and invasion through 3D culture and extracellular matrix. Perfusion is possibleTypical, immunodeficient models otherwise murine immune systemHuman immune system targetsYesYesYesOnly in immunodeficient xenograftsModelling matrix interactionsNoNoYesYesLabour intensivenessLowLow-IntermediateIntermediateHigh
Conclusions and future directions
Childhood cancers are relatively uncommon, with a low mutational burden compared to adult cancers. While immunotherapy holds great promise, it still faces many challenges in treating neuroblastoma. Probably the most significant two are the selection of neoantigens and the immune status of a cancer patient. Neuroblastoma patients undergo intensive, immune-depleting treatments, prior to the introduction of immunotherapy. Patients that do progress to immunotherapeutic approaches are often those with the most aggressive disease and under-performing anti-tumour immunity. A major addition to the field would be more complex understanding of the neuroblastoma-immune microenvironment and indeed, the propensity for immune activation in treated patients. Multiantigen approaches combined with immunomodulators can provide better recognition of tumour cells and its heterogeneity by the patient immune system.
To progress multiantigen approaches actionable TAAs are a must. This is, where Artificial Intelligence (AI) comes at a play to interrogate vast datasets in automating quantitative fashion to predict candidates for immunotherapy [80]. By harnessing and scrutinising biomedical data, including omics, radiology, pathology, and clinical data, AI algorithms bolster their capacity to execute the five steps of TAA prediction. This includes somatic mutation calling, MHC typing, assessment of peptide-HLA binding affinity, TCR-pMHC binding prediction, and prediction of the immunogenicity of TAA candidates. Paired with sc-RNAseq datasets of immune status of patients with neuroblastoma, AI can model potential responses to immunotherapy in silico.
Within the realm of 3D tumour-immune models, this review primarily focuses on a few relevant examples related to neuroblastoma. By leveraging bio- and tissue engineering advances, we can meticulously reconstruct the interactions between neuroblastoma and diverse immune cells. This approach offers profound insights into the cell-to-cell interactions that contribute to the 'cold' immune microenvironment of neuroblastoma (Table 1). The future holds promise for advancements in microfluidic models that explore the interactions between tumour and immune cells extravasating from synthetic blood vessels to simulate immune invasion. This aspect of the immune response is a significant limitation of current in vitro models when compared to their in vivo counterparts.
Enhanced with AI, these 3D models can also help study the effects of immunomodulating agents on TAA expression and immune cell activation, thus accelerating the drug discovery pipeline for personalised immunotherapies.
Disclosure
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors declare that they have no conflict of interest.
Declaration of generative AI and AI-assisted technologies in the writing process
During the preparation of this work the authors used Grammarly in order to improve language and readability, with caution. After using this tool, the authors reviewed and edited the content as needed and took full responsibility for the content of the publication.
CRediT authorship contribution statement
Ellen King: Writing – review & editing, Writing – original draft, Visualization, Conceptualization. Ronja Struck: Writing – review & editing, Writing – original draft, Visualization, Funding acquisition, Conceptualization. Olga Piskareva: Writing – review & editing, Writing – original draft, Supervision, Resources, Project administration, Funding acquisition, Conceptualization.
Declaration of competing interest
On behalf of my co-authors, I am submitting the enclosed original review article for consideration for publication in Translational Oncology. It is not under consideration for publication elsewhere, nor has it been published in whole or in part elsewhere. All the authors were fully involved in the manuscript preparation and agreed on its submission to Translational Oncology. None of the authors have any conflicts of interest. |
Title: Number of lymph nodes retrieved in patients with locally advanced rectal cancer after total neoadjuvant therapy: post-hoc analysis from the STELLAR trial | Body:
Introduction
Colorectal cancer (CRC) is the second most common cause of cancer-related death worldwide1. Rectal cancer in particular accounts for nearly 30% of patients with CRC, with a higher incidence among males2. For patients who have undergone curative resection, accurate identification of lymph node metastasis (LNM) is crucial for improved prognostic assessment and treatment decision-making3. Identifying the optimal number of lymph nodes (LNs) is essential for accurate nodal staging.
Currently, the National Comprehensive Cancer Network (NCCN) guidelines recommend retrieving a minimum of 12 LNs3. However, these recommendations are suitable only for colon cancer and cannot be applied for rectal cancer, particularly for those patients receiving neoadjuvant therapy (NAT). Meanwhile, the first-line standard treatment for mid- and distal locally advanced rectal cancer (LARC) is total neoadjuvant therapy (TNT) followed by total mesorectal excision (TME)3–5. However, data on the optimal number of LNs to retrieve in patients with LARC after TNT are lacking.
To better address these issues, a post-hoc analysis based on a multicentre, open-label, randomized phase III trial—namely, the STELLAR study—was conducted. The aim of this study was to explore the optimal number of LNs in rectal cancer patients undergoing TME after TNT and its correlation with survival.
Methods
Study design and participants
This study was conducted according to the ethical principles for medical research involving human participants stated in the Declaration of Helsinki and approved by the Institutional Review Board of the National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College. All patients provided written informed consent, and the protocol was approved by the local ethics committee and registered at Clinical-Trials.gov (identifier: NCT02533271). All participating centres were tertiary hospitals in different regions of China. This study was reported following the strengthening the reporting of cohort, cross-sectional and case–control studies in surgery (STROCSS) reporting guidelines6.
The STELLAR study enrolled patients with mid- and distal-LARC with biopsy-proven adenocarcinoma. The inclusion criteria were as follows: 18–70 years of age, Eastern Cooperative Oncology Group (ECOG) score 0–1, clinical primary tumour (cT) stage 3–4 and/or regional lymph node (N) positivity without distant metastases, white blood cell count ≥ 3.5 × 109/l, haemoglobin ≥ 100 g/l, platelet count ≥ 100 × 109/l and creatinine ≤ 1.0× the upper limit of normal. The exclusion criteria were as follows: recurrent disease, a medical contraindication to the planned treatment or MRI, or a second primary malignancy. All patients underwent pretreatment and post-treatment radiological assessments centrally and independently by three radiologists.
Patients were randomly assigned to short-term radiotherapy followed by chemotherapy (TNT group) or long-term concurrent chemotherapy (CRT group). Random assignment was carried out by computer-generated allocation stratified by location, clinical stage, and mesorectal fascia (MRF) status. A telephone call to an independent central trial office was used to ensure blindness from the random assignment. Those in the TNT group underwent short-term radiotherapy (5 Gy × 5) followed by four cycles of CAPOX (oxaliplatin (130 mg/m2, once a day, on day 1) and capecitabine (1000 mg/m2, twice a day, from day 1 to day 14)) at 7–14 days after the completion of radiotherapy. Those in the CRT group underwent treatment with 50 Gy in 25 fractions over 5 weeks concurrently with capecitabine (825 mg/m2, twice a day). Postoperative chemotherapy comprised two cycles of CAPOX in the TNT group or six cycles of CAPOX in the CRT group. The TME procedure was performed in both groups 6–8 weeks after preoperative therapy. Acute adverse events (AEs) were codified using the National Cancer Institute Common Terminology Criteria for Adverse Events (NCI-CTCAE) v4.0.
Data collection and follow-up
Pelvic MRI was required to identify the T/N stage, MRF status and extramural vascular invasion (EMVI) status appropriately. The MRF was defined as the fascia surrounding the mesorectum. In addition, chest CT and liver MRI/CT were performed to rule out metastatic disease. The distance from the lower pole of the tumour to the anal verge was measured during the colonoscopy examination. Pathological staging was performed by examining the surgical specimen. R1 resection was defined by the presence of tumour tissue in the surgical margin. A pathological complete response (pCR) was defined as the absence of tumour cells at the primary site and in regional LNs. The collected patient characteristics included age, sex, ECOG score, MRI T/N stage, clinical stage, MRF involvement, EMVI status, distance to anal verge, severe AEs, extent of resection, MRI ypT/N stage, adjuvant chemotherapy (ACT) information, and the number of LNs/positive LNs (pLNs).
Outcomes of interest
The primary outcome of this study was overall survival (OS), defined as the time from randomization to death due to any cause. The second outcome of this study was disease-free survival (DFS), defined as the time from randomization to the first locoregional recurrence. Follow-up examinations were scheduled every 3 months during the first 2 years, every 6 months for the next 3 years, and annually thereafter.
LN subgroups
A restricted cubic spline (RCS) function was applied to present linear or non-linear prognostic profiles of LNs retrieved and to identify the optimal cut-off value. Subgroups were defined based on a limited LN retrieval (below the median value) or extended LN retrieval (greater/equal than the median value).
Statistical analysis
Categorical variables were presented as absolute frequencies and percentages and were compared using Fisher’s exact test or the χ2 test. Continuous variables were reported as medians with interquartile ranges (25–75th percentiles) and were compared using the non-parametric Mann–Whitney U test. The distributions of stage changes after preoperative treatment and the number of LNs retrieved were plotted in bar charts. The differences in the number of LNs retrieved between different subgroups were also illustrated through bar charts. Survival was analysed using the Kaplan–Meier (K-M) method and the log-rank test and Cox regression model. The false discovery rate (FDR) method was used to adjust the values of P, considering the sample size, in order to reduce the probability of false positives and demonstrate the validity of the subgroup analysis.
Multivariate Cox proportional hazards regression analysis was used to mitigate confounding biases and adjust the parameters. Statistical tests were carried out at a two-sided significance level of 0.05. All the statistical analyses and data visualizations were performed using R 4.2.1 software.
This study was conducted according to the ethical principles for medical research involving human participants stated in the Declaration of Helsinki and approved by the Institutional Review Board of the National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College. All patients provided written informed consent, and the protocol was approved by the local ethics committee and registered at Clinical-Trials.gov (identifier: NCT02533271).
Study design and participants
This study was conducted according to the ethical principles for medical research involving human participants stated in the Declaration of Helsinki and approved by the Institutional Review Board of the National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College. All patients provided written informed consent, and the protocol was approved by the local ethics committee and registered at Clinical-Trials.gov (identifier: NCT02533271). All participating centres were tertiary hospitals in different regions of China. This study was reported following the strengthening the reporting of cohort, cross-sectional and case–control studies in surgery (STROCSS) reporting guidelines6.
The STELLAR study enrolled patients with mid- and distal-LARC with biopsy-proven adenocarcinoma. The inclusion criteria were as follows: 18–70 years of age, Eastern Cooperative Oncology Group (ECOG) score 0–1, clinical primary tumour (cT) stage 3–4 and/or regional lymph node (N) positivity without distant metastases, white blood cell count ≥ 3.5 × 109/l, haemoglobin ≥ 100 g/l, platelet count ≥ 100 × 109/l and creatinine ≤ 1.0× the upper limit of normal. The exclusion criteria were as follows: recurrent disease, a medical contraindication to the planned treatment or MRI, or a second primary malignancy. All patients underwent pretreatment and post-treatment radiological assessments centrally and independently by three radiologists.
Patients were randomly assigned to short-term radiotherapy followed by chemotherapy (TNT group) or long-term concurrent chemotherapy (CRT group). Random assignment was carried out by computer-generated allocation stratified by location, clinical stage, and mesorectal fascia (MRF) status. A telephone call to an independent central trial office was used to ensure blindness from the random assignment. Those in the TNT group underwent short-term radiotherapy (5 Gy × 5) followed by four cycles of CAPOX (oxaliplatin (130 mg/m2, once a day, on day 1) and capecitabine (1000 mg/m2, twice a day, from day 1 to day 14)) at 7–14 days after the completion of radiotherapy. Those in the CRT group underwent treatment with 50 Gy in 25 fractions over 5 weeks concurrently with capecitabine (825 mg/m2, twice a day). Postoperative chemotherapy comprised two cycles of CAPOX in the TNT group or six cycles of CAPOX in the CRT group. The TME procedure was performed in both groups 6–8 weeks after preoperative therapy. Acute adverse events (AEs) were codified using the National Cancer Institute Common Terminology Criteria for Adverse Events (NCI-CTCAE) v4.0.
Data collection and follow-up
Pelvic MRI was required to identify the T/N stage, MRF status and extramural vascular invasion (EMVI) status appropriately. The MRF was defined as the fascia surrounding the mesorectum. In addition, chest CT and liver MRI/CT were performed to rule out metastatic disease. The distance from the lower pole of the tumour to the anal verge was measured during the colonoscopy examination. Pathological staging was performed by examining the surgical specimen. R1 resection was defined by the presence of tumour tissue in the surgical margin. A pathological complete response (pCR) was defined as the absence of tumour cells at the primary site and in regional LNs. The collected patient characteristics included age, sex, ECOG score, MRI T/N stage, clinical stage, MRF involvement, EMVI status, distance to anal verge, severe AEs, extent of resection, MRI ypT/N stage, adjuvant chemotherapy (ACT) information, and the number of LNs/positive LNs (pLNs).
Outcomes of interest
The primary outcome of this study was overall survival (OS), defined as the time from randomization to death due to any cause. The second outcome of this study was disease-free survival (DFS), defined as the time from randomization to the first locoregional recurrence. Follow-up examinations were scheduled every 3 months during the first 2 years, every 6 months for the next 3 years, and annually thereafter.
LN subgroups
A restricted cubic spline (RCS) function was applied to present linear or non-linear prognostic profiles of LNs retrieved and to identify the optimal cut-off value. Subgroups were defined based on a limited LN retrieval (below the median value) or extended LN retrieval (greater/equal than the median value).
Statistical analysis
Categorical variables were presented as absolute frequencies and percentages and were compared using Fisher’s exact test or the χ2 test. Continuous variables were reported as medians with interquartile ranges (25–75th percentiles) and were compared using the non-parametric Mann–Whitney U test. The distributions of stage changes after preoperative treatment and the number of LNs retrieved were plotted in bar charts. The differences in the number of LNs retrieved between different subgroups were also illustrated through bar charts. Survival was analysed using the Kaplan–Meier (K-M) method and the log-rank test and Cox regression model. The false discovery rate (FDR) method was used to adjust the values of P, considering the sample size, in order to reduce the probability of false positives and demonstrate the validity of the subgroup analysis.
Multivariate Cox proportional hazards regression analysis was used to mitigate confounding biases and adjust the parameters. Statistical tests were carried out at a two-sided significance level of 0.05. All the statistical analyses and data visualizations were performed using R 4.2.1 software.
This study was conducted according to the ethical principles for medical research involving human participants stated in the Declaration of Helsinki and approved by the Institutional Review Board of the National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College. All patients provided written informed consent, and the protocol was approved by the local ethics committee and registered at Clinical-Trials.gov (identifier: NCT02533271).
Results
Clinical characteristics
From 30 August 2015 to 27 August 2018, a total of 629 patients were screened; 451 underwent TME and had complete statistical data available, and these patients were enrolled in the final analysis. Of these, 227 patients were assigned to the TNT group and 224 patients were assigned to the CRT group (Fig. 1). The clinical characteristics were well balanced between the groups (Table 1). The proportions of females in the TNT and CRT groups were 29.5% and 30.8% respectively. The median age at diagnosis was 55 years in the TNT group and 56 years in the CRT group. The number of LNs retrieved was 11.0 (i.q.r. 7.0–16.0) in the TNT group and 11.0 (i.q.r. 7.0–17.0) in the CRT group.
Fig. 1
Study profile
TNT, total neoadjuvant therapy; CRT, chemoradiotherapy; NOM, non-operative management.
Table 1
Baseline characteristics
Characteristics
TNT group no. (%)
CRT group no. (%)
P
Total
227
224
Age, median (i.q.r.), years
55.0 (47.0, 62.0)
56.0 (49.0, 62.0)
0.672
Sex
0.766
Male
160 (70.5)
155 (69.2)
Female
67 (29.5)
69 (30.8)
ECOG score
0.499
0
86 (37.9)
78 (34.8)
1
141 (62.1)
146 (65.2)
MRI T stage
0.157
cT2
4 (1.8)
7 (3.1)
cT3
186 (81.9)
193 (86.2)
cT3a-b
111 (48.9)
115 (51.4)
cT3c-d
75 (33.0)
78 (34.8)
cT4
37 (16.3)
24 (10.7)
cT4a
25 (11.0)
9 (4.0)
cT4b
12 (5.3)
15 (6.7)
MRI N stage
0.328
cN0
25 (11.0)
35 (15.6)
cN1
119 (52.4)
115 (51.4)
cN2
83 (36.6)
74 (33.0)
Clinical stage
0.149
II
25 (11.0)
35 (15.6)
III
202 (89.0)
189 (84.4)
Distance to anal verge (cm)
0.634
≤5
140 (61.7)
143 (63.8)
>5
87 (38.3)
81 (36.2)
MRF involvement
126 (55.5)
125 (55.8)
0.949
EMVI
133 (58.6)
109 (48.7)
0.034
Extent of resection
0.151
R0
208 (91.6)
196 (87.5)
R1
19 (8.4)
28 (12.5)
ypT stage
0.766
ypT0
39 (17.2)
31 (13.8)
ypT1
9 (4.0)
10 (4.5)
ypT2
68 (30.0)
63 (28.1)
ypT3
104 (45.7)
110 (49.1)
ypT4
7 (3.1)
10 (4.5)
ypN stage
0.702
ypN0
160 (70.5)
153 (68.3)
ypN1
55 (24.2)
55 (24.6)
ypN2
12 (5.3)
16 (7.1)
pCR
0.157
Yes
38 (16.7)
27 (12.1)
No
189 (83.3)
197 (87.9)
ACT
0.395
Yes
176 (77.5)
166 (74.1)
No
51 (22.5)
58 (25.9)
No. of LNs, median (i.q.r.)
11.0 (7.0, 16.0)
11.0 (7.0, 17.0)
0.739
No. of pLNs, median (i.q.r.)
0.0 (0.0, 1.0)
0.0 (0.0, 1.0)
0.273
ACT, adjuvant chemotherapy; c, clinical; CRT, chemoradiotherapy; ECOG, Eastern Cooperative Oncology Group; EMVI, extramural vascular invasion; i.q.r., interquartile range; LNs, lymph nodes; MRF, mesorectal fascia; MRI, magnetic resonance imaging; N, regional lymph node; p, positive; pCR, pathological complete response; T, primary tumour; TNT, total neoadjuvant therapy.
Figure 2
illustrates the difference in downstaging between the two groups. T downstaging was achieved in 134 (59.0%) patients in the TNT group, representing a significantly greater level of downstaging than that achieved in the CRT group (108 patients, 48.2%; P = 0.027). However, this was not observed in N staging (72.7% and 67.4%, in the TNT and CRT groups respectively).
Fig. 2
Changes in T stage and N stage after neoadjuvant treatment
a Changes in T stage after TNT. b Changes in T stage after CRT. c Bar chart reflecting T downstaging after TNT and CRT. d Changes in N stage after TNT. e Changes in N stage after CRT. f Bar chart reflecting N downstaging after TNT and CRT. c, clinical; p, pathological; TNT, total neoadjuvant therapy; CRT, chemoradiotherapy.
Distribution of LNs retrieved
The distributions of LNs retrieved in the TNT and CRT groups are shown in Fig. 3. The median number of LNs retrieved was 11.0 in both groups. Furthermore, several clinical characteristics were associated with the number of LNs retrieved (Supplementary Materials, Figs. S1, S2). Fewer LNs were retrieved in patients with LARC who achieved a pCR than those who did not achieve a pCR in the TNT group (8.0 [i.q.r. 4.3–13.0] versus 12.0 [i.q.r. 8.0–16.0], P = 0.024). The same result was also found in the CRT group (8.0 [i.q.r. 4.0–12.0] versus 12.0 [i.q.r. 8.0–17.0], P = 0.020). More LNs were retrieved in patients younger than 60 years in the TNT group (12.0 [i.q.r. 7.0–17.0] versus 10.0 [i.q.r. 7.0–14.0], P = 0.042). Although this trend was not observed in the CRT group, fewer LNs were retrieved in patients who underwent TME and achieved R0 resection (11.0 [i.q.r. 7.0–17.0] versus 16.5 [i.q.r. 9.8–22.0], P = 0.036).
Fig. 3
Distribution of the number of lymph nodes retrieved in the TNT and CRT groups
TNT, total neoadjuvant therapy; CRT, chemoradiotherapy.
Identification of the cut-off value for LN yield in the TNT group
The RCS functions reflecting the number of LNs retrieved showed that prognostic factors presented a linear relationship (non-linearity P = 0.718; Fig. 4). This result suggested that the number of LNs retrieved could be modelled as a continuous linear variable. A cut-off value (n = 11) was used for affecting clinical outcomes for which the HR was = 1.00. Patients were thus divided into two subgroups: the extended lymphadenectomy subgroup (with ≥11 LNs retrieved) and the limited lymphadenectomy subgroup (with <11 LNs retrieved).
Fig. 4
Restricted cubic spline curve of the number of lymph nodes
HR, hazard ratio.
Among the 227 patients, 104 patients (45.8%) were in the limited subgroup, whereas 123 patients (54.2%) were in the extended subgroup. The survival curve showed that patients who underwent limited LN retrieval had worse OS than those who underwent extended LN retrieval (HR 2.95 (95% c.i. 1.47 to 5.92), P = 0.001; Fig. 5). After adjustment for clinical confounders (sex, age, ECOG score, distance to anal verge, MRF status, EMVI status, clinical stage, severe AEs, extent of resection, ypT stage and ACT), limited LN retrieval was associated with worse OS (HR 3.01 (95% c.i. 1.43 to 6.34), P < 0.001; Supplementary Materials, Table S1). However, a significant difference in DFS between the two groups of patients was not documented (HR 1.33 (95% c.i. 0.84 to 2.13), P = 0.224; Supplementary Materials, Fig. S3). Furthermore, OS survival analyses were performed for the ypN0 and ypN1 subgroups. As expected, limited LN retrieval led to poorer OS in both subgroups (Fig. 5). However, there was no significant difference between the ypN0-limited subgroup and the ypN1-adequate subgroup (HR 0.38 (95% c.i. 0.11 to 1.30), P = 0.109; Fig. 5d). Despite adjustment for clinical confounders, patients in the ypN0-limited subgroup still had a similar prognosis as patients in the ypN1-adequate subgroup (HR 0.32 (95% c.i. 0.08 to 1.26), P = 0.102; Supplementary Materials, Table S2).
Fig. 5
Survival analyses comparing limited and extended lymphadenectomy subgroups
a Kaplan–Meier curve of stratified OS between the limited lymphadenectomy subgroup and the extended subgroup. b Kaplan–Meier curve of stratified OS between the ypN0-limited subgroup and the ypN0-extended subgroup. c Kaplan–Meier curve of stratified OS between the ypN1-limited subgroup and the ypN1-extended subgroup. d Kaplan–Meier curve of stratified OS between the ypN0-limited subgroup and the ypN1-extended subgroup. p, pathological; OS, overall survival.
Adjuvant chemotherapy for limited LN retrieval in the TNT group
In the TNT group, 176 of the 227 patients (77.5%) received ACT, whereas 51 (22.5%) did not. Figure 6 shows that ACT was associated with better OS than no ACT (HR 0.30 (95% c.i. 0.15 to 0.59), P < 0.001). After adjustment for clinical confounders, ACT was still associated with improved OS (HR 0.37 (95% c.i. 0.18 to 0.75), P < 0.001; Supplementary Materials, Table S3). Among the patients who underwent limited LN retrieval, ACT also resulted in a better prognosis (HR 0.25 (95% c.i. 0.11 to 0.57), P < 0.001; Fig. 6). Remarkably, there was no significant difference between the ACT and no ACT subgroups when an extended number of LNs was retrieved (HR 0.90 (95% c.i. 0.20 to 4.10), P = 0.887; Fig. 6). The same results were documented in the DFS survival analyses (Fig. 6). ACT was associated with better DFS than no ACT (HR 0.37 (95% c.i. 0.23 to 0.61), P < 0.001). After adjustment for clinical confounders, ACT was still associated with improved DFS (HR 0.42 (95% c.i. 0.26 to 0.70), P < 0.001; Supplementary Materials, Table S4). It was also found that ACT showed better DFS outcomes in patients who underwent limited LN retrieval (HR 0.28 (95% c.i. 0.14 to 0.54), P < 0.001; Fig. 6) but not in those who underwent an extended lymphadenectomy (HR 0.60 (95% c.i. 0.27 to 1.31), P = 0.192; Fig. 6).
Fig. 6
Survival analyses between ACT and no ACT subgroups
a Kaplan–Meier curve of stratified OS between the ACT and no ACT subgroups. b Kaplan–Meier curve of stratified OS between the ACT and no ACT subgroups in the limited lymphadenectomy subgroup. c Kaplan–Meier curve of stratified OS between the ACT and no ACT subgroups in the extended lymphadenectomy subgroup. d Kaplan–Meier curve of stratified DFS between the ACT and no ACT subgroups. e Kaplan–Meier curve of stratified DFS between the ACT and no ACT subgroups in the limited lymphadenectomy subgroup. f Kaplan–Meier curve of stratified DFS between the ACT and no ACT subgroups in the extended lymphadenectomy subgroup. ACT, adjuvant chemotherapy; OS, overall survival; DFS, disease-free survival.
Clinical characteristics
From 30 August 2015 to 27 August 2018, a total of 629 patients were screened; 451 underwent TME and had complete statistical data available, and these patients were enrolled in the final analysis. Of these, 227 patients were assigned to the TNT group and 224 patients were assigned to the CRT group (Fig. 1). The clinical characteristics were well balanced between the groups (Table 1). The proportions of females in the TNT and CRT groups were 29.5% and 30.8% respectively. The median age at diagnosis was 55 years in the TNT group and 56 years in the CRT group. The number of LNs retrieved was 11.0 (i.q.r. 7.0–16.0) in the TNT group and 11.0 (i.q.r. 7.0–17.0) in the CRT group.
Fig. 1
Study profile
TNT, total neoadjuvant therapy; CRT, chemoradiotherapy; NOM, non-operative management.
Table 1
Baseline characteristics
Characteristics
TNT group no. (%)
CRT group no. (%)
P
Total
227
224
Age, median (i.q.r.), years
55.0 (47.0, 62.0)
56.0 (49.0, 62.0)
0.672
Sex
0.766
Male
160 (70.5)
155 (69.2)
Female
67 (29.5)
69 (30.8)
ECOG score
0.499
0
86 (37.9)
78 (34.8)
1
141 (62.1)
146 (65.2)
MRI T stage
0.157
cT2
4 (1.8)
7 (3.1)
cT3
186 (81.9)
193 (86.2)
cT3a-b
111 (48.9)
115 (51.4)
cT3c-d
75 (33.0)
78 (34.8)
cT4
37 (16.3)
24 (10.7)
cT4a
25 (11.0)
9 (4.0)
cT4b
12 (5.3)
15 (6.7)
MRI N stage
0.328
cN0
25 (11.0)
35 (15.6)
cN1
119 (52.4)
115 (51.4)
cN2
83 (36.6)
74 (33.0)
Clinical stage
0.149
II
25 (11.0)
35 (15.6)
III
202 (89.0)
189 (84.4)
Distance to anal verge (cm)
0.634
≤5
140 (61.7)
143 (63.8)
>5
87 (38.3)
81 (36.2)
MRF involvement
126 (55.5)
125 (55.8)
0.949
EMVI
133 (58.6)
109 (48.7)
0.034
Extent of resection
0.151
R0
208 (91.6)
196 (87.5)
R1
19 (8.4)
28 (12.5)
ypT stage
0.766
ypT0
39 (17.2)
31 (13.8)
ypT1
9 (4.0)
10 (4.5)
ypT2
68 (30.0)
63 (28.1)
ypT3
104 (45.7)
110 (49.1)
ypT4
7 (3.1)
10 (4.5)
ypN stage
0.702
ypN0
160 (70.5)
153 (68.3)
ypN1
55 (24.2)
55 (24.6)
ypN2
12 (5.3)
16 (7.1)
pCR
0.157
Yes
38 (16.7)
27 (12.1)
No
189 (83.3)
197 (87.9)
ACT
0.395
Yes
176 (77.5)
166 (74.1)
No
51 (22.5)
58 (25.9)
No. of LNs, median (i.q.r.)
11.0 (7.0, 16.0)
11.0 (7.0, 17.0)
0.739
No. of pLNs, median (i.q.r.)
0.0 (0.0, 1.0)
0.0 (0.0, 1.0)
0.273
ACT, adjuvant chemotherapy; c, clinical; CRT, chemoradiotherapy; ECOG, Eastern Cooperative Oncology Group; EMVI, extramural vascular invasion; i.q.r., interquartile range; LNs, lymph nodes; MRF, mesorectal fascia; MRI, magnetic resonance imaging; N, regional lymph node; p, positive; pCR, pathological complete response; T, primary tumour; TNT, total neoadjuvant therapy.
Figure 2
illustrates the difference in downstaging between the two groups. T downstaging was achieved in 134 (59.0%) patients in the TNT group, representing a significantly greater level of downstaging than that achieved in the CRT group (108 patients, 48.2%; P = 0.027). However, this was not observed in N staging (72.7% and 67.4%, in the TNT and CRT groups respectively).
Fig. 2
Changes in T stage and N stage after neoadjuvant treatment
a Changes in T stage after TNT. b Changes in T stage after CRT. c Bar chart reflecting T downstaging after TNT and CRT. d Changes in N stage after TNT. e Changes in N stage after CRT. f Bar chart reflecting N downstaging after TNT and CRT. c, clinical; p, pathological; TNT, total neoadjuvant therapy; CRT, chemoradiotherapy.
Distribution of LNs retrieved
The distributions of LNs retrieved in the TNT and CRT groups are shown in Fig. 3. The median number of LNs retrieved was 11.0 in both groups. Furthermore, several clinical characteristics were associated with the number of LNs retrieved (Supplementary Materials, Figs. S1, S2). Fewer LNs were retrieved in patients with LARC who achieved a pCR than those who did not achieve a pCR in the TNT group (8.0 [i.q.r. 4.3–13.0] versus 12.0 [i.q.r. 8.0–16.0], P = 0.024). The same result was also found in the CRT group (8.0 [i.q.r. 4.0–12.0] versus 12.0 [i.q.r. 8.0–17.0], P = 0.020). More LNs were retrieved in patients younger than 60 years in the TNT group (12.0 [i.q.r. 7.0–17.0] versus 10.0 [i.q.r. 7.0–14.0], P = 0.042). Although this trend was not observed in the CRT group, fewer LNs were retrieved in patients who underwent TME and achieved R0 resection (11.0 [i.q.r. 7.0–17.0] versus 16.5 [i.q.r. 9.8–22.0], P = 0.036).
Fig. 3
Distribution of the number of lymph nodes retrieved in the TNT and CRT groups
TNT, total neoadjuvant therapy; CRT, chemoradiotherapy.
Identification of the cut-off value for LN yield in the TNT group
The RCS functions reflecting the number of LNs retrieved showed that prognostic factors presented a linear relationship (non-linearity P = 0.718; Fig. 4). This result suggested that the number of LNs retrieved could be modelled as a continuous linear variable. A cut-off value (n = 11) was used for affecting clinical outcomes for which the HR was = 1.00. Patients were thus divided into two subgroups: the extended lymphadenectomy subgroup (with ≥11 LNs retrieved) and the limited lymphadenectomy subgroup (with <11 LNs retrieved).
Fig. 4
Restricted cubic spline curve of the number of lymph nodes
HR, hazard ratio.
Among the 227 patients, 104 patients (45.8%) were in the limited subgroup, whereas 123 patients (54.2%) were in the extended subgroup. The survival curve showed that patients who underwent limited LN retrieval had worse OS than those who underwent extended LN retrieval (HR 2.95 (95% c.i. 1.47 to 5.92), P = 0.001; Fig. 5). After adjustment for clinical confounders (sex, age, ECOG score, distance to anal verge, MRF status, EMVI status, clinical stage, severe AEs, extent of resection, ypT stage and ACT), limited LN retrieval was associated with worse OS (HR 3.01 (95% c.i. 1.43 to 6.34), P < 0.001; Supplementary Materials, Table S1). However, a significant difference in DFS between the two groups of patients was not documented (HR 1.33 (95% c.i. 0.84 to 2.13), P = 0.224; Supplementary Materials, Fig. S3). Furthermore, OS survival analyses were performed for the ypN0 and ypN1 subgroups. As expected, limited LN retrieval led to poorer OS in both subgroups (Fig. 5). However, there was no significant difference between the ypN0-limited subgroup and the ypN1-adequate subgroup (HR 0.38 (95% c.i. 0.11 to 1.30), P = 0.109; Fig. 5d). Despite adjustment for clinical confounders, patients in the ypN0-limited subgroup still had a similar prognosis as patients in the ypN1-adequate subgroup (HR 0.32 (95% c.i. 0.08 to 1.26), P = 0.102; Supplementary Materials, Table S2).
Fig. 5
Survival analyses comparing limited and extended lymphadenectomy subgroups
a Kaplan–Meier curve of stratified OS between the limited lymphadenectomy subgroup and the extended subgroup. b Kaplan–Meier curve of stratified OS between the ypN0-limited subgroup and the ypN0-extended subgroup. c Kaplan–Meier curve of stratified OS between the ypN1-limited subgroup and the ypN1-extended subgroup. d Kaplan–Meier curve of stratified OS between the ypN0-limited subgroup and the ypN1-extended subgroup. p, pathological; OS, overall survival.
Adjuvant chemotherapy for limited LN retrieval in the TNT group
In the TNT group, 176 of the 227 patients (77.5%) received ACT, whereas 51 (22.5%) did not. Figure 6 shows that ACT was associated with better OS than no ACT (HR 0.30 (95% c.i. 0.15 to 0.59), P < 0.001). After adjustment for clinical confounders, ACT was still associated with improved OS (HR 0.37 (95% c.i. 0.18 to 0.75), P < 0.001; Supplementary Materials, Table S3). Among the patients who underwent limited LN retrieval, ACT also resulted in a better prognosis (HR 0.25 (95% c.i. 0.11 to 0.57), P < 0.001; Fig. 6). Remarkably, there was no significant difference between the ACT and no ACT subgroups when an extended number of LNs was retrieved (HR 0.90 (95% c.i. 0.20 to 4.10), P = 0.887; Fig. 6). The same results were documented in the DFS survival analyses (Fig. 6). ACT was associated with better DFS than no ACT (HR 0.37 (95% c.i. 0.23 to 0.61), P < 0.001). After adjustment for clinical confounders, ACT was still associated with improved DFS (HR 0.42 (95% c.i. 0.26 to 0.70), P < 0.001; Supplementary Materials, Table S4). It was also found that ACT showed better DFS outcomes in patients who underwent limited LN retrieval (HR 0.28 (95% c.i. 0.14 to 0.54), P < 0.001; Fig. 6) but not in those who underwent an extended lymphadenectomy (HR 0.60 (95% c.i. 0.27 to 1.31), P = 0.192; Fig. 6).
Fig. 6
Survival analyses between ACT and no ACT subgroups
a Kaplan–Meier curve of stratified OS between the ACT and no ACT subgroups. b Kaplan–Meier curve of stratified OS between the ACT and no ACT subgroups in the limited lymphadenectomy subgroup. c Kaplan–Meier curve of stratified OS between the ACT and no ACT subgroups in the extended lymphadenectomy subgroup. d Kaplan–Meier curve of stratified DFS between the ACT and no ACT subgroups. e Kaplan–Meier curve of stratified DFS between the ACT and no ACT subgroups in the limited lymphadenectomy subgroup. f Kaplan–Meier curve of stratified DFS between the ACT and no ACT subgroups in the extended lymphadenectomy subgroup. ACT, adjuvant chemotherapy; OS, overall survival; DFS, disease-free survival.
Discussion
This research, based on the STELLAR trial, investigated the number of LNs in patients with LARC after short-term radiotherapy plus neoadjuvant chemotherapy (TNT). The results documented that postoperative ACT significantly improved OS and DFS in patients with limited lymphadenectomy. Nevertheless, patients in whom an adequate number of LNs was retrieved did not show improvement in OS or DFS with ACT. Based on these findings, at least 11 LNs should be retrieved in these patients to achieve optimal prognostic benefits, accurate N staging and better decision-making regarding ACT.
Additionally, one of the main findings of the present study was superior T downstaging in the TNT group compared with the CRT group, which could be attributed to the fact that TNT has a superior pCR rate compared with CRT7,8; other reasons could include the higher single radiation dose in short-term radiotherapy and the effects of the four cycles of neoadjuvant chemotherapy. Moreover, NAT can also lead to an improvement in N staging9. N downstaging was nearly 70% of patients in both groups, which was greater than the extent of T downstaging. These findings indicate that NAT plays a crucial role in treating regional LNM.
Although NAT can affect the number of harvested LNs10,11, few previous studies have reported the optimal number of LNs to retrieve in CRC patients after NAT. The latest clinical guidelines suggest that at least 12 LNs should be retrieved in CRC patients to accurately define the LNM status and that re-examination should be performed if fewer than 12 LNs are retrieved3. With continuing improvements in pathological examinations and advancements in surgical techniques, the number of retrieved LNs has significantly increased; for example, others suggested that the minimum number of LNs retrieved should be 16 for node-negative CRC patients based on real-world data12, whereas other Italian groups have proposed that the number of LNs retrieved has no prognostic impact in node-negative rectal cancer patients after NAT13. Moreover, although a worse OS was documented in the limited LN retrieval subgroup than the extended LN retrieval subgroup, no difference was observed in DFS between the two groups. This suggests that the number of lymph nodes retrieved may not significantly impact the local recurrence of LARC. However, as patients require further treatment to control the disease upon recurrence, those who undergo adequate LN retrieval might benefit more from such treatment, potentially improving their OS. Considering that adequate LN retrieval is still a critical component of high-quality surgical standards in CRC patients14, this study suggests that the retrieval of ≥11 LNs may be considered sufficient for adequate LN retrieval in patients with LARC after TNT.
In addition to indicating differences in patient prognosis, adequate LN retrieval is essential for ensuring accurate N staging12,15. According to the AJCC nodal staging criteria, ypN0 patients are expected to have a better prognosis than ypN1 patients. However, a similar OS in ypN0-limited and ypN1-extended patients was reported here. This prognostic difference showed that ypN0 patients could be upstaged to ypN1 if adequate LN retrieval could not be achieved to determine their LN status, which is consistent with the findings of previous studies of non–neoadjuvant-treated CRC patients16,17.
Previous studies have demonstrated that postoperative ACT after long-term concurrent CRT cannot substantially improve OS because of poor patient tolerance and compliance18–20. However, in the present study, patients in the TNT group who underwent postoperative ACT exhibited more favourable prognostic outcomes, both OS and DFS, than those who did not. After adjusting for confounders, it was found that patients with LARC could benefit from postoperative ACT after short-term radiotherapy plus neoadjuvant chemotherapy in patients with <11 LNs retrieved. On the other hand, we found that patients who underwent extended LN retrieval did not have the same benefit from ACT. Taken together, these findings suggest that postoperative ACT should be considered for patients with LARC who have undergone limited LN retrieval after TNT (short-term radiotherapy plus neoadjuvant chemotherapy).
A strength of this study is that it was based on a multicentre, open-label, randomized phase III trial. Patients were enrolled from 16 hospitals in 11 provinces of China, effectively representing the characteristics of the Chinese population.
Still, there are several limitations. First, the number of LNs examined can be affected by various factors, such as the extent of surgical resection, the thoroughness of the pathological examination, host immune status and the use of neoadjuvant treatment21–23. Differences in these factors could have led to heterogeneity in findings. Second, another limitation could be the relatively small sample size, even though this cohort was derived from a high-quality phase III trial.
Overall, this research documented that retrieving 11 or more LNs could be sufficient for adequate LN retrieval and accurate N staging in patients with LARC after short-term radiotherapy plus neoadjuvant chemotherapy (TNT), suggesting that postoperative ACT should be considered for those patients who have undergone inadequate LN lymphadenectomy.
Author contributions
Yueyang Zhang (Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing—original draft, Writing—review & editing), Yuan Tang (Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Supervision, Visualization, Writing—review & editing), Huiying Ma (Data curation, Formal analysis, Resources, Validation, Writing—original draft), Hao Su (Conceptualization, Formal analysis, Methodology, Visualization, Writing—original draft, Writing—review & editing), Zheng Xu (Conceptualization, Data curation, Writing—original draft), Changyuan Gao (Conceptualization, Data curation, Validation, Writing—original draft), Haitao Zhou (Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Resources, Validation, Visualization, Writing—review & editing), and Jing Jin (Conceptualization, Data curation, Funding acquisition, Investigation, Project administration, Supervision, Visualization, Writing—review & editing)
Supplementary Material
zrae118_Supplementary_Data |
Title: Ethical Decision-Making Regarding Life Sustaining Treatment in End-Of-Life Care: A Scoping Review of the Similarities and Differences Between Two Viewpoints | Body:
1. Introduction:
A terminal or dying patient is one who suffers from an advanced incurable disease and does not respond to active treatments. Death is inevitable within a short period of time in the case of such patients, but supportive treatments can improve the quality of their lives in the final stages (1). Physicians constantly encounter such patients and can keep them alive through supportive or life-prolonging treatments, without changing or reversing their underlying illness. During the past decades, however, cardio pulmonary resuscitation (CPR) has gone from being an advanced intervention for saving those who suffer reversible cardiac arrest to being used in all cases of death in hospitals. As a result, advanced medical centers throughout the world implement “Do Not Resuscitate” (DNR) orders, and refer to it as an order to allow natural death, believing that we must know when to withhold resuscitation efforts. Consequently, healthcare providers face ethical challenges regarding death in end-of-life care (2).
Three major issues are discussed in end-of-life care. The first is that considering disease patterns and the prevalence of chronic illnesses, a great number of patients in different societies rely upon end-of-life care, and therefore the quality of such care is an important healthcare problem. The second issue is that despite the existing guidelines, the boundaries for end-of-life care remain vague and unclear. The decision whether to start or withdraw life sustaining treatment, therefore, poses numerous ethical problems. Finaly, where palliative sedation represents the optimal treatment approach, even though it may superficially resemble euthanasia or physician-assisted suicide. The distinction between the two has, therefore, been repeatedly and emphatically pointed out by thinkers and scholars, and is an important issue in end-of-life care.
As a matter of fact, any perspective on withdrawing or withholding life-sustaining treatments is contingent on the evaluation of the assumed fundamental ethical principles in taking human life (3). This issue overshadows many subjects, such as the debate over futility of treatment and the right to refuse it, the nature of professional responsibility, and the most desirable approach to discussing end-of-life options with patients and their families.
Islamic teachings offer notable instructions regarding the final stages of illness where there is no hope for improvement and death is imminent in a short period of time, so that the patient will receive the blessings of God almighty and experience a good death. Nonetheless, verses of the Holy Quran highlight the sanctity of human life and God’s absolute power over man’s life and death, and forbid taking human life for any reason (4).
As a result, it appears that health care providers need to set and follow certain criteria for starting or continuing life-prolonging treatments in terminal patients in order to better the quality of care and specify boundaries. Considering the criteria for offering such treatments, the next great challenge is determining who should perform evaluations and make the final decision. Patients, their families, or the physician cannot make the final decision alone and they must decide based on suitable criteria. This issue usually is a morally stressful subject for physicians which cause conflict between obligations to patients and society (5-7). Currently, there is no ethical framework in Iran that can simply and unequivocally be used to make decisions about withholding or withdrawing life-prolonging treatments. Occasionally, the best efforts and wisest decisions create ethical problems and appear rather inhuman. Therefore, this study attempted to compare the Islamic and secular medical ethical viewpoints on the two major issues in end-of-life care, that is, the criteria for withdrawing life-prolonging treatments of terminal patients such as CPR, and the final decision maker, in the hope that it can assist health care providers in making decisions.
2. Methods:
2.1 Study design and setting
This study was designed as a scoping review to compare Islamic and secular perspectives on end-of-life care, utilizing the PRISMA-ScR (“Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews”) guideline (8) to ensure transparency and rigor. The primary objective of this review was to comprehensively assess the existing literature to address two key ethical questions in end-of-life care:
What are the determining ethical criteria for withdrawing life-prolonging treatments, such as CPR, in terminal or dying patients?
In cases of uncertainty or disagreement regarding the continuation or withdrawal of life-prolonging treatments, who should be responsible for making the final decision?
The scoping review aimed to clarify the ethical concepts from both Islamic and secular viewpoints, focusing on these two main questions. This involved a systematic drafting, reviewing, and extraction of relevant texts, followed by a description and comparison of the identified ethical concepts.
2.2 Search strategy and data gathering
To gather relevant literature on Islamic and secular opinions regarding end-of-life care, a systematic search was conducted across three major international databases: Web of Science, PubMed, and Scopus. The search employed a combination of keywords, including: “ethics,” “end-of-life care,” “withdraw,” “withhold,” “dying patient,” “terminal patient,” “life-sustaining treatment,” “life-prolonging treatment,” “advance care,” “ethical decision-making,” “Islam,” and “secular.” The time frame for inclusion was from January 2000 to December 2022, with an additional search conducted in 2023 to capture any recent publications.
To include Persian-language sources, searches were also performed in Magiran and SID databases using equivalent Persian keywords. Furthermore, Google Scholar, Google, and reputable websites were searched to identify additional documents, such as books and ethical guidelines, relevant to end-of-life care. For Islamic references, specific articles and books written by Muslim scholars, available in electronic format, were included for Quranic citations. The search strategy was developed and refined by two investigators (MM and AZ), and a representative search query used for PubMed was:
(("End-of-Life Care"[Title/Abstract] OR "Terminal Patient"[Title/Abstract] OR "Life sustaining Treatment"[Title/Abstract]) OR ("Terminal Care"[Mesh])) AND (withdraw [Title/Abstract] OR withhold [Title/Abstract] OR "Ethical Decision-Making"[Title/Abstract] OR "dying patients"[Title/Abstract] OR "life prolonging treatments"[Title/Abstract] OR "advance care"[Title/Abstract]).
2.3 Selection of relevant studies
Studies were eligible for inclusion if they provided data on ethical criteria for withdrawing life-prolonging treatments and decision-making for end-stage cancer patients. Articles were limited to those published between January 2000 and December 2022, and only those written in English or Persian were considered. Exclusion criteria included studies that focused on chronic obstructive pulmonary disease (COPD), or end-stage kidney or heart disease. The database search and initial screening of titles and abstracts were conducted by one author (MM), while duplicates were removed using EndNote version 8. The eligibility of the studies was then assessed independently by two authors (MM and FZ). Full-text articles that appeared potentially relevant were further reviewed. Priority was given to national and international surveys, reviews, and guidelines on life support limitation policies from both Islamic and secular perspectives. Reference lists of selected studies were also examined to identify additional relevant documents. Data collection continued until data saturation was achieved, ensuring a comprehensive overview of the literature. Any disagreements regarding study selection were resolved through discussions with the research team.
2.4 The process of extracting, summarizing, and reporting the relevant studies
The researchers (MM and FZ) screened documents, yielded by the search to match the two questions of this study, based on titles and abstracts. After reviewing these documents, documents were selected for the final review. We found that 45 documents could help answer the two main questions of the study including 4 guidelines, 5 books or book chapters and 36 articles, which are presented in Table1 by title, first author, publication year, journal, country, method, language, study design, population, data collection instrument, reliability and validity, and outcome. After finalizing the list of selected articles, the full texts were thoroughly reviewed, and the relevant data were systematically extracted. The authors (BL, MM, FZ, MT) examined documents, systematically reporting and comparing data from the main studies where possible. To identify key concepts and primary themes, each selected article was meticulously analyzed. Upon completing the data extraction based on the main questions, the researchers presented the identified concepts to the other members of the research team for achieving a consensus. Studies that did not meet the inclusion criteria, as judged by the research team, were excluded. To ensure the comprehensiveness of the obtained data, information related to two main questions was collected from various guidelines, based on availability, ease of access, and citation frequency. This approach aimed to capture a broad spectrum of perspectives on end-of-life ethical decision-making. We had two main questions and managed the documents based on the main concepts including sanctity of life, benefit of treatments, and authority of decision maker. The study described areas of agreement and disagreement between Islamic and secular ethical viewpoints on these topics. However, it is important to note that the researchers did not analyze these perspectives themselves. To maintain objectivity, the authors explicitly set aside personal views during the data analysis process, striving to present an impartial academic description. The researchers made deliberate efforts to ensure that their findings did not favor either Islamic or secular viewpoints. The process of document extraction is illustrated in Figure 1.
2.5 Ethical consideration
The authors strictly followed principles of publishing ethics, ensuring the process was conducted with integrity, transparency, and respect for all sources throughout the literature review. In addition, they carefully addressed ethical considerations during the review, selection, description, and analysis of key concepts to answer the two main questions of the scoping review by applying ethical standards in line with Iran's publication ethics guideline and the Committee on Publication Ethics (COPE) Code of Conduct. The authors observed the reliability, validity, and overall quality of the sources used by evaluating source credibility, methodological rigor, transparency and reporting, consistency and consensus, timeliness of data, and limitations of the data.
3. Results:
A total of 13,208 articles were identified. After removing duplicates, 8332 articles remained. By an initial screening of titles and abstracts 7877 articles were excluded. Of the remaining 455 records, the full texts of 379 articles were retrieved and assessed for eligibility. After further review, 300 articles were excluded due to inappropriate study design or lack of relevance to the research questions and objectives (Figure 1). Finally, 45 documents were selected that could help answer the two main questions of the study including 4 guidelines, 5 books or book chapters and 36 articles. Bibliographic profile of final included studies is presented in Table 1. After extracting relevant documents, the results of this study have been examined under the three topics including sanctity of life, goals and benefits of treatment, authority of the physician and the patient’s family. Results from comparisons in each topic have been presented briefly in Table 2.
3.1 Sanctity of life
a) Islamic perspective
According to the comprehensive and valuable teachings of Islam and Quran, human life is extremely sanctified, and can be ended only through the Will of God. Every moment of a person’s life is precious, even if its quality is low (9). In patients who are dependent on medical assistance to stay alive, measures taken by physicians, patients, or their families that shorten their lives are unacceptable (10). Preservation of life is a duty, and it is the responsibility of both the patient and the physician to save it (11), and taking it is considered a great sin (9). It is stated in Sura Al-Ma’ida, Verse 32 (“If someone kills a person in the land who is not guilty of murder or depravity, then it is as if he has killed all mankind, and if someone saves one person from death, then it is as if he has given life to all mankind.”). This verse legitimates the implementation of medical advances in saving lives, and forbids suicide and euthanasia. In fact, human life falls under the category of the Lord’s rights and His authority. It seems that no principle, including man’s autonomy, can permit people to exercise their authority in this area (12). In Sura At-Tawbah, Verse 116, the authority of God over life and death has been emphasized (“He is the Lord of heaven and earth, and He is the one who gives and takes life.”).
The pivotal value and sanctity of life are not obstacles to our decisions. Thus, regarding the sanctity of human life and its existential value, therapies that cause serious complications and lead to loss of human dignity should not continue (4). The values such as sanctity of life will lead to equilibrium and proportionality in providing health care.
b) Secular perspective
Secular guidelines on end-of-life care points to the necessity of different treatment of terminal patients. In the case of such patients, the focus is not on the value of the patient’s life, but rather on an acceptable justification of life-prolonging treatments and palliative care (13, 14). This does not mean that the patient’s life has no value, but that any medical intervention should be justifiable based on its benefits for the patient, and the patient’s opinions, values, wishes, and philosophy must be considered in this respect (14). One of the authors discussed medical interventions against their benefits. They believe that medical treatments should not target an anatomical, physiological, or chemical unit in the patient’s body, but rather his or her welfare, then life-prolonging treatments must be offered as they are to the patient’s benefit. Furthermore, physicians should empower patients’ autonomy to decide about care of end of life by informing them and sharing decision making (15). Accordingly, relational autonomy concept suggests that the patients do not separate from their life environment and need to interact with families and the other relatives for making decision in end-of-life care (16).
3.2 Benefits of treatment
a) Islamic perspective
From the Islamic perspective, evaluation of benefits and harms is influenced by the value of human life, spiritual growth and development, and happiness in afterlife and the final stages of life. Therefore, the quality and quantity of a patient’s life in the last days through medical treatments, sedation and relief are not the only factors that come into question in this evaluation. In the Islamic worldview, a good life results from a balance between partaking of the blessings and riches of earthly life, and achieving the true purpose of life, that is, spiritual elevation through obeying and praising the Lord. Thus, both body and soul are the criteria for measuring the quality of life.
From the Islamic viewpoint, saving all forms of life is equally valuable. The care should be offered based on the Islamic principle that regards life as a divine gift, not just the prognosis that can determine the benefits of treatments (12, 17).
Numerous studies emphasize the sensibility of saving the patient’s life, and note that in Islam, one obligation toward patients is to not prolong the process of dying. Also, they believe that having clear insight and attitude towards death, along with other influential factors, will help making decisions with less internal challenges (4, 11, 18). Based on Islamic Code of Ethics, by accepting death as a phase in man’s spiritual life, Islam does not approve of prolongation of the dying process through futile medical technology in all patients. In addition to the concepts above, many Muslim authors (19, 20) cite principles of Fiqh such as illegitimacy of harm (la¯ d∙arar wa la¯ d∙ira¯r), financial hardship (al-‘usr wa al-h.araj), and prudence (mas.lah.a) in their analyses of issues related to end-of-life care for terminal patients. The reason is that there are rules in Fiqh that expound the principles of beneficence and non-maleficence in Islamic ethics. One well-known principle of Fiqh often cited in relation to worship and transactions is illegitimacy of harm. This principle has two implications: God has not set any harmful rules in primary Islamic commandments, and if a rule is generally and by nature harmless, but can in practice harm a Muslim, it will be waived in that special case. Likewise, Islamic scholars emphasized that if the treating physician finds out that the treatment is not beneficial to the patient or that it causes them more suffering, the treatment should be stopped. This has been mentioned regarding withholding unhelpful or harmful treatments, since the principle of non-maleficence is the base of Islamic medical ethics (21). In a narrative review study, the researchers found that the basic criteria for withholding or withdrawing life-sustaining treatment in Islamic view point are “futility of continued therapy, depressed neurological status of the patient, and compounding harms from continued clinical care”. Also, the assessment of these conditions should be evaluated by expert physicians (22). Furthermore, Stable (Mustaqarr) and Unstable life (Ghayr- Mustaqarr) (20) are the other concepts in Islamic teachings, which may be useful in ethical decision making to care for terminal patients who are not dead according to any of the criteria (brain or heartbeat and respiration). Based on the concept of unstable life, they are already moving toward the separation of body and soul (23), and therefore measures such as cardiopulmonary resuscitation cannot help them. It seems that, futile care takes on a clearer meaning by applying these concepts.
b) Secular perspective
Hippocrates believed that one of the medical goals is to relieve patients’ pain and suffering and one of the duties of physicians is to refrain from treating patients who are defeated by disease. For all patients in the final stages, if life-prolonging treatments fail, medical care should aim at pain relief and sedation. Sometimes treatments focus on physiological goals, but are not beneficial to the patient overall. At other times treatment benefits are limited to sustaining the patient’s condition and stopping it from deteriorating (24, 25). In some cases, treatments may keep the patient alive, but cannot prevent the course of illness, and there is no hope that the patient will achieve the required level of consciousness to interact with others. In many of the above cases, caregivers will decide to stop treatments aimed at improvement, and to focus on relieving symptoms and preparing the patients for their passing. Such decisions are made based on certain rules. For instance, if there is no chance of recovery or a level of improvement that is beneficial to the patient, CPR is not performed (26).
Any decision on withholding or withdrawing treatment must be based on the best clinical evidence, and guidelines for identification and investigation of the situation need to be observed. Consequently, evaluations must establish whether any given accessible treatment is beneficial for the patient or not (27).
However, the concept of futility in medicine is a difficult and confusing concept. Pope believed that “futile treatment” is a misleading terminology, furthermore, we should use “inappropriate or non-beneficial treatment” as a value-laden concept to decide withholding or withdrawing life-sustaining treatment (28). Futile treatment should concisely be discussed based on a specific intervention and its outcome for end stage patients. Thus, the concept of futility reminds us that in the provision of therapeutic measures such as lifesaving treatment only the assessment of benefits and risks of treatment based on the knowledge and beliefs of health care providers about end stage disease must be taken into consideration (29).
3.3 Authority of the physician and the patient’s family
a) Islamic perspective
Some Muslim thinkers believe that regarding decisions to continue life-prolonging treatments, opinion of the physician and the patient’s family can be considered (4, 10). Based on the principle of illegitimacy of harm (la¯ d∙arar wa la¯ d∙ira¯r), for a patient who is in the final stages and cannot be cured by a certain treatment, the benefits should be weighed against the harms that it can inflict upon the community, the families of patients, and sometimes even the patients themselves (20).
There are numerous fatwas on stopping resuscitation efforts in cases of brain death, and it has been mentioned that artificial ventilation can be withdrawn in such cases. It has been emphasized, however, that this should be done through medical diagnosis, and not by the family’s request alone (12). Likewise, Islamic Code of Ethics stated that if the treating physician finds out that the treatment is not beneficial to the patient, or that it causes them more suffering, the treatment should be stopped (21). As regards life-sustaining treatments, decisions need to be made by a committee of treating physicians, philosophy, ethics, jurisprudence, and law experts (4).
b) Secular perspective
From the point of view of modern medical ethics, capacitated patients must be encouraged to express their wishes for future treatments. Physicians are therefore obligated to adjust care to the patient’s best interest. A surrogate decision-maker may be appointed to give consent on behalf of the incapacitated patient, and this person must be consulted about the medical procedures (30). If a surrogate decision-maker has not been appointed, physicians will do what they deem advisable under the circumstances to improve the mental or physical well-being of the patient. There are different opinions on the factors that need to be considered in decisions about life-prolonging treatments beneficial to these patients. There are inner values in being alive, and therefore prolonging life is always a form of benefit (31). The following criteria must be considered in evaluations concerning life-prolonging treatments: patients’ wishes and values (where they can be established); clinical judgment of how effective certain treatments can be, including their benefits and harms; possibility of the patient experiencing great and unmanageable pain and suffering; patient’s level of consciousness of self and surroundings, the ability to interact with others, the capacity to perform self-directed activities, or control over different aspects of one’s life; chance and extent of improvements in the patient’s condition through treatments; justifiability of invasive procedures; viewpoints of the patient’s family; and perspectives of the surrogate decision-maker regarding benefits of the treatment. Discussions with the patients’ families can often provide valuable insight into whether the patients would consider life-prolonging treatments to be beneficial. The patient’s outlook on harm or danger that is considered acceptable can affect treatment decisions greatly. Thus, capacitated patients must be conferred with so it can be determined whether the benefits of CPR outweigh its harms (10). Foster emphasized that uncertainties in life-sustaining treatments are ethically acceptable for these interventions and should not to be withdrawn (32).
Sometimes the worthy goal of these patients is to continue living for a farewell to one of the relatives. Thus, unilateral decision-making by physician without the involvement of patient and his/her family about withdrawing of life saving treatments is considered an old and unacceptable view. However, discussion with these patients and their families about providing advanced end of life care need inter-professional collaboration (25).
On the other hand, lack of communication can be a significant cause of distress for health care providers. Furthermore, when there appears to be little hope, decisions regarding withdrawing or withholding life-sustaining treatments by physicians alone could be very difficult and complex (33). European Resuscitation Council Guidelines 2021 emphasized that withdrawing or withholding life-sustaining treatments as a collaborative process, which involved patients and their families as well as health care team (34). In a study, researchers recommended that physicians should inform the patient and their relatives about the incurable disease, so that they can participate in an active and collaborative decision-making process with the physician (35). In a study, it was shown that ICU specialists can help the patient's family to decide to withdraw life-sustaining treatments and avoid futile interventions (36). The authors of a scoping review have expressed that the role of family for decision-making in end-of-life care has been emphasized in different literature now (37).
Figure 1
The flowchart of the extracted articles.
Table 1
Bibliographic profile of final included studies
Title
First Author
Publication Year
Journal
Country
Method
Language
Study design
Population
Data gathering
instrument
Reliability and Validity
Outcome
Islamic Theology and the Principles of Palliative Care
Mohammad Zafir Al-Shahri
2016
Palliative & Supportive Care
Saudi Arabia
Review
English
Employs a literature review and analysis of Islamic theology to explore principles relevant to palliative care
The Muslim population, particularly patients receiving palliative care
Not applicable
This is not an empirical study
Discusses how Islamic theology can inform and support the principles of palliative care, emphasizing values such as compassion, relief of suffering, and respect for patient dignity within an Islamic framework.
[Do-not-resuscitate order across societies and the necessity of a national ethical guideline]
Maryam Peimani
2012
Iranian Journal of Medical Ethics and History of Medicine (IJMEHM)
Iran
Review
Persian
A review of literature, ethical guidelines, and case studies to discuss DNR orders
Do-Not-Resuscitate (DNR) orders across different societies, particularly focusing on Iran
Not applicable
Based on the thoroughness of its ethical analysis and the extent to which it incorporates both national and international perspectives on DNR orders.
The necessity for a national ethical guideline on DNR orders in Iran, informed by an analysis of international practices and cultural considerations
Life-Sustaining Treatment and Euthanasia: I. Ethical Aspects
Dan W Brock
2004
Book
USA
Book
English
A review of ethical theories, legal precedents, and case studies, providing a philosophical and bioethical analysis of life-sustaining treatments and euthanasia
All Population
Not applicable
Brock is a well-known bioethicist, which enhances the credibility of his arguments.
Key ethical considerations surrounding decisions about life-sustaining treatment and euthanasia
End-of-Life Care Ethical Decision-Making: Shiite Scholars' Views
Mina Mobasher
2014
Journal of Medical Ethics and History of Medicine (JMEHM)
Iran
Original
English
Qualitative
Eight Shiite experts in Islamic studies
Structured interview
Based on the thoroughness of the analysis and the accuracy of the representation of Shiite scholars' views
The ethical frameworks and opinions of Shiite scholars regarding decisions about end-of-life care, highlighting principles such as the sanctity of life, patient autonomy, and permissible withdrawal of life-sustaining treatment under specific circumstances
The Relationship Between Moral Distress and Perception of Futile Care in the Critical Care Unit
Melinda J Mobley
2007
Intensive and Critical Care Nursing
USA
Original
English
Quantitative
A cross-sectional survey consisting of 38 clinical situations
Questionnaire
Dependent on the robustness of the instruments used to measure moral distress and perceptions of futile care
A relationship between higher levels of moral distress and the perception that care being provided to critically ill patients is futile
Causes of Moral Distress in the Intensive Care Unit: A Qualitative Study
Natalie J Henrich
2016
Journal of Critical Care
Canada
Original
English
Qualitative
59 Healthcare professionals working in Intensive Care Units (ICUs)
Focus group
Would be enhanced by the use of well-established qualitative research methods
Identified several key causes of moral distress in ICU settings
Organizational Influences on Health Professionals' Experiences of Moral Distress in PICUs
Sarah Wall
2016
HEC Forum
Canada
Original
English
Qualitative
Healthcare professionals working in Pediatric Intensive Care Units (PICUs)
Interviews or surveysContent analysis of
Depends on the rigor of the qualitative methods
Specific organizational factors contributing to moral distress in PICU staff
Bioethics for Clinicians: 21. Islamic Bioethics
Abdallah S. Daar
2001
Canadian Medical Association Journal (CMAJ)
Canada
Review
English
A review of Islamic bioethical literature and religious texts
Healthcare professionals, specifically clinicians
Not applicable
Dr. A.S. Daar is a well-known expert in this field, enhancing the article's credibility.
Key Islamic bioethical principles, emphasizing the importance of patient autonomy, beneficence, and the role of religious authority in decision-making
End of Life Ethical Issues and Islamic Views
Farzaneh Zahedi
2007
Iranian Journal of Allergy, Asthma, and Immunology
Iran
Review
English
A review and analysis of Islamic religious texts, bioethical literature, and the viewpoints of Islamic scholars
Healthcare professionals treating Muslim patients
Not applicable
From the expertise of the authors in the field of Islamic bioethics and medical ethics
The balance between religious obligations and ethical decision-making in medical practice from an Islamic viewpoint
Brain-Dead Patients Are Not Cadavers: The Need to Revise the Definition of Death in Muslim Communities
Mohamed Y. Rady
2012
HEC Forum
USA
Review
English
A review and analysis of medical and bioethical literature, Islamic jurisprudence, and religious texts
Muslim communities and the healthcare professionals working with Muslim patients
Not applicable
Are grounded in the expertise of the authors, who are well-versed in both medical ethics and Islamic bioethics
The conventional medical definition of brain deathBrain-dead patients should not be considered cadavers according to Islamic law
Euthanasia: Ethical Explanation and Analysis
Alireza Parsapour
2008
Iranian Journal of Medical Ethics and History of Medicine (IJMEHM)
Iran
Review
English
Qualitative analysis of ethical considerations surrounding euthanasia
Various stakeholders in healthcare
Ethical frameworks and philosophical analysis
Does not have traditional reliability and validity measures
Exploration and analysis of ethical dimensions of euthanasia
WMA Declaration of Venice on End-of-Life Medical Care
World Medical Association
2022
Not applicable
USA
Internet article
English
Development through consensus among medical professionals
Healthcare professionals and patients in end-of-life care
Ethical principles and guidelines for end-of-life care
Does not have traditional measures of reliability and validity
Guidelines and ethical principles for medical professionals regarding end-of-life care
Withholding and Withdrawing Life-Prolonging Medical Treatment: Guidance for Decision Making
British Medical Association
2008
Book
UK
Book
English
Consensus development among medical professionals and ethical analysis
Relevant to healthcare providers, patients, and families involved in decision-making
Ethical frameworks and clinical guidelines
Not applicable
Guidance for healthcare professionals on making decisions about withholding or withdrawing life-prolonging treatment
Dealing with Family Conflicts in Decision-making in End-of-Life Care of Advanced Cancer Patients
Katsiaryna Laryionava
2021
Current Oncology Reports
Germany
Review
English
Qualitative analysis of family dynamics and conflicts in end-of-life care
Family members of advanced cancer patients and healthcare providers involved in end-of-life decision-making
Ethical frameworks and decision-making models
Typically evaluated through peer review and expert consensus in qualitative studies
Applicable to multiple countries, with potential focus on western healthcare settings
Research Priorities in Geriatric Palliative Care: Informal Caregiving
Richard Schulz
2013
Journal of Palliative Medicine
USA
Review
English
Review and synthesis of existing research to identify priorities in geriatric palliative care
Older adults receiving palliative care and their informal caregivers (family and friends)
Surveys, interviews, or frameworks related to informal caregiving in palliative contexts
Not specifically stated
Key research areas related to informal caregiving in geriatric palliative care
From Quality of Life to Value of Life: An Islamic Ethical Perspective
Waseem M. Fathalla
2010
Ibnosina Journal of Medicine and Biomedical Sciences
UAE
Review
English
Theoretical exploration and ethical analysis
Not explicitly stated; likely relevant to healthcare professionals and scholars interested in Islamic ethics
Ethical frameworks and Islamic ethical principles
Not specified
Discussion on the transition from quality of life considerations to the value of life from an Islamic ethical perspective
A Discussion on Some Ontological Components of Death in the Holy Quran
Bagher Larijani
2015
Iranian Journal of Medical Ethics and History of Medicine (IJMEHM)
Iran
Review
Persian
Philosophical/theological analysis of the concept of death as presented in the Quran
Not referring to a specific group of people
Qualitative tools such as textual analysis
The consistency of Quranic interpretation and alignment with Islamic jurisprudence and ethical theory
The ontological aspects of death as described in the Quran
Euthanasia from Islam and Modern Medical Ethics Perspectives
Zahra Hashemi
2008
Iranian Journal of Medical Ethics and History of Medicine (IJMEHM)
Iran
Review
Persian
Qualitative, involving comparative analysis
This is a comparative ethical discussion
Textual and comparative analysis
Reliability would be tied to the accuracy and consistency of interpretations of Islamic teachings and ethical principles. Validity would relate to how well the authors compare these perspectives.
A comparative perspective on euthanasia, highlighting differences and potential common ground between Islamic ethics and modern medical ethics regarding end-of-life care.
Death and Dying (Chapter)Islamic Biomedical Ethics: Principles and Application
Abdulaziz Sachedina
2009
Book
USA
Book
English
A qualitative and theological approach
Not applicable
Theoretical analysis of religious texts (Quran, Hadith) and ethical principles in biomedical contexts.
Sachedina is a well-known scholar in the field, which supports the credibility of the analysis.
In-depth exploration of Islamic perspectives on death and dying, and how these principles can be applied in contemporary biomedical ethics
Medical Figh
Seyyed Mostafa Mohaghegh Damad
2012
Book
Iran
Book
English
Qualitative analysis and Islamic legal reasoning, focusing on Fiqh principles
Not applicable
Legal analysis and jurisprudential reasoning
Mohaghegh-Damad is a respected scholar in Islamic law, lending credibility to the analysis.
A comprehensive legal framework for addressing modern medical issues through the lens of Islamic jurisprudence
When Can Muslims Withdraw or Withhold Life Support? A Narrative Review of Islamic Juridical Rulings
Mohiuddin A.
2020
Global Bioethics
USA
Review
English
A narrative review methodology
Muslims, particularly patients and families facing decisions regarding life support withdrawal, as well as Islamic scholars and healthcare professionals
Textual analysis of Islamic legal opinions (Fatwas), scholarly articles, and bioethical literature
The involvement of A.I. Padela, a well-known scholar in Islamic bioethics, enhances the credibility of the review.
An overview of Islamic juridical rulings concerning the withdrawal or withholding of life support
Surveying Brain Death from the Perspectives of Jurisprudence and Criminal Law
Mohammad Rahmati
2011
Iranian Journal of Medical Ethics and History of Medicine (IJMEHM)
Iran
Review
Persian
Qualitative analysis
Muslims
Textual analysis of Islamic legal rulings (Fatwas), legal texts, and possibly criminal law cases involving brain death
Depend on the scholarly rigor
An analysis of how brain death is viewed from Islamic jurisprudence and Iranian criminal law perspectives
The clinical value of quality-of-life assessment in oncology practice—a qualitative study of patient and physician views
Galina Velikova
2008
Psycho-Oncology
Denmark
Original
English
Qualitative
Oncology patients and physicians
Interviews or focus groups (common in qualitative research to gather perspectives).
Are ensured through methods like triangulation, member checking, and clear documentation of coding and themes
The perceived value of quality-of-life (qol) assessments in oncology from the perspectives of both patients and physicians
Perceived Barriers to Goals of Care Discussions with Patients with Advanced Cancer and Their Families in the Ambulatory Setting: A Multicenter Survey of Oncologists
Josee-Lyne Ethier
2018
Journal of Palliative Care
Canada
Original
English
Quantitative
Oncologists
Survey questionnaire to assess perceived barriers to goals of care discussions
Assessed through measures such as pre-testing the survey instrument or using validated survey questions
Perceived barriers to discussing goals of care with patients with advanced cancer and their families in ambulatory settings
Why active euthanasia and physician-assisted suicide should be legalized
Len Doyal
2001
BMJ (British Medical Journal)
UK
Editorial
English
An argumentative essay
It does not focus on a specific study population.
Not applicable
Lies in its ethical reasoning and analysis of existing legal and ethical frameworks.
In favor of the legalization of active euthanasia and physician-assisted suicide based on ethical reasoning and patient autonomy
Caring for patients at the end of life
Veronica English
2012
Medical Ethics Today: The BMA’s Handbook of Ethics and Law
UK
Book
English
Ethical and legal analysis concerning end-of-life care, drawing from case studies, laws, and ethical principles
Healthcare professionals and stakeholders involved in end-of-life care decisions
Not applicable
Its grounding in UK law and British Medical Association (BMA) ethical guidelines
Guidance on best practices for healthcare professionals.
Medical futility and potentially inappropriate treatment: Better ethics with more precise definitions and language
Thaddeus Mason Pope
2018
Perspectives in Biology and Medicine
USA
Comment
English
A theoretical exploration of the ethical language and definitions surrounding medical futility and inappropriate treatments
Focuses on healthcare professionals, ethicists, and policymakers involved in end-of-life decision-making.
Not applicable
Is grounded in the ethical and philosophical rigor of the author's arguments
Definitions regarding "medical futility" and "inappropriate treatment," to improve ethical decision-making in healthcare, particularly at the end of life.
Barriers to goals of care discussions with seriously ill hospitalized patients and their families: A multicenter survey of clinicians
John J. You
2015
JAMA Internal Medicine
Canada
Original
English
Quantitative
Physicians and healthcare professionals
Questionnaire
Ensured through survey design (e.g., pre-testing, using validated questions) and appropriate sampling methods.
The key barriers clinicians face when having goals of care discussions with seriously ill patients and their families
Improvements in advance care planning in the Veterans Affairs system: Results of a multifaceted intervention
Robert A. Pearlman
2005
Archives of Internal Medicine
USA
Original
English
Quantitative
Veterans receiving care through the U.S. Veterans Affairs (VA) healthcare system
Questionnaire
The design of the intervention, appropriate sampling, and the use of validated tools for measuring outcomes
Improvements in advance care planning practices within the VA system
Medical treatment for adults with incapacity: Guidance on ethical and medico-legal issues in Scotland.
British Medical Association
2009
Book
UK
Book
English
Ethical and medico-legal guidance
Healthcare professionals and legal stakeholders
Not applicable
Use of established legal and ethical standards
Provides healthcare professionals with a framework to make ethical and legally sound decisions when treating adults who are unable to make decisions for themselves.
It is never lawful or ethical to withdraw life-sustaining treatment from patients with prolonged disorders of consciousness
Charles Foster
2019
Journal of Medical Ethics
UK
Extended essay
English
Ethical and legal arguments against the withdrawal of life-sustaining treatment
Patients in a prolonged disorder of consciousness
Not applicable
Stems from the rigor of ethical and legal analysis
Discusses legal and ethical issues, likely focusing on the united kingdom's legal framework.
Guardianship and end-of-life decision making
Andrew B. Cohen
2015
JAMA Internal Medicine
USA
Original
English
A review and analysis of guardianship and its implications for end-of-life decision-making
Individuals requiring guardianship for end-of-life decision-making
Not applicable
Is grounded in the use of established legal and ethical standards.
Explores the complexities of guardianship in relation to end-of-life decision-making.
European Resuscitation Council Guidelines 2021: Ethics of resuscitation and end of life decisions.
Spyros D Mentzelopoulos
2021
Resuscitation
Greece, UK, Belgium, The Netherlands, Croatia, Czech Republic, Cyprus, Serbia, Sweden
Guideline
English
A synthesis of ethical principles and guidelines concerning resuscitation and end-of-life decision-making
Healthcare professionals
Not applicable
Based on the consensus of expert opinions and evidence-based practices; and referencing current ethical standards and clinical evidence.
The guidelines provide a framework for ethical decision-making regarding resuscitation efforts and end-of-life care.
Association of illness understanding with advance care planning and end-of-life care preferences for advanced cancer patients and their family members.
Shin Hye Yoo
2021
Supportive Care in Cancer
Korea
Original
English
A prospective cohort study
Advanced cancer patients and their family members
Questionnaire
Ensured through validated instruments for measuring understanding and preferences and the study's design.
Highlighting the importance of effective communication and education in cancer care
Decision-making regarding withdrawal of life-sustaining treatment and the role of intensivists in the intensive care unit: a single-center study
Seo In Lee
2020
Acute and Critical Care
Korea
Original
English
Qualitative and/or quantitative methods
Patients in the intensive care unit (ICU) and intensivists (critical care physicians) involved in end-of-life decision-making
The forms for the decision to withdraw or withhold LST
From the study's design and its focus on real-world decision-making
The complexities involved in decision-making regarding the withdrawal of life-sustaining treatment
Discordance and concordance on perception of quality care at end of life between older patients, caregivers and clinicians: a scoping review
Joan Carlini
2022
European Geriatric Medicine
Australia
Review
English
A scoping review
Older patients, caregivers, and clinicians involved in end-of-life care
Thematic analysis
Is grounded in the systematic approach to reviewing literature.
Areas of concordance and discordance in perceptions of quality care at the end of life among older patients, caregivers, and clinicians
Islamic bioethics: Problems and perspectives
Dariusch Atighetchi
2007
Book
USA
Book
English
A philosophical and analytical approach
Scholars, healthcare professionals, and ethicists interested in bioethics from an Islamic perspective
Not applicable
Rigorous analysis and scholarly approach
Provides insights into the complexities of Islamic bioethics.
Truth-telling at the end of life: a pilot study on the perspective of patients and professional caregivers
Reginald Deschepper
2008
Patient Education and Counseling
Belgium
Original
English
Qualitative
Patients at the end of life and professional caregivers involved in their care
Interviews, focus groups
Is grounded in the careful collection and analysis of qualitative data.
Highlighting the importance of honesty in communication during end-of-life care.
Ethical issues in the end of life care for cancer patients in Iran
Mina Mobasher
2013
Iranian Journal of Public Health
Iran
Original
English
Qualitative
Cancer patients receiving end-of-life care in Iran
Structured Interviews
A systematic approach to data collection
Various ethical dilemmas related to end-of-life care for cancer patients in Iran
Religious perspectives on withdrawal of treatment from patients with multiple organ failure.
Rachel A Ankeny
2005
Medical Journal of Australia
Australia
Original
English
Qualitative
Patients with multiple organ failure and healthcare professionals involved in their care
Qualitative interviews or surveys
Ensured through systematic qualitative analysis.
Various religious viewpoints on the ethical implications of withdrawing treatment from patients with multiple organ failure
Euthanasia from Christian and Islamic point of view.
Sayyed Hasan Eslami
2006
Journal of Philosophy and Theory Research
Iran
Review
Persian
Comparative analysis
Various theological and philosophical circles within Christianity and Islam
Not applicable
Ensured through rigorous analysis of religious texts and ethical arguments.
Provides insights into the differing views on euthanasia within Christianity and Islam.
Decisions relating to cardiopulmonary resuscitation. Guidance from the British Medical Association, the Resuscitation Council (UK) and the Royal College of Nursing
Guidance from the British Medical Association, the Resuscitation Council (UK) and the Royal College of Nursing
2016
Guideline
UK
Guideline
English
A review of current evidence, clinical practices, and ethical considerations regarding CPR decision-making
Healthcare professionals
Not applicable
Ensured through the involvement of reputable organizations (British Medical Association, Resuscitation Council UK, and Royal College of Nursing).
A framework for making informed decisions about CPR
Do not attempt resuscitation decisions in a cancer center: Addressing difficult ethical and communication issues
C Reid
2002
British Journal of Cancer
UK
Original
English
Qualitative analysis
Patients with cancer receiving care in a cancer center, as well as healthcare professionals involved in making resuscitation decisions
Case studies or interviews
A systematic discussion of cases and experiences
Discusses the ethical and communication issues related to DNR decisions in cancer patients.
Ethical issues of resuscitation: An American perspective
C A Marco
2005
Postgraduate Medical Journal
USA
Review
English
A philosophical and ethical approach
Healthcare professionals
Not applicable
Is ensured through the author’s expertise and thorough analysis of relevant literature.
Provides insights into the ethical dilemmas surrounding resuscitation practices.
Making end-of-life care decisions for older adults subject to guardianship
Zachary Sager
2019
Elder Law Journal
USA
Original
English
Qualitative analysis
Older adults
Structured interviews
Systematic analysis of qualitative data
Challenges and considerations in making end-of-life care decisions for older adults under guardianship
CPR: cardiopulmonary resuscitation; DNR: do not resuscitate.
Table 2
Comparison between the Secular and Islamic perspectives regarding end-of-life care
Perspective
Secular
Islamic
Sanctity of Life
The focus is not on the value of human life, but rather on indications of the treatments. This does not, however, negate the value of man’s existence and life.
It is emphasized that human life is valuable, and man must not be allowed to end it. It is very important to note that God alone has authority over life, and therefore everyone is obligated to save the patient’s life.
Benefits of Treatment
The focus is on the indication and justification of treatment. Every treatment must be justifiable based on correct clinical judgment.
In addition to medical indications and patient’s prognosis, it is important to note the value of human life and appreciate this divine gift. Futility of treatment should, nevertheless, be considered in the light of medical knowledge, and principles of Fiqh such as illegitimacy of harm (la¯ d∙arar wa la¯ d∙ira¯r) and financial hardship (al-‘usr wa al-h.araj) must be taken into account.
Authority of the Physician and the Patient’s Family
Patients’ wishes regarding treatments and prolongation of life are respected, and health care providers are obligated to speak with patients and their families to determine the patients’ wishes and preferences, and to apply an active and collaborative decision-making process.
Patients, their families, or physicians cannot make the decision to prolong or end the patient’s life. The final decision must be made by a committee of treating physicians, philosophy, ethics, jurisprudence, and law experts. It is an obligation, not to prolong the process of dying, and therefore the patient can request it.
4. Discussion
This scoping review was conducted to explore and compare the ethical considerations surrounding end-of-life care from Islamic and secular perspectives, by focusing on two main questions: the criteria for withdrawing life-prolonging treatments and the determination of decision-making authority when disagreements arise. The review revealed that both perspectives emphasize the importance of weighing the benefits of life-sustaining treatments for the patient and the broader healthcare goals. In both views, although the roles and hierarchies differ slightly, patients, their families, physicians, and medical teams are involved in the decision-making process.
4.1 Islamic Perspective
The Islamic ethical framework places a strong emphasis on the sanctity of life, guided by religious texts and interpretations of Sharia law. Life is considered sacred, and the prolongation of life is viewed as a moral obligation, particularly when the treatment is not deemed futile. However, Islamic ethics also recognize to allow for the withdrawal of treatment in certain cases where it causes excessive suffering, pain, or when the treatment is medically futile. When a patient is in the normal process of dying, acceptance of death as the Will of God is unavoidable. It will therefore be permissible not to use procedures that based on medical knowledge are considered futile, will not save the patient, and cannot stop the process of death (12, 38). On the other hand, based on Islamic Code of Ethics, prolongation of this process is not acceptable from the Islamic perspective, and if physicians do not see any chance of recovery, they must not cause pain and suffering for patients and their families, and continue supportive treatments only to prolong the process of dying. Therefore, physicians are allowed to withdraw treatments that are considered futile according to medical knowledge (4, 20). Even if the patient or the family requests resuscitation, there is no need for resuscitation and physicians can refrain from offering it when they believe it is not beneficial (21).
Uncertainty is of great importance in establishing the final stages of illnesses such as advanced cancers, as uncertain prognosis makes it hard to decide whether to start advanced and invasive treatment, and if so, when, and how to stop (39, 40). If a patient suffers from multiple organ failure but is not brain dead, however, there is the possibility of an active and conscious life, and death is not certain, so the patient must be allowed to live to the last minute. In such cases many Islamic experts believe that stopping treatment is the same as non-voluntary active euthanasia, which is forbidden from the Islamic perspective. Therefore, most Islamic experts are of the opinion that one should wait for the normal process of nature to take over, until artificial ventilation and other forms of support can be withdrawn (2, 41). One Muslim author has emphasized this issue and believes that there is a difference between withholding medical interventions while the normal process of dying is in progress, and active euthanasia and assisted suicide. Some philosophers in ethics draw a distinction between active and passive euthanasia, and consider the latter acceptable as they see a difference between killing a person and withholding life-sustaining procedures. In fact, no one has the right to kill another, but keeping people alive is not an obligation (21, 42). A survey of Islamic sources reveals that to this point there has been no clear directive in Sharia regarding DNR and discontinuation of futile treatments, and science and human intellect must be the guiding lights in these issues or making Islamic laws in this respect (2).
4.2 Secular perspective
In the secular perspective, the criteria for withdrawing life-prolonging treatments, such as cardiopulmonary resuscitation (CPR) or mechanical ventilation, are often guided by patient autonomy, quality of life, and medical futility. Patient autonomy is emphasized as a cornerstone of ethical decision-making, with the belief that individuals have the right to refuse or withdraw treatment, especially when it no longer offers therapeutic benefits or contributes to an improved quality of life. The concept of medical futility is critical in secular ethics, focusing on the balance between the burdens and benefits of treatment, and is often grounded in evidence-based clinical guidelines and the physician's professional judgment. So, there is a fundamental difference between avoiding treatment that cannot benefit the patient overall, and an intentional act of hastening death (14). In any event, this is an ongoing discussion from the legal point of view as well. The law, however, clearly states that neither the physician nor any other person has the right to hasten the death of a dying patient, much in the same way as a healthy person. Withholding or withdrawing treatment is not the same as the intent to kill, but rather avoidance of offering treatment that cannot have any benefits for the patient (27). Respiratory or cardiac failure is part of the dying process, and in theory, everyone can be given resuscitation before death (43). For patients whose death is inevitable and who experience cardiac or respiratory arrest due to a terminal illness, sustaining treatment such as CPR is inappropriate. In many DNR directives, a lot of the problems associated with communication with patients and their families have not been resolved. These directives are nevertheless useful with regard to CPR for the purpose of establishing ethical and legal standards in care planning (44). Decisions on resuscitation are part of the patient’s treatment planning that are discussed with the patient much in the same way as the other aspects of health care. The decision not to resuscitate should be taken only after proper consultation and a comprehensive evaluation of the patient’s condition (43).
When making decisions about offering life-prolonging treatments, both medical and economic factors need to be considered. These procedures should not be performed on patients with poor prognosis as part of their standard care. In fact, some treatments must be withheld due to financial considerations, and under certain circumstances, expenses and treatment benefits for patients, their families, and the society should also be examined (45). Furthermore, some guidelines emphasize some points regarding the DNR order, the most important of which are: the necessity to ascertain that resuscitation is pointless; observance of the patients’ rights in discussing DNR with them or their legal guardian, and the need for their consent to DNR; (44) transparent policies that everyone can acquire information on; and the necessity for supervision, and gaining the trust of the National Health System. On the other hand, one author believed that three categories of patients need end of life care and decision making to withdraw life sustaining treatment. They believed that patients commonly suffer from organ failure, especially both heart and lung disease are very complicated for decision making to care and determine their prognosis. Because terminal phase of their diseases is not clear and predictable (46).
The findings of this review have important implications for clinical practice and policy-making in multicultural and religiously diverse contexts. Understanding these ethical differences is essential for healthcare providers, particularly those working in environments where patients and families from different cultural or religious backgrounds are making decisions about end-of-life care. Clinicians must not only provide medical advice but also navigate the complex ethical terrain shaped by religious and secular values. In Islamic sciences saving human life is of utmost importance, and therefore the criteria for treatment benefits, indications and goals should all be evaluated in the light of this concept. Consequently, this evaluation cannot be left to patients, their families, or the physician alone. For this reason, examination of medical criteria for initiating or continuing life-prolonging procedures needs to be done with the help of a team of experts, and in view of the value and sanctity of each moment of a person’s life. From the secular perspective, although, human life is considered valuable, but due to the emphasis on freedom and the individuals’ interest in their life, the medical criteria for starting or continuing life-prolonging treatments, namely the aims and benefits of treatment, can be assessed by patients, their surrogate decision-makers, or the medical team.
5. Limitations
Despite the comprehensive approach employed in this scoping review, several limitations must be acknowledged. First, in this study, we used with a few articles by Islamic Shiite experts due to accessibility issues. Moreover, the inclusion of literature was limited to English and Persian languages, potentially excluding relevant studies published in other languages. While these documents may have provided additional insights into Islamic or secular perspectives on end-of-life care. Second, the search was restricted to publications from the year 2000 onward. This time frame was chosen to reflect more contemporary ethical debates, but it may limit the scope of historical perspectives. Third, we used end of life care guidelines more than articles in secular standpoints because we think that they show the common perspective in secular societies. In a scoping review, the evaluation process is not as rigorous as a systematic review. Additionally, the study focused primarily on end-of-life care in terminal cancer patients, which may not fully represent ethical considerations in other terminal conditions, such as heart failure or chronic obstructive pulmonary disease (COPD). The exclusion of studies on these conditions may have resulted in a narrower scope of the review. Forth, this scoping review could provide a general perspective of the topic and identify concepts about the two main ethical questions regarding end-of-life care. Thus, this study provides an overview of general trends and serves as a foundation for future research on the comparison of Islamic and secular perspectives on end-of-life care. Finally, by identifying key ethical concepts and gaps in the literature, this review can influence subsequent research addressing the critical issues of this study-namely, the criteria for withdrawing life-prolonging treatments and determining decision-making authority in end-of-life care as well as clinical practice aimed at improving the quality of care for terminally ill patients.
6. Conclusions:
This review revealed that while both Islamic and secular frameworks engage deeply with these issues, both Islamic and secular perspectives emphasize the importance of weighing the benefits of life-sustaining treatments for the patient and the broader healthcare goals. In both views, patients, their families, physicians, and medical teams are involved in the decision-making process, although the roles and hierarchies differ slightly. Apparently, one important similarity between Islamic and secular perspectives is that both of them respect the patient’s request not to prolong the process of dying, or to receive futile care. Moreover, both views consider medical principles to be the most important factor in determination of treatment benefits and futile care. |
Title: 鼻咽癌放疗后颈内动脉爆裂综合征的治疗策略 | Body: |
Title: A Framework for Interpretability in Machine Learning for Medical Imaging | Body: |
Title: Repurposing methuosis-inducing anticancer drugs for anthelmintic therapy | Body:
A battle against parasitic nematodes
A nematode’s dynamic adaptability and simple body structure make it remarkably resilient to harsh environmental conditions. Disease and death caused by parasitic nematodes in humans, livestock, and plants are enormous [1]. In recent years, pathogenic nematodes have evolved to adapt to many lifestyles and have shown remarkable ability to expand their host range [2]. Consequently, they are becoming more resilient to environmental conditions, host responses, and anthelmintics. Three decades after its discovery, ivermectin and its derivatives are still widely used to control and eradicate nematodes [3]. Ivermectin derivatives, for instance, function by preferentially paralyzing the nematodes, making them inert and unable to reproduce [4]. However, a few parasitic nematodes have already developed resistance to ivermectin, and resistance to these anthelmintic treatments is likely to emerge in the future [5]. Furthermore, it is possible for resistance genes to disseminate within clades. Hence, research ought to concentrate on screening anthelmintics that kill and destroy them or repurpose drugs that have cleared clinical trials. Here, we discuss a new therapeutic approach that involves repurposing anticancer drugs that could potentially kill nematodes via methuosis, a process of nematode and cell death marked by accumulation of vacuoles [6].
Methuosis—A death by vacuolation
Methuotic death in nematodes is characterized by formation of multiple tiny vacuoles, their subsequent fusion to form giant vacuoles, and the rupture of the cuticle layer [6]. Originally, methuosis was regarded as a nonapoptotic cell death phenotype derived from the Greek word “methuo” (to drink to intoxication) [7,8]. Methuosis and drugs that induce methuosis are extensively researched in cancer biology [9]. Multiple pathways have been reportedly associated with methuosis, with researchers actively engaged in bridging the existing knowledge gaps. The most studied pathways in cancer cells include the macropinosomes trafficking pathway governed by Ras cell signaling pathway [10]. The most striking characteristic of cells that undergo methuosis is the accumulation of large cytoplasmic vacuoles that are formed by the fusion of macropinosomes. Succinctly, following H-Ras overactivation, the cell develops a lamellipodia, or ruffle, which allows nutrients and fluid tracers to descend inside and form macropinocytic sinks. Further, macropinocytic sinks coalesce into macropinosomes through a cascade of GTPase activation. A typical scenario involves mature macropinosomes being recycled while some, expressing the late endosomal markers (Rab7 and LAMP1), fuse with endocytic pathway organelles such as endosomes and lysosomes, undergoing a sequential process of cell lysis and nutrient release [11]. During cancerous growth, macropinosomes fail to recruit early endosomal proteins, preventing them from fusing with lysosomes and recycling. Instead, they merge to form giant vacuoles that rupture and cause cell death by methuosis (Fig 1). In the last few years, several small molecules have been reported to induce methuosis in a variety of cancer cell lines (Table 1), while a few others were effectual in inducing vacuoles and methuotic death in nematode models (Fig 2). The text that follows will focus on these compounds that induce methuosis and provide a quick overview of the Structure–Activity Relationship (SAR) and mechanistic study with the aim to encourage repurposing anticancer drugs for anthelmintic therapy.
10.1371/journal.ppat.1012475.g001
Fig 1
Molecular pathways that lead to methuosis in cancer cells.
Briefly, Lamellipodia, or ruffles, allow nutrients and liquid tracer to enter cells, forming macropinocytic sinks, which coalesce into macropinosomes. The merger of macropinosomes produces giant vacuoles, which rupture and cause the death of cells by methuosis (refer text for details).
10.1371/journal.ppat.1012475.g002
Fig 2
Organic acids with mono- or dicarboxy groups and indole derivatives with iodine or fluorine that cause vacuoles in nematodes (a), electronegative interactions between iodine and fluorine in 5F4IPP may be responsible for better glutamate-gated chloride channel (GluCl) receptor interactions and suppressed methuosis (b), and death phenotypes in pinewood nematode treated with 5-iodoindole (c).
10.1371/journal.ppat.1012475.t001
Table 1
Methuosis-inducing anticancer chemicals/agents that can be effectively repurposed for anthelmintic therapy.
Anticancer agents
Functional group (s)
Relevent function
Cancer cell lines
Death phenotype
Reference
Isobavachalcone
-Cl, -CO, -OH
V-ATPase, AKT
Myeloid cell lines (NB4, U937)
Methuosis-like cell death
[29]
Vacquinol-1
-Cl, -OH
Antitumor immune response
Human and rat glioblastoma models, RG2 and NS1
Macropinocytosis inducer
[21]
Tubeimoside 1
-OH, -CH3, -CO, -O-,-COO-
Inactivation of VEGF-A/VEGFR2/ERK signaling
SW480, CRC, NSCLC
Macropinocytosis hyperstimulation
[30]
Ursolic acid derivatives (C17)
-CN, -COOH, -CH3
Anticancer activity
HeLa cells
Macropinocytosis hyperstimulation
[18]
Indolyl-Pyridinyl-Propenone
-CH3, -CO, -O-, -OH
PIKFYVE inhibitor
HCT116, U251 glioblastoma
Methuosis, microtubule disruption
[31]
Indole-based chalcones (MIPP, MOMIPP)
-CO, -CH3, -O-
Inhibition of endosomal trafficking, targeting Rab5 and Rab7
U251 glioblastoma, breast cancer cell
Methuosis
[32]
Platycarya strobilacea Sieb. Et Zucc (PSZ) (Extract)
n/a
Rac1 overexpression
Human nasopharyngeal carcinoma cells (CNE1 and CNE2 cells)
Methuosis
[33]
Jaspine B
-OH, -NH2, -C14
Ceramide synthase inhibitor
HGC-27 gastric cancer
Vacuolation related to methuosis
[34]
F14512
-CO, -NH2, -OH, -OCH3
Topoisomerase II inhibitor
A549 nonsmall cell lung cancer cells
Electron-lucent (methuosis-like)
[35]
DZ-514
-Br, -CO, -O-
Activation of ROS-MKK4-p38 axis
Breast cancer
Methuosis
[22]
Pyrimidinediamine derivatives (JH530)
-Br, -CO, -O-, -S-
Antitumor activitiy
Breast cancer
Methuosis
[36]
Tubeimoside-2
OH, -CH3, -O-, -COO-
MKK4-p38α Axis
Hepatocarcinoma cells
Methuosis
[37]
Spiropachysine A
-CO, CH3
Ras/Rac1 signal pathways
Hepatocellular carcinoma proliferation
Methuosis
[38]
Maduramicin
OH, -CH3, -OCH3, -O-,-COOH, NH3
Activation H-Ras-Rac1 signaling pathway
Myocardial cell H9c2
Methuosis
[39]
Silmitasertib (CX-4945)
-Cl, -COOH
Rac-1 activation
HepG2 cells
Methuosis
[40]
Epimedokoreanin C
-OH, -CO, -CH3
Regulation of Rac1 and Arf6
Lung cancer NCI-H292 and A549 cells
Methuosis-like cell death
[41]
Nutlin-3a
-Cl, -CO, -OCH3,—CH3
Inhibited the KRAS-PI3K/Akt-mTOR pathway
KRAS mutant NSCLC (nonsmall cell lung cancer) cells
Methuosis-like cell death
L22
-NH2, -CH3
Interaction with PIKfyve kinase
HeLa and MDA-MB-231 cells
Methuosis
[36]
C13 (azaindole-based compounds)
-CO, -CF3, -CH3
-
MDA-MB-231, A375, HCT116, and MCF-7
Methuosis
[42]
DMBP (methyl 2,4-dihydroxy-3-(3-methyl-2-butenyl)-6-phenethylbenzoate)
-OH, -COO-, -CH3
Inhibited autophagic flux in cancer cells by inhibiting the function of VPS41
A549 and Panc-1 cell viability
Methuosis
[43]
Compounds 20 and 22
-CO, -NH2
H-Ras activation
-
Methuosis
[44]
Microbial-derived amphiphilic CLP bacillomycin Lb (B-Lb)
-COOH, -OH, -CO, -NH2, -CH3
Triggered by cytoplasmic vacuolation through macropinocytosis
MDA-MB-231-Luc and MCF-7 cells
Methuosis-like cell death
[45]
2-Amino-14,16-dimethyloctadecan-3-ol
-OH, -NH2, -CH3
Disturbs later stages of endolysosomal process
HepG2
Vacuolation, partial macropinocytosis induction
[46]
HZX-02-059
-CF3, -CO, -CH3
PIKfyve and tubulin dual-target inhibitor
DHL cell lines WILL-2, LR, TMD8
Methuosis and cell cycle arrest
[47]
Ezetimibe
-F, -CO, -OH
NPC1L1 inhibitor
Human cancer cell line Du145/Du145TXR and MCF-7/MCF-7ADR cells
Methuosis
[48]
Glycosylated antitumor ether lipids (GAELs)
n/a
n/a
Epithelial cancer cell lines and BT474 cancer stem cells; MDA-MB-231, JIMT-1, and DU-145; MDA-MB-468, Hs578t, and MDA-MB-453 cell lines
Methuosis
[49]
Methanphetamine
-CH3
Ras and Rac1 activation
SH-SY5Y neuroblastoma cells
Hyperstimulation of macropinocytosis
[50]
BAPT compounds
-S
Endolysosomal trafficking defects that prevent recycling of lysosomes and cause lysosome-to-nucleus signaling defect
HCT-116 colon cancer cell line
Dual action Methuophagy
[51]
5-((4-(pyridin-3-yl)pyrimidin-2-yl)amino)-1H-Indole-2-Carbohydrazide derivatives (Compound 12A)
-CO, -CH3
MAPK/JNK signalling pathway
HepG2, HeLa, MDA-MB-231, MCF-7, MCF-10A, LO2 cells
Methuosis
[52]
Bacoside A
-OH, -CH3, -O-
Excessive phosphorylation of calcium/calmodulin-dependent protein kinase IIA (CaMKIIA/CaMK2A) enzyme
GBM patient-derived glioblastoma cells
Hyperstimulation of macropinocytosis
[53]
Meridianin C
-Br, -NH2
Reducing the cellular level of Dickkopf-related protein-3 (DKK-3)
YD-10B human tongue cancer cells
Methuosis-like cell death
[54]
WJ-644A
Br-, -OCH3
Activation of unfolded protein response(UPR)
Human prostate cancer cell lines, DU145, PC3M, PC3, 22RV1, LNCAP, VCAP
Methuosis
[55]
Vacuolar phenotypes and carboxyl functional groups
Vacuoles are the visual hallmark of methuosis in nematodes [6]. Vacuolar death was first spotted in plant parasitic nematode Meloidogyne incognita or the root-knot nematode, following treatment with carboxylic acids. Acetic acid, lactic acid, and their mixtures induced vacuolation in M. incognita juveniles [12]. Mixtures of organic acids consisting of acetic acid, lactic acid, malic acid, and succinic acid in Lactobacillus brevis WiKim0069 culture filtrates also induce vacuoles in M. incognita [13]. More pronounced phenotypes were observed when M. incognita J2 was treated with oxalic acid, a dicarboxylic acid [14]. Secondary metabolites from Fusarium oxysporum strain Fo162 that consisted of gibepyrone D, indole-3-acetic acid, and 4-hydroxybenzoic acid also induced vacuoles in M. incognita J2 [15]. Based on the SAR analysis, we speculate that the presence of carboxyl functional group as a key for the vacuolar phenotypes (Fig 2A). Carbonyl groups (C = O) and hydroxyl groups (O–H) make up the carboxyl group. The design and development of drugs relies heavily on compounds that contain carboxylic acids moieties [16]. Worldwide, more than 450 drugs with carboxylic acid moieties are marketed [17]. In most cases, carboxylic acid–containing drugs often trigger idiosyncratic reactions and cause idiopathic effects. There remains a lack of clear understanding regarding the mechanism of action. It is possible that vacuolation and methuosis contribute to disruption of cellular function, eventually causing death in nematodes. Two of the anticancer drugs containing carboxyl group, namely, ursolic acid (C17) and silmitasertib (CX-4945), induced methuosis in HeLa and colon cancer cell lines, respectively [18,19]. C17 specifically induced death by hyperstimulation of macropinocytosis, while CX-4945 triggered methuosis-like cell death accompanied by catastrophic vacuolation. Due to the presence of the carboxyl group, it is conceivable that both chemicals may induce similar vacuolation in nematodes, akin to their effect on cancer cells. In general, it would be interesting to repurpose small molecule inhibitors with carboxylic acid groups among the 450 FDA drug candidates for use as anthelmintics as well.
Halogenated organic compounds and methuosis
The majority of anthelmintics contain one or more halogen substitutes [6]. We demonstrated that 5-iodoindole and 7-iodoindole selectively killed nematodes by triggering vacuolar phenotypes [6]. Iodine in the indole ring is the key factor in triggering methuosis, whereas fluorine (in 7-fluro 5-iodoindole) mitigates methuosis as an iodine antagonist (Fig 2B) [20]. Nematodes undergoing methuosis revealed several hallmarks and intriguing phenotypes. Small vacuoles formed inside the nematode’s body, which merged into larger ones and eventually ruptured, thereby killing the nematode. There was also evidence of cuticle damage, central voiding, and internal organ disruption in the nematodes and their eggs (Fig 2C).
Interestingly, many halogenated anticancer agents were found to have potential to induce methuosis and methuosis-like cell death (Table 1). It was first reported that a chalcone derivative named 3-(5-Methoxy-2-methyl-1H-indol-3-yl)-1-(4-pyridinyl)-2-propen-1-one (MIPP), along with its 5-brominated derivative (BMIPP), to trigger methuosis in glioblastoma cells [7]. They also found that MIPP possesses the ability to induce methuosis in various other cell lines, showing that these compounds have broad-spectrum activity. Vacquinol-1 (Vac), a quinolone derivative, was also reported to induce rapid methuosis-like cell death in glioblastoma cells [21]. The possible mode of action of Vac-induced methuosis is based on the ATP-inducible and carvacrol-sensitive ion channel TRPM7. Other compounds like meridianin A-G, an indole alkaloid, induced vacuolation by reducing the levels of Dickkopf-related protein-3 (DKK-3), a known negative regulator of macropinocytosis. CX-4945 (silmitasertib), a potent ATP-competitive inhibitor of CK2, with the unusual structural feature of having a free carboxylic acid and chlorine, could induce vacuolization in the cytoplasm of cholangiocarcinoma cells [19]. It is noteworthy to mention that CX-4945 was approved by the FDA for cholangiocarcinoma (bile duct cancer) in 2017 with an orphan drug designation [19]. DZ-514, a derivative of N-phenyl-4-pyrimidine diamine, induced time-dependent vacuolation in cancer cells, partially facilitated through the activation of the ROS-MKK4-p38 signaling pathway.
Exploring the potential of these small molecule inhibitors containing halogen groups that induce methuosis against nematodes as broad-spectrum nematicides would be intriguing. Our research, alongside studies on 5-iodoindole, Vacquinol-1, and DZ-514, respectively, indicates that these methuosis inducers have promising prospects for in vivo applications as well [22,23].
Repurposing drugs: An unexplored panacea
Parasitologists, especially those in veterinary medicine, face a growing challenge of anthelmintic resistance [24]. Repurposing existing drugs as anthelmintics reduces the clinical trial burdens since drug screening is cumbersome, exorbitant, and time-consuming. The market offers a wide range of drugs that have passed clinical trials and are considered safe for use on plants, animals, and humans. Repurposing of an existing old drug/chemical offers possibilities of inexpensive, readily available solutions with extensive safety profiles. Although the repurposing approach is being pursued in many directions, we focalize on anticancer drugs that trigger vacuolation and cause methuosis-like death. While it would be challenging to establish a direct correlation in the mode of action of these drugs between nematodes and mammalian cells, there’s a flicker of hope that they could induce vacuolar death in nematodes. Furthermore, several recent studies suggest anthelmintic drugs may function as effective cancer therapeutics [25]. This is most likely owing to the fact that some helminths (intestinal parasitic helminths) can cause cancer and multiply rapidly in immunocompromised patients undergoing cancer chemotherapy [26–28]. The coexistence of cancer and helminth infections can be a circumstance necessitating drugs like methuosis inducers, which can mitigate both conditions. Currently, compounds like CX-4945 and MOMIPP are in various stages of clinical trials [19] but may be able to treat helminthic infections in the future. It may not be so farfetched to develop a panacea approach to these diseases. The repurposing of cancer drugs as anthelmintics and vice versa may be possible while simultaneously treating both conditions.
Concluding remarks and future perspectives
In total, we discuss the biological effects and SAR analysis of small molecule methuosis inducers that may spur parasite death by causing methuosis. Methuosis-based therapeutic approaches have not been adopted against parasitic nematodes, so information on the topic is very limited. As we gain greater knowledge of the mechanisms of vacuolization in parasitic nematodes, we will be able to create more realistic perceptions of how parasites behave and respond to their environment. Repurposing strategies will encourage employing multiomics methodologies to explore the impact and mechanism of action of these methuosis-inducing anticancer agents against parasitic nematodes. Overall, this approach will likely pave the way for broad-spectrum anthelmintic and anticancer agents in the future as well as reveal the biological similarity between cancer cells and nematode cells in responding to these inducers. |
Title: Dynamic changes in insulin-like growth factor binding protein expression occur between embryonic and early post-hatch development in broiler chickens | Body:
INTRODUCTION
Modern commercial broiler chickens are capable of rapid growth and muscle accretion during post-hatch development (Bartov, 1982; Goddard et al., 1988; Havenstein et al., 1994; Berrong and Washburn, 1998; Havenstein et al., 2003; Collins et al., 2014). Though the molecular mechanisms behind these traits have yet to be fully elucidated, they are associated with highly conserved endocrine systems known to regulate vertebrate growth and metabolism. One of these systems is the somatotropic axis, which has been shown to be influenced by genetic selection of commercial broilers (Vaccaro et al., 2022a) and is known to induce growth via cellular proliferation and protein accretion in muscle and bone (Clark and Robinson, 1996; Levine, 2012; Gahete et al., 2016). Many of these processes are indirectly induced by growth hormone (GH) binding to the GH receptor (GHR) and increasing insulin-like growth factor (IGF) 1 and IGF2 production and signaling. Circulating IGFs are synthesized in the liver (Kajimoto and Rotwein, 1989; Stewart and Rotwein, 1996; Dewil et al., 1999; Woelfle et al., 2005) and influence growth by downregulating apoptosis and increasing cellular proliferation after binding the type I IGF receptor (IGFR1) (Girbau et al., 1989; Duclos and Goddard, 1990; D'Costa et al., 1998).
In mammals, IGF1 contributes to growth and body weight (Stratikopoulos et al., 2008), and a lack of IGF1 is typically associated with dwarfism (Yakar et al., 2002). However, direct or correlative relationships between IGF signaling and growth in birds is less clear. Though exogenous administration of human recombinant IGF1, but not IGF2, has been shown to moderately increase average daily gain, feed efficiency, and lean tissue growth in young female chickens (Tomas et al., 1998), these effects are not consistently observed. Daily injection (McGuinness and Cogburn, 1991) or continuous infusion (Huybrechts et al., 1992) of human recombinant IGF1 over a 2-wk period did not influence body weight or feed efficiency in juvenile male or female broilers, respectively, but did decrease abdominal fat in females at the highest dose administered. Similarly, chronic infusion of chicken IGF1 did not influence growth rate but did reduce skeletal muscle weight in 3-wk-old male broilers (Czerwinski et al., 1998). While elevated levels of IGF1 mRNA have been demonstrated in chickens selected for high body weight when compared those selected for low body weight (Beccavin et al., 2001) and higher plasma IGF1 and IGF2 levels were also observed in broilers selected for high juvenile body weight (Scanes et al., 1989), another study found no differences in hepatic IGF1 mRNA or circulating IGF1 between broilers of differing growth potential through 42 days of age (Giachetto et al., 2004). Circulating IGF1 and IGF2 concentrations and levels of mRNA for these proteins in liver also did not differ between modern commercial Ross 308 and legacy Athens Canadian Random Bred (ACRB) broilers, despite Ross 308 chickens having significantly greater body weights from late embryonic development onwards (Vaccaro et al., 2022a; Vaccaro et al., 2022b).
IGF-binding proteins (IGFBPs) modulate IGF activity by influencing its stability in circulation, receptor affinity, and local tissue-specific effects (Baxter, 1991; Kelley et al., 2002; Kim, 2010; Baxter, 2023). There are seven IGFBPs in mammals (IGFBP1 – 7), though IGFBP7 exhibits a lower affinity for IGFs than the others (Kim et al., 1997); avian species lack IGFBP6 but do have genes for the other family members (Daza et al., 2011). Several tissue-specific and context-dependent effects of IGFBPs on IGF action have been observed in mammalian and some avian models, and IGFBPs have also been shown to have effects independent of the IGFs in mammalian cells. They can prevent binding of IGFs to their receptor, as is the case with IGFBP1 that blocks IGF1-induced protein synthesis in human skeletal muscle (Frost and Lang, 1999) and IGFBP2 and IGFBP4 that prevent long bone growth stimulated by IGF in mice and chicken embryos (Mohan et al., 1995; Fisher et al., 2005). They can also have differential effects depending on which IGF they are bound to. For example, IGFBP5 enhances rat myoblast differentiation into myotubes when bound to IGF1 but inhibits this process when bound to IGF2 (Ewton et al., 1998). Demonstrated IGF-independent effects of IGFBPs include upregulation of apoptosis in sarcoma and breast cancer cells by IGFBP2 (Schutt et al., 2004; Klaus et al., 2006) and stimulation of mouse osteoblast cell proliferation by IGFBP5 (Mohan et al., 1995). Studies investigating functionality of IGFBPs in birds are lacking, and understanding their developmental expression profiles in targets of the somatotropic axis could shed light on their actions in key tissues like liver and skeletal muscle.
The somatotropic axis is highly conserved across vertebrates, and IGFBPs exhibit a multiplicity of functions. As a result, it is important to understand their impact on economically important traits in poultry, such as growth, muscle accretion, and feed efficiency. In a previous study, no differences in circulating IGFs were observed between between modern commercial (Ross 308) and legacy [Athens-Canadian Random Bred (ACRB)] broilers, though IGFBP mRNA expression in liver and muscle did differ between lines during embryogenesis and post-hatch (Vaccaro et al., 2022a). Due to observed differences between the lines in expression of select IGFBPs in both tissues and a lack of difference in circulating hormone levels, it is likely that that the increased growth rate, greater muscle accretion, and improved feed efficiency observed in modern broilers is facilitated, at least in part, by IGFBP action. Further, based on gene expression patterns that were apparent between embryonic day (e) 10 and e18 and from post-hatch day (d) 10 through d40, it is possible that the influence of IGFBPs on broiler growth and body composition is distinct across developmental periods. The previous study examined developmental patterns in somatotropic axis gene expression and circulating IGF levels in 2 separate experiments (Vaccaro et al., 2022a) and, as a result, was not able to investigate changes that occurred between e18 and d10. This period represents critical times of rapid growth, proliferation and development of muscle satellite cells, and the metabolic transition in energy source from lipid-rich yolk to carbohydrate-rich diets (Noble, 1986; Noble and Cocchi, 1990; Halevy et al., 2000; Halevy et al., 2004). As such, understanding how the somatotropic axis, particularly the IGF-IGFBP system, influences these developmental processes is important to further determine its contribution to the aforementioned economically valuable traits. Therefore, the objective of this study was to evaluate developmental mRNA expression of GHR, IGFs, IGFR1, and IGFBPs in commercial broiler chickens between mid-embryogenesis and 3 wk post-hatch, as well as evaluate circulating IGF levels post-hatch.
MATERIALS AND METHODS
Animals and Tissue Collection
Tissues used in this study were collected from embryonic and post-hatch male Ross 308 broilers hatched from a breeder flock raised at the University of Georgia's Poultry Research Center farm. All procedures using animals were approved by the University of Georgia's Institutional Animal Care and Use Committee.
Fertile Ross 308 eggs were obtained and incubated under standard conditions (37.5°C and 60% humidity, rotation every 2–3 h), with the day eggs were set defined as e0. After hatching, birds were raised in floor pens (n = 6 pens) with free access to water and a 2 phase commercial-type broiler diet. Birds were fed starter (21.3% crude protein, 1.2% digestible lysine, 3,050 kcal/kg metabolizable energy, 0.95% calcium and 0.48% available phosphorus) from d0 to d14 and grower (19.6% crude protein, 1.09% digestible lysine, 3120 kcal/kg metabolizable energy, 0.85% calcium and 0.43% available phosphorus) from d14 to d21.
On e12, e14, e16, e18, and e20, twelve embryos were euthanized by decapitation prior to collection of skin, liver, and breast muscle. Genomic DNA was extracted from skin as previously described (Vaccaro et al., 2022a; Vaccaro et al., 2022b), and embryos were sexed through PCR amplification of the chromo-helicase-DNA binding protein gene (Fridolfsson and Ellegren, 1999). Liver and breast muscle from six male embryos at each age (n = 6) were used for gene expression analysis as described below.
One bird was selected from each floor pen on d0 (day of hatch), d1, d3, d5, d7, d10, d14, and d21 from which blood and tissues were harvested. Blood was collected from a cardiac puncture into heparinized tubes, stored on ice until centrifugation at 1,500 x g for 10 min at 4°C, and plasma was collected and stored at -20°C prior to analysis of circulating hormone levels. After blood collection, birds were euthanized by cervical dislocation, sexed by visual identification of the gonads, and only males were used for liver and muscle collection (n = 6). Tissues were flash frozen in liquid nitrogen and stored at -80°C prior to total RNA extraction for gene expression analysis.
Reverse Transcription-Quantitative PCR
Total RNA was isolated from liver and breast muscle using RNeasy Mini kits (Qiagen, Valencia, CA) with modifications for lipid-rich or fibrous tissues, respectively and analyzed by RT-qPCR using primers as previously described (Vaccaro et al., 2022a; Vaccaro et al., 2022b). Transcripts were normalized to 18s ribosomal rRNA (18s rRNA), which was not affected by age in either liver or muscle (P > 0.05; Figure S1). For qPCR reactions where 18S levels were analyzed, cDNA was diluted an additional 50-fold beyond that for target gene amplification to ensure that CTs were comparable. The equation (2ΔCt)target/(2ΔCt)18s, where ΔCt = Ctno RT – CTsample, was used to transform and normalize data as previously described (Ellestad et al., 2009; Ellestad and Porter, 2013; Ellestad et al., 2015; Payne et al., 2019; Vaccaro et al., 2022a; Vaccaro et al., 2022b). Data are expressed relative to the age with the highest mRNA level. As a result, the age with the highest expression level is 100% in all cases.
IGF Enzyme-Linked Immunosorbent Assays
Samples were analyzed with commercial competitive-binding ELISAs (Cusabio, Houston, TX) for IGF1 and IGF2 as described previously (Vaccaro et al., 2022a). These assays were validated for parallelism and sensitivity using a series of five 2-fold dilutions of 4 independent broiler chicken sample pools that were initially diluted 1:3 (IGF1) or 1:100 (IGF2) in sample diluent. Parallelism was exhibited through the manufacturer's reported sensitivities of 125 pg/mL for IGF1 and 62.5 pg/mL for IGF2. All samples were analyzed in a single ELISA plate for IGF1 and 2 ELISA plates for IGF2. The intra-assay coefficient of variation (CV) for the IGF1 ELISA was determined to be 8.1%, and the and intra-assay and inter-assay CVs for IGF2 were determined to be 7.9% and 10.1%, respectively.
Statistical Analysis
Data were analyzed with a one-way analysis of variance (ANOVA) using the Fit Model Procedure of JMP Pro 14 (SAS Institute, Cary, NC). Gene expression data were log2-transformed prior to statistical analysis to correct for non-normal distribution of relative data. When ANOVA indicated a significant effect of age, post hoc means comparisons were performed using the test of least significant difference. All differences were considered significant at P ≤ 0.05.
Animals and Tissue Collection
Tissues used in this study were collected from embryonic and post-hatch male Ross 308 broilers hatched from a breeder flock raised at the University of Georgia's Poultry Research Center farm. All procedures using animals were approved by the University of Georgia's Institutional Animal Care and Use Committee.
Fertile Ross 308 eggs were obtained and incubated under standard conditions (37.5°C and 60% humidity, rotation every 2–3 h), with the day eggs were set defined as e0. After hatching, birds were raised in floor pens (n = 6 pens) with free access to water and a 2 phase commercial-type broiler diet. Birds were fed starter (21.3% crude protein, 1.2% digestible lysine, 3,050 kcal/kg metabolizable energy, 0.95% calcium and 0.48% available phosphorus) from d0 to d14 and grower (19.6% crude protein, 1.09% digestible lysine, 3120 kcal/kg metabolizable energy, 0.85% calcium and 0.43% available phosphorus) from d14 to d21.
On e12, e14, e16, e18, and e20, twelve embryos were euthanized by decapitation prior to collection of skin, liver, and breast muscle. Genomic DNA was extracted from skin as previously described (Vaccaro et al., 2022a; Vaccaro et al., 2022b), and embryos were sexed through PCR amplification of the chromo-helicase-DNA binding protein gene (Fridolfsson and Ellegren, 1999). Liver and breast muscle from six male embryos at each age (n = 6) were used for gene expression analysis as described below.
One bird was selected from each floor pen on d0 (day of hatch), d1, d3, d5, d7, d10, d14, and d21 from which blood and tissues were harvested. Blood was collected from a cardiac puncture into heparinized tubes, stored on ice until centrifugation at 1,500 x g for 10 min at 4°C, and plasma was collected and stored at -20°C prior to analysis of circulating hormone levels. After blood collection, birds were euthanized by cervical dislocation, sexed by visual identification of the gonads, and only males were used for liver and muscle collection (n = 6). Tissues were flash frozen in liquid nitrogen and stored at -80°C prior to total RNA extraction for gene expression analysis.
Reverse Transcription-Quantitative PCR
Total RNA was isolated from liver and breast muscle using RNeasy Mini kits (Qiagen, Valencia, CA) with modifications for lipid-rich or fibrous tissues, respectively and analyzed by RT-qPCR using primers as previously described (Vaccaro et al., 2022a; Vaccaro et al., 2022b). Transcripts were normalized to 18s ribosomal rRNA (18s rRNA), which was not affected by age in either liver or muscle (P > 0.05; Figure S1). For qPCR reactions where 18S levels were analyzed, cDNA was diluted an additional 50-fold beyond that for target gene amplification to ensure that CTs were comparable. The equation (2ΔCt)target/(2ΔCt)18s, where ΔCt = Ctno RT – CTsample, was used to transform and normalize data as previously described (Ellestad et al., 2009; Ellestad and Porter, 2013; Ellestad et al., 2015; Payne et al., 2019; Vaccaro et al., 2022a; Vaccaro et al., 2022b). Data are expressed relative to the age with the highest mRNA level. As a result, the age with the highest expression level is 100% in all cases.
IGF Enzyme-Linked Immunosorbent Assays
Samples were analyzed with commercial competitive-binding ELISAs (Cusabio, Houston, TX) for IGF1 and IGF2 as described previously (Vaccaro et al., 2022a). These assays were validated for parallelism and sensitivity using a series of five 2-fold dilutions of 4 independent broiler chicken sample pools that were initially diluted 1:3 (IGF1) or 1:100 (IGF2) in sample diluent. Parallelism was exhibited through the manufacturer's reported sensitivities of 125 pg/mL for IGF1 and 62.5 pg/mL for IGF2. All samples were analyzed in a single ELISA plate for IGF1 and 2 ELISA plates for IGF2. The intra-assay coefficient of variation (CV) for the IGF1 ELISA was determined to be 8.1%, and the and intra-assay and inter-assay CVs for IGF2 were determined to be 7.9% and 10.1%, respectively.
Statistical Analysis
Data were analyzed with a one-way analysis of variance (ANOVA) using the Fit Model Procedure of JMP Pro 14 (SAS Institute, Cary, NC). Gene expression data were log2-transformed prior to statistical analysis to correct for non-normal distribution of relative data. When ANOVA indicated a significant effect of age, post hoc means comparisons were performed using the test of least significant difference. All differences were considered significant at P ≤ 0.05.
RESULTS
IGF and Hormone Receptor Expression
Distinct developmental expression patterns were detected for IGF1, IGF2, IGFR1, and GHR in the liver (Figure 1; P ≤ 0.05). Expression of IGF1 began to increase on d5 and continued steadily rising through d21 (Figure 1A). Levels of IGF2 decreased between e12 and e16, increased transiently on e20 before dropping again on d0, and then steadily increased after hatch (Figure 1B). Unlike IGF1 and IGF2, IGFR1 expression dropped between e12 and d0 and increased again to intermediate levels on d5, after which it remained constant (Figure 1C). Expression of GHR did not change beween e12 and 18, decreased 10-fold between e18 and d1, and then steadily increased again after hatch through d21 (Figure 1D).Figure 1Relative mRNA expression of (A) IGF1, (B) IGF2, (C) IGFR1, and (D) GHR in liver on embryonic days (e) 12, 14, 16, 18, and 20, day of hatch (d0), and post-hatch days (d) 1, 3, 5, 7, 10, 14, and 21 in Ross 308 male broilers. Relative expression levels were measured using RT-qPCR and normalized to 18S rRNA. The data (mean + SEM) are expressed relative to the age with the highest expression level (equivalent to 100%). Data were analyzed by one-way ANOVA followed by Fisher's least significant difference test. All genes exhibited an effect of age, and values without a common letter are significantly different (P ≤ 0.05; n = 6 replicate birds at each age).Figure 1
As in liver, expression of these genes was also dynamic in breast muscle between mid-embryonic development and 3 wk post-hatch (Figure 2; P ≤ 0.05). A cyclical expression pattern was observed for IGF1 mRNA, with a transient decrease observed in the peri-hatch period and a second decline between d7 and d21 (Figure 2A). Levels of IGF2 increased slightly between e12 and e16 and remained at that level with the exception of subtle and inconsistent decreases observed on d5 and d14 (Figure 2B). Expression of IGFR1 in breast muscle was highest on e12 to e16, decreased through d3, and remained at that level through d21 (Figure 2C). Expression of GHR exhibited a similar pattern, though there was a transient increase on d1 and d3 and the overall difference in expression across the ages was smaller (Figure 2D).Figure 2Relative mRNA expression of (A) IGF1, (B) IGF2, (C) IGFR1, and (D) GHR in breast muscle on embryonic days (e) 12, 14, 16, 18, and 20, day of hatch (d0), and post-hatch days (d) 1, 3, 5, 7, 10, 14, and 21 in Ross 308 male broilers. Relative expression levels were measured using RT-qPCR and normalized to 18S rRNA. The data (mean + SEM) are expressed relative to the age with the highest expression level (equivalent to 100%). Data were analyzed by one-way ANOVA followed by Fisher's least significant difference test. All genes exhibited an effect of age, and values without a common letter are significantly different (P ≤ 0.05; n = 6 replicate birds at each age).Figure 2
IGFBP Expression
Dynamic expression patterns were exhibited between the developmental stages for all IGFBPs produced in the liver (Figure 3; P ≤ 0.05). There was a transient decrease in IGFBP1 on d0 and d1, with its expression at other ages remaining relatively stable (Figure 3A). Expression of IGFBP2 increased from e12 to d0, dropped sharply between d0 and d1 to levels not different from e12, and remained low through d21 (Figure 3B). A decrease was observed for IGFBP3 from e18 to d0, and it remained at low-to-intermediate levels after hatch (Figure 3C). Only IGFBP4 expression increased consistently in the liver throughout embryogenesis and after hatch, with significant increases occuring from e16 to e20, d3 to d5, and d10 to d21 (Figure 3D). A decrease in IGFBP5 mRNA was observed at and just after hatch, but expression was restored to embryonic levels by d5 (Figure 3E). Much like expression of IGFBP1 and IGFBP5, IGFBP7 levels decreased transiently after e20 before increasing again between d3 and d5 (Figure 3F).Figure 3Relative mRNA expression of (A) IGFBP1, (B) IGFBP2, (C) IGFBP3, (D) IGFBP4, (E) IGFBP5, and (F) IGFBP7 in liver on embryonic days (e) 12, 14, 16, 18, and 20, day of hatch (d0), and post-hatch days (d) 1, 3, 5, 7, 10, 14, and 21 in Ross 308 male broilers. Relative expression levels were measured using RT-qPCR and normalized to 18S rRNA. The data (mean + SEM) are expressed relative to the age with the highest expression level (equivalent to 100%). Data were analyzed by one-way ANOVA followed by Fisher's least significant difference test. All genes exhibited an effect of age, and values without a common letter are significantly different (P ≤ 0.05; n = 6 replicate birds at each age).Figure 3
Expression of all IGFBPs except IGFBP7 changed between developmental stages in breast muscle (Figure 4; P ≤ 0.05). Transcripts of IGFBP1 could not be detected in this tissue, which is consistent with our previous findings (Vaccaro et al., 2022a). Levels of IGFBP2 mRNA increased between e12 and e16, decreased between e20 and d3, and remained low through d21 (Figure 4A). Expression of IGFBP3 diminished between e14 and e16 and returned to e14 levels on d1. A second decrease occurred between d1 and d5 and expression remained relatively lower through d10 before increasing again on d14 and d21 (Figure 4B). Levels of IGFBP4 mRNA were highest between e12 and e18, decreased to intermediate levels on e20 through d1, and further decreased to its lowest levels on d3 through d14 (Figure 4C). For IGFBP5, expression steadily declined from e20 to d3 and tended to be lower between d3 and d21 than at earlier ages (Figure 4D). No significant differences were detected for IGFBP7 in bresast muscle (Figure 4E; P > 0.05).Figure 4Relative mRNA expression of (A) IGFBP2, (B) IGFBP3, (C) IGFBP4, (D) IGFBP5, and (E) IGFBP7 in breast muscle on embryonic days (e) 12, 14, 16, 18, and 20, day of hatch (d0), and post-hatch days (d) 1, 3, 5, 7, 10, 14, and 21 in Ross 308 male broilers. Relative expression levels were measured using RT-qPCR and normalized to 18S rRNA. The data (mean + SEM) are expressed relative to the age with the highest expression level (equivalent to 100%). Data were analyzed by one-way ANOVA followed by Fisher's least significant difference test. For genes demonstrating an effect of age, values without a common letter are significantly different (P ≤ 0.05; n = 6 replicate birds at each age). Transcripts of IGFBP1 were not detected in muscle.Figure 4
Circulating IGFs in Post-Hatch Plasma
Circulating IGF concentrations did not change during the first 3 wk of post-hatch development (Figure 5; P > 0.05). However, changes in levels of IGF2 approached significance (Figure 5B; P = 0.0661), with concentrations appearing to rise between d1 and d3 before dropping after d7.Figure 5Circulating (A) IGF1 and (B) IGF2 in Ross 308 male broilers on post-hatch days (d) 1, 3, 5, 7, 14, and 21 were determined using an IGF1 and IGF2 ELISA, respectively. Data were analyzed by one-way ANOVA followed by Fisher's least significant difference test. The data (mean + SEM) are presented as the average hormone level at each age (pg/mL). No significant age effects were observed for (A) IGF1 or (B) IGF2 (P > 0.05; n = 6 replicate birds at each age).Figure 5
IGF and Hormone Receptor Expression
Distinct developmental expression patterns were detected for IGF1, IGF2, IGFR1, and GHR in the liver (Figure 1; P ≤ 0.05). Expression of IGF1 began to increase on d5 and continued steadily rising through d21 (Figure 1A). Levels of IGF2 decreased between e12 and e16, increased transiently on e20 before dropping again on d0, and then steadily increased after hatch (Figure 1B). Unlike IGF1 and IGF2, IGFR1 expression dropped between e12 and d0 and increased again to intermediate levels on d5, after which it remained constant (Figure 1C). Expression of GHR did not change beween e12 and 18, decreased 10-fold between e18 and d1, and then steadily increased again after hatch through d21 (Figure 1D).Figure 1Relative mRNA expression of (A) IGF1, (B) IGF2, (C) IGFR1, and (D) GHR in liver on embryonic days (e) 12, 14, 16, 18, and 20, day of hatch (d0), and post-hatch days (d) 1, 3, 5, 7, 10, 14, and 21 in Ross 308 male broilers. Relative expression levels were measured using RT-qPCR and normalized to 18S rRNA. The data (mean + SEM) are expressed relative to the age with the highest expression level (equivalent to 100%). Data were analyzed by one-way ANOVA followed by Fisher's least significant difference test. All genes exhibited an effect of age, and values without a common letter are significantly different (P ≤ 0.05; n = 6 replicate birds at each age).Figure 1
As in liver, expression of these genes was also dynamic in breast muscle between mid-embryonic development and 3 wk post-hatch (Figure 2; P ≤ 0.05). A cyclical expression pattern was observed for IGF1 mRNA, with a transient decrease observed in the peri-hatch period and a second decline between d7 and d21 (Figure 2A). Levels of IGF2 increased slightly between e12 and e16 and remained at that level with the exception of subtle and inconsistent decreases observed on d5 and d14 (Figure 2B). Expression of IGFR1 in breast muscle was highest on e12 to e16, decreased through d3, and remained at that level through d21 (Figure 2C). Expression of GHR exhibited a similar pattern, though there was a transient increase on d1 and d3 and the overall difference in expression across the ages was smaller (Figure 2D).Figure 2Relative mRNA expression of (A) IGF1, (B) IGF2, (C) IGFR1, and (D) GHR in breast muscle on embryonic days (e) 12, 14, 16, 18, and 20, day of hatch (d0), and post-hatch days (d) 1, 3, 5, 7, 10, 14, and 21 in Ross 308 male broilers. Relative expression levels were measured using RT-qPCR and normalized to 18S rRNA. The data (mean + SEM) are expressed relative to the age with the highest expression level (equivalent to 100%). Data were analyzed by one-way ANOVA followed by Fisher's least significant difference test. All genes exhibited an effect of age, and values without a common letter are significantly different (P ≤ 0.05; n = 6 replicate birds at each age).Figure 2
IGFBP Expression
Dynamic expression patterns were exhibited between the developmental stages for all IGFBPs produced in the liver (Figure 3; P ≤ 0.05). There was a transient decrease in IGFBP1 on d0 and d1, with its expression at other ages remaining relatively stable (Figure 3A). Expression of IGFBP2 increased from e12 to d0, dropped sharply between d0 and d1 to levels not different from e12, and remained low through d21 (Figure 3B). A decrease was observed for IGFBP3 from e18 to d0, and it remained at low-to-intermediate levels after hatch (Figure 3C). Only IGFBP4 expression increased consistently in the liver throughout embryogenesis and after hatch, with significant increases occuring from e16 to e20, d3 to d5, and d10 to d21 (Figure 3D). A decrease in IGFBP5 mRNA was observed at and just after hatch, but expression was restored to embryonic levels by d5 (Figure 3E). Much like expression of IGFBP1 and IGFBP5, IGFBP7 levels decreased transiently after e20 before increasing again between d3 and d5 (Figure 3F).Figure 3Relative mRNA expression of (A) IGFBP1, (B) IGFBP2, (C) IGFBP3, (D) IGFBP4, (E) IGFBP5, and (F) IGFBP7 in liver on embryonic days (e) 12, 14, 16, 18, and 20, day of hatch (d0), and post-hatch days (d) 1, 3, 5, 7, 10, 14, and 21 in Ross 308 male broilers. Relative expression levels were measured using RT-qPCR and normalized to 18S rRNA. The data (mean + SEM) are expressed relative to the age with the highest expression level (equivalent to 100%). Data were analyzed by one-way ANOVA followed by Fisher's least significant difference test. All genes exhibited an effect of age, and values without a common letter are significantly different (P ≤ 0.05; n = 6 replicate birds at each age).Figure 3
Expression of all IGFBPs except IGFBP7 changed between developmental stages in breast muscle (Figure 4; P ≤ 0.05). Transcripts of IGFBP1 could not be detected in this tissue, which is consistent with our previous findings (Vaccaro et al., 2022a). Levels of IGFBP2 mRNA increased between e12 and e16, decreased between e20 and d3, and remained low through d21 (Figure 4A). Expression of IGFBP3 diminished between e14 and e16 and returned to e14 levels on d1. A second decrease occurred between d1 and d5 and expression remained relatively lower through d10 before increasing again on d14 and d21 (Figure 4B). Levels of IGFBP4 mRNA were highest between e12 and e18, decreased to intermediate levels on e20 through d1, and further decreased to its lowest levels on d3 through d14 (Figure 4C). For IGFBP5, expression steadily declined from e20 to d3 and tended to be lower between d3 and d21 than at earlier ages (Figure 4D). No significant differences were detected for IGFBP7 in bresast muscle (Figure 4E; P > 0.05).Figure 4Relative mRNA expression of (A) IGFBP2, (B) IGFBP3, (C) IGFBP4, (D) IGFBP5, and (E) IGFBP7 in breast muscle on embryonic days (e) 12, 14, 16, 18, and 20, day of hatch (d0), and post-hatch days (d) 1, 3, 5, 7, 10, 14, and 21 in Ross 308 male broilers. Relative expression levels were measured using RT-qPCR and normalized to 18S rRNA. The data (mean + SEM) are expressed relative to the age with the highest expression level (equivalent to 100%). Data were analyzed by one-way ANOVA followed by Fisher's least significant difference test. For genes demonstrating an effect of age, values without a common letter are significantly different (P ≤ 0.05; n = 6 replicate birds at each age). Transcripts of IGFBP1 were not detected in muscle.Figure 4
Circulating IGFs in Post-Hatch Plasma
Circulating IGF concentrations did not change during the first 3 wk of post-hatch development (Figure 5; P > 0.05). However, changes in levels of IGF2 approached significance (Figure 5B; P = 0.0661), with concentrations appearing to rise between d1 and d3 before dropping after d7.Figure 5Circulating (A) IGF1 and (B) IGF2 in Ross 308 male broilers on post-hatch days (d) 1, 3, 5, 7, 14, and 21 were determined using an IGF1 and IGF2 ELISA, respectively. Data were analyzed by one-way ANOVA followed by Fisher's least significant difference test. The data (mean + SEM) are presented as the average hormone level at each age (pg/mL). No significant age effects were observed for (A) IGF1 or (B) IGF2 (P > 0.05; n = 6 replicate birds at each age).Figure 5
DISCUSSION
A comprehensive evaluation of somatotropic gene expression from mid-embryogenesis through the first 3 wk post-hatch has provided insight into how this axis regulates growth and metabolism during distinct developmental periods. The ages examined were chosen based on prior work demonstrating that the somatotropic axis becomes active during the last week of embryogenesis, although IGF1 production does not appear GH-dependent until the axis is fully mature during the early post-hatch period (Porter et al., 1995; Ellestad et al., 2011; Vaccaro et al., 2022a). Additionally, expression of genes within the somatotropic axis was observed to differ between Ross 308 modern commercial broilers and ACRB legacy broilers during both embryonic and post-hatch development (Vaccaro et al., 2022a), indicating that this axis is involved in regulating growth and development of modern broilers to such a degree its activity has been altered by commercial genetic selection. By examining additional ages that encompass times of rapid growth, development of muscle, and the metabolic transition between yolk and grain-based diets (Noble, 1986; Noble and Cocchi, 1990; Halevy et al., 2000; Halevy et al., 2004), potential roles for the IGF system in these processes can be inferred.
Levels of IGF1 mRNA in liver increased almost 50-fold after hatch, while in breast muscle, expression diminished about 5- to 10-fold during the peri-hatch period but increased again during the first week post-hatch. Of the 2 IGFs, circulating IGF1 is thought to be the more important regulator of post-natal growth in mammals (Stratikopoulos et al., 2008). Our results revealed that, in broilers, the pattern of hepatic IGF1 mRNA expression is consistent with previous reports that the highest levels occur during the 2.5 to 3.5-wk post-hatch period in which weight gain is most rapid in broilers (Giachetto et al., 2004; Liu et al., 2016; Vaccaro et al., 2022a). Additionally, low embryonic expression of IGF1 in the liver coincided with greater GHR mRNA in the same tissue. This indicates that GH signaling via GHR likely does not control IGF1 production until after hatch, when the somatotropic axis is thought to become fully mature (Ellestad et al., 2011). Additional evidence for the development of negative feedback comes from decreases in muscle IGFR1 mRNA post-hatch, which is consistent with observations that binding of radiolabeled IGF1 to partially purified IGFR1 on breast and leg muscle plasma membranes decreased between 1 and 7 weeks of age (Oudin et al., 1998). Levels of IGF2 mRNA in the liver were expressed at comparable levels both pre- and post-hatch, while hepatic IGF1 mRNA was much lower during embryonic development than after hatch. Based on relative levels of hepatic IGF1 and IGF2 mRNA during each developmental phase, IGF2 could have a reduced role in post-hatch growth and development as IGF1 production increases, similar to what is seen in mammals (DeChiara et al., 1991). In chickens, the cation-independent mannose 6-phosphate receptor does not bind IGF2 as it does in mammals (Canfield and Kornfeld, 1989), and IFGR1 exhibits similar affinities for both IGF1 and IGF2 (Oudin et al., 1998). Therefore, relatively higher levels of IGF1 than IGF2 after hatch that were reflected in hepatic mRNA expression and circulating hormone concentrations measured here and previously observed (Radecki et al., 1997) would indicate that it is the primary IGF regulating growth after hatch.
The production of IGF1 in breast muscle also suggests that local synthesis of IGF1 is critical for growth and development of this tissue, as previous data have implicated in chickens (Guernec et al., 2003; Guernec et al., 2004; Vaccaro et al., 2022a). This is consistent with studies in mice, where it has been demonstrated via tissue-specific knockout of hepatic IGF1 that liver is the main source of circulating IGF1 but does not appear to control overall body growth (Sjögren et al., 1999; Yakar et al., 1999). Circulating IGF levels have sometimes (Scanes et al., 1989; Beccavin et al., 2001) but not always (Giachetto et al., 2004; Vaccaro et al., 2022a) correlated with growth rate in chickens, suggesting that autocrine or paracrine action might also be important in controlling growth or body weight in this species. A drop in IGF1 levels in muscle was observed between e12 and e20 and levels increased greatly again between e20 and d3. This was followed by a decrease after d7. As the first week post-hatch represents a period of rapid satellite cell expansion (Halevy et al., 2000; Halevy et al., 2004), this increase in IGF1 suggests its importance in local regulation of this process. Comparatively, IGF2 may also facilitate muscle growth and satellite cell mitotic activity, as expression of IGF2 mRNA in breast muscle rose between e12 and e16 and remained constant thereafter. Thus, rapid muscle accretion induced by satellite muscle cell proliferation observed shortly post-hatch in broilers (Halevy et al., 2000; Halevy et al., 2004) could be facilitated by locally produced IGF1, whereas IGF2 might perform a role in maintaining muscle tissue throughout both embryogenesis and post-hatch. Additionally, IGFR1 expression may change in the wake of increased paracrine IGF1 signaling. By d3, IGFR1 levels decreased in breast muscle, concomitant with the increase in IGF1 observed during the same time. This suggests that IGFR1 mRNA production diminishes post-hatch in breast muscle following increased IGF1 production, potentially as part of a negative feedback loop.
Expression of IGFBPs demonstrated dynamic changes in both tissues between developmental stages. In the liver, IGFBP1, IGFBP3, IGFBP5, and IGFBP7 dropped transiently during the peri-hatch period. On the other hand, hepatic IGFPB2 levels dropped substantially just after hatch, while IGFBP4 increased steadily from mid-embryogenesis until 3 wk post-hatch in this tissue. A similar decrease in expression shortly after hatch was observed for IGFBP2 in breast muscle, though the change was more gradual. In contrast to liver, IGFBP4 expression in muscle also decreased shortly after hatch on d3 and remained relatively low. Despite these dynamic changes in expression in liver and breast muscle, circulating IGF1 and IGF2 concentrations in plasma did not change throughout the first 3 wk post-hatch. Collectively, these results provide further evidence that IGFBPs, rather than circulating IGF levels, play a substantial role in mediating signaling (Vaccaro et al., 2022a). Dynamic fluctuations in IGFBP expression indicate that the activity of these proteins varies based on developmental stage, with transient or consistent downregulation of several in the liver and muscle necessary for the transition between embryogenesis and post-hatch growth and development.
The IGFBPs can inhibit growth by preventing IGF access to IGFR1, ultimately interfering with IGF-IGFR1 mediated siganling (Baxter, 1991). Therefore, diminished expression of multiple IGFBPs in liver or muscle post-hatch could allow for greater access of circulating or local IGFs to IGFR1, ultimately promoting growth in a holistic or tissue-specific manner. Expression of IGFBP2 decreased at or shortly after hatch in both liver and muscle; IGFBP3 and IGFBP4 expression was reduced in liver or breast muscle, respectively. These developmental patterns suggest that hepatic IGFBP2 and IGFBP3 could have an inhibitory effect on post-hatch growth in chickens. Similar results were found in a prior study, where post-hatch expression of IGFBP2 mRNA was higher in the liver and IGFBP3 mRNA was higher in the breast muscle of slower growing ACRB broilers when compared to faster growing modern Ross 308 broilers (Vaccaro et al., 2022a). Overexpression of IGFBP2 in mice slows the development of myofibers, causing total lower body protein and reduced muscle mass (Rehfeldt, 2008; Rehfeldt et al., 2010). In this study, IGFBP2 mRNA was observed to increase between e12 and e16 in breast muscle but began to decrease afterward to very low levels throughout the post-hatch period. Levels of IGFBP4, which also decreased in this study throughout late embryonic development and after hatch, is also involved in myofiber development through blocking differentiation in mouse myoblasts (James et al., 1993; Rotwein et al., 1995; Mukherjee et al., 2008). If IGFBP2 and IGFBP4 act similarly in avian muscle as they do in mammalian muscle, a reduction in expression would be required to facilitate rapid development and accretion of this tissue that is observed after hatch in broilers. Taken together, IGFBP2 and IGFBP4 appear to act in a paracrine, inhibitory fashion in breast muscle prior to hatch and are subsequently downregulated to allow for rapid muscle growth after hatch.
In addition to acting in an inhibitory fashion, select IGFBPs can also promote IGF action and have effects independent of IGF (Schutt et al., 2004; Klaus et al., 2006). The results observed in this study suggest that IGFBP4 functions as an promoter or inhibitor of growth in a tissue-specific manner. Throughout embryonic and post-hatch development, IGFBP4 mRNA levels increased in liver and decreased in breast muscle. When released into circulation from the liver, IGFBPs bind to IGFs in plasma to extend their half-life. Therefore, higher IGFBP4 expression in broiler liver post-hatch suggests it may promote growth via stabilizing IGF in circulation and, therefore, promoting its signaling, similar to mammals (Awede et al., 1999). However, IGFBP4 mRNA decreased at d3 in breast muscle. A reduction in local IGFBP4 activity in broiler muscle could facilitate growth of this tissue as described above, because less IGFBP4 would allow for increased IGF access to IGFR1. This inhibitory effect of IGFBP4 in mammals has been established to occur via paracrine signaling, including acting as an IGF antagonist in mouse smooth muscle cells (Jones and Clemmons, 1995; Florini et al., 1996; Wang et al., 1998) and rat skeletal muscle cells (Silverman et al., 1995). These data and our results suggest that IGFBP4 has multiple roles in the context of growth, including both positive and negative IGF interactions that may be tissue-specific (Ning et al., 2008), and these effects appear conserved between mammals and birds.
The effects of IGFBPs on IGF signaling can affect biological processes outside of cellular proliferation and differentiation. Hepatic IGFBP1, IGFBP5, and IGFBP7 transiently decreased between e20 and d1, only to return to embryonic levels within the first week of hatch. These changes during the peri-hatch period may be the result of the known metabolic switch from pre- to post-hatch. The majority of energy utilized by the chick embryo is sourced from lipoproteins in the yolk. This occurs until around d3 (Noble, 1986; Noble and Cocchi, 1990), after which energy is obtained from a carbohydrate-based diet (Sklan, 2003). The peri-hatch downregulation of these IGFBPs coincides with this metabolic transition, as the IGFs begin to function in glucose homeostasis via GH suppression (Clemmons, 2006). There is precedence for IGFBP involvement in mediating glucose utilization in mammals, and this may ultimately impact hepatic metabolism and growth. Overexpression of human IGFBP3 in fasted mice induced hyperglycemia and impaired glucose tolerance (Silha et al., 2002), and in a mouse model where IGFBP3 was deleted, knockouts had larger livers and higher body weights than controls (Yakar et al., 2009; Yamada et al., 2010).
Circulating IGF levels did not change significantly during post-hatch development, although IGF2 tended to decrease over time. This suggests that, as IGF1 concentrations stay relatively stable in plasma, its effects are modulated largely by IGFBP activity and IGFR1 sensitivity. It should be noted that the lack of developmental changes in circulating IGF1 is in contrast to prior studies that have shown an increase after the early post-hatch period, with levels plateauing sometime between 3 and 6 weeks of age (Radecki et al., 1997; Beccavin et al., 2001; Giachetto et al., 2004; Guernec et al., 2004; Vaccaro et al., 2022a). The absolute values measured in the current study and Vaccaro et al. (2022a) were also about an order of magnitude lower than those in prior reports, which ranged from approximately 5 to 60 ng/mL (Radecki et al., 1997; Beccavin et al., 2001; Giachetto et al., 2004; Guernec et al., 2004). It is possible that genetic differences in the chickens used contribute to the inconsistencies, as the previous studies used non-commercial broilers divergently selected for body weight (Beccavin et al., 2001), commercial and non-commercial broilers from the early 2000s (Giachetto et al., 2004; Guernec et al., 2004), or layers (Radecki et al., 1997). The older studies also used a heterologous radioimmunoassays validated for chickens (Huybrechts et al., 1985) and not an ELISA as used here, which could have contributed to differences. The slight decrease observed in circulating IGF2 suggests that IGF-induced post-hatch growth is primarily carried out by signaling and modulation of IGF1. Relatively stable or reducing circulating IGF2 levels post-hatch that are somewhat lower than IGF1 have been noted previously (Radecki et al., 1997; Vaccaro et al., 2022a) and support this. The IGFBPs may alter endocrine IGF signaling by directly binding to IGFs and either preventing them from binding IGFR1 or facilitating their transport to IGF-sensitive tissues expressing the receptor (Baxter, 1991; Kelley et al., 2002; Kim, 2010; Baxter, 2023), though these functions have not been established in chickens. Therefore, the IGFBPs are critical for controlling endocrine IGF action. However, as both IGF1 and IGF2 were detected in breast muscle tissue, locally-produced IGFs that signal in a paracrine fashion are likely important for breast muscle growth, as discussed earlier.
Results presented here indicate the dynamic and tissue-specific nature of broiler somatotropic gene expression between mid-embryogenesis and the first 3-wk post-hatch. This suggests these genes play important roles in regulating growth, development, and metabolic transitions during this period. Post-hatch, IGF1 appears to become the primary regulator of growth, particularly at the paracrine level, as its expression patterns are much more dynamic in both liver and muscle at this time. Effects of IGFBPs likely differ depending on developmental stage, tissue of origin, and mode of action. As the results are limited to changes in mRNA levels, inferences surrounding potentional functionality should be interpreted with caution but do provide a basis for hypothesis generation to guide future investigations, especially since functional data pertaining to the IGF-IGFBP system are lacking in chickens. For example, hepatic IGFBP4 expression was unique in increasing in a manner similar to hepatic IGF1, suggesting it could promote IGF signaling and ultimately growth, perhaps through extending the half-life of IGF1 in circulation. Similarly, the transient decrease in hepatic expression of several IGFBPs around hatch indicates that this might be necessary to facilitate the metabolic switch between lipid and carbohydrate utilization that must occur at this time. In breast muscle, IGFBPs with downregulated expression after hatch would be more likely function in an inhibitory, paracrine fashion since a decrease in their levels would allow for enhanced sensitivity to circulating and locally-produced IGFs necessary for the rapid accretion of this tissue in modern commercial broilers. In conclusion, differential expression of most IGFBPs across developmental stages reinforces the idea that they are critical regulators of IGF signaling that contribute to broiler growth, metabolism, and muscle accretion and provide a basis for future studies investigating their functionality.
DISCLOSURES
The authors declare no conflicts of interest. |
Title: Dietary high lipid and high plant-protein affected growth performance, liver health, bile acid metabolism and gut microbiota in groupers | Body:
1
Introduction
The grouper, a prominent marine fish, recorded a production of 205,816 t in China in 2022, marking a 0.83% increase on the previous year (Bureau of Fisheries of MARA, 2023). Notably, the pearl gentian grouper (Epinephelus fuscoguttatus♂ × Epinephelus lanceolatus♀), highly prized by farmers and consumers alike, is extensively cultivated along the coasts of Southeast Asia and China (Xu et al., 2022a). As a carnivorous species, grouper feed typically contains 50% protein, with fishmeal serving as the predominant ingredient due to its palatability, balanced amino acid profile, and essential nutrients (Shapawi et al., 2018). However, the escalating demand coupled with limited fishmeal production has resulted in increased prices, significantly affecting feeding costs (Liang et al., 2013). Consequently, there has been a growing adoption of high plant-protein diets (HPD) and high lipid diets (HLD) to diminish fishmeal usage, fostering sustainable grouper aquaculture (Pan et al., 2016; Zhang et al., 2022a).
Previous research demonstrated that partial substitution of fishmeal with cottonseed protein concentrate (CPC) did not impact the growth performance of largemouth bass (Micropterus salmoides), large yellow croaker (Larimichthys crocea), rainbow trout (Oncorhynchus mykiss), and pearl gentian groupers (He et al., 2021, 2022; Liu et al., 2022; Tian et al., 2022). However, it is imperative to consider that excessive levels of substitution could detrimentally affect the survival, growth, and enterohepatic health of aquatic animals (He et al., 2022; Xie et al., 2023). Additionally, HPD have been linked to adverse effects on liver health in common carp (Cyprinus carpio L.) and Amur sturgeon (Acipenser schrenckii), manifesting as disrupted bile acid (BA) circulation and diminished BA levels (Wei et al., 2020; Yao et al., 2021). Notably, liver damage in common carp and shrimp (Litopenaeus vannamei) caused by HPD was mitigated by BA supplementation (Li et al., 2022b; Yao et al., 2021). This suggests that the liver injury induced by HPD may be attributed to an imbalance in BA homeostasis.
Additionally, some researchers have observed that HLD may disrupt lipid metabolism, potentially leading to tissue damage in organs such as the intestines and liver over time, and in severe cases, even growth and survival, as observed in common carp (Yang et al., 2023), yellow catfish (Zheng et al., 2023) and largemouth bass (Yin et al., 2021). In our previous study, we noted that a HLD resulted in reduced BA levels in the pearl gentian grouper, leading to impaired liver health (Xu et al., 2022b). Similarly, Zheng et al. (2017) demonstrated that the impact of a HLD on hepatic fat deposition in mice may be correlated with its effect on the BA pool size. Moreover, following BA supplementation, pearl gentian groupers and Chinese perch (Siniperca chuatsi) exhibited enhanced lipid metabolism and antioxidant capacity, indicative of healthy livers (Xu et al., 2022b; Zhang et al., 2022b). These findings suggest that disruption of BA homeostasis may significantly contribute to HLD-induced liver damage in fish.
BA, a class of steroidal carboxylic acids derived from cholesterol, serve dual roles as emulsifiers that enhance nutrient absorption and transport, and as intricate metabolic integrators and signaling agents that modulate diverse metabolic pathways within the body (He et al., 2023; Zheng et al., 2023). The enterohepatic circulation of BA involves the liver producing primary BA, which are then transformed by the intestinal microbiota into secondary BA. These are subsequently reabsorbed and returned to the liver (Xiong et al., 2022). Notably, the intestinal microbiota generates essential enzymes that are critical for BA metabolism in the enterohepatic circulation, thereby significantly affecting host metabolism (Luo et al., 2023). Therefore, changes in BA profiles mediated by the microbiota may be crucial in regulating host health. Nonetheless, the exact mechanisms by which gut microbiota influence BA homeostasis and the effects of gut microbe-BA interactions on fish health require further clarification.
While both HLD and HPD are trending in the aquafeed industry, and they have been found to induce liver damage and disruption of BA homeostasis in fish, few studies have focused on how these two feed environments impair liver health by modulating BA homeostasis, and the similarities and differences in the mechanisms of action behind them. On the other hand, liver damage is an important factor limiting the promotion and application of low fishmeal feeds (Nankervis et al., 2022; Zhou et al., 2023). Although both HLD and HPD have the ability to save fishmeal, little attention has been paid to the potential to save fishmeal when the two are applied in combination (high lipid-high plant-protein diet, HLPD), and the limiting factors behind this. Consequently, this study aimed to explore the effects of HLD and HPD on the intestinal microbiota, BA metabolism, and liver health in pearl gentian grouper. Furthermore, we assessed the impacts of HLD, HPD, and their combination on grouper liver health to evaluate the potential of HLPD in reducing fishmeal in feeds. The primary objective of this research is to elucidate the mechanisms by which gut microbes and BA metabolism interact to regulate liver health in fish. As the challenge of balancing fishmeal production with aquaculture expansion persists, discovering more efficient strategies to decrease fishmeal content in diets and understanding the limitations of low-fishmeal diets will become increasingly vital for the aquaculture sector.
2
Materials and methods
2.1
Animal ethics statement
Present study followed the recommendations of Care and Use of Laboratory Animals in China, Animal Ethical and Welfare Committee of China Experimental Animal Society. The protocol was approved by the Ethical Committee of the Guangxi Academy of Sciences, Nanning, China, and processing ID: GXAS-EC-202303157215.
2.2
Chemicals
The study employed primary antibodies against sterol-regulator element-binding protein 1 (srebp1, ab28481), nuclear factor (erythroid-derived 2)-like 2 (nrf2, ab137550) and Kelch-like erythroid cell-derived protein 1 (keap1, ab227828) from Abcam Co (Cambridge, UK). The antibody against glyceraldehyde-phosphate dehydrogenase (GAPDH, 2118S) was purchased from Cell Signaling Technology (MA, USA). The secondary antibody (AB0101) was purchased from Abways (Shanghai, China). The kits of total protein (TP, A045-4-2), triglyceride (TG, A110-1-1), total cholesterol (T-CHO, A111-1-1), aspartate aminotransferase (AST, C010-2-1), alanine aminotransferase (ALT, C009-2-1), malondialdehyde (MDA, A003-1-2), total bile acids (TBA, E003-2-1), superoxide dismutase (SOD, A001-3-2) and catalase (CAT, A007-1-1) were purchased from Nanjing Jiancheng Bioengineering Institute (Nanjing, China).
2.3
Diet preparation, animals and sample collection
Previous studies have demonstrated that an optimal dietary lipid of the pearl gentian grouper level falls within the range of 7% to 13%, while a lipid content of ≥15% leads to fat accumulation in the liver (Zou et al., 2019). Thus, in this study, a HLD was prepared by adding soybean oil to a control diet (CD), resulting in a crude lipid content of 16.70% for the HLD and 9.48% for the CD (Table 1). To formulate a HPD, we replaced 50% of the fishmeal in the CD diet (base level of 500 g/kg) with CPC, as previous laboratory studies have shown that the maximum level that CPC could replace fishmeal is 50% (Ye et al., 2020). In addition, the HLPD (16.67% crude lipid) with 50% fishmeal replaced by CPC was prepared.Table 1Composition and nutrient levels of diets (air-dry basis, %).Table 1ItemDiet1CDHLDHPDHLPDIngredientsFishmeal50.0050.0025.0025.00Vital wheat gluten8.008.008.008.00Wheat flour12.5012.5012.5012.50Cottonseed protein concentrate25.2025.20Corn gluten meal5.005.005.005.00Casein5.005.005.005.00Gelatine1.001.001.001.00Fish oil1.501.503.453.45Soybean oil1.509.001.509.00Soybean lecithin2.002.002.002.00Calcium monophosphate1.001.001.001.00Vitamin C0.030.030.030.03Choline chloride0.500.500.500.50Vitamin premix20.500.500.500.50Mineral premix30.500.500.500.50Antioxidant40.050.050.050.05Attractant50.100.100.100.10Cellulose microcrystalline10.823.327.810.31Methionine0.310.31Lysine0.550.55Total100.00100.00100.00100.00Nutrient levelsCrude protein46.2146.3746.5046.54Crude lipid9.4816.709.3816.67Moisture9.158.319.098.741CD means control diet, 46.21% crude protein, 9.48% crude lipid; HLD means high lipid diet, 46.37% crude protein, 16.70% crude lipid; HPD means high plant-protein diet, 46.50% crude protein, 9.38% crude lipid; HLPD means high lipid-high plant-protein diet, 46.54% crude protein, 16.67% crude lipid.2Vitamin premix (g/kg vitamin premix): vitamin B1 17.00 g, vitamin B2 16.67 g, vitamin B6 33.33 g, vitamin B12 0.07 g, vitamin K 3.33 g, vitamin E 66.00 g, retinyl acetate 6.67 g, vitamin D 33.33 g, nicotinic acid 67.33 g, D-calcium pantothenate 40.67 g, biotin 16.67 g, folic acid 4.17 g, inositol 102.04 g, cellulose 592.72 g. All ingredients were diluted with corn starch to 1 kg. The mixture was provided by Beijing Enhalor International Tech Co., Ltd., Beijing, China.3Mineral premix (g/kg mineral premix): CaCO3 350 g, NaH2PO4·H2O 200 g, KH2PO4 200 g, NaCl 12 g, MgSO4·7H2O 10 g, FeSO4·7H2O 2 g, MnSO4·7H2O 2 g, AlCl3·6H2O 1 g, CuCl2·2H2O 1 g, KF, 1 g, NaMoO4·2H2O 0.5 g, NaSeO3 0.4 g, CoCl2·6H2O 0.1 g, KI, 0.1 g, zeolite powder 219.9 g. The mixture was provided by Beijing Enhalor International Tech Co., Ltd., Beijing, China.4Antioxidant: ethoxyquin, provided by Shanghai Aladdin Biochemical Technology Co., Ltd., Shanghai, China.5Attractant: betaine, provided by Shanghai Aladdin Biochemical Technology Co., Ltd., Shanghai, China.
Juvenile healthy pearl gentian groupers (E. fuscog-uttatus♀ × E. lanceolatus♂) were carefully selected from a local fish farm in Beihai, Guangxi Province. The fish were fed high-quality commercial diets with 50% crude protein and 10% crude fat, and were expertly domesticated for 7 d in salinity of 28‰. Then, 300 fish were randomly divided into 4 diet treatments (ensuring 3 tanks replicates of each diet treatment, each tank containing 25 fish, for a total of 12 experimental tanks). During the feeding period, two-thirds of the tank water daily was replaced. The water temperature, dissolved oxygen concentration, ammonia nitrogen, and nitrate concentration throughout the experimental process were maintained at optimal levels of 28 to 30 °C, 7 mg/L, less than 0.03 mg/L, and less than 0.03 mg/L, respectively (Xu et al., 2022b). Our previous study provided a detailed description of the methods for diet preparation and rearing conditions (Xu et al., 2022b).
At the end of the 8-week feeding period, the fish in each tank were anaesthetized using MS-222 (Sigma Aldrich, MO, USA) at a concentration of 100 mg/L following 24 h of fasting. The final body weight of all fish in each tank, along with the weight and length of three randomly selected fish, were recorded. Following euthanasia and dissection, 15 fish from each tank were randomly chosen for sampling. Whole-body, blood, muscle, and liver samples were collected. Muscle and whole-body samples were frozen at −20 °C for composition analysis. Blood was drawn from the caudal vein with sterile syringes, centrifuged at 3000 × g and 4 °C for 10 min to isolate serum, which was then rapidly frozen in liquid nitrogen and stored at −80 °C for enzyme activity and biochemical indicator measurements. The distal intestinal contents were carefully collected, frozen in liquid nitrogen, and stored at −80 °C till the bacterial composition analysis. Each liver was quartered: one part was fixed in 4% paraformaldehyde for histology, another was stored at −20 °C for composition analysis, and the remaining portions were quick-frozen in liquid nitrogen and stored at −80 °C for biochemical testing, gene, and protein expression analysis.
2.4
Growth performance analyses
The growth parameters were calculated:Weight gain rate (WGR, %) = 100 × (final body weight - initial body weight)/initial body weight;Specific growth rate (SGR, %/d) = 100 × (Ln final body weight - Ln initial body weight)/experiment duration;Feed intake (FI, %/fish) = 100 × diet fed/(final body weight + initial body weight)/(2 × experimental duration).
2.5
Nutrient composition of diets and body tissues
Following the previously described methods (AOAC, 2002), the proximate composition of crude protein (method 968.06), crude lipid (method 922.06) and moisture contents (method 926.08) in diets, whole-body, muscle and liver were analyzed. In brief, the level of crude protein (N × 6.25) was measured by the Kjeldahl nitrogen method via Auto Kjeldahl System usage (8400-Autoanalyzer, FOSS, Hoganas, Sweden); the level of crude lipid was measured by the Soxhlet method via ether extraction system (Tecator, Sweden); the level of moisture was measured by oven drying the sample at 105 °C until constant weight.
2.6
Liver staining
Liver tissue samples were fixed, dehydrated, and paraffin-embedded before being stained with hematoxylin and eosin (H&E). The stained sections were observed under 200× magnification (Olympus, Tokyo, Japan) to examine the morphology of the hepatocyte and the unstained fatty vacuole area.
2.7
Enzyme activity, biochemical assays and real-time fluorescence quantitative polymerasechain reaction (RT-qPCR) analyses
Commercial kits were used to determine the levels of TP, TG, T-CHO, AST, ALT, MDA, TBA, SOD and CAT (Chiu et al., 2018). The reagent configuration, sample pre-treatment and determination steps were carried out according to the instructions of the kit manufacturers. The concentration of total RNA in all samples was diluted to 1000 ng/μL. Reverse transcription was performed using the Evo M-MLV kit with gDNA Eraser (Accurate Biotechnology (Hunan) Co., Ltd, Hunan, China). The RT-qPCR assay was carried out using SYBR Green Pro Taq HS (Accurate Biotechnology) on the qTower3G IVD system (Jena, Germany). Primers were designed based on the full-length sequence of the pearl gentian grouper transcriptome. The relative expression of genes was calculated using the 2−ΔΔCt method with 18s rRNA (18s) and β-actin as reference genes (Xu et al., 2022c). The following genes were tested in the present study (Table S2): lipogenesis (acetyl-CoA carboxylase [acc], fatty acid synthase [fas]); lipolysis (adipose triglyceride lipase [atgl], carnitine palmitoyltransferase 1 [cpt1], hormone-sensitive lipase [hsl]); transcriptional factors (liver X receptor alpha [lxr], peroxisome proliferator-activated receptor alpha [pparα], srebp1); keap1/nrf2 pathway (keap1, nrf2, heme oxygenase-1 [ho-1]); pro-inflammatory cytokines (interleukin 1β [il1β] and tumor necrosis factor-alpha [tnfα]); BA synthesis (cholesterol 7α-hydroxylase [cpy7a1], sterol-27-hydroxylase [cyp27a1]); BA transport (bile salt export pump [bsep], multidrug resistance protein 3 [mdr3]); BA reabsorption (apical sodium-dependent BA transporter [asbt], multidrug resistance-associated protein 3 [mrp3]; BA recycling (organic anion transporters 1 [oatp1], microsomal epoxide hydrolase [meh]); BA receptors (farnesoid X receptor [fxr] and G protein-coupled bile acid receptor 1 [tgr5]).
2.8
Western blot analyses
The Western blot analysis was conducted following the protocol outlined in our recent study (Xu et al., 2022c). Three replicate wells were made for each protein sample. In detail, liver samples were lysed in radio immunoprecipitation assay lysis buffer (RIPA) buffer to extract protein. The protein was denatured in a boiling water bath and electrophoretically separated on a 10% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) gel containing. The separated proteins were then transferred to a polyvinylidene fluoride (PVDF) membrane. The membrane was incubated with primary antibodies: srebp1 (1:1000), nrf2 (1:500), keap1 (1:1500), and GAPDH (1:1000). Horseradish peroxidase (HRP)-conjugated anti-rabbit secondary antibodies were used to detect the probes. The target protein bands were visualized using enhanced chemiluminescence (ECL) reagent (Billerica, MA, USA) and quantified using Image J software (version 1.42, National Institutes of Health, USA).
2.9
Untargeted metabolomics analyses
The untargeted metabolomics analyses were performed by Suzhou BioNovo Gene Company. The distal intestine was taken and weighed accurately. Tissue extracts were added, thoroughly ground, centrifuged, and dried. The samples were then re-dissolved by adding 2-chloro-l-phenylalanine (dissolved in an acetonitrile solution). The supernatant was filtered and transferred to a detection vial for LC-MS analysis. The Vanquish UHPLC system (Thermo Fisher Scientific, USA) equipped with an ACQUITY UPLC HSS T3 (2.1 mm × 100 mm, 1.8 μm) (Waters, Milford, MA, USA) was used for chromatographic separations. The eluents employed were 0.1% formic acid acetonitrile (vol/vol) (eluent B2) and acetonitrile solution (eluent B3). The elution gradients were carried out as follows: 0 to 1 min, 8% B; 1 to 8 min, 8% to 98% B; 8 to 10 min, 98% B; 10 to 10.1 min, 98% to 8% B; 10.1 to 12 min, 8% B. The LC-ESI (+)-MS analysis elution solution was B2, while the LC-ESI (−)-MS analysis was B3. The parameters for the ESI Ion Source mass spectrometry of the Orbitrap Exploris 120 (Thermo Fisher Scientific, USA) were set as follows: sheath gas pressure = 40 arb; auxiliary gas flow = 10 arb; spray voltage: ESI (+) was set to 3.50 kV, while ESI (−) was set to −2.50 kV. The capillary temperature was maintained at 325 °C. The MS1 range was set to m/z 100 to 1000 with a resolution of 60,000 FWHM. The number of correlation scans per cycle of data was 4. The MS/MS resolution was set to 15,000 FWHM, with a normalised collision energy of 30%. The dynamic exclusion time was set to automatic. The metabolomics raw data (accession number: MTBLS9403) have been deposited in the MetaboLights database.
For metabolomics analysis, raw Triple-TOF/MS data were processed using Progenesis QI 2.0 (Waters, MA, USA) for nonlinear alignment and normalization. The software automatically handled deconvolution of the total ion chromatogram, data normalization, peak picking, ion peak alignment, and extraction of ion chromatograms using default settings. It assessed all runs for automatic alignment and peak picking with a default threshold of 3. Metabolite annotation involved matching MS and MS/MS data against the METLIN and HMDB databases with a mass error tolerance of 5 mg/L. Chemical similarity enrichment analysis (ChemRICH) was employed for statistical enrichment analysis based on chemical similarity. Subsequently, we normalized the peak areas of the BA metabolites and expressed them as the relative contents of BA. These values were then subjected to further analysis and comparison in subsequent steps.
2.10
16S rRNA of microbial samples
The total genome DNA from bacteria in the intestinal content of groupers was extracted using a TIANamp Marine Animals DNA Kit (TIANGEN, Beijing, China) as per the manufacturer's protocol. The sample size was 3 for the CD, HLD and HLPD groups, while 4 for the HPD group. The V3–V4 regions of the 16S rRNA genes were amplified using primers 338-forward (5′-ACTCCTACGGGAGGCAGCA-3′) and 806-reverse (5′-GGCTACHVGGGTWTCTAAT-3′). Sequencing libraries were prepared with the TruSeq DNA PCR-Free Kit (Illumina, California, USA), incorporating index barcodes, and sequenced on an Illumina NovaSeq platform to produce 250 bp paired-end reads. These were merged with FLASH (v1.2.7) to form raw tags. Qualified reads were clustered into amplicon sequence variants (ASV) at 97% similarity using UPARSE (v 7.1), and taxonomic information was annotated against the Silva Database via the Mothur algorithm. To study the phylogenetic relationship of different ASV, and the differences in terms of dominant species in different groups, multiple sequence alignments were conducted using the MUSCLE software (v 3.8.31). ASV abundance information was normalized using a standard of the sequence number corresponding to the sample with the fewest sequences. The α diversity indices were computed using QIIME2 (2019.4) and displayed using R software (v 2.15.3). Alpha diversity was estimated using various metrics, such as Shannon index, Simpson index, Chao1 index, Observed-species, Faith-pd, Pielou-e, and Goods-coverage. Unweighted Pair Group Method with Arithmetic mean (UPGMA) trees (Jaccard distance) were created. Beta diversity was calculated using weighted and unweighted UniFrac and principal coordinate analysis (PCoA). In addition, linear discriminant analysis (LDA) effect size (LEfSe) algorithm was performed combining Kruskal–Wallis test or Wilcoxon rank-sum test with LDA scores to estimate the effect size of differentially abundant features with biological consistency and statistical significance. Differentially expressed genes (DEG) were identified between libraries at a false discovery rate (FDR) threshold of less than 0.05. Subsequently, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed on these DEG. The microbial sequences obtained in this study are available in NCBI's SRA database under accession number PRJNA1065578.
2.11
Statistical analyses
The results were presented as mean ± standard deviation (SD). Normality and homogeneity of variance were tested using the Shapiro–Wilk and Levene tests, respectively. Data were evaluated using one-way ANOVA and two-way ANOVA. Multiple comparisons of means were performed using Duncan's multiple range tests when significant differences were found between groups. A significance level of 0.05 was used. The statistical analysis was performed using SPSS 23.0 (IBM, Armonk, NY, USA).
The mathematical model for one-way ANOVA can be expressed as:Yij = μ + αi + ϵi,where Yij is the j-th observation under the i-th level; μ is the overall mean, representing the grand mean of all observations; αi is the main effect of the i-th level, indicating the influence of that level on the dependent variable; ϵij is the random error term, representing the influence of other random factors beyond the independent variable on the dependent variable.
The mathematical model for two-way ANOVA can be expressed as:Yijk = μ + αi + βj + (αβ)ij + ϵijk,where two factors of lipid level and plant-protein level, labeled as L and HP, each with different denoted as L1, L2, …, Lj for factor L and HP1, HP2, …, HPk for factor HP; Yijk is the value of the dependent variable for the k-th observation at the j-th level of factor HP within the i-th level of factor L; αi is the main effect of the i-th level of factor L, indicating the influence of factor L on the dependent variable; βj is the main effect of the j-th level of factor HP, indicating the influence of factor HP on the dependent variable; (αβ)ij is the interaction effect between factor L and factor HP, representing the combined influence of both factors on the dependent variable; ϵijk is the random error term, representing the influence of other random factors beyond factors L and HP.
2.1
Animal ethics statement
Present study followed the recommendations of Care and Use of Laboratory Animals in China, Animal Ethical and Welfare Committee of China Experimental Animal Society. The protocol was approved by the Ethical Committee of the Guangxi Academy of Sciences, Nanning, China, and processing ID: GXAS-EC-202303157215.
2.2
Chemicals
The study employed primary antibodies against sterol-regulator element-binding protein 1 (srebp1, ab28481), nuclear factor (erythroid-derived 2)-like 2 (nrf2, ab137550) and Kelch-like erythroid cell-derived protein 1 (keap1, ab227828) from Abcam Co (Cambridge, UK). The antibody against glyceraldehyde-phosphate dehydrogenase (GAPDH, 2118S) was purchased from Cell Signaling Technology (MA, USA). The secondary antibody (AB0101) was purchased from Abways (Shanghai, China). The kits of total protein (TP, A045-4-2), triglyceride (TG, A110-1-1), total cholesterol (T-CHO, A111-1-1), aspartate aminotransferase (AST, C010-2-1), alanine aminotransferase (ALT, C009-2-1), malondialdehyde (MDA, A003-1-2), total bile acids (TBA, E003-2-1), superoxide dismutase (SOD, A001-3-2) and catalase (CAT, A007-1-1) were purchased from Nanjing Jiancheng Bioengineering Institute (Nanjing, China).
2.3
Diet preparation, animals and sample collection
Previous studies have demonstrated that an optimal dietary lipid of the pearl gentian grouper level falls within the range of 7% to 13%, while a lipid content of ≥15% leads to fat accumulation in the liver (Zou et al., 2019). Thus, in this study, a HLD was prepared by adding soybean oil to a control diet (CD), resulting in a crude lipid content of 16.70% for the HLD and 9.48% for the CD (Table 1). To formulate a HPD, we replaced 50% of the fishmeal in the CD diet (base level of 500 g/kg) with CPC, as previous laboratory studies have shown that the maximum level that CPC could replace fishmeal is 50% (Ye et al., 2020). In addition, the HLPD (16.67% crude lipid) with 50% fishmeal replaced by CPC was prepared.Table 1Composition and nutrient levels of diets (air-dry basis, %).Table 1ItemDiet1CDHLDHPDHLPDIngredientsFishmeal50.0050.0025.0025.00Vital wheat gluten8.008.008.008.00Wheat flour12.5012.5012.5012.50Cottonseed protein concentrate25.2025.20Corn gluten meal5.005.005.005.00Casein5.005.005.005.00Gelatine1.001.001.001.00Fish oil1.501.503.453.45Soybean oil1.509.001.509.00Soybean lecithin2.002.002.002.00Calcium monophosphate1.001.001.001.00Vitamin C0.030.030.030.03Choline chloride0.500.500.500.50Vitamin premix20.500.500.500.50Mineral premix30.500.500.500.50Antioxidant40.050.050.050.05Attractant50.100.100.100.10Cellulose microcrystalline10.823.327.810.31Methionine0.310.31Lysine0.550.55Total100.00100.00100.00100.00Nutrient levelsCrude protein46.2146.3746.5046.54Crude lipid9.4816.709.3816.67Moisture9.158.319.098.741CD means control diet, 46.21% crude protein, 9.48% crude lipid; HLD means high lipid diet, 46.37% crude protein, 16.70% crude lipid; HPD means high plant-protein diet, 46.50% crude protein, 9.38% crude lipid; HLPD means high lipid-high plant-protein diet, 46.54% crude protein, 16.67% crude lipid.2Vitamin premix (g/kg vitamin premix): vitamin B1 17.00 g, vitamin B2 16.67 g, vitamin B6 33.33 g, vitamin B12 0.07 g, vitamin K 3.33 g, vitamin E 66.00 g, retinyl acetate 6.67 g, vitamin D 33.33 g, nicotinic acid 67.33 g, D-calcium pantothenate 40.67 g, biotin 16.67 g, folic acid 4.17 g, inositol 102.04 g, cellulose 592.72 g. All ingredients were diluted with corn starch to 1 kg. The mixture was provided by Beijing Enhalor International Tech Co., Ltd., Beijing, China.3Mineral premix (g/kg mineral premix): CaCO3 350 g, NaH2PO4·H2O 200 g, KH2PO4 200 g, NaCl 12 g, MgSO4·7H2O 10 g, FeSO4·7H2O 2 g, MnSO4·7H2O 2 g, AlCl3·6H2O 1 g, CuCl2·2H2O 1 g, KF, 1 g, NaMoO4·2H2O 0.5 g, NaSeO3 0.4 g, CoCl2·6H2O 0.1 g, KI, 0.1 g, zeolite powder 219.9 g. The mixture was provided by Beijing Enhalor International Tech Co., Ltd., Beijing, China.4Antioxidant: ethoxyquin, provided by Shanghai Aladdin Biochemical Technology Co., Ltd., Shanghai, China.5Attractant: betaine, provided by Shanghai Aladdin Biochemical Technology Co., Ltd., Shanghai, China.
Juvenile healthy pearl gentian groupers (E. fuscog-uttatus♀ × E. lanceolatus♂) were carefully selected from a local fish farm in Beihai, Guangxi Province. The fish were fed high-quality commercial diets with 50% crude protein and 10% crude fat, and were expertly domesticated for 7 d in salinity of 28‰. Then, 300 fish were randomly divided into 4 diet treatments (ensuring 3 tanks replicates of each diet treatment, each tank containing 25 fish, for a total of 12 experimental tanks). During the feeding period, two-thirds of the tank water daily was replaced. The water temperature, dissolved oxygen concentration, ammonia nitrogen, and nitrate concentration throughout the experimental process were maintained at optimal levels of 28 to 30 °C, 7 mg/L, less than 0.03 mg/L, and less than 0.03 mg/L, respectively (Xu et al., 2022b). Our previous study provided a detailed description of the methods for diet preparation and rearing conditions (Xu et al., 2022b).
At the end of the 8-week feeding period, the fish in each tank were anaesthetized using MS-222 (Sigma Aldrich, MO, USA) at a concentration of 100 mg/L following 24 h of fasting. The final body weight of all fish in each tank, along with the weight and length of three randomly selected fish, were recorded. Following euthanasia and dissection, 15 fish from each tank were randomly chosen for sampling. Whole-body, blood, muscle, and liver samples were collected. Muscle and whole-body samples were frozen at −20 °C for composition analysis. Blood was drawn from the caudal vein with sterile syringes, centrifuged at 3000 × g and 4 °C for 10 min to isolate serum, which was then rapidly frozen in liquid nitrogen and stored at −80 °C for enzyme activity and biochemical indicator measurements. The distal intestinal contents were carefully collected, frozen in liquid nitrogen, and stored at −80 °C till the bacterial composition analysis. Each liver was quartered: one part was fixed in 4% paraformaldehyde for histology, another was stored at −20 °C for composition analysis, and the remaining portions were quick-frozen in liquid nitrogen and stored at −80 °C for biochemical testing, gene, and protein expression analysis.
2.4
Growth performance analyses
The growth parameters were calculated:Weight gain rate (WGR, %) = 100 × (final body weight - initial body weight)/initial body weight;Specific growth rate (SGR, %/d) = 100 × (Ln final body weight - Ln initial body weight)/experiment duration;Feed intake (FI, %/fish) = 100 × diet fed/(final body weight + initial body weight)/(2 × experimental duration).
2.5
Nutrient composition of diets and body tissues
Following the previously described methods (AOAC, 2002), the proximate composition of crude protein (method 968.06), crude lipid (method 922.06) and moisture contents (method 926.08) in diets, whole-body, muscle and liver were analyzed. In brief, the level of crude protein (N × 6.25) was measured by the Kjeldahl nitrogen method via Auto Kjeldahl System usage (8400-Autoanalyzer, FOSS, Hoganas, Sweden); the level of crude lipid was measured by the Soxhlet method via ether extraction system (Tecator, Sweden); the level of moisture was measured by oven drying the sample at 105 °C until constant weight.
2.6
Liver staining
Liver tissue samples were fixed, dehydrated, and paraffin-embedded before being stained with hematoxylin and eosin (H&E). The stained sections were observed under 200× magnification (Olympus, Tokyo, Japan) to examine the morphology of the hepatocyte and the unstained fatty vacuole area.
2.7
Enzyme activity, biochemical assays and real-time fluorescence quantitative polymerasechain reaction (RT-qPCR) analyses
Commercial kits were used to determine the levels of TP, TG, T-CHO, AST, ALT, MDA, TBA, SOD and CAT (Chiu et al., 2018). The reagent configuration, sample pre-treatment and determination steps were carried out according to the instructions of the kit manufacturers. The concentration of total RNA in all samples was diluted to 1000 ng/μL. Reverse transcription was performed using the Evo M-MLV kit with gDNA Eraser (Accurate Biotechnology (Hunan) Co., Ltd, Hunan, China). The RT-qPCR assay was carried out using SYBR Green Pro Taq HS (Accurate Biotechnology) on the qTower3G IVD system (Jena, Germany). Primers were designed based on the full-length sequence of the pearl gentian grouper transcriptome. The relative expression of genes was calculated using the 2−ΔΔCt method with 18s rRNA (18s) and β-actin as reference genes (Xu et al., 2022c). The following genes were tested in the present study (Table S2): lipogenesis (acetyl-CoA carboxylase [acc], fatty acid synthase [fas]); lipolysis (adipose triglyceride lipase [atgl], carnitine palmitoyltransferase 1 [cpt1], hormone-sensitive lipase [hsl]); transcriptional factors (liver X receptor alpha [lxr], peroxisome proliferator-activated receptor alpha [pparα], srebp1); keap1/nrf2 pathway (keap1, nrf2, heme oxygenase-1 [ho-1]); pro-inflammatory cytokines (interleukin 1β [il1β] and tumor necrosis factor-alpha [tnfα]); BA synthesis (cholesterol 7α-hydroxylase [cpy7a1], sterol-27-hydroxylase [cyp27a1]); BA transport (bile salt export pump [bsep], multidrug resistance protein 3 [mdr3]); BA reabsorption (apical sodium-dependent BA transporter [asbt], multidrug resistance-associated protein 3 [mrp3]; BA recycling (organic anion transporters 1 [oatp1], microsomal epoxide hydrolase [meh]); BA receptors (farnesoid X receptor [fxr] and G protein-coupled bile acid receptor 1 [tgr5]).
2.8
Western blot analyses
The Western blot analysis was conducted following the protocol outlined in our recent study (Xu et al., 2022c). Three replicate wells were made for each protein sample. In detail, liver samples were lysed in radio immunoprecipitation assay lysis buffer (RIPA) buffer to extract protein. The protein was denatured in a boiling water bath and electrophoretically separated on a 10% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) gel containing. The separated proteins were then transferred to a polyvinylidene fluoride (PVDF) membrane. The membrane was incubated with primary antibodies: srebp1 (1:1000), nrf2 (1:500), keap1 (1:1500), and GAPDH (1:1000). Horseradish peroxidase (HRP)-conjugated anti-rabbit secondary antibodies were used to detect the probes. The target protein bands were visualized using enhanced chemiluminescence (ECL) reagent (Billerica, MA, USA) and quantified using Image J software (version 1.42, National Institutes of Health, USA).
2.9
Untargeted metabolomics analyses
The untargeted metabolomics analyses were performed by Suzhou BioNovo Gene Company. The distal intestine was taken and weighed accurately. Tissue extracts were added, thoroughly ground, centrifuged, and dried. The samples were then re-dissolved by adding 2-chloro-l-phenylalanine (dissolved in an acetonitrile solution). The supernatant was filtered and transferred to a detection vial for LC-MS analysis. The Vanquish UHPLC system (Thermo Fisher Scientific, USA) equipped with an ACQUITY UPLC HSS T3 (2.1 mm × 100 mm, 1.8 μm) (Waters, Milford, MA, USA) was used for chromatographic separations. The eluents employed were 0.1% formic acid acetonitrile (vol/vol) (eluent B2) and acetonitrile solution (eluent B3). The elution gradients were carried out as follows: 0 to 1 min, 8% B; 1 to 8 min, 8% to 98% B; 8 to 10 min, 98% B; 10 to 10.1 min, 98% to 8% B; 10.1 to 12 min, 8% B. The LC-ESI (+)-MS analysis elution solution was B2, while the LC-ESI (−)-MS analysis was B3. The parameters for the ESI Ion Source mass spectrometry of the Orbitrap Exploris 120 (Thermo Fisher Scientific, USA) were set as follows: sheath gas pressure = 40 arb; auxiliary gas flow = 10 arb; spray voltage: ESI (+) was set to 3.50 kV, while ESI (−) was set to −2.50 kV. The capillary temperature was maintained at 325 °C. The MS1 range was set to m/z 100 to 1000 with a resolution of 60,000 FWHM. The number of correlation scans per cycle of data was 4. The MS/MS resolution was set to 15,000 FWHM, with a normalised collision energy of 30%. The dynamic exclusion time was set to automatic. The metabolomics raw data (accession number: MTBLS9403) have been deposited in the MetaboLights database.
For metabolomics analysis, raw Triple-TOF/MS data were processed using Progenesis QI 2.0 (Waters, MA, USA) for nonlinear alignment and normalization. The software automatically handled deconvolution of the total ion chromatogram, data normalization, peak picking, ion peak alignment, and extraction of ion chromatograms using default settings. It assessed all runs for automatic alignment and peak picking with a default threshold of 3. Metabolite annotation involved matching MS and MS/MS data against the METLIN and HMDB databases with a mass error tolerance of 5 mg/L. Chemical similarity enrichment analysis (ChemRICH) was employed for statistical enrichment analysis based on chemical similarity. Subsequently, we normalized the peak areas of the BA metabolites and expressed them as the relative contents of BA. These values were then subjected to further analysis and comparison in subsequent steps.
2.10
16S rRNA of microbial samples
The total genome DNA from bacteria in the intestinal content of groupers was extracted using a TIANamp Marine Animals DNA Kit (TIANGEN, Beijing, China) as per the manufacturer's protocol. The sample size was 3 for the CD, HLD and HLPD groups, while 4 for the HPD group. The V3–V4 regions of the 16S rRNA genes were amplified using primers 338-forward (5′-ACTCCTACGGGAGGCAGCA-3′) and 806-reverse (5′-GGCTACHVGGGTWTCTAAT-3′). Sequencing libraries were prepared with the TruSeq DNA PCR-Free Kit (Illumina, California, USA), incorporating index barcodes, and sequenced on an Illumina NovaSeq platform to produce 250 bp paired-end reads. These were merged with FLASH (v1.2.7) to form raw tags. Qualified reads were clustered into amplicon sequence variants (ASV) at 97% similarity using UPARSE (v 7.1), and taxonomic information was annotated against the Silva Database via the Mothur algorithm. To study the phylogenetic relationship of different ASV, and the differences in terms of dominant species in different groups, multiple sequence alignments were conducted using the MUSCLE software (v 3.8.31). ASV abundance information was normalized using a standard of the sequence number corresponding to the sample with the fewest sequences. The α diversity indices were computed using QIIME2 (2019.4) and displayed using R software (v 2.15.3). Alpha diversity was estimated using various metrics, such as Shannon index, Simpson index, Chao1 index, Observed-species, Faith-pd, Pielou-e, and Goods-coverage. Unweighted Pair Group Method with Arithmetic mean (UPGMA) trees (Jaccard distance) were created. Beta diversity was calculated using weighted and unweighted UniFrac and principal coordinate analysis (PCoA). In addition, linear discriminant analysis (LDA) effect size (LEfSe) algorithm was performed combining Kruskal–Wallis test or Wilcoxon rank-sum test with LDA scores to estimate the effect size of differentially abundant features with biological consistency and statistical significance. Differentially expressed genes (DEG) were identified between libraries at a false discovery rate (FDR) threshold of less than 0.05. Subsequently, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed on these DEG. The microbial sequences obtained in this study are available in NCBI's SRA database under accession number PRJNA1065578.
2.11
Statistical analyses
The results were presented as mean ± standard deviation (SD). Normality and homogeneity of variance were tested using the Shapiro–Wilk and Levene tests, respectively. Data were evaluated using one-way ANOVA and two-way ANOVA. Multiple comparisons of means were performed using Duncan's multiple range tests when significant differences were found between groups. A significance level of 0.05 was used. The statistical analysis was performed using SPSS 23.0 (IBM, Armonk, NY, USA).
The mathematical model for one-way ANOVA can be expressed as:Yij = μ + αi + ϵi,where Yij is the j-th observation under the i-th level; μ is the overall mean, representing the grand mean of all observations; αi is the main effect of the i-th level, indicating the influence of that level on the dependent variable; ϵij is the random error term, representing the influence of other random factors beyond the independent variable on the dependent variable.
The mathematical model for two-way ANOVA can be expressed as:Yijk = μ + αi + βj + (αβ)ij + ϵijk,where two factors of lipid level and plant-protein level, labeled as L and HP, each with different denoted as L1, L2, …, Lj for factor L and HP1, HP2, …, HPk for factor HP; Yijk is the value of the dependent variable for the k-th observation at the j-th level of factor HP within the i-th level of factor L; αi is the main effect of the i-th level of factor L, indicating the influence of factor L on the dependent variable; βj is the main effect of the j-th level of factor HP, indicating the influence of factor HP on the dependent variable; (αβ)ij is the interaction effect between factor L and factor HP, representing the combined influence of both factors on the dependent variable; ϵijk is the random error term, representing the influence of other random factors beyond factors L and HP.
3
Results
3.1
Dietary high lipid/plant-protein impaired the growth performance and affected the lipid deposition of groupers
The HLD and HPD significantly decreased the FBW, WGR, SGR and FI in comparison to the CD group (P ＜ 0.05) (Table 2). In addition, compared to the HLD or HPD groups, the HLPD significantly decreased the FBW, WGR, SGR and FI (P ＜ 0.05). Thus, the HLD and HPD impaired growth performance, and their combined effects (HLPD) further undermined it.Table 2The growth performance and lipid deposition in groupers1.Table 2ItemDiet2P-value3CDHLDHPDHLPDLHPL × HPIBW, g15.21 ± 0.02715.21 ± 0.02715.24 ± 0.02315.20 ± 0.023–––FBW, g84.70 ± 2.633c80.39 ± 2.054b76.78 ± 1.656b71.28 ± 2.418a0.005＜0.0010.065WGR, %456.70 ± 15.857c428.41 ± 11.976b403.79 ± 9.745b368.96 ± 17.047a0.004＜0.0010.696SGR, %/d3.07 ± 0.051c2.97 ± 0.041b2.89 ± 0.034b2.76 ± 0.066a0.005＜0.0010.550FI, %/fish2.06 ± 0.004c1.97 ± 0.027b2.01 ± 0.019b1.91 ± 0.022a＜0.0010.0010.792TG in liver, mmol/g prot0.37 ± 0.012a0.54 ± 0.064b0.30 ± 0.047a0.52 ± 0.064b＜0.0010.1450.435T-CHO in liver, mmol/g prot0.06 ± 0.000b0.07 ± 0.002c0.05 ± 0.003b0.04 ± 0.005a0.029＜0.0010.024TG in serum, mmol/L1.88 ± 0.113ab3.28 ± 0.338c1.65 ± 0.029a2.25 ± 0.126b＜0.001＜0.0010.006T-CHO in serum, mmol/L1.37 ± 0.010b1.67 ± 0.090c1.07 ± 0.025a1.07 ± 0.053a0.005＜0.0010.065IBW = initial body weight; FBW = final body weight; WGR = weight gain rate; SGR = specific growth rate; FI = feed intake; HSI = hepatosomatic index; TG = triglyceride; T-CHO = total cholesterol.a-c Different letters indicate significant differences between groups (P < 0.05).1Values are presented as means ± standard deviation (SD).2CD means control diet, 46.21% crude protein, 9.48% crude lipid; HLD means high lipid diet, 46.37% crude protein, 16.70% crude lipid; HPD means high plant-protein diet, 46.50% crude protein, 9.38% crude lipid; HLPD means high lipid-high plant-protein diet, 46.54% crude protein, 16.67% crude lipid.3L means lipid level; HP means plant-protein level; L × HP means lipids and plant-protein interacted level.
Furthermore, compared to the CD group, the content of T-CHO and TG in liver and serum were significantly increased in the HLD group, whereas the content of T-CHO in serum was significantly decreased in the HPD group (P ＜ 0.05) (Table 2). In addition, compared to the HPD group, the content of T-CHO in liver was significantly decreased, and the TG in liver and serum were significantly increased in the HLPD group (P ＜ 0.05).
The HLD significantly increased the levels of crude lipid in whole-body, muscle and liver, compared with the CD group (P ＜ 0.05) (Table 3). The HPD did not significantly alter the levels of crude lipid in the whole-body, muscle and liver compared with the CD group (P ＞ 0.05) (Table 3). When compared to the HLD/HPD group, the crude lipid in whole-body, muscle and liver were significantly decreased/increased in the HLPD group (P ＜ 0.05）.Table 3Average proximate composition of the whole-body, muscle, and liver in groupers (% of wet matter)1.Table 3ItemDiet2P-value3CDHLDHPDHLPDLHPL × HPWhole-bodyMoisture77.16 ± 0.061d71.73 ± 0.068a75.85 ± 0.095c73.93 ± 0.085b＜0.001＜0.001＜0.001Crude protein11.97 ± 0.286a14.70 ± 0.205c13.86 ± 0.142b14.40 ± 0.190c＜0.001＜0.001＜0.001Crude lipid6.21 ± 0.325a9.52 ± 0.177c6.01 ± 0.306a7.47 ± 0.292b＜0.001＜0.001＜0.001MuscleMoisture80.59 ± 0.332b79.42 ± 0.184a80.51 ± 0.277b80.75 ± 0.110b0.0100.0020.001Crude protein16.12 ± 0.61816.73 ± 0.15416.38 ± 0.54416.29 ± 0.8330.4720.7860.336Crude lipid1.84 ± 0.070ab2.49 ± 0.046c1.76 ± 0.115a2.03 ± 0.162b＜0.0010.0020.013LiverMoisture68.08 ± 0.173b67.46 ± 0.136a68.25 ± 0.125b68.31 ± 0.156b0.010＜0.0010.004Crude lipid4.37 ± 0.151a5.69 ± 0.287c4.17 ± 0.146a4.93 ± 0.120b＜0.0010.0020.032a-d Different letters indicate significant differences between groups (P < 0.05).1Values are presented as means ± standard deviation (SD).2CD means control diet, 46.21% crude protein, 9.48% crude lipid; HLD means high lipid diet, 46.37% crude protein, 16.70% crude lipid; HPD means high plant-protein diet, 46.50% crude protein, 9.38% crude lipid; HLPD means high lipid-high plant-protein diet, 46.54% crude protein, 16.67% crude lipid.3L means lipid level; HP means plant-protein level; L × HP means lipids and plant-protein interacted level.
3.2
Dietary high lipid/plant-protein impaired the liver health of grouper
The fish fed CD exhibited normal morphology, with most hepatocytes having regular round nuclei (Fig. 1A). However, hepatocyte swelling with large numbers of diffuse lipid vacuoles was more common, accompanied by lysis of some hepatocyte nuclei in the HLD group. In the HPD and HLPD group, hepatocyte nuclear abnormalities, including deviation and cleavage, were more common. Moreover, the HLD significantly increased the activities of ALT and AST in serum compared to the CD group (P ＜ 0.05). Likewise, the HPD resulted in a significant increase in the activity of ALT in serum (P = 0.001) (Table 4). The HLPD significantly decreased the activity of AST in serum compared to the HLD group (P = 0.031).Fig. 1The liver health in response to dietary high lipid and high plant-protein in groupers. (A) Photomicrographs of representative hematoxylin and eosin-stained histological liver sections in 200× magnification. (B) The mRNA relative expression of inflammatory cytokines. (C) The Western blot analysis, three replicate wells were made for each protein sample. (D-E) The relative quantification of protein levels. CD means control diet, 46.21% crude protein, 9.48% crude lipid; HLD means high lipid diet, 46.37% crude protein, 16.70% crude lipid; HPD means high plant-protein diet, 46.50% crude protein, 9.38% crude lipid; HLPD means high lipid-high plant-protein diet, 46.54% crude protein, 16.67% crude lipid; L means lipid level; HP means plant-protein level; L × HP means lipids and plant-protein interacted level. Arrows indicate examples of a: normal hepatocytes with a regular, round nucleus; b: abnormal nucleus; c: swollen hepatocytes with large diffused lipid vacuoles; d: absent nucleus. Values are presented as means ± error bars (SD). tnfα = tumor necrosis factor-alpha; il1β = interleukin 1β; KEAP1 = Kelch-like erythroid cell-derived protein 1; NRF2 = nuclear factor (erythroid-derived 2)-like 2; GADPH = glyceraldehyde-phosphate dehydrogenase. a-c Different letters indicate significant differences between groups (P < 0.05).Fig. 1Table 4The levels of markers of liver impairment in grouper1.Table 4ItemDiet2P-value3CDHLDHPDHLPDLHPL × HPALT in serum, U/L55.36 ± 3.008a79.48 ± 5.318b68.92 ± 1.905b77.47 ± 1.133b0.1130.0010.043AST in serum, U/L21.59 ± 0.448a25.72 ± 0.574b22.66 ± 1.607a22.01 ± 0.453a0.1850.0920.031CAT in liver, U/mg prot12.10 ± 0.197c6.64 ± 0.629a8.19 ± 0.404b9.07 ± 0.330b0.1150.001＜0.001SOD in liver, U/mg prot29.41 ± 1.315b23.08 ± 1.625a23.25 ± 0.815a34.66 ± 1.504c0.0790.097＜0.001MDA in liver, nmol/mg prot1.06 ± 0.0251.03 ± 0.0621.12 ± 0.1091.03 ± 0.1070.7580.4710.737ALT = alanine aminotransferase; AST = aspartate aminotransferase; CAT = catalase; SOD = superoxide dismutase; MDA = malondialdehyde.a-c Different letters indicate significant differences between groups (P < 0.05).1Values are presented as means ± standard deviation (SD).2CD means control diet, 46.21% crude protein, 9.48% crude lipid; HLD means high lipid diet, 46.37% crude protein, 16.70% crude lipid; HPD means high plant-protein diet, 46.50% crude protein, 9.38% crude lipid; HLPD means high lipid-high plant-protein diet, 46.54% crude protein, 16.67% crude lipid.3L means lipid level; HP means plant-protein level; L × HP means lipids and plant-protein interacted level.
In liver, the mRNA relative expression of il1β and tnfα was significantly increased in the HLD and HPD groups, compared to the CD group (P ＜ 0.05) (Fig. 1B). Compared to the HLD or HPD groups, the mRNA relative expression of il1β was significantly decreased, and the mRNA relative expression of tnfα was significantly increased in the HLPD group (P ＜ 0.05). Furthermore, compared to the CD group, the activities of CAT and SOD in liver were significantly decreased in the HLD and HPD groups (P ＜ 0.05) (Table 4). Compared to the HPD group, the activity of SOD was significantly increased in the HLPD group (P ＜ 0.001). Meanwhile, compared to the CD group, the expression of NRF2 was significantly increased in the HLD group, while the expression of KEAP1 and NRF2 was significantly increased in liver of the HPD group (P ＜ 0.05) (Fig. 1D–E). Compared to the HLD/HPD groups, the HLPD treatment significantly increased/decreased the expression of KEAP1/NRF2 in liver (P ＜ 0.05).
3.3
Dietary high lipid/plant-protein altered the intestinal microbiota of grouper
For alpha diversity (Fig. 2A–G), compared to the CD group, the HLD treatment significantly increased the Pielou_e and Goods_coverage indexse (P ＜ 0.05); the HPD treatment significantly increased the Simpson, Shannon and Pielou_e indexse (P ＜ 0.05); the HLPD treatment significantly increased the Simpson, Shannon, Pielou_e, and Goods_coverage indexse (P ＜ 0.05). Furthermore, there was a statistically significant effect on the clustering of these communities (P = 0.001, Jaccard-based Adonis), and 29.15% of this variance was accounted for by the difference among groups (PERMANOVA R2 = 0.890918) (Table S2).Fig. 2The structure of gut microbiotas in grouper. (A-G) The alpha diversity of the intestinal microbiota. (H) The Unweighted Pair Group Method with Arithmetic mean (UPGMA) tree with Jaccard distances. (I-L) The proportion of microbiotas at phylum levels. CD means control diet, 46.21% crude protein, 9.48% crude lipid; HLD means high lipid diet, 46.37% crude protein, 16.70% crude lipid; HPD means high plant-protein diet, 46.50% crude protein, 9.38% crude lipid; HLPD means high lipid-high plant-protein diet, 46.54% crude protein, 16.67% crude lipid; L means lipid level; HP means plant-protein level; L × HP means lipids and plant-protein interacted level. Values are presented as means ± error bars (SD). F/B = the ratio of Firmicutes/Bacteroidetes. a,b Different letters indicate significant differences between groups (P < 0.05).Fig. 2
Un-weighted UniFrac based PCoA plots revealed that the samples from the CD and HLD groups were distributed together and were not well differentiated (Fig. 3A). Whereas the samples of the HPD group were distributed independently from those of the CD group along the direction of Axis1, there was an independent distribution of the samples of the HPLD group from the CD group along both Axis1 and Axis2. UPGMA analysis based on the Jaccard similarity revealed that the samples in the CD, HLD and HPD groups were broadly distinguishable (Fig. 2H). Meanwhile, the samples in the HLPD group had some distributional randomness among the HLD and HPD groups, which further confirmed the synergistic effects (HLD and HPD) on microbiota structure in the HLPD group. The LEfSe algorithm revealed that the main biomarkers (LDA score > 4.0) of microbiota in the CD group were: phylum Firmicutes, class Bacilli, species Lactobacillus_delbrueckii, genera Lactobacillus, order Lactobacillales, families Lactobacillaceae; whereas in the HPD group it was the genera Bosea, species Bosea_genosp, families Bradyrhizobiaceae (Fig. 3B).Fig. 3The beta diversity and LEfSe algorithm of gut microbiota. (A) The unweighted UniFrac based principal coordinate analysis (PCoA) plots. (B) The linear discriminatory analysis (LDA) effect size (LEfSe). CD means control diet, 46.21% crude protein, 9.48% crude lipid; HLD means high lipid diet, 46.37% crude protein, 16.70% crude lipid; HPD means high plant-protein diet, 46.50% crude protein, 9.38% crude lipid; HLPD means high lipid-high plant-protein diet, 46.54% crude protein, 16.67% crude lipid.Fig. 3
Compared to the CD group, the HLPD significantly increased the relative abundance of Proteobacteria and decreased that of Firmicutes (P ＜ 0.05) (Fig. 2I-L). By two-way ANOVA, the dietary lipid significantly affected the relative abundance of Bosea and Chryseobacterium (P ＜ 0.05), which might be the sensitive microorganisms to lipid levels (Fig. 4). The dietary plant-protein significantly affected the relative abundance of unclassified_Peptostreptococcaceae, Lactobacillus, Lactococcus, Staphylococcus, Clostridium, and Bosea (P ＜ 0.05), which might be the sensitive microorganisms to plant-protein levels. Meanwhile, the relative abundance of Streptococcus, Lactobacillus and Bosea, were affected by the interaction of dietary lipid and plant-protein (P < 0.05) (Fig. 4).Fig. 4The proportion of genus of gut microbiotas in groupers. (A) Unclassified_Peptostreptococcaceae. (B) Streptococcus. (C) Lactobacillus. (D) Lactococcus. (E) Staphylococcus. (F) Clostridium. (G) Bosea. (H) Chryseobacterium. CD means control diet, 46.21% crude protein, 9.48% crude lipid; HLD means high lipid diet, 46.37% crude protein, 16.70% crude lipid; HPD means high plant-protein diet, 46.50% crude protein, 9.38% crude lipid; HLPD means high lipid-high plant-protein diet, 46.54% crude protein, 16.67% crude lipid; L means lipid level; HP means plant-protein level; L × HP means lipids and plant-protein interacted level. Values are presented as means ± error bars (SD). a,b Different letters indicate significant differences between groups (P < 0.05).Fig. 4
3.4
Dietary high lipid/plant-protein impacted the metabolomics of the intestine of groupers
The hierarchical clustering dendrograms roughly separated samples from the CD and HLD groups, while samples from the HPD and HLPD groups were mixed together in both positive and negative ion patterns (Fig. S1A-B). Meanwhile, the results of principal component analysis (PCA) indicated that the metabolite structures of the samples from the CD, HLD, and HPD groups were mostly separable along the axial direction with a slight overlap. However, the metabolite structures of the samples from the HLPD and HPD groups showed a large overlap (Fig. S2 and S3). In addition, compared to the CD group, there were 87 differential expressions of metabolites (DEM) in the HLD group, while 76 DEM in the HPD group (Fig. S3C). Compared to the HLD group, there were 67 DEM in HLPD group. Compared to the HPD group, there were 22 DEM in the HLPD group (Fig. S1C).
In the identified 14 kinds of BA (Table S3), dietary lipid significantly affected the levels of allocholic acid (ACA), gly-chenodeoxycholic acid (GCDCA) and taurohyocholate (THCA), while the plant-protein significantly affected the levels of ACA, GCDCA and 3b-Hydroxy-5-Cholenoic acid (P < 0.05). In detail, compared to the CD group, the levels of ACA and GCDCA were significantly decreased in the HLD group (P < 0.05). Compared to the CD group, the levels of ACA, GCDCA and 3b-Hydroxy-5-Cholenoic acid were significantly decreased in the HPD group, while the levels of taurocholic acid (TCA) were significantly increased (P < 0.05). In addition, compared to the HLD group, the HLPD treatment significantly increased the level of deoxycholic acid (DCA). Conclusively, these results demonstrated that HLD and HPD mainly affected the levels of ACA, GCDCA, THCA, TCA and DCA.
To further determine the association between changes in the profiles of intestinal BA and the abundance of microorganisms sensitive to high lipid or plant-protein levels, spearman correlation analysis was conducted (Fig. 5). The results showed that the level of ACA was positively correlated with the relative abundances of unclassified_Peptostreptococcaceae and Clostridium (P < 0.01), and negatively correlated with the relative abundances of Lactobacillus and Lactococcus (P < 0.05); the level of TCA was positively correlated with the relative abundances of Streptococcus and Lactococcus (P < 0.05); the level of GCDCA was positively correlated with the relative abundances of unclassified_Peptostreptococcaceae and Clostridium (P < 0.01); the level of THCA was positively correlated with the relative abundance of Streptococcus (P < 0.05), and negatively correlated with the relative abundances of unclassified_Peptostreptococcaceae and Clostridium (P < 0.01). These results showed that HLD and HPD could induce the variations in BA profiles by altering the unclassified_Peptostreptococcaceae, Streptococcus, Lactobacillus, Lactococcus, Staphylococcus and Clostridium.Fig. 5Spearman's correlation analysis of BA profiles and representative genus in grouper. BA = bile acids. ∗ P < 0.05; ∗∗ P < 0.01.Fig. 5
3.5
Dietary high lipid/plant-protein affected the lipid metabolism of groupers
All high lipid or plant-protein treatments effectively suppressed the body's BA level (BA pool), as revealed by the significantly lower levels of TBA in the intestine, liver, serum in HLD, HPD and HLPD groups (P ＜ 0.05) (Table 5), which further confirmed the alterations of BA profiles in metabolomics. Furthermore, compared to the CD group, the mRNA relative expression of cyp7a1, bsep and mdr3 was significantly increased in the HLD group, whereas the mRNA relative expression of cyp27a1, meh, asbt, mrp3, fxr and tgr5 was significantly decreased (P ＜ 0.05) (Fig. 6). Compared to the CD group, the mRNA relative expression of mdr3, asbt, mrp3, oatp1, meh, fxr and tgr5 was significantly decreased in the HPD group (P ＜ 0.05). Compared to the HPD group, the mRNA relative expression of cyp27a1, bsep, mdr3 and tgr5 was significantly increased in the HLPD group, while the mRNA relative expression of asbt was significantly decreased (P ＜ 0.05).Table 5The concentrations of TBA in the intestine, liver and serum tissues1.Table 5ItemDiet2P-value3CDHLDHPDHLPDLHPL × HPIntestine, μmol/mg prot114.70 ± 9.291b72.85 ± 13.921a82.84 ± 9.092a81.16 ± 7.653a0.0060.0820.010Liver, μmol/mg prot67.62 ± 5.689b45.89 ± 2.924a52.79 ± 5.838a39.36 ± 8.217a0.0010.0150.263Serum, μmol/L65.34 ± 5.251b46.90 ± 0.782a53.22 ± 4.136a46.17 ± 2.071a0.1050.0070.144TBA= total bile acids.a,b Different letters indicate significant differences between groups (P < 0.05).1Values are presented as means ± standard deviation (SD).2CD means control diet, 46.21% crude protein, 9.48% crude lipid; HLD means high lipid diet, 46.37% crude protein, 16.70% crude lipid; HPD means high plant-protein diet, 46.50% crude protein, 9.38% crude lipid; HLPD means high lipid-high plant-protein diet, 46.54% crude protein, 16.67% crude lipid.3L means lipid level; HP means plant-protein level; L × HP means lipids and plant-protein interacted level.Fig. 6The mRNA relative expression of BA enterohepatic circulation genes in grouper. cpy7a1 = cholesterol 7α-hydroxylase; cyp27a1 = sterol-27-hydroxylase; bsep = bile salt export pump; mdr3 = multidrug resistance protein 3; asbt = apical sodium-dependent BAs transporter; mrp3 = multidrug resistance-associated protein 3; oatp1 = organic anion transporters 1; meh = microsomal epoxide hydrolase; fxr = farnesoid X receptor; tgr = G protein-coupled bile acid receptor. CD means control diet, 46.21% crude protein, 9.48% crude lipid; HLD means high lipid diet, 46.37% crude protein, 16.70% crude lipid; HPD means high plant-protein diet, 46.50% crude protein, 9.38% crude lipid; HLPD means high lipid-high plant-protein diet, 46.54% crude protein, 16.67% crude lipid; L means lipid level; HP means plant-protein level; L × HP means lipids and plant-protein interacted level. Values are presented as means ± error bars (SD). a-c Different letters indicate significant differences between groups (P < 0.05).Fig. 6
Compared to the CD group, the mRNA relative expression of acc, fas, lxr, pparα and srebp1 was significantly increased in HLD group (P ＜ 0.05), while the mRNA relative expression of atgl and hsl was significantly decreased (P ＜ 0.05) (Fig. 7A). Compared to the CD group, the mRNA relative expression of atgl, hsl and pparα was significantly increased in the HPD group, while the mRNA relative expression of lxr and srebp1 was significantly decreased (P ＜ 0.05). Compared to the HPD group, the mRNA relative expression of acc, fas, lxr and srebp1 was significantly increased in the HLPD group, while the mRNA relative expression of atgl and hsl was significantly decreased (P ＜ 0.05). In addition, compared to the CD group, the expression of SREBP1 was significantly increased in the HLD and HPD groups, and compared to the HLD group, the HLPD treatment significantly increased the mRNA relative expression of SREBP1 (P ＜ 0.05; Fig. 7B-C).Fig. 7The lipid metabolism in response to dietary high lipid and high plant-protein in grouper. (A) The mRNA relative expression of lipid metabolism genes. (B) The Western blot analysis, three replicate wells were made for each protein sample. (C) The relative quantification of protein levels. acc = acetyl-CoA carboxylase; fas = fatty acid synthase; atgl = adipose triglyceride lipase; cpt1 = carnitine palmitoyltransferase 1; hsl = hormone-sensitive lipase; lxr = liver X receptor alpha; pparα = peroxisome proliferator-activated receptor alpha; srebp1 = sterol responsive element binding protein 1; GADPH = glyceraldehyde-phosphate dehydrogenase. CD means control diet, 46.21% crude protein, 9.48% crude lipid; HLD means high lipid diet, 46.37% crude protein, 16.70% crude lipid; HPD means high plant-protein diet, 46.50% crude protein, 9.38% crude lipid; HLPD means high lipid-high plant-protein diet, 46.54% crude protein, 16.67% crude lipid; L means lipid level; HP means plant-protein level; L × HP means lipids and plant-protein interacted level. Values are presented as means ± error bars (SD). a-d Different letters indicate significant differences between groups (P < 0.05).Fig. 7
3.1
Dietary high lipid/plant-protein impaired the growth performance and affected the lipid deposition of groupers
The HLD and HPD significantly decreased the FBW, WGR, SGR and FI in comparison to the CD group (P ＜ 0.05) (Table 2). In addition, compared to the HLD or HPD groups, the HLPD significantly decreased the FBW, WGR, SGR and FI (P ＜ 0.05). Thus, the HLD and HPD impaired growth performance, and their combined effects (HLPD) further undermined it.Table 2The growth performance and lipid deposition in groupers1.Table 2ItemDiet2P-value3CDHLDHPDHLPDLHPL × HPIBW, g15.21 ± 0.02715.21 ± 0.02715.24 ± 0.02315.20 ± 0.023–––FBW, g84.70 ± 2.633c80.39 ± 2.054b76.78 ± 1.656b71.28 ± 2.418a0.005＜0.0010.065WGR, %456.70 ± 15.857c428.41 ± 11.976b403.79 ± 9.745b368.96 ± 17.047a0.004＜0.0010.696SGR, %/d3.07 ± 0.051c2.97 ± 0.041b2.89 ± 0.034b2.76 ± 0.066a0.005＜0.0010.550FI, %/fish2.06 ± 0.004c1.97 ± 0.027b2.01 ± 0.019b1.91 ± 0.022a＜0.0010.0010.792TG in liver, mmol/g prot0.37 ± 0.012a0.54 ± 0.064b0.30 ± 0.047a0.52 ± 0.064b＜0.0010.1450.435T-CHO in liver, mmol/g prot0.06 ± 0.000b0.07 ± 0.002c0.05 ± 0.003b0.04 ± 0.005a0.029＜0.0010.024TG in serum, mmol/L1.88 ± 0.113ab3.28 ± 0.338c1.65 ± 0.029a2.25 ± 0.126b＜0.001＜0.0010.006T-CHO in serum, mmol/L1.37 ± 0.010b1.67 ± 0.090c1.07 ± 0.025a1.07 ± 0.053a0.005＜0.0010.065IBW = initial body weight; FBW = final body weight; WGR = weight gain rate; SGR = specific growth rate; FI = feed intake; HSI = hepatosomatic index; TG = triglyceride; T-CHO = total cholesterol.a-c Different letters indicate significant differences between groups (P < 0.05).1Values are presented as means ± standard deviation (SD).2CD means control diet, 46.21% crude protein, 9.48% crude lipid; HLD means high lipid diet, 46.37% crude protein, 16.70% crude lipid; HPD means high plant-protein diet, 46.50% crude protein, 9.38% crude lipid; HLPD means high lipid-high plant-protein diet, 46.54% crude protein, 16.67% crude lipid.3L means lipid level; HP means plant-protein level; L × HP means lipids and plant-protein interacted level.
Furthermore, compared to the CD group, the content of T-CHO and TG in liver and serum were significantly increased in the HLD group, whereas the content of T-CHO in serum was significantly decreased in the HPD group (P ＜ 0.05) (Table 2). In addition, compared to the HPD group, the content of T-CHO in liver was significantly decreased, and the TG in liver and serum were significantly increased in the HLPD group (P ＜ 0.05).
The HLD significantly increased the levels of crude lipid in whole-body, muscle and liver, compared with the CD group (P ＜ 0.05) (Table 3). The HPD did not significantly alter the levels of crude lipid in the whole-body, muscle and liver compared with the CD group (P ＞ 0.05) (Table 3). When compared to the HLD/HPD group, the crude lipid in whole-body, muscle and liver were significantly decreased/increased in the HLPD group (P ＜ 0.05）.Table 3Average proximate composition of the whole-body, muscle, and liver in groupers (% of wet matter)1.Table 3ItemDiet2P-value3CDHLDHPDHLPDLHPL × HPWhole-bodyMoisture77.16 ± 0.061d71.73 ± 0.068a75.85 ± 0.095c73.93 ± 0.085b＜0.001＜0.001＜0.001Crude protein11.97 ± 0.286a14.70 ± 0.205c13.86 ± 0.142b14.40 ± 0.190c＜0.001＜0.001＜0.001Crude lipid6.21 ± 0.325a9.52 ± 0.177c6.01 ± 0.306a7.47 ± 0.292b＜0.001＜0.001＜0.001MuscleMoisture80.59 ± 0.332b79.42 ± 0.184a80.51 ± 0.277b80.75 ± 0.110b0.0100.0020.001Crude protein16.12 ± 0.61816.73 ± 0.15416.38 ± 0.54416.29 ± 0.8330.4720.7860.336Crude lipid1.84 ± 0.070ab2.49 ± 0.046c1.76 ± 0.115a2.03 ± 0.162b＜0.0010.0020.013LiverMoisture68.08 ± 0.173b67.46 ± 0.136a68.25 ± 0.125b68.31 ± 0.156b0.010＜0.0010.004Crude lipid4.37 ± 0.151a5.69 ± 0.287c4.17 ± 0.146a4.93 ± 0.120b＜0.0010.0020.032a-d Different letters indicate significant differences between groups (P < 0.05).1Values are presented as means ± standard deviation (SD).2CD means control diet, 46.21% crude protein, 9.48% crude lipid; HLD means high lipid diet, 46.37% crude protein, 16.70% crude lipid; HPD means high plant-protein diet, 46.50% crude protein, 9.38% crude lipid; HLPD means high lipid-high plant-protein diet, 46.54% crude protein, 16.67% crude lipid.3L means lipid level; HP means plant-protein level; L × HP means lipids and plant-protein interacted level.
3.2
Dietary high lipid/plant-protein impaired the liver health of grouper
The fish fed CD exhibited normal morphology, with most hepatocytes having regular round nuclei (Fig. 1A). However, hepatocyte swelling with large numbers of diffuse lipid vacuoles was more common, accompanied by lysis of some hepatocyte nuclei in the HLD group. In the HPD and HLPD group, hepatocyte nuclear abnormalities, including deviation and cleavage, were more common. Moreover, the HLD significantly increased the activities of ALT and AST in serum compared to the CD group (P ＜ 0.05). Likewise, the HPD resulted in a significant increase in the activity of ALT in serum (P = 0.001) (Table 4). The HLPD significantly decreased the activity of AST in serum compared to the HLD group (P = 0.031).Fig. 1The liver health in response to dietary high lipid and high plant-protein in groupers. (A) Photomicrographs of representative hematoxylin and eosin-stained histological liver sections in 200× magnification. (B) The mRNA relative expression of inflammatory cytokines. (C) The Western blot analysis, three replicate wells were made for each protein sample. (D-E) The relative quantification of protein levels. CD means control diet, 46.21% crude protein, 9.48% crude lipid; HLD means high lipid diet, 46.37% crude protein, 16.70% crude lipid; HPD means high plant-protein diet, 46.50% crude protein, 9.38% crude lipid; HLPD means high lipid-high plant-protein diet, 46.54% crude protein, 16.67% crude lipid; L means lipid level; HP means plant-protein level; L × HP means lipids and plant-protein interacted level. Arrows indicate examples of a: normal hepatocytes with a regular, round nucleus; b: abnormal nucleus; c: swollen hepatocytes with large diffused lipid vacuoles; d: absent nucleus. Values are presented as means ± error bars (SD). tnfα = tumor necrosis factor-alpha; il1β = interleukin 1β; KEAP1 = Kelch-like erythroid cell-derived protein 1; NRF2 = nuclear factor (erythroid-derived 2)-like 2; GADPH = glyceraldehyde-phosphate dehydrogenase. a-c Different letters indicate significant differences between groups (P < 0.05).Fig. 1Table 4The levels of markers of liver impairment in grouper1.Table 4ItemDiet2P-value3CDHLDHPDHLPDLHPL × HPALT in serum, U/L55.36 ± 3.008a79.48 ± 5.318b68.92 ± 1.905b77.47 ± 1.133b0.1130.0010.043AST in serum, U/L21.59 ± 0.448a25.72 ± 0.574b22.66 ± 1.607a22.01 ± 0.453a0.1850.0920.031CAT in liver, U/mg prot12.10 ± 0.197c6.64 ± 0.629a8.19 ± 0.404b9.07 ± 0.330b0.1150.001＜0.001SOD in liver, U/mg prot29.41 ± 1.315b23.08 ± 1.625a23.25 ± 0.815a34.66 ± 1.504c0.0790.097＜0.001MDA in liver, nmol/mg prot1.06 ± 0.0251.03 ± 0.0621.12 ± 0.1091.03 ± 0.1070.7580.4710.737ALT = alanine aminotransferase; AST = aspartate aminotransferase; CAT = catalase; SOD = superoxide dismutase; MDA = malondialdehyde.a-c Different letters indicate significant differences between groups (P < 0.05).1Values are presented as means ± standard deviation (SD).2CD means control diet, 46.21% crude protein, 9.48% crude lipid; HLD means high lipid diet, 46.37% crude protein, 16.70% crude lipid; HPD means high plant-protein diet, 46.50% crude protein, 9.38% crude lipid; HLPD means high lipid-high plant-protein diet, 46.54% crude protein, 16.67% crude lipid.3L means lipid level; HP means plant-protein level; L × HP means lipids and plant-protein interacted level.
In liver, the mRNA relative expression of il1β and tnfα was significantly increased in the HLD and HPD groups, compared to the CD group (P ＜ 0.05) (Fig. 1B). Compared to the HLD or HPD groups, the mRNA relative expression of il1β was significantly decreased, and the mRNA relative expression of tnfα was significantly increased in the HLPD group (P ＜ 0.05). Furthermore, compared to the CD group, the activities of CAT and SOD in liver were significantly decreased in the HLD and HPD groups (P ＜ 0.05) (Table 4). Compared to the HPD group, the activity of SOD was significantly increased in the HLPD group (P ＜ 0.001). Meanwhile, compared to the CD group, the expression of NRF2 was significantly increased in the HLD group, while the expression of KEAP1 and NRF2 was significantly increased in liver of the HPD group (P ＜ 0.05) (Fig. 1D–E). Compared to the HLD/HPD groups, the HLPD treatment significantly increased/decreased the expression of KEAP1/NRF2 in liver (P ＜ 0.05).
3.3
Dietary high lipid/plant-protein altered the intestinal microbiota of grouper
For alpha diversity (Fig. 2A–G), compared to the CD group, the HLD treatment significantly increased the Pielou_e and Goods_coverage indexse (P ＜ 0.05); the HPD treatment significantly increased the Simpson, Shannon and Pielou_e indexse (P ＜ 0.05); the HLPD treatment significantly increased the Simpson, Shannon, Pielou_e, and Goods_coverage indexse (P ＜ 0.05). Furthermore, there was a statistically significant effect on the clustering of these communities (P = 0.001, Jaccard-based Adonis), and 29.15% of this variance was accounted for by the difference among groups (PERMANOVA R2 = 0.890918) (Table S2).Fig. 2The structure of gut microbiotas in grouper. (A-G) The alpha diversity of the intestinal microbiota. (H) The Unweighted Pair Group Method with Arithmetic mean (UPGMA) tree with Jaccard distances. (I-L) The proportion of microbiotas at phylum levels. CD means control diet, 46.21% crude protein, 9.48% crude lipid; HLD means high lipid diet, 46.37% crude protein, 16.70% crude lipid; HPD means high plant-protein diet, 46.50% crude protein, 9.38% crude lipid; HLPD means high lipid-high plant-protein diet, 46.54% crude protein, 16.67% crude lipid; L means lipid level; HP means plant-protein level; L × HP means lipids and plant-protein interacted level. Values are presented as means ± error bars (SD). F/B = the ratio of Firmicutes/Bacteroidetes. a,b Different letters indicate significant differences between groups (P < 0.05).Fig. 2
Un-weighted UniFrac based PCoA plots revealed that the samples from the CD and HLD groups were distributed together and were not well differentiated (Fig. 3A). Whereas the samples of the HPD group were distributed independently from those of the CD group along the direction of Axis1, there was an independent distribution of the samples of the HPLD group from the CD group along both Axis1 and Axis2. UPGMA analysis based on the Jaccard similarity revealed that the samples in the CD, HLD and HPD groups were broadly distinguishable (Fig. 2H). Meanwhile, the samples in the HLPD group had some distributional randomness among the HLD and HPD groups, which further confirmed the synergistic effects (HLD and HPD) on microbiota structure in the HLPD group. The LEfSe algorithm revealed that the main biomarkers (LDA score > 4.0) of microbiota in the CD group were: phylum Firmicutes, class Bacilli, species Lactobacillus_delbrueckii, genera Lactobacillus, order Lactobacillales, families Lactobacillaceae; whereas in the HPD group it was the genera Bosea, species Bosea_genosp, families Bradyrhizobiaceae (Fig. 3B).Fig. 3The beta diversity and LEfSe algorithm of gut microbiota. (A) The unweighted UniFrac based principal coordinate analysis (PCoA) plots. (B) The linear discriminatory analysis (LDA) effect size (LEfSe). CD means control diet, 46.21% crude protein, 9.48% crude lipid; HLD means high lipid diet, 46.37% crude protein, 16.70% crude lipid; HPD means high plant-protein diet, 46.50% crude protein, 9.38% crude lipid; HLPD means high lipid-high plant-protein diet, 46.54% crude protein, 16.67% crude lipid.Fig. 3
Compared to the CD group, the HLPD significantly increased the relative abundance of Proteobacteria and decreased that of Firmicutes (P ＜ 0.05) (Fig. 2I-L). By two-way ANOVA, the dietary lipid significantly affected the relative abundance of Bosea and Chryseobacterium (P ＜ 0.05), which might be the sensitive microorganisms to lipid levels (Fig. 4). The dietary plant-protein significantly affected the relative abundance of unclassified_Peptostreptococcaceae, Lactobacillus, Lactococcus, Staphylococcus, Clostridium, and Bosea (P ＜ 0.05), which might be the sensitive microorganisms to plant-protein levels. Meanwhile, the relative abundance of Streptococcus, Lactobacillus and Bosea, were affected by the interaction of dietary lipid and plant-protein (P < 0.05) (Fig. 4).Fig. 4The proportion of genus of gut microbiotas in groupers. (A) Unclassified_Peptostreptococcaceae. (B) Streptococcus. (C) Lactobacillus. (D) Lactococcus. (E) Staphylococcus. (F) Clostridium. (G) Bosea. (H) Chryseobacterium. CD means control diet, 46.21% crude protein, 9.48% crude lipid; HLD means high lipid diet, 46.37% crude protein, 16.70% crude lipid; HPD means high plant-protein diet, 46.50% crude protein, 9.38% crude lipid; HLPD means high lipid-high plant-protein diet, 46.54% crude protein, 16.67% crude lipid; L means lipid level; HP means plant-protein level; L × HP means lipids and plant-protein interacted level. Values are presented as means ± error bars (SD). a,b Different letters indicate significant differences between groups (P < 0.05).Fig. 4
3.4
Dietary high lipid/plant-protein impacted the metabolomics of the intestine of groupers
The hierarchical clustering dendrograms roughly separated samples from the CD and HLD groups, while samples from the HPD and HLPD groups were mixed together in both positive and negative ion patterns (Fig. S1A-B). Meanwhile, the results of principal component analysis (PCA) indicated that the metabolite structures of the samples from the CD, HLD, and HPD groups were mostly separable along the axial direction with a slight overlap. However, the metabolite structures of the samples from the HLPD and HPD groups showed a large overlap (Fig. S2 and S3). In addition, compared to the CD group, there were 87 differential expressions of metabolites (DEM) in the HLD group, while 76 DEM in the HPD group (Fig. S3C). Compared to the HLD group, there were 67 DEM in HLPD group. Compared to the HPD group, there were 22 DEM in the HLPD group (Fig. S1C).
In the identified 14 kinds of BA (Table S3), dietary lipid significantly affected the levels of allocholic acid (ACA), gly-chenodeoxycholic acid (GCDCA) and taurohyocholate (THCA), while the plant-protein significantly affected the levels of ACA, GCDCA and 3b-Hydroxy-5-Cholenoic acid (P < 0.05). In detail, compared to the CD group, the levels of ACA and GCDCA were significantly decreased in the HLD group (P < 0.05). Compared to the CD group, the levels of ACA, GCDCA and 3b-Hydroxy-5-Cholenoic acid were significantly decreased in the HPD group, while the levels of taurocholic acid (TCA) were significantly increased (P < 0.05). In addition, compared to the HLD group, the HLPD treatment significantly increased the level of deoxycholic acid (DCA). Conclusively, these results demonstrated that HLD and HPD mainly affected the levels of ACA, GCDCA, THCA, TCA and DCA.
To further determine the association between changes in the profiles of intestinal BA and the abundance of microorganisms sensitive to high lipid or plant-protein levels, spearman correlation analysis was conducted (Fig. 5). The results showed that the level of ACA was positively correlated with the relative abundances of unclassified_Peptostreptococcaceae and Clostridium (P < 0.01), and negatively correlated with the relative abundances of Lactobacillus and Lactococcus (P < 0.05); the level of TCA was positively correlated with the relative abundances of Streptococcus and Lactococcus (P < 0.05); the level of GCDCA was positively correlated with the relative abundances of unclassified_Peptostreptococcaceae and Clostridium (P < 0.01); the level of THCA was positively correlated with the relative abundance of Streptococcus (P < 0.05), and negatively correlated with the relative abundances of unclassified_Peptostreptococcaceae and Clostridium (P < 0.01). These results showed that HLD and HPD could induce the variations in BA profiles by altering the unclassified_Peptostreptococcaceae, Streptococcus, Lactobacillus, Lactococcus, Staphylococcus and Clostridium.Fig. 5Spearman's correlation analysis of BA profiles and representative genus in grouper. BA = bile acids. ∗ P < 0.05; ∗∗ P < 0.01.Fig. 5
3.5
Dietary high lipid/plant-protein affected the lipid metabolism of groupers
All high lipid or plant-protein treatments effectively suppressed the body's BA level (BA pool), as revealed by the significantly lower levels of TBA in the intestine, liver, serum in HLD, HPD and HLPD groups (P ＜ 0.05) (Table 5), which further confirmed the alterations of BA profiles in metabolomics. Furthermore, compared to the CD group, the mRNA relative expression of cyp7a1, bsep and mdr3 was significantly increased in the HLD group, whereas the mRNA relative expression of cyp27a1, meh, asbt, mrp3, fxr and tgr5 was significantly decreased (P ＜ 0.05) (Fig. 6). Compared to the CD group, the mRNA relative expression of mdr3, asbt, mrp3, oatp1, meh, fxr and tgr5 was significantly decreased in the HPD group (P ＜ 0.05). Compared to the HPD group, the mRNA relative expression of cyp27a1, bsep, mdr3 and tgr5 was significantly increased in the HLPD group, while the mRNA relative expression of asbt was significantly decreased (P ＜ 0.05).Table 5The concentrations of TBA in the intestine, liver and serum tissues1.Table 5ItemDiet2P-value3CDHLDHPDHLPDLHPL × HPIntestine, μmol/mg prot114.70 ± 9.291b72.85 ± 13.921a82.84 ± 9.092a81.16 ± 7.653a0.0060.0820.010Liver, μmol/mg prot67.62 ± 5.689b45.89 ± 2.924a52.79 ± 5.838a39.36 ± 8.217a0.0010.0150.263Serum, μmol/L65.34 ± 5.251b46.90 ± 0.782a53.22 ± 4.136a46.17 ± 2.071a0.1050.0070.144TBA= total bile acids.a,b Different letters indicate significant differences between groups (P < 0.05).1Values are presented as means ± standard deviation (SD).2CD means control diet, 46.21% crude protein, 9.48% crude lipid; HLD means high lipid diet, 46.37% crude protein, 16.70% crude lipid; HPD means high plant-protein diet, 46.50% crude protein, 9.38% crude lipid; HLPD means high lipid-high plant-protein diet, 46.54% crude protein, 16.67% crude lipid.3L means lipid level; HP means plant-protein level; L × HP means lipids and plant-protein interacted level.Fig. 6The mRNA relative expression of BA enterohepatic circulation genes in grouper. cpy7a1 = cholesterol 7α-hydroxylase; cyp27a1 = sterol-27-hydroxylase; bsep = bile salt export pump; mdr3 = multidrug resistance protein 3; asbt = apical sodium-dependent BAs transporter; mrp3 = multidrug resistance-associated protein 3; oatp1 = organic anion transporters 1; meh = microsomal epoxide hydrolase; fxr = farnesoid X receptor; tgr = G protein-coupled bile acid receptor. CD means control diet, 46.21% crude protein, 9.48% crude lipid; HLD means high lipid diet, 46.37% crude protein, 16.70% crude lipid; HPD means high plant-protein diet, 46.50% crude protein, 9.38% crude lipid; HLPD means high lipid-high plant-protein diet, 46.54% crude protein, 16.67% crude lipid; L means lipid level; HP means plant-protein level; L × HP means lipids and plant-protein interacted level. Values are presented as means ± error bars (SD). a-c Different letters indicate significant differences between groups (P < 0.05).Fig. 6
Compared to the CD group, the mRNA relative expression of acc, fas, lxr, pparα and srebp1 was significantly increased in HLD group (P ＜ 0.05), while the mRNA relative expression of atgl and hsl was significantly decreased (P ＜ 0.05) (Fig. 7A). Compared to the CD group, the mRNA relative expression of atgl, hsl and pparα was significantly increased in the HPD group, while the mRNA relative expression of lxr and srebp1 was significantly decreased (P ＜ 0.05). Compared to the HPD group, the mRNA relative expression of acc, fas, lxr and srebp1 was significantly increased in the HLPD group, while the mRNA relative expression of atgl and hsl was significantly decreased (P ＜ 0.05). In addition, compared to the CD group, the expression of SREBP1 was significantly increased in the HLD and HPD groups, and compared to the HLD group, the HLPD treatment significantly increased the mRNA relative expression of SREBP1 (P ＜ 0.05; Fig. 7B-C).Fig. 7The lipid metabolism in response to dietary high lipid and high plant-protein in grouper. (A) The mRNA relative expression of lipid metabolism genes. (B) The Western blot analysis, three replicate wells were made for each protein sample. (C) The relative quantification of protein levels. acc = acetyl-CoA carboxylase; fas = fatty acid synthase; atgl = adipose triglyceride lipase; cpt1 = carnitine palmitoyltransferase 1; hsl = hormone-sensitive lipase; lxr = liver X receptor alpha; pparα = peroxisome proliferator-activated receptor alpha; srebp1 = sterol responsive element binding protein 1; GADPH = glyceraldehyde-phosphate dehydrogenase. CD means control diet, 46.21% crude protein, 9.48% crude lipid; HLD means high lipid diet, 46.37% crude protein, 16.70% crude lipid; HPD means high plant-protein diet, 46.50% crude protein, 9.38% crude lipid; HLPD means high lipid-high plant-protein diet, 46.54% crude protein, 16.67% crude lipid; L means lipid level; HP means plant-protein level; L × HP means lipids and plant-protein interacted level. Values are presented as means ± error bars (SD). a-d Different letters indicate significant differences between groups (P < 0.05).Fig. 7
4
Discussion
4.1
High lipid diet impaired the growth performance and liver damage via regulating intestinal microbiotas, BA metabolism and lipid metabolism
In this study, the HLD negatively impacted the growth performance of pearl gentian groupers. Previous research reported mixed outcomes regarding HLD's effect on fish growth—ranging from beneficial (Xu et al., 2022a) to neutral (Zheng et al., 2023) and detrimental (Yang et al., 2023). It has been observed that as dietary lipid content increases, fish tend to exhibit a reduced tolerance to lipid-rich feeds, transitioning from growth enhancement to suppression (Xun et al., 2021; Yue et al., 2020). Such variability in growth performance outcomes under HLD is likely due to differences in lipid tolerance among fish subjected to varying experimental conditions, encompassing base diet formulation, culture environment, and genetic makeup.
Echoing the findings related to other fish species (He et al., 2022; Yang et al., 2023; Zheng et al., 2023), our study also found that HLD led to hepatocyte abnormalities, including nuclear migration and vacuolization, and liver damage. This was evidenced by increased activities of AST and ALT, elevated levels of pro-inflammatory factors, decreased antioxidant enzyme activities, and the activation of the KEAP1/NRF2 pathway, further corroborating the adverse effects of high lipid intake on fish health.
Dietary composition markedly influences the intestinal microbial community, impacting the population size and metabolism of crucial symbiotic species and consequently eliciting significant biological alterations in the host (Ye et al., 2020). In our study, the HLD treatment augmented certain indicators of α-diversity. However, research on golden pompano (Trachinotus ovatus) indicates that an HLD does not significantly affect most α-diversity indices (Fang et al., 2021; Xun et al., 2021). Furthermore, the relative abundance of Clostridium diminished in grouper subjected to an HLD, aligning with observations in rice field eel (Peng et al., 2019). Conversely, the use of an HLD in mice was associated with an increase in the proportions of Clostridium and Lactobacillus (He et al., 2023). Considering that Clostridium is a lipase-producing bacterium in aquatic animals (Wu et al., 2023), these findings imply that an HLD can modulate BA metabolism by adjusting specific intestinal microbiota, with BA metabolic pathways possibly interconnected with lipid metabolism through these microbial populations.
The intestine serves as a critical organ for assessing the BA profile and pool in fish (Wang et al., 2023). This study observed a marked decrease in the levels of ACA and GCDCA in the distal gut of the HLD group. Consistent with some findings, the HLD elevated GCDCA levels in the gallbladder of Tiger puffer (Takifugu rubripes) but did not affect it in the liver of yellow catfish (Liao et al., 2020; Zheng et al., 2023). The size of the circulating BA pool in the body is governed by hepatic BA synthesis, transport, intestinal reabsorption, and recycling to the liver (Luo et al., 2023). In humans, elevated lipid consumption stimulates hepatic BA synthesis, leading to increased BA that are reabsorbed by the ileum (Ocvirk, O'Keefe, 2021). Partially corroborating a previous report (Xu et al., 2022b), this study revealed that HLD augmented BA synthesis and transport, while diminishing BA reabsorption and recycling. Given that BA are efficiently reabsorbed (>95%) from the gut content into enterocytes, and this process is vital for maintaining BA homeostasis (Jia et al., 2018), inflammation-induced impairment of intestinal function significantly reduces the BA pool size (Romano et al., 2020). Although intestinal health was not directly assessed in this study, based on prior research (He et al., 2023; Krogdahl et al., 2015), it was hypothesized that HLD treatment might have diminished BA absorption and pool size due to compromised gut function under these dietary conditions.
Given that BA metabolism is known to regulate lipid and glucose homeostasis through BA receptor activation, alterations in lipid metabolism might be associated with the mRNA relative expression of fxr and tgr5 in this study. Previous findings indicated that feeding a HLD decreased fxr mRNA relative expression in Tiger puffer and both fxr and tgr5 mRNA relative expression in pearl gentian groupers (Liao et al., 2020; Xu et al., 2022a). Consistent with these findings, our study showed that HLD, HPD, and HLPD treatments significantly decreased fxr and tgr5 mRNA relative expression. The reduced fxr mRNA relative expression might be due to altered BA composition and reduced BA pool size, as GCDCA has been shown to promote FXR expression in rat pancreatic acinar-like AR42J cells and gallbladder cancer cells (Wang et al., 2020; Zhou et al., 2017). Furthermore, the FXR-SHP and FXR-LXR axis inhibited the mRNA relative expression of srebp1 and its downstream lipogenic genes in the liver (Watanabe et al., 2004; Yang, Wu, 2022). Activation of FXR also upregulated the expression of PPARα and its targets related to fatty acid oxidation (Cyphert et al., 2012). Previous studies have shown that feeding HLD to groupers increases the expression of lipogenic genes such as srebp1 and fas, while decreasing the mRNA relative expression of lipolytic genes such as cpt1 and ppar (Li et al., 2022a; Xu et al., 2022a; Zheng et al., 2023). Consistent with these findings, our study confirmed that the HLD enhanced lipogenesis. This enhancement of lipogenesis is one reason why the HLD promoted fat deposition, as evidenced by increased crude lipid in tissues and elevated hepatic or systemic lipid levels in pearl gentian grouper. Overall, dietary high lipid intake impaired intestinal microbiota, BA metabolism, and lipid metabolism, which in turn induced lipid deposition and liver damage, ultimately impairing the growth performance of pearl gentian groupers.
4.2
High plant-protein diet impaired the growth performance and liver damage via regulating gut microbiota, BA metabolism and inflammatory response
In previous studies, diets enriched with CPC, substituting for fishmeal at approximately 50%, either attenuated or did not impact fish growth performance (Liu et al., 2022; Tian et al., 2022; Xie et al., 2023). Consistent with these observations, the HPD also adversely affected the growth performance of pearl gentian groupers in this study. Reflecting outcomes from research on largemouth bass, where a HPD detrimentally influenced liver functionality (Xie et al., 2023), the current study similarly identified that the HPD induced hepatocyte abnormalities and liver impairment in groupers.
Prior research revealed that a HPD augmented the α-diversity of the gut microbiome in L. vannamei (Wang et al., 2022). In alignment with these findings, the present study observed an enhancement in specific α-diversity indicators under the HPD treatment. Given the antimicrobial properties of BA that inhibit the proliferation of pathogenic bacteria (He et al., 2023), it is hypothesized that diminished BA levels could increase intestinal bacterial abundance, consequently enriching microbial diversity within the HPD group. Moreover, variations in protein intake levels have been shown to influence Lactobacillus abundance in mice (He et al., 2023). Echoing these insights, our study documented a reduction in Clostridium abundance and an elevation in Lactobacillus within the HPD group, highlighting the linkage of these genera with BA metabolism. This suggests that feeding an HPD may modulate BA metabolism by altering specific intestinal microbiotas.
Significant reductions in ACA and GCDCA levels were observed in the distal gut of the HPD groups. Partially aligning with our findings, feeding an HPD elevated ACA levels and reduced GCDCA levels in the hemolymph of L. vannamei (Li et al., 2023). Correlation analysis indicated that variations in BA profiles (ACA, TCA, GCDCA, and THCA) correlated with changes in the relative abundance of unclassified_Peptostreptococcaceae, Streptococcus, Lactobacillus, Lactococcus, Staphylococcus and Clostridium. Similarly, in mouse liver, TCA levels were negatively correlated with the relative abundance of Clostridium and Lactobacillus (Chen et al., 2022). Despite Lactobacillus's notable capacity to hydrolyze GCDCA (Liang et al., 2021), no association was found in the current study, signaling a need for further research to elucidate the complex interactions between gut microbiota and BA metabolism.
As fishmeal alternatives often disturb BA homeostasis by either increasing BA excretion/decreasing intestinal reabsorption or decreasing synthesis due to lower cholesterol levels (a BA precursor) and the ability of certain plant-protein compounds to bind bile salts, preventing their reabsorption (Romano et al., 2020), these mechanisms may explain the observed inhibitory effects on BA synthesis, transport, reabsorption, and recycling in grouper attributable to the HPD in our study.
Furthermore, in the present study, the HPD significantly downregulated the mRNA relative expression of fxr and tgr5, genes essential in regulating fat metabolism. Interestingly, despite HPD treatment, adipogenic genes and regulatory factors did not exhibit upregulation, except for a significant increase in SREBP1 protein expression. Conversely, expressions of lipolysis genes (atgl, cpt1, and hsl) were significantly upregulated. Given that crude lipid levels in liver, serum, and whole-body of fish remained unchanged, it appears that the HPD had minimal impact on lipid metabolism, contrasting with previous findings that indicated the HPD could influence lipid deposition in other fish species (He et al., 2022; Tian et al., 2022; Xie et al., 2023). These discrepancies warrant further investigation.
Additionally, recent studies, including our own, suggest that tgr5 and fxr signaling pathways may mitigate the inflammatory response by inhibiting the nuclear translocation of nuclear factor-κB (NF-κB) in grouper, mice, and humans (Chiang, 2013; Ticho et al., 2019; Xu et al., 2022d, 2023). In accordance with these findings, we observed a significant inverse correlation between the expression of BA receptor genes and pro-inflammatory factors, potentially contributing to liver damage in grouper. Overall, our findings propose a potential mechanism by which feeding an HPD influences gut microbiota, BA metabolism, and inflammatory response, thereby affecting the expression of BA receptors and ultimately compromising liver health and growth performance in pearl gentian grouper.
4.3
High lipid-high plant-protein diet did not further impair liver health, but disrupted growth performance
The current study has elucidated that both the HLD and HPD detrimentally impact the growth performance and liver health of groupers. Notably, the synergistic effects of the HLD and HPD, as observed in the HLPD group, primarily compromised growth performance without exacerbating hepatic damage. Specifically, the serum activities of ALT and AST, and the degree of hepatocyte microstructural damage in the HLPD group were not significantly divergent from those in the HLD and/or HPD groups. Similarly, the HLPD did not augment indicators related to antioxidant capacity, inflammatory response, and lipid metabolism in the liver, nor did it modify the principal composition of the intestinal microbiota when juxtaposed with the HLD and HPD treatments. This observation implies that groupers exhibit a robust capacity for self-regulation of hepatic damage, thereby mitigating the potential exacerbation of these indicators by the combined effects of the HLD and HPD. Consequently, the HLPD could be regarded as a viable alternative for preserving feed fishmeal with respect to liver health, albeit with the proviso that specific applications necessitate further scrutiny.
Moreover, given that the HLPD group exhibited the lowest feed intake, it is hypothesized that the compromised growth performance in this cohort is attributable not to hepatic health impairment but to diminished feed intake. This finding underscores the imperative to address issues of palatability and attractiveness in HLPD feeds prior to their implementation. However, this hypothesis remains provisional and requires empirical validation to substantiate the findings of this investigation.
Additionally, examination of primary and secondary metabolite identification maps within the metabolome, incorporating PCA, disclosed a substantial overlap in sample distribution between the HPD and HLPD groups. This observation suggests that the HPD component significantly modulates the effects of the HLPD treatment on the intestinal metabolome. Furthermore, the HLPD treatment mirrored the HLD treatment in terms of lipid deposition, lipolysis, and lipid synthesis in grouper, indicating that the HLD treatment is a primary determinant of the impact of the HLPD treatment on lipid homeostasis. Considering the scarcity of research on HLPD feeds in fish, our findings underscore the necessity for further investigation to elucidate these phenomena.
4.1
High lipid diet impaired the growth performance and liver damage via regulating intestinal microbiotas, BA metabolism and lipid metabolism
In this study, the HLD negatively impacted the growth performance of pearl gentian groupers. Previous research reported mixed outcomes regarding HLD's effect on fish growth—ranging from beneficial (Xu et al., 2022a) to neutral (Zheng et al., 2023) and detrimental (Yang et al., 2023). It has been observed that as dietary lipid content increases, fish tend to exhibit a reduced tolerance to lipid-rich feeds, transitioning from growth enhancement to suppression (Xun et al., 2021; Yue et al., 2020). Such variability in growth performance outcomes under HLD is likely due to differences in lipid tolerance among fish subjected to varying experimental conditions, encompassing base diet formulation, culture environment, and genetic makeup.
Echoing the findings related to other fish species (He et al., 2022; Yang et al., 2023; Zheng et al., 2023), our study also found that HLD led to hepatocyte abnormalities, including nuclear migration and vacuolization, and liver damage. This was evidenced by increased activities of AST and ALT, elevated levels of pro-inflammatory factors, decreased antioxidant enzyme activities, and the activation of the KEAP1/NRF2 pathway, further corroborating the adverse effects of high lipid intake on fish health.
Dietary composition markedly influences the intestinal microbial community, impacting the population size and metabolism of crucial symbiotic species and consequently eliciting significant biological alterations in the host (Ye et al., 2020). In our study, the HLD treatment augmented certain indicators of α-diversity. However, research on golden pompano (Trachinotus ovatus) indicates that an HLD does not significantly affect most α-diversity indices (Fang et al., 2021; Xun et al., 2021). Furthermore, the relative abundance of Clostridium diminished in grouper subjected to an HLD, aligning with observations in rice field eel (Peng et al., 2019). Conversely, the use of an HLD in mice was associated with an increase in the proportions of Clostridium and Lactobacillus (He et al., 2023). Considering that Clostridium is a lipase-producing bacterium in aquatic animals (Wu et al., 2023), these findings imply that an HLD can modulate BA metabolism by adjusting specific intestinal microbiota, with BA metabolic pathways possibly interconnected with lipid metabolism through these microbial populations.
The intestine serves as a critical organ for assessing the BA profile and pool in fish (Wang et al., 2023). This study observed a marked decrease in the levels of ACA and GCDCA in the distal gut of the HLD group. Consistent with some findings, the HLD elevated GCDCA levels in the gallbladder of Tiger puffer (Takifugu rubripes) but did not affect it in the liver of yellow catfish (Liao et al., 2020; Zheng et al., 2023). The size of the circulating BA pool in the body is governed by hepatic BA synthesis, transport, intestinal reabsorption, and recycling to the liver (Luo et al., 2023). In humans, elevated lipid consumption stimulates hepatic BA synthesis, leading to increased BA that are reabsorbed by the ileum (Ocvirk, O'Keefe, 2021). Partially corroborating a previous report (Xu et al., 2022b), this study revealed that HLD augmented BA synthesis and transport, while diminishing BA reabsorption and recycling. Given that BA are efficiently reabsorbed (>95%) from the gut content into enterocytes, and this process is vital for maintaining BA homeostasis (Jia et al., 2018), inflammation-induced impairment of intestinal function significantly reduces the BA pool size (Romano et al., 2020). Although intestinal health was not directly assessed in this study, based on prior research (He et al., 2023; Krogdahl et al., 2015), it was hypothesized that HLD treatment might have diminished BA absorption and pool size due to compromised gut function under these dietary conditions.
Given that BA metabolism is known to regulate lipid and glucose homeostasis through BA receptor activation, alterations in lipid metabolism might be associated with the mRNA relative expression of fxr and tgr5 in this study. Previous findings indicated that feeding a HLD decreased fxr mRNA relative expression in Tiger puffer and both fxr and tgr5 mRNA relative expression in pearl gentian groupers (Liao et al., 2020; Xu et al., 2022a). Consistent with these findings, our study showed that HLD, HPD, and HLPD treatments significantly decreased fxr and tgr5 mRNA relative expression. The reduced fxr mRNA relative expression might be due to altered BA composition and reduced BA pool size, as GCDCA has been shown to promote FXR expression in rat pancreatic acinar-like AR42J cells and gallbladder cancer cells (Wang et al., 2020; Zhou et al., 2017). Furthermore, the FXR-SHP and FXR-LXR axis inhibited the mRNA relative expression of srebp1 and its downstream lipogenic genes in the liver (Watanabe et al., 2004; Yang, Wu, 2022). Activation of FXR also upregulated the expression of PPARα and its targets related to fatty acid oxidation (Cyphert et al., 2012). Previous studies have shown that feeding HLD to groupers increases the expression of lipogenic genes such as srebp1 and fas, while decreasing the mRNA relative expression of lipolytic genes such as cpt1 and ppar (Li et al., 2022a; Xu et al., 2022a; Zheng et al., 2023). Consistent with these findings, our study confirmed that the HLD enhanced lipogenesis. This enhancement of lipogenesis is one reason why the HLD promoted fat deposition, as evidenced by increased crude lipid in tissues and elevated hepatic or systemic lipid levels in pearl gentian grouper. Overall, dietary high lipid intake impaired intestinal microbiota, BA metabolism, and lipid metabolism, which in turn induced lipid deposition and liver damage, ultimately impairing the growth performance of pearl gentian groupers.
4.2
High plant-protein diet impaired the growth performance and liver damage via regulating gut microbiota, BA metabolism and inflammatory response
In previous studies, diets enriched with CPC, substituting for fishmeal at approximately 50%, either attenuated or did not impact fish growth performance (Liu et al., 2022; Tian et al., 2022; Xie et al., 2023). Consistent with these observations, the HPD also adversely affected the growth performance of pearl gentian groupers in this study. Reflecting outcomes from research on largemouth bass, where a HPD detrimentally influenced liver functionality (Xie et al., 2023), the current study similarly identified that the HPD induced hepatocyte abnormalities and liver impairment in groupers.
Prior research revealed that a HPD augmented the α-diversity of the gut microbiome in L. vannamei (Wang et al., 2022). In alignment with these findings, the present study observed an enhancement in specific α-diversity indicators under the HPD treatment. Given the antimicrobial properties of BA that inhibit the proliferation of pathogenic bacteria (He et al., 2023), it is hypothesized that diminished BA levels could increase intestinal bacterial abundance, consequently enriching microbial diversity within the HPD group. Moreover, variations in protein intake levels have been shown to influence Lactobacillus abundance in mice (He et al., 2023). Echoing these insights, our study documented a reduction in Clostridium abundance and an elevation in Lactobacillus within the HPD group, highlighting the linkage of these genera with BA metabolism. This suggests that feeding an HPD may modulate BA metabolism by altering specific intestinal microbiotas.
Significant reductions in ACA and GCDCA levels were observed in the distal gut of the HPD groups. Partially aligning with our findings, feeding an HPD elevated ACA levels and reduced GCDCA levels in the hemolymph of L. vannamei (Li et al., 2023). Correlation analysis indicated that variations in BA profiles (ACA, TCA, GCDCA, and THCA) correlated with changes in the relative abundance of unclassified_Peptostreptococcaceae, Streptococcus, Lactobacillus, Lactococcus, Staphylococcus and Clostridium. Similarly, in mouse liver, TCA levels were negatively correlated with the relative abundance of Clostridium and Lactobacillus (Chen et al., 2022). Despite Lactobacillus's notable capacity to hydrolyze GCDCA (Liang et al., 2021), no association was found in the current study, signaling a need for further research to elucidate the complex interactions between gut microbiota and BA metabolism.
As fishmeal alternatives often disturb BA homeostasis by either increasing BA excretion/decreasing intestinal reabsorption or decreasing synthesis due to lower cholesterol levels (a BA precursor) and the ability of certain plant-protein compounds to bind bile salts, preventing their reabsorption (Romano et al., 2020), these mechanisms may explain the observed inhibitory effects on BA synthesis, transport, reabsorption, and recycling in grouper attributable to the HPD in our study.
Furthermore, in the present study, the HPD significantly downregulated the mRNA relative expression of fxr and tgr5, genes essential in regulating fat metabolism. Interestingly, despite HPD treatment, adipogenic genes and regulatory factors did not exhibit upregulation, except for a significant increase in SREBP1 protein expression. Conversely, expressions of lipolysis genes (atgl, cpt1, and hsl) were significantly upregulated. Given that crude lipid levels in liver, serum, and whole-body of fish remained unchanged, it appears that the HPD had minimal impact on lipid metabolism, contrasting with previous findings that indicated the HPD could influence lipid deposition in other fish species (He et al., 2022; Tian et al., 2022; Xie et al., 2023). These discrepancies warrant further investigation.
Additionally, recent studies, including our own, suggest that tgr5 and fxr signaling pathways may mitigate the inflammatory response by inhibiting the nuclear translocation of nuclear factor-κB (NF-κB) in grouper, mice, and humans (Chiang, 2013; Ticho et al., 2019; Xu et al., 2022d, 2023). In accordance with these findings, we observed a significant inverse correlation between the expression of BA receptor genes and pro-inflammatory factors, potentially contributing to liver damage in grouper. Overall, our findings propose a potential mechanism by which feeding an HPD influences gut microbiota, BA metabolism, and inflammatory response, thereby affecting the expression of BA receptors and ultimately compromising liver health and growth performance in pearl gentian grouper.
4.3
High lipid-high plant-protein diet did not further impair liver health, but disrupted growth performance
The current study has elucidated that both the HLD and HPD detrimentally impact the growth performance and liver health of groupers. Notably, the synergistic effects of the HLD and HPD, as observed in the HLPD group, primarily compromised growth performance without exacerbating hepatic damage. Specifically, the serum activities of ALT and AST, and the degree of hepatocyte microstructural damage in the HLPD group were not significantly divergent from those in the HLD and/or HPD groups. Similarly, the HLPD did not augment indicators related to antioxidant capacity, inflammatory response, and lipid metabolism in the liver, nor did it modify the principal composition of the intestinal microbiota when juxtaposed with the HLD and HPD treatments. This observation implies that groupers exhibit a robust capacity for self-regulation of hepatic damage, thereby mitigating the potential exacerbation of these indicators by the combined effects of the HLD and HPD. Consequently, the HLPD could be regarded as a viable alternative for preserving feed fishmeal with respect to liver health, albeit with the proviso that specific applications necessitate further scrutiny.
Moreover, given that the HLPD group exhibited the lowest feed intake, it is hypothesized that the compromised growth performance in this cohort is attributable not to hepatic health impairment but to diminished feed intake. This finding underscores the imperative to address issues of palatability and attractiveness in HLPD feeds prior to their implementation. However, this hypothesis remains provisional and requires empirical validation to substantiate the findings of this investigation.
Additionally, examination of primary and secondary metabolite identification maps within the metabolome, incorporating PCA, disclosed a substantial overlap in sample distribution between the HPD and HLPD groups. This observation suggests that the HPD component significantly modulates the effects of the HLPD treatment on the intestinal metabolome. Furthermore, the HLPD treatment mirrored the HLD treatment in terms of lipid deposition, lipolysis, and lipid synthesis in grouper, indicating that the HLD treatment is a primary determinant of the impact of the HLPD treatment on lipid homeostasis. Considering the scarcity of research on HLPD feeds in fish, our findings underscore the necessity for further investigation to elucidate these phenomena.
5
Conclusion
The present study substantiates the hypothesis that dietary high lipid intake detrimentally affects the intestinal microbiota, BA metabolism, and lipid metabolism, culminating in lipid deposition, liver damage, and impaired growth performance of pearl gentian groupers (Fig. 8). Moreover, the investigation has delineated a plausible mechanism by which HPD feeds influence the gut microbiota, BA metabolism, inflammatory responses, and the expression of BA receptors, thereby compromising liver health and growth performance in grouper. Notably, our findings reveal that HLPD feeds do not further impair (compared to the HLD and HPD) liver health in groupers; however, they significantly disrupt growth performance. This research highlights the pivotal role of the interplay between intestinal microbiota and BA metabolism as a critical mechanism by which various diets modulate liver health in fish and possibly other animals. Additionally, this study advances our comprehension of the physiological implications of HLPD feeds and provides fresh feasibility into strategies for minimizing dietary fishmeal levels.Fig. 8The potential mechanism by which the high lipid and high plant-protein diets impaired the liver health in grouper. ACA = allocholic acid; GCDCA = gly-chenodeoxycholic acid; THCA = taurohyocholate; BA = bile acids metabolism; FXR = farnesoid X receptor; TGR5 = G protein-coupled bile acid receptor 5. CD means control diet, 46.21% crude protein, 9.48% crude lipid; HLD means high lipid diet, 46.37% crude protein, 16.70% crude lipid; HPD means high plant-protein diet, 46.50% crude protein, 9.38% crude lipid.Fig. 8
Credit author contribution statement
Jia Xu: Writing – original draft, Validation, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation. Fan Wang: Writing – original draft, Investigation, Formal analysis, Data curation. Chaoqun Hu: Writing – review & editing, Validation, Resources, Conceptualization. Junxiang Lai: Writing – review & editing, Investigation, Conceptualization. Shiwei Xie: Writing – review & editing, Conceptualization. Kefu Yu: Project administration, Funding acquisition, Conceptualization. Fajun Jiang: Writing – review & editing, Project administration, Conceptualization.
Declaration of competing interest
We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, and there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the content of this paper. |
Title: Activation of MEK‐ERK‐c‐MYC signaling pathway promotes splenic M2-like macrophage polarization to inhibit PHcH-liver cirrhosis | Body:
1
Introduction
Cirrhotic portal hypertension (PH) is a clinical syndrome characterized by portal collateral circulation, splenomegaly, and ascites (1). Patients often show hypersplenism, which is a common and frequently occurring complication in China. Although the pathogenesis of hypersplenism is still unclear, splenic macrophage phagocytosis, destruction, and hemocytosis are involved in its pathogenesis (2, 3). Macrophages are innate immune cells participating in homeostasis and defense. This depends on their activated phenotypes, including the typical macrophage phenotype (M1-like) and the alternatively activated macrophage (M2-like) (4, 5). Previous studies reported that activation of splenic macrophages is involved in liver cirrhosis-related splenic hyperfunction in PH (3, 6, 7).
Transcription factors are involved in the polarization of macrophages, which secrete IL-12 or IL-10, both regulatory cytokines for the production of IFNγ and development of Th1 cells, called M1 polarization (4, 8, 9). M2 macrophages are activated by helper T cytokines 2 (Th2) such as IL-4 and IL-13 to induce high expression of arginase-1 (arg1), while also producing higher levels of the anti-inflammatory cytokines such as mannose receptor-1 (Mrc1), and IL-10 involved in tissue repair (5, 10–13). The MYC protooncogene is represented downstream of receptor signal transduction pathways, resulting in positive or negative regulation of the MYC gene. c-Myc controls 45% of the genes related to M2 macrophage activation, including the direct upregulation of SCARB1, ALOX15, and MRC1 expression (14). IL-4 induces the transformation of M1 macrophages to the M2 phenotype by up regulating the expression of JNK and its downstream transcription factor c-myc (15). Our previous study on hypersplenism spleen transcription factor chip showed that the activity of c-Myc in hypersplenism spleen macrophages is significantly higher than that in normal spleen macrophages. The c-Myc gene is a transcription factor involved in the polarization of M2-like macrophages, but its role in the regulation of macrophage polarization in patients with PH hypersplenism and its mechanism have not yet been studied and reported.
Our team previously demonstrated the activation of the LPS/TLR/NF-κB signaling pathway in splenic macrophages with hypersplenism; their phagocytic function and antigen presentation function are stronger than normal cells (16). Mitogen activated protein kinases (MAPKs) transduce extracellular stimulation signals into cells and nuclei, leading to cell proliferation, differentiation, transformation, and apoptosis (17). Several studies have reported that MAPK pathway is involved in promoting M2-like macrophage activation and immune regulation (11, 18, 19). c-Myc is also involved in the activation of splenic macrophages in PH hypersplenism through the MAPK signaling pathway.
2
Materials and methods
2.1
Clinical spleen samples
Fresh human normal spleen samples (n = 15) were obtained from patients with traumatic splenic rupture who underwent splenectomy (Nor group); splenic samples (n = 36) of hepatitis B cirrhosis accompanied by PH were obtained from patients with PH hypersplenism who underwent splenectomy (PH group). The following individuals were excluded from the study: patients with hypersplenism caused by non-hepatitis B virus infection such as hepatitis C and autoimmune hepatitis and patients without hepatitis accompanied by tumor splenectomy. All procedures involving human sample collection were approved by the ethics committee of the Second Affiliated Hospital of Xi’an Jiaotong University (ID:2016127) and were performed according to the principles of the declaration of Helsinki. Signed informed consent was obtained by all individuals or their families.
2.2
Generation of genetically modified mice
The Cre gene in Lyz cre mice is mainly expressed in mature macrophages, and the Cre recombinase produced can mediate the cleavage of loxP on both sides of the c-Myc gene. c-Myc loxP
+/+
and Lyz cre+/- mice used in this study were donated by the Department of pathology of the University of Pittsburgh in the United States. We selected Lyz cre+/- to hybridize with c-Myc loxp+/+mice to obtain Lyz cre+/- c-Myc loxp+/-, and then backcrossed with c-Myc loxp+/+mice to obtain mice with specific knockout of c-Myc in macrophages. The genotype was Lyz cre+/- c-Myc loxp+/+(KO group), and the littermate c-Myc Flox+/+mice were used as controls (WT group).In this model, the fifth exon of c-Myc gene was knocked out. The primers used for genotype identification are shown in
Supplementary Table S3
.
2.3
Animals and treatment
All animal experimental protocols were approved by the Animal care and Use Committee of Xi’an Jiaotong University and carried out in accordance with the guidelines and recommendations issued by the National Institutes of health of China. C57BL/6 mice aged 6-8 weeks were purchased from the experimental animal center of Xi’an Jiaotong University (Xi’an, China). Two mouse liver fibrosis models were constructed using carbon tetrachloride (CCl4) and thioacetamide (TAA) for in vivo animal experiments: CCl4 induces mouse liver injury, and TAA simulates chronic liver fibrosis. CCl4 was intraperitoneally injected twice a week for 6 weeks (CCl4: olive oil = 1:3, 2 μL/g body weight), to establish the CCl4-induced liver fibrosis model (20). The hepatic fibrosis model induced by thioacetamide (TAA) solution was established by the administration of TAA (V900086; 300 mg/L; Sigma Aldrich), into the drinking water for 27 weeks (21).
2.4
Isolation of monocyte-macrophages by immunomagnetic beads
Density gradient centrifugation was used to obtain mononuclear cells from spleen samples. CD14 antigen is highly expressed in most monocyte-macrophages (22); thus patients’ spleen monocyte-macrophages were isolated using human CD14 MicroBeads (130-050-201; Milteny Biotec; Auburn, CA). Mouse spleen monocyte-macrophages were purified and isolated using CD11b MicroBeads (130-093-634; Milteny Biotec; Auburn, CA). The protocol steps for sorting monocyte-macrophages were provided by the instructions of immune magnetic beads of Milteny Biotec.
2.5
Hematoxylin-eosin and Sirius red staining
Hematoxylin-eosin staining was performed on the liver and spleen samples of mice in different groups of the two models to evaluate the pathological changes of liver and spleen. Sirius red staining was used to evaluate liver injury and collagen fiber deposition. At least five different regions of each mouse were selected for image acquisition and quantified by Image Pro Plus 6.0 software. At least 5-10 mice were included in the drug treatment group and KO group.
2.6
Immunofluorescence
The proportion of CD206+ M2-like macrophages in the spleen of patients was evaluated by double immunofluorescence using rabbit anti-CD11b antibody (1:200; ab133357; Abcam) and mouse anti-CD206 (1:100; sc-58986; SANTA CRUZ),. Evaluation of the proportion of CD206+ M2 like macrophages in mouse spleen using rabbit anti-F4/80 antibody (1:200; ab300421; Abcam) and mouse anti-CD206. Simultaneously setting up a blank control and a negative control without primary antibody. Followed by the second antibody CoraLite488-reconciled goat anti-rabbit IgG (1:100; SA00013-2; Proteintech) and CoraLite594-reconciled goat anti-mouse IgG (1:100; SA00013-3; Proteintech).
2.7
Flow cytometry
Flow cytometry (FACS) was used to analyze the macrophage ratio in the spleen cells of patients and mice. The antibodies are shown in
Supplementary Table S1
. Intracytoplasmic staining was performed using an Intraprep permeabilization reagent (A07803; Beckman Coulter). BD-FACS Canto II cytometer (BD Bioscienses) was used to measure the cell count, which was analyzed with FlowJo software (FlowJo 10, LLC, Ashland, OR).
2.8
Western blot analysis
Total proteins were isolated from spleen tissue or macrophages cell samples using radio immunoprecipitation assay buffer (RIPA) according to the manufacturer’s instructions (Beyotime, China). Protein concentration was measured using the bicinchoninic acid (BCA) assay. Optical density was detected by Bio Rad imaging system. All the antibodies used in this study are listed in the Supporting
Supplementary Table S2
.
2.9
Quantitative real-time PCR
Total RNA was extracted from cells using Trizol Regent, and the amplification products were quantified and analyzed by SYBR premix ex Taq II (RR820A; Takara) and ABI 7500 rapid instruments (ABI Life technologies). GAPDH was used as the housekeeping gene for the calculation of relative gene expression. The primers used in this study are listed in Supporting
Supplementary Table S3
.
2.10
Protein phosphorylation array
Phosphorylation of MAPK signaling proteins (p-CREB, p-ERK1/2, p-GSK3a, p-GSK3b, p-MEK, and p-MSK2) was detected using a human/mouse MAPK pathway phosphorylation array C1 (#AAH-MAPK-1-8; RayBiotech) according to the manufacturer’s protocol. The gray value of each image was detected by ImageJ software. The negative control value (NEG mean) was removed from the positive samples to obtain the actual expression, and the ratio (phosphorylated protein/positive control group) was expressed as the relative protein expression of the positive control group.
2.11
Primary cell culture and MEK1/2 inhibitor
Fresh spleen samples were collected from patients with traumatic spleen and PH spleen, and spleen mononuclear cells were extracted. The number of mononuclear cells used for in vitro culture of each sample should not be less than 3×108. The same sample was divided into a control group and MEK inhibitor group (HY-14691; Refametinib), and titrate the inhibitor concentration with CCK8 (abs50003; Absin), with an inhibitor dose of 47 nM. Both groups of cells were cultured at 37 °C for 18 hours. A small amount of cultured mononuclear cells was used for nucleic acid and protein validation. Most of the remaining cells were used to sort macrophages by CD11b magnetic beads and used for subsequent experiments.
2.12
RNA sequencing and date processing
RNA sequencing analysis was performed on 12 samples of splenic monocytesc-macrophages: 6 samples from traumatic spleen and 6 samples from PH spleen. The construction and sequencing of the cDNA library for all RNA samples were performed by Baimike Biotechnology Co., Ltd. Functional annotation and Kyoto Encyclopedia of genes and genomes (KEGG) analysis were performed on two groups of differentially expressed genes (DEGs). Two criteria were used to screen DEGs: (1) fold change greater than 1.5 and (2) corresponding adjusted p value less than 0.05.
2.13
Chromatin immunoprecipitation
Chromatin immunoprecipitation (Chip) assay was performed using One-Day Chromatin IP Kits (17-10086; EZ-Magna Chip™ A/G; Millipore),. The simple process is as follows: the monocyte-macrophages sorted by magnetic beads were treated with formaldehyde to crosslink the protein with DNA, and the chromatin was cut to the size of 200-1000 BP by an ultrasonic cell breaker. Rabbit anti-c-Myc antibody (#13987; CST), and rabbit derived negative control IgG (#2729; CST), were used. After reverse cross-linking, DNA was purified by purification column, using 5% sample as input. The purified DNA was controlled by ordinary PCR, and the expected size was 166 bp. In addition, the reported downstream genes HK2, NCL (23), PER1, CRY1 (24) and NPM1 (#4779; CST), were used for ChIP quality control by qRT-PCR. The primers are listed in
Supplementary Table S3
.
2.14
Chip sequencing
The c-Myc gene chip pull-down samples and input samples were sequenced, as well as one sample of normal trauma spleen and one sample of hypersplenism spleen by Kang Cheng Biotechnology Co., Ltd. The differentially enriched regions for Peak Promoter Annotation in the experimental group versus control group were analyzed, and the expression changes of c-Myc dependent genes in M2 macrophages were compared (14, 25); qPCR was performed on spleen macrophage samples to verify some regulatory genes involved in M2-like macrophage cell activation (14). The sequencing primers are listed in
Supplementary Table S3
.
2.15
Statistical analysis
All plotting and statistical analysis were performed using GraphPad prism 8 (GraphPad Software Inc., San Diego, CA). Normally distributed data were compared using Student’s t-test or one-way or two-way analysis of variance (ANOVA). Parametric statistical analysis of non-normally distributed data was performed using Mann Whitney U test. Results were expressed as mean ± standard deviation, and a value of P < 0.05 was considered statistically significant.
2.1
Clinical spleen samples
Fresh human normal spleen samples (n = 15) were obtained from patients with traumatic splenic rupture who underwent splenectomy (Nor group); splenic samples (n = 36) of hepatitis B cirrhosis accompanied by PH were obtained from patients with PH hypersplenism who underwent splenectomy (PH group). The following individuals were excluded from the study: patients with hypersplenism caused by non-hepatitis B virus infection such as hepatitis C and autoimmune hepatitis and patients without hepatitis accompanied by tumor splenectomy. All procedures involving human sample collection were approved by the ethics committee of the Second Affiliated Hospital of Xi’an Jiaotong University (ID:2016127) and were performed according to the principles of the declaration of Helsinki. Signed informed consent was obtained by all individuals or their families.
2.2
Generation of genetically modified mice
The Cre gene in Lyz cre mice is mainly expressed in mature macrophages, and the Cre recombinase produced can mediate the cleavage of loxP on both sides of the c-Myc gene. c-Myc loxP
+/+
and Lyz cre+/- mice used in this study were donated by the Department of pathology of the University of Pittsburgh in the United States. We selected Lyz cre+/- to hybridize with c-Myc loxp+/+mice to obtain Lyz cre+/- c-Myc loxp+/-, and then backcrossed with c-Myc loxp+/+mice to obtain mice with specific knockout of c-Myc in macrophages. The genotype was Lyz cre+/- c-Myc loxp+/+(KO group), and the littermate c-Myc Flox+/+mice were used as controls (WT group).In this model, the fifth exon of c-Myc gene was knocked out. The primers used for genotype identification are shown in
Supplementary Table S3
.
2.3
Animals and treatment
All animal experimental protocols were approved by the Animal care and Use Committee of Xi’an Jiaotong University and carried out in accordance with the guidelines and recommendations issued by the National Institutes of health of China. C57BL/6 mice aged 6-8 weeks were purchased from the experimental animal center of Xi’an Jiaotong University (Xi’an, China). Two mouse liver fibrosis models were constructed using carbon tetrachloride (CCl4) and thioacetamide (TAA) for in vivo animal experiments: CCl4 induces mouse liver injury, and TAA simulates chronic liver fibrosis. CCl4 was intraperitoneally injected twice a week for 6 weeks (CCl4: olive oil = 1:3, 2 μL/g body weight), to establish the CCl4-induced liver fibrosis model (20). The hepatic fibrosis model induced by thioacetamide (TAA) solution was established by the administration of TAA (V900086; 300 mg/L; Sigma Aldrich), into the drinking water for 27 weeks (21).
2.4
Isolation of monocyte-macrophages by immunomagnetic beads
Density gradient centrifugation was used to obtain mononuclear cells from spleen samples. CD14 antigen is highly expressed in most monocyte-macrophages (22); thus patients’ spleen monocyte-macrophages were isolated using human CD14 MicroBeads (130-050-201; Milteny Biotec; Auburn, CA). Mouse spleen monocyte-macrophages were purified and isolated using CD11b MicroBeads (130-093-634; Milteny Biotec; Auburn, CA). The protocol steps for sorting monocyte-macrophages were provided by the instructions of immune magnetic beads of Milteny Biotec.
2.5
Hematoxylin-eosin and Sirius red staining
Hematoxylin-eosin staining was performed on the liver and spleen samples of mice in different groups of the two models to evaluate the pathological changes of liver and spleen. Sirius red staining was used to evaluate liver injury and collagen fiber deposition. At least five different regions of each mouse were selected for image acquisition and quantified by Image Pro Plus 6.0 software. At least 5-10 mice were included in the drug treatment group and KO group.
2.6
Immunofluorescence
The proportion of CD206+ M2-like macrophages in the spleen of patients was evaluated by double immunofluorescence using rabbit anti-CD11b antibody (1:200; ab133357; Abcam) and mouse anti-CD206 (1:100; sc-58986; SANTA CRUZ),. Evaluation of the proportion of CD206+ M2 like macrophages in mouse spleen using rabbit anti-F4/80 antibody (1:200; ab300421; Abcam) and mouse anti-CD206. Simultaneously setting up a blank control and a negative control without primary antibody. Followed by the second antibody CoraLite488-reconciled goat anti-rabbit IgG (1:100; SA00013-2; Proteintech) and CoraLite594-reconciled goat anti-mouse IgG (1:100; SA00013-3; Proteintech).
2.7
Flow cytometry
Flow cytometry (FACS) was used to analyze the macrophage ratio in the spleen cells of patients and mice. The antibodies are shown in
Supplementary Table S1
. Intracytoplasmic staining was performed using an Intraprep permeabilization reagent (A07803; Beckman Coulter). BD-FACS Canto II cytometer (BD Bioscienses) was used to measure the cell count, which was analyzed with FlowJo software (FlowJo 10, LLC, Ashland, OR).
2.8
Western blot analysis
Total proteins were isolated from spleen tissue or macrophages cell samples using radio immunoprecipitation assay buffer (RIPA) according to the manufacturer’s instructions (Beyotime, China). Protein concentration was measured using the bicinchoninic acid (BCA) assay. Optical density was detected by Bio Rad imaging system. All the antibodies used in this study are listed in the Supporting
Supplementary Table S2
.
2.9
Quantitative real-time PCR
Total RNA was extracted from cells using Trizol Regent, and the amplification products were quantified and analyzed by SYBR premix ex Taq II (RR820A; Takara) and ABI 7500 rapid instruments (ABI Life technologies). GAPDH was used as the housekeeping gene for the calculation of relative gene expression. The primers used in this study are listed in Supporting
Supplementary Table S3
.
2.10
Protein phosphorylation array
Phosphorylation of MAPK signaling proteins (p-CREB, p-ERK1/2, p-GSK3a, p-GSK3b, p-MEK, and p-MSK2) was detected using a human/mouse MAPK pathway phosphorylation array C1 (#AAH-MAPK-1-8; RayBiotech) according to the manufacturer’s protocol. The gray value of each image was detected by ImageJ software. The negative control value (NEG mean) was removed from the positive samples to obtain the actual expression, and the ratio (phosphorylated protein/positive control group) was expressed as the relative protein expression of the positive control group.
2.11
Primary cell culture and MEK1/2 inhibitor
Fresh spleen samples were collected from patients with traumatic spleen and PH spleen, and spleen mononuclear cells were extracted. The number of mononuclear cells used for in vitro culture of each sample should not be less than 3×108. The same sample was divided into a control group and MEK inhibitor group (HY-14691; Refametinib), and titrate the inhibitor concentration with CCK8 (abs50003; Absin), with an inhibitor dose of 47 nM. Both groups of cells were cultured at 37 °C for 18 hours. A small amount of cultured mononuclear cells was used for nucleic acid and protein validation. Most of the remaining cells were used to sort macrophages by CD11b magnetic beads and used for subsequent experiments.
2.12
RNA sequencing and date processing
RNA sequencing analysis was performed on 12 samples of splenic monocytesc-macrophages: 6 samples from traumatic spleen and 6 samples from PH spleen. The construction and sequencing of the cDNA library for all RNA samples were performed by Baimike Biotechnology Co., Ltd. Functional annotation and Kyoto Encyclopedia of genes and genomes (KEGG) analysis were performed on two groups of differentially expressed genes (DEGs). Two criteria were used to screen DEGs: (1) fold change greater than 1.5 and (2) corresponding adjusted p value less than 0.05.
2.13
Chromatin immunoprecipitation
Chromatin immunoprecipitation (Chip) assay was performed using One-Day Chromatin IP Kits (17-10086; EZ-Magna Chip™ A/G; Millipore),. The simple process is as follows: the monocyte-macrophages sorted by magnetic beads were treated with formaldehyde to crosslink the protein with DNA, and the chromatin was cut to the size of 200-1000 BP by an ultrasonic cell breaker. Rabbit anti-c-Myc antibody (#13987; CST), and rabbit derived negative control IgG (#2729; CST), were used. After reverse cross-linking, DNA was purified by purification column, using 5% sample as input. The purified DNA was controlled by ordinary PCR, and the expected size was 166 bp. In addition, the reported downstream genes HK2, NCL (23), PER1, CRY1 (24) and NPM1 (#4779; CST), were used for ChIP quality control by qRT-PCR. The primers are listed in
Supplementary Table S3
.
2.14
Chip sequencing
The c-Myc gene chip pull-down samples and input samples were sequenced, as well as one sample of normal trauma spleen and one sample of hypersplenism spleen by Kang Cheng Biotechnology Co., Ltd. The differentially enriched regions for Peak Promoter Annotation in the experimental group versus control group were analyzed, and the expression changes of c-Myc dependent genes in M2 macrophages were compared (14, 25); qPCR was performed on spleen macrophage samples to verify some regulatory genes involved in M2-like macrophage cell activation (14). The sequencing primers are listed in
Supplementary Table S3
.
2.15
Statistical analysis
All plotting and statistical analysis were performed using GraphPad prism 8 (GraphPad Software Inc., San Diego, CA). Normally distributed data were compared using Student’s t-test or one-way or two-way analysis of variance (ANOVA). Parametric statistical analysis of non-normally distributed data was performed using Mann Whitney U test. Results were expressed as mean ± standard deviation, and a value of P < 0.05 was considered statistically significant.
3
Results
3.1
c-Myc increased and M2-like macrophages were activated in the spleen of PH patients
This study used CD14 magnetic bead sorting of monocyte-macrophages showed high specificity, and high-purity macrophages were obtained from patients with normal traumatic spleen and splenic hypersplenism (92.24% and 89.94%) (
Supplementary Figures S1A, B
). When using CD14 and CD11b antibodies to label and sort positive cells simultaneously, the proportion of CD14+ cells and CD11b+ cells was similar (90.2% vs 89%) (
Supplementary Figures S1A, B
). C-Myc and MAX mRNA expression in splenic macrophages was increased compared to that in normal traumatic spleen (
Figure 1A
). Similarly, c-Myc and phosphorylated c-Myc protein expression significantly increased (P<0.05,
Figure 1B
). Further FACS analysis revealed changes in the proportion of CD86+ M1-like macrophage and CD206+ M2-like macrophage in the spleen (PH vs Nor); the proportion of CD206+ M2-like macrophages (CD11b+CD86-CD206+) or (CD11b+CD11c-CD206+) in PH patients significantly increased (
Figure 1C
,
Supplementary Figure S1C
), while the change in CD86+ M1-like macrophages (CD11b+ CD86+ CD206-) or (CD11b+ CD11c+ CD206-) was not significant (
Figure 1C
,
Supplementary Figure S2C
). CD206+ M2-like macrophages (CD11b+ CD206+) showed an increasing trend in the PH spleen (P < 0.05,
Figure 1D
). The mRNA expression of the M1-like related inflammatory factors CD86, IL-6, IL-1α, and IL-1β showed an upward trend in splenic macrophages of PH patients (
Figure 1E
). The mRNA expression of M2-like inflammatory factors CD206, CD163, IL-10, and related ARG1 is significantly upregulated (P < 0.05,
Figure 1F
). These results demonstrated that the expression of the c-Myc gene was increased in patients with hypersplenism of PH; M2-like macrophages were significantly activated in patients with hypersplenism.
Figure 1
c-Myc increased and M2-like spleen macrophages were activated in PH patients. (A) c-Myc and Max mRNA expression in spleen macrophages sorted by magnetic beads (CD14+) in the Nor group and PH group, analyzed by qPCR (8 samples in the Nor group and 16 samples in the pH group). (B) p-c-Myc and c-Myc protein expressions were higher in PH macrophages than in Nor macrophages, as revealed by western blot (3 samples/group). (C) Ratio of CD86+ M1-like macrophages (CD11b+CD86+CD206-) and CD206+ M2-like macrophages (CD11b+CD86-CD206 +) in splenocyte suspension analyzed by FACS, and the significant difference between the two groups was assessed (6 samples in the Nor group and 9 samples in the PH group). (D) Positive cells of CD206+ M2-like macrophages (CD11b+CD206+) in the spleen of patients, as detected by immunofluorescence double staining. (E, F) Relative mRNA expression of M1-like and M2-like macrophage related inflammatory factors in the spleen (8 samples in the Nor group and 16 samples in the PH group). *p<0.05, **p<0.01, ***p<0.001, ns p>0.05.
3.2
MAPK signaling pathway was activated in spleen macrophages of PH patients
JAK-STAT and TLR4/NF-κB signaling pathways in the PH group were activated compared to the Nor group (
Supplementary Figures S2A, B
), consistent with the reported results in previous studies (16). KEGG analysis showed that MAPK signaling pathway was activated in patients with hypersplenism (
Figures 2A, B
). In addition, the MAPK signaling pathway was significantly activated, showing that the expression of MEK (P-S217/221), ERK1 (P-T202/Y203), and ERK2 (P-Y185/Y187) phosphorylation in macrophages of PH patients was significantly increased, as well as the expression of downstream phosphorylated MSK2 and the expression of CREB phosphorylation s133 site (P < 0.05) (
Figure 2C
,
Supplementary Figure S2C
). The expression of phosphorylated MEK1/2 and phosphorylated ERK1/2 in pH spleen macrophages significantly increased compared to that in normal spleen (P < 0.05). The expression of the upstream Ras, p-c-Raf, c-Raf, and B-Raf significantly increased as well as that of the downstream p-MSK1 and CREB (P < 0.05) (
Figures 2D–F
). Previous studies reported that the activity of PPARγ is inhibited by MAPK phosphorylation (26). The expression of PPARγ protein in macrophages was significantly reduced in the PH spleen (P < 0.01), further suggesting the activation of the MAPK pathway (
Figure 2F
). These results demonstrated that the MAPK signaling pathway in PH patients was activated through the Ras-Raf-MEK-ERK axis.
Figure 2
MAPK signaling pathway was activated in spleen macrophages of hypersplenism patients with portal hypertension (PH). (A, B) RNA-seq was performed on spleen macrophages (CD14+) in the Nor group and PH group (6 samples in each group), and the volcano plot showed the distribution of the differentially expressed genes (DEGs). KEGG enrichment analysis of RNA-seq data showing the expression of the related pathways in the Nor group vs the pH group. (C) Expression of MAPK pathway related proteins was further detected by MAPK pathway phosphorylation array (2 samples in each group), and the relative expression of CREB, ERK1/2, GSK3a/b, MEK, and MSK2 proteins in the Nor group vs PH group was assessed. (D–F) p-MEK1/2, MEK1/2, p-ERK1/2, ERK1/2, Ras, p-c-Raf, c-Raf, B-Raf, p-MSK1, PPARγ, and CREB protein expression in spleen macrophages of the Nor group and pH group (3 samples in each group) by western blot. β-actin was used as the loading control. A two-tailed Student t-test was used to examine the significance of each gene. Results are expressed as mean ± SD. Nor, normal traumatic spleen; PH, hypersplenism spleen; CREB, cAMP-response element binding protein; ERK1/2, extracellular regulated protein kinases1/2; GSK3a/b, glycogen synthase kinase3a/b; PPARγ, peroxisome proliferators-activated receptor. *p<0.05, ns p>0.05.
3.3
PH patients induce M2-like activation through the MEK-ERK-c-MYC axis
Further in vitro cytology experiments were conducted to add MEK inhibitors to the mononuclear cells of the patient’s spleen, and to investigate the activation of the MEK/ERK signaling pathway in macrophages. Use CCK8 to titrate the drug concentration of refametinib (MEK inhibitor) (
Supplementary Figure S3A
). At the same time, Trametinib, selumetinib, and refametinib had a significant inhibitory effect on MEK1/2 in macrophages, with a reduced downstream c-Myc expression. Among them, refametinib exerted the best inhibitory effect (
Supplementary Figures S3B, C
). The expression of phosphorylated ERK1/2 and phosphorylated c-Myc increased in macrophages of patients with hypersplenism compared with that in macrophages of Nor patients spleen, suggesting the activation of MAPK signaling pathway (
Figures 3A, B
). The expression of phosphorylated ERK1/2 and phosphorylated c-Myc in macrophages of the Nor group and hypersplenism spleen was decreased after the administration of the inhibitor compared with that in the macrophages of the control group. A significant difference was observed in the increase of phosphorylated c-Myc (P < 0.05) (
Figures 3A, B
). Based on these results, inhibiting the activation of the MAPK signaling pathway affects the expression of phosphorylated c-Myc protein in PH patients. Therefore, c-Myc is involved in the activation of the MAPK pathway in PH patients (
Figures 3A, B
). We further conducted a C-Myc ChIP studies on splenic macrophages and performed PCR quality controls and qPCR fold enrichment analysis on each Chip sample (
Supplementary Figure S4
). We also performed Chip-seq analysis on two samples. Compare the expression changes of c-Myc-dependent genes in PH and Nor M2 like macrophages. Among the 56 related genes analyzed, 51 genes showed an increased expression (
Figure 3C
). The expression of SCARB1, ALOX15, STAT6, and IL4 was up-regulated in hypersplenism, while the PPARγ expression was down-regulated (
Figure 3D
). Moreover, IFN-γ genes in patients with hypersplenism were downregulated at both molecular and protein levels, with significant differences (
Figures 3E, F
). The comprehensive results indicated that patients with hypersplenism showed an induction of macrophage M2-like activation through the MEK-ERK-c-MYC axis. c-Myc was involved in regulating the expression of alternative activation pathway-dependent genes, probably through the upregulation of IL-4-mediated signal transduction downregulating PPARγ and IFN-γ.
Figure 3
c-Myc participated in the inflammatory response of pH patients through the MEK/ERK signaling pathway. (A, B) p-MEK, MEK, p-c-Myc, and c-Myc protein expression in spleen macrophages of the Nor group and PH group by western blot after the in vitro culture and treatment with Refametinib (2 samples in each group). (C) Expression of c-Myc-dependent genes in PH vs Nor M2-like macrophages compared by immuno-coprecipitation analysis (chip) and chip seq detection. (D) SCARB1, ALOX15, STAT6, PPARγ and IL4 mRNA expression in PH vs Nor macrophages by qPCR (E, F) IFN-γ mRNA and protein expression in PH vs Nor macrophages by qPCR and western blot. *p<0.05, **p<0.01, ns p>0.05.
3.4
c-Myc increased and M2-like macrophages were activated in liver fibrosis mice
Using CD11b magnetic beads to sort mouse spleen Monocyte-macrophages, the results showed that CD11b has high specificity, and different treatment groups can obtain high-purity monocytes-macrophages(CD11b+) (
Supplementary Figure S5A
). When using CD11b and F4/80 antibodies to label and sort positive cells simultaneously, the proportion of CD11b+cells and F4/80+cells was similar (82.7% vs 83.7%), and the proportion of CD11b+F4/80+cells was 74.2% (
Supplementary Figure S5B
) This result suggests that mouse spleen macrophages account for a high proportion of positive cells sorted by CD11b magnetic beads, which can be used for subsequent research.
In addition, the expression of c-Myc and Max mRNA was upregulated in the spleen macrophages of CCl4-induced liver fibrosis mice (
Figure 4A
), and the expression of phosphorylated c-Myc protein was increased in the spleen macrophages of the model mice compared with the control group (P < 0.05,
Figure 4B
). The expression of c-Myc and Max mRNA was upregulated in the spleen macrophages of TAA-induced liver fibrosis mice, and the Max difference is not significant (
Figure 4C
), and the expression of phosphorylated c-Myc protein was increased in the spleen macrophages of TAA model mice (P < 0.05,
Figure 4D
). The mRNA expression of the M2-related inflammatory factors IL4, IL10 and Arg1 was significantly upregulated in spleen macrophages in CCl4 model mice (P < 0.05,
Figure 4E
). The mRNA expression of the M2-like related inflammatory factors IL4, IL10 and arg1 in TAA model mice increased in spleen macrophages, with a significant difference between IL4 and IL10 (P < 0.05) (
Figure 4F
). These results suggested that the increased expression of the c-Myc gene in spleen macrophages of CCl4 and TAA-induced hepatic fibrosis mice might be involved in hepatic fibrosis inflammation and M2-like macrophage activation in hepatic fibrosis mice.
Figure 4
Increased c-Myc expression and activation of M2-like macrophages in CCl4 and TAA-induced liver fibrosis mice. (A) Expression of CD11b + c-Myc and Max mRNA in mouse spleen macrophages sorted by magnetic beads (CD11b +) in the WT group and WT-CCl4 group, analyzed by qPCR (6 samples in the WT group and 5 samples in the WT-CCl4 group). (B) p-c-Myc and c-Myc protein expression in spleen macrophages of the WT group and WT-CCl4 group (2 samples in each group). (C) c-Myc and Max mRNA expression in spleen macrophages of the WT group and WT-TAA group (6 samples in each group). (D) p-c-Myc and c-Myc protein expression in spleen macrophages of the WT group and WT-TAA group by western blot (2 samples in each group). (E, F) Spleen M2-like macrophage-related inflammatory factors IL4, IL10, Arg1 mRNA expression in the CCl4 and TAA model group by qPCR (6 samples in each group). *p<0.05, ns p>0.05.
3.5
Loss of c-Myc in macrophages aggravated CCl4 and TAA induced hepatic fibrosis and inflammation
c-Myc and Max mRNA expression in the KO group decreased compared with that in the WT group (
Figures 5A, B
). p-c-Myc protein expression in spleen macrophages was significantly lower than that in liver, heart, and kidney macrophages (P < 0.05,
Figure 5C
). c-Myc protein expression was not significantly decreased, which was related to the splicing position of exon (
Figure 5C
), In this mice model (Lyz cre+/- c-Myc loxp+/-) the C-Myc gene in KO mice was specifically knocked out in macrophages.
Figure 5
c-Myc deletion in macrophages aggravated CCl4 and TAA-induced liver fibrosis and inflammation (A) Protocol to obtain macrophage specific c-Myc knockout mice. (B) c-Myc and Max mRNA expression in spleen macrophages of knockout mice by qPCR (5 samples in each group). (C) p-c-Myc and c-Myc protein expressions in the liver, heart, kidney, and spleen macrophages of WT and KO mice analyzed and compared by western blot. (D–F) Liver and spleen morphology, spleen index, and liver index in the knockout group and model mice of the CCl4 induced liver fibrosis model compared to the control group (9 samples in WT and KO groups, 17 samples in WT-CCl4 group and 12 samples in KO-CCl4 group) (G, H). In the CCl4 induced liver fibrosis model, the pathological changes of the liver were analyzed by HE staining, and the degree of liver fibrosis was evaluated by Sirus red staining. (I) Quantification of the fibrosis. (J–L) TAA-induced liver fibrosis model, as shown by with HE staining and Sirus red staining, and quantification of the fibrosis (5 samples in WT and KO groups, 9 samples in WT-TAA group, and 10 samples in KO-TAA group). * p<0.05, ** p<0.01, *** p<0.001, **** p<0.0001, ns p>0.05.
Compared with the control group, both CCL4 and TAA models showed an increase in liver index and spleen index in mice, with a significant difference in spleen index (P<0.05) (
Figures 5D–F
;
Supplementary Figures S6A–C
). At the same time, research found that the liver index and spleen index of KO group mice further increased under modeling conditions, with significant differences (P<0.01) (WT-CCL4 vs KO KO-CCL4; WT-TAA vs KO-TAA) (
Figures 5D-F
;
Supplementary Figures S6A–C
). These results suggest that the liver and spleen of mice increased after modeling, while the liver and spleen of C-Myc KO mice showed an increasing trend of enlargement.
The liver of CCl4 and TTA model mice showed evident inflammatory cell infiltration and collagen fiber proliferation than the control group; c-Myc KO model mice showed more severe liver inflammation, and the spleen of the model mice showed evident disorder of splenosome marginal area (WT-CCL4 vs KO KO-CCL4; WT-TAA vs KO-TAA) (
Figures 5G, J
;
Supplementary Figure S6D
). The liver of CCl4 and TTA model mice showed more evident liver fibrosis than the control group, and the liver fibrosis in the c-Myc KO model mice (KO-CCl4 and KO-TAA) was aggravated (
Figures 5H, K
;
Supplementary Figure S6E
). These results showed that liver fibrosis and spleen inflammation in spleen macrophage-specific c-Myc knockout mice were aggravated in the liver fibrosis model induced by CCL4 and TAA.
3.6
c-myc gene regulated the polarization of macrophages to M2-like in a mouse model of liver fibrosis
The proportion of CD206+ M2-like macrophages (F4/80+ CD11c- CD206+) in the WT-CCL4 and WT-TAA group increased compared with that in the WT group mice, with a significant difference in the CCL4 model group (WT vs WT-CCl4), P < 0.05 (
Figures 6A, B
). The proportion of CD206+ M2-like type macrophages (F4/80+ CD11c- CD206+) in the KO-CCL4 and KO-TAA group decreased compared with that in the KO group, with a significant difference in the KO-CCl4 group (KO vs KO-CCl4), P < 0.05 (
Figures 6A, B
). The proportion of total macrophages (F4/80+) and CD11c+ M1-like macrophages (F4/80+ CD11C+ CD206-) decreased compared to that in the KO group (
Supplementary Figure S7
). CD206+ M2-like macrophages (F4/80+/CD206+) showed an increasing trend in the PH spleen (P < 0.05,
Figure 1D
). In histology, the proportion of CD206+M2 like macrophages (F4/80+/CD206+) showed an increasing trend in liver fibrosis mice (WT vs WT-CCL4, WT VS WT-TAA), P<0.05,
Figure 1D
). In macrophage c-Myc knockout mice, the proportion of CD206+M2 like macrophages in the modeling group showed a downward trend (KO vs KO-CCL4, KO vs KO-TAA), with significant differences observed in the CCL4 modeling group (P<0.05,
Figure 1D
). Moreover, the mRNA expression of the M2-like related inflammatory factors CD206 and IL-4 in the KO-CCL4 group significantly decreased compared with that in the KO group (P < 0.05) (
Figure 6D
). The mRNA expression of the M2-like related inflammatory factors CD206, IL-4 and IL-10 in the KO-TAA group was down-regulated compared with that in the KO group, and the down-regulation of the CD206 gene was significant (
Figure 6E
). These results showed that c-Myc deletion reduced the proportion and polarization of M2-like macrophages in CCL4 and TAA-induced liver fibrosis model, and c-Myc gene regulated the polarization of M2-like macrophages.
Figure 6
c-Myc deletion in macrophages reduced the polarization of M2-like macrophages in mice with liver fibrosis. (A, B) Proportion of spleen CD206+ M2-like macrophages (F4/80 + CD11c- CD206+) in the WT and KO group after CCl4 and TAA administration by FACS (5 samples in each group). (C) Positive cells of CD206+ M2-like macrophages (F4/80+CD206+) in the spleen of mouse, as detected by immunofluorescence double staining. (D, E) Relative mRNA expression of M2-like macrophage-related inflammatory factors CD206, IL4, IL10, and Arg1 in different groups after CCl4 and TAA modeling, by qPCR (5 samples in each group). *p<0.05, **p<0.01, ***p<0.001, ns p>0.05.
3.7
C-Myc deletion inhibited the activation of MAPK signaling pathway
The expression of the phosphorylated c-Myc in spleen macrophages of WT-CCL4 mice was significantly higher than that of WT mouse. The expression of phosphorylated c-Myc significantly decreased in the KO-CCL4 group compared with that in the WT-CCL4 group (WT-CCL4 vs KO-CCL4, P < 0.001) (
Figure 7A
). The expression of phosphorylated MEK1/2 and phosphorylated ERK1/2 in spleen macrophages of the WT-CCL4 group mice significantly increased compared with that in the spleen macrophages of the WT group. The expression of phosphorylated MEK1/2 and phosphorylated ERK1/2 significantly decreased in the KO-CCL4 group compared with that in the WT-CCL4 group (P < 0.001) (
Figures 7B, C
). Mouse spleen macrophages PPARγ gene expression was downregulated in the CCL4 group (WT vs KO-CCL4). c-Myc gene knockout significantly upregulated PPARγ expression (WT-CCL4 vs KO-CCL4) (P < 0.001) (
Figure 7D
). IFN-γRβ expression increased in the KO group mice compared with that in the WT group mice. IFN-γRβ expression in CCL4 and TAA model group was significantly reduced compared with that in the control group (WT-CCL4 vs WT, KO-CCL4 vs KO) (P < 0.01) (
Figure 7E
). The MEK/ERK signaling pathway was activated in spleen macrophages of CCl4 or TAA-induced liver fibrosis mice. The c-Myc gene feedback regulated the activation of MAPK signaling pathway and regulated the PPARγ and IFN-γ expression.
Figure 7
c-Myc deletion in spleen macrophages reduced the activation of MAPK signaling pathway. (A) p-c-Myc and c-Myc protein expression in splenic macrophages in each group, and the significant difference among the groups was calculated (2 samples in each group). (B, C) p-MEK1/2, MEK1/2, p-ERK1/2, and ERK1/2 protein expression in splenic macrophages in each group, and the significant difference among the groups was calculated (2 samples in each group). (C) MEK pathway inhibition. (D) Downstream PPARγ gene protein expression and significant difference between groups (2 samples in each group). (E) IFN-γRβ protein expression in splenic macrophages in each group. * p<0.05, ** p<0.01, *** p<0.001, **** p<0.0001, ns p>0.05.
3.1
c-Myc increased and M2-like macrophages were activated in the spleen of PH patients
This study used CD14 magnetic bead sorting of monocyte-macrophages showed high specificity, and high-purity macrophages were obtained from patients with normal traumatic spleen and splenic hypersplenism (92.24% and 89.94%) (
Supplementary Figures S1A, B
). When using CD14 and CD11b antibodies to label and sort positive cells simultaneously, the proportion of CD14+ cells and CD11b+ cells was similar (90.2% vs 89%) (
Supplementary Figures S1A, B
). C-Myc and MAX mRNA expression in splenic macrophages was increased compared to that in normal traumatic spleen (
Figure 1A
). Similarly, c-Myc and phosphorylated c-Myc protein expression significantly increased (P<0.05,
Figure 1B
). Further FACS analysis revealed changes in the proportion of CD86+ M1-like macrophage and CD206+ M2-like macrophage in the spleen (PH vs Nor); the proportion of CD206+ M2-like macrophages (CD11b+CD86-CD206+) or (CD11b+CD11c-CD206+) in PH patients significantly increased (
Figure 1C
,
Supplementary Figure S1C
), while the change in CD86+ M1-like macrophages (CD11b+ CD86+ CD206-) or (CD11b+ CD11c+ CD206-) was not significant (
Figure 1C
,
Supplementary Figure S2C
). CD206+ M2-like macrophages (CD11b+ CD206+) showed an increasing trend in the PH spleen (P < 0.05,
Figure 1D
). The mRNA expression of the M1-like related inflammatory factors CD86, IL-6, IL-1α, and IL-1β showed an upward trend in splenic macrophages of PH patients (
Figure 1E
). The mRNA expression of M2-like inflammatory factors CD206, CD163, IL-10, and related ARG1 is significantly upregulated (P < 0.05,
Figure 1F
). These results demonstrated that the expression of the c-Myc gene was increased in patients with hypersplenism of PH; M2-like macrophages were significantly activated in patients with hypersplenism.
Figure 1
c-Myc increased and M2-like spleen macrophages were activated in PH patients. (A) c-Myc and Max mRNA expression in spleen macrophages sorted by magnetic beads (CD14+) in the Nor group and PH group, analyzed by qPCR (8 samples in the Nor group and 16 samples in the pH group). (B) p-c-Myc and c-Myc protein expressions were higher in PH macrophages than in Nor macrophages, as revealed by western blot (3 samples/group). (C) Ratio of CD86+ M1-like macrophages (CD11b+CD86+CD206-) and CD206+ M2-like macrophages (CD11b+CD86-CD206 +) in splenocyte suspension analyzed by FACS, and the significant difference between the two groups was assessed (6 samples in the Nor group and 9 samples in the PH group). (D) Positive cells of CD206+ M2-like macrophages (CD11b+CD206+) in the spleen of patients, as detected by immunofluorescence double staining. (E, F) Relative mRNA expression of M1-like and M2-like macrophage related inflammatory factors in the spleen (8 samples in the Nor group and 16 samples in the PH group). *p<0.05, **p<0.01, ***p<0.001, ns p>0.05.
3.2
MAPK signaling pathway was activated in spleen macrophages of PH patients
JAK-STAT and TLR4/NF-κB signaling pathways in the PH group were activated compared to the Nor group (
Supplementary Figures S2A, B
), consistent with the reported results in previous studies (16). KEGG analysis showed that MAPK signaling pathway was activated in patients with hypersplenism (
Figures 2A, B
). In addition, the MAPK signaling pathway was significantly activated, showing that the expression of MEK (P-S217/221), ERK1 (P-T202/Y203), and ERK2 (P-Y185/Y187) phosphorylation in macrophages of PH patients was significantly increased, as well as the expression of downstream phosphorylated MSK2 and the expression of CREB phosphorylation s133 site (P < 0.05) (
Figure 2C
,
Supplementary Figure S2C
). The expression of phosphorylated MEK1/2 and phosphorylated ERK1/2 in pH spleen macrophages significantly increased compared to that in normal spleen (P < 0.05). The expression of the upstream Ras, p-c-Raf, c-Raf, and B-Raf significantly increased as well as that of the downstream p-MSK1 and CREB (P < 0.05) (
Figures 2D–F
). Previous studies reported that the activity of PPARγ is inhibited by MAPK phosphorylation (26). The expression of PPARγ protein in macrophages was significantly reduced in the PH spleen (P < 0.01), further suggesting the activation of the MAPK pathway (
Figure 2F
). These results demonstrated that the MAPK signaling pathway in PH patients was activated through the Ras-Raf-MEK-ERK axis.
Figure 2
MAPK signaling pathway was activated in spleen macrophages of hypersplenism patients with portal hypertension (PH). (A, B) RNA-seq was performed on spleen macrophages (CD14+) in the Nor group and PH group (6 samples in each group), and the volcano plot showed the distribution of the differentially expressed genes (DEGs). KEGG enrichment analysis of RNA-seq data showing the expression of the related pathways in the Nor group vs the pH group. (C) Expression of MAPK pathway related proteins was further detected by MAPK pathway phosphorylation array (2 samples in each group), and the relative expression of CREB, ERK1/2, GSK3a/b, MEK, and MSK2 proteins in the Nor group vs PH group was assessed. (D–F) p-MEK1/2, MEK1/2, p-ERK1/2, ERK1/2, Ras, p-c-Raf, c-Raf, B-Raf, p-MSK1, PPARγ, and CREB protein expression in spleen macrophages of the Nor group and pH group (3 samples in each group) by western blot. β-actin was used as the loading control. A two-tailed Student t-test was used to examine the significance of each gene. Results are expressed as mean ± SD. Nor, normal traumatic spleen; PH, hypersplenism spleen; CREB, cAMP-response element binding protein; ERK1/2, extracellular regulated protein kinases1/2; GSK3a/b, glycogen synthase kinase3a/b; PPARγ, peroxisome proliferators-activated receptor. *p<0.05, ns p>0.05.
3.3
PH patients induce M2-like activation through the MEK-ERK-c-MYC axis
Further in vitro cytology experiments were conducted to add MEK inhibitors to the mononuclear cells of the patient’s spleen, and to investigate the activation of the MEK/ERK signaling pathway in macrophages. Use CCK8 to titrate the drug concentration of refametinib (MEK inhibitor) (
Supplementary Figure S3A
). At the same time, Trametinib, selumetinib, and refametinib had a significant inhibitory effect on MEK1/2 in macrophages, with a reduced downstream c-Myc expression. Among them, refametinib exerted the best inhibitory effect (
Supplementary Figures S3B, C
). The expression of phosphorylated ERK1/2 and phosphorylated c-Myc increased in macrophages of patients with hypersplenism compared with that in macrophages of Nor patients spleen, suggesting the activation of MAPK signaling pathway (
Figures 3A, B
). The expression of phosphorylated ERK1/2 and phosphorylated c-Myc in macrophages of the Nor group and hypersplenism spleen was decreased after the administration of the inhibitor compared with that in the macrophages of the control group. A significant difference was observed in the increase of phosphorylated c-Myc (P < 0.05) (
Figures 3A, B
). Based on these results, inhibiting the activation of the MAPK signaling pathway affects the expression of phosphorylated c-Myc protein in PH patients. Therefore, c-Myc is involved in the activation of the MAPK pathway in PH patients (
Figures 3A, B
). We further conducted a C-Myc ChIP studies on splenic macrophages and performed PCR quality controls and qPCR fold enrichment analysis on each Chip sample (
Supplementary Figure S4
). We also performed Chip-seq analysis on two samples. Compare the expression changes of c-Myc-dependent genes in PH and Nor M2 like macrophages. Among the 56 related genes analyzed, 51 genes showed an increased expression (
Figure 3C
). The expression of SCARB1, ALOX15, STAT6, and IL4 was up-regulated in hypersplenism, while the PPARγ expression was down-regulated (
Figure 3D
). Moreover, IFN-γ genes in patients with hypersplenism were downregulated at both molecular and protein levels, with significant differences (
Figures 3E, F
). The comprehensive results indicated that patients with hypersplenism showed an induction of macrophage M2-like activation through the MEK-ERK-c-MYC axis. c-Myc was involved in regulating the expression of alternative activation pathway-dependent genes, probably through the upregulation of IL-4-mediated signal transduction downregulating PPARγ and IFN-γ.
Figure 3
c-Myc participated in the inflammatory response of pH patients through the MEK/ERK signaling pathway. (A, B) p-MEK, MEK, p-c-Myc, and c-Myc protein expression in spleen macrophages of the Nor group and PH group by western blot after the in vitro culture and treatment with Refametinib (2 samples in each group). (C) Expression of c-Myc-dependent genes in PH vs Nor M2-like macrophages compared by immuno-coprecipitation analysis (chip) and chip seq detection. (D) SCARB1, ALOX15, STAT6, PPARγ and IL4 mRNA expression in PH vs Nor macrophages by qPCR (E, F) IFN-γ mRNA and protein expression in PH vs Nor macrophages by qPCR and western blot. *p<0.05, **p<0.01, ns p>0.05.
3.4
c-Myc increased and M2-like macrophages were activated in liver fibrosis mice
Using CD11b magnetic beads to sort mouse spleen Monocyte-macrophages, the results showed that CD11b has high specificity, and different treatment groups can obtain high-purity monocytes-macrophages(CD11b+) (
Supplementary Figure S5A
). When using CD11b and F4/80 antibodies to label and sort positive cells simultaneously, the proportion of CD11b+cells and F4/80+cells was similar (82.7% vs 83.7%), and the proportion of CD11b+F4/80+cells was 74.2% (
Supplementary Figure S5B
) This result suggests that mouse spleen macrophages account for a high proportion of positive cells sorted by CD11b magnetic beads, which can be used for subsequent research.
In addition, the expression of c-Myc and Max mRNA was upregulated in the spleen macrophages of CCl4-induced liver fibrosis mice (
Figure 4A
), and the expression of phosphorylated c-Myc protein was increased in the spleen macrophages of the model mice compared with the control group (P < 0.05,
Figure 4B
). The expression of c-Myc and Max mRNA was upregulated in the spleen macrophages of TAA-induced liver fibrosis mice, and the Max difference is not significant (
Figure 4C
), and the expression of phosphorylated c-Myc protein was increased in the spleen macrophages of TAA model mice (P < 0.05,
Figure 4D
). The mRNA expression of the M2-related inflammatory factors IL4, IL10 and Arg1 was significantly upregulated in spleen macrophages in CCl4 model mice (P < 0.05,
Figure 4E
). The mRNA expression of the M2-like related inflammatory factors IL4, IL10 and arg1 in TAA model mice increased in spleen macrophages, with a significant difference between IL4 and IL10 (P < 0.05) (
Figure 4F
). These results suggested that the increased expression of the c-Myc gene in spleen macrophages of CCl4 and TAA-induced hepatic fibrosis mice might be involved in hepatic fibrosis inflammation and M2-like macrophage activation in hepatic fibrosis mice.
Figure 4
Increased c-Myc expression and activation of M2-like macrophages in CCl4 and TAA-induced liver fibrosis mice. (A) Expression of CD11b + c-Myc and Max mRNA in mouse spleen macrophages sorted by magnetic beads (CD11b +) in the WT group and WT-CCl4 group, analyzed by qPCR (6 samples in the WT group and 5 samples in the WT-CCl4 group). (B) p-c-Myc and c-Myc protein expression in spleen macrophages of the WT group and WT-CCl4 group (2 samples in each group). (C) c-Myc and Max mRNA expression in spleen macrophages of the WT group and WT-TAA group (6 samples in each group). (D) p-c-Myc and c-Myc protein expression in spleen macrophages of the WT group and WT-TAA group by western blot (2 samples in each group). (E, F) Spleen M2-like macrophage-related inflammatory factors IL4, IL10, Arg1 mRNA expression in the CCl4 and TAA model group by qPCR (6 samples in each group). *p<0.05, ns p>0.05.
3.5
Loss of c-Myc in macrophages aggravated CCl4 and TAA induced hepatic fibrosis and inflammation
c-Myc and Max mRNA expression in the KO group decreased compared with that in the WT group (
Figures 5A, B
). p-c-Myc protein expression in spleen macrophages was significantly lower than that in liver, heart, and kidney macrophages (P < 0.05,
Figure 5C
). c-Myc protein expression was not significantly decreased, which was related to the splicing position of exon (
Figure 5C
), In this mice model (Lyz cre+/- c-Myc loxp+/-) the C-Myc gene in KO mice was specifically knocked out in macrophages.
Figure 5
c-Myc deletion in macrophages aggravated CCl4 and TAA-induced liver fibrosis and inflammation (A) Protocol to obtain macrophage specific c-Myc knockout mice. (B) c-Myc and Max mRNA expression in spleen macrophages of knockout mice by qPCR (5 samples in each group). (C) p-c-Myc and c-Myc protein expressions in the liver, heart, kidney, and spleen macrophages of WT and KO mice analyzed and compared by western blot. (D–F) Liver and spleen morphology, spleen index, and liver index in the knockout group and model mice of the CCl4 induced liver fibrosis model compared to the control group (9 samples in WT and KO groups, 17 samples in WT-CCl4 group and 12 samples in KO-CCl4 group) (G, H). In the CCl4 induced liver fibrosis model, the pathological changes of the liver were analyzed by HE staining, and the degree of liver fibrosis was evaluated by Sirus red staining. (I) Quantification of the fibrosis. (J–L) TAA-induced liver fibrosis model, as shown by with HE staining and Sirus red staining, and quantification of the fibrosis (5 samples in WT and KO groups, 9 samples in WT-TAA group, and 10 samples in KO-TAA group). * p<0.05, ** p<0.01, *** p<0.001, **** p<0.0001, ns p>0.05.
Compared with the control group, both CCL4 and TAA models showed an increase in liver index and spleen index in mice, with a significant difference in spleen index (P<0.05) (
Figures 5D–F
;
Supplementary Figures S6A–C
). At the same time, research found that the liver index and spleen index of KO group mice further increased under modeling conditions, with significant differences (P<0.01) (WT-CCL4 vs KO KO-CCL4; WT-TAA vs KO-TAA) (
Figures 5D-F
;
Supplementary Figures S6A–C
). These results suggest that the liver and spleen of mice increased after modeling, while the liver and spleen of C-Myc KO mice showed an increasing trend of enlargement.
The liver of CCl4 and TTA model mice showed evident inflammatory cell infiltration and collagen fiber proliferation than the control group; c-Myc KO model mice showed more severe liver inflammation, and the spleen of the model mice showed evident disorder of splenosome marginal area (WT-CCL4 vs KO KO-CCL4; WT-TAA vs KO-TAA) (
Figures 5G, J
;
Supplementary Figure S6D
). The liver of CCl4 and TTA model mice showed more evident liver fibrosis than the control group, and the liver fibrosis in the c-Myc KO model mice (KO-CCl4 and KO-TAA) was aggravated (
Figures 5H, K
;
Supplementary Figure S6E
). These results showed that liver fibrosis and spleen inflammation in spleen macrophage-specific c-Myc knockout mice were aggravated in the liver fibrosis model induced by CCL4 and TAA.
3.6
c-myc gene regulated the polarization of macrophages to M2-like in a mouse model of liver fibrosis
The proportion of CD206+ M2-like macrophages (F4/80+ CD11c- CD206+) in the WT-CCL4 and WT-TAA group increased compared with that in the WT group mice, with a significant difference in the CCL4 model group (WT vs WT-CCl4), P < 0.05 (
Figures 6A, B
). The proportion of CD206+ M2-like type macrophages (F4/80+ CD11c- CD206+) in the KO-CCL4 and KO-TAA group decreased compared with that in the KO group, with a significant difference in the KO-CCl4 group (KO vs KO-CCl4), P < 0.05 (
Figures 6A, B
). The proportion of total macrophages (F4/80+) and CD11c+ M1-like macrophages (F4/80+ CD11C+ CD206-) decreased compared to that in the KO group (
Supplementary Figure S7
). CD206+ M2-like macrophages (F4/80+/CD206+) showed an increasing trend in the PH spleen (P < 0.05,
Figure 1D
). In histology, the proportion of CD206+M2 like macrophages (F4/80+/CD206+) showed an increasing trend in liver fibrosis mice (WT vs WT-CCL4, WT VS WT-TAA), P<0.05,
Figure 1D
). In macrophage c-Myc knockout mice, the proportion of CD206+M2 like macrophages in the modeling group showed a downward trend (KO vs KO-CCL4, KO vs KO-TAA), with significant differences observed in the CCL4 modeling group (P<0.05,
Figure 1D
). Moreover, the mRNA expression of the M2-like related inflammatory factors CD206 and IL-4 in the KO-CCL4 group significantly decreased compared with that in the KO group (P < 0.05) (
Figure 6D
). The mRNA expression of the M2-like related inflammatory factors CD206, IL-4 and IL-10 in the KO-TAA group was down-regulated compared with that in the KO group, and the down-regulation of the CD206 gene was significant (
Figure 6E
). These results showed that c-Myc deletion reduced the proportion and polarization of M2-like macrophages in CCL4 and TAA-induced liver fibrosis model, and c-Myc gene regulated the polarization of M2-like macrophages.
Figure 6
c-Myc deletion in macrophages reduced the polarization of M2-like macrophages in mice with liver fibrosis. (A, B) Proportion of spleen CD206+ M2-like macrophages (F4/80 + CD11c- CD206+) in the WT and KO group after CCl4 and TAA administration by FACS (5 samples in each group). (C) Positive cells of CD206+ M2-like macrophages (F4/80+CD206+) in the spleen of mouse, as detected by immunofluorescence double staining. (D, E) Relative mRNA expression of M2-like macrophage-related inflammatory factors CD206, IL4, IL10, and Arg1 in different groups after CCl4 and TAA modeling, by qPCR (5 samples in each group). *p<0.05, **p<0.01, ***p<0.001, ns p>0.05.
3.7
C-Myc deletion inhibited the activation of MAPK signaling pathway
The expression of the phosphorylated c-Myc in spleen macrophages of WT-CCL4 mice was significantly higher than that of WT mouse. The expression of phosphorylated c-Myc significantly decreased in the KO-CCL4 group compared with that in the WT-CCL4 group (WT-CCL4 vs KO-CCL4, P < 0.001) (
Figure 7A
). The expression of phosphorylated MEK1/2 and phosphorylated ERK1/2 in spleen macrophages of the WT-CCL4 group mice significantly increased compared with that in the spleen macrophages of the WT group. The expression of phosphorylated MEK1/2 and phosphorylated ERK1/2 significantly decreased in the KO-CCL4 group compared with that in the WT-CCL4 group (P < 0.001) (
Figures 7B, C
). Mouse spleen macrophages PPARγ gene expression was downregulated in the CCL4 group (WT vs KO-CCL4). c-Myc gene knockout significantly upregulated PPARγ expression (WT-CCL4 vs KO-CCL4) (P < 0.001) (
Figure 7D
). IFN-γRβ expression increased in the KO group mice compared with that in the WT group mice. IFN-γRβ expression in CCL4 and TAA model group was significantly reduced compared with that in the control group (WT-CCL4 vs WT, KO-CCL4 vs KO) (P < 0.01) (
Figure 7E
). The MEK/ERK signaling pathway was activated in spleen macrophages of CCl4 or TAA-induced liver fibrosis mice. The c-Myc gene feedback regulated the activation of MAPK signaling pathway and regulated the PPARγ and IFN-γ expression.
Figure 7
c-Myc deletion in spleen macrophages reduced the activation of MAPK signaling pathway. (A) p-c-Myc and c-Myc protein expression in splenic macrophages in each group, and the significant difference among the groups was calculated (2 samples in each group). (B, C) p-MEK1/2, MEK1/2, p-ERK1/2, and ERK1/2 protein expression in splenic macrophages in each group, and the significant difference among the groups was calculated (2 samples in each group). (C) MEK pathway inhibition. (D) Downstream PPARγ gene protein expression and significant difference between groups (2 samples in each group). (E) IFN-γRβ protein expression in splenic macrophages in each group. * p<0.05, ** p<0.01, *** p<0.001, **** p<0.0001, ns p>0.05.
4
Discussion
Hypersplenic function is a common manifestation of liver cirrhosis, which leads to a decrease in the number of third line cells and induces upper gastrointestinal bleeding, posing a serious threat to the patient’s life. Splenectomy is usually performed on patients with liver cirrhosis and hepatitis, which improves immune response, and reduces the risk of liver cancer (27–29). However, still many complications can occur; splenectomy increases the risk of portal vein thrombosis in patients with liver cirrhosis by at least 10 times (30). Our previous studies revealed that macrophage phagocytose blood cells in the spleen of patients with splenomegaly lead to the abnormal activation of splenic macrophages. However, the type and mechanism of activation are unclear.
Splenectomy regulates macrophages phenotypic conversion in hepatic fibrosis. promotes fibrosis and fibrosis ablation in the progression and regression of liver cirrhosis, respectively (21, 31, 32). The study found that macrophages are recruited to the tissue injury area at different stages of liver fibrosis, and they switch between different states, including pro-inflammatory M1 and anti-inflammatory M2, affecting liver fibrosis through phenotypic transformation (12, 33–35). Knockout of mir-98-5p alleviates inflammatory bowel disease symptoms by increasing the expression of Trib1 and changing the polarization of macrophages to M2 phenotype (36). In kidney injury inflammation and fibrosis, macrophages differentiate into M2-like by releasing IL10, arginase, TGF-β, and HO-1 to play an anti-inflammatory role (12, 37). The results of this study also confirmed that M2-like macrophages were activated in patients with hypersplenism, and the inhibition of M2-like polarization of macrophages led to increased inflammation of liver fibrosis in mice. Therefore, the intervention on M2-like polarization has become an important approach of alleviating liver inflammation and splenomegaly.
MYC produces the transcription factor Myc, which dimerizes with Max and binds target DNA sequences or E boxes (with the sequence 50-CANNTG-30) to regulate the transcription of genes involved in cell growth and proliferation (38). Multiple studies reported that the c-Myc gene is involved in regulating the inflammatory response of M2 macrophage (14, 39–41). In this study, the expression of c-Myc in splenic macrophages was upregulated in patients with hypersplenism and in two types of liver fibrosis model mice. Macrophage c-Myc specific knockout inhibited M2-like polarization and affected liver fibrosis inflammation. The c-Myc gene in splenic hypersplenism might be a transcription factor of M2-like macrophages, participating in the regulation of macrophages in patients with PH splenic hypersplenism. Therefore, regulating the expression of the c-Myc gene might alter the inflammation and microenvironment of patients with hypersplenism.
ERKs can not only phosphorylate envelope proteins, but also some nuclear transcription factors, such as c-fos, c-jun, c-Myc, and ATF2, which are involved in the regulation of cell proliferation and differentiation (42). c-Myc is a downstream effector of ERK signaling, and the central metabolic axis of MEK-ERK-c-Myc is also crucial in combating tumor progression (43). There are also many reports revealing that downstream c-Myc genes are involved in the regulation of negative feedback pathways in tumors (44, 45). It has been reported that ERK also phosphorylates the upstream proteins of its pathway, including neg receptor, SOS, Raf-1, and MEK, and then regulates the signaling pathway by its own negative feedback, participating in tumor regulation (46). This study confirmed the activation of the MEK/ERK signaling pathway in splenic macrophages in both hypersplenism patients and liver fibrosis mice. c-Myc is a downstream transcription factor of the MEK/ERK pathway. Patient samples showed the inhibition of MEK pathway activation and the downregulation of c-Myc gene expression. In liver fibrosis mice, c-Myc gene knockout reduced the activation of the MEK/ERK pathway; thus, c-Myc might participate in the polarization of M2-like macrophages through a feedback regulation of MAPK signaling pathway activation.
Previous studies showed that IL4 induces the activation of M2-like macrophages through different pathways (14, 47, 48). c-Myc directly regulates and replaces activation related genes, upregulates signaling mediators involved in IL4, and transcriptional activators STAS6 and PPARγ involved in the expression of tumor related macrophages (14). Research reports that IL-4-induced and MEK/ERK-mediated PPARγ and retinoic acid (RA) signaling are required for M2-like macrophage polarization (48). In this study, the results of patients with hypersplenism showed that the c-Myc regulatory alternative activation pathway dependent genes by upregulation of IL-4 mediated signal transduction to participate in M2-like polarization and downregulation of PPARγ and IFN-γ gene expression. Animal experiments showed that c-Myc knockout led to a decrease in macrophage IL-4 expression and increase PPARγ activation, while the expression of IFN-γRβ was suppressed, further supporting the results obtained with patients. The specific mechanism of IFN-γ And PPARγ involved in the polarization of M2-like macrophages still needs further studies in patients with PH.
In conclusion, c-Myc regulates the activation of M2-like macrophages through the MEK-ERK-c-Myc axis in patients with hypersplenism. The c-Myc gene may exert anti-inflammatory effects by upregulating IL-4-mediated signal transduction to promote M2-like differentiation and anti-inflammatory cytokine secretion (
Figure 8
). Therefore, c-Myc in macrophages may become an important target for polarization therapy.
Figure 8
Schematic explanation of the mechanism of action: the transformation into M2-like of spleen macrophages in patients with PH is activated, which in turn activates the Ras-Raf-MEK-ERK-c-Myc signaling pathway axis. The inhibition of the MEK signaling pathway reduces the expression of c-Myc. Macrophage c-Myc specific knockout inhibits macrophage activation into the M2-like, reduces repair ability, and exacerbates fibrotic liver inflammation. c-Myc might provide feedback on the activation of MAPK signaling pathway and upregulate the expression of IL4 and M2-like related genes. |
Title: Effects of Different No-Ozone Cold Plasma Treatment Methods on Mouse Osteoblast Proliferation and Differentiation | Body:
1. Introduction
Osteoporosis is a systemic skeletal disease that increases the risk of bone fractures, owing to decreased bone density and mass [1]. According to the International Osteoporosis Foundation, one in five women and one in five men older than 50 years worldwide have osteoporosis [2]. The main causes of osteoporosis are aging and, in women, a rapid decrease in estrogen levels owing to menopause [3]. Therefore, the prevention and treatment of osteoporosis are crucial.
Bone tissue comprises hydroxyapatite crystals, collagen, and non-collagenous proteins [4]. Most bone-related diseases are associated with decreased expression of factors related to bone formation [5]. Additionally, hormonal imbalances during bone remodeling can lead to bone loss [6] Consequently, maintaining bone homeostasis, which requires a balance between osteoblasts (bone-forming cells) and osteoclasts (bone-absorbing cells), is crucial for preventing osteoporosis [7]. Current treatments for osteoporosis include drug therapy; however, they have a limited effect in terms of long-term safety [8,9]. In contrast, recent studies suggest that enhancing osteoblast differentiation may represent a new method for preventing and treating osteoporosis [10].
Cold plasma, the fourth state of matter after solids, liquids, and gases, is being extensively studied for its medical applications [11]. Studies have investigated its use in killing bacteria [12], regenerating damaged nerves [13], inducing cancer cell death [14], and treating oral cavity infections [15]. Additionally, several studies have explored the role of cold plasma in osteoblast differentiation [16]. Cold plasma treatment increases β-catenin expression after one day of differentiation in human periodontal ligament cells and elevates BMP2 and Runx2 levels after three days of differentiation. This process controls differentiation into osteoblasts through Wnt/β-catenin signaling, ultimately leading to increased levels of ALP and related factors, which are indicators of osteoblast differentiation [17].
Cold plasma emits various active species, such as reactive oxygen species (ROS) and hydroxyl radicals (∙OH), depending on the processing method, and ionizes gases using existing energy sources [18]. Cold plasma-generating gases include argon, helium, and oxygen, whose effects vary depending on the gas used [19]. Cold plasma processing methods are largely divided into direct and indirect methods, with different outcomes based on the processing method used. Direct treatment of microorganisms with cold plasma has a much faster effect than indirect treatment and has been shown to kill streptococci, staphylococci, and yeasts [20]. Additionally, studies on Cutibacterium acnes have shown that direct cold plasma treatment is more effective in killing bacteria than indirect plasma-activated water [20].
However, direct treatment methods result in the generation of higher ozone levels, reported at 57 ppm (compared to indirect methods) [12]. Although ozone has potential therapeutic applications, excessive amounts of it pose health risks. Moreover, ozone has been considered a toxic gas until the 16th century; however, it has recently been used for the potential treatment of various diseases [21]. Specifically, it is effective against skin diseases [22], oral diseases [23], and cancer [24]. Despite its potential, the safety of ozone remains a concern since acute exposure to it can cause serious problems such as decreased lung function and temporary abnormalities in respiratory function [25]. Accordingly, the Food and Drug Administration (FDA) has set the acceptable ozone generation standard for medical devices to <0.05 ppm over a specified period [26].
Therefore, in this study, we evaluated bone formation in mouse osteoblasts based on the plasma treatment method using the No-ozone Cold Plasma (NCP) device developed by our research team.
2. Materials and Methods
2.1. NCP Generation
The NCP used in this experiment was generated using a device called Periplapy, developed by Feagle Co., Ltd. (Yangsan, Republic of Korea), which is a registered patent, trademark and design at the Korean Intellectual Property Office. The device was tested by the Korea Testing and Research Institute and generates a maximum ozone concentration of 0.008 ppm, nitrogen monoxide of <0.001 ppm, and nitrogen dioxide of 0.007 ppm. Figure 1 shows a photograph and a schematic diagram of the NCP device.
The device consists of a switched-mode power supply, a mainboard for control, a high-voltage circuit, a pressure sensor, and a regulator. It has an LCD panel and a handpiece as an attachment (hand-held). When the power button is pressed on, power is supplied to the main body, and argon gas is delivered to the handpiece at a constant flow rate. NCP is generated inside the nozzle of the handpiece when argon gas flows through it and AC high voltage is applied to the inner and outer electrodes of the nozzle. The NCP intensity can be set to one of three modes. In this study it was set to Mode 3 at 4.5 kVpp, 1.25 slm ± 20%. We opted for this experimental setting to maximize the effectiveness of the NCP treatment while keeping the duration brief.
2.2. Cell Culture
MC3T3-E1 cells were purchased from ATCC (Manassas, VA, USA). The MC3T3-E1 cells were cultured in α-MEM (Gibco BRL, Gaithersburg, MD, USA) containing 10% fetal bovine serum (FBS) (Gibco BRL), and 1% antibiotics (Gibco BRL) and incubated at 37 °C in an atmosphere of 5% CO2.
The following osteogenic differentiation induction medium was used for the osteoblast differentiation experiment: α-MEM supplemented with 10% FBS, 10 mM beta-glycerophosphate (Sigma-Aldrich, St Louis, MO, USA), 50 µg/mL ascorbic acid (Sigma-Aldrich), and 100 nM dexamethasone (Gibco BRL). Conventional media were used for testing the cell proliferation rates across all groups, while osteogenic induction media were used when differentiation began. The differentiation control group was treated with the osteogenic culture medium.
2.3. NCP Treatment
The cells were divided into the following four groups:First group: not treated with NCP (NT);Second group: treated directly with NCP in the dish with cells (DT);Third group: treated directly with NCP in the dish, and the medium was immediately replaced (MC);Fourth group: treated with NCP in a 35 mm dish without cells for 1 min, and then transferred to the dish with cells (PAM).
The treatment time for all NCP applications was 1 min, and the distance between the NCP device and the cells was 20 mm. All NCP groups were cultured after one treatment with NCP.
2.4. Water-Soluble Tetrazolium 1 (WST-1) Salt Assay
MC3T3-E1 cells were seeded at a density of 2 × 105 cells in a 35 mm dish and incubated at 37 °C for 24 h. Each treatment group was treated with NCP and cultured for 24 or 48 h. Subsequently, the WST-1 solution (EX-Cytox, DoGenBio, Seoul, Republic of Korea) was diluted with media in a 1:9 ratio, and 1 mL of the resulting media was added to each well. After culturing in an incubator at 37 °C for 2 h, cell viability was measured using a microplate reader (Thermo Fisher Scientific, Darmstadt, Germany) at 450 nm [27]. All experiments were conducted independently and repeated in triplicate.
2.5. Alkaline Phosphatase (ALP) Staining
MC3T3-E1 cells were seeded at a density of 2 × 105 cells in a 35 mm dish and incubated at 37 °C for 24 h. Each NCP-treated group was cultured in osteogenic medium for 1 or 2 weeks, with medium changes every 2–3 days. After incubation, the cells were fixed for 1 min using 3.7% formaldehyde (Sigma-Aldrich, St Louis, MO, USA) and 90% ethanol solution (Sigma-Aldrich, St Louis, MO, USA), followed by washing with TBS solution. Staining was performed in a dark room for 20 min with a fast 5-bromo-4-chloro-3-indolyl phosphate and nitroblue tetrazolium (BCIP/NBT) ALP substrate solution (B6404, Sigma-Aldrich, St Louis, MO, USA) [28]. Subsequently, the cells were washed four times with distilled water (DW) and photographed using a camera.
2.6. ALP Activity
MC3T3-E1 cells were seeded at a density of 2 × 105 cells in a 35 mm dish and incubated at 37 °C for 24 h. Subsequently, each NCP-treated group was cultured in osteogenic medium for 2 weeks, with medium changes every 2–3 days. Cells were washed with phosphate buffered saline (PBS), sonicated in 0.1 M Tris buffer containing 0.5% Triton X-100 solution on ice at 4 °C, centrifuged at 14,000 rpm for 10 min. The supernatant was used for Bradford analysis. ALP was measured using a laboratory assay ALP kit (Wako Pure Chemicals, Osaka, Japan) and a microplate reader at 450 nm [29]. Data analysis was performed according to the manufacturer’s instructions.
2.7. Alizarin Red S Staining
MC3T3-E1 cells were seeded at a density of 2 × 105 cells in a 35 mm dish and incubated at 37 °C for 24 h. Subsequently, each NCP-treated group was cultured in osteoinduction medium for 2 weeks, with medium changes every 2–3 days. After incubation, the cells were washed with PBS and fixed with 10% formaldehyde (Sigma-Aldrich) for 15 min. After washing twice with DW, the fixed cells were stained with 2% Alizarin Red S (pH 4.2) (Sigma-Aldrich) for 2 min, washed with DW, dried, and imaged using a camera. After imaging, the stained cells were exposed to 100 mM cetylpyridinium chloride (Sigma-Aldrich) for 2 h and the absorbance of the eluted supernatant was measured at 575 nm using a microplate reader [28].
2.8. Real-Time Polymerase Chain Reaction (qPCR)
MC3T3-E1 cells were seeded at a density of 2 × 105 cells in a 35 mm dish and incubated at 37 °C for 24 h. Each NCP-treated group was cultured in osteoinduction medium for 2 weeks, with medium changes every 2–3 days. After incubation, total RNA was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA, USA). cDNA was prepared by reverse transcription (One-step PreMix kit; iNtRON Biotechnology Inc., Seoul, Republic of Korea) on a T100 Thermal Cycler (Bio-Rad, Hercules, CA, USA), and PCR amplification was performed using a SensiFAT SYBR No ROX kit (Bioline, OH, USA) on a CFX 96 Real-Time System (Bio-Rad, Hercules, CA, USA). Relative quantification was achieved using the comparative 2−ΔΔCt method. All samples were run in triplicate and normalized to the housekeeping gene, GAPDH [17]. The following primers were used: alkaline phosphatase-1 Forward, 5′-ACGAGGTCACGTCCATCCT-3′; alkaline phosphatase-1 Reverse, 5′-CCGAGTGGTGGTCACGAT-3′, osteocalcin Forward, 5′-ACAGACAAGTCCCACACAGCAACT-3′; osteocalcin Reverse 5′-CCTGCTTGGACATGAAGGCTTTGT-3′, GAPDH Forward 5′-TGGGAAGCTGGTCATCAAC-3′; GAPDH Reverse 5′-GCATCACCCCATTTGATGTT3-3′.
2.9. Statistical Analysis
All experiments were independently repeated three times (n = 3). The results were graphed using GraphPad Prism 5.0, and statistical analysis was performed using the SPSS (version 24, Chicago, IL, USA) statistical software package. Data analysis was performed using one-way ANOVA, followed by Duncan’s post hoc analysis. The different letters shown in the figures indicate significant differences among the groups after post-analysis. In other words, the difference between groups with the same letter is not statistically significant, whereas the difference between groups with different letters is statistically significant.
2.1. NCP Generation
The NCP used in this experiment was generated using a device called Periplapy, developed by Feagle Co., Ltd. (Yangsan, Republic of Korea), which is a registered patent, trademark and design at the Korean Intellectual Property Office. The device was tested by the Korea Testing and Research Institute and generates a maximum ozone concentration of 0.008 ppm, nitrogen monoxide of <0.001 ppm, and nitrogen dioxide of 0.007 ppm. Figure 1 shows a photograph and a schematic diagram of the NCP device.
The device consists of a switched-mode power supply, a mainboard for control, a high-voltage circuit, a pressure sensor, and a regulator. It has an LCD panel and a handpiece as an attachment (hand-held). When the power button is pressed on, power is supplied to the main body, and argon gas is delivered to the handpiece at a constant flow rate. NCP is generated inside the nozzle of the handpiece when argon gas flows through it and AC high voltage is applied to the inner and outer electrodes of the nozzle. The NCP intensity can be set to one of three modes. In this study it was set to Mode 3 at 4.5 kVpp, 1.25 slm ± 20%. We opted for this experimental setting to maximize the effectiveness of the NCP treatment while keeping the duration brief.
2.2. Cell Culture
MC3T3-E1 cells were purchased from ATCC (Manassas, VA, USA). The MC3T3-E1 cells were cultured in α-MEM (Gibco BRL, Gaithersburg, MD, USA) containing 10% fetal bovine serum (FBS) (Gibco BRL), and 1% antibiotics (Gibco BRL) and incubated at 37 °C in an atmosphere of 5% CO2.
The following osteogenic differentiation induction medium was used for the osteoblast differentiation experiment: α-MEM supplemented with 10% FBS, 10 mM beta-glycerophosphate (Sigma-Aldrich, St Louis, MO, USA), 50 µg/mL ascorbic acid (Sigma-Aldrich), and 100 nM dexamethasone (Gibco BRL). Conventional media were used for testing the cell proliferation rates across all groups, while osteogenic induction media were used when differentiation began. The differentiation control group was treated with the osteogenic culture medium.
2.3. NCP Treatment
The cells were divided into the following four groups:First group: not treated with NCP (NT);Second group: treated directly with NCP in the dish with cells (DT);Third group: treated directly with NCP in the dish, and the medium was immediately replaced (MC);Fourth group: treated with NCP in a 35 mm dish without cells for 1 min, and then transferred to the dish with cells (PAM).
The treatment time for all NCP applications was 1 min, and the distance between the NCP device and the cells was 20 mm. All NCP groups were cultured after one treatment with NCP.
2.4. Water-Soluble Tetrazolium 1 (WST-1) Salt Assay
MC3T3-E1 cells were seeded at a density of 2 × 105 cells in a 35 mm dish and incubated at 37 °C for 24 h. Each treatment group was treated with NCP and cultured for 24 or 48 h. Subsequently, the WST-1 solution (EX-Cytox, DoGenBio, Seoul, Republic of Korea) was diluted with media in a 1:9 ratio, and 1 mL of the resulting media was added to each well. After culturing in an incubator at 37 °C for 2 h, cell viability was measured using a microplate reader (Thermo Fisher Scientific, Darmstadt, Germany) at 450 nm [27]. All experiments were conducted independently and repeated in triplicate.
2.5. Alkaline Phosphatase (ALP) Staining
MC3T3-E1 cells were seeded at a density of 2 × 105 cells in a 35 mm dish and incubated at 37 °C for 24 h. Each NCP-treated group was cultured in osteogenic medium for 1 or 2 weeks, with medium changes every 2–3 days. After incubation, the cells were fixed for 1 min using 3.7% formaldehyde (Sigma-Aldrich, St Louis, MO, USA) and 90% ethanol solution (Sigma-Aldrich, St Louis, MO, USA), followed by washing with TBS solution. Staining was performed in a dark room for 20 min with a fast 5-bromo-4-chloro-3-indolyl phosphate and nitroblue tetrazolium (BCIP/NBT) ALP substrate solution (B6404, Sigma-Aldrich, St Louis, MO, USA) [28]. Subsequently, the cells were washed four times with distilled water (DW) and photographed using a camera.
2.6. ALP Activity
MC3T3-E1 cells were seeded at a density of 2 × 105 cells in a 35 mm dish and incubated at 37 °C for 24 h. Subsequently, each NCP-treated group was cultured in osteogenic medium for 2 weeks, with medium changes every 2–3 days. Cells were washed with phosphate buffered saline (PBS), sonicated in 0.1 M Tris buffer containing 0.5% Triton X-100 solution on ice at 4 °C, centrifuged at 14,000 rpm for 10 min. The supernatant was used for Bradford analysis. ALP was measured using a laboratory assay ALP kit (Wako Pure Chemicals, Osaka, Japan) and a microplate reader at 450 nm [29]. Data analysis was performed according to the manufacturer’s instructions.
2.7. Alizarin Red S Staining
MC3T3-E1 cells were seeded at a density of 2 × 105 cells in a 35 mm dish and incubated at 37 °C for 24 h. Subsequently, each NCP-treated group was cultured in osteoinduction medium for 2 weeks, with medium changes every 2–3 days. After incubation, the cells were washed with PBS and fixed with 10% formaldehyde (Sigma-Aldrich) for 15 min. After washing twice with DW, the fixed cells were stained with 2% Alizarin Red S (pH 4.2) (Sigma-Aldrich) for 2 min, washed with DW, dried, and imaged using a camera. After imaging, the stained cells were exposed to 100 mM cetylpyridinium chloride (Sigma-Aldrich) for 2 h and the absorbance of the eluted supernatant was measured at 575 nm using a microplate reader [28].
2.8. Real-Time Polymerase Chain Reaction (qPCR)
MC3T3-E1 cells were seeded at a density of 2 × 105 cells in a 35 mm dish and incubated at 37 °C for 24 h. Each NCP-treated group was cultured in osteoinduction medium for 2 weeks, with medium changes every 2–3 days. After incubation, total RNA was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA, USA). cDNA was prepared by reverse transcription (One-step PreMix kit; iNtRON Biotechnology Inc., Seoul, Republic of Korea) on a T100 Thermal Cycler (Bio-Rad, Hercules, CA, USA), and PCR amplification was performed using a SensiFAT SYBR No ROX kit (Bioline, OH, USA) on a CFX 96 Real-Time System (Bio-Rad, Hercules, CA, USA). Relative quantification was achieved using the comparative 2−ΔΔCt method. All samples were run in triplicate and normalized to the housekeeping gene, GAPDH [17]. The following primers were used: alkaline phosphatase-1 Forward, 5′-ACGAGGTCACGTCCATCCT-3′; alkaline phosphatase-1 Reverse, 5′-CCGAGTGGTGGTCACGAT-3′, osteocalcin Forward, 5′-ACAGACAAGTCCCACACAGCAACT-3′; osteocalcin Reverse 5′-CCTGCTTGGACATGAAGGCTTTGT-3′, GAPDH Forward 5′-TGGGAAGCTGGTCATCAAC-3′; GAPDH Reverse 5′-GCATCACCCCATTTGATGTT3-3′.
2.9. Statistical Analysis
All experiments were independently repeated three times (n = 3). The results were graphed using GraphPad Prism 5.0, and statistical analysis was performed using the SPSS (version 24, Chicago, IL, USA) statistical software package. Data analysis was performed using one-way ANOVA, followed by Duncan’s post hoc analysis. The different letters shown in the figures indicate significant differences among the groups after post-analysis. In other words, the difference between groups with the same letter is not statistically significant, whereas the difference between groups with different letters is statistically significant.
3. Results
3.1. Effect of NCP on MC3T3-E1 Cell Proliferation
After 24 h of treatment with NCP, cells in the NT, DT, MC, and PAM groups exhibited proliferation rates of 100, 108, 103, and 106%, respectively (p < 0.038). After 48 h, the cell proliferation rates were 100, 98, 121, and 120% for the NT, DT, MC, and PAM groups, respectively (p < 0.000). At 24 h, the cell proliferation rates in the DT and PAM groups were significantly increased compared to the control (NT) group, whereas at 48 h, the MC and PAM groups showed a statistically significant increase compared to the NT group (Figure 2). Consequently, after 24 and 48 h, the PAM group showed the most significant effect on cell proliferation among all NCP methods, with none of them exhibiting cytotoxicity.
3.2. ALP Activity in the NCP-Treated MC3T3-E1 Cells According to NCP Treatment Method
ALP staining was increased in the DT, MC and PAM groups compared to the NT group at 1 week. The MC group exhibited increased staining even at 2 weeks. Moreover, an increase in ALP activity was observed in the DT and PAM groups compared to the untreated group at 1 week (p < 0.115). The DT, MC, and PAM groups showed a statistically significant increase at 2 weeks (p < 0.000) compared to the NT group, with the highest activity observed in the MC group (Figure 3).
3.3. Calcium Deposition in NCP-Treated MC3T3-E1 Cells
Alizarin Red S staining did not show a significant visual difference between the groups at 2 and 3 weeks. However, OD values of eluted stained cells indicated increased calcium deposition in the DT, MC, and PAM groups at 2 and 3 weeks compared to the NT group (p < 0.000). The highest deposition was observed in the DT group at 2 weeks and the MC group at 3 weeks (Figure 4).
3.4. mRNA Expression Changes in NCP-Treated MC3T3-E1 Cells
Real-time PCR analysis showed that ALP mRNA expression levels were lower in the DT and MC groups than in the NT and PAM groups at 1 week (p < 0.000). Additionally, the MC group had the highest levels of ALP mRNA expression compared to the other groups at 2 weeks (p < 0.014). No significant difference was observed between the groups at 3 weeks (p < 0.293).
The DT group showed lower osteocalcin expression compared to the NT and PAM groups at 1 week (p < 0.109); however, no significant difference was observed compared to the MC group. The MC group showed the highest osteocalcin expression compared to the other groups at 2 weeks (p < 0.000), similar to the ALP results. In contrast, the PAM group showed the highest osteocalcin expression at 3 weeks, followed by the MC group (p < 0.000). Although the MC and DT groups showed significant differences, neither showed significant differences compared to NT in the post-test. Statistically significant differences in ALP expression were observed at weeks 1 and 2, and in osteocalcin expression at weeks 2 and 3 (Figure 5). Our results showed that the expression of ALP and osteocalcin increased in all NCP treatment groups except the DT group. This suggests that, among the NCP treatment groups, MC and PAM treatment methods promoted differentiation into osteoblasts more effectively.
3.1. Effect of NCP on MC3T3-E1 Cell Proliferation
After 24 h of treatment with NCP, cells in the NT, DT, MC, and PAM groups exhibited proliferation rates of 100, 108, 103, and 106%, respectively (p < 0.038). After 48 h, the cell proliferation rates were 100, 98, 121, and 120% for the NT, DT, MC, and PAM groups, respectively (p < 0.000). At 24 h, the cell proliferation rates in the DT and PAM groups were significantly increased compared to the control (NT) group, whereas at 48 h, the MC and PAM groups showed a statistically significant increase compared to the NT group (Figure 2). Consequently, after 24 and 48 h, the PAM group showed the most significant effect on cell proliferation among all NCP methods, with none of them exhibiting cytotoxicity.
3.2. ALP Activity in the NCP-Treated MC3T3-E1 Cells According to NCP Treatment Method
ALP staining was increased in the DT, MC and PAM groups compared to the NT group at 1 week. The MC group exhibited increased staining even at 2 weeks. Moreover, an increase in ALP activity was observed in the DT and PAM groups compared to the untreated group at 1 week (p < 0.115). The DT, MC, and PAM groups showed a statistically significant increase at 2 weeks (p < 0.000) compared to the NT group, with the highest activity observed in the MC group (Figure 3).
3.3. Calcium Deposition in NCP-Treated MC3T3-E1 Cells
Alizarin Red S staining did not show a significant visual difference between the groups at 2 and 3 weeks. However, OD values of eluted stained cells indicated increased calcium deposition in the DT, MC, and PAM groups at 2 and 3 weeks compared to the NT group (p < 0.000). The highest deposition was observed in the DT group at 2 weeks and the MC group at 3 weeks (Figure 4).
3.4. mRNA Expression Changes in NCP-Treated MC3T3-E1 Cells
Real-time PCR analysis showed that ALP mRNA expression levels were lower in the DT and MC groups than in the NT and PAM groups at 1 week (p < 0.000). Additionally, the MC group had the highest levels of ALP mRNA expression compared to the other groups at 2 weeks (p < 0.014). No significant difference was observed between the groups at 3 weeks (p < 0.293).
The DT group showed lower osteocalcin expression compared to the NT and PAM groups at 1 week (p < 0.109); however, no significant difference was observed compared to the MC group. The MC group showed the highest osteocalcin expression compared to the other groups at 2 weeks (p < 0.000), similar to the ALP results. In contrast, the PAM group showed the highest osteocalcin expression at 3 weeks, followed by the MC group (p < 0.000). Although the MC and DT groups showed significant differences, neither showed significant differences compared to NT in the post-test. Statistically significant differences in ALP expression were observed at weeks 1 and 2, and in osteocalcin expression at weeks 2 and 3 (Figure 5). Our results showed that the expression of ALP and osteocalcin increased in all NCP treatment groups except the DT group. This suggests that, among the NCP treatment groups, MC and PAM treatment methods promoted differentiation into osteoblasts more effectively.
4. Discussion
Bone remodeling occurs continuously throughout life, involving osteoblasts that form bone osteoclasts that resorb bone [30]. However, osteoporosis occurs when the balance between bone formation and resorption is disrupted due to aging or disease, leading to weakened bones. Therefore, in this study, we compared the effects of direct and indirect cold plasma treatments on osteoblast stimulation, focusing on cell proliferation and differentiation.
When cells are treated with cold plasma, the effects differ depending on the treatment method (direct or indirect). Direct NCP treatment involves charged particles, such as argon ions and electrons, directly impacting cells. The charged particles accumulate on the outer surface of the cell membrane. For instance, they destroy the cell membrane and cell wall when exposed to bacteria, leading to bacterial inactivation [31].
However, during indirect treatment, NCP is applied directly to liquid-like media, which generates chemical elements such as ROS and reactive nitrogen species (RNS) within the media. The cells are then indirectly treated with NCP by exposing them to the media, which may lead to cell damage. The various active species, including ROS and RNS, are effective against cancer cells [32]. Although low levels of ROS are essential for cell proliferation and growth, excessive accumulation of ROS within healthy cells ultimately leads to cell death [33]. Therefore, an appropriate cold plasma treatment method for inducing cell activity without causing cell damage is crucial.
Previous studies have shown that NCP can regulate E-cadherin in skin cells, indicating the significant role of charged particles in plasma [34]. However, OH and NO do not have significant effects [34]. Similarly, a study on oral bacteria highlighted the importance of charged particles in bacterial inactivation [35].
In this study, cells were treated with direct and indirect plasma treatment. In particular, the cell medium was exchanged as a method to minimize the influence of the indirect medium treated with plasma among the direct treatment methods (MC). We evaluated the effects of NCP on osteoblasts using different treatment methods and found that the cell proliferation rate increased in all experimental groups during the 24 h cell culture period without cytotoxicity. After 48 h, the MC and PAM groups showed a statistically significant increase (about 20%) in proliferation rate compared to the NT group. These findings align with the results of a previous study in which human periodontal ligament cells were treated with NCP [17].
ALP is an indicator of the early stage of osteoblast differentiation [36]. It is secreted into the extracellular matrix and can be observed through cellular staining or mRNA levels. As bone begins to form, the activity of extracellular matrix proteins such as osteocalcin, an indicator of mature osteoblasts, increases, and mineral crystals such as calcium are deposited. These calcium deposits can be observed through Alizarin Red S staining [37]. In this study, the NCP-treated mouse osteoblasts in the MC group showed stronger positive cytochemical labeling for ALP and higher ALP activity than those in the other groups. This is consistent with other studies that showed that ALP levels usually increase to a maximum at week 2 [38].
Calcium deposition experiments using ARS staining yielded similar results, with a significant increase in the number of mineralized nodules in the MC group at 3 weeks. These findings suggest that direct NCP treatment enhances osteoblast differentiation owing to increased ALP activity. However, ALP mRNA levels in the MC group were initially lower than in the NT group at week 1 but increased at week 2, indicating a peak in ALP levels at 2 weeks. Therefore, our results suggest that direct NCP treatment, followed by media replacement, is the optimal treatment method that leads to increased ALP levels. Similarly, osteocalcin levels increased in the MC group at 2 weeks, and PAM levels were the highest at 3 weeks. In another study, osteocalcin exhibited as the differentiation period progressed until the fourth week [39]. Conversely, we only analyzed the results up to the third week. Therefore, a longer analysis period will be required in the future.
In summary, the findings from this study demonstrated that NCP treatment, both direct and indirect, induces mouse osteoblast differentiation. The direct treatment in the MC group was the most effective method. These findings hold promise for treating patients with osteoporosis or bone defects. Additionally, because the production of ozone is minimized, it can be safely used in the human body when developed as a medical device.
The limitations of this study include the small sample size and the lack of a mechanism to explain the osteoblast differentiation effect observed in each NCP group. Additionally, as this study was conducted in vitro, its applicability to humans is limited. Therefore, further in vitro and in vivo studies are necessary to elucidate the process of controlling bone formation using NCP.
5. Conclusions
Direct and indirect NCP treatments of mouse-derived osteoblasts increase cell proliferation and promote osteoblast differentiation. Direct (DT, MC) and indirect (PAM) treatment effects varied, with MC treatment having a more significant impact on osteoblast activity. Enhanced cell differentiation into osteoblasts was observed in the MC group, indicating that direct NCP treatment followed by media replacement was the most effective treatment method to improve bone formation in vitro.
6. Patents
The Periplapy (Feagle Co., Ltd., Yangsan, Republic of Korea) is registered as a patent (10-2568541), design (30-1171975-0000) and trademark (401-824232-0000) at the Korean Intellectual Property Office. |
Title: Early management of adult sepsis and septic shock: Korean clinical practice guidelines | Body:
INTRODUCTION
Sepsis is a life-threatening condition characterized by a dysregulated host response to infection, leading to organ dysfunction. It is a significant global health issue, affecting approximately 50 million individuals annually and causing at least 11 million deaths worldwide [1,2]. In recent decades, advances in sepsis recognition and supportive care have led to improved outcomes. However, studies indicate that mortality rates in Korea remain higher than in Western countries, and compliance with sepsis bundle components is notably low across Asia [3,4]. Specifically, a Korean Sepsis Alliance (KSA) report highlights significant regional and hospital-level variations in sepsis mortality and bundle compliance within Korea [5]. This underscores the growing awareness of the need for standardized sepsis treatment protocols and performance improvements.
Two decades ago, the European Society of Intensive Care Medicine and the Society of Critical Care Medicine consortium developed the Surviving Sepsis Campaign (SSC) international guidelines, which are revised every 4 years [6]. The United Kingdom also has the National Institute for Health and Care Excellence guidelines on sepsis management [7]. Among Asian countries, Japan developed a version of sepsis guidelines in 2016 and 2020 [8,9]. However, sepsis guidelines incorporating comprehensive systematic review and meta-analysis had yet to be developed in Korea. Hence, in recognition of the clinical circumstances in Korea, the KSA, an organization affiliated with the Korean Society of Critical Care Medicine (KSCCM), applied for research funding from the Korean Disease Control and Prevention Agency and developed the first sepsis treatment guidelines with a comprehensive systematic review and meta-analysis, involving multidisciplinary departments.
The present guidelines comprise 12 key questions (KQs) and their recommendations, focusing mainly on early sepsis treatments such as fluids, vasopressors, and prompt antibiotic administration. Diagnostic methods, management after initial resuscitation, and adjunctive therapies are not covered. Early recognition is crucial, but the topic could not be covered in these guidelines because of constraints. These guidelines are intended to support medical professionals in making appropriate decisions for treating sepsis and septic shock. The original Korean version of the guidelines obtained approvals from the KCDA and KSCCM and was subsequently endorsed by seven academic societies. The Korean version was first released on the official website of the KSCCM (https://www.ksccm.org/html/).
MATERIALS AND METHODS
For development of the guidelines, we employed a de novo method to account for the unique epidemiological and clinical characteristics of sepsis patients in Korea. Supplementary Material 1 provides detailed processes, and Table 1 summarizes the recommendations.
Organization of Committee Members
The guideline committees consisted of a steering committee (n=9), a working committee (n=24 from 9 departments), an advisory committee (external consultants, n=2; from the National Evidence-based Healthcare Collaborating Agency [NECA] and Korean Society of Anesthesiologists), and external reviewers (n=9). Guideline development was carried out through regular online meetings.
Key Questions and PICO
After an initial survey of landmark articles and international guidelines, the working committee identified 14 candidate KQs deemed most urgent and essential for treating sepsis and septic shock in the Korean clinical context. The 12 KQs with the highest votes from the working group members were selected from these. In a single working group, two members were assigned to each KQ and established the PICO (patients, intervention, comparator, and outcomes). For each PICO, the working group classified outcomes as either “critical” or “important.”
Literature Search and Selection
We collaborated with a professional literature search agency for a comprehensive literature search. A literature search was performed using PubMed, Embase, Cochrane Library, and KMbase and was supplemented by a manual search and reference assessment. The literature search was performed through December 2022. The inclusion and exclusion criteria for study selection was based on the PICO elements and study design of each KQ. Screening was initially performed using titles and abstracts by two members of each KQ group. Thereafter, the two members independently conducted a full-text review and reached a consensus. Disagreements were resolved by a third member or by discussion with the steering committee.
Assessment of Risk of Bias (Quality)
For the selected articles, two working members independently assessed the risk of bias and reached a consensus. In the event of disagreements, an external consultant (e.g., a NECA methodologist) was involved. For randomized controlled trials (RCTs), the Cochrane risk of bias (RoB) tool was used, and for non-RCTs, RoB for nonrandomized studies 2.0 (RoBANS 2.0) was used.
Level of Evidence and Grade of Recommendations
A meta-analysis was performed if quantitative synthesis was possible, and qualitative description was used if meta-analysis was not possible. A random-effects model was applied when heterogeneity was high. Publication bias was assessed using Egger’s test and the trim-and-fill method when the number of included studies was 10 or more. In these guidelines, Review Manager (RevMan) version 5.4 (The Nordic Cochrane Center) was used for meta-analyses [10]. The certainty of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach, where the level was rated as “high,” “moderate,” “low,” or “very low” [11]. The direction and strength of the recommendations were determined from four factors: “certainty of evidence,” “magnitude of effects (benefits and harms),” “patient values and preferences,” and “resources used.” Definitions for the certainty of evidence and grade of recommendations are summarized in Tables 2 and 3.
Drafting of Recommendation and Consensus Process
The working group members in each KQ drafted the recommendations. Subsequently, consensus was reached through online meetings attended by the majority of working group members. In cases of disagreement, the steering committee intervened to make the final decision. After internal (peer) and external review and a public hearing, some minor modifications were made to the recommendations (Supplementary Material 1).
Organization of Committee Members
The guideline committees consisted of a steering committee (n=9), a working committee (n=24 from 9 departments), an advisory committee (external consultants, n=2; from the National Evidence-based Healthcare Collaborating Agency [NECA] and Korean Society of Anesthesiologists), and external reviewers (n=9). Guideline development was carried out through regular online meetings.
Key Questions and PICO
After an initial survey of landmark articles and international guidelines, the working committee identified 14 candidate KQs deemed most urgent and essential for treating sepsis and septic shock in the Korean clinical context. The 12 KQs with the highest votes from the working group members were selected from these. In a single working group, two members were assigned to each KQ and established the PICO (patients, intervention, comparator, and outcomes). For each PICO, the working group classified outcomes as either “critical” or “important.”
Literature Search and Selection
We collaborated with a professional literature search agency for a comprehensive literature search. A literature search was performed using PubMed, Embase, Cochrane Library, and KMbase and was supplemented by a manual search and reference assessment. The literature search was performed through December 2022. The inclusion and exclusion criteria for study selection was based on the PICO elements and study design of each KQ. Screening was initially performed using titles and abstracts by two members of each KQ group. Thereafter, the two members independently conducted a full-text review and reached a consensus. Disagreements were resolved by a third member or by discussion with the steering committee.
Assessment of Risk of Bias (Quality)
For the selected articles, two working members independently assessed the risk of bias and reached a consensus. In the event of disagreements, an external consultant (e.g., a NECA methodologist) was involved. For randomized controlled trials (RCTs), the Cochrane risk of bias (RoB) tool was used, and for non-RCTs, RoB for nonrandomized studies 2.0 (RoBANS 2.0) was used.
Level of Evidence and Grade of Recommendations
A meta-analysis was performed if quantitative synthesis was possible, and qualitative description was used if meta-analysis was not possible. A random-effects model was applied when heterogeneity was high. Publication bias was assessed using Egger’s test and the trim-and-fill method when the number of included studies was 10 or more. In these guidelines, Review Manager (RevMan) version 5.4 (The Nordic Cochrane Center) was used for meta-analyses [10]. The certainty of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach, where the level was rated as “high,” “moderate,” “low,” or “very low” [11]. The direction and strength of the recommendations were determined from four factors: “certainty of evidence,” “magnitude of effects (benefits and harms),” “patient values and preferences,” and “resources used.” Definitions for the certainty of evidence and grade of recommendations are summarized in Tables 2 and 3.
Drafting of Recommendation and Consensus Process
The working group members in each KQ drafted the recommendations. Subsequently, consensus was reached through online meetings attended by the majority of working group members. In cases of disagreement, the steering committee intervened to make the final decision. After internal (peer) and external review and a public hearing, some minor modifications were made to the recommendations (Supplementary Material 1).
KEY QUESTIONS AND RECOMMENDATIONS
The guidelines present 12 KQs along with their respective recommendations. For each recommendation, background information, a summary of evidence, and relevant comments are provided. However, due to space constraints, the summary of findings (tables), the assessment of RoB, and the meta-analyses (e.g., forest plots), are presented in Supplementary Material 1. Some data accompanying KQ 7 (e.g., timing of vasopressors) were previously reported in another article [12].
KQ 1. Lactate clearance
When performing fluid resuscitation in patients with sepsis or septic shock, is the use of lactate clearance recommended as an indicator rather than central venous oxygen saturation (ScvO2)?
Recommendation
When performing fluid resuscitation in patients with sepsis or septic shock, the use of lactate clearance is suggested as an indicator rather than ScvO2 (Recommendation strength B, conditional recommendation for intervention; Certainty of evidence: moderate).
Background
Lactate is a marker of metabolic stress and tissue hypoxia, while ScvO2 reflects the balance between oxygen delivery and consumption (e.g., the amount of oxygen remaining in the blood after circulating through the tissues). Previously, ScvO2 was used as a target for quantitative resuscitation of sepsis patients in the early SSC international guidelines [13]. However, there are some barriers to using ScvO2 as a quantitative resuscitation goal, such as time, technology, and need for measurement equipment [14,15]. Of note, protocoled quantitative resuscitation, known as early goal-directed therapy (EGDT), failed to show a reduction in mortality in subsequent large multicenter RCTs and was removed from the recommendations when the SSC guidelines were revised in 2016 [16-18]. However, the relationship between lactate concentration and mortality in sepsis patients is well known [19,20], and lactate measurement is recommended as a component of the Hour-1 bundle by the SSC guideline. Elevated levels of lactate (>2 mmol/L) are also included as a criterion for septic shock in the Sepsis-3 definition [21].
Summary of Evidence
Of 16,822 articles found through the initial literature search, 12,462 were screened. Seventy-five full-text articles were reviewed, and four RCTs were finally selected [15,22-24]. All four studies looked at in-hospital mortality as the primary clinical outcome, and the lengths of intensive care unit (ICU) days and mechanical ventilation (MV) were confirmed in two studies each. In the studies, the lactate clearance and ScvO2 measurement groups were compared during initial fluid resuscitation. The meta-analysis showed that the lactate clearance group, compared to the ScvO2 group, had a significantly lower in-hospital mortality rate (risk ratio [RR], 0.74; 95% CI, 0.59–0.93). No significant differences were observed between the two groups in the periods of MV application and length of ICU stay, but the period of MV application tended to be lower in the lactate clearance group (mean difference [MD], 10.74 hours; 95% CI, 23.86 to 2.38). Despite unclear information about the concealment of group allocation in two studies, the RoB in other areas was low, and the level of evidence was moderate in all four RCTs. The overall level of evidence was determined as moderate, considering the level of in-hospital mortality, which is a crucial outcome indicator. The benefits are higher than the risks because lactate levels can be measured quickly without inserting a central venous catheter.
Comments
In a KSA survey on obstacles to performing the sepsis bundle, the most common reason for difficulty or delay in measuring lactate levels was a shortage of doctors or nurses (43.6%). The second most common reason was a lack of awareness among medical staff regarding the importance of lactate measurement (21.5%) [25]. Therefore, continuous promotion and education on the importance of lactate measurement remain essential. Emphasis should be placed on lactate clearance, rather than just measuring lactate levels. Additionally, clinicians should be aware of other conditions involving increase in lactate levels, such as medications (metformin, epinephrine, etc.), excessive exercise, alcohol, convulsions, liver disease, and tumors. Interestingly, recent studies on resuscitation using capillary refill time showed a trend toward a lower 28-day mortality rate and improvement in major organ function at three days compared to lactate levels [26,27]. Thus, capillary refill time may be helpful in resource-limited countries where a prompt lactate measurement is not feasible.
KQ 2. Fluid resuscitation
Should at least 30 ml/kg of crystalloid fluids be administered within 3 hours of starting resuscitation in adult patients with sepsis or septic shock and hypoperfusion?
Recommendation
In adult patients with sepsis or septic shock accompanied by hypotension or hypoperfusion, administration of 30 ml/kg of crystalloid fluids within the first 3 hours is suggested (Recommendation strength B, conditional recommendation for intervention; Certainty of evidence: low).
Background
Fluid therapy is essential to early sepsis resuscitation, increasing circulating blood volume and cardiac output. The 2021 SSC international guidelines recommend administering 30 ml/kg of crystalloid fluids within 3 hours in patients with tissue hypoperfusion due to sepsis [20]. That dosage was based on the amount of fluid administered in previous sepsis studies. However, excessive fluid treatment carries the risk of complications such as fluid overload, pulmonary edema, and prolonged MV. In studies by Boyd et al. [28] and Sakr et al. [29], positive fluid balance was associated with increased mortality. In a study by Marik et al. [30], fluid treatment of greater than 5 L on the first day was associated with a high mortality rate. However, many studies were not limited to sepsis patients but covered a broad range of critically ill patients. In observational studies, it is essential to consider that greater severity may correlate with a higher likelihood of receiving larger fluid volumes early in treatment.
To date, no prospective study has examined the association between fluid dose and treatment outcomes in sepsis patients. Specifically, the timing of fluid administration (early vs. delayed fluid administrations) and treatment results differed from study to study. The presently considered guideline reviewed and analyzed studies that specified the initial fluid dose and administration time for patients with sepsis or septic shock.
Summary of Evidence
From 3,720 papers retrieved through the literature search strategy, 2,948 studies were initially screened, excluding duplicates. Among them, 20 full-text articles were reviewed, and finally, five RCTs and two retrospective cohort studies were selected. In a retrospective cohort study by Kuttab et al. [31], sepsis or septic shock was defined using International Classification of Diseases (ICD) codes. That study found that, compared to patients who received 30 ml/kg crystalloids within the first 3 hours of sepsis (509 patients), the in-hospital mortality rate (odds ratio [OR], 1.52; 95% CI, 1.03–2.24) and the length of ICU stay were significantly increased in patients who did not that treatment (523 patients) [31]. Since no other articles addressing the PICO were identified, the scope was extended to secure as much evidence as possible for the clinical question by including one observational study and five RCTs as indirect evidence.
Seymour et al. [32] analyzed 26,978 patients who received 30 ml/kg of fluid within 12 hours among 49,331 patients with sepsis or septic shock who visited the emergency rooms of 149 hospitals. The mortality rate did not increase when the completion time of 30 ml/kg fluid administration was delayed (OR, 1.01; 95% CI, 0.99–1.02). Additionally, no significant difference was found between patients who received 30 ml/kg fluid within 6 hours and those who experienced it between 6 and 12 hours (OR, 1.02; 95% CI, 0.92–1.14) [32]. However, because no accurate data on the number of patients were available, our meta-analysis did not include that study. In the meta-analysis using the five RCTs on the EGDT (Rivers et al. [33], ProCESS [17], ARISE [16], ProMISe [18], and Andrews et al. [34]), the risk for in-hospital mortality and 28-day mortality was 1.17 (0.91–1.51) and 1.10 (0.92–1.32), respectively. A non-significant difference occurred between the early resuscitation and the control groups. In one of these studies, the mortality rate was higher in the resuscitation group [34]. However, since the EGDT study by Rivers et al. [33], early fluid therapy has been considered important and is included in the usual care of patients with sepsis or septic shock. In RCTs, such as ARISE, ProCESS, and ProMISe, approximately 2.0 L of fluids was administered within the first 6 hours, even in the usual care group [35]. In particular, the dose-response relationship between the amounts of initial fluids and outcomes in a retrospective study by Kuttab et al. [31] may emphasize the importance of early fluid administration. Based on this evidence, the recommendation grade for treatment was determined as conditional, and the level of evidence was judged to be low due to the lack of related research.
Comments
There is insufficient evidence to determine if at least 30 ml/kg of crystalloid fluids should be administered within the first 3 hours for management in adults with sepsis or septic shock accompanied by hypoperfusion. However, the CLASSIC trial used four noteworthy conditions (e.g., conditions for intravenous fluid administration in the restrictive fluid group): severe hypoperfusion, which was defined as lactate levels of at least 4 mmol/L; a mean arterial pressure (MAP) below 50 mm Hg despite infusion of a vasopressor or an inotropic agent; mottling beyond the edge of the kneecap (mottling score >2); and a urine output < 0.1 ml/kg/hr for the first 2 hours [36,37]. Again, although fluid therapy is critical for increasing cardiac output and tissue perfusion, the disadvantages should be considered. Excessive fluid administration can cause worsening pulmonary edema, a decrease in cardiac function, and increases in the duration of MV or ICU stay. Therefore, the decision to administer fluids should be made with caution, considering the risks and benefits.
KQ 3. Fluid type
When performing fluid resuscitation in patients with sepsis, does the use of balanced crystalloid, compared to 0.9% saline, reduce mortality rates and incidence of acute kidney injury?
Recommendation
Balanced crystalloids or saline (0.9% saline) can be used during fluid resuscitation in patients with sepsis (Recommendation strength B, conditional recommendation for intervention; Certainty of evidence: moderate)
Background
Crystalloid fluids are recommended during fluid resuscitation in sepsis patients [20]. However, recent studies have reported that intravenous 0.9% saline promotes hyperchloremic metabolic acidosis, increases the possibility of acute kidney injury (AKI) [38-40], and increases mortality [41-43]. Therefore, more attention has been given to the usefulness of balanced solutions with electrolyte components more closely resembling those of plasma-Ringer’s Lactate solution and Plasma-Lyte A solution [42]. However, conclusive evidence on the choice of crystalloid fluids on patient outcomes is lacking [44,45]. Therefore, the present guideline analyzed and compared the effects of the two fluid types on patient outcomes.
Summary of Evidence
Through the literature search, a total of 23,338 articles was identified. After excluding duplicate studies, the titles and abstracts of 19,788 studies were assessed, and the full texts of 11,312 studies were reviewed. Among these, six RCTs were finally selected [40,46-50]. Of the six studies, only one targeted patients with sepsis, while the other five targeted those admitted to ICUs, although the number of participating patients was more significant. In the analysis, mortality (in-hospital, 28-, 30-, and 90-day mortality) was considered the critical outcome, and the incidence of AKI was analyzed as an essential outcome. Among the six selected studies, that by Semler et al. (SALT trial) [47] reported death and renal damage as a composite outcome. Therefore, we performed a meta-analysis on mortality using five studies. There was no significant difference in mortality between the two fluid therapies (RR, 0.95; 95% CI, 0.87–1.02). For the incidence of AKI, our meta-analysis was conducted using two studies (by Young et al. [46] and Kumar et al. [48]), and no significant difference was found between the two fluids (RR, 0.71; 95% CI, 0.47–1.06). In an open-label study by Kumar et al. [48] (also published by Golla et al. in 2022), the concentration of chloride ion and the incidences of AKI at 24 and 48 hours were significantly increased in the group receiving 0.9% saline compared to those receiving balanced crystalloids. However, during the entire hospitalization period, there was no difference in the chloride ion concentration, AKI incidence, and mortality rate [48]. In the SMART study by Semler et al. [40], which targeted 7,942 critically ill patients admitted to ICUs, balanced crystalloids reduced mortality, use of renal replacement therapy (RRT), and persistent renal function decline in all enrolled patients with no statistical significance, but there was a tendency favoring balanced crystalloids in sepsis patients. In a study by Brown et al. [43], a secondary analysis of the SMART trial, balanced crystalloids significantly reduced the 30-day in-hospital mortality rate (adjusted OR, 0.74; 95% CI, 0.59–0.93; P=0.01) with improved renal outcomes. However, this was a single-center study, and fluid resuscitation was not assigned using a blinded method. There is also a possibility of misclassification due to the use of ICD-10 codes. In a 2015 study by Young et al. [46] of patients in medical ICUs, no differences were identified either in the incidence of AKI or the use of RRT between the two fluid groups. In our meta-analysis, no significant differences were identified between 0.9% saline and balanced crystalloid groups; except for the study by Seymour et al. [32], there was no significant difference in primary outcomes. Therefore, the level of evidence was lowered by one grade due to inconsistency. Additionally, since meta-analysis was conducted using data from subgroup populations, it is difficult to rule out RoB. Based on this, the evidence level of the recommendation was judged as moderate.
Comments
Despite no significant difference in the critical outcomes in the meta-analysis, the results from recent analyses by Zampieri et al. [51,52] are noteworthy. In their secondary analyses of the original BaSICS (Balanced Solutions in Intensive Care Study) trial, the beneficial effects of balanced solution over 0.9% saline were more apparent in septic patients likely to have unplanned ICU admission and the need for higher fluid volumes. Based on their results, a balanced solution may be preferred to 0.9% saline in septic conditions where a large volume of fluids is needed, such as peritonitis or pancreatitis. However, it is important to consider the association of volume overload with worse outcomes regardless of the type of fluid, especially in septic patients. In addition, the use of balanced crystalloids may exert detrimental effects in patients with traumatic brain injury [49]. Finally, given the statistically significant increase in chloride ion concentration (or hyperchloremic acidosis) in the 0.9% saline group [48], the choice of crystalloid fluids can depend on the circumstances, especially when the patient has hyperkalemia, hyperchloremia, or AKI [46,48].
KQ 4. Target blood pressure
In adult patients with septic shock, can a target MAP ≥65 mm Hg improve the outcomes of patients compared to targeting a higher MAP?
Recommendation
In adult patients with septic shock, we suggest a target MAP ≥65 mm Hg over higher MAP targets (Recommendation strength B, conditional recommendation for intervention; Certainty of evidence: moderate).
Background
The 2021 SSC international guidelines recommend maintaining the initial target MAP above 65 mm Hg in adult patients with septic shock using vasopressors as a strong recommendation with moderate quality of evidence [20]. MAP is the primary determinant of systemic filling pressure and the main driver of venous return and cardiac output. Thus, as MAP increases, tissue blood flow also increases. Specific organs, such as the brain and kidneys, can autoregulate blood flow, but when MAP falls below approximately 60 mm Hg, tissue perfusion decreases proportionally. Therefore, an adequate MAP is crucial in patients with septic shock.
Summary of Evidence
A total of 8,386 studies was identified; after excluding duplicates, we reviewed the titles and abstracts of 6,750 studies. Among these, after excluding 6,736 articles, we reviewed the full texts of 14 articles. Finally, three RCTs were selected to compare patients who maintained MAP above 65 mm Hg (control) with those who targeted an MAP higher than 65 mm Hg (intervention). In a prospective, open-label RCT by Asfar et al. [53], 776 patients with septic shock were divided into a high-target group (n=338, MAP target 80 to 85 mm Hg) and a low-target group (n=338, MAP target 65 to 70 mm Hg). On day 28, 142 people (36.6%) in the higher MAP group and 132 people (34.0%) in the lower MAP group had died, with no significant difference between the two groups (hazard ratio [HR], 1.07; 95 % CI, 0.84–1.38; P=0.57). The 90-day mortality rate also showed no significant difference between the two groups. However, atrial fibrillation was more frequent and use of RRT was less frequent in the higher MAP group. In a prospective, multicenter RCT, Lamontagne et al. [54] investigated 118 patients with septic shock, with 58 in the high MAP group (MAP target 75 to 80 mm Hg) and 60 in the lower MAP group (MAP target 60 to 65 mm Hg). The primary outcome was the separate measurement of MAPs in each group, and secondary outcomes were in-hospital, 28-day, and 6-month mortality rates. The 28-day mortality rate did not differ significantly between the higher and lower MAP groups (46% vs. 44%, P=0.21). In a study by 65 clinical trial investigators (Mouncey et al. [55]), a prospective multicenter pragmatic RCT, 1,291 patients in the permissive hypotension group (MAP of 60–65 mm Hg) were compared to 1,307 patients in the usual care group. The average MAP up to 7 days after the application of vasopressors was 67.6 mm Hg in the permissive hypotension group and 72.9 mm Hg in the usual care group. At 90 days, the mortality rate was not different between the two groups (41.0% vs. 43.8%, P=0.154). After controlling for pre-specified variables, the OR for 90-day mortality was 0.82, favoring the permissive hypotension group more than the usual care group. Our meta-analysis found no significant benefit of maintaining a target MAP higher than 65 mm Hg (e.g., a higher MAP group). In the RCTs included, there were no significant problems with random sequence generation, allocation concealment, or blinding of participants and personnel. However, in some studies, blinding of interventions was incomplete, and missing data were identified. Additionally, the underlying conditions of the enrolled patients varied, and the criteria for low and high MAPs differed slightly. Considering this, the overall recommendation strength for this clinical question was assessed as conditional for the intervention (e.g., target MAP ≥65 mm Hg), and the evidence level was moderate.
Comments
Currently, there is no evidence that a higher MAP target, compared to maintaining an MAP ≥65 mm Hg, improves patient outcomes. In the SEPSISPAM study by Asfar et al. [53], the incidence of atrial fibrillation was higher but renal replacement therapy was less frequent in the higher MAP target group. Importantly, it is necessary to consider the accuracy of measurements because the value of 65 mm Hg measured with invasive methods may differ from that measured with non-invasive methods. Large-scale comparative studies on the currently suggested level of MAP (65 mm Hg) are needed.
KQ 5. Dynamic parameters
In adult patients with sepsis or septic shock, can fluid therapy using dynamic parameters compared to static parameters or usual treatments reduce mortality rate?
Recommendation
If additional fluids are required after initial fluid resuscitation in adult patients with sepsis or septic shock, fluid therapy using dynamic parameters is suggested (Recommendation strength B, conditional recommendation for intervention; Certainty of evidence: moderate).
Background
In clinical practice, parameters that represent the filling pressure of the heart, such as central venous pressure (CVP) or pulmonary artery pressure, have been widely used as static parameters. This hypothesis assumes that, as ventricular volume increases, heart-filling pressure increases proportionally. However, this is only true if ventricular compliance, which determines the pressure-volume relationship in the heart, remains constant. The actual compliance of the ventricles varies from patient to patient because it is affected by myocardial ischemia or infarction, myocardial hypertrophy, or cardiomyopathy. Even in the same patient, compliance of the ventricles varies depending on positive end-expiratory pressure and changes in cardiac function [56]. As a result, heart-filling pressure may remain the same despite varying volume states [56,57]. Additionally, changes in filling pressure can differ with increasing preload, highlighting a limitation of static parameters. Conversely, representative dynamic parameters include pulse pressure variation (PPV) and stroke volume variation (SVV), with larger values indicating a hypovolemic state and more significant variation in the respiratory cycle [58,59]. However, these dynamic parameters may not be accurate when an arrhythmia or increased intra-abdominal pressure is present, when vascular tension significantly changes, when tidal volume is low, or when there is spontaneous breathing effort [60-63]. In patients with spontaneous breathing, the passive leg raising (PLR) test is especially helpful in predicting responsiveness to fluid therapy. When a patient's leg is lifted to 45°, approximately 300 ml of blood moves from the periphery to the heart, and cardiac output changes accordingly.
Summary of Evidence
Among a total of 20,463 documents found through a literature search strategy, 18,502 were selected after excluding 1,961 duplicates. Among these, 67 full-text articles were reviewed (among 68 selected papers), and four RCTs were ultimately selected [64-67]. In an RCT by Richard et al. [65], the intervention group (n=30) received fluid therapy using SVV with PLR test or PPV (for patients on MV), and the control group (n=30) was given fluid therapy using CVP. Chen et al. [64] conducted a study of patients with septic shock who had received vasopressors for more than 12 hours and used the changes in PPV, inferior vena cava distension index, and stroke volume index after PLR (41 patients in each group). A study by Kuan et al. [66] targeted patients with sepsis with serum lactate levels of 3.0 mmol/L or higher and examined the changes in stroke volume index after PLR (61 vs. 61 patients in intervention vs. control groups). Finally, Douglas et al. [67] performed a PLR test in sepsis patients with persistent refractory hypotension or expected to be admitted to the ICU. They compared fluid therapy using changes in cardiac output with usual care (83 vs. 41 patients).
In this guideline, the critical outcomes were overall mortality and 28- or 30-day mortality rates, and other important outcomes were duration of MV and fluid balance on day 3. Of the four RCTs, three reported 28- or 30-day mortality rates [65-67], but one study only reported in-hospital mortality [64]. When combining all four studies, the risk of mortality was lower in the group that used dynamic parameters (intervention group) than the group that did not (usual care group), with no significant difference (RR, 0.81; 95% CI, 0.59–1.11). Regarding 28- or 30-day mortality (after exclusion of the study reporting in-hospital mortality), the risk of mortality in the intervention group was lower than in the usual care group, with statistical significance (RR, 0.62; 95% CI, 0.39–0.99). The duration of MV was shorter by 2.48 days in the intervention group than the usual care group (MD, –2.48 days; 95% CI, –3.61 to –1.35) [64,67], and fluid balance on day 3 was no different between the two groups (MD, –0.62 L; 95% CI, –1.31 to 0.08 L) [64,65,67]. As the four studies were RCTs, heterogeneity was not high. The level of evidence, which was lowered by one step due to imprecision, was finally determined to be moderate.
Comments
According to a study by Richard et al. [65], there was no difference in time to shock resolution when comparing fluid therapy using dynamic parameters with usual care (median [interquartile range]: 2.3 days [1.4–5.6] vs. 2.0 days [1.2–3.1], P=0.29). However, in our meta-analysis of subgroups (three RCTs), a significant reduction in 28- 30-day mortality rates and the period of MV was found. Therefore, fluid therapy using dynamic parameters can be considered beneficial to patients. Douglas et al. [67] compared major cardiovascular endpoints, including cardiovascular death, non-fatal myocardial infarction, and non-fatal stroke, between the two groups and found no significant difference. Therefore, compared to usual care or fluid therapy using static parameters, fluid therapy using dynamic parameters has no obvious harm, and the benefits may be more significant [65,67]. However, the primary obstacle to using dynamic parameters may be the absence of equipment monitoring cardiac output or PPV (due to its high costs). Additionally, healthcare insurance does not cover the PLR test, which may be another barrier.
KQ 6-1. Antibiotics
In adult septic shock patients, does administering antibiotics within 1 hour of sepsis recognition improve mortality compared to administering antibiotics at 1 hour or later?
Recommendation
In adult patients with septic shock, we suggest administering antibiotics within 1 hour of septic shock recognition (Recommendation strength B, conditional recommendation for intervention; Certainty of evidence: low).
KQ 6-2. Antibiotics
In adult sepsis patients, does administering antibiotics within 3 hours of sepsis recognition improve mortality compared to administering antibiotics at 3 hours or later?
Recommendation
In adult patients with sepsis, we suggest administering antibiotics within 3 hours of sepsis recognition (Recommendation strength E, expert consensus; Certainty of evidence: very low).
Background
Early administration of appropriate antibiotics is one of the most effective treatments for lowering the mortality rate of sepsis patients [32,68,69]. However, there are controversies about the relationship between timing of antibiotic administration and mortality in patients with sepsis or septic shock [70,71]. The 2016 SSC international guidelines were not approved by the Infectious Diseases Society of America (IDSA) because of concerns about antibiotic overuse, overdiagnosis of sepsis, lack of data to support time-to-antibiotic administration goals, and difficulty in distinguishing between patients with sepsis and septic shock [72]. In 2018, the SSC committee consolidated the 3-hour and 6-hour bundles into a 1-hour bundle and recommended that the Hour-1 bundle be implemented promptly [73]. However, concerns were raised about insufficient evidence to support these changes in emergency room care. The Society of Critical Care Medicine (SCCM) and the American College of Emergency Physicians (ACEP) issued a joint statement and announced that the Hour-1 bundle will not be immediately applied to hospitals in the United States [74]. In 2020, the IDSA highlighted the lack of evidence to support early antibiotic administration in patients with suspected sepsis without shock, the risk of antibiotic overuse, and the complexity of the “time zero” definition. They recommended modifications to the Severe Sepsis and Septic Shock Early Management (SEP-1) bundle, suggesting that sepsis without shock excluded from the bundle treatment, broad-spectrum antibiotics should be started within 1 hour of time zero in septic shock, and the definition of time zero should be clear and reproducible [75].
In 2021, the ACEP issued guidelines for the initial treatment of sepsis in emergency settings, which the IDSA and SCCM endorsed. Although antibiotics should be administered promptly when sepsis is diagnosed, there is insufficient evidence to recommend a specific time standard for antibiotic administration [76]. Accordingly, the SSC committee received feedback from other expert groups and distributed a new version of the guidelines in 2021. In the revised guidelines, the antibiotic administration time is divided according to the presence of shock and the possibility of sepsis. In patients with septic shock or sepsis with a high risk of infection, antibiotics are administered within 1 hour. However, diagnostic tests should be conducted promptly in sepsis with a low risk of infection, and antibiotics should be treated within 3 hours if infection concerns persist [20].
Summary of Evidence
The literature search strategy initially found 14,670 articles. Of these articles, 12,257 were screened, and 65 full-text articles were reviewed. For this guideline, 33 cohort studies were ultimately selected, with no RCTs identified.
When “time zero” is defined as the moment when sepsis or septic shock is recognized
Thirteen articles defined “time zero” as the moment of sepsis or septic shock recognition [68,77-88]. In our meta-analysis of patients with sepsis or septic shock, there was no significant difference in mortality between those who were given antibiotics within 1 hour of recognition of sepsis or septic shock and those given antibiotics after 1 hour (RR, 0.87; 95% CI, 0.75–1.01). However, in a subgroup analysis targeting only patients with septic shock, the mortality rate was significantly lower in those who were given antibiotics within 1 hour than in those given antibiotics after 1 hour (RR, 0.89; 95% CI, 0.88–0.90). In patients with sepsis or septic shock, the mortality rate was significantly lower in those who were given antibiotics within 3 hours of sepsis or septic shock recognition than in those given antibiotics after 3 hours (OR, 0.67; 95% CI, 0.53–0.86). In a subgroup analysis targeting only patients with septic shock, the mortality rate was significantly lower in those given antibiotics within 3 hours than those given antibiotics after 3 hours (OR, 0.65; 95% CI, 0.51–0.83).
Since only two observational studies were included in the analysis of antibiotic administration within 3 hours in patients with septic shock, it was inappropriate to recommend antibiotics within 3 hours in this group. Therefore, in adult patients with septic shock, we recommend administering antibiotics within 1 hour of recognizing septic shock (Recommendation strength B, conditional recommendation for intervention; Certainty of evidence: low). Regarding antibiotic administration within 3 hours of time zero, no articles targeted only sepsis patients. However, among the observational studies in the meta-analysis, which included both sepsis and septic shock cases, sepsis accounted for most of the cases. Considering an increased mortality rate due to delayed administration of antibiotics, we can assume that the beneficial effects of antibiotics within 3 hours will also be greater than their harmful effects in patients with sepsis (Recommendation strength E, expert consensus; Certainty of evidence: very low).
When "time zero" is defined as the moment of emergency department triage
A total of 20 papers defined the ‘time zero’ as the time of emergency department triage [32,70,89-106]. In our meta-analysis on patients with sepsis or septic shock, there was no significant difference in mortality rate in those who were given antibiotics within 1 hour of the triage compared to those given antibiotics after 1 hour (OR, 0.92; 95% CI, 0.85–1.00). This was also the case in a subgroup analysis targeting only patients with septic shock (OR, 0.91; 95% CI, 0.60–1.39). In patients with sepsis or septic shock, mortality was not significantly different in those who were given antibiotics within 3 hours of the triage compared to those given antibiotics after 3 hours (OR, 0.90; 95% CI, 0.76–1.07). This was also the case in a subgroup analysis targeting only patients with septic shock (OR, 1.08; 95% CI, 0.54–2.12).
Comments
In our meta-analyses, when “time zero” was defined as the time of triage, no significant differences were found in mortality rates according to the timing of antibiotic administration among patients with sepsis or septic shock. Therefore, it seems better to define “time zero” as the time of sepsis or septic shock recognition rather than the time of emergency department triage. However, as described above, unconditional rapid administration of antibiotics can cause various problems, such as antibiotic overuse, overdiagnosis of sepsis, and increased burden on medical staff and costs. Hence, sufficient effort is needed to make an accurate diagnosis and find the source of infection. Conversely, in patients who require antibiotics, maximum effort and improved performance are needed to ensure that antibiotic administration is not delayed after recognition of septic shock. Given the absence of large-scale RCTs on this topic, there is a need for additional well-designed large-scale RCTs.
KQ 7. Timing of vasopressors
When should vasopressors be administered to adult patients with septic shock?
Recommendation
In adult patients with septic shock, early administration of vasopressors is suggested if necessary to ensure hemodynamic stability during initial fluid therapy (Recommendation strength B, conditional recommendation for intervention; Certainty of evidence: moderate).
Background
Vasopressors can increase blood perfusion of organs and correct hypotension. They are essential for treating septic shock, along with fluid and antibiotic therapies [20]. The 2021 SSC international guidelines recommend administering fluids and vasopressors with an MAP ≥65 mm Hg as the initial hemodynamic goal. They also recommend administering vasopressors using a peripheral venous catheter rather than delaying the treatment for CVC insertion [20]. However, the appropriate timing of vasopressor administration in patients with septic shock is controversial, with conflicting research results [107-109].
Summary of Evidence
Through the literature search strategy, four RCTs [110-113] and eight cohort studies [109,114-120] were ultimately selected. The RCTs included two studies using restrictive fluid and early vasopressor strategies [110,113]. In the meta-analysis, the mortality rate tended to be lower in the early vasopressor group versus the late group, regardless of whether they only included RCTs (RR, 0.76; 95% CI, 0.53–1.09) or observational studies (RR, 0.84; 95% CI, 0.66–1.07), with no statistical significance. In the RCTs, there was no significant difference in the length of ICU stay, duration of MV, vasopressor-free days, RRT-free days, or incidence of arrhythmia. However, the incidence of pulmonary edema was significantly lower in the early treatment group [110,112,113]. In the observational studies, although no difference was found in the length of ICU stay, a significantly shorter period was reported in MV, use of vasopressors, and RRT in the early vasopressor group compared to the late group. However, the number of studies included in the analysis was limited. In terms of fluid volume, there was a tendency for the 6-hour and 24-hour fluid doses to be lower in the early group, with no significant difference. In a subgroup analysis of two RCTs that implemented a restrictive fluid strategy, no significant difference was found in mortality rate [110,113]. However, in the two studies not using a fluid restriction strategy, the mortality rate was significantly lower in the early vasopressor group [111,112], consistent with the results of a previous meta-analysis [108].
Among the studies included in the analysis, the overall level of evidence from RCTs was assessed as moderate, while that for observational studies was very low. Accordingly, the overall level of evidence for this clinical question was moderate according to the level of evidence in RCTs.
Comments
Considering the following, we recommend early administration of vasopressors for adult patients with septic shock. First, although no significant difference was found in mortality between the early and delayed vasopressor administration groups, some results suggest a therapeutic benefit in early administration in secondary endpoints such as pulmonary edema. Second, a reduction in mortality was observed in a subgroup analysis including two RCTs in which a fluid restriction strategy was not implemented. Third, no significant worsening prognosis or side effects were observed in the group receiving the early administration of vasopressors. Finally, the correlation between the duration of hypotension and increased mortality has been well established [121].
However, the effects of early vasopressor use might differ depending on certain factors such as vasopressor dose, volume status (or fluid volume administered), severity of sepsis, and corticosteroids (e.g., hydrocortisone). In particular, fluid volume and vasopressor timing may have interactions with mortality. In all the studies included in our analysis, initial fluid therapy was administered before vasopressor infusions. Hence, early administration of vasopressors alone without fluid therapy is not recommended. In most studies, the difference in timing of vasopressor administration between the early and delayed groups was not remarkable, and it did not specify an optimal time for vasopressor initiation. Therefore, an individualized approach that depends on the severity and clinical course of the septic shock is needed.
KQ 8. Vasopressor type
Should norepinephrine be used preferentially over other vasopressors in adult patients with septic shock?
Recommendation
We suggest that norepinephrine be used in preference to other vasopressors in adult patients with septic shock (Recommendation strength A, strong recommendation for intervention).
Quality of evidence:
• Norepinephrine vs. dopamine: high quality
• Norepinephrine vs. vasopressin: moderate quality
• Norepinephrine vs. epinephrine: low quality
• Phenylephrine: very low quality
• Norepinephrine vs. terlipressin: low quality
Background
According to international guidelines, norepinephrine is recommended as the first-line vasopressor to maintain the target MAP of 65 mm Hg [20]. If norepinephrine is 0.25–0.5 μg/kg/min and the target MAP is not reached, vasopressin is recommended as the second-line drug. When norepinephrine is not available, dopamine or epinephrine can be used as a substitute [20]. Norepinephrine is a powerful α1 adrenergic receptor agonist with moderate β-agonist activity, exerting strong vasoconstriction but less direct cardiac contractility. Therefore, norepinephrine primarily increases systolic and diastolic pressure and has a minimal effect on heart rate.
Dopamine is an endogenous central neurotransmitter precursor of norepinephrine and acts on dopamine and adrenergic receptors. Low doses (<3 μg/kg/min) stimulate dopamine receptors in the coronary arteries, kidneys, and cerebrum, promoting vasodilation and increased blood flow to tissues. At medium doses (5–10 μg/kg/min), dopamine binds to β1 adrenergic receptors and promotes the release of norepinephrine, increases cardiac contractility and heart rate (chronotropic), and slightly increases systemic vascular resistance (SVR). High doses (10–20 μg/kg/min) act on α1 adrenergic receptors, resulting in dominant vasoconstriction. However, dose-dependent activation of β1 adrenergic receptors may cause arrhythmia.
Vasopressin is an endogenous peptide hormone produced in the hypothalamus and stored and released in the posterior pituitary gland. Vasopressin binds to the V1 receptor of the vascular smooth muscle and the V2 receptor of the renal collecting duct. Hence, it induces vascular smooth muscle contraction through V1 stimulation, increasing arterial blood pressure and water reabsorption through the V2 receptor. Vasopressin also causes less direct coronary and cerebral vascular constriction than catecholamines while increasing SVR dose-dependently.
Epinephrine is an endogenous catecholamine with a high affinity for β1, β2, and α1-receptors in cardiac and vascular smooth muscles. It has the characteristics of more pronounced β1 adrenergic effects at low doses but more pronounced α1 adrenergic effects at high doses. At low doses, it mainly acts on β1 adrenergic receptors to increase cardiac output and reduce SVR, whereas at high doses it increases cardiac output and SVR. Potential side effects of epinephrine include arrhythmia and disruption of the splanchnic blood circulation.
Summary of Evidence
The literature search strategy identified 10,926 studies. After excluding 1,993 duplicates, 8,933 studies were screened. A total of 40 full-text articles was reviewed, and 16 RCTs and 6 cohort studies were ultimately selected.
Norepinephrine vs. dopamine
There was no significant difference in overall mortality between the norepinephrine and dopamine groups from the analysis of six RCTs (RR, 0.93; 95% CI, 0.84–1.02) [122-127]. However, a significant reduction was found in the norepinephrine group in one cohort study (RR, 0.67; 95% CI, 0.55–0.82) [128]. Additionally, when analyzing four RCTs, the ICU mortality rate was significantly reduced in the norepinephrine group compared to the dopamine group (RR, 0.90; 95% CI, 0.82–0.99) [122,124,125,129]. In the analysis of three RCTs, the incidence of arrhythmia was significantly lower in the norepinephrine group than in the dopamine group (RR, 0.49; 95% CI, 0.40–0.59) [125-127]. However, no significant difference was found in the length of ICU stay between the two groups in the analysis of two RCTs [125,126].
Norepinephrine vs. vasopressin
There was no significant difference in overall mortality between the norepinephrine and vasopressin groups in all four RCTs (RR, 1.09; 95% CI, 0.94–1.26) [129-132] and in three cohort studies (RR, 1.14; 95% CI, 0.79–1.65) [133-135]. There was no statistically significant difference in ICU mortality between the norepinephrine and vasopressin groups in three RCTs (RR, 0.94; 95% CI, 0.71–1.24) [129,131,132]. Regarding AKI, no difference was found between the norepinephrine and vasopressin groups in two RCTs, but the use of RRT was less frequent in the vasopressin group (RR, 1.44; 95% CI, 1.09–1.90) [129,132]. However, in the analysis of two cohort studies [133,135], no difference was found in the rate of RRT between the two groups. In terms of the length of ICU stay, it was shorter in the norepinephrine group compared to the vasopressin group in the three RCTs (MD, –1.55 days; 95% CI, –2.52 to –0.58 days) [129,131,132], but no difference was found in the analysis of two cohort studies [133,135].
Norepinephrine vs. epinephrine
In one RCT, overall mortality between the norepinephrine and epinephrine groups was not significantly different (RR, 1.13; 95% CI, 0.80–1.60) [136]. Vasopressin-free days were also not different between the two groups in the study.
Norepinephrine vs. phenylephrine
The overall mortality rate was not different between the norepinephrine and phenylephrine groups in one RCT [137]. However, the incidence of arrhythmia was significantly lower in the phenylephrine group compared to the norepinephrine group in a cohort study (RR, 1.20; 95% CI, 1.09–1.33) [138].
Norepinephrine vs. terlipressin
The overall mortality rate was not different between the norepinephrine and terlipressin groups in three RCTs (RR, 1.02; 95% CI, 0.74–1.42) [131,139,140]. Additionally, no differences were noted between the two groups in the RCT for both length of ICU stay and vasopressor-free days. Regarding the selection of the first vasopressor to be used in adult patients with septic shock, studies comparing norepinephrine with five other vasopressors (dopamine, vasopressin, epinephrine, phenylephrine, and terlipressin) were analyzed, and recommendations for each drug are given in this guideline. The overall level of RCTs comparing norepinephrine with the five other vasopressors varied: high for dopamine, moderate for vasopressin, low for epinephrine and terlipressin, and very low for phenylephrine. However, unlike the RCTs, the evidence for cohort studies comparing norepinephrine with five other vasopressors was all confirmed as very low.
Comments
Dopamine mainly increases cardiac output and MAP by increasing stroke volume (SV) and heart rate, while norepinephrine increases MAP through vasoconstriction without significant changes in SV and heart rate. In an RCT by the SOAP (The Sepsis Occurrence in Acutely Ill Patients) II investigators, more arrhythmic events were observed in the dopamine group compared to the norepinephrine group, and a higher 28-day mortality was also noted in the former group among patients with cardiogenic shock [125]. The results of our meta-analysis showed that norepinephrine reduced the rates of ICU mortality and arrhythmia compared to the use of dopamine. Therefore, we recommend that norepinephrine be preferred to dopamine in patients with septic shock. When used at low doses, vasopressin increases blood pressure in patients who do not respond to other vasopressors. Conversely, high-dose vasopressin can be associated with ischemia in the heart, extremities, and intestine [141]. Our meta-analysis showed that norepinephrine, compared to vasopressin, reduced the length of ICU stay despite no difference in mortality. However, the incidence of RRT was lower in the vasopressin group. The VASST (Vasopressin and Septic Shock Trial) study, which examined the effect of co-administering low-dose vasopressin (0.01 to 0.03 units/min) with norepinephrine, found in subgroup analysis that adding low-dose vasopressin to norepinephrine (5–14 μg/min) improved survival rates compared to using norepinephrine alone [142]. This suggests that vasopressin should be initiated at an early stage of septic shock, particularly in less severe cases.
Epinephrine is associated with side effects such as arrhythmia, lactic acidemia, and splanchnic circulation disorders [143]. However, there was no significant difference in mortality in studies comparing the drug with norepinephrine, and the results of our meta-analysis also showed no difference between the two drugs. The 2021 SSC international guidelines suggest using epinephrine when the optimal blood pressure is not achieved despite the combined use of norepinephrine and vasopressin in patients with septic shock [1]. Epinephrine may be useful in patients with refractory septic shock and cardiac dysfunction. Phenylephrine results in less frequent tachycardia (compared to norepinephrine) but can induce splanchnic vasoconstriction. Given that only one RCT with a small number of patients (n=32) was included in our analysis, it was not possible to draw any conclusions about the effects of the drug on clinical outcomes. Regarding the use of terlipressin, no differences were found in our meta-analysis between the norepinephrine and terlipressin groups in terms of mortality, length of ICU stay, and vasopressor-free days. However, serious adverse events occurred more significantly with terlipressin use.
KQ 9. Vasopressin
In adult patients with septic shock, when appropriate MAP is not maintained despite the use of norepinephrine, is the addition of vasopressin better than increasing norepinephrine dose?
Recommendation
In adult patients with septic shock, when appropriate MAP is not maintained despite the usual dose of norepinephrine, we suggest adding vasopressin rather than increasing norepinephrine dose (Recommendation strength B, conditional recommendation for intervention; Quality of evidence: moderate).
Clinical Considerations
Additional research is needed on the timing of vasopressin administration. However, based on the results of previous RCTs, it seems appropriate to consider adding vasopressin when the norepinephrine concentration exceeds 0.25 μg/kg/min.
Background
In adult septic shock, when it is difficult to maintain a target MAP, even with appropriate fluid therapy, the use of vasopressors should be considered. Norepinephrine is an α1, β1, and β2 adrenergic receptor agonist that constricts blood vessels, increasing MAP. Based on many RCTs, it is recommended as a first-line vasopressor in adult sepsis [144]. When it is difficult to achieve an appropriate MAP with norepinephrine, addition of epinephrine or vasopressin can be considered. Several physiological advantages can be anticipated regarding the use of vasopressin. First, previous studies reported a relatively low concentration of endogenous vasopressin in patients with septic shock [145]. Second, when administering norepinephrine, the adrenergic receptors are probably already saturated. Finally, catecholamine-saving effects can be obtained using vasopressin [146]. Therefore, vasopressin is prioritized as a secondary vasopressor [20].
Summary of Evidence
For this clinical question, a three-step strategy literature search recovered 6,789 articles. After excluding 1,364 duplicates, 5,425 documents were selected using titles and abstracts. A full-text review was performed on seven RCTs [129,130,132,142,147-149], five of which were selected for our analysis [129,130,132,142,148]. Most studies used first-line vasopressors to correct blood pressure after diagnosis of septic shock and before randomization [129,132,142,148]. After randomization, the study drug dose was increased if the MAP did not achieve the target value. The studies included in the meta-analysis are summarized in Table 4.
A meta-analysis was conducted on 28-day and ICU mortality rates, the incidence of AKI, and the application of RRT. In four RCTs where 28-day mortality was addressed, vasopressin plus norepinephrine showed no significant difference in 28-day mortality rate compared to norepinephrine alone (RR, 0.98; 95% CI, 0.86–1.12) [129,132,142,148]. Additionally, ICU mortality was not different in three of those studies [129,130,148]. Regarding AKI, no difference was found between the combination treatment and norepinephrine alone [129,132,142]. However, the incidence of RRT was significantly lower with the combination treatment (RR, 0.69; 95% CI, 0.89–1.06) [129,132,148].
Among all studies included in the meta-analysis, the primary endpoints varied (e.g., hemodynamic variables, 28-day mortality, AKI-free day, and lactate clearance). The baseline characteristics and disease severity of the enrolled patients were also different. In addition, because several studies did not include critical outcome variables, the meta-analyses were performed using subgroups of the studies that reported each outcome. For the three RCTs included in the analysis of ICU mortality, the level of evidence decreased due to the imprecision caused by the small number of events [129,130,148]. Therefore, the overall level of evidence for the clinical questions was downgraded to moderate.
Comments
In the meta-analysis, the combined use of norepinephrine plus vasopressin showed no significant difference in mortality rate compared to norepinephrine alone but significantly reduced the rate of RRT. However, the timing of vasopressin initiation needs to be noted. In an RCT (the VASST trial) by Russell et al. [142], vasopressin administration was associated with lower mortality in the low-severity group of patients in whom norepinephrine concentration was <15 μg/min (<0.25 μg/min/kg for 60 kg). In another RCT (by Gordon et al. [129]), the norepinephrine concentration when vasopressin was initiated was 0.1 to 0.3 μg/kg/min. The 2021 SSC international guidelines recently recommended the concurrent use of vasopressin when the norepinephrine dose reaches 0.25 to 0.5 μg/kg/min [20]. Additionally, they suggested that intravenous corticosteroids (hydrocortisone, 200 mg/day) be commenced at a dose of norepinephrine ≥0.25 μg/kg/min at least 4 hours after initiation.
Since most previous studies used vasopressin as a second-line drug in addition to the use of other vasopressors [129,132,142,148], additional research is needed to determine the benefits of combination therapy and the appropriate dose of norepinephrine when vasopressin infusion is started.
KQ 10. Dobutamine
In adult patients with septic shock accompanied by decreased cardiac function, does adding dobutamine to existing treatments reduce mortality?
Recommendation
In adult septic shock patients with decreased cardiac function and hypoperfusion, the use of dobutamine may be considered (Recommendation strength E, expert opinion; Quality of evidence: very low).
Background
In patients with septic shock, cardiac dysfunction is a major cause of hemodynamic instability and is associated with worsening prognosis [150]. Dobutamine can increase cardiac output, increasing visceral perfusion and tissue oxygenation and improving intramucosal metabolic acidosis and hyperlactatemia. However, this effect is difficult to predict, and hypotension may occur due to vasodilation. Additionally, there are cases where the heart rate increases without the expected increase in cardiac output. The 2021 SSC guidelines suggest the use of dobutamine in patients with persistent hypoperfusion accompanied by acute myocardial dysfunction despite appropriate fluid therapy, but the level of evidence is very low [20]. In particular, most studies have focused on physiological variables rather than clinical indicators, resulting in a very limited number of studies on which the guidelines are based, with no relevant RCTs. However, several retrospective observational studies have emerged since the guidelines were published [151-153], and an RCT is in progress (NCT04166331) [154].
Summary of Evidence
A total of 8,049 articles was found through the literature search. After excluding duplicates, 1,363 articles were screened, and 65 full-text articles were reviewed. However, no studies addressed the key question (patients with septic shock and decreased cardiac function). As an alternative, studies targeting patients with sepsis and septic shock were selected (16 studies), with 4 RCTs [155-158] and 12 non-RCTs (9 prospective before-after studies [159-167] and 3 retrospective cohort studies [152,153,168]).
To date, there are no RCTs examining the effect of dobutamine use on mortality in patients with sepsis or septic shock. In a retrospective study by Wilkman et al. [168], among 420 patients with septic shock, the mortality rate was significantly higher in the dobutamine group than in the non-administration group (44.0% vs. 24.2%, P<0.001). However, our meta-analysis, including 4 non-RCTs, showed that dobutamine did not affect mortality in patients with sepsis or septic shock (RR, 1.22; 95% CI, 0.86–1.73). The length of ICU stay was no different between the two groups when using two retrospective studies [152,153]. For tissue perfusion, a meta-analysis was conducted on renal (urine output), gastrointestinal, and peripheral tissue perfusion indices, using data from one RCT and three non-RCTs [158,159,162,166]. There was no significant difference in urine output between the dobutamine and non-dobutamine groups (MD, –11.60 ml/hr; –24.93 to 1.74 ml/hr). In terms of gastrointestinal perfusion, there were no significant differences in gastric mucosal pH [156,157,165] or gastric mucosal-arterial blood carbon dioxide partial pressure difference (ΔPaCO2) between the two groups [155,156,158,161].
A recently published network meta-analysis showed that, among various drug combinations, that of norepinephrine and dobutamine was associated with lower 28-day mortality in patients with septic shock accompanied by decreased cardiac function [169]. Despite being small RCTs, the data on use of dobutamine showed some positive results on tissue perfusion [155,161]. Given that the network meta-analysis shows the best results from the combination of norepinephrine and dobutamine [169], we may consider using dobutamine while carefully monitoring patients with septic shock. However, the RCTs included in the meta-analysis had a high RoB, and they investigated physiological indicators rather than clinical parameters. Additionally, the risk of inconsistency and imprecision was high considering the different patient groups and insufficient subjects. In this guideline, the level of evidence was very low, and the recommendation grade was expert opinion.
Comments
Despite the improved tissue perfusion mentioned above, several studies have reported a higher mortality rate or increased length of ICU stay in the dobutamine group. Dobutamine can sometimes lower blood pressure due to its vasodilation effect. It can also destabilize the vital signs of sepsis patients by increasing heart rate without increasing SV. To date, no RCTs have included the effect of dobutamine administration on mortality or length of ICU stay. However, the results of our meta-analysis showed that the use of dobutamine had no influence on the mortality rate or length of ICU stay in patients with sepsis or septic shock. Therefore, it is advisable to make decisions on the use of the drug after carefully reviewing the condition of the patient. Additionally, these recommendations may change depending on the results of a large-scale RCT currently in progress [154].
KQ 11. Extracorporeal membrane oxygenation (ECMO)
Is ECMO treatment effective in adult patients with septic shock?
Recommendation
1. In patients with acute respiratory distress syndrome due to sepsis who do not respond to existing standard treatments, we suggest performing venovenous (VV) ECMO (Recommendation strength E, expert opinion; Quality of evidence: none).
2. In patients with septic shock and decreased cardiac function who do not respond to existing standard treatments, venous arterial (VA) ECMO can be applied (Recommendation strength B, conditional recommendation for intervention; Quality of evidence: low).
Clinical Considerations
ECMO is not recommended for patients with septic shock accompanied by multi-organ failure. When ECMO treatment is considered in these patients, the benefits and risks of the treatment should be assessed.
Background
ECMO is a method of treatment that supports cardiopulmonary function through an extracorporeal circulation device consisting of an artificial oxygenator and a blood pump. It is used in patients with severe heart failure or severe acute respiratory failure who do not respond to standard treatments and have no other treatment options. A recent multicenter international report published by the Extracorporeal Life Support Organization found that the number of ECMO applications is increasing every year. The discharge rate of live patients after ECMO treatment is 45% and 58% in adults with acute heart failure and acute respiratory failure, respectively [170]. However, because ECMO is an invasive treatment and serious life-threatening complications occur at a considerable rate, the choice of ECMO treatment must be made carefully.
Summary of Evidence
Through a literature search strategy, 6,776 studies were retrieved. In the literature selection process, 4,975 studies were screened using titles and abstracts, with duplicates excluded. Afterward, 504 original texts were reviewed, and three cohort studies were ultimately selected. A study by Takauji et al. [171] included both patients with septic shock due to severe respiratory failure without respiratory infections and those with respiratory infections. Their multicenter retrospective observational study used propensity score matching (conservative treatment group, n=239; VV-ECMO group, n=65). A publication by Bréchot et al. [172] also involved an international multicenter retrospective observational study. They included 212 patients with sepsis-induced cardiogenic shock and compared 90-day mortality rates between the conservative treatment (n=130) and VA-ECMO groups (n=82) after propensity score weighting. A study by Zha et al. [173] conducted a propensity score matching analysis among 255 patients with septic shock, respiratory infection, or respiratory failure. They compared 30- and 90-day mortality rates between conservative treatment (n=31) and VV-ECMO treatment groups (n=31).
Among the selected studies (n=3), the ECMO treatment group had a lower risk of death than the conservative treatment group (RR, 0.69; 95% CI, 0.51–0.93). Two studies reported serious adverse reactions (AKI, RRT, stroke, bleeding, etc.), and bleeding complications were more likely to occur in the ECMO treatment group than in the conservative treatment group (RR, 2.60; 95% CI, 1.64–4.14) [171,173]. In three studies reporting critical outcomes, the heterogeneity was high, so the recommendation grade was lowered by one step due to inconsistency and publication bias. Another step reduction was due to imprecision and effect size related to the small number of subjects and events. Additionally, the criteria for selecting ECMO treatment were diverse among the studies (e.g., selection bias). Based on these factors, the level of evidence for this clinical question was evaluated as low.
Comments
Since all the studies included in this guideline were not RCTs but retrospective observational studies, evaluating benefits and risks is subject to major limitations. However, given the results of a recent large-scale RCT (EOLIA [Extracorporeal Membrane Oxygenation for Severe Acute Respiratory Syndrome]) [174], VV-ECMO can be considered in patients with acute respiratory distress syndrome due to sepsis refractory to standard treatments if they have no multi-organ failures. Although there are no RCTs on patients with refractory septic shock, an international retrospective analysis by Ling et al. [175] showed that VA-ECMO significantly improved survival in patients with sepsis-induced cardiogenic shock. Moreover, in an individual participant data meta-regression analysis by Ling et al. [175], VA-ECMO showed improved survival in adults with septic shock and sepsis-induced myocardial depression. However, the treatment was associated with poor outcomes among those with septic shock without severe left ventricular depression. Therefore, VA-ECMO may be a viable treatment option in selected adult patients with refractory septic shock and left ventricular dysfunction.
In Korea, the influenza pandemic and the Middle East respiratory syndrome (MERS) epidemic have led to accumulated and widely shared knowledge and experience in the managing of ECMO cases of various causes among healthcare providers. ECMO has also been recognized as an important treatment option for severe cases of coronavirus disease 2019 (COVID-19). However, no RCTs have evaluated the effect of ECMO in patients with refractory septic shock. Therefore, the treatment should be carefully considered in the ICU.
KQ 12. Echocardiography
Is echocardiography recommended to assess cardiac function in adult patients with sepsis?
Recommendation
We suggest echocardiography to assess cardiac function and hemodynamics in adult patients with sepsis (Recommendation strength B, conditional recommendation for intervention; Quality of evidence: very low).
Background
Reduced or hyperdynamic LV systolic function is a risk factor for increased mortality in patients with sepsis [176]. Sepsis-induced cardiomyopathy (SICM) or sepsis-induced myocardial dysfunction can be expressed as a temporary cardiac dysfunction in sepsis patients. Although its importance in determining the prognosis of sepsis patients continues to evolve, there is no widely accepted definition. Transthoracic echocardiography (TTE) is a commonly used instrument in many clinical fields because it is non-invasive and easily accessible. The 2021 SSC international guidelines recommend echocardiography as a dynamic indicator to evaluate fluid responsiveness during the initial fluid treatment in sepsis patients. However, there is no specific mention of its use to evaluate cardiac function [20].
Summary of Evidence
Of the 8,795 articles found through the literature search strategy, 8,776 were excluded using the title and abstract, and a full-text review was performed on 19 articles. Given that the existing sepsis treatment guidelines did not cover the topic in detail, it was difficult to find studies that provided evidence related to the PICO. Finally, four retrospective cohort studies were selected and reviewed (Table 5) [177-180].
In a study using the Medical Information Mart for Intensive Care (MIMIC) III database by Feng et al., when comparing two propensity-matched cohorts (1,626 patients in each group), the 28-day mortality rate was significantly lower in the TTE group than the non-TTE group (OR, 0.78; P<0.001). In addition, the former group was able to stop vasopressors earlier than the latter (vasopressor-free days, 21 vs. 19; P=0.004) [177]. Lan et al. [178] also used the MIMIC-III database and a propensity score matched analysis (1,289 patients in each group). They found that the 28-day mortality rate in the TTE group was significantly lower than in the non-TTE group (HR, 0.83; P=0.005). Hanumanthu et al. [179] conducted a single-center retrospective cohort study using data on patients with sepsis but without acute coronary syndrome. When comparing the SICM group (n=19) and the non-SICM group (n=340), with TTE used for diagnosis confirmation, the in-hospital mortality rate was significantly higher in the SICM group (OR, 4.46; P=0.03). Another retrospective cohort study using the MIMIC-III database by Zheng et al. [180] compared 28-day mortality rates between an early TTE group (within 10 hours of admission to the ICU, n=544) and a delayed TTE group (>10 hours of admission to the ICU, n=2,027). They found that the early TTE group had a significantly lower 28-day mortality rate compared to the delayed TTE group (HR, 0.73–0.78; P<0.05) [180].
In the meta-analysis of the three observational studies that reported critical outcomes, a significantly lower 28-day mortality rate was noted in the TTE group compared to the non-TTE group (RR, 0.79; 95% CI, 0.71–0.88) [175-177]. However, there is a high RoB because only retrospective observational studies were used in the meta-analysis. Additionally, the study period and inclusion criteria differ in two of the three studies that used the MIMIC-III database [177,178]. Due to these limitations, the current level of evidence was determined as very low.
Comments
TTE is a non-invasive test that can be performed at the bedside with no serious complications. Although the 2021 SSC international guidelines recommend echocardiography as a dynamic indicator to evaluate fluid responsiveness, we analyzed the role of TTE from a different perspective, and the results indicate that the 28-day mortality rate is significantly lower in the group who underwent TTE compared to those who did not. Therefore, TTE itself might be beneficial in adult patients with sepsis or septic shock. This implies that TTE can affect treatment strategies or help predict prognosis in patients with sepsis. However, echocardiography is operator-dependent, and the accuracy of results can vary based on the clinician's skill and experience. Additionally, further research is warranted since the evidence remains unclear on the indicators to be used as references in echocardiography.
Recommendation
When performing fluid resuscitation in patients with sepsis or septic shock, the use of lactate clearance is suggested as an indicator rather than ScvO2 (Recommendation strength B, conditional recommendation for intervention; Certainty of evidence: moderate).
Background
Lactate is a marker of metabolic stress and tissue hypoxia, while ScvO2 reflects the balance between oxygen delivery and consumption (e.g., the amount of oxygen remaining in the blood after circulating through the tissues). Previously, ScvO2 was used as a target for quantitative resuscitation of sepsis patients in the early SSC international guidelines [13]. However, there are some barriers to using ScvO2 as a quantitative resuscitation goal, such as time, technology, and need for measurement equipment [14,15]. Of note, protocoled quantitative resuscitation, known as early goal-directed therapy (EGDT), failed to show a reduction in mortality in subsequent large multicenter RCTs and was removed from the recommendations when the SSC guidelines were revised in 2016 [16-18]. However, the relationship between lactate concentration and mortality in sepsis patients is well known [19,20], and lactate measurement is recommended as a component of the Hour-1 bundle by the SSC guideline. Elevated levels of lactate (>2 mmol/L) are also included as a criterion for septic shock in the Sepsis-3 definition [21].
Summary of Evidence
Of 16,822 articles found through the initial literature search, 12,462 were screened. Seventy-five full-text articles were reviewed, and four RCTs were finally selected [15,22-24]. All four studies looked at in-hospital mortality as the primary clinical outcome, and the lengths of intensive care unit (ICU) days and mechanical ventilation (MV) were confirmed in two studies each. In the studies, the lactate clearance and ScvO2 measurement groups were compared during initial fluid resuscitation. The meta-analysis showed that the lactate clearance group, compared to the ScvO2 group, had a significantly lower in-hospital mortality rate (risk ratio [RR], 0.74; 95% CI, 0.59–0.93). No significant differences were observed between the two groups in the periods of MV application and length of ICU stay, but the period of MV application tended to be lower in the lactate clearance group (mean difference [MD], 10.74 hours; 95% CI, 23.86 to 2.38). Despite unclear information about the concealment of group allocation in two studies, the RoB in other areas was low, and the level of evidence was moderate in all four RCTs. The overall level of evidence was determined as moderate, considering the level of in-hospital mortality, which is a crucial outcome indicator. The benefits are higher than the risks because lactate levels can be measured quickly without inserting a central venous catheter.
Comments
In a KSA survey on obstacles to performing the sepsis bundle, the most common reason for difficulty or delay in measuring lactate levels was a shortage of doctors or nurses (43.6%). The second most common reason was a lack of awareness among medical staff regarding the importance of lactate measurement (21.5%) [25]. Therefore, continuous promotion and education on the importance of lactate measurement remain essential. Emphasis should be placed on lactate clearance, rather than just measuring lactate levels. Additionally, clinicians should be aware of other conditions involving increase in lactate levels, such as medications (metformin, epinephrine, etc.), excessive exercise, alcohol, convulsions, liver disease, and tumors. Interestingly, recent studies on resuscitation using capillary refill time showed a trend toward a lower 28-day mortality rate and improvement in major organ function at three days compared to lactate levels [26,27]. Thus, capillary refill time may be helpful in resource-limited countries where a prompt lactate measurement is not feasible.
KQ 2. Fluid resuscitation
Should at least 30 ml/kg of crystalloid fluids be administered within 3 hours of starting resuscitation in adult patients with sepsis or septic shock and hypoperfusion?
Recommendation
In adult patients with sepsis or septic shock accompanied by hypotension or hypoperfusion, administration of 30 ml/kg of crystalloid fluids within the first 3 hours is suggested (Recommendation strength B, conditional recommendation for intervention; Certainty of evidence: low).
Background
Fluid therapy is essential to early sepsis resuscitation, increasing circulating blood volume and cardiac output. The 2021 SSC international guidelines recommend administering 30 ml/kg of crystalloid fluids within 3 hours in patients with tissue hypoperfusion due to sepsis [20]. That dosage was based on the amount of fluid administered in previous sepsis studies. However, excessive fluid treatment carries the risk of complications such as fluid overload, pulmonary edema, and prolonged MV. In studies by Boyd et al. [28] and Sakr et al. [29], positive fluid balance was associated with increased mortality. In a study by Marik et al. [30], fluid treatment of greater than 5 L on the first day was associated with a high mortality rate. However, many studies were not limited to sepsis patients but covered a broad range of critically ill patients. In observational studies, it is essential to consider that greater severity may correlate with a higher likelihood of receiving larger fluid volumes early in treatment.
To date, no prospective study has examined the association between fluid dose and treatment outcomes in sepsis patients. Specifically, the timing of fluid administration (early vs. delayed fluid administrations) and treatment results differed from study to study. The presently considered guideline reviewed and analyzed studies that specified the initial fluid dose and administration time for patients with sepsis or septic shock.
Summary of Evidence
From 3,720 papers retrieved through the literature search strategy, 2,948 studies were initially screened, excluding duplicates. Among them, 20 full-text articles were reviewed, and finally, five RCTs and two retrospective cohort studies were selected. In a retrospective cohort study by Kuttab et al. [31], sepsis or septic shock was defined using International Classification of Diseases (ICD) codes. That study found that, compared to patients who received 30 ml/kg crystalloids within the first 3 hours of sepsis (509 patients), the in-hospital mortality rate (odds ratio [OR], 1.52; 95% CI, 1.03–2.24) and the length of ICU stay were significantly increased in patients who did not that treatment (523 patients) [31]. Since no other articles addressing the PICO were identified, the scope was extended to secure as much evidence as possible for the clinical question by including one observational study and five RCTs as indirect evidence.
Seymour et al. [32] analyzed 26,978 patients who received 30 ml/kg of fluid within 12 hours among 49,331 patients with sepsis or septic shock who visited the emergency rooms of 149 hospitals. The mortality rate did not increase when the completion time of 30 ml/kg fluid administration was delayed (OR, 1.01; 95% CI, 0.99–1.02). Additionally, no significant difference was found between patients who received 30 ml/kg fluid within 6 hours and those who experienced it between 6 and 12 hours (OR, 1.02; 95% CI, 0.92–1.14) [32]. However, because no accurate data on the number of patients were available, our meta-analysis did not include that study. In the meta-analysis using the five RCTs on the EGDT (Rivers et al. [33], ProCESS [17], ARISE [16], ProMISe [18], and Andrews et al. [34]), the risk for in-hospital mortality and 28-day mortality was 1.17 (0.91–1.51) and 1.10 (0.92–1.32), respectively. A non-significant difference occurred between the early resuscitation and the control groups. In one of these studies, the mortality rate was higher in the resuscitation group [34]. However, since the EGDT study by Rivers et al. [33], early fluid therapy has been considered important and is included in the usual care of patients with sepsis or septic shock. In RCTs, such as ARISE, ProCESS, and ProMISe, approximately 2.0 L of fluids was administered within the first 6 hours, even in the usual care group [35]. In particular, the dose-response relationship between the amounts of initial fluids and outcomes in a retrospective study by Kuttab et al. [31] may emphasize the importance of early fluid administration. Based on this evidence, the recommendation grade for treatment was determined as conditional, and the level of evidence was judged to be low due to the lack of related research.
Comments
There is insufficient evidence to determine if at least 30 ml/kg of crystalloid fluids should be administered within the first 3 hours for management in adults with sepsis or septic shock accompanied by hypoperfusion. However, the CLASSIC trial used four noteworthy conditions (e.g., conditions for intravenous fluid administration in the restrictive fluid group): severe hypoperfusion, which was defined as lactate levels of at least 4 mmol/L; a mean arterial pressure (MAP) below 50 mm Hg despite infusion of a vasopressor or an inotropic agent; mottling beyond the edge of the kneecap (mottling score >2); and a urine output < 0.1 ml/kg/hr for the first 2 hours [36,37]. Again, although fluid therapy is critical for increasing cardiac output and tissue perfusion, the disadvantages should be considered. Excessive fluid administration can cause worsening pulmonary edema, a decrease in cardiac function, and increases in the duration of MV or ICU stay. Therefore, the decision to administer fluids should be made with caution, considering the risks and benefits.
KQ 3. Fluid type
When performing fluid resuscitation in patients with sepsis, does the use of balanced crystalloid, compared to 0.9% saline, reduce mortality rates and incidence of acute kidney injury?
Recommendation
Balanced crystalloids or saline (0.9% saline) can be used during fluid resuscitation in patients with sepsis (Recommendation strength B, conditional recommendation for intervention; Certainty of evidence: moderate)
Background
Crystalloid fluids are recommended during fluid resuscitation in sepsis patients [20]. However, recent studies have reported that intravenous 0.9% saline promotes hyperchloremic metabolic acidosis, increases the possibility of acute kidney injury (AKI) [38-40], and increases mortality [41-43]. Therefore, more attention has been given to the usefulness of balanced solutions with electrolyte components more closely resembling those of plasma-Ringer’s Lactate solution and Plasma-Lyte A solution [42]. However, conclusive evidence on the choice of crystalloid fluids on patient outcomes is lacking [44,45]. Therefore, the present guideline analyzed and compared the effects of the two fluid types on patient outcomes.
Summary of Evidence
Through the literature search, a total of 23,338 articles was identified. After excluding duplicate studies, the titles and abstracts of 19,788 studies were assessed, and the full texts of 11,312 studies were reviewed. Among these, six RCTs were finally selected [40,46-50]. Of the six studies, only one targeted patients with sepsis, while the other five targeted those admitted to ICUs, although the number of participating patients was more significant. In the analysis, mortality (in-hospital, 28-, 30-, and 90-day mortality) was considered the critical outcome, and the incidence of AKI was analyzed as an essential outcome. Among the six selected studies, that by Semler et al. (SALT trial) [47] reported death and renal damage as a composite outcome. Therefore, we performed a meta-analysis on mortality using five studies. There was no significant difference in mortality between the two fluid therapies (RR, 0.95; 95% CI, 0.87–1.02). For the incidence of AKI, our meta-analysis was conducted using two studies (by Young et al. [46] and Kumar et al. [48]), and no significant difference was found between the two fluids (RR, 0.71; 95% CI, 0.47–1.06). In an open-label study by Kumar et al. [48] (also published by Golla et al. in 2022), the concentration of chloride ion and the incidences of AKI at 24 and 48 hours were significantly increased in the group receiving 0.9% saline compared to those receiving balanced crystalloids. However, during the entire hospitalization period, there was no difference in the chloride ion concentration, AKI incidence, and mortality rate [48]. In the SMART study by Semler et al. [40], which targeted 7,942 critically ill patients admitted to ICUs, balanced crystalloids reduced mortality, use of renal replacement therapy (RRT), and persistent renal function decline in all enrolled patients with no statistical significance, but there was a tendency favoring balanced crystalloids in sepsis patients. In a study by Brown et al. [43], a secondary analysis of the SMART trial, balanced crystalloids significantly reduced the 30-day in-hospital mortality rate (adjusted OR, 0.74; 95% CI, 0.59–0.93; P=0.01) with improved renal outcomes. However, this was a single-center study, and fluid resuscitation was not assigned using a blinded method. There is also a possibility of misclassification due to the use of ICD-10 codes. In a 2015 study by Young et al. [46] of patients in medical ICUs, no differences were identified either in the incidence of AKI or the use of RRT between the two fluid groups. In our meta-analysis, no significant differences were identified between 0.9% saline and balanced crystalloid groups; except for the study by Seymour et al. [32], there was no significant difference in primary outcomes. Therefore, the level of evidence was lowered by one grade due to inconsistency. Additionally, since meta-analysis was conducted using data from subgroup populations, it is difficult to rule out RoB. Based on this, the evidence level of the recommendation was judged as moderate.
Comments
Despite no significant difference in the critical outcomes in the meta-analysis, the results from recent analyses by Zampieri et al. [51,52] are noteworthy. In their secondary analyses of the original BaSICS (Balanced Solutions in Intensive Care Study) trial, the beneficial effects of balanced solution over 0.9% saline were more apparent in septic patients likely to have unplanned ICU admission and the need for higher fluid volumes. Based on their results, a balanced solution may be preferred to 0.9% saline in septic conditions where a large volume of fluids is needed, such as peritonitis or pancreatitis. However, it is important to consider the association of volume overload with worse outcomes regardless of the type of fluid, especially in septic patients. In addition, the use of balanced crystalloids may exert detrimental effects in patients with traumatic brain injury [49]. Finally, given the statistically significant increase in chloride ion concentration (or hyperchloremic acidosis) in the 0.9% saline group [48], the choice of crystalloid fluids can depend on the circumstances, especially when the patient has hyperkalemia, hyperchloremia, or AKI [46,48].
KQ 4. Target blood pressure
In adult patients with septic shock, can a target MAP ≥65 mm Hg improve the outcomes of patients compared to targeting a higher MAP?
Recommendation
In adult patients with septic shock, we suggest a target MAP ≥65 mm Hg over higher MAP targets (Recommendation strength B, conditional recommendation for intervention; Certainty of evidence: moderate).
Background
The 2021 SSC international guidelines recommend maintaining the initial target MAP above 65 mm Hg in adult patients with septic shock using vasopressors as a strong recommendation with moderate quality of evidence [20]. MAP is the primary determinant of systemic filling pressure and the main driver of venous return and cardiac output. Thus, as MAP increases, tissue blood flow also increases. Specific organs, such as the brain and kidneys, can autoregulate blood flow, but when MAP falls below approximately 60 mm Hg, tissue perfusion decreases proportionally. Therefore, an adequate MAP is crucial in patients with septic shock.
Summary of Evidence
A total of 8,386 studies was identified; after excluding duplicates, we reviewed the titles and abstracts of 6,750 studies. Among these, after excluding 6,736 articles, we reviewed the full texts of 14 articles. Finally, three RCTs were selected to compare patients who maintained MAP above 65 mm Hg (control) with those who targeted an MAP higher than 65 mm Hg (intervention). In a prospective, open-label RCT by Asfar et al. [53], 776 patients with septic shock were divided into a high-target group (n=338, MAP target 80 to 85 mm Hg) and a low-target group (n=338, MAP target 65 to 70 mm Hg). On day 28, 142 people (36.6%) in the higher MAP group and 132 people (34.0%) in the lower MAP group had died, with no significant difference between the two groups (hazard ratio [HR], 1.07; 95 % CI, 0.84–1.38; P=0.57). The 90-day mortality rate also showed no significant difference between the two groups. However, atrial fibrillation was more frequent and use of RRT was less frequent in the higher MAP group. In a prospective, multicenter RCT, Lamontagne et al. [54] investigated 118 patients with septic shock, with 58 in the high MAP group (MAP target 75 to 80 mm Hg) and 60 in the lower MAP group (MAP target 60 to 65 mm Hg). The primary outcome was the separate measurement of MAPs in each group, and secondary outcomes were in-hospital, 28-day, and 6-month mortality rates. The 28-day mortality rate did not differ significantly between the higher and lower MAP groups (46% vs. 44%, P=0.21). In a study by 65 clinical trial investigators (Mouncey et al. [55]), a prospective multicenter pragmatic RCT, 1,291 patients in the permissive hypotension group (MAP of 60–65 mm Hg) were compared to 1,307 patients in the usual care group. The average MAP up to 7 days after the application of vasopressors was 67.6 mm Hg in the permissive hypotension group and 72.9 mm Hg in the usual care group. At 90 days, the mortality rate was not different between the two groups (41.0% vs. 43.8%, P=0.154). After controlling for pre-specified variables, the OR for 90-day mortality was 0.82, favoring the permissive hypotension group more than the usual care group. Our meta-analysis found no significant benefit of maintaining a target MAP higher than 65 mm Hg (e.g., a higher MAP group). In the RCTs included, there were no significant problems with random sequence generation, allocation concealment, or blinding of participants and personnel. However, in some studies, blinding of interventions was incomplete, and missing data were identified. Additionally, the underlying conditions of the enrolled patients varied, and the criteria for low and high MAPs differed slightly. Considering this, the overall recommendation strength for this clinical question was assessed as conditional for the intervention (e.g., target MAP ≥65 mm Hg), and the evidence level was moderate.
Comments
Currently, there is no evidence that a higher MAP target, compared to maintaining an MAP ≥65 mm Hg, improves patient outcomes. In the SEPSISPAM study by Asfar et al. [53], the incidence of atrial fibrillation was higher but renal replacement therapy was less frequent in the higher MAP target group. Importantly, it is necessary to consider the accuracy of measurements because the value of 65 mm Hg measured with invasive methods may differ from that measured with non-invasive methods. Large-scale comparative studies on the currently suggested level of MAP (65 mm Hg) are needed.
KQ 5. Dynamic parameters
In adult patients with sepsis or septic shock, can fluid therapy using dynamic parameters compared to static parameters or usual treatments reduce mortality rate?
Recommendation
If additional fluids are required after initial fluid resuscitation in adult patients with sepsis or septic shock, fluid therapy using dynamic parameters is suggested (Recommendation strength B, conditional recommendation for intervention; Certainty of evidence: moderate).
Background
In clinical practice, parameters that represent the filling pressure of the heart, such as central venous pressure (CVP) or pulmonary artery pressure, have been widely used as static parameters. This hypothesis assumes that, as ventricular volume increases, heart-filling pressure increases proportionally. However, this is only true if ventricular compliance, which determines the pressure-volume relationship in the heart, remains constant. The actual compliance of the ventricles varies from patient to patient because it is affected by myocardial ischemia or infarction, myocardial hypertrophy, or cardiomyopathy. Even in the same patient, compliance of the ventricles varies depending on positive end-expiratory pressure and changes in cardiac function [56]. As a result, heart-filling pressure may remain the same despite varying volume states [56,57]. Additionally, changes in filling pressure can differ with increasing preload, highlighting a limitation of static parameters. Conversely, representative dynamic parameters include pulse pressure variation (PPV) and stroke volume variation (SVV), with larger values indicating a hypovolemic state and more significant variation in the respiratory cycle [58,59]. However, these dynamic parameters may not be accurate when an arrhythmia or increased intra-abdominal pressure is present, when vascular tension significantly changes, when tidal volume is low, or when there is spontaneous breathing effort [60-63]. In patients with spontaneous breathing, the passive leg raising (PLR) test is especially helpful in predicting responsiveness to fluid therapy. When a patient's leg is lifted to 45°, approximately 300 ml of blood moves from the periphery to the heart, and cardiac output changes accordingly.
Summary of Evidence
Among a total of 20,463 documents found through a literature search strategy, 18,502 were selected after excluding 1,961 duplicates. Among these, 67 full-text articles were reviewed (among 68 selected papers), and four RCTs were ultimately selected [64-67]. In an RCT by Richard et al. [65], the intervention group (n=30) received fluid therapy using SVV with PLR test or PPV (for patients on MV), and the control group (n=30) was given fluid therapy using CVP. Chen et al. [64] conducted a study of patients with septic shock who had received vasopressors for more than 12 hours and used the changes in PPV, inferior vena cava distension index, and stroke volume index after PLR (41 patients in each group). A study by Kuan et al. [66] targeted patients with sepsis with serum lactate levels of 3.0 mmol/L or higher and examined the changes in stroke volume index after PLR (61 vs. 61 patients in intervention vs. control groups). Finally, Douglas et al. [67] performed a PLR test in sepsis patients with persistent refractory hypotension or expected to be admitted to the ICU. They compared fluid therapy using changes in cardiac output with usual care (83 vs. 41 patients).
In this guideline, the critical outcomes were overall mortality and 28- or 30-day mortality rates, and other important outcomes were duration of MV and fluid balance on day 3. Of the four RCTs, three reported 28- or 30-day mortality rates [65-67], but one study only reported in-hospital mortality [64]. When combining all four studies, the risk of mortality was lower in the group that used dynamic parameters (intervention group) than the group that did not (usual care group), with no significant difference (RR, 0.81; 95% CI, 0.59–1.11). Regarding 28- or 30-day mortality (after exclusion of the study reporting in-hospital mortality), the risk of mortality in the intervention group was lower than in the usual care group, with statistical significance (RR, 0.62; 95% CI, 0.39–0.99). The duration of MV was shorter by 2.48 days in the intervention group than the usual care group (MD, –2.48 days; 95% CI, –3.61 to –1.35) [64,67], and fluid balance on day 3 was no different between the two groups (MD, –0.62 L; 95% CI, –1.31 to 0.08 L) [64,65,67]. As the four studies were RCTs, heterogeneity was not high. The level of evidence, which was lowered by one step due to imprecision, was finally determined to be moderate.
Comments
According to a study by Richard et al. [65], there was no difference in time to shock resolution when comparing fluid therapy using dynamic parameters with usual care (median [interquartile range]: 2.3 days [1.4–5.6] vs. 2.0 days [1.2–3.1], P=0.29). However, in our meta-analysis of subgroups (three RCTs), a significant reduction in 28- 30-day mortality rates and the period of MV was found. Therefore, fluid therapy using dynamic parameters can be considered beneficial to patients. Douglas et al. [67] compared major cardiovascular endpoints, including cardiovascular death, non-fatal myocardial infarction, and non-fatal stroke, between the two groups and found no significant difference. Therefore, compared to usual care or fluid therapy using static parameters, fluid therapy using dynamic parameters has no obvious harm, and the benefits may be more significant [65,67]. However, the primary obstacle to using dynamic parameters may be the absence of equipment monitoring cardiac output or PPV (due to its high costs). Additionally, healthcare insurance does not cover the PLR test, which may be another barrier.
KQ 6-1. Antibiotics
In adult septic shock patients, does administering antibiotics within 1 hour of sepsis recognition improve mortality compared to administering antibiotics at 1 hour or later?
Recommendation
In adult patients with septic shock, we suggest administering antibiotics within 1 hour of septic shock recognition (Recommendation strength B, conditional recommendation for intervention; Certainty of evidence: low).
KQ 6-2. Antibiotics
In adult sepsis patients, does administering antibiotics within 3 hours of sepsis recognition improve mortality compared to administering antibiotics at 3 hours or later?
Recommendation
In adult patients with sepsis, we suggest administering antibiotics within 3 hours of sepsis recognition (Recommendation strength E, expert consensus; Certainty of evidence: very low).
Background
Early administration of appropriate antibiotics is one of the most effective treatments for lowering the mortality rate of sepsis patients [32,68,69]. However, there are controversies about the relationship between timing of antibiotic administration and mortality in patients with sepsis or septic shock [70,71]. The 2016 SSC international guidelines were not approved by the Infectious Diseases Society of America (IDSA) because of concerns about antibiotic overuse, overdiagnosis of sepsis, lack of data to support time-to-antibiotic administration goals, and difficulty in distinguishing between patients with sepsis and septic shock [72]. In 2018, the SSC committee consolidated the 3-hour and 6-hour bundles into a 1-hour bundle and recommended that the Hour-1 bundle be implemented promptly [73]. However, concerns were raised about insufficient evidence to support these changes in emergency room care. The Society of Critical Care Medicine (SCCM) and the American College of Emergency Physicians (ACEP) issued a joint statement and announced that the Hour-1 bundle will not be immediately applied to hospitals in the United States [74]. In 2020, the IDSA highlighted the lack of evidence to support early antibiotic administration in patients with suspected sepsis without shock, the risk of antibiotic overuse, and the complexity of the “time zero” definition. They recommended modifications to the Severe Sepsis and Septic Shock Early Management (SEP-1) bundle, suggesting that sepsis without shock excluded from the bundle treatment, broad-spectrum antibiotics should be started within 1 hour of time zero in septic shock, and the definition of time zero should be clear and reproducible [75].
In 2021, the ACEP issued guidelines for the initial treatment of sepsis in emergency settings, which the IDSA and SCCM endorsed. Although antibiotics should be administered promptly when sepsis is diagnosed, there is insufficient evidence to recommend a specific time standard for antibiotic administration [76]. Accordingly, the SSC committee received feedback from other expert groups and distributed a new version of the guidelines in 2021. In the revised guidelines, the antibiotic administration time is divided according to the presence of shock and the possibility of sepsis. In patients with septic shock or sepsis with a high risk of infection, antibiotics are administered within 1 hour. However, diagnostic tests should be conducted promptly in sepsis with a low risk of infection, and antibiotics should be treated within 3 hours if infection concerns persist [20].
Summary of Evidence
The literature search strategy initially found 14,670 articles. Of these articles, 12,257 were screened, and 65 full-text articles were reviewed. For this guideline, 33 cohort studies were ultimately selected, with no RCTs identified.
When “time zero” is defined as the moment when sepsis or septic shock is recognized
Thirteen articles defined “time zero” as the moment of sepsis or septic shock recognition [68,77-88]. In our meta-analysis of patients with sepsis or septic shock, there was no significant difference in mortality between those who were given antibiotics within 1 hour of recognition of sepsis or septic shock and those given antibiotics after 1 hour (RR, 0.87; 95% CI, 0.75–1.01). However, in a subgroup analysis targeting only patients with septic shock, the mortality rate was significantly lower in those who were given antibiotics within 1 hour than in those given antibiotics after 1 hour (RR, 0.89; 95% CI, 0.88–0.90). In patients with sepsis or septic shock, the mortality rate was significantly lower in those who were given antibiotics within 3 hours of sepsis or septic shock recognition than in those given antibiotics after 3 hours (OR, 0.67; 95% CI, 0.53–0.86). In a subgroup analysis targeting only patients with septic shock, the mortality rate was significantly lower in those given antibiotics within 3 hours than those given antibiotics after 3 hours (OR, 0.65; 95% CI, 0.51–0.83).
Since only two observational studies were included in the analysis of antibiotic administration within 3 hours in patients with septic shock, it was inappropriate to recommend antibiotics within 3 hours in this group. Therefore, in adult patients with septic shock, we recommend administering antibiotics within 1 hour of recognizing septic shock (Recommendation strength B, conditional recommendation for intervention; Certainty of evidence: low). Regarding antibiotic administration within 3 hours of time zero, no articles targeted only sepsis patients. However, among the observational studies in the meta-analysis, which included both sepsis and septic shock cases, sepsis accounted for most of the cases. Considering an increased mortality rate due to delayed administration of antibiotics, we can assume that the beneficial effects of antibiotics within 3 hours will also be greater than their harmful effects in patients with sepsis (Recommendation strength E, expert consensus; Certainty of evidence: very low).
When "time zero" is defined as the moment of emergency department triage
A total of 20 papers defined the ‘time zero’ as the time of emergency department triage [32,70,89-106]. In our meta-analysis on patients with sepsis or septic shock, there was no significant difference in mortality rate in those who were given antibiotics within 1 hour of the triage compared to those given antibiotics after 1 hour (OR, 0.92; 95% CI, 0.85–1.00). This was also the case in a subgroup analysis targeting only patients with septic shock (OR, 0.91; 95% CI, 0.60–1.39). In patients with sepsis or septic shock, mortality was not significantly different in those who were given antibiotics within 3 hours of the triage compared to those given antibiotics after 3 hours (OR, 0.90; 95% CI, 0.76–1.07). This was also the case in a subgroup analysis targeting only patients with septic shock (OR, 1.08; 95% CI, 0.54–2.12).
When “time zero” is defined as the moment when sepsis or septic shock is recognized
Thirteen articles defined “time zero” as the moment of sepsis or septic shock recognition [68,77-88]. In our meta-analysis of patients with sepsis or septic shock, there was no significant difference in mortality between those who were given antibiotics within 1 hour of recognition of sepsis or septic shock and those given antibiotics after 1 hour (RR, 0.87; 95% CI, 0.75–1.01). However, in a subgroup analysis targeting only patients with septic shock, the mortality rate was significantly lower in those who were given antibiotics within 1 hour than in those given antibiotics after 1 hour (RR, 0.89; 95% CI, 0.88–0.90). In patients with sepsis or septic shock, the mortality rate was significantly lower in those who were given antibiotics within 3 hours of sepsis or septic shock recognition than in those given antibiotics after 3 hours (OR, 0.67; 95% CI, 0.53–0.86). In a subgroup analysis targeting only patients with septic shock, the mortality rate was significantly lower in those given antibiotics within 3 hours than those given antibiotics after 3 hours (OR, 0.65; 95% CI, 0.51–0.83).
Since only two observational studies were included in the analysis of antibiotic administration within 3 hours in patients with septic shock, it was inappropriate to recommend antibiotics within 3 hours in this group. Therefore, in adult patients with septic shock, we recommend administering antibiotics within 1 hour of recognizing septic shock (Recommendation strength B, conditional recommendation for intervention; Certainty of evidence: low). Regarding antibiotic administration within 3 hours of time zero, no articles targeted only sepsis patients. However, among the observational studies in the meta-analysis, which included both sepsis and septic shock cases, sepsis accounted for most of the cases. Considering an increased mortality rate due to delayed administration of antibiotics, we can assume that the beneficial effects of antibiotics within 3 hours will also be greater than their harmful effects in patients with sepsis (Recommendation strength E, expert consensus; Certainty of evidence: very low).
When "time zero" is defined as the moment of emergency department triage
A total of 20 papers defined the ‘time zero’ as the time of emergency department triage [32,70,89-106]. In our meta-analysis on patients with sepsis or septic shock, there was no significant difference in mortality rate in those who were given antibiotics within 1 hour of the triage compared to those given antibiotics after 1 hour (OR, 0.92; 95% CI, 0.85–1.00). This was also the case in a subgroup analysis targeting only patients with septic shock (OR, 0.91; 95% CI, 0.60–1.39). In patients with sepsis or septic shock, mortality was not significantly different in those who were given antibiotics within 3 hours of the triage compared to those given antibiotics after 3 hours (OR, 0.90; 95% CI, 0.76–1.07). This was also the case in a subgroup analysis targeting only patients with septic shock (OR, 1.08; 95% CI, 0.54–2.12).
Comments
In our meta-analyses, when “time zero” was defined as the time of triage, no significant differences were found in mortality rates according to the timing of antibiotic administration among patients with sepsis or septic shock. Therefore, it seems better to define “time zero” as the time of sepsis or septic shock recognition rather than the time of emergency department triage. However, as described above, unconditional rapid administration of antibiotics can cause various problems, such as antibiotic overuse, overdiagnosis of sepsis, and increased burden on medical staff and costs. Hence, sufficient effort is needed to make an accurate diagnosis and find the source of infection. Conversely, in patients who require antibiotics, maximum effort and improved performance are needed to ensure that antibiotic administration is not delayed after recognition of septic shock. Given the absence of large-scale RCTs on this topic, there is a need for additional well-designed large-scale RCTs.
KQ 7. Timing of vasopressors
When should vasopressors be administered to adult patients with septic shock?
Recommendation
In adult patients with septic shock, early administration of vasopressors is suggested if necessary to ensure hemodynamic stability during initial fluid therapy (Recommendation strength B, conditional recommendation for intervention; Certainty of evidence: moderate).
Background
Vasopressors can increase blood perfusion of organs and correct hypotension. They are essential for treating septic shock, along with fluid and antibiotic therapies [20]. The 2021 SSC international guidelines recommend administering fluids and vasopressors with an MAP ≥65 mm Hg as the initial hemodynamic goal. They also recommend administering vasopressors using a peripheral venous catheter rather than delaying the treatment for CVC insertion [20]. However, the appropriate timing of vasopressor administration in patients with septic shock is controversial, with conflicting research results [107-109].
Summary of Evidence
Through the literature search strategy, four RCTs [110-113] and eight cohort studies [109,114-120] were ultimately selected. The RCTs included two studies using restrictive fluid and early vasopressor strategies [110,113]. In the meta-analysis, the mortality rate tended to be lower in the early vasopressor group versus the late group, regardless of whether they only included RCTs (RR, 0.76; 95% CI, 0.53–1.09) or observational studies (RR, 0.84; 95% CI, 0.66–1.07), with no statistical significance. In the RCTs, there was no significant difference in the length of ICU stay, duration of MV, vasopressor-free days, RRT-free days, or incidence of arrhythmia. However, the incidence of pulmonary edema was significantly lower in the early treatment group [110,112,113]. In the observational studies, although no difference was found in the length of ICU stay, a significantly shorter period was reported in MV, use of vasopressors, and RRT in the early vasopressor group compared to the late group. However, the number of studies included in the analysis was limited. In terms of fluid volume, there was a tendency for the 6-hour and 24-hour fluid doses to be lower in the early group, with no significant difference. In a subgroup analysis of two RCTs that implemented a restrictive fluid strategy, no significant difference was found in mortality rate [110,113]. However, in the two studies not using a fluid restriction strategy, the mortality rate was significantly lower in the early vasopressor group [111,112], consistent with the results of a previous meta-analysis [108].
Among the studies included in the analysis, the overall level of evidence from RCTs was assessed as moderate, while that for observational studies was very low. Accordingly, the overall level of evidence for this clinical question was moderate according to the level of evidence in RCTs.
Comments
Considering the following, we recommend early administration of vasopressors for adult patients with septic shock. First, although no significant difference was found in mortality between the early and delayed vasopressor administration groups, some results suggest a therapeutic benefit in early administration in secondary endpoints such as pulmonary edema. Second, a reduction in mortality was observed in a subgroup analysis including two RCTs in which a fluid restriction strategy was not implemented. Third, no significant worsening prognosis or side effects were observed in the group receiving the early administration of vasopressors. Finally, the correlation between the duration of hypotension and increased mortality has been well established [121].
However, the effects of early vasopressor use might differ depending on certain factors such as vasopressor dose, volume status (or fluid volume administered), severity of sepsis, and corticosteroids (e.g., hydrocortisone). In particular, fluid volume and vasopressor timing may have interactions with mortality. In all the studies included in our analysis, initial fluid therapy was administered before vasopressor infusions. Hence, early administration of vasopressors alone without fluid therapy is not recommended. In most studies, the difference in timing of vasopressor administration between the early and delayed groups was not remarkable, and it did not specify an optimal time for vasopressor initiation. Therefore, an individualized approach that depends on the severity and clinical course of the septic shock is needed.
KQ 8. Vasopressor type
Should norepinephrine be used preferentially over other vasopressors in adult patients with septic shock?
Recommendation
We suggest that norepinephrine be used in preference to other vasopressors in adult patients with septic shock (Recommendation strength A, strong recommendation for intervention).
Quality of evidence:
• Norepinephrine vs. dopamine: high quality
• Norepinephrine vs. vasopressin: moderate quality
• Norepinephrine vs. epinephrine: low quality
• Phenylephrine: very low quality
• Norepinephrine vs. terlipressin: low quality
Background
According to international guidelines, norepinephrine is recommended as the first-line vasopressor to maintain the target MAP of 65 mm Hg [20]. If norepinephrine is 0.25–0.5 μg/kg/min and the target MAP is not reached, vasopressin is recommended as the second-line drug. When norepinephrine is not available, dopamine or epinephrine can be used as a substitute [20]. Norepinephrine is a powerful α1 adrenergic receptor agonist with moderate β-agonist activity, exerting strong vasoconstriction but less direct cardiac contractility. Therefore, norepinephrine primarily increases systolic and diastolic pressure and has a minimal effect on heart rate.
Dopamine is an endogenous central neurotransmitter precursor of norepinephrine and acts on dopamine and adrenergic receptors. Low doses (<3 μg/kg/min) stimulate dopamine receptors in the coronary arteries, kidneys, and cerebrum, promoting vasodilation and increased blood flow to tissues. At medium doses (5–10 μg/kg/min), dopamine binds to β1 adrenergic receptors and promotes the release of norepinephrine, increases cardiac contractility and heart rate (chronotropic), and slightly increases systemic vascular resistance (SVR). High doses (10–20 μg/kg/min) act on α1 adrenergic receptors, resulting in dominant vasoconstriction. However, dose-dependent activation of β1 adrenergic receptors may cause arrhythmia.
Vasopressin is an endogenous peptide hormone produced in the hypothalamus and stored and released in the posterior pituitary gland. Vasopressin binds to the V1 receptor of the vascular smooth muscle and the V2 receptor of the renal collecting duct. Hence, it induces vascular smooth muscle contraction through V1 stimulation, increasing arterial blood pressure and water reabsorption through the V2 receptor. Vasopressin also causes less direct coronary and cerebral vascular constriction than catecholamines while increasing SVR dose-dependently.
Epinephrine is an endogenous catecholamine with a high affinity for β1, β2, and α1-receptors in cardiac and vascular smooth muscles. It has the characteristics of more pronounced β1 adrenergic effects at low doses but more pronounced α1 adrenergic effects at high doses. At low doses, it mainly acts on β1 adrenergic receptors to increase cardiac output and reduce SVR, whereas at high doses it increases cardiac output and SVR. Potential side effects of epinephrine include arrhythmia and disruption of the splanchnic blood circulation.
Summary of Evidence
The literature search strategy identified 10,926 studies. After excluding 1,993 duplicates, 8,933 studies were screened. A total of 40 full-text articles was reviewed, and 16 RCTs and 6 cohort studies were ultimately selected.
Norepinephrine vs. dopamine
There was no significant difference in overall mortality between the norepinephrine and dopamine groups from the analysis of six RCTs (RR, 0.93; 95% CI, 0.84–1.02) [122-127]. However, a significant reduction was found in the norepinephrine group in one cohort study (RR, 0.67; 95% CI, 0.55–0.82) [128]. Additionally, when analyzing four RCTs, the ICU mortality rate was significantly reduced in the norepinephrine group compared to the dopamine group (RR, 0.90; 95% CI, 0.82–0.99) [122,124,125,129]. In the analysis of three RCTs, the incidence of arrhythmia was significantly lower in the norepinephrine group than in the dopamine group (RR, 0.49; 95% CI, 0.40–0.59) [125-127]. However, no significant difference was found in the length of ICU stay between the two groups in the analysis of two RCTs [125,126].
Norepinephrine vs. vasopressin
There was no significant difference in overall mortality between the norepinephrine and vasopressin groups in all four RCTs (RR, 1.09; 95% CI, 0.94–1.26) [129-132] and in three cohort studies (RR, 1.14; 95% CI, 0.79–1.65) [133-135]. There was no statistically significant difference in ICU mortality between the norepinephrine and vasopressin groups in three RCTs (RR, 0.94; 95% CI, 0.71–1.24) [129,131,132]. Regarding AKI, no difference was found between the norepinephrine and vasopressin groups in two RCTs, but the use of RRT was less frequent in the vasopressin group (RR, 1.44; 95% CI, 1.09–1.90) [129,132]. However, in the analysis of two cohort studies [133,135], no difference was found in the rate of RRT between the two groups. In terms of the length of ICU stay, it was shorter in the norepinephrine group compared to the vasopressin group in the three RCTs (MD, –1.55 days; 95% CI, –2.52 to –0.58 days) [129,131,132], but no difference was found in the analysis of two cohort studies [133,135].
Norepinephrine vs. epinephrine
In one RCT, overall mortality between the norepinephrine and epinephrine groups was not significantly different (RR, 1.13; 95% CI, 0.80–1.60) [136]. Vasopressin-free days were also not different between the two groups in the study.
Norepinephrine vs. phenylephrine
The overall mortality rate was not different between the norepinephrine and phenylephrine groups in one RCT [137]. However, the incidence of arrhythmia was significantly lower in the phenylephrine group compared to the norepinephrine group in a cohort study (RR, 1.20; 95% CI, 1.09–1.33) [138].
Norepinephrine vs. terlipressin
The overall mortality rate was not different between the norepinephrine and terlipressin groups in three RCTs (RR, 1.02; 95% CI, 0.74–1.42) [131,139,140]. Additionally, no differences were noted between the two groups in the RCT for both length of ICU stay and vasopressor-free days. Regarding the selection of the first vasopressor to be used in adult patients with septic shock, studies comparing norepinephrine with five other vasopressors (dopamine, vasopressin, epinephrine, phenylephrine, and terlipressin) were analyzed, and recommendations for each drug are given in this guideline. The overall level of RCTs comparing norepinephrine with the five other vasopressors varied: high for dopamine, moderate for vasopressin, low for epinephrine and terlipressin, and very low for phenylephrine. However, unlike the RCTs, the evidence for cohort studies comparing norepinephrine with five other vasopressors was all confirmed as very low.
Norepinephrine vs. dopamine
There was no significant difference in overall mortality between the norepinephrine and dopamine groups from the analysis of six RCTs (RR, 0.93; 95% CI, 0.84–1.02) [122-127]. However, a significant reduction was found in the norepinephrine group in one cohort study (RR, 0.67; 95% CI, 0.55–0.82) [128]. Additionally, when analyzing four RCTs, the ICU mortality rate was significantly reduced in the norepinephrine group compared to the dopamine group (RR, 0.90; 95% CI, 0.82–0.99) [122,124,125,129]. In the analysis of three RCTs, the incidence of arrhythmia was significantly lower in the norepinephrine group than in the dopamine group (RR, 0.49; 95% CI, 0.40–0.59) [125-127]. However, no significant difference was found in the length of ICU stay between the two groups in the analysis of two RCTs [125,126].
Norepinephrine vs. vasopressin
There was no significant difference in overall mortality between the norepinephrine and vasopressin groups in all four RCTs (RR, 1.09; 95% CI, 0.94–1.26) [129-132] and in three cohort studies (RR, 1.14; 95% CI, 0.79–1.65) [133-135]. There was no statistically significant difference in ICU mortality between the norepinephrine and vasopressin groups in three RCTs (RR, 0.94; 95% CI, 0.71–1.24) [129,131,132]. Regarding AKI, no difference was found between the norepinephrine and vasopressin groups in two RCTs, but the use of RRT was less frequent in the vasopressin group (RR, 1.44; 95% CI, 1.09–1.90) [129,132]. However, in the analysis of two cohort studies [133,135], no difference was found in the rate of RRT between the two groups. In terms of the length of ICU stay, it was shorter in the norepinephrine group compared to the vasopressin group in the three RCTs (MD, –1.55 days; 95% CI, –2.52 to –0.58 days) [129,131,132], but no difference was found in the analysis of two cohort studies [133,135].
Norepinephrine vs. epinephrine
In one RCT, overall mortality between the norepinephrine and epinephrine groups was not significantly different (RR, 1.13; 95% CI, 0.80–1.60) [136]. Vasopressin-free days were also not different between the two groups in the study.
Norepinephrine vs. phenylephrine
The overall mortality rate was not different between the norepinephrine and phenylephrine groups in one RCT [137]. However, the incidence of arrhythmia was significantly lower in the phenylephrine group compared to the norepinephrine group in a cohort study (RR, 1.20; 95% CI, 1.09–1.33) [138].
Norepinephrine vs. terlipressin
The overall mortality rate was not different between the norepinephrine and terlipressin groups in three RCTs (RR, 1.02; 95% CI, 0.74–1.42) [131,139,140]. Additionally, no differences were noted between the two groups in the RCT for both length of ICU stay and vasopressor-free days. Regarding the selection of the first vasopressor to be used in adult patients with septic shock, studies comparing norepinephrine with five other vasopressors (dopamine, vasopressin, epinephrine, phenylephrine, and terlipressin) were analyzed, and recommendations for each drug are given in this guideline. The overall level of RCTs comparing norepinephrine with the five other vasopressors varied: high for dopamine, moderate for vasopressin, low for epinephrine and terlipressin, and very low for phenylephrine. However, unlike the RCTs, the evidence for cohort studies comparing norepinephrine with five other vasopressors was all confirmed as very low.
Comments
Dopamine mainly increases cardiac output and MAP by increasing stroke volume (SV) and heart rate, while norepinephrine increases MAP through vasoconstriction without significant changes in SV and heart rate. In an RCT by the SOAP (The Sepsis Occurrence in Acutely Ill Patients) II investigators, more arrhythmic events were observed in the dopamine group compared to the norepinephrine group, and a higher 28-day mortality was also noted in the former group among patients with cardiogenic shock [125]. The results of our meta-analysis showed that norepinephrine reduced the rates of ICU mortality and arrhythmia compared to the use of dopamine. Therefore, we recommend that norepinephrine be preferred to dopamine in patients with septic shock. When used at low doses, vasopressin increases blood pressure in patients who do not respond to other vasopressors. Conversely, high-dose vasopressin can be associated with ischemia in the heart, extremities, and intestine [141]. Our meta-analysis showed that norepinephrine, compared to vasopressin, reduced the length of ICU stay despite no difference in mortality. However, the incidence of RRT was lower in the vasopressin group. The VASST (Vasopressin and Septic Shock Trial) study, which examined the effect of co-administering low-dose vasopressin (0.01 to 0.03 units/min) with norepinephrine, found in subgroup analysis that adding low-dose vasopressin to norepinephrine (5–14 μg/min) improved survival rates compared to using norepinephrine alone [142]. This suggests that vasopressin should be initiated at an early stage of septic shock, particularly in less severe cases.
Epinephrine is associated with side effects such as arrhythmia, lactic acidemia, and splanchnic circulation disorders [143]. However, there was no significant difference in mortality in studies comparing the drug with norepinephrine, and the results of our meta-analysis also showed no difference between the two drugs. The 2021 SSC international guidelines suggest using epinephrine when the optimal blood pressure is not achieved despite the combined use of norepinephrine and vasopressin in patients with septic shock [1]. Epinephrine may be useful in patients with refractory septic shock and cardiac dysfunction. Phenylephrine results in less frequent tachycardia (compared to norepinephrine) but can induce splanchnic vasoconstriction. Given that only one RCT with a small number of patients (n=32) was included in our analysis, it was not possible to draw any conclusions about the effects of the drug on clinical outcomes. Regarding the use of terlipressin, no differences were found in our meta-analysis between the norepinephrine and terlipressin groups in terms of mortality, length of ICU stay, and vasopressor-free days. However, serious adverse events occurred more significantly with terlipressin use.
KQ 9. Vasopressin
In adult patients with septic shock, when appropriate MAP is not maintained despite the use of norepinephrine, is the addition of vasopressin better than increasing norepinephrine dose?
Recommendation
In adult patients with septic shock, when appropriate MAP is not maintained despite the usual dose of norepinephrine, we suggest adding vasopressin rather than increasing norepinephrine dose (Recommendation strength B, conditional recommendation for intervention; Quality of evidence: moderate).
Clinical Considerations
Additional research is needed on the timing of vasopressin administration. However, based on the results of previous RCTs, it seems appropriate to consider adding vasopressin when the norepinephrine concentration exceeds 0.25 μg/kg/min.
Background
In adult septic shock, when it is difficult to maintain a target MAP, even with appropriate fluid therapy, the use of vasopressors should be considered. Norepinephrine is an α1, β1, and β2 adrenergic receptor agonist that constricts blood vessels, increasing MAP. Based on many RCTs, it is recommended as a first-line vasopressor in adult sepsis [144]. When it is difficult to achieve an appropriate MAP with norepinephrine, addition of epinephrine or vasopressin can be considered. Several physiological advantages can be anticipated regarding the use of vasopressin. First, previous studies reported a relatively low concentration of endogenous vasopressin in patients with septic shock [145]. Second, when administering norepinephrine, the adrenergic receptors are probably already saturated. Finally, catecholamine-saving effects can be obtained using vasopressin [146]. Therefore, vasopressin is prioritized as a secondary vasopressor [20].
Summary of Evidence
For this clinical question, a three-step strategy literature search recovered 6,789 articles. After excluding 1,364 duplicates, 5,425 documents were selected using titles and abstracts. A full-text review was performed on seven RCTs [129,130,132,142,147-149], five of which were selected for our analysis [129,130,132,142,148]. Most studies used first-line vasopressors to correct blood pressure after diagnosis of septic shock and before randomization [129,132,142,148]. After randomization, the study drug dose was increased if the MAP did not achieve the target value. The studies included in the meta-analysis are summarized in Table 4.
A meta-analysis was conducted on 28-day and ICU mortality rates, the incidence of AKI, and the application of RRT. In four RCTs where 28-day mortality was addressed, vasopressin plus norepinephrine showed no significant difference in 28-day mortality rate compared to norepinephrine alone (RR, 0.98; 95% CI, 0.86–1.12) [129,132,142,148]. Additionally, ICU mortality was not different in three of those studies [129,130,148]. Regarding AKI, no difference was found between the combination treatment and norepinephrine alone [129,132,142]. However, the incidence of RRT was significantly lower with the combination treatment (RR, 0.69; 95% CI, 0.89–1.06) [129,132,148].
Among all studies included in the meta-analysis, the primary endpoints varied (e.g., hemodynamic variables, 28-day mortality, AKI-free day, and lactate clearance). The baseline characteristics and disease severity of the enrolled patients were also different. In addition, because several studies did not include critical outcome variables, the meta-analyses were performed using subgroups of the studies that reported each outcome. For the three RCTs included in the analysis of ICU mortality, the level of evidence decreased due to the imprecision caused by the small number of events [129,130,148]. Therefore, the overall level of evidence for the clinical questions was downgraded to moderate.
Comments
In the meta-analysis, the combined use of norepinephrine plus vasopressin showed no significant difference in mortality rate compared to norepinephrine alone but significantly reduced the rate of RRT. However, the timing of vasopressin initiation needs to be noted. In an RCT (the VASST trial) by Russell et al. [142], vasopressin administration was associated with lower mortality in the low-severity group of patients in whom norepinephrine concentration was <15 μg/min (<0.25 μg/min/kg for 60 kg). In another RCT (by Gordon et al. [129]), the norepinephrine concentration when vasopressin was initiated was 0.1 to 0.3 μg/kg/min. The 2021 SSC international guidelines recently recommended the concurrent use of vasopressin when the norepinephrine dose reaches 0.25 to 0.5 μg/kg/min [20]. Additionally, they suggested that intravenous corticosteroids (hydrocortisone, 200 mg/day) be commenced at a dose of norepinephrine ≥0.25 μg/kg/min at least 4 hours after initiation.
Since most previous studies used vasopressin as a second-line drug in addition to the use of other vasopressors [129,132,142,148], additional research is needed to determine the benefits of combination therapy and the appropriate dose of norepinephrine when vasopressin infusion is started.
KQ 10. Dobutamine
In adult patients with septic shock accompanied by decreased cardiac function, does adding dobutamine to existing treatments reduce mortality?
Recommendation
In adult septic shock patients with decreased cardiac function and hypoperfusion, the use of dobutamine may be considered (Recommendation strength E, expert opinion; Quality of evidence: very low).
Background
In patients with septic shock, cardiac dysfunction is a major cause of hemodynamic instability and is associated with worsening prognosis [150]. Dobutamine can increase cardiac output, increasing visceral perfusion and tissue oxygenation and improving intramucosal metabolic acidosis and hyperlactatemia. However, this effect is difficult to predict, and hypotension may occur due to vasodilation. Additionally, there are cases where the heart rate increases without the expected increase in cardiac output. The 2021 SSC guidelines suggest the use of dobutamine in patients with persistent hypoperfusion accompanied by acute myocardial dysfunction despite appropriate fluid therapy, but the level of evidence is very low [20]. In particular, most studies have focused on physiological variables rather than clinical indicators, resulting in a very limited number of studies on which the guidelines are based, with no relevant RCTs. However, several retrospective observational studies have emerged since the guidelines were published [151-153], and an RCT is in progress (NCT04166331) [154].
Summary of Evidence
A total of 8,049 articles was found through the literature search. After excluding duplicates, 1,363 articles were screened, and 65 full-text articles were reviewed. However, no studies addressed the key question (patients with septic shock and decreased cardiac function). As an alternative, studies targeting patients with sepsis and septic shock were selected (16 studies), with 4 RCTs [155-158] and 12 non-RCTs (9 prospective before-after studies [159-167] and 3 retrospective cohort studies [152,153,168]).
To date, there are no RCTs examining the effect of dobutamine use on mortality in patients with sepsis or septic shock. In a retrospective study by Wilkman et al. [168], among 420 patients with septic shock, the mortality rate was significantly higher in the dobutamine group than in the non-administration group (44.0% vs. 24.2%, P<0.001). However, our meta-analysis, including 4 non-RCTs, showed that dobutamine did not affect mortality in patients with sepsis or septic shock (RR, 1.22; 95% CI, 0.86–1.73). The length of ICU stay was no different between the two groups when using two retrospective studies [152,153]. For tissue perfusion, a meta-analysis was conducted on renal (urine output), gastrointestinal, and peripheral tissue perfusion indices, using data from one RCT and three non-RCTs [158,159,162,166]. There was no significant difference in urine output between the dobutamine and non-dobutamine groups (MD, –11.60 ml/hr; –24.93 to 1.74 ml/hr). In terms of gastrointestinal perfusion, there were no significant differences in gastric mucosal pH [156,157,165] or gastric mucosal-arterial blood carbon dioxide partial pressure difference (ΔPaCO2) between the two groups [155,156,158,161].
A recently published network meta-analysis showed that, among various drug combinations, that of norepinephrine and dobutamine was associated with lower 28-day mortality in patients with septic shock accompanied by decreased cardiac function [169]. Despite being small RCTs, the data on use of dobutamine showed some positive results on tissue perfusion [155,161]. Given that the network meta-analysis shows the best results from the combination of norepinephrine and dobutamine [169], we may consider using dobutamine while carefully monitoring patients with septic shock. However, the RCTs included in the meta-analysis had a high RoB, and they investigated physiological indicators rather than clinical parameters. Additionally, the risk of inconsistency and imprecision was high considering the different patient groups and insufficient subjects. In this guideline, the level of evidence was very low, and the recommendation grade was expert opinion.
Comments
Despite the improved tissue perfusion mentioned above, several studies have reported a higher mortality rate or increased length of ICU stay in the dobutamine group. Dobutamine can sometimes lower blood pressure due to its vasodilation effect. It can also destabilize the vital signs of sepsis patients by increasing heart rate without increasing SV. To date, no RCTs have included the effect of dobutamine administration on mortality or length of ICU stay. However, the results of our meta-analysis showed that the use of dobutamine had no influence on the mortality rate or length of ICU stay in patients with sepsis or septic shock. Therefore, it is advisable to make decisions on the use of the drug after carefully reviewing the condition of the patient. Additionally, these recommendations may change depending on the results of a large-scale RCT currently in progress [154].
KQ 11. Extracorporeal membrane oxygenation (ECMO)
Is ECMO treatment effective in adult patients with septic shock?
Recommendation
1. In patients with acute respiratory distress syndrome due to sepsis who do not respond to existing standard treatments, we suggest performing venovenous (VV) ECMO (Recommendation strength E, expert opinion; Quality of evidence: none).
2. In patients with septic shock and decreased cardiac function who do not respond to existing standard treatments, venous arterial (VA) ECMO can be applied (Recommendation strength B, conditional recommendation for intervention; Quality of evidence: low).
Clinical Considerations
ECMO is not recommended for patients with septic shock accompanied by multi-organ failure. When ECMO treatment is considered in these patients, the benefits and risks of the treatment should be assessed.
Background
ECMO is a method of treatment that supports cardiopulmonary function through an extracorporeal circulation device consisting of an artificial oxygenator and a blood pump. It is used in patients with severe heart failure or severe acute respiratory failure who do not respond to standard treatments and have no other treatment options. A recent multicenter international report published by the Extracorporeal Life Support Organization found that the number of ECMO applications is increasing every year. The discharge rate of live patients after ECMO treatment is 45% and 58% in adults with acute heart failure and acute respiratory failure, respectively [170]. However, because ECMO is an invasive treatment and serious life-threatening complications occur at a considerable rate, the choice of ECMO treatment must be made carefully.
Summary of Evidence
Through a literature search strategy, 6,776 studies were retrieved. In the literature selection process, 4,975 studies were screened using titles and abstracts, with duplicates excluded. Afterward, 504 original texts were reviewed, and three cohort studies were ultimately selected. A study by Takauji et al. [171] included both patients with septic shock due to severe respiratory failure without respiratory infections and those with respiratory infections. Their multicenter retrospective observational study used propensity score matching (conservative treatment group, n=239; VV-ECMO group, n=65). A publication by Bréchot et al. [172] also involved an international multicenter retrospective observational study. They included 212 patients with sepsis-induced cardiogenic shock and compared 90-day mortality rates between the conservative treatment (n=130) and VA-ECMO groups (n=82) after propensity score weighting. A study by Zha et al. [173] conducted a propensity score matching analysis among 255 patients with septic shock, respiratory infection, or respiratory failure. They compared 30- and 90-day mortality rates between conservative treatment (n=31) and VV-ECMO treatment groups (n=31).
Among the selected studies (n=3), the ECMO treatment group had a lower risk of death than the conservative treatment group (RR, 0.69; 95% CI, 0.51–0.93). Two studies reported serious adverse reactions (AKI, RRT, stroke, bleeding, etc.), and bleeding complications were more likely to occur in the ECMO treatment group than in the conservative treatment group (RR, 2.60; 95% CI, 1.64–4.14) [171,173]. In three studies reporting critical outcomes, the heterogeneity was high, so the recommendation grade was lowered by one step due to inconsistency and publication bias. Another step reduction was due to imprecision and effect size related to the small number of subjects and events. Additionally, the criteria for selecting ECMO treatment were diverse among the studies (e.g., selection bias). Based on these factors, the level of evidence for this clinical question was evaluated as low.
Comments
Since all the studies included in this guideline were not RCTs but retrospective observational studies, evaluating benefits and risks is subject to major limitations. However, given the results of a recent large-scale RCT (EOLIA [Extracorporeal Membrane Oxygenation for Severe Acute Respiratory Syndrome]) [174], VV-ECMO can be considered in patients with acute respiratory distress syndrome due to sepsis refractory to standard treatments if they have no multi-organ failures. Although there are no RCTs on patients with refractory septic shock, an international retrospective analysis by Ling et al. [175] showed that VA-ECMO significantly improved survival in patients with sepsis-induced cardiogenic shock. Moreover, in an individual participant data meta-regression analysis by Ling et al. [175], VA-ECMO showed improved survival in adults with septic shock and sepsis-induced myocardial depression. However, the treatment was associated with poor outcomes among those with septic shock without severe left ventricular depression. Therefore, VA-ECMO may be a viable treatment option in selected adult patients with refractory septic shock and left ventricular dysfunction.
In Korea, the influenza pandemic and the Middle East respiratory syndrome (MERS) epidemic have led to accumulated and widely shared knowledge and experience in the managing of ECMO cases of various causes among healthcare providers. ECMO has also been recognized as an important treatment option for severe cases of coronavirus disease 2019 (COVID-19). However, no RCTs have evaluated the effect of ECMO in patients with refractory septic shock. Therefore, the treatment should be carefully considered in the ICU.
KQ 12. Echocardiography
Is echocardiography recommended to assess cardiac function in adult patients with sepsis?
Recommendation
We suggest echocardiography to assess cardiac function and hemodynamics in adult patients with sepsis (Recommendation strength B, conditional recommendation for intervention; Quality of evidence: very low).
Background
Reduced or hyperdynamic LV systolic function is a risk factor for increased mortality in patients with sepsis [176]. Sepsis-induced cardiomyopathy (SICM) or sepsis-induced myocardial dysfunction can be expressed as a temporary cardiac dysfunction in sepsis patients. Although its importance in determining the prognosis of sepsis patients continues to evolve, there is no widely accepted definition. Transthoracic echocardiography (TTE) is a commonly used instrument in many clinical fields because it is non-invasive and easily accessible. The 2021 SSC international guidelines recommend echocardiography as a dynamic indicator to evaluate fluid responsiveness during the initial fluid treatment in sepsis patients. However, there is no specific mention of its use to evaluate cardiac function [20].
Summary of Evidence
Of the 8,795 articles found through the literature search strategy, 8,776 were excluded using the title and abstract, and a full-text review was performed on 19 articles. Given that the existing sepsis treatment guidelines did not cover the topic in detail, it was difficult to find studies that provided evidence related to the PICO. Finally, four retrospective cohort studies were selected and reviewed (Table 5) [177-180].
In a study using the Medical Information Mart for Intensive Care (MIMIC) III database by Feng et al., when comparing two propensity-matched cohorts (1,626 patients in each group), the 28-day mortality rate was significantly lower in the TTE group than the non-TTE group (OR, 0.78; P<0.001). In addition, the former group was able to stop vasopressors earlier than the latter (vasopressor-free days, 21 vs. 19; P=0.004) [177]. Lan et al. [178] also used the MIMIC-III database and a propensity score matched analysis (1,289 patients in each group). They found that the 28-day mortality rate in the TTE group was significantly lower than in the non-TTE group (HR, 0.83; P=0.005). Hanumanthu et al. [179] conducted a single-center retrospective cohort study using data on patients with sepsis but without acute coronary syndrome. When comparing the SICM group (n=19) and the non-SICM group (n=340), with TTE used for diagnosis confirmation, the in-hospital mortality rate was significantly higher in the SICM group (OR, 4.46; P=0.03). Another retrospective cohort study using the MIMIC-III database by Zheng et al. [180] compared 28-day mortality rates between an early TTE group (within 10 hours of admission to the ICU, n=544) and a delayed TTE group (>10 hours of admission to the ICU, n=2,027). They found that the early TTE group had a significantly lower 28-day mortality rate compared to the delayed TTE group (HR, 0.73–0.78; P<0.05) [180].
In the meta-analysis of the three observational studies that reported critical outcomes, a significantly lower 28-day mortality rate was noted in the TTE group compared to the non-TTE group (RR, 0.79; 95% CI, 0.71–0.88) [175-177]. However, there is a high RoB because only retrospective observational studies were used in the meta-analysis. Additionally, the study period and inclusion criteria differ in two of the three studies that used the MIMIC-III database [177,178]. Due to these limitations, the current level of evidence was determined as very low.
Comments
TTE is a non-invasive test that can be performed at the bedside with no serious complications. Although the 2021 SSC international guidelines recommend echocardiography as a dynamic indicator to evaluate fluid responsiveness, we analyzed the role of TTE from a different perspective, and the results indicate that the 28-day mortality rate is significantly lower in the group who underwent TTE compared to those who did not. Therefore, TTE itself might be beneficial in adult patients with sepsis or septic shock. This implies that TTE can affect treatment strategies or help predict prognosis in patients with sepsis. However, echocardiography is operator-dependent, and the accuracy of results can vary based on the clinician's skill and experience. Additionally, further research is warranted since the evidence remains unclear on the indicators to be used as references in echocardiography. |
Title: Distinct clinical features of urothelial carcinoma with low-expressing human epidermal growth factor receptor 2 status | Body:
Key findings
• The traditional view was that human epidermal growth factor receptor 2 (HER2) expression was associated with worse prognosis. However, with the emergence of anti-HER2 therapies, a more refined assessment of HER2 status is needed to inform clinical decisions. This study demonstrated that the HER2 three-tier scoring system provides a better assessment of HER2 expression level compared to binary scoring. The updated classification method takes into account current therapeutic advances and can better guide clinical management of breast cancer patients. Therefore, the three-tier HER2 scoring system has greater clinical relevance considering today’s treatment landscape.
What is known and what is new?
• The monoclonal antibodies and tyrosine kinase inhibitors targeting HER2 failed to show clinical benefits in metastatic urothelial carcinoma until the novel antibody-drug conjugate (ADC) trastuzumab deruxtecan (RC48) demonstrated better efficacy in patients with advanced urothelial carcinoma after failure of conventional treatment. These new breakthroughs have made HER2 a research hotspot in urothelial carcinoma again.
• In this study, we re-evaluated HER2 expression in urothelial carcinoma based on the three-tier classification method for HER2 in breast cancer. The results showed that the three-tier classification method for HER2 has higher efficacy in predicting prognosis compared to the traditional binary classification method.
What is the implication, and what should change now?
• The HER2 three-tier classification can effectively incorporate HER2-low expressing patients into the treatment scope of ADC drugs. While effectively predicting prognosis, it optimizes patient screening. The HER2 three-tier classification has practical guidance for the work of pathologists and clinicians.
Introduction
Urothelial carcinoma is one of the common malignant tumors originating from the urinary system (1). The incidence of urothelial carcinoma is higher in males compared to females. Approximately 90% of urothelial carcinomas occur in the bladder, while 5–10% originate from the renal pelvis and ureter (2). Previous research has shown that among Chinese patients, the proportion of urothelial carcinoma arising from the renal pelvis and ureter can be as high as 20–30%, which is more prevalent compared to Western countries (3). The prognosis of patients with advanced urothelial carcinoma is poor (4). Platinum-based chemotherapy is the standard first-line treatment for locally advanced or metastatic urothelial carcinoma. However, some patients are ineligible for chemotherapy and the regimen caused hematologic severe adverse events (AEs), necessitating exploration of other effective therapeutic strategies (5).
Human epidermal growth factor receptor 2 (HER2) protein overexpression, gene amplification or mutations exist in various malignant tumors including breast cancer, gastric cancer and urothelial carcinoma, and play important roles (6). A study has shown that high HER2 expression is also associated with poor prognosis in advanced urothelial carcinoma (7). The anti-HER2 targeted drug trastuzumab has achieved significant clinical benefits in breast cancer and gastric cancer treatment, improving the prognosis of advanced HER2 positive breast cancer and gastric cancer patients, prolonging median overall survival, and becoming the standard treatment choice for HER2 positive patients (8,9). However, anti-HER2 monoclonal antibodies alone and anti-HER2 tyrosine kinase inhibitors have not shown significant clinical efficacy in metastatic urothelial carcinoma with HER2 overexpression (10).
Study has revealed that anti-HER2-antibody-drug conjugates (ADC) drugs demonstrate significant clinical efficacy in locally advanced or metastatic urothelial carcinoma patients with HER2 overexpression [immunohistochemistry (IHC) 2+ and 3+] progressing after first-line chemotherapy, with an objective response rate of 51.2% and progression-free survival of 6.9 months. Therefore, accurate detection of HER2 protein expression status is clinically significant for screening potential beneficiaries of anti-HER2-ADC drug treatment among urothelial carcinoma patients (11).
Determination of urothelial carcinoma HER2 status primarily refers to the classification method for breast cancer (12). In recent years, with the breakthrough of ADC drugs in the treatment of HER2-low expressing breast cancer patients, which has changed the traditional landscape of anti-HER2 treatment and brought targeted benefits to more breast cancer patients (13). Breast cancer HER2 testing and interpretation have also progressed from the original dichotomous to a three-tier system, proposing more precise requirements for pathological diagnosis (14).
This study reinterpreted HER2 expression in urothelial carcinoma based on the consensus of clinical pathological criteria for urothelial carcinoma HER2 testing established for the first time in China combined with the new three-tier system for breast cancer (15). We studied the relationship between expression patterns and clinical pathological information of urothelial carcinoma cases. This study retrospectively reinterpreted HER2 expression in urothelial carcinoma and analyzed the relationship between HER2 expression and prognosis to further explore the clinical translation and actual value of the new HER2 classification. We present this article in accordance with the REMARK reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-24-354/rc).
Methods
Patient collection
We retrospectively analyzed 75 patients with histo-pathologically diagnosed urothelial carcinoma from Jiangsu Cancer Hospital. HER2 expression was re-scored by IHC and fluorescence in situ hybridization (FISH) according to HER2 three-tier assessment standards. Its relationship with various pathological parameters of urothelial carcinoma and its correlation with prognosis were analyzed.
Inclusion criteria: (I) diagnosed as urothelial carcinoma by ultrasonography and computed Tomography (CT) urologic imaging with histologically confirmed; (II) undergoing initial renal pelvic or vesicoureter resection; (III) without neoadjuvant chemotherapy; (IV) with complete demographic data; (V) adequate organ function. Exclusion criteria: (I) with congenital or acquired immunodeficiency; (II) a diagnosis of other malignant tumors within the previous 5 years; (III) with surgical contraindications; (IV) allergic or intolerant to anesthetic drugs. There is no bias in the study. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the ethics board of Jiangsu Cancer Hospital (No. 2021-042) and individual consent for this retrospective analysis was waived due to the retrospective nature.
Collection of clinical data
Patients received treatments including transurethral resection of bladder tumor or radical cystectomy. Some patients undergo platinum-based chemotherapies after surgery, such as gemcitabine plus cisplatin. Tumor tissues from urothelial carcinoma patients were collected, fixed with 10% formalin, and embedded in paraffin. Complete clinical pathological results were collected, including P53 and Ki-67 status. Correlations between HER2 expression and demographic, clinical, pathological and follow-up parameters were analyzed. The tumor grade and HER2 expression were confirmed by two experienced pathologists.
IHC detection of HER2 protein expression
Tumor tissue sections of urothelial carcinoma were prepared for dewaxing, hydration, and heat-induced epitope retrieval. After cooling, sections were washed and incubated with primary antibody overnight at 4 ℃. Sections were incubated with secondary antibody at room temperature for 30 min after rewarming at room temperature for 60 min, followed by washing, coloring, staining, differentiation, dehydration. After transparent in xylene, sections were mounted with neutral gum. HER2 positive breast cancer sections served as positive control and phosphate buffered saline replaced primary antibody as negative control.
FISH detection
Paraffin sections (3 µm) of urothelial carcinoma tumor tissues were prepared for deparaffinization, hydration, washing, distilled water pretreated, washed after cooling, digested with protease (pH 1.8–2.3) at 54 ℃. After cooling, sections were washed, dehydrated and air dried. Probes of 5 µL were added and cover slipped, sealed. Hybridization was performed at 76 ℃ for 6 min and 42 ℃ for 9 min. On the next day, the cover glass was removed and washed with water. After dehydration, DAPI staining solution was added and cover slipped. Twenty tumor cells were observed under a fluorescence microscope and the number and ratio of red and green signals were recorded. FISH detection of HER2 gene amplification was defined as HER2 positive. The HER2 scoring system is now being replaced by a three-tiered system (16): (I) HER2-positive (including 3+ score and 2+ score accompanied by HER2 amplification), (II) HER2-negative (0 score), and (III) HER2-low (1+ score and 2+ score without HER2 amplification).
Survival analysis
The progression-free rate refers to the proportion of patients who have not experienced disease progression after receiving treatment within a certain follow-up period. The proportion of patients with imaging-confirmed disease progression was calculated as the number of patients with progression in that particular follow-up year divided by the total number of disease-free patients at the end of that year, excluding patients lost to follow-up. Data cut-off date was October 31, 2023. And validate in The Cancer Genome Atlas (TCGA) cohort.
Statistical analysis
Mutational profiles in the HER2-positive, HER2-low and HER2-negative groups were compared using Fisher’s exact test and Wilcoxon signed-rank test. Survival analysis was performed using the Kaplan-Meier method and log-rank test. Multivariate Cox proportional hazards regression modeling was used to analyze prognostic factors affecting patient outcomes. Statistical significance was set at P<0.05. All analyses were performed using IBM SPSS software version 24.0 (IBM Corporation, Armonk, NY, USA).
Patient collection
We retrospectively analyzed 75 patients with histo-pathologically diagnosed urothelial carcinoma from Jiangsu Cancer Hospital. HER2 expression was re-scored by IHC and fluorescence in situ hybridization (FISH) according to HER2 three-tier assessment standards. Its relationship with various pathological parameters of urothelial carcinoma and its correlation with prognosis were analyzed.
Inclusion criteria: (I) diagnosed as urothelial carcinoma by ultrasonography and computed Tomography (CT) urologic imaging with histologically confirmed; (II) undergoing initial renal pelvic or vesicoureter resection; (III) without neoadjuvant chemotherapy; (IV) with complete demographic data; (V) adequate organ function. Exclusion criteria: (I) with congenital or acquired immunodeficiency; (II) a diagnosis of other malignant tumors within the previous 5 years; (III) with surgical contraindications; (IV) allergic or intolerant to anesthetic drugs. There is no bias in the study. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the ethics board of Jiangsu Cancer Hospital (No. 2021-042) and individual consent for this retrospective analysis was waived due to the retrospective nature.
Collection of clinical data
Patients received treatments including transurethral resection of bladder tumor or radical cystectomy. Some patients undergo platinum-based chemotherapies after surgery, such as gemcitabine plus cisplatin. Tumor tissues from urothelial carcinoma patients were collected, fixed with 10% formalin, and embedded in paraffin. Complete clinical pathological results were collected, including P53 and Ki-67 status. Correlations between HER2 expression and demographic, clinical, pathological and follow-up parameters were analyzed. The tumor grade and HER2 expression were confirmed by two experienced pathologists.
IHC detection of HER2 protein expression
Tumor tissue sections of urothelial carcinoma were prepared for dewaxing, hydration, and heat-induced epitope retrieval. After cooling, sections were washed and incubated with primary antibody overnight at 4 ℃. Sections were incubated with secondary antibody at room temperature for 30 min after rewarming at room temperature for 60 min, followed by washing, coloring, staining, differentiation, dehydration. After transparent in xylene, sections were mounted with neutral gum. HER2 positive breast cancer sections served as positive control and phosphate buffered saline replaced primary antibody as negative control.
FISH detection
Paraffin sections (3 µm) of urothelial carcinoma tumor tissues were prepared for deparaffinization, hydration, washing, distilled water pretreated, washed after cooling, digested with protease (pH 1.8–2.3) at 54 ℃. After cooling, sections were washed, dehydrated and air dried. Probes of 5 µL were added and cover slipped, sealed. Hybridization was performed at 76 ℃ for 6 min and 42 ℃ for 9 min. On the next day, the cover glass was removed and washed with water. After dehydration, DAPI staining solution was added and cover slipped. Twenty tumor cells were observed under a fluorescence microscope and the number and ratio of red and green signals were recorded. FISH detection of HER2 gene amplification was defined as HER2 positive. The HER2 scoring system is now being replaced by a three-tiered system (16): (I) HER2-positive (including 3+ score and 2+ score accompanied by HER2 amplification), (II) HER2-negative (0 score), and (III) HER2-low (1+ score and 2+ score without HER2 amplification).
Survival analysis
The progression-free rate refers to the proportion of patients who have not experienced disease progression after receiving treatment within a certain follow-up period. The proportion of patients with imaging-confirmed disease progression was calculated as the number of patients with progression in that particular follow-up year divided by the total number of disease-free patients at the end of that year, excluding patients lost to follow-up. Data cut-off date was October 31, 2023. And validate in The Cancer Genome Atlas (TCGA) cohort.
Statistical analysis
Mutational profiles in the HER2-positive, HER2-low and HER2-negative groups were compared using Fisher’s exact test and Wilcoxon signed-rank test. Survival analysis was performed using the Kaplan-Meier method and log-rank test. Multivariate Cox proportional hazards regression modeling was used to analyze prognostic factors affecting patient outcomes. Statistical significance was set at P<0.05. All analyses were performed using IBM SPSS software version 24.0 (IBM Corporation, Armonk, NY, USA).
Results
Clinical patient baseline
A total of 75 cases of urothelial carcinoma according to the selective criteria were included in the study, comprising 59 males and 16 females with a median age of 68 (range, 44–89) years. Among them, 33 patients developed lymph node metastasis and 28 developed distant metastasis; 66 cases were high-grade urothelial carcinoma and 9 cases were low-grade urothelial carcinoma; 33 cases were at clinical stage I–II and 42 cases were at clinical stage III–IV. Among these variables, 39 cases had a Ki-67 score greater than or equal to 50% and 57 cases had a P53 mutation (Table 1).
Table 1
Clinicopathological characteristics of the cohort
Clinico-pathological features
Value
Age (years), median [range]
68 [44–89]
Gender, n (%)
Male
59 (78.7)
Female
16 (21.3)
Tumor (T) stage, n (%)
T1–2
35 (46.7)
T3–4
40 (53.3)
Clinical lymph node (N) stage, n (%)
N0
42 (56.0)
N1–3
33 (44.0)
Clinical metastasis (M) stage, n (%)
M0
47 (62.6)
M1
28 (37.3)
TNM stage, n (%)
I
8 (10.7)
II–III
42 (56.0)
IV
25 (33.3)
Histological grade, n (%)
Low
9 (12.0)
High
66 (88.0)
Ki67 expression status (cut-off 50%), n (%)
<50%
36 (48.0)
≥50%
39 (52.0)
P53 expression, n (%)
Mut-type
57 (76.0)
Wild-type
18 (24.0)
TNM, tumor-nodes-metastasis.
Immunohistochemical studies of HER2 expression
Based on the completeness, intensity, and percentage of cells exhibiting staining, HER2 IHC was scored using a three-tiered scoring system (Figures 1,2). The 75 cases were rescored as: 13 cases HER2 negative (17.3%), 46 cases HER2 low (61.3%), and 16 cases HER2 positive (21.3% of all cases) (Table 2).
Figure 1
Algorithm for defining the HER2 expression spectrum according to ASCO/CAP guidelines. HER2-low expression is defined as IHC score 2+ with negative FISH results, or IHC score 1+. HER2, human epidermal growth factor receptor 2; IHC, immunohistochemistry; FISH, fluorescence in situ hybridization; ASCO/CAP, American Society of Clinical Oncology/College of American Pathologists.
Figure 2
The HER2 immunohistochemical protein expression score was defined as: score 0 (no staining) (A); score 1+ (incomplete membrane staining) (B); score 2+ (complete but weak membrane staining in >10% of tumor cells) (C); score 3+ (strong membrane staining in >30% of tumor cells) (D); existence of HER2 gene amplification (E); absence of HER2 gene amplification (F). Magnification, 200×. HER2, human epidermal growth factor receptor 2.
Table 2
HER2 immunohistochemistry scores in excisions
Score
Value, n (%)
Initial score
0
13 (17.3)
1+
20 (26.7)
2+
29 (38.7)
3+
13 (17.3)
Re-score
Negative
13 (17.3)
Low
46 (61.3)
Positive
16 (21.3)
HER2, human epidermal growth factor receptor 2.
Association of HER2 expression with clinical features
Table 3 summarizes the clinical pathological characteristics of the entire cohort and between HER2 subcategories. The majority of cases exhibited HER2 expression (62 out of 75 cases, accounting for 82.7%), only 13 cases (accounting for 17.3% of 75 cases) exhibited HER2 negative expression; among which HER2 low expression accounted for 46 cases and 61.3% of all cases. HER2 expression was comparable between high-grade and low-grade urothelial carcinoma, with no statistically significant difference. HER2-negative, HER2-low and HER2-positive groups were significantly associated with the tumor-nodes-metastasis (TNM) grade of urothelial carcinoma (P=0.04). No relevance was found between HER2 expression and gender (P=0.12), lymph node (N) stage (P=0.99), metastasis (M) stage (P=0.81), histological stage (P=0.87), Ki67 (P=0.39) and P53 expression (P=0.31).
Table 3
Correlation between HER2 expression and clinico-pathological features
Clinicopathological characteristics
Total
IHC/FISH-based HER2 status subgroup
HER2-negative
HER2-low
HER2-positive
P value
Age (years), median [range]
68 [44–89]
–
–
–
–
Gender
0.12
Male
59
13
34
12
Female
16
0
12
4
Tumor (T) stage
0.74
T1–2
35
5
23
7
T3–4
40
8
23
9
Clinical lymph node (N) stage
0.99
N0
42
7
26
9
N1–3
33
6
20
7
Clinical metastasis (M) stage
0.81
M0
47
8
30
9
M1
28
5
16
7
TNM stage
0.043
I–II
33
5
25
3
III–IV
42
8
21
13
Histological grade
0.87
Low
9
1
6
2
High
66
12
40
14
Ki67 expression status
0.39
<50%
36
4
24
8
≥50%
39
9
22
8
P53 expression
0.31
Mut-type
57
12
33
12
Wild-type
18
1
13
4
HER2, human epidermal growth factor receptor 2; IHC, immunohistochemistry; FISH, fluorescence in situ hybridization; TNM, tumor-nodes-metastasis.
Analysis of factors influencing prognosis
The Cox univariate analysis showed that HER2 low expression status was associated with prognosis of urothelial carcinoma (Table 4). The factors with P values less than 0.1 in the univariate analysis were included in the multivariate analysis. The results demonstrated that HER2 low expression status was an independent poor prognostic factor for urothelial carcinoma patients (P=0.04) (Figure 3).
Table 4
Cox univariate analysis of prognostic factors of the cohort
Variables
β
SE
Z
P
HR (95% CI)
Gender
Female
1.00 (reference)
Male
−0.59
1.08
−0.55
0.59
0.55 (0.07–4.62)
Age
0.00
0.03
0.02
0.98
1.00 (0.94–1.07)
Stage
I
1.00 (reference)
II
−0.58
0.94
−0.62
0.53
0.56 (0.09–3.51)
III
−1.07
1.12
−0.96
0.34
0.34 (0.04–3.09)
IV
−1.47
0.94
−1.56
0.12
0.23 (0.04–1.45)
Grade
Low
1.00 (reference)
High
−0.04
0.78
−0.05
0.96
0.96 (0.21–4.41)
P53
Mut-type
1.00 (reference)
Wild-type
0.13
0.55
0.24
0.81
1.14 (0.39–3.31)
KI67
0.00
0.02
0.23
0.82
1.00 (0.97–1.04)
HER2
Negative
1.00 (reference)
Low
2.08
0.89
2.34
0.02
7.98 (1.40–45.49)
Positive
0.77
0.92
0.84
0.40
2.17 (0.36–13.17)
SE, standard error; HR, hazard ratio; CI, confidence interval; HER2, human epidermal growth factor receptor 2.
Figure 3
The multivariate analysis showed that HER2-low was an independent poor prognostic factor for patients with urothelial carcinoma (P=0.04). *, P<0.05, AIC, Akaike information criterion; HER2, human epidermal growth factor receptor 2.
Association between HER2 low and patient survival
There was a statistically significant difference in progression-free survival time between different HER2 expression subgroups at the time of diagnosis. Among the three groups, HER2-low had the worst progression rate compared with HER2-negative (P=0.02) and HER2 positive (P=0.02) (Figure 4A) and also had the highest progression rate in TCGA cohort (P=0.02) (Figure 4B). We classified patients and assessed the clinical significance of HER2 expression and progression-free survival time. The results revealed that HER2-low could effectively predict the progression-free survival time of urothelial carcinoma patients (AUC 1 year =0.83; AUC 3 years =0.86; AUC 5 years =0.85) (Figure 5).
Figure 4
The progression-free survival rate of urothelial carcinoma patients with HER2 expression was significantly higher than that of HER2-negative patients, with statistically significant differences. (A) Patients with low HER2 expression in sample cohort had the highest progression rate (P=0.02). (B) Patients with low HER2 expression in TCGA cohort had the highest progression rate (P=0.02). HER2, human epidermal growth factor receptor 2.
Figure 5
The receiver operating characteristic curves showed that low HER2 expression could effectively predict the prognosis. AUC, area under the curve; HER2, human epidermal growth factor receptor 2.
Clinical patient baseline
A total of 75 cases of urothelial carcinoma according to the selective criteria were included in the study, comprising 59 males and 16 females with a median age of 68 (range, 44–89) years. Among them, 33 patients developed lymph node metastasis and 28 developed distant metastasis; 66 cases were high-grade urothelial carcinoma and 9 cases were low-grade urothelial carcinoma; 33 cases were at clinical stage I–II and 42 cases were at clinical stage III–IV. Among these variables, 39 cases had a Ki-67 score greater than or equal to 50% and 57 cases had a P53 mutation (Table 1).
Table 1
Clinicopathological characteristics of the cohort
Clinico-pathological features
Value
Age (years), median [range]
68 [44–89]
Gender, n (%)
Male
59 (78.7)
Female
16 (21.3)
Tumor (T) stage, n (%)
T1–2
35 (46.7)
T3–4
40 (53.3)
Clinical lymph node (N) stage, n (%)
N0
42 (56.0)
N1–3
33 (44.0)
Clinical metastasis (M) stage, n (%)
M0
47 (62.6)
M1
28 (37.3)
TNM stage, n (%)
I
8 (10.7)
II–III
42 (56.0)
IV
25 (33.3)
Histological grade, n (%)
Low
9 (12.0)
High
66 (88.0)
Ki67 expression status (cut-off 50%), n (%)
<50%
36 (48.0)
≥50%
39 (52.0)
P53 expression, n (%)
Mut-type
57 (76.0)
Wild-type
18 (24.0)
TNM, tumor-nodes-metastasis.
Immunohistochemical studies of HER2 expression
Based on the completeness, intensity, and percentage of cells exhibiting staining, HER2 IHC was scored using a three-tiered scoring system (Figures 1,2). The 75 cases were rescored as: 13 cases HER2 negative (17.3%), 46 cases HER2 low (61.3%), and 16 cases HER2 positive (21.3% of all cases) (Table 2).
Figure 1
Algorithm for defining the HER2 expression spectrum according to ASCO/CAP guidelines. HER2-low expression is defined as IHC score 2+ with negative FISH results, or IHC score 1+. HER2, human epidermal growth factor receptor 2; IHC, immunohistochemistry; FISH, fluorescence in situ hybridization; ASCO/CAP, American Society of Clinical Oncology/College of American Pathologists.
Figure 2
The HER2 immunohistochemical protein expression score was defined as: score 0 (no staining) (A); score 1+ (incomplete membrane staining) (B); score 2+ (complete but weak membrane staining in >10% of tumor cells) (C); score 3+ (strong membrane staining in >30% of tumor cells) (D); existence of HER2 gene amplification (E); absence of HER2 gene amplification (F). Magnification, 200×. HER2, human epidermal growth factor receptor 2.
Table 2
HER2 immunohistochemistry scores in excisions
Score
Value, n (%)
Initial score
0
13 (17.3)
1+
20 (26.7)
2+
29 (38.7)
3+
13 (17.3)
Re-score
Negative
13 (17.3)
Low
46 (61.3)
Positive
16 (21.3)
HER2, human epidermal growth factor receptor 2.
Association of HER2 expression with clinical features
Table 3 summarizes the clinical pathological characteristics of the entire cohort and between HER2 subcategories. The majority of cases exhibited HER2 expression (62 out of 75 cases, accounting for 82.7%), only 13 cases (accounting for 17.3% of 75 cases) exhibited HER2 negative expression; among which HER2 low expression accounted for 46 cases and 61.3% of all cases. HER2 expression was comparable between high-grade and low-grade urothelial carcinoma, with no statistically significant difference. HER2-negative, HER2-low and HER2-positive groups were significantly associated with the tumor-nodes-metastasis (TNM) grade of urothelial carcinoma (P=0.04). No relevance was found between HER2 expression and gender (P=0.12), lymph node (N) stage (P=0.99), metastasis (M) stage (P=0.81), histological stage (P=0.87), Ki67 (P=0.39) and P53 expression (P=0.31).
Table 3
Correlation between HER2 expression and clinico-pathological features
Clinicopathological characteristics
Total
IHC/FISH-based HER2 status subgroup
HER2-negative
HER2-low
HER2-positive
P value
Age (years), median [range]
68 [44–89]
–
–
–
–
Gender
0.12
Male
59
13
34
12
Female
16
0
12
4
Tumor (T) stage
0.74
T1–2
35
5
23
7
T3–4
40
8
23
9
Clinical lymph node (N) stage
0.99
N0
42
7
26
9
N1–3
33
6
20
7
Clinical metastasis (M) stage
0.81
M0
47
8
30
9
M1
28
5
16
7
TNM stage
0.043
I–II
33
5
25
3
III–IV
42
8
21
13
Histological grade
0.87
Low
9
1
6
2
High
66
12
40
14
Ki67 expression status
0.39
<50%
36
4
24
8
≥50%
39
9
22
8
P53 expression
0.31
Mut-type
57
12
33
12
Wild-type
18
1
13
4
HER2, human epidermal growth factor receptor 2; IHC, immunohistochemistry; FISH, fluorescence in situ hybridization; TNM, tumor-nodes-metastasis.
Analysis of factors influencing prognosis
The Cox univariate analysis showed that HER2 low expression status was associated with prognosis of urothelial carcinoma (Table 4). The factors with P values less than 0.1 in the univariate analysis were included in the multivariate analysis. The results demonstrated that HER2 low expression status was an independent poor prognostic factor for urothelial carcinoma patients (P=0.04) (Figure 3).
Table 4
Cox univariate analysis of prognostic factors of the cohort
Variables
β
SE
Z
P
HR (95% CI)
Gender
Female
1.00 (reference)
Male
−0.59
1.08
−0.55
0.59
0.55 (0.07–4.62)
Age
0.00
0.03
0.02
0.98
1.00 (0.94–1.07)
Stage
I
1.00 (reference)
II
−0.58
0.94
−0.62
0.53
0.56 (0.09–3.51)
III
−1.07
1.12
−0.96
0.34
0.34 (0.04–3.09)
IV
−1.47
0.94
−1.56
0.12
0.23 (0.04–1.45)
Grade
Low
1.00 (reference)
High
−0.04
0.78
−0.05
0.96
0.96 (0.21–4.41)
P53
Mut-type
1.00 (reference)
Wild-type
0.13
0.55
0.24
0.81
1.14 (0.39–3.31)
KI67
0.00
0.02
0.23
0.82
1.00 (0.97–1.04)
HER2
Negative
1.00 (reference)
Low
2.08
0.89
2.34
0.02
7.98 (1.40–45.49)
Positive
0.77
0.92
0.84
0.40
2.17 (0.36–13.17)
SE, standard error; HR, hazard ratio; CI, confidence interval; HER2, human epidermal growth factor receptor 2.
Figure 3
The multivariate analysis showed that HER2-low was an independent poor prognostic factor for patients with urothelial carcinoma (P=0.04). *, P<0.05, AIC, Akaike information criterion; HER2, human epidermal growth factor receptor 2.
Association between HER2 low and patient survival
There was a statistically significant difference in progression-free survival time between different HER2 expression subgroups at the time of diagnosis. Among the three groups, HER2-low had the worst progression rate compared with HER2-negative (P=0.02) and HER2 positive (P=0.02) (Figure 4A) and also had the highest progression rate in TCGA cohort (P=0.02) (Figure 4B). We classified patients and assessed the clinical significance of HER2 expression and progression-free survival time. The results revealed that HER2-low could effectively predict the progression-free survival time of urothelial carcinoma patients (AUC 1 year =0.83; AUC 3 years =0.86; AUC 5 years =0.85) (Figure 5).
Figure 4
The progression-free survival rate of urothelial carcinoma patients with HER2 expression was significantly higher than that of HER2-negative patients, with statistically significant differences. (A) Patients with low HER2 expression in sample cohort had the highest progression rate (P=0.02). (B) Patients with low HER2 expression in TCGA cohort had the highest progression rate (P=0.02). HER2, human epidermal growth factor receptor 2.
Figure 5
The receiver operating characteristic curves showed that low HER2 expression could effectively predict the prognosis. AUC, area under the curve; HER2, human epidermal growth factor receptor 2.
Discussion
Urothelial carcinoma is the second most common malignancy of the urinary system worldwide. It exhibits heterogeneity, with different clinical outcomes often observed among patients with the same clinical stage and pathological grade. There is an urgent need for personalized treatment approaches in urothelial carcinoma (17).
The HER2 gene is located on chromosome 17q and encodes a transmembrane receptor protein with tyrosine kinase activity. When HER2 binds to epidermal growth factor, it becomes phosphorylated and regulates the activation of the RAS signaling pathway, promoting cell growth and proliferation. The epidermal growth factor receptor (EGFR) family consists of four members: HER1 (EGFR), HER2 (ERBB2), HER3 and HER4 (18). HER2 normally exists in an un-activated monomeric form on the cell membrane and is activated upon homodimerization or heterodimerization with other EGFR family members upon ligand stimulation. Upon activation, HER2 further activates downstream signals such as the MAPK and PI3K-AKT signaling pathways, ultimately promoting cell proliferation, inhibiting apoptosis, increasing angiogenesis, reducing cell adhesion, and playing an important role in tumor occurrence and progression (19). HER2 gene amplification and protein overexpression exist in breast cancer, lung cancer, gastric cancer and urothelial carcinoma, making it an important therapeutic target for these cancers (20).
Previous studies found that HER2 gene amplification and HER2 protein overexpression also exist in urothelial carcinoma. Urothelial carcinomas with HER2 protein overexpression and HER2 gene amplification are more likely to invade lymphatic vessels and blood vessels, and had significantly higher recurrence and mortality rates (11). The interpretation of HER2 analysis results in urothelial carcinoma is basically based on guidelines for HER2 testing in breast cancer. HER2 is the main driving molecule and therapeutic target in breast cancer (21). Clinically, HER2 status is detected by IHC and FISH techniques. Although HER2-positive breast cancer patients (defined as IHC 3+ or FISH+) could benefit from anti-HER2 targeted therapy, this population accounts for only 15–20% of all breast cancers. In recent years, the development of ADC drugs targeting HER2 has broken the traditional dichotomous HER2-positive/negative classification paradigm and refreshed our understanding of breast cancer subtypes and treatments. “HER2-low expression” has successfully entered clinical physicians’ field of vision and attracted great attention (22). However, there is currently a lack of corresponding clinical data in urothelial carcinoma to support such a new three-tier classification method. This study reinterpreted HER2 in initially diagnosed urothelial carcinoma cases based on the three-tier classification method for breast cancer and observed the correlation between the new HER2 grading method and clinical pathological data.
This study retrospectively rescored HER2 expression of 75 urothelial carcinoma patients in light of the current hotspots in anti-HER2 treatment. Results showed that after reinterpretation, three additional cases were classified as HER2-positive, as the combination of IHC and FISH increased the detection rate. Among the 75 cases, 62 expressed HER2, of which HER2-low expression accounted for 61.3%, consistent with the current trend of HER2-low expression in breast cancer patients (23). This also reflected the clinical translational value of HER2-low expression research. Further statistical analysis with clinical data found that the three-tier expression of HER2 was significantly correlated with the TNM stage of urothelial carcinoma, consistent with previous studies.
Through follow-up analysis of progression-free survival time, the study found that the progression rate of HER2-expressing patients was higher than that of HER2-negative patients, with earlier progression; and found that the progression rate of HER2-low expressing patients was the highest, with the shortest progression-free time, higher than the HER2-positive group. At the same time, we have compiled data from 448 TCGA cases, the progression rate of HER2-low expressing patients was also the highest. These data were mainly related to the current targeted drugs for HER2. HER2-low expression is a new classification which lack accurate therapeutic method, and the previous acceptance of ADC drugs was also lower, which leads to this follow-up result (24). In order to further validate the clinical predictive value of this three-tier classification method, we found through modeling analysis that HER2-low expression can effectively predict recurrence-free survival time, with reliable AUC values. This also explains the superiority and clinical value of HER2 in this three-tier system.
ADCs are currently one of the hotspots in anticancer drug research (25). HER2 is a known oncogene that can drive the occurrence and development of various tumor types, and HER2 is an important tumor target for early-approved ADCs for solid tumors (26). For urothelial carcinoma, DS8201-A-U105 is an exploratory study of T-DXd in combination with trastuzumab for HER2-expressing urothelial carcinoma Ib. Cohorts 3 and 4 enrolled HER2-expressing urothelial carcinoma patients who failed prior chemotherapy. The primary endpoints were maximum tolerated dose/recommended expansion dose and objective response rate based on independent imaging assessments. As of July 2021, cohort 3 enrolled 22 patients with HER2 IHC2+, cohort 4 enrolled 4 patients with HER2 IHC1+; the objective response rate of HER2 IHC2+ patients in cohort 3 was 27.3% (6/22), 2 patients achieved partial response (PR) and 1 had stable disease and 1 had disease progression among the 4 HER2 IHC1+ patients in cohort 4 (27). The RC48-C014 I/II clinical study is expected to enroll 36 patients with locally advanced and metastatic urothelial tumors. Results published in ASCO 2022 showed that the objective response rate was 100% for HER2 IHC3+ patients, 77.8% for HER2 IHC2+ patients, and 66.7% for HER2 IHC1+ patients, 50% for HER2 (IHC0) patients (28). Based on the observed antitumor activity of novel HER2-targeted ADC drugs in patients with IHC scores of 1+ and 2+ (non-amplified), the amplification concept that is closely related to treatment efficacy is gradually changing.
These clinical trial results indicate the effectiveness of drugs targeting HER2-positive and low-expressing patients, further illustrating the value of precision classification and assessment for future drug efficacy. One study established and validated a new method for detecting HER2 expression in tissue samples based on quantitative immunofluorescence and cell lines expressing HER2 protein but without ERBB2 gene amplification, which measures HER2 protein amounts by mass spectrometry and standardization in array format (29).
HER2-low expression is an emerging subtype accounting for over half of all patients. The results of this study provide evidence for more precise management of HER2-low expressing urothelial carcinoma in the future. In summary, based on the new classification method for breast cancer HER2-low expression proposed after 2021, this study retrospectively reinterpreted HER2 grading and analyzed its correlation with various clinical pathological data results. The results showed that the HER2 three-tier system was significantly correlated with the pathological grade of urothelial carcinoma. Follow-up found that patients with HER2-low expression had the highest recurrence rate, which is closely related to the current treatment situation. Finally, modeling found that this classification method has very good clinical predictive value. There are certain limitations in this study. Further efforts are still needed to improve the sample size and statistically analyze patient prognosis in subsequent studies.
Conclusions
The study suggests that we further refine the precise interpretation of HER2, HER2 expression is closely related to the TNM stage of urothelial carcinoma and HER2 three-tier assessment can effectively predict clinical prognosis of urothelial carcinoma. While also jointly anticipating the publication of clinical study results for HER2-low expressing urothelial carcinoma and the coming of the HER2-low expression era.
Supplementary
The article’s supplementary files as
10.21037/tau-24-354
10.21037/tau-24-354 |
Title: Implementation of the Package of Essential Non-Communicable (PEN) Disease Interventions in Low-Resource Settings: A Systematic Review | Body:
Introduction
One of the challenges of health systems is the rising prevalence of non-communicable diseases. The proportion of deaths associated with noncommunicable diseases is projected to increase from 59% in 2002 to 69% in 2030 (1). NCDs are responsible for the deaths of 41 million people annually, which accounts for 74% of global fatalities. Around 17 million individuals die from an NCCD before they reach their 70th year old; 86% of these premature deaths occur in low- and middle-income countries. Low- and middle-income countries have a 77% mortality rate due to NCDs. NCD deaths occur more frequently in cardiovascular diseases, with 17.9 million deaths annually, while cancers and chronic respiratory diseases account for the majority (4.1 million), and diabetes causes the most deaths (2.0 million including kidney disease deaths caused by diabetes). These four diseases are responsible for more than 80% of all premature deaths caused by NCDs.(2, 3)
Hypertension and diabetes are the most common conditions that significantly contribute to the burden of non-communicable diseases. Therefore, the WHO designed the Package of Essential Noncommunicable Disease (NCDs) Interventions for Primary Health Care should begin to prevent heart attack, stroke, and kidney disease through integrated management of hypertension and diabetes. Once primary healthcare workers develop the skills to implement effectively the protocol, the portfolio can be expanded to other NCDs (4).
Package of Essential Non-communicable Disease Interventions for Primary Health Care (WHO PEN) sets minimum standards for the management of non-communicable diseases. Thus, its implementation leads to strengthening the national capacity to integrate and scale up care for heart disease, stroke, cardiovascular risk, diabetes, cancer, asthma, and chronic obstructive pulmonary disease in primary health care in low-resource settings (5).
The purpose of the package is to provide an equitable framework for initiating action to develop primary care in countries that strive to achieve universal access to the health sector. Therefore, the WHO PEN should be an integral part of pro-poor health care programs that target vulnerable and disadvantaged groups. As noted, the package should be considered as a set of minimum standard interventions and only as a starting point for action to address NCDs in primary healthcare in restricted-resourced areas (5).
There is little global evidence about the implementation and effectiveness of these interventions in the areas where this program is implemented, and the little available evidence has conflicting results (6, 7). The findings of this review, to identify global experiences on the implementation of this WHO PEN, can provide useful information to the health sector policymakers to identify and eliminate the gaps and resolve the challenges of this policy as well as deal with chronic diseases through any national policy.
Methods
The protocol of this review is registered in PROS-PERO( CRD42021256242).
Types of Studies and Data Sources
A systematic literature search of PubMed, Scopus, Web of Science, Cochrane Library, and Scientific Information Database (SID) was conducted without a time limit at the end of Dec 2020. Google Scholar was also used for citation track chasing.
Search strategies
The search strategy involved systematic use of keywords in various combinations. To find relevant articles, general keywords were selected (“Noncommunicable Diseases” (MeSH), “NCD”, “Non-communicable Diseases”, “Non communicable Diseases”, “Chronic Diseases”, “WHOPEN”, “WHO PEN”, “PEN”, “Non-infectious Diseases”, “Non infectious Diseases”, “Noninfectious Disease”, “Non-communicable Chronic Disease”, “Non-infectious Disease”) combined using the AND and OR operators to ensure a comprehensive and complete search process. Detailed search strategy including electronic search strategy for at least one database is shown in supplementary file1.
Eligibility (inclusion and exclusion) criteria
The present systematic review included articles that were written in English and Persian language. As we aimed to include only original studies and cross-sectional studies resulting from studies related to the WHO PEN package, any type of other studies including review articles, letters to the editor, and commentary/perspective articles, were excluded.
Data collection process
According to the search strategy of each database, articles were extracted and imported into the End-Note software (V.X8), and duplicate articles were removed. Then two independent reviewers (AA and MA) screened the articles based on the title, abstract, and full text. If the two reviewers disagreed, the third reviewer’s opinion was used. Furthermore, the reevaluation of the full-text article was performed based on inclusion and exclusion criteria.
Quality Assessment of Studies
The quality of articles was evaluated by using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS), Appraisal Tool for Cross-Sectional Studies (AXIS), Critical Appraisal Skills Programme (CASP), and Mixed Methods Appraisal Tool (MMAT) for assessing Economic Evaluation, Cross-Sectional, Qualitative and Mixed Methods studies.
To evaluate the quality of studies, special tools were used according to the type of study. Studies were scored based on four groups (Table 1). For example, cross-sectional studies with a score of 20–16 were in the category of good-quality studies and economic evaluation studies with a score of 24–20 were in this group. Studies belonging to the average or below-average quality assessment group were excluded from the study and 15 articles remained (Table 2). To reduce bias, the quality of the articles was assessed by two independent evaluators, and disagreements were discussed and resolved.
Table 1:
Four groups of study quality assessment criteria
Variable
AXIS
MMAT
CASP
CHEER
1
Good
20–16
20–16
10–8
24–20
2
Fair
16–12
16–12
8–6
19–17
3
Average
12–8
12–8
6–4
16–14
4
Below average
8–4
8–4
4–2
13–11
Table 2:
Characteristics of the included studies
Authors (year)
Location/Setting
Study design
Aims of the study
Finding/Effectiveness/Impact
Quality Rating of Study
1
Dukpa et al (2015) (10)
Bhutan
A model-based economic evaluation
Assessing the cost-effectiveness of the PEN project implemented in Bhutan and analyzing the costs and health consequences of the program in both the short and long term.
The results support the WHO’s standpoint, which indicates that the WHO PEN is very cost-effective and feasible to implement in all countries.
Good
2
Wangchuk et al (2014) (7)
Bhutan
A performance assessment study
The performance of the PEN project in detecting and managing non-communicable diseases (NCDs) and their risk factors was assessed.
Implementation of the PEN intervention in the primary healthcare setting of Bhutan led to improvement in blood pressure and diabetes control and a reduction in CVD risk.
Good
3
AlHelo et al (2019) (11)
Gaza/Palestine
A cross-sectional study
To evaluate the impact of these interventions in reducing cardiovascular disease risk as these interventions are newly implemented.
There was no statistically the significant difference between pre- and post-parameter interventions for systolic and diastolic blood pressure, waist circumference, weight, body mass index, and cholesterol, as well as for tobacco use and cardiovascular risk (p > 0.05).In contrast, fasting blood sugar was lower, with a statistically significant difference (p = 0.049).
Good
4
Gyamfi et al (2020) (12)
Ghana
A qualitativestudy
To identify and describe the community health nurses’ (CHNs) perceptions of facilitators and chalenges faced with TASSH implementation.
Three themes emerged following deductive analysis using the Consolidated Framework for Implementation Research, including (1) Patient health goal a setting-relative priority and positive feedback from nurses; (2) Leadership engagement; (3) Availability of resources, with limited space and personnel time to carry out TASSH duties, limited blood pressure (BP) monitoring equipment, and transportation, listed as barriers to effective implementation.
Good
5
Hadava nd Siri et al (InPersian)(2020) (13)
Iran
A cross-sectional study
To examine the client’s adherence to the visitation schedule recommended by health centers in Ira-PEN, based on their cardiovascular risks.
Adherence was low in all four evaluated cities. The overall adherence rate was 19.73% in this study, and timely referral was higher in women than men (21.98% vs. 16.37%).
Good
6
Etemad et al (2016) (14)
Iran
A qualitativestudy
To determine the challenges of implementing this package in Iran’s healthcare system.
The findings were categorized into nine groups of challenges, including management, organizational, functional, intelligence, political, demographic, economic, cultural, and educational
Fair
7
Tahir Ahmed et al (2019 )(23)
Iraq
A cross-sectional design
To explore the factors that affect the weak response of attendants to primary health care centers for the diagnostic second visit following the first screening one.
The factors affecting adherence are as follows:
Socioeconomic as long-distance from treatment settings,Health care workers related factors, such as a lack of knowledge of health professionals about the program,The nature of the illness andPatient-related factors.
Below average
8
Collins et al (2017)(15)
Kyrgyzstan
The qualitative evaluation
To identify opportunities to improve the implementation of PEN in Kyrgyzstan.
Qualitative analysis found 11 themes that seemed to help explain the quantitative findings.Themes include mainstreaming of PEN protocols, use of lifestyle interventions, training for PEN protocols, understanding and use of cardiovascular risk charts, use of drug treatment, lack of human resources, lack of educational materials for patients, population health promotion campaigns, modern technologies, patent demographics, access to risk estimation.
Good
9
Laatikainen et al (2020)(16)
Moldova
A cross-Sectional study
To determine the feasibility of implementing and evaluating the WHO PEN approach in primary healthcare in the Republic of Moldova.
It is feasible to implement and evaluate interventions for the prevention of CVD in the Republic of Moldova using routine clinical data from paper-based records.
Good
10
Aye et al (2020) (17)
Myanmar
A sequential explanatory QUAN-QUAL mixed methods design
To assess the implementation of PEN, and its effectiveness, and understand the facilitators and barriers in its implementation.
High loss to follow-up, poor recording of CVD risk score, and lack of essential medicines and equipment were the key challenges identified that need to be addressed before further expansion of the PEN project to other townships.
Good
11
Rawal LB, et al (2020) (18)
Nepal
A qualitative study
To explore the barriers and facilitators to engaging community health workers (CHWs) for NCDs prevention and control in Nepal.
Some challenges and barriers were identified, including inadequate NCD training, high workload, poor system-level support, inadequate remuneration, and inadequate supply of logistics and drugs.The facilitating factors included government priority, formation of NCD-related policies, community support systems, social prestige, and staff motivation.
Good
12
Agrawal, et al (2018) (9)
Nepal
A cross-sectional study
To evaluate the care delivery in diabetes patients in a rural primary care hospital that had implemented the WHO PEN protocol.
The results revealed adherence to the PEN protocol and identified several areas of improvement in diabetes care delivery in rural hospital functioning. The results reflected the need for regular CME programs on diabetes for our team of healthcare providers.
Good
13
Kontsevaya et al (2017) (19)
Kyrgyzstan
A cross-sectional study
To compare the inputs, outputs, and outcomes for PEN pilot sites versus non-PEN pilot sites in Bishkek city for 12 months; To assess the possibility of and perspectives for developing an approach to the economic evaluation of PEN implementation in Kyrgyzstan
The evaluation in PEN centers did not show clear and significant evidence of a real impact resulting from the implementation of the PEN protocols on primary care, so there were no arguments for performing a long-term economic analysis of the combination of the effects and costs.
Fair
14
Basu et al (2019) (20)
South Africa
A microsimulation and cost-effectiveness analysis
To assess how cardiovascular risk factors are distributed across sub-populations and to identify which cardiovascular treatments should be prioritized. To investigate whether implementation of either guideline would lead to a reduction in premature mortality
Implementation of South Africa’s Primary Care101 guidelines averted slightly more overall DALYs and had better cost-effectiveness than implemented of the WHO PEN guidelines.
Good
15
Rattanavipapong et al (2016) (21)
Indonesia
Model-Based Economic Evaluation
The objective of the quantitative assessment is to evaluate the cost-effectiveness of the PEN program compared to a “no screening” policy choice.
Providing the current PEN policy had the greatest health benefits in terms of the lowest DALYs lost or highest DALYs averted compared to no screening. In addition, adopting policy option 2 requires a slightly lower budget for the first year compared to the current policy (the PEN program)
Good
16
Collins et al (2017) (22)
Jordan
A mixed method
to identify opportunities to improve total CVD risk-based guidance for humanitarian settings.
Few patients had a documented and correct CVD risk score, and half of high-risk patients were not prescribed lipid-lowering treatment.The qualitative analysis found nine themes.
Good
Data Extraction
After the qualified papers were determined, data were extracted based on an extraction form, that encompassed authors’ information, publication year, type of study, study setting, study objectives, methodology, results of actions, and key findings. To critical appraisal and improve accuracy, data extraction was conducted by independent researchers, and disagreements between researchers (AA and MA) were resolved through discussion.
The method for thematic analysis
Braun & Clarke’s six-phase framework was used to guide thematic analysis. 1) Become familiar with the data, 2) Generate initial codes, 3) Search for themes, 4) Review themes, 5) Define themes, 6) Write up (8).
Types of Studies and Data Sources
A systematic literature search of PubMed, Scopus, Web of Science, Cochrane Library, and Scientific Information Database (SID) was conducted without a time limit at the end of Dec 2020. Google Scholar was also used for citation track chasing.
Search strategies
The search strategy involved systematic use of keywords in various combinations. To find relevant articles, general keywords were selected (“Noncommunicable Diseases” (MeSH), “NCD”, “Non-communicable Diseases”, “Non communicable Diseases”, “Chronic Diseases”, “WHOPEN”, “WHO PEN”, “PEN”, “Non-infectious Diseases”, “Non infectious Diseases”, “Noninfectious Disease”, “Non-communicable Chronic Disease”, “Non-infectious Disease”) combined using the AND and OR operators to ensure a comprehensive and complete search process. Detailed search strategy including electronic search strategy for at least one database is shown in supplementary file1.
Eligibility (inclusion and exclusion) criteria
The present systematic review included articles that were written in English and Persian language. As we aimed to include only original studies and cross-sectional studies resulting from studies related to the WHO PEN package, any type of other studies including review articles, letters to the editor, and commentary/perspective articles, were excluded.
Data collection process
According to the search strategy of each database, articles were extracted and imported into the End-Note software (V.X8), and duplicate articles were removed. Then two independent reviewers (AA and MA) screened the articles based on the title, abstract, and full text. If the two reviewers disagreed, the third reviewer’s opinion was used. Furthermore, the reevaluation of the full-text article was performed based on inclusion and exclusion criteria.
Quality Assessment of Studies
The quality of articles was evaluated by using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS), Appraisal Tool for Cross-Sectional Studies (AXIS), Critical Appraisal Skills Programme (CASP), and Mixed Methods Appraisal Tool (MMAT) for assessing Economic Evaluation, Cross-Sectional, Qualitative and Mixed Methods studies.
To evaluate the quality of studies, special tools were used according to the type of study. Studies were scored based on four groups (Table 1). For example, cross-sectional studies with a score of 20–16 were in the category of good-quality studies and economic evaluation studies with a score of 24–20 were in this group. Studies belonging to the average or below-average quality assessment group were excluded from the study and 15 articles remained (Table 2). To reduce bias, the quality of the articles was assessed by two independent evaluators, and disagreements were discussed and resolved.
Table 1:
Four groups of study quality assessment criteria
Variable
AXIS
MMAT
CASP
CHEER
1
Good
20–16
20–16
10–8
24–20
2
Fair
16–12
16–12
8–6
19–17
3
Average
12–8
12–8
6–4
16–14
4
Below average
8–4
8–4
4–2
13–11
Table 2:
Characteristics of the included studies
Authors (year)
Location/Setting
Study design
Aims of the study
Finding/Effectiveness/Impact
Quality Rating of Study
1
Dukpa et al (2015) (10)
Bhutan
A model-based economic evaluation
Assessing the cost-effectiveness of the PEN project implemented in Bhutan and analyzing the costs and health consequences of the program in both the short and long term.
The results support the WHO’s standpoint, which indicates that the WHO PEN is very cost-effective and feasible to implement in all countries.
Good
2
Wangchuk et al (2014) (7)
Bhutan
A performance assessment study
The performance of the PEN project in detecting and managing non-communicable diseases (NCDs) and their risk factors was assessed.
Implementation of the PEN intervention in the primary healthcare setting of Bhutan led to improvement in blood pressure and diabetes control and a reduction in CVD risk.
Good
3
AlHelo et al (2019) (11)
Gaza/Palestine
A cross-sectional study
To evaluate the impact of these interventions in reducing cardiovascular disease risk as these interventions are newly implemented.
There was no statistically the significant difference between pre- and post-parameter interventions for systolic and diastolic blood pressure, waist circumference, weight, body mass index, and cholesterol, as well as for tobacco use and cardiovascular risk (p > 0.05).In contrast, fasting blood sugar was lower, with a statistically significant difference (p = 0.049).
Good
4
Gyamfi et al (2020) (12)
Ghana
A qualitativestudy
To identify and describe the community health nurses’ (CHNs) perceptions of facilitators and chalenges faced with TASSH implementation.
Three themes emerged following deductive analysis using the Consolidated Framework for Implementation Research, including (1) Patient health goal a setting-relative priority and positive feedback from nurses; (2) Leadership engagement; (3) Availability of resources, with limited space and personnel time to carry out TASSH duties, limited blood pressure (BP) monitoring equipment, and transportation, listed as barriers to effective implementation.
Good
5
Hadava nd Siri et al (InPersian)(2020) (13)
Iran
A cross-sectional study
To examine the client’s adherence to the visitation schedule recommended by health centers in Ira-PEN, based on their cardiovascular risks.
Adherence was low in all four evaluated cities. The overall adherence rate was 19.73% in this study, and timely referral was higher in women than men (21.98% vs. 16.37%).
Good
6
Etemad et al (2016) (14)
Iran
A qualitativestudy
To determine the challenges of implementing this package in Iran’s healthcare system.
The findings were categorized into nine groups of challenges, including management, organizational, functional, intelligence, political, demographic, economic, cultural, and educational
Fair
7
Tahir Ahmed et al (2019 )(23)
Iraq
A cross-sectional design
To explore the factors that affect the weak response of attendants to primary health care centers for the diagnostic second visit following the first screening one.
The factors affecting adherence are as follows:
Socioeconomic as long-distance from treatment settings,Health care workers related factors, such as a lack of knowledge of health professionals about the program,The nature of the illness andPatient-related factors.
Below average
8
Collins et al (2017)(15)
Kyrgyzstan
The qualitative evaluation
To identify opportunities to improve the implementation of PEN in Kyrgyzstan.
Qualitative analysis found 11 themes that seemed to help explain the quantitative findings.Themes include mainstreaming of PEN protocols, use of lifestyle interventions, training for PEN protocols, understanding and use of cardiovascular risk charts, use of drug treatment, lack of human resources, lack of educational materials for patients, population health promotion campaigns, modern technologies, patent demographics, access to risk estimation.
Good
9
Laatikainen et al (2020)(16)
Moldova
A cross-Sectional study
To determine the feasibility of implementing and evaluating the WHO PEN approach in primary healthcare in the Republic of Moldova.
It is feasible to implement and evaluate interventions for the prevention of CVD in the Republic of Moldova using routine clinical data from paper-based records.
Good
10
Aye et al (2020) (17)
Myanmar
A sequential explanatory QUAN-QUAL mixed methods design
To assess the implementation of PEN, and its effectiveness, and understand the facilitators and barriers in its implementation.
High loss to follow-up, poor recording of CVD risk score, and lack of essential medicines and equipment were the key challenges identified that need to be addressed before further expansion of the PEN project to other townships.
Good
11
Rawal LB, et al (2020) (18)
Nepal
A qualitative study
To explore the barriers and facilitators to engaging community health workers (CHWs) for NCDs prevention and control in Nepal.
Some challenges and barriers were identified, including inadequate NCD training, high workload, poor system-level support, inadequate remuneration, and inadequate supply of logistics and drugs.The facilitating factors included government priority, formation of NCD-related policies, community support systems, social prestige, and staff motivation.
Good
12
Agrawal, et al (2018) (9)
Nepal
A cross-sectional study
To evaluate the care delivery in diabetes patients in a rural primary care hospital that had implemented the WHO PEN protocol.
The results revealed adherence to the PEN protocol and identified several areas of improvement in diabetes care delivery in rural hospital functioning. The results reflected the need for regular CME programs on diabetes for our team of healthcare providers.
Good
13
Kontsevaya et al (2017) (19)
Kyrgyzstan
A cross-sectional study
To compare the inputs, outputs, and outcomes for PEN pilot sites versus non-PEN pilot sites in Bishkek city for 12 months; To assess the possibility of and perspectives for developing an approach to the economic evaluation of PEN implementation in Kyrgyzstan
The evaluation in PEN centers did not show clear and significant evidence of a real impact resulting from the implementation of the PEN protocols on primary care, so there were no arguments for performing a long-term economic analysis of the combination of the effects and costs.
Fair
14
Basu et al (2019) (20)
South Africa
A microsimulation and cost-effectiveness analysis
To assess how cardiovascular risk factors are distributed across sub-populations and to identify which cardiovascular treatments should be prioritized. To investigate whether implementation of either guideline would lead to a reduction in premature mortality
Implementation of South Africa’s Primary Care101 guidelines averted slightly more overall DALYs and had better cost-effectiveness than implemented of the WHO PEN guidelines.
Good
15
Rattanavipapong et al (2016) (21)
Indonesia
Model-Based Economic Evaluation
The objective of the quantitative assessment is to evaluate the cost-effectiveness of the PEN program compared to a “no screening” policy choice.
Providing the current PEN policy had the greatest health benefits in terms of the lowest DALYs lost or highest DALYs averted compared to no screening. In addition, adopting policy option 2 requires a slightly lower budget for the first year compared to the current policy (the PEN program)
Good
16
Collins et al (2017) (22)
Jordan
A mixed method
to identify opportunities to improve total CVD risk-based guidance for humanitarian settings.
Few patients had a documented and correct CVD risk score, and half of high-risk patients were not prescribed lipid-lowering treatment.The qualitative analysis found nine themes.
Good
Data Extraction
After the qualified papers were determined, data were extracted based on an extraction form, that encompassed authors’ information, publication year, type of study, study setting, study objectives, methodology, results of actions, and key findings. To critical appraisal and improve accuracy, data extraction was conducted by independent researchers, and disagreements between researchers (AA and MA) were resolved through discussion.
The method for thematic analysis
Braun & Clarke’s six-phase framework was used to guide thematic analysis. 1) Become familiar with the data, 2) Generate initial codes, 3) Search for themes, 4) Review themes, 5) Define themes, 6) Write up (8).
Results
Overall, 404 articles were extracted through database searches. Search results from each database were stored in EndNote X8 and 62 duplicate articles were removed. By screening the title and abstract based on the inclusion and exclusion criteria 291 papers were also excluded. In the last step, 51 full-text articles were assessed for the eligibility criteria, and 16 articles were included. After quality assessment, one more article was excluded and 15 articles were included for the final analysis. Of the 15 articles included in the study, the quality of 13 articles was good, and 2 articles were fair. Fig. 1 shows the steps for selecting articles based on the PRISMA flowchart.
Fig. 1:
PRISMA flowchart for study inclusion
Characteristics of the included studies
The characteristics of the included studies (7, 9–22), are displayed in Table 2. There were 15 studies published during 2014–2020. Six studies were conducted as performance evaluation studies (7,9,11,13,16,19), 3 studies were conducted as economic evaluation of the WHO PEN package (10,20,21), 4 studies were conducted as qualitative studies (12,14,15,18), and 2 studies were conducted as the mixed method (17,22).
Finally, by conducting the thematic analysis of the articles, 2 main themes and 7 sub-themes were identified, and for each sub-theme, effective factors including facilitators and barriers to the successful implementation of WHO PEN were identified discussed as follows (Table 3).
Table 3:
Main themes of facilitators and barriers to the WHO PEN implementation
Theme
Sub-theme
Effective factors
Facilitators and Barriers
Internal organizational factors
Human resource
Education & training motivation
Facilitators
Job Satisfaction
Providing services
Adhere to program protocols
Facilitators
Unit process and feedback
Lifestyle interventions
Risk assessment protocol referral system
Patient follow-up visits
Structure
Suitability of health system structure with the needs of non-communicable diseases
Facilitators
Expanding the scope of the program
Health system readiness
Up-to-date health system
Strengthen PHC
Too much bureaucracy
Barrier
leadership/Governance
Government’s commitment and consideration of health benefits in government policies
Facilitators
Consulting with implementation science experts
Cooperation between related ministries and interdepartmental cooperation
Support managers and leaders
Program priority for the Ministry of Health
Sustainability at managerial and executive levels and short-term management period
Expert support for management decisions
Haste in planning
Barrier
Data and information
Completeness of information
Facilitators
Reliable data
Lack of systematic registration and reporting
Barrier
Resources and Financing
Shortage of capacity and resources in primary care centers
Barriers
High cost of care for NCDs and increased cost of treatment
Increasing the unbearable costs of patients paying out-of-pocket
Low cost of preparation and implementation
Facilitators
Providing free services
Insurance
External organizational factors
knowledge and culture, Economic and social factors
Access to health facilities
Facilitators
More use of services by women
The importance and priority of health for the patient
Acceptance of the program by most patients
Gender restriction
Barriers
Lack of trust in healthcare providers
Non-cooperation of clients
Internal organizational factors
Human resources
In this theme, most of the articles refer to employee training. In Iran, Nepal, and Myanmar, lack of appropriate and sufficient training (7, 14, 18) has been recognized as one of the barriers to the implementation of this program. Moreover, in a study conducted to improve the implementation of this program in Kyrgyzstan, one of the eleven themes discovered in the study was the lack of systematic training for new employees (15). In other studies, not achieving health education goals (22) and financing educational needs (20) were among the barriers to implementing this program.
Another effective factor in this theme is the motivation and desire and job satisfaction among employees to provide services to clients (9, 18). The feedback from patients about the effect of this program on their health plays a key role in creating motivation and high willingness of staff (12).
The delivery of healthcare services
This category is related to the way of providing services and following WHO PEN protocols. Some studies reported non-adherence to the WHO PEN protocols (15, 17, 22). In Ghana, integrating new tasks with other routine tasks and responsibilities has been difficult for some nurses (12). Failure to provide timely services and the non-availability of free essential drugs in the healthcare center (18) are also other problems and barriers on the way to providing services based on the WHO PEN protocols.
Increasing awareness of non-communicable disease management, referral criteria, and use of monitoring tools (11) is one of the influencing factors on how the delivery of healthcare services. Planning to improve the participation of nurses in counseling (15) will be an effective factor. Barriers in this field have been reported as the tendency to provide lifestyle intervention services as the first line of treatment, contrary to the protocols (15, 22). In addition, among the other barriers was the limited understanding of physicians regarding the use of treatment protocols at the level of primary prevention (22).
Referral of patients outside of the WHO PEN protocols due to lack of resources and capacity in primary healthcare centers and the inefficiency of the existing referral mechanism, well as, the insufficient referral mechanism and the lack of a systematic approach to refer and follow patients from one health center to another were among the other problems of referral in this program (18).
In the field of risk assessment protocols of this program, one of the major problems is reported the inability of employees to perform the risk assessment (15, 17, 22). Practical barriers to receiving risk assessment services such as long distances and possible difficulty of traveling to healthcare centers (15) have been stated among other things related to providing risk assessment service.
Compliance with regular follow-up of patients in the WHO PEN protocols (17, 18), was one of the factors affecting the successful implementation of the program. In Bhutan, only 10% of patients missed their treatment follow-up visits (7). In Nepal, about 13% failed to follow up, this could be due to migration of patients, social stigma due to chronic drug use, and death (9).
The structure
In general, the readiness of the health system to reduce the increasing burden of non-communicable diseases, (18) the proportion of the structure of the health system with the needs of non-communicable diseases, (14) strengthening the PHC system (11) and the need to expand and develop PEN WHO intervention throughout the country, (17, 18) are the factors reported in this theme.
Leadership/Governance
Studies reported certain factors that worked for the implementation of the WHO PEN including the high level of commitment of the government (14, 18), considering health benefits in government (14), and the development and implementation of policies and programs related to non-communicable diseases (18), the high priority of prevention and control of non-communicable diseases for the Ministry of Health (18), the need for cooperation with other relevant ministries and related sectors other than health sector (18), the inter-sectoral coordination (14), and the importance of continuous guidance from the implementation science experts (12), the support of leaders or managers of health centers (12, 15), and creating the media campaigns to improve the health of the population (22).
Information
Barriers that were reported in the field of data and information, included the unavailability of systematic reporting and recording systems (17, 18), lack of comprehensive information and of data dealing with patients with NCDs at the health facility (14, 17, 20), and lack of reliable data (20).
Resources and Financing
In Indonesia, providing the current PEN policy had the greatest health benefits in terms of the lowest DALYs lost or highest DALYs averted compared to no screening (21). In Bhutan, the current PEN program and universal screening are certainly cost-effective and show they were cost-saving interventions (10). According to studies, availability of insurance (14), provision of care, medication, and free equipment (11, 17), low costs of preparation and implementation of PEN compared to non-implementation, cost savings, and cost-effectiveness of PEN implementation will be among the facilitators of the implementation of this program (10, 19–21).
Lack of internal resources and capacity, for example, trained human resources, regular supply of drugs, appropriate equipment, logistics-related challenges such as lack of adequate space, lack of furniture, and time, is a commonly reported barrier (9, 11, 12, 15, 17, 18). Due to the high costs of care for non-communicable patients (18, 20) financial resources instability and economic sanctions (14), and budget volatility (20), financing this program will be a challenge. Increasing costs of treating hypertension and dyslipidemia (20), as well as lack of timely supply and availability of free essential drugs at the health center, forces patients to purchase NCD drugs from private pharmacies and as a result, it adds to the unbearable out-of-pocket costs of patients (18).
External organizational factors: knowledge, culture, and economic and social factors
This theme includes things that are rooted in the knowledge and awareness and culture of patients and people covered by health centers. In this regard, the facilitators reported by studies in this field include the need to inform and increase public awareness (14, 15), motivate acceptance of interventions by most patients (12), promote the use of services by women (7, 16) and increase adherence of women to regular visits (13), and increase the importance and priority of health and its consequences for patients (12). On the other hand, in some countries, women have more challenges than men in sports due to gender, cultural, or security restrictions (14, 22).
Studies have shown different barriers and challenges including lack of cooperation by clients for receiving care (12), non-adherence of high-risk patients to drug interventions (22), not following referrals to specialist practitioners in patients (17), lack of interest or ability to exercise (22), as well as patients’ self-report of their condition, which may lead to overestimation or underestimation of treatment levels due to social acceptability bias (20), can under the program in achieving its goals (15). Overall, barriers to access to health care including culture, trust, and financial implications of care in the poor (20), transportation problems and associated costs for patients and clinic staff (9, 12), missed follow-up visits due to the inability to walk long distances by elderly, disability following stroke and reluctance to travel by bus due to motion sickness (7), and the patients’ spiritual beliefs determine their response to the disease and the strategies they use to deal with it. For example, the patient believed that accepting she had hypertension would accelerate her death (12), mistrust of health care providers (22), patients’ embarrassment from full disclosure of psychological, social, or occupational background, were also among the barriers to implementation of the WHO PEN (22).
Characteristics of the included studies
The characteristics of the included studies (7, 9–22), are displayed in Table 2. There were 15 studies published during 2014–2020. Six studies were conducted as performance evaluation studies (7,9,11,13,16,19), 3 studies were conducted as economic evaluation of the WHO PEN package (10,20,21), 4 studies were conducted as qualitative studies (12,14,15,18), and 2 studies were conducted as the mixed method (17,22).
Finally, by conducting the thematic analysis of the articles, 2 main themes and 7 sub-themes were identified, and for each sub-theme, effective factors including facilitators and barriers to the successful implementation of WHO PEN were identified discussed as follows (Table 3).
Table 3:
Main themes of facilitators and barriers to the WHO PEN implementation
Theme
Sub-theme
Effective factors
Facilitators and Barriers
Internal organizational factors
Human resource
Education & training motivation
Facilitators
Job Satisfaction
Providing services
Adhere to program protocols
Facilitators
Unit process and feedback
Lifestyle interventions
Risk assessment protocol referral system
Patient follow-up visits
Structure
Suitability of health system structure with the needs of non-communicable diseases
Facilitators
Expanding the scope of the program
Health system readiness
Up-to-date health system
Strengthen PHC
Too much bureaucracy
Barrier
leadership/Governance
Government’s commitment and consideration of health benefits in government policies
Facilitators
Consulting with implementation science experts
Cooperation between related ministries and interdepartmental cooperation
Support managers and leaders
Program priority for the Ministry of Health
Sustainability at managerial and executive levels and short-term management period
Expert support for management decisions
Haste in planning
Barrier
Data and information
Completeness of information
Facilitators
Reliable data
Lack of systematic registration and reporting
Barrier
Resources and Financing
Shortage of capacity and resources in primary care centers
Barriers
High cost of care for NCDs and increased cost of treatment
Increasing the unbearable costs of patients paying out-of-pocket
Low cost of preparation and implementation
Facilitators
Providing free services
Insurance
External organizational factors
knowledge and culture, Economic and social factors
Access to health facilities
Facilitators
More use of services by women
The importance and priority of health for the patient
Acceptance of the program by most patients
Gender restriction
Barriers
Lack of trust in healthcare providers
Non-cooperation of clients
Internal organizational factors
Human resources
In this theme, most of the articles refer to employee training. In Iran, Nepal, and Myanmar, lack of appropriate and sufficient training (7, 14, 18) has been recognized as one of the barriers to the implementation of this program. Moreover, in a study conducted to improve the implementation of this program in Kyrgyzstan, one of the eleven themes discovered in the study was the lack of systematic training for new employees (15). In other studies, not achieving health education goals (22) and financing educational needs (20) were among the barriers to implementing this program.
Another effective factor in this theme is the motivation and desire and job satisfaction among employees to provide services to clients (9, 18). The feedback from patients about the effect of this program on their health plays a key role in creating motivation and high willingness of staff (12).
The delivery of healthcare services
This category is related to the way of providing services and following WHO PEN protocols. Some studies reported non-adherence to the WHO PEN protocols (15, 17, 22). In Ghana, integrating new tasks with other routine tasks and responsibilities has been difficult for some nurses (12). Failure to provide timely services and the non-availability of free essential drugs in the healthcare center (18) are also other problems and barriers on the way to providing services based on the WHO PEN protocols.
Increasing awareness of non-communicable disease management, referral criteria, and use of monitoring tools (11) is one of the influencing factors on how the delivery of healthcare services. Planning to improve the participation of nurses in counseling (15) will be an effective factor. Barriers in this field have been reported as the tendency to provide lifestyle intervention services as the first line of treatment, contrary to the protocols (15, 22). In addition, among the other barriers was the limited understanding of physicians regarding the use of treatment protocols at the level of primary prevention (22).
Referral of patients outside of the WHO PEN protocols due to lack of resources and capacity in primary healthcare centers and the inefficiency of the existing referral mechanism, well as, the insufficient referral mechanism and the lack of a systematic approach to refer and follow patients from one health center to another were among the other problems of referral in this program (18).
In the field of risk assessment protocols of this program, one of the major problems is reported the inability of employees to perform the risk assessment (15, 17, 22). Practical barriers to receiving risk assessment services such as long distances and possible difficulty of traveling to healthcare centers (15) have been stated among other things related to providing risk assessment service.
Compliance with regular follow-up of patients in the WHO PEN protocols (17, 18), was one of the factors affecting the successful implementation of the program. In Bhutan, only 10% of patients missed their treatment follow-up visits (7). In Nepal, about 13% failed to follow up, this could be due to migration of patients, social stigma due to chronic drug use, and death (9).
The structure
In general, the readiness of the health system to reduce the increasing burden of non-communicable diseases, (18) the proportion of the structure of the health system with the needs of non-communicable diseases, (14) strengthening the PHC system (11) and the need to expand and develop PEN WHO intervention throughout the country, (17, 18) are the factors reported in this theme.
Leadership/Governance
Studies reported certain factors that worked for the implementation of the WHO PEN including the high level of commitment of the government (14, 18), considering health benefits in government (14), and the development and implementation of policies and programs related to non-communicable diseases (18), the high priority of prevention and control of non-communicable diseases for the Ministry of Health (18), the need for cooperation with other relevant ministries and related sectors other than health sector (18), the inter-sectoral coordination (14), and the importance of continuous guidance from the implementation science experts (12), the support of leaders or managers of health centers (12, 15), and creating the media campaigns to improve the health of the population (22).
Information
Barriers that were reported in the field of data and information, included the unavailability of systematic reporting and recording systems (17, 18), lack of comprehensive information and of data dealing with patients with NCDs at the health facility (14, 17, 20), and lack of reliable data (20).
Resources and Financing
In Indonesia, providing the current PEN policy had the greatest health benefits in terms of the lowest DALYs lost or highest DALYs averted compared to no screening (21). In Bhutan, the current PEN program and universal screening are certainly cost-effective and show they were cost-saving interventions (10). According to studies, availability of insurance (14), provision of care, medication, and free equipment (11, 17), low costs of preparation and implementation of PEN compared to non-implementation, cost savings, and cost-effectiveness of PEN implementation will be among the facilitators of the implementation of this program (10, 19–21).
Lack of internal resources and capacity, for example, trained human resources, regular supply of drugs, appropriate equipment, logistics-related challenges such as lack of adequate space, lack of furniture, and time, is a commonly reported barrier (9, 11, 12, 15, 17, 18). Due to the high costs of care for non-communicable patients (18, 20) financial resources instability and economic sanctions (14), and budget volatility (20), financing this program will be a challenge. Increasing costs of treating hypertension and dyslipidemia (20), as well as lack of timely supply and availability of free essential drugs at the health center, forces patients to purchase NCD drugs from private pharmacies and as a result, it adds to the unbearable out-of-pocket costs of patients (18).
Human resources
In this theme, most of the articles refer to employee training. In Iran, Nepal, and Myanmar, lack of appropriate and sufficient training (7, 14, 18) has been recognized as one of the barriers to the implementation of this program. Moreover, in a study conducted to improve the implementation of this program in Kyrgyzstan, one of the eleven themes discovered in the study was the lack of systematic training for new employees (15). In other studies, not achieving health education goals (22) and financing educational needs (20) were among the barriers to implementing this program.
Another effective factor in this theme is the motivation and desire and job satisfaction among employees to provide services to clients (9, 18). The feedback from patients about the effect of this program on their health plays a key role in creating motivation and high willingness of staff (12).
The delivery of healthcare services
This category is related to the way of providing services and following WHO PEN protocols. Some studies reported non-adherence to the WHO PEN protocols (15, 17, 22). In Ghana, integrating new tasks with other routine tasks and responsibilities has been difficult for some nurses (12). Failure to provide timely services and the non-availability of free essential drugs in the healthcare center (18) are also other problems and barriers on the way to providing services based on the WHO PEN protocols.
Increasing awareness of non-communicable disease management, referral criteria, and use of monitoring tools (11) is one of the influencing factors on how the delivery of healthcare services. Planning to improve the participation of nurses in counseling (15) will be an effective factor. Barriers in this field have been reported as the tendency to provide lifestyle intervention services as the first line of treatment, contrary to the protocols (15, 22). In addition, among the other barriers was the limited understanding of physicians regarding the use of treatment protocols at the level of primary prevention (22).
Referral of patients outside of the WHO PEN protocols due to lack of resources and capacity in primary healthcare centers and the inefficiency of the existing referral mechanism, well as, the insufficient referral mechanism and the lack of a systematic approach to refer and follow patients from one health center to another were among the other problems of referral in this program (18).
In the field of risk assessment protocols of this program, one of the major problems is reported the inability of employees to perform the risk assessment (15, 17, 22). Practical barriers to receiving risk assessment services such as long distances and possible difficulty of traveling to healthcare centers (15) have been stated among other things related to providing risk assessment service.
Compliance with regular follow-up of patients in the WHO PEN protocols (17, 18), was one of the factors affecting the successful implementation of the program. In Bhutan, only 10% of patients missed their treatment follow-up visits (7). In Nepal, about 13% failed to follow up, this could be due to migration of patients, social stigma due to chronic drug use, and death (9).
The structure
In general, the readiness of the health system to reduce the increasing burden of non-communicable diseases, (18) the proportion of the structure of the health system with the needs of non-communicable diseases, (14) strengthening the PHC system (11) and the need to expand and develop PEN WHO intervention throughout the country, (17, 18) are the factors reported in this theme.
Leadership/Governance
Studies reported certain factors that worked for the implementation of the WHO PEN including the high level of commitment of the government (14, 18), considering health benefits in government (14), and the development and implementation of policies and programs related to non-communicable diseases (18), the high priority of prevention and control of non-communicable diseases for the Ministry of Health (18), the need for cooperation with other relevant ministries and related sectors other than health sector (18), the inter-sectoral coordination (14), and the importance of continuous guidance from the implementation science experts (12), the support of leaders or managers of health centers (12, 15), and creating the media campaigns to improve the health of the population (22).
Information
Barriers that were reported in the field of data and information, included the unavailability of systematic reporting and recording systems (17, 18), lack of comprehensive information and of data dealing with patients with NCDs at the health facility (14, 17, 20), and lack of reliable data (20).
Resources and Financing
In Indonesia, providing the current PEN policy had the greatest health benefits in terms of the lowest DALYs lost or highest DALYs averted compared to no screening (21). In Bhutan, the current PEN program and universal screening are certainly cost-effective and show they were cost-saving interventions (10). According to studies, availability of insurance (14), provision of care, medication, and free equipment (11, 17), low costs of preparation and implementation of PEN compared to non-implementation, cost savings, and cost-effectiveness of PEN implementation will be among the facilitators of the implementation of this program (10, 19–21).
Lack of internal resources and capacity, for example, trained human resources, regular supply of drugs, appropriate equipment, logistics-related challenges such as lack of adequate space, lack of furniture, and time, is a commonly reported barrier (9, 11, 12, 15, 17, 18). Due to the high costs of care for non-communicable patients (18, 20) financial resources instability and economic sanctions (14), and budget volatility (20), financing this program will be a challenge. Increasing costs of treating hypertension and dyslipidemia (20), as well as lack of timely supply and availability of free essential drugs at the health center, forces patients to purchase NCD drugs from private pharmacies and as a result, it adds to the unbearable out-of-pocket costs of patients (18).
External organizational factors: knowledge, culture, and economic and social factors
This theme includes things that are rooted in the knowledge and awareness and culture of patients and people covered by health centers. In this regard, the facilitators reported by studies in this field include the need to inform and increase public awareness (14, 15), motivate acceptance of interventions by most patients (12), promote the use of services by women (7, 16) and increase adherence of women to regular visits (13), and increase the importance and priority of health and its consequences for patients (12). On the other hand, in some countries, women have more challenges than men in sports due to gender, cultural, or security restrictions (14, 22).
Studies have shown different barriers and challenges including lack of cooperation by clients for receiving care (12), non-adherence of high-risk patients to drug interventions (22), not following referrals to specialist practitioners in patients (17), lack of interest or ability to exercise (22), as well as patients’ self-report of their condition, which may lead to overestimation or underestimation of treatment levels due to social acceptability bias (20), can under the program in achieving its goals (15). Overall, barriers to access to health care including culture, trust, and financial implications of care in the poor (20), transportation problems and associated costs for patients and clinic staff (9, 12), missed follow-up visits due to the inability to walk long distances by elderly, disability following stroke and reluctance to travel by bus due to motion sickness (7), and the patients’ spiritual beliefs determine their response to the disease and the strategies they use to deal with it. For example, the patient believed that accepting she had hypertension would accelerate her death (12), mistrust of health care providers (22), patients’ embarrassment from full disclosure of psychological, social, or occupational background, were also among the barriers to implementation of the WHO PEN (22).
Discussion
Using the thematic analysis of the articles, this review summarized the facilitators and barriers to the WHO PEN implementation reported over the past 10 years. As a result of this analysis, certain organizational factors including human resources, service delivery, structure, leadership/governance, data and information, resources, and financing were identified from the studies. knowledge and culture, economic and social factors were identified as beyond organizational factors.
Employee motivation has been identified as one of the factors affecting this program. Many studies have been conducted in the field of job motivation of human resources working in the health sector (24). The results of some studies emphasized the importance of internal factors and others emphasized the importance of external factors (25, 26).
Lack of internal resources and capacity is a commonly reported barrier. Little information is known about the capacity of PHCs (Primary health care) in LMICs (Low- and middle-income countries) to meet the needs of people with NCDs. Although NCD interventions (e.g., diagnosis and treatment) are universal, effective care delivery strategies for people to access common socioeconomic, cultural, and health scenarios differ in LMICs compared to high-income countries (27).
One of the other factors affecting the implementation of the PEN program is the training of employees (28). Comprehensive training and development programs can help to focus trainees on skills, attitudes, and knowledge to achieve goals and create competitive advantages for the organization (29). Many studies have revealed the impact of training on organizational performance (30–33). Effective service delivery in compliance with PEN program protocols will play a role in its successful implementation. healthcare delivery means providing effective services to people with diseases for which there are proven treatments (34). Well-designed healthcare delivery systems are powerful resources for economic development (34).
Another effective factor identified in this study is the structure of the health system to face the crisis of non-communicable diseases. Weak regulatory structures have been identified as one of the barriers to effective surveillance of non-communicable diseases in low- and middle-income countries (35).
The reviewed studies reported leadership and governance as factors influencing the implementation of this program. One of the most important and vital factors for the successful completion of projects is the support of senior management (36, 37). Many researchers also agree that top management commitment is critical (38–42). Senior management must not only demonstrate commitment and leadership but also must strive to create interest in implementing and communicating change to everyone in the organization (40).
Strengths and limitations
This study has a protocol registered in PROS-PERO, which is an international database of prospectively registered systematic reviews in health and social care, which increases the validity of the results. The review provides novel findings that can inform the design of future studies on the implementation of this WHO PEN package and its impacts. Moreover, the assessment of review quality was used to assign the strength of evidence to the findings. This systematic review has some limitations. Although five well-known databases were used, only studies in English and Persian studies were included in this review.
Strengths and limitations
This study has a protocol registered in PROS-PERO, which is an international database of prospectively registered systematic reviews in health and social care, which increases the validity of the results. The review provides novel findings that can inform the design of future studies on the implementation of this WHO PEN package and its impacts. Moreover, the assessment of review quality was used to assign the strength of evidence to the findings. This systematic review has some limitations. Although five well-known databases were used, only studies in English and Persian studies were included in this review.
Conclusion
The effective factors that include facilitators and barriers to the implementation of this program are divided into two groups, external and internal organizational effective factors, and most of the factors identified in the studies are related to internal organizational factors. The study identified and explained the factors influencing the implementation of the program (WHO PEN) that facilitate the successful implementation of this program, or the barriers to its implementation to support its successful implementation in primary healthcare requires that they be removed. Therefore, according to the effective factors identified in this study, policymakers and managers of the health system will be more successful in implementing this package (WHO PEN).
Journalism Ethics considerations
Ethical issues (Including plagiarism, informed consent, misconduct, data fabrication and/or falsification, double publication and/or submission, redundancy, etc.) have been completely observed by the authors. |
Title: A Transfer Learning-Based Framework for Classifying Lymph Node Metastasis in Prostate Cancer Patients | Body:
1. Introduction
According to the American Cancer Society, prostate cancer is the second most common cancer in American men after skin cancer. As per their estimates, about 248,530 new cases and 34,130 deaths occurred from prostate cancer in 2021 alone [1]. Often, prostate cancer is slow-growing and initially confined to the prostate gland [2]. In these instances, patients may need no treatment, opting instead for active surveillance. Other patients may need surgery, chemotherapy, immunotherapy, radiation therapy, or often a combination of these. The decision to intervene, and the best intervention, hinges on the cancer’s stage. Staging cancer depends on primary tumor growth, for example, growth into adjacent organs such as the seminal vesicles or the urinary bladder. Staging also depends on the secondary, metastatic extent. Prostate cancer has a predilection for spreading to pelvic/retroperitoneal lymph nodes and bones. As the overall prostate cancer tumor burden often determines the treatments offered, reliable staging is important, and medical imaging often plays a key role [3,4]. Magnetic resonance imaging (MRI) has emerged as an important imaging modality for the assessment of tumor invasion and pelvic lymph node metastases [5]. However, the determination of lymph node metastatic status can be challenging because abnormal (cancerous) and normal lymph nodes often appear similar on MRI. As such, the sensitivity of imaging for lymph node metastasis in prostate cancer is low [6].
To address the aforementioned challenges, artificial intelligence (AI)-based research has gained special prominence in medical imaging diagnosis. Earlier efforts mainly focused on texture analysis as a means of feature extraction from medical images, which were in turn fed into machine learning models for classification. Texture analysis refers to the segregation of the different regions in an image, based on their physical characteristics or intensity distribution. There are different texture analysis algorithms, including but not limited to the gray-level co-occurrence matrix (GLCM), local binary patterns (LBPs), and Gabor filters. Researchers often utilize texture-based algorithms in combination with features that are representative of the imaging modality in question [7]. For example, ref. [8] designed a multifractal feature descriptor to classifying non-neoplastic tissues and tumors, as well as to grade hepatocellular carcinoma tissues into five stages. For both of those tasks, the proposed feature descriptor outperformed GLCM-, LBP-, and Gabor-based features. Ref. [9] utilized features from the GLCM, LBP, and Gabor filters to classify benign vs. malignant pulmonary nodules using the Support Vector Machine (SVM) classifier. The results showed a similar performance accuracy of 90.00% for all three of these features but a highest area under the curve (AUC) of 92.70% for GLCM. Ref. [10] made use of a dataset of 22 patients with glioblastoma to perform classification between true progression and pseudoprogression from T2-weighted MRI images. Five GLCM features, namely homogeneity, entropy, energy, correlation, and contrast were extracted, out of which correlation generated the best classification performance, with an accuracy of 86.40%, an AUC of 89.20%, an sensitivity of 75.00%, and a specificity of 100%. Ref. [11] reported a sensitivity of 98.00%, a specificity of 99.25%, an accuracy of 99.07%, and an AUC of 99.80% using GLCM and morphological features classified by the k-Nearest Neighbor–Cosine (KNN-Cosine) classifier, for the detection of carcinoma on prostate cancer patients. The GLCM features extracted were contrast, correlation, dissimilarity, energy, entropy, homogeneity, mean, variance, standard deviation, skewness, kurtosis, and root mean squared (RMS)—each extracted along four different orientations (0∘, 45∘, 90∘, and 135∘). The morphological features extracted include area, perimeter, maximum radius, minimum radius, Euler number, eccentricity, equivalent diameter, elongatedness, entropy, circularity 1, circularity 2, compactness, dispersion, standard deviation, and shape index. Ref. [12] made use of GLCM-based features (namely contrast, homogeneity, difference variance, dissimilarity, and inverse difference) for predicting the Gleason score (GS) of patients with prostate cancer from T2-weighted standard MRI images. Three GS groupings were defined based on range: G1 when GS≤6; G2 when GS=3+4; and G3 when GS≥4+3. The authors reported a prediction AUC of 78.40% for G1, 82.35% for G2, and 64.76% for G3. Ref. [13] made use of contrast and homogeneity GLCM features, in combination with mean, median, and 10th- and 25th-percentile values, to predict prostate cancer aggressiveness from T2-weighted (T2w) and Apparent Diffusion Coefficient (ADC) MRI scans of 45 patients before prostatectomy. Out of these 45 patients, 41 had one clinically significant tumor focus, and 4 had two clinically significant tumor foci. The study reported AUCs of 94.50% and 96.20%, for T2w and ADC, respectively. Ref. [14] utilized Gabor and LBP features to perform Gleason grading of 160 prostate cancer patients from H&E stained histological images into four Gleason categories, namely benign, grade 3, grade 4 and grade 5. The study reported a performance accuracy of 98.30% with the 10-fold KNN classifier. Ref. [15] made use of texture analysis for the detection of prostate cancer from MRI and Magnetic Resonance Spectroscopy (MRS) scans. Haar features were extracted from the MRS images, and Gabor features were extracted from the MRI scans. Subsequently, dimensionality reduction was performed, followed by classification using the Random Forest (RF) classifier, which provided an AUC of (89.00 ± 2.00)%. Ref. [16] utilized texture features to perform prostate cancer detection on digital biopsies. A total of 10 features were finally chosen using an AdaBoost ensemble method, from an initially extracted set of 900 first-order statistical, second-order statistical, and Gabor features, for the purpose of prostate cancer detection on 100 images extracted from 58 patients. The study reported AUCs of 84.00%, 83.00%, and 76.00% on low, medium, and high image resolutions, respectively. Ref. [17] performed feature extraction using first-order statistical features, second-order statistical, or co-occurrence matrix features and Gabor filter-based features and achieved a best sensitivity of 78% and a False Positive Rate (FPR) of 6% with the combined feature set for prostate cancer diagnosis.
While texture analysis-based investigation is still underway, it is worth noting that recent efforts in deep neural-network-based approaches have gained popularity among researchers owing to their automatic feature extraction capabilities from images. The most common type of deep neural network is the convolutional neural network (CNN), which has been used extensively in imaging, video and audio applications. CNNs have been studied for detection, classification and segmentation tasks in medical research. For example, ref. [18] utilized three different CNN architectures (Inception V3, Inception-ResNet V2 and ResNet-101) to detect axillary lymph node metastasis from primary breast cancer patients. Inception V3, the best-performing model, reported an area under curve (AUC) of 89.00%, a sensitivity of 85.00%, and a specificity of 73.00%. In comparison, the radiologists achieved 73.00% sensitivity and 63.00% specificity. Ref. [19] made use of eight different pre-trained CNN models to diagnose cervical lymph node metastasis from 995 axial CT scans of patients with thyroid cancer. Res-Net-50 was the best-performing model, with an AUC of 95.30% and an accuracy of 90.40%. Ref. [20] utilized ten CNN-based architectures on a small dataset of 218 patients to perform classification on lymph node metastasis. They reported a mean AUC of 68.00%, an accuracy of 61.00%, a sensitivity of 53.00%, and a specificity of 70.00%. The research reviewed above employed end-to-end neural-network-based approaches wherein training and validation were performed on the same dataset. A prevalent criticism against such approaches is that they require large datasets to achieve desirable performance. Researchers therefore increasingly employ the concept of transfer learning—that is, pre-training a deep model on a large imaging dataset (such as ImageNet), and subsequently fine-tuning the weights on smaller target data. Table 1 provides a summary of the proposed work, in comparison to similar works from the literature.
In this research, since the dataset is small (126 lymph node images), instead of taking an end-to-end deep learning approach, we decided to make use of transfer learning to address issues relating to overfitting. We utilized a CNN model, ResNet-18, which was pre-trained on ImageNet (a public dataset with a large volume of data) and then transferred to our prostate image dataset to extract features. The features were then utilized to perform classification between normal and metastatic lymph nodes. For the purpose of dimensionality reduction on our relatively small dataset, we developed a feature selection procedure comprising supervised and unsupervised feature selection techniques. The most important features obtained from the feature selection step were subsequently used for classification using the 10-fold decision tree (DT) classifier, which achieved an accuracy of 76.19%, a sensitivity of 79.76%, a specificity of 69.05%, a precision of 83.75%, and an F1-score of 81.71%. In addition, to investigate the efficacy of the features extracted from the pre-trained deep model over texture features, we implemented two texture analysis algorithms: GLCM and Gabor. Following the same workflow in terms of feature selection and classification, the DT classifier trained on GLCM features achieved its best classification accuracy of 61.90%, a sensitivity of 74.07%, a specificity of 42.86%, a precision of 71.43%, and an F1-score of 72.73%. The same DT, when trained on Gabor features, achieved a best classification accuracy of 65.08%, a sensitivity of 73.49%, a specificity of 52.50%, a precision of 76.25%, and an F1-score of 74.84%. These sets of experiments show that the proposed combined deep-learning-machine-learning architecture is promising for the automatic classification of normal vs. metastatic prostate cancer lymph nodes.
2. Materials and Methods
First, we performed feature extraction from the image regions of interest (ROIs) using a pre-trained ResNet-18 deep neural network (we also extracted texture-based features for the purpose of comparison). Next, we performed feature selection, which was particularly important because our dataset contains very few samples and is therefore prone to overfitting without proper feature selection. Subsequently, we performed classification between cancer and normal ROIs using statistical machine learning models.
Figure 1 provides a schematic representation of the overall workflow, starting from the raw images of lymph nodes to their automatic categorization as normal vs. metastatic. The methodology includes feature extraction, feature selection, and classification. In the proposed hybrid framework, we first extracted a 512-element feature vector per image from the average pooling layer of the ResNet-18 pre-trained model. For the purpose of comparison, we also extracted features using two texture analysis-based approaches, namely GLCM and Gabor. Next, we implemented our feature-selection algorithm on this feature vector. The decision tree (DT) classifier was finally trained on the selected features.
2.1. Feature Extraction
2.1.1. Texture Feature Extraction
Two texture algorithms for feature extraction were implemented: GLCM and Gabor. For GLCM, 11 features, namely contrast, correlation, energy, entropy, homogeneity, variance, sum of average, sum of variance, sum of entropy, difference of variance, and difference of entropy, were extracted from each of the ROI scans. The scalar distance was selected as s = 2, and the orientations were selected as θ=0∘,45∘,90∘, and 135∘ corresponding to 4 different GLCM matrices. Subsequently, the corresponding features from each orientation were averaged to generate the final 11 GLCM features. For Gabor, the feature extraction methodology was inspired by [22]—where a filter bank of 40 filters (five different scales, with eight orientations per scale) was utilized to extract features. A uniform input image size of 28 × 28 was used, resulting in a total of 28 × 28 × 40 = 31,360 features extracted per ROI scan. Subsequently—in order to remove the redundancy of features extracted [22,23,24]—we decided to downsample the features by a scale of four, thereby reducing the feature vector size to 31,360/(4 × 4) = 1960 features per scan.
2.1.2. ResNet-18 for Feature Extraction
A 71-layer ResNet-18 model pre-trained on the ImageNet dataset was used to extract features from the raw scans. Weights from the fifth and last pooling layer were extracted and used as weights for classification between malignant and non-malignant scans. Note that the weights obtained using this approach were concatenated to generate a 512-element feature vector corresponding to each lymph node scan. Figure 2 provides a pictorial representation of the ResNet-18 model that has been utilized in this work. The model comprised a total of 71 layers, out of which the trained weights from the “average pooling” layer were used for classification.
2.2. Feature Selection Mechanism
We employed an ensemble technique to select the most important features from the originally extracted feature vector—which was obtained using the pre-trained ResNet-18 model, GLCM, and Gabor filter banks, respectively, to perform the classification of normal vs. metastatic lymph nodes. The steps involved in the feature selection process are described in detail in Table 2. We employed the Random Forest algorithm, one of the most widely used algorithms in machine learning for the purpose of classification as well as feature selection.
2.3. Machine Learning Classification
The selected features obtained from the feature selection algorithm were fed into a machine learning classifier to differentiate between normal vs. metastatic lymph nodes. In order to investigate the classification performance of the proposed model, 10-fold cross-validation (CV) was utilized. The dataset was randomly shuffled and split evenly into 10 different groups. In each iteration, nine of these groups were used for training, and the remaining one was used for testing. The iteration process was repeated 10 times, and each time, the test set was a different group. The results obtained across the 10 iterations were finally averaged and reported. It must be noted that the groups were generated in such a manner so as to ensure that the training and test sets were mutually exclusive of one another (that is, no overlap of samples). The decision tree (DT) classifier was the only classifier that performed well in differentiating metastatic versus normal lymph nodes on the selected feature set, which makes sense because the feature selection itself was performed by Random Forest, which is a boosted decision tree algorithm.
The decision tree is a hierarchical, non-parametric, supervised learning algorithm that learns via a series of if–then–else decisions. Each node in a decision tree represents a “test” (also known as “question”) on a particular feature, which in the case of a binary decision tree, can be visualized as a coin toss. The edge entering a particular node represents the decision made in the previous step, and the edges leaving the node represent the “decisions” (formally known as “outcomes”) being made at the current node (in the case of a coin toss test, the two outcomes are heads and tails). Each node in a decision tree, via training, learns the optimal threshold of the feature associated with that particular node, which maximizes classification performance.
The training set contains a set of characteristics (also known as “features”—the same as the features described above)—and corresponding labels—often depicted as [X,y]=(x1,x2,x3,…,xF,…xn−1,xn)∈Rm×(n+1). These particular training data comprise a total of n features, xF depicting feature F, and a label associated with each training sample—the label set as a whole depicted as y∈Rm×1; the training set here has a total of m samples.
There are several techniques for computing impurity metrics that help decide the optimal split at each node, the two most commonly used being Gini impurity and Shannon’s entropy. In this work, we utilized the Gini index.
Machine learning classifiers try to improve performance by reducing impurity, in other words, maximizing information gain. Information gain is formally defined as follows:IGX,XF(X,f)=I(X)−I(X/f)
where, IGX,XF(X,f) is the information gain (also known as mutual information) of a random variable (r.v.) X resulting from an r.v. XF assuming the value of f; I(X) is the impurity measure of r.v. X obtained in the previous step, i.e., before r.v. XF assuming the value of f; I(X/f) is the conditional impurity measure of r.v. X given that the value of r.v. XF=f.
Here, r.v. X denotes the set of all features associated with the training set defined above, and f everywhere in the equation denotes feature XFinX assuming the value of f.
The decision tree algorithm tries to learn the optimal value of feature XF by learning XF=f such that I(X/f) is minimized, thereby maximizing information gain IGX,XF(X,f).
2.4. Evaluation Metrics
In order to evaluate the efficacy of the classification model, the following five metrics were used:(1)Accuracy=TP+TNTP+TN+FP+FN
(2)Sensitivity=TPTP+FN
(3)Specificity=TNTN+FP
(4)Precision=TPTP+FP
(5)F1=2×Precision×SensitivityPrecision+Sensitivity
where TP is the # of true positives, TN is the # of true negatives, FP is the # of false positives, and FN is the # of false negatives. In addition, the area under the curve (AUC) was used. For detailed formulations on AUC, interested readers are referred to [25].
2.1. Feature Extraction
2.1.1. Texture Feature Extraction
Two texture algorithms for feature extraction were implemented: GLCM and Gabor. For GLCM, 11 features, namely contrast, correlation, energy, entropy, homogeneity, variance, sum of average, sum of variance, sum of entropy, difference of variance, and difference of entropy, were extracted from each of the ROI scans. The scalar distance was selected as s = 2, and the orientations were selected as θ=0∘,45∘,90∘, and 135∘ corresponding to 4 different GLCM matrices. Subsequently, the corresponding features from each orientation were averaged to generate the final 11 GLCM features. For Gabor, the feature extraction methodology was inspired by [22]—where a filter bank of 40 filters (five different scales, with eight orientations per scale) was utilized to extract features. A uniform input image size of 28 × 28 was used, resulting in a total of 28 × 28 × 40 = 31,360 features extracted per ROI scan. Subsequently—in order to remove the redundancy of features extracted [22,23,24]—we decided to downsample the features by a scale of four, thereby reducing the feature vector size to 31,360/(4 × 4) = 1960 features per scan.
2.1.2. ResNet-18 for Feature Extraction
A 71-layer ResNet-18 model pre-trained on the ImageNet dataset was used to extract features from the raw scans. Weights from the fifth and last pooling layer were extracted and used as weights for classification between malignant and non-malignant scans. Note that the weights obtained using this approach were concatenated to generate a 512-element feature vector corresponding to each lymph node scan. Figure 2 provides a pictorial representation of the ResNet-18 model that has been utilized in this work. The model comprised a total of 71 layers, out of which the trained weights from the “average pooling” layer were used for classification.
2.1.1. Texture Feature Extraction
Two texture algorithms for feature extraction were implemented: GLCM and Gabor. For GLCM, 11 features, namely contrast, correlation, energy, entropy, homogeneity, variance, sum of average, sum of variance, sum of entropy, difference of variance, and difference of entropy, were extracted from each of the ROI scans. The scalar distance was selected as s = 2, and the orientations were selected as θ=0∘,45∘,90∘, and 135∘ corresponding to 4 different GLCM matrices. Subsequently, the corresponding features from each orientation were averaged to generate the final 11 GLCM features. For Gabor, the feature extraction methodology was inspired by [22]—where a filter bank of 40 filters (five different scales, with eight orientations per scale) was utilized to extract features. A uniform input image size of 28 × 28 was used, resulting in a total of 28 × 28 × 40 = 31,360 features extracted per ROI scan. Subsequently—in order to remove the redundancy of features extracted [22,23,24]—we decided to downsample the features by a scale of four, thereby reducing the feature vector size to 31,360/(4 × 4) = 1960 features per scan.
2.1.2. ResNet-18 for Feature Extraction
A 71-layer ResNet-18 model pre-trained on the ImageNet dataset was used to extract features from the raw scans. Weights from the fifth and last pooling layer were extracted and used as weights for classification between malignant and non-malignant scans. Note that the weights obtained using this approach were concatenated to generate a 512-element feature vector corresponding to each lymph node scan. Figure 2 provides a pictorial representation of the ResNet-18 model that has been utilized in this work. The model comprised a total of 71 layers, out of which the trained weights from the “average pooling” layer were used for classification.
2.2. Feature Selection Mechanism
We employed an ensemble technique to select the most important features from the originally extracted feature vector—which was obtained using the pre-trained ResNet-18 model, GLCM, and Gabor filter banks, respectively, to perform the classification of normal vs. metastatic lymph nodes. The steps involved in the feature selection process are described in detail in Table 2. We employed the Random Forest algorithm, one of the most widely used algorithms in machine learning for the purpose of classification as well as feature selection.
2.3. Machine Learning Classification
The selected features obtained from the feature selection algorithm were fed into a machine learning classifier to differentiate between normal vs. metastatic lymph nodes. In order to investigate the classification performance of the proposed model, 10-fold cross-validation (CV) was utilized. The dataset was randomly shuffled and split evenly into 10 different groups. In each iteration, nine of these groups were used for training, and the remaining one was used for testing. The iteration process was repeated 10 times, and each time, the test set was a different group. The results obtained across the 10 iterations were finally averaged and reported. It must be noted that the groups were generated in such a manner so as to ensure that the training and test sets were mutually exclusive of one another (that is, no overlap of samples). The decision tree (DT) classifier was the only classifier that performed well in differentiating metastatic versus normal lymph nodes on the selected feature set, which makes sense because the feature selection itself was performed by Random Forest, which is a boosted decision tree algorithm.
The decision tree is a hierarchical, non-parametric, supervised learning algorithm that learns via a series of if–then–else decisions. Each node in a decision tree represents a “test” (also known as “question”) on a particular feature, which in the case of a binary decision tree, can be visualized as a coin toss. The edge entering a particular node represents the decision made in the previous step, and the edges leaving the node represent the “decisions” (formally known as “outcomes”) being made at the current node (in the case of a coin toss test, the two outcomes are heads and tails). Each node in a decision tree, via training, learns the optimal threshold of the feature associated with that particular node, which maximizes classification performance.
The training set contains a set of characteristics (also known as “features”—the same as the features described above)—and corresponding labels—often depicted as [X,y]=(x1,x2,x3,…,xF,…xn−1,xn)∈Rm×(n+1). These particular training data comprise a total of n features, xF depicting feature F, and a label associated with each training sample—the label set as a whole depicted as y∈Rm×1; the training set here has a total of m samples.
There are several techniques for computing impurity metrics that help decide the optimal split at each node, the two most commonly used being Gini impurity and Shannon’s entropy. In this work, we utilized the Gini index.
Machine learning classifiers try to improve performance by reducing impurity, in other words, maximizing information gain. Information gain is formally defined as follows:IGX,XF(X,f)=I(X)−I(X/f)
where, IGX,XF(X,f) is the information gain (also known as mutual information) of a random variable (r.v.) X resulting from an r.v. XF assuming the value of f; I(X) is the impurity measure of r.v. X obtained in the previous step, i.e., before r.v. XF assuming the value of f; I(X/f) is the conditional impurity measure of r.v. X given that the value of r.v. XF=f.
Here, r.v. X denotes the set of all features associated with the training set defined above, and f everywhere in the equation denotes feature XFinX assuming the value of f.
The decision tree algorithm tries to learn the optimal value of feature XF by learning XF=f such that I(X/f) is minimized, thereby maximizing information gain IGX,XF(X,f).
2.4. Evaluation Metrics
In order to evaluate the efficacy of the classification model, the following five metrics were used:(1)Accuracy=TP+TNTP+TN+FP+FN
(2)Sensitivity=TPTP+FN
(3)Specificity=TNTN+FP
(4)Precision=TPTP+FP
(5)F1=2×Precision×SensitivityPrecision+Sensitivity
where TP is the # of true positives, TN is the # of true negatives, FP is the # of false positives, and FN is the # of false negatives. In addition, the area under the curve (AUC) was used. For detailed formulations on AUC, interested readers are referred to [25].
3. Software and Tools
MATLAB R2021b was used in order to extract features and subsequently perform machine-learning-based classification of the imaging data. (Five pre-trained models, namely AlexNet, GoogleNet, InceptionV3, ResNet50, and XceptionNet, all which are available as part of the Deep Learning Toolbox of Matlab, were used for neural-network-based classification. The built-in Statistics and Machine Learning Toolbox of Matlab was used to train four classifiers, namely decision tree, Linear Discriminant Analysis, Support Vector Machine, and Naive Bayes.) The feature selection framework and code for plot generation were programmed using Python (version 3.7). Data cleaning, pre-processing, and a majority of the image processing framework was developed using the same installation of Python. ImageJ (version 1.53) was used to visualize and analyze the grayscale images. BioRender [26] was used for the purpose of figure generation for the paper.
4. Dataset
A radiological dataset was provided by Mayo Clinic, Scottsdale, Arizona. The dataset comprised multiple de-identified gray-level MRI scans of lymph nodes, obtained using a varied range of diffusion-weighted image (DWI) contrast types from a prospective clinical trial of 15 high-risk (Gleason ≥ 8) prostate cancer patients. The patients underwent prostate MRI before prostatectomy and pelvic lymph node dissection as part of a trial. The tissue was submitted for pathologist review. The location of each lymph node was confirmed, and labels of the pre-operative MRI data were generated. The labels were “metastatic” (meaning harboring metastatic cancer cells) and “normal” (meaning no cancer metastases were found). As per the TNM cancer staging protocol (see Table 3 and Figure 3), for this particular project, we only focused on M1 (labeled “metastatic”) versus M0 (labeled “normal”) classification.
There were a total of 126 lymph node images: 41 metastatic and 85 normal. The four MRI sequences, each with different tissue contrast characteristics, were as follows: Apparent Diffusion Coefficient (ADC), Fast Recovery Fast Spin Echo (FRFSE), Pelvis (T2 FatSat) and MRI with Gadolinium Contrast (Water-GAD). Figure 4 snlws a prostate MRI from the same patient; four MRI sequences are shown. Table 4 provides an overview of the lymph node scans, distributed over the four aforementioned MRI sequences (ADC, FRFSE, Pelvis, Water-GAD) and two class labels (metastatic, normal).
The dataset comprises normal and metastatic samples at a ratio of approximately 2:1 (85 normal, 41 metastatic). The distribution of normal and metastatic samples in each of the four the MRI sequences is consistent with the overall data (see Figure 5).
4.1. Signal Weighting
An isotropic DWI can be obtained from an ADC image and an image with signal intensity of b=0 using the following formulation:(6)Sb=S0e−bD
where Sb is the diffusion-weighted image, S0 is the image with no diffusion weighting (b=0), b is the level of diffusion weighting that has been applied to the image signal, and D is the ADC image, also known as the diffusion coefficient signal.
4.1.1. ADC
ADC voxel values (also known as the diffusion coefficient) are calculated from a series of conventional DWI images and are meant to show a greater level of diffusion than DWIs by getting rid of the T2-weighting coefficient.
As can be derived from Equation (6), an ADC image can be obtained from an isotropic DWI image and an image with signal intensity of b=0 using the following formulation:(7)D=−1bloge(SbS0)
where the symbols Sb, S0, b and D are the same as defined in Equation (6).
4.1.2. FRFSE
FRFSE is a T2-weighted MRI contrast type that has consistently and significantly managed to reduce imaging time while maintaining T2 weighting since the Fast Spin Echo (FSE) and Turbo Spin Echo (TSE) techniques developed in the 1990s, both of which are T2W imaging techniques as well [27]. There are two main types of FRFSE image acquisition techniques: the breath-hold (BH) technique; and the respiratory triggering (RT) technique—also known as the non-breath-hold technique. The BH-FRFSE technique is especially popular in imaging and identifying liver lesions [27,28]. It also helps reduce image artifacts, owing to having a short imaging time [29]. Several studies have shown significantly better performances of BH techniques in comparison to RT techniques in general [30] and BH-FRFSE in particular when compared against primitive non-BH techniques such as the RT-FSE [29]. BH-FSE-based techniques have been shown to cut down on image acquisition time, reduce artifacts, improve lesion sizing, and capture better structural characteristics as compared to their traditional, Spin Echo (SE) counterparts.
4.1.3. T2 FatSat
The T2w Fat Saturation (T2 FatSat) technique helps detect a minimal presence of water in the pelvis. Fats often mimic high-signal-intensity fluids, thereby making it difficult to distinguish the two. The T2 FatSat protocol makes distinguishing fats from pelvic fluids, particularly those with high signal intensities, easier [31].
4.1.4. Water-GAD
Water-GAD is a T1-weighted MRI contrast type, imaged using Gadolinium metal as a contrast agent. T1w images bring out the differences in T1 relaxation times between different types of tissue. Fat shows up in the form of bright voxels, while water shows up in the form of dark voxels, in T1w images.
4.2. Image Preprocessing
Min–max normalization was performed on each image individually, in order to account for varying intensity ranges across the database. Additionally, gradient-descent-based optimization algorithms have not only been shown to converge significantly faster [32] on scaled data compared to unscaled data, but also perform better [33,34]. Since our proposed classification model extracts data using pre-trained neural networks, stochastic gradient descent is extensively used. Although we do not expect to see a conceivable difference in runtime complexity, owing to the small sample size of our data when scaled on bigger datasets, the difference is expected to be significant. Moreover, feature scaling has also been shown to aid in improving the speed, performance, and often both during the process of convergence in various statistical machine learning algorithms [33,34], for example, Linear Discriminant Analysis [35], kNN [33,36,37], SVM [38], and Naive Bayes [39]. In fact, decision trees (and their variants such as the Random Forest classifier, which is a boosted decision tree algorithm) are among the very few mainstream statistical learning-based algorithms that are completely independent of the variance in the training data and therefore fare no better in terms of performance or optimization time whatsoever with feature scaling [33].
Min–max normalization transforms features to fall in the range of [0,1], in this context, meaning the lowest pixel value in the transformed image is 0 and the highest pixel value is 1.
Each pixel p in the normalized image matrix X′ (denoted as Xp′), can be obtained from the corresponding pixel p in the original image matrix X (denoted as Xp) using the following formulation:(8)Xp′=Xp−min(X)max(X)−min(X)∀p∈[1,np]
where np is the total number of pixels in the original (and normalized) image, min(X) is the value of the pixel with the lowest intensity in the original image X, and max(X) is the value of the pixel with the highest intensity in the original image X.
We performed min–max normalization on each image in the dataset using scikit-learn’s sklearn.preprocessing.MinMaxScaler function, with the default parameter settings: feature_range=(0, 1), copy=True, and clip=False.
4.1. Signal Weighting
An isotropic DWI can be obtained from an ADC image and an image with signal intensity of b=0 using the following formulation:(6)Sb=S0e−bD
where Sb is the diffusion-weighted image, S0 is the image with no diffusion weighting (b=0), b is the level of diffusion weighting that has been applied to the image signal, and D is the ADC image, also known as the diffusion coefficient signal.
4.1.1. ADC
ADC voxel values (also known as the diffusion coefficient) are calculated from a series of conventional DWI images and are meant to show a greater level of diffusion than DWIs by getting rid of the T2-weighting coefficient.
As can be derived from Equation (6), an ADC image can be obtained from an isotropic DWI image and an image with signal intensity of b=0 using the following formulation:(7)D=−1bloge(SbS0)
where the symbols Sb, S0, b and D are the same as defined in Equation (6).
4.1.2. FRFSE
FRFSE is a T2-weighted MRI contrast type that has consistently and significantly managed to reduce imaging time while maintaining T2 weighting since the Fast Spin Echo (FSE) and Turbo Spin Echo (TSE) techniques developed in the 1990s, both of which are T2W imaging techniques as well [27]. There are two main types of FRFSE image acquisition techniques: the breath-hold (BH) technique; and the respiratory triggering (RT) technique—also known as the non-breath-hold technique. The BH-FRFSE technique is especially popular in imaging and identifying liver lesions [27,28]. It also helps reduce image artifacts, owing to having a short imaging time [29]. Several studies have shown significantly better performances of BH techniques in comparison to RT techniques in general [30] and BH-FRFSE in particular when compared against primitive non-BH techniques such as the RT-FSE [29]. BH-FSE-based techniques have been shown to cut down on image acquisition time, reduce artifacts, improve lesion sizing, and capture better structural characteristics as compared to their traditional, Spin Echo (SE) counterparts.
4.1.3. T2 FatSat
The T2w Fat Saturation (T2 FatSat) technique helps detect a minimal presence of water in the pelvis. Fats often mimic high-signal-intensity fluids, thereby making it difficult to distinguish the two. The T2 FatSat protocol makes distinguishing fats from pelvic fluids, particularly those with high signal intensities, easier [31].
4.1.4. Water-GAD
Water-GAD is a T1-weighted MRI contrast type, imaged using Gadolinium metal as a contrast agent. T1w images bring out the differences in T1 relaxation times between different types of tissue. Fat shows up in the form of bright voxels, while water shows up in the form of dark voxels, in T1w images.
4.1.1. ADC
ADC voxel values (also known as the diffusion coefficient) are calculated from a series of conventional DWI images and are meant to show a greater level of diffusion than DWIs by getting rid of the T2-weighting coefficient.
As can be derived from Equation (6), an ADC image can be obtained from an isotropic DWI image and an image with signal intensity of b=0 using the following formulation:(7)D=−1bloge(SbS0)
where the symbols Sb, S0, b and D are the same as defined in Equation (6).
4.1.2. FRFSE
FRFSE is a T2-weighted MRI contrast type that has consistently and significantly managed to reduce imaging time while maintaining T2 weighting since the Fast Spin Echo (FSE) and Turbo Spin Echo (TSE) techniques developed in the 1990s, both of which are T2W imaging techniques as well [27]. There are two main types of FRFSE image acquisition techniques: the breath-hold (BH) technique; and the respiratory triggering (RT) technique—also known as the non-breath-hold technique. The BH-FRFSE technique is especially popular in imaging and identifying liver lesions [27,28]. It also helps reduce image artifacts, owing to having a short imaging time [29]. Several studies have shown significantly better performances of BH techniques in comparison to RT techniques in general [30] and BH-FRFSE in particular when compared against primitive non-BH techniques such as the RT-FSE [29]. BH-FSE-based techniques have been shown to cut down on image acquisition time, reduce artifacts, improve lesion sizing, and capture better structural characteristics as compared to their traditional, Spin Echo (SE) counterparts.
4.1.3. T2 FatSat
The T2w Fat Saturation (T2 FatSat) technique helps detect a minimal presence of water in the pelvis. Fats often mimic high-signal-intensity fluids, thereby making it difficult to distinguish the two. The T2 FatSat protocol makes distinguishing fats from pelvic fluids, particularly those with high signal intensities, easier [31].
4.1.4. Water-GAD
Water-GAD is a T1-weighted MRI contrast type, imaged using Gadolinium metal as a contrast agent. T1w images bring out the differences in T1 relaxation times between different types of tissue. Fat shows up in the form of bright voxels, while water shows up in the form of dark voxels, in T1w images.
4.2. Image Preprocessing
Min–max normalization was performed on each image individually, in order to account for varying intensity ranges across the database. Additionally, gradient-descent-based optimization algorithms have not only been shown to converge significantly faster [32] on scaled data compared to unscaled data, but also perform better [33,34]. Since our proposed classification model extracts data using pre-trained neural networks, stochastic gradient descent is extensively used. Although we do not expect to see a conceivable difference in runtime complexity, owing to the small sample size of our data when scaled on bigger datasets, the difference is expected to be significant. Moreover, feature scaling has also been shown to aid in improving the speed, performance, and often both during the process of convergence in various statistical machine learning algorithms [33,34], for example, Linear Discriminant Analysis [35], kNN [33,36,37], SVM [38], and Naive Bayes [39]. In fact, decision trees (and their variants such as the Random Forest classifier, which is a boosted decision tree algorithm) are among the very few mainstream statistical learning-based algorithms that are completely independent of the variance in the training data and therefore fare no better in terms of performance or optimization time whatsoever with feature scaling [33].
Min–max normalization transforms features to fall in the range of [0,1], in this context, meaning the lowest pixel value in the transformed image is 0 and the highest pixel value is 1.
Each pixel p in the normalized image matrix X′ (denoted as Xp′), can be obtained from the corresponding pixel p in the original image matrix X (denoted as Xp) using the following formulation:(8)Xp′=Xp−min(X)max(X)−min(X)∀p∈[1,np]
where np is the total number of pixels in the original (and normalized) image, min(X) is the value of the pixel with the lowest intensity in the original image X, and max(X) is the value of the pixel with the highest intensity in the original image X.
We performed min–max normalization on each image in the dataset using scikit-learn’s sklearn.preprocessing.MinMaxScaler function, with the default parameter settings: feature_range=(0, 1), copy=True, and clip=False.
5. Results
Using the pre-trained ResNet-18 model, 512 features were originally extracted, 9 of which were selected using our aforementioned feature selection mechanism. For GLCM, 11 features were originally extracted, out of which 5 features were finally selected. For Gabor, 1960 features were first extracted, 15 of which were ultimately selected. Table 5 summarizes the classification performance of the DT classifier on the original and selected features from ResNet-18, GLCM, and Gabor. Using the selected features, features selected from the ResNet-18 features outperform both GLCM and Gabor. Specifically, using DT as a classifier, (1) Resnet-18 reported the highest classification accuracy of 76.19%, a sensitivity of 79.76%, a specificity of 69.05%, a precision of 83.75%, and an F1-score of 81.71%; (2) GLCM achieved an accuracy of 61.90%, a sensitivity of 74.07%, a specificity of 42.86%, a precision of 71.43%, and an F1-score of 72.73%; (3) Gabor achieved an accuracy of 65.08%, a sensitivity of 73.49%, a specificity of 52.50%, a precision of 76.25%, and an F1-score of 74.84%. In addition, feature selection helped significantly improve performance compared to classification on original feature sets. It was observed that (1) for ResNet-18, the improvements in accuracy, sensitivity, specificity, precision, and F1-score were 19.05%, 17.41%, 22.71%, 13.08%, and 15.46%, respectively; (2) for GLCM, the improvements in accuracy, sensitivity, specificity, precision, and F1-score were 3.96%, 1.23%, 9.53%, 3.61%, and 2.49%, respectively; (3) for Gabor wavelet-based features, the improvements in accuracy, sensitivity, specificity, precision, and F1-score were 5.56%, 6.02%, 5.00%, 3.52%, and 4.84%, respectively.
Table 5 provides the AUC values of each of the three sets of features before and after feature selection using the 10-fold DT classifier. The area under the curve (AUC) of the six experiments further exhibit the efficacy of the feature selection algorithm over using the original feature set for classification. Specifically, (1) the 512 original ResNet-18-based features, which provided unstable and varying AUC values across implementations, after feature selection achieved a 94.59% on the top 9 features; (2) GLCM shows a slight improvement from an AUC of 92.11% on the 11 original features to an AUC of 95.12% on the 5 selected features; (3) Gabor feature performances remain comparable between the original feature set of 1960 features, which had an AUC of 99.20%, and the selected feature set of 15 features, which had an AUC of 98.98%.
6. Discussion and Conclusions
The results presented in this research show that the features extracted by the pre-trained ResNet-18 model outperform both the GLCM and the Gabor filter features after feature selection. Although it is not entirely clear to us why the original ResNet-18-based features, without feature selection, achieved unstable and variable AUC values across implementations, we contend that since we have only 126 samples and 512 original features (a feature-to-sample ratio of 4:1), the model might be overfitting the training set and struggling to attain generalizability on the validation set. This problem, however, is countered by utilizing the feature selection mechanism, which, when applied to the ResNet-18 model achieves, the highest AUC of 94.59% and an F1-score of 81.71% on the top nine deep features after classification using the 10-fold DT classifier. It is also observed that all three feature extractors yield a higher accuracy, sensitivity, specificity, precision, and F1-score after feature selection than on the original set of features—thereby exhibiting the efficacy of the feature selection algorithm in general and the Random Forest feature importance calculator in particular.
The AUC of 94.59% obtained by our approach had a higher value compared to those reported in some of the prior studies relying on imaging alone. For example, [6] states that the AUC varies between 69.00% and 81.00% for prostate cancer detection over multi-parametric MRIs, which includes diffusion-weighted imaging (DWI). The authors of [40] designed an automatic deep CNN-based architecture to detect prostate cancer on diffusion-weighted magnetic resonance imaging (DWI). The database comprised DWI images of 427 patients (175 prostate cancer and 252 healthy patients). The model yielded an AUC of 87.00%. The authors of [41], the runners up of the 2017 PROSTATEx challenge, designed a deep learning model called the XmasNet, which was based on deep CNNs, for the purpose of performing classification on prostate cancer lesions utilizing 3D multiparametric MRIs. They achieved an AUC of 84.00%. The database comprised 341 patients, each having Diffusion Weighted Images (DWI), Apparent Diffusion Coefficient (ADC) scans, Ktrans, and T2w images. The authors of [42] designed a radiomics signature by selecting 9 out of 150 manually extracted GLCM and gray-level histogram features from the lymph node CT scans of 118 patients. A radiomic nomogram was subsequently generated using the logistic regression model, which achieved an AUC of 89.86%. The authors of [43] utilized a total of 103 T2-weighted MRIs to perform the classification of lymph node metastasis on bladder cancer patients. A total of 718 features—comprising textural features, shape-based features, wavelet features, and first-order statistics features—were manually extracted from the bladder scans, and a nomogram was generated using logistic regression. Feature selection was performed, which reduced the number of important features from 718 to 9. Classification was subsequently performed on these 9 important features, generating a validation AUC of 84.47%. The sensitivity of 79.76%, a specificity of 69.05%, accuracy of 76.19%, and an AUC of 94.59% obtained by our approach performed significantly better than the sensitivity of 53.00%, the specificity of 70.00%, theaccuracy of 61.00%, and the mean AUC of 68.00% reported in [20], which utilized ten CNN-based architectures on a small dataset of 218 patients to perform the classification of lymph node metastasis.
In medical imaging research, one of the primary challenges is that interpretability varies greatly between radiologists. The PI-RADS architecture is pervasively utilized for the purpose of image interpretation. However, ref. [44] exhibits many impending issues of inter-observer interpretability associated with the PI-RADS model. Owing to the innate difficulties associated with identifying anomalies in prostate MRIs, there seems to be varying consensus among researchers and radiologists alike, with respect to determining the best identification methodologies. Sample size—as in the case of our study as well—often proves to be an important factor that determines the selection of the classification model. While we know that machine learning performs better on datasets with limited samples, we also acknowledge the capability of deep models to extract more meaningful features. In [45], the authors demonstrated that on a dataset of multiparametric MRIs obtained from 52 prostate cancer patients, hierarchical clustering performed better than deep models in differentiating between normal and tumor prostate tissues.
While the statistical measures that we have employed to evaluate our model seem to work well, we acknowledge that there might be more medically sound metrics for measuring performance, which, to a certain degree, predict the chances of survival as well. In order to evaluate such parameters, we need to have an in-depth understanding of associated biomedical processes, as well as access to a wide range of radiological features. The authors of [46] have identified certain measures and classifiers that outperform others when it comes to prostate cancer with lymph node metastasis. A sample size of 1400 patients—all with metastatic prostate cancer of the lymph node—was employed in this study. Univariate analysis revealed that age, Gleason score, radiotherapy history, T stage, log odds of metastatic lymph node (LODDS) classifier, lymph node ratio (LNR) classifier, and number of metastatic lymph node (NMLN) classifier except for the total number of lymph nodes examined (TNLE) were some of the most consequential predictors of patient survival. Multivariate analysis suggested that LODDS, LNR, and NMLN except the TNLE classifiers were some of the most important parameters for measuring survival rate.
7. Future Work
As with most medical imaging data, our dataset also suffered from a significant class-wise imbalance of samples. The negative class contained 84 ROIs, which was twice that of the positive class (42 ROIs). Therefore, the training process was heavily biased in favor of the negative class. The problem of imbalanced classes was addressed by employing the Synthetic Minority Oversampling Technique (SMOTE), which is used to generate synthetic instances of the class with fewer samples. However, in this case, the artificially generated samples further confused the training model, thereby yielding even poorer results. Simple physical transformations of the ROIs such as rotation, flipping, et cetera were also applied to increase the sample size of the normal class but without any improvements in classification performance. In the future, we intend to extend this work by performing neural-network-based ROI augmentation, which includes the application of generative adversarial networks (GANs).
In addition to performing the classification of lymph node metastasis in prostate cancer patients, we also intend to extend our analysis to subsequently be able to localize the lesion region in lymph node images where cancer has been detected. From previous experience working on similar medical imaging datasets, we have noticed that having an additional set of “difference features” helps in not only localizing the lesion region but also monitoring the progression of lesion over time. This involves subtracting time-variant images from fixed baseline data. In this case, since we have a control image of an unaffected portion of the lymph node provided, corresponding to every cancerous region of the lymph node, we could use the control as a baseline image to then perform localization. However, for that to work effectively, we need more positively labeled (or cancerous) samples. Currently, we have too few lesion lymph node images to train a full-fledged model to perform localization. We are in the process of acquiring more samples to enable us to explore the use of the classifier in cascade with the localization framework, which is a future effort. |
Title: Study of long-term effects of pelvic radiotherapy on the function of bone marrow in recurrent cervical cancer patients | Body:
Introduction
Cervical cancer is the fourth most common cancer in incidence and mortality in women worldwide with about 13,960 new cases diagnosed and 4,310 cases dead from cervical cancer in United state in 2023 1. Several risk factors, such as human sustainable papillomavirus (HPV) infection, smoking, active sexual history, and long-term use of oral contraceptives, increase the chance of developing cervical cancer 2. With the cancer progression, cervical cancer commonly causes local spread (pelvic, vaginal), lymph node metastasis and other distant metastasis (lung, bone, brain) 3, 4. Surgery and radiotherapy are the main two therapeutic strategies for treating cervical cancer and chemotherapy is employed as alternative adjuvant therapy according to the risk factors individually. For early stages of cervical cancer, either surgery or radiation combined with chemotherapy lead to a well prognosis with 60-70% five-year survival. For later stages, radiotherapy combined with chemotherapy is the most recommended treatment strategy with a relatively poor prognosis compared to patients with early stages 5. The patients with cervical cancer recurrence usually exhibit poor prognosis and high mortality rates and nearly 30% patients with invasive carcinoma die owing to recurrence or metastasis 6, 7. Notably, due to the lack of effective treatment strategies, the treatment of recurrent cervical cancer patients still faces significant clinical challenges. In addition to surgery, radiotherapy and chemotherapy are usually used in the treatment of recurrent cervical cancer patients, attention needs to be paid to whether these patients are more prone to bone marrow suppression 5, 8.
Radiotherapy is the most-effective cytotoxic therapy in treating patients with cervical cancer 9. Concurrent chemoradiotherapy is the standard strategy for cervical cancer patients with stage IB3 to IVA disease 10. Radiotherapy for patients with cervical cancer includes external beam radiation therapy (EBRT) and brachytherapy, and the dose of radiation for EBRT and brachytherapy for patients with cervical cancer are commonly 40-45 Gary (Gy) and 30-40 Gy, respectively, according to the National Comprehensive Cancer Network (NCCN) guidelines. EBRT is considered to have certain hematologic toxicity (HT) for cancer patients since the approximately 50% of active bone marrow is located in pelvic and lower spine bones and active bone marrow is the most sensitive tissue to ionizing radiation, thereby increasing the risk of bone marrow suppression especially when combined with chemotherapy with inhibitory effects on bone marrow function 11-14. Furthermore, the range of irradiation field and dose of irradiation may need to be expanded individually when lymph node metastasis is suspected based on radiographic evidence or pathological confirmed. The latest guideline for delineation of clinical target volume (CTV) for pelvic radiotherapy in patients with cervical cancer was updated by the Radiation Therapy Oncology Group (RTOG) in 2021 and highlighted that the para-aortic nodal CTV should include the lymph node groups at risk adjacent to the aorta and inferior vena cava (IVC): the paracaval, precaval, retrocaval, deep and superficial intercavo-aortic, para-aortic, preaortic, and retro-aortic nodes 15, 16. Given that irradiated areas have damage to most of tissues (from the skin through to the bone marrow) and the high sensitivity of the hematopoietic system to irradiation, bone marrow failure with neutropenia, anemia and thrombocytopenia were commonly occurred, which finally contribute to increased risk of lethal hemorrhage or infection and the interruption of chemotherapy administration or treatment failure 17, 18.
The pathogenesis of HT after irradiation is complex which remains poorly understood. Treating rats (right distal femur and proximal tibia) with irradiation at a dose of 20 Gy induced bone loss of non-irradiated bone and increased marrow adiposity at 12 weeks post-irradiation. Importantly, expression of runt-related transcription factor-2 by bone mesenchymal stem cells (BMSCs) decreased after irradiation by 88.0 % (P < 0.01) at the contralateral and 82.3 % (P < 0.01) at the irradiation site 2 weeks post-irradiation and decreased by 94.5 % (P < 0.001) at the contralateral and 44.1 % (P < 0.05) at the irradiation site 12 weeks post-irradiation, indicating that radiation-induced bone complications were partly BMSC-mediated and localized irradiation may trigger remote changes in bone which called indirect effects 19. After irradiation with a single dose of X-rays to the left hind limbs of rats, isolated BMSCs from direct and indirect irradiated bone tissue exhibited bigger cell bodies and increased granules compared to control group and the proliferation of BMMSCs decreased both in the direct irradiated and non‑irradiated bone tissue, which may be the mechanism of radiation-induced abscopal impairment to the skeleton in the cancer radiotherapy-induced bone loss 20. Increased expression of peroxisome proliferator-activated receptor gamma (PPARγ) and decreased expression of runt-related transcription factor 2 (RUNX2), accompanied by upregulated adipogenesis and downregulated osteogenesis of BMSCs as well as increased B cells and CD8+T lymphocytes in the blood were also observed at 12 weeks post-irradiation in four-month-old male Sprague-Dawley rats. These results suggested the multifaceted effects of irradiation on the blood and immune systems 21. Additionally, it has been well-discussed in a systematic review that the dose of irradiation was also identified as an influence factor of HT that needs further studied among patients with different types of cancer 22. Though it has been confirmed that prior radiotherapy does harm to bone marrow hematopoietic function of cancer patients, to date no effective drug has been reported that can help prevent bone marrow failure 23. Meanwhile, it is not yet clear whether the hematopoietic system damage induced by irradiation affect the long-term bone marrow function for cancer patients 22. Given the increasing emphasis on the quality of life of cancer patients, reducing the short-term and long-term side effects followed by radiotherapy become an urgent issue that need to be solved clinically.
To investigate and assess the effects of prior pelvic radiotherapy on long-term bone marrow function and potential risk factors of bone marrow suppression during chemotherapy in recurrent cervical cancer patients, this current study retrospectively analyzed 129 recurrent cervical cancer cases with and without prior radiotherapy, collected general clinical information, radiotherapy methods, grade bone marrow suppression of all patients and compared the rate of bone marrow suppression between the two groups by using univariable and multivariable analysis, with the hope of elucidating the risk factors of bone marrow suppression followed by chemotherapy in recurrent cervical cancer patients and trying to provide clinical evidence regarding the long-term possible effects of pelvic radiotherapy.
Methods and materials
Research objects
A total of 129 cases of recurrent cervical cancer that accepted primary treatment in our hospital from 2010 to 2021 were enrolled in this study, including 77 cases (59.7%) with the pelvic radiotherapy history and 52 patients (40.3%) without pelvic radiotherapy history, serving as the control group. The patients were staged according to the FIGO staging of cervical cancer in 2018 24. General information, including age, body mass index (BMI), disease free survival (DFS), FIGO stage, pathological type, treatment protocols, types of the relapse, interruption of chemotherapy due to severe bone marrow suppression, usage of bevacizumab, results of blood routine test during radiotherapy, before chemotherapy and in the period of chemotherapy after relapse were collected. Hematologic toxicity was defined by using the Common Terminology Criteria for Adverse Events (version 4.0) 25. The inclusion standards were as follows: (1) patients with cervical cancer treated in our hospital, (2) patients who chose the paclitaxel in the combination of carboplatin regimen for supplementary chemotherapy after relapse, (3) patients who formulate and finish prior radiotherapy in our hospital, (4) patients with regular follow-up. Exclusion criteria were as follows: (1) patients who didn't finish the whole treatment of supplementary chemotherapy due to various reasons, including chemotherapy allergy or serious side effects, (2) patients without regular follow-up and blood routine test in our hospital, (3) patients didn't use paclitaxel combined with carboplatin as chemotherapy regimen or change chemotherapy regimen since insensitivity, (4) patients with severe medical or surgical complications.
Radiotherapy
The prior radiotherapy plan was formulated and operated in the department of radiotherapy clinic in our hospital. Principles of radiation therapy are based on the NCCN guidelines for cervical cancer. The volume of EBRT covers the gross disease (if present), parametria, uterosacral ligaments, sufficient vaginal margin from the gross disease (at least 3 cm), presacral nodes, and other nodal volumes at risk. For patients with negative nodes on surgical or radiologic imaging, the radiation volume includes the entirety of the external iliac, internal iliac, obturator, and presacral nodal basins. For patients deemed at higher risk of lymph node involvement (eg, bulkier tumors; suspected or confirmed nodes confined to the low true pelvis), the radiation volume should be increased to cover the common iliacs as well. In patients with documented common iliac and/or para-aortic nodal involvement, extended-field pelvic and para-aortic radiotherapy is recommended, up to the level of the renal vessels (or even more cephalad as directed by involved nodal distribution). For patients with lower 1/3 vaginal involvement, the bilateral groins should be covered as well. EBRT is directed to the pelvis with or without para-aortic region and majority of patients receive concurrent platinum-containing chemotherapy during the time of EBRT. Brachytherapy is performed by using an intracavitary or an interstitial approach serving as an irreplaceable part of definitive radiotherapy. According to NCCN guidelines, in patients with an intact cervix, the dose of definitive EBRT is approximately 45 Gy (40-50 Gy) to treat primary tumor and regional lymphatics at risk and 192Ir-brachytherapy is used to boost primary cervical tumor with an additional 30 to 40 Gy. In highly selected, very early diseases (ie, stage IA2), brachytherapy alone (without EBRT) may be an option, especially for patients with positive vaginal margins. Extended-filed radiotherapy was applied when metastasis of para-aortic lymph node was confirmed. Patients who experienced EBRT underwent concurrent platinum-containing chemotherapy (cisplatin 40 mg per 7 days, 4-6 times) to increase the sensitivity of radiotherapy. The detailed plan of radiotherapy was presented in Figure 1.
Determination of radiation field: The patient drank water one hour before positioning CT scan until the bladder was filled and was placed in a supine position with both hands holding their heads. The position was fixed with a vacuum pad and a thermoplastic mold, and enhanced CT scan was used for positioning with a slice spacing of 5mm. After scanning, transfer the image data to the radiotherapy planning system for target area delineation 26, 27.
Definition of target area for curative radiotherapy: Gross tumor volume (GTV) is the primary tumor based on enhanced MRI/CT or PET-CT imaging; GTVnd: Metastatic lymph nodes in the pelvic cavity or adjacent to the abdominal aorta as indicated by imaging examination; CTV1: including GTV + uterine body + cervix; CTV2: including parametrial/vaginal tissue, bilateral adnexa, and proximal vagina (if the vagina is not invaded, including 1/2 of the upper segment; if the upper segment of the vagina is invaded, including 2/3 of the upper segment; if the vagina is extensively invaded, including the entire vagina); CTV3: including the iliac, iliac, iliac, sacral, and obturator lymph drainage areas and patients with clear or high-risk lymph node metastasis in the paraaortic region should include the paraaortic lymph drainage area 26, 27.
Chemotherapy
For chemotherapy, if surgical patients have high-risk factors (such as parametrial invasion, positive lymph nodes, positive surgical margins, and adverse pathological types) after primary surgery, sequential radiotherapy and chemotherapy (4-6 times of platinum containing chemotherapy) will be supplemented. When recurrence was confirmed, all of the patients underwent a chemotherapy regimen of paclitaxel combined with carboplatin, with a carboplatin dose of area under the curve (AUC) 5 to AUC6 and a paclitaxel dose of (135-175) mg / m2 * body surface area (m2) per 21 days for 5 to 6 times totally. Patients who underwent combination treatment with bevacizumab received simultaneously administering bevacizumab on the day of chemotherapy. The dose of bevacizumab was 7.5mg/kg*body weight (kg). The decision on the dosage of chemotherapy drugs and bevacizumab was made by two attending doctors with at least 5 years of experience in the treatment of gynecological oncology in our hospital.
Blood parameter analysis
Hematological toxicity refers to the decrease in blood cell count caused by radiation induced decrease in bone marrow hematopoietic function. According to the World Health Organization's classification criteria for acute and subacute hematological toxic reactions to the treatment, we assessed the bone marrow function of patients by doing routine blood test weekly. Given that hematological toxicities decrease production of red blood cells (anemia), production of white blood cells (neutropenia or granulocytopenia), and production of platelets (thrombocytopenia), we collected the information of hemoglobin (HGB), White Cell Count (WCC), Absolute Neutral Count (ANC), and Platelets (PLT) of each patient and the lowest blood cell count for each patient during radiotherapy was recorded and used to analyze further (Table 1) 11, 28.
HGB, WCC, ANC and PLT were determined from blood samples collected at baseline and weekly before and in the period of entire chemotherapy. Maximum toxicity grading and the type of HT during previous radiotherapy and chemotherapy at relapse were noted for each patient.
Follow-up checkups
The follow-up was scheduled for 3 to 6 months within the first three years after surgery or radical radiotherapy and then every 6 months for the next two years, and one for every year after 5 years. Physical examination, gynecologic examination, thinprep cytologic test (TCT), imaging examination (X-ray and pelvic ultrasound), computed tomography (CT) or magnetic resonance imaging (MRI) are selected to assess disease condition for every follow-up. All cases were under regular follow-up. Disease free survival of patients was also collected, which refers to the time from disease-free survival after surgery to the occurrence of disease recurrence or metastasis.
Statistical Analysis
SPSS 26.0 was used for data analysis. The measurement data of normal distribution were expressed as the mean ± standard deviation. Descriptive statistics were performed on all variables. Comparisons between the two groups were performed with the t test, analysis of variance (ANOVA) and χ2-test, as appropriate. A logistic regression model was used for multivariable analysis. GraphPad Prism 8 (La Jolla, CA, USA) was employed to present all graphs. P values of < 0.05 were considered to have significant group differences.
Research objects
A total of 129 cases of recurrent cervical cancer that accepted primary treatment in our hospital from 2010 to 2021 were enrolled in this study, including 77 cases (59.7%) with the pelvic radiotherapy history and 52 patients (40.3%) without pelvic radiotherapy history, serving as the control group. The patients were staged according to the FIGO staging of cervical cancer in 2018 24. General information, including age, body mass index (BMI), disease free survival (DFS), FIGO stage, pathological type, treatment protocols, types of the relapse, interruption of chemotherapy due to severe bone marrow suppression, usage of bevacizumab, results of blood routine test during radiotherapy, before chemotherapy and in the period of chemotherapy after relapse were collected. Hematologic toxicity was defined by using the Common Terminology Criteria for Adverse Events (version 4.0) 25. The inclusion standards were as follows: (1) patients with cervical cancer treated in our hospital, (2) patients who chose the paclitaxel in the combination of carboplatin regimen for supplementary chemotherapy after relapse, (3) patients who formulate and finish prior radiotherapy in our hospital, (4) patients with regular follow-up. Exclusion criteria were as follows: (1) patients who didn't finish the whole treatment of supplementary chemotherapy due to various reasons, including chemotherapy allergy or serious side effects, (2) patients without regular follow-up and blood routine test in our hospital, (3) patients didn't use paclitaxel combined with carboplatin as chemotherapy regimen or change chemotherapy regimen since insensitivity, (4) patients with severe medical or surgical complications.
Radiotherapy
The prior radiotherapy plan was formulated and operated in the department of radiotherapy clinic in our hospital. Principles of radiation therapy are based on the NCCN guidelines for cervical cancer. The volume of EBRT covers the gross disease (if present), parametria, uterosacral ligaments, sufficient vaginal margin from the gross disease (at least 3 cm), presacral nodes, and other nodal volumes at risk. For patients with negative nodes on surgical or radiologic imaging, the radiation volume includes the entirety of the external iliac, internal iliac, obturator, and presacral nodal basins. For patients deemed at higher risk of lymph node involvement (eg, bulkier tumors; suspected or confirmed nodes confined to the low true pelvis), the radiation volume should be increased to cover the common iliacs as well. In patients with documented common iliac and/or para-aortic nodal involvement, extended-field pelvic and para-aortic radiotherapy is recommended, up to the level of the renal vessels (or even more cephalad as directed by involved nodal distribution). For patients with lower 1/3 vaginal involvement, the bilateral groins should be covered as well. EBRT is directed to the pelvis with or without para-aortic region and majority of patients receive concurrent platinum-containing chemotherapy during the time of EBRT. Brachytherapy is performed by using an intracavitary or an interstitial approach serving as an irreplaceable part of definitive radiotherapy. According to NCCN guidelines, in patients with an intact cervix, the dose of definitive EBRT is approximately 45 Gy (40-50 Gy) to treat primary tumor and regional lymphatics at risk and 192Ir-brachytherapy is used to boost primary cervical tumor with an additional 30 to 40 Gy. In highly selected, very early diseases (ie, stage IA2), brachytherapy alone (without EBRT) may be an option, especially for patients with positive vaginal margins. Extended-filed radiotherapy was applied when metastasis of para-aortic lymph node was confirmed. Patients who experienced EBRT underwent concurrent platinum-containing chemotherapy (cisplatin 40 mg per 7 days, 4-6 times) to increase the sensitivity of radiotherapy. The detailed plan of radiotherapy was presented in Figure 1.
Determination of radiation field: The patient drank water one hour before positioning CT scan until the bladder was filled and was placed in a supine position with both hands holding their heads. The position was fixed with a vacuum pad and a thermoplastic mold, and enhanced CT scan was used for positioning with a slice spacing of 5mm. After scanning, transfer the image data to the radiotherapy planning system for target area delineation 26, 27.
Definition of target area for curative radiotherapy: Gross tumor volume (GTV) is the primary tumor based on enhanced MRI/CT or PET-CT imaging; GTVnd: Metastatic lymph nodes in the pelvic cavity or adjacent to the abdominal aorta as indicated by imaging examination; CTV1: including GTV + uterine body + cervix; CTV2: including parametrial/vaginal tissue, bilateral adnexa, and proximal vagina (if the vagina is not invaded, including 1/2 of the upper segment; if the upper segment of the vagina is invaded, including 2/3 of the upper segment; if the vagina is extensively invaded, including the entire vagina); CTV3: including the iliac, iliac, iliac, sacral, and obturator lymph drainage areas and patients with clear or high-risk lymph node metastasis in the paraaortic region should include the paraaortic lymph drainage area 26, 27.
Chemotherapy
For chemotherapy, if surgical patients have high-risk factors (such as parametrial invasion, positive lymph nodes, positive surgical margins, and adverse pathological types) after primary surgery, sequential radiotherapy and chemotherapy (4-6 times of platinum containing chemotherapy) will be supplemented. When recurrence was confirmed, all of the patients underwent a chemotherapy regimen of paclitaxel combined with carboplatin, with a carboplatin dose of area under the curve (AUC) 5 to AUC6 and a paclitaxel dose of (135-175) mg / m2 * body surface area (m2) per 21 days for 5 to 6 times totally. Patients who underwent combination treatment with bevacizumab received simultaneously administering bevacizumab on the day of chemotherapy. The dose of bevacizumab was 7.5mg/kg*body weight (kg). The decision on the dosage of chemotherapy drugs and bevacizumab was made by two attending doctors with at least 5 years of experience in the treatment of gynecological oncology in our hospital.
Blood parameter analysis
Hematological toxicity refers to the decrease in blood cell count caused by radiation induced decrease in bone marrow hematopoietic function. According to the World Health Organization's classification criteria for acute and subacute hematological toxic reactions to the treatment, we assessed the bone marrow function of patients by doing routine blood test weekly. Given that hematological toxicities decrease production of red blood cells (anemia), production of white blood cells (neutropenia or granulocytopenia), and production of platelets (thrombocytopenia), we collected the information of hemoglobin (HGB), White Cell Count (WCC), Absolute Neutral Count (ANC), and Platelets (PLT) of each patient and the lowest blood cell count for each patient during radiotherapy was recorded and used to analyze further (Table 1) 11, 28.
HGB, WCC, ANC and PLT were determined from blood samples collected at baseline and weekly before and in the period of entire chemotherapy. Maximum toxicity grading and the type of HT during previous radiotherapy and chemotherapy at relapse were noted for each patient.
Follow-up checkups
The follow-up was scheduled for 3 to 6 months within the first three years after surgery or radical radiotherapy and then every 6 months for the next two years, and one for every year after 5 years. Physical examination, gynecologic examination, thinprep cytologic test (TCT), imaging examination (X-ray and pelvic ultrasound), computed tomography (CT) or magnetic resonance imaging (MRI) are selected to assess disease condition for every follow-up. All cases were under regular follow-up. Disease free survival of patients was also collected, which refers to the time from disease-free survival after surgery to the occurrence of disease recurrence or metastasis.
Statistical Analysis
SPSS 26.0 was used for data analysis. The measurement data of normal distribution were expressed as the mean ± standard deviation. Descriptive statistics were performed on all variables. Comparisons between the two groups were performed with the t test, analysis of variance (ANOVA) and χ2-test, as appropriate. A logistic regression model was used for multivariable analysis. GraphPad Prism 8 (La Jolla, CA, USA) was employed to present all graphs. P values of < 0.05 were considered to have significant group differences.
Results
General clinical information of recurrent cervical cancer patients
A total of 129 patients with recurrent cervical cancer were included in this study. All of these patients were primarily diagnosed and treated in our hospital with 77 cases had radiotherapy history at the point of recurrence, whereas 52 cases didn't undergo prior radiotherapy were used as control group. The age, BMI, DFS, FIGO stages, pathological types, radiotherapy methods, bone marrow suppression during previous radiotherapy, extended-field radiotherapy, relapse site, completion of chemotherapy after relapse, usage of bevacizumab was collected. The general clinical information of the two groups is listed in Table 2. No significant differences were observed between the two groups in age, BMI, DFS and pathological types (P > 0.05). FIGO stages and radiotherapy methods were significantly different between the two groups (P < 0.05). The proportion of patients with stages I-III in the radiation group was far higher than that in no radiotherapy history group. By utilizing blood routine test, 50.5% (n=39) of patients with previous radiotherapy exhibited bone marrow suppression in the period of radiotherapy, whereas 49.5% didn't showed hematological events (P>0.05). For patients with history of previous radiotherapy, 6.5% (n=5) of them underwent extended field irradiation due to para-aorta lymph node metastasis and 71.4% (n=55) of them were found to relapse inside the previous irradiated field, whereas 28.6% (n=22) of them relapsed outside the previous irradiated field. Notably, patients who experienced severe bone marrow suppression may face to anemia, infection or even lethal bleeding which force them to interrupt chemotherapy. Our results showed that 16.9% and 5.8% of patients with and without previous radiotherapy interrupted chemotherapy due to severe bone marrow suppression (P=0.060). Besides, there were 13.0% and 13.5% of patients underwent chemotherapy combined with bevacizumab after recurrence in the radiation group and control group, respectively.
Comparison of HT3+ risk between two groups by univariable analysis
According to the grading criteria for bone marrow suppression, we defined grade 3 and grade 4 bone marrow suppression as HT3+, which were considered severe side effects of chemotherapy, and analyzed the incidence of HT3+ in patients with or without previous radiotherapy, The result showed that patients with prior radiotherapy history exhibited higher risk of HT3+ during chemotherapy, compared with those without prior radiotherapy history (χ2=12.939, P = 0.005). Furthermore, patients accepted brachytherapy combined with EBRT showed the highest incidence of HT3+ (44.9%), compared to that in patients with brachytherapy alone (25.0%) and EBRT alone (40.0%). Given that equal or over grade 3 HT (HT3+) required clinical intervention and treatment, we analyzed the potential risk factors for HT3+ in recurrent cervical cancer patients with or without radiotherapy history by using t test, ANOVA, and χ2-test, as appropriate. The age, BMI, DFS, FIGO stages, pathological types and methods of radiotherapy, bone marrow suppression during previous radiotherapy were included (Table 2). Univariable analysis results showed that age, BMI, DFS, FIGO stages and pathological types had no significant differences between patients with or without HT3+ during the period of chemotherapy after recurrence (P > 0.05). For 39 recurrent patients have bone marrow suppression during the previous radiotherapy, 18 cases (18/39, 46.2%) of them exhibited HT3+ in the period of chemotherapy after relapse, whereas 15 cases (15/38, 39.5%) of no-bone marrow suppression during the previous radiotherapy exhibited HT3+ in the period of chemotherapy after relapse (χ2=0.351, P = 0.554). Combination of bevacizumab with chemotherapy didn't increase the incidence of HT3+ in recurrent cancer patients (P=0.626). When there is suspicion or postoperative confirmation of paraaortic lymph node metastasis, extended-field radiotherapy is required to achieve satisfactory therapeutic effects. In our study, there were 5 patients who underwent extended-field radiotherapy and 80.0% (n=4) of these 5 patients experienced HT3+ in the period of chemotherapy, whereas 40.3% (29/43) of patients without extended-field radiotherapy exhibited HT3+ (P=0.083). Although the difference between the two groups was not statistically significant, patients with extended-field radiotherapy have a higher trend of severe bone marrow suppression. The possible explanation of this phenomenon was that extended-field radiotherapy leads to further accumulation of doses of bone marrow and a decrease in bone marrow function, suggesting that patients with a history of extended-field radiotherapy should be more vigilant about the risk of bone marrow suppression.
To assess whether the baseline of WCC, HGB and PLT affecting the incidence of HT3+, we calculated the accounts of WCC, HGB and PLT before the first treatment of chemotherapy at relapse by using blood routine test (Table 3). Beyond expectation, the baseline of WCC, HGB and PLT before chemotherapy at relapse in patients with or without HT3+ showed no significant differences, indicating that the baseline of blood cells was not related to the risk of HT3+ for cervical cancer patients.
Comparison of HT3+ risk between two groups by multivariable analysis
To further evaluate the relationship between the above factors and the risk of HT3+ in recurrent cervical cancer patients during chemotherapy, binary logistic regression model was employed to perform multivariable analysis. The final logistic model is statistically significant, χ2=16.975, P=0.001). Similar to the results of univariable analysis, prior radiotherapy and extended-field radiotherapy were independent risk factor of HT3+, and the OR of brachytherapy, EBRT and brachytherapy combined with EBRT were 3.884, 9.410 and 15.462, respectively. Extended-field radiotherapy also increased the risk of HT3+ and the OR of it was 15.320. The model can correctly classify 69.8% of the research objects. The other factors in the model did not show significant impact on severe bone marrow suppression for recurrent cervical cancer patients (Table 4).
The relationship between baseline of blood cell levels post-radiation and blood events during chemotherapy
To investigate whether the history of pelvic radiotherapy affect the baseline of blood cells, including WCC, HGB and PLT, we collected the relative information by using blood routine test of cervical cancer patients with or without history of radiotherapy before the first course of chemotherapy after relapse. The baseline of three types of blood cells was shown in the Table 5 as mean ± SD in two groups. Results showed that there were no differences between the baseline of patients with or without history of pelvic radiotherapy in the counts of WCC, HGB and PLT.
We also analyzed the type of HT3+ in recurrent cervical cancer patients with HT3+. A total of 39 cases experienced HT3+ during chemotherapy after relapse, of which 32 cases (82.1%) had prior radiotherapy history and 7 cases without. Among these 39 cases, 33.4% (n=13) of patients showed severe decreased WCC during chemotherapy, 12.8% (n=5) of patients showed decreased HGB alone, 12.8% (n=5) of patients exhibited reduced PLT alone, 28.2% (n=11) of patients showed decreased WCC combined with PLT, and the rest (12.8%, n=5) showed decreased WCC combined with HGB (Figure 2A). The proportion of different types of HT3+ was similar between the two groups (Figure 2B). Although both WCC and PLT showed a high probability of reduction, our data showed a significant decrease in WCC, and the trend of decreasing blood cell types tends to be the same between the two groups.
General clinical information of recurrent cervical cancer patients
A total of 129 patients with recurrent cervical cancer were included in this study. All of these patients were primarily diagnosed and treated in our hospital with 77 cases had radiotherapy history at the point of recurrence, whereas 52 cases didn't undergo prior radiotherapy were used as control group. The age, BMI, DFS, FIGO stages, pathological types, radiotherapy methods, bone marrow suppression during previous radiotherapy, extended-field radiotherapy, relapse site, completion of chemotherapy after relapse, usage of bevacizumab was collected. The general clinical information of the two groups is listed in Table 2. No significant differences were observed between the two groups in age, BMI, DFS and pathological types (P > 0.05). FIGO stages and radiotherapy methods were significantly different between the two groups (P < 0.05). The proportion of patients with stages I-III in the radiation group was far higher than that in no radiotherapy history group. By utilizing blood routine test, 50.5% (n=39) of patients with previous radiotherapy exhibited bone marrow suppression in the period of radiotherapy, whereas 49.5% didn't showed hematological events (P>0.05). For patients with history of previous radiotherapy, 6.5% (n=5) of them underwent extended field irradiation due to para-aorta lymph node metastasis and 71.4% (n=55) of them were found to relapse inside the previous irradiated field, whereas 28.6% (n=22) of them relapsed outside the previous irradiated field. Notably, patients who experienced severe bone marrow suppression may face to anemia, infection or even lethal bleeding which force them to interrupt chemotherapy. Our results showed that 16.9% and 5.8% of patients with and without previous radiotherapy interrupted chemotherapy due to severe bone marrow suppression (P=0.060). Besides, there were 13.0% and 13.5% of patients underwent chemotherapy combined with bevacizumab after recurrence in the radiation group and control group, respectively.
Comparison of HT3+ risk between two groups by univariable analysis
According to the grading criteria for bone marrow suppression, we defined grade 3 and grade 4 bone marrow suppression as HT3+, which were considered severe side effects of chemotherapy, and analyzed the incidence of HT3+ in patients with or without previous radiotherapy, The result showed that patients with prior radiotherapy history exhibited higher risk of HT3+ during chemotherapy, compared with those without prior radiotherapy history (χ2=12.939, P = 0.005). Furthermore, patients accepted brachytherapy combined with EBRT showed the highest incidence of HT3+ (44.9%), compared to that in patients with brachytherapy alone (25.0%) and EBRT alone (40.0%). Given that equal or over grade 3 HT (HT3+) required clinical intervention and treatment, we analyzed the potential risk factors for HT3+ in recurrent cervical cancer patients with or without radiotherapy history by using t test, ANOVA, and χ2-test, as appropriate. The age, BMI, DFS, FIGO stages, pathological types and methods of radiotherapy, bone marrow suppression during previous radiotherapy were included (Table 2). Univariable analysis results showed that age, BMI, DFS, FIGO stages and pathological types had no significant differences between patients with or without HT3+ during the period of chemotherapy after recurrence (P > 0.05). For 39 recurrent patients have bone marrow suppression during the previous radiotherapy, 18 cases (18/39, 46.2%) of them exhibited HT3+ in the period of chemotherapy after relapse, whereas 15 cases (15/38, 39.5%) of no-bone marrow suppression during the previous radiotherapy exhibited HT3+ in the period of chemotherapy after relapse (χ2=0.351, P = 0.554). Combination of bevacizumab with chemotherapy didn't increase the incidence of HT3+ in recurrent cancer patients (P=0.626). When there is suspicion or postoperative confirmation of paraaortic lymph node metastasis, extended-field radiotherapy is required to achieve satisfactory therapeutic effects. In our study, there were 5 patients who underwent extended-field radiotherapy and 80.0% (n=4) of these 5 patients experienced HT3+ in the period of chemotherapy, whereas 40.3% (29/43) of patients without extended-field radiotherapy exhibited HT3+ (P=0.083). Although the difference between the two groups was not statistically significant, patients with extended-field radiotherapy have a higher trend of severe bone marrow suppression. The possible explanation of this phenomenon was that extended-field radiotherapy leads to further accumulation of doses of bone marrow and a decrease in bone marrow function, suggesting that patients with a history of extended-field radiotherapy should be more vigilant about the risk of bone marrow suppression.
To assess whether the baseline of WCC, HGB and PLT affecting the incidence of HT3+, we calculated the accounts of WCC, HGB and PLT before the first treatment of chemotherapy at relapse by using blood routine test (Table 3). Beyond expectation, the baseline of WCC, HGB and PLT before chemotherapy at relapse in patients with or without HT3+ showed no significant differences, indicating that the baseline of blood cells was not related to the risk of HT3+ for cervical cancer patients.
Comparison of HT3+ risk between two groups by multivariable analysis
To further evaluate the relationship between the above factors and the risk of HT3+ in recurrent cervical cancer patients during chemotherapy, binary logistic regression model was employed to perform multivariable analysis. The final logistic model is statistically significant, χ2=16.975, P=0.001). Similar to the results of univariable analysis, prior radiotherapy and extended-field radiotherapy were independent risk factor of HT3+, and the OR of brachytherapy, EBRT and brachytherapy combined with EBRT were 3.884, 9.410 and 15.462, respectively. Extended-field radiotherapy also increased the risk of HT3+ and the OR of it was 15.320. The model can correctly classify 69.8% of the research objects. The other factors in the model did not show significant impact on severe bone marrow suppression for recurrent cervical cancer patients (Table 4).
The relationship between baseline of blood cell levels post-radiation and blood events during chemotherapy
To investigate whether the history of pelvic radiotherapy affect the baseline of blood cells, including WCC, HGB and PLT, we collected the relative information by using blood routine test of cervical cancer patients with or without history of radiotherapy before the first course of chemotherapy after relapse. The baseline of three types of blood cells was shown in the Table 5 as mean ± SD in two groups. Results showed that there were no differences between the baseline of patients with or without history of pelvic radiotherapy in the counts of WCC, HGB and PLT.
We also analyzed the type of HT3+ in recurrent cervical cancer patients with HT3+. A total of 39 cases experienced HT3+ during chemotherapy after relapse, of which 32 cases (82.1%) had prior radiotherapy history and 7 cases without. Among these 39 cases, 33.4% (n=13) of patients showed severe decreased WCC during chemotherapy, 12.8% (n=5) of patients showed decreased HGB alone, 12.8% (n=5) of patients exhibited reduced PLT alone, 28.2% (n=11) of patients showed decreased WCC combined with PLT, and the rest (12.8%, n=5) showed decreased WCC combined with HGB (Figure 2A). The proportion of different types of HT3+ was similar between the two groups (Figure 2B). Although both WCC and PLT showed a high probability of reduction, our data showed a significant decrease in WCC, and the trend of decreasing blood cell types tends to be the same between the two groups.
Discussion
Radiation therapy is the main treatment for cervical cancer patients at early stages with high risk factors confirmed by postoperative pathology and for those at late stages (IIB-IV). The bone marrow, as a hematopoietic organ, consists of up to 50% hematopoietic site in adults and is highly sensitive to radiotherapy. Exposure of bone marrow microvasculature to irradiation not only hinders the blood supply to the bone marrow, but also has direct or indirect adverse effects on other normal physiological functions 29. In an animal model, the dynamic balance between the internal and external blood vessels in the bone marrow of mice is disrupted after one week of radiation with 9.5Gy. Further study suggested that ionizing radiation damaged the sinusoids of the bone marrow and caused dilation and bleeding of the sinusoids 30. In addition to the reduction of lymphocytes caused by short-term ischemia or direct radiation damage, the long-term changes in bone tissue after radiotherapy are due to a decrease in the number of bone marrow cells at the radiation therapy site, which are replaced by adipose tissue, resulting in new and progressive bone marrow replacement, tumor recurrence, or radiation induced osteonecrosis after radiation therapy 31. Patients with pelvic lymph node metastasis of cervical cancer have a larger target area and higher dose compared to those without lymph node metastasis and these patients are more susceptible to extensive and high-dose irradiation of the bone marrow, and combined with synchronous platinum chemotherapy, acute bone marrow suppression can occur in a short period of time, thereby affecting the treatment effectiveness of patients 27, 32. In this study, we analyzed the effects of prior radiotherapy on the risk of bone marrow suppression in recurrent cervical cancer cases and found that the history of pelvic radiotherapy significantly increased the incidence of bone marrow suppression during chemotherapy in recurrent patients. A total of 77 recurrent patients have received prior radiotherapy in this study, of which 32 (41.6%) had severe bone marrow suppression (grade III-IV) during TC regimen chemotherapy after recurrence, whereas 13.3% of patients (4/52) with no prior radiotherapy exhibited grade III-IV bone marrow suppression. Our results showed that both univariable and multivariable analysis showed that the history of prior radiotherapy was an independent risk factor for HT3+ in recurrent cervical cancer patients undergoing TC regimen chemotherapy (P < 0.05). Meanwhile, extended-field radiotherapy also increases the risk of severe bone marrow suppression in recurrent patients to a certain extent (80.0% vs 40.3%, P < 0.05). Additionally, based on our results, combination of bevacizumab and chemotherapy did not increase the risk of HT3+ of recurrent patients with cervical cancer by using univariable and multivariable analysis. These results indicated that prior pelvic radiotherapy had a long-term effect on the function of pelvic bone marrow and patients with prior radiotherapy were more prone to severe bone marrow suppression. Although, in the study, we were unable to measure the percentage increase in dose received by the active bone marrow from e extended-field radiotherapy compared to normal area of pelvic irradiation, our results pointed out that patients with cervical cancer who received extended-field radiotherapy had higher risk for bone marrow suppression when facing the challenge of hematological toxicities.
The ability of bone marrow to regenerate after pelvic radiation depends on the amount of radiation it receives 33. Notably, when high-dose radiotherapy is used, the bone marrow requires more time to repair, and some bone marrow injuries are irreversible 34. Increased irradiation dose of pelvic was associated with reduced nadir white cell count and absolute neutrophil count in patients with anal cancer and led to a higher risk of early hematological adverse events grade 3 or greater 33. A retrospective study evaluated the hematological events of 35 patients with locally advanced rectal cancer who received preoperative radiotherapy followed by postoperative 5-fluorouracil combined oxaliplatin (oxf) chemotherapy during treatment. The results showed that preoperative radiotherapy had a lasting effect on the function of pelvic bone marrow and greatly increased the risk of grade ≥ 3 hematological toxicities during postoperative chemotherapy 35. In patients with locally advanced cervical cancer who received concurrent chemoradiotherapy, three different irradiation doses for fractionated treatment, 10 Gy, 20 Gy and 40 Gy, were shown to be related to the risk of hematological toxicity and 20 Gy was identified as the most significant toxic radiotherapy dosage threshold clinically 22. Compared to cervical cancer patients with pelvic bone V10 < 90%, patients with pelvic bone V10 ≥ 90% had a higher proportion of grade 2 or higher leukopenia and neutropenia 36. In esophageal cancer patients receiving radiotherapy and chemotherapy, the relative volume size of sternum bone marrow irradiated with over 20 Gy dose of radiation was significantly negatively correlated with the level of peripheral blood lymphocytes 37. Conducting various bone structure dosimetry parameters related to acute hematological toxicity in patients with anal squamous cell carcinoma receiving radiotherapy and chemotherapy, the results showed a positive relationship between average dose in the entire bone or bone marrow cavity of the lumbosacral vertebrae and the risk of grade 3 and above hematological toxicity above level 3 38. In our study, patients receiving brachytherapy with lower radiation doses had a lower risk of bone marrow suppression during subsequent chemotherapy compared to those received EBRT (25.0% vs 40.0%, P<0.05). Our findings emphasized the long-term damage that traditional two-dimensional pelvic radiation caused to the pelvic bone marrow of cancer patients, and due to the need for treatment, the dosage used in radiotherapy cannot be reduced. The improvement of radiotherapy technology may be an effective way to solve this problem.
With the progress of radiotherapy technology, intensity modulated radiotherapy (IMRT) and three-dimensional conformal radiotherapy, which can individualize irradiation area and dose, are gradually applied to clinics and are identified with lower risk of HT3+. For 231 patients with anal cancer who received radiotherapy, IMRT effectively reduced the hematologic toxicity caused by radiotherapy and minimize the interruption of treatment caused by acute toxicity compared with traditional technology 39. Importantly, IMRT was better than 3D-conformal technology in reducing the acute radiotherapy response of large intestine and small intestine in patients with cervical cancer and endometrial cancer 31. A single center study also found that the application of pelvic bone marrow sparing IMRT can reduce hematologic toxicity. The incidence of equal or over grade II hematologic toxicity (50.0%) was significantly lower than that of the control group (69.5%), P=0.02 40. Despite IMRT significantly reduced radiotherapy dose of pelvic bone marrow, its ability to reducing hematological toxicity after fluorothymidine-18 based chemotherapy remained challenging for the slow inhibition of bone marrow activity with an irradiation dose greater than 35 Gy in patients with pelvic cancer (n=32) 41. These results emphasized the benefits of new radiotherapy technologies, including IMRT in reducing bone marrow damage caused by radiotherapy. Although the current evidence on how much dose received by active bone marrow can be reduced by IMRT and its association with radiotherapy regimens is still limited, this is positive news for reducing the short- and long-term pelvic bone marrow suppression in cervical cancer patients.
Neutropenia or deficiency is a common complication of chemotherapy. According to existing retrospective studies, chemotherapy-induced neutropenia occurred in nearly 50.5% (n=147) gynecologic patients over 23.4% (378) chemotherapy cycles, associating with older age (over 70 years), less than five previous chemotherapy cycles, disseminated disease, platinum-based regimens and taxane-containing regimens 42. In this study, all recurrent cervical cancer patients have received platinum-based and paclitaxel-containing combined chemotherapy after recurrence and 17 patients underwent a combination treatment of bevacizumab in addition to chemotherapy. Compared to patients without prior pelvic radiotherapy, prior pelvic radiotherapy did not reduce the baseline of blood cell (WBC, nadir ALC, HGB and PLT) significantly (P > 0.05) of cancer patients. In 39 cases with HT3+ during chemotherapy after relapse, 33.4% (n=13), 12.8% (n=5), 12.8% (n=5) patients experienced sharp reduction in WCC, HGB and PLT, respectively. Besides, another 28.2% (n=11) and12.8% (n=5) of HT3+ cases exhibited combined reduction in WCC and PLT, WCC and HGB, respectively. Given the severe hematologic toxicity, 12.4% of patients (16/129) experienced unexpected interruption of chemotherapy due to severe bone marrow suppression. Among them, severely decreased WCC and PLT were the most common factors that limiting chemotherapy when monitored hematopoietic capacity of bone marrow by using weekly blood routine test in the period of chemotherapy. Severe leukopenia and thrombocytopenia can cause fever, infection, and fatal bleeding, which not only increases the readmission rate of patients, but also increases the medical and economic burden. Optimistically, the usage of hematopoietic colony-stimulating factors (CSFs) in clinical has gradually become universal, and their application in cancer patients has greatly reduced the incidence of hematological complications after chemotherapy 43. Therefore, more attention should be paid to hematological complications in patients with a history of pelvic radiation therapy and promptly consider the use of CSFs.
Conclusion
In conclusion, our current findings demonstrate, for the first time, that pelvic radiotherapy increased the risk of severe HT and lead to interruptions of chemotherapy for recurrent cervical cancer patients. Extended-field radiotherapy was also a risk factor for HT3+, while combination of bevacizumab and platinum-based chemotherapy showed no significant impacts on the incidence of HT3+. Moreover, WCC and PLT were the main blood indicators affected during chemotherapy after relapse. Little effects of prior pelvic radiotherapy for patients with cervical cancer on the long-term baseline counts of blood cells were observed. Our results identified the risk factors of sever HT of recurrent cervical cancer patients and provided a limited long-term observation of the impact of pelvic radiotherapy on bone marrow function, with the hope of promoting the understanding of treatment for recurrent cervical cancer patients. |
Title: Special Issue “Drug Discovery and Application of New Technologies” | Body:
1. Overview of Technological Innovations in Drug Discovery
Historically, drug discovery and development have proven to be time-consuming and costly, with the process averaging around 15 years and costing approximately USD 2 billion to bring a new small-molecule drug to market [1]. The landscape of drug discovery has undergone a significant transformation in recent years, largely driven by advances in computational approaches [2] and structural biology techniques. These innovations are reshaping the traditional approaches, enabling more efficient and targeted drug development. The most impactful advancement is the comprehensive application of artificial intelligence technology in new drug research and development.
Artificial Intelligence for Drug Discovery (AIDD) leverages the collection and organization of extensive biochemical datasets, compound representation, and advanced AI algorithms, including generative adversarial networks and other deep learning models, to generate novel compound structures. Additionally, it develops machine learning models that assist scientists in predicting drug–target structures, drug–target interactions, binding affinities, drug toxicity, drug bioactivity, and physicochemical properties [3,4]. Proteomics and metabolomics employ technologies such as mass spectrometry and nuclear magnetic resonance to investigate small molecules and proteins within biological samples, significantly advancing the drug discovery process by elucidating targets, drug mechanisms, and biomarkers [5,6]. Next-generation sequencing technology facilitates the rapid acquisition of genomic, transcriptomic, and epigenomic information, yielding a vast array of data for target identification. This technology also enables scientists to elucidate drug resistance mechanisms, optimize drug combinations, and enhance therapeutic efficacy, thereby expediting the development of new drugs [7,8,9]. In the field of nanotechnology, nano-carriers such as nanoparticles, nano-emulsions, liposomes, and micelles significantly enhance the stability and solubility of drug molecules, thereby improving their bioavailability and playing a crucial role in drug discovery [10,11,12].
As the integration of artificial intelligence and structural biology techniques continues to advance, the drug discovery process becomes more streamlined. Trends like the use of ultra-large virtual screening libraries and the application of deep learning for protein structure prediction are driving the field forward. These innovations promise to accelerate the discovery of new drugs and broaden the scope of therapeutic targets, including those considered challenging or “undruggable”.
2. Key Challenges and Future Directions
Despite significant progress in the field of drug discovery, several challenges continue to hinder the seamless translation of these innovations into clinical success. (1) Artificial Intelligence for Drug Discovery remains heavily reliant on high-quality chemical and biological data, and the current scarcity of biological data and limited samples for machine learning pose significant challenges to its further development. (2) In genomics and transcriptomics, the substantial volume of data generated by high-throughput sequencing necessitates proficiency in bioinformatic tools for effective data processing and analysis. Individual variability and biological variation can result in inconsistent outcomes, thereby affecting data authenticity and the feasibility of clinical applications. In metabolomics and proteomics, scientists encounter challenges in sample handling and data analysis, which demand a high level of expertise in research when applying omics technologies to drug discovery. (3) The intricate design and modification of biological systems in synthetic biology necessitate the effective integration of multiple biological components and pathways. Addressing the challenges of translating laboratory findings in synthetic biology into clinical applications and drug development will be crucial areas of research in the future. (4) The synthesis of nanomaterials presents challenges due to its relative complexity and high cost, and the long-term biocompatibility and toxicity of these materials within biological systems remain inadequately understood. Moreover, effectively controlling the timing and rate of drug release continues to be a significant challenge.
3. Overview of the Contributions in the Issue
This Special Issue brings together groundbreaking studies that explore novel methodologies, technologies, and compounds in drug discovery, with applications ranging from viral infection treatment to cancer therapy. The advancements presented here reflect the power of integrating cutting-edge technologies, including molecular docking, ultrafiltration techniques, and bispecific antibody–drug conjugates (bsADCs), in regard to handling some of the most pressing challenges in therapeutic development.
A study by Wang et al. demonstrates the power of traditional compounds in modern drug discovery. Curcumin, derived from turmeric, was shown to inhibit Porcine Deltacoronavirus (PDCoV) replication through a combination of molecular docking and network pharmacology techniques. This study identifies curcumin’s binding to key viral targets, such as IL-6 and TNF signaling pathways, providing an avenue for antiviral drug development [13]. Zhuang et al. present a novel approach to improving the efficacy of antibody–drug conjugates (ADCs) for HER2-low-expressing tumors. By engineering a SORT1×HER2 bispecific antibody–drug conjugate, they demonstrate enhanced internalization and antitumor activity compared to conventional HER2-targeted therapies. This technology represents a promising strategy for treating HER2-low-expressing cancers that are resistant to standard therapies [14]. Alostath et al. found that CHX-CaCl2 surface crystallization is a new drug technology for controlled and sustained CHX release; its antibacterial effectiveness makes the drug an ideal adjunct following clinical and surgical procedures in terms of maintaining oral hygiene and preventing surgical site infections [15]. Lu et al. conducted an initial exploration of potential α-glucosidase inhibitors stemmed from siraitia grosvenorii roots. Sixteen potential inhibitors were successfully isolated from siraitia grosvenorii roots, including lignans, cucurbitacins, and cucurbitane glycosides [16]. Li et al.’ study provides insights into the dimerization patterns of chemokine receptors and the functional significance of their truncated isoforms [17]. Bai et al. demonstrated that an amyloid antibody could be engineered by a few mutations to bind new amyloid sequences, providing an efficient way to reposition a therapeutic antibody to target different amyloid diseases [18]. Liu et al. developed a formulation of liposomes co-loaded with Panax notoginseng saponins (PNSs) and a Ginsenoside Rg3 (Lip-Rg3/PNS) novel nano-delivery system to treat ischemic stroke via intranasal administration [19]. Finally, Torres-Jaramillo et al. reported the synthesis of 2-(4-alkyloxyphenyl)-imidazoline and imidazole derivates and found two new and potent antiprotozoal on L. mexicana and T. cruzi [20].
4. Final Reflections
As we look to the future, drug discovery and the application of new technologies will enhance our understanding of the potential of drug discovery in meeting the growing demand for safe and effective therapeutics against human diseases. The integration of new technologies into drug discovery holds the promise of bringing more effective and personalized treatments to patients faster than ever before. We hope that the collection of articles will serve as a valuable resource for researchers, fostering innovation and collaboration in the pursuit of better therapies. |
Title: Biomarker-based prediction of sinus rhythm in atrial fibrillation patients: the EAST-AFNET 4 biomolecule study | Body:
Introduction
In addition to improving atrial fibrillation (AF)-related symptoms,1 rhythm control therapy2 can prevent AF-related cardiovascular events such as stroke, heart failure hospitalizations, and cardiovascular death.3 The cardiovascular complication-reducing effect of early rhythm control therapy shown in the EAST-AFNET 4 study is mainly mediated by attaining sinus rhythm at 12-month follow-up.4 This potentially reflects a reduced AF burden5 and lack of progression to non-paroxysmal patterns of AF.6,7 Predicting sinus rhythm at 12 months could therefore help to identify patients requiring intensive rhythm control, e.g. with AF ablation.3,8 Knowledge of treatable processes contributing to AF at 12-month follow-up can help to develop adjunct therapies aimed at maintaining sinus rhythm and preventing AF progression. Several chronic, interdependent disease processes9,10 contribute to AF. Such processes can be aggravated by presence of AF, attenuated by rhythm control, or exist independent of AF.1,11 Circulating biomarkers provide quantitative proxies for cardiomyocyte death or injury [troponin (TnT)]; atrial metabolic dysfunction and stress [bone morphogenetic protein 10 (BMP10), fatty acid binding protein 3 (FABP3), and insulin-like growth factor binding protein 7 (IGFBP7)]12,13; thrombo-inflammation [D-dimer, C-reactive protein (CRP), interleukin-6 (IL-6)]14,15; vascular and endothelial dysfunction [angiopoietin 2 (ANGPT2), endothelial specific molecule 1 (ESM1)]14,15; frailty [growth differentiation factor 15 (GDF-15)]; and cardiac load [natriuretic peptides like N-terminal pro-B-type natriuretic peptide (NT-proBNP)].16 Quantification of biomarkers selected to reflect these disease processes in a single blood draw identifies patient clusters with different risk of cardiovascular events.17 Whether the disease processes reflected by these biomarkers modify future rhythm in patients with AF has not been investigated.
This analysis of the EAST-AFNET 4 biomolecule study embedded into the Early treatment of Atrial fibrillation for STroke prevention (EAST-AFNET 4) trial2 quantified 14 biomarkers reflecting different disease processes in AF that were defined a priori.9 The ability of each biomarker to predict sinus rhythm at 12-month follow-up in patients with and without early rhythm control therapy was evaluated (Structured Graphical Abstract).
Validation was performed internally at 24 months, by comparing biomarker-based clusters at baseline by association with sinus rhythm at 12- and 24-month follow-up and by machine learning integrating biomarkers and clinical parameters. Clinical utility was assessed by defining and testing threshold values and by comparison with a clinical score. External validation was performed in two independent datasets of patients with AF.
Methods
Details of the prespecified analysis plan of the EAST-AFNET 4 biomolecule study can be found in a separate Supplementary material file (Supplementary file Statistical analysis plan SAP). Post hoc exploratory analyses were added to gain more insight into the main findings.
Derivation dataset (EAST-AFNET 4)
EAST-AFNET 4 randomized patients with recently diagnosed AF and stroke risk factors to systematic early rhythm control or usual care including symptom-based rhythm control.2 All patients were followed up for a median of 5.1 years. The EAST-AFNET 4 biomolecule study collected a baseline blood sample in 1586 patients enrolled in the EAST-AFNET 4 trial.17,18 In brief, all consenting patients provided a blood sample at baseline. Samples were shipped to the core biostorage facility at UKE Hamburg, spun, shock-frozen, and stored at −80°C. EAST-AFNET 4 and its biomolecule study were approved at all participating study sites. Written informed consent was obtained from all patients.
Validation datasets
AXAFA-AFNET 5
The Anticoagulation using the direct factor Xa inhibitor apixaban during Atrial Fibrillation catheter Ablation: Comparison to vitamin K antagonist therapy (AXAFA-AFNET 519) trial was a randomized, investigator-initiated trial comparing continuous vitamin K antagonist therapy to apixaban in 633 patients undergoing a first AF ablation in 49 European and US American study sites. The same 14 biomarkers quantified in the derivation dataset were quantified in the AXAFA-AFNET 5 blood samples using the same assays.20 The outcome of interest was rhythm at the final follow-up visit, 120 days after enrolment.19 All patients provided written informed consent.
BBC-AF atrial fibrillation snapshot
Details of the BBC-AF cohort have been described before.21 In brief, consecutive patients eligible for recruitment had ECG-diagnosed AF or presented with at least two cardiovascular conditions (congestive heart failure, hypertension, diabetes, prior stroke, or vascular disease) to a large teaching hospital (Sandwell and West Birmingham NHS Trust). Patients who did not have a diagnosis of AF underwent 7-day ambulatory ECG monitoring to rule out undiagnosed ECG-documented AF. For this analysis, only patients with ECG-documented AF were included. Follow-up data were collected by assessing local hospital records corroborated against Hospital Episode Statistics data, general practitioner records, and mortality data from NHS Digital, up to 2.5 years after the final patient was recruited.22 This study complied with the Declaration of Helsinki, was approved by the National Research Ethics Service Committee (IRAS ID 97753), and was sponsored by the University of Birmingham. All patients provided written informed consent.
TRUST snapshot
A snapshot of patients enrolled in the Long-term Outcome and Predictors for Recurrence after Medical and Interventional Treatment of Arrhythmias study (TRUST; NCT05521451), with biomarker concentrations and 12-month rhythm status was created. All patients provided written informed consent. A snapshot of all patients with biomarker concentrations and ECG follow-up at 12–18 months was obtained in June 2024 for validation.
Selection of biomarkers and their quantification
Circulating biomarkers were selected by scientists from the EU-funded CATCH-ME consortium based on relevant disease processes and available high-precision high-throughput assays.9 Biomarkers were selected in four steps: (i) members of the consortium identified candidate biomarkers reflecting disease processes known to contribute to AF and its complications, (ii) deep literature and patent searches for candidate biomarkers and additional novel biomarkers were performed, (iii) expert discussion and Delphi-like votes by the consortium defined most promising candidates, and (iv) availability and feasibility checks to perform measurements of thousands of samples with high precision.
Fourteen biomarkers were selected (Table 1 following clinical characteristics); ANGPT2, BMP10, cancer antigen 125 (CA125), CRP, D-dimer, ESM1, FABP3, fibroblast growth factor 23 (FGF23), GDF15, IGFBP7, IL-6, NT-proBNP, TnT, and serum creatinine (sCr).
Table 1
Baseline characteristics and biomarkers in the EAST-AFNET 4 biomolecule study
Treatment group
Early rhythm control
Usual care
P-value*
n
800
786
Sex: female
355 (44%)
358 (46%)
.639
Age (years)
71 [66, 75]
71 [66, 76]
.711
BMI
28.7 [25.6, 32.1]
29.0 [25.6, 32.5]
.699
Blood pressure (systolic, mmHg)
135 [123, 150]
135 [125, 148]
.730
Blood pressure (diastolic, mmHg)
80 [74, 90]
80 [74, 90]
.716
LVEF (%)
60 [55, 65]
60 [55, 65]
.873
AF type (first episode)
290 (36%)
270 (34%)
AF type (paroxysmal)
302 (38%)
288 (37%)
.839
AF type (persistent)
208 (26%)
228 (29%)
.202
Other clinical characteristics
Diabetes
207 (26%)
189 (24%)
.400
Hypertension
494 (62%)
512 (65%)
.170
Chronic kidney disease
98 (12%)
97 (12%)
.956
Estimated glomerular filtration rate (mL/min 1.73 m²)
75 [63–87]
76 [64–87]
.734
Previous stroke or transient ischaemic attack
114 (14%)
81 (10%)
.017
Chronic obstructive pulmonary disease
63 (8%)
61 (8%)
.991
Diastolic LA diameter (mm)
42 [38, 47]
43 [39, 47]
.730
NYHA class
No heart failure
523 (65%)
509 (65%)
I
82 (10%)
88 (11%)
.555
II
164 (21%)
160 (20%)
.985
III
31 (4%)
29 (4%)
.882
EHRA score
I
232 (29%)
236 (30%)
II
386 (48%)
374 (48%)
.679
III
122 (15%)
122 (15%)
.914
IV
8 (1%)
9 (1%)
.839
Missing
52 (7%)
45 (6%)
Biomarker (unit)
Coefficient of variation
NT-proBNP (pg/mL)
1.51
441 [175–966]
467 [187–1036]
.537
ANGPT2 (ng/mL)
.70
2.53 [1.87–3.65]
2.53 [1.87–3.75]
.456
BMP10 (ng/mL)
.24
2.10 [1.82–2.41]
2.11 [1.83–2.45]
.507
FGF23 (pg/mL)
1.27
155 [115–218]
153 [115–211]
.244
ESM1 (ng/mL)
.76
2.04 [1.64–2.59]
2.05 [1.63–2.63]
.818
GDF15 (pg/mL)
.80
1333 [990–2000]
1359 [971–2005]
.078
IGFBP7 (ng/mL)
.26
102 [90.7–117]
102 [90.1–117]
.457
IL-6 (pg/mL)
6.62
2.56 [1.64–4.04]
2.68 [1.67–4.18]
.479
FABP3 (ng/mL)
.50
32.0 [26.3–39.6]
31.9 [26.4–39.6]
.837
D-dimer (µg/mL)
1.74
.17 [.09–.34]
.16 [.08–.36]
.506
TnT (ng/L)
2.26
11.1 [8.02–16.6]
11.4 [8.21–16.7]
.337
CRP (mg/L)
3.28
2.02 [.96–4.99]
2.38 [1.04–4.75]
.392
sCr (µmol/L)
.29
81.7 [70.7–95.5]
80.4 [70.0–94.5]
.771
CA125 (U/mL)
1.51
11.5 [8.08–15.9]
11.1 [7.93–16.1]
.433
Estimated glomerular filtration rate (eGFR) was calculated as CKD EPI, Chronic Kidney Disease Epidemiology Collaboration.
AF, atrial fibrillation; SR, sinus rhythm; ERC, early rhythm control; UC, usual care; BMI, body mass index; LVEF, left ventricular ejection fraction; LA, left atrium; NYHA, New York Heart Association Functional Classification of heart failure; EHRA, European Heart Rhythm Association score; ANGPT2, angiopoietin 2; BMP10, bone morphogenetic protein 10; CA125, cancer antigen 125; CRP, C-reactive protein; ESM1, endothelial specific molecule 1; FABP3, fatty acid binding protein 3; FGF23, fibroblast growth factor 23; GDF15, growth differentiation factor 15; IGFBP7, insulin-like growth factor binding protein 7; IL-6, interleukin-6; NT-proBNP, N-terminal pro-B-type natriuretic peptide; TnT, cardiac troponin; sCr, serum creatinine.
*
P-values were calculated on the unimputed dataset using mixed logistic regression model with site as random effect, for biomarkers additionally adjusted for sex, age, body mass index, diastolic blood pressure, left ventricular ejection fraction, and AF type. Distributions are shown as mean and SD for normally distributed values, as median and IQR for non-normally distributed values and biomarkers, and as frequency (percentage) for nominal features.
Blood samples were collected at all participating sites and shipped to the core lab at University Heart and Vascular Center (UHZ) Hamburg by courier at ambient temperatures (24–48 h transport time). Upon arrival at UHZ, samples were spun, shock-frozen, and stored at −80°C for analysis. Biomarkers were centrally quantified using pre-commercial and commercial high-throughput, high-precision platforms (Roche, Penzberg, Germany) in EDTA plasma. The biomarker quantification was provided as an in-kind contribution of Roche to the CATCH ME consortium. Blood samples were shipped to, and quantifications were conducted at the Roche biomarker research facility in Penzberg, Germany.
Statistical methods
As rhythm is a secondary outcome analysis of the EAST-AFNET 4 trial, all results are exploratory. Biomarker concentrations were 1% winsorized23 from above and logarithmically transformed (log base e) to normalize skewed concentration ranges for all datasets. Concentrations below the detection limit for CA-125 and D-dimer were replaced with the lowest available value. For the initial testing of prespecified hypotheses, all 14 biomarkers were used. Validations were done with predictive biomarkers. This analysis does not take into account the probability of chance findings because of performance of multiple comparisons with 14 biomarkers. As a consequence, results should be interpreted as explorative/hypothesis generating and call for further validation.
Patients in AF at the time of blood sampling showed higher concentrations in most biomarkers (see Supplementary data online, Table S1). Rhythm at time of blood sampling was therefore included as a confounder in all subsequent analyses in addition to the features predicting rhythm at 12 months in the main EAST-AFNET 4 dataset.4
Mixed logistic regression models were used to assess the predictive value of the 14 biomarkers on rhythm at 12 months, with study centre as a random intercept. The lme4 R package24 was used. Each biomarker was assessed in a separate model adjusted for sex, age, rhythm at baseline, body mass index, diastolic blood pressure, AF pattern (first episode, paroxysmal, persistent), left ventricular ejection fraction, rhythm at baseline, and randomized group (usual care or early rhythm control).4 Nested models with additional interaction terms between treatment type and the biomarker of interest were constructed. To obtain P-values for the interaction, each nested model pair was compared by ANOVA for their goodness of fit. Odds ratios (ORs) and P-values for the biomarker effects under different treatment types were calculated by reference cell coding.25 Missing values in heart rhythm and left ventricular ejection fraction were imputed in a 60-times multiple imputed dataset as described earlier,2 following the recommendations of White, Royston, and Wood.26,27 A sensitivity analysis constructed prediction models for recurrent AF at 12- and 24-month follow-up without imputation.
To further explore the effect of rhythm on the biomarkers, mixed regression models were repeated in subgroups split by baseline rhythm (sinus rhythm or AF) and by rhythm control therapy (early rhythm control or usual care) and ORs for the outcome sinus rhythm at 12 months were calculated using the methods described above.
As internal validation, analysis was repeated for sinus rhythm at 24-month follow-up. As sensitivity analysis, the analysis was repeated for recurrent AF up to 24 months.
As additional internal validation, patient clusters formed using all biomarker concentrations agnostic to clinical features17 were tested for prediction of presence of sinus rhythm at 12- and 24-month follow-up. The lowest-risk cluster was used as a reference.
As another means of internal validation, we applied a random forest machine learning model (ML) and made use of a mixed effect random forest (MERF) wrapper to account for the centre as a random effect. The ML model was fitted with the features used for confounding the generalized linear model as well as of all 14 biomarkers at once. To assess the variable importance, we used the models’ inherent Gini-based feature importance as well as the model agnostic SHapley Additive exPlanations (SHAP) values.
Clinical utility
Cut-off values for clinically useful probabilities of sinus rhythm at 12 months (80%) and for AF at 12 months (40%) were determined for all biomarkers that predicted the main outcome. A clinical risk score was developed based on a recent meta-analysis28: three accepted clinical features predicting recurrent AF, namely left atrial size, AF pattern, and age, were dichotomized with a point scored for persistent AF yes, anterior–posterior left atrial diameter > 50 mm, age > 75 years (see Supplementary data online, Table S2). As many patients with one of these three features attain sinus rhythm at 12 months, the score was considered as predictive of high risk of AF at 12 months if at least two of the three factors were present. Each of the biomarkers that were independently associated with sinus rhythm at 12 months was added to this clinical score separately, as well as in combination. If at least one biomarker was above the cut-off value, the patient was regarded as high risk of not attaining sinus rhythm. The confusion matrices for correctly and incorrectly classified patients at high-risk-classified of not attaining sinus rhythm were calculated for the reference clinical score alone and all additional, biomarker-enriched scores.
Biomarkers’ predictive values were tested in the validation datasets using univariate and multivariate models restricted to the features that predicted sinus rhythm at 12 months in the derivation dataset.
Python version 3.8.13 was employed for data pre-processing and visualization, R version 4.2.2 for statistical computations.29 Relevant code will be made publicly available (https://github.com/UCCSHH).
Derivation dataset (EAST-AFNET 4)
EAST-AFNET 4 randomized patients with recently diagnosed AF and stroke risk factors to systematic early rhythm control or usual care including symptom-based rhythm control.2 All patients were followed up for a median of 5.1 years. The EAST-AFNET 4 biomolecule study collected a baseline blood sample in 1586 patients enrolled in the EAST-AFNET 4 trial.17,18 In brief, all consenting patients provided a blood sample at baseline. Samples were shipped to the core biostorage facility at UKE Hamburg, spun, shock-frozen, and stored at −80°C. EAST-AFNET 4 and its biomolecule study were approved at all participating study sites. Written informed consent was obtained from all patients.
Validation datasets
AXAFA-AFNET 5
The Anticoagulation using the direct factor Xa inhibitor apixaban during Atrial Fibrillation catheter Ablation: Comparison to vitamin K antagonist therapy (AXAFA-AFNET 519) trial was a randomized, investigator-initiated trial comparing continuous vitamin K antagonist therapy to apixaban in 633 patients undergoing a first AF ablation in 49 European and US American study sites. The same 14 biomarkers quantified in the derivation dataset were quantified in the AXAFA-AFNET 5 blood samples using the same assays.20 The outcome of interest was rhythm at the final follow-up visit, 120 days after enrolment.19 All patients provided written informed consent.
BBC-AF atrial fibrillation snapshot
Details of the BBC-AF cohort have been described before.21 In brief, consecutive patients eligible for recruitment had ECG-diagnosed AF or presented with at least two cardiovascular conditions (congestive heart failure, hypertension, diabetes, prior stroke, or vascular disease) to a large teaching hospital (Sandwell and West Birmingham NHS Trust). Patients who did not have a diagnosis of AF underwent 7-day ambulatory ECG monitoring to rule out undiagnosed ECG-documented AF. For this analysis, only patients with ECG-documented AF were included. Follow-up data were collected by assessing local hospital records corroborated against Hospital Episode Statistics data, general practitioner records, and mortality data from NHS Digital, up to 2.5 years after the final patient was recruited.22 This study complied with the Declaration of Helsinki, was approved by the National Research Ethics Service Committee (IRAS ID 97753), and was sponsored by the University of Birmingham. All patients provided written informed consent.
TRUST snapshot
A snapshot of patients enrolled in the Long-term Outcome and Predictors for Recurrence after Medical and Interventional Treatment of Arrhythmias study (TRUST; NCT05521451), with biomarker concentrations and 12-month rhythm status was created. All patients provided written informed consent. A snapshot of all patients with biomarker concentrations and ECG follow-up at 12–18 months was obtained in June 2024 for validation.
AXAFA-AFNET 5
The Anticoagulation using the direct factor Xa inhibitor apixaban during Atrial Fibrillation catheter Ablation: Comparison to vitamin K antagonist therapy (AXAFA-AFNET 519) trial was a randomized, investigator-initiated trial comparing continuous vitamin K antagonist therapy to apixaban in 633 patients undergoing a first AF ablation in 49 European and US American study sites. The same 14 biomarkers quantified in the derivation dataset were quantified in the AXAFA-AFNET 5 blood samples using the same assays.20 The outcome of interest was rhythm at the final follow-up visit, 120 days after enrolment.19 All patients provided written informed consent.
BBC-AF atrial fibrillation snapshot
Details of the BBC-AF cohort have been described before.21 In brief, consecutive patients eligible for recruitment had ECG-diagnosed AF or presented with at least two cardiovascular conditions (congestive heart failure, hypertension, diabetes, prior stroke, or vascular disease) to a large teaching hospital (Sandwell and West Birmingham NHS Trust). Patients who did not have a diagnosis of AF underwent 7-day ambulatory ECG monitoring to rule out undiagnosed ECG-documented AF. For this analysis, only patients with ECG-documented AF were included. Follow-up data were collected by assessing local hospital records corroborated against Hospital Episode Statistics data, general practitioner records, and mortality data from NHS Digital, up to 2.5 years after the final patient was recruited.22 This study complied with the Declaration of Helsinki, was approved by the National Research Ethics Service Committee (IRAS ID 97753), and was sponsored by the University of Birmingham. All patients provided written informed consent.
TRUST snapshot
A snapshot of patients enrolled in the Long-term Outcome and Predictors for Recurrence after Medical and Interventional Treatment of Arrhythmias study (TRUST; NCT05521451), with biomarker concentrations and 12-month rhythm status was created. All patients provided written informed consent. A snapshot of all patients with biomarker concentrations and ECG follow-up at 12–18 months was obtained in June 2024 for validation.
Selection of biomarkers and their quantification
Circulating biomarkers were selected by scientists from the EU-funded CATCH-ME consortium based on relevant disease processes and available high-precision high-throughput assays.9 Biomarkers were selected in four steps: (i) members of the consortium identified candidate biomarkers reflecting disease processes known to contribute to AF and its complications, (ii) deep literature and patent searches for candidate biomarkers and additional novel biomarkers were performed, (iii) expert discussion and Delphi-like votes by the consortium defined most promising candidates, and (iv) availability and feasibility checks to perform measurements of thousands of samples with high precision.
Fourteen biomarkers were selected (Table 1 following clinical characteristics); ANGPT2, BMP10, cancer antigen 125 (CA125), CRP, D-dimer, ESM1, FABP3, fibroblast growth factor 23 (FGF23), GDF15, IGFBP7, IL-6, NT-proBNP, TnT, and serum creatinine (sCr).
Table 1
Baseline characteristics and biomarkers in the EAST-AFNET 4 biomolecule study
Treatment group
Early rhythm control
Usual care
P-value*
n
800
786
Sex: female
355 (44%)
358 (46%)
.639
Age (years)
71 [66, 75]
71 [66, 76]
.711
BMI
28.7 [25.6, 32.1]
29.0 [25.6, 32.5]
.699
Blood pressure (systolic, mmHg)
135 [123, 150]
135 [125, 148]
.730
Blood pressure (diastolic, mmHg)
80 [74, 90]
80 [74, 90]
.716
LVEF (%)
60 [55, 65]
60 [55, 65]
.873
AF type (first episode)
290 (36%)
270 (34%)
AF type (paroxysmal)
302 (38%)
288 (37%)
.839
AF type (persistent)
208 (26%)
228 (29%)
.202
Other clinical characteristics
Diabetes
207 (26%)
189 (24%)
.400
Hypertension
494 (62%)
512 (65%)
.170
Chronic kidney disease
98 (12%)
97 (12%)
.956
Estimated glomerular filtration rate (mL/min 1.73 m²)
75 [63–87]
76 [64–87]
.734
Previous stroke or transient ischaemic attack
114 (14%)
81 (10%)
.017
Chronic obstructive pulmonary disease
63 (8%)
61 (8%)
.991
Diastolic LA diameter (mm)
42 [38, 47]
43 [39, 47]
.730
NYHA class
No heart failure
523 (65%)
509 (65%)
I
82 (10%)
88 (11%)
.555
II
164 (21%)
160 (20%)
.985
III
31 (4%)
29 (4%)
.882
EHRA score
I
232 (29%)
236 (30%)
II
386 (48%)
374 (48%)
.679
III
122 (15%)
122 (15%)
.914
IV
8 (1%)
9 (1%)
.839
Missing
52 (7%)
45 (6%)
Biomarker (unit)
Coefficient of variation
NT-proBNP (pg/mL)
1.51
441 [175–966]
467 [187–1036]
.537
ANGPT2 (ng/mL)
.70
2.53 [1.87–3.65]
2.53 [1.87–3.75]
.456
BMP10 (ng/mL)
.24
2.10 [1.82–2.41]
2.11 [1.83–2.45]
.507
FGF23 (pg/mL)
1.27
155 [115–218]
153 [115–211]
.244
ESM1 (ng/mL)
.76
2.04 [1.64–2.59]
2.05 [1.63–2.63]
.818
GDF15 (pg/mL)
.80
1333 [990–2000]
1359 [971–2005]
.078
IGFBP7 (ng/mL)
.26
102 [90.7–117]
102 [90.1–117]
.457
IL-6 (pg/mL)
6.62
2.56 [1.64–4.04]
2.68 [1.67–4.18]
.479
FABP3 (ng/mL)
.50
32.0 [26.3–39.6]
31.9 [26.4–39.6]
.837
D-dimer (µg/mL)
1.74
.17 [.09–.34]
.16 [.08–.36]
.506
TnT (ng/L)
2.26
11.1 [8.02–16.6]
11.4 [8.21–16.7]
.337
CRP (mg/L)
3.28
2.02 [.96–4.99]
2.38 [1.04–4.75]
.392
sCr (µmol/L)
.29
81.7 [70.7–95.5]
80.4 [70.0–94.5]
.771
CA125 (U/mL)
1.51
11.5 [8.08–15.9]
11.1 [7.93–16.1]
.433
Estimated glomerular filtration rate (eGFR) was calculated as CKD EPI, Chronic Kidney Disease Epidemiology Collaboration.
AF, atrial fibrillation; SR, sinus rhythm; ERC, early rhythm control; UC, usual care; BMI, body mass index; LVEF, left ventricular ejection fraction; LA, left atrium; NYHA, New York Heart Association Functional Classification of heart failure; EHRA, European Heart Rhythm Association score; ANGPT2, angiopoietin 2; BMP10, bone morphogenetic protein 10; CA125, cancer antigen 125; CRP, C-reactive protein; ESM1, endothelial specific molecule 1; FABP3, fatty acid binding protein 3; FGF23, fibroblast growth factor 23; GDF15, growth differentiation factor 15; IGFBP7, insulin-like growth factor binding protein 7; IL-6, interleukin-6; NT-proBNP, N-terminal pro-B-type natriuretic peptide; TnT, cardiac troponin; sCr, serum creatinine.
*
P-values were calculated on the unimputed dataset using mixed logistic regression model with site as random effect, for biomarkers additionally adjusted for sex, age, body mass index, diastolic blood pressure, left ventricular ejection fraction, and AF type. Distributions are shown as mean and SD for normally distributed values, as median and IQR for non-normally distributed values and biomarkers, and as frequency (percentage) for nominal features.
Blood samples were collected at all participating sites and shipped to the core lab at University Heart and Vascular Center (UHZ) Hamburg by courier at ambient temperatures (24–48 h transport time). Upon arrival at UHZ, samples were spun, shock-frozen, and stored at −80°C for analysis. Biomarkers were centrally quantified using pre-commercial and commercial high-throughput, high-precision platforms (Roche, Penzberg, Germany) in EDTA plasma. The biomarker quantification was provided as an in-kind contribution of Roche to the CATCH ME consortium. Blood samples were shipped to, and quantifications were conducted at the Roche biomarker research facility in Penzberg, Germany.
Statistical methods
As rhythm is a secondary outcome analysis of the EAST-AFNET 4 trial, all results are exploratory. Biomarker concentrations were 1% winsorized23 from above and logarithmically transformed (log base e) to normalize skewed concentration ranges for all datasets. Concentrations below the detection limit for CA-125 and D-dimer were replaced with the lowest available value. For the initial testing of prespecified hypotheses, all 14 biomarkers were used. Validations were done with predictive biomarkers. This analysis does not take into account the probability of chance findings because of performance of multiple comparisons with 14 biomarkers. As a consequence, results should be interpreted as explorative/hypothesis generating and call for further validation.
Patients in AF at the time of blood sampling showed higher concentrations in most biomarkers (see Supplementary data online, Table S1). Rhythm at time of blood sampling was therefore included as a confounder in all subsequent analyses in addition to the features predicting rhythm at 12 months in the main EAST-AFNET 4 dataset.4
Mixed logistic regression models were used to assess the predictive value of the 14 biomarkers on rhythm at 12 months, with study centre as a random intercept. The lme4 R package24 was used. Each biomarker was assessed in a separate model adjusted for sex, age, rhythm at baseline, body mass index, diastolic blood pressure, AF pattern (first episode, paroxysmal, persistent), left ventricular ejection fraction, rhythm at baseline, and randomized group (usual care or early rhythm control).4 Nested models with additional interaction terms between treatment type and the biomarker of interest were constructed. To obtain P-values for the interaction, each nested model pair was compared by ANOVA for their goodness of fit. Odds ratios (ORs) and P-values for the biomarker effects under different treatment types were calculated by reference cell coding.25 Missing values in heart rhythm and left ventricular ejection fraction were imputed in a 60-times multiple imputed dataset as described earlier,2 following the recommendations of White, Royston, and Wood.26,27 A sensitivity analysis constructed prediction models for recurrent AF at 12- and 24-month follow-up without imputation.
To further explore the effect of rhythm on the biomarkers, mixed regression models were repeated in subgroups split by baseline rhythm (sinus rhythm or AF) and by rhythm control therapy (early rhythm control or usual care) and ORs for the outcome sinus rhythm at 12 months were calculated using the methods described above.
As internal validation, analysis was repeated for sinus rhythm at 24-month follow-up. As sensitivity analysis, the analysis was repeated for recurrent AF up to 24 months.
As additional internal validation, patient clusters formed using all biomarker concentrations agnostic to clinical features17 were tested for prediction of presence of sinus rhythm at 12- and 24-month follow-up. The lowest-risk cluster was used as a reference.
As another means of internal validation, we applied a random forest machine learning model (ML) and made use of a mixed effect random forest (MERF) wrapper to account for the centre as a random effect. The ML model was fitted with the features used for confounding the generalized linear model as well as of all 14 biomarkers at once. To assess the variable importance, we used the models’ inherent Gini-based feature importance as well as the model agnostic SHapley Additive exPlanations (SHAP) values.
Clinical utility
Cut-off values for clinically useful probabilities of sinus rhythm at 12 months (80%) and for AF at 12 months (40%) were determined for all biomarkers that predicted the main outcome. A clinical risk score was developed based on a recent meta-analysis28: three accepted clinical features predicting recurrent AF, namely left atrial size, AF pattern, and age, were dichotomized with a point scored for persistent AF yes, anterior–posterior left atrial diameter > 50 mm, age > 75 years (see Supplementary data online, Table S2). As many patients with one of these three features attain sinus rhythm at 12 months, the score was considered as predictive of high risk of AF at 12 months if at least two of the three factors were present. Each of the biomarkers that were independently associated with sinus rhythm at 12 months was added to this clinical score separately, as well as in combination. If at least one biomarker was above the cut-off value, the patient was regarded as high risk of not attaining sinus rhythm. The confusion matrices for correctly and incorrectly classified patients at high-risk-classified of not attaining sinus rhythm were calculated for the reference clinical score alone and all additional, biomarker-enriched scores.
Biomarkers’ predictive values were tested in the validation datasets using univariate and multivariate models restricted to the features that predicted sinus rhythm at 12 months in the derivation dataset.
Python version 3.8.13 was employed for data pre-processing and visualization, R version 4.2.2 for statistical computations.29 Relevant code will be made publicly available (https://github.com/UCCSHH).
Results
Derivation analysis dataset
The 1586 patients with a recent history of AF and stroke risk factors (age 71 years, 45% women) with clinical features, biomarker concentrations, and cardiovascular outcomes were equally assigned to both randomized treatment groups (Table 1, Supplementary data online, Figure S1).
Association of biomarker concentrations with attaining sinus rhythm at 12 months
Three biomarkers (ANGPT2, BMP10, and NT-proBNP) showed lower concentrations at baseline in patients who were in sinus rhythm at the 12-month follow-up (Figure 1A). These three biomarkers were independently associated with sinus rhythm at the 12-month follow-up after multiple corrections for clinical features, early rhythm control, and baseline rhythm (Figure 1A). NT-proBNP interacted with early rhythm control therapy at 12-month follow-up (P = .033) and low NT-proBNP concentrations only predicted sinus rhythm at 12 months in patients randomized to usual care (Figure 1B). Early rhythm control impacted on the rhythm-predicting effect of NT-proBNP and dampened its predictive value in this group. There was no significant interaction detected between early rhythm control and any of the other 13 biomarkers in this dataset (Figure 1B).
Figure 1
Low concentrations of the biomarkers NT-proBNP, angiopoietin 2, and bone morphogenetic protein 10 predict sinus rhythm at 12-month follow-up in the derivation dataset (EAST-AFNET 4). Odds ratios for sinus rhythm at 12-month follow-up (A) and odds ratios by randomized treatment group (B). Forest plot showing odds ratios for each biomarker for the outcome sinus rhythm at 12-month follow-up and 95% confidence intervals. The odds ratio for NT-proBNP shows an interaction between NT-proBNP concentrations and randomized treatment group (early rhythm control or usual care). All odds ratios are corrected for clinical features, age, sex, EAST study centre, rhythm at baseline, atrial fibrillation type, randomized treatment group, body mass index, diastolic blood pressure, and left ventricular ejection fraction. Even after multiple confounding, high biomarker concentrations indicate lower odds of sinus rhythm at 12-month follow-up. Low concentrations of NT-proBNP predict sinus rhythm at 12-month follow-up in patients with usual care (only symptomatic rhythm control). High concentrations of NT-proBNP do not necessarily predict lack of sinus rhythm at 12 months if patients receive early rhythm control. ANGPT2, angiopoietin 2; BMP10, bone morphogenetic protein 10; CA125, cancer antigen 125; CRP, C-reactive protein; D-dimer, ESM1, endothelial specific molecule 1; FABP3, fatty acid binding protein 3; FGF23, fibroblast growth factor 23; GDF15, growth differentiation factor 15; IGFBP7, insulin-like growth factor binding protein 7; IL-6, interleukin-6; NT-proBNP, N-terminal pro-B-type natriuretic peptide; TnT, cardiac troponin; sCr, serum creatinine
Biomarker concentrations distributions depicted in violin plots after log transformation (Figure 2) show lower concentrations in sinus rhythm vs. AF at 12 months. Numbers of mean biomarker concentrations by rhythm at 12-month follow-up and by randomized treatment group are given in Table 2.
Figure 2
Biomarker concentration distributions at baseline in patients with sinus rhythm (teal right part of each plot) or atrial fibrillation (orange left part of each plot) at 12-month follow-up. Violin plot of the distribution of log-transformed biomarker concentrations for each of 14 biomarkers at baseline, split by the outcome of rhythm at 12-month follow-up. Log-transformed biomarker concentrations are shown on the y-axis and the kernel estimated frequency on the x-axis. Central thick horizontal lines are the median and the thinner lines represent interquartile range. N-terminal pro-B-type natriuretic peptide, angiopoietin 2, and bone morphogenetic protein 10 show an association with sinus rhythm at 12-month follow-up based on the acceptance of a Type 1 error of 5%. P-values were calculated using mixed logistic regression model with site as random effect, adjusted for age, sex, rhythm at baseline, randomized group (early rhythm control or usual care), body mass index, diastolic blood pressure, and left ventricular ejection fraction, those clinical features that were associated with outcomes including sinus rhythm in the main EAST-AFNET 4 trial. ANGPT2, angiopoietin 2; BMP10, bone morphogenetic protein 10; CA125, cancer antigen 125; CRP, C-reactive protein; D-dimer, ESM1, endothelial specific molecule 1; FABP3, fatty acid binding protein 3; FGF23, fibroblast growth factor 23; GDF15, growth differentiation factor 15; IGFBP7, insulin-like growth factor binding protein 7; IL-6, interleukin-6; NT-proBNP, N-terminal pro-B-type natriuretic peptide; TnT, cardiac troponin; sCr, serum creatinine
Table 2
Baseline biomarker concentrations are shown split by rhythm at 12-month follow-up (sinus rhythm or atrial fibrillation) and by randomized group (early rhythm control or usual care)
Randomization group
Early rhythm control
Usual care
SR vs. AF 12 months
Rhythm at 12-month follow-up
Sinus rhythm 12-month FU
Atrial fibrillation 12-month FU
Sinus rhythm 12-month FU
Atrial fibrillation 12-month FU
P-value*
NT-proBNP (pg/mL)
377[164–859]
750[376–1351]
294[127–700]
782[437–1454]
.001
ANGPT2 (ng/mL)
2.34[1.78–3.41]
3.45[2.43–5.62]
2.24[1.7–3.09]
3.31[2.15–4.62]
.001
BMP10 (ng/mL)
2.08[1.8–2.39]
2.21[1.96–2.58]
2.04[1.79–2.34]
2.24[1.94–2.66]
.010
FGF23 (pg/mL)
151[112–209]
179[125–238]
141[108–197]
168[129–226]
.429
ESM1 (ng/mL)
2.01[1.61–2.52]
2.17[1.75–2.88]
1.98[1.57–2.52]
2.09[1.73–2.66]
.218
GDF15 (pg/mL)
1304[958–1934]
1441[997–2008]
1254[911–1782]
1589[1071–2347]
.461
IGFBP7 (ng/mL)
100[89–114]
108[93–126]
98.8[88.5–110]
104[94.7–119]
.487
IL-6 (pg/mL)
2.47[1.57–3.88]
2.6[1.76–4.62]
2.37[1.56–3.6]
3.02[1.98–4.65]
.417
FABP3 (ng/mL)
31.4[25.6–39]
35.3[28.3–43.4]
30.4[25.7–37.8]
33.5[28.0–42.0]
.151
D-dimer (µg/mL)
.17[.08–.33]
.19[.1–.36]
.16[.08–.32]
.16[.08–.32]
.638
TnT (ng/L)
10.6[7.81–15.7]
13.0[9–17.6]
10.3[7.53–15.5]
12.5[8.68–17.7]
.415
CRP (mg/L)
2[.95–4.65]
1.97[.9–4.63]
2.07[.93–4.37]
2.52[1.12–4.87]
.910
sCr (µmol/L)
81.3[70–95]
83[72.7–94.8]
79.5[68.0–91.9]
84.4[72–97.2]
.541
CA125 (U/mL)
11.4[8.0–15.8]
12.3[8.3–17.1]
10.8[7.8–15.7]
11.4[7.96–15.9]
.779
AF, atrial fibrillation; SR, sinus rhythm; FU, follow-up; ANGPT2, angiopoietin 2; BMP10, bone morphogenetic protein 10; CA125, cancer antigen 125; CRP, C-reactive protein; D-dimer, ESM1, endothelial specific molecule 1; FABP3, fatty acid binding protein 3; FGF23, fibroblast growth factor 23; GDF15, growth differentiation factor 15; IGFBP7, insulin-like growth factor binding protein 7; IL-6, interleukin-6; NT-proBNP, N-terminal pro-B-type natriuretic peptide; TnT, cardiac troponin; sCr, serum creatinine.
*
P-values were calculated using mixed logistic regression model with site as random effect, adjusted for sex, age, body mass index, diastolic blood pressure, left ventricular ejection fraction. Values are shown as median [IQR].
Baseline biomarker concentrations depending on baseline rhythm in the derivation dataset and clinical features are shown in Table 3, extended information shown in Supplementary data online, Table S1. Post hoc subgroup analyses by rhythm at the time of baseline assessment (sinus rhythm or AF) and by randomized group (early rhythm control or usual care) find NT-proBNP mainly associated with sinus rhythm at 12 months in patients under usual care. BMP10 and ANGPT2 retained their predictive ability shown in the joint group of all patients also if only the subgroup patients in AF at the time of blood sampling were analysed (Figure 3).
Figure 3
Biomarkers measured at baseline predicting sinus rhythm at 12-month follow-up in all participants of the biomarker study, separately analysed by rhythm at baseline (atrial fibrillation at baseline or sinus rhythm at baseline) and randomized treatment group (early rhythm control or usual care), respectively, in a post hoc analysis. Of the three biomarkers identified to be predictive of sinus rhythm in the whole cohort, NT-proBNP, ANGPT2, and BMP10, all three biomarkers retained their predictive value in the subgroup of patients randomized to usual care. All three biomarkers also retained their predictive value in the subgroup of patients in atrial fibrillation during blood draw at baseline. ANGPT2, angiopoietin 2; BMP10, bone morphogenetic protein 10; NT-proBNP, N-terminal pro-B-type natriuretic peptide
Table 3
Baseline clinical characteristics used as confounders and biomarker concentrations in the derivation dataset (EAST-AFNET 4 biomolecule study) at baseline by randomized group and by baseline rhythm
Group
Early rhythm control
Usual care
P-value
Baseline rhythm
Sinus rhythm
Atrial fibrillation
Sinus rhythm
Atrial fibrillation
Rhythm*
N
452
348
438
348
Women
220 (49%)
135 (39%)
221 (51%)
137 (39%)
<.001
Age, years
70[65–75]
71[67–76]
71[66–75]
72[66–76]
.035
BMI
28.7[25.8–31.6]
28.4[25.5–32.9]
28.4[25.4–31.4]
29.4[25.9–33.3]
.022
Blood pressure (diastolic) (mmHg)
80[72, 87]
80[76, 90]
80[71, 89]
80[76, 90]
<.001
LVEF (%)
60[57, 65]
59[50, 64]
60[59, 65]
60[51, 64]
<.001
AF type: first episode
172 (38%)
118 (34%)
155 (35%)
115 (33%)
AF type: paroxysmal
235 (52%)
67 (19%)
223 (51%)
65 (19%)
<.001
AF type: persistent or long-standing persistent
45 (10%)
163 (47%)
60 (14%)
168 (48%)
<.001
Biomarker concentrations
NT-proBNP (pg/mL)
228[121–467]
890[506–1496]
253[124–504]
934[529–1603]
<.001
ANGPT2 (ng/mL)
2.20[1.65–2.76]
3.39[2.29–5.14]
2.12[1.63–3.00]
3.35[2.31–4.81]
<.001
BMP10 (ng/mL)
2.03[1.73–2.30]
2.22[1.96–2.58]
2.01[1.76–2.29]
2.25[1.96–2.69]
<.001
FGF23 (pg/mL)
139[106–194]
178[128–247]
140[110–192]
170[130–243]
.003
ESM1 (ng/mL)
1.97[1.58–2.44]
2.14[1.74–2.84]
1.96[1.57–2.56]
2.15[1.74–2.78]
.002
GDF15 (pg/mL)
1251[938–1847]
1478[1058–2188]
1259[914–1761]
1585[1065–2272]
<.001
IGFBP7 (ng/mL)
99.0[89.3–111.2]
106.8[93.6–125]
99.2[87.9–111]
105[93.8–123]
<.001
IL-6 (pg/mL)
2.22[1.50–3.58]
3.03[1.99–4.88]
2.42[1.58–3.89]
3.02[1.95–4.59]
.041
FABP3 (ng/mL)
30.2[25.1–38.1]
34.2[28.2–42.1]
30.9[25.6–37.9]
33.3[27.1–42.6]
.020
D-dimer (µg/mL)
.17[.08–.32]
.18[.09–.36]
.15[.08–.32]
.18[.09–.4]
.267
TnT (ng/L)
10.1[7.39–14.5]
12.7[9–18.8]
10.7[7.6–15.7]
12.5[8.73–18.3]
.436
CRP (mg/L)
1.76[.87–4.29]
2.48[1.09–5.78]
2.08[.93–4.52]
2.58[1.26–5.03]
.130
sCr (µmol/L)
80.0[69.0–93.7]
84.0[71.0–97.0]
79.6[68.1–92.0]
83.9[71.0–97.2]
.296
CA125 (U/mL)
11.1[8.01–14.9]
12.3[8.4–16.9]
10.8[8.02–15.7]
11.4[7.84–16.7]
.052
Rhythm at time of blood sampling was included as a fix factor in the analyses of outcome. Distributions are shown as mean and SD for normally distributed values, as median and IQR for non-normal distributed values and biomarkers, and as frequency (percentage) for nominal features. For biomarker concentrations, there were no differences between the randomized groups, but differences between sinus rhythm and AF during the baseline visit.
AF, atrial fibrillation; ERC, early rhythm control; UC, usual care; BMI, body mass index; AF, atrial fibrillation; LVEF, left ventricular ejection fraction; ANGPT2, angiopoietin 2; BMP10, bone morphogenetic protein 10; CA125, cancer antigen 125; CRP, C-reactive protein; D-dimer, ESM1, endothelial specific molecule 1; FABP3, fatty acid binding protein 3; FGF23, fibroblast growth factor 23; GDF15, growth differentiation factor 15; IGFBP7, insulin-like growth factor binding protein 7; IL-6, interleukin-6; NT-proBNP, N-terminal pro-B-type natriuretic peptide; TnT, cardiac troponin; sCr, serum creatinine.
*
P-values were calculated in the unimputed, pooled dataset (ERC and UC combined) using mixed logistic regression model with site as random effect, for the biomarkers additionally adjusted for sex, age, body mass index, diastolic blood pressure, left ventricular ejection fraction, and AF type, the clinical features that were associated with outcomes including sinus rhythm in the main EAST-AFNET 4 dataset.4
Internal validations
As a first internal validation, the same analysis was performed for the 24-month follow-up. The same biomarkers, ANGPT2, BMP10, and NT-proBNP, were consistently associated with sinus rhythm at 24-month follow-up (Figure 4).
Figure 4
Internal validation: angiopoietin 2, bone morphogenetic protein 10, and NT-proBNP biomarkers at baseline predict sinus rhythm at 24-month follow-up even after correction for multiple confounders. Odds ratios are shown for sinus rhythm at 24-month follow-up. This analysis provides an internal validation of the biomarkers predicting sinus rhythm at 12-month follow-up (Figure 1). All odds ratios are corrected for clinical age, sex, study site, rhythm at baseline, randomized treatment group (early rhythm control or usual care), body mass index, diastolic blood pressure, and left ventricular ejection fraction, those clinical features that were associated with outcomes including sinus rhythm in the main EAST-AFNET 4 trial.4 Low concentrations of NT-proBNP, ANGPT2, and BMP10 predict sinus rhythm at 24-month follow-up in patients. Accordingly, high concentrations predict lack of sinus rhythm at 24-month follow-up. ANGPT2, angiopoietin 2; BMP10, bone morphogenetic protein 10; CA125, cancer antigen 125; CRP, C-reactive protein; D-dimer, ESM1, endothelial specific molecule 1; FABP3, fatty acid binding protein 3; FGF23, fibroblast growth factor 23; GDF15, growth differentiation factor 15; IGFBP7, insulin-like growth factor binding protein 7; IL-6, interleukin-6; NT-proBNP, N-terminal pro-B-type natriuretic peptide; TnT, cardiac troponin; sCr, serum creatinine
Repeating the analysis for recurrent AF up to 24 months showed similar results (see Supplementary data online, Table S3). As further internal validation analysis, unsupervised biomarker-based clustering of EAST patients previously performed was applied to sinus rhythm at 12-month follow-up. Clusters separated by risk of cardiovascular complications, with patients assigned to the high-risk cardiovascular outcome cluster showing a lower likelihood of sinus rhythm at 12 months, patients in the two intermediate cardiovascular risk biomarker clusters showing an intermediate likelihood of sinus rhythm, all tested against the low cardiovascular risk cluster, with the low-risk outcome patient cluster showing the highest likelihood of sinus rhythm at 12 months (Figure 5A). These findings were consistent for the biomarker-based clusters at 24-month follow-up (Figure 5B).
Figure 5
Validation applying biomarker-based clusters indicating cardiovascular outcome risk: patients at high risk of cardiovascular complications as estimated by biomarker-based clusters have reduced odds of sinus rhythm at 12-month and 24-month follow-up. Odds ratio for the high cardiovascular outcome risk (red) and intermediate cardiovascular outcome risk biomarker clusters (orange and green) for sinus rhythm at 12-month follow-up (A, above) and at 24-month follow-up (B, bottom) tested against the low cardiovascular risk cluster (not depicted as used as reference). All odds ratios are corrected for age, sex, study centre, rhythm at baseline, atrial fibrillation type (depicted in grey odds ratios below the cluster odds ratios), randomized treatment group (early rhythm control or usual care), as well as body mass index, diastolic blood pressure, and left ventricular ejection fraction, the clinical features that were associated with outcomes including sinus rhythm in the main EAST-FNET 4 trial.4 AF, atrial fibrillation
As further internal validation, a random forest classifier was trained on the EAST-AFNET 4 dataset. Its feature performance evaluation confirmed the importance of the three biomarkers alongside AF pattern, rhythm at baseline, and early rhythm control for the outcome of sinus rhythm (Figure 6).
Figure 6
Validation by random forest analyses identified highest importance for similar biomarkers, alongside rhythm at baseline and AF pattern, as predictors of sinus rhythm at 12-month follow-up (A—importance, B—SHAP value). AF, atrial fibrillation; ANGPT2, angiopoietin 2; BL, baseline; BMI, body mass index; BMP10, bone morphogenetic protein 10; CA125, cancer antigen 125; CRP, C-reactive protein; D-dimer, ESM1, endothelial specific molecule 1; FABP3, fatty acid binding protein 3; FGF23, fibroblast growth factor 23; GDF15, growth differentiation factor 15; IGFBP7, insulin-like growth factor binding protein 7; IL-6, interleukin-6; MERF, mixed effect random forest; NT-proBNP, N-terminal pro-B-type natriuretic peptide; TnT, cardiac troponin; sCr, serum creatinine; SHAP, SHapley Additive exPlanations
Clinical utility
Thresholds to predict a high probability of attaining sinus rhythm (>80%, low risk of AF) or a high probability of recurrent AF at follow-up (>40%, high risk of AF) were determined for each biomarker (Table 4, Supplementary data online, Figures S2–S4). To compare them to clinical features predicting sinus rhythm, a score combining clinical features predicting recurrent AF was created integrating age, left atrial size, and AF pattern28 (see Supplementary data online, Table S2). Adding biomarkers using these thresholds improved identification of patients at risk of not attaining sinus rhythm at 12-month follow-up (Table 5, Supplementary data online, Table S4).
Table 4
Threshold concentrations for NT-proBNP, BMP10, and ANGPT2 determined in the derivation dataset (EAST-AFNET 4 biomolecule study)
Biomarker
Low threshold (>80% sinus rhythm at 12 months)
High threshold (>40% AF at 12 months)
NT-proBNP(pg/mL)
<1000
>1500
BMP10(ng/mL)
<2
>3
ANGPT2(ng/mL)
<3.5
>3.5
The lower threshold was defined as the nearest round concentration below which 80% of patients attained sinus rhythm at 12 months. The higher threshold was defined as the nearest rounded concentration above which 40% of patients were in AF at 12 months.
AF, atrial fibrillation; ANGPT2, angiopoietin 2; BMP10, bone morphogenetic protein 10; NT-proBNP, N-terminal pro-B-type natriuretic peptide.
Table 5
Estimated clinical utility of adding NT-proBNP, BMP10, and ANGPT2 alone or in combination to a clinical risk score to predict sinus rhythm at 12 months
Patients reclassified as high risk of not attaining sinus rhythm at 12 M (N)
Confusion matrix
Predicted sinus rhythm (actual patients in SR: N = 1081)
Predicted: AF (actual patients in AF: N = 365)
Patients in sinus rhythm at 12 M
Patients in AF at 12 M
Patients in AF at 12 M
Patients in sinus rhythm at 12 M
Clinical model (LA size > 50 mm, persistent AF, age > 75 years)
Reference
813 (77%)
245 (23%)
75 (40%)
112 (60%)
+NT-proBNP
135
743 (79%)
201 (21%)
129 (40%)
191 (60%)
+BMP10
240
670 (79%)
175 (21%)
161 (37%)
279 (63%)
+ANGPT2
301
650 (82%)
145 (18%)
198 (40%)
303 (60%)
+NT-proBNP and BMP10
298
638 (80%)
158 (20%)
183 (37%)
315 (63%)
+NT-proBNP and ANGPT2
345
625 (83%)
130 (17%)
215 (39%)
332 (61%)
+ANGPT2 and BMP10
410
570 (82%)
125 (18%)
223 (36%)
394 (64%)
+NT-proBNP and BMP10 and ANGPT2
441
551 (83%)
115 (17%)
234 (36%)
416 (64%)
Sinus rhythm at 12 months was initially predicted by a clinical risk score based on three validated clinical features (LA size > 50 mm, persistent AF, age > 75 years) alone. This reference score was then combined with one, a combination of two, or all three binarized predictive biomarkers (biomarker thresholds: NT-proBNP < 1000 pg/mL or >1500 pg/mL, ANGPT2 < 3.5 ng/mL or >3.5 ng/mL, BMP10 < 2 ng/mL or >3 ng/mL, Table 4). If either the clinical risk score is ≥2 or any of the biomarkers added to the model surpasses its threshold, the model predicts failure to attain sinus rhythm at 12-month follow-up and predicts AF instead. All numbers indicate number of patients with percentages of the predicted class in brackets. There were 140 missing values in outcomes and 225 missing values in LA size. The additional use of biomarkers for prediction can lead to differing missing values in predictions made for participants with available outcome data.
AF, atrial fibrillation; ANGPT2, angiopoietin 2; BMP10, bone morphogenetic protein 10; NT-proBNP, N-terminal pro-B-type natriuretic peptide; SR, sinus rhythm; 12 M, 12-month follow-up.
External validation
Several separate validation datasets (AXAFA-AFNET 5 trial, BBC-AF, and TRUST cohort snapshot Supplementary data online, Tables S5–S7) were used. The biomarkers NT-proBNP, BMP10, and ANGPT2 were confirmed as predictive of sinus rhythm in the final follow-up in AXAFA-AFNET 5 (Figure 7, Structured Graphical Abstract). The clinical utility of adding the biomarkers to clinical predictors was validated in both cohorts using the thresholds derived in EAST-AFNET 4 (see Supplementary data online, Tables S8 and S9).
Figure 7
External validation of the prediction of sinus rhythm at the end of follow-up by baseline biomarkers in AXAFA-AFNET 5. AXAFA-AFNET 5 enrolled 674 patients undergoing a first AF ablation with at least one stroke risk factor. Patients were randomized to apixaban or vitamin K antagonist therapy without affecting rhythm. Individual models with rhythm at baseline, age, and sex were constructed to determine whether each biomarker predicts sinus rhythm at the end of follow-up 120 days after randomization, 549 patients with sinus rhythm, 71 patients with atrial fibrillation, 620 patients with baseline biomarkers completed follow-up. *P-values were calculated using logistic regression, adjusted for sex, age, rhythm at baseline, and treatment group. ANGPT2, angiopoietin 2; BMP10, bone morphogenetic protein 10; NT-proBNP, N-terminal pro-B-type natriuretic peptide
Derivation analysis dataset
The 1586 patients with a recent history of AF and stroke risk factors (age 71 years, 45% women) with clinical features, biomarker concentrations, and cardiovascular outcomes were equally assigned to both randomized treatment groups (Table 1, Supplementary data online, Figure S1).
Association of biomarker concentrations with attaining sinus rhythm at 12 months
Three biomarkers (ANGPT2, BMP10, and NT-proBNP) showed lower concentrations at baseline in patients who were in sinus rhythm at the 12-month follow-up (Figure 1A). These three biomarkers were independently associated with sinus rhythm at the 12-month follow-up after multiple corrections for clinical features, early rhythm control, and baseline rhythm (Figure 1A). NT-proBNP interacted with early rhythm control therapy at 12-month follow-up (P = .033) and low NT-proBNP concentrations only predicted sinus rhythm at 12 months in patients randomized to usual care (Figure 1B). Early rhythm control impacted on the rhythm-predicting effect of NT-proBNP and dampened its predictive value in this group. There was no significant interaction detected between early rhythm control and any of the other 13 biomarkers in this dataset (Figure 1B).
Figure 1
Low concentrations of the biomarkers NT-proBNP, angiopoietin 2, and bone morphogenetic protein 10 predict sinus rhythm at 12-month follow-up in the derivation dataset (EAST-AFNET 4). Odds ratios for sinus rhythm at 12-month follow-up (A) and odds ratios by randomized treatment group (B). Forest plot showing odds ratios for each biomarker for the outcome sinus rhythm at 12-month follow-up and 95% confidence intervals. The odds ratio for NT-proBNP shows an interaction between NT-proBNP concentrations and randomized treatment group (early rhythm control or usual care). All odds ratios are corrected for clinical features, age, sex, EAST study centre, rhythm at baseline, atrial fibrillation type, randomized treatment group, body mass index, diastolic blood pressure, and left ventricular ejection fraction. Even after multiple confounding, high biomarker concentrations indicate lower odds of sinus rhythm at 12-month follow-up. Low concentrations of NT-proBNP predict sinus rhythm at 12-month follow-up in patients with usual care (only symptomatic rhythm control). High concentrations of NT-proBNP do not necessarily predict lack of sinus rhythm at 12 months if patients receive early rhythm control. ANGPT2, angiopoietin 2; BMP10, bone morphogenetic protein 10; CA125, cancer antigen 125; CRP, C-reactive protein; D-dimer, ESM1, endothelial specific molecule 1; FABP3, fatty acid binding protein 3; FGF23, fibroblast growth factor 23; GDF15, growth differentiation factor 15; IGFBP7, insulin-like growth factor binding protein 7; IL-6, interleukin-6; NT-proBNP, N-terminal pro-B-type natriuretic peptide; TnT, cardiac troponin; sCr, serum creatinine
Biomarker concentrations distributions depicted in violin plots after log transformation (Figure 2) show lower concentrations in sinus rhythm vs. AF at 12 months. Numbers of mean biomarker concentrations by rhythm at 12-month follow-up and by randomized treatment group are given in Table 2.
Figure 2
Biomarker concentration distributions at baseline in patients with sinus rhythm (teal right part of each plot) or atrial fibrillation (orange left part of each plot) at 12-month follow-up. Violin plot of the distribution of log-transformed biomarker concentrations for each of 14 biomarkers at baseline, split by the outcome of rhythm at 12-month follow-up. Log-transformed biomarker concentrations are shown on the y-axis and the kernel estimated frequency on the x-axis. Central thick horizontal lines are the median and the thinner lines represent interquartile range. N-terminal pro-B-type natriuretic peptide, angiopoietin 2, and bone morphogenetic protein 10 show an association with sinus rhythm at 12-month follow-up based on the acceptance of a Type 1 error of 5%. P-values were calculated using mixed logistic regression model with site as random effect, adjusted for age, sex, rhythm at baseline, randomized group (early rhythm control or usual care), body mass index, diastolic blood pressure, and left ventricular ejection fraction, those clinical features that were associated with outcomes including sinus rhythm in the main EAST-AFNET 4 trial. ANGPT2, angiopoietin 2; BMP10, bone morphogenetic protein 10; CA125, cancer antigen 125; CRP, C-reactive protein; D-dimer, ESM1, endothelial specific molecule 1; FABP3, fatty acid binding protein 3; FGF23, fibroblast growth factor 23; GDF15, growth differentiation factor 15; IGFBP7, insulin-like growth factor binding protein 7; IL-6, interleukin-6; NT-proBNP, N-terminal pro-B-type natriuretic peptide; TnT, cardiac troponin; sCr, serum creatinine
Table 2
Baseline biomarker concentrations are shown split by rhythm at 12-month follow-up (sinus rhythm or atrial fibrillation) and by randomized group (early rhythm control or usual care)
Randomization group
Early rhythm control
Usual care
SR vs. AF 12 months
Rhythm at 12-month follow-up
Sinus rhythm 12-month FU
Atrial fibrillation 12-month FU
Sinus rhythm 12-month FU
Atrial fibrillation 12-month FU
P-value*
NT-proBNP (pg/mL)
377[164–859]
750[376–1351]
294[127–700]
782[437–1454]
.001
ANGPT2 (ng/mL)
2.34[1.78–3.41]
3.45[2.43–5.62]
2.24[1.7–3.09]
3.31[2.15–4.62]
.001
BMP10 (ng/mL)
2.08[1.8–2.39]
2.21[1.96–2.58]
2.04[1.79–2.34]
2.24[1.94–2.66]
.010
FGF23 (pg/mL)
151[112–209]
179[125–238]
141[108–197]
168[129–226]
.429
ESM1 (ng/mL)
2.01[1.61–2.52]
2.17[1.75–2.88]
1.98[1.57–2.52]
2.09[1.73–2.66]
.218
GDF15 (pg/mL)
1304[958–1934]
1441[997–2008]
1254[911–1782]
1589[1071–2347]
.461
IGFBP7 (ng/mL)
100[89–114]
108[93–126]
98.8[88.5–110]
104[94.7–119]
.487
IL-6 (pg/mL)
2.47[1.57–3.88]
2.6[1.76–4.62]
2.37[1.56–3.6]
3.02[1.98–4.65]
.417
FABP3 (ng/mL)
31.4[25.6–39]
35.3[28.3–43.4]
30.4[25.7–37.8]
33.5[28.0–42.0]
.151
D-dimer (µg/mL)
.17[.08–.33]
.19[.1–.36]
.16[.08–.32]
.16[.08–.32]
.638
TnT (ng/L)
10.6[7.81–15.7]
13.0[9–17.6]
10.3[7.53–15.5]
12.5[8.68–17.7]
.415
CRP (mg/L)
2[.95–4.65]
1.97[.9–4.63]
2.07[.93–4.37]
2.52[1.12–4.87]
.910
sCr (µmol/L)
81.3[70–95]
83[72.7–94.8]
79.5[68.0–91.9]
84.4[72–97.2]
.541
CA125 (U/mL)
11.4[8.0–15.8]
12.3[8.3–17.1]
10.8[7.8–15.7]
11.4[7.96–15.9]
.779
AF, atrial fibrillation; SR, sinus rhythm; FU, follow-up; ANGPT2, angiopoietin 2; BMP10, bone morphogenetic protein 10; CA125, cancer antigen 125; CRP, C-reactive protein; D-dimer, ESM1, endothelial specific molecule 1; FABP3, fatty acid binding protein 3; FGF23, fibroblast growth factor 23; GDF15, growth differentiation factor 15; IGFBP7, insulin-like growth factor binding protein 7; IL-6, interleukin-6; NT-proBNP, N-terminal pro-B-type natriuretic peptide; TnT, cardiac troponin; sCr, serum creatinine.
*
P-values were calculated using mixed logistic regression model with site as random effect, adjusted for sex, age, body mass index, diastolic blood pressure, left ventricular ejection fraction. Values are shown as median [IQR].
Baseline biomarker concentrations depending on baseline rhythm in the derivation dataset and clinical features are shown in Table 3, extended information shown in Supplementary data online, Table S1. Post hoc subgroup analyses by rhythm at the time of baseline assessment (sinus rhythm or AF) and by randomized group (early rhythm control or usual care) find NT-proBNP mainly associated with sinus rhythm at 12 months in patients under usual care. BMP10 and ANGPT2 retained their predictive ability shown in the joint group of all patients also if only the subgroup patients in AF at the time of blood sampling were analysed (Figure 3).
Figure 3
Biomarkers measured at baseline predicting sinus rhythm at 12-month follow-up in all participants of the biomarker study, separately analysed by rhythm at baseline (atrial fibrillation at baseline or sinus rhythm at baseline) and randomized treatment group (early rhythm control or usual care), respectively, in a post hoc analysis. Of the three biomarkers identified to be predictive of sinus rhythm in the whole cohort, NT-proBNP, ANGPT2, and BMP10, all three biomarkers retained their predictive value in the subgroup of patients randomized to usual care. All three biomarkers also retained their predictive value in the subgroup of patients in atrial fibrillation during blood draw at baseline. ANGPT2, angiopoietin 2; BMP10, bone morphogenetic protein 10; NT-proBNP, N-terminal pro-B-type natriuretic peptide
Table 3
Baseline clinical characteristics used as confounders and biomarker concentrations in the derivation dataset (EAST-AFNET 4 biomolecule study) at baseline by randomized group and by baseline rhythm
Group
Early rhythm control
Usual care
P-value
Baseline rhythm
Sinus rhythm
Atrial fibrillation
Sinus rhythm
Atrial fibrillation
Rhythm*
N
452
348
438
348
Women
220 (49%)
135 (39%)
221 (51%)
137 (39%)
<.001
Age, years
70[65–75]
71[67–76]
71[66–75]
72[66–76]
.035
BMI
28.7[25.8–31.6]
28.4[25.5–32.9]
28.4[25.4–31.4]
29.4[25.9–33.3]
.022
Blood pressure (diastolic) (mmHg)
80[72, 87]
80[76, 90]
80[71, 89]
80[76, 90]
<.001
LVEF (%)
60[57, 65]
59[50, 64]
60[59, 65]
60[51, 64]
<.001
AF type: first episode
172 (38%)
118 (34%)
155 (35%)
115 (33%)
AF type: paroxysmal
235 (52%)
67 (19%)
223 (51%)
65 (19%)
<.001
AF type: persistent or long-standing persistent
45 (10%)
163 (47%)
60 (14%)
168 (48%)
<.001
Biomarker concentrations
NT-proBNP (pg/mL)
228[121–467]
890[506–1496]
253[124–504]
934[529–1603]
<.001
ANGPT2 (ng/mL)
2.20[1.65–2.76]
3.39[2.29–5.14]
2.12[1.63–3.00]
3.35[2.31–4.81]
<.001
BMP10 (ng/mL)
2.03[1.73–2.30]
2.22[1.96–2.58]
2.01[1.76–2.29]
2.25[1.96–2.69]
<.001
FGF23 (pg/mL)
139[106–194]
178[128–247]
140[110–192]
170[130–243]
.003
ESM1 (ng/mL)
1.97[1.58–2.44]
2.14[1.74–2.84]
1.96[1.57–2.56]
2.15[1.74–2.78]
.002
GDF15 (pg/mL)
1251[938–1847]
1478[1058–2188]
1259[914–1761]
1585[1065–2272]
<.001
IGFBP7 (ng/mL)
99.0[89.3–111.2]
106.8[93.6–125]
99.2[87.9–111]
105[93.8–123]
<.001
IL-6 (pg/mL)
2.22[1.50–3.58]
3.03[1.99–4.88]
2.42[1.58–3.89]
3.02[1.95–4.59]
.041
FABP3 (ng/mL)
30.2[25.1–38.1]
34.2[28.2–42.1]
30.9[25.6–37.9]
33.3[27.1–42.6]
.020
D-dimer (µg/mL)
.17[.08–.32]
.18[.09–.36]
.15[.08–.32]
.18[.09–.4]
.267
TnT (ng/L)
10.1[7.39–14.5]
12.7[9–18.8]
10.7[7.6–15.7]
12.5[8.73–18.3]
.436
CRP (mg/L)
1.76[.87–4.29]
2.48[1.09–5.78]
2.08[.93–4.52]
2.58[1.26–5.03]
.130
sCr (µmol/L)
80.0[69.0–93.7]
84.0[71.0–97.0]
79.6[68.1–92.0]
83.9[71.0–97.2]
.296
CA125 (U/mL)
11.1[8.01–14.9]
12.3[8.4–16.9]
10.8[8.02–15.7]
11.4[7.84–16.7]
.052
Rhythm at time of blood sampling was included as a fix factor in the analyses of outcome. Distributions are shown as mean and SD for normally distributed values, as median and IQR for non-normal distributed values and biomarkers, and as frequency (percentage) for nominal features. For biomarker concentrations, there were no differences between the randomized groups, but differences between sinus rhythm and AF during the baseline visit.
AF, atrial fibrillation; ERC, early rhythm control; UC, usual care; BMI, body mass index; AF, atrial fibrillation; LVEF, left ventricular ejection fraction; ANGPT2, angiopoietin 2; BMP10, bone morphogenetic protein 10; CA125, cancer antigen 125; CRP, C-reactive protein; D-dimer, ESM1, endothelial specific molecule 1; FABP3, fatty acid binding protein 3; FGF23, fibroblast growth factor 23; GDF15, growth differentiation factor 15; IGFBP7, insulin-like growth factor binding protein 7; IL-6, interleukin-6; NT-proBNP, N-terminal pro-B-type natriuretic peptide; TnT, cardiac troponin; sCr, serum creatinine.
*
P-values were calculated in the unimputed, pooled dataset (ERC and UC combined) using mixed logistic regression model with site as random effect, for the biomarkers additionally adjusted for sex, age, body mass index, diastolic blood pressure, left ventricular ejection fraction, and AF type, the clinical features that were associated with outcomes including sinus rhythm in the main EAST-AFNET 4 dataset.4
Internal validations
As a first internal validation, the same analysis was performed for the 24-month follow-up. The same biomarkers, ANGPT2, BMP10, and NT-proBNP, were consistently associated with sinus rhythm at 24-month follow-up (Figure 4).
Figure 4
Internal validation: angiopoietin 2, bone morphogenetic protein 10, and NT-proBNP biomarkers at baseline predict sinus rhythm at 24-month follow-up even after correction for multiple confounders. Odds ratios are shown for sinus rhythm at 24-month follow-up. This analysis provides an internal validation of the biomarkers predicting sinus rhythm at 12-month follow-up (Figure 1). All odds ratios are corrected for clinical age, sex, study site, rhythm at baseline, randomized treatment group (early rhythm control or usual care), body mass index, diastolic blood pressure, and left ventricular ejection fraction, those clinical features that were associated with outcomes including sinus rhythm in the main EAST-AFNET 4 trial.4 Low concentrations of NT-proBNP, ANGPT2, and BMP10 predict sinus rhythm at 24-month follow-up in patients. Accordingly, high concentrations predict lack of sinus rhythm at 24-month follow-up. ANGPT2, angiopoietin 2; BMP10, bone morphogenetic protein 10; CA125, cancer antigen 125; CRP, C-reactive protein; D-dimer, ESM1, endothelial specific molecule 1; FABP3, fatty acid binding protein 3; FGF23, fibroblast growth factor 23; GDF15, growth differentiation factor 15; IGFBP7, insulin-like growth factor binding protein 7; IL-6, interleukin-6; NT-proBNP, N-terminal pro-B-type natriuretic peptide; TnT, cardiac troponin; sCr, serum creatinine
Repeating the analysis for recurrent AF up to 24 months showed similar results (see Supplementary data online, Table S3). As further internal validation analysis, unsupervised biomarker-based clustering of EAST patients previously performed was applied to sinus rhythm at 12-month follow-up. Clusters separated by risk of cardiovascular complications, with patients assigned to the high-risk cardiovascular outcome cluster showing a lower likelihood of sinus rhythm at 12 months, patients in the two intermediate cardiovascular risk biomarker clusters showing an intermediate likelihood of sinus rhythm, all tested against the low cardiovascular risk cluster, with the low-risk outcome patient cluster showing the highest likelihood of sinus rhythm at 12 months (Figure 5A). These findings were consistent for the biomarker-based clusters at 24-month follow-up (Figure 5B).
Figure 5
Validation applying biomarker-based clusters indicating cardiovascular outcome risk: patients at high risk of cardiovascular complications as estimated by biomarker-based clusters have reduced odds of sinus rhythm at 12-month and 24-month follow-up. Odds ratio for the high cardiovascular outcome risk (red) and intermediate cardiovascular outcome risk biomarker clusters (orange and green) for sinus rhythm at 12-month follow-up (A, above) and at 24-month follow-up (B, bottom) tested against the low cardiovascular risk cluster (not depicted as used as reference). All odds ratios are corrected for age, sex, study centre, rhythm at baseline, atrial fibrillation type (depicted in grey odds ratios below the cluster odds ratios), randomized treatment group (early rhythm control or usual care), as well as body mass index, diastolic blood pressure, and left ventricular ejection fraction, the clinical features that were associated with outcomes including sinus rhythm in the main EAST-FNET 4 trial.4 AF, atrial fibrillation
As further internal validation, a random forest classifier was trained on the EAST-AFNET 4 dataset. Its feature performance evaluation confirmed the importance of the three biomarkers alongside AF pattern, rhythm at baseline, and early rhythm control for the outcome of sinus rhythm (Figure 6).
Figure 6
Validation by random forest analyses identified highest importance for similar biomarkers, alongside rhythm at baseline and AF pattern, as predictors of sinus rhythm at 12-month follow-up (A—importance, B—SHAP value). AF, atrial fibrillation; ANGPT2, angiopoietin 2; BL, baseline; BMI, body mass index; BMP10, bone morphogenetic protein 10; CA125, cancer antigen 125; CRP, C-reactive protein; D-dimer, ESM1, endothelial specific molecule 1; FABP3, fatty acid binding protein 3; FGF23, fibroblast growth factor 23; GDF15, growth differentiation factor 15; IGFBP7, insulin-like growth factor binding protein 7; IL-6, interleukin-6; MERF, mixed effect random forest; NT-proBNP, N-terminal pro-B-type natriuretic peptide; TnT, cardiac troponin; sCr, serum creatinine; SHAP, SHapley Additive exPlanations
Clinical utility
Thresholds to predict a high probability of attaining sinus rhythm (>80%, low risk of AF) or a high probability of recurrent AF at follow-up (>40%, high risk of AF) were determined for each biomarker (Table 4, Supplementary data online, Figures S2–S4). To compare them to clinical features predicting sinus rhythm, a score combining clinical features predicting recurrent AF was created integrating age, left atrial size, and AF pattern28 (see Supplementary data online, Table S2). Adding biomarkers using these thresholds improved identification of patients at risk of not attaining sinus rhythm at 12-month follow-up (Table 5, Supplementary data online, Table S4).
Table 4
Threshold concentrations for NT-proBNP, BMP10, and ANGPT2 determined in the derivation dataset (EAST-AFNET 4 biomolecule study)
Biomarker
Low threshold (>80% sinus rhythm at 12 months)
High threshold (>40% AF at 12 months)
NT-proBNP(pg/mL)
<1000
>1500
BMP10(ng/mL)
<2
>3
ANGPT2(ng/mL)
<3.5
>3.5
The lower threshold was defined as the nearest round concentration below which 80% of patients attained sinus rhythm at 12 months. The higher threshold was defined as the nearest rounded concentration above which 40% of patients were in AF at 12 months.
AF, atrial fibrillation; ANGPT2, angiopoietin 2; BMP10, bone morphogenetic protein 10; NT-proBNP, N-terminal pro-B-type natriuretic peptide.
Table 5
Estimated clinical utility of adding NT-proBNP, BMP10, and ANGPT2 alone or in combination to a clinical risk score to predict sinus rhythm at 12 months
Patients reclassified as high risk of not attaining sinus rhythm at 12 M (N)
Confusion matrix
Predicted sinus rhythm (actual patients in SR: N = 1081)
Predicted: AF (actual patients in AF: N = 365)
Patients in sinus rhythm at 12 M
Patients in AF at 12 M
Patients in AF at 12 M
Patients in sinus rhythm at 12 M
Clinical model (LA size > 50 mm, persistent AF, age > 75 years)
Reference
813 (77%)
245 (23%)
75 (40%)
112 (60%)
+NT-proBNP
135
743 (79%)
201 (21%)
129 (40%)
191 (60%)
+BMP10
240
670 (79%)
175 (21%)
161 (37%)
279 (63%)
+ANGPT2
301
650 (82%)
145 (18%)
198 (40%)
303 (60%)
+NT-proBNP and BMP10
298
638 (80%)
158 (20%)
183 (37%)
315 (63%)
+NT-proBNP and ANGPT2
345
625 (83%)
130 (17%)
215 (39%)
332 (61%)
+ANGPT2 and BMP10
410
570 (82%)
125 (18%)
223 (36%)
394 (64%)
+NT-proBNP and BMP10 and ANGPT2
441
551 (83%)
115 (17%)
234 (36%)
416 (64%)
Sinus rhythm at 12 months was initially predicted by a clinical risk score based on three validated clinical features (LA size > 50 mm, persistent AF, age > 75 years) alone. This reference score was then combined with one, a combination of two, or all three binarized predictive biomarkers (biomarker thresholds: NT-proBNP < 1000 pg/mL or >1500 pg/mL, ANGPT2 < 3.5 ng/mL or >3.5 ng/mL, BMP10 < 2 ng/mL or >3 ng/mL, Table 4). If either the clinical risk score is ≥2 or any of the biomarkers added to the model surpasses its threshold, the model predicts failure to attain sinus rhythm at 12-month follow-up and predicts AF instead. All numbers indicate number of patients with percentages of the predicted class in brackets. There were 140 missing values in outcomes and 225 missing values in LA size. The additional use of biomarkers for prediction can lead to differing missing values in predictions made for participants with available outcome data.
AF, atrial fibrillation; ANGPT2, angiopoietin 2; BMP10, bone morphogenetic protein 10; NT-proBNP, N-terminal pro-B-type natriuretic peptide; SR, sinus rhythm; 12 M, 12-month follow-up.
External validation
Several separate validation datasets (AXAFA-AFNET 5 trial, BBC-AF, and TRUST cohort snapshot Supplementary data online, Tables S5–S7) were used. The biomarkers NT-proBNP, BMP10, and ANGPT2 were confirmed as predictive of sinus rhythm in the final follow-up in AXAFA-AFNET 5 (Figure 7, Structured Graphical Abstract). The clinical utility of adding the biomarkers to clinical predictors was validated in both cohorts using the thresholds derived in EAST-AFNET 4 (see Supplementary data online, Tables S8 and S9).
Figure 7
External validation of the prediction of sinus rhythm at the end of follow-up by baseline biomarkers in AXAFA-AFNET 5. AXAFA-AFNET 5 enrolled 674 patients undergoing a first AF ablation with at least one stroke risk factor. Patients were randomized to apixaban or vitamin K antagonist therapy without affecting rhythm. Individual models with rhythm at baseline, age, and sex were constructed to determine whether each biomarker predicts sinus rhythm at the end of follow-up 120 days after randomization, 549 patients with sinus rhythm, 71 patients with atrial fibrillation, 620 patients with baseline biomarkers completed follow-up. *P-values were calculated using logistic regression, adjusted for sex, age, rhythm at baseline, and treatment group. ANGPT2, angiopoietin 2; BMP10, bone morphogenetic protein 10; NT-proBNP, N-terminal pro-B-type natriuretic peptide
Discussion
Main findings
Three out of 14 candidate biomarkers, BMP10, ANGPT2, and NT-proBNP, are associated with sinus rhythm at 12-month and 24-month follow-up after correcting for clinical features. Low NT-proBNP, low ANGPT2, and low BMP10 concentrations independently predict sinus rhythm in patients at follow-up. NT-proBNP is less predictive of rhythm in patients receiving rhythm control therapy. Adding these biomarkers to a clinical score identifying patients with a low probability of sinus rhythm at 12 months (positive with two out of three features: left atrial size > 50 mm, persistent AF, or age > 75 years) refined risk prediction (Structured Graphical Abstract).
Relevance for clinical care and research
In view of the growing choice of medical,2,30 interventional,2,31 and surgical32 treatment options for patients with AF, selecting the best strategy and the patients most benefitting from rhythm control therapy gains importance. Biomarker-based risk estimators have so far mainly been developed to refine anticoagulation decisions in patients with AF.33–35 Actionable biomarkers to guide rhythm control therapy are lacking. Similar to stroke prevention estimators, rhythm estimators face the challenge of random factors determining a binary outcome (AF or sinus rhythm). The present results suggest that NT-proBNP, BMP10, and ANGPT2 can stratify patients at high and low risk of attaining sinus rhythm alone and in combination. These biomarkers reflect and identify diseases processes that promote future AF, pointing to potential therapeutic targets for adjunct therapy supporting rhythm control. While a simple clinical score combining enlarged left atrial size, persistent AF, and older age predicted future sinus rhythm reasonably well, adding biomarkers reclassifies a clinically relevant number of patients at high risk of not attaining sinus rhythm at the price of also classifying more patients in sinus rhythm as high risk.
Effect of baseline rhythm on biomarker concentrations
This study shows that ANGPT2 and BMP10 provide additional information on future sinus rhythm when combined with NT-proBNP, especially in patients who are in AF at the time of blood sampling. Most biomarkers studied were elevated when the blood sample was taken in AF. Furthermore, NT-proBNP lost its ability to predict sinus rhythm in patients on rhythm control therapy.20,36 The effects of baseline rhythm on the concentrations and predictive ability of biomarkers should be further investigated in patients with AF undergoing rhythm control therapy.
Interpretation of NT-proBNP
NT-proBNP is released by atrial cardiomyocytes in response to stretch and strain, thereby acutely regulating fluid balance in the body, resulting in high concentrations during AF.37 In heart failure, NT-proBNP is also released by ventricular cardiomyocytes, further enhancing its concentrations. Atrial stretch has proarrhythmic effects including shortening of the atrial effective refractory period38 and conduction slowing,39 partially explaining its prediction of sinus rhythm in this study. NT-proBNP reflects short- and mid-term cardiac load in patients with AF, probably explaining its interaction with rhythm control. The possibility that elevated NT-proBNP concentrations predict rhythm during follow-up have been reported before.40–47 NT-proBNP is also associated with incident AF48–51 and with cardiovascular events in patients with and without AF and heart failure.22 This analysis demonstrates that the rhythm-predicting ability of NT-proBNP is reduced in patients treated with rhythm control therapy.
The NT-proBNP thresholds associated with a high risk of AF at 12 months in this study (>1500 pg/mL) are comparable to the thresholds associated with cardiovascular events, but higher than currently used thresholds e.g. for AF screening52 or for diagnosing heart failure with AF and heart failure with preserved ejection fraction.53 Based on the present analysis, higher thresholds may have better clinical utility. This warrants further analysis.
Interpretation of BMP10 and ANGPT2
BMP10 and ANGPT2 are tightly regulated circulating biomarkers, illustrating their signalling roles in regulating disease processes contributing to AF.3 Mechanistic studies of their role in AF are needed to define more precise clinical use cases for these biomarkers in patients with AF.
ANGPT2 is a vascular growth factor required for angiogenic remodelling.54 Overexpression of ANGPT2 in murine models promotes perivascular cardiac inflammation and fibrosis.55 Pro-inflammatory molecules such as thrombin increase ANGPT2 expression in vitro56 and inhibition of thrombin in animals with persistent AF improves atrial cardiomyopathy.15 Thus, ANGPT2 mediates the inflammatory communication between endothelial cells and myocardium in AF. Low ANGPT2 might reflect preserved vascular integrity, reducing the inflammatory burden in atrial vascular beds and thereby slowing AF progression.
ANGPT2 is associated with recurrent AF in patients after AF ablation20 and with prevalent AF in unselected hospitalized patients.57 ANGPT2 is elevated in patients with kidney disease,58 acute lung injury,59 and sepsis,60 conditions associated with AF. ANGPT2 can also predict heart failure hospitalization in patients with AF,61 similar to NT-proBNP.22 This study is the first to suggest that ANGPT2 can predict sinus rhythm in patients with AF with and without rhythm control therapy. Further research into treatable atrial disease processes regulated by ANGPT2 is warranted.
BMP10 is selectively expressed in and released by atrial cardiomyocytes.16,62 BMP10 is part of the TGFβ growth factor family and regulates vascular smooth muscle cell tone.63 Its function in the atria is not well known. BMP10 concentrations are reduced in hereditary forms of pulmonary arterial hypertension,64 possibly reflecting reduced atrial metabolism. Its inverse correlation and possible repression by PITX2 in atrial cardiomyocytes16,65 may suggest that elevated BMP10 concentrations could identify a reversible atrial metabolic defect13,17 that may be aggravated by the genomic basis of AF on chromosome 4q25.13
High concentrations of BMP10 are associated with recurrent AF,57,66 and with cardiovascular events17,67 and stroke in patients with AF. BMP10 may also be associated with atrial fibrosis.68 Lower BMP10 concentrations in patients in sinus rhythm,20 combined with its prediction of future sinus rhythm (Figure 1) suggest that a possible BMP10-mediated metabolic defect could partially be secondary to the metabolic demands of AF. Taken together, these results suggest that BMP10 is a potentially actionable biomarker indicative of atrial myopathy and atrial metabolic dysfunction. Further research into the atrial effects of BMP10 and its relation to AF burden5 are warranted.
Biomolecule-based clustering of patients agnostic to clinical features previously identified four subgroups of patients with AF with a gradual increase in cardiovascular events.17 The three biomarkers associated with sinus rhythm at 12 months in this study are among the six dominant biomarkers defining these patient clusters.17 The biomarker-based clusters show a risk gradient for sinus rhythm at 12 months (Figure 5). At difference to the prior study that defined patient clusters based on biomarker concentrations agnostic to clinical information, this analysis shows that the three biomarkers NT-proBNP, ANGPT2, and BMP10 predict sinus rhythm in context with clinical parameters. Of note, a simple clinical score (see Supplementary data online, Table S2) was already quite useful in identifying patients who will attain sinus rhythm. This information can help clinicians to select different intensities of rhythm control therapy depending on the likelihood of attaining sinus rhythm. NT-proBNP, ANGPT2, and BMP10 can refine that selection (Table 5). The present result and the biomarker-clustering also identify potentially treatable drivers of recurrent AF and or cardiovascular events in patients with AF. Based on the known atrial effects of BMP10 and ANGPT2, antihypertensive therapy and metabolic interventions such as SGLT2 inhibitor therapy12 could have beneficial effects in patients with elevated BMP10 and ANGPT2 concentrations.16,67,69 The underlying disease processes suggest that the same biomarkers could also be useful to identify patients at risk of AF. The present analysis identifies potentially actionable biomarkers suitable to select the intensity of rhythm control therapy. Further research into the mechanistic links between these biomarkers with baseline and future rhythm, and further evaluations of their clinical utility in different scenarios are warranted.
Strengths and limitations
Central quantification of the biomarkers using high-precision assays combined with the rigorous, near-complete follow-up at 12 and 24 months in a controlled clinical trial is a strength of this analysis. The consistent findings at both time points may suggest that the effects can be extrapolated to even longer follow-up, but this would require validation. Another strength of the analysis is the collection of samples in a broad range of care settings in adequately treated patients with AF, and external validation both in a controlled clinical trial and in cohorts of patients with AF enrolled in routine care settings. Validation of the findings using the same assays in different clinical datasets is a strength, but also limits the findings to the assays used in this study.
The study has important limitations. Although the statistical analysis plan was prespecified and validation was possible in different datasets, all results are explorative. This study is limited to 14 preselected biomarkers. Selected biomarkers intentionally reflect overlapping disease processes, creating redundancy that enables robust definition of disease pathways. Collinearity of biomarkers was more deeply investigated in a previous study defining biomarker-based patient clusters agnostic to clinical features.17
Additional biomarkers in AF may emerge from hypothesis-free quantification of many molecules at once e.g. by RNA-sequencing of cardiac tissue,70 quantification of circulating RNAs, and by proteomics.71,72 Repeat blood samples were not obtained and no information on changes over time is available. Some data on the changes of BMP10 and NT-proBNP over time have been published.20,36
While NT-proBNP can be measured in clinical routine as in vitro diagnostic devices with regulatory approval, the assays for ANGPT2 and BMP10 are not approved for clinical use, restricting them to research settings. Only the consenting portion of the total EAST-AFNET study participants was included in the biomarker study (two-thirds), hence there could be a considerable selection bias. Due to time required to setup the biobank, the first 400 patients were not invited to participate in the biomarker study.
The present study used serum creatinine rather than estimated glomerular filtration rate in the analyses as the formulas used to estimate kidney function rely on clinical parameters that are used in the regression model, including age, sex, and body mass index. Serum creatinine was not a major predictor of sinus rhythm. Whether estimated kidney function is a better predictor of sinus rhythm was not studied.
Validation datasets were smaller than the derivation dataset and therefore did not allow for multiple confounding. Post hoc subgroup analysis by baseline rhythm in EAST-AFNET 4 may have underestimated effects due to smaller group sizes. Almost all patients received guideline-recommended anticoagulation, rate and rhythm control, and often effective treatment of concomitant conditions. Twenty-four hour blood pressure may provide more granular prognostic information than office-based blood pressure, but 24 h blood pressure readings were not available for this analysis.
Left atrial size was used in the clinical score rather than left atrial volume. Indexed left atrial volume can provide more detailed information on left atrial size compared to left atrial diameter, but the predictive value of left atrial volume for recurrent AF is less well established than left atrial size.28 Indexed left atrial volume was not available in a sufficient number of patients to be assessed in this study. The predictive ability of the different biomarker-based models is only valid for the specific AF prevalences in the cohorts studied. Further research into the clinical utility of the biomarkers identified here is warranted.
The blood samples studied here stem from patients with predominantly Caucasian ethnicity, which may limit the generalizability of the findings to other ethnic groups. Validation in other ethnicities is therefore needed.
Testing the relationship between specific blood biomarker levels and a remote outcome observed 12 months later is challenging. In order to limit acute effects of the specific biomarker levels at baseline, we corrected for the acute rhythm at baseline, among other clinical parameters. Prediction of future rhythm by biomarkers depends on several factors, including the underlying biology of each biomarker, spontaneous variations in concentrations, and assay quality. Lack of predictive ability in this study does not rule out relevant biological function of a given molecule. The proposed interventions countering the disease processes associated with biomarkers require further testing.
Main findings
Three out of 14 candidate biomarkers, BMP10, ANGPT2, and NT-proBNP, are associated with sinus rhythm at 12-month and 24-month follow-up after correcting for clinical features. Low NT-proBNP, low ANGPT2, and low BMP10 concentrations independently predict sinus rhythm in patients at follow-up. NT-proBNP is less predictive of rhythm in patients receiving rhythm control therapy. Adding these biomarkers to a clinical score identifying patients with a low probability of sinus rhythm at 12 months (positive with two out of three features: left atrial size > 50 mm, persistent AF, or age > 75 years) refined risk prediction (Structured Graphical Abstract).
Relevance for clinical care and research
In view of the growing choice of medical,2,30 interventional,2,31 and surgical32 treatment options for patients with AF, selecting the best strategy and the patients most benefitting from rhythm control therapy gains importance. Biomarker-based risk estimators have so far mainly been developed to refine anticoagulation decisions in patients with AF.33–35 Actionable biomarkers to guide rhythm control therapy are lacking. Similar to stroke prevention estimators, rhythm estimators face the challenge of random factors determining a binary outcome (AF or sinus rhythm). The present results suggest that NT-proBNP, BMP10, and ANGPT2 can stratify patients at high and low risk of attaining sinus rhythm alone and in combination. These biomarkers reflect and identify diseases processes that promote future AF, pointing to potential therapeutic targets for adjunct therapy supporting rhythm control. While a simple clinical score combining enlarged left atrial size, persistent AF, and older age predicted future sinus rhythm reasonably well, adding biomarkers reclassifies a clinically relevant number of patients at high risk of not attaining sinus rhythm at the price of also classifying more patients in sinus rhythm as high risk.
Effect of baseline rhythm on biomarker concentrations
This study shows that ANGPT2 and BMP10 provide additional information on future sinus rhythm when combined with NT-proBNP, especially in patients who are in AF at the time of blood sampling. Most biomarkers studied were elevated when the blood sample was taken in AF. Furthermore, NT-proBNP lost its ability to predict sinus rhythm in patients on rhythm control therapy.20,36 The effects of baseline rhythm on the concentrations and predictive ability of biomarkers should be further investigated in patients with AF undergoing rhythm control therapy.
Interpretation of NT-proBNP
NT-proBNP is released by atrial cardiomyocytes in response to stretch and strain, thereby acutely regulating fluid balance in the body, resulting in high concentrations during AF.37 In heart failure, NT-proBNP is also released by ventricular cardiomyocytes, further enhancing its concentrations. Atrial stretch has proarrhythmic effects including shortening of the atrial effective refractory period38 and conduction slowing,39 partially explaining its prediction of sinus rhythm in this study. NT-proBNP reflects short- and mid-term cardiac load in patients with AF, probably explaining its interaction with rhythm control. The possibility that elevated NT-proBNP concentrations predict rhythm during follow-up have been reported before.40–47 NT-proBNP is also associated with incident AF48–51 and with cardiovascular events in patients with and without AF and heart failure.22 This analysis demonstrates that the rhythm-predicting ability of NT-proBNP is reduced in patients treated with rhythm control therapy.
The NT-proBNP thresholds associated with a high risk of AF at 12 months in this study (>1500 pg/mL) are comparable to the thresholds associated with cardiovascular events, but higher than currently used thresholds e.g. for AF screening52 or for diagnosing heart failure with AF and heart failure with preserved ejection fraction.53 Based on the present analysis, higher thresholds may have better clinical utility. This warrants further analysis.
Interpretation of BMP10 and ANGPT2
BMP10 and ANGPT2 are tightly regulated circulating biomarkers, illustrating their signalling roles in regulating disease processes contributing to AF.3 Mechanistic studies of their role in AF are needed to define more precise clinical use cases for these biomarkers in patients with AF.
ANGPT2 is a vascular growth factor required for angiogenic remodelling.54 Overexpression of ANGPT2 in murine models promotes perivascular cardiac inflammation and fibrosis.55 Pro-inflammatory molecules such as thrombin increase ANGPT2 expression in vitro56 and inhibition of thrombin in animals with persistent AF improves atrial cardiomyopathy.15 Thus, ANGPT2 mediates the inflammatory communication between endothelial cells and myocardium in AF. Low ANGPT2 might reflect preserved vascular integrity, reducing the inflammatory burden in atrial vascular beds and thereby slowing AF progression.
ANGPT2 is associated with recurrent AF in patients after AF ablation20 and with prevalent AF in unselected hospitalized patients.57 ANGPT2 is elevated in patients with kidney disease,58 acute lung injury,59 and sepsis,60 conditions associated with AF. ANGPT2 can also predict heart failure hospitalization in patients with AF,61 similar to NT-proBNP.22 This study is the first to suggest that ANGPT2 can predict sinus rhythm in patients with AF with and without rhythm control therapy. Further research into treatable atrial disease processes regulated by ANGPT2 is warranted.
BMP10 is selectively expressed in and released by atrial cardiomyocytes.16,62 BMP10 is part of the TGFβ growth factor family and regulates vascular smooth muscle cell tone.63 Its function in the atria is not well known. BMP10 concentrations are reduced in hereditary forms of pulmonary arterial hypertension,64 possibly reflecting reduced atrial metabolism. Its inverse correlation and possible repression by PITX2 in atrial cardiomyocytes16,65 may suggest that elevated BMP10 concentrations could identify a reversible atrial metabolic defect13,17 that may be aggravated by the genomic basis of AF on chromosome 4q25.13
High concentrations of BMP10 are associated with recurrent AF,57,66 and with cardiovascular events17,67 and stroke in patients with AF. BMP10 may also be associated with atrial fibrosis.68 Lower BMP10 concentrations in patients in sinus rhythm,20 combined with its prediction of future sinus rhythm (Figure 1) suggest that a possible BMP10-mediated metabolic defect could partially be secondary to the metabolic demands of AF. Taken together, these results suggest that BMP10 is a potentially actionable biomarker indicative of atrial myopathy and atrial metabolic dysfunction. Further research into the atrial effects of BMP10 and its relation to AF burden5 are warranted.
Biomolecule-based clustering of patients agnostic to clinical features previously identified four subgroups of patients with AF with a gradual increase in cardiovascular events.17 The three biomarkers associated with sinus rhythm at 12 months in this study are among the six dominant biomarkers defining these patient clusters.17 The biomarker-based clusters show a risk gradient for sinus rhythm at 12 months (Figure 5). At difference to the prior study that defined patient clusters based on biomarker concentrations agnostic to clinical information, this analysis shows that the three biomarkers NT-proBNP, ANGPT2, and BMP10 predict sinus rhythm in context with clinical parameters. Of note, a simple clinical score (see Supplementary data online, Table S2) was already quite useful in identifying patients who will attain sinus rhythm. This information can help clinicians to select different intensities of rhythm control therapy depending on the likelihood of attaining sinus rhythm. NT-proBNP, ANGPT2, and BMP10 can refine that selection (Table 5). The present result and the biomarker-clustering also identify potentially treatable drivers of recurrent AF and or cardiovascular events in patients with AF. Based on the known atrial effects of BMP10 and ANGPT2, antihypertensive therapy and metabolic interventions such as SGLT2 inhibitor therapy12 could have beneficial effects in patients with elevated BMP10 and ANGPT2 concentrations.16,67,69 The underlying disease processes suggest that the same biomarkers could also be useful to identify patients at risk of AF. The present analysis identifies potentially actionable biomarkers suitable to select the intensity of rhythm control therapy. Further research into the mechanistic links between these biomarkers with baseline and future rhythm, and further evaluations of their clinical utility in different scenarios are warranted.
Strengths and limitations
Central quantification of the biomarkers using high-precision assays combined with the rigorous, near-complete follow-up at 12 and 24 months in a controlled clinical trial is a strength of this analysis. The consistent findings at both time points may suggest that the effects can be extrapolated to even longer follow-up, but this would require validation. Another strength of the analysis is the collection of samples in a broad range of care settings in adequately treated patients with AF, and external validation both in a controlled clinical trial and in cohorts of patients with AF enrolled in routine care settings. Validation of the findings using the same assays in different clinical datasets is a strength, but also limits the findings to the assays used in this study.
The study has important limitations. Although the statistical analysis plan was prespecified and validation was possible in different datasets, all results are explorative. This study is limited to 14 preselected biomarkers. Selected biomarkers intentionally reflect overlapping disease processes, creating redundancy that enables robust definition of disease pathways. Collinearity of biomarkers was more deeply investigated in a previous study defining biomarker-based patient clusters agnostic to clinical features.17
Additional biomarkers in AF may emerge from hypothesis-free quantification of many molecules at once e.g. by RNA-sequencing of cardiac tissue,70 quantification of circulating RNAs, and by proteomics.71,72 Repeat blood samples were not obtained and no information on changes over time is available. Some data on the changes of BMP10 and NT-proBNP over time have been published.20,36
While NT-proBNP can be measured in clinical routine as in vitro diagnostic devices with regulatory approval, the assays for ANGPT2 and BMP10 are not approved for clinical use, restricting them to research settings. Only the consenting portion of the total EAST-AFNET study participants was included in the biomarker study (two-thirds), hence there could be a considerable selection bias. Due to time required to setup the biobank, the first 400 patients were not invited to participate in the biomarker study.
The present study used serum creatinine rather than estimated glomerular filtration rate in the analyses as the formulas used to estimate kidney function rely on clinical parameters that are used in the regression model, including age, sex, and body mass index. Serum creatinine was not a major predictor of sinus rhythm. Whether estimated kidney function is a better predictor of sinus rhythm was not studied.
Validation datasets were smaller than the derivation dataset and therefore did not allow for multiple confounding. Post hoc subgroup analysis by baseline rhythm in EAST-AFNET 4 may have underestimated effects due to smaller group sizes. Almost all patients received guideline-recommended anticoagulation, rate and rhythm control, and often effective treatment of concomitant conditions. Twenty-four hour blood pressure may provide more granular prognostic information than office-based blood pressure, but 24 h blood pressure readings were not available for this analysis.
Left atrial size was used in the clinical score rather than left atrial volume. Indexed left atrial volume can provide more detailed information on left atrial size compared to left atrial diameter, but the predictive value of left atrial volume for recurrent AF is less well established than left atrial size.28 Indexed left atrial volume was not available in a sufficient number of patients to be assessed in this study. The predictive ability of the different biomarker-based models is only valid for the specific AF prevalences in the cohorts studied. Further research into the clinical utility of the biomarkers identified here is warranted.
The blood samples studied here stem from patients with predominantly Caucasian ethnicity, which may limit the generalizability of the findings to other ethnic groups. Validation in other ethnicities is therefore needed.
Testing the relationship between specific blood biomarker levels and a remote outcome observed 12 months later is challenging. In order to limit acute effects of the specific biomarker levels at baseline, we corrected for the acute rhythm at baseline, among other clinical parameters. Prediction of future rhythm by biomarkers depends on several factors, including the underlying biology of each biomarker, spontaneous variations in concentrations, and assay quality. Lack of predictive ability in this study does not rule out relevant biological function of a given molecule. The proposed interventions countering the disease processes associated with biomarkers require further testing.
Conclusion
In conclusion, these findings suggest that NT-proBNP, ANGPT2, and BMP10 can be combined to identify patients with AF at high risk of not attaining sinus rhythm. The disease processes related to ANGPT2 and BMP10 emerge as likely contributors to future rhythm in patients with and without rhythm control therapy. NT-proBNP elevations interact with early rhythm control, potentially suggesting repeat assessment of NT-proBNP to monitor the effectiveness of rhythm control.
Supplementary Material
ehae611_Supplementary_Data |
Title: Encorafenib and binimetinib followed by radiotherapy for patients with BRAF | Body:
Patients and Methods
Study Population and Procedures
The EBRAIN (GEM-1802) was a prospective, multicenter, single-arm, phase II trial led by the Spanish Multidisciplinary Melanoma Group (GEM; clinicaltrials.gov NCT03898908). The study was conducted at 15 sites in Spain.
Supplementary Figure 1 summarizes the study design, including the treatment and efficacy evaluation plans. The study included adult (≥18 years old) patients with BRAFV600-mutant melanoma detected locally by validated tests, brain metastases, an Eastern Cooperative Oncology Group (ECOG) of ≤ 2, at least one RECIST-modified measurable lesion (5 mm in the longest diameter by magnetic resonance imaging (MRI) but no lesions larger than 50 mm), and no prior targeted therapy for advanced melanoma. Previously targeted therapies were permitted only in the adjuvant setting. Previous immunotherapy was allowed in both adjuvant and advanced settings, before developing brain metastasis (BM). Previous local surgery was allowed but previous brain radiotherapy (RDT) was not allowed. Antiepileptic and supportive care treatments were allowed in cases where no major interactions with encorafenib and binimetinib were present. Corticosteroids for control of symptoms were allowed, with no dose limit. Patients with only leptomeningeal involvement were excluded.
Initially, 2 cohorts of patients with BRAFV600-mutant melanoma and brain metastases were planned: One main cohort of patients with asymptomatic brain metastases at enrollment and one exploratory cohort of patients with symptomatic brain metastases. Due to slow accrual and the results from previous studies in patients with MBM5 and a non-preplanned interim analysis10 showing that no differences in icRR between the 2 cohorts were expected, the steering committee decided to analyze both asymptomatic and symptomatic as one single cohort. Moreover, this decision was undertaken due to the evolving and changing definition of symptomatic that may limit the interpretation of the results as already discussed in recent studies.7
The patients were treated with standard doses of encorafenib (450 mg PO every 24 hours) and binimetinib (45 mg PO every 12 hours) for 2 months, after which a brain MRI and body computed tomography (CT) scan were performed. If according to the investigator, no signs of progressive disease were detected in either the brain or body, brain RDT was offered to the patients, with the exceptions of: Intracranial complete response (CR), in which RDT was spared for further progressive disease; or patient’s refusal to be treated with RDT (in this later case after discussing with medical monitor). RDT treatment was recommended but not compulsory. The different RDT techniques, fractions, and doses were decided as per institutional guidelines according to the investigator’s team radiation oncologist and a guidance summarized in Supplementary Table 1. RDT schemes different from those proposed in current guidelines were discussed with the radiation oncology coordinator.
Encorafenib and binimetinib were discontinued 24 hours before, during and 24 hours after RDT, and then continued until disease progression, unacceptable toxicity, death, or the patient’s decision. Brain MRI and body CT scan were performed every 2 months during the first year, and as per institutional guidelines thereafter. Demographic patient characteristics and previous cancer history were documented at baseline. Patients underwent clinical and physical examinations including documentation of vital signs, ECOG performance status, and laboratory assessments (including hematologic blood counts, blood chemistry panel, and urinalysis) every 4 weeks during treatment and at the safety visit (within 28 days after the last dose of study treatment). The blood chemistry panel included: lactate dehydrogenase (LDH), CK (CPK), gamma-glutamyltransferase (GGT), creatinine, creatinine clearance, total bilirubin, alkaline phosphatase, aspartate aminotransferase (AST) or GOT, alanine aminotransferase (ALT) or GPT, calcium (Ca), potassium (K), and magnesium (Mg). EORTC QLQ 30 quality of life (QoL) questionnaires were completed before any study-specific determination at the following visits: baseline, week 8 (after the first 2 months of treatment with encorafenib and binimetinib), and week 24. Basic neurological assessments according to clinical practice and the Modified Barthel Index were collected at baseline, and weeks 4, 8, 24, and every 4 weeks thereafter until disease progression. Safety was assessed by means of frequency and severity of adverse events (AEs) according to the Common Terminology Criteria for AEs (CTCAE—version 4.03) at each patient visit (continuously) from the time informed consent was signed until the end of the study. The causality and outcomes for each AE were assessed and reported by the principal investigator. All AEs were followed until resolution. Cardiac function was assessed through standard 12-lead ECG at baseline, after 4 weeks of treatment, after every 12 weeks, and at the safety visit. Additional ECGs may be performed at the discretion of the investigator, and LFEV was assessed at baseline and as per institutional guidelines. Concomitant medications were recorded continuously from 30 days before starting the trial until the end of the study. All women with reproductive capacity must have a negative urine pregnancy test within 72 hours before receiving study treatment and the pregnancy tests were repeated every 4 weeks until the end of treatment.
Objectives
The initial primary endpoint was to assess the efficacy of the study treatment by means of icRR at 2 months after starting treatment with encorafenib and binimetinib (prior to any dose of radiotherapy), as evaluated by the investigator and according to the modified RECIST criteria (mRECIST, see Supplementary Table 2) in asymptomatic patients. No confirmation of responses was requested, as depending on the first tumor assessment patients could undergo RDT and this would have interfered with tumor assessments. After a non-preplanned analysis, the protocol was amended to evaluate as the primary objective the icRR according to mRECIST with asymptomatic and symptomatic patients together.
Secondary objectives included assessing the efficacy by means of intracranial PFS (icPFS), extracranial PFS (ecPFS), OS, QoL, and safety in the global cohort and disaggregated into 2 post hoc subgroups: (1) patients with asymptomatic brain metastases and no need for steroids for symptoms control; and (2) patients that at the time of treatment initiation had neurological symptoms either uncontrolled or controlled with corticosteroids. icPFS was defined as the time from inclusion (first dose of treatment) until intracranial tumor progression (mRECIST). ecPFS was defined as the time from inclusion (first dose of treatment) until extracranial tumor progression (RECIST 1.1) or death. Patients without any of the previous events are censored at the date of the last available tumor assessment.
Statistical Methods
The required sample size was initially calculated for the primary endpoint, the intracranial response rate, for the cohort of asymptomatic patients using Fleming’s single-stage procedure. Investigators assessed icRR in COMBI-MB, a benchmark study for TKIs in patients with melanoma BM, varied across subgroups from 44 to 59%.5 Therefore, we hypothesized that the experimental treatment would achieve an icORR ≤40% (null hypothesis), and an icORR of ≥60% was adopted as an alternative hypothesis. Employing an alpha error of 0.05 and a power of 80%, the expected total sample size required was 38 patients. Factoring in an estimated 20% attrition due to loss of follow-up before day 56 without undergoing the first tumor assessment, the initial sample size was set at 48 patients, to ensure obtaining 38 evaluable patients. The cohort of symptomatic patients was exploratory and no formal sample calculation was performed, anticipating the inclusion of up to 15 patients. An interim analysis revealed no differences between cohorts.10 Consequently, the trial was amended and the icORR was analyzed irrespective of symptomatology in all enrolled patients. The null and alternative hypothesis for sample size calculations were maintained for the pooled analysis of all patients based on evidence from previous studies and our own interim report showing no differences in ORR between symptomatic and asymptomatic patients.5,10 Thus, the trial required a total of 38 evaluable patients to maintain adequate statistical power to observe differences as initially proposed.
The efficacy analysis was based on the intention-to-treat population, including all patients enrolled in the trial. Additional analysis was performed in the per-protocol population, including all patients fulfilling all eligibility criteria and having at least 2 valid tumor assessments (baseline and one evaluation post-baseline by MRI or CT-scan) without any protocol deviation, invalidating the patient for primary endpoint evaluation. A secondary analysis was also performed in 2 groups defined post hoc based on their symptomatology and the use of corticosteroids. Comparisons between patients who received RDT or not were post hoc. Safety was assessed on all patients who received at least one dose of treatment. Descriptive statistics were used to evaluate baseline demographic characteristics. Continuous variables were summarized using descriptive statistics (n, median, mean, standard deviation, range, or 95% confidence interval [CI]), as applicable. Categorical data were represented as frequency counts and percentages of patients within each category. Time-to-event endpoints (icPFS, ecPFS, and OS) were estimated using the Kaplan–Meier method.
All statistical analyses were performed using R and SPSS softwares (IBM SPSS Statistics Version 26). Figures and tables were generated using RStudio (Version 1.2.5033 2009-2019 RStudio).
Ethical Considerations
The E-BRAIN / GEM-1802 study was approved by independent ethics committees and competent authorities in Spain (Agencia Española del Medicamento y Productos Sanitarios, AEMPS), and performed in accordance with the Declaration of Helsinki, International Conference on Harmonization Guidelines for Good Clinical Practice, and applicable local laws. Written informed consent was obtained from all patients before to study enrollment. This study was registered in EudraCT (2018-002530-20) and www.clinicaltrials.gov (NCT03898908).
Data Availability
The Supplementary Information file contains the study protocol. Additional data are available from the corresponding author upon reasonable request. Data will be provided anonymously, with no identifiable data.
Study Population and Procedures
The EBRAIN (GEM-1802) was a prospective, multicenter, single-arm, phase II trial led by the Spanish Multidisciplinary Melanoma Group (GEM; clinicaltrials.gov NCT03898908). The study was conducted at 15 sites in Spain.
Supplementary Figure 1 summarizes the study design, including the treatment and efficacy evaluation plans. The study included adult (≥18 years old) patients with BRAFV600-mutant melanoma detected locally by validated tests, brain metastases, an Eastern Cooperative Oncology Group (ECOG) of ≤ 2, at least one RECIST-modified measurable lesion (5 mm in the longest diameter by magnetic resonance imaging (MRI) but no lesions larger than 50 mm), and no prior targeted therapy for advanced melanoma. Previously targeted therapies were permitted only in the adjuvant setting. Previous immunotherapy was allowed in both adjuvant and advanced settings, before developing brain metastasis (BM). Previous local surgery was allowed but previous brain radiotherapy (RDT) was not allowed. Antiepileptic and supportive care treatments were allowed in cases where no major interactions with encorafenib and binimetinib were present. Corticosteroids for control of symptoms were allowed, with no dose limit. Patients with only leptomeningeal involvement were excluded.
Initially, 2 cohorts of patients with BRAFV600-mutant melanoma and brain metastases were planned: One main cohort of patients with asymptomatic brain metastases at enrollment and one exploratory cohort of patients with symptomatic brain metastases. Due to slow accrual and the results from previous studies in patients with MBM5 and a non-preplanned interim analysis10 showing that no differences in icRR between the 2 cohorts were expected, the steering committee decided to analyze both asymptomatic and symptomatic as one single cohort. Moreover, this decision was undertaken due to the evolving and changing definition of symptomatic that may limit the interpretation of the results as already discussed in recent studies.7
The patients were treated with standard doses of encorafenib (450 mg PO every 24 hours) and binimetinib (45 mg PO every 12 hours) for 2 months, after which a brain MRI and body computed tomography (CT) scan were performed. If according to the investigator, no signs of progressive disease were detected in either the brain or body, brain RDT was offered to the patients, with the exceptions of: Intracranial complete response (CR), in which RDT was spared for further progressive disease; or patient’s refusal to be treated with RDT (in this later case after discussing with medical monitor). RDT treatment was recommended but not compulsory. The different RDT techniques, fractions, and doses were decided as per institutional guidelines according to the investigator’s team radiation oncologist and a guidance summarized in Supplementary Table 1. RDT schemes different from those proposed in current guidelines were discussed with the radiation oncology coordinator.
Encorafenib and binimetinib were discontinued 24 hours before, during and 24 hours after RDT, and then continued until disease progression, unacceptable toxicity, death, or the patient’s decision. Brain MRI and body CT scan were performed every 2 months during the first year, and as per institutional guidelines thereafter. Demographic patient characteristics and previous cancer history were documented at baseline. Patients underwent clinical and physical examinations including documentation of vital signs, ECOG performance status, and laboratory assessments (including hematologic blood counts, blood chemistry panel, and urinalysis) every 4 weeks during treatment and at the safety visit (within 28 days after the last dose of study treatment). The blood chemistry panel included: lactate dehydrogenase (LDH), CK (CPK), gamma-glutamyltransferase (GGT), creatinine, creatinine clearance, total bilirubin, alkaline phosphatase, aspartate aminotransferase (AST) or GOT, alanine aminotransferase (ALT) or GPT, calcium (Ca), potassium (K), and magnesium (Mg). EORTC QLQ 30 quality of life (QoL) questionnaires were completed before any study-specific determination at the following visits: baseline, week 8 (after the first 2 months of treatment with encorafenib and binimetinib), and week 24. Basic neurological assessments according to clinical practice and the Modified Barthel Index were collected at baseline, and weeks 4, 8, 24, and every 4 weeks thereafter until disease progression. Safety was assessed by means of frequency and severity of adverse events (AEs) according to the Common Terminology Criteria for AEs (CTCAE—version 4.03) at each patient visit (continuously) from the time informed consent was signed until the end of the study. The causality and outcomes for each AE were assessed and reported by the principal investigator. All AEs were followed until resolution. Cardiac function was assessed through standard 12-lead ECG at baseline, after 4 weeks of treatment, after every 12 weeks, and at the safety visit. Additional ECGs may be performed at the discretion of the investigator, and LFEV was assessed at baseline and as per institutional guidelines. Concomitant medications were recorded continuously from 30 days before starting the trial until the end of the study. All women with reproductive capacity must have a negative urine pregnancy test within 72 hours before receiving study treatment and the pregnancy tests were repeated every 4 weeks until the end of treatment.
Objectives
The initial primary endpoint was to assess the efficacy of the study treatment by means of icRR at 2 months after starting treatment with encorafenib and binimetinib (prior to any dose of radiotherapy), as evaluated by the investigator and according to the modified RECIST criteria (mRECIST, see Supplementary Table 2) in asymptomatic patients. No confirmation of responses was requested, as depending on the first tumor assessment patients could undergo RDT and this would have interfered with tumor assessments. After a non-preplanned analysis, the protocol was amended to evaluate as the primary objective the icRR according to mRECIST with asymptomatic and symptomatic patients together.
Secondary objectives included assessing the efficacy by means of intracranial PFS (icPFS), extracranial PFS (ecPFS), OS, QoL, and safety in the global cohort and disaggregated into 2 post hoc subgroups: (1) patients with asymptomatic brain metastases and no need for steroids for symptoms control; and (2) patients that at the time of treatment initiation had neurological symptoms either uncontrolled or controlled with corticosteroids. icPFS was defined as the time from inclusion (first dose of treatment) until intracranial tumor progression (mRECIST). ecPFS was defined as the time from inclusion (first dose of treatment) until extracranial tumor progression (RECIST 1.1) or death. Patients without any of the previous events are censored at the date of the last available tumor assessment.
Statistical Methods
The required sample size was initially calculated for the primary endpoint, the intracranial response rate, for the cohort of asymptomatic patients using Fleming’s single-stage procedure. Investigators assessed icRR in COMBI-MB, a benchmark study for TKIs in patients with melanoma BM, varied across subgroups from 44 to 59%.5 Therefore, we hypothesized that the experimental treatment would achieve an icORR ≤40% (null hypothesis), and an icORR of ≥60% was adopted as an alternative hypothesis. Employing an alpha error of 0.05 and a power of 80%, the expected total sample size required was 38 patients. Factoring in an estimated 20% attrition due to loss of follow-up before day 56 without undergoing the first tumor assessment, the initial sample size was set at 48 patients, to ensure obtaining 38 evaluable patients. The cohort of symptomatic patients was exploratory and no formal sample calculation was performed, anticipating the inclusion of up to 15 patients. An interim analysis revealed no differences between cohorts.10 Consequently, the trial was amended and the icORR was analyzed irrespective of symptomatology in all enrolled patients. The null and alternative hypothesis for sample size calculations were maintained for the pooled analysis of all patients based on evidence from previous studies and our own interim report showing no differences in ORR between symptomatic and asymptomatic patients.5,10 Thus, the trial required a total of 38 evaluable patients to maintain adequate statistical power to observe differences as initially proposed.
The efficacy analysis was based on the intention-to-treat population, including all patients enrolled in the trial. Additional analysis was performed in the per-protocol population, including all patients fulfilling all eligibility criteria and having at least 2 valid tumor assessments (baseline and one evaluation post-baseline by MRI or CT-scan) without any protocol deviation, invalidating the patient for primary endpoint evaluation. A secondary analysis was also performed in 2 groups defined post hoc based on their symptomatology and the use of corticosteroids. Comparisons between patients who received RDT or not were post hoc. Safety was assessed on all patients who received at least one dose of treatment. Descriptive statistics were used to evaluate baseline demographic characteristics. Continuous variables were summarized using descriptive statistics (n, median, mean, standard deviation, range, or 95% confidence interval [CI]), as applicable. Categorical data were represented as frequency counts and percentages of patients within each category. Time-to-event endpoints (icPFS, ecPFS, and OS) were estimated using the Kaplan–Meier method.
All statistical analyses were performed using R and SPSS softwares (IBM SPSS Statistics Version 26). Figures and tables were generated using RStudio (Version 1.2.5033 2009-2019 RStudio).
Ethical Considerations
The E-BRAIN / GEM-1802 study was approved by independent ethics committees and competent authorities in Spain (Agencia Española del Medicamento y Productos Sanitarios, AEMPS), and performed in accordance with the Declaration of Helsinki, International Conference on Harmonization Guidelines for Good Clinical Practice, and applicable local laws. Written informed consent was obtained from all patients before to study enrollment. This study was registered in EudraCT (2018-002530-20) and www.clinicaltrials.gov (NCT03898908).
Data Availability
The Supplementary Information file contains the study protocol. Additional data are available from the corresponding author upon reasonable request. Data will be provided anonymously, with no identifiable data.
Results
Patient Characteristics
Between July 2019 and October 2022, 48 patients were enrolled, with 47 (97.9%) patients receiving a minimum of 2 months of treatment and being evaluable for the primary endpoint (Supplementary Figure 2). One patient discontinued the study treatment after the first month following the investigator criteria. The study analyzed descriptively also 2 post hoc subgroups: (1) totally asymptomatic patients [N = 25], and (2) patients with neurological symptoms before start of treatment, either non-controlled or controlled with corticosteroids [N = 23] Patient characteristics are described in Table 1. Among these 23 symptomatic patients, corticosteroids were used prior to study entry for symptom control in 8 (34.8%) patients achieving symptom disappearance and 14 (61%) patients with persistent symptomatology. Among asymptomatic patients, 9 (36%) received corticosteroids prior to study entry for reasons unrelated to intracranial symptoms, primarily for the management of previous toxicities from immunotherapy in 5 patients (20%), asymptomatic perilesional edema in 2 (8%), arthritis in 1 (4%), and symptomatic spinal metastasis in 1 (4%). A detailed description of the symptoms present in patients is provided in Supplementary Table 3.
Table 1.
Patient Characteristics
Characteristic
TotalN = 48
median Age (range); years
54 (18–88)
Sex; n (%)
Male
24 (50)
Female
24 (50)
ECOG PS; n (%)
0
26 (54.2)
1
20 (41.7)
2
2 (4.2)
Barthel index; n (%)
Total dependent (0–4)
3 (6.3)
Severe dependent (5–12)
4 (8.3)
Moderate dependent(13–18)
6 (12.5)
Slight dependent (19–20)
33 (68.9)
NA
2 (4.2)
BRAF genotype; n (%)*
V600E
41 (87.2)
V600K
11 (22.9)
V600R
6 (12.5)
V600 other
25 (52.1)
Brain symptoms; n (%)
Asymptomatic
25 (52.1)
Symptomatic
23 (47.9)
Median SLD of target intracranial lesions (range); mm
26.5 (6–134)
Num of brain target lesion; n (%)
1
21 (43.8)
2
15 (31.3)
3 or more
12 (25)
Mean num of brain target lesions (range)
2 (1–5)
Extracranial metastases; n (%)
Yes
41 (85.4)
No
7 (14.6)
LDH, n (%)
≤ULN
26 (54.2)
>ULN
21 (43.8)
Unknown
1 (2.1)
Baseline corticosteroids before first dose of study treatment n (%)
Yes
33 (68.8)
No
15 (31.2)
Median steroids dose before first dose of study treatment (dexametasone equivalent) mg (range) per day
8 (1–16)
Previous anti-PD-1 based immunotherapy; n (%)
Anti PD-1
11 (22.9)
Anti PD-1 / anti CTLA-4
5 (10.4)
No
32 (66.7)
Radiotherapy received in the EBRAIN trial; n (%)
RS
10 (20.8)
FSRT
6 (12.5)
WBRT
15 (31.2)
No
17 (35.4)
Abbreviations: ECOG PS, Eastern Cooperative Oncology Group Performance Status; RS, radiosurgery; FSRT, fractionated stereotactic radiotherapy; LDH, lactate dehydrogenase; SLD, sum of largest diameters; ULN, upper limit normal; WBRT, whole brain radiotherapy. # Had no symptoms related to brain metastasis and received corticosteroids for other reasons. * Patients may harbor more than one mutation and/or the analytical technique detects the presence of one out of several V600 alterations.
Primary Endpoint: Intracranial Response Rate According to mRECIST
With a median follow-up (reverse censoring) of 24.9 months (95% CI: 21.8), the icRR at 2 months (before radiotherapy, in the first assessment) was 70.8% (95% CI: 55.9–83.1), with 5 (10.4%) CR, and 29 (60.4%) partial responses (PR). Patients with stable disease (SD) were 13 (27.1%) and 1 (2%) patient was not evaluated because he withdrew from the study before the assessment (Figure 1). Response rate was 80% in neurologically asymptomatic patients and 60.9% in symptomatic patients. The median duration of response was 5.6 (95% CI: 3.6–7.5) months.
Figure 1.
Confirmed maximum reduction in intracranial target lesion (at any time) in patients with symptomatic and asymptomatic brain metastasis. Dashed red lines at 20% and –30% represent the threshold of progression and partial response respectively. Crosses mark patients who were receiving corticosteroids at baseline. 1 patient was not evaluated for response.
Secondary Endpoints
The median icPFS was 8.5 (95% CI: 6.4–11.8) months with 29.5% free of progression at one year (Figure 2A). Regarding extracranial disease, median ecPFS was 7.7 (95% CI: 6.1–11.8) months (Figure 2B). Median OS was 15.9 (95% CI: 10.7–21.4) and 59.2% of patients were alive at 1 year (Figure 2C). Four patients remained alive 2 years after the start of the study treatment. Supplementary Figure 3 illustrates the results of icPFS, ecPFS, and OS in the 2 post hoc subgroups.
Figure 2.
Kaplan–Meier graphs showing intracranial PFS (A), extracranial PFS (B), and OS (C) in the overall population. Median survivals for each subgroup are included in the graph. Censored patients are marked with a cross line. Dashed lines represent the 50% rate of events.
RDT was offered to patients achieving partial response or SD, aiming to prolong those responses. A total of 31 (64.6%) patients underwent radiotherapy, with 16 (33.3%) receiving localized therapies and 15 (31.3%) undergoing whole brain radiotherapy (WBRT). There were 11 patients who were eligible for RDT according to protocol recommendations but did not start the local treatment following the investigator’s decision. The median icPFS was 8.3 (95% CI: 6.1–9.5) months and 16.4 (95% CI: 5.4–NR) months for patients receiving and not receiving RDT, respectively (Supplementary Figure 4A). The patients who received RDT showed a duration of response of 5.6 months (95% CI: 3–7.5). The median OS was 13.9 (95% CI:10.6–21) months and 22.4 (95% CI: 10.6–NR) months for patients receiving and not receiving RDT, respectively (Supplementary Figure 4B). The 1-year icPFS rate was 20.8% and 54.1%; the 1-year OS rate was 56.7% versus 64.8% for patients receiving and not receiving RDT, respectively.
Encorafenib and binimetinib were discontinued in 44 (91.7%) patients at the time of the analysis (November 2023), mainly due to: disease progression 31 (64.6%), toxicities 6 (12.5%), investigator decision 3 (6.3%), patient consent withdrawal 2 (4.2%), and surgical intervention 2 (4.2%). Treatment-related AEs leading to discontinuation of the study treatment included transaminitis, muscle weakness, anemia, creatinine increase, general discomfort, and cardiac toxicity (decrease in LVEF that coursed with muscle weakness and edema in lower limbs). Temporary treatment interruptions and dose reductions for AEs management were reported in 24 (50%) and 13 (27.1%) patients, respectively. The AEs that led to dose modification were mainly transaminitis (10.4%), and diarrhea (6.3%).
The most frequent treatment-related AEs were diarrhea (27%), fatigue (25%), nausea (22.9%), elevated alanine aminotransferase (ALT), and aspartate aminotransferase (AST; 18.8% and 16.7%, respectively). Most events were low-grade and were appropriately managed with dose modifications or rescue medications. Grade 3–4 toxicities were reported in 12 (25%) patients, all related to encorafenib and binimetinib and none to radiotherapy. The most common grades 3–4 were elevated ALT (10.4%), elevated AST (8.3%), and diarrhea (4.2%). No toxic deaths were reported. Figure 3 and Supplementary Table 4 summarize treatment-related AEs (encorafenib and binimetinib, RDT, or both) according to the investigator´s criteria.
Baseline QoL questionnaires were completed by 35 (72.9%) patients. The overall QLQ-C30 score exhibited a significant transient improvement at week 8 (P = .015) after treatment initiation (Supplementary Figure 5). The global health status showed no statistically significant improvement at weeks 8 and 24. Notably, in patients with symptoms (N = 15), the global health status showed an improvement that was statistically significant at weeks 8 (P = .046) and 24 (P = .022; Supplementary Figure 5). Consistent with this result, the QLQ-C30 score showed a significant transient improvement at week 8 (P = .033), while physical, social, and role functioning showed no statistically significant improvement. A significant improvement in insomnia was reported at week 8 (P = .004) and was maintained at week 24 (P = .02) in symptomatic patients. Conversely, no changes in insomnia were reported in asymptomatic patients, who already had lower insomnia levels than baseline that were maintained. Symptoms such as pain, fatigue, and appetite loss showed non-significant progressive improvement from baseline in the follow-up visits. Changes observed in QoL showed a similar trend in patients who underwent radiotherapy of any type, or WBRT specifically. No significant worsening was observed with WBRT (Supplementary Figure 6).
Patient Characteristics
Between July 2019 and October 2022, 48 patients were enrolled, with 47 (97.9%) patients receiving a minimum of 2 months of treatment and being evaluable for the primary endpoint (Supplementary Figure 2). One patient discontinued the study treatment after the first month following the investigator criteria. The study analyzed descriptively also 2 post hoc subgroups: (1) totally asymptomatic patients [N = 25], and (2) patients with neurological symptoms before start of treatment, either non-controlled or controlled with corticosteroids [N = 23] Patient characteristics are described in Table 1. Among these 23 symptomatic patients, corticosteroids were used prior to study entry for symptom control in 8 (34.8%) patients achieving symptom disappearance and 14 (61%) patients with persistent symptomatology. Among asymptomatic patients, 9 (36%) received corticosteroids prior to study entry for reasons unrelated to intracranial symptoms, primarily for the management of previous toxicities from immunotherapy in 5 patients (20%), asymptomatic perilesional edema in 2 (8%), arthritis in 1 (4%), and symptomatic spinal metastasis in 1 (4%). A detailed description of the symptoms present in patients is provided in Supplementary Table 3.
Table 1.
Patient Characteristics
Characteristic
TotalN = 48
median Age (range); years
54 (18–88)
Sex; n (%)
Male
24 (50)
Female
24 (50)
ECOG PS; n (%)
0
26 (54.2)
1
20 (41.7)
2
2 (4.2)
Barthel index; n (%)
Total dependent (0–4)
3 (6.3)
Severe dependent (5–12)
4 (8.3)
Moderate dependent(13–18)
6 (12.5)
Slight dependent (19–20)
33 (68.9)
NA
2 (4.2)
BRAF genotype; n (%)*
V600E
41 (87.2)
V600K
11 (22.9)
V600R
6 (12.5)
V600 other
25 (52.1)
Brain symptoms; n (%)
Asymptomatic
25 (52.1)
Symptomatic
23 (47.9)
Median SLD of target intracranial lesions (range); mm
26.5 (6–134)
Num of brain target lesion; n (%)
1
21 (43.8)
2
15 (31.3)
3 or more
12 (25)
Mean num of brain target lesions (range)
2 (1–5)
Extracranial metastases; n (%)
Yes
41 (85.4)
No
7 (14.6)
LDH, n (%)
≤ULN
26 (54.2)
>ULN
21 (43.8)
Unknown
1 (2.1)
Baseline corticosteroids before first dose of study treatment n (%)
Yes
33 (68.8)
No
15 (31.2)
Median steroids dose before first dose of study treatment (dexametasone equivalent) mg (range) per day
8 (1–16)
Previous anti-PD-1 based immunotherapy; n (%)
Anti PD-1
11 (22.9)
Anti PD-1 / anti CTLA-4
5 (10.4)
No
32 (66.7)
Radiotherapy received in the EBRAIN trial; n (%)
RS
10 (20.8)
FSRT
6 (12.5)
WBRT
15 (31.2)
No
17 (35.4)
Abbreviations: ECOG PS, Eastern Cooperative Oncology Group Performance Status; RS, radiosurgery; FSRT, fractionated stereotactic radiotherapy; LDH, lactate dehydrogenase; SLD, sum of largest diameters; ULN, upper limit normal; WBRT, whole brain radiotherapy. # Had no symptoms related to brain metastasis and received corticosteroids for other reasons. * Patients may harbor more than one mutation and/or the analytical technique detects the presence of one out of several V600 alterations.
Primary Endpoint: Intracranial Response Rate According to mRECIST
With a median follow-up (reverse censoring) of 24.9 months (95% CI: 21.8), the icRR at 2 months (before radiotherapy, in the first assessment) was 70.8% (95% CI: 55.9–83.1), with 5 (10.4%) CR, and 29 (60.4%) partial responses (PR). Patients with stable disease (SD) were 13 (27.1%) and 1 (2%) patient was not evaluated because he withdrew from the study before the assessment (Figure 1). Response rate was 80% in neurologically asymptomatic patients and 60.9% in symptomatic patients. The median duration of response was 5.6 (95% CI: 3.6–7.5) months.
Figure 1.
Confirmed maximum reduction in intracranial target lesion (at any time) in patients with symptomatic and asymptomatic brain metastasis. Dashed red lines at 20% and –30% represent the threshold of progression and partial response respectively. Crosses mark patients who were receiving corticosteroids at baseline. 1 patient was not evaluated for response.
Secondary Endpoints
The median icPFS was 8.5 (95% CI: 6.4–11.8) months with 29.5% free of progression at one year (Figure 2A). Regarding extracranial disease, median ecPFS was 7.7 (95% CI: 6.1–11.8) months (Figure 2B). Median OS was 15.9 (95% CI: 10.7–21.4) and 59.2% of patients were alive at 1 year (Figure 2C). Four patients remained alive 2 years after the start of the study treatment. Supplementary Figure 3 illustrates the results of icPFS, ecPFS, and OS in the 2 post hoc subgroups.
Figure 2.
Kaplan–Meier graphs showing intracranial PFS (A), extracranial PFS (B), and OS (C) in the overall population. Median survivals for each subgroup are included in the graph. Censored patients are marked with a cross line. Dashed lines represent the 50% rate of events.
RDT was offered to patients achieving partial response or SD, aiming to prolong those responses. A total of 31 (64.6%) patients underwent radiotherapy, with 16 (33.3%) receiving localized therapies and 15 (31.3%) undergoing whole brain radiotherapy (WBRT). There were 11 patients who were eligible for RDT according to protocol recommendations but did not start the local treatment following the investigator’s decision. The median icPFS was 8.3 (95% CI: 6.1–9.5) months and 16.4 (95% CI: 5.4–NR) months for patients receiving and not receiving RDT, respectively (Supplementary Figure 4A). The patients who received RDT showed a duration of response of 5.6 months (95% CI: 3–7.5). The median OS was 13.9 (95% CI:10.6–21) months and 22.4 (95% CI: 10.6–NR) months for patients receiving and not receiving RDT, respectively (Supplementary Figure 4B). The 1-year icPFS rate was 20.8% and 54.1%; the 1-year OS rate was 56.7% versus 64.8% for patients receiving and not receiving RDT, respectively.
Encorafenib and binimetinib were discontinued in 44 (91.7%) patients at the time of the analysis (November 2023), mainly due to: disease progression 31 (64.6%), toxicities 6 (12.5%), investigator decision 3 (6.3%), patient consent withdrawal 2 (4.2%), and surgical intervention 2 (4.2%). Treatment-related AEs leading to discontinuation of the study treatment included transaminitis, muscle weakness, anemia, creatinine increase, general discomfort, and cardiac toxicity (decrease in LVEF that coursed with muscle weakness and edema in lower limbs). Temporary treatment interruptions and dose reductions for AEs management were reported in 24 (50%) and 13 (27.1%) patients, respectively. The AEs that led to dose modification were mainly transaminitis (10.4%), and diarrhea (6.3%).
The most frequent treatment-related AEs were diarrhea (27%), fatigue (25%), nausea (22.9%), elevated alanine aminotransferase (ALT), and aspartate aminotransferase (AST; 18.8% and 16.7%, respectively). Most events were low-grade and were appropriately managed with dose modifications or rescue medications. Grade 3–4 toxicities were reported in 12 (25%) patients, all related to encorafenib and binimetinib and none to radiotherapy. The most common grades 3–4 were elevated ALT (10.4%), elevated AST (8.3%), and diarrhea (4.2%). No toxic deaths were reported. Figure 3 and Supplementary Table 4 summarize treatment-related AEs (encorafenib and binimetinib, RDT, or both) according to the investigator´s criteria.
Baseline QoL questionnaires were completed by 35 (72.9%) patients. The overall QLQ-C30 score exhibited a significant transient improvement at week 8 (P = .015) after treatment initiation (Supplementary Figure 5). The global health status showed no statistically significant improvement at weeks 8 and 24. Notably, in patients with symptoms (N = 15), the global health status showed an improvement that was statistically significant at weeks 8 (P = .046) and 24 (P = .022; Supplementary Figure 5). Consistent with this result, the QLQ-C30 score showed a significant transient improvement at week 8 (P = .033), while physical, social, and role functioning showed no statistically significant improvement. A significant improvement in insomnia was reported at week 8 (P = .004) and was maintained at week 24 (P = .02) in symptomatic patients. Conversely, no changes in insomnia were reported in asymptomatic patients, who already had lower insomnia levels than baseline that were maintained. Symptoms such as pain, fatigue, and appetite loss showed non-significant progressive improvement from baseline in the follow-up visits. Changes observed in QoL showed a similar trend in patients who underwent radiotherapy of any type, or WBRT specifically. No significant worsening was observed with WBRT (Supplementary Figure 6).
Discussion
This is the final analysis of the EBRAIN / GEM-1802 trial, which constitutes to our knowledge the first prospective study that shows intracranial activity from encorafenib and binimetinib, in addition to the feasibility of combination with radiotherapy. The primary endpoint of intracranial response rate was achieved, with 70.8% of both asymptomatic and symptomatic patients exhibiting a response to treatment and surpassing the expected futility threshold. Intracranial response rate was in line with previous reports using BRAF and MEK inhibitors in patients with metastatic melanoma with BM.5 Despite the good response rates, the duration of the response was shorter than in patients without BM, 5.6 months versus 18.6 months reported in the COLUMBUS trial, this is also true for OS (15.9 months in our study versus 33.6 months respectively).8 These differences in PFS and OS were also present when comparing dabrafenib and trametinib in patients with5 and without6 brain metastases in pivotal clinical trials (COMBI-MB).
Intracranial responses were observed in patients with neurological symptoms and also in patients with concomitant use of corticosteroids, suggesting that the use of corticosteroids likely did not interfere with the efficacy observed in this trial. This is particularly noteworthy as it remains a limitation with immunotherapies, especially in symptomatic patients.11 The observed intracranial response rate was higher than that reported for immune checkpoint inhibitors.2,3 Moreover, triple combinations of immune checkpoint inhibitors with BRAF/MEK inhibitors (atezolizumab plus vemurafenib plus cobimetinib) showed comparable or lower icRR7 than those in the current study. However, the prognosis of patients with symptoms and need for corticosteroid therapy is still poor and will require the implementation of novel therapeutic approaches.
The safety profile of encorafenib and binimetinib in this study was similar to that reported in previous studies in patients with metastatic melanoma.8 Gastrointestinal events secondary to treatment were predominantly mild, with only 2 patients suffering grade 3 diarrhea, effectively managed with dose modifications/interruptions. Pyrexia occurred in 14.6% of patients, a lower rate than observed with other BRAF / MEK inhibitor combinations such as dabrafenib and trametinib.5 Thus, the combination of encorafenib and binimetinib demonstrated a manageable safety profile in patients with melanoma BM. Patient self-reported outcomes, also strengthened the safety profile, and exhibited significant improvement in symptoms and global status in patients with symptoms, who had worse self-perceived health status at baseline.
Regarding the additional value of radiotherapy for patients with intracranial PR or SD, our findings indicate the feasibility of this combination in terms of safety. No new safety concerns were raised with the combination. However, given the exploratory nature of this objective, and the absence of clear differences between patients who received brain radiotherapy and those who did not, which may be the consequence of a potential selection bias, no conclusions for the recommendation for this radiation approach are feasible.
The main limitation of this study lies in its non-randomized design, precluding direct comparisons with immunotherapy and other BRAF / MEK inhibitors currently approved for the treatment of patients with metastatic melanoma. The performance of an interim analysis may have an impact on type I errors. The small sample size of subgroups only allowed an exploratory analysis for hypothesis generation. Moreover, these subgroup analyses were performed post hoc and were not statistically powered to observe differences so we could not discard effects which are not seen. The lack of standardization in radiotherapy techniques introduced variability between sites. In addition, some patients were given WBRT even though current guidelines suggest that its use should be restricted to carefully selected patients.12 Nevertheless, no concerns were raised from QoL in patients who underwent WBRT. Another limitation, common with other clinical trials dedicated to patients with melanoma and brain metastases, is the efficacy criteria used. In our case, we decided to use the modified RECIST criteria used in the COMBI-MB clinical trial, since this clinical trial also treated patients with BRAF and MEK inhibitors (dabrafenib and trametinib). Whether other evaluation criteria dedicated to brain involvement such as Response Assessment in Neuro-Oncology for BM (RANO-MB)13 could better evaluate the outcomes of patients with melanoma and brain metastases is a question to explore in future clinical trials. Central imaging review would have reinforced our findings.
Lastly, we concluded that in contrast with immunotherapy, the response to encorafenib and binimetinib was independent of the presence of symptoms. Although the data collection design and investigator assessment aimed to unequivocally indicate the clear presence or absence of symptoms related to brain metastases, the definition, and categorization of these symptoms lack a globally recognized international consensus. This was another study limitation that led to a change in study design to combine the initial 2 cohorts to avoid the classification of patients according to symptoms. Therefore, future studies involving patients with brain metastases, especially those experiencing symptoms, would benefit from the establishment of a standardized and internationally accepted framework for the harmonization of symptom definitions.
In conclusion, our findings provide evidence that encorafenib plus binimetinib have a potential clinically significant benefit in patients with BRAFV600-mutant melanoma and BM, especially in patients with symptomatic MBM, and maintain tolerable safety profiles. These findings serve as a framework for future research and confirm the usefulness of BRAF and MEK inhibitors in brain metastases as an alternative treatment, particularly in cases where immunotherapy failed, is contraindicated, or is expected to be less effective.
Supplementary material
Supplementary material is available online at Neuro-Oncology (https://academic.oup.com/neuro-oncology).
Figure 3.
Most common treatment-related adverse events. Bars represent the percentage of patients who experience each event type. Numbers within the bar show the number of patients who had each event. The minimum frequency threshold used for the figure is 10%.
noae116_suppl_Supplementary_Material |
Title: Advances in the pharmacological mechanisms of berberine in the treatment of fibrosis | Body:
1 Introduction
Fibrosis can affect various tissues and organs, with the prevalence of fibrosis-related diseases approaching 5%, and the mortality rate due to fibrosis can be as high as 45% in some regions, resulting in a significant economic burden worldwide (Henderson et al., 2020; Zhao X. et al., 2020). The main pathological changes are excessive deposition of extracellular matrix (ECM), proliferation of fibrous connective tissue and reduction of parenchymal cells in organ tissues, which ultimately leads to structural destruction and functional decline of organs (Friedman et al., 2013; Henderson et al., 2020). Epithelial and endothelial cell damage, inflammatory responses, oxidative stress by various stimuli are common reasons of fibrosis formation and progression (Henderson et al., 2020; Wynn and Ramalingam, 2012; Zhao X. et al., 2020). Common diseases associated with fibrosis include cirrhosis, heart failure, idiopathic pulmonary fibrosis (IPF), nephropathy, diabetes and scleroderma (Zhao X. et al., 2020). For example, hepatic fibrosis (HF) is the result of acute or chronic liver injury, but the result of progressive ECM deposition can lead to histologic cirrhosis (Saxena and Anania, 2015; Zhao X. et al., 2020). Reactive and progressive interstitial fibrosis caused by persistent activation of myocardial fibroblasts leads to myocardial stiffness and ultimately to ventricular dysfunction (Wynn and Ramalingam, 2012). Multiple gene regulatory pathways are critical for driving the function of MFBs, including molecules downstream of the TGF-β receptor, including Smads (Inagaki and Okazaki, 2007; Zhao X. et al., 2020), and extracellular signaling regulators of AMP-activated protein kinase (AMPK), nuclear factor-erythroid 2-related factor 2 (Nrf2), and the inflammatory pathway (Zhao X. et al., 2020), which normally control fibrotic cell motility, proliferation and morphology through well-characterized intracellular signaling pathways (Casaletto and McClatchey, 2012) (Figure 1). Although a small number of drugs are available for the treatment of fibrosis, such as Nidanib and Pirfenidone, long drug cycles, adverse effects, and the lack of drugs targeting different organs remain a challenge to clinical treatment. Therefore, effective therapeutic strategies for fibrosis are urgently required.
FIGURE 1
Pathologic processes and signaling cascades associated with fibrosis. Related signaling pathways include TGF-β, AMPK, Nrf2, and inflammatory pathway.
Natural products offer clear advantages in the treatment of diseases due to their high safety profile and diverse efficacy (Liu et al., 2024; Wang et al., 2024a). Rhizoma Coptidis is well known traditional Chinese medicine used in the treatment of fibrosis, inflammation, diabetes, etc. (Ma et al., 2024; Xiao et al., 2021; Yang et al., 2024). Rhizoma Coptidis was first described in Shennong Bencao Jing (Tan et al., 2016), and its anti-diabetic properties were first documented in Note of Elite Physicians (Zhang Q. et al., 2014). Berberine (BBR), one of the representative active ingredients of Rhizoma Coptidis (Shao et al., 2021), is an isoquinoline alkaloid and is also found in the leaves, twigs, barks, rhizomes, roots and stems of large numbers of plants, including Berberis kansuensis C.K. Schneid. (Berberidaceae), Poppyaceae, Coscinium fenestratum (Goetgh.) Colebr. (Menispermaceae), Phellodendron amurense Rupr. (Rutaceae), and Argemone mexicana L. (Papaveraceae) (Feng X. et al., 2019; Habtemariam, 2020; Imenshahidi and Hosseinzadeh, 2016; Singh and Mahajan 2013; Sun et al., 2023; Xu X. et al., 2021). It is an over-the-counter drug for the treatment of bacterial diarrhea and has a long history of medicinal use in both traditional Chinese medicines (Dang et al., 2020; Kong et al., 2020). Multiple studies have demonstrated that BBR has anti-inflammatory, anti-bacterial, anti-viral and anti-fibrotic, and has attracted intensive interest in the treatment of fibrosis of liver, heart, lung, kidney, pancreatic and other organs (Bansod et al., 2020; Che et al., 2019; Chitra et al., 2013; Gao et al., 2024; Tan E. et al., 2023; Xu X. et al., 2021; Zhang Z. et al., 2016).
Literature searches were performed using PubMed, Web of Science, and Google Scholar databases, and no filters were set for the searches in these three databases. The keywords included “berberine”, “fibrosis”, “anti-fibrotic effect”, “pharmacological effect”, “pharmacological mechanism”, “pharmacokinetics”, “safety” and “toxicity”. All articles included were published between 2004 and 2024. The purpose of this review is to focus on the therapeutic role and mechanism studies of BBR in fibrosis, and for this purpose, we read the titles, abstracts, and full text of publications from the past 20 years, removed publications that were not relevant to the topic of this review, and categorized them according to the type of fibrosis and mechanism. We also performed a correlation search for citations to relevant studies and review literature. Duplicates were removed using Endnote 20’s automatic and manual duplicate detection system, and a total of 168 references were finally included.
2 Anti-fibrotic effect of BBR
2.1 Hepatic fibrosis
The main pathological features of HF are activation of hepatic stellate cells (HSCs) and excessive deposition of protofibrillar collagen (Tacke and Weiskirchen, 2012; Yi et al., 2021). After liver injury, multiple factors such as cytokines, chemokines or reactive oxygen species (ROS) induce the differentiation of HSCs from quiescent to myofibroblasts (MFBs), and the overproduction of smooth muscle α-actinin (α-SMA), which ultimately leads to the overproduction and deposition of ECM components and the development of HF (Kong et al., 2012; Sanchez-Valle et al., 2012; Zimmermann and Tacke, 2011). BBR inhibits the activation of HSCs and the generation of α-SMA and suppresses the development of HF through multiple mechanisms (Figure 2).
FIGURE 2
The effect of BBR on the hepatic fibrosis. ERS, AMPK, Ferroptosis, ROS, FoxO, Lipid metabolism, SCFAs and microRNA are involved in BBR for hepatic fibrosis.
2.1.1 Endoplasmic reticulum stress
Fibrotic signaling triggers transcription of procollagen I, which enters the endoplasmic reticulum and is released into the ECM via the endoplasmic reticulum (ER)-Golgi secretion compartment (Ajoolabady et al., 2023; Maiers et al., 2017). Perturbation of ER export of procollagen I induced ER stress (ERS) and UPR activation, leading to apoptosis in HSCs and upregulates fibrotic genes and Smad2 expression, accelerated progression of HF (Koo et al., 2016; Maiers et al., 2017; Xuan et al., 2021).
Previous studies clarified that 200 mg/kg of BBR for 5 weeks downregulated the expression of α-SMA, CoI, alanine aminotransferase (ALT), azelaic transaminase (AST), platelet-derived growth factor (PDGF), connective tissue growth factor (CTGF), metalloproteinase-1 (MMP-1), and TGF-β1 via ERS, and attenuated chronic liver injury, inflammation and fibrosis in vivo (Zhang Z. et al., 2016). On the other hand, BBR attenuated tunicamycin-induced triglyceride (TG) and collagen deposition in the liver of mice, compared to the tunicamycin group, BBR (75, 150 and 300 mg/kg) all reversed the levels of unfolded protein response (UPR)-related genes (CHOP, GRP78 and ATF6) that it upregulated, mainly by attenuating ERS (Yang et al., 2022). In summary, BBR can improve HF by modulating ERS.
2.1.2 AMPK pathway
AMPK, acts as a “metabolic sensor”, to regulate energy homeostasis and metabolic processes (Hardie et al., 2012; Li et al., 2014; Zhao P. et al., 2020). Researchers find AMPK regulates the activation of HSCs and inhibits TGF-β-induced fibrotic properties of HSCs, ameliorating liver injury and fibrosis (da Silva Morais et al., 2009; Zhao P. et al., 2020).
BBR (50 mg/kg) attenuated carbon tetrachloride (CCl4)-induced hepatic histological changes in mice, such as hepatocyte necrosis, adjacent hepatocyte steatosis, ballooning degeneration, lymphocyte infiltration, pseudofollicular and bridging formation through up-regulating the p-AMPK/AMPK ratio in activated HSCs, thereby decreasing the ratio of p-Akt to total Akt, strongly increased serum and liver tissue superoxidedismutase (SOD) activity in CCl4-induced hepatic injury in mice, decreased serum Malondialdehyde (MDA) level and NADPH oxidase 4 (NOX4) expression in liver tissues, reducing ROS production and effectively preventing HF (Li et al., 2014).
2.1.3 Ferroptosis
Emerging research suggests that ferroptosis is characterized by redox-active iron accumulation, ROS generation, lipid peroxidation, and glutathione depletion, and the iron death inhibitor fer1 helps to inhibit the activation of HSCs in vitro (Yi et al., 2021).
In thioacetamide (TAA) and CCl4 induced HF in mice, compared to the model group, another proposed mechanism of BBR associated anti-fibrotic effect was based on inhibits HSCs activation through ferroptosis, by which BBR reduced expression of hyaluronic acid (HA), p75NTR (HSC activation marker), ALT, AST and iron deposition, as well as lowering of Ishak score (Passino et al., 2007; Yi et al., 2021). Furthermore, treatment with 21 µM BBR for 24 h affected HSCs but not hepatocytes and reduced TAA-induced α-SMA expression, modulating the viability and proliferation of HSCs in a dose- and time-dependent manner (Yi et al., 2021). Besides, studies have shown that BBR treatment can decreased LC3B and FTH1 expression, enhanced Ptgs2 and ROS levels, promoted ferritin hydrolysis, and increased iron overload in HSCs, suggesting that BBR enhances HSCs ferroptosis through inhibition of autophagy, which is beneficial to HF (Yi et al., 2021). In addition, BBR inhibited the proliferation of HSCs in a dose-dependent manner and its IC50 value is 66.86 mM, but combination with sorafenib (10 µM) reduced it to 15.61 µM; molecular docking experiments further demonstrated that BBR binds to PEBP1 (can trigger ferroptosis), with a maximum binding energy of −8.51 kcal/mol; so BBR can promote HSC ferroptosis to alleviate HF through binding to PEBP1 (Xie et al., 2023).
2.1.4 Oxidative stress
Oxidative stress is a vital factor in the pathogenesis of HF. Injured hepatocytes release ROS and inflammatory cytokines which are involved in HSCs activation and recruitment of immune cells to liver tissue (Che et al., 2023; Li N. et al., 2021; Xu et al., 2022). Among them, interleukin 17 (IL-17) promotes ECM production by HSCs and exacerbates HF (Ma et al., 2020; Meng et al., 2012; Seo et al., 2016). Thus, hepatic ROS trigger complex interactions between activated HSCs and recruited immune cells, thereby exacerbating fibrosis and inflammation within the liver.
Accumulating evidences demonstrated that BBR hinder HF through oxidative stress. In the CCL4-induced HF model in Balb/c mice, different doses of BBR (9 and 50 mg/kg) were treated for 2 weeks that ameliorated the increase in plasma enzyme activities and oxidative stress, decreased tumor necrosis factor-α (TNF-α), α-SMA, transforming growth factor beta 1 (TGF-β1) and MMP-9 expression, the levels of MMP-2 is increased, and induced the Cu/Zn SOD activity to be normalization (Domitrovic et al., 2013b). BBR reduced ALT, AST, MDA and hydroxyproline (HYP) levels, as well as increased SOD levels, compared with the CCL4-induced fibrosis group of male Wistar rats (Zhang et al., 2008). BBR (5 µM) can reduced cellular steatosis, hindered ROS, inflammatory cytokines and collagen production in vitro (Rafiei et al., 2023). This indicate that BBR treatment can promote liver repair by ameliorating oxidative stress.
2.1.5 Metabolism
Disorders of lipid metabolism, such as fatty acid and cholesterol metabolism, are one of the pathologic bases of many liver diseases (Arain et al., 2017; Huang et al., 2021; Liu et al., 2022; Scorletti and Carr, 2022). In non-alcoholic fatty liver disease, dysregulation of the lipid balance in the body can lead to a severe accumulation of triacylglycerols in the liver cells, which can progress to non-alcoholic steatohepatitis, hepatic fibrosis and even cirrhosis (Grabner et al., 2021). Lipid metabolism in the liver is regulated by various mechanisms, including peroxisome proliferator-activated receptors (PPARs) and sterol regulatory element binding protein (SREBP) (Grabner et al., 2021).
BBR downregulates the expression of the lipid metabolism-related gene stearoyl coenzyme A desaturase 1 (SCD1) (Yang et al., 2022), reduces TG biosynthesis and enhances TG oxidation to ameliorate hepatic steatosis and HF in vivo (Bansod et al., 2021; Boudaba et al., 2018; Huang et al., 2021; Wang et al., 2016; Zhang et al., 2019). Other studies have reported that BBR supplementation improves total cholesterol, low-density lipoprotein C and high-density lipoprotein C in the blood and accelerates cholesterol excretion through inhibition of adipocyte enhancer-binding protein one or enhancement of cholesterol-binding receptor, which explains its hepatoprotective properties in vivo and in vitro (Derosa et al., 2013; Kong et al., 2004; Wang and Zidichouski, 2018; Wei et al., 2016).
PPARs are considered important metabolic regulators of hepatic lipid metabolism and inflammation (Boyer-Diaz et al., 2021; Chen et al., 2023). PPAR-γ inhibits the activation of HSCs and regulates the expression of genes related to adipogenesis and fibrogenesis to prevent HF progression (Brunner et al., 2019; Perez-Carreon et al., 2010; Sanyal et al., 2010; Yuan et al., 2004). BBR enhances the expression of the gene encoding the FAO carnitine palmitoyl transferase IA by interacting with PPAR-α to inhibit the production of TG in vivo (Yu et al., 2016; Zhou et al., 2008). BBR contains potential agonists of all PPAR isoforms (Xia et al., 2013; Yu et al., 2016), and these could act as ligands to regulate the progression of HF. Taken together, BBR may restore lipid homeostasis and regulate liver function by modulating the PPARs signaling cascade.
SREBP-1c, CHREBP, FAs and C/EBPβ as adipogenic regulators, are involved in the regulation of steatosis, inflammation and fibrosis, however, BBR downregulates their mRNA levels in tunicamycin-induced mice, and mRNA levels of ROS, CYP2E1 (a major mediator of lipid peroxidation), and the expression of TNF-α, IL-6, α-SMA, TIMP-1 and TGF-β1 that are the anti-fibrotic effect of BBR was fully utilized (Zhang Z. et al., 2016).
2.1.6 Gut microbiota
Gut microbiota is a complex system that regulates certain biochemical, physiological and immune responses to maintain the health of the organism (Mishra et al., 2021). It alters the microenvironment of the organism by directly influencing metabolic signaling and energy metabolism either by itself or by producing certain metabolites, which can lead to inflammation, autoimmunity and metabolic disorders (Thursby and Juge, 2017). Thus, a growing body of research suggests that ecological dysregulation of the gut microbiota and impairment of its composition and function are strongly associated with many metabolic diseases (Cai and Kang, 2023; Wang et al., 2021).
Short-chain fatty acids (SCFAs), the end products of anaerobic microbial fermentation of indigestible carbohydrates, have a profound impact on gut function and host energy metabolism (Nicholson et al., 2012). BBR (100 mg/kg) treatment increased the concentration of SCFAs in the gut, increased the production of gut flora-derived butyrate, and decreased lipopolysaccharide (LPS) binding protein (LBP), monocyte chemotactic protein-1 (MCP-1), leptin and lipocalin, and promotes homeostasis of the hepatic microenvironment in vivo (Zhang et al., 2012).
2.1.7 Forkhead box O pathway
Forkhead box O (FoxO) is an essential class of transcription factors involved in numerous biological processes, such as cell cycle, cell proliferation, apoptosis and anti-oxidative stress (Calissi et al., 2021; Sun et al., 2009). p21 and p27 are FoxO-specific transcriptional targets that are associated with G1-phase cell cycle arrest (Roy et al., 2011; Sun et al., 2009). p27 is the key downstream target of FoxO1 to control proliferation and differentiation of HSCs (Sun et al., 2009). The protein kinase B (Akt) signaling cascade controls the transcriptional activity of p21 and p27 through phosphorylating FoxO1 reversing its transcriptional activity (Sun et al., 2009).
This study reported that BBR (5, 10 and 20 μg/mL) induced subcellular redistribution of FoxO1 from the cytoplasm to the nucleus in hepatic stellate cells (CFSCs) in vitro, which reduced the number of activated HSCs, fibrotic septa, and hepatic collagen. Furthermore, it decreases p-FoxO1 (Ser-256) and p-Akt, increases p21 and p27 expression, and inducts G1 blockade, which directly inhibited the CFSC proliferation, which has a protective effect on the liver (Sun et al., 2009).
2.1.8 Another pathway
Numerous studies have proved that enhanced autophagy can induce fibrotic diseases in many organs (Marcelin et al., 2020; Shang et al., 2020; Zhang et al., 2020). After liver injury, increased autophagy promotes the activation of HSCs (Thoen et al., 2011; Wang B. et al., 2017; Zhang et al., 2017). Different microRNAs (miR) involved in various fibrotic diseases. MiR-15 family mainly promotes cell proliferation and apoptosis. MiR-199 and miR-200 families are responsible for ECM deposition and pro-fibrotic cytokine release (Jiang et al., 2017; Schmiedel et al., 2015).
BBR reduced the expression of ATG5, BCL-2, HYP, α-SMA, collagen type I alpha (COL1A1), LC3, ALT, and AST, attenuated collagen deposition and inflammatory cell infiltration, and up-regulating the expression of p53, BAX, and cleaved PARP, and enhanced hepatic repair through up-regulating the expression of miR-30a-5p in vivo and in vitro (Tan Y. et al., 2023). Cyclooxygenase-2 (COX-2) is rapidly synthesized by cells in response to various stimuli involving different pathophysiological processes. BBR can inhibit the expression of COX-2 in vivo and in vitro to protect cells from excessive inflammatory responses, which is beneficial to HF (Feng et al., 2012; Guo et al., 2008; Li et al., 2012; Zeng et al., 2011).
Overall, BBR can mediate different signaling cascades such as ERS, AMPK, ferroptosis, oxidative stress, metabolism, gut microbiota, and FoxO to exert anti-fibrotic effects (Figure 2). However, HF is a complex process, and the activation of HSCs and excessive deposition of collagen fibers are necessary processes that must be taken into account in the future development of new target mechanisms and drugs.
2.2 Myocardial fibrosis
One of the major events that occurs in myocardial fibrosis (MF) when the heart is injured is the activation and differentiation of cardiac fibroblasts (CFs) into MFBs, which contribute to ECM turnover and collagen deposition (Liu et al., 2021). Activated CFs are central cellular effectors of MF and are the main source of ECM proteins in MF (Frangogiannis, 2021). BBR hinders CFs activation and differentiation, and reduces ECM production to achieve anti-MF (Figure 3).
FIGURE 3
The effect of BBR on the myocardial fibrosis. Related signaling pathways include TGF-β, AMPK and Nrf2 pathway.
2.2.1 TGF-β/smad pathway
Previous studies elaborated that TGF-β producing inflammatory cells are essential in the critical cellular event of CFs activation and ECM deposition (Frangogiannis, 2020; Liu et al., 2021). In isoprenaline (ISO)-induced MF in Sprague-Dawley rats, BBR (10, 30 and 60 mg/kg) attenuated macrophage infiltration, altered macrophage phenotype, reduced the expression of COL1A1, collagen type III alpha (COL3A1), CTGF, TGF-β1, and α-SMA, and inhibited the proliferation of CFs, all of which were achieved by blocking the activation of the TGF-β1/Smad signaling cascade (Che et al., 2019).
2.2.2 AMPK pathway
Several studies have confirmed that BBR downregulates the p-mTOR, p-4EBP1 and p-p70S6K (Thr389), inhibits the proliferation of CFs and their conversion to MFBs, reduces the expression of CoI, CoIII, TGF-β1, MMP-2 and MMP-9, shrinks the size of the MFs, increases the secretion of IL-10, and ultimately inhibits MFs and improves cardiac dysfunction in vivo and in vitro. The pharmacological mechanism of the above results is strongly related to the phosphorylation of AMPK (Ai et al., 2015; Chen et al., 2020).
2.2.3 Nuclear factor-erythroid 2-related factor 2 pathway
Nuclear factor-erythroid 2-related factor 2 (Nrf2) translocate into the nucleus as a transcription factor and induces the expression of downstream anti-oxidant and detoxification enzymes, such as heme oxidase-1 (HO-1), SOD, Glutathione peroxidase (GPx) (Wang et al., 2023). Nrf2 can counterbalances oxidative stress, also affects TGF-β1-mediated phosphorylation of Smad3, which is indispensable for fibrosis development (Kobayashi et al., 2016).
Recent studies have elucidated that in the DOX-induced MF model in male Sprague-Dawley rats, BBR (60 mg/kg) targeting Nrf2 can downregulated the expression of α-SMA, CoI, CoIII, and MDA, inhibited the differentiation of CFs to MFBs, and increased SOD activity, HO-1, and mitochondrial transcription factor A (TFAM), rescuing mitochondrial morphology damage and loss of membrane potential (Wang et al., 2023), further protecting the normal functioning of the heart.
Taken together, the conversion of CFs to MFBs occurs at a central event in CF, and BBR prevents this event through the TGF-β/Smad, AMPK, and Nrf2 pathways (Figure 3). Hitherto, there is still a critical lack of research on the target and mechanism of BBR in MF treatment.
2.3 Pulmonary fibrosis
Pulmonary fibrosis (PF) is a heterogeneous lung mesenchymal disorder that includes persistent epithelial injury, abnormal wound healing, and excessive ECM deposition (Dong and Ma, 2016). MFBs, fibroblasts, and other cell types that secrete numerous of ECM into the alveolar structures, continued differentiation, proliferation, and migration of fibroblasts triggered by a variety of fibrotic mediators contribute to the formation of fibrotic foci and subsequent lung structural remodeling (Wang et al., 2024b). However, BBR plays a unique role in the treatment of PF (Figure 4).
FIGURE 4
The effect of BBR on the pulmonary fibrosis. Inflammatory, AMPK, TGF-β, Nrf2, Notch/Snail and SIRT2 are involved in BBR for pulmonary fibrosis.
2.3.1 TGF-β pathway
TGF-β, a critical inducer of epithelial-mesenchymal transition (EMT), promotes EMT in alveolar epithelial and endothelial cells, restores tissue morphology and structure, and induces fibroblasts to convert to MFBs, synthesize and secrete ECM (Li et al., 2011; Tew et al., 2020). TGF-β-mediated Smad and non-Smad signaling cascades are thought to be major players in accelerated PF(Higashiyama et al., 2007; Wynn, 2011). In the BLM-induced PF in male Wistar albino rats, BBR (200 mg/kg) reverses bleomycin (BLM)-induced lung ultrastructural changes, enhances Smad7 expression, and downregulates α-SMA, CoI and CoIII expression through inhibits BLM-induced elevation of p-Smad 2/3 (Chitra et al., 2015; Tan E. et al., 2023; Palanivel et al., 2015). Another study clarified that BBR reduced the levels of HYP, CXCL14, CXCR4, CoI/III, MMP-2, MMP-9, α-SMA and p-Smad 2/3, and prevented the activation of Smad2/3, ensured recovery of lung status and function (Li et al., 2019).
2.3.2 Inflammatory
Pathologically, PF is always first accompanied by an inflammatory response (Feng F. et al., 2019), inflammatory cells multitask at the wound site by facilitating wound debridement and producing chemokines and growth factors (Eming et al., 2017).
In the PM2.5-induced MF in male C57BL/6 mice and BLM-induecd MF in male Kunming mice, BBR (50 mg/kg) treatment reduces inflammatory cell aggregation in the lungs and decreases the expression of pro-inflammatory cytokines TNF-α, IL-8, IL-1β, and IL-6; it also downregulates the levels of COI, COIII, TGF-β1, PDGF-AB, HYP, α-SMA, p38 MAPKα, and p38 MAPKα (pT180/Y182) and inhibits collagen production and deposition (Alnuqaydan et al., 2022; Beik et al., 2023; Tew et al., 2020; Yang et al., 2023; Zhao et al., 2023).
2.3.3 Stromal cell-derived factor-1/CXCR4 pathway
CXCL12 is involved in the mobilization of bone marrow-derived stem cells through Stromal cell-derived factor-1 (SDF-1) receptor C-X-C chemokine receptor 4 (CXCR4), which is abundantly expressed on a wide range of cells and may be considered a permanent reservoir for fibroblasts (Ahmedy et al., 2023).
Recently, a study found that in the BLM-induecd MF in albino male mice, BBR (5 mg/kg) greatly inhibited BLM-induced weight loss and elevated lung index, preserved lung structure and attenuated bronchoalveolar injury, and ameliorated lung injury, which through reduced expression of SDF1 and CXCR4 (Ahmedy et al., 2023). And further inhibited the expression of TGF-β, p-Smad2/3, α-SMA, NF-κB p65, TNF-α, IL-6, MDA, GSH, SOD and CAT, effectively attenuated oxidative stress, enhanced the expression of Nrf2, and ultimately attenuated epithelial mesenchymal transition of PF (Ahmedy et al., 2023).
2.3.4 PPAR-γ pathway
BBR can directly enter the cytoplasm of fibroblasts and act as a PPAR-γ agonist, up-regulating the nuclear translocation, DNA-binding activity, and transcriptional activity of PPAR-γ, and reversing PM2.5-induced collagen deposition, the expression of fibroblast markers (TGF-β1, FN, α-SMA, COI, and COIII), and the expression of E cadherin expression upregulation, promotes CD36 and AP2 mRNA expression, HGF and PTEN protein levels, and attenuates oxidative and inflammatory factor-mediated PF in BLM-induecd female ICR mice (Guan et al., 2018; Zhao et al., 2023).
2.3.5 Nuclear factor-κB pathway
The BBR-mediated nuclear factor-κB (NF-κB) pathway attenuated the extent of lung oxidative damage and PF. In BLM-induecd male wistar albino rats, BBR (200 mg/kg) action was manifested as inhibition of TGF-β1, ROS, iNOS, TNF-α, MDA, OH, NO, and myeloperoxidase (MPO) levels through downregulation of the phosphorylation of Nrf2, IκB, and NF-κB p65; inhibition of HYP and histamine release, significant reduction of mast cell recruitment, and reversal of BLM-induced SOD, CAT, GPx and GR activities, and restored the levels of non-enzymatic antioxidant status of glutathione, vitamin A, vitamin C and vitamin E (Chitra et al., 2013; Sun et al., 2022).
The special structure of the lung poses a challenge for the treatment of PF, and although BBR can protect the lung structure and reduce lung damage through various pathways (such as TGF-β) (Figure 4), researchers are encouraged to conduct in-depth studies, especially clinical studies, to provide stronger and more comprehensive evidence for the treatment of fibrotic diseases with BBR.
2.4 Renal fibrosis
Renal fibrosis (RF) is a common manifestation of various chronic kidney diseases and representative events include increased matrix production and inhibited degradation to promote intercellular matrix interactions, cyst-like cell and fibroblast activation, tubular epithelial-mesenchymal transition, MFBs activation, immune cell infiltration and apoptosis (Sun et al., 2022). Almost all cells in the kidney are associated with the fibrotic process, including fibroblasts, tubular epithelial cells, endothelial cells, lymphocytes and macrophages (Sun et al., 2022). Thus, BBR may achieve renal protection through these cellular interactions and associated factors (Figure 5).
FIGURE 5
The effect of BBR on the renal fibrosis. Related signaling pathways include TGF-β, inflammatory, SDF-1/CXCR4, PPAR-γ and NF-κB pathway.
2.4.1 TGF-β pathway
TGF-β1 is a major driver of RF, and is thought to act either directly or indirectly on a variety of cell types of the kidney to promote the fibrotic process (Meng et al., 2016).
BBR intervention can downregulate TGF-β, p-Smad3, α-SMA, vimentin, NF-κB mRNA and protein levels, relieves renal injury and inhibits RF in vivo and in vitro (Hassanein et al., 2022; Li and Zhang, 2017; Wang et al., 2014). BBR alleviates adriamycin (DOX)-induced RF through reduced TGF-β, caspase-3 and NF-κB expression and collagen deposition in vivo, ultimately slowing down the progression of RF (Ibrahim Fouad and Ahmed, 2021; Song et al., 2024).
2.4.2 Inflammatory
RF pathogenesis mainly includes inflammation-induced tubular epithelial cell (TEC) injury, and inhibition of inflammatory progression can delay or reverse RF (Song et al., 2024; Tan E. et al., 2023). BBR (30 μM and 50 mg/kg) attenuated the extent of interstitial fibrosis and histopathological damage through inhibiting the activation of NLRP3 inflammasome and IL-1β level, which reduced the expression of F4/80, MCP-1, CoI, CoIV, and α-SMA, upregulated the level of E-cadherin, and alleviated the abnormalities of serum creatinine and urea nitrogen levels in vivo and in vitro (Song et al., 2024; Tan E. et al., 2023). In summary, BBR has a protective effect on RF.
2.4.3 AMPK pathway
AMPK is a key factor contributing to the onset and progression of renal interstitial fibrosis. It can enhance mitochondrial FAO in response to decreased ATP levels (Li et al., 2022). BBR (30 μM and 50 mg/kg) intervention reversed the downregulation of p-AMPK in the kidney and reversed the downregulation of FAO-associated proteins in vivo and in vitro, including CPT1A and PPAR-α (Tan E. et al., 2023).
2.4.4 Nrf2 pathway
On the one hand, BBR (200 mg/kg) was found to attenuate RF induced by STZ in C57BL/6J mice by activating the Nrf2 signaling pathway in Diabetic Nephropathy (DN); on the other hand, knockdown of Nrf2 not only counteracts BBR-induced HO-1 and NQO-1 expression, but also reverses the inhibitory effect of BBR on high glucose (HG)-induced TGF-β/Smad signaling activation and anti-fibrotic effect (Hassanein et al., 2022; Zhang X. et al., 2016).
2.4.5 Notch/snail pathway
The Notch pathway has been shown to mediate cellular fibrosis such as EMT in epithelial cells in DN and is associated with TGF-β1 (Yang et al., 2017). Snail1 expression is directly regulated by the Notch signaling, and the notch/snail pathway is an important mechanism in renal interstitial fibrosis in DN (Yang et al., 2017).
BBR (30 μM and 150 mg/kg) blocked HG-induced EMT events, and inhibited expression of notch and snail1 in renal tubular epithelial cells, suppressing tubular EMT and renal interstitial fibrosis in vivo and in vitro (Yang et al., 2017).
2.4.6 Sirtuin 2/murine double minute2 pathway
Class III histone deacetylase Sirtuin 2 (SIRT2) is a vital member of nicotinamide adenine dinucleotide-dependent protein deacetylases, which occupy a key position in inflammation and fibrogenesis (Ahmedy et al., 2022). BBR (200 mg/kg) inhibited cisplatin-induced SIRT2 and Murine double minute2 (MDM2) expression, further reduced hemagglutinin-3 (Gal-3), α-SMA, TNF-α, DUSP6, and reduced the mRNA levels of P2X7R and p-ERK1/2 to achieve relief of RF induced by cisplatin in female Wistar rats (Ahmedy et al., 2022).
During the development of RF, fibrotic tissue replaces nephrons in the kidneys, and the problem of increased tissue stiffness brought about by excessive accumulation of ECM hinders drug distribution and efficacy. Although various studies have shown that BBR has multiple targets and mechanisms for targeting RF (Figure 5), the above problems cannot be ignored and more researchers are called to consider them in future studies.
2.5 Pancreatic fibrosis
Pancreatic fibrosis is primarily associated with the activation of pancreatic stellate cells (PSCs), which results in the secretion of excess ECM proteins (An et al., 2023; Masamune et al., 2009). Pro-fibrotic mediators activate quiescent PSCs into MFBs, such as TGF-β (Masamune et al., 2009). AMPK is a metabolite sensor protein that is predominantly expressed in all organs and ameliorates inflammation and fibrosis by regulating macrophage polarization.
In the cerulein-induced pancreatic fibrosis in male Swiss albino mice, BBR dose-dependently decreased levels of pancreatic MDA, nitrite, TNF-α, IL-6, IL-1β, TGF-β1, α-SMA, COL1A, COL3A, and p-Smad2/3, reduced inflammatory cell infiltration, vesicular cell atrophy, exocrine pancreatic vacuolization, ECM deposition and EMT program; increased the content of GSH, and the expression of E-cadherin, p-AMPKα, p-AMPKβ, ACC and Smad7 expression (Bansod et al., 2020). Taken together, BBR prevented the progression of chronic pancreatitis and associated fibrosis in an AMPK-dependent manner by inhibiting TGF-β/Smad signaling and regulating macrophage polarization (Bansod et al., 2020).
2.6 Adipose tissue fibrosis
In the presence of overnutrition, adipose tissue undergoes rapid dynamic remodeling through adipocyte hypertrophy and hyperplasia (Koenen et al., 2021; Sun et al., 2011), accompanied by elevated accumulation of immune cells and overproduction of ECM, leading to the development of fibrosis (Li et al., 2022; Wang et al., 2018). Adipose tissue fibrosis is a hallmark of obesity-related adipose tissue dysfunction (Hu et al., 2018).
2.6.1 AMPK pathway
Previous studies have shown that TGF-β signaling is associated with adipose ECM remodeling and that inhibition of TGF-β by activated AMPK alleviates adipose tissue fibrosis (Wang et al., 2018; Xu X. et al., 2021). Besides, BBR inhibits TGF-β1/Smad3 in HFD-induced white adipose tissues by attenuating macrophage infiltration and polarization, and by activating the AMPK pathway. TGF-β1/Smad3 signaling and ameliorated obesity-associated adipose tissue fibrosis (Wang et al., 2018; Xu X. et al., 2021). Notably, the inhibitory effect of BBR on adipose tissue fibrosis was blocked by compound C (an AMPK inhibitor) (Wang et al., 2018; Xu X. et al., 2021). The C57BL/6 mice adipose tissue fibrosis model used in the above study was induced by the HFD. The above results provide sufficient evidence that BBR attenuates adipose tissue fibrosis through AMPK pathway.
2.6.2 Hypoxia-inducible factor 1α pathway
Hypoxia is an early event in adipose tissue dysfunction, and hypoxic conditions promote the expression of hypoxia-inducible factor 1α (HIF-1α). HIF-1α-induced transcriptional program that leads to the enhanced synthesis of ECM components and ultimately promotes the development of fibrosis in white adipose tissue (Hu et al., 2018). BBR attenuated HFD-induced fibrosis and fibroblast proliferation through reducing CoI, α-SMA, platelet-derived growth factor receptor α (PDGFR-α) and HIF-1α expression, and inhibiting aberrant ECM protein synthesis in vivo. All of the above results were achieved by reversing HFD-induced HIF-1α activation and transcription by BBR (Hu et al., 2018).
2.6.3 Another pathway
Adipose tissue macrophages are found in up to 50% of adipose tissue in obese rodents and humans. Under obesity, the M2 macrophage phenotype is transformed into M1 macrophage phenotype. BBR regulates macrophage infiltration and polarization by decreasing the expression of iNOS, COX-2, IL-1β, IFN-γ, F4/80, MCP-1, and MMP-1α, reducing abnormal ECM deposition to reduce inflammation and fibrosis in adipose tissue through down-regulating the expression of COI, α-SMA, α-SMA, MMP-9 and TIMP-1 in vivo (Li et al., 2022).
2.7 Epidural fibrosis
Epidural fibrosis (EF) is the result of a physiological cyclic defense response during wound healing, a cycle in which fibroblasts in the healing area proliferate rapidly in response to inflammatory mediators and growth factors, and an excessive healing response leads to increased formation of scar tissue, resulting in excessive and disorganized matrix deposition (Keskin et al., 2022).
During wound healing, immune cells (such as monocytes and macrophages) together with fibroblasts and smooth muscle cells produce high levels of TGF-β1, further causes proliferation and accumulation of fibroblasts in the ECM, leading to a vicious cycle of EF after laminectomy (Gorgulu et al., 2004). In the Laminectomy-induced EF in Wistar albino rats, it's worth noting that BBR downregulates the expression of TGF-β1 further attenuate EF (Gorgulu et al., 2004). It has also been shown that BBR prevents apoptosis in human-derived myeloid cells by reducing oxidative stress induced by autophagy and endoplasmic reticulum stress induced by Ca2+ dysregulation (Luo et al., 2019). Other study found that BBR reduced HYP expression stopping the development of EF (Keskin et al., 2022).
Briefly, BBR plays a unique role in pancreatic, adipose tissue, and epidural fibrosis using different mechanisms, but relevant studies are insufficient to illustrate the therapeutic role of BBR in various types of organ fibrosis. Therefore, studies on the anti-fibrotic effects of BBR are indispensable in the future.
2.1 Hepatic fibrosis
The main pathological features of HF are activation of hepatic stellate cells (HSCs) and excessive deposition of protofibrillar collagen (Tacke and Weiskirchen, 2012; Yi et al., 2021). After liver injury, multiple factors such as cytokines, chemokines or reactive oxygen species (ROS) induce the differentiation of HSCs from quiescent to myofibroblasts (MFBs), and the overproduction of smooth muscle α-actinin (α-SMA), which ultimately leads to the overproduction and deposition of ECM components and the development of HF (Kong et al., 2012; Sanchez-Valle et al., 2012; Zimmermann and Tacke, 2011). BBR inhibits the activation of HSCs and the generation of α-SMA and suppresses the development of HF through multiple mechanisms (Figure 2).
FIGURE 2
The effect of BBR on the hepatic fibrosis. ERS, AMPK, Ferroptosis, ROS, FoxO, Lipid metabolism, SCFAs and microRNA are involved in BBR for hepatic fibrosis.
2.1.1 Endoplasmic reticulum stress
Fibrotic signaling triggers transcription of procollagen I, which enters the endoplasmic reticulum and is released into the ECM via the endoplasmic reticulum (ER)-Golgi secretion compartment (Ajoolabady et al., 2023; Maiers et al., 2017). Perturbation of ER export of procollagen I induced ER stress (ERS) and UPR activation, leading to apoptosis in HSCs and upregulates fibrotic genes and Smad2 expression, accelerated progression of HF (Koo et al., 2016; Maiers et al., 2017; Xuan et al., 2021).
Previous studies clarified that 200 mg/kg of BBR for 5 weeks downregulated the expression of α-SMA, CoI, alanine aminotransferase (ALT), azelaic transaminase (AST), platelet-derived growth factor (PDGF), connective tissue growth factor (CTGF), metalloproteinase-1 (MMP-1), and TGF-β1 via ERS, and attenuated chronic liver injury, inflammation and fibrosis in vivo (Zhang Z. et al., 2016). On the other hand, BBR attenuated tunicamycin-induced triglyceride (TG) and collagen deposition in the liver of mice, compared to the tunicamycin group, BBR (75, 150 and 300 mg/kg) all reversed the levels of unfolded protein response (UPR)-related genes (CHOP, GRP78 and ATF6) that it upregulated, mainly by attenuating ERS (Yang et al., 2022). In summary, BBR can improve HF by modulating ERS.
2.1.2 AMPK pathway
AMPK, acts as a “metabolic sensor”, to regulate energy homeostasis and metabolic processes (Hardie et al., 2012; Li et al., 2014; Zhao P. et al., 2020). Researchers find AMPK regulates the activation of HSCs and inhibits TGF-β-induced fibrotic properties of HSCs, ameliorating liver injury and fibrosis (da Silva Morais et al., 2009; Zhao P. et al., 2020).
BBR (50 mg/kg) attenuated carbon tetrachloride (CCl4)-induced hepatic histological changes in mice, such as hepatocyte necrosis, adjacent hepatocyte steatosis, ballooning degeneration, lymphocyte infiltration, pseudofollicular and bridging formation through up-regulating the p-AMPK/AMPK ratio in activated HSCs, thereby decreasing the ratio of p-Akt to total Akt, strongly increased serum and liver tissue superoxidedismutase (SOD) activity in CCl4-induced hepatic injury in mice, decreased serum Malondialdehyde (MDA) level and NADPH oxidase 4 (NOX4) expression in liver tissues, reducing ROS production and effectively preventing HF (Li et al., 2014).
2.1.3 Ferroptosis
Emerging research suggests that ferroptosis is characterized by redox-active iron accumulation, ROS generation, lipid peroxidation, and glutathione depletion, and the iron death inhibitor fer1 helps to inhibit the activation of HSCs in vitro (Yi et al., 2021).
In thioacetamide (TAA) and CCl4 induced HF in mice, compared to the model group, another proposed mechanism of BBR associated anti-fibrotic effect was based on inhibits HSCs activation through ferroptosis, by which BBR reduced expression of hyaluronic acid (HA), p75NTR (HSC activation marker), ALT, AST and iron deposition, as well as lowering of Ishak score (Passino et al., 2007; Yi et al., 2021). Furthermore, treatment with 21 µM BBR for 24 h affected HSCs but not hepatocytes and reduced TAA-induced α-SMA expression, modulating the viability and proliferation of HSCs in a dose- and time-dependent manner (Yi et al., 2021). Besides, studies have shown that BBR treatment can decreased LC3B and FTH1 expression, enhanced Ptgs2 and ROS levels, promoted ferritin hydrolysis, and increased iron overload in HSCs, suggesting that BBR enhances HSCs ferroptosis through inhibition of autophagy, which is beneficial to HF (Yi et al., 2021). In addition, BBR inhibited the proliferation of HSCs in a dose-dependent manner and its IC50 value is 66.86 mM, but combination with sorafenib (10 µM) reduced it to 15.61 µM; molecular docking experiments further demonstrated that BBR binds to PEBP1 (can trigger ferroptosis), with a maximum binding energy of −8.51 kcal/mol; so BBR can promote HSC ferroptosis to alleviate HF through binding to PEBP1 (Xie et al., 2023).
2.1.4 Oxidative stress
Oxidative stress is a vital factor in the pathogenesis of HF. Injured hepatocytes release ROS and inflammatory cytokines which are involved in HSCs activation and recruitment of immune cells to liver tissue (Che et al., 2023; Li N. et al., 2021; Xu et al., 2022). Among them, interleukin 17 (IL-17) promotes ECM production by HSCs and exacerbates HF (Ma et al., 2020; Meng et al., 2012; Seo et al., 2016). Thus, hepatic ROS trigger complex interactions between activated HSCs and recruited immune cells, thereby exacerbating fibrosis and inflammation within the liver.
Accumulating evidences demonstrated that BBR hinder HF through oxidative stress. In the CCL4-induced HF model in Balb/c mice, different doses of BBR (9 and 50 mg/kg) were treated for 2 weeks that ameliorated the increase in plasma enzyme activities and oxidative stress, decreased tumor necrosis factor-α (TNF-α), α-SMA, transforming growth factor beta 1 (TGF-β1) and MMP-9 expression, the levels of MMP-2 is increased, and induced the Cu/Zn SOD activity to be normalization (Domitrovic et al., 2013b). BBR reduced ALT, AST, MDA and hydroxyproline (HYP) levels, as well as increased SOD levels, compared with the CCL4-induced fibrosis group of male Wistar rats (Zhang et al., 2008). BBR (5 µM) can reduced cellular steatosis, hindered ROS, inflammatory cytokines and collagen production in vitro (Rafiei et al., 2023). This indicate that BBR treatment can promote liver repair by ameliorating oxidative stress.
2.1.5 Metabolism
Disorders of lipid metabolism, such as fatty acid and cholesterol metabolism, are one of the pathologic bases of many liver diseases (Arain et al., 2017; Huang et al., 2021; Liu et al., 2022; Scorletti and Carr, 2022). In non-alcoholic fatty liver disease, dysregulation of the lipid balance in the body can lead to a severe accumulation of triacylglycerols in the liver cells, which can progress to non-alcoholic steatohepatitis, hepatic fibrosis and even cirrhosis (Grabner et al., 2021). Lipid metabolism in the liver is regulated by various mechanisms, including peroxisome proliferator-activated receptors (PPARs) and sterol regulatory element binding protein (SREBP) (Grabner et al., 2021).
BBR downregulates the expression of the lipid metabolism-related gene stearoyl coenzyme A desaturase 1 (SCD1) (Yang et al., 2022), reduces TG biosynthesis and enhances TG oxidation to ameliorate hepatic steatosis and HF in vivo (Bansod et al., 2021; Boudaba et al., 2018; Huang et al., 2021; Wang et al., 2016; Zhang et al., 2019). Other studies have reported that BBR supplementation improves total cholesterol, low-density lipoprotein C and high-density lipoprotein C in the blood and accelerates cholesterol excretion through inhibition of adipocyte enhancer-binding protein one or enhancement of cholesterol-binding receptor, which explains its hepatoprotective properties in vivo and in vitro (Derosa et al., 2013; Kong et al., 2004; Wang and Zidichouski, 2018; Wei et al., 2016).
PPARs are considered important metabolic regulators of hepatic lipid metabolism and inflammation (Boyer-Diaz et al., 2021; Chen et al., 2023). PPAR-γ inhibits the activation of HSCs and regulates the expression of genes related to adipogenesis and fibrogenesis to prevent HF progression (Brunner et al., 2019; Perez-Carreon et al., 2010; Sanyal et al., 2010; Yuan et al., 2004). BBR enhances the expression of the gene encoding the FAO carnitine palmitoyl transferase IA by interacting with PPAR-α to inhibit the production of TG in vivo (Yu et al., 2016; Zhou et al., 2008). BBR contains potential agonists of all PPAR isoforms (Xia et al., 2013; Yu et al., 2016), and these could act as ligands to regulate the progression of HF. Taken together, BBR may restore lipid homeostasis and regulate liver function by modulating the PPARs signaling cascade.
SREBP-1c, CHREBP, FAs and C/EBPβ as adipogenic regulators, are involved in the regulation of steatosis, inflammation and fibrosis, however, BBR downregulates their mRNA levels in tunicamycin-induced mice, and mRNA levels of ROS, CYP2E1 (a major mediator of lipid peroxidation), and the expression of TNF-α, IL-6, α-SMA, TIMP-1 and TGF-β1 that are the anti-fibrotic effect of BBR was fully utilized (Zhang Z. et al., 2016).
2.1.6 Gut microbiota
Gut microbiota is a complex system that regulates certain biochemical, physiological and immune responses to maintain the health of the organism (Mishra et al., 2021). It alters the microenvironment of the organism by directly influencing metabolic signaling and energy metabolism either by itself or by producing certain metabolites, which can lead to inflammation, autoimmunity and metabolic disorders (Thursby and Juge, 2017). Thus, a growing body of research suggests that ecological dysregulation of the gut microbiota and impairment of its composition and function are strongly associated with many metabolic diseases (Cai and Kang, 2023; Wang et al., 2021).
Short-chain fatty acids (SCFAs), the end products of anaerobic microbial fermentation of indigestible carbohydrates, have a profound impact on gut function and host energy metabolism (Nicholson et al., 2012). BBR (100 mg/kg) treatment increased the concentration of SCFAs in the gut, increased the production of gut flora-derived butyrate, and decreased lipopolysaccharide (LPS) binding protein (LBP), monocyte chemotactic protein-1 (MCP-1), leptin and lipocalin, and promotes homeostasis of the hepatic microenvironment in vivo (Zhang et al., 2012).
2.1.7 Forkhead box O pathway
Forkhead box O (FoxO) is an essential class of transcription factors involved in numerous biological processes, such as cell cycle, cell proliferation, apoptosis and anti-oxidative stress (Calissi et al., 2021; Sun et al., 2009). p21 and p27 are FoxO-specific transcriptional targets that are associated with G1-phase cell cycle arrest (Roy et al., 2011; Sun et al., 2009). p27 is the key downstream target of FoxO1 to control proliferation and differentiation of HSCs (Sun et al., 2009). The protein kinase B (Akt) signaling cascade controls the transcriptional activity of p21 and p27 through phosphorylating FoxO1 reversing its transcriptional activity (Sun et al., 2009).
This study reported that BBR (5, 10 and 20 μg/mL) induced subcellular redistribution of FoxO1 from the cytoplasm to the nucleus in hepatic stellate cells (CFSCs) in vitro, which reduced the number of activated HSCs, fibrotic septa, and hepatic collagen. Furthermore, it decreases p-FoxO1 (Ser-256) and p-Akt, increases p21 and p27 expression, and inducts G1 blockade, which directly inhibited the CFSC proliferation, which has a protective effect on the liver (Sun et al., 2009).
2.1.8 Another pathway
Numerous studies have proved that enhanced autophagy can induce fibrotic diseases in many organs (Marcelin et al., 2020; Shang et al., 2020; Zhang et al., 2020). After liver injury, increased autophagy promotes the activation of HSCs (Thoen et al., 2011; Wang B. et al., 2017; Zhang et al., 2017). Different microRNAs (miR) involved in various fibrotic diseases. MiR-15 family mainly promotes cell proliferation and apoptosis. MiR-199 and miR-200 families are responsible for ECM deposition and pro-fibrotic cytokine release (Jiang et al., 2017; Schmiedel et al., 2015).
BBR reduced the expression of ATG5, BCL-2, HYP, α-SMA, collagen type I alpha (COL1A1), LC3, ALT, and AST, attenuated collagen deposition and inflammatory cell infiltration, and up-regulating the expression of p53, BAX, and cleaved PARP, and enhanced hepatic repair through up-regulating the expression of miR-30a-5p in vivo and in vitro (Tan Y. et al., 2023). Cyclooxygenase-2 (COX-2) is rapidly synthesized by cells in response to various stimuli involving different pathophysiological processes. BBR can inhibit the expression of COX-2 in vivo and in vitro to protect cells from excessive inflammatory responses, which is beneficial to HF (Feng et al., 2012; Guo et al., 2008; Li et al., 2012; Zeng et al., 2011).
Overall, BBR can mediate different signaling cascades such as ERS, AMPK, ferroptosis, oxidative stress, metabolism, gut microbiota, and FoxO to exert anti-fibrotic effects (Figure 2). However, HF is a complex process, and the activation of HSCs and excessive deposition of collagen fibers are necessary processes that must be taken into account in the future development of new target mechanisms and drugs.
2.1.1 Endoplasmic reticulum stress
Fibrotic signaling triggers transcription of procollagen I, which enters the endoplasmic reticulum and is released into the ECM via the endoplasmic reticulum (ER)-Golgi secretion compartment (Ajoolabady et al., 2023; Maiers et al., 2017). Perturbation of ER export of procollagen I induced ER stress (ERS) and UPR activation, leading to apoptosis in HSCs and upregulates fibrotic genes and Smad2 expression, accelerated progression of HF (Koo et al., 2016; Maiers et al., 2017; Xuan et al., 2021).
Previous studies clarified that 200 mg/kg of BBR for 5 weeks downregulated the expression of α-SMA, CoI, alanine aminotransferase (ALT), azelaic transaminase (AST), platelet-derived growth factor (PDGF), connective tissue growth factor (CTGF), metalloproteinase-1 (MMP-1), and TGF-β1 via ERS, and attenuated chronic liver injury, inflammation and fibrosis in vivo (Zhang Z. et al., 2016). On the other hand, BBR attenuated tunicamycin-induced triglyceride (TG) and collagen deposition in the liver of mice, compared to the tunicamycin group, BBR (75, 150 and 300 mg/kg) all reversed the levels of unfolded protein response (UPR)-related genes (CHOP, GRP78 and ATF6) that it upregulated, mainly by attenuating ERS (Yang et al., 2022). In summary, BBR can improve HF by modulating ERS.
2.1.2 AMPK pathway
AMPK, acts as a “metabolic sensor”, to regulate energy homeostasis and metabolic processes (Hardie et al., 2012; Li et al., 2014; Zhao P. et al., 2020). Researchers find AMPK regulates the activation of HSCs and inhibits TGF-β-induced fibrotic properties of HSCs, ameliorating liver injury and fibrosis (da Silva Morais et al., 2009; Zhao P. et al., 2020).
BBR (50 mg/kg) attenuated carbon tetrachloride (CCl4)-induced hepatic histological changes in mice, such as hepatocyte necrosis, adjacent hepatocyte steatosis, ballooning degeneration, lymphocyte infiltration, pseudofollicular and bridging formation through up-regulating the p-AMPK/AMPK ratio in activated HSCs, thereby decreasing the ratio of p-Akt to total Akt, strongly increased serum and liver tissue superoxidedismutase (SOD) activity in CCl4-induced hepatic injury in mice, decreased serum Malondialdehyde (MDA) level and NADPH oxidase 4 (NOX4) expression in liver tissues, reducing ROS production and effectively preventing HF (Li et al., 2014).
2.1.3 Ferroptosis
Emerging research suggests that ferroptosis is characterized by redox-active iron accumulation, ROS generation, lipid peroxidation, and glutathione depletion, and the iron death inhibitor fer1 helps to inhibit the activation of HSCs in vitro (Yi et al., 2021).
In thioacetamide (TAA) and CCl4 induced HF in mice, compared to the model group, another proposed mechanism of BBR associated anti-fibrotic effect was based on inhibits HSCs activation through ferroptosis, by which BBR reduced expression of hyaluronic acid (HA), p75NTR (HSC activation marker), ALT, AST and iron deposition, as well as lowering of Ishak score (Passino et al., 2007; Yi et al., 2021). Furthermore, treatment with 21 µM BBR for 24 h affected HSCs but not hepatocytes and reduced TAA-induced α-SMA expression, modulating the viability and proliferation of HSCs in a dose- and time-dependent manner (Yi et al., 2021). Besides, studies have shown that BBR treatment can decreased LC3B and FTH1 expression, enhanced Ptgs2 and ROS levels, promoted ferritin hydrolysis, and increased iron overload in HSCs, suggesting that BBR enhances HSCs ferroptosis through inhibition of autophagy, which is beneficial to HF (Yi et al., 2021). In addition, BBR inhibited the proliferation of HSCs in a dose-dependent manner and its IC50 value is 66.86 mM, but combination with sorafenib (10 µM) reduced it to 15.61 µM; molecular docking experiments further demonstrated that BBR binds to PEBP1 (can trigger ferroptosis), with a maximum binding energy of −8.51 kcal/mol; so BBR can promote HSC ferroptosis to alleviate HF through binding to PEBP1 (Xie et al., 2023).
2.1.4 Oxidative stress
Oxidative stress is a vital factor in the pathogenesis of HF. Injured hepatocytes release ROS and inflammatory cytokines which are involved in HSCs activation and recruitment of immune cells to liver tissue (Che et al., 2023; Li N. et al., 2021; Xu et al., 2022). Among them, interleukin 17 (IL-17) promotes ECM production by HSCs and exacerbates HF (Ma et al., 2020; Meng et al., 2012; Seo et al., 2016). Thus, hepatic ROS trigger complex interactions between activated HSCs and recruited immune cells, thereby exacerbating fibrosis and inflammation within the liver.
Accumulating evidences demonstrated that BBR hinder HF through oxidative stress. In the CCL4-induced HF model in Balb/c mice, different doses of BBR (9 and 50 mg/kg) were treated for 2 weeks that ameliorated the increase in plasma enzyme activities and oxidative stress, decreased tumor necrosis factor-α (TNF-α), α-SMA, transforming growth factor beta 1 (TGF-β1) and MMP-9 expression, the levels of MMP-2 is increased, and induced the Cu/Zn SOD activity to be normalization (Domitrovic et al., 2013b). BBR reduced ALT, AST, MDA and hydroxyproline (HYP) levels, as well as increased SOD levels, compared with the CCL4-induced fibrosis group of male Wistar rats (Zhang et al., 2008). BBR (5 µM) can reduced cellular steatosis, hindered ROS, inflammatory cytokines and collagen production in vitro (Rafiei et al., 2023). This indicate that BBR treatment can promote liver repair by ameliorating oxidative stress.
2.1.5 Metabolism
Disorders of lipid metabolism, such as fatty acid and cholesterol metabolism, are one of the pathologic bases of many liver diseases (Arain et al., 2017; Huang et al., 2021; Liu et al., 2022; Scorletti and Carr, 2022). In non-alcoholic fatty liver disease, dysregulation of the lipid balance in the body can lead to a severe accumulation of triacylglycerols in the liver cells, which can progress to non-alcoholic steatohepatitis, hepatic fibrosis and even cirrhosis (Grabner et al., 2021). Lipid metabolism in the liver is regulated by various mechanisms, including peroxisome proliferator-activated receptors (PPARs) and sterol regulatory element binding protein (SREBP) (Grabner et al., 2021).
BBR downregulates the expression of the lipid metabolism-related gene stearoyl coenzyme A desaturase 1 (SCD1) (Yang et al., 2022), reduces TG biosynthesis and enhances TG oxidation to ameliorate hepatic steatosis and HF in vivo (Bansod et al., 2021; Boudaba et al., 2018; Huang et al., 2021; Wang et al., 2016; Zhang et al., 2019). Other studies have reported that BBR supplementation improves total cholesterol, low-density lipoprotein C and high-density lipoprotein C in the blood and accelerates cholesterol excretion through inhibition of adipocyte enhancer-binding protein one or enhancement of cholesterol-binding receptor, which explains its hepatoprotective properties in vivo and in vitro (Derosa et al., 2013; Kong et al., 2004; Wang and Zidichouski, 2018; Wei et al., 2016).
PPARs are considered important metabolic regulators of hepatic lipid metabolism and inflammation (Boyer-Diaz et al., 2021; Chen et al., 2023). PPAR-γ inhibits the activation of HSCs and regulates the expression of genes related to adipogenesis and fibrogenesis to prevent HF progression (Brunner et al., 2019; Perez-Carreon et al., 2010; Sanyal et al., 2010; Yuan et al., 2004). BBR enhances the expression of the gene encoding the FAO carnitine palmitoyl transferase IA by interacting with PPAR-α to inhibit the production of TG in vivo (Yu et al., 2016; Zhou et al., 2008). BBR contains potential agonists of all PPAR isoforms (Xia et al., 2013; Yu et al., 2016), and these could act as ligands to regulate the progression of HF. Taken together, BBR may restore lipid homeostasis and regulate liver function by modulating the PPARs signaling cascade.
SREBP-1c, CHREBP, FAs and C/EBPβ as adipogenic regulators, are involved in the regulation of steatosis, inflammation and fibrosis, however, BBR downregulates their mRNA levels in tunicamycin-induced mice, and mRNA levels of ROS, CYP2E1 (a major mediator of lipid peroxidation), and the expression of TNF-α, IL-6, α-SMA, TIMP-1 and TGF-β1 that are the anti-fibrotic effect of BBR was fully utilized (Zhang Z. et al., 2016).
2.1.6 Gut microbiota
Gut microbiota is a complex system that regulates certain biochemical, physiological and immune responses to maintain the health of the organism (Mishra et al., 2021). It alters the microenvironment of the organism by directly influencing metabolic signaling and energy metabolism either by itself or by producing certain metabolites, which can lead to inflammation, autoimmunity and metabolic disorders (Thursby and Juge, 2017). Thus, a growing body of research suggests that ecological dysregulation of the gut microbiota and impairment of its composition and function are strongly associated with many metabolic diseases (Cai and Kang, 2023; Wang et al., 2021).
Short-chain fatty acids (SCFAs), the end products of anaerobic microbial fermentation of indigestible carbohydrates, have a profound impact on gut function and host energy metabolism (Nicholson et al., 2012). BBR (100 mg/kg) treatment increased the concentration of SCFAs in the gut, increased the production of gut flora-derived butyrate, and decreased lipopolysaccharide (LPS) binding protein (LBP), monocyte chemotactic protein-1 (MCP-1), leptin and lipocalin, and promotes homeostasis of the hepatic microenvironment in vivo (Zhang et al., 2012).
2.1.7 Forkhead box O pathway
Forkhead box O (FoxO) is an essential class of transcription factors involved in numerous biological processes, such as cell cycle, cell proliferation, apoptosis and anti-oxidative stress (Calissi et al., 2021; Sun et al., 2009). p21 and p27 are FoxO-specific transcriptional targets that are associated with G1-phase cell cycle arrest (Roy et al., 2011; Sun et al., 2009). p27 is the key downstream target of FoxO1 to control proliferation and differentiation of HSCs (Sun et al., 2009). The protein kinase B (Akt) signaling cascade controls the transcriptional activity of p21 and p27 through phosphorylating FoxO1 reversing its transcriptional activity (Sun et al., 2009).
This study reported that BBR (5, 10 and 20 μg/mL) induced subcellular redistribution of FoxO1 from the cytoplasm to the nucleus in hepatic stellate cells (CFSCs) in vitro, which reduced the number of activated HSCs, fibrotic septa, and hepatic collagen. Furthermore, it decreases p-FoxO1 (Ser-256) and p-Akt, increases p21 and p27 expression, and inducts G1 blockade, which directly inhibited the CFSC proliferation, which has a protective effect on the liver (Sun et al., 2009).
2.1.8 Another pathway
Numerous studies have proved that enhanced autophagy can induce fibrotic diseases in many organs (Marcelin et al., 2020; Shang et al., 2020; Zhang et al., 2020). After liver injury, increased autophagy promotes the activation of HSCs (Thoen et al., 2011; Wang B. et al., 2017; Zhang et al., 2017). Different microRNAs (miR) involved in various fibrotic diseases. MiR-15 family mainly promotes cell proliferation and apoptosis. MiR-199 and miR-200 families are responsible for ECM deposition and pro-fibrotic cytokine release (Jiang et al., 2017; Schmiedel et al., 2015).
BBR reduced the expression of ATG5, BCL-2, HYP, α-SMA, collagen type I alpha (COL1A1), LC3, ALT, and AST, attenuated collagen deposition and inflammatory cell infiltration, and up-regulating the expression of p53, BAX, and cleaved PARP, and enhanced hepatic repair through up-regulating the expression of miR-30a-5p in vivo and in vitro (Tan Y. et al., 2023). Cyclooxygenase-2 (COX-2) is rapidly synthesized by cells in response to various stimuli involving different pathophysiological processes. BBR can inhibit the expression of COX-2 in vivo and in vitro to protect cells from excessive inflammatory responses, which is beneficial to HF (Feng et al., 2012; Guo et al., 2008; Li et al., 2012; Zeng et al., 2011).
Overall, BBR can mediate different signaling cascades such as ERS, AMPK, ferroptosis, oxidative stress, metabolism, gut microbiota, and FoxO to exert anti-fibrotic effects (Figure 2). However, HF is a complex process, and the activation of HSCs and excessive deposition of collagen fibers are necessary processes that must be taken into account in the future development of new target mechanisms and drugs.
2.2 Myocardial fibrosis
One of the major events that occurs in myocardial fibrosis (MF) when the heart is injured is the activation and differentiation of cardiac fibroblasts (CFs) into MFBs, which contribute to ECM turnover and collagen deposition (Liu et al., 2021). Activated CFs are central cellular effectors of MF and are the main source of ECM proteins in MF (Frangogiannis, 2021). BBR hinders CFs activation and differentiation, and reduces ECM production to achieve anti-MF (Figure 3).
FIGURE 3
The effect of BBR on the myocardial fibrosis. Related signaling pathways include TGF-β, AMPK and Nrf2 pathway.
2.2.1 TGF-β/smad pathway
Previous studies elaborated that TGF-β producing inflammatory cells are essential in the critical cellular event of CFs activation and ECM deposition (Frangogiannis, 2020; Liu et al., 2021). In isoprenaline (ISO)-induced MF in Sprague-Dawley rats, BBR (10, 30 and 60 mg/kg) attenuated macrophage infiltration, altered macrophage phenotype, reduced the expression of COL1A1, collagen type III alpha (COL3A1), CTGF, TGF-β1, and α-SMA, and inhibited the proliferation of CFs, all of which were achieved by blocking the activation of the TGF-β1/Smad signaling cascade (Che et al., 2019).
2.2.2 AMPK pathway
Several studies have confirmed that BBR downregulates the p-mTOR, p-4EBP1 and p-p70S6K (Thr389), inhibits the proliferation of CFs and their conversion to MFBs, reduces the expression of CoI, CoIII, TGF-β1, MMP-2 and MMP-9, shrinks the size of the MFs, increases the secretion of IL-10, and ultimately inhibits MFs and improves cardiac dysfunction in vivo and in vitro. The pharmacological mechanism of the above results is strongly related to the phosphorylation of AMPK (Ai et al., 2015; Chen et al., 2020).
2.2.3 Nuclear factor-erythroid 2-related factor 2 pathway
Nuclear factor-erythroid 2-related factor 2 (Nrf2) translocate into the nucleus as a transcription factor and induces the expression of downstream anti-oxidant and detoxification enzymes, such as heme oxidase-1 (HO-1), SOD, Glutathione peroxidase (GPx) (Wang et al., 2023). Nrf2 can counterbalances oxidative stress, also affects TGF-β1-mediated phosphorylation of Smad3, which is indispensable for fibrosis development (Kobayashi et al., 2016).
Recent studies have elucidated that in the DOX-induced MF model in male Sprague-Dawley rats, BBR (60 mg/kg) targeting Nrf2 can downregulated the expression of α-SMA, CoI, CoIII, and MDA, inhibited the differentiation of CFs to MFBs, and increased SOD activity, HO-1, and mitochondrial transcription factor A (TFAM), rescuing mitochondrial morphology damage and loss of membrane potential (Wang et al., 2023), further protecting the normal functioning of the heart.
Taken together, the conversion of CFs to MFBs occurs at a central event in CF, and BBR prevents this event through the TGF-β/Smad, AMPK, and Nrf2 pathways (Figure 3). Hitherto, there is still a critical lack of research on the target and mechanism of BBR in MF treatment.
2.2.1 TGF-β/smad pathway
Previous studies elaborated that TGF-β producing inflammatory cells are essential in the critical cellular event of CFs activation and ECM deposition (Frangogiannis, 2020; Liu et al., 2021). In isoprenaline (ISO)-induced MF in Sprague-Dawley rats, BBR (10, 30 and 60 mg/kg) attenuated macrophage infiltration, altered macrophage phenotype, reduced the expression of COL1A1, collagen type III alpha (COL3A1), CTGF, TGF-β1, and α-SMA, and inhibited the proliferation of CFs, all of which were achieved by blocking the activation of the TGF-β1/Smad signaling cascade (Che et al., 2019).
2.2.2 AMPK pathway
Several studies have confirmed that BBR downregulates the p-mTOR, p-4EBP1 and p-p70S6K (Thr389), inhibits the proliferation of CFs and their conversion to MFBs, reduces the expression of CoI, CoIII, TGF-β1, MMP-2 and MMP-9, shrinks the size of the MFs, increases the secretion of IL-10, and ultimately inhibits MFs and improves cardiac dysfunction in vivo and in vitro. The pharmacological mechanism of the above results is strongly related to the phosphorylation of AMPK (Ai et al., 2015; Chen et al., 2020).
2.2.3 Nuclear factor-erythroid 2-related factor 2 pathway
Nuclear factor-erythroid 2-related factor 2 (Nrf2) translocate into the nucleus as a transcription factor and induces the expression of downstream anti-oxidant and detoxification enzymes, such as heme oxidase-1 (HO-1), SOD, Glutathione peroxidase (GPx) (Wang et al., 2023). Nrf2 can counterbalances oxidative stress, also affects TGF-β1-mediated phosphorylation of Smad3, which is indispensable for fibrosis development (Kobayashi et al., 2016).
Recent studies have elucidated that in the DOX-induced MF model in male Sprague-Dawley rats, BBR (60 mg/kg) targeting Nrf2 can downregulated the expression of α-SMA, CoI, CoIII, and MDA, inhibited the differentiation of CFs to MFBs, and increased SOD activity, HO-1, and mitochondrial transcription factor A (TFAM), rescuing mitochondrial morphology damage and loss of membrane potential (Wang et al., 2023), further protecting the normal functioning of the heart.
Taken together, the conversion of CFs to MFBs occurs at a central event in CF, and BBR prevents this event through the TGF-β/Smad, AMPK, and Nrf2 pathways (Figure 3). Hitherto, there is still a critical lack of research on the target and mechanism of BBR in MF treatment.
2.3 Pulmonary fibrosis
Pulmonary fibrosis (PF) is a heterogeneous lung mesenchymal disorder that includes persistent epithelial injury, abnormal wound healing, and excessive ECM deposition (Dong and Ma, 2016). MFBs, fibroblasts, and other cell types that secrete numerous of ECM into the alveolar structures, continued differentiation, proliferation, and migration of fibroblasts triggered by a variety of fibrotic mediators contribute to the formation of fibrotic foci and subsequent lung structural remodeling (Wang et al., 2024b). However, BBR plays a unique role in the treatment of PF (Figure 4).
FIGURE 4
The effect of BBR on the pulmonary fibrosis. Inflammatory, AMPK, TGF-β, Nrf2, Notch/Snail and SIRT2 are involved in BBR for pulmonary fibrosis.
2.3.1 TGF-β pathway
TGF-β, a critical inducer of epithelial-mesenchymal transition (EMT), promotes EMT in alveolar epithelial and endothelial cells, restores tissue morphology and structure, and induces fibroblasts to convert to MFBs, synthesize and secrete ECM (Li et al., 2011; Tew et al., 2020). TGF-β-mediated Smad and non-Smad signaling cascades are thought to be major players in accelerated PF(Higashiyama et al., 2007; Wynn, 2011). In the BLM-induced PF in male Wistar albino rats, BBR (200 mg/kg) reverses bleomycin (BLM)-induced lung ultrastructural changes, enhances Smad7 expression, and downregulates α-SMA, CoI and CoIII expression through inhibits BLM-induced elevation of p-Smad 2/3 (Chitra et al., 2015; Tan E. et al., 2023; Palanivel et al., 2015). Another study clarified that BBR reduced the levels of HYP, CXCL14, CXCR4, CoI/III, MMP-2, MMP-9, α-SMA and p-Smad 2/3, and prevented the activation of Smad2/3, ensured recovery of lung status and function (Li et al., 2019).
2.3.2 Inflammatory
Pathologically, PF is always first accompanied by an inflammatory response (Feng F. et al., 2019), inflammatory cells multitask at the wound site by facilitating wound debridement and producing chemokines and growth factors (Eming et al., 2017).
In the PM2.5-induced MF in male C57BL/6 mice and BLM-induecd MF in male Kunming mice, BBR (50 mg/kg) treatment reduces inflammatory cell aggregation in the lungs and decreases the expression of pro-inflammatory cytokines TNF-α, IL-8, IL-1β, and IL-6; it also downregulates the levels of COI, COIII, TGF-β1, PDGF-AB, HYP, α-SMA, p38 MAPKα, and p38 MAPKα (pT180/Y182) and inhibits collagen production and deposition (Alnuqaydan et al., 2022; Beik et al., 2023; Tew et al., 2020; Yang et al., 2023; Zhao et al., 2023).
2.3.3 Stromal cell-derived factor-1/CXCR4 pathway
CXCL12 is involved in the mobilization of bone marrow-derived stem cells through Stromal cell-derived factor-1 (SDF-1) receptor C-X-C chemokine receptor 4 (CXCR4), which is abundantly expressed on a wide range of cells and may be considered a permanent reservoir for fibroblasts (Ahmedy et al., 2023).
Recently, a study found that in the BLM-induecd MF in albino male mice, BBR (5 mg/kg) greatly inhibited BLM-induced weight loss and elevated lung index, preserved lung structure and attenuated bronchoalveolar injury, and ameliorated lung injury, which through reduced expression of SDF1 and CXCR4 (Ahmedy et al., 2023). And further inhibited the expression of TGF-β, p-Smad2/3, α-SMA, NF-κB p65, TNF-α, IL-6, MDA, GSH, SOD and CAT, effectively attenuated oxidative stress, enhanced the expression of Nrf2, and ultimately attenuated epithelial mesenchymal transition of PF (Ahmedy et al., 2023).
2.3.4 PPAR-γ pathway
BBR can directly enter the cytoplasm of fibroblasts and act as a PPAR-γ agonist, up-regulating the nuclear translocation, DNA-binding activity, and transcriptional activity of PPAR-γ, and reversing PM2.5-induced collagen deposition, the expression of fibroblast markers (TGF-β1, FN, α-SMA, COI, and COIII), and the expression of E cadherin expression upregulation, promotes CD36 and AP2 mRNA expression, HGF and PTEN protein levels, and attenuates oxidative and inflammatory factor-mediated PF in BLM-induecd female ICR mice (Guan et al., 2018; Zhao et al., 2023).
2.3.5 Nuclear factor-κB pathway
The BBR-mediated nuclear factor-κB (NF-κB) pathway attenuated the extent of lung oxidative damage and PF. In BLM-induecd male wistar albino rats, BBR (200 mg/kg) action was manifested as inhibition of TGF-β1, ROS, iNOS, TNF-α, MDA, OH, NO, and myeloperoxidase (MPO) levels through downregulation of the phosphorylation of Nrf2, IκB, and NF-κB p65; inhibition of HYP and histamine release, significant reduction of mast cell recruitment, and reversal of BLM-induced SOD, CAT, GPx and GR activities, and restored the levels of non-enzymatic antioxidant status of glutathione, vitamin A, vitamin C and vitamin E (Chitra et al., 2013; Sun et al., 2022).
The special structure of the lung poses a challenge for the treatment of PF, and although BBR can protect the lung structure and reduce lung damage through various pathways (such as TGF-β) (Figure 4), researchers are encouraged to conduct in-depth studies, especially clinical studies, to provide stronger and more comprehensive evidence for the treatment of fibrotic diseases with BBR.
2.3.1 TGF-β pathway
TGF-β, a critical inducer of epithelial-mesenchymal transition (EMT), promotes EMT in alveolar epithelial and endothelial cells, restores tissue morphology and structure, and induces fibroblasts to convert to MFBs, synthesize and secrete ECM (Li et al., 2011; Tew et al., 2020). TGF-β-mediated Smad and non-Smad signaling cascades are thought to be major players in accelerated PF(Higashiyama et al., 2007; Wynn, 2011). In the BLM-induced PF in male Wistar albino rats, BBR (200 mg/kg) reverses bleomycin (BLM)-induced lung ultrastructural changes, enhances Smad7 expression, and downregulates α-SMA, CoI and CoIII expression through inhibits BLM-induced elevation of p-Smad 2/3 (Chitra et al., 2015; Tan E. et al., 2023; Palanivel et al., 2015). Another study clarified that BBR reduced the levels of HYP, CXCL14, CXCR4, CoI/III, MMP-2, MMP-9, α-SMA and p-Smad 2/3, and prevented the activation of Smad2/3, ensured recovery of lung status and function (Li et al., 2019).
2.3.2 Inflammatory
Pathologically, PF is always first accompanied by an inflammatory response (Feng F. et al., 2019), inflammatory cells multitask at the wound site by facilitating wound debridement and producing chemokines and growth factors (Eming et al., 2017).
In the PM2.5-induced MF in male C57BL/6 mice and BLM-induecd MF in male Kunming mice, BBR (50 mg/kg) treatment reduces inflammatory cell aggregation in the lungs and decreases the expression of pro-inflammatory cytokines TNF-α, IL-8, IL-1β, and IL-6; it also downregulates the levels of COI, COIII, TGF-β1, PDGF-AB, HYP, α-SMA, p38 MAPKα, and p38 MAPKα (pT180/Y182) and inhibits collagen production and deposition (Alnuqaydan et al., 2022; Beik et al., 2023; Tew et al., 2020; Yang et al., 2023; Zhao et al., 2023).
2.3.3 Stromal cell-derived factor-1/CXCR4 pathway
CXCL12 is involved in the mobilization of bone marrow-derived stem cells through Stromal cell-derived factor-1 (SDF-1) receptor C-X-C chemokine receptor 4 (CXCR4), which is abundantly expressed on a wide range of cells and may be considered a permanent reservoir for fibroblasts (Ahmedy et al., 2023).
Recently, a study found that in the BLM-induecd MF in albino male mice, BBR (5 mg/kg) greatly inhibited BLM-induced weight loss and elevated lung index, preserved lung structure and attenuated bronchoalveolar injury, and ameliorated lung injury, which through reduced expression of SDF1 and CXCR4 (Ahmedy et al., 2023). And further inhibited the expression of TGF-β, p-Smad2/3, α-SMA, NF-κB p65, TNF-α, IL-6, MDA, GSH, SOD and CAT, effectively attenuated oxidative stress, enhanced the expression of Nrf2, and ultimately attenuated epithelial mesenchymal transition of PF (Ahmedy et al., 2023).
2.3.4 PPAR-γ pathway
BBR can directly enter the cytoplasm of fibroblasts and act as a PPAR-γ agonist, up-regulating the nuclear translocation, DNA-binding activity, and transcriptional activity of PPAR-γ, and reversing PM2.5-induced collagen deposition, the expression of fibroblast markers (TGF-β1, FN, α-SMA, COI, and COIII), and the expression of E cadherin expression upregulation, promotes CD36 and AP2 mRNA expression, HGF and PTEN protein levels, and attenuates oxidative and inflammatory factor-mediated PF in BLM-induecd female ICR mice (Guan et al., 2018; Zhao et al., 2023).
2.3.5 Nuclear factor-κB pathway
The BBR-mediated nuclear factor-κB (NF-κB) pathway attenuated the extent of lung oxidative damage and PF. In BLM-induecd male wistar albino rats, BBR (200 mg/kg) action was manifested as inhibition of TGF-β1, ROS, iNOS, TNF-α, MDA, OH, NO, and myeloperoxidase (MPO) levels through downregulation of the phosphorylation of Nrf2, IκB, and NF-κB p65; inhibition of HYP and histamine release, significant reduction of mast cell recruitment, and reversal of BLM-induced SOD, CAT, GPx and GR activities, and restored the levels of non-enzymatic antioxidant status of glutathione, vitamin A, vitamin C and vitamin E (Chitra et al., 2013; Sun et al., 2022).
The special structure of the lung poses a challenge for the treatment of PF, and although BBR can protect the lung structure and reduce lung damage through various pathways (such as TGF-β) (Figure 4), researchers are encouraged to conduct in-depth studies, especially clinical studies, to provide stronger and more comprehensive evidence for the treatment of fibrotic diseases with BBR.
2.4 Renal fibrosis
Renal fibrosis (RF) is a common manifestation of various chronic kidney diseases and representative events include increased matrix production and inhibited degradation to promote intercellular matrix interactions, cyst-like cell and fibroblast activation, tubular epithelial-mesenchymal transition, MFBs activation, immune cell infiltration and apoptosis (Sun et al., 2022). Almost all cells in the kidney are associated with the fibrotic process, including fibroblasts, tubular epithelial cells, endothelial cells, lymphocytes and macrophages (Sun et al., 2022). Thus, BBR may achieve renal protection through these cellular interactions and associated factors (Figure 5).
FIGURE 5
The effect of BBR on the renal fibrosis. Related signaling pathways include TGF-β, inflammatory, SDF-1/CXCR4, PPAR-γ and NF-κB pathway.
2.4.1 TGF-β pathway
TGF-β1 is a major driver of RF, and is thought to act either directly or indirectly on a variety of cell types of the kidney to promote the fibrotic process (Meng et al., 2016).
BBR intervention can downregulate TGF-β, p-Smad3, α-SMA, vimentin, NF-κB mRNA and protein levels, relieves renal injury and inhibits RF in vivo and in vitro (Hassanein et al., 2022; Li and Zhang, 2017; Wang et al., 2014). BBR alleviates adriamycin (DOX)-induced RF through reduced TGF-β, caspase-3 and NF-κB expression and collagen deposition in vivo, ultimately slowing down the progression of RF (Ibrahim Fouad and Ahmed, 2021; Song et al., 2024).
2.4.2 Inflammatory
RF pathogenesis mainly includes inflammation-induced tubular epithelial cell (TEC) injury, and inhibition of inflammatory progression can delay or reverse RF (Song et al., 2024; Tan E. et al., 2023). BBR (30 μM and 50 mg/kg) attenuated the extent of interstitial fibrosis and histopathological damage through inhibiting the activation of NLRP3 inflammasome and IL-1β level, which reduced the expression of F4/80, MCP-1, CoI, CoIV, and α-SMA, upregulated the level of E-cadherin, and alleviated the abnormalities of serum creatinine and urea nitrogen levels in vivo and in vitro (Song et al., 2024; Tan E. et al., 2023). In summary, BBR has a protective effect on RF.
2.4.3 AMPK pathway
AMPK is a key factor contributing to the onset and progression of renal interstitial fibrosis. It can enhance mitochondrial FAO in response to decreased ATP levels (Li et al., 2022). BBR (30 μM and 50 mg/kg) intervention reversed the downregulation of p-AMPK in the kidney and reversed the downregulation of FAO-associated proteins in vivo and in vitro, including CPT1A and PPAR-α (Tan E. et al., 2023).
2.4.4 Nrf2 pathway
On the one hand, BBR (200 mg/kg) was found to attenuate RF induced by STZ in C57BL/6J mice by activating the Nrf2 signaling pathway in Diabetic Nephropathy (DN); on the other hand, knockdown of Nrf2 not only counteracts BBR-induced HO-1 and NQO-1 expression, but also reverses the inhibitory effect of BBR on high glucose (HG)-induced TGF-β/Smad signaling activation and anti-fibrotic effect (Hassanein et al., 2022; Zhang X. et al., 2016).
2.4.5 Notch/snail pathway
The Notch pathway has been shown to mediate cellular fibrosis such as EMT in epithelial cells in DN and is associated with TGF-β1 (Yang et al., 2017). Snail1 expression is directly regulated by the Notch signaling, and the notch/snail pathway is an important mechanism in renal interstitial fibrosis in DN (Yang et al., 2017).
BBR (30 μM and 150 mg/kg) blocked HG-induced EMT events, and inhibited expression of notch and snail1 in renal tubular epithelial cells, suppressing tubular EMT and renal interstitial fibrosis in vivo and in vitro (Yang et al., 2017).
2.4.6 Sirtuin 2/murine double minute2 pathway
Class III histone deacetylase Sirtuin 2 (SIRT2) is a vital member of nicotinamide adenine dinucleotide-dependent protein deacetylases, which occupy a key position in inflammation and fibrogenesis (Ahmedy et al., 2022). BBR (200 mg/kg) inhibited cisplatin-induced SIRT2 and Murine double minute2 (MDM2) expression, further reduced hemagglutinin-3 (Gal-3), α-SMA, TNF-α, DUSP6, and reduced the mRNA levels of P2X7R and p-ERK1/2 to achieve relief of RF induced by cisplatin in female Wistar rats (Ahmedy et al., 2022).
During the development of RF, fibrotic tissue replaces nephrons in the kidneys, and the problem of increased tissue stiffness brought about by excessive accumulation of ECM hinders drug distribution and efficacy. Although various studies have shown that BBR has multiple targets and mechanisms for targeting RF (Figure 5), the above problems cannot be ignored and more researchers are called to consider them in future studies.
2.4.1 TGF-β pathway
TGF-β1 is a major driver of RF, and is thought to act either directly or indirectly on a variety of cell types of the kidney to promote the fibrotic process (Meng et al., 2016).
BBR intervention can downregulate TGF-β, p-Smad3, α-SMA, vimentin, NF-κB mRNA and protein levels, relieves renal injury and inhibits RF in vivo and in vitro (Hassanein et al., 2022; Li and Zhang, 2017; Wang et al., 2014). BBR alleviates adriamycin (DOX)-induced RF through reduced TGF-β, caspase-3 and NF-κB expression and collagen deposition in vivo, ultimately slowing down the progression of RF (Ibrahim Fouad and Ahmed, 2021; Song et al., 2024).
2.4.2 Inflammatory
RF pathogenesis mainly includes inflammation-induced tubular epithelial cell (TEC) injury, and inhibition of inflammatory progression can delay or reverse RF (Song et al., 2024; Tan E. et al., 2023). BBR (30 μM and 50 mg/kg) attenuated the extent of interstitial fibrosis and histopathological damage through inhibiting the activation of NLRP3 inflammasome and IL-1β level, which reduced the expression of F4/80, MCP-1, CoI, CoIV, and α-SMA, upregulated the level of E-cadherin, and alleviated the abnormalities of serum creatinine and urea nitrogen levels in vivo and in vitro (Song et al., 2024; Tan E. et al., 2023). In summary, BBR has a protective effect on RF.
2.4.3 AMPK pathway
AMPK is a key factor contributing to the onset and progression of renal interstitial fibrosis. It can enhance mitochondrial FAO in response to decreased ATP levels (Li et al., 2022). BBR (30 μM and 50 mg/kg) intervention reversed the downregulation of p-AMPK in the kidney and reversed the downregulation of FAO-associated proteins in vivo and in vitro, including CPT1A and PPAR-α (Tan E. et al., 2023).
2.4.4 Nrf2 pathway
On the one hand, BBR (200 mg/kg) was found to attenuate RF induced by STZ in C57BL/6J mice by activating the Nrf2 signaling pathway in Diabetic Nephropathy (DN); on the other hand, knockdown of Nrf2 not only counteracts BBR-induced HO-1 and NQO-1 expression, but also reverses the inhibitory effect of BBR on high glucose (HG)-induced TGF-β/Smad signaling activation and anti-fibrotic effect (Hassanein et al., 2022; Zhang X. et al., 2016).
2.4.5 Notch/snail pathway
The Notch pathway has been shown to mediate cellular fibrosis such as EMT in epithelial cells in DN and is associated with TGF-β1 (Yang et al., 2017). Snail1 expression is directly regulated by the Notch signaling, and the notch/snail pathway is an important mechanism in renal interstitial fibrosis in DN (Yang et al., 2017).
BBR (30 μM and 150 mg/kg) blocked HG-induced EMT events, and inhibited expression of notch and snail1 in renal tubular epithelial cells, suppressing tubular EMT and renal interstitial fibrosis in vivo and in vitro (Yang et al., 2017).
2.4.6 Sirtuin 2/murine double minute2 pathway
Class III histone deacetylase Sirtuin 2 (SIRT2) is a vital member of nicotinamide adenine dinucleotide-dependent protein deacetylases, which occupy a key position in inflammation and fibrogenesis (Ahmedy et al., 2022). BBR (200 mg/kg) inhibited cisplatin-induced SIRT2 and Murine double minute2 (MDM2) expression, further reduced hemagglutinin-3 (Gal-3), α-SMA, TNF-α, DUSP6, and reduced the mRNA levels of P2X7R and p-ERK1/2 to achieve relief of RF induced by cisplatin in female Wistar rats (Ahmedy et al., 2022).
During the development of RF, fibrotic tissue replaces nephrons in the kidneys, and the problem of increased tissue stiffness brought about by excessive accumulation of ECM hinders drug distribution and efficacy. Although various studies have shown that BBR has multiple targets and mechanisms for targeting RF (Figure 5), the above problems cannot be ignored and more researchers are called to consider them in future studies.
2.5 Pancreatic fibrosis
Pancreatic fibrosis is primarily associated with the activation of pancreatic stellate cells (PSCs), which results in the secretion of excess ECM proteins (An et al., 2023; Masamune et al., 2009). Pro-fibrotic mediators activate quiescent PSCs into MFBs, such as TGF-β (Masamune et al., 2009). AMPK is a metabolite sensor protein that is predominantly expressed in all organs and ameliorates inflammation and fibrosis by regulating macrophage polarization.
In the cerulein-induced pancreatic fibrosis in male Swiss albino mice, BBR dose-dependently decreased levels of pancreatic MDA, nitrite, TNF-α, IL-6, IL-1β, TGF-β1, α-SMA, COL1A, COL3A, and p-Smad2/3, reduced inflammatory cell infiltration, vesicular cell atrophy, exocrine pancreatic vacuolization, ECM deposition and EMT program; increased the content of GSH, and the expression of E-cadherin, p-AMPKα, p-AMPKβ, ACC and Smad7 expression (Bansod et al., 2020). Taken together, BBR prevented the progression of chronic pancreatitis and associated fibrosis in an AMPK-dependent manner by inhibiting TGF-β/Smad signaling and regulating macrophage polarization (Bansod et al., 2020).
2.6 Adipose tissue fibrosis
In the presence of overnutrition, adipose tissue undergoes rapid dynamic remodeling through adipocyte hypertrophy and hyperplasia (Koenen et al., 2021; Sun et al., 2011), accompanied by elevated accumulation of immune cells and overproduction of ECM, leading to the development of fibrosis (Li et al., 2022; Wang et al., 2018). Adipose tissue fibrosis is a hallmark of obesity-related adipose tissue dysfunction (Hu et al., 2018).
2.6.1 AMPK pathway
Previous studies have shown that TGF-β signaling is associated with adipose ECM remodeling and that inhibition of TGF-β by activated AMPK alleviates adipose tissue fibrosis (Wang et al., 2018; Xu X. et al., 2021). Besides, BBR inhibits TGF-β1/Smad3 in HFD-induced white adipose tissues by attenuating macrophage infiltration and polarization, and by activating the AMPK pathway. TGF-β1/Smad3 signaling and ameliorated obesity-associated adipose tissue fibrosis (Wang et al., 2018; Xu X. et al., 2021). Notably, the inhibitory effect of BBR on adipose tissue fibrosis was blocked by compound C (an AMPK inhibitor) (Wang et al., 2018; Xu X. et al., 2021). The C57BL/6 mice adipose tissue fibrosis model used in the above study was induced by the HFD. The above results provide sufficient evidence that BBR attenuates adipose tissue fibrosis through AMPK pathway.
2.6.2 Hypoxia-inducible factor 1α pathway
Hypoxia is an early event in adipose tissue dysfunction, and hypoxic conditions promote the expression of hypoxia-inducible factor 1α (HIF-1α). HIF-1α-induced transcriptional program that leads to the enhanced synthesis of ECM components and ultimately promotes the development of fibrosis in white adipose tissue (Hu et al., 2018). BBR attenuated HFD-induced fibrosis and fibroblast proliferation through reducing CoI, α-SMA, platelet-derived growth factor receptor α (PDGFR-α) and HIF-1α expression, and inhibiting aberrant ECM protein synthesis in vivo. All of the above results were achieved by reversing HFD-induced HIF-1α activation and transcription by BBR (Hu et al., 2018).
2.6.3 Another pathway
Adipose tissue macrophages are found in up to 50% of adipose tissue in obese rodents and humans. Under obesity, the M2 macrophage phenotype is transformed into M1 macrophage phenotype. BBR regulates macrophage infiltration and polarization by decreasing the expression of iNOS, COX-2, IL-1β, IFN-γ, F4/80, MCP-1, and MMP-1α, reducing abnormal ECM deposition to reduce inflammation and fibrosis in adipose tissue through down-regulating the expression of COI, α-SMA, α-SMA, MMP-9 and TIMP-1 in vivo (Li et al., 2022).
2.6.1 AMPK pathway
Previous studies have shown that TGF-β signaling is associated with adipose ECM remodeling and that inhibition of TGF-β by activated AMPK alleviates adipose tissue fibrosis (Wang et al., 2018; Xu X. et al., 2021). Besides, BBR inhibits TGF-β1/Smad3 in HFD-induced white adipose tissues by attenuating macrophage infiltration and polarization, and by activating the AMPK pathway. TGF-β1/Smad3 signaling and ameliorated obesity-associated adipose tissue fibrosis (Wang et al., 2018; Xu X. et al., 2021). Notably, the inhibitory effect of BBR on adipose tissue fibrosis was blocked by compound C (an AMPK inhibitor) (Wang et al., 2018; Xu X. et al., 2021). The C57BL/6 mice adipose tissue fibrosis model used in the above study was induced by the HFD. The above results provide sufficient evidence that BBR attenuates adipose tissue fibrosis through AMPK pathway.
2.6.2 Hypoxia-inducible factor 1α pathway
Hypoxia is an early event in adipose tissue dysfunction, and hypoxic conditions promote the expression of hypoxia-inducible factor 1α (HIF-1α). HIF-1α-induced transcriptional program that leads to the enhanced synthesis of ECM components and ultimately promotes the development of fibrosis in white adipose tissue (Hu et al., 2018). BBR attenuated HFD-induced fibrosis and fibroblast proliferation through reducing CoI, α-SMA, platelet-derived growth factor receptor α (PDGFR-α) and HIF-1α expression, and inhibiting aberrant ECM protein synthesis in vivo. All of the above results were achieved by reversing HFD-induced HIF-1α activation and transcription by BBR (Hu et al., 2018).
2.6.3 Another pathway
Adipose tissue macrophages are found in up to 50% of adipose tissue in obese rodents and humans. Under obesity, the M2 macrophage phenotype is transformed into M1 macrophage phenotype. BBR regulates macrophage infiltration and polarization by decreasing the expression of iNOS, COX-2, IL-1β, IFN-γ, F4/80, MCP-1, and MMP-1α, reducing abnormal ECM deposition to reduce inflammation and fibrosis in adipose tissue through down-regulating the expression of COI, α-SMA, α-SMA, MMP-9 and TIMP-1 in vivo (Li et al., 2022).
2.7 Epidural fibrosis
Epidural fibrosis (EF) is the result of a physiological cyclic defense response during wound healing, a cycle in which fibroblasts in the healing area proliferate rapidly in response to inflammatory mediators and growth factors, and an excessive healing response leads to increased formation of scar tissue, resulting in excessive and disorganized matrix deposition (Keskin et al., 2022).
During wound healing, immune cells (such as monocytes and macrophages) together with fibroblasts and smooth muscle cells produce high levels of TGF-β1, further causes proliferation and accumulation of fibroblasts in the ECM, leading to a vicious cycle of EF after laminectomy (Gorgulu et al., 2004). In the Laminectomy-induced EF in Wistar albino rats, it's worth noting that BBR downregulates the expression of TGF-β1 further attenuate EF (Gorgulu et al., 2004). It has also been shown that BBR prevents apoptosis in human-derived myeloid cells by reducing oxidative stress induced by autophagy and endoplasmic reticulum stress induced by Ca2+ dysregulation (Luo et al., 2019). Other study found that BBR reduced HYP expression stopping the development of EF (Keskin et al., 2022).
Briefly, BBR plays a unique role in pancreatic, adipose tissue, and epidural fibrosis using different mechanisms, but relevant studies are insufficient to illustrate the therapeutic role of BBR in various types of organ fibrosis. Therefore, studies on the anti-fibrotic effects of BBR are indispensable in the future.
3 Anti-fibrotic effects of BBR derivatives
The absolute bioavailability of BBR is low and more than half of the original BBR is not absorbed by the intestinal tract, however, BBR is converted by the intestinal flora into absorbable metabolites such as compounds like dihydroberberberine (dhBBR), oxyberberine (OBB), canadine, and others (Chen et al., 2011; Feng et al., 2015; Liu et al., 2010). Previous studies have demonstrated that dhBBR reduces the release of caspase-1, apoptosis-associated speck-like protein (ASC), and IL-1β through NLRP3 inflammatory vesicle-associated mechanisms to inhibit pyroptosis (a form of programmed cell death that occurs in HF) (de Carvalho Ribeiro and Szabo, 2022; Xu L. et al., 2021). OBB treatment has been reported to increase SOD, catalase (CAT), and GPx activities, and decrease ROS, MDA, and MPO concentrations, thereby reducing oxidative stress (Dou et al., 2021). Therefore, OBB-mediated recovery of liver function may impede the progression of liver disease and promote liver regeneration (Dou et al., 2021). OBB has also been clarified to ameliorate pathological deterioration of adipocytes and hepatocytes via the AMPK pathway and to stimulate energy expenditure to control lipid homeostasis at a smaller dose than BBR. In addition, OBB was shown to inhibit macrophage migration and promote phenotypic conversion of M1 macrophages to M2 macrophages, ultimately reducing the inflammatory burden of the liver (Li Q. P. et al., 2021).
BBR hydrochloride (BH), which is another BBR derivative, ameliorated PM2.5-induced PF by inhibiting oxidative stress and inflammation (Zhao et al., 2023). BH concentration-dependently decreased the expression of TGF-β1, CTGF, ICAM-1, IL-1β, and p-P38, exerting anti-inflammatory and anti-fibrotic effects (Zhao et al., 2023). Postoperative adhesions are a common cause of peritoneal fibrosis. Studies have delayed that in the Surgery-induced peritoneal fibrosis in male Sprague-Dawley rats, BH reduces the expression levels of IL-1β, IL-6, TGF-β, TNF-α, ICAM-1, p-JNK and p-NF-κB, prevents adhesions after abdominal surgery, and reduces inflammation and prevents peritoneal fibrosis through inhibiting TAK1/JNK and TAK1/NF-κB signaling (Zhang Y. et al., 2014).
Additionally, it has been reported that proto-berberine alkaloids attenuate skin fibrosis by modulating mitochondrial dehydrogenase activity, cell proliferation, collagen production, and the ability of inflammatory cytokines (IL-1β and IL-6) production in vitro (Pietra et al., 2015).
4 Pharmacokinetics
BBR has a low oral bioavailability due to poor intestinal absorption and rapid metabolism, Hua et al. used the liquid chromatography-electrospray ionization-mass spectrometry method to demonstrate that plasma concentrations were only 0.4 ng/mL at an oral dose of 400 mg (Hua et al., 2007). Oral bioavailability of BBR is hampered by intestinal first-pass elimination, hepatic distribution and p-glycoprotein (Pg-P) pumps (Imenshahidi and Hosseinzadeh, 2016; Liu et al., 2010). After 4 h of oral administration of BBR (200 mg/kg) to rats, the distribution of BBR in organs was at least 10–30 times higher than that in plasma, with the highest distribution in the liver, followed by the kidney, this may provide an explanation for the pharmacologic effects of BBR in clinical human disease (Kumar et al., 2015; Tan et al., 2013; Wang et al., 2005). The metabolites of BBR are mainly berberrubine (up to 65.1%), thalifendine, demethylene-berberine and jatrorrhizine, which are ultimately excreted in urine, bile and feces (Feng X. et al., 2019).
The bioavailability of BBR is the biggest hindrance to its clinical application, and several strategies have been found to improve the bioavailability of BBR (Imenshahidi and Hosseinzadeh, 2016). For example, Chitosan-N-Acetylcysteine and β-cyclodextrin modulated Pg-P activity to further enhanced the intestinal absorption of BBR (Imenshahidi and Hosseinzadeh, 2016). Chen et al. found that 2.5% D-α-tocopherol polyethylene glycol 1,000 succinate increased the area under the curve (AUC) (0–36) of BBR up to 1.9-fold, possibly through inhibition of Pg-P activity (Chen et al., 2011). Godugu et al. prepared a mucoadhesive microparticle formulation of BBR that enhanced the AUC of BBR up to 6.98-fold (Godugu et al., 2014), whereas oral BBR microemulsion formulation increased the bioavailability of BBR up to 6.47-fold (Gui et al., 2008). Wang et al. improved the oral bioavailability of BBR up to 2.4-fold by an anhydrous reverse micelle delivery system (Wang et al., 2011). Current clinical studies could not adequately address the issue of BBR bioavailability, and there is still a need to construct more drug formulations to address this challenge in the future, so as to increase the utilization and the application range of BBR.
5 Safety and toxicology
In general, BBR is virtually safe at routine doses, with low toxicity and side effects (Imenshahidi and Hosseinzadeh, 2016; Liu et al., 2016), such as only mild gastrointestinal reactions (diarrhea and constipation) reported in clinical studies (Zhang et al., 2010). On the one hand, BBR may prevent toxic reactions and side effects associated with some anti-tumor and analgesic drugs, such as cisplatin, cyclophosphamide, bleomycin, and acetaminophen (Chitra et al., 2013; Domitrovic et al., 2013a; Germoush and Mahmoud, 2014). On the other hand, in some cases, other adverse effects may occur. 10 mg/kg of BBR can suppress immune function in mice (Mahmoudi et al., 2016), and BBR interaction with macrolides and statins may lead to cardiac arrhythmias and reduce the efficacy of the drug (Feng P. et al., 2018; Holy et al., 2009; Zhi et al., 2015). Therefore, we conclude that BBR is safe for use in oral formulations based on traditional dosages and indications.
6 Discussion
Although a small number of drugs are available for the treatment of fibrosis, such as Nidanib and Pirfenidone, long drug cycles, adverse effects, and the lack of drugs targeting different organs remain a challenge to clinical treatment. Therefore, effective therapeutic strategies for fibrosis are urgently required. Natural products have become a hotspot for research and new drug development in recent years due to their advantages of multiple actions, targets and pathways, and more and more natural products have been reported with the utilization of various biological techniques. Studies over the past 2 decades have convincingly demonstrated the therapeutic effects of BBR on various types of fibrosis. The efficacy of BBR in the treatment of different types of fibrosis is mediated by its multi-target pharmacological profile, including the modulation of the TGF-β/Smad, inflammation, AMPK, and Nrf2 pathways, among others (Table 1). Although numerous studies have described the antifibrotic effects of BBR, most of them are based on cellular and animal studies, and comprehensive clinical studies are still lacking.
TABLE 1
Anti-fibrotic effects of BBR.
Disease
In vivo/vitro
Animal/cell model
Dosage
Duration
Mode of administration
Described effects
Pathways
Refers
Hepatic fibrosis
In vivo
Db/db male mice
200 mg/kg
5 weeks
Gavage
↓: α-SMA, CoI, ALT, AST, PDGF, CTGF, MMP-1, SREBP-1c, CHREBP, FAS, C/EBPβ, ROS, CYP2E1, ATF6, XBP1, ATF4, CHOP, TNF-α, IL-6, α-SMA, TIMP-1, TGF-β1
Endoplasmic reticulum stress; Metabolism
Zhang et al. (2016b)
In vivo
Tunicamycin-induced C57BL/6 mice
75, 150, 300 mg/kg
72 h
Gavage
↓: CHOP, Grp78, ATF6, SCD1
Endoplasmic reticulum stress; Metabolism
Yang et al. (2022)
In vivo
CCl4-induced ICR mice
25, 50 mg/kg
6 weeks
Gavage
↑: p-AMPK/AMPK, SOD↓: p-Akt/Akt, MDA, NOX4, ROS
AMPK
Li et al. (2014)
In vivo
CCl4-induced male mice
200 mg/kg
6 weeks
Gavage
↑: Ptgs2, ROS↓: HA, ALT, AST, p75NTR, α-SMA, LC3B, FTH1
Ferroptosis
Yi et al. (2021)
In vivo
CCl4-induced male Balb/c miceCCl4-induced Male Wistar ratsCCl4-induced male KM mice
3, 9 mg/kg50, 100, 200 mg/kg120 mg/kg
2 weeks4 weeks7 weeks
Gavage
↑: MMP-2, SOD↓: TNF-α, α-SMA, TGF-β1, MMP-9, ALT, AST, MDA, HYP
Oxidative Stress
Domitrovic et al. (2013b),
Zhang et al. (2008)
In vitro
HepG2; LX-2; THP-1
5 µM
72 h
—
↓: ROS, MIP-1α, MIP1β, MCP-1, IL-7, CoI
Oxidative Stress
Rafiei et al. (2023)
In vivo
HFD-induced male SpragueDawley rats
100 mg/kg
16 weeks
Gavage
↑: SIRT3, p-AMPK, p-ACC, CPT-1A, HDL-C↓: TC, TG, LDL-C, ALT, AST
Metabolism
Zhang et al. (2019)
In vivo
LPS-induced male Sprague–Dawley rats
100 mg/kg
0, 24, 48 h
Gavage
↓: COX-2, p-p38, p-ATF2, ATF2, ATF3
Metabolism
Feng et al. (2012)
In vitro
LPS-stimulated PBMC
25, 50, 100 µM
6, 12, 24 h
—
↓: COX-2
Metabolism
Guo et al. (2008)
In vivo
Streptozotocin-induced rats
75, 150, 300 mg/kg
16 weeks
Gavage
↑: HDL-C, Apo-AI, PPAR-α↓: TC, TG, LDL-c, ApoB, PPAR-γ
Metabolism/PPARs
Zhou et al. (2008)
In vivo
HFD-induced male Wistar rats
100 mg/kg
18 weeks
Gavage
↓: LBP, MCP-1
Gut Microbiota
Zhang et al. (2012)
In vitro
In vivo
CFCSCCl4-induced male C57BL/6 mice
5, 10, 20 μg/mL100, 200, 400 mg/kg
48 h6 weeks
Gavage
↑: p21, p27↓: p-FoxO1 (Ser-256), p-Akt
FoxO
Sun et al. (2009)
In vitro
In vivo
HSCs; LX-2CCl4-induced male C57BL/6 mice
0, 1, 5, 10, 20 µM50 mg/kg
0, 3, 6, 12, 24 h4 weeks
Gavage
↑: miR-30a-5p, p53, BAX, cleaved PARP, p62↓: ATG5, BCL-2, HYP, α-SMA, 1-A1, LC-3, LC3-II
miR-30a-5p/ATG5/Autophagy
Tan et al. (2023b)
Myocardial fibrosis
In vivo
ISO-induced male SD rats
10, 30, 60 mg/kg
10 days
Gavage
↓: TGF-β1, COL1A1, COL3A1, CTGF, α-SMA
TGF-β/Smad
Che et al. (2019)
In vivo
In vitro
H9c2; Ang II-induced cardiac fibroblastsTAC-induced male SD rats
0, 1, 10 µM10 mg/kg
6 weeks
Gavage
↑: IL-10, p-AMPK↓: p-mTOR, p-4EBP1, p-p70S6K (Thr389), CoI, CoIII, TGF-β1, MMP-2, MMP-9
AMPK
Chen et al. (2020)
In vivo
DOX-induced male Sprague-Dawley rats
60 mg/kg
3 weeks
Gavage
↑: SOD, ROS, MDA↓: α-SMA, CoI, CoIII, MDA
Nrf2
Wang et al. (2023)
Pulmonary fibrosis
In vivo
BLM-induced male Wistar albino rats
100, 150, 200, 250, 300 mg/kg
1, 2, 3 weeks
Gavage
↑: Samd7, PTEN, Beclin-1, LC3-II↓: p-Smad2, α-SMA, CoI, CoIII, p-FAK, p-PI3K, p-Akt, p-mTOR
TGF-β/Smad
Chitra et al. (2013)
In vivo
BLM-induced male Wistar albino rats
200 mg/kg
2 weeks
Gavage
↓: HYP, CXCL14, CXCR4, α-SMA, CoI, CoIII, MMP-2, MMP-9, p-Smad2/3
TGF-β/Smad
Li et al. (2019)
In vivo
PM2.5-induced male C57BL/6 mice
50 mg/kg
45 days
Gavage
↑: E-cadherin, PPAR-γ↓: HYP, LDH, MDA, IL-6, IL-1β, TNF-α, caspase3, caspase9, FN, COI, COIII, α-SMA
Inflammatory/PPAR-γ
Zhao et al. (2023)
In vivo
BLM-induecd male Kunming mice
50 mg/kg
2, 6 weeks
Gavage
↓: TNF-α, IL-6, IL-8, TGF-β1, PDGF-AB, HYP, α-SMA, p38MAPKα, p38MAPKα (pT180/Y182)
Inflammatory
Yang et al. (2023)
In vivo
BLM-induecd albino male mice
5 mg/kg
2 weeks
Gavage
↑: Nrf2↓: SDF-1, CXCR4, TGF-β, p-Smad2/3, α-SMA, NF-κB p65, TNF-α, IL-6, MDA, GSH, SOD, CAT
SDF-1/CXCR4
Ahmedy et al. (2023)
In vivo
BLM-induecd female ICR mice
50, 100, 200 mg/kg
3 weeks
Gavage
↑: HGF, PTEN, PPAR-γ, CD36, AP2↓: HYP
PPAR-γ
Guan et al. (2018)
In vivo
BLM-induecd male wistar albino rats
200 mg/kg
4 weeks
Intravenous injection
↑: Nrf2, SOD, CAT, GPx, GR↓: MDA, OH, NO, MPO, IκB, NF-κB p65, iNOS, TNF-α, TGF-β1
NF-κB
Chitra et al. (2013)
Renal fibrosis
In vivo
In vitro
TGF-β1-stimulated HK-2UUO-induced male C57BL/6 mice
30 µM50 mg/kg
24 h2 weeks
Gavage
↑: p-AMPK, CRT1A, PPAR-α, E-cadherin, FAO↓: F4/80, MCP-1, NLRP3, IL-1β, Bcl-2, Bax, caspase-3, α-SMA
TGF-β/Inflammatory/AMPK
Tan et al. (2023a)
In vivo
UUO-induced male Sprague–Dawley rats
200 mg/kg
2 weeks
Gavage
↑: SOD, CAT↓: MDA, ED-1, MPO, TGF-β1, p-Smad3, α-SMA
TGF-β
Wang et al. (2014)
In vivo
STZ-induced male Wistar rats
400 mg/kg
12 weeks
Gavage
↑: SOD, CAT↓: Scr, BUN, TGF-β, α-SMA, vimentin
TGF-β
Li and Zhang, (2017)
In vivo
DOX-induced male Wistar rats
50 mg/kg
2 weeks
Gavage
↑: SOD, CAT↓: MDA, H2O2, TGF-β1, caspase-3, NF-κBp65
TGF-β
Ibrahim Fouad and Ahmed, (2021)
In vivo
STZ-induced C57BL/6J mice
200 mg/kg
3 weeks
Gavage
↑: Nrf2, NQO1, HO-1↓: α-SMA, CoI, E-cadherin, p-Smad2/3
Nrf2
Zhang et al. (2016a)
In vivo
In vitro
mRTECsFemale KKAy mice
30 µM150 mg/kg
40 min4 weeks
Gavage
↑: Nrf2, NQO1, HO-1, E-cadherin↓: Cr, BUN, α-SMA, snail, jagged1, notch1, hes1
Notch/snail
Yang et al. (2017)
In vivo
Cisplatin-induced female Wistar rats
200 mg/kg
2 weeks
Gavage
↑: Nrf2, NQO1, HO-1, E-cadherin↓: BUN, KIM-1, Gal-3, α-SMA, TNF-α, P2X7R, p-ERK1/2, DUSP6, SIRT2, MDM2
SIRT2/MDM2
Ahmedy et al. (2022)
Pancreatic fibrosis
In vivo
Cerulein-induced male Swiss albino mice
3, 10 mg/kg
3 weeks
Intravenous injection
↑: GSH, p-AMPKα, p-AMPKβ↓: MDA, TNF-α, IL-6, IL-1β, TGF-β1, α-SMA, COI1A, COI3A, p-Smad2/3, CD206
AMPK
Bansod et al. (2020)
Adipose tissue fibrosis
In vivo
HFD-induced male C57BL/6 mice
75, 150 mg/kg
4 weeks
Gavage
↓: COI1A1, COI3A1, CoI6A3, MMP-2, MMP-9, TIMP-1, LOX, TGF-β1, p-Smad3, TNF-α, iNOS
AMPK
Wang et al. (2018)
In vivo
HFD-induced male C57BL/6 mice
100, 200, 300 mg/kg
8 weeks
Gavage
↓: COI, α-SMA, PDGFR-α, HIF-1α
HIF-1α
Hu et al. (2018)
In vivo
HFD-induced male C57BL/6J mice
200 mg/kg
20 weeks
Gavage
↑: SIRT3↓: TC, TG, LDL-C, NEFA, iNOS, COX-2, IL-1β, IFN-γ, F4/80, MCP-1, MMP-1α, Ccl5, Ccl11, Cx3cl1, Cxcl10, COI, α-SMA, PDGFR-α, HIF-1α, α-SMA, MMP-9, TIMP-1, p-ERK, p-JNK, p-p38, p-IKKα/β, p-IκBα, p-p65
SIRT3/Inflammatory
Li et al. (2022)
Epidural fibrosis
In vivo
Laminectomy-induced Wistar albino rats
10, 60 mg/kg
1 week
Gavage
↓: HYP
HYP
Keskin et al. (2022)
Peritoneal fibrosis
In vivo
Surgery-induced male Sprague-Dawley rats
0.75, 1.5 mg/mL
2 weeks
Dropped into the abdominal cavity
↓: IL-1β, IL-6, TGF-β, TNF-α, ICAM-1, p-JNK, p-NF-κB, TAK1
Inflammatory
Zhang et al. (2014b)
Skin fibrosis
In vitro
H2O2/TGF-β1-stimulated HDF
1–10 µM
18, 42 h
—
↓: TGF-β1, IL-1β, IL-6
Inflammatory
Pietra et al. (2015)
As mentioned earlier, the low bioavailability of BBR is the biggest hindrance to its practical application, and its preparation into modern oral dosage forms is essential, such as a mucoadhesive microparticle formulation and microemulsion formulation, as it reduces side effects and achieves better therapeutic effect (Feng X. et al., 2019). Besides, the development and advancement of various biological technologies can also solve this problem, such as an anhydrous reverse micelle delivery system, polymer materials and nanotechnology. Generally, BBR at routine doses is almost safe with low toxicity and side effects, the adverse effects of BBR under special circumstances may also limit the clinical application of BBR, so it needs to be used according to conventional dosage and indications.
Interestingly, despite the poor bioavailability of BBR, this does not explain why it is so effective in vivo. Growing research suggests that BBR may target the gut microbiota as a target for multifunctional action, exerting therapeutic effects by reversing the structure and value of the gut microbiota under pathological conditions (Feng X. et al., 2019). A study on beagle dogs showed that continuous oral administration of BBR for 7 days upregulated the levels of butyrate (a product of bacteria) and nitroreductases-producing bacteria in the plasma (Feng R. et al., 2018). Wang et al. (2017) found different bioavailability of BBR in obese and lean animals due to different structural distribution and values of microbiota. Alioga et al. (2016) studied the differential expression of gut microbiota between populations from different countries, emphasizing that the gut microbiota is closely related to inter-individual and inter-ethnic differences in drug metabolism. BBR is rapidly metabolized in vivo, and the gut microbiota enhances the bioactivity of BBR by converting it to dhBBR. Nevertheless, the effects of dhBBR treatment given alone differed from those described above, suggesting that the anti-fibrotic effect of BBR is not only due to its metabolites. Another study confirmed that the intestinal absorption of dhBBR in the body is 5-fold higher than that of BBR, and the absorbed dhBBR is oxidized to BBR and re-enters the bloodstream (Feng et al., 2015). There is also a study that explains this, where BBR undergoes extrahepatic metabolism and intestinal conversion to OBB, which are mainly transported intracellularly in a protein-bound form to form hepatocyte-targeted and enterohepatic circulations (Huang et al., 2023). We believe that the distribution of BBR and its bioactive metabolites in vivo may partially explain this.
7 Final considerations and prospect
Natural products are derivatives of traditional Chinese medicines and are the material basis for the pharmacological effects. They have the advantages of multiple actions, multiple targets and multiple pathways, and have become a hot spot for research and new drug development in recent years. BBR has attracted much attention due to its multiple pharmacological effects. Numerous studies on BBR affecting various types of fibrosis have found that its mechanisms of action include, but are not limited to the regulation of collagen homeostasis, anti-inflammation, anti-oxidative stress, and the prevention of tissue damage through different signaling cascades, and the mechanisms of action vary in different tissues or organs. We are only reviewing the completed studies with some limitations. Firstly, the bioavailability of BBR is insufficient, and the discovery of good drug carriers is important for improving its solubility, enhancing tissue targeting, and expanding the range of clinical applications. Secondly, further comprehensive studies, especially clinical trials, are urgently needed, which are essential for elucidating other mechanisms and molecular targets of BBR in the treatment of fibrosis as well as evaluating its efficacy and safety. |
Title: Protein Immobilization on Bacterial Cellulose for Biomedical Application | Body:
1. Introduction
Protein immobilization is a biotechnological technique in which a protein is fixed in a suitable matrix to limit mobility, increase stability, and, in the case of an enzyme protein, allow reuse while retaining the immobilized protein activity [1]. The immobilized proteins and enzymes have been used in biomedicine, including the detection and treatment of many diseases. The immobilization process allows protein functionality to be optimized for specific tasks. For example, immobilized antibodies, receptors, or enzymes are used in biosensors and ELISA to detect various bioactive substances in disease diagnosis. Immobilized enzymes are also used in bioreactors to remove waste metabolites and correct congenital metabolic deficiencies. Today, artificial cells are being developed and controlled-release drug delivery systems based on the release of encapsulated enzymes or proteins are being created [2,3]. Immobilization of the protein improves its stability by preventing environmentally induced structural denaturation, allowing it to remain active under various conditions [4,5]. The incorporation of proteins into polymer biomaterials enables the acquisition of pro-adhesive properties, a modification of the biomaterial’s hydrophilicity, the introduction of supplementary functional groups that facilitate cellular activity, an alteration of the surface stiffness, and a modification of the biomaterial’s degradation rate [6].
The matrix for protein immobilization should be inert, stable, accessible, resistant to mechanical stress, and biocompatible without disrupting the protein structure. Among the matrixes, natural polymers as carriers have attracted considerable attention [7]. Polysaccharides, cellulose, chitosan, alginate, and their derivatives are widely used in protein immobilization [8]. Recently, cellulose materials have been studied and used due to their good renewability, availability, biodegradability, and biocompatibility [9]. Cellulose is a robust, dual hydrophilic/hydrophobic, non-toxic, and chemically inert material under physiological conditions [10]. Cellulose is a versatile material that can be used as a reinforcement material or as a matrix, depending on the specific requirements of the task at hand. Its well-organized fibrous network structure allows it to enclose nanoparticles, acting as a matrix. Similarly, cellulose nanofibers can be used to reinforce other materials, cells, and tissues [11]. BC has similar chemistry and superior physical properties to plant cellulose, making it an ideal choice for manufacturing a range of composite materials for diverse applications [12]. The composite BC with protein can utilize the dual properties of both materials [13]. In addition, several BC-based composites were created that showed high protein immobilization efficiency compared to other materials [14,15,16].
BC is produced by gram-positive bacteria such as Sarcina and gram-negative bacteria strains the Acetobacter, Rhizobium, Agrobacterium, Aerobacter, Achromobacter, Azotobacter, Salmonella, Escherichia [17,18]. Moreover, BC can be synthesized in a cell-free system [19]. BC is a biopolymer consisting of linear chains of covalently linked glucose residues between carbon 1 and 4—β (1,4)-bound d-glucopyranosyl [20]. BC does not contain lignin and hemicellulose, rendering it more pure than plant cellulose [21]. BC has high porosity, water-holding capacity biocompatibility, low toxicity, and non-immunogenicity and can be readily and safely sterilized [22]. Moreover, BC has enhanced mechanical strength, biodegradability, and high crystallinity [14] and increased the surface-to-volume ratio [23,24].
BC composites are prepared to adapt their characteristics for a specific application [25] and overcome the disadvantages of native BC. By in situ modification, different structures can be formed during fermentation to produce materials for various applications. In addition, BC can be modified ex situ by adding a functional compound or polymer coating [26]. Previously has been demonstrated that the combination of nanoparticles (Ag, ZnO, etc.) and polymers (e.g., polyaniline, chitosan, polyethylene glycol) can be used to create BC composites with bactericidal and conductive properties [12], enhance antimicrobial, antiviral and anticancer activities [27]. BC-based nanocomposites were used for phage immobilization to detect live S. aureus [28], incorporation of probiotic bacteria Lactobacillus acidophilus 016 [29], and yeast immobilization [30]. BC is utilized in the development of drug delivery systems, scaffolds, tissue and organ regeneration, and wound healing [31].
The latest drug delivery research has further demonstrated various advantages of BC, including its promising effects on controlling drug release, biocompatibility, low immunogenicity, and ease of production and handling [32]. BC films have been successfully used for the immobilization of antibiotics (amoxicillin [33], tetracycline [34], levofloxacin [35], ciprofloxacin [36], gentamycin [37]), anesthetics (lidocaine [38]), anticancer drugs (doxorubicin [39], curcumin [40], paclitaxel [41], immune checkpoint blocking antibodies [32]). The porous structure and increased surface area of BC make it useful for the immobilization of enzymes and bioactive compounds [42]. Various types of BC and activation methods have been investigated to immobilize glucoamylase [43,44], glutamate decarboxylase [45], and laccase [14].
The commercialization of BC has increased markedly recently, driven by its growing applications in a range of fields. The global BC market was valued at approximately USD 426.7 million in 2022 [46] and is projected to reach approximately USD 777 million by 2027 [47]. Among the major trends in the BC market today, the main interest is in the creation of BC-based composites for wound care products, dental treatment, organ regeneration, and BC for drug delivery [46]. Soon, BC can potentially be widely applied in bioprinting techniques [48]. Furthermore, as regulatory agencies promote the use of xeno-free biomaterials in human care and medicine delivery, BC has gained popularity due to its animal- and human-free origin [49]. Currently, an increasing number of studies [50] have been dedicated to the utilization of BC in the development of protein composites for wound healing, tissue engineering, three-dimensional (3D) cell culture systems, drug delivery systems, and enzyme immobilization matrices. This review concentrates on the employment of BC for protein immobilization in biomedical applications, the diverse forms of protein immobilization and the modifications of BC for protein immobilization, and the advantages and disadvantages of employing BC as a carrier.
2. Structure of Bacterial Cellulose
BC is a linear glucose biopolymer produced primarily by the bacterium Gluconacetobacter xylinus (G. xylinus) in both synthetic and non-synthetic environments via oxidative fermentation. This non-photosynthetic organism can obtain sugars, glycerol, glucose, and various other organic molecules before converting them to pure cellulose [51,52]. Bacteria strains typically produce and use BC as a protective envelope against harsh environmental conditions or as a component of their cell walls. The bacteria release nanofibrils that self-assemble into larger fibers that interconnect to form a BC matrix of increasing volume and density. The BC matrix forms a visible hydrogel called a pellicle at the air–liquid interface of the working culture [53].
BC is composed of β-(1 → 4) glucan chains with fiber widths of 25–100 nm and lengths of several microns. It is predominantly twisted to the left. BC is the “gold standard” for nanocellulose because one of its dimensions is on the nanometer scale, and it is released in a controlled environment by bacteria. The monomer unit has a cyclic structure with reactive primary and secondary hydroxyl groups (Figure 1). The β-D-glucopyranose ring and all -OH groups are free and play an important role in intermolecular hydrogen bonding between adjacent chains [20]. Each monomer unit is rotated 180° relative to its neighbor. These chains first form nanofibrils, then microfibrils, and finally macrofibrils (Figure 1) [54]. The microfibrils exhibit distinctive characteristics, including unidirectional polarity and variable thickness [55].
Although both BC and plant cellulose have structural similarities, their distinct nanofibrous structures give them different physical and chemical properties [57]. BC is classified as a cellulose subtype Iα, while plant cellulose is mainly classified as Iβ, which differ in their crystalline structure [54], molecular conformation, and hydrogen bonding, and these differences may influence the physical properties of the cellulose [56]. In contrast to plant cellulose, BC comprises a fully crystalline core encased in a less crystalline zone interposed by the amorphous form of cellulose, along with fibers arranged in a 3D lattice. Due to the robust intermolecular interactions and the presence of hydroxyl groups, the fibers exhibit a proclivity for self-assembly. These fibers give rise to a network structure interconnected by intramolecular hydrogen bonds, resulting in the formation of sheets with a high surface area and porosity [20].
BC exhibits ultrafine fibers (width 50–80 nm and thickness 3–8 nm) [14], and BC is thinner than plant cellulose [23,24]. Furthermore, in contrast to plant cellulose, which typically consists of fibers with 10,000–15,000 degrees of polymerization, BC fibers consist of the pure form of the polymer with a degree of polymerization of 2000–8000. Such structural characteristics render microbial cellulose with improved mechanical properties, such as strength and toughness, compared to plant cellulose with a similar chemical structure [53]. Different morphologies of BC have a direct effect on its mechanical properties and cell attachment to the material. Tang et al. [58] produced BC mats with different aperture sizes and porosities when the fermentation conditions and post-treatment interventions were changed. Backdahl et al. [59] invented a novel approach to introduce microporosity in BC tubes, which are intended to be used as scaffolds for tissue-engineered blood vessels [51]. BC, like plant cellulose, contains reactive hydroxyl groups that can be modified [25]. The increased surface area-to-volume ratio allows for greater interaction with the components of BC-derived composites, making them ideal for medical applications.
3. Biomedical Application of Bacterial Cellulose
The use of BC for biomedical applications has attracted considerable interest over the past decade [60]. BC is a promising biomaterial due to its physical and chemical properties [24,25,61]. Various surface functionalizations by biosynthetic or chemical modification can enhance the functionality of BC and broaden their potential applications [62].
Both native and modified BC have been used in various applications, including tissue engineering, the fabrication of artificial organs, and the development of scaffolds and drug delivery systems (Figure 2) [63]. Previously, the most diversified application of BC in biomedicine has been the creation of drug delivery systems. A significant number of published research studies have attempted to develop an effective BC-based system for delivering specific drugs to the wound or diseased tissue [64]. The majority of BC carrier research focuses on loading cellulose (often a dressing) with small molecules, including analgesics and anti-inflammatory drugs, bacteriostatic agents such as metal ions, antibiotics, or other chemicals [65].
The latest developments in the field of BC focus on new carrier materials for cell culture, cell encapsulation or enzyme immobilization, medical device coating, and the production of BC materials with soft tissue implants and materials for bone regeneration [66]. To achieve this, it is important to immobilize larger molecules such as proteins (e.g., albumin, lysozyme, lipase, or phospholipase) and growth factors in the membrane after changing the porosity of the cellulose nanostructure. In recent times, BC-based materials have been employed in the field of cancer therapeutics [24,61]. BC-based materials are also suitable for the delivery of proteins and nucleic acids [67,68].
3.1. Wound Healing and Antibacterial Wound Dressings
A wound is a break in the continuity of the skin. Microbial infection and inflammation are the most common wound-related conditions [69]. Wound healing requires the coordinated and balanced activity of inflammation and vascularization of connective tissue and epithelial cells. All of these stages require an extracellular matrix (ECM) and various growth factors to support the healing process [70]. The primary proteins of the ECM are collagen, non-collagen fractions, and proteoglycans [71]. These proteins serve as scaffolds and mediate cell attachment, cellular proliferation, differentiation, and migration. Each organ has a unique ECMs [20].
ECM proteins, a plethora of growth factors, and antidiabetic wound-healing agents (insulin) play a pivotal role in the process of tissue regeneration [72]. Proteins and growth factors are used to create nanopreparations with wound healing and regenerative effects to help reduce inflammation, cell proliferation, and remodeling. These formulations release proteins over time, increasing the efficacy and impact on the wound area, promoting targeted drug delivery, improving solubility and biocompatibility, and helping wounds heal without complications [72]. Enzymatic wound treatment with proteases is already used in surgical practice [73]. Proteolytic enzymes (serine proteases, metalloproteinases, cysteine proteases, and aspartate proteases) contribute positively to tissue regeneration processes [74].
Despite the advances in wound healing management, the treatment of the majority of skin lesions remains a significant challenge for the biomedical and pharmaceutical industries [75,76,77]. Alternative approaches, such as the use of hydrogels as wound dressings, can be used when surgery is not feasible due to patient circumstances or when there is insufficient necrotic tissue [78]. Wound healing materials must be able to keep the wound moist, absorb wound fluid, promote new skin growth, let in oxygen and other gases, and fight infection. They must also be safe for the body [79]. In addition, the material must act as a suitable interphase to support the complex interactions that occur during wound healing involving various cells, soluble substances, and ECM components. Therefore, the aforementioned properties of BC make it ideal for this purpose [25,80].
For the first time, BC was used in medicine as a wound dressing to promote tissue regeneration [81]. A moist environment, non-toxicity and non-allergy, promotion of thermal insulation, and ease of gas transfer are the primary requirements for effective wound dressings [82]. The use of constructed BC composite scaffolds can extend cell adhesion, proliferation, and transplantation of scaffold-seeded cells, enhancing their biocompatibility [81]. It has been shown that BC and BC-composites are biocompatible with skin tissues [83]. BC is appropriate for skin care applications because it can relieve discomfort, speed healing, and fit the body well [82]. Water-holding is an important factor in the healing process because dry wounds need more moisture to promote tissue regeneration and prevent necrosis [84,85]. BC has a 30% higher water absorption capacity and a 33% longer drying time than cotton gauze [86]. In terms of maintaining a moist wound environment, reducing discomfort, accelerating tissue re-epithelialization, and minimizing scarring, BC-based wound dressings outperformed conventional wound dressings [31,87]. By retaining liquid, a hydrogel can be used to load liquid medications and physiologically active substances close to the dressing material [88].
In vivo studies have proved that BC wound dressings in porous form outperform compacted form in terms of wound healing performance [13,89]. Long-term clinical trials have shown that BC-based dressings are more cost-effective than standard fiber dressings (surgical pads, tulle grass, and saline-impregnated gas), synthetic foams, and alginate dressings. The high surface area and porosity of BC allow for the integration of additional wound-healing-promoting agents [12,90]. A number of commercial BC-based dressings have been developed, including BioFill®, Bioprocess®, Gengiflex® (BioFill Produtos Biotecnológicos, Curitiba, Brazil), Xcell® (Xylos Corporation, Langhorne, PA, USA), Dermafill® (Seven Indústria de Produtos Biotecnológicos Ltda, Londrina, Brazil), and Epiprotect® (Royal Wootton Bassett, UK). The efficacy of BC-based dressings has been investigated in clinical trials, with the results demonstrating that the BC membrane can significantly reduce pain and facilitate the healing of a range of wounds [83,91]. The dressings based on BC can vary considerably in composition, from relatively simple films to highly complex constructions. They often incorporate various biomolecules, pharmaceutical agents, and polymers [92].
There are several limitations to the use of BC-based dressings. Due to the slow degradation of BC, several attempts have been made to improve the degradability of BC for wound healing. Several approaches have been used: oxidation with periodate, γ-irradiation, or incorporation of the enzyme cellulase into BC [93]. Compared to more expensive protein-based materials, BC shows limited cell attachment and proliferation, especially in fibroblast cells in the wound. Several modification strategies have been developed to improve the cellular response of fibroblasts to various materials. Techniques include biopolymer/protein adsorption, gas plasma surface modification, and self-assembled monolayers [94].
Regarding passive wound healing, BC can be combined with various compounds, including antimicrobial polymers, photosensitizers, metallic nanoparticles, antibiotics, antimicrobial peptides, and antiseptics to accelerate wound healing. Researchers often use BC doped with conductive chemicals to locate wound healing sites. This allows the circuit to be carried over a large area and stimulates skin cell behavior, resulting in faster healing [81,91]. BC-based composites can help retain and slowly release antibiotics (Figure 3); however, the issue of antibiotic resistance is still being investigated [95]. Antibacterial nanomaterials may replace antibiotics, but nanoparticles have drawbacks such as easy aggregation, an unpredictable tendency to release ions, and potential cytotoxicity that limit their use [96]. Due to the large number of active functional groups, BC and its modified derivatives can be used as a template or immobilization material for antibacterial nanoparticles, which helps to reduce nanoparticle agglomeration and control the release rate. As a result, there is no defect-free BC-based wound dressing material, and the development of multifunctional BC-based composites is an important area of future research [97].
There are a number of techniques that can be employed to modify BC in order to address the shortcomings of the native material while simultaneously optimizing its biocompatibility, water uptake and release, and antibacterial activity [97]. Given that BC lacks intrinsic antimicrobial properties [78], it is modified by incorporating other polymers or inorganic materials. Modification of BC has several goals, including improving mechanical properties, changing some physical parameters such as water-holding capacity, water retention rate, and water vapor transmission rate, or even imparting antimicrobial activity [98]. Various techniques, including microbial fermentation, physical modification, chemical modification, and combination modification, have been used to enhance the biocompatibility and antibacterial activity of BC to ensure better use in wound healing [25,83]. For dental and drug delivery applications, the degradability of BC mediated by oxidation is very important [25], as BC is slow to degrade in the human body [99,100]. Also, composites are created with collagen [101,102,103,104] and sericin [105]. These polymers strengthen the BC structure, improve its mechanical properties, and accelerate wound healing [78,106]. β-keratose [107], ECMs (collagen, elastin, and hyaluronan) and growth factors (B-FGF, H-EGF, and KGF) [70], laccase [108], soybean protein isolate [13], involucrin antibody SY5 [109], papain [110] have been used for the development of BC-based wound dressings. The BC with antibacterial activity was obtained with lysozyme [111,112] and such peptides as nisin [113], ε-poly-l-Lysine [79], and bacteriocins from Lactobacillus sakei [114,115].
3.2. Tissue Engineering
Tissue engineering involves developing scaffolds and growth factors that influence the regeneration or replacement of damaged tissue [106,116,117]. The scaffold material plays a pivotal role in providing the biological and physical environment essential for tissue growth, including the ECM. This is achieved by facilitating cell adhesion, development, and differentiation [71,118]. The replacement of natural ECMs is becoming an increasingly crucial and promising aspect of tissue engineering, as they facilitate the localization and transportation of cells to specific regions of the body [119].
BC structurally resembles natural ECM [120], therefore, BC has been believed to be a promising material for tissue engineering scaffolds [121]. The BC products have been approved by the FDA for use as tissue replacement due to their low endotoxin levels (less than 20 EU per device) [122]. BC scaffolds (Figure 4) have been shown to have the potential to serve as a viable material for chondrogenesis applications due to their ability to successfully regenerate cartilage using human mesenchymal stem cells [123]. In vivo studies consistently demonstrate that BC membranes or scaffolds are typically well tolerated by host tissues after implantation, with no significant adverse effects [124]. Several studies have shown good in vivo biocompatibility of BC-based scaffolds, giving the material potential for use as a scaffold in tissue engineering [21,117,125,126,127].
While the suitability of BC as a raw material for the fabrication of tissue engineering grafts has been demonstrated, the native form of BC is deficient in certain fundamental characteristics that are essential for its utilization in tissue engineering. These include limited biocompatibility, which is a prerequisite for effective tissue regeneration, and inadequate mechanical strength, which is a prerequisite for high-strength applications such as bone and cartilage tissue engineering [128]. Moreover, there is currently no research available that investigates the potential for BC to calcify in vivo over a long-term period [129]. The lack of macropores in native BC also limits its widespread use in tissue engineering [117], as calcification is largely dependent on the porosity of the material and the length of exposure. It should be emphasized that while calcification is undesirable, the degree of calcification will vary depending on the tissue into which the biomaterial is to be incorporated. Appropriate porosity can prevent calcification processes by ensuring angiogenesis and adequate nutrient supply to the cells [129]. In addition, BC has strong mechanical properties, although the presence of many pores limits its stress-bearing capacity [12]. Furthermore, there is a possibility of immunological rejection [106]. To overcome these limitations, the most commonly studied strategies include the production of BC-based nanocomposites with bioactive components, such as polymers and nanomaterials [128].
The majority of research studies have focused on modifying the microporosity of BC to produce materials with the desired properties for replacement or regeneration applications [118,130,131]. Collagen was primarily employed in the development of composite materials with BC for tissue engineering [132,133,134,135,136]. In comparison to the use of collagen composites alone, it has been demonstrated that BC-collagen composites facilitate enhanced cell adhesion and proliferation [132]. In addition, modification BC by proteins such as osteopontin and bone morphogenetic protein 2 (BMP-2) can provide a novel alternative to collagen in the guided bone regeneration field [120,137,138]. In several studies, BC–gelatin composite scaffolds were fabricated for bone tissue engineering applications [117,118,139,140]. BC-keratin composite scaffolds [141] were created for skin tissue engineering. In one study, BC was functionalized by recombinant IKVAV peptide for nerve tissue engineering. BC has also been used for nerve regeneration. BC functionalized with recombinant proteins IKVAV-CBM3 (Ile-Lys-Val-Ala-Val fused with cellulose-binding module) and (19)IKVAV-CBM3 increased mesenchymal stem cell adhesion, cell survival, and neurotrophin expression, which promoted neuronal regeneration [142]. A soy protein isolate (SPI) has been compounded on a double-modified BC to provide a new material for urethral reconstruction [143].
3.3. Artificial Blood Vessels
The limited availability of veins in the human body and the potential for severe rejection caused by allografts necessitates the development of artificial veins as a replacement. The most commonly employed clinical artificial vessels are currently constructed of expanded polytetrafluoroethylene (ePTEE, Gore-Tex), polyglycolic acid (PGA), and poly-l-lactic acid (PLLA). However, these materials exhibit several inadequacies that facilitate the formation of thrombi and intimal thickening, which must be addressed in the design of new artificial vein models [144]. In addition, when the procoagulant properties of PET (polyethylene terephthalate, Dacron®, DuPont de Nemours, Inc., Wilmington, DE, USA) and ePTFE (Gore-Tex®, W. L. Gore & Associates, Newark, NJ, USA) vascular graft materials were compared with BC grafts, BC was shown not to significantly induce plasma coagulation. Compared to PET and ePTFE, BC was found to induce the least and slowest activation of the coagulation cascade and is, therefore, considered a potential vascular graft material [145].
BC and its composites are great options for artificial blood vessels [100,144] by promoting neovascularization. A major problem that can arise in the construction of tubular tissue engineering constructs is the attempt to seed the construct with cells in a tubular state, whereas cells in culture adhere much more readily to a flat scaffold [25]. In hemocompatibility tests, BC typically exhibits a low hemolysis rate, and the mechanical properties of BC-based artificial vessels frequently necessitate enhancement. Modifications of the BC surface through chemical functionalization, in addition to alterations in the manufacturing process, can influence the optimization of BC properties. BC has been functionalized with several macromolecules, including peptides, proteins, and polysaccharides, to improve its hemostatic characteristics. Amino acids have varying electrostatic and hydrophobic characteristics, allowing them to interact with platelets and other blood components via physical and chemical mechanisms to induce hemostasis [146]. To solve this challenge, researchers have mostly focused on making artificial blood vessels with the desired properties and have examined the fermentation procedures and conditions that influence the production of BC tubes [144].
The team of Dieter Klemm was the first research organization to investigate and apply artificial vascular substitute obtained with biomaterials from BC [147] and described a clinical product named BActerial SYnthesized Cellulose (BASYC®, Jena, Germany) [100,147,148,149]. The use of BC for the creation of vascular grafts has also been the subject of other studies. Various ECM proteins are used to improve the biofunctional properties of BC membranes and to create a functional endothelial layer [146]. BC/fibrin composites have been developed for the fabrication of artificial blood vessels. However, an investigation of BC/fibrin composites revealed only a slight increase in mechanical properties over those of native BC [150]. In another study, Andrade et al. used BC modified with a recombinant CBM-2 protein and an adhesion peptide tripeptide Arg-Gly-Asp (RGD) to create hemocompatible material [151]. The incorporation of functional peptides usually facilitates protein interaction of ECM by acting as docking sites [152]. In a study conducted by Leitão et al., a novel graft material was created from unmodified, small-caliber, minimally processed BC. The graft’s luminal surface had a similar topography to native vessels. Neovascularization and endothelialization of the graft resulted in the restoration of patency within one month [153].
3.4. Cell Culture System
Three-dimensional culture systems are gaining increasing popularity due to their capacity to more effectively mimic tissue-like structures than monolayer cultures [154]. Three-dimensional cell culture models are employed in the prediction of responses to anticancer treatments. In order to accurately and successfully predict treatment options, 3D cell culture must be capable of mimicking the ECM of cancer cells in an artificial environment [155]. Early studies have shown that using commercial hydrophobic proteins enables the hydrophobization of cellulosic cotton fiber [156], and impregnating zein [157] into BC can increase the hydrophobicity of BC surface as well as enhance cell attachment and proliferation when it is used as cell culture scaffold (Figure 4).
Today, natural and synthetic polymers and their composites are employed in the fabrication of 3D scaffolds for tissue engineering and even 3D cancer cell cultures. Cancer cells within a solid tumor maintain close and continual contact with the ECM [158]. Currently, 3D systems used to research tumor behavior, such as MatrigelVR and GeltrexVR, are based on natural ECM components. However, chemical variability resulting from the presence of numerous growth factors and proteins, in addition to batch-to-batch variability in these matrices, may interfere with signaling pathway biological reasoning or drug-induced effect outcomes. Consequently, the development of new biomaterials is essential to overcome the limitations of natural ECM [159].
Many researchers have conducted studies to explore the application of BC as a scaffold for 3D in vitro cancer cell models [160,161]. To mimic the characteristics of the tumor microenvironment, BC-based scaffolds were synthesized and evaluated in vitro for their ability to support cancer cell growth [162]. Modifying the pore structure of the BC scaffold can influence the behavior of cancer cells, thereby representing an effective approach for the design and fabrication of in vitro models for the study of cancer biology, potential application in cancer diagnosis, and the development of cancer treatments [121,163,164]. Some molecules, such as hyaluronic acid, chitosan, and gelatin, have been incorporated into the BC network to enhance the mechanical strength, cell adhesion, and cell growth properties of composite scaffolds [155,164,165,166,167].
One of the drawbacks of using BC to make small 3D spheroids is the millimeter scale and low precision of manual fabrication. To produce smaller or more precise shapes from BC-based engineered living materials, it is proposed to use molds or recently developed 3D printing methods for bacterial cultures, such as the «functional living ink» (FLINK) method or a hybrid approach in which a 3D printer is programmed to dispense BC spheroids precisely with different functionalizations [168]. Another challenge of using BC for 3D culture is that it is an uneven three-dimensional substrate for cell attachment with limited integrin binding sites, and cells did not adhere well to BC without surface modification. This can affect cell motility and migration on the surface, as cells migrate toward each other and form huge aggregates that spread in three planes, in contrast to cells cultured on conventional cell plastic, which spread evenly across the surface with no evidence of migration. Morphological variations of individual cells were also observed. To improve cell adhesion and cell proliferation, BC can be modified with the use of different porogens [169] or biodegradable polymers [170] to develop 3D biomimetic scaffolds with interconnected macropores and nanofibrous structure, mimicking the physical structure of ECM. The development of simple, effective physicochemical techniques for surface modification of BC with alternative ECM, growth factors, or other materials could enhance its biocompatibility and biodegradability in vivo [128].
In order to model the tumor microenvironment, it was synthesized and studied BC/gelatin hydrogels as scaffolds for the human breast cancer cell line MDA-MB-231 in vitro culture [171]. BC hydrogel was used for the immobilization of a laminin peptide (IKVAV) to mimic human melanoma cells’ microenvironment and to evaluate the influence of the microstructure and modified chemical surface properties of the resulting matrix [159]. Recently, the BC matrix has been proposed as a tool for trapping and localizing tumor cells within a predetermined region that can be targeted with therapy. The BC scaffold is placed at the tumor site after excision to attract and trap residual cancer cells. Once the cells are immobilized on the BC scaffold, they can be killed by targeted treatment. For example, the chemoattractant human serum albumin (HSA) has been used to capture cancer cells [25]. Biofunctionalized BC scaffold was created for cell replacement therapies in Parkinson’s disease. Human embryonic stem cell-derived progenitor cells were cultured on BC with growth factors and laminin that were covalently functionalized to the surface via silanization [172].
3.5. Targeted Drug Delivery System
The development of a targeted drug delivery system has the potential to markedly enhance the therapeutic efficacy of drugs. Antibodies represent one of the most commonly utilized targeting molecules [173]. BC-based drug delivery systems have been a subject of considerable interest recently [32,174]. BC is often mixed with other materials to provide controlled drug release mechanisms. It has been shown that the properties of BC composites can be changed [175], allowing these composites to be tuned for use in a wide range of biomedical applications requiring varying drug release rates. BC has been shown to be a viable substance for long-term drug release, making it an excellent carrier for cancer therapy [174]. A key advantage of using a drug carrier such as BC is that it allows for controlled and localized treatment, which can increase drug concentrations at the tumor site [25] (Figure 5).
Two transdermal delivery systems for immune checkpoints (anti-CTLA-4 antibody [32], 131I-αPD-L1 antibody [176]) to treat melanoma cells have been developed based on BC. These novel approaches offer distinct advantages that can be leveraged to enhance the efficacy of immunotherapy. The controlled release of antibodies via delivery systems such as BC represents a highly attractive strategy for reducing the systemic dissemination of antibodies and potentially mitigating the adverse effects associated with checkpoint therapy [32].
A recent study has indicated that the BC may be an appropriate nanocarrier for developing vaccines for aquatic animals. The use of carboxylated BC by 2,2,6,6-tetramethyl-1-piperidinoxyl (TEMPO) oxidization was employed to conjugate ribavirin to the NbE4 nanobody, with the objective of developing a drug system against the largemouth bass virus [173]. In another study, a system for delivering cyano-phycocyanin to the gastrointestinal tract was developed based on BC nanocrystals (BCNC). This system has been shown to protect phycocyanin release from degradation by gastric fluid until phycocyanin reaches target sites [177].
Although researchers have reported encouraging results with the use of BC-based materials for drug delivery, most studies are still in their early stages. Some research focuses solely on in vitro drug release [98], while others primarily use animal models. As a result, more clinical trials are needed to ensure the safety and efficacy of BC-based materials before they can be commercialized. In addition, most of the drugs used in BC-based drug delivery systems are model drugs. More sophisticated drugs used to treat specific diseases are unlikely to be useful due to uncertain interactions between BC and other treatments. To improve the efficiency of drug delivery, ensure biocompatibility, and adjust the hydrophilicity/hydrophobicity of BC, it is necessary to optimize the composite formula based on BC; some drugs need additional research and integration of 3D printing [174].
3.6. Enzyme Immobilization
Enzyme immobilization reduces operating costs, extends enzyme life, increases enzyme stability, and facilitates recovery and reuse [178]. The immobilization of the enzyme on BC ensures high contact between the substrate and the immobilized enzyme due to the fast diffusion of substrates and products into the aqueous solution through the network of BC nanofibrils, which ensures high turnover of the enzyme [179].
The most common and most effective way to immobilize enzymes is to attach them to highly activated supports. The enzyme’s primary amino groups are good at reacting with activated supports, and they do not need to be activated [180]. The primary amino groups in enzymes can be classified as either highly reactive at neutral pH or low reactive at neutral pH due to the high pK (10.5) of lysine residues. Although the less reactive lysine residues in this area are 1000 times less reactive than the single N-terminus, immobilization of the enzyme through this region is necessary to achieve the best multipoint immobilization [180].
One of the disadvantages of utilizing both BC and the majority of carriers is the necessity to activate them prior to immobilization, as well as the relatively low efficiency of immobilization, especially with BC [14]. Furthermore, immobilization may result in enzyme inactivation or a low initial activity of the immobilized enzyme. This may be due to limited diffusion of the substrate and products through the cellulose matrix in which the hybrid protein is embedded. The enzyme activity may also be affected by the method of BC drying. This may affect the access of water to the enzymes bound in the inner parts of the membrane, thereby changing their hydrolytic activity [178].
One way to alter the BC matrix used for enzyme immobilization is to add substances such as carboxymethylcellulose (CMC), chitosan, alginate, and lignin derivatives to the culture medium. Similarly, modifying the drying conditions of BC membranes can change the physical and chemical characteristics of BC, as it impacts the membrane’s porosity and its capacity to adsorb enzymes [13,14,15,16]. These treatments significantly enhance the potential for using BC as a carrier of enzymes or other active compounds [181].
To date, both unmodified and modified BC have been successfully employed for the immobilization of numerous enzymes, including papain [110], lysozyme [111], lipase [14,182,183,184], β-galactosidase [178], horseradish peroxidase (HRP) [185], superoxide dismutase (SOD) [4], glutamate decarboxylase (GAD) [45], laccase [108,186], lecitase [181], urease [187], L-asparaginase [188], and others. The enzymes were immobilized using unmodified BC and modified by chitosan BC hydrogel beads, as well as preactivated BC with glutaraldehyde or oxidized BC with sodium periodate.
3.1. Wound Healing and Antibacterial Wound Dressings
A wound is a break in the continuity of the skin. Microbial infection and inflammation are the most common wound-related conditions [69]. Wound healing requires the coordinated and balanced activity of inflammation and vascularization of connective tissue and epithelial cells. All of these stages require an extracellular matrix (ECM) and various growth factors to support the healing process [70]. The primary proteins of the ECM are collagen, non-collagen fractions, and proteoglycans [71]. These proteins serve as scaffolds and mediate cell attachment, cellular proliferation, differentiation, and migration. Each organ has a unique ECMs [20].
ECM proteins, a plethora of growth factors, and antidiabetic wound-healing agents (insulin) play a pivotal role in the process of tissue regeneration [72]. Proteins and growth factors are used to create nanopreparations with wound healing and regenerative effects to help reduce inflammation, cell proliferation, and remodeling. These formulations release proteins over time, increasing the efficacy and impact on the wound area, promoting targeted drug delivery, improving solubility and biocompatibility, and helping wounds heal without complications [72]. Enzymatic wound treatment with proteases is already used in surgical practice [73]. Proteolytic enzymes (serine proteases, metalloproteinases, cysteine proteases, and aspartate proteases) contribute positively to tissue regeneration processes [74].
Despite the advances in wound healing management, the treatment of the majority of skin lesions remains a significant challenge for the biomedical and pharmaceutical industries [75,76,77]. Alternative approaches, such as the use of hydrogels as wound dressings, can be used when surgery is not feasible due to patient circumstances or when there is insufficient necrotic tissue [78]. Wound healing materials must be able to keep the wound moist, absorb wound fluid, promote new skin growth, let in oxygen and other gases, and fight infection. They must also be safe for the body [79]. In addition, the material must act as a suitable interphase to support the complex interactions that occur during wound healing involving various cells, soluble substances, and ECM components. Therefore, the aforementioned properties of BC make it ideal for this purpose [25,80].
For the first time, BC was used in medicine as a wound dressing to promote tissue regeneration [81]. A moist environment, non-toxicity and non-allergy, promotion of thermal insulation, and ease of gas transfer are the primary requirements for effective wound dressings [82]. The use of constructed BC composite scaffolds can extend cell adhesion, proliferation, and transplantation of scaffold-seeded cells, enhancing their biocompatibility [81]. It has been shown that BC and BC-composites are biocompatible with skin tissues [83]. BC is appropriate for skin care applications because it can relieve discomfort, speed healing, and fit the body well [82]. Water-holding is an important factor in the healing process because dry wounds need more moisture to promote tissue regeneration and prevent necrosis [84,85]. BC has a 30% higher water absorption capacity and a 33% longer drying time than cotton gauze [86]. In terms of maintaining a moist wound environment, reducing discomfort, accelerating tissue re-epithelialization, and minimizing scarring, BC-based wound dressings outperformed conventional wound dressings [31,87]. By retaining liquid, a hydrogel can be used to load liquid medications and physiologically active substances close to the dressing material [88].
In vivo studies have proved that BC wound dressings in porous form outperform compacted form in terms of wound healing performance [13,89]. Long-term clinical trials have shown that BC-based dressings are more cost-effective than standard fiber dressings (surgical pads, tulle grass, and saline-impregnated gas), synthetic foams, and alginate dressings. The high surface area and porosity of BC allow for the integration of additional wound-healing-promoting agents [12,90]. A number of commercial BC-based dressings have been developed, including BioFill®, Bioprocess®, Gengiflex® (BioFill Produtos Biotecnológicos, Curitiba, Brazil), Xcell® (Xylos Corporation, Langhorne, PA, USA), Dermafill® (Seven Indústria de Produtos Biotecnológicos Ltda, Londrina, Brazil), and Epiprotect® (Royal Wootton Bassett, UK). The efficacy of BC-based dressings has been investigated in clinical trials, with the results demonstrating that the BC membrane can significantly reduce pain and facilitate the healing of a range of wounds [83,91]. The dressings based on BC can vary considerably in composition, from relatively simple films to highly complex constructions. They often incorporate various biomolecules, pharmaceutical agents, and polymers [92].
There are several limitations to the use of BC-based dressings. Due to the slow degradation of BC, several attempts have been made to improve the degradability of BC for wound healing. Several approaches have been used: oxidation with periodate, γ-irradiation, or incorporation of the enzyme cellulase into BC [93]. Compared to more expensive protein-based materials, BC shows limited cell attachment and proliferation, especially in fibroblast cells in the wound. Several modification strategies have been developed to improve the cellular response of fibroblasts to various materials. Techniques include biopolymer/protein adsorption, gas plasma surface modification, and self-assembled monolayers [94].
Regarding passive wound healing, BC can be combined with various compounds, including antimicrobial polymers, photosensitizers, metallic nanoparticles, antibiotics, antimicrobial peptides, and antiseptics to accelerate wound healing. Researchers often use BC doped with conductive chemicals to locate wound healing sites. This allows the circuit to be carried over a large area and stimulates skin cell behavior, resulting in faster healing [81,91]. BC-based composites can help retain and slowly release antibiotics (Figure 3); however, the issue of antibiotic resistance is still being investigated [95]. Antibacterial nanomaterials may replace antibiotics, but nanoparticles have drawbacks such as easy aggregation, an unpredictable tendency to release ions, and potential cytotoxicity that limit their use [96]. Due to the large number of active functional groups, BC and its modified derivatives can be used as a template or immobilization material for antibacterial nanoparticles, which helps to reduce nanoparticle agglomeration and control the release rate. As a result, there is no defect-free BC-based wound dressing material, and the development of multifunctional BC-based composites is an important area of future research [97].
There are a number of techniques that can be employed to modify BC in order to address the shortcomings of the native material while simultaneously optimizing its biocompatibility, water uptake and release, and antibacterial activity [97]. Given that BC lacks intrinsic antimicrobial properties [78], it is modified by incorporating other polymers or inorganic materials. Modification of BC has several goals, including improving mechanical properties, changing some physical parameters such as water-holding capacity, water retention rate, and water vapor transmission rate, or even imparting antimicrobial activity [98]. Various techniques, including microbial fermentation, physical modification, chemical modification, and combination modification, have been used to enhance the biocompatibility and antibacterial activity of BC to ensure better use in wound healing [25,83]. For dental and drug delivery applications, the degradability of BC mediated by oxidation is very important [25], as BC is slow to degrade in the human body [99,100]. Also, composites are created with collagen [101,102,103,104] and sericin [105]. These polymers strengthen the BC structure, improve its mechanical properties, and accelerate wound healing [78,106]. β-keratose [107], ECMs (collagen, elastin, and hyaluronan) and growth factors (B-FGF, H-EGF, and KGF) [70], laccase [108], soybean protein isolate [13], involucrin antibody SY5 [109], papain [110] have been used for the development of BC-based wound dressings. The BC with antibacterial activity was obtained with lysozyme [111,112] and such peptides as nisin [113], ε-poly-l-Lysine [79], and bacteriocins from Lactobacillus sakei [114,115].
3.2. Tissue Engineering
Tissue engineering involves developing scaffolds and growth factors that influence the regeneration or replacement of damaged tissue [106,116,117]. The scaffold material plays a pivotal role in providing the biological and physical environment essential for tissue growth, including the ECM. This is achieved by facilitating cell adhesion, development, and differentiation [71,118]. The replacement of natural ECMs is becoming an increasingly crucial and promising aspect of tissue engineering, as they facilitate the localization and transportation of cells to specific regions of the body [119].
BC structurally resembles natural ECM [120], therefore, BC has been believed to be a promising material for tissue engineering scaffolds [121]. The BC products have been approved by the FDA for use as tissue replacement due to their low endotoxin levels (less than 20 EU per device) [122]. BC scaffolds (Figure 4) have been shown to have the potential to serve as a viable material for chondrogenesis applications due to their ability to successfully regenerate cartilage using human mesenchymal stem cells [123]. In vivo studies consistently demonstrate that BC membranes or scaffolds are typically well tolerated by host tissues after implantation, with no significant adverse effects [124]. Several studies have shown good in vivo biocompatibility of BC-based scaffolds, giving the material potential for use as a scaffold in tissue engineering [21,117,125,126,127].
While the suitability of BC as a raw material for the fabrication of tissue engineering grafts has been demonstrated, the native form of BC is deficient in certain fundamental characteristics that are essential for its utilization in tissue engineering. These include limited biocompatibility, which is a prerequisite for effective tissue regeneration, and inadequate mechanical strength, which is a prerequisite for high-strength applications such as bone and cartilage tissue engineering [128]. Moreover, there is currently no research available that investigates the potential for BC to calcify in vivo over a long-term period [129]. The lack of macropores in native BC also limits its widespread use in tissue engineering [117], as calcification is largely dependent on the porosity of the material and the length of exposure. It should be emphasized that while calcification is undesirable, the degree of calcification will vary depending on the tissue into which the biomaterial is to be incorporated. Appropriate porosity can prevent calcification processes by ensuring angiogenesis and adequate nutrient supply to the cells [129]. In addition, BC has strong mechanical properties, although the presence of many pores limits its stress-bearing capacity [12]. Furthermore, there is a possibility of immunological rejection [106]. To overcome these limitations, the most commonly studied strategies include the production of BC-based nanocomposites with bioactive components, such as polymers and nanomaterials [128].
The majority of research studies have focused on modifying the microporosity of BC to produce materials with the desired properties for replacement or regeneration applications [118,130,131]. Collagen was primarily employed in the development of composite materials with BC for tissue engineering [132,133,134,135,136]. In comparison to the use of collagen composites alone, it has been demonstrated that BC-collagen composites facilitate enhanced cell adhesion and proliferation [132]. In addition, modification BC by proteins such as osteopontin and bone morphogenetic protein 2 (BMP-2) can provide a novel alternative to collagen in the guided bone regeneration field [120,137,138]. In several studies, BC–gelatin composite scaffolds were fabricated for bone tissue engineering applications [117,118,139,140]. BC-keratin composite scaffolds [141] were created for skin tissue engineering. In one study, BC was functionalized by recombinant IKVAV peptide for nerve tissue engineering. BC has also been used for nerve regeneration. BC functionalized with recombinant proteins IKVAV-CBM3 (Ile-Lys-Val-Ala-Val fused with cellulose-binding module) and (19)IKVAV-CBM3 increased mesenchymal stem cell adhesion, cell survival, and neurotrophin expression, which promoted neuronal regeneration [142]. A soy protein isolate (SPI) has been compounded on a double-modified BC to provide a new material for urethral reconstruction [143].
3.3. Artificial Blood Vessels
The limited availability of veins in the human body and the potential for severe rejection caused by allografts necessitates the development of artificial veins as a replacement. The most commonly employed clinical artificial vessels are currently constructed of expanded polytetrafluoroethylene (ePTEE, Gore-Tex), polyglycolic acid (PGA), and poly-l-lactic acid (PLLA). However, these materials exhibit several inadequacies that facilitate the formation of thrombi and intimal thickening, which must be addressed in the design of new artificial vein models [144]. In addition, when the procoagulant properties of PET (polyethylene terephthalate, Dacron®, DuPont de Nemours, Inc., Wilmington, DE, USA) and ePTFE (Gore-Tex®, W. L. Gore & Associates, Newark, NJ, USA) vascular graft materials were compared with BC grafts, BC was shown not to significantly induce plasma coagulation. Compared to PET and ePTFE, BC was found to induce the least and slowest activation of the coagulation cascade and is, therefore, considered a potential vascular graft material [145].
BC and its composites are great options for artificial blood vessels [100,144] by promoting neovascularization. A major problem that can arise in the construction of tubular tissue engineering constructs is the attempt to seed the construct with cells in a tubular state, whereas cells in culture adhere much more readily to a flat scaffold [25]. In hemocompatibility tests, BC typically exhibits a low hemolysis rate, and the mechanical properties of BC-based artificial vessels frequently necessitate enhancement. Modifications of the BC surface through chemical functionalization, in addition to alterations in the manufacturing process, can influence the optimization of BC properties. BC has been functionalized with several macromolecules, including peptides, proteins, and polysaccharides, to improve its hemostatic characteristics. Amino acids have varying electrostatic and hydrophobic characteristics, allowing them to interact with platelets and other blood components via physical and chemical mechanisms to induce hemostasis [146]. To solve this challenge, researchers have mostly focused on making artificial blood vessels with the desired properties and have examined the fermentation procedures and conditions that influence the production of BC tubes [144].
The team of Dieter Klemm was the first research organization to investigate and apply artificial vascular substitute obtained with biomaterials from BC [147] and described a clinical product named BActerial SYnthesized Cellulose (BASYC®, Jena, Germany) [100,147,148,149]. The use of BC for the creation of vascular grafts has also been the subject of other studies. Various ECM proteins are used to improve the biofunctional properties of BC membranes and to create a functional endothelial layer [146]. BC/fibrin composites have been developed for the fabrication of artificial blood vessels. However, an investigation of BC/fibrin composites revealed only a slight increase in mechanical properties over those of native BC [150]. In another study, Andrade et al. used BC modified with a recombinant CBM-2 protein and an adhesion peptide tripeptide Arg-Gly-Asp (RGD) to create hemocompatible material [151]. The incorporation of functional peptides usually facilitates protein interaction of ECM by acting as docking sites [152]. In a study conducted by Leitão et al., a novel graft material was created from unmodified, small-caliber, minimally processed BC. The graft’s luminal surface had a similar topography to native vessels. Neovascularization and endothelialization of the graft resulted in the restoration of patency within one month [153].
3.4. Cell Culture System
Three-dimensional culture systems are gaining increasing popularity due to their capacity to more effectively mimic tissue-like structures than monolayer cultures [154]. Three-dimensional cell culture models are employed in the prediction of responses to anticancer treatments. In order to accurately and successfully predict treatment options, 3D cell culture must be capable of mimicking the ECM of cancer cells in an artificial environment [155]. Early studies have shown that using commercial hydrophobic proteins enables the hydrophobization of cellulosic cotton fiber [156], and impregnating zein [157] into BC can increase the hydrophobicity of BC surface as well as enhance cell attachment and proliferation when it is used as cell culture scaffold (Figure 4).
Today, natural and synthetic polymers and their composites are employed in the fabrication of 3D scaffolds for tissue engineering and even 3D cancer cell cultures. Cancer cells within a solid tumor maintain close and continual contact with the ECM [158]. Currently, 3D systems used to research tumor behavior, such as MatrigelVR and GeltrexVR, are based on natural ECM components. However, chemical variability resulting from the presence of numerous growth factors and proteins, in addition to batch-to-batch variability in these matrices, may interfere with signaling pathway biological reasoning or drug-induced effect outcomes. Consequently, the development of new biomaterials is essential to overcome the limitations of natural ECM [159].
Many researchers have conducted studies to explore the application of BC as a scaffold for 3D in vitro cancer cell models [160,161]. To mimic the characteristics of the tumor microenvironment, BC-based scaffolds were synthesized and evaluated in vitro for their ability to support cancer cell growth [162]. Modifying the pore structure of the BC scaffold can influence the behavior of cancer cells, thereby representing an effective approach for the design and fabrication of in vitro models for the study of cancer biology, potential application in cancer diagnosis, and the development of cancer treatments [121,163,164]. Some molecules, such as hyaluronic acid, chitosan, and gelatin, have been incorporated into the BC network to enhance the mechanical strength, cell adhesion, and cell growth properties of composite scaffolds [155,164,165,166,167].
One of the drawbacks of using BC to make small 3D spheroids is the millimeter scale and low precision of manual fabrication. To produce smaller or more precise shapes from BC-based engineered living materials, it is proposed to use molds or recently developed 3D printing methods for bacterial cultures, such as the «functional living ink» (FLINK) method or a hybrid approach in which a 3D printer is programmed to dispense BC spheroids precisely with different functionalizations [168]. Another challenge of using BC for 3D culture is that it is an uneven three-dimensional substrate for cell attachment with limited integrin binding sites, and cells did not adhere well to BC without surface modification. This can affect cell motility and migration on the surface, as cells migrate toward each other and form huge aggregates that spread in three planes, in contrast to cells cultured on conventional cell plastic, which spread evenly across the surface with no evidence of migration. Morphological variations of individual cells were also observed. To improve cell adhesion and cell proliferation, BC can be modified with the use of different porogens [169] or biodegradable polymers [170] to develop 3D biomimetic scaffolds with interconnected macropores and nanofibrous structure, mimicking the physical structure of ECM. The development of simple, effective physicochemical techniques for surface modification of BC with alternative ECM, growth factors, or other materials could enhance its biocompatibility and biodegradability in vivo [128].
In order to model the tumor microenvironment, it was synthesized and studied BC/gelatin hydrogels as scaffolds for the human breast cancer cell line MDA-MB-231 in vitro culture [171]. BC hydrogel was used for the immobilization of a laminin peptide (IKVAV) to mimic human melanoma cells’ microenvironment and to evaluate the influence of the microstructure and modified chemical surface properties of the resulting matrix [159]. Recently, the BC matrix has been proposed as a tool for trapping and localizing tumor cells within a predetermined region that can be targeted with therapy. The BC scaffold is placed at the tumor site after excision to attract and trap residual cancer cells. Once the cells are immobilized on the BC scaffold, they can be killed by targeted treatment. For example, the chemoattractant human serum albumin (HSA) has been used to capture cancer cells [25]. Biofunctionalized BC scaffold was created for cell replacement therapies in Parkinson’s disease. Human embryonic stem cell-derived progenitor cells were cultured on BC with growth factors and laminin that were covalently functionalized to the surface via silanization [172].
3.5. Targeted Drug Delivery System
The development of a targeted drug delivery system has the potential to markedly enhance the therapeutic efficacy of drugs. Antibodies represent one of the most commonly utilized targeting molecules [173]. BC-based drug delivery systems have been a subject of considerable interest recently [32,174]. BC is often mixed with other materials to provide controlled drug release mechanisms. It has been shown that the properties of BC composites can be changed [175], allowing these composites to be tuned for use in a wide range of biomedical applications requiring varying drug release rates. BC has been shown to be a viable substance for long-term drug release, making it an excellent carrier for cancer therapy [174]. A key advantage of using a drug carrier such as BC is that it allows for controlled and localized treatment, which can increase drug concentrations at the tumor site [25] (Figure 5).
Two transdermal delivery systems for immune checkpoints (anti-CTLA-4 antibody [32], 131I-αPD-L1 antibody [176]) to treat melanoma cells have been developed based on BC. These novel approaches offer distinct advantages that can be leveraged to enhance the efficacy of immunotherapy. The controlled release of antibodies via delivery systems such as BC represents a highly attractive strategy for reducing the systemic dissemination of antibodies and potentially mitigating the adverse effects associated with checkpoint therapy [32].
A recent study has indicated that the BC may be an appropriate nanocarrier for developing vaccines for aquatic animals. The use of carboxylated BC by 2,2,6,6-tetramethyl-1-piperidinoxyl (TEMPO) oxidization was employed to conjugate ribavirin to the NbE4 nanobody, with the objective of developing a drug system against the largemouth bass virus [173]. In another study, a system for delivering cyano-phycocyanin to the gastrointestinal tract was developed based on BC nanocrystals (BCNC). This system has been shown to protect phycocyanin release from degradation by gastric fluid until phycocyanin reaches target sites [177].
Although researchers have reported encouraging results with the use of BC-based materials for drug delivery, most studies are still in their early stages. Some research focuses solely on in vitro drug release [98], while others primarily use animal models. As a result, more clinical trials are needed to ensure the safety and efficacy of BC-based materials before they can be commercialized. In addition, most of the drugs used in BC-based drug delivery systems are model drugs. More sophisticated drugs used to treat specific diseases are unlikely to be useful due to uncertain interactions between BC and other treatments. To improve the efficiency of drug delivery, ensure biocompatibility, and adjust the hydrophilicity/hydrophobicity of BC, it is necessary to optimize the composite formula based on BC; some drugs need additional research and integration of 3D printing [174].
3.6. Enzyme Immobilization
Enzyme immobilization reduces operating costs, extends enzyme life, increases enzyme stability, and facilitates recovery and reuse [178]. The immobilization of the enzyme on BC ensures high contact between the substrate and the immobilized enzyme due to the fast diffusion of substrates and products into the aqueous solution through the network of BC nanofibrils, which ensures high turnover of the enzyme [179].
The most common and most effective way to immobilize enzymes is to attach them to highly activated supports. The enzyme’s primary amino groups are good at reacting with activated supports, and they do not need to be activated [180]. The primary amino groups in enzymes can be classified as either highly reactive at neutral pH or low reactive at neutral pH due to the high pK (10.5) of lysine residues. Although the less reactive lysine residues in this area are 1000 times less reactive than the single N-terminus, immobilization of the enzyme through this region is necessary to achieve the best multipoint immobilization [180].
One of the disadvantages of utilizing both BC and the majority of carriers is the necessity to activate them prior to immobilization, as well as the relatively low efficiency of immobilization, especially with BC [14]. Furthermore, immobilization may result in enzyme inactivation or a low initial activity of the immobilized enzyme. This may be due to limited diffusion of the substrate and products through the cellulose matrix in which the hybrid protein is embedded. The enzyme activity may also be affected by the method of BC drying. This may affect the access of water to the enzymes bound in the inner parts of the membrane, thereby changing their hydrolytic activity [178].
One way to alter the BC matrix used for enzyme immobilization is to add substances such as carboxymethylcellulose (CMC), chitosan, alginate, and lignin derivatives to the culture medium. Similarly, modifying the drying conditions of BC membranes can change the physical and chemical characteristics of BC, as it impacts the membrane’s porosity and its capacity to adsorb enzymes [13,14,15,16]. These treatments significantly enhance the potential for using BC as a carrier of enzymes or other active compounds [181].
To date, both unmodified and modified BC have been successfully employed for the immobilization of numerous enzymes, including papain [110], lysozyme [111], lipase [14,182,183,184], β-galactosidase [178], horseradish peroxidase (HRP) [185], superoxide dismutase (SOD) [4], glutamate decarboxylase (GAD) [45], laccase [108,186], lecitase [181], urease [187], L-asparaginase [188], and others. The enzymes were immobilized using unmodified BC and modified by chitosan BC hydrogel beads, as well as preactivated BC with glutaraldehyde or oxidized BC with sodium periodate.
4. Methods of Protein Immobilization on Bacterial Cellulose
The latest advances in nanostructured carrier material and immobilization technique development now allow for precise protein immobilization, including on BC [189]. Protein immobilization on BC can be achieved through a number of different methods, including covalent binding, adsorption, crosslinking, and entrapment (Figure 6).
Physical immobilization techniques utilize physical interactions to stabilize proteins and enzymes onto cellulose substrates. This approach frequently results in only minor alterations to the protein [10,178]. Compared to other immobilization techniques, physical immobilization methods are relatively straightforward and cost-effective. The primary physical immobilization methods are entrapment and adsorption [10].
Among protein and enzyme immobilization methods, adsorption is the simplest. Adsorption is usually accomplished by hydrophobic interactions and salt bridges, and protein function is largely preserved because the bond between the support and the protein or enzyme is minimal [190]. The degree of protein adsorption on BC depends on the characteristics of the native form of BC, such as the porosity, the degree of cross-linking of the nanofibrils, and other material properties, depending on the culture time and the composition of the medium [191]. The efficiency of protein immobilization on BC can reach over 93,5% with physical adsorption [14]. Recently, a protein imprinted material based on a BC composite carrier and a metal–organic framework, zeolite imidazolate framework-67 (ZIF-67), was developed for the isolation of bovine serum albumin. This material exhibited ultra-high adsorption capacity (1017.0 mg/g), excellent recognition (IF = 5.98), and fast adsorption equilibrium time (50 min) [16].
The cellulose matrix is one of the most optimal substrates for the immobilization of enzymes by entrapment [178]. Entrapment methods do not place proteins directly on the surface of their supports, in contrast to adsorption approaches. Instead, they are physically entrapped within the polymer matrix. Entrapment protects proteins and enzymes from hostile conditions and improves their stability [192]. Nevertheless, due to the weak interactions involved, physical methods often result in enzyme degradation during washing, which can lead to loss of functionality. It is, therefore, essential to employ enzyme purification as a preliminary step in such cases [178].
Protein immobilization by crosslinking is the process of chemically joining two or more molecules by a covalent bond [193]. The protein is typically adsorbed to the nanocarrier and then cross-linked using a bifunctional agent like glutaraldehyde [194]. The combination of two groups in a single protein results in intramolecular cross-links, which reinforce the protein’s tertiary or quaternary structure. Intermolecular cross-links are formed when groups of two distinct proteins bind together, creating a stable protein-protein connection [193]. This method improves enzyme stability and reduces leakage, which is common with non-covalently attached enzymes on supports [194].
Covalent bonds between protein functional groups and support materials are often used for immobilization [192]. In this approach, proteins are securely attached to the surface by covalent bonds [195]. In addition, covalent bonding has a high loading efficiency [196], which is not possible with other methods [8]. The covalent attachment process involves the binding of amino acid residues (i.e., –NH2, –COOH, –SH) to support matrices [48]. Hydroxyl groups on a cellulose surface may interact with proteins, but this is not sufficient for the covalent immobilization process. Therefore, additional functionalization procedures must be conducted to achieve a robust covalent immobilization. The incorporation of functional groups into cellulose surfaces that may react with amino acid residues was accomplished through modification of the cellulose matrix [10].
There are two methods for functionalizing cellulose. The first method entails the addition of amino groups to the surface of cellulose, thereby enabling the formation of a reactive complex with the carboxylic acid groups of amino acids. The second approach involves the introduction of an aldehyde, carboxyl, or epoxide moiety that can engage in a reaction with the amino (–NH₂) group. Another strategy for immobilizing proteins and enzymes on cellulose is to use protein carboxyl groups to react with matrix functionality, including the amino groups present in the cellulose matrix. Activated groups like carboxyl and aldehyde can attach to protein amino groups. To produce aldehyde or carboxyl groups, several chemical methods were employed to oxidize the hydroxyl groups of cellulose [10].
Covalent binding is the most stable approach for enzyme immobilization on cellulosic supports, which can increase the activity and thermal stability of the immobilized enzyme [63,197]. However, protein immobilization by covalent coupling to polymeric materials offers several outstanding advantages for a wide variety of applications, yet coupling techniques are typically limited by their high cost or complexity [198]. The efficiency of protein immobilization by covalent binding to BC may vary depending on the method used. The immobilization efficiency of conjugated recombinant human osteopontin, coupled with the poly(acrylic acid) (PAA)-grafted BC, has been determined to be 97% [15]. However, the strong binding has implications as the enzymes are chemically modified and lose some of their catalytic activity [8]. The use of chemical coupling agents can potentially inactivate the protein or enzyme [10,178]. In some cases, chemical modification of BC (for instance, by diphenyltetrazole) may lead to ineffective protein conjugation [178,199]. Examples of BC modification for protein immobilization in biomedicine will be discussed in detail in the next chapter.
5. Ex Situ and In Situ Modifications of Bacterial Cellulose for Protein Immobilization
Despite the development of new BC-based composites, there are still obstacles to overcome before BC can be fully utilized as a biomedical material. The main issues that need to be addressed are optimizing culture conditions to control the porosity of the BC scaffold, incorporating functional groups into the BC matrix, and increasing the degradation rate of BC to suit specific applications [99,200]. Furthermore, the utilization of native BC presents a challenge in the form of dehydration during the drying process. To prevent dehydration, BC can be modified by various methods to achieve the desired properties [201]. Although BC has a high degree of crystallinity and a single functional hydroxyl group, providing low solubility and limiting its application, BC contains a high concentration of hydroxyl groups on its surface that can be modified. The focus is on modified BC with multiple functional groups that exhibit diverse surface properties, such as lipophilic-hydrophilic properties and magnetic and optical capabilities, along with a regulated specified functionalization pattern [62].
To achieve these goals, BC has been modified in a variety of ways, including chemical modifications (changes in chemical structure and functionality) and physical modifications (changes in porosity, crystallinity, and fiber density). In general, there are two main approaches to implementing these changes: ex situ and in situ [202] (Figure 7).
An ex situ modification is the most common modification of BC [203]. Ex situ modification of BC occurs when an exogenous macromolecule interacts primarily with the BC surface and can penetrate through membrane pores. Changes in the physical-chemical parameters of the BC composite are determined by the degree of exogenous molecule incorporation into the membrane [98]. The key benefits of ex situ modification of BC include a wide range of composite synthesis techniques, the ability to utilize liquefied and suspended materials [204], the maintenance of BC’s primary structural characteristics [11], and the avoidance of issues associated with the incorporation of antimicrobial materials [205].
However, the size and nature of the exogenous molecule (reinforcing material) pose the greatest challenge to ex situ composite synthesis. Only submicron to nanoscale materials can be implanted into the BC matrix. This is because larger particles cannot pass through the BC pores, and hydrophobic materials cannot combine with BC. In addition, the structural arrangement of the BC fibrils is not always regular, so penetrating materials may not be uniformly distributed throughout the BC matrix [12]. In addition, unless a covalent chemical modification is performed, the interaction between the BC membrane and the exogenous molecule is weaker compared to the in situ process [98]. The influence of many modifications on environmental and host physiological conditions is still not fully known and, therefore, requires further investigation [174]. For example, treatment of cells with NaIO4 has been shown to result in the formation of free surface aldehydes that lead to cross-linking between cells via a Schiff base and cause cytotoxicity [206].
There are two forms of ex situ modification: chemical and physical. Physical ex situ modification is usually achieved by physical absorption—a porous BC matrix can be filled with solutions or particle suspensions—the presence of hydroxyl groups on the cellulose chains often results in strong hydrogen bonding between the BC molecules and the absorbed molecules to achieve modification [80,202,207].
In the case of chemical ex situ modification, a reaction with chemicals to change BC’s chemical composition takes place. Since the chemical nature of BC is cellulose, it can be phosphorylated and then modified by graft copolymerization or crosslinking [208]. Carboxymethylation [209], acetylation [203], phosphorylation [210], esterification [211], and other graft copolymerization and crosslinking processes on the BC surface have produced a wide range of BC derivatives with unique structures and properties.
Chemical modification of the BC structure disrupts the ordered crystal-forming hydrogen bonds and increases the water solubility of even hydrophobic derivatives [25]. Incorporation of additional functional groups into the BC structure can impart to BC hydrophobicity, ion adsorption capacity, and optical properties while retaining the characteristic three-dimensional nanostructure and superior mechanical properties of BC. For example, oxidation under moderate aqueous conditions can preserve the crystallinity and size of BC. Recently, acetylation of BC via a non-swelling reaction mechanism has been reported to increase its dispersibility and compatibility in various solvents or matrices suitable for nanocomposite fabrication [212]. The hydrophobicity of the acetylated surface is beneficial for maintaining a large surface area after drying from water and also makes the microfibrils compatible with other hydrophobic materials [62]. The surface modification of BC matrices can improve drug loading and release capabilities. The results indicated that surface modification of BC matrices can alter the surface properties [207]. Most BC modifications are aimed at improving its applicability and performance in a variety of applications (Table S1 in Supplementary Materials).
Biosynthetic (in situ) modification of BC represents an environmentally friendly method that is also simple and cost-effective. This process can be combined with various different chemical additives present in the culture solution to create scalable nanocomposites [213]. In contrast to ex situ modification, in situ modification is relatively straightforward to perform and exhibits uniformity in modification effect. The application of in situ modification of BC presents certain challenges, including the precipitation of additional compounds, the inability to successfully incorporate reinforcement materials into the pellicle [214], and the stringent conditions required for bacterial growth [203]. Although in situ BC modification is commonly used in tissue engineering applications, the stringent microbial fermentation conditions limit the entry of a wider range of additives. Other concerns with the in situ modification process, such as interactions between externally added additives and BC fibril formation, as well as structure controls of BC nanofibers, need to be addressed [202]. In addition, there are limitations in the synthesis of BC composites with antimicrobials [215], the use of BC composites produced by the agitation technique [216], and the disturbance of the main structural features of BC [217]. By incorporating liquid and nanoparticles into the structural matrix of the prepared BC, some problems associated with the in situ synthesis of BC composites can be solved [12].
5.1. Ex Situ Bacterial Cellulose Modification
5.1.1. Native Bacterial Cellulose
Native BC (Figure 8) exhibits superior mechanical strength and stability [150] and high water absorption capacity [218] in the wet state. Implants created from native BC exhibit gradual, non-enzymatic hydrolysis, which is determined by the chemical composition of the main chain and side groups, aggregation state and shape, hydrophilic-hydrophobic balance, surface, and other variables. This process is of utility in certain applications [219], for example drug delivery or tissue engineering. It was shown that unmodified BC did not affect the antibody binding efficacy [32]. Furthermore, unmodified BC membranes do not possess any inflammatory or immunogenic properties [20,32,65,220]. A grafted, native BC membrane serves as a physical barrier, reducing pain and the risk of infection, and allows drug delivery to the wound [221]. In contrast, unmodified BCs are characterized by the immediate release of drugs, regardless of the solubility of drugs in water and the dose [207].
There are several studies on the use of native BC as a carrier for proteins. A macroporous BC hydrogel was developed for wound healing through a process of physical punching with a stainless mold to generate uniform holes with a size of 0.5 mm in diameter, separated by a constant distance of 2 mm. The generation of the macroporous BC hydrogel was achieved by direct layering of the BC hydrogel on top of an alginate solution, with CaCl2 promoting the integration of the alginate into the BC. Then, BC hydrogel was immersed in ECMs (collagen, elastin, and hyaluronan) and growth factors (B-FGF, H-EGF, and KGF). The modified BC hydrogels were shown to be biodegradable under physiological conditions, and growth factors were gradually released. The H-EGF and collagen-modified BCHG were found to support the growth of human skin fibroblasts [70].
A BC–sericin composite was developed for wound healing. For this purpose, BC was impregnated with a sericin solution for 24 h with stirring. The resulting composites exhibited a homogeneous, highly porous structure, a smaller pore size, and a high swelling capacity when compared to BC. However, no significant difference was observed between the effect of BC and the BC–sericin composite on the behavior of keratinocyte cells during cultivation. Additionally, no significant changes were noted in the thermal and mechanical stability of the BC network after the addition of sericin [105].
Modification of native BC with soybean isolate protein was utilized for wound healing treatment. The surface roughness and hydrophilicity of BC–soy protein composites are reduced compared to native BC, and soy protein could be stably released. The resulting composites promoted improved adhesion and proliferation of normal human dermal fibroblast culture and type I collagen expression in vitro compared to the control. At the same time, cell viability increased by almost 50% compared to BC. The composites promoted accelerated wound healing (17 days versus 21 days for wound treatment by control). In addition, BC–soy protein composites stimulated collagen deposition (five times higher than the control), enhanced angiogenesis and hair follicle regeneration, and helped reduce scarring and skin inflammation in rats [13].
Nisin-loaded BC membranes were developed to preserve food quality and inhibit the growth of microbial contaminants. When the antimicrobial activity of the resulting membranes was evaluated by minimum inhibitory concentration and agar diffusion assay using Staphylococcus aureus (S. aureus), Escherichia coli (E. coli), and Pseudomonas aeruginosa (P. aeruginosa), it was shown that nisin in combination with EDTA exhibited significant antimicrobial and antioxidant activity against S. aureus (MIC was 15.63 μg/mL) and E. coli (MIC was 31.25 μg/mL). No antimicrobial activity was observed against P. aeruginosa [113].
Two distinct methodologies were employed to functionalize BC with the antimicrobial peptide ε-poly-l-lysine. The first strategy involved adding ε-PLL to CMC-functionalized BC membranes using EDC (N-(3-dimethylaminopropyl)-N’-ethylcarbodiimide hydrochloride) and NHS (N-hydroxysuccinimide) to form the amide bond [190]. The second strategy involved directly crosslinking ε-PLL with the BC structure using carbodiimide chemistry to form a stable interpenetrating network. Both techniques yielded membranes that were biocompatible with human fibroblasts and capable of inhibiting S. epidermidis development upon contact [222].
Bacteriocins from Lactobacillus sakei subsp. sakei 2a (Lb. sakei 2a) strains were immobilized on BC membranes to enhance their antimicrobial activity against Listeria monocytogenes (L. monocytogenes, foodborne pathogen). Immobilized bacteriocins were significantly (p < 0.05) more effective in controlling pathogen growth than the free bacteriocins throughout the study period [115]. In another study, commercial laccase and silver nanoparticles were physically adsorbed onto BC for wound dressings. The specific activities of immobilized and free laccase were similar. However, the value of the Michaelis–Menten constant (Km) for immobilized laccase (0.77 mM) was almost twice that of the free enzyme. The antimicrobial effect of laccase on medically relevant strains was 92% (S. aureus) and 26% (E. coli), while the composite had no cytotoxicity on fibroblasts [108].
To enhance the biocompatibility and osteoinductivity of BC, Huang et al. developed a porous BC scaffold modified with gelatin and coated with hydroxyapatite. Gelatin was introduced into the surfaces of BC nanofibres by physical adsorption or via the procyanidins crosslinking technique. The results demonstrated that procyanidine crosslinking led to a greater improvement in Young’s modulus and maximum load of BC scaffolds compared to physical crosslinking. A notable increase in mechanical properties was observed in the order of BC, BC/gelatin, BC/procyanidine/gelatin, and BC/procyanidine/gelatin/hydroxyapatite scaffolds. The BC/procyanidine/gelatin/hydroxyapatite scaffold exhibited superior adhesion, viability, proliferation, and osteogenic differentiation of human bone marrow stromal cells. In vivo studies in nude mice and rabbits demonstrated that the BC/procyanidine/gelatin/hydroxyapatite composite exhibited the most favorable osteogenic properties [117].
A recent study demonstrated that BC could serve as a carrier of BMP-2 (an osteoinductive cytokine) for bone regeneration. The rabbits treated with the BC/BMP-2 composite exhibited significantly more newly formed bone than the other groups. The new bone was found to have a markedly higher number of PCNA-positive cells compared to sites away from the composite. After eight weeks, the composite exhibited continuous release of BMP-2. Additionally, no rabbits showed any noticeable inflammation, and no capsules developed around the BC or the BC/BMP-2 combination [138].
A novel keratin-containing BC nanocomposite with the potential to enhance skin fibroblast adherence to the BC surface was developed and characterized by Lin et al. The BC-containing keratin composites were obtained through both in situ and ex situ modification. In comparison to native BC and in situ modified BC/keratin, the viability of keratinocytes and fibroblasts on post-modified BC/keratin nanocomposites was found to be higher. In vitro cell culture studies have demonstrated that cutaneous fibroblasts have good attachment and proliferation on post-modified BC/keratin nanocomposites [141].
Different recombinant bioactive peptides IKVAV, (19)IKVAV, and RGD were fused to a CBM3 to functionalize BC surfaces to promote neuronal and mesenchymal stem cell (MSC) adhesion. It was demonstrated that there was an improvement of almost 100% in cell adhesion for PC12 cells and 30% for MSCs. The RGD-CBM3 protein also exhibited the capacity to enhance the adhesion of N1E-115 and mesenchymal cells. Additionally, the IKVAV-CBM3 facilitated the release of neurotrophic factor (NGF) secreted by MSCs into the culture medium [142].
One study utilized native BC, which was dissolved in N-methylmorpholine N-oxide (NMMO), followed by the addition of porogen (sodium chloride) and then gelatin to create a composite that can be applied in tissue engineering. The resulting composite had high porosity and rapid swelling. In vitro biological tests demonstrated that animal fibroblast cells (NIH 3T3) adhered well and proliferated well on the BC–gelatin composite scaffolds. Increased expression of metalloproteases indicated that long-term incubation of cells can lead to the formation of ECM within the resulting 3D scaffolds [118].
Surface modification of native BC was performed with tripeptide Arg-Gly-Asp (RGD) fused to a cellulose-binding module for the development of hemocompatible material. The RGD is found in many adhesive plasma and ECM proteins and has been shown to improve cell adhesion. The plasma recalcification time and whole blood clotting results demonstrated that BC did not interfere with the coagulation process. A significant amount of plasma protein was adsorbed to BC fibers, and the adsorption of plasma proteins to the BC fiber surface did not affect its protein structure. Human microvascular endothelial cells grown on RGD-modified BC developed a confluent cell layer, which inhibited platelet attachment [151].
Human serum albumin (HSA) was chosen as a model protein to study the loading of chemoattractants onto and released from BC membranes in the F98 rat glioma model. The BC membrane was found to confine F98 tumor cells, preventing their migration once attached to the membrane surface (even in the presence of an attractive medium in the environment). F98 cells trapped on BC remained viable and retained the ability to grow, adopting a spheroid pattern of growth [223,224].
BC was studied as a carrier for antibody delivery. To investigate the release of antibodies in vitro and in vivo, BC was loaded with a model IgG antibody and an anti-CTLA-4 antibody. In vitro experiments demonstrated that IgG was released within 24–48 h. Experiments on cell cultures indicated that BC did not have a cytotoxic effect on the M39 cell line and did not cause activation of dendritic cells. In vivo investigations in serum demonstrated that BC hydrogels significantly reduced the levels of IgG and anti-CTLA-4 antibodies when compared to the levels of antibodies in PBS. The antibodies loaded in the BC retained their binding capacity, as compared to antibodies from a stock solution, after 14 days of implantation [32]. Additionally, BC films were utilized to facilitate the delivery of L-asparaginase to melanoma cells. L-asparaginase was immobilized via physical adsorption. The maximum adsorption of L-asparaginase was observed among BC films grown for 96 h, reaching 84.5 ± 5.7%. Uveal melanoma cells (A875) demonstrated sensitivity to L-asparaginase, with an IC50 value of 0.03. L-asparaginase immobilized on BC caused the death of over 90% of tumor cells after 72 h [188].
BC has also been used to immobilize superoxide dismutase (SOD) to increase its stability at high temperatures and protect fibroblasts against oxidative damage. The results demonstrated that the immobilized SOD was stable at pH levels ranging from 4 to 8, with approximately 70% of the remaining activity. In contrast, the free SOD lost 70% of its initial activity. At temperatures ranging from 25 to 40 °C, the immobilized SOD retained more than 80% of its residual activity. The residual activity of immobilized SOD exhibited a gradual decline from 40 to 45 °C, reaching 30% of the initial activity at 50 °C. In comparison, the activity of free SOD demonstrated a precipitous decline at temperatures above 40 °C. The fibroblast cells that were incubated with BC/SOD and subsequently treated with hydrogen peroxide demonstrated a cell viability of 78.46%, which was higher than that observed in the induced fibroblast cells [4].
Unmodified or dry BC membranes were used to immobilize wild-type β-galactosidase and β-galactosidase with a thermostable module CBM2 (TmLac). The CBM2 domain allows direct immobilization of cellulose substrates with high specificity. The binding efficiency of the TmLac-CBM2 hybrid was similar to hydrated BC and freeze-dried BC. The TmLac-CBM2 protein bound to BC more strongly at pH 6.5 than at pH 8.5 and with high specificity compared to the wild-type enzyme. The CBM2 module fused to the enzyme provided a stable attachment to cellulose at 75 °C. The efficiency of lactose hydrolysis was similar between the three forms of β-galactosidase. Enzyme recycling was limited by the instability of the β-galactosidase module, whereas the attachment of CBM2 to cellulose was stable even at 75 °C for 3 h [178].
5.1.2. Bacterial Cellulose Nanoparticles
BC has a wide range of applications in the biomedical field. However, the usage of BC in the form of films or membranes, which is produced by static culture fermentation, limits its applicability [225]. Furthermore, BC scaffolds have some other drawbacks, such as a lack of antimicrobial properties (for use as dressings) and modest mechanical strength [62,226].
Nanoparticles derived from BC can be classified into two categories: BCNCs and BCNFs (Figure 9) [227]. BCNC and BCNF can be obtained from BC using acid hydrolysis and mechanical homogenization, respectively [228,229,230]. Unlike the hydrogel structure of BC in its natural form, BCNC, and BCNF can be dispersed in an aqueous solution and easily incorporated into polymer networks that act as reinforcing agents [231]. These nanoparticles have different sizes, shapes, and properties [227].
Bacterial Cellulose Nanofibrils
BCNFs make up over half of the research on nanocellulose and have been a European bioeconomic priority since 2008 [232]. BCNFs, like CNFs, are flexible, nanosized fibrils with a high aspect ratio. They can form strong, entangled, and disordered networks. BCNFs are long and flexible nanofibers that contain both crystalline and amorphous areas. They consist of fibrillar elements that are 10–50 nm wide and several micrometers long [227]. The BCNF solution is stable, which enhances the versatility and performance of this cellulose material [225]. BCNFs interact with other inorganic particles or biomass components (such as polyphenols, polysaccharides, or proteins) to form unique complex structures [233]. BCNFs are considered safe biomaterials in accordance with the FDA’s Generally Recognized as Safe (GRAS) standard [122].
In a recent study, BCNF was utilized to immobilize lysozyme through a process of physical absorption. After immobilization, lysozyme activity decreased by approximately 12%, but storage stability was improved, and immobilized lysozyme retained more than 70% of its original activity after nine cycles of use. Immobilized lysozyme showed enhanced antimicrobial activity against S. aureus, E. coli, L. monocytogenes, Yersinia entrocolitica, Aspergillus niger, and Saccharomyces sereviseae [111].
A BCNF–zein composite with controlled surface hydrophobicity was created for tissue engineering by Wang et al. The use of zein was based on the ability of zein to self-assemble into various microstructures upon solvent evaporation, as well as its good biodegradability and high biocompatibility. First, BCNF was immersed in zein solutions with gentle stirring, then self-assembly of zein molecules occurred under evaporation, followed by hot pressing. An increase in surface roughness and hydrophobicity of BCNF was observed with the addition of zein at low concentrations (5 mg/mL), while the opposite effect was observed with a higher zein concentration (2%). The incorporation of zein on the surface of BCNF did not significantly alter the internal structure and mechanical properties of BCNF. Compared to pure BC, BCNF–zein composites showed significantly increased adhesion and proliferation of fibroblast cells [157].
BCNFs have been used to construct a delivery system for radiotherapy and immunotherapy in the treatment of metastatic cancer. To address the challenges associated with the clinical application of immune checkpoint blockade and the nonspecific distribution of radioisotopes, an injectable suspension of 131I-labeled antibody against programmed cell death ligand 1 (αPD-L1) immobilized on BC was developed. The resulting composites were targeted specifically to the tumor and stimulated the immune response to achieve specific cancer radioimmunotherapy. The biocompatibility, long-term antibody retention, and immunostimulatory effects of 131I-αPD-L1/BC were confirmed in vitro and in vivo. After long-term treatment with 131I-αPD-L1/BC, T cells in lymph nodes were polarized to CD8+ CTL, which killed cancer cells in the tumor. Radioimmunotherapy prevented cancer from spreading in a breast cancer model [176].
BCNF-chitosan composite hydrogel beads were prepared as scaffolds for the immobilization of Candida rugosa lipase. To prepare BC-chitosan hydrogel beads, chitosan was dissolved in 1-ethyl-3-methylimidazolium acetate. BC powder was added, and the mixture was stirred and dried. The amino groups of chitosan were converted to aldehyde groups after treatment with GA. Lipase was immobilized by crosslinking GA or physical adsorption. Cross-linked lipases showed higher stability than adsorbed and free lipases. After 30 min incubation at 60 °C, the residual activity of BC2 was 76%, while free lipase retained 43% of initial activity. After 10 h incubation, the residual activity of BC2 was 44%, while that of free lipase was 15%. The half-life time of lipase adsorbed on cellulose-chitosan beads was found to be 2.7–3.7 times higher than that of free lipase. The half-life of lipase cross-linked to BC-chitosan beads at 60 °C was 22.7 times that of free lipase [183].
Bacterial Cellulose Nanocrystals
BCNCs are rod-like nanoparticles created from BC after selecting and eliminating the amorphous region. They have a high crystallinity and a rigid structure with a length of 100–1000 nm and a width of 10–50 nm [227]. BC can be hydrolyzed using strong acids such as H2SO4 and HCl to generate a stable solution of BCNC, which provides the material with new functionality [234]. However, acid hydrolysis removes the amorphous portion of cellulose, reducing yield [231]. BCNCs can be used as building blocks for a wide range of applications [234].
Sakacin-A/BCNC conjugates have been developed for use in antimicrobial food packaging. Sakacin-A is an anti-Listeria bacteriocin produced by Lb. sakei. The resulting conjugates were found to be stable when incubated in neutral and mildly acidic solutions (pH 5), but Sakacin-A completely dissociated from BCNCs in alkaline conditions (pH 11). The Sakacin-A/BCNCs conjugate-coated samples exhibited superior surface roughness and tensile strength compared to the paper substrate. The antimicrobial packaging was effective in both in vitro and cheese experiments. The paper samples coated with Sakacin-A and Sakacin-A/BCNCs conjugates had similar antimicrobial activity [114].
A 3D-printed scaffold comprising BCNC, gelatin (GEL), polycaprolactone (PCL), and hydroxyapatite (HA) was developed for use in bone tissue engineering. The 3D printing procedure was used to create four different scaffold compositions with 50%, 60%, 70%, and 80% infill rates. The 3D scaffolds with an 80% infill rate exhibited a pore size (~300 µm) that was suitable for bone tissue engineering. These scaffolds demonstrated a uniformity ratio exceeding 90%. The incorporation of BC and HA into the PCL/GEL scaffold enhanced the growth and attachment of human osteoblast cells. The 3D-printed scaffolds exhibited osteoblast cells with large cytoplasmic dendritic structures, which resembled the appearance of osteocytes [140].
In work [182], lipase was immobilized on BC and BCNC. BC and BCNC were functionalized with succinic acid as a linker [235], and the lipase was then conjugated to succinylated cellulose using EDC/NHS. After immobilization, the enzyme retained its activity in both BCNC and BC membrane, and the amount of protein immobilized on BCNC was 2.75 times higher than that in BC membrane. The BCNC was also employed for the immobilization of urease. The immobilized urease demonstrated superior tolerance to changes in pH (5.5–9) and temperature (30–80 °C) when compared to the free urease. Furthermore, the immobilized urease retained approximately 81% and 68% of its initial activity following 15 and 20 cycles of reuse, respectively. It also exhibited significantly enhanced storage stability for 20 days [187].
5.1.3. Crosslinking
Crosslinking is defined as the induction of chemical or physical links among polymer chains [236]. The crosslinking of materials can be achieved through a variety of methods, including physical processes, chemical processes, and enzymatic processes [237]. The chemical crosslinking method makes it possible to obtain an irreversible or permanent hydrogel. The physical crosslinking method can produce a hydrogel that can be reversed since the forces involved are hydrophilic interaction, electrostatic, and hydrogen bonding. Crosslinking improves the thermal and mechanical stability of the matrix and can be tailored to modify the release rate of the incorporated active agents [238].
The abundant hydroxyl functional groups in the BC molecular chains make BC an excellent candidate for modifications by crosslinking [202]. Crosslinking plays a pivotal role in the drying process [68,239], prevents the collapse of 3D network BC in the drying process [163,240,241], and improves water absorption [242]. In this sense, cross-linked samples may absorb water faster than pure samples, and their spongy structure results in a significant variation in surface shape compared to native samples [243]. The chemical structural similarities between BC and plant cellulose make it possible to utilize plant cellulose crosslinkers [163,240,241].
Chemical Crosslinking
Chemical crosslinking is the process of incorporating monomers into polymers or joining two polymer chains using crosslinking agents [9,244]. Several different types of crosslinking agents have been used in published studies of cellulose-based hydrogel materials. Typically, when considering crosslinking agents, one is interested in compounds that are capable of forming pairs of covalent bonds, thereby linking polymeric segments together [245].
Glutaraldehyde
GA is widely used as a crosslinking agent in biomedical applications such as enzyme and cell immobilization and hydrogel formation. It is a dialdehyde with highly reactive aldehydic groups capable of forming covalent bonds with functional groups such as amines, thiols, phenols, hydroxyls, and imidazoles [246]. The optimal concentration of glutaraldehyde for chemical crosslinking is 1.0% (v/v), which preserves the cytocompatibility of composites [247]. GA is also employed as an activation reagent [14]. The GA stabilizes the composite by forming chemical bonds between the GA and the components [248].
A number of composite materials have been developed by crosslinking BC with gelatin and fibrin for tissue engineering. The amine groups of gelatin (or fibrin) and the hydroxyl groups of BC cellulose underwent a chemical reaction, forming hydrogen bonds [249].
A composite of BC and gelatin was synthesized for use as a small-diameter artificial blood vessel. After introducing fish gelatin into the fiber network of BC tubes, GA was used as a chemical crosslinking agent. The results demonstrated that the BC/gelatin composite tubes exhibited superior water permeability and nutrient transportation through the tube walls compared to BC. All BC/gelatin composite tubes could withstand pressure three times or higher than normal human blood pressure. In vitro cell culture experiments showed that human venous endothelial cells (HUVECs) and human smooth muscle cells seeded on the inner walls of BC/gelatin tubes had improved adhesion, proliferation, and differentiation potential, as well as anticoagulant and mechanical properties. Whole blood coagulation tests showed that BC/gelatin tubes outperformed BC tubes, with all samples exhibiting a hemolysis rate of less than 1.0%, meeting international medical device criteria [247].
Artificial blood vessels have been developed based on GA-treated BC/fibrin composites. The Young’s modulus and the time-dependent viscoelastic behavior of the composite were comparable to a reference small-diameter blood vessel. However, the strain at the break of the composite material (0.33) was still significantly lower than that of the native blood vessel (1.07) [250].
In certain studies, GA was utilized as a pre-activating agent for BC, facilitating the subsequent covalent immobilization of enzymes onto the activated BC (Figure 10).
The development of an in vitro enzymatic conversion process with GAD immobilized on the BC membrane was proposed for the efficient production of γ-Aminobutyric acid. Three different immobilization methods were compared. In the case of pre-activation BC with GA followed by adsorption, the enzyme was immobilized on BC via covalent binding and retained more than 96% of its original activity after seven cycles and had 89.17% of the activity of the native enzyme. After adsorption followed by crosslinking, the GAD activity on BC was 60.1% of the initial value and decreased rapidly over seven cycles. The membrane prepared by the physical adsorption method showed the highest GAD activity (95.11%), but the enzyme activity decreased exponentially with increasing cycle number of uses [45]. In another study using lipase as a model enzyme, the two-step immobilization strategy involving BC activation by GA and enzyme binding was also the most effective. The activity of the immobilized enzyme was 93.5% of that of the free enzyme. After six reactions, the activity was 76.7% (330 U) of that in the first reaction. Immobilized and free enzymes had identical activity at different pH and temperature levels. In addition, the immobilized lipase retained 60% of its initial activity after 15 cycles of use, in contrast to the free enzyme [14].
BCNC crosslinked by GA was used for the delivery of cyano-phycocyanin (C-PC). The C-PC was loaded onto the BCNC to make a carrier that keeps the C-PC stable, increases the time of drug release, and improves absorption in the gastrointestinal tract. The crosslinking process resulted in a BCNC structure with larger pores and enhanced stability. C-PC was immobilized on BCNC by physical adsorption, reaching 65.3% adsorption in 3 h. After 24 h, 69.46% of the C-PC was released from the BC, 47.39% from the BCNC, and 43.21% from the BCNC-GA [177].
CDAP
The organic cyanating reagent 1-cyano-4-dimethylaminopyridinium tetrafluoroborate (CDAP) is used to activate polysaccharides, which could subsequently react with spacer reagents or directly with protein. In comparison to CNBr, CDAP is simpler to utilize, may be used at lower pH, has fewer negative effects and proteins can be directly linked to CDAP-activated polysaccharides [251,252]. Proteins interact with the cyanoester of CDAP-activated polysaccharides primarily through the unprotonated ε-amines of surface lysine residues, forming an isourea bond. It is possible that numerous multipoint connections may form between the polysaccharide and the protein, given that proteins have a large number of lysine residues that can react with the activated polysaccharide [253].
Acellular tissue-engineered vascular grafts have been developed based on BC. BCs were modified with heparin-chitosan, albumin, and fibronectin to encourage the growth of HUVECs or endothelial progenitor cells (EPCs). To prepare scaffolds with albumin or fibronectin, the BC surface was activated with CDAP. A portion of the BCs were coated with heparin by immersion in an EDC/NHS. BC-based grafts were characterized by good surgical controllability and burst pressure in a physiological environment. Fibronectin coating significantly promoted the adhesion and growth of VECs and EPCs, while albumin only promoted the adhesion of VECs, but the cells were functionally impaired. At the same time, fibronectin-modified surfaces were capable of capturing platelets via β1 and αIIbβ3 integrins, which can lead to increased thrombogenicity. Heparin-chitosan coating significantly improved EPC adhesion but not VEC adhesion [254].
Citric acid
Citric acid (CA) is regarded as a safe and non-toxic crosslinker. CA has been employed to cross-link nanofibers and preserve the 3D structure of BC pellicles. One potential mechanism for the crosslinking process of BC with CA is the dehydration of BC as a result of the esterification process. This reaction produces an ester group that can bind to the accessible hydroxyl group of cellulose (Figure 11) [243].
The crosslinking of BC with CA and the functionalization of the resulting material with Ni- and Mg-ferrite magnetic nanoparticles were employed for the immobilization of lipase B from Candida antarctica and phospholipase A from Aspergillus oryzae. Both enzymes demonstrated significantly enhanced thermal stability at 60 °C, with a notable retention of residual activity at 70 °C, in contrast with the free form, which exhibited a notable decline. After 10 cycles of repeated use, the analyzed enzymes retained a notable amount of residual activity. However, there was a decline in the catalytic efficiency of phospholipase A and lipase after immobilization. In addition, there was a notable difference in immobilization efficiency between phospholipase A and lipase B [191].
Physical crosslinking
Physical crosslinking has attracted much interest in hydrogel formation due to its ease of formulation and the fact that it does not require crosslinking chemicals [255]. This approach is primarily based on the generation of free radicals, which are then exposed to a high-energy source such as an electron beam, x-ray, or gamma ray [256,257].
Electron beam irradiation can be used to induce physical crosslinking (Figure 12), resulting in pure, sterile, and residue-free hydrogels. In addition, electron beam irradiation does not require the use of a radioactive source, reducing the possibility of harmful toxicity [258]. The electron beam creates patterns on BC’s surface without changing its chemical or crystalline structure [259]. BC membranes that have been irradiated are more swollen and biodegradable than non-irradiated ones [120]. These features make them excellent materials for use in drug delivery applications [106,260].
Stimuli-responsive BC-g-poly(acrylic acid) hydrogels were prepared by electron beam irradiation for the oral delivery of proteins. Bovine serum albumin (BSA) was a model protein. The cumulative release of BSA was less than 10% in simulated gastric fluid, demonstrating the ability of the hydrogels to protect BSA from the acidic environment of the stomach. Hydrogels had the maximum adhesiveness among the described hydrogels. The in vitro cytotoxicity test of the hydrogel demonstrated that the viability of Caco-2 cells was well above 90%. In addition, an ex vivo penetration study indicated that BSA penetration increased throughout the intestinal mucosal tissue, and acute oral toxicity tests demonstrated the safety of these hydrogels for in vivo applications [261].
5.1.4. Modification of the Chemical Structure of Bacterial Cellulose
The hydroxyl groups of BC form both intra- and intermolecular hydrogen bonds, which limits the reactivity of the hydroxyl groups [262]. As a result, the hydroxyl groups cannot react directly with the functional groups present in proteins [199]. Furthermore, the loading of substances into BC by physical absorption does not result in sufficient interactions to enable long-term immobilization of protein molecules. The incorporation of functional groups [185] into the BC generates a carrier that can covalently immobilize biomolecules like proteins. For example, modification of its chemical structure of cellulose (derivatization) promotes the solubility in water of even hydrophobic derivatives [25].
The techniques for chemical modification of cellulose mostly include esterification, oxidation, etherification, carbamation, and amination, which are commonly carried out by creating reactive functional and charged groups on the surface by utilizing free hydroxyl groups [100,207]. Among the three hydroxyl groups, including two secondary hydroxyl groups and one primary hydroxyl group, the relative reactivity can generally be expressed in the following order: OH-C6 ≫ OH-C2 > OH-C3 [185,263]. Chemical coupling agents must be added to the main and secondary hydroxyl groups of the cellulose structure to immobilize proteins [44].
Oxidation
In order to regulate its degradability in vivo, BC can be selectively oxidized by oxidants, including TEMPO–NaClO–NaBr and NaIO4 [264]. The oxidized BC (OBC) membrane could act as a support matrix for the covalent immobilization of proteins [110]. For the improvement of the immobilization stability, the protein could be covalently conjugated to the OBC membrane via a Schiff base reaction [265]. It has been shown that oxidation affects the swelling and crystallinity of BC [106]. Furthermore, oxidation allows the optimization of BC degradability. Biodegradability through oxidation treatment and bioresorbability are aspects of BC that are being widely investigated for potential application in tissue engineering [266].
Periodate
Periodate ions (IO4−) can selectively oxidize secondary hydroxyl groups in cellulose (Figure 13) [267]. The aldehyde groups are useful for introducing various substituent groups such as carboxylic acids, hydroxyls, or imines [7]. In addition, the oxidation process reduces the negative charge density, increasing the possibility of intermolecular interactions with enzymes [7].
Porous 3D BC microspheres with collagen (COL) and bone morphogenetic protein 2 (BMP-2) were developed for bone tissue engineering. The resulting COL/BC/BMP-2 microspheres exhibited a porous structure with numerous interconnecting voids and a rough surface. These microspheres exhibited biocompatibility and enhanced the adhesion, proliferation, and differentiation of the mouse osteogenic cell line MC3T3-E1 cells. The adhesion rate of MC3T3-E1 cells to collagen was 86.7%, while the adhesion rate to BC was 66.7%. The mice MC3T3-E1 cells on COL/BC porous microspheres and COL/BC/BMP-2 microspheres produced more calcium nodules than the control [268].
Novel composite membranes created by Gorgieva et al. using BC membranes and gelatin biopolymers for tissue regeneration. The conjugation of OBC membranes with EDC significantly increased the physiological stability of gelatin. As a result, the resulting BC-gelatin membranes were found to have high swelling, degradation rate, and pH retention. MRC-5 cells adhered well to the porous gelatin regions of the resulting membrane, and it was not cytotoxic [139].
Double-modified BC (DMBC) was combined with soy protein isolate (SPI) to create a novel urethral scaffold. The number of adipose stem cells adhered to DMBC and DMBC/SPI material was significantly higher than that adhered to BC. The DMBC/SPI composite had low cytotoxicity but good biocompatibility on the third day of incubation with adipose stem cells. The in vivo results in New Zealand rabbits showed that DMBC/SPI did not induce an inflammatory response. Two weeks after surgery, inorganic tissue with a smooth urethral surface and continuous mucosa was produced in the DMBC/SPI group, while organic tissue was produced in the BC group. The DMBC/SPI acquired good degradation ability in vivo, while BC was not degradable in the rabbit body [143].
The DMBC was also created for the purpose of loading FGFR2-modified adipose-derived stem cells (ADSCs) for urethral repair. ADSCs are frequently employed in tissue repair due to their accessibility and capacity to release a variety of cytokines [269,270]. The secretory function and repair effects of ADSCs may be improved by targeted overexpression of FGFR2. The modification of BC involved oxidizing BC and followed sulfonation with NaHSO3. The lentiviral transfection with plasmid systems was used to create FGFR2-expressing ADSCs. The results demonstrated that the new composite with FGFR2-expressing ADSCs exhibited excellent repairability, with this ability correlated with angiogenesis. FGFR2 enhanced the osteogenic capacity of ADSCs without significantly affecting lipogenic capacity [271].
Vasconcelos et al. developed a bioactive dressing by immobilizing papain on oxidized BC (OBC) membranes. The OBC membrane was able to immobilize papain via covalent bonding (–C–NHR) and adsorption (ion exchange), with a recovered activity of 93.3%, an immobilization efficiency of 49.4%, and superior thermal properties OBC over BC. The release mechanisms of the BC–Papain and OBC–Papain membranes were anomalous and predominantly non-Fickian diffusion. The activities measured for wet oxidized BC and BC membranes showed no significant difference [110].
A spherical OBC was used as a carrier for the immobilization of industrial lipases. The stability and hydrolytic activity of lipase immobilized by covalent binding on spherical BC were significantly improved. Two optimal pH values (5 and 8) and a relatively low active temperature (30 °C) were achieved for optimal hydrolytic activity of lipases immobilized on BC, which was superior to free lipase (pH 9 and 40 °C) [184].
BC spheres also served as a carrier to immobilize the Lecitase® Ultra (E.C.3.1.1.32, Sigma-Aldrich, St. Louis, MO, USA) enzyme. The OBC spheres were incubated with PEI and saturated with a mixture of Fe2+/Fe3+ ions. The obtained spheres OBC were then activated with 1% GA to immobilize the enzyme. The maximum yield for Lecitase® Ultra immobilization was 70%. The immobilized enzyme exhibited had no significant impact on the enzyme’s KM value. The immobilized enzyme retained more than 70% of its original activity after eight cycles of use and excellent storage stability, retaining 80% of its initial activity after four weeks at 4 °C [181].
Laccase and TiO2 were added to OBC to create a composite with the biocatalytic properties of laccase and the photocatalytic properties of TiO2. Immobilized laccase outperformed free laccase in terms of pH and temperature stability. The optimal pH for dye degradation was 5.0–6.0, while the optimal temperature was 40 °C. In addition, the immobilized laccase maintained a relative activity of 67% after ten cycles. Under UV irradiation, the oxidized BC/TiO2-Laccase composite degraded 95% of the dye within 3 h [186].
TEMPO
One of the most effective pretreatments for BC is TEMPO oxidation, which selectively modifies the polymer under mild aqueous conditions (Figure 14). TEMPO oxidation creates carboxylate groups (–COO-Na+) that increase interchain electrostatic repulsion, leading to nanofibril disaggregation. BC membranes degraded by TEMPO oxidation have been investigated as stabilizers for food, topical, and pharmaceutical emulsions, replacing surfactants that often cause irritating reactions [272,273]. TEMPO-mediated oxidized BC scaffolds alone operate as potential tissue engineering scaffolds [274].
A bioadhesive composite based on the conjugation of involucrin antibody SY5 and BCNF has been developed. This system enables antigen–antibody interaction between SY5 on BCNF and involucrin exposed in the stratum corneum and epidermis. The antibodies covalently conjugated to oxidized BCNF using EDC/NHS. The BCNF–SY5 composite exhibited 2- to 3.5-fold higher adhesion than albumin-conjugated BCNF. BCNF-SY5 composite has been shown to effectively adhere to damaged skin and stimulate cell proliferation while preserving the intrinsic properties of the antibody [109].
The BC membranes oxidized by TEMPO have been utilized in the development of vaccines for aquatic animals. For this purpose, oxidized BC membranes were conjugated with ribavirin and NbE4 nanobody using EDC/NHS. The results of RT-qPCR analysis of the major capsid protein of LMBV demonstrated that the BC-ribavirin-NbE4 (BRN) therapy resulted in a notable reduction in virus abundance in infected largemouth bass. Furthermore, following the appropriate treatment, the BRN group exhibited reduced levels of inflammation-related factors. After seven days of treatment, the BRN group exhibited reduced expression levels of IRF-3 and IRF-6, indicating that IRF-3 and IRF-6 in the BRN group had returned to normal or pre-infectious levels [173].
Polymer Grafting on BC
Grafting is a process in which a parent polymer serves as the backbone, and branches of a second polymer are attached at various points. Polymer grafting increases the functional properties of the polymer by performing sulfonation, phosphorylation, carboxymethylation, and acetylation [275]. The most prevalent methods for BC modification include surface-induced atom transfer radical polymerization (ATRP), the conventional synthesis approach, and the crosslinking of silane coupling agents [174].
Silanization of cellulose materials is a unique process that serves two distinct purposes. Firstly, it is used as an independent functionalization of cellulose. Secondly, it is employed as an intermediate step to introduce the necessary functionality for further modification. A highly efficient surface functionalization approach is the direct introduction of amino groups into BC using a silane coupling technique [185]. The silane agent most commonly used in this process is (aminopropyl)triethoxysilane (APTES) [276]. APTES is used to conduct the amination of BC (Figure 15). APTES does not alter the water absorption characteristics of BC [277]. It was found that the functionalization of the BC membrane with APTES introduces a new opportunity in click chemistry [278].
Ying et al. first used silanization of BC to immobilize HRP by binding GA. The amino-functionalized BC was activated with GA, and the HRP was covalently attached via its amino groups. The activity and reusability of the immobilized HRP were compared with those of the free enzyme. The optimal pH range for immobilized HRP (pH 5.5–8.5) was greater than that of the free enzyme (pH 6–8), and immobilized HRP was well adapted to ambient alkalinity. The relative activity of immobilized HRP was found to be higher by 90% than that of free HRP at temperatures 25–40 °C. Moreover, BC-immobilized HRP was reused effectively for 10 cycles, exhibiting greater than 70% of its original activity retention [185].
A BC scaffold functionalized with laminin and growth factors was prepared as a support structure for patterning and expansion of human embryonic stem (hES) cell-derived progenitor cells. Dopaminergic ventral midbrain (VM) progenitor cells are being used in cell replacement therapies for Parkinson’s disease. The BC was modified using the silanization method, and laminin and the growth factors BDNF and GDNF were immobilized on the BC surface via a covalent bond. The functionalization of BC resulted in an improved differentiation rate of cells after plating on BC. The viability of hES-derived VM progenitor cells seeded on different substrates was not affected by cellulose functionalization, regardless of whether BC had been modified with laminin + BDNF + GDNF or laminin alone. Furthermore, the expression of early dopaminergic markers in these cells was enhanced by the growth factor functionalization of BC. The modification of BC with growth factors prevents protein leakage while also providing cells with a long-term supply of growth factors required for proper differentiation and development of VM progenitor cells [172].
An active, non-resorbable guided tissue regeneration membrane by conjugating BC with recombinant human osteopontin (OPN) was proposed by Klinthoopthamrong et al. Surface-initiated reversible addition-fragmentation chain transfer (RAFT) polymerization was used to graft PAA onto the surface of BC. Then, p-rhOPN or rhOPN (commercial preparation) was conjugated to the PAA-grafted BC. Conjugated p-rhOPN has an immobilization efficiency of 97%. Both p-rhOPN-BC and rhOPN-BC demonstrated enhanced capabilities in promoting human periodontal ligament stem cell adhesion, osteogenic differentiation, and calcium deposition levels when compared to BC alone [15].
The addition of phosphate moieties to the cellulose backbone represents a significant approach for the preparation of a diverse array of phosphate derivatives from cellulosic materials. Furthermore, phosphate-ester functionalized cellulosic materials are compatible with calcium phosphate, thereby enabling the formation of novel hybrid materials that can be utilized in bone tissue engineering and drug delivery applications. It has been widely reported that concentrated H3PO4 has been used extensively as an effective phosphating agent (Figure 16) [279].
Phosphorylated BC (PBC) was found to be an attractive adsorbent with a large adsorption capacity for proteins [280]. In the investigation of the adsorption of proteins on obtained PBC, it was discovered that it has a much larger specific surface area than phosphorylated plant cellulose (PPC). The adsorption capacity for the protein increased as the percentage of phosphorylation increased. The adsorption capacity of PBC was much higher than that of PPC, even though their phosphorylation percentages were similar [280].
5.2. In Situ Bacterial Cellulose Modification
A variety of materials, including polyaniline [281], collagen [282], hyaluronan [155,166], xanthan gum [283], CMC [284,285,286], and sodium alginate [287,288] have been already utilized to modify BC in situ. These modifications were intended to enhance the morphological and physicochemical properties of BCs for biomedical applications [202]. To date, several modifications of BC in situ have been performed for protein immobilization in tissue engineering. The application of proteins as functional molecules to modify BC during microbial fermentation, as an alternative to conventional chemical techniques, has the potential to result in a more sustainable process. The in situ-modified materials are biocompatible and can undergo decay in a regulated manner [289].
A collagen-BC composite was prepared by incorporating collagen into the incubation medium of A. xylinum. The collagen–BC composite exhibited a well-interconnected porous network structure and a large surface area required for cell attachment and vascularization. The crystal structure of BC also underwent a transformation when collagen was introduced into the incubation medium of A. xylinum [282].
A novel approach for 3D cell culture of tumor cells has recently been developed using BC. The BC was modified in situ with hyaluronic acid and gelatin to create a bioengineered tumor model containing a network of nanofibers and a human glioblastoma cell line (U251). Their findings indicated that the BC/hyaluronic acid/gelatin composite scaffold exhibited moderately hydrophilic properties, influencing cell adhesion and proliferation behavior significantly. The U251 cells exhibited normal morphology and good adhesion and demonstrated excellent viability, forming multilayered and compact cell clusters [155].
In situ modification of BC with CMC and conjugation with anti-HSA affibody was performed to use BC as a matrix for selective biofiltration of blood proteins. CMC–BC composites with conjugated anti-HSA affibodies demonstrated superior binding efficacy for HSA compared to TEMPO-oxidized BC composites. The carboxylated cellulose conjugated with anti-HSA via EDC/NHS exhibited approximately eight-fold higher HSA-specific binding capacity than the carboxylated cellulose surface with physically adsorbed anti-HSA. Affibody conjugation increased the affinity and specificity of CMC–BC tubes to capture target molecules, and the presence of CMC in the BC network reduced irreversible structural changes during the drying process [286].
The protein BslA (B. subtilis biofilm protein, bacterial hydrophobin) was employed for the modification of BC in a localized manner [289]. BslA has the capacity to form a hydrophobic film that coats the biofilm surface, rendering it water-repellent [290]. The results of in situ modification demonstrate that BslA has the potential to cause structural and mechanical changes to the BC fiber network, thereby creating a stronger, less brittle material with enhanced potential for use in a wide range of applications. However, higher concentrations of BslA and BslA–CBM have been observed to delay the formation of the BC pellicle [289].
5.1. Ex Situ Bacterial Cellulose Modification
5.1.1. Native Bacterial Cellulose
Native BC (Figure 8) exhibits superior mechanical strength and stability [150] and high water absorption capacity [218] in the wet state. Implants created from native BC exhibit gradual, non-enzymatic hydrolysis, which is determined by the chemical composition of the main chain and side groups, aggregation state and shape, hydrophilic-hydrophobic balance, surface, and other variables. This process is of utility in certain applications [219], for example drug delivery or tissue engineering. It was shown that unmodified BC did not affect the antibody binding efficacy [32]. Furthermore, unmodified BC membranes do not possess any inflammatory or immunogenic properties [20,32,65,220]. A grafted, native BC membrane serves as a physical barrier, reducing pain and the risk of infection, and allows drug delivery to the wound [221]. In contrast, unmodified BCs are characterized by the immediate release of drugs, regardless of the solubility of drugs in water and the dose [207].
There are several studies on the use of native BC as a carrier for proteins. A macroporous BC hydrogel was developed for wound healing through a process of physical punching with a stainless mold to generate uniform holes with a size of 0.5 mm in diameter, separated by a constant distance of 2 mm. The generation of the macroporous BC hydrogel was achieved by direct layering of the BC hydrogel on top of an alginate solution, with CaCl2 promoting the integration of the alginate into the BC. Then, BC hydrogel was immersed in ECMs (collagen, elastin, and hyaluronan) and growth factors (B-FGF, H-EGF, and KGF). The modified BC hydrogels were shown to be biodegradable under physiological conditions, and growth factors were gradually released. The H-EGF and collagen-modified BCHG were found to support the growth of human skin fibroblasts [70].
A BC–sericin composite was developed for wound healing. For this purpose, BC was impregnated with a sericin solution for 24 h with stirring. The resulting composites exhibited a homogeneous, highly porous structure, a smaller pore size, and a high swelling capacity when compared to BC. However, no significant difference was observed between the effect of BC and the BC–sericin composite on the behavior of keratinocyte cells during cultivation. Additionally, no significant changes were noted in the thermal and mechanical stability of the BC network after the addition of sericin [105].
Modification of native BC with soybean isolate protein was utilized for wound healing treatment. The surface roughness and hydrophilicity of BC–soy protein composites are reduced compared to native BC, and soy protein could be stably released. The resulting composites promoted improved adhesion and proliferation of normal human dermal fibroblast culture and type I collagen expression in vitro compared to the control. At the same time, cell viability increased by almost 50% compared to BC. The composites promoted accelerated wound healing (17 days versus 21 days for wound treatment by control). In addition, BC–soy protein composites stimulated collagen deposition (five times higher than the control), enhanced angiogenesis and hair follicle regeneration, and helped reduce scarring and skin inflammation in rats [13].
Nisin-loaded BC membranes were developed to preserve food quality and inhibit the growth of microbial contaminants. When the antimicrobial activity of the resulting membranes was evaluated by minimum inhibitory concentration and agar diffusion assay using Staphylococcus aureus (S. aureus), Escherichia coli (E. coli), and Pseudomonas aeruginosa (P. aeruginosa), it was shown that nisin in combination with EDTA exhibited significant antimicrobial and antioxidant activity against S. aureus (MIC was 15.63 μg/mL) and E. coli (MIC was 31.25 μg/mL). No antimicrobial activity was observed against P. aeruginosa [113].
Two distinct methodologies were employed to functionalize BC with the antimicrobial peptide ε-poly-l-lysine. The first strategy involved adding ε-PLL to CMC-functionalized BC membranes using EDC (N-(3-dimethylaminopropyl)-N’-ethylcarbodiimide hydrochloride) and NHS (N-hydroxysuccinimide) to form the amide bond [190]. The second strategy involved directly crosslinking ε-PLL with the BC structure using carbodiimide chemistry to form a stable interpenetrating network. Both techniques yielded membranes that were biocompatible with human fibroblasts and capable of inhibiting S. epidermidis development upon contact [222].
Bacteriocins from Lactobacillus sakei subsp. sakei 2a (Lb. sakei 2a) strains were immobilized on BC membranes to enhance their antimicrobial activity against Listeria monocytogenes (L. monocytogenes, foodborne pathogen). Immobilized bacteriocins were significantly (p < 0.05) more effective in controlling pathogen growth than the free bacteriocins throughout the study period [115]. In another study, commercial laccase and silver nanoparticles were physically adsorbed onto BC for wound dressings. The specific activities of immobilized and free laccase were similar. However, the value of the Michaelis–Menten constant (Km) for immobilized laccase (0.77 mM) was almost twice that of the free enzyme. The antimicrobial effect of laccase on medically relevant strains was 92% (S. aureus) and 26% (E. coli), while the composite had no cytotoxicity on fibroblasts [108].
To enhance the biocompatibility and osteoinductivity of BC, Huang et al. developed a porous BC scaffold modified with gelatin and coated with hydroxyapatite. Gelatin was introduced into the surfaces of BC nanofibres by physical adsorption or via the procyanidins crosslinking technique. The results demonstrated that procyanidine crosslinking led to a greater improvement in Young’s modulus and maximum load of BC scaffolds compared to physical crosslinking. A notable increase in mechanical properties was observed in the order of BC, BC/gelatin, BC/procyanidine/gelatin, and BC/procyanidine/gelatin/hydroxyapatite scaffolds. The BC/procyanidine/gelatin/hydroxyapatite scaffold exhibited superior adhesion, viability, proliferation, and osteogenic differentiation of human bone marrow stromal cells. In vivo studies in nude mice and rabbits demonstrated that the BC/procyanidine/gelatin/hydroxyapatite composite exhibited the most favorable osteogenic properties [117].
A recent study demonstrated that BC could serve as a carrier of BMP-2 (an osteoinductive cytokine) for bone regeneration. The rabbits treated with the BC/BMP-2 composite exhibited significantly more newly formed bone than the other groups. The new bone was found to have a markedly higher number of PCNA-positive cells compared to sites away from the composite. After eight weeks, the composite exhibited continuous release of BMP-2. Additionally, no rabbits showed any noticeable inflammation, and no capsules developed around the BC or the BC/BMP-2 combination [138].
A novel keratin-containing BC nanocomposite with the potential to enhance skin fibroblast adherence to the BC surface was developed and characterized by Lin et al. The BC-containing keratin composites were obtained through both in situ and ex situ modification. In comparison to native BC and in situ modified BC/keratin, the viability of keratinocytes and fibroblasts on post-modified BC/keratin nanocomposites was found to be higher. In vitro cell culture studies have demonstrated that cutaneous fibroblasts have good attachment and proliferation on post-modified BC/keratin nanocomposites [141].
Different recombinant bioactive peptides IKVAV, (19)IKVAV, and RGD were fused to a CBM3 to functionalize BC surfaces to promote neuronal and mesenchymal stem cell (MSC) adhesion. It was demonstrated that there was an improvement of almost 100% in cell adhesion for PC12 cells and 30% for MSCs. The RGD-CBM3 protein also exhibited the capacity to enhance the adhesion of N1E-115 and mesenchymal cells. Additionally, the IKVAV-CBM3 facilitated the release of neurotrophic factor (NGF) secreted by MSCs into the culture medium [142].
One study utilized native BC, which was dissolved in N-methylmorpholine N-oxide (NMMO), followed by the addition of porogen (sodium chloride) and then gelatin to create a composite that can be applied in tissue engineering. The resulting composite had high porosity and rapid swelling. In vitro biological tests demonstrated that animal fibroblast cells (NIH 3T3) adhered well and proliferated well on the BC–gelatin composite scaffolds. Increased expression of metalloproteases indicated that long-term incubation of cells can lead to the formation of ECM within the resulting 3D scaffolds [118].
Surface modification of native BC was performed with tripeptide Arg-Gly-Asp (RGD) fused to a cellulose-binding module for the development of hemocompatible material. The RGD is found in many adhesive plasma and ECM proteins and has been shown to improve cell adhesion. The plasma recalcification time and whole blood clotting results demonstrated that BC did not interfere with the coagulation process. A significant amount of plasma protein was adsorbed to BC fibers, and the adsorption of plasma proteins to the BC fiber surface did not affect its protein structure. Human microvascular endothelial cells grown on RGD-modified BC developed a confluent cell layer, which inhibited platelet attachment [151].
Human serum albumin (HSA) was chosen as a model protein to study the loading of chemoattractants onto and released from BC membranes in the F98 rat glioma model. The BC membrane was found to confine F98 tumor cells, preventing their migration once attached to the membrane surface (even in the presence of an attractive medium in the environment). F98 cells trapped on BC remained viable and retained the ability to grow, adopting a spheroid pattern of growth [223,224].
BC was studied as a carrier for antibody delivery. To investigate the release of antibodies in vitro and in vivo, BC was loaded with a model IgG antibody and an anti-CTLA-4 antibody. In vitro experiments demonstrated that IgG was released within 24–48 h. Experiments on cell cultures indicated that BC did not have a cytotoxic effect on the M39 cell line and did not cause activation of dendritic cells. In vivo investigations in serum demonstrated that BC hydrogels significantly reduced the levels of IgG and anti-CTLA-4 antibodies when compared to the levels of antibodies in PBS. The antibodies loaded in the BC retained their binding capacity, as compared to antibodies from a stock solution, after 14 days of implantation [32]. Additionally, BC films were utilized to facilitate the delivery of L-asparaginase to melanoma cells. L-asparaginase was immobilized via physical adsorption. The maximum adsorption of L-asparaginase was observed among BC films grown for 96 h, reaching 84.5 ± 5.7%. Uveal melanoma cells (A875) demonstrated sensitivity to L-asparaginase, with an IC50 value of 0.03. L-asparaginase immobilized on BC caused the death of over 90% of tumor cells after 72 h [188].
BC has also been used to immobilize superoxide dismutase (SOD) to increase its stability at high temperatures and protect fibroblasts against oxidative damage. The results demonstrated that the immobilized SOD was stable at pH levels ranging from 4 to 8, with approximately 70% of the remaining activity. In contrast, the free SOD lost 70% of its initial activity. At temperatures ranging from 25 to 40 °C, the immobilized SOD retained more than 80% of its residual activity. The residual activity of immobilized SOD exhibited a gradual decline from 40 to 45 °C, reaching 30% of the initial activity at 50 °C. In comparison, the activity of free SOD demonstrated a precipitous decline at temperatures above 40 °C. The fibroblast cells that were incubated with BC/SOD and subsequently treated with hydrogen peroxide demonstrated a cell viability of 78.46%, which was higher than that observed in the induced fibroblast cells [4].
Unmodified or dry BC membranes were used to immobilize wild-type β-galactosidase and β-galactosidase with a thermostable module CBM2 (TmLac). The CBM2 domain allows direct immobilization of cellulose substrates with high specificity. The binding efficiency of the TmLac-CBM2 hybrid was similar to hydrated BC and freeze-dried BC. The TmLac-CBM2 protein bound to BC more strongly at pH 6.5 than at pH 8.5 and with high specificity compared to the wild-type enzyme. The CBM2 module fused to the enzyme provided a stable attachment to cellulose at 75 °C. The efficiency of lactose hydrolysis was similar between the three forms of β-galactosidase. Enzyme recycling was limited by the instability of the β-galactosidase module, whereas the attachment of CBM2 to cellulose was stable even at 75 °C for 3 h [178].
5.1.2. Bacterial Cellulose Nanoparticles
BC has a wide range of applications in the biomedical field. However, the usage of BC in the form of films or membranes, which is produced by static culture fermentation, limits its applicability [225]. Furthermore, BC scaffolds have some other drawbacks, such as a lack of antimicrobial properties (for use as dressings) and modest mechanical strength [62,226].
Nanoparticles derived from BC can be classified into two categories: BCNCs and BCNFs (Figure 9) [227]. BCNC and BCNF can be obtained from BC using acid hydrolysis and mechanical homogenization, respectively [228,229,230]. Unlike the hydrogel structure of BC in its natural form, BCNC, and BCNF can be dispersed in an aqueous solution and easily incorporated into polymer networks that act as reinforcing agents [231]. These nanoparticles have different sizes, shapes, and properties [227].
Bacterial Cellulose Nanofibrils
BCNFs make up over half of the research on nanocellulose and have been a European bioeconomic priority since 2008 [232]. BCNFs, like CNFs, are flexible, nanosized fibrils with a high aspect ratio. They can form strong, entangled, and disordered networks. BCNFs are long and flexible nanofibers that contain both crystalline and amorphous areas. They consist of fibrillar elements that are 10–50 nm wide and several micrometers long [227]. The BCNF solution is stable, which enhances the versatility and performance of this cellulose material [225]. BCNFs interact with other inorganic particles or biomass components (such as polyphenols, polysaccharides, or proteins) to form unique complex structures [233]. BCNFs are considered safe biomaterials in accordance with the FDA’s Generally Recognized as Safe (GRAS) standard [122].
In a recent study, BCNF was utilized to immobilize lysozyme through a process of physical absorption. After immobilization, lysozyme activity decreased by approximately 12%, but storage stability was improved, and immobilized lysozyme retained more than 70% of its original activity after nine cycles of use. Immobilized lysozyme showed enhanced antimicrobial activity against S. aureus, E. coli, L. monocytogenes, Yersinia entrocolitica, Aspergillus niger, and Saccharomyces sereviseae [111].
A BCNF–zein composite with controlled surface hydrophobicity was created for tissue engineering by Wang et al. The use of zein was based on the ability of zein to self-assemble into various microstructures upon solvent evaporation, as well as its good biodegradability and high biocompatibility. First, BCNF was immersed in zein solutions with gentle stirring, then self-assembly of zein molecules occurred under evaporation, followed by hot pressing. An increase in surface roughness and hydrophobicity of BCNF was observed with the addition of zein at low concentrations (5 mg/mL), while the opposite effect was observed with a higher zein concentration (2%). The incorporation of zein on the surface of BCNF did not significantly alter the internal structure and mechanical properties of BCNF. Compared to pure BC, BCNF–zein composites showed significantly increased adhesion and proliferation of fibroblast cells [157].
BCNFs have been used to construct a delivery system for radiotherapy and immunotherapy in the treatment of metastatic cancer. To address the challenges associated with the clinical application of immune checkpoint blockade and the nonspecific distribution of radioisotopes, an injectable suspension of 131I-labeled antibody against programmed cell death ligand 1 (αPD-L1) immobilized on BC was developed. The resulting composites were targeted specifically to the tumor and stimulated the immune response to achieve specific cancer radioimmunotherapy. The biocompatibility, long-term antibody retention, and immunostimulatory effects of 131I-αPD-L1/BC were confirmed in vitro and in vivo. After long-term treatment with 131I-αPD-L1/BC, T cells in lymph nodes were polarized to CD8+ CTL, which killed cancer cells in the tumor. Radioimmunotherapy prevented cancer from spreading in a breast cancer model [176].
BCNF-chitosan composite hydrogel beads were prepared as scaffolds for the immobilization of Candida rugosa lipase. To prepare BC-chitosan hydrogel beads, chitosan was dissolved in 1-ethyl-3-methylimidazolium acetate. BC powder was added, and the mixture was stirred and dried. The amino groups of chitosan were converted to aldehyde groups after treatment with GA. Lipase was immobilized by crosslinking GA or physical adsorption. Cross-linked lipases showed higher stability than adsorbed and free lipases. After 30 min incubation at 60 °C, the residual activity of BC2 was 76%, while free lipase retained 43% of initial activity. After 10 h incubation, the residual activity of BC2 was 44%, while that of free lipase was 15%. The half-life time of lipase adsorbed on cellulose-chitosan beads was found to be 2.7–3.7 times higher than that of free lipase. The half-life of lipase cross-linked to BC-chitosan beads at 60 °C was 22.7 times that of free lipase [183].
Bacterial Cellulose Nanocrystals
BCNCs are rod-like nanoparticles created from BC after selecting and eliminating the amorphous region. They have a high crystallinity and a rigid structure with a length of 100–1000 nm and a width of 10–50 nm [227]. BC can be hydrolyzed using strong acids such as H2SO4 and HCl to generate a stable solution of BCNC, which provides the material with new functionality [234]. However, acid hydrolysis removes the amorphous portion of cellulose, reducing yield [231]. BCNCs can be used as building blocks for a wide range of applications [234].
Sakacin-A/BCNC conjugates have been developed for use in antimicrobial food packaging. Sakacin-A is an anti-Listeria bacteriocin produced by Lb. sakei. The resulting conjugates were found to be stable when incubated in neutral and mildly acidic solutions (pH 5), but Sakacin-A completely dissociated from BCNCs in alkaline conditions (pH 11). The Sakacin-A/BCNCs conjugate-coated samples exhibited superior surface roughness and tensile strength compared to the paper substrate. The antimicrobial packaging was effective in both in vitro and cheese experiments. The paper samples coated with Sakacin-A and Sakacin-A/BCNCs conjugates had similar antimicrobial activity [114].
A 3D-printed scaffold comprising BCNC, gelatin (GEL), polycaprolactone (PCL), and hydroxyapatite (HA) was developed for use in bone tissue engineering. The 3D printing procedure was used to create four different scaffold compositions with 50%, 60%, 70%, and 80% infill rates. The 3D scaffolds with an 80% infill rate exhibited a pore size (~300 µm) that was suitable for bone tissue engineering. These scaffolds demonstrated a uniformity ratio exceeding 90%. The incorporation of BC and HA into the PCL/GEL scaffold enhanced the growth and attachment of human osteoblast cells. The 3D-printed scaffolds exhibited osteoblast cells with large cytoplasmic dendritic structures, which resembled the appearance of osteocytes [140].
In work [182], lipase was immobilized on BC and BCNC. BC and BCNC were functionalized with succinic acid as a linker [235], and the lipase was then conjugated to succinylated cellulose using EDC/NHS. After immobilization, the enzyme retained its activity in both BCNC and BC membrane, and the amount of protein immobilized on BCNC was 2.75 times higher than that in BC membrane. The BCNC was also employed for the immobilization of urease. The immobilized urease demonstrated superior tolerance to changes in pH (5.5–9) and temperature (30–80 °C) when compared to the free urease. Furthermore, the immobilized urease retained approximately 81% and 68% of its initial activity following 15 and 20 cycles of reuse, respectively. It also exhibited significantly enhanced storage stability for 20 days [187].
5.1.3. Crosslinking
Crosslinking is defined as the induction of chemical or physical links among polymer chains [236]. The crosslinking of materials can be achieved through a variety of methods, including physical processes, chemical processes, and enzymatic processes [237]. The chemical crosslinking method makes it possible to obtain an irreversible or permanent hydrogel. The physical crosslinking method can produce a hydrogel that can be reversed since the forces involved are hydrophilic interaction, electrostatic, and hydrogen bonding. Crosslinking improves the thermal and mechanical stability of the matrix and can be tailored to modify the release rate of the incorporated active agents [238].
The abundant hydroxyl functional groups in the BC molecular chains make BC an excellent candidate for modifications by crosslinking [202]. Crosslinking plays a pivotal role in the drying process [68,239], prevents the collapse of 3D network BC in the drying process [163,240,241], and improves water absorption [242]. In this sense, cross-linked samples may absorb water faster than pure samples, and their spongy structure results in a significant variation in surface shape compared to native samples [243]. The chemical structural similarities between BC and plant cellulose make it possible to utilize plant cellulose crosslinkers [163,240,241].
Chemical Crosslinking
Chemical crosslinking is the process of incorporating monomers into polymers or joining two polymer chains using crosslinking agents [9,244]. Several different types of crosslinking agents have been used in published studies of cellulose-based hydrogel materials. Typically, when considering crosslinking agents, one is interested in compounds that are capable of forming pairs of covalent bonds, thereby linking polymeric segments together [245].
Glutaraldehyde
GA is widely used as a crosslinking agent in biomedical applications such as enzyme and cell immobilization and hydrogel formation. It is a dialdehyde with highly reactive aldehydic groups capable of forming covalent bonds with functional groups such as amines, thiols, phenols, hydroxyls, and imidazoles [246]. The optimal concentration of glutaraldehyde for chemical crosslinking is 1.0% (v/v), which preserves the cytocompatibility of composites [247]. GA is also employed as an activation reagent [14]. The GA stabilizes the composite by forming chemical bonds between the GA and the components [248].
A number of composite materials have been developed by crosslinking BC with gelatin and fibrin for tissue engineering. The amine groups of gelatin (or fibrin) and the hydroxyl groups of BC cellulose underwent a chemical reaction, forming hydrogen bonds [249].
A composite of BC and gelatin was synthesized for use as a small-diameter artificial blood vessel. After introducing fish gelatin into the fiber network of BC tubes, GA was used as a chemical crosslinking agent. The results demonstrated that the BC/gelatin composite tubes exhibited superior water permeability and nutrient transportation through the tube walls compared to BC. All BC/gelatin composite tubes could withstand pressure three times or higher than normal human blood pressure. In vitro cell culture experiments showed that human venous endothelial cells (HUVECs) and human smooth muscle cells seeded on the inner walls of BC/gelatin tubes had improved adhesion, proliferation, and differentiation potential, as well as anticoagulant and mechanical properties. Whole blood coagulation tests showed that BC/gelatin tubes outperformed BC tubes, with all samples exhibiting a hemolysis rate of less than 1.0%, meeting international medical device criteria [247].
Artificial blood vessels have been developed based on GA-treated BC/fibrin composites. The Young’s modulus and the time-dependent viscoelastic behavior of the composite were comparable to a reference small-diameter blood vessel. However, the strain at the break of the composite material (0.33) was still significantly lower than that of the native blood vessel (1.07) [250].
In certain studies, GA was utilized as a pre-activating agent for BC, facilitating the subsequent covalent immobilization of enzymes onto the activated BC (Figure 10).
The development of an in vitro enzymatic conversion process with GAD immobilized on the BC membrane was proposed for the efficient production of γ-Aminobutyric acid. Three different immobilization methods were compared. In the case of pre-activation BC with GA followed by adsorption, the enzyme was immobilized on BC via covalent binding and retained more than 96% of its original activity after seven cycles and had 89.17% of the activity of the native enzyme. After adsorption followed by crosslinking, the GAD activity on BC was 60.1% of the initial value and decreased rapidly over seven cycles. The membrane prepared by the physical adsorption method showed the highest GAD activity (95.11%), but the enzyme activity decreased exponentially with increasing cycle number of uses [45]. In another study using lipase as a model enzyme, the two-step immobilization strategy involving BC activation by GA and enzyme binding was also the most effective. The activity of the immobilized enzyme was 93.5% of that of the free enzyme. After six reactions, the activity was 76.7% (330 U) of that in the first reaction. Immobilized and free enzymes had identical activity at different pH and temperature levels. In addition, the immobilized lipase retained 60% of its initial activity after 15 cycles of use, in contrast to the free enzyme [14].
BCNC crosslinked by GA was used for the delivery of cyano-phycocyanin (C-PC). The C-PC was loaded onto the BCNC to make a carrier that keeps the C-PC stable, increases the time of drug release, and improves absorption in the gastrointestinal tract. The crosslinking process resulted in a BCNC structure with larger pores and enhanced stability. C-PC was immobilized on BCNC by physical adsorption, reaching 65.3% adsorption in 3 h. After 24 h, 69.46% of the C-PC was released from the BC, 47.39% from the BCNC, and 43.21% from the BCNC-GA [177].
CDAP
The organic cyanating reagent 1-cyano-4-dimethylaminopyridinium tetrafluoroborate (CDAP) is used to activate polysaccharides, which could subsequently react with spacer reagents or directly with protein. In comparison to CNBr, CDAP is simpler to utilize, may be used at lower pH, has fewer negative effects and proteins can be directly linked to CDAP-activated polysaccharides [251,252]. Proteins interact with the cyanoester of CDAP-activated polysaccharides primarily through the unprotonated ε-amines of surface lysine residues, forming an isourea bond. It is possible that numerous multipoint connections may form between the polysaccharide and the protein, given that proteins have a large number of lysine residues that can react with the activated polysaccharide [253].
Acellular tissue-engineered vascular grafts have been developed based on BC. BCs were modified with heparin-chitosan, albumin, and fibronectin to encourage the growth of HUVECs or endothelial progenitor cells (EPCs). To prepare scaffolds with albumin or fibronectin, the BC surface was activated with CDAP. A portion of the BCs were coated with heparin by immersion in an EDC/NHS. BC-based grafts were characterized by good surgical controllability and burst pressure in a physiological environment. Fibronectin coating significantly promoted the adhesion and growth of VECs and EPCs, while albumin only promoted the adhesion of VECs, but the cells were functionally impaired. At the same time, fibronectin-modified surfaces were capable of capturing platelets via β1 and αIIbβ3 integrins, which can lead to increased thrombogenicity. Heparin-chitosan coating significantly improved EPC adhesion but not VEC adhesion [254].
Citric acid
Citric acid (CA) is regarded as a safe and non-toxic crosslinker. CA has been employed to cross-link nanofibers and preserve the 3D structure of BC pellicles. One potential mechanism for the crosslinking process of BC with CA is the dehydration of BC as a result of the esterification process. This reaction produces an ester group that can bind to the accessible hydroxyl group of cellulose (Figure 11) [243].
The crosslinking of BC with CA and the functionalization of the resulting material with Ni- and Mg-ferrite magnetic nanoparticles were employed for the immobilization of lipase B from Candida antarctica and phospholipase A from Aspergillus oryzae. Both enzymes demonstrated significantly enhanced thermal stability at 60 °C, with a notable retention of residual activity at 70 °C, in contrast with the free form, which exhibited a notable decline. After 10 cycles of repeated use, the analyzed enzymes retained a notable amount of residual activity. However, there was a decline in the catalytic efficiency of phospholipase A and lipase after immobilization. In addition, there was a notable difference in immobilization efficiency between phospholipase A and lipase B [191].
Physical crosslinking
Physical crosslinking has attracted much interest in hydrogel formation due to its ease of formulation and the fact that it does not require crosslinking chemicals [255]. This approach is primarily based on the generation of free radicals, which are then exposed to a high-energy source such as an electron beam, x-ray, or gamma ray [256,257].
Electron beam irradiation can be used to induce physical crosslinking (Figure 12), resulting in pure, sterile, and residue-free hydrogels. In addition, electron beam irradiation does not require the use of a radioactive source, reducing the possibility of harmful toxicity [258]. The electron beam creates patterns on BC’s surface without changing its chemical or crystalline structure [259]. BC membranes that have been irradiated are more swollen and biodegradable than non-irradiated ones [120]. These features make them excellent materials for use in drug delivery applications [106,260].
Stimuli-responsive BC-g-poly(acrylic acid) hydrogels were prepared by electron beam irradiation for the oral delivery of proteins. Bovine serum albumin (BSA) was a model protein. The cumulative release of BSA was less than 10% in simulated gastric fluid, demonstrating the ability of the hydrogels to protect BSA from the acidic environment of the stomach. Hydrogels had the maximum adhesiveness among the described hydrogels. The in vitro cytotoxicity test of the hydrogel demonstrated that the viability of Caco-2 cells was well above 90%. In addition, an ex vivo penetration study indicated that BSA penetration increased throughout the intestinal mucosal tissue, and acute oral toxicity tests demonstrated the safety of these hydrogels for in vivo applications [261].
5.1.4. Modification of the Chemical Structure of Bacterial Cellulose
The hydroxyl groups of BC form both intra- and intermolecular hydrogen bonds, which limits the reactivity of the hydroxyl groups [262]. As a result, the hydroxyl groups cannot react directly with the functional groups present in proteins [199]. Furthermore, the loading of substances into BC by physical absorption does not result in sufficient interactions to enable long-term immobilization of protein molecules. The incorporation of functional groups [185] into the BC generates a carrier that can covalently immobilize biomolecules like proteins. For example, modification of its chemical structure of cellulose (derivatization) promotes the solubility in water of even hydrophobic derivatives [25].
The techniques for chemical modification of cellulose mostly include esterification, oxidation, etherification, carbamation, and amination, which are commonly carried out by creating reactive functional and charged groups on the surface by utilizing free hydroxyl groups [100,207]. Among the three hydroxyl groups, including two secondary hydroxyl groups and one primary hydroxyl group, the relative reactivity can generally be expressed in the following order: OH-C6 ≫ OH-C2 > OH-C3 [185,263]. Chemical coupling agents must be added to the main and secondary hydroxyl groups of the cellulose structure to immobilize proteins [44].
Oxidation
In order to regulate its degradability in vivo, BC can be selectively oxidized by oxidants, including TEMPO–NaClO–NaBr and NaIO4 [264]. The oxidized BC (OBC) membrane could act as a support matrix for the covalent immobilization of proteins [110]. For the improvement of the immobilization stability, the protein could be covalently conjugated to the OBC membrane via a Schiff base reaction [265]. It has been shown that oxidation affects the swelling and crystallinity of BC [106]. Furthermore, oxidation allows the optimization of BC degradability. Biodegradability through oxidation treatment and bioresorbability are aspects of BC that are being widely investigated for potential application in tissue engineering [266].
Periodate
Periodate ions (IO4−) can selectively oxidize secondary hydroxyl groups in cellulose (Figure 13) [267]. The aldehyde groups are useful for introducing various substituent groups such as carboxylic acids, hydroxyls, or imines [7]. In addition, the oxidation process reduces the negative charge density, increasing the possibility of intermolecular interactions with enzymes [7].
Porous 3D BC microspheres with collagen (COL) and bone morphogenetic protein 2 (BMP-2) were developed for bone tissue engineering. The resulting COL/BC/BMP-2 microspheres exhibited a porous structure with numerous interconnecting voids and a rough surface. These microspheres exhibited biocompatibility and enhanced the adhesion, proliferation, and differentiation of the mouse osteogenic cell line MC3T3-E1 cells. The adhesion rate of MC3T3-E1 cells to collagen was 86.7%, while the adhesion rate to BC was 66.7%. The mice MC3T3-E1 cells on COL/BC porous microspheres and COL/BC/BMP-2 microspheres produced more calcium nodules than the control [268].
Novel composite membranes created by Gorgieva et al. using BC membranes and gelatin biopolymers for tissue regeneration. The conjugation of OBC membranes with EDC significantly increased the physiological stability of gelatin. As a result, the resulting BC-gelatin membranes were found to have high swelling, degradation rate, and pH retention. MRC-5 cells adhered well to the porous gelatin regions of the resulting membrane, and it was not cytotoxic [139].
Double-modified BC (DMBC) was combined with soy protein isolate (SPI) to create a novel urethral scaffold. The number of adipose stem cells adhered to DMBC and DMBC/SPI material was significantly higher than that adhered to BC. The DMBC/SPI composite had low cytotoxicity but good biocompatibility on the third day of incubation with adipose stem cells. The in vivo results in New Zealand rabbits showed that DMBC/SPI did not induce an inflammatory response. Two weeks after surgery, inorganic tissue with a smooth urethral surface and continuous mucosa was produced in the DMBC/SPI group, while organic tissue was produced in the BC group. The DMBC/SPI acquired good degradation ability in vivo, while BC was not degradable in the rabbit body [143].
The DMBC was also created for the purpose of loading FGFR2-modified adipose-derived stem cells (ADSCs) for urethral repair. ADSCs are frequently employed in tissue repair due to their accessibility and capacity to release a variety of cytokines [269,270]. The secretory function and repair effects of ADSCs may be improved by targeted overexpression of FGFR2. The modification of BC involved oxidizing BC and followed sulfonation with NaHSO3. The lentiviral transfection with plasmid systems was used to create FGFR2-expressing ADSCs. The results demonstrated that the new composite with FGFR2-expressing ADSCs exhibited excellent repairability, with this ability correlated with angiogenesis. FGFR2 enhanced the osteogenic capacity of ADSCs without significantly affecting lipogenic capacity [271].
Vasconcelos et al. developed a bioactive dressing by immobilizing papain on oxidized BC (OBC) membranes. The OBC membrane was able to immobilize papain via covalent bonding (–C–NHR) and adsorption (ion exchange), with a recovered activity of 93.3%, an immobilization efficiency of 49.4%, and superior thermal properties OBC over BC. The release mechanisms of the BC–Papain and OBC–Papain membranes were anomalous and predominantly non-Fickian diffusion. The activities measured for wet oxidized BC and BC membranes showed no significant difference [110].
A spherical OBC was used as a carrier for the immobilization of industrial lipases. The stability and hydrolytic activity of lipase immobilized by covalent binding on spherical BC were significantly improved. Two optimal pH values (5 and 8) and a relatively low active temperature (30 °C) were achieved for optimal hydrolytic activity of lipases immobilized on BC, which was superior to free lipase (pH 9 and 40 °C) [184].
BC spheres also served as a carrier to immobilize the Lecitase® Ultra (E.C.3.1.1.32, Sigma-Aldrich, St. Louis, MO, USA) enzyme. The OBC spheres were incubated with PEI and saturated with a mixture of Fe2+/Fe3+ ions. The obtained spheres OBC were then activated with 1% GA to immobilize the enzyme. The maximum yield for Lecitase® Ultra immobilization was 70%. The immobilized enzyme exhibited had no significant impact on the enzyme’s KM value. The immobilized enzyme retained more than 70% of its original activity after eight cycles of use and excellent storage stability, retaining 80% of its initial activity after four weeks at 4 °C [181].
Laccase and TiO2 were added to OBC to create a composite with the biocatalytic properties of laccase and the photocatalytic properties of TiO2. Immobilized laccase outperformed free laccase in terms of pH and temperature stability. The optimal pH for dye degradation was 5.0–6.0, while the optimal temperature was 40 °C. In addition, the immobilized laccase maintained a relative activity of 67% after ten cycles. Under UV irradiation, the oxidized BC/TiO2-Laccase composite degraded 95% of the dye within 3 h [186].
TEMPO
One of the most effective pretreatments for BC is TEMPO oxidation, which selectively modifies the polymer under mild aqueous conditions (Figure 14). TEMPO oxidation creates carboxylate groups (–COO-Na+) that increase interchain electrostatic repulsion, leading to nanofibril disaggregation. BC membranes degraded by TEMPO oxidation have been investigated as stabilizers for food, topical, and pharmaceutical emulsions, replacing surfactants that often cause irritating reactions [272,273]. TEMPO-mediated oxidized BC scaffolds alone operate as potential tissue engineering scaffolds [274].
A bioadhesive composite based on the conjugation of involucrin antibody SY5 and BCNF has been developed. This system enables antigen–antibody interaction between SY5 on BCNF and involucrin exposed in the stratum corneum and epidermis. The antibodies covalently conjugated to oxidized BCNF using EDC/NHS. The BCNF–SY5 composite exhibited 2- to 3.5-fold higher adhesion than albumin-conjugated BCNF. BCNF-SY5 composite has been shown to effectively adhere to damaged skin and stimulate cell proliferation while preserving the intrinsic properties of the antibody [109].
The BC membranes oxidized by TEMPO have been utilized in the development of vaccines for aquatic animals. For this purpose, oxidized BC membranes were conjugated with ribavirin and NbE4 nanobody using EDC/NHS. The results of RT-qPCR analysis of the major capsid protein of LMBV demonstrated that the BC-ribavirin-NbE4 (BRN) therapy resulted in a notable reduction in virus abundance in infected largemouth bass. Furthermore, following the appropriate treatment, the BRN group exhibited reduced levels of inflammation-related factors. After seven days of treatment, the BRN group exhibited reduced expression levels of IRF-3 and IRF-6, indicating that IRF-3 and IRF-6 in the BRN group had returned to normal or pre-infectious levels [173].
Polymer Grafting on BC
Grafting is a process in which a parent polymer serves as the backbone, and branches of a second polymer are attached at various points. Polymer grafting increases the functional properties of the polymer by performing sulfonation, phosphorylation, carboxymethylation, and acetylation [275]. The most prevalent methods for BC modification include surface-induced atom transfer radical polymerization (ATRP), the conventional synthesis approach, and the crosslinking of silane coupling agents [174].
Silanization of cellulose materials is a unique process that serves two distinct purposes. Firstly, it is used as an independent functionalization of cellulose. Secondly, it is employed as an intermediate step to introduce the necessary functionality for further modification. A highly efficient surface functionalization approach is the direct introduction of amino groups into BC using a silane coupling technique [185]. The silane agent most commonly used in this process is (aminopropyl)triethoxysilane (APTES) [276]. APTES is used to conduct the amination of BC (Figure 15). APTES does not alter the water absorption characteristics of BC [277]. It was found that the functionalization of the BC membrane with APTES introduces a new opportunity in click chemistry [278].
Ying et al. first used silanization of BC to immobilize HRP by binding GA. The amino-functionalized BC was activated with GA, and the HRP was covalently attached via its amino groups. The activity and reusability of the immobilized HRP were compared with those of the free enzyme. The optimal pH range for immobilized HRP (pH 5.5–8.5) was greater than that of the free enzyme (pH 6–8), and immobilized HRP was well adapted to ambient alkalinity. The relative activity of immobilized HRP was found to be higher by 90% than that of free HRP at temperatures 25–40 °C. Moreover, BC-immobilized HRP was reused effectively for 10 cycles, exhibiting greater than 70% of its original activity retention [185].
A BC scaffold functionalized with laminin and growth factors was prepared as a support structure for patterning and expansion of human embryonic stem (hES) cell-derived progenitor cells. Dopaminergic ventral midbrain (VM) progenitor cells are being used in cell replacement therapies for Parkinson’s disease. The BC was modified using the silanization method, and laminin and the growth factors BDNF and GDNF were immobilized on the BC surface via a covalent bond. The functionalization of BC resulted in an improved differentiation rate of cells after plating on BC. The viability of hES-derived VM progenitor cells seeded on different substrates was not affected by cellulose functionalization, regardless of whether BC had been modified with laminin + BDNF + GDNF or laminin alone. Furthermore, the expression of early dopaminergic markers in these cells was enhanced by the growth factor functionalization of BC. The modification of BC with growth factors prevents protein leakage while also providing cells with a long-term supply of growth factors required for proper differentiation and development of VM progenitor cells [172].
An active, non-resorbable guided tissue regeneration membrane by conjugating BC with recombinant human osteopontin (OPN) was proposed by Klinthoopthamrong et al. Surface-initiated reversible addition-fragmentation chain transfer (RAFT) polymerization was used to graft PAA onto the surface of BC. Then, p-rhOPN or rhOPN (commercial preparation) was conjugated to the PAA-grafted BC. Conjugated p-rhOPN has an immobilization efficiency of 97%. Both p-rhOPN-BC and rhOPN-BC demonstrated enhanced capabilities in promoting human periodontal ligament stem cell adhesion, osteogenic differentiation, and calcium deposition levels when compared to BC alone [15].
The addition of phosphate moieties to the cellulose backbone represents a significant approach for the preparation of a diverse array of phosphate derivatives from cellulosic materials. Furthermore, phosphate-ester functionalized cellulosic materials are compatible with calcium phosphate, thereby enabling the formation of novel hybrid materials that can be utilized in bone tissue engineering and drug delivery applications. It has been widely reported that concentrated H3PO4 has been used extensively as an effective phosphating agent (Figure 16) [279].
Phosphorylated BC (PBC) was found to be an attractive adsorbent with a large adsorption capacity for proteins [280]. In the investigation of the adsorption of proteins on obtained PBC, it was discovered that it has a much larger specific surface area than phosphorylated plant cellulose (PPC). The adsorption capacity for the protein increased as the percentage of phosphorylation increased. The adsorption capacity of PBC was much higher than that of PPC, even though their phosphorylation percentages were similar [280].
5.1.1. Native Bacterial Cellulose
Native BC (Figure 8) exhibits superior mechanical strength and stability [150] and high water absorption capacity [218] in the wet state. Implants created from native BC exhibit gradual, non-enzymatic hydrolysis, which is determined by the chemical composition of the main chain and side groups, aggregation state and shape, hydrophilic-hydrophobic balance, surface, and other variables. This process is of utility in certain applications [219], for example drug delivery or tissue engineering. It was shown that unmodified BC did not affect the antibody binding efficacy [32]. Furthermore, unmodified BC membranes do not possess any inflammatory or immunogenic properties [20,32,65,220]. A grafted, native BC membrane serves as a physical barrier, reducing pain and the risk of infection, and allows drug delivery to the wound [221]. In contrast, unmodified BCs are characterized by the immediate release of drugs, regardless of the solubility of drugs in water and the dose [207].
There are several studies on the use of native BC as a carrier for proteins. A macroporous BC hydrogel was developed for wound healing through a process of physical punching with a stainless mold to generate uniform holes with a size of 0.5 mm in diameter, separated by a constant distance of 2 mm. The generation of the macroporous BC hydrogel was achieved by direct layering of the BC hydrogel on top of an alginate solution, with CaCl2 promoting the integration of the alginate into the BC. Then, BC hydrogel was immersed in ECMs (collagen, elastin, and hyaluronan) and growth factors (B-FGF, H-EGF, and KGF). The modified BC hydrogels were shown to be biodegradable under physiological conditions, and growth factors were gradually released. The H-EGF and collagen-modified BCHG were found to support the growth of human skin fibroblasts [70].
A BC–sericin composite was developed for wound healing. For this purpose, BC was impregnated with a sericin solution for 24 h with stirring. The resulting composites exhibited a homogeneous, highly porous structure, a smaller pore size, and a high swelling capacity when compared to BC. However, no significant difference was observed between the effect of BC and the BC–sericin composite on the behavior of keratinocyte cells during cultivation. Additionally, no significant changes were noted in the thermal and mechanical stability of the BC network after the addition of sericin [105].
Modification of native BC with soybean isolate protein was utilized for wound healing treatment. The surface roughness and hydrophilicity of BC–soy protein composites are reduced compared to native BC, and soy protein could be stably released. The resulting composites promoted improved adhesion and proliferation of normal human dermal fibroblast culture and type I collagen expression in vitro compared to the control. At the same time, cell viability increased by almost 50% compared to BC. The composites promoted accelerated wound healing (17 days versus 21 days for wound treatment by control). In addition, BC–soy protein composites stimulated collagen deposition (five times higher than the control), enhanced angiogenesis and hair follicle regeneration, and helped reduce scarring and skin inflammation in rats [13].
Nisin-loaded BC membranes were developed to preserve food quality and inhibit the growth of microbial contaminants. When the antimicrobial activity of the resulting membranes was evaluated by minimum inhibitory concentration and agar diffusion assay using Staphylococcus aureus (S. aureus), Escherichia coli (E. coli), and Pseudomonas aeruginosa (P. aeruginosa), it was shown that nisin in combination with EDTA exhibited significant antimicrobial and antioxidant activity against S. aureus (MIC was 15.63 μg/mL) and E. coli (MIC was 31.25 μg/mL). No antimicrobial activity was observed against P. aeruginosa [113].
Two distinct methodologies were employed to functionalize BC with the antimicrobial peptide ε-poly-l-lysine. The first strategy involved adding ε-PLL to CMC-functionalized BC membranes using EDC (N-(3-dimethylaminopropyl)-N’-ethylcarbodiimide hydrochloride) and NHS (N-hydroxysuccinimide) to form the amide bond [190]. The second strategy involved directly crosslinking ε-PLL with the BC structure using carbodiimide chemistry to form a stable interpenetrating network. Both techniques yielded membranes that were biocompatible with human fibroblasts and capable of inhibiting S. epidermidis development upon contact [222].
Bacteriocins from Lactobacillus sakei subsp. sakei 2a (Lb. sakei 2a) strains were immobilized on BC membranes to enhance their antimicrobial activity against Listeria monocytogenes (L. monocytogenes, foodborne pathogen). Immobilized bacteriocins were significantly (p < 0.05) more effective in controlling pathogen growth than the free bacteriocins throughout the study period [115]. In another study, commercial laccase and silver nanoparticles were physically adsorbed onto BC for wound dressings. The specific activities of immobilized and free laccase were similar. However, the value of the Michaelis–Menten constant (Km) for immobilized laccase (0.77 mM) was almost twice that of the free enzyme. The antimicrobial effect of laccase on medically relevant strains was 92% (S. aureus) and 26% (E. coli), while the composite had no cytotoxicity on fibroblasts [108].
To enhance the biocompatibility and osteoinductivity of BC, Huang et al. developed a porous BC scaffold modified with gelatin and coated with hydroxyapatite. Gelatin was introduced into the surfaces of BC nanofibres by physical adsorption or via the procyanidins crosslinking technique. The results demonstrated that procyanidine crosslinking led to a greater improvement in Young’s modulus and maximum load of BC scaffolds compared to physical crosslinking. A notable increase in mechanical properties was observed in the order of BC, BC/gelatin, BC/procyanidine/gelatin, and BC/procyanidine/gelatin/hydroxyapatite scaffolds. The BC/procyanidine/gelatin/hydroxyapatite scaffold exhibited superior adhesion, viability, proliferation, and osteogenic differentiation of human bone marrow stromal cells. In vivo studies in nude mice and rabbits demonstrated that the BC/procyanidine/gelatin/hydroxyapatite composite exhibited the most favorable osteogenic properties [117].
A recent study demonstrated that BC could serve as a carrier of BMP-2 (an osteoinductive cytokine) for bone regeneration. The rabbits treated with the BC/BMP-2 composite exhibited significantly more newly formed bone than the other groups. The new bone was found to have a markedly higher number of PCNA-positive cells compared to sites away from the composite. After eight weeks, the composite exhibited continuous release of BMP-2. Additionally, no rabbits showed any noticeable inflammation, and no capsules developed around the BC or the BC/BMP-2 combination [138].
A novel keratin-containing BC nanocomposite with the potential to enhance skin fibroblast adherence to the BC surface was developed and characterized by Lin et al. The BC-containing keratin composites were obtained through both in situ and ex situ modification. In comparison to native BC and in situ modified BC/keratin, the viability of keratinocytes and fibroblasts on post-modified BC/keratin nanocomposites was found to be higher. In vitro cell culture studies have demonstrated that cutaneous fibroblasts have good attachment and proliferation on post-modified BC/keratin nanocomposites [141].
Different recombinant bioactive peptides IKVAV, (19)IKVAV, and RGD were fused to a CBM3 to functionalize BC surfaces to promote neuronal and mesenchymal stem cell (MSC) adhesion. It was demonstrated that there was an improvement of almost 100% in cell adhesion for PC12 cells and 30% for MSCs. The RGD-CBM3 protein also exhibited the capacity to enhance the adhesion of N1E-115 and mesenchymal cells. Additionally, the IKVAV-CBM3 facilitated the release of neurotrophic factor (NGF) secreted by MSCs into the culture medium [142].
One study utilized native BC, which was dissolved in N-methylmorpholine N-oxide (NMMO), followed by the addition of porogen (sodium chloride) and then gelatin to create a composite that can be applied in tissue engineering. The resulting composite had high porosity and rapid swelling. In vitro biological tests demonstrated that animal fibroblast cells (NIH 3T3) adhered well and proliferated well on the BC–gelatin composite scaffolds. Increased expression of metalloproteases indicated that long-term incubation of cells can lead to the formation of ECM within the resulting 3D scaffolds [118].
Surface modification of native BC was performed with tripeptide Arg-Gly-Asp (RGD) fused to a cellulose-binding module for the development of hemocompatible material. The RGD is found in many adhesive plasma and ECM proteins and has been shown to improve cell adhesion. The plasma recalcification time and whole blood clotting results demonstrated that BC did not interfere with the coagulation process. A significant amount of plasma protein was adsorbed to BC fibers, and the adsorption of plasma proteins to the BC fiber surface did not affect its protein structure. Human microvascular endothelial cells grown on RGD-modified BC developed a confluent cell layer, which inhibited platelet attachment [151].
Human serum albumin (HSA) was chosen as a model protein to study the loading of chemoattractants onto and released from BC membranes in the F98 rat glioma model. The BC membrane was found to confine F98 tumor cells, preventing their migration once attached to the membrane surface (even in the presence of an attractive medium in the environment). F98 cells trapped on BC remained viable and retained the ability to grow, adopting a spheroid pattern of growth [223,224].
BC was studied as a carrier for antibody delivery. To investigate the release of antibodies in vitro and in vivo, BC was loaded with a model IgG antibody and an anti-CTLA-4 antibody. In vitro experiments demonstrated that IgG was released within 24–48 h. Experiments on cell cultures indicated that BC did not have a cytotoxic effect on the M39 cell line and did not cause activation of dendritic cells. In vivo investigations in serum demonstrated that BC hydrogels significantly reduced the levels of IgG and anti-CTLA-4 antibodies when compared to the levels of antibodies in PBS. The antibodies loaded in the BC retained their binding capacity, as compared to antibodies from a stock solution, after 14 days of implantation [32]. Additionally, BC films were utilized to facilitate the delivery of L-asparaginase to melanoma cells. L-asparaginase was immobilized via physical adsorption. The maximum adsorption of L-asparaginase was observed among BC films grown for 96 h, reaching 84.5 ± 5.7%. Uveal melanoma cells (A875) demonstrated sensitivity to L-asparaginase, with an IC50 value of 0.03. L-asparaginase immobilized on BC caused the death of over 90% of tumor cells after 72 h [188].
BC has also been used to immobilize superoxide dismutase (SOD) to increase its stability at high temperatures and protect fibroblasts against oxidative damage. The results demonstrated that the immobilized SOD was stable at pH levels ranging from 4 to 8, with approximately 70% of the remaining activity. In contrast, the free SOD lost 70% of its initial activity. At temperatures ranging from 25 to 40 °C, the immobilized SOD retained more than 80% of its residual activity. The residual activity of immobilized SOD exhibited a gradual decline from 40 to 45 °C, reaching 30% of the initial activity at 50 °C. In comparison, the activity of free SOD demonstrated a precipitous decline at temperatures above 40 °C. The fibroblast cells that were incubated with BC/SOD and subsequently treated with hydrogen peroxide demonstrated a cell viability of 78.46%, which was higher than that observed in the induced fibroblast cells [4].
Unmodified or dry BC membranes were used to immobilize wild-type β-galactosidase and β-galactosidase with a thermostable module CBM2 (TmLac). The CBM2 domain allows direct immobilization of cellulose substrates with high specificity. The binding efficiency of the TmLac-CBM2 hybrid was similar to hydrated BC and freeze-dried BC. The TmLac-CBM2 protein bound to BC more strongly at pH 6.5 than at pH 8.5 and with high specificity compared to the wild-type enzyme. The CBM2 module fused to the enzyme provided a stable attachment to cellulose at 75 °C. The efficiency of lactose hydrolysis was similar between the three forms of β-galactosidase. Enzyme recycling was limited by the instability of the β-galactosidase module, whereas the attachment of CBM2 to cellulose was stable even at 75 °C for 3 h [178].
5.1.2. Bacterial Cellulose Nanoparticles
BC has a wide range of applications in the biomedical field. However, the usage of BC in the form of films or membranes, which is produced by static culture fermentation, limits its applicability [225]. Furthermore, BC scaffolds have some other drawbacks, such as a lack of antimicrobial properties (for use as dressings) and modest mechanical strength [62,226].
Nanoparticles derived from BC can be classified into two categories: BCNCs and BCNFs (Figure 9) [227]. BCNC and BCNF can be obtained from BC using acid hydrolysis and mechanical homogenization, respectively [228,229,230]. Unlike the hydrogel structure of BC in its natural form, BCNC, and BCNF can be dispersed in an aqueous solution and easily incorporated into polymer networks that act as reinforcing agents [231]. These nanoparticles have different sizes, shapes, and properties [227].
Bacterial Cellulose Nanofibrils
BCNFs make up over half of the research on nanocellulose and have been a European bioeconomic priority since 2008 [232]. BCNFs, like CNFs, are flexible, nanosized fibrils with a high aspect ratio. They can form strong, entangled, and disordered networks. BCNFs are long and flexible nanofibers that contain both crystalline and amorphous areas. They consist of fibrillar elements that are 10–50 nm wide and several micrometers long [227]. The BCNF solution is stable, which enhances the versatility and performance of this cellulose material [225]. BCNFs interact with other inorganic particles or biomass components (such as polyphenols, polysaccharides, or proteins) to form unique complex structures [233]. BCNFs are considered safe biomaterials in accordance with the FDA’s Generally Recognized as Safe (GRAS) standard [122].
In a recent study, BCNF was utilized to immobilize lysozyme through a process of physical absorption. After immobilization, lysozyme activity decreased by approximately 12%, but storage stability was improved, and immobilized lysozyme retained more than 70% of its original activity after nine cycles of use. Immobilized lysozyme showed enhanced antimicrobial activity against S. aureus, E. coli, L. monocytogenes, Yersinia entrocolitica, Aspergillus niger, and Saccharomyces sereviseae [111].
A BCNF–zein composite with controlled surface hydrophobicity was created for tissue engineering by Wang et al. The use of zein was based on the ability of zein to self-assemble into various microstructures upon solvent evaporation, as well as its good biodegradability and high biocompatibility. First, BCNF was immersed in zein solutions with gentle stirring, then self-assembly of zein molecules occurred under evaporation, followed by hot pressing. An increase in surface roughness and hydrophobicity of BCNF was observed with the addition of zein at low concentrations (5 mg/mL), while the opposite effect was observed with a higher zein concentration (2%). The incorporation of zein on the surface of BCNF did not significantly alter the internal structure and mechanical properties of BCNF. Compared to pure BC, BCNF–zein composites showed significantly increased adhesion and proliferation of fibroblast cells [157].
BCNFs have been used to construct a delivery system for radiotherapy and immunotherapy in the treatment of metastatic cancer. To address the challenges associated with the clinical application of immune checkpoint blockade and the nonspecific distribution of radioisotopes, an injectable suspension of 131I-labeled antibody against programmed cell death ligand 1 (αPD-L1) immobilized on BC was developed. The resulting composites were targeted specifically to the tumor and stimulated the immune response to achieve specific cancer radioimmunotherapy. The biocompatibility, long-term antibody retention, and immunostimulatory effects of 131I-αPD-L1/BC were confirmed in vitro and in vivo. After long-term treatment with 131I-αPD-L1/BC, T cells in lymph nodes were polarized to CD8+ CTL, which killed cancer cells in the tumor. Radioimmunotherapy prevented cancer from spreading in a breast cancer model [176].
BCNF-chitosan composite hydrogel beads were prepared as scaffolds for the immobilization of Candida rugosa lipase. To prepare BC-chitosan hydrogel beads, chitosan was dissolved in 1-ethyl-3-methylimidazolium acetate. BC powder was added, and the mixture was stirred and dried. The amino groups of chitosan were converted to aldehyde groups after treatment with GA. Lipase was immobilized by crosslinking GA or physical adsorption. Cross-linked lipases showed higher stability than adsorbed and free lipases. After 30 min incubation at 60 °C, the residual activity of BC2 was 76%, while free lipase retained 43% of initial activity. After 10 h incubation, the residual activity of BC2 was 44%, while that of free lipase was 15%. The half-life time of lipase adsorbed on cellulose-chitosan beads was found to be 2.7–3.7 times higher than that of free lipase. The half-life of lipase cross-linked to BC-chitosan beads at 60 °C was 22.7 times that of free lipase [183].
Bacterial Cellulose Nanocrystals
BCNCs are rod-like nanoparticles created from BC after selecting and eliminating the amorphous region. They have a high crystallinity and a rigid structure with a length of 100–1000 nm and a width of 10–50 nm [227]. BC can be hydrolyzed using strong acids such as H2SO4 and HCl to generate a stable solution of BCNC, which provides the material with new functionality [234]. However, acid hydrolysis removes the amorphous portion of cellulose, reducing yield [231]. BCNCs can be used as building blocks for a wide range of applications [234].
Sakacin-A/BCNC conjugates have been developed for use in antimicrobial food packaging. Sakacin-A is an anti-Listeria bacteriocin produced by Lb. sakei. The resulting conjugates were found to be stable when incubated in neutral and mildly acidic solutions (pH 5), but Sakacin-A completely dissociated from BCNCs in alkaline conditions (pH 11). The Sakacin-A/BCNCs conjugate-coated samples exhibited superior surface roughness and tensile strength compared to the paper substrate. The antimicrobial packaging was effective in both in vitro and cheese experiments. The paper samples coated with Sakacin-A and Sakacin-A/BCNCs conjugates had similar antimicrobial activity [114].
A 3D-printed scaffold comprising BCNC, gelatin (GEL), polycaprolactone (PCL), and hydroxyapatite (HA) was developed for use in bone tissue engineering. The 3D printing procedure was used to create four different scaffold compositions with 50%, 60%, 70%, and 80% infill rates. The 3D scaffolds with an 80% infill rate exhibited a pore size (~300 µm) that was suitable for bone tissue engineering. These scaffolds demonstrated a uniformity ratio exceeding 90%. The incorporation of BC and HA into the PCL/GEL scaffold enhanced the growth and attachment of human osteoblast cells. The 3D-printed scaffolds exhibited osteoblast cells with large cytoplasmic dendritic structures, which resembled the appearance of osteocytes [140].
In work [182], lipase was immobilized on BC and BCNC. BC and BCNC were functionalized with succinic acid as a linker [235], and the lipase was then conjugated to succinylated cellulose using EDC/NHS. After immobilization, the enzyme retained its activity in both BCNC and BC membrane, and the amount of protein immobilized on BCNC was 2.75 times higher than that in BC membrane. The BCNC was also employed for the immobilization of urease. The immobilized urease demonstrated superior tolerance to changes in pH (5.5–9) and temperature (30–80 °C) when compared to the free urease. Furthermore, the immobilized urease retained approximately 81% and 68% of its initial activity following 15 and 20 cycles of reuse, respectively. It also exhibited significantly enhanced storage stability for 20 days [187].
Bacterial Cellulose Nanofibrils
BCNFs make up over half of the research on nanocellulose and have been a European bioeconomic priority since 2008 [232]. BCNFs, like CNFs, are flexible, nanosized fibrils with a high aspect ratio. They can form strong, entangled, and disordered networks. BCNFs are long and flexible nanofibers that contain both crystalline and amorphous areas. They consist of fibrillar elements that are 10–50 nm wide and several micrometers long [227]. The BCNF solution is stable, which enhances the versatility and performance of this cellulose material [225]. BCNFs interact with other inorganic particles or biomass components (such as polyphenols, polysaccharides, or proteins) to form unique complex structures [233]. BCNFs are considered safe biomaterials in accordance with the FDA’s Generally Recognized as Safe (GRAS) standard [122].
In a recent study, BCNF was utilized to immobilize lysozyme through a process of physical absorption. After immobilization, lysozyme activity decreased by approximately 12%, but storage stability was improved, and immobilized lysozyme retained more than 70% of its original activity after nine cycles of use. Immobilized lysozyme showed enhanced antimicrobial activity against S. aureus, E. coli, L. monocytogenes, Yersinia entrocolitica, Aspergillus niger, and Saccharomyces sereviseae [111].
A BCNF–zein composite with controlled surface hydrophobicity was created for tissue engineering by Wang et al. The use of zein was based on the ability of zein to self-assemble into various microstructures upon solvent evaporation, as well as its good biodegradability and high biocompatibility. First, BCNF was immersed in zein solutions with gentle stirring, then self-assembly of zein molecules occurred under evaporation, followed by hot pressing. An increase in surface roughness and hydrophobicity of BCNF was observed with the addition of zein at low concentrations (5 mg/mL), while the opposite effect was observed with a higher zein concentration (2%). The incorporation of zein on the surface of BCNF did not significantly alter the internal structure and mechanical properties of BCNF. Compared to pure BC, BCNF–zein composites showed significantly increased adhesion and proliferation of fibroblast cells [157].
BCNFs have been used to construct a delivery system for radiotherapy and immunotherapy in the treatment of metastatic cancer. To address the challenges associated with the clinical application of immune checkpoint blockade and the nonspecific distribution of radioisotopes, an injectable suspension of 131I-labeled antibody against programmed cell death ligand 1 (αPD-L1) immobilized on BC was developed. The resulting composites were targeted specifically to the tumor and stimulated the immune response to achieve specific cancer radioimmunotherapy. The biocompatibility, long-term antibody retention, and immunostimulatory effects of 131I-αPD-L1/BC were confirmed in vitro and in vivo. After long-term treatment with 131I-αPD-L1/BC, T cells in lymph nodes were polarized to CD8+ CTL, which killed cancer cells in the tumor. Radioimmunotherapy prevented cancer from spreading in a breast cancer model [176].
BCNF-chitosan composite hydrogel beads were prepared as scaffolds for the immobilization of Candida rugosa lipase. To prepare BC-chitosan hydrogel beads, chitosan was dissolved in 1-ethyl-3-methylimidazolium acetate. BC powder was added, and the mixture was stirred and dried. The amino groups of chitosan were converted to aldehyde groups after treatment with GA. Lipase was immobilized by crosslinking GA or physical adsorption. Cross-linked lipases showed higher stability than adsorbed and free lipases. After 30 min incubation at 60 °C, the residual activity of BC2 was 76%, while free lipase retained 43% of initial activity. After 10 h incubation, the residual activity of BC2 was 44%, while that of free lipase was 15%. The half-life time of lipase adsorbed on cellulose-chitosan beads was found to be 2.7–3.7 times higher than that of free lipase. The half-life of lipase cross-linked to BC-chitosan beads at 60 °C was 22.7 times that of free lipase [183].
Bacterial Cellulose Nanocrystals
BCNCs are rod-like nanoparticles created from BC after selecting and eliminating the amorphous region. They have a high crystallinity and a rigid structure with a length of 100–1000 nm and a width of 10–50 nm [227]. BC can be hydrolyzed using strong acids such as H2SO4 and HCl to generate a stable solution of BCNC, which provides the material with new functionality [234]. However, acid hydrolysis removes the amorphous portion of cellulose, reducing yield [231]. BCNCs can be used as building blocks for a wide range of applications [234].
Sakacin-A/BCNC conjugates have been developed for use in antimicrobial food packaging. Sakacin-A is an anti-Listeria bacteriocin produced by Lb. sakei. The resulting conjugates were found to be stable when incubated in neutral and mildly acidic solutions (pH 5), but Sakacin-A completely dissociated from BCNCs in alkaline conditions (pH 11). The Sakacin-A/BCNCs conjugate-coated samples exhibited superior surface roughness and tensile strength compared to the paper substrate. The antimicrobial packaging was effective in both in vitro and cheese experiments. The paper samples coated with Sakacin-A and Sakacin-A/BCNCs conjugates had similar antimicrobial activity [114].
A 3D-printed scaffold comprising BCNC, gelatin (GEL), polycaprolactone (PCL), and hydroxyapatite (HA) was developed for use in bone tissue engineering. The 3D printing procedure was used to create four different scaffold compositions with 50%, 60%, 70%, and 80% infill rates. The 3D scaffolds with an 80% infill rate exhibited a pore size (~300 µm) that was suitable for bone tissue engineering. These scaffolds demonstrated a uniformity ratio exceeding 90%. The incorporation of BC and HA into the PCL/GEL scaffold enhanced the growth and attachment of human osteoblast cells. The 3D-printed scaffolds exhibited osteoblast cells with large cytoplasmic dendritic structures, which resembled the appearance of osteocytes [140].
In work [182], lipase was immobilized on BC and BCNC. BC and BCNC were functionalized with succinic acid as a linker [235], and the lipase was then conjugated to succinylated cellulose using EDC/NHS. After immobilization, the enzyme retained its activity in both BCNC and BC membrane, and the amount of protein immobilized on BCNC was 2.75 times higher than that in BC membrane. The BCNC was also employed for the immobilization of urease. The immobilized urease demonstrated superior tolerance to changes in pH (5.5–9) and temperature (30–80 °C) when compared to the free urease. Furthermore, the immobilized urease retained approximately 81% and 68% of its initial activity following 15 and 20 cycles of reuse, respectively. It also exhibited significantly enhanced storage stability for 20 days [187].
5.1.3. Crosslinking
Crosslinking is defined as the induction of chemical or physical links among polymer chains [236]. The crosslinking of materials can be achieved through a variety of methods, including physical processes, chemical processes, and enzymatic processes [237]. The chemical crosslinking method makes it possible to obtain an irreversible or permanent hydrogel. The physical crosslinking method can produce a hydrogel that can be reversed since the forces involved are hydrophilic interaction, electrostatic, and hydrogen bonding. Crosslinking improves the thermal and mechanical stability of the matrix and can be tailored to modify the release rate of the incorporated active agents [238].
The abundant hydroxyl functional groups in the BC molecular chains make BC an excellent candidate for modifications by crosslinking [202]. Crosslinking plays a pivotal role in the drying process [68,239], prevents the collapse of 3D network BC in the drying process [163,240,241], and improves water absorption [242]. In this sense, cross-linked samples may absorb water faster than pure samples, and their spongy structure results in a significant variation in surface shape compared to native samples [243]. The chemical structural similarities between BC and plant cellulose make it possible to utilize plant cellulose crosslinkers [163,240,241].
Chemical Crosslinking
Chemical crosslinking is the process of incorporating monomers into polymers or joining two polymer chains using crosslinking agents [9,244]. Several different types of crosslinking agents have been used in published studies of cellulose-based hydrogel materials. Typically, when considering crosslinking agents, one is interested in compounds that are capable of forming pairs of covalent bonds, thereby linking polymeric segments together [245].
Glutaraldehyde
GA is widely used as a crosslinking agent in biomedical applications such as enzyme and cell immobilization and hydrogel formation. It is a dialdehyde with highly reactive aldehydic groups capable of forming covalent bonds with functional groups such as amines, thiols, phenols, hydroxyls, and imidazoles [246]. The optimal concentration of glutaraldehyde for chemical crosslinking is 1.0% (v/v), which preserves the cytocompatibility of composites [247]. GA is also employed as an activation reagent [14]. The GA stabilizes the composite by forming chemical bonds between the GA and the components [248].
A number of composite materials have been developed by crosslinking BC with gelatin and fibrin for tissue engineering. The amine groups of gelatin (or fibrin) and the hydroxyl groups of BC cellulose underwent a chemical reaction, forming hydrogen bonds [249].
A composite of BC and gelatin was synthesized for use as a small-diameter artificial blood vessel. After introducing fish gelatin into the fiber network of BC tubes, GA was used as a chemical crosslinking agent. The results demonstrated that the BC/gelatin composite tubes exhibited superior water permeability and nutrient transportation through the tube walls compared to BC. All BC/gelatin composite tubes could withstand pressure three times or higher than normal human blood pressure. In vitro cell culture experiments showed that human venous endothelial cells (HUVECs) and human smooth muscle cells seeded on the inner walls of BC/gelatin tubes had improved adhesion, proliferation, and differentiation potential, as well as anticoagulant and mechanical properties. Whole blood coagulation tests showed that BC/gelatin tubes outperformed BC tubes, with all samples exhibiting a hemolysis rate of less than 1.0%, meeting international medical device criteria [247].
Artificial blood vessels have been developed based on GA-treated BC/fibrin composites. The Young’s modulus and the time-dependent viscoelastic behavior of the composite were comparable to a reference small-diameter blood vessel. However, the strain at the break of the composite material (0.33) was still significantly lower than that of the native blood vessel (1.07) [250].
In certain studies, GA was utilized as a pre-activating agent for BC, facilitating the subsequent covalent immobilization of enzymes onto the activated BC (Figure 10).
The development of an in vitro enzymatic conversion process with GAD immobilized on the BC membrane was proposed for the efficient production of γ-Aminobutyric acid. Three different immobilization methods were compared. In the case of pre-activation BC with GA followed by adsorption, the enzyme was immobilized on BC via covalent binding and retained more than 96% of its original activity after seven cycles and had 89.17% of the activity of the native enzyme. After adsorption followed by crosslinking, the GAD activity on BC was 60.1% of the initial value and decreased rapidly over seven cycles. The membrane prepared by the physical adsorption method showed the highest GAD activity (95.11%), but the enzyme activity decreased exponentially with increasing cycle number of uses [45]. In another study using lipase as a model enzyme, the two-step immobilization strategy involving BC activation by GA and enzyme binding was also the most effective. The activity of the immobilized enzyme was 93.5% of that of the free enzyme. After six reactions, the activity was 76.7% (330 U) of that in the first reaction. Immobilized and free enzymes had identical activity at different pH and temperature levels. In addition, the immobilized lipase retained 60% of its initial activity after 15 cycles of use, in contrast to the free enzyme [14].
BCNC crosslinked by GA was used for the delivery of cyano-phycocyanin (C-PC). The C-PC was loaded onto the BCNC to make a carrier that keeps the C-PC stable, increases the time of drug release, and improves absorption in the gastrointestinal tract. The crosslinking process resulted in a BCNC structure with larger pores and enhanced stability. C-PC was immobilized on BCNC by physical adsorption, reaching 65.3% adsorption in 3 h. After 24 h, 69.46% of the C-PC was released from the BC, 47.39% from the BCNC, and 43.21% from the BCNC-GA [177].
CDAP
The organic cyanating reagent 1-cyano-4-dimethylaminopyridinium tetrafluoroborate (CDAP) is used to activate polysaccharides, which could subsequently react with spacer reagents or directly with protein. In comparison to CNBr, CDAP is simpler to utilize, may be used at lower pH, has fewer negative effects and proteins can be directly linked to CDAP-activated polysaccharides [251,252]. Proteins interact with the cyanoester of CDAP-activated polysaccharides primarily through the unprotonated ε-amines of surface lysine residues, forming an isourea bond. It is possible that numerous multipoint connections may form between the polysaccharide and the protein, given that proteins have a large number of lysine residues that can react with the activated polysaccharide [253].
Acellular tissue-engineered vascular grafts have been developed based on BC. BCs were modified with heparin-chitosan, albumin, and fibronectin to encourage the growth of HUVECs or endothelial progenitor cells (EPCs). To prepare scaffolds with albumin or fibronectin, the BC surface was activated with CDAP. A portion of the BCs were coated with heparin by immersion in an EDC/NHS. BC-based grafts were characterized by good surgical controllability and burst pressure in a physiological environment. Fibronectin coating significantly promoted the adhesion and growth of VECs and EPCs, while albumin only promoted the adhesion of VECs, but the cells were functionally impaired. At the same time, fibronectin-modified surfaces were capable of capturing platelets via β1 and αIIbβ3 integrins, which can lead to increased thrombogenicity. Heparin-chitosan coating significantly improved EPC adhesion but not VEC adhesion [254].
Citric acid
Citric acid (CA) is regarded as a safe and non-toxic crosslinker. CA has been employed to cross-link nanofibers and preserve the 3D structure of BC pellicles. One potential mechanism for the crosslinking process of BC with CA is the dehydration of BC as a result of the esterification process. This reaction produces an ester group that can bind to the accessible hydroxyl group of cellulose (Figure 11) [243].
The crosslinking of BC with CA and the functionalization of the resulting material with Ni- and Mg-ferrite magnetic nanoparticles were employed for the immobilization of lipase B from Candida antarctica and phospholipase A from Aspergillus oryzae. Both enzymes demonstrated significantly enhanced thermal stability at 60 °C, with a notable retention of residual activity at 70 °C, in contrast with the free form, which exhibited a notable decline. After 10 cycles of repeated use, the analyzed enzymes retained a notable amount of residual activity. However, there was a decline in the catalytic efficiency of phospholipase A and lipase after immobilization. In addition, there was a notable difference in immobilization efficiency between phospholipase A and lipase B [191].
Physical crosslinking
Physical crosslinking has attracted much interest in hydrogel formation due to its ease of formulation and the fact that it does not require crosslinking chemicals [255]. This approach is primarily based on the generation of free radicals, which are then exposed to a high-energy source such as an electron beam, x-ray, or gamma ray [256,257].
Electron beam irradiation can be used to induce physical crosslinking (Figure 12), resulting in pure, sterile, and residue-free hydrogels. In addition, electron beam irradiation does not require the use of a radioactive source, reducing the possibility of harmful toxicity [258]. The electron beam creates patterns on BC’s surface without changing its chemical or crystalline structure [259]. BC membranes that have been irradiated are more swollen and biodegradable than non-irradiated ones [120]. These features make them excellent materials for use in drug delivery applications [106,260].
Stimuli-responsive BC-g-poly(acrylic acid) hydrogels were prepared by electron beam irradiation for the oral delivery of proteins. Bovine serum albumin (BSA) was a model protein. The cumulative release of BSA was less than 10% in simulated gastric fluid, demonstrating the ability of the hydrogels to protect BSA from the acidic environment of the stomach. Hydrogels had the maximum adhesiveness among the described hydrogels. The in vitro cytotoxicity test of the hydrogel demonstrated that the viability of Caco-2 cells was well above 90%. In addition, an ex vivo penetration study indicated that BSA penetration increased throughout the intestinal mucosal tissue, and acute oral toxicity tests demonstrated the safety of these hydrogels for in vivo applications [261].
Chemical Crosslinking
Chemical crosslinking is the process of incorporating monomers into polymers or joining two polymer chains using crosslinking agents [9,244]. Several different types of crosslinking agents have been used in published studies of cellulose-based hydrogel materials. Typically, when considering crosslinking agents, one is interested in compounds that are capable of forming pairs of covalent bonds, thereby linking polymeric segments together [245].
Glutaraldehyde
GA is widely used as a crosslinking agent in biomedical applications such as enzyme and cell immobilization and hydrogel formation. It is a dialdehyde with highly reactive aldehydic groups capable of forming covalent bonds with functional groups such as amines, thiols, phenols, hydroxyls, and imidazoles [246]. The optimal concentration of glutaraldehyde for chemical crosslinking is 1.0% (v/v), which preserves the cytocompatibility of composites [247]. GA is also employed as an activation reagent [14]. The GA stabilizes the composite by forming chemical bonds between the GA and the components [248].
A number of composite materials have been developed by crosslinking BC with gelatin and fibrin for tissue engineering. The amine groups of gelatin (or fibrin) and the hydroxyl groups of BC cellulose underwent a chemical reaction, forming hydrogen bonds [249].
A composite of BC and gelatin was synthesized for use as a small-diameter artificial blood vessel. After introducing fish gelatin into the fiber network of BC tubes, GA was used as a chemical crosslinking agent. The results demonstrated that the BC/gelatin composite tubes exhibited superior water permeability and nutrient transportation through the tube walls compared to BC. All BC/gelatin composite tubes could withstand pressure three times or higher than normal human blood pressure. In vitro cell culture experiments showed that human venous endothelial cells (HUVECs) and human smooth muscle cells seeded on the inner walls of BC/gelatin tubes had improved adhesion, proliferation, and differentiation potential, as well as anticoagulant and mechanical properties. Whole blood coagulation tests showed that BC/gelatin tubes outperformed BC tubes, with all samples exhibiting a hemolysis rate of less than 1.0%, meeting international medical device criteria [247].
Artificial blood vessels have been developed based on GA-treated BC/fibrin composites. The Young’s modulus and the time-dependent viscoelastic behavior of the composite were comparable to a reference small-diameter blood vessel. However, the strain at the break of the composite material (0.33) was still significantly lower than that of the native blood vessel (1.07) [250].
In certain studies, GA was utilized as a pre-activating agent for BC, facilitating the subsequent covalent immobilization of enzymes onto the activated BC (Figure 10).
The development of an in vitro enzymatic conversion process with GAD immobilized on the BC membrane was proposed for the efficient production of γ-Aminobutyric acid. Three different immobilization methods were compared. In the case of pre-activation BC with GA followed by adsorption, the enzyme was immobilized on BC via covalent binding and retained more than 96% of its original activity after seven cycles and had 89.17% of the activity of the native enzyme. After adsorption followed by crosslinking, the GAD activity on BC was 60.1% of the initial value and decreased rapidly over seven cycles. The membrane prepared by the physical adsorption method showed the highest GAD activity (95.11%), but the enzyme activity decreased exponentially with increasing cycle number of uses [45]. In another study using lipase as a model enzyme, the two-step immobilization strategy involving BC activation by GA and enzyme binding was also the most effective. The activity of the immobilized enzyme was 93.5% of that of the free enzyme. After six reactions, the activity was 76.7% (330 U) of that in the first reaction. Immobilized and free enzymes had identical activity at different pH and temperature levels. In addition, the immobilized lipase retained 60% of its initial activity after 15 cycles of use, in contrast to the free enzyme [14].
BCNC crosslinked by GA was used for the delivery of cyano-phycocyanin (C-PC). The C-PC was loaded onto the BCNC to make a carrier that keeps the C-PC stable, increases the time of drug release, and improves absorption in the gastrointestinal tract. The crosslinking process resulted in a BCNC structure with larger pores and enhanced stability. C-PC was immobilized on BCNC by physical adsorption, reaching 65.3% adsorption in 3 h. After 24 h, 69.46% of the C-PC was released from the BC, 47.39% from the BCNC, and 43.21% from the BCNC-GA [177].
CDAP
The organic cyanating reagent 1-cyano-4-dimethylaminopyridinium tetrafluoroborate (CDAP) is used to activate polysaccharides, which could subsequently react with spacer reagents or directly with protein. In comparison to CNBr, CDAP is simpler to utilize, may be used at lower pH, has fewer negative effects and proteins can be directly linked to CDAP-activated polysaccharides [251,252]. Proteins interact with the cyanoester of CDAP-activated polysaccharides primarily through the unprotonated ε-amines of surface lysine residues, forming an isourea bond. It is possible that numerous multipoint connections may form between the polysaccharide and the protein, given that proteins have a large number of lysine residues that can react with the activated polysaccharide [253].
Acellular tissue-engineered vascular grafts have been developed based on BC. BCs were modified with heparin-chitosan, albumin, and fibronectin to encourage the growth of HUVECs or endothelial progenitor cells (EPCs). To prepare scaffolds with albumin or fibronectin, the BC surface was activated with CDAP. A portion of the BCs were coated with heparin by immersion in an EDC/NHS. BC-based grafts were characterized by good surgical controllability and burst pressure in a physiological environment. Fibronectin coating significantly promoted the adhesion and growth of VECs and EPCs, while albumin only promoted the adhesion of VECs, but the cells were functionally impaired. At the same time, fibronectin-modified surfaces were capable of capturing platelets via β1 and αIIbβ3 integrins, which can lead to increased thrombogenicity. Heparin-chitosan coating significantly improved EPC adhesion but not VEC adhesion [254].
Citric acid
Citric acid (CA) is regarded as a safe and non-toxic crosslinker. CA has been employed to cross-link nanofibers and preserve the 3D structure of BC pellicles. One potential mechanism for the crosslinking process of BC with CA is the dehydration of BC as a result of the esterification process. This reaction produces an ester group that can bind to the accessible hydroxyl group of cellulose (Figure 11) [243].
The crosslinking of BC with CA and the functionalization of the resulting material with Ni- and Mg-ferrite magnetic nanoparticles were employed for the immobilization of lipase B from Candida antarctica and phospholipase A from Aspergillus oryzae. Both enzymes demonstrated significantly enhanced thermal stability at 60 °C, with a notable retention of residual activity at 70 °C, in contrast with the free form, which exhibited a notable decline. After 10 cycles of repeated use, the analyzed enzymes retained a notable amount of residual activity. However, there was a decline in the catalytic efficiency of phospholipase A and lipase after immobilization. In addition, there was a notable difference in immobilization efficiency between phospholipase A and lipase B [191].
Physical crosslinking
Physical crosslinking has attracted much interest in hydrogel formation due to its ease of formulation and the fact that it does not require crosslinking chemicals [255]. This approach is primarily based on the generation of free radicals, which are then exposed to a high-energy source such as an electron beam, x-ray, or gamma ray [256,257].
Electron beam irradiation can be used to induce physical crosslinking (Figure 12), resulting in pure, sterile, and residue-free hydrogels. In addition, electron beam irradiation does not require the use of a radioactive source, reducing the possibility of harmful toxicity [258]. The electron beam creates patterns on BC’s surface without changing its chemical or crystalline structure [259]. BC membranes that have been irradiated are more swollen and biodegradable than non-irradiated ones [120]. These features make them excellent materials for use in drug delivery applications [106,260].
Stimuli-responsive BC-g-poly(acrylic acid) hydrogels were prepared by electron beam irradiation for the oral delivery of proteins. Bovine serum albumin (BSA) was a model protein. The cumulative release of BSA was less than 10% in simulated gastric fluid, demonstrating the ability of the hydrogels to protect BSA from the acidic environment of the stomach. Hydrogels had the maximum adhesiveness among the described hydrogels. The in vitro cytotoxicity test of the hydrogel demonstrated that the viability of Caco-2 cells was well above 90%. In addition, an ex vivo penetration study indicated that BSA penetration increased throughout the intestinal mucosal tissue, and acute oral toxicity tests demonstrated the safety of these hydrogels for in vivo applications [261].
5.1.4. Modification of the Chemical Structure of Bacterial Cellulose
The hydroxyl groups of BC form both intra- and intermolecular hydrogen bonds, which limits the reactivity of the hydroxyl groups [262]. As a result, the hydroxyl groups cannot react directly with the functional groups present in proteins [199]. Furthermore, the loading of substances into BC by physical absorption does not result in sufficient interactions to enable long-term immobilization of protein molecules. The incorporation of functional groups [185] into the BC generates a carrier that can covalently immobilize biomolecules like proteins. For example, modification of its chemical structure of cellulose (derivatization) promotes the solubility in water of even hydrophobic derivatives [25].
The techniques for chemical modification of cellulose mostly include esterification, oxidation, etherification, carbamation, and amination, which are commonly carried out by creating reactive functional and charged groups on the surface by utilizing free hydroxyl groups [100,207]. Among the three hydroxyl groups, including two secondary hydroxyl groups and one primary hydroxyl group, the relative reactivity can generally be expressed in the following order: OH-C6 ≫ OH-C2 > OH-C3 [185,263]. Chemical coupling agents must be added to the main and secondary hydroxyl groups of the cellulose structure to immobilize proteins [44].
Oxidation
In order to regulate its degradability in vivo, BC can be selectively oxidized by oxidants, including TEMPO–NaClO–NaBr and NaIO4 [264]. The oxidized BC (OBC) membrane could act as a support matrix for the covalent immobilization of proteins [110]. For the improvement of the immobilization stability, the protein could be covalently conjugated to the OBC membrane via a Schiff base reaction [265]. It has been shown that oxidation affects the swelling and crystallinity of BC [106]. Furthermore, oxidation allows the optimization of BC degradability. Biodegradability through oxidation treatment and bioresorbability are aspects of BC that are being widely investigated for potential application in tissue engineering [266].
Periodate
Periodate ions (IO4−) can selectively oxidize secondary hydroxyl groups in cellulose (Figure 13) [267]. The aldehyde groups are useful for introducing various substituent groups such as carboxylic acids, hydroxyls, or imines [7]. In addition, the oxidation process reduces the negative charge density, increasing the possibility of intermolecular interactions with enzymes [7].
Porous 3D BC microspheres with collagen (COL) and bone morphogenetic protein 2 (BMP-2) were developed for bone tissue engineering. The resulting COL/BC/BMP-2 microspheres exhibited a porous structure with numerous interconnecting voids and a rough surface. These microspheres exhibited biocompatibility and enhanced the adhesion, proliferation, and differentiation of the mouse osteogenic cell line MC3T3-E1 cells. The adhesion rate of MC3T3-E1 cells to collagen was 86.7%, while the adhesion rate to BC was 66.7%. The mice MC3T3-E1 cells on COL/BC porous microspheres and COL/BC/BMP-2 microspheres produced more calcium nodules than the control [268].
Novel composite membranes created by Gorgieva et al. using BC membranes and gelatin biopolymers for tissue regeneration. The conjugation of OBC membranes with EDC significantly increased the physiological stability of gelatin. As a result, the resulting BC-gelatin membranes were found to have high swelling, degradation rate, and pH retention. MRC-5 cells adhered well to the porous gelatin regions of the resulting membrane, and it was not cytotoxic [139].
Double-modified BC (DMBC) was combined with soy protein isolate (SPI) to create a novel urethral scaffold. The number of adipose stem cells adhered to DMBC and DMBC/SPI material was significantly higher than that adhered to BC. The DMBC/SPI composite had low cytotoxicity but good biocompatibility on the third day of incubation with adipose stem cells. The in vivo results in New Zealand rabbits showed that DMBC/SPI did not induce an inflammatory response. Two weeks after surgery, inorganic tissue with a smooth urethral surface and continuous mucosa was produced in the DMBC/SPI group, while organic tissue was produced in the BC group. The DMBC/SPI acquired good degradation ability in vivo, while BC was not degradable in the rabbit body [143].
The DMBC was also created for the purpose of loading FGFR2-modified adipose-derived stem cells (ADSCs) for urethral repair. ADSCs are frequently employed in tissue repair due to their accessibility and capacity to release a variety of cytokines [269,270]. The secretory function and repair effects of ADSCs may be improved by targeted overexpression of FGFR2. The modification of BC involved oxidizing BC and followed sulfonation with NaHSO3. The lentiviral transfection with plasmid systems was used to create FGFR2-expressing ADSCs. The results demonstrated that the new composite with FGFR2-expressing ADSCs exhibited excellent repairability, with this ability correlated with angiogenesis. FGFR2 enhanced the osteogenic capacity of ADSCs without significantly affecting lipogenic capacity [271].
Vasconcelos et al. developed a bioactive dressing by immobilizing papain on oxidized BC (OBC) membranes. The OBC membrane was able to immobilize papain via covalent bonding (–C–NHR) and adsorption (ion exchange), with a recovered activity of 93.3%, an immobilization efficiency of 49.4%, and superior thermal properties OBC over BC. The release mechanisms of the BC–Papain and OBC–Papain membranes were anomalous and predominantly non-Fickian diffusion. The activities measured for wet oxidized BC and BC membranes showed no significant difference [110].
A spherical OBC was used as a carrier for the immobilization of industrial lipases. The stability and hydrolytic activity of lipase immobilized by covalent binding on spherical BC were significantly improved. Two optimal pH values (5 and 8) and a relatively low active temperature (30 °C) were achieved for optimal hydrolytic activity of lipases immobilized on BC, which was superior to free lipase (pH 9 and 40 °C) [184].
BC spheres also served as a carrier to immobilize the Lecitase® Ultra (E.C.3.1.1.32, Sigma-Aldrich, St. Louis, MO, USA) enzyme. The OBC spheres were incubated with PEI and saturated with a mixture of Fe2+/Fe3+ ions. The obtained spheres OBC were then activated with 1% GA to immobilize the enzyme. The maximum yield for Lecitase® Ultra immobilization was 70%. The immobilized enzyme exhibited had no significant impact on the enzyme’s KM value. The immobilized enzyme retained more than 70% of its original activity after eight cycles of use and excellent storage stability, retaining 80% of its initial activity after four weeks at 4 °C [181].
Laccase and TiO2 were added to OBC to create a composite with the biocatalytic properties of laccase and the photocatalytic properties of TiO2. Immobilized laccase outperformed free laccase in terms of pH and temperature stability. The optimal pH for dye degradation was 5.0–6.0, while the optimal temperature was 40 °C. In addition, the immobilized laccase maintained a relative activity of 67% after ten cycles. Under UV irradiation, the oxidized BC/TiO2-Laccase composite degraded 95% of the dye within 3 h [186].
TEMPO
One of the most effective pretreatments for BC is TEMPO oxidation, which selectively modifies the polymer under mild aqueous conditions (Figure 14). TEMPO oxidation creates carboxylate groups (–COO-Na+) that increase interchain electrostatic repulsion, leading to nanofibril disaggregation. BC membranes degraded by TEMPO oxidation have been investigated as stabilizers for food, topical, and pharmaceutical emulsions, replacing surfactants that often cause irritating reactions [272,273]. TEMPO-mediated oxidized BC scaffolds alone operate as potential tissue engineering scaffolds [274].
A bioadhesive composite based on the conjugation of involucrin antibody SY5 and BCNF has been developed. This system enables antigen–antibody interaction between SY5 on BCNF and involucrin exposed in the stratum corneum and epidermis. The antibodies covalently conjugated to oxidized BCNF using EDC/NHS. The BCNF–SY5 composite exhibited 2- to 3.5-fold higher adhesion than albumin-conjugated BCNF. BCNF-SY5 composite has been shown to effectively adhere to damaged skin and stimulate cell proliferation while preserving the intrinsic properties of the antibody [109].
The BC membranes oxidized by TEMPO have been utilized in the development of vaccines for aquatic animals. For this purpose, oxidized BC membranes were conjugated with ribavirin and NbE4 nanobody using EDC/NHS. The results of RT-qPCR analysis of the major capsid protein of LMBV demonstrated that the BC-ribavirin-NbE4 (BRN) therapy resulted in a notable reduction in virus abundance in infected largemouth bass. Furthermore, following the appropriate treatment, the BRN group exhibited reduced levels of inflammation-related factors. After seven days of treatment, the BRN group exhibited reduced expression levels of IRF-3 and IRF-6, indicating that IRF-3 and IRF-6 in the BRN group had returned to normal or pre-infectious levels [173].
Polymer Grafting on BC
Grafting is a process in which a parent polymer serves as the backbone, and branches of a second polymer are attached at various points. Polymer grafting increases the functional properties of the polymer by performing sulfonation, phosphorylation, carboxymethylation, and acetylation [275]. The most prevalent methods for BC modification include surface-induced atom transfer radical polymerization (ATRP), the conventional synthesis approach, and the crosslinking of silane coupling agents [174].
Silanization of cellulose materials is a unique process that serves two distinct purposes. Firstly, it is used as an independent functionalization of cellulose. Secondly, it is employed as an intermediate step to introduce the necessary functionality for further modification. A highly efficient surface functionalization approach is the direct introduction of amino groups into BC using a silane coupling technique [185]. The silane agent most commonly used in this process is (aminopropyl)triethoxysilane (APTES) [276]. APTES is used to conduct the amination of BC (Figure 15). APTES does not alter the water absorption characteristics of BC [277]. It was found that the functionalization of the BC membrane with APTES introduces a new opportunity in click chemistry [278].
Ying et al. first used silanization of BC to immobilize HRP by binding GA. The amino-functionalized BC was activated with GA, and the HRP was covalently attached via its amino groups. The activity and reusability of the immobilized HRP were compared with those of the free enzyme. The optimal pH range for immobilized HRP (pH 5.5–8.5) was greater than that of the free enzyme (pH 6–8), and immobilized HRP was well adapted to ambient alkalinity. The relative activity of immobilized HRP was found to be higher by 90% than that of free HRP at temperatures 25–40 °C. Moreover, BC-immobilized HRP was reused effectively for 10 cycles, exhibiting greater than 70% of its original activity retention [185].
A BC scaffold functionalized with laminin and growth factors was prepared as a support structure for patterning and expansion of human embryonic stem (hES) cell-derived progenitor cells. Dopaminergic ventral midbrain (VM) progenitor cells are being used in cell replacement therapies for Parkinson’s disease. The BC was modified using the silanization method, and laminin and the growth factors BDNF and GDNF were immobilized on the BC surface via a covalent bond. The functionalization of BC resulted in an improved differentiation rate of cells after plating on BC. The viability of hES-derived VM progenitor cells seeded on different substrates was not affected by cellulose functionalization, regardless of whether BC had been modified with laminin + BDNF + GDNF or laminin alone. Furthermore, the expression of early dopaminergic markers in these cells was enhanced by the growth factor functionalization of BC. The modification of BC with growth factors prevents protein leakage while also providing cells with a long-term supply of growth factors required for proper differentiation and development of VM progenitor cells [172].
An active, non-resorbable guided tissue regeneration membrane by conjugating BC with recombinant human osteopontin (OPN) was proposed by Klinthoopthamrong et al. Surface-initiated reversible addition-fragmentation chain transfer (RAFT) polymerization was used to graft PAA onto the surface of BC. Then, p-rhOPN or rhOPN (commercial preparation) was conjugated to the PAA-grafted BC. Conjugated p-rhOPN has an immobilization efficiency of 97%. Both p-rhOPN-BC and rhOPN-BC demonstrated enhanced capabilities in promoting human periodontal ligament stem cell adhesion, osteogenic differentiation, and calcium deposition levels when compared to BC alone [15].
The addition of phosphate moieties to the cellulose backbone represents a significant approach for the preparation of a diverse array of phosphate derivatives from cellulosic materials. Furthermore, phosphate-ester functionalized cellulosic materials are compatible with calcium phosphate, thereby enabling the formation of novel hybrid materials that can be utilized in bone tissue engineering and drug delivery applications. It has been widely reported that concentrated H3PO4 has been used extensively as an effective phosphating agent (Figure 16) [279].
Phosphorylated BC (PBC) was found to be an attractive adsorbent with a large adsorption capacity for proteins [280]. In the investigation of the adsorption of proteins on obtained PBC, it was discovered that it has a much larger specific surface area than phosphorylated plant cellulose (PPC). The adsorption capacity for the protein increased as the percentage of phosphorylation increased. The adsorption capacity of PBC was much higher than that of PPC, even though their phosphorylation percentages were similar [280].
5.2. In Situ Bacterial Cellulose Modification
A variety of materials, including polyaniline [281], collagen [282], hyaluronan [155,166], xanthan gum [283], CMC [284,285,286], and sodium alginate [287,288] have been already utilized to modify BC in situ. These modifications were intended to enhance the morphological and physicochemical properties of BCs for biomedical applications [202]. To date, several modifications of BC in situ have been performed for protein immobilization in tissue engineering. The application of proteins as functional molecules to modify BC during microbial fermentation, as an alternative to conventional chemical techniques, has the potential to result in a more sustainable process. The in situ-modified materials are biocompatible and can undergo decay in a regulated manner [289].
A collagen-BC composite was prepared by incorporating collagen into the incubation medium of A. xylinum. The collagen–BC composite exhibited a well-interconnected porous network structure and a large surface area required for cell attachment and vascularization. The crystal structure of BC also underwent a transformation when collagen was introduced into the incubation medium of A. xylinum [282].
A novel approach for 3D cell culture of tumor cells has recently been developed using BC. The BC was modified in situ with hyaluronic acid and gelatin to create a bioengineered tumor model containing a network of nanofibers and a human glioblastoma cell line (U251). Their findings indicated that the BC/hyaluronic acid/gelatin composite scaffold exhibited moderately hydrophilic properties, influencing cell adhesion and proliferation behavior significantly. The U251 cells exhibited normal morphology and good adhesion and demonstrated excellent viability, forming multilayered and compact cell clusters [155].
In situ modification of BC with CMC and conjugation with anti-HSA affibody was performed to use BC as a matrix for selective biofiltration of blood proteins. CMC–BC composites with conjugated anti-HSA affibodies demonstrated superior binding efficacy for HSA compared to TEMPO-oxidized BC composites. The carboxylated cellulose conjugated with anti-HSA via EDC/NHS exhibited approximately eight-fold higher HSA-specific binding capacity than the carboxylated cellulose surface with physically adsorbed anti-HSA. Affibody conjugation increased the affinity and specificity of CMC–BC tubes to capture target molecules, and the presence of CMC in the BC network reduced irreversible structural changes during the drying process [286].
The protein BslA (B. subtilis biofilm protein, bacterial hydrophobin) was employed for the modification of BC in a localized manner [289]. BslA has the capacity to form a hydrophobic film that coats the biofilm surface, rendering it water-repellent [290]. The results of in situ modification demonstrate that BslA has the potential to cause structural and mechanical changes to the BC fiber network, thereby creating a stronger, less brittle material with enhanced potential for use in a wide range of applications. However, higher concentrations of BslA and BslA–CBM have been observed to delay the formation of the BC pellicle [289].
6. Conclusions
BC has recently emerged as one of the most popular biomaterials among engineers and scientists [82]. BC is a promising flexible material with a high water-holding capacity, outstanding mechanical strength, and a low cost [24], making it an ideal solution for immobilizing a wide range of molecules, nanoparticles, and cells. Due to its unique mechanical properties, its renewable, bio-based, biodegradable properties [53], and its lack of contaminants [214], there has been considerable effort to incorporate BC into a variety of commercial products. A nontoxic, biocompatible wound dressing (Cellulose Solutions LLC), antimicrobial (Axcelon Biopolymers, JeNacell), and other commercial BC-based dressings (Biofill®, Cellulon®, and Gengiflex®) have been developed recently [25]. In addition, Kusano Sakko Inc. (KSI) has reported utilizing BC as a matrix for the delivery of anti-cancer medications with controlled drug release [50].
BC is a material that is employed in a multitude of biomedical developments due to its capacity to assume novel properties through the process of modification [291]. Recently, it has been demonstrated that the incorporation of BCNF into fish myofibrillar protein substrate can impart beneficial effects on the physical properties of the resulting films, leading to an enhancement in water resistance compared to the control film [292]. Due to its similarity to human tissues, BC has great potential for application in regenerative therapy, organ replacement, targeting the capability of drug delivery systems, and immobilizing proteins [62]. Immobilization of proteins on BC is one method to improve protein properties [293] and protection of their 3D structure and activity [49,294]. Currently, the majority of published articles on protein immobilization on BC are devoted to the creation of wound healing, tissue scaffolds, and the immobilization of enzymes. The BC has been compounded with proteins for applications such as enhancing osteoblast cell growth in bone regeneration, guiding fibroblast/endothelial cells in wound healing [295], blood vessel replacement, composite with antibacterial properties, to trap tumor cells, for developing attractive adsorbent, improving enzyme stability, etc. [25]. Several studies have indicated that BC can be used as a delivery system for both protein and non-protein drugs [293]. Immobilization could not only increase the activity of enzymes but also allow them to be reused [296].
Despite its remarkable potential, BC is currently too expensive to produce on a large scale. As a result, it is not commonly used as a replacement for plant cellulose [297]. The high production cost of BC due to expensive intermediate components, as well as the low yield and productivity, are significant barriers to the commercialization of BC-based products. The issue of high production cost due to the use of expensive intermediate components has been addressed to some extent by exploring the possibility of various agro-industrial wastes and low-cost substrates [298,299]. In addition, the use of low-cost agro-industrial waste products such as corn steep liquor in biomass production can not only reduce costs but also minimize environmental impact [300]. While biomaterials typically result in reduced greenhouse gas emissions compared to conventional materials, they may potentially contribute to increased eutrophication and stratospheric ozone depletion [301].
The yield and productivity of BC have been significantly increased through the development of modern reactors, the investigation of efficient bacterial strains, and the generation of novel strains [128,297,302,303]. Furthermore, co-cultivation represents an effective approach to enhancing BC production and obtaining modified BC pellicles [304]. It has been demonstrated that BC generated through the co-cultivation of Aureobasidium pullulans and BC producer G. hansenii exhibits 4.5–6 times greater elasticity and a 22.4% increase in BC production compared to that of BC produced through other methods [305].
Another serious problem is that unmodified BC does not have antibacterial, antioxidant, anticancer, or electromagnetic properties [306] and has low biocompatibility due to the lack of attachment sites. The physical stability and relatively low degradation rate of cellulose in the human body may pose challenges for BC in certain biomedical applications. Therefore, several strategies have been proposed to improve the biocompatibility of BC, including surface modification, porosity modification, and preparation of BC composites [307]. BC can be functionalized with polymers, nanomaterials, antibiotics, peptides, etc. [299,302,308]. A major focus of current BC modification research has been the development of cheaper and more environmentally friendly methods. Despite many years of research on BC modification, there is still much to be discovered in biological applications [174]. |
Title: Guidelines for Scrutiny of Death Associated With Surgery and Anesthesia | Body:
Introduction and background
The phrase "perioperative death or death associated with surgery and anesthesia" is vague, and because physicians at all levels are uninformed of their legal obligations, deaths resulting from invasive diagnostic procedures and anesthesia are frequently not reported to the proper authority [1]. Anesthetic death is defined as death occurring within 24 hours of administration of anesthesia due to causes related to anesthesia. However, death may occur even afterward due to its complications [2]. Surgical death is defined as individuals undergoing surgical interventions who may die during the intervention (intraoperative deaths) or in the postoperative period (following transfer from the post-anesthesia care unit to an intensive care unit or general ward). Deaths relating to the operation may occur as a result of issues quite some time after surgery. Death may be due to the disease for which the operation was performed, a complication of the operation and its anesthetic, or an unrelated factor [3].
Four subgroups can be identified based on the cause of death for these cases [4], namely: 1) Those directly brought on by the illness or injury that the invasive operation or anesthesia was used to treat; 2) Those brought on by an illness or problem other than the one for which the surgery was carried out; 3) Those brought on by a mistake or complication that arose during the invasive operation; 4) Those brought on by a mistake or complication that arose while performing an aesthetic operation.
The guidelines are methodically formulated statements grounded in the most reliable data to aid and encourage the choices made by practitioners. The guidelines function as instruments for executing and propagating optimal methodologies. The Royal College of Pathologists gave the guidelines "Guidelines on autopsy practice: Postoperative deaths" to cover most typical situations. Nevertheless, not every pathology specimen or clinical situation can be considered by those standards. It may, therefore, be necessary to occasionally deviate from this guideline's recommended approach to document a case in a manner most advantageous to forensic doctors, authorities, and the family of the dead [3]. They adhere to standard postmortem examination criteria when it comes to analyzing deaths involving surgery and anesthesia, although some of them specifically address such deaths (Table 1).
Table 1
Guidelines for investigating deaths related to surgery and anesthesia
ACP: Advance Care Planning
Guidelines
References
1
ACP Best Practice no.155: Pathological investigation of deaths following surgery, anesthesia, and medical procedures
[5]
2
Guidance for pathologists conducting postmortem examinations on individuals with implanted electronic medical devices
[6]
3
Guidelines on autopsy practice: Sudden death with likely cardiac pathology
[7]
4
Guidelines on autopsy practice: Autopsy when drug overdose or poisoning may be involved
[8]
5
National Confidential Enquiry into Patient Outcome and Death (NCEPOD)
[9]
6
Guidelines on autopsy practice: Postoperative deaths
[10]
7
Guidelines on autopsy practice: Autopsy examination following bariatric surgery
[11]
8
Guidance for doctors completing medical certificates of cause of death in England and Wales
[12]
9
Guidelines for best practice: Principles for radiographers and imaging practitioners providing forensic imaging services
[13]
Introduction
Postoperative fatalities, which happen during or within 30 days of a surgical procedure, are common. Forensic pathologists may encounter challenging autopsies during the investigation of these fatalities. The patient often has a long clinical history; their anatomy is deformed by disease and therapy, and many clinical questions need to be answered [14]. The lack of an agreed-upon definition of anesthesia and perioperative-related deaths is a serious difficulty in the forensic area, and the terms "operative deaths" and "anesthetic deaths" are sometimes used interchangeably in the medico-legal novel. From the perspective of forensic pathology, at least, these incidents involving perioperative mortality permit a fine differentiation between natural and unnatural death. In this sector, iatrogenic deaths can be divided into multiple groups based on whether they resulted from the surgical process itself (e.g., perioperative cardiac and cerebrovascular events) or from the adverse circumstances of patients' prior conditions [15]. When a patient is moved from the post-anesthesia care unit to an intensive care unit, they may pass away during the procedure (intraoperative fatalities) or after (postoperative deaths) [16]. Complications that develop after the procedure may be the cause of death due to the procedure. Death may result from an unrelated source, a consequence of the operation and anesthesia, or the disease for which the procedure was conducted. The next autopsy will differ in complexity; therefore, a comprehensive, methodical approach is necessary. With the coroner's approval, it could be advantageous to have the operational surgeon and other physicians engaged in the patient's care attend the autopsy, even though the pathologist performing the autopsy should reach their conclusions [10].
A vast and growing list of disorders can be surgically treated, palliated, or cured, making surgery a vital component of health care systems. Three hundred thirteen million surgical procedures are performed worldwide every year. In low- and middle-income nations, emergency patients account for 60% of surgical operations performed annually, yet an additional 143 million surgical procedures are required to prevent disability and save lives. Surgical operations are not without risk, and there is always a risk of death owing to the process or the anesthesia used during the treatment, as well as postoperative complications when considering the patient's condition at the time of surgery [17]. Ischemic heart disease and stroke are thought to be the two leading causes of death worldwide, with postoperative deaths coming in third. Half of postoperative fatalities occur in low- and middle-income countries, even though 4.2 million people die within 30 days of surgery each year, and postoperative fatalities account for 7.7% of all deaths worldwide [18]. The postoperative mortality rate for elective procedures in Africa is twice as high as the global norm, according to the Lancet Commission on Global Surgery (LCoGS) [19]. Usually, with the aid of an autopsy, the proper medico-legal authority (coroner or procurator fiscal) will look into intra- and postoperative deaths. It is commonly known that a well-executed autopsy will identify a huge number of cases in which death could have been prevented if the right diagnosis had been made earlier in life [20]. An autopsy can help establish or disprove the clinical diagnosis of the cause of death in cases where malpractice is suspected. It can also reveal pathologies and complications that were not suspected during the patient's lifetime but may have affected the cause of death and management choices [21]. One would expect the autopsy to be carried out to a high standard given the frequency of these deaths each year and the fact that these investigations are of interest to the bereaved relatives of the deceased as well as to the surgeons, anesthesiologists, and other staff members who cared for the patient during his or her life [22]. Excluding reports involving suspected homicide, the National Confidential Enquiry into Patient Outcome and Death (NCEPOD) evaluated the quality of coronial autopsy reports in 2006 and found that over 25% of them were inadequate or unsatisfactory. The Royal College of Pathologists recently released guidelines to raise the standard of autopsies performed during and after surgery [9].
Investigating the cause of death prerequisites broad discussion between forensic pathologists, surgeons, and anesthetists in order to arrive at the best consensus of opinion to offer the investigating authority and courts of law. They must come out with protocols to be followed in different clinical situations. This review aims to demonstrate the guidelines for investigating deaths related to surgery and anesthesia, with a focus on deaths after bariatric surgery, individuals with medical devices, drug overdoses, and anesthesia-related deaths. The significance of the autopsy report and the interpretation of the cause of death are also considered.
Review
Postmortem examination (autopsy)
Objectives of the Autopsy
An autopsy is performed when a patient passes away following surgery in order to know the cause of death and the influence, if any, that the procedure may have had on it. Surgical or postoperative deaths can occur for several reasons. The investigator's role is to answer the following questions [23]: Was the medical procedure responsible for or contributed to the death, and if so, how? Did the death result from a natural disease or other preexisting conditions, and if so, how? Did the patient provide a correct pre-surgical assessment, especially regarding the nature and severity of the extent to which the procedure was being performed and the operating risk? Was the quality of medical care before, during, and following the procedure sufficient? The pathologist or investigator will respond to the preceding questions based on the operation's nature, the patient's age, comorbidities present or absent, and the time between surgery and death.
Preparation for Autopsy and Postmortem Examination
The pathologist should be able to identify the main topics for study after carefully reviewing the case; if there is any ambiguity, further evaluation and consultation may be necessary [5, 24]. The investigation of surgical and anesthetic-related deaths brings up certain difficulties. The autopsy may be technically challenging, and there may be few or no gross morphological findings due to the surgical procedure. Normal anatomy can be distorted by exudates, infections, adhesions, hemorrhage, and edema; therefore, it is critical to understand all previous surgeries. It is important to consider keeping blood and bodily fluid samples after death. While biochemistry and hematology laboratories may discard samples immediately, blood transfusion and serology laboratories normally keep samples for days or weeks. These could be useful in evaluating creatine phosphokinase activity in malignant hyperthermia, confirming disseminated intravascular coagulation or identifying problems with blood transfusion.
The reporting pathologist should also go over both recent and older histology specimens. Requesting laboratory test data obtained during life but unavailable at the time of death can be helpful. Although interpretation may be difficult, blood cultures and cardiac enzyme levels may provide useful information regarding the cause of death. Several medical devices, such as subcutaneous pacemakers, nasogastric tubes, urine catheters, wound drains, chest drains, and metal or plastic prostheses, may have been implanted in the patient. No article must be removed before the autopsy, so pathologists must make advance arrangements with wards, intensive care units, imaging departments, and operating rooms to avoid any of these from being removed. It may be necessary to confirm the location and patency of all these devices during necropsy. All anesthetic machines must use the same technique. When there are many tubes around, things can go wrong. Mistakes may occur, for instance, if a prescription drug is injected into the wrong tube, like a central venous line filled with stomach medication.
Timing of the Autopsy
The role of infections in the cause of death is a prominent concern in autopsies looking into postoperative deaths. Within an hour of death, postmortem bacterial translocation and putrefactive decomposition start, which ultimately results in corpse breakdown. Thus, the necropsy can be started following a comprehensive examination of the patient's antemortem history, including pertinent serum investigations and positive and negative cultures. The proper personal protection equipment must be used to prevent catching an infectious disease or transferring it to others [25]. It is often desirable to start the autopsy as soon as possible.
Important Information Before the Autopsy
Ideally, autopsies for perioperative deaths ought to be carried out only following a review of the clinical history. This aids the autopsy pathologist in organizing the autopsy examination and deciding which questions to try to answer during the autopsy. The surgical, anesthetic, and nursing teams should be consulted for any concerns. According to Jenkins et al., it is important to be particularly aware of the following details when examining the clinical history [22]: pre-admission and admission clerking (prior diagnosis, medication history, and allergy history); Obtaining consent forms prior to the procedure; operation notes; drug identification cards; postoperative treatment; any results from laboratory chemistry, hematology, microbiology, or antemortem histology (they help provide a complete picture and may avoid additional tests).
Autopsy Dissection (Postmortem Examination)
External examination: After a perioperative death, an external examination usually determines the route to internal structures and any devices present. Consequently, a complete physical examination is necessary to detect any surgical incisions or scars, cannulae, and previous sites of line, catheter, drain, fixator, etc. If relevant, these should be noted along with their size and any indications of infection, poor healing, or other illnesses [20, 26].
Saukko et al. recommended documenting all anesthesia and surgical equipment, preferably on a body chart [27]. Similarly, precise anatomical descriptions and dimensions should be recorded for all surgical incisions and scars, including those from ileostomies and colostomies. Documentation is necessary to determine the kind and state of any current dentition and the extent of cyanosis, jaundice, and edema. Skin rashes are common and can be severe, especially when immunosuppression, septicemia, internal malignancies, and drug reactions are present. All bruise regions and locations related to probable external device insertions should be noted. Other reasons for bruising, such as anticoagulant therapy or thrombocytopenia, should be thoroughly investigated.
Nutritional status, edema, jaundice, tissue swelling, and ulceration should all be considered when assessing general body health (often decubitus). Photography can reveal signs of a sepsis-related condition, such as marbling and disseminated intravascular coagulation. If sepsis is suspected, relevant microbiology samples should be collected before opening the body [28].
Internal examination (autopsy procedures): A standard Y-shaped incision should be made to open the body methodically. Digital autopsy and digital photography are combined in postmortem computed tomography (PMCT). A methodical approach to evisceration is necessary; the body should be opened using a standard Y-shaped incision unless the scope of the interior inspection will be restricted after PMCT.
The importance of digital autopsies and photos: According to Vester et al., PMCT is a growing trend in digital autopsies [29]. While it is widely utilized worldwide, it is not yet available in every center. It has been demonstrated that PMCT reduces the need for intrusive autopsy examinations in patients who passed away naturally. It can identify mortality connected to infections when used with minimally invasive sampling. It has been suggested in recent years to employ PMCT as a non-invasive, quick, inexpensive, and widely accessible method of figuring out the cause of death. This could enhance mortality statistics, healthcare quality management, preventative programs, and perhaps even the identification of genetic illnesses that have already been discovered. When an autopsy is not a possibility due to trauma, PMCT has shown to be a useful substitute. In addition to helping with cause of death diagnosis, PMCT can help identify underlying disorders.
Incisions: The standards of a routine autopsy will usually apply to the dissection of the body; no equipment or prior surgical or medicinal intervention will be used. The type and placement of the primary incision will depend on each case's specifics and personal preference. It is customary to start incisions for the head and neck area behind each ear, proceed over the posterior-lateral aspect of the neck, and cross the clavicles over the outside third of the neck. Then, a curve connecting these two incisions is made, meeting above the sternum. This combination makes the neck structures visible and makes installing any indwelling devices precisely possible [30].
Usually, the midline anteriorly is where incisions are done in the chest and belly. To ensure proper placement assessment, carefully dissecting inserted devices is important. Air embolism, pneumothorax, and surgical emphysema must be ruled out at this early stage. All body organs and cavities are visible after the anterior rib cage is removed, and the skin is reflected before further dissection. All pus, hemorrhages, effusions, and other fluids should be documented and sampled. Some bleeding is normal at surgical sites, although more than 250 ml is uncommon [27].
A thorough autopsy necessitates knowledge of the previous surgical interaction since once lines and electrodes are cut, they can never be fully evaluated. Photographs taken during dissection often yield valuable information for evaluation later [31, 32].
According to Diac et al., in order to prevent contamination from external sources, blood and microbiological samples should be obtained before further dissection [33]. Getting blood cultures from the heart or spleen after the surface has been scorched to eliminate impurities is ideal. After cardiothoracic surgery, sepsis should be looked for in the thorax, subphrenic, and pelvic regions and surrounding the surgical site in cases where septicemia is known. It is important to rule out immunodeficiency, malnourishment, persistent drinking, and malignancy as probable causes of septicemia. To educate them and evaluate the case within the framework of the entire institution, hospital-acquired infections should be revised with the infection control unit. After abdominal surgery, the intestines should only be examined very carefully. Autolysis can complicate appearances, particularly in cases where anastomosis sites have recently undergone suture lines. During a necropsy, careless tissue handling could rupture important structures and impede an accurate assessment. Accurately detecting local postoperative problems can greatly benefit from the surgeon's presence. Surgical sites must always be inspected in situ before dissection; otherwise, artifactual abnormalities in organs and anastomoses may occur [34].
Organ removal (evisceration): As usual, the body cavities must be opened sequentially. Incisions made outside conventional protocols may be necessary to check for drainage lines and other devices in the subcutaneous and dermal tissues, depending on the situation [35]. Unconventional methods of opening the bodily cavities may also be used to check for implant-related issues. It is often better to assess the position and status of the device before the beginning of evisceration, like disembowelment, rather than after organs and tissues have been removed (e.g., bleeding, infection, misplacement). Photography of any pathology is helpful as the situation develops [9, 27].
Evisceration should, therefore, include [22]: i. accessing the internal organs; ii. physical examination of the contents of bodily cavities before organ removal; iii. observing the location of the device and whether local issues are present; iv. removal of devices such as nerve stimulators, pacemakers, and defibrillators. This might be simpler if radiology has established the devices' location and range. A thorough inspection of the tissue parenchyma may reveal lesions such as scarring and infection because the devices traverse the boundaries of skin and other tissue zones; v. checking of surgical anastomoses for dehiscence indications. It is crucial to avoid applying tension or traction to the anastomotic site when dissecting anastomotic sites, especially those in the intestine, as this could unintentionally rupture the tissues.; vi. Obtaining pictures of devices and organs in situ; vii. organ removal as usual and being cautious not to induce artifactual dehiscence or place tension on anastomoses.
Specific issues to be considered during postoperative death autopsy
Surgical Scars
Scars should always be mentioned in the report, even if only to state that there are or are not visible surgical scars. It is not necessary to categorize scars as tiny, medium, or large based on the anatomical location in which they are located. Scars can be raised, flat, curved, uneven, round, or linear, among other shapes. If there are several scars in a case of surgery-related death, it is helpful to have a distinct paragraph for scars. Scars should be thoroughly described, including the age and whether or not there are any indications of infection [34].
Organ Preservation
In most situations, entire organ preservation is unnecessary, particularly when imagery is available. According to the Guidelines on Autopsy Practice: Sudden Death Due to Cardiac Pathology, the heart may be preserved after difficult cardiac surgery for extensive examination by a cardiac pathologist [36].
Suspected Viral Infections
Virology samples do not have to be kept with every autopsy. Samples should be taken for virology whenever there is a possibility that a viral infection caused or contributed to the death. It is necessary to have pieces of brain tissue, myocardium, and solid lung tissue in addition to a nasal or bronchial swab. For analysis, save any pertinent fluid specimens, such as cerebrospinal (CSF) fluid. Caution must be exercised when dealing with suspected COVID-19 cases [37].
If an autopsy is performed on a suspected COVID-19 case, the following postmortem swab specimens should be collected and and tests should be performed for SARS-CoV-2 testing [38]: swab of the upper respiratory tract: nasopharyngeal swab; lower respiratory tract swab: swab from each lung. For testing for clinical diagnostic detection of SARS-CoV-2, reverse transcription polymerase chain reaction (RT-PCR) remains the "gold standard." In case of influenza virus and other respiratory pathogens, postmortem swab specimens should be collected. For additional postmortem microbiologic and infectious disease testing, autopsy tissues from the lung, upper airway, and other major organs that have been formalin-fixed paraffin-embedded (FFPE) (e.g., heart, liver, kidney) must be collected. In some cases, submitting FFPE autopsy tissues to the CDC for SARS-CoV-2 testing may be necessary.
Tissue Specimens for Histopathological Examination
Lucas et al. addressed how gross examination may miss infections, ischemia, and embolic events; as a result, it is usually advisable to have the major organs, generally the heart, lungs, liver, and kidneys, histopathologically examined. Although it may not always be necessary to sample these four primary tissues, it can yield valuable insights into the dying individual's overall pathophysiology and any possible contributing factors to their death, such as sepsis, disseminated neoplasia, or disseminated intravascular coagulation [39].
Heart samples can include one or more native/graft vascular segments. These segments can usually be placed in the same block, but thorough investigation often necessitates decalcification. One or two cardiac tissue blocks may be used for examination, but it may also include many samples from the tissues at the midventricular level and the cardiac conduction tissues (pacemaker cases) [3].
Tissue Sampling
Recommended tissue sampling according to Rous et al. [3] is described in Table 2.
Table 2
Recommended tissue sampling
Source: [3]
Organ
Recommended sampling
Heart
Five blocks from a mid-horizontal slice: the anterior and posterior right ventricles and the four quadrants of the left ventricle. If stenosed, epicardial coronary arteries
Lungs
If a death has occurred following orthopedic surgery, one sample from each lower lobe is used for the frozen section, and Oil Red O staining is used to look for fat emboli.
Spleen
Whole organ
Liver
250 gm of tissue
Pancreas
Collect a sample if pancreatitis is suspected or if death occurs after pancreatic surgery.
Kidney
100 gm from both kidneys
Brain
100 gm of brain tissue
Bone
The lumbar vertebral bone is where surgery has been undertaken to treat osteoporotic fractures. This also allows CD68 assessment of marrow in cases of sepsis.
Other
Anastomotic sites, septic foci not already sampled, histopathological examination of any specimens removed at surgery
Toxicological Analysis
If the clinical history indicates that a drug overdose or metabolic disease (such as ketoacidosis) was the cause of death, samples ought to be gathered for toxicological examination. The pathologist should look into the availability of blood samples obtained during life, as these are frequently the most important for analysis. Blood, urine, vitreous humor, and stomach contents ought to be taken at the autopsy [15].
Toxicological analysis samples are always taken from a complete autopsy. Nonetheless, "needle autopsies," which are minimally invasive techniques for taking bodily fluid samples in place of a full-scale autopsy, have been used in some labs. Blood and urine from "needle autopsies" form the basis for 40%-60% of all toxicological investigations [40].
Microbiology Analysis
Before opening the body, blood samples for culture from the neck, veins, or the heart must be taken if the clinical history suggests sepsis as the cause of death. Extra samples may be necessary if the history and macroscopic findings indicate a specific infection focus. Bacterial samples for microscopy, culture, and sensitivity must be collected when there is a clear focus. If a patient's history suggests sepsis but there is no clear focus, the following samples mentioned in Table 3 must be collected [10].
Table 3
Recommended microbiological sampling
Source: [39]
Tissue
Samples
Blood
0–20 ml from the neck veins or heart must be aspirated before opening the body
Lungs
One sample from each lower lobe.; each sample must be collected using new sterile instruments.
Spleen
One sample to be collected with sterile instruments when opening the abdomen
Bile
5–10 ml to be aspirated from the fundus of the gallbladder on opening the abdomen
Urine
5–10 ml to be aspirated from the fundus of the urinary bladder either before opening the body or upon opening the abdomen. The bladder is to be opened using sterile instruments and a bacteriological swab if the bladder is empty.
CSF
5–10 ml to be aspirated from the cisterna magna before opening the body
Others
Any apparent focus of infection is to be swabbed; The sites of occult sepsis – discitis, psoas abscess, and middle ears are to be examined
Autopsy Imaging
To determine the accurate cause of death and provide pre-dissection information, autopsy imaging is utilized. Postmortem imaging, which excludes pathological dissection, is simply imaging that is done after a person has passed away. Conversely, autopsy imaging offers imaging results that direct dissection and feedback autopsy imaging based on dissection results serves a supplementary function [41]. For many years, body imaging has been possible. Plain X-rays have traditionally been employed, although postmortem computed tomography and magnetic resonance imaging are now available to varying degrees. An external evaluation is recommended for autopsy cases requiring imaging assessments before proceeding to radiological review unless the mortuary scans all cases. This is especially useful for assessing bone structures and bodily compartments using transverse sections or reconstructed images after numerous traumas. Injecting dye into veins or hollow structures can help identify leaks, thrombosis, and blockages [42]. Current guidelines from the Royal College of Pathologists/Radiologists state that a comprehensive exterior examination of the body by a medical practitioner with the necessary training and credentials should be done before performing an imaging-based postmortem examination [43].
Guidelines for performing autopsy: special cases
Autopsy Examination After Bariatric Surgery Following Guidelines on Autopsy Practice (April 2019)
In such a case, the autopsy's role is to determine whether the bariatric surgical treatment caused the death, whether it was unrelated, whether it was natural, to assess the surgical anastomoses' integrity, and to find any comorbidities that might have aided in the patient's demise. Bariatric patients present a unique health and safety problem for the mortuary. Trays, trolleys, and tables must be able to support more weight, but refrigerated rooms must also be the proper size to accommodate a patient. The mortuary should ensure it has the tools needed to weigh and transport an increasing number of bariatric patients, and the scales must be calibrated regularly. If local storage is full or otherwise unavailable, contingency plans, such as service level agreements, should be in place to serve bariatric patients at other facilities [3]. Performing a bariatric postmortem requires substantial manual handling. A lateral movement is required to convey the corpse from the trolley to the postmortem table. Transferring 15-20 kg at waist level is advised by manual handling rules; this requires many workers to guarantee safety. The deceased is rolled so the pathologist can examine the back. Exercise caution to reduce the danger of musculoskeletal issues. Future mortuary designs could benefit from trays that lock onto tables to accommodate bariatric patients. Obese patients face technical challenges during evisceration due to increased subcutaneous fat, which can hinder skin reflection.
Additionally, anatomical pathology technicians and pathologists may struggle to visualize the blade during the procedure. Reducing neck extension makes removing the brain through a respectable incision more difficult. Excess soft tissue might cause the sutured skin incision to pull open, making body restoration more challenging. Local practice involves stitching the deep fascia before the skin to reduce tension on the suture line [11].
Postoperative Death When Drug Overdose or Poisoning Is Suspected
When drug overdose or poisoning may be present, an autopsy is performed [8]. The purpose of an autopsy is to determine whether a drug or toxin caused the postoperative death, whether it was due to another cause (such as positional asphyxia/pneumonia or a combination of both), what the pathological effects of drug use or misuse were, whether prior drug use caused any traumatic injuries, whether there was a preexisting illness that could have made a person more susceptible to the effects of a drug or toxin, whether the toxicity could have been treated to avoid death, and to gather suitable samples for toxicological analysis. Information needed before the autopsy is crucial since it will be included in the final report, along with relevant case history and information source data. It is impossible to overstate the value of a comprehensive history. The toxicology lab should receive all samples that have been gathered (Table 4).
Table 4
Types of samples for toxicological analysis with a special intimation
Source: [8]
Samples
Blood
In most postmortem cases, blood remains the most important specimen to analyze. At least 10 ml of peripheral blood (femoral or iliac) is suggested.
Caution: the gels used in many serum gel tubes may absorb drugs and thus affect the blood concentration.
Urine
At least 20 ml of urine should be collected in postmortem cases.
Vitreous humor
Samples should be collected routinely in appropriate cases. At present, vitreous humor is used primarily to quantitate ethanol, urea, electrolytes, and beta-hydroxybutyrate.
All vitreous humor from both eyes should be collected; however, it can be collected into a single container. Following removal, the shape of the eyes can be restored by injecting water.
Gastric contents
The most important investigation is the observation of undigested pills and tablets. If these are present, they should be separated and placed into plastic pillboxes for analysis.
Stomach content is heterogeneous. If only an aliquot of stomach content is collected, the total volume/weight should be recorded.
Other samples
Injection site (skin)
It may be useful in determining the type of substance that has been injected, such as insulin or heroin. Again, it is rarely required but needs to be considered. Always send a control site sample for comparison.
Excise a wide skin ellipse down to subcutaneous tissue to sample the injection site. Place the specimens in clean, labeled universal containers. If the specimen is for histology, add neutral buffered formalin. When fixed, examine and serially slice; if a tract is not identified, submit the entire specimen for histological examination. Otherwise, do not fix the specimen; instead, send the specimen immediately to the laboratory.
Lung tissues
Approximately 2 cm cubed, sealed in a glass airtight container or universally wrapped in parafilm, may be useful.
Bile
It can be useful for screening (but not quantitation) if no other samples are available.
Liver (deep within right lobe)
It can be useful for screening, but quantitation is hindered by poor databases of reference values.
Wilkins et al. state that the following are examples of ideal sampling for the majority of medicinal and illegal drugs: antemortem samples (blood and urine); postmortem femoral/iliac venous blood; postmortem urine; vitreous (ideally fluoride oxalate preserved) [8].
Guidelines on Autopsy Practice on Individuals With Medical Devices
The following are Johnson et al.'s guidelines for pathologists performing postmortem investigations for patients who have implanted medical devices [6].
Risk assessment for the examination: The use of implanted defibrillators, which have the potential to shock anyone who comes into contact with them if they are not deactivated before a postmortem, has brought attention to potential risks for pathologists and other mortuary staff. Therefore, before beginning a postmortem, it is imperative to ensure that any such device is completely disabled [44]. Medical equipment can be divided into two major categories. The first category consists of the well-known intrauterine contraceptive devices and metal prosthetic devices used in joint surgery. Moreover, mesh devices are used to rebuild the abdomen and, occasionally, the perineum. Considering any signs of sepsis and evident sclerosis near these things is crucial [31]. According to Johnson et al., the second type of device is more interactive and includes traditional permanent pacemakers. However, defibrillator pacemakers are already widely used in medical therapy. Standard autopsy ought to reveal the existence of auditory loops, bladder stimulators (for spinal/cord sickness), and central nervous system stimulators for epilepsy.
Guidelines for pathologists who examine individuals who have implanted electronic medical devices after death [6]: An autopsy pathologist must consider the removal of bone-lengthening devices, such as fixation, before cremation. Penile pumps are among the strange equipment found in unusual places. Saukko et al. suggest that these can cause fatal occult sepsis [27].
Guidelines on Autopsy Practice: Procedures in Anesthesia-Related Deaths
These are according to Helbert et al. and Attri et al. [5, 45]. In these cases, the main goal of the necropsy is to confirm or rule out natural sickness. It is difficult to investigate the pharmacological aspects of mortality connected to anesthesia. Studies on toxicology are usually useless, particularly when it comes to overdosing on drugs like barbiturates or adrenaline (epinephrine). Blood gas measurements cannot demonstrate hypoxia, and quantitative evaluations of volatile chemicals during necropsy are unreliable. When malignant hyperpyrexia is suspected antemortem blood samples must be drawn and examined.
The patient might have had several anesthetic devices during treatment, such as electrodes, catheters, cannulas, and endotracheal tubes. After death, things must stay where they are to guarantee correct placement and patency. Pre-necropsy imaging can assist in determining accurate anatomical positioning in cases when clinical data casts doubt on the location. Although esophageal intubation is uncommon, a primary midline neck incision can confirm it. A ring of edematous esophageal mucosa may be visible close to the inflated component if the position was adjusted after death.
Distention of the stomach and intestines can be caused by anesthetic gas. However, it is usually impossible to determine the exact source. An extensive histological investigation is advised in order to rule out occult intercurrent disease and gauge the extent and severity of the condition that needs to be surgically removed. Certain anesthetic effects, such as halothane hepatitis, can also be verified by histology. For neuropathological examination, keeping and analyzing the intact brain is essential, yet many deaths happen before hypoxia-related changes manifest.
Local and epidural anesthesia: Pöpping et al. documented that local anesthetics rarely cause death. The main risks include hypersensitivity and escape of adrenergic medications, which can lead to overdose. Diffusion from the injection site can result in vasoconstriction and cardiac collapse. Anesthetic concentrations in CSF fluid from necropsies should be examined. To examine the placement and patency of epidural catheters, a dye must be used and be carefully dissected [46].
Malignant hyperthermia: The diagnosis of malignant hyperthermia is based on clinical evidence, as there are no notable necropsy findings. Antemortem blood testing can verify the increased creatine phosphokinase and aldolase activity in 70% of carriers. It is advisable to test other family members for the condition. Certain anesthetic medications may present challenges when administered. Some anesthetic drugs can cause difficulties during administration. Repeated use of halothane anesthesia might lead to hepatitis. Excessive use of barbiturates during induction, including thiopentone (thiopental), can cause cardiac arrest [47, 48].
Autopsy report and clinicopathological summary
These are as stated in the Guidelines on Autopsy Practice: Postoperative Fatalities (May 2019) and ACP Best Practice no. 155: Pathological Investigation of Deaths Following Surgery, Anesthesia, and Medical Procedures (2000) [49,5]. Guidelines for autopsy reports have been established, including standards for substance and timeliness. Individual cases may require specific reports based on the surgical treatment performed. All reports should include three parts of information [50] which are as follows: actual findings of the autopsy; a brief explanation of the cause of death given; take into account if the death happened because of or despite the surgery; consider if the autopsy indicates any surgical or anesthetic error indications; if the cause of death and pathology are unclear, seek assistance from a more competent pathologist.
Explanation of These Findings and Conclusions
The pathological sequence should be logically laid forth in the final autopsy report and the clinicopathological summary. Clinicians, family members, solicitors, and members of the public and press will all closely examine the autopsy report. With input from surgeons, anesthesiologists, and other clinicians, a multidisciplinary approach may help the pathologist determine the most plausible and reasonable cause of death. In certain perioperative and postoperative deaths, the cause of death may not always be ascertained by the autopsy alone. One area fraught with difficulty is the declaration of the "cause of death." The British format is a recognized format that provides the reason for death. It indicates what caused the death; whether it was a disease or illness that caused death directly; other diseases or conditions, if any, leading to death; significant conditions that were not associated with the disease or condition that caused the death but contributed to its outcome.
Uncertain parts should be written in the opinions section, whereas genuine elements can be stated in the cause of death. If the cause of death cannot be identified, phrases like "undetermined" or "multiple factors" can be used. The ultimate phrasing is up to the competent medico-legal authorities.
Many pathologists have difficulty pinpointing the cause of death in postsurgical fatalities. The main question is whether the death would have happened without the procedure. In some circumstances, this may be impossible to identify. Any doubt should be clearly stated in the necropsy report. It might be challenging to determine the role of trauma and surgery in a death. When a patient dies from a sickness not related to the operation, it is important to distinguish between known and unknown disorders. When the disease is present, examine whether the operation was justified.
Unknown illnesses are only a concern if appropriate measures are not taken to identify common risk factors. In rare cases, a surgical or anesthetic procedure failure could be fatal. This could have been an accident, an inadvertent consequence of an extraordinarily challenging operation, anomalous anatomy, or malfunctioning equipment. Legal action for negligence may be taken if there is any possibility of individual error or incompetence and the pathologist must take great care to provide a thorough, unbiased, and objective autopsy report.
The autopsy report must meet the demands of several recipients, including medico-legal authorities, clinicians, family doctors, relatives, hospital management, medical insurers, and lawyers representing multiple parties. While it is hard to produce a report with a vocabulary everyone can understand, writing as simply as possible to convey technical information is important. The local coroner or equivalent medico-legal authority controls access to autopsy reports, which is contentious.
Local norms are crucial for conducting necropsies during surgical operations and anesthesia, which can pose confidentiality and disclosure challenges. Pathologists and doctors require adequate medico-legal training to collaborate with local coroners effectively.
Examples of cause of death opinions or assertions
Since surgery is a contributing factor to death, it needs to be taken into account when calculating the cause of death. It is not always a sign of error when surgical treatments are included in the cause-of-death determination (Table 5).
Table 5
Common causes of death formulation contributed to death after surgical interventions
Source: [9]
System
Common causes of death
Common pathologies that are less often the direct cause of death
Cardiac
Cardiac tamponade, right ventricle rupture by temporary pacing wire, and complete heart block following myocardial infarct
Ischemic heart disease, hypertensive heart disease, cardiac air embolus, and central venous catheter placement
Respiratory
Pulmonary thromboembolism, hemothorax, laceration of the right subclavian vein, thoracoscopic pulmonary lobectomy for adenocarcinoma of the lung, developmental respiratory diseases, neoplasms of the respiratory system, pulmonary heart disease or diseases of pulmonary circulation, sleep-related breathing disorders, and diseases of the respiratory system complicating pregnancy, childbirth, or the puerperium
Gastrointestinal
Anastomotic breakdown, particularly bowel fecal peritonitis, ischaemic anastomotic dehiscence, and sigmoid colectomy for adenocarcinoma of the sigmoid colon
Chronic obstructive pulmonary disease exacerbation following abdominal surgery and reduced mobility following right hemicolectomy for adenocarcinoma of the ascending colon
Bone
Bone cement implantation syndrome, cemented right hip hemiarthroplasty, and osteoporotic fracture of the right femoral neck
Reduced mobility and osteoporotic fracture of the left femoral neck (operated)
Sepsis
Bacterial sepsis (including pneumonia, urinary tract infection, wound infection, and peritonitis), and Escherichia coli septicemia
Escherichia coli-infected sacral pressure sores
Miscellaneous
Hemorrhage, potentially at any site
Intracranial hemorrhage, malignant hyperpyrexia, halothane hepatitis, drug-induced hepatitis (e.g., antibiotic-induced), and anaphylaxis
Medico-legal aspects of perioperative deaths
When a patient passes away during a surgical treatment carried out under anesthesia, the surgeon or anesthesiologist is frequently falsely blamed for the death. When a patient passes away while undergoing surgery while sedated, the anesthesiologist or surgeon should notify the police right away so that an inquest can be held. All fatalities after surgery and anesthesia should be considered unnatural and reported to the police. During the trial, the chief judicial officer may evaluate the following questions [15]: 1. Anesthesiologists are responsible for attending patients the day before surgery, doing a pre-anesthetic check-up, and conducting investigations. Before consent, the anesthesiologist should fully explain the operation, anesthetic drug, side effects, complications, and hazards to the patient; 2. Informed written consent: Before administering anesthesia, anesthesiologists must get written consent; 3. Anesthesiologists must use reasonable expertise and care while selecting anesthetic agents and performing procedures. Anesthesiologists must conform to conventional practices and institutional protocols.
Anesthesiologists may be liable for negligence if their actions or omissions result in patient harm, sickness, or death. Anesthesiologists can be held liable for negligence if injuries were caused solely by deviations from normal protocols during anesthesia operations. The plaintiff has the burden of showing the anesthesiologist's negligence. The court permits both parties to show their case through evidence. This could be records, books, journals, or expert testimonies. Carelessness is considered res ipsa loquitur when it is clear to a layperson; for instance, if a pre-anesthetic examination is not performed before administering anesthesia or if an inexplicable cardiac arrest during anesthesia results in death, this is considered carelessness. An explosion happened during the administration of anesthesia to a patient, despite the procedure being often employed without incident [51].
The defendant physician's responsibility, not the plaintiff's, is to demonstrate that their carelessness did not cause the accident. The court determined that the anesthetic agent was not contaminated and that the staff had taken the required procedures to disinfect themselves before the operation in a case where a patient acquired meningitis following spinal anesthesia. The hospital was fined for a sterilization process error. However, the anesthesiologist was not guilty [15].
In a notable medical malpractice case involving spinal anesthesia, the court highlighted the legal principle that it is the defendant physician's responsibility to demonstrate that their actions did not contribute to the adverse outcome, rather than the plaintiff's obligation to prove causation. In this instance, the patient developed meningitis postoperatively. The court found that the anesthetic agent used was uncontaminated and that the medical staff had adhered to all required disinfection protocols prior to the procedure. Despite the unfortunate outcome for the patient, the anesthesiologist was not found to be negligent. However, the hospital was penalized due to a failure in ensuring proper sterilization processes. This case underscores the complexities involved in medical malpractice litigation and the essential role of demonstrating adherence to medical standards of care [52].
Precaution and defense
Surgeons and anesthesiologists should maintain current, comprehensive patient records in addition to maintaining ongoing professional development. Before using any equipment, he/she must inspect it, check for drugs known to induce allergic responses and stay with the patient until they have fully recovered from the anesthetic's effects. An anesthesiologist can defend himself/herself against a negligence lawsuit by demonstrating that he/she used a fair amount of skill and care when performing anesthetic operations. A doctor is not negligent if he/she follows a practice approved by a responsible organization of medical professionals with training in that field prevalent at that time, even if other doctors may take a different course of action. This is Bolam's Law [53]. He/she is not liable as long as the physician exercised reasonable competence and care. The patient may also suffer harm due to therapeutic error, medical mishap, unanticipated injury, or the emergence of a new disease [54].
By helping to investigate the causes of perioperative accidents, one can streamline the auditing process, manage different clinical reactions, and determine whether doing so can alter unfavorable outcomes in the medico-legal field of interest. This theory states that the methods for patient safety that Madea et al. mentioned may assist in implementing in anesthetic-surgical practice will be revealed by a thorough multidisciplinary investigation that incorporates medico-legal data from autopsies, such as gross findings, additional laboratory analyses, and a recent molecular autopsy that revealed diseases that genes may prevent [55].
For the defense of anesthesiologists, Lee et al. recommended standard-of-care issues that indicated whether or not guidelines are applied are used to make choices about medical negligence, all within the framework of a medico-legal strategy [56].
Defensive medicine (DM)
Sadly, DM is gradually but steadily making its way into Egypt. Medical choices and activities regarding patient treatment should always be directed toward improving the patient's health. Whether it is an inquiry, a recommendation, a referral, or a procedure, the patient's best interests should always come first and foremost. This also holds for any guidance or instruction. However, even in these perfect circumstances, physicians occasionally find themselves in front of the courts, charged with malpractice for allegedly failing to take sufficient care of their patients. As medical malpractice claims against physicians became more likely than not, many doctors decided it was appropriate to incorporate certain measures into patient care that were not intended to improve the patient's health but rather to safeguard themselves in a malpractice lawsuit [57].
Multiple studies have highlighted how lawsuits negatively impact physicians, causing them stress and thereby threatening their future performance, according to Frati et al. [58]. Furthermore, "significant pressure on health professionals, particularly in some specialized branches more exposed to this risk," is also due to it. De Ville et al. state that "physicians are likely to look to the law first, not afterward, and are often preoccupied with maintaining the safest legal procedure possible" [59]. On the other hand, Frati et al. emphasize that "the idea that a fear of lawsuits can lower the rate of medical errors is not supported by the literature" [58].
According to Tuers et al., DM comes in two main forms. In an active form, which is often referred to as "positive," the doctor will request additional tests and procedures.". In terms of avoiding high-risk patients, the other is "passive or negative." Defensive actions can also be divided into two main categories: "avoidance practices, which try to keep away from the treatment of the high patients, and assurance practices, which needlessly overinvestigate the low-risk patients" [60].
Baungaart et al. point out that fear of litigation is not the only existing form. These other forms, motivated by "self-protective motives," fall into four categories: fear of patient dissatisfaction, fear of failing to recognize a serious diagnosis, fear of bad press, and unconscious defensive medicine [61]. According to this viewpoint, doctors who face legal action are more cautious in their activities and protocols to avoid medical claims "rather than to promote the patient's best interest," disobeying medical ethics [62].
These additional kinds, driven by "self-protective motives," can be broadly classified into four categories: fear of negative press, worry of failing to recognize a dangerous diagnosis, fear of patient dissatisfaction, and unconscious defensive medicine. This argument holds that 21 physicians under legal threat behave more cautiously and follow procedures "rather than to promote the patient's best interest," which is against medical ethics [63].
The fact that DM is neither harmless nor innocuous is another issue. Due to the likelihood of lawsuits against surgeons performing preoperative or surgical treatments, requests for unnecessary tests before procedures can improve the whole process [63].
Recommendations
A comprehensive multidisciplinary investigation that includes medico-legal data from autopsies, such as gross findings, additional laboratory analyses, and a recent molecular autopsy that revealed diseases that genes may prevent, will reveal the patient's safety strategies. In addition, standard-of-care issues that show whether or not guidelines are applied are utilized to make decisions concerning medical negligence, all within the framework of a medico-legal strategy.
Careful analysis and inquiry are needed for deaths that result from medical procedures and complications. The cause of death sequence should state whether the operation was a factor in the death. Every report should include a clinicopathological correlation that evaluates the case and the reliability of the findings drawn from the data.
Local guidelines and nationally consistent standards and criteria should be in place to investigate reported fatalities. This covers the diagnostic stage of the inquiry, as autopsies related to surgery and anesthesia would most likely raise delicate questions about privacy and transparency.
Independent peers should regularly review autopsy reports and procedures to ensure uniformity of established standards and accountability. In addition, pathologists and coroners should review each other's autopsy reports and related documents while undergoing training and as part of their ongoing professional development.
It is important to create additional perioperative quality control initiatives and examine their applicability to medico-legal assessments.
Conclusions
Among the most taxing events experienced by surgeons and anesthesiologists is death associated with surgery and anesthesia. Surgeons and anesthesiologists are caught at least once in this tragedy. Many pathologists have problems determining the cause of perioperative deaths. Their main role is to determine if death occurred due to a disease other than the procedure for which it was performed. A multidisciplinary approach with input from surgeons, anesthesiologists, and other clinicians may help the pathologist establish the most likely or reasonable cause of death. An autopsy alone may not be able to determine the cause of death in all perioperative deaths.
In order to develop a strategy for averting possibly deadly complications, forensic pathology professionals need to take into account such fatal events in terms of malpractice claims. So, efforts are being made to release guidelines for forensic pathologists to follow in order not to miss any leading information that could help in detecting the cause of death. Local rules are crucial since autopsies related to surgery and anesthesia will always bring up challenging questions about disclosure and secrecy. Errors in judgment and performance occur and can have serious consequences. |
Title: Unveiling the hidden interactome of CRBN molecular glues with chemoproteomics | Body:
INTRODUCTION
Targeted Protein Degradation (TPD) represents a promising therapeutic approach to remove disease-associated proteins from cells1,2. The process of TPD involves hijacking the ubiquitylation machinery for the covalent attachment of ubiquitin molecules to a desired protein of interest, which in turn leads to degradation by the proteasome3,4. Ubiquitin-mediated TPD utilizes two types of small molecules, molecular glues5 and heterobifunctional degraders (also known as PROteolysis Targeting Chimeras, or PROTACs)3, both of which chemically induce ternary complex formation between a protein target and a ubiquitin E3 ligase, followed by proximity-driven ubiquitylation and subsequent degradation.
Despite the rapid growth of TPD as a therapeutic strategy, the discovery and development of effective degraders remains challenging. Heterobifunctional degraders rely on a linker to connect two binding warheads: one for the ligase and one for the protein of interest3,6,7. Although this modular design offers the flexibility to target any protein with a known binder, the resulting molecules often possess poor drug-like properties due to their large size. Molecular glues present a possible alternative due to their small size and improved drug-like properties. However, as they lack binding to their target protein and instead enhance protein-protein interactions (PPI) between a ligase and substrate, rational design of molecular glues is far more challenging8. Over the last decade, the discovery of new molecular glue degraders has largely relied on serendipity through phenotypic screening of large libraries of molecules and retrospective identification of their degradation targets. Although FDA-approved molecular glue degraders exist, including the immunomodulatory drugs (IMiDs) thalidomide, lenalidomide, and pomalidomide, they have all been characterized as molecular glues in retrospect after their serendipitous discovery. IMiD molecules bind to the CUL4-RBX1-DDB1-CRBN (CRL4CRBN) E3 ligase5,9–13 creating a favorable surface for new proteins (neo-substrates) to bind for induced degradation. Since this discovery, significant efforts into the design and screening of new IMiD analogs have revealed up to 50 neo-substrates in the public domain, all carrying a glycine containing β-hairpin structural degron5,11,14–22. Remarkably, computational modeling of the AlphaFold2 (AF2) structures available in the Protein Data Bank (PDB) suggest that we are just scraping the surface of what is targetable by these molecules14.
Given the mechanism of action (MoA) of degraders, global chemoproteomics has proven to be an effective tool for the identification of protein degradation targets19,23–25. Using this approach, the target space of degraders for multiple therapeutic target classes have been extensively mapped for tractable targets, including kinases23,26,27, bromo-domains28–30, HDACs31 and zinc finger (ZF) transcription factors19. Although this method has greatly expanded the repertoire of known targets, limited sensitivity has restricted the ability to identify proteins with low expression levels without screening libraries of cell lines or using target enrichment methods. This approach also remains blind to a key aspect of these molecules: the identification of proteins that are recruited to the ligase but do not ultimately get degraded. Such “non-degrading glue” targets may be subject to poor lysine accessibility, lack of degradative ubiquitin chain formation32, high deubiquitinase activity, poor proteasome access, or other resistance mechanisms. However, these targets still represent important therapeutic targets if these factors can be overcome to convert silent molecular glues into molecular glue degraders or functional modulators of the target.
Methods to identify chemically induced protein-protein interactions include immunoprecipitation mass spectrometry (IP-MS)33 and proximity labeling approaches coupled to mass spectrometry34,35. IP-MS approaches have been employed for the identification of direct protein interactions, whereas proximity labeling approaches are commonly employed for the mapping of proximity interactomes in cells and in vivo34,36–38, enabling the identification of protein interactions within a 10–30 nm radius of the epitope tagged protein of interest34,36,37,39. Although these in-cell methods have demonstrated successful identification of chemically induced interactions, they often require extensive fine tuning of various factors including noise, sensitivity, variability and scalability.
In this study, we establish a simple, robust and sensitive workflow to facilitate high throughput discovery of degrader-induced protein-protein interactions and develop them into selective tools and therapeutic candidates. We use this method to build a comprehensive inventory of 298 distinct protein targets recruited to CRBN including many new zinc finger (ZF) transcription factors and proteins from new target classes, including RNA-recognition motif (RRM) domain proteins. We evaluate the binding potential of these targets through structural alignment with IMiD-bound CRBN and performed biochemical and structural validation studies on a series of non-ZF targets. We then screened a library of ~6000 IMiD analogs against a novel non-ZF target, PPIL4, identifying a selective lead degrader molecule, thereby presenting a blueprint for the effective discovery of novel molecular glue degraders.
RESULTS
Unbiased identification of degrader-induced interactors in-lysate
To establish a workflow for the identification of chemically induced protein-protein interactions, we set out to simplify traditional IP-MS methods. We hypothesized that we could create a controlled environment with reduced biological variability and enhanced scalability by establishing a workflow in cell lysate using spiked in recombinant protein as the bait. Our workflow harnesses small molecule degrader-induced ternary complex formation in cell lysate using recombinant FLAG-tagged CRBN in complex with DDB1 excluding the BPB domain (ΔB), which prevents CUL4 interaction to inhibit ubiquitylation of the recruited target, with a small molecule degrader. After incubation, we enrich with a highly selective antibody for the FLAG epitope tag followed by label free quantitative proteomic assessment to identify interactors (Figure 1A). To benchmark and explore the viability of this approach for identification of protein-protein interactions, we selected two representative degrader molecules that have been thoroughly profiled in published reports – pomalidomide, a IMiD molecular glue19,40 and SB1-G-187, a kinase-targeted heterobifunctional degrader26 (Figure 1B). We profiled these two molecules across two different cell lines, MOLT4 and Kelly, selected for their orthogonal expression profiles and broad coverage of known CRBN neo-substrates including IKZF1/3 (MOLT4) and SALL4 (Kelly)19. The pomalidomide screen revealed 11 different enriched proteins across these two cell lines (9 in MOLT4 and 4 in Kelly cells), which revealed three novel targets, ASS1, ZBED3 and ZNF219 (Figure 1C, Figure S1A–B, Table S1). We then validated recruitment of these three novel targets to CRBN using dose response immunoblot or TR-FRET analysis (Figure S1C–D).
To assess the overlap of these enriched targets with published degradation data for pomalidomide, we performed a hit comparison with publicly available global proteomics data (http://proteomics.fischerlab.org), which includes ten independent pomalidomide treatments spanning HEK293T, Kelly and MOLT4 cell lines (Figure 1D). Like our enrichment data, the global degradation data also maps 11 targets as degradable, however only 4 of these targets (IKZF1, IKZF3, ZFP91 and SALL4) overlap with those that we see enriched in this dataset. The SB1-G-187 kinase degrader screen identified 18 enriched targets across the two cell lines (16 in MOLT4 and 7 in Kelly cells) including multiple non-protein kinase targets which raised the question of how these proteins are being recruited to CRBN by a kinase degrader (Figure 1E, Figure S1E–F, Table S1). Assessment of the non-kinase targets revealed that several are known to form complexes with different kinases, such as TAB1 and TAB2 which form a functional kinase complex with MAP3K741,42, and UNC119 which binds to myristoylated SRC to regulate cellular localization43. This data suggests that these non-kinases are being recruited to CRBN through piggybacking on their kinase binding partners. Of the other recruited non-kinase targets, ZBED3 is also identified in the pomalidomide treatment suggesting recruitment through the IMiD handle of the degrader and SDR39U1 was reported as a non-kinase target in a chemoproteomics profiling study of kinase inhibitor probes44. Next, we assessed the differences and overlap in hits between publicly available degradation data in MOLT4 and Kelly cells for kinase-targeted heterobifunctional degrader, SB1-G-187, and enrichment data (Figure 1E). We found 6 overlapping hits - all protein kinases - including CDK1, IRAK1, LCK, LYN, MAP3K7 and SRC. Like the pomalidomide data, we observe similar numbers of proteins identified in either degradation data (15) or enrichment data (12), demonstrating that these two methods complement each other to expand the target scope of these molecules. Together, the data collected for these two degrader molecules demonstrate the value of our workflow for identifying chemically induced protein-protein interactions invisible to degradation assays, while also highlighting opportunities for improving sensitivity.
Mapping the interactomes of IMiD molecular glue degraders
Next, using the functional enrichment method as a basis, we set out to optimize and address the critical need for sensitivity and high throughput. IP-MS experiments typically require labor-intensive sample preparation steps which create a significant source of variability and lead to high background and false positive rates while also placing limits on the number of samples that can be prepared in parallel. To address these limitations, we automated the enrichment and sample preparation procedures to enable effective mapping of interaction targets across libraries of molecules at scale. We incorporated a cost effective Opentrons OT-2 liquid handling platform to automate the sample preparation process from addition of all immunoprecipitation components to tryptic digestion for 96 samples in parallel (Figure 2A). To address throughput and depth of the proteomics workflow, we took advantage of recent updates in instrumentation (timsTOF Pro2, Bruker) and acquisition methods (diaPASEF)45 that allow for significant improvements in sensitivity (Figure 2A). In contrast to the data-dependent acquisition (DDA) data collected in our proof-of-concept analysis (Figure 1), diaPASEF measures peptides by systematically sampling all precursor ions within a specified m/z range regardless of their abundance which enhances the reproducibility and depth of peptide coverage to allow for more accurate and robust quantification of peptides in complex samples. Comparison of the average numbers of proteins and peptides quantified between the DDA collection (Figure 1A) and diaPASEF collection (Figure 2A) revealed a >5-fold increase in proteins and a ~9-fold increase in peptides quantified (Figure S2A–B) confirming a significant improvement in depth and sensitivity.
Work over the last several years has led to the identification of a growing list of ~50 neo-substrates that are recruited to CRBN by IMiD analogs for chemically induced degradation14. Validated targets include a large number of C2H2 zinc finger (ZF) transcription factors such as IKZF1/35, ZFP9118, or SALL415,19, but only a few non-ZF proteins such as G1 to S phase transition protein 1 (GSPT1)11 and casein kinase 1 alpha (CK1α)12,17. These targets do not possess any similarity, but instead all share a common structural CRBN binding motif consisting of an 8-residue loop that connects the two strands of a β-hairpin and has a glycine at the sixth position (G-loop)11,12,18. Remarkably, a recently reported analysis of available AlphaFold2 (AF2) predicted structures for proteins in the human proteome uncovered over 2,500 proteins that harbor a G-loop potentially compatible with IMiD-recruitment to CRBN, with C2H2 ZF proteins revealing themselves as the most prevalent domain class, aligning with the dominance that this class has amongst the experimentally confirmed targets14. Due to the extensive range of proteins that are predicted to be chemically recruitable to CRBN, we asked how many of these proteins are already targeted by existing chemistry, but not yet identified due to lack of sensitivity of existing methods. To explore the range of proteins chemically recruited to CRBN, we screened a curated library of 20 different IMiD analogs through our automated lysate-based IP workflow. We assembled this library to incorporate a broad range of IMiD-based scaffolds including the parental FDA-approved IMiDs (thalidomide, lenalidomide, pomalidomide)46,47, where there is a high value to identifying new targets for drug repurposing efforts. We included a series of IMiD analogs that are undergoing clinical trials (CC-220, CC-92480, CC-90009)48–50 and molecules that have demonstrated promiscuity (FPFT-2216, CC-122)51,52. Finally, we included a series of in-house synthesized scaffolds developed in the context of targeting Helios (IKZF2)53 or part of an effort to diversify IMiDs with the addition of fragments on an extended linker (Figure 2B). We screened this library at 1 μM concentration across MOLT4 and Kelly cell lines (including a second 5 μM concentration for pomalidomide) and identified proteins that were enriched in the degrader compared to DMSO control IP treatment (Figure 2C, Figures S3–4, Tables S2–3). Using significance cutoffs of fold change (FC) >1.5, P-value <0.001 and combining the data from both cell lines, we identified a total of 298 enriched proteins (Table S4). We rationalized that the likelihood of observing the same proteins enriched as false positives across multiple treatments with similar IMiD analog molecules is low and therefore used ‘frequency of enrichment’ as a measure of confidence. We observed 102 proteins enriched in at least three independent IPs, and each of the top 5 proteins (PATZ1, ZBED3, WIZ, IKZF2 and ASS1) enriched in more than 20 independent IPs across the database (Figure 2D, Table S4). Surprisingly, although published reports have confirmed degradation of PATZ1, WIZ and IKZF2, none of these top 5 enriched proteins regularly feature amongst those proteins that we commonly see reported in existing unbiased screens of IMiD-based molecules indicating the orthogonal data generated by this profiling method, identifying targets that might otherwise be overlooked. ZBED3 and ASS1 showed frequent enrichment across our database without any prior reporting of degradation, even at concentrations up to 5 μM of pomalidomide (Figure S2C, Table S4), suggesting the first reported examples of targets that are chemically glued to CRBN but lack productive degradation, thus emphasizing the benefit of alternative binding focused approaches for target identification. Also, important to note is that the new targets identified in this study are not only targets of new IMiD analogs but are also identified as targets of IMiDs in clinical trials and with FDA approval.
To assess the fraction of newly identified IMiD targets, we compared the 298 enriched proteins to a list of literature reported targets and discovered an overlap of only 28 targets. We identified 270 novel targets and found only 22 targets were reported in the literature but not identified as hits in our study (Figure 2E, Table S4)14. Considering the prevalence of C2H2 ZF transcription factors amongst reported IMiD targets, we asked whether this dominance holds true across our extended list of targets. To assess this, we extracted superfamily, family and domain information from curated databases including InterPro54,55, Uniprot56 and Superfamily57 to categorize the targets based on studied features (Figure 2F–G, Table S4). Of the 298 targets identified, after C-terminal domain classification, the C2H2 ZF superfamily represents the largest segment, accounting for >14% of the targets in the top 10 enriched superfamilies. This is followed by RNA-recognition motif domain proteins (RRM, >13%) and nucleotide-binding alpha-beta plait domain superfamilies (α-β plait domain, >12%). Notably, protein kinase-like domain proteins also features on this top 10 list of superfamilies (kinase-like domain, >6.4%) which aligns with our knowledge that kinases can be targets of IMiD molecular glues (eg, CSNK1A1 or WEE1)12,17,58 and suggests that molecular glues may be a viable alternative to PROTACs, which are currently widely explored for kinase targeting. Exploration of the top 10 domain classifications across the dataset shows a similar trend with C2H2 ZF, RRM, ZF, protein kinases and BTB/POZ domains showing the highest representation across the targets identified (Figure 2G).
This dataset builds upon previous identifications of protein kinases as targets of IMiD molecules17,22,59, and further extends the kinase list adding CDK7, IRAK1 and TBK1 as novel putative molecular glue targets. It also broadens the scope of tractable targets by introducing multiple new families as targetable by CRBN-based molecular glues, illustrating the extensive potential of these molecules. Through the application of our unbiased target enrichment workflow, we have significantly increased the number of experimentally detected IMiD targets, expanding beyond the C2H2 ZF protein family to a wide range of protein families including protein kinases and proteins involved in RNA metabolism.
Zinc finger transcription factors enriched among targets
To validate the 270 previously unreported targets, we sought to establish a computational screening pipeline to score the compatibility of targets for recruitment to CRBN. Structural studies on IMiD-mediated CRBN neo-substrates, both natural and designed, have established the common G-loop motif that is recognized by the CRBN-IMiD complex12,60. We used MASTER61 to mine the AF2 database62 for proteins containing G-loops with similar backbone architecture to the G-loop in known neo-substrate CSNK1A1 (PDB: 5FQD, aa35–42) resulting in a set of 46,040 loops across 10,926 proteins (Figure 3A).
Due to structural constraints, not all these proteins are compatible with CRBN. To identify CRBN-compatible proteins, we first extracted domains containing the G-loops based on domain definitions from DPAM, a tool that parses domains from AF models based upon predicted aligned errors (PAE) and evolutionary classification63. Next, we aligned the domains to our reference CSNK1A1-IMiD-CRBN-DDB1ΔB structure based on the G-loop and calculated a clash score. We used the van der Waals force term for interchain contacts in Rosetta’s low-resolution mode64 to obtain a side-chain independent clash estimate. Out of 16 known neo-substrates with validated G-loops (Table S5, Figure S5), 15 had clash scores below 2, while ZNF65440 had a score of 172, indicating a minor clash. The clash was caused by a low confidence region in the AF2 structure and could be resolved by relaxing the complex with Rosetta (Figure S6A)65. On the other hand, a protein with no evidence supporting it being a neo-substrate, PAAF1, had a major clash with a score of 1,551 which could not be resolved by relaxation (Figure S6B). Based on these examples, and analysis of the clash scores of all hit proteins containing a clear structural G-loop in AF2 (Figure S6C), we filtered out domains with scores greater than 200 resulting in a list of 14,189 loops across 6,018 proteins with nonexistent, or marginal clashes with CRBN (Figure 3A, Table S5).
Of the 298 total enriched candidates, 199 were found to have a clear structural G-loop with 162 having a clash score below 200. Given the high proportion of ZF proteins identified as targets across this enrichment database (Figure 2F), we mapped the fold change in enrichment for proteins with an annotated ZF domain across all 20 degraders for both MOLT4 (Figure 3B) and Kelly cells (Figure 3C). Across these two cell lines, we identified 19 previously reported and 28 new neo-substrates as chemically recruited to CRBN. We then used our G-loop database to inform on which of these targets have a tractable G-loop and found that only five of the 57 identified targets do not contain a structural G-loop (Figure 3C, Table S5). Given what we know about the recruitment and binding of CRBN neo-substrates, targets usually bind through a dominant structural hairpin. Since we do not have validated degron information for all these ZF targets, we assumed that the G-loop with the lowest clash score has the highest likelihood to bind and therefore proceeded with evaluation of a single G-loop for each target. To gauge how the clash scores for these ZF targets compare to all hits in the G-loop database, we compared the clash scores for our ZF targets to those of all hits (Figure S6C) demonstrating a pronounced trend towards lower scores for ZF targets suggesting fewer unfavorable interactions (Figure S5A–B). Notably, when we explored the ZF hits with higher clash scores (>10) and >3 hit frequency, we realized that almost all of these have a reported association with at least one of the validated hits – ZMYND8 (cs 455, binds to ZNF687), and RNF166 (cs 17, binds to ZNF653/ZBTB39/ZNF827) – which also offers the possibility that these proteins could be collateral targets, recruited via piggybacking on their binding partners, the direct binders (Table S4). Finally, we compared ZF targets across the two cell lines as an additional means for validation, and found 10 overlapping proteins, 6 of which are novel recruited targets (ZBED3, MNAT1, MTA2, ZBTB44, TRIM28) (Figure 3D).
There are many factors to take into consideration when looking to predict target degradability, such as ternary complex formation26,31,66 and target ubiquitylation12,32,67–69, and multiple studies have placed an emphasis on exploring their role in driving productive degradation70,71. For degrader-induced degradation to occur, a ternary complex consisting of ligase-degrader-target needs to form for proximity-mediated ubiquitin transfer to the target protein. Because ternary complex formation is necessary for successful protein degradation, we set out to explore the relationship between ternary complex formation and degradation for ZF targets identified in this study. We focused our evaluation on the parental IMiD molecules which have been subjected to degradation target profiling using unbiased global proteomics analysis across a panel of four cell lines (SK-N-DZ, Kelly, MM.1S, hES)19. Comparison of the enriched ZF targets to the published degradation data shows a consistent trend across the three IMiDs where only ~30% of the enriched targets that were quantified in global proteomics studies were degraded, with ~ 60% of the targets quantified but not reported as degraded (Figure 3E, Table S5). The data was then grouped to allow a global comparison of the enriched versus degraded IMiD targets. The comparison revealed that of the 31 ZF targets enriched across these three molecular glues, only 11 of the 29 proteins quantified in global proteomics experiments were found to be degraded (Figure 3F, Table S5). 18 proteins were quantified in global proteomics but were not identified as degraded. This prompted us to question whether these targets were resistant to degradation by IMiDs and their analogs, or if they were not identified as degraded due to experimental limitations such as inadequate sensitivity to detect minor changes in protein abundance, rapid protein turnover or suboptimal experimental conditions. We found that although several of the targets (WIZ, PATZ1, ZNF687, ZMYM2 and HIC2) were not reported as degraded in Donovan et al.19, they have since been reported as degraded in other published studies40,72,73 confirming that IPs provide a complementary approach able to overcome limitations in sensitivity. The absence of degradation data for the remaining targets could imply that these targets are resistant to degradation, or similar to the above proteins, the appropriate degradation experiment has yet to be performed. These data demonstrate that our IP workflow provides a significant advantage over global proteomics analysis by enabling selective isolation and enrichment of targets that may be below the change in abundance threshold for consistent identification with global proteomics approaches.
IMiD derived molecular glues recruit hundreds of non-zinc finger proteins
The largest target class of CRBN neo-substrates today are ZF containing proteins, however, of the ~20,000 proteins in the human proteome, ZF containing proteins only make up a relatively small proportion with about ~1700 ZF proteins reported74. So far, only a handful of targets are reported to lack a ZF motif, which includes GSPT111, CK1α12,17, PDE6D75, and RAB2875. With this in mind, we examined our list of targeted proteins with a focus on those that do not contain a reported ZF domain and found 251 non-ZF proteins enriched across the IP dataset (Figure 4A, Table S4). These non-ZF proteins include a wide range of families such as protein kinases (IRAK1, TBK1, CDK7), RNA recognition motif proteins (ELAVL1, PPIL4, CSTF2, RBM45), metabolic enzymes (ASS1, PAICS, ACLY, CS, ACADVL), translational proteins (MARS1, ETF1, EEF1E1, EIF4B) and more spanning different biological pathways. To assist in establishing confidence in some of these targets, we performed a comparison of the non-ZF targets enriched in the two tested cell lines and found 39 targets were identified in both MOLT4 and Kelly cells, including the four above mentioned targets (Figure 4A–B). We then assessed the AF2 structures of each of these 39 proteins and found that almost all of them (33/39) contain a structural G-loop (Figure 4B, Figure S5, Table S5).
Given the large number of non-ZF targets identified in this study and the lack of emphasis in the public domain with regards to non-ZF CRBN neo-substrates, we selected a series of non-ZF proteins for further experimental validation. Firstly, to demonstrate that these neo-substrates are directly recruited to CRBN, we examined ternary complex formation using recombinant purified proteins. Using two of the more promiscuous molecular glues, pomalidomide and FPFT-2216, we tested previous reported degradation targets PDE6D, RAB28 and DTWD1, along with a newly discovered target PPIL4. Indeed, PDE6D, DTWD1, and PPIL4 formed compound dependent ternary complex with CRBN at varying effective concentrations (Figure 4C). However, RAB28, which was previously reported to be degraded by IMiDs19 and FPFT-221675, did not show any evidence for direct binding to CRBN using purified proteins. Since RAB28 has previously been reported as a CRBN neo-substrate and consistently scored across our enrichment study, we explored whether there was any evidence suggesting that RAB28 could be a collateral target. Exploration of protein-protein interaction databases including BioPlex33 and STRING-DB76 revealed that RAB28 is known to bind to two validated IMiD-CRBN neo-substrates PDE6D and ZNF653 (Table S4), suggesting that RAB28 is likely an indirectly recruited target. These data demonstrate that in addition to identifying direct binders, we can also identify indirect binding partners that may be simultaneously recruited together with direct binding neo-substrates. Given that targeted protein degradation requires not only recruitment to CRBN, but also CRBN mediated ubiquitin transfer for degradation, we also monitored whether the recruited proteins can be ubiquitylated by CRL4CRBN. In vitro ubiquitylation assays showed robust ubiquitin modification on all 3 recruited non-ZF proteins in the presence of pomalidomide or FPFT-2216 (Figure 4D). In addition, all three of these targets were degraded in response to IMiD treatment as observed by global proteomics analysis (Figure S6D, Table S5). Using structural G-loop alignments, we then assessed the potential for each of these three proteins to bind to IMiD-CRBN and found that all three proteins had a G-loop with a clash score of <200 (Figure 4E). However, the aligned clash score for DTWD1 was relatively high and approaching the upper 200 threshold (cs 198). We performed relaxation with Rosetta and found that this reduced the clash score to 1.58 by allowing minor shifts in the overall conformation while retaining the structural G-loop (Figure S6E). This process demonstrates that in some cases, clash scores can be relieved through minor structural rearrangements using Rosetta relax.
To expand our understanding of the recruitment of non-ZF targets, we determined cryo-EM structures of CRBN-DDB1ΔB-FPFT-2216 bound to PPIL4 and PDE6D, respectively (Figure 4F, Figure S7, Table 1). The complex structures were both refined to a global resolution of around 3.4 Å and the quality of the resulting maps were sufficient to dock the complex components, but the flexibly tethered PPIL4 resulted in a lower local resolution. We were able to observe PPIL4 engagement with FPFT-2216-CRBN via its Gly278 harboring G-loop as expected from the G-loop alignment, as well as for PDE6D via its Gly28 G-loop. Furthermore, overall density allowed fitting of FPFT-2216 in bulk although the reduced resolution in that region did not permit exact positioning of the molecule. Nevertheless, we were able to see that the glutarimide ring engages CRBN’s binding pocket in a similar manner to other IMiD molecular glues. The triazole interacts with the backbone of the G-loop, and the methoxythiophene moiety potentially contacts both the PPIL4 backbone of the G-loop and Arg273. This suggests that the triazole and the methoxythiophene moieties could provide specificity elements to FPFT-2216 mediated neo-substrate recruitment. The methoxythiophene moiety also engaged Arg23 of PDE6D, indicating that FPFT-2216 might derive specificity in engaging an arginine residue from its neo-substrates. Analysis of the non-ZF targets of FPFT-2216 revealed several other proteins harboring an arginine or a lysine residue at this sequence location (PDE6D, SCYL1, RBM45, PPIL4). Finally, we compared the experimental structure to the AF2 predicted G-loop aligned structure of PPIL4 (Figure S6F). The G-loop aligned structure of PPIL4 presented a clash score of 3.38, which showed the C-terminal region of CRBN around residue Arg373 to be clashing with PPIL4’s loop harboring residue Val250. Although the low resolution permitted only backbone level fitting of PPIL4, we observed that the cryo-EM structure revealed a minor shift in the RRM domain of PPIL4 to accommodate this minor clash suggested in the G-loop aligned structure while retaining overall conformational similarity of the G-loop (Figure S6F). Meanwhile, PDE6D retained overall similar conformation with minor shifts that did not alter the interaction with CRBN (Figure S6G).
These data demonstrate that RRM domain containing proteins represent a new class of proteins targetable through CRBN dependent molecular glues. Using structural modeling we increase confidence in these new targets while also providing a reminder that structural analysis and AF2 predicted structures are static models and although they provide excellent structural guidance, we need to keep in mind that proteins in solution are flexible and dynamic.
Discovery of new and selective molecular glue for PPIL4
While the proteomics-based screening workflow identifies novel putative CRBN targets and provides initial chemical matter, it does not necessarily provide the best starting point for developing a chemical probe or therapeutic due to the limited number of molecules screened. We hypothesized that this limitation could be overcome by following up proteomics screening with a target centric screen of a larger CRBN binder library to identify the optimal chemical starting point. To test this, we set out to identify PPIL4 targeting molecular glues with improved selectivity and lacking the triazole moiety. We employed an IMiD molecular glue library consisting of ~6000 compounds of various IMiD analogs that were either synthesized in-house or purchased externally. We screened this library against PPIL4 using TR-FRET to measure compound-induced PPIL4 recruitment to CRBN (Figure 5A). TR-FRET ratios were obtained by incubating the library with GFP fused CRBN-DDB1ΔB, biotinylated PPIL4, and Tb-labeled streptavidin that binds to the biotinylated PPIL4. The library was compared relative to the positive control, whereby the 520/490 ratio of FPFT-2216 at 10 μM was normalized as 1, and compounds were tested at 1.66 μM or 3.33 μM to find hits with equal or improved efficacy in directly recruiting PPIL4 to CRBN-DDB1ΔB. We were able to narrow down the library to two molecules that performed similar or better than FPFT-2216 (Figure 5B). These lead compounds were subject to a full titration to assess recruitment efficacy by TR-FRET. Ultimately, after recognizing one of the two hits was due to autofluorescence, we were able to identify a molecule, Z6466608628, that produced a higher 520/490 ratio, and a better EC50 of 0.34 μM compared to FPFT-2216, measured at 1.05 μM in this experiment (Figure 5C–D).
To test the efficacy and selectivity of our lead compound, we first performed IP-MS in comparison with FPFT-2216 in Kelly cell lysate. While FPFT-2216 recruited many proteins, Z6466608628 selectively recruited PPIL4, along with its binding partner DHX40 (Figure 5E, Table S5). We then performed global proteomics in MOLT4 cells to confirm that Z6466608628 can induce selective downregulation of PPIL4 (Figure 5F, Table S5). These data collectively demonstrate the complete workflow, starting from the identification of a novel non-ZF target PPIL4 in a chemoproteomics screen, to the discovery of a new PPIL4 selective molecular glue that would serve as an excellent lead molecule for structural optimization.
Unbiased identification of degrader-induced interactors in-lysate
To establish a workflow for the identification of chemically induced protein-protein interactions, we set out to simplify traditional IP-MS methods. We hypothesized that we could create a controlled environment with reduced biological variability and enhanced scalability by establishing a workflow in cell lysate using spiked in recombinant protein as the bait. Our workflow harnesses small molecule degrader-induced ternary complex formation in cell lysate using recombinant FLAG-tagged CRBN in complex with DDB1 excluding the BPB domain (ΔB), which prevents CUL4 interaction to inhibit ubiquitylation of the recruited target, with a small molecule degrader. After incubation, we enrich with a highly selective antibody for the FLAG epitope tag followed by label free quantitative proteomic assessment to identify interactors (Figure 1A). To benchmark and explore the viability of this approach for identification of protein-protein interactions, we selected two representative degrader molecules that have been thoroughly profiled in published reports – pomalidomide, a IMiD molecular glue19,40 and SB1-G-187, a kinase-targeted heterobifunctional degrader26 (Figure 1B). We profiled these two molecules across two different cell lines, MOLT4 and Kelly, selected for their orthogonal expression profiles and broad coverage of known CRBN neo-substrates including IKZF1/3 (MOLT4) and SALL4 (Kelly)19. The pomalidomide screen revealed 11 different enriched proteins across these two cell lines (9 in MOLT4 and 4 in Kelly cells), which revealed three novel targets, ASS1, ZBED3 and ZNF219 (Figure 1C, Figure S1A–B, Table S1). We then validated recruitment of these three novel targets to CRBN using dose response immunoblot or TR-FRET analysis (Figure S1C–D).
To assess the overlap of these enriched targets with published degradation data for pomalidomide, we performed a hit comparison with publicly available global proteomics data (http://proteomics.fischerlab.org), which includes ten independent pomalidomide treatments spanning HEK293T, Kelly and MOLT4 cell lines (Figure 1D). Like our enrichment data, the global degradation data also maps 11 targets as degradable, however only 4 of these targets (IKZF1, IKZF3, ZFP91 and SALL4) overlap with those that we see enriched in this dataset. The SB1-G-187 kinase degrader screen identified 18 enriched targets across the two cell lines (16 in MOLT4 and 7 in Kelly cells) including multiple non-protein kinase targets which raised the question of how these proteins are being recruited to CRBN by a kinase degrader (Figure 1E, Figure S1E–F, Table S1). Assessment of the non-kinase targets revealed that several are known to form complexes with different kinases, such as TAB1 and TAB2 which form a functional kinase complex with MAP3K741,42, and UNC119 which binds to myristoylated SRC to regulate cellular localization43. This data suggests that these non-kinases are being recruited to CRBN through piggybacking on their kinase binding partners. Of the other recruited non-kinase targets, ZBED3 is also identified in the pomalidomide treatment suggesting recruitment through the IMiD handle of the degrader and SDR39U1 was reported as a non-kinase target in a chemoproteomics profiling study of kinase inhibitor probes44. Next, we assessed the differences and overlap in hits between publicly available degradation data in MOLT4 and Kelly cells for kinase-targeted heterobifunctional degrader, SB1-G-187, and enrichment data (Figure 1E). We found 6 overlapping hits - all protein kinases - including CDK1, IRAK1, LCK, LYN, MAP3K7 and SRC. Like the pomalidomide data, we observe similar numbers of proteins identified in either degradation data (15) or enrichment data (12), demonstrating that these two methods complement each other to expand the target scope of these molecules. Together, the data collected for these two degrader molecules demonstrate the value of our workflow for identifying chemically induced protein-protein interactions invisible to degradation assays, while also highlighting opportunities for improving sensitivity.
Mapping the interactomes of IMiD molecular glue degraders
Next, using the functional enrichment method as a basis, we set out to optimize and address the critical need for sensitivity and high throughput. IP-MS experiments typically require labor-intensive sample preparation steps which create a significant source of variability and lead to high background and false positive rates while also placing limits on the number of samples that can be prepared in parallel. To address these limitations, we automated the enrichment and sample preparation procedures to enable effective mapping of interaction targets across libraries of molecules at scale. We incorporated a cost effective Opentrons OT-2 liquid handling platform to automate the sample preparation process from addition of all immunoprecipitation components to tryptic digestion for 96 samples in parallel (Figure 2A). To address throughput and depth of the proteomics workflow, we took advantage of recent updates in instrumentation (timsTOF Pro2, Bruker) and acquisition methods (diaPASEF)45 that allow for significant improvements in sensitivity (Figure 2A). In contrast to the data-dependent acquisition (DDA) data collected in our proof-of-concept analysis (Figure 1), diaPASEF measures peptides by systematically sampling all precursor ions within a specified m/z range regardless of their abundance which enhances the reproducibility and depth of peptide coverage to allow for more accurate and robust quantification of peptides in complex samples. Comparison of the average numbers of proteins and peptides quantified between the DDA collection (Figure 1A) and diaPASEF collection (Figure 2A) revealed a >5-fold increase in proteins and a ~9-fold increase in peptides quantified (Figure S2A–B) confirming a significant improvement in depth and sensitivity.
Work over the last several years has led to the identification of a growing list of ~50 neo-substrates that are recruited to CRBN by IMiD analogs for chemically induced degradation14. Validated targets include a large number of C2H2 zinc finger (ZF) transcription factors such as IKZF1/35, ZFP9118, or SALL415,19, but only a few non-ZF proteins such as G1 to S phase transition protein 1 (GSPT1)11 and casein kinase 1 alpha (CK1α)12,17. These targets do not possess any similarity, but instead all share a common structural CRBN binding motif consisting of an 8-residue loop that connects the two strands of a β-hairpin and has a glycine at the sixth position (G-loop)11,12,18. Remarkably, a recently reported analysis of available AlphaFold2 (AF2) predicted structures for proteins in the human proteome uncovered over 2,500 proteins that harbor a G-loop potentially compatible with IMiD-recruitment to CRBN, with C2H2 ZF proteins revealing themselves as the most prevalent domain class, aligning with the dominance that this class has amongst the experimentally confirmed targets14. Due to the extensive range of proteins that are predicted to be chemically recruitable to CRBN, we asked how many of these proteins are already targeted by existing chemistry, but not yet identified due to lack of sensitivity of existing methods. To explore the range of proteins chemically recruited to CRBN, we screened a curated library of 20 different IMiD analogs through our automated lysate-based IP workflow. We assembled this library to incorporate a broad range of IMiD-based scaffolds including the parental FDA-approved IMiDs (thalidomide, lenalidomide, pomalidomide)46,47, where there is a high value to identifying new targets for drug repurposing efforts. We included a series of IMiD analogs that are undergoing clinical trials (CC-220, CC-92480, CC-90009)48–50 and molecules that have demonstrated promiscuity (FPFT-2216, CC-122)51,52. Finally, we included a series of in-house synthesized scaffolds developed in the context of targeting Helios (IKZF2)53 or part of an effort to diversify IMiDs with the addition of fragments on an extended linker (Figure 2B). We screened this library at 1 μM concentration across MOLT4 and Kelly cell lines (including a second 5 μM concentration for pomalidomide) and identified proteins that were enriched in the degrader compared to DMSO control IP treatment (Figure 2C, Figures S3–4, Tables S2–3). Using significance cutoffs of fold change (FC) >1.5, P-value <0.001 and combining the data from both cell lines, we identified a total of 298 enriched proteins (Table S4). We rationalized that the likelihood of observing the same proteins enriched as false positives across multiple treatments with similar IMiD analog molecules is low and therefore used ‘frequency of enrichment’ as a measure of confidence. We observed 102 proteins enriched in at least three independent IPs, and each of the top 5 proteins (PATZ1, ZBED3, WIZ, IKZF2 and ASS1) enriched in more than 20 independent IPs across the database (Figure 2D, Table S4). Surprisingly, although published reports have confirmed degradation of PATZ1, WIZ and IKZF2, none of these top 5 enriched proteins regularly feature amongst those proteins that we commonly see reported in existing unbiased screens of IMiD-based molecules indicating the orthogonal data generated by this profiling method, identifying targets that might otherwise be overlooked. ZBED3 and ASS1 showed frequent enrichment across our database without any prior reporting of degradation, even at concentrations up to 5 μM of pomalidomide (Figure S2C, Table S4), suggesting the first reported examples of targets that are chemically glued to CRBN but lack productive degradation, thus emphasizing the benefit of alternative binding focused approaches for target identification. Also, important to note is that the new targets identified in this study are not only targets of new IMiD analogs but are also identified as targets of IMiDs in clinical trials and with FDA approval.
To assess the fraction of newly identified IMiD targets, we compared the 298 enriched proteins to a list of literature reported targets and discovered an overlap of only 28 targets. We identified 270 novel targets and found only 22 targets were reported in the literature but not identified as hits in our study (Figure 2E, Table S4)14. Considering the prevalence of C2H2 ZF transcription factors amongst reported IMiD targets, we asked whether this dominance holds true across our extended list of targets. To assess this, we extracted superfamily, family and domain information from curated databases including InterPro54,55, Uniprot56 and Superfamily57 to categorize the targets based on studied features (Figure 2F–G, Table S4). Of the 298 targets identified, after C-terminal domain classification, the C2H2 ZF superfamily represents the largest segment, accounting for >14% of the targets in the top 10 enriched superfamilies. This is followed by RNA-recognition motif domain proteins (RRM, >13%) and nucleotide-binding alpha-beta plait domain superfamilies (α-β plait domain, >12%). Notably, protein kinase-like domain proteins also features on this top 10 list of superfamilies (kinase-like domain, >6.4%) which aligns with our knowledge that kinases can be targets of IMiD molecular glues (eg, CSNK1A1 or WEE1)12,17,58 and suggests that molecular glues may be a viable alternative to PROTACs, which are currently widely explored for kinase targeting. Exploration of the top 10 domain classifications across the dataset shows a similar trend with C2H2 ZF, RRM, ZF, protein kinases and BTB/POZ domains showing the highest representation across the targets identified (Figure 2G).
This dataset builds upon previous identifications of protein kinases as targets of IMiD molecules17,22,59, and further extends the kinase list adding CDK7, IRAK1 and TBK1 as novel putative molecular glue targets. It also broadens the scope of tractable targets by introducing multiple new families as targetable by CRBN-based molecular glues, illustrating the extensive potential of these molecules. Through the application of our unbiased target enrichment workflow, we have significantly increased the number of experimentally detected IMiD targets, expanding beyond the C2H2 ZF protein family to a wide range of protein families including protein kinases and proteins involved in RNA metabolism.
Zinc finger transcription factors enriched among targets
To validate the 270 previously unreported targets, we sought to establish a computational screening pipeline to score the compatibility of targets for recruitment to CRBN. Structural studies on IMiD-mediated CRBN neo-substrates, both natural and designed, have established the common G-loop motif that is recognized by the CRBN-IMiD complex12,60. We used MASTER61 to mine the AF2 database62 for proteins containing G-loops with similar backbone architecture to the G-loop in known neo-substrate CSNK1A1 (PDB: 5FQD, aa35–42) resulting in a set of 46,040 loops across 10,926 proteins (Figure 3A).
Due to structural constraints, not all these proteins are compatible with CRBN. To identify CRBN-compatible proteins, we first extracted domains containing the G-loops based on domain definitions from DPAM, a tool that parses domains from AF models based upon predicted aligned errors (PAE) and evolutionary classification63. Next, we aligned the domains to our reference CSNK1A1-IMiD-CRBN-DDB1ΔB structure based on the G-loop and calculated a clash score. We used the van der Waals force term for interchain contacts in Rosetta’s low-resolution mode64 to obtain a side-chain independent clash estimate. Out of 16 known neo-substrates with validated G-loops (Table S5, Figure S5), 15 had clash scores below 2, while ZNF65440 had a score of 172, indicating a minor clash. The clash was caused by a low confidence region in the AF2 structure and could be resolved by relaxing the complex with Rosetta (Figure S6A)65. On the other hand, a protein with no evidence supporting it being a neo-substrate, PAAF1, had a major clash with a score of 1,551 which could not be resolved by relaxation (Figure S6B). Based on these examples, and analysis of the clash scores of all hit proteins containing a clear structural G-loop in AF2 (Figure S6C), we filtered out domains with scores greater than 200 resulting in a list of 14,189 loops across 6,018 proteins with nonexistent, or marginal clashes with CRBN (Figure 3A, Table S5).
Of the 298 total enriched candidates, 199 were found to have a clear structural G-loop with 162 having a clash score below 200. Given the high proportion of ZF proteins identified as targets across this enrichment database (Figure 2F), we mapped the fold change in enrichment for proteins with an annotated ZF domain across all 20 degraders for both MOLT4 (Figure 3B) and Kelly cells (Figure 3C). Across these two cell lines, we identified 19 previously reported and 28 new neo-substrates as chemically recruited to CRBN. We then used our G-loop database to inform on which of these targets have a tractable G-loop and found that only five of the 57 identified targets do not contain a structural G-loop (Figure 3C, Table S5). Given what we know about the recruitment and binding of CRBN neo-substrates, targets usually bind through a dominant structural hairpin. Since we do not have validated degron information for all these ZF targets, we assumed that the G-loop with the lowest clash score has the highest likelihood to bind and therefore proceeded with evaluation of a single G-loop for each target. To gauge how the clash scores for these ZF targets compare to all hits in the G-loop database, we compared the clash scores for our ZF targets to those of all hits (Figure S6C) demonstrating a pronounced trend towards lower scores for ZF targets suggesting fewer unfavorable interactions (Figure S5A–B). Notably, when we explored the ZF hits with higher clash scores (>10) and >3 hit frequency, we realized that almost all of these have a reported association with at least one of the validated hits – ZMYND8 (cs 455, binds to ZNF687), and RNF166 (cs 17, binds to ZNF653/ZBTB39/ZNF827) – which also offers the possibility that these proteins could be collateral targets, recruited via piggybacking on their binding partners, the direct binders (Table S4). Finally, we compared ZF targets across the two cell lines as an additional means for validation, and found 10 overlapping proteins, 6 of which are novel recruited targets (ZBED3, MNAT1, MTA2, ZBTB44, TRIM28) (Figure 3D).
There are many factors to take into consideration when looking to predict target degradability, such as ternary complex formation26,31,66 and target ubiquitylation12,32,67–69, and multiple studies have placed an emphasis on exploring their role in driving productive degradation70,71. For degrader-induced degradation to occur, a ternary complex consisting of ligase-degrader-target needs to form for proximity-mediated ubiquitin transfer to the target protein. Because ternary complex formation is necessary for successful protein degradation, we set out to explore the relationship between ternary complex formation and degradation for ZF targets identified in this study. We focused our evaluation on the parental IMiD molecules which have been subjected to degradation target profiling using unbiased global proteomics analysis across a panel of four cell lines (SK-N-DZ, Kelly, MM.1S, hES)19. Comparison of the enriched ZF targets to the published degradation data shows a consistent trend across the three IMiDs where only ~30% of the enriched targets that were quantified in global proteomics studies were degraded, with ~ 60% of the targets quantified but not reported as degraded (Figure 3E, Table S5). The data was then grouped to allow a global comparison of the enriched versus degraded IMiD targets. The comparison revealed that of the 31 ZF targets enriched across these three molecular glues, only 11 of the 29 proteins quantified in global proteomics experiments were found to be degraded (Figure 3F, Table S5). 18 proteins were quantified in global proteomics but were not identified as degraded. This prompted us to question whether these targets were resistant to degradation by IMiDs and their analogs, or if they were not identified as degraded due to experimental limitations such as inadequate sensitivity to detect minor changes in protein abundance, rapid protein turnover or suboptimal experimental conditions. We found that although several of the targets (WIZ, PATZ1, ZNF687, ZMYM2 and HIC2) were not reported as degraded in Donovan et al.19, they have since been reported as degraded in other published studies40,72,73 confirming that IPs provide a complementary approach able to overcome limitations in sensitivity. The absence of degradation data for the remaining targets could imply that these targets are resistant to degradation, or similar to the above proteins, the appropriate degradation experiment has yet to be performed. These data demonstrate that our IP workflow provides a significant advantage over global proteomics analysis by enabling selective isolation and enrichment of targets that may be below the change in abundance threshold for consistent identification with global proteomics approaches.
IMiD derived molecular glues recruit hundreds of non-zinc finger proteins
The largest target class of CRBN neo-substrates today are ZF containing proteins, however, of the ~20,000 proteins in the human proteome, ZF containing proteins only make up a relatively small proportion with about ~1700 ZF proteins reported74. So far, only a handful of targets are reported to lack a ZF motif, which includes GSPT111, CK1α12,17, PDE6D75, and RAB2875. With this in mind, we examined our list of targeted proteins with a focus on those that do not contain a reported ZF domain and found 251 non-ZF proteins enriched across the IP dataset (Figure 4A, Table S4). These non-ZF proteins include a wide range of families such as protein kinases (IRAK1, TBK1, CDK7), RNA recognition motif proteins (ELAVL1, PPIL4, CSTF2, RBM45), metabolic enzymes (ASS1, PAICS, ACLY, CS, ACADVL), translational proteins (MARS1, ETF1, EEF1E1, EIF4B) and more spanning different biological pathways. To assist in establishing confidence in some of these targets, we performed a comparison of the non-ZF targets enriched in the two tested cell lines and found 39 targets were identified in both MOLT4 and Kelly cells, including the four above mentioned targets (Figure 4A–B). We then assessed the AF2 structures of each of these 39 proteins and found that almost all of them (33/39) contain a structural G-loop (Figure 4B, Figure S5, Table S5).
Given the large number of non-ZF targets identified in this study and the lack of emphasis in the public domain with regards to non-ZF CRBN neo-substrates, we selected a series of non-ZF proteins for further experimental validation. Firstly, to demonstrate that these neo-substrates are directly recruited to CRBN, we examined ternary complex formation using recombinant purified proteins. Using two of the more promiscuous molecular glues, pomalidomide and FPFT-2216, we tested previous reported degradation targets PDE6D, RAB28 and DTWD1, along with a newly discovered target PPIL4. Indeed, PDE6D, DTWD1, and PPIL4 formed compound dependent ternary complex with CRBN at varying effective concentrations (Figure 4C). However, RAB28, which was previously reported to be degraded by IMiDs19 and FPFT-221675, did not show any evidence for direct binding to CRBN using purified proteins. Since RAB28 has previously been reported as a CRBN neo-substrate and consistently scored across our enrichment study, we explored whether there was any evidence suggesting that RAB28 could be a collateral target. Exploration of protein-protein interaction databases including BioPlex33 and STRING-DB76 revealed that RAB28 is known to bind to two validated IMiD-CRBN neo-substrates PDE6D and ZNF653 (Table S4), suggesting that RAB28 is likely an indirectly recruited target. These data demonstrate that in addition to identifying direct binders, we can also identify indirect binding partners that may be simultaneously recruited together with direct binding neo-substrates. Given that targeted protein degradation requires not only recruitment to CRBN, but also CRBN mediated ubiquitin transfer for degradation, we also monitored whether the recruited proteins can be ubiquitylated by CRL4CRBN. In vitro ubiquitylation assays showed robust ubiquitin modification on all 3 recruited non-ZF proteins in the presence of pomalidomide or FPFT-2216 (Figure 4D). In addition, all three of these targets were degraded in response to IMiD treatment as observed by global proteomics analysis (Figure S6D, Table S5). Using structural G-loop alignments, we then assessed the potential for each of these three proteins to bind to IMiD-CRBN and found that all three proteins had a G-loop with a clash score of <200 (Figure 4E). However, the aligned clash score for DTWD1 was relatively high and approaching the upper 200 threshold (cs 198). We performed relaxation with Rosetta and found that this reduced the clash score to 1.58 by allowing minor shifts in the overall conformation while retaining the structural G-loop (Figure S6E). This process demonstrates that in some cases, clash scores can be relieved through minor structural rearrangements using Rosetta relax.
To expand our understanding of the recruitment of non-ZF targets, we determined cryo-EM structures of CRBN-DDB1ΔB-FPFT-2216 bound to PPIL4 and PDE6D, respectively (Figure 4F, Figure S7, Table 1). The complex structures were both refined to a global resolution of around 3.4 Å and the quality of the resulting maps were sufficient to dock the complex components, but the flexibly tethered PPIL4 resulted in a lower local resolution. We were able to observe PPIL4 engagement with FPFT-2216-CRBN via its Gly278 harboring G-loop as expected from the G-loop alignment, as well as for PDE6D via its Gly28 G-loop. Furthermore, overall density allowed fitting of FPFT-2216 in bulk although the reduced resolution in that region did not permit exact positioning of the molecule. Nevertheless, we were able to see that the glutarimide ring engages CRBN’s binding pocket in a similar manner to other IMiD molecular glues. The triazole interacts with the backbone of the G-loop, and the methoxythiophene moiety potentially contacts both the PPIL4 backbone of the G-loop and Arg273. This suggests that the triazole and the methoxythiophene moieties could provide specificity elements to FPFT-2216 mediated neo-substrate recruitment. The methoxythiophene moiety also engaged Arg23 of PDE6D, indicating that FPFT-2216 might derive specificity in engaging an arginine residue from its neo-substrates. Analysis of the non-ZF targets of FPFT-2216 revealed several other proteins harboring an arginine or a lysine residue at this sequence location (PDE6D, SCYL1, RBM45, PPIL4). Finally, we compared the experimental structure to the AF2 predicted G-loop aligned structure of PPIL4 (Figure S6F). The G-loop aligned structure of PPIL4 presented a clash score of 3.38, which showed the C-terminal region of CRBN around residue Arg373 to be clashing with PPIL4’s loop harboring residue Val250. Although the low resolution permitted only backbone level fitting of PPIL4, we observed that the cryo-EM structure revealed a minor shift in the RRM domain of PPIL4 to accommodate this minor clash suggested in the G-loop aligned structure while retaining overall conformational similarity of the G-loop (Figure S6F). Meanwhile, PDE6D retained overall similar conformation with minor shifts that did not alter the interaction with CRBN (Figure S6G).
These data demonstrate that RRM domain containing proteins represent a new class of proteins targetable through CRBN dependent molecular glues. Using structural modeling we increase confidence in these new targets while also providing a reminder that structural analysis and AF2 predicted structures are static models and although they provide excellent structural guidance, we need to keep in mind that proteins in solution are flexible and dynamic.
Discovery of new and selective molecular glue for PPIL4
While the proteomics-based screening workflow identifies novel putative CRBN targets and provides initial chemical matter, it does not necessarily provide the best starting point for developing a chemical probe or therapeutic due to the limited number of molecules screened. We hypothesized that this limitation could be overcome by following up proteomics screening with a target centric screen of a larger CRBN binder library to identify the optimal chemical starting point. To test this, we set out to identify PPIL4 targeting molecular glues with improved selectivity and lacking the triazole moiety. We employed an IMiD molecular glue library consisting of ~6000 compounds of various IMiD analogs that were either synthesized in-house or purchased externally. We screened this library against PPIL4 using TR-FRET to measure compound-induced PPIL4 recruitment to CRBN (Figure 5A). TR-FRET ratios were obtained by incubating the library with GFP fused CRBN-DDB1ΔB, biotinylated PPIL4, and Tb-labeled streptavidin that binds to the biotinylated PPIL4. The library was compared relative to the positive control, whereby the 520/490 ratio of FPFT-2216 at 10 μM was normalized as 1, and compounds were tested at 1.66 μM or 3.33 μM to find hits with equal or improved efficacy in directly recruiting PPIL4 to CRBN-DDB1ΔB. We were able to narrow down the library to two molecules that performed similar or better than FPFT-2216 (Figure 5B). These lead compounds were subject to a full titration to assess recruitment efficacy by TR-FRET. Ultimately, after recognizing one of the two hits was due to autofluorescence, we were able to identify a molecule, Z6466608628, that produced a higher 520/490 ratio, and a better EC50 of 0.34 μM compared to FPFT-2216, measured at 1.05 μM in this experiment (Figure 5C–D).
To test the efficacy and selectivity of our lead compound, we first performed IP-MS in comparison with FPFT-2216 in Kelly cell lysate. While FPFT-2216 recruited many proteins, Z6466608628 selectively recruited PPIL4, along with its binding partner DHX40 (Figure 5E, Table S5). We then performed global proteomics in MOLT4 cells to confirm that Z6466608628 can induce selective downregulation of PPIL4 (Figure 5F, Table S5). These data collectively demonstrate the complete workflow, starting from the identification of a novel non-ZF target PPIL4 in a chemoproteomics screen, to the discovery of a new PPIL4 selective molecular glue that would serve as an excellent lead molecule for structural optimization.
DISCUSSION
Targeted protein degradation and induced proximity are part of a rapidly expanding field focused on the development of small molecules that leverage induced neo-protein-protein interactions to drive pharmacology. In this study, we develop and showcase a new workflow for high sensitivity, unbiased target identification of degraders and non-degrading molecular glues, identifying more than 290 targets recruited to CRBN by IMiD-like molecules. We demonstrate that this new approach to target identification can reveal critical insights and new targets that are missed by traditional screening methodologies and provide a blueprint from discovery to optimization and structure guided design of new molecular glue degraders.
Thalidomide and its derivatives, lenalidomide and pomalidomide (IMiDs), have had a checkered past. These molecules have been in use for a variety of indications, on and off, since the 1950’s and have experienced perhaps the greatest turnaround in drug history. From devastating birth defects to effective hematological cancer therapy, and more recently, significant investment in utilizing these molecules for TPD-based therapeutics. While a decade of research has slowly uncovered around 50 reported neo-substrates of IMiD’s, thousands of proteins harbor G-loops that have the potential for recruitment to CRBN by IMiD molecules. Our simple, cost effective and highly scalable unbiased screening workflow combines whole cell lysate with recombinant Flag-CRBN and degraders to enrich target binders from the complex proteome. Through an IMiD-analog diversity screen across two cell lines, we mapped a significant expansion of the neo-substrate repertoire by identifying 298 proteins recruited to CRBN, with 270 of these being novel targets. Unlike many current high throughput screening workflows that focus on the end point – degradation, this workflow allows us to explore the fundamental first step of proximity induced degradation – recruitment, where we are now able to identify targets that are directly or indirectly recruited to CRBN. This sensitive workflow sheds light onto a previously unchartered element of the molecular glue mechanism of action and establishes insights into how and why certain molecular glues may exhibit higher efficacy than others. Surprisingly, we discovered targets recruited to CRBN that are resistant to degradation, demonstrating the first examples of targets being glued to CRBN without productive degradation. Exploration of two of these targets, ASS1 and ZBED3, does not offer any clues as to why they are not degraded since both have reported ubiquitylated sites77. Numerous possibilities exist, from these targets being tightly preoccupied by other binding partners, geometric constraints leading to inaccessibility of lysines, removal of ubiquitylation by deubiquitinases, or preclusion of the catalytic sites due to size and shape preventing active ubiquitylation. It is important to note that the non-degrading functions of these molecular glues may have interesting degradation-independent pharmacology that have not yet been investigated, thus providing an opportunity for future experimental research.
The comprehensive G-loop database provided us with prefiltered insights as to whether these targets have the potential to be recruited to CRBN through the currently established mechanism of G-loop binding. However, although most targets identified in this study do have a structural G-loop, we do have numerous instances of proteins that do not harbor a G-loop. Some of these targets do have a structurally similar hairpin motif but are lacking the ‘essential’ glycine in position six. Whereas other targets did not have this structural motif at all. These findings indicate the potential for alternative recruitment mechanisms such as proteins piggybacking on a direct G-loop carrying target. This concept of collateral (or bystander) targeting was also demonstrated in a study exploring HDAC degradability, where it was found that both HDACs, and their known complex binding partners can be degraded31. Alternatively, and perhaps more intriguingly, the potential for recruitment of proteins through a distinct structural motif suggesting there may be new binding mechanisms that are pending discovery. The potential capacity for IMiDs to yield interfaces favorable for recruitment of various structural motifs would considerably expand the diversity CRBN neo-substrates and broaden therapeutic applications.
Amongst the targets identified in this study, we not only discovered many new C2H2 ZF transcription factor targets but also extended targets beyond C2H2 ZF proteins, into additional classes of proteins such as those containing RNA recognition motif (RRM) domain and kinase domains, confirming that CRBN is an incredibly versatile ligase and very well suited to hijacking for TPD applications. We reveal 251 non-ZF targets, a dramatic increase in the breadth and number of proteins targeted by CRBN from the currently reported targets of less than a dozen. Direct binding data using TR-FRET on a selection of these targets validates their direct binding mechanism, and structural characterization further corroborates this binding while validating the generated G-loop alignment database as a tool to assist prioritization of targets using clash score assessment. Using the accumulative data, we selected a novel non-ZF neo-substrate, PPIL4, for additional screening to illustrate the utility of this workflow for prioritization efforts. After a biochemical ternary complex recruitment screen of around 6,000 IMiD analogs, we selected a single hit compound and used chemoproteomics to confirm selective recruitment of PPIL4 to CRBN. Genomic studies have reported that PPIL4 is essential for brain specific angiogenesis and has implications in intracranial aneurysms78, and is known to regulate the catalytic activation of the spliceosome79. Thus, this new molecular glue could be of great interest to target the splicing pathway, in relation to intracranial aneurysms, or in other contexts.
We believe our strategic workflow and comprehensive data package, along with outlining specific applications of these, provides a valuable resource for the chemical biology, drug discovery and induced proximity communities. Importantly, the workflow is neither limited to CRBN nor to TPD, but rather can be applied to any induced proximity application. We expect the enrichment workflow will provide a blueprint for expansion into target identification for induced proximity platforms as well as further expansion of targets for protein degraders beyond molecular glues. Through initial scouting efforts on heterobifunctional degraders and additional ligases we are confident there are many novel discoveries to be made with already existing chemistry and we envision this as an evolving resource where we will continue to release data as it becomes available.
SIGNIFICANCE
Degraders and molecular glues are small molecules that can target and promote the degradation of specific proteins providing a novel approach for modulating protein function. Currently available unbiased methods to identify targets of degraders, although successful in identifying transient and/or degraded targets, are limited in sensitivity and ability to identify direct binders of these molecules, prohibiting identification of targets that have weak expression changes or are glued and not degraded. Here, we develop an automatable high throughput method for the identification of chemically-induced binders. We demonstrate the ability to comprehensively identify new targets by identifying 298 neo-substrates of CRBN, significantly expanding the repertoire of actionable targets. We then used structural and biochemical characterization alongside a computational structural alignment workflow to validate hit targets and selected one novel target, PPIL4, to perform a focused biochemical screen for the identification of a new lead molecule. CRBN is the most targeted ligase in the TPD field, with molecules FDA approved and more in clinical trials it is important that we understand the complete cellular and molecular impact of targeting this ligase. The findings presented in this study, open a new and complementary avenue for target identification and create a valuable data resource mapping a wide range of neo-substrates of the CRBN ligase. Through expansion of the range of CRBN targets, we not only enhance our knowledge of newly druggable targets and offer new avenues for therapeutic development, but we also enhance our understanding of the molecular mechanisms and cellular pathways that are influenced by existing and future IMiD molecules providing opportunities for improved drug design.
RESOURCES AVAILABILITY
Lead Contact
Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Eric Fischer ([email protected]).
Materials Availability
Small molecules described in this study will be made available on request, upon completion of a Materials Transfer Agreement.
Lead Contact
Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Eric Fischer ([email protected]).
Materials Availability
Small molecules described in this study will be made available on request, upon completion of a Materials Transfer Agreement.
EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS
Mammalian Cell culture
MOLT4 and Kelly cells were cultured in RPMI-1640 media supplemented with 10% fetal bovine serum and 2 mM L-glutamine and grown in a 37 °C incubator with 5% CO2. MOLT4 cells are derived from 19 year old male, Kelly cells are derived from 1 year old female.
Insect cell culture
High Five insect cells were cultured at 27 °C in SF-4 Baculo Express ICM medium (BioConcept) in suspension. Sf9 insect cells were cultured at 27 °C in ESF 921 medium (Expression Systems).
Mammalian Cell culture
MOLT4 and Kelly cells were cultured in RPMI-1640 media supplemented with 10% fetal bovine serum and 2 mM L-glutamine and grown in a 37 °C incubator with 5% CO2. MOLT4 cells are derived from 19 year old male, Kelly cells are derived from 1 year old female.
Insect cell culture
High Five insect cells were cultured at 27 °C in SF-4 Baculo Express ICM medium (BioConcept) in suspension. Sf9 insect cells were cultured at 27 °C in ESF 921 medium (Expression Systems).
METHOD DETAILS
Immunoprecipitation
A total of 1 × 107 cells per IP were collected and lysed in lysis buffer (50 mM Tris pH 8, 200 mM NaCl, 2 mM TCEP, 0.1% NP-40, 10 units turbonuclease/200 μL buffer, 1x cOmplete protease inhibitor tablet/5 mL buffer) and sonicated on ice for 5 rounds of 2 seconds followed by 10 second pauses at 25% amplitude. After centrifugation clarification, lysate was transferred to new lobind tubes. 20 μg of Flag-CRBN-DDB1ΔB, 1 μM of MLN4924 and CSN5i-3 (neddylation trap)96, and 1 μM of selected degraders or DMSO vehicle control were added to each lysate and incubated with end-over-end rotation for 1 hour in the cold room. 20 μL of pre-washed and resuspended M2-Flag magnetic bead slurry was added to each IP and incubated with end-over-end rotation for 1 hour in the cold room. Beads were washed with wash buffer (50 mM Tris pH 8, 2 mM TCEP, 0.1% NP-40, 1x cOmplete protease inhibitor tablet/5 mL buffer) containing the respective compounds followed by specific elution method for the next application (immunoblot or mass spectrometry).
Samples prepared on the OT2 followed the same procedures as above but using scaled down equivalents of reagents. After the three detergent washes described above, the samples were washed an additional three times with non-detergent buffer (50 mM Tris pH 8, 2 mM TCEP, 1x cOmplete protease inhibitor tablet/5 mL buffer) containing the required compounds prior to appropriate elution described below.
Immunoblot
IP samples were eluted by resuspension in SDS sample buffer and heated at 95 °C for 5 minutes. Samples were run on 4–20% Mini-PROTEAN TGX Precast Protein gels (Bio-rad). Protein was transferred to PVDF membranes using the iBlot 2.0 dry blotting system (Thermo Fisher Scientific). Membranes were blocked with Intercept blocking solution (LiCor), washed three times with PBST and incubated with primary antibodies diluted using Intercept® T20 (PBS) Antibody Diluent, overnight at 4 °C, followed by three washes with PBST and incubation with secondary antibodies for 1 hour in the dark. The membrane was washed three final times in PBST and imaged on the Odyssey LCx imaging system (LiCor).
Cloning and Protein Expression
All proteins are derived from human origin sequences. ZBED3 (residues 39–108), ZNF219 (residues 53–110) were cloned into pGEX4T1-TEV vectors with C-terminal Avi-Strep fusion. These were expressed in LOBSTR BL21(DE3) E. coli, purified by GST-affinity, followed by liberation of the GST-tag by overnight TEV protease incubation, ion exchange chromatography, and size exclusion chromatography into a final buffer of 25 mM HEPES, 200 mM NaCl, and 1 mM TCEP, pH 7.5. UBE2D3, UBE2G1, and UBE2M were cloned into pGEX4T1-TEV vectors, and NEDD8 was cloned into pGEX4T1–3C vector. Purification of these proteins were performed as described above. APPBP1-UBA3 was cloned into a pET based vector and expressed in LOBSTR BL21(DE3) E. coli, purified by nickel affinity, ion exchange chromatography, and size exclusion chromatography into a final buffer of 25 mM HEPES, 200 mM NaCl, and 1 mM TCEP, pH 7.5. Ubiquitin was cloned into a pET3a vector and was expressed in Rosetta BL21(DE3) E. coli. Ubiquitin was purified by SP Sepharose resin at pH 4, and was subject to size exclusion chromatography into a final buffer of 25 mM HEPES, 150 mM NaCl, 1 mM TCEP, pH 7.5. PDE6D, DTWD1, PPIL4, PPIL4 RRM (residues 240–318), and RAB28 were cloned into pAC8-Strep-Avi vectors. Baculovirus was generated in Spodoptera frugiperda cells, and proteins were expressed in Trichoplusia ni cells. These were purified by Strep-affinity, followed by ion exchange chromatography and size exclusion chromatography into 25 mM HEPES, 200 mM NaCl, and 1 mM TCEP, pH 7.5. GST-TEV-CRBN, GST-TEV-eGFP-CRBN, DDB1 FL, GST-TEV-UBA1, CUL4A (residues 38-C), RBX1(residues 5-C) were cloned into pLIB vectors. His-TEV-DDB1ΔBPB (Δresidues 396–705 with a GNGNSG linker) and Flag-Spy-CRBN were cloned into a pAC8 vector. CRBN and DDB1 were coexpressed in the following pairs, CRBN-DDB1, CRBN-DDB1ΔB, Flag-CRBN-DDB1ΔB, and eGFP-CRBN-DDB1ΔB, purified by GST-affinity, followed by TEV cleavage overnight, ion exchange chromatography, and size exclusion chromatography into 25 mM HEPES, 150 mM NaCl, 1 mM TCEP, pH 7.5. CUL4A and RBX1 were coexpressed, CUL4-RBX1, and UBA1 were purified as described above.
Time-Resolved Fluorescence Energy Transfer (TR-FRET)
Ternary complex formation of CRBN-DDB1, neo-substrate, and compound was measured by TR-FRET. 20 nM biotinylated neo-substrate, 2 nM Tb (CoraFluor-1)-labeled Streptavidin, and 200 nM eGFP-CRBN-DDB1ΔB were added in assay buffer (25 mM HEPES, 100 mM NaCl, 0.5% Tween-20, and 0.5% BSA). This reaction mix was added to a 384 well microplate, and compound was titrated to indicated concentrations using a D300e Digital Dispenser (HP). Reaction was incubated at room temperature for 1 hour and was measured using a PHERAstar FS microplate reader (BMG Labtech). The 520 nm/490 nm signal ratio was measured to calculate ternary complex formation, and datapoints were plotted to calculate EC50 values by using agonist versus response variable slope (four parameter model) in Graphpad Prism (v10.1.1).
In-vitro ubiquitylation assay
Ubiquitylation of neo-substrates was performed by premixing 500 nM neddylated CRL4CRBN, 2 μM UBE2D3, 2 μM UBE2G1, 200 nM UBA1, 500 nM Strep tagged-neo-substrate, and 10 μM compound in assay buffer (25 mM HEPES, 100 mM NaCl, 10 mM MgCl2, 5 mM ATP, pH 7.5). This mixture was incubated for 15 min on ice, and reaction was started by adding 60 μM ubiquitin at room temperature. Sample was quenched by addition of SDS sample buffer and reaction products were separated on 4–20% SDS-PAGE gels. Assay was analyzed by immunoblotting as described above. Neddylation of CUL4-RBX1 was performed as described in Scott et al., 201497. In brief, 12 μM CUL4-RBX1, 1 μM UBE2M, 0.2 μM APPBP1-UBA3, 25 μM NEDD8 was incubated at room temperature for 10 min in assay buffer (25 mM HEPES, 100 mM NaCl, 10 mM MgCl2, 5 mM ATP, pH 7.5). Reaction was quenched by addition of 10mM DTT and was purified by size exclusion chromatography into buffer containing 25 mM HEPES, 150 mM NaCl, 1 mM TCEP, pH 7.5.
Cryo-EM sample preparation and Data processing
10 μM CRBN-DDB1ΔB, 20 μM PPIL4 RRM, and 100 μM FPFT-2216 were incubated on ice for 30 min in buffer containing 25 mM HEPES, 150 mM NaCl, 1 mM TCEP, pH 7.5. The protein complex was diluted to 1.5 μM final concentration before application to grids. Quantifoil UltraAuFoil grids (R0.6/1) were glow discharged at 20 mA for 2 min. Prior to complex application, 4 μL of 10 μM CRBN-agnostic IKZF1 (residues 140–196, harboring mutants Q146A, G151N) was applied to saturate the air-water interface82. After 1 min, the grid was blotted from the back, and complex was applied, and immediately plunged into liquid ethane. Dataset was collected on a Talos Arctica at 200 kV equipped with a Gatan K3 direct detector in counting mode. Movies were collected with a total dose of 54 e−/Å2 over 40 frames, at 1.1 Å/pixel with a nominal magnification of 36,000x, with a defocus range of −0.8 μm to −2.0 μm.
Sample preparation for immunoprecipitation mass spectrometry (IP-MS)
Immunoprecipitation (IP) was performed as described above. After the final wash step, samples were eluted using 0.1 M Glycine-HCl, pH 2.7. Tris (1M, pH 8.5) was added to elution to reach a pH of 8. Samples were then reduced with 10 mM TCEP for 30 min at room temperature, followed by alkylation with 15 mM iodoacetamide for 45 min at room temperature in the dark. Alkylation was quenched by the addition of 10 mM DTT. Proteins were isolated by methanol-chloroform precipitation (only for manual IPs. Automated IPs undergo three non-detergent washes instead). The protein pellets were dried and then resuspended in 50 μL 200 mM EPPS pH 8.0. The resuspended protein samples were digested with 2 μg LysC and 1 μg Trypsin overnight at 37°C. Sample digests were acidified with formic acid to a pH of 2–3 prior to desalting using C18 solid phase extraction plates (SOLA, Thermo Fisher Scientific). Desalted peptides were dried in a vacuum-centrifuged and reconstituted in 0.1% formic acid for LC-MS analysis.
Label free quantitative mass spectrometry with DDA and data analysis
Data were collected using an Orbitrap Exploris 480 mass spectrometer (Thermo Fisher Scientific) equipped with a FAIMS Pro Interface and coupled with a UltiMate 3000 RSLCnano System. Peptides were separated on an Aurora 25 cm × 75 μm inner diameter microcapillary column (IonOpticks), and using a 60 min gradient of 5 – 25% acetonitrile in 1.0% formic acid with a flow rate of 250 nL/min. Each analysis used a TopN data-dependent method. The data were acquired using a mass range of m/z 350 – 1200, resolution 60,000, 300% normalized AGC target, auto maximum injection time, dynamic exclusion of 30 sec, and charge states of 2–6. TopN 40 data-dependent MS2 spectra were acquired with a scan range starting at m/z 110, resolution 15,000, isolation window of 1.4 m/z, normalized collision energy (NCE) set at 30%, standard AGC target and the automatic maximum injection time.
Proteome Discoverer 2.5 (Thermo Fisher Scientific) was used for .RAW file processing and controlling peptide and protein level false discovery rates, assembling proteins from peptides, and protein quantification from peptides. MS/MS spectra were searched against a Swissprot human database (January 2021) with both the forward and reverse sequences as well as known contaminants such as human keratins. Database search criteria were as follows: tryptic with two missed cleavages, a precursor mass tolerance of 10 ppm, fragment ion mass tolerance of 0.03 Da, static alkylation of cysteine (57.0215 Da) and variable oxidation of methionine (15.995 Da), N-terminal acetylation (42.011 Da) and phosphorylation of serine, threonine and tyrosine (75.966 Da). Peptides were quantified using the MS1 Intensity, and peptide abundance values were summed to yield the protein abundance values. Resulting data was filtered to only include proteins that had a minimum of 3 counts in at least 3 replicates of each independent comparison of treatment sample to the DMSO control. Protein abundances were globally normalized and scaled using in-house scripts in the R framework (R Development Core Team, 2014). Proteins with missing values were imputed by random selection from a Gaussian distribution either with a mean of the non-missing values for that treatment group or with a mean equal to the median of the background (in cases when all values for a treatment group are missing). Significant changes comparing the relative protein abundance of these treatment to DMSO control comparisons were assessed by moderated t-test as implemented in the limma package within the R framework80.
Label free quantitative mass spectrometry with diaPASEF and data analysis
Data were collected using a TimsTOF Pro2 (Bruker Daltonics, Bremen, Germany) coupled to a nanoElute LC pump (Bruker Daltonics, Bremen, Germany) via a CaptiveSpray nano-electrospray source. Peptides were separated on a reversed-phase C18 column (25 cm × 75 μM ID, 1.6 μM, IonOpticks, Australia) containing an integrated captive spray emitter. Peptides were separated using a 50 min gradient of 2 – 30% buffer B (acetonitrile in 0.1% formic acid) with a flow rate of 250 nL/min and column temperature maintained at 50 °C. To perform diaPASEF, the precursor distribution in the DDA m/z-ion mobility plane was used to design an acquisition scheme for DIA data collection which included two windows in each 50 ms diaPASEF scan. Data was acquired using sixteen of these 25 Da precursor double window scans (creating 32 windows) which covered the diagonal scan line for doubly and triply charged precursors, with singly charged precursors able to be excluded by their position in the m/z-ion mobility plane. These precursor isolation windows were defined between 400 – 1200 m/z and 1/k0 of 0.7 – 1.3 V.s/cm2.
The diaPASEF raw file processing and controlling peptide and protein level false discovery rates, assembling proteins from peptides, and protein quantification from peptides was performed using library free analysis in DIA-NN 1.898. Library free mode performs an in-silico digestion of a given protein sequence database alongside deep learning-based predictions to extract the DIA precursor data into a collection of MS2 spectra. The search results are then used to generate a spectral library which is then employed for the targeted analysis of the DIA data searched against a Swissprot human database (January 2021). Database search criteria largely followed the default settings for directDIA including: tryptic with two missed cleavages, carbamidomethylation of cysteine, and oxidation of methionine and precursor Q-value (FDR) cut-off of 0.01. Precursor quantification strategy was set to Robust LC (high accuracy) with RT-dependent cross run normalization. Resulting data was filtered to only include proteins that had a minimum of 3 counts in at least 4 replicates of each independent comparison of treatment sample to the DMSO control. Protein abundances were globally normalized using in-house scripts in the R framework (R Development Core Team, 2014). Proteins with missing values were imputed by random selection from a Gaussian distribution either with a mean of the non-missing values for that treatment group or with a mean equal to the median of the background (in cases when all values for a treatment group are missing). Significant changes comparing the relative protein abundance of these treatment to DMSO control comparisons were assessed by moderated t-test as implemented in the limma package within the R framework80.
TMT quantitative global proteomics
Cells were lysed by the addition of lysis buffer (8 M urea, 50 mM NaCl, 50 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (EPPS) pH 8.5, Protease and Phosphatase inhibitors) followed by manual homogenization by 20 passes through a 21-gauge (1.25 in. long) needle. Lysate was clarified by centrifugation and protein quantified using bradford (Bio-Rad) assay. 100 μg of protein for each sample was reduced, alkylated and precipitated using methanol/chloroform as previously described19 and the resulting washed precipitated protein was allowed to air dry. Precipitated protein was resuspended in 4 M urea, 50 mM HEPES pH 7.4, buffer for solubilization, followed by dilution to 1 M urea with the addition of 200 mM EPPS, pH 8. Proteins were digested for 12 hours at room temperature with LysC (1:50 ratio), followed by dilution to 0.5 M urea and a second digestion step was performed by addition of trypsin (1:50 ratio) for 6 hours at 37 °C. Anhydrous ACN was added to each peptide sample to a final concentration of 30%, followed by addition of Tandem mass tag (TMT) reagents at a labelling ratio of 1:4 peptide:TMT label. TMT labelling occurred over a 1.5 hour incubation at room temperature followed by quenching with the addition of hydroxylamine to a final concentration of 0.3%. Each of the samples were combined using adjusted volumes and dried down in a speed vacuum followed by desalting with C18 SPE (Sep-Pak, Waters). The sample was offline fractionated into 96 fractions by high pH reverse-phase HPLC (Agilent LC1260) through an aeris peptide xb-c18 column (phenomenex) with mobile phase A containing 5% acetonitrile and 10 mM NH4HCO3 in LC-MS grade H2O, and mobile phase B containing 90% acetonitrile and 5 mM NH4HCO3 in LC-MS grade H2O (both pH 8.0). The resulting 96 fractions were recombined in a non-contiguous manner into 24 fractions and desalted using C18 solid phase extraction plates (SOLA, Thermo Fisher Scientific) followed by subsequent mass spectrometry analysis. Data were collected using an Orbitrap Fusion Lumos mass spectrometer (Thermo Fisher Scientific, San Jose, CA, USA) coupled with a Proxeon EASY-nLC 1200 LC system (Thermo Fisher Scientific, San Jose, CA, USA). Peptides were separated on a 1) 50 cm 75 μm inner diameter EasySpray ES903 microcapillary column (Thermo Fisher Scientific). Peptides were separated over a 190 min gradient of 6 – 27% acetonitrile in 1.0% formic acid with a flow rate of 300 nL/min. Quantification was performed using a MS3-based TMT method as described previously99. The data were acquired using a mass range of m/z 340 – 1350, resolution 120,000, AGC target 5 × 105, maximum injection time 100 ms, dynamic exclusion of 120 seconds for the peptide measurements in the Orbitrap. Data dependent MS2 spectra were acquired in the ion trap with a normalized collision energy (NCE) set at 35%, AGC target set to 1.8 × 104 and a maximum injection time of 120 ms. MS3 scans were acquired in the Orbitrap with HCD collision energy set to 55%, AGC target set to 2 × 105, maximum injection time of 150 ms, resolution at 50,000 and with a maximum synchronous precursor selection (SPS) precursors set to 10.
TMT quantitative global proteomics LC-MS data analysis
Proteome Discoverer 2.2 (Thermo Fisher Scientific) was used for .RAW file processing and controlling peptide and protein level false discovery rates, assembling proteins from peptides, and protein quantification from peptides. The MS/MS spectra were searched against a Swissprot human database (January 2021) containing both the forward and reverse sequences. Searches were performed using a 20 ppm precursor mass tolerance, 0.6 Da fragment ion mass tolerance, tryptic peptides containing a maximum of two missed cleavages, static alkylation of cysteine (57.02146 Da), static TMT labelling of lysine residues and N-termini of peptides (229.1629 Da), and variable oxidation of methionine (15.99491 Da). TMT reporter ion intensities were measured using a 0.003 Da window around the theoretical m/z for each reporter ion in the MS3 scan. The peptide spectral matches with poor quality MS3 spectra were excluded from quantitation (summed signal-to-noise across channels < 110 and precursor isolation specificity < 0.5), and the resulting data was filtered to only include proteins with a minimum of 2 unique peptides quantified. Reporter ion intensities were normalized and scaled using in-house scripts in the R framework100. Statistical analysis was carried out using the limma package within the R framework80.
Searching for CRBN-compatible G-loops in the AlphaFold2 database
Using MASTER v1.661, the AlphaFold2 human database (v4)62 comprising 23,391 protein structures was queried for 8-residue loops with backbone root-mean-squared-deviation less than 1.5 Å to CK1α residues 35–42 (extracted from PDB 5FQD12. Loops not having a glycine at the sixth residue were filtered out resulting in a set of 46,040 G-loops from 10,926 proteins. Next, domains containing the G-loops were extracted using domain definitions from DPAM63 and aligned to the casein kinase I α reference loop and CRBN from 5FQD. To estimate backbone clashes, each structure was coarse-grained to represent the side chain as a pseudoatom and scored using Rosetta v13.364. Coarse-graining the structure makes the score rotamer-independent. The interchain_vdw term of the score function was used as a clash score and represents a modified Lennard-Jones 6–12 potential that penalizes atoms overlapping at the interface101. Domains with a clash score of greater than 200 were filtered out resulting in a list 16,0901 loops from 7,111 proteins.
Relieving minor clashes with Rosetta refinement
For select domains with low clash scores, clashes were relieved by relaxing the neo-substrate with Rosetta FastRelax64,65 while holding the G-loop in place. No energy minimization or rotamer optimization was performed on CRBN. Rigid body translation between CRBN and the neo-substrate was disabled. Of the 10 independent trajectories run, the one with the lowest total score was selected and then the clash score was calculated as above.
Quantification and statistical analysis
Statistical methods are described in the according figure legends, and methods. Cryo-EM statistics are based on the gold-standard FSC=0.143 to determine resolution. For quantitative proteomics experiments, statistical analysis was carried out using the limma package within the R framework80.
Immunoprecipitation
A total of 1 × 107 cells per IP were collected and lysed in lysis buffer (50 mM Tris pH 8, 200 mM NaCl, 2 mM TCEP, 0.1% NP-40, 10 units turbonuclease/200 μL buffer, 1x cOmplete protease inhibitor tablet/5 mL buffer) and sonicated on ice for 5 rounds of 2 seconds followed by 10 second pauses at 25% amplitude. After centrifugation clarification, lysate was transferred to new lobind tubes. 20 μg of Flag-CRBN-DDB1ΔB, 1 μM of MLN4924 and CSN5i-3 (neddylation trap)96, and 1 μM of selected degraders or DMSO vehicle control were added to each lysate and incubated with end-over-end rotation for 1 hour in the cold room. 20 μL of pre-washed and resuspended M2-Flag magnetic bead slurry was added to each IP and incubated with end-over-end rotation for 1 hour in the cold room. Beads were washed with wash buffer (50 mM Tris pH 8, 2 mM TCEP, 0.1% NP-40, 1x cOmplete protease inhibitor tablet/5 mL buffer) containing the respective compounds followed by specific elution method for the next application (immunoblot or mass spectrometry).
Samples prepared on the OT2 followed the same procedures as above but using scaled down equivalents of reagents. After the three detergent washes described above, the samples were washed an additional three times with non-detergent buffer (50 mM Tris pH 8, 2 mM TCEP, 1x cOmplete protease inhibitor tablet/5 mL buffer) containing the required compounds prior to appropriate elution described below.
Immunoblot
IP samples were eluted by resuspension in SDS sample buffer and heated at 95 °C for 5 minutes. Samples were run on 4–20% Mini-PROTEAN TGX Precast Protein gels (Bio-rad). Protein was transferred to PVDF membranes using the iBlot 2.0 dry blotting system (Thermo Fisher Scientific). Membranes were blocked with Intercept blocking solution (LiCor), washed three times with PBST and incubated with primary antibodies diluted using Intercept® T20 (PBS) Antibody Diluent, overnight at 4 °C, followed by three washes with PBST and incubation with secondary antibodies for 1 hour in the dark. The membrane was washed three final times in PBST and imaged on the Odyssey LCx imaging system (LiCor).
Cloning and Protein Expression
All proteins are derived from human origin sequences. ZBED3 (residues 39–108), ZNF219 (residues 53–110) were cloned into pGEX4T1-TEV vectors with C-terminal Avi-Strep fusion. These were expressed in LOBSTR BL21(DE3) E. coli, purified by GST-affinity, followed by liberation of the GST-tag by overnight TEV protease incubation, ion exchange chromatography, and size exclusion chromatography into a final buffer of 25 mM HEPES, 200 mM NaCl, and 1 mM TCEP, pH 7.5. UBE2D3, UBE2G1, and UBE2M were cloned into pGEX4T1-TEV vectors, and NEDD8 was cloned into pGEX4T1–3C vector. Purification of these proteins were performed as described above. APPBP1-UBA3 was cloned into a pET based vector and expressed in LOBSTR BL21(DE3) E. coli, purified by nickel affinity, ion exchange chromatography, and size exclusion chromatography into a final buffer of 25 mM HEPES, 200 mM NaCl, and 1 mM TCEP, pH 7.5. Ubiquitin was cloned into a pET3a vector and was expressed in Rosetta BL21(DE3) E. coli. Ubiquitin was purified by SP Sepharose resin at pH 4, and was subject to size exclusion chromatography into a final buffer of 25 mM HEPES, 150 mM NaCl, 1 mM TCEP, pH 7.5. PDE6D, DTWD1, PPIL4, PPIL4 RRM (residues 240–318), and RAB28 were cloned into pAC8-Strep-Avi vectors. Baculovirus was generated in Spodoptera frugiperda cells, and proteins were expressed in Trichoplusia ni cells. These were purified by Strep-affinity, followed by ion exchange chromatography and size exclusion chromatography into 25 mM HEPES, 200 mM NaCl, and 1 mM TCEP, pH 7.5. GST-TEV-CRBN, GST-TEV-eGFP-CRBN, DDB1 FL, GST-TEV-UBA1, CUL4A (residues 38-C), RBX1(residues 5-C) were cloned into pLIB vectors. His-TEV-DDB1ΔBPB (Δresidues 396–705 with a GNGNSG linker) and Flag-Spy-CRBN were cloned into a pAC8 vector. CRBN and DDB1 were coexpressed in the following pairs, CRBN-DDB1, CRBN-DDB1ΔB, Flag-CRBN-DDB1ΔB, and eGFP-CRBN-DDB1ΔB, purified by GST-affinity, followed by TEV cleavage overnight, ion exchange chromatography, and size exclusion chromatography into 25 mM HEPES, 150 mM NaCl, 1 mM TCEP, pH 7.5. CUL4A and RBX1 were coexpressed, CUL4-RBX1, and UBA1 were purified as described above.
Time-Resolved Fluorescence Energy Transfer (TR-FRET)
Ternary complex formation of CRBN-DDB1, neo-substrate, and compound was measured by TR-FRET. 20 nM biotinylated neo-substrate, 2 nM Tb (CoraFluor-1)-labeled Streptavidin, and 200 nM eGFP-CRBN-DDB1ΔB were added in assay buffer (25 mM HEPES, 100 mM NaCl, 0.5% Tween-20, and 0.5% BSA). This reaction mix was added to a 384 well microplate, and compound was titrated to indicated concentrations using a D300e Digital Dispenser (HP). Reaction was incubated at room temperature for 1 hour and was measured using a PHERAstar FS microplate reader (BMG Labtech). The 520 nm/490 nm signal ratio was measured to calculate ternary complex formation, and datapoints were plotted to calculate EC50 values by using agonist versus response variable slope (four parameter model) in Graphpad Prism (v10.1.1).
In-vitro ubiquitylation assay
Ubiquitylation of neo-substrates was performed by premixing 500 nM neddylated CRL4CRBN, 2 μM UBE2D3, 2 μM UBE2G1, 200 nM UBA1, 500 nM Strep tagged-neo-substrate, and 10 μM compound in assay buffer (25 mM HEPES, 100 mM NaCl, 10 mM MgCl2, 5 mM ATP, pH 7.5). This mixture was incubated for 15 min on ice, and reaction was started by adding 60 μM ubiquitin at room temperature. Sample was quenched by addition of SDS sample buffer and reaction products were separated on 4–20% SDS-PAGE gels. Assay was analyzed by immunoblotting as described above. Neddylation of CUL4-RBX1 was performed as described in Scott et al., 201497. In brief, 12 μM CUL4-RBX1, 1 μM UBE2M, 0.2 μM APPBP1-UBA3, 25 μM NEDD8 was incubated at room temperature for 10 min in assay buffer (25 mM HEPES, 100 mM NaCl, 10 mM MgCl2, 5 mM ATP, pH 7.5). Reaction was quenched by addition of 10mM DTT and was purified by size exclusion chromatography into buffer containing 25 mM HEPES, 150 mM NaCl, 1 mM TCEP, pH 7.5.
Cryo-EM sample preparation and Data processing
10 μM CRBN-DDB1ΔB, 20 μM PPIL4 RRM, and 100 μM FPFT-2216 were incubated on ice for 30 min in buffer containing 25 mM HEPES, 150 mM NaCl, 1 mM TCEP, pH 7.5. The protein complex was diluted to 1.5 μM final concentration before application to grids. Quantifoil UltraAuFoil grids (R0.6/1) were glow discharged at 20 mA for 2 min. Prior to complex application, 4 μL of 10 μM CRBN-agnostic IKZF1 (residues 140–196, harboring mutants Q146A, G151N) was applied to saturate the air-water interface82. After 1 min, the grid was blotted from the back, and complex was applied, and immediately plunged into liquid ethane. Dataset was collected on a Talos Arctica at 200 kV equipped with a Gatan K3 direct detector in counting mode. Movies were collected with a total dose of 54 e−/Å2 over 40 frames, at 1.1 Å/pixel with a nominal magnification of 36,000x, with a defocus range of −0.8 μm to −2.0 μm.
Sample preparation for immunoprecipitation mass spectrometry (IP-MS)
Immunoprecipitation (IP) was performed as described above. After the final wash step, samples were eluted using 0.1 M Glycine-HCl, pH 2.7. Tris (1M, pH 8.5) was added to elution to reach a pH of 8. Samples were then reduced with 10 mM TCEP for 30 min at room temperature, followed by alkylation with 15 mM iodoacetamide for 45 min at room temperature in the dark. Alkylation was quenched by the addition of 10 mM DTT. Proteins were isolated by methanol-chloroform precipitation (only for manual IPs. Automated IPs undergo three non-detergent washes instead). The protein pellets were dried and then resuspended in 50 μL 200 mM EPPS pH 8.0. The resuspended protein samples were digested with 2 μg LysC and 1 μg Trypsin overnight at 37°C. Sample digests were acidified with formic acid to a pH of 2–3 prior to desalting using C18 solid phase extraction plates (SOLA, Thermo Fisher Scientific). Desalted peptides were dried in a vacuum-centrifuged and reconstituted in 0.1% formic acid for LC-MS analysis.
Label free quantitative mass spectrometry with DDA and data analysis
Data were collected using an Orbitrap Exploris 480 mass spectrometer (Thermo Fisher Scientific) equipped with a FAIMS Pro Interface and coupled with a UltiMate 3000 RSLCnano System. Peptides were separated on an Aurora 25 cm × 75 μm inner diameter microcapillary column (IonOpticks), and using a 60 min gradient of 5 – 25% acetonitrile in 1.0% formic acid with a flow rate of 250 nL/min. Each analysis used a TopN data-dependent method. The data were acquired using a mass range of m/z 350 – 1200, resolution 60,000, 300% normalized AGC target, auto maximum injection time, dynamic exclusion of 30 sec, and charge states of 2–6. TopN 40 data-dependent MS2 spectra were acquired with a scan range starting at m/z 110, resolution 15,000, isolation window of 1.4 m/z, normalized collision energy (NCE) set at 30%, standard AGC target and the automatic maximum injection time.
Proteome Discoverer 2.5 (Thermo Fisher Scientific) was used for .RAW file processing and controlling peptide and protein level false discovery rates, assembling proteins from peptides, and protein quantification from peptides. MS/MS spectra were searched against a Swissprot human database (January 2021) with both the forward and reverse sequences as well as known contaminants such as human keratins. Database search criteria were as follows: tryptic with two missed cleavages, a precursor mass tolerance of 10 ppm, fragment ion mass tolerance of 0.03 Da, static alkylation of cysteine (57.0215 Da) and variable oxidation of methionine (15.995 Da), N-terminal acetylation (42.011 Da) and phosphorylation of serine, threonine and tyrosine (75.966 Da). Peptides were quantified using the MS1 Intensity, and peptide abundance values were summed to yield the protein abundance values. Resulting data was filtered to only include proteins that had a minimum of 3 counts in at least 3 replicates of each independent comparison of treatment sample to the DMSO control. Protein abundances were globally normalized and scaled using in-house scripts in the R framework (R Development Core Team, 2014). Proteins with missing values were imputed by random selection from a Gaussian distribution either with a mean of the non-missing values for that treatment group or with a mean equal to the median of the background (in cases when all values for a treatment group are missing). Significant changes comparing the relative protein abundance of these treatment to DMSO control comparisons were assessed by moderated t-test as implemented in the limma package within the R framework80.
Label free quantitative mass spectrometry with diaPASEF and data analysis
Data were collected using a TimsTOF Pro2 (Bruker Daltonics, Bremen, Germany) coupled to a nanoElute LC pump (Bruker Daltonics, Bremen, Germany) via a CaptiveSpray nano-electrospray source. Peptides were separated on a reversed-phase C18 column (25 cm × 75 μM ID, 1.6 μM, IonOpticks, Australia) containing an integrated captive spray emitter. Peptides were separated using a 50 min gradient of 2 – 30% buffer B (acetonitrile in 0.1% formic acid) with a flow rate of 250 nL/min and column temperature maintained at 50 °C. To perform diaPASEF, the precursor distribution in the DDA m/z-ion mobility plane was used to design an acquisition scheme for DIA data collection which included two windows in each 50 ms diaPASEF scan. Data was acquired using sixteen of these 25 Da precursor double window scans (creating 32 windows) which covered the diagonal scan line for doubly and triply charged precursors, with singly charged precursors able to be excluded by their position in the m/z-ion mobility plane. These precursor isolation windows were defined between 400 – 1200 m/z and 1/k0 of 0.7 – 1.3 V.s/cm2.
The diaPASEF raw file processing and controlling peptide and protein level false discovery rates, assembling proteins from peptides, and protein quantification from peptides was performed using library free analysis in DIA-NN 1.898. Library free mode performs an in-silico digestion of a given protein sequence database alongside deep learning-based predictions to extract the DIA precursor data into a collection of MS2 spectra. The search results are then used to generate a spectral library which is then employed for the targeted analysis of the DIA data searched against a Swissprot human database (January 2021). Database search criteria largely followed the default settings for directDIA including: tryptic with two missed cleavages, carbamidomethylation of cysteine, and oxidation of methionine and precursor Q-value (FDR) cut-off of 0.01. Precursor quantification strategy was set to Robust LC (high accuracy) with RT-dependent cross run normalization. Resulting data was filtered to only include proteins that had a minimum of 3 counts in at least 4 replicates of each independent comparison of treatment sample to the DMSO control. Protein abundances were globally normalized using in-house scripts in the R framework (R Development Core Team, 2014). Proteins with missing values were imputed by random selection from a Gaussian distribution either with a mean of the non-missing values for that treatment group or with a mean equal to the median of the background (in cases when all values for a treatment group are missing). Significant changes comparing the relative protein abundance of these treatment to DMSO control comparisons were assessed by moderated t-test as implemented in the limma package within the R framework80.
TMT quantitative global proteomics
Cells were lysed by the addition of lysis buffer (8 M urea, 50 mM NaCl, 50 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (EPPS) pH 8.5, Protease and Phosphatase inhibitors) followed by manual homogenization by 20 passes through a 21-gauge (1.25 in. long) needle. Lysate was clarified by centrifugation and protein quantified using bradford (Bio-Rad) assay. 100 μg of protein for each sample was reduced, alkylated and precipitated using methanol/chloroform as previously described19 and the resulting washed precipitated protein was allowed to air dry. Precipitated protein was resuspended in 4 M urea, 50 mM HEPES pH 7.4, buffer for solubilization, followed by dilution to 1 M urea with the addition of 200 mM EPPS, pH 8. Proteins were digested for 12 hours at room temperature with LysC (1:50 ratio), followed by dilution to 0.5 M urea and a second digestion step was performed by addition of trypsin (1:50 ratio) for 6 hours at 37 °C. Anhydrous ACN was added to each peptide sample to a final concentration of 30%, followed by addition of Tandem mass tag (TMT) reagents at a labelling ratio of 1:4 peptide:TMT label. TMT labelling occurred over a 1.5 hour incubation at room temperature followed by quenching with the addition of hydroxylamine to a final concentration of 0.3%. Each of the samples were combined using adjusted volumes and dried down in a speed vacuum followed by desalting with C18 SPE (Sep-Pak, Waters). The sample was offline fractionated into 96 fractions by high pH reverse-phase HPLC (Agilent LC1260) through an aeris peptide xb-c18 column (phenomenex) with mobile phase A containing 5% acetonitrile and 10 mM NH4HCO3 in LC-MS grade H2O, and mobile phase B containing 90% acetonitrile and 5 mM NH4HCO3 in LC-MS grade H2O (both pH 8.0). The resulting 96 fractions were recombined in a non-contiguous manner into 24 fractions and desalted using C18 solid phase extraction plates (SOLA, Thermo Fisher Scientific) followed by subsequent mass spectrometry analysis. Data were collected using an Orbitrap Fusion Lumos mass spectrometer (Thermo Fisher Scientific, San Jose, CA, USA) coupled with a Proxeon EASY-nLC 1200 LC system (Thermo Fisher Scientific, San Jose, CA, USA). Peptides were separated on a 1) 50 cm 75 μm inner diameter EasySpray ES903 microcapillary column (Thermo Fisher Scientific). Peptides were separated over a 190 min gradient of 6 – 27% acetonitrile in 1.0% formic acid with a flow rate of 300 nL/min. Quantification was performed using a MS3-based TMT method as described previously99. The data were acquired using a mass range of m/z 340 – 1350, resolution 120,000, AGC target 5 × 105, maximum injection time 100 ms, dynamic exclusion of 120 seconds for the peptide measurements in the Orbitrap. Data dependent MS2 spectra were acquired in the ion trap with a normalized collision energy (NCE) set at 35%, AGC target set to 1.8 × 104 and a maximum injection time of 120 ms. MS3 scans were acquired in the Orbitrap with HCD collision energy set to 55%, AGC target set to 2 × 105, maximum injection time of 150 ms, resolution at 50,000 and with a maximum synchronous precursor selection (SPS) precursors set to 10.
TMT quantitative global proteomics LC-MS data analysis
Proteome Discoverer 2.2 (Thermo Fisher Scientific) was used for .RAW file processing and controlling peptide and protein level false discovery rates, assembling proteins from peptides, and protein quantification from peptides. The MS/MS spectra were searched against a Swissprot human database (January 2021) containing both the forward and reverse sequences. Searches were performed using a 20 ppm precursor mass tolerance, 0.6 Da fragment ion mass tolerance, tryptic peptides containing a maximum of two missed cleavages, static alkylation of cysteine (57.02146 Da), static TMT labelling of lysine residues and N-termini of peptides (229.1629 Da), and variable oxidation of methionine (15.99491 Da). TMT reporter ion intensities were measured using a 0.003 Da window around the theoretical m/z for each reporter ion in the MS3 scan. The peptide spectral matches with poor quality MS3 spectra were excluded from quantitation (summed signal-to-noise across channels < 110 and precursor isolation specificity < 0.5), and the resulting data was filtered to only include proteins with a minimum of 2 unique peptides quantified. Reporter ion intensities were normalized and scaled using in-house scripts in the R framework100. Statistical analysis was carried out using the limma package within the R framework80.
Searching for CRBN-compatible G-loops in the AlphaFold2 database
Using MASTER v1.661, the AlphaFold2 human database (v4)62 comprising 23,391 protein structures was queried for 8-residue loops with backbone root-mean-squared-deviation less than 1.5 Å to CK1α residues 35–42 (extracted from PDB 5FQD12. Loops not having a glycine at the sixth residue were filtered out resulting in a set of 46,040 G-loops from 10,926 proteins. Next, domains containing the G-loops were extracted using domain definitions from DPAM63 and aligned to the casein kinase I α reference loop and CRBN from 5FQD. To estimate backbone clashes, each structure was coarse-grained to represent the side chain as a pseudoatom and scored using Rosetta v13.364. Coarse-graining the structure makes the score rotamer-independent. The interchain_vdw term of the score function was used as a clash score and represents a modified Lennard-Jones 6–12 potential that penalizes atoms overlapping at the interface101. Domains with a clash score of greater than 200 were filtered out resulting in a list 16,0901 loops from 7,111 proteins.
Relieving minor clashes with Rosetta refinement
For select domains with low clash scores, clashes were relieved by relaxing the neo-substrate with Rosetta FastRelax64,65 while holding the G-loop in place. No energy minimization or rotamer optimization was performed on CRBN. Rigid body translation between CRBN and the neo-substrate was disabled. Of the 10 independent trajectories run, the one with the lowest total score was selected and then the clash score was calculated as above.
Quantification and statistical analysis
Statistical methods are described in the according figure legends, and methods. Cryo-EM statistics are based on the gold-standard FSC=0.143 to determine resolution. For quantitative proteomics experiments, statistical analysis was carried out using the limma package within the R framework80.
Supplementary Material
1 |
Title: Esophageal Cancer-Related Gene-4 Contributes to Lipopolysaccharide-Induced Ion Channel Dysfunction in hiPSC-Derived Cardiomyocytes | Body:
Introduction
Sepsis, caused by severe infection, may result in multiple organ dysfunction, such as heart failure and arrhythmias.1,2 Lipopolysaccharide (LPS), a key component of the outer membrane of Gram-negative bacteria, is an important pathogenic factor in sepsis. Heart dysfunction is commonly documented in patients with sepsis and LPS-treated animals.3,4 LPS in circulation can stimulate the innate immune system and, in turn, leads to a regional or systemic inflammatory reaction. In addition, LPS may activate non-immune cells and evoke inflammation. The LPS receptor, Toll-like receptor 4 (TLR4), has been detected in cardiomyocytes.5,6 Hence, the innate inflammatory reaction can be stimulated in cardiomyocytes by LPS, without the participation of immune cells. Despite the advancements in the investigation of infection and sepsis, the exact mechanisms of cardiac dysfunctions, particularly arrhythmias, remain to be clarified.
As hiPSC-CMs can be a good platform for modeling cardiac diseases including myocarditis, in our previous study, we established a model of inflammation using hiPSC-CMs challenged by LPS.7 In this model, we demonstrated that hiPSC-CMs responded to LPS-challenging. The hiPSC-CMs possess LPS-receptor, TLR4, and its associated signaling proteins, including TIRAP, RelA, MyD88 and NFκB1. LPS-treatment induced an increase in proinflammatory factors and chemotactic cytokines in a dose-dependent manner. These results suggested that hiPSC-CMs can be used as a model of inflammation in cardiomyocytes. We also found that hiPSC-CMs challenged by LPS exhibited a prolonged action potential duration (APD). Decreased ISK and increased INCX contributed to the ADP prolongation. Enhanced INCX may cause delayed afterdepolarization (DAD), which may lead to tachyarrhythmias. Nevertheless, it is unknown whether LPS directly induces ion channel dysfunction or whether other factors are involved in this process.
ECRG4 was cloned by mRNA differential display between normal and cancerous epithelium of esophagus. It was originally recognized as a tumor suppressor gene.8 Studies have shown that ECRG4 is constitutively expressed in normal epithelium of esophagus and downregulated in esophageal cancer, and ECRG4 levels are directly correlated with prognosis.9 Besides epithelium, ECRG4 has been reported to be widely expressed in many other types of cells, including skeletal muscle, cardiomyocytes, pancreatic exocrine cells and hepatocytes.10 Similar to mouse and rat, in adult human heart, the ECRG4 expression is higher in atrial myocardium than in left ventricular myocardium.11,12
ECRG4 expression was documented to be downregulated after injury and infection, similar to its downregulation in tumors.13–15 Nonetheless, upregulation of ECRG4 gene was observed in chronic inflammation as well.16,17 In adult human heart, Huang et al detected that the ECRG4 expression level was largely reduced in atrial appendages of atrial fibrillation (AF) patients compared with the sinus rhythm (SR) group. In rat neonatal cardiomyocytes, the knockdown of ECRG4 significantly shortened the APDs and upregulated genes involved in proinflammatory cascades and heart remodeling. These data indicate that ECRG4 may play an important role in the pathogenesis of AF.18 In agreement with the possible role of ECRG4 in atrial electric remodeling, a significant decrease in ECRG4 level in HL1 cells was observed after 24 hours of rapid electric stimulation.19 Taken together, ECRG4 may participate in the modulation of ion channel function and electrical activity of cardiomyocytes, but the electrophysiological mechanism is unclear.
In human granulocytes, ECRG4 was found to exist in the innate immunity complex TLR4-MD2-CD14 and modulated inflammation through non-canonical, NFκB signal transduction.20 Given that LPS can induce arrhythmia through TLR4 receptor, we hypothesize that ECRG4 may contribute to inflammation-induced ion channel dysfunctions in cardiomyocytes. The objective of this study was to evaluate the effects of ECRG4 on LPS-induced ion channel dysfunctions in hiPSC-CMs.
Methods
Ethics Statement
Written consents for skin biopsies, blood and urine were obtained from three healthy donors, from whom three human-induced pluripotent stem cell (hiPSC) lines were generated, respectively. One hiPS cell line was obtained from the University Medical Center Göttingen, and the study plan was approved by the Ethical Committee of the Medical Faculty Mannheim, University of Heidelberg (approval number: 2018–565N-MA), and by the Ethical Committee of University Medical Center Göttingen (approval number: 10/9/15). The other two hiPS cell lines were obtained from Beijing Cellapy Biotechnology Co., LTD with informed consent for scientific research and commercial provision (Cat. No.: CA4025106; CA4027106). The study was performed in accordance with the approved guidelines and fulfilled following the Helsinki Declaration of 1975, as revised in 1983. The participants gave their written informed consent prior to the study commencing.
Generation of Human iPS Cells
The first cell line UMGi014-B clone 1 (ipWT1.1) of hiPSCs was generated from primary human fibroblasts derived from a skin biopsy. This hiPSC line was generated under feeder-free culture conditions using the integration-free episomal 4-in-1 CoMiP reprogramming plasmid (Addgene, #63726) with the reprogramming factors OCT4, KLF4, SOX2, c-MYC, and short hairpin RNA against p53, as previously described with modifications.21 The second (B1) and third (U2) hiPS cell lines were generated from primary human blood cells and epithelial cells in urine, respectively. These two hiPSC lines were generated under feeder-free culture conditions using lentiviral particles carrying the transactivator (TA) and an inducible polycistronic cassette containing the reprogramming factors OCT4, SOX2, KLF4 and c-MYC. Generated hiPSCs were cultured under feeder-free conditions and were validated for pluripotency.
Generation of hiPSC-CMs
Frozen aliquots of hiPSCs were thawed and cultured under feeder cell-free condition and differentiated into cardiomyocytes (hiPSC-CMs). Culture dishes and wells were coated with Matrigel (Corning, USA). Culture medium for hiPSCs was TeSR-E8 (Stemcell Technologies, USA), and the medium for hiPSC-CMs was RPMI 1640 GlutaMax (Thermo Fisher Scientific, USA) containing Penicillin/Streptomycin and B27 with and without insulin (Thermo Fisher Scientific, USA). After the third passaging, hiPSC colonies were passaged by Versene solution (Thermo Fisher Scientific, USA) and transferred to feeder-free 24 well plates. When cells were cultured for expansion to 90% −95% confluence, the cardiomyocyte differentiation was initiated. Application of CHIR99021 (Stemcell Technologies, USA) and IWP-2 (Stemcell Technologies, USA) at different time points induced the cells to differentiate into hiPSC-CMs in the first two weeks after the start of differentiation. Beating cardiac colonies can be observed as early as eight days later. In the third week, a selection medium of lactate (Sigma, USA) containing RPMI 1640 medium without glucose and glutamine (WKS, Germany) was used to select cardiomyocytes. From days 40 to 60, the cells were cultured with basic culture medium and were used for further experiments. For patch-clamp measurements, the cardiomyocytes were dissociated from 24 well plates by Collagenase I and plated on Matrigel-coated 3.5 cm petri dishes as single cells.
Real-Time PCR Assays
To measure the mRNA expression in hiPSC-CMs, total RNA was extracted using GeneJET RNA Purification Kit (Thermo Fisher Scientific, USA) which includes DNAse. The RNA was reversed transcribed to cDNA using a high-capacity cDNA reverse transcription kit (Applied Biosystems, USA). cDNA was amplified by real-time PCR on ABI real-time cycler (Applied Biosystems, USA) using QuantiNovaTM SYBR Green PCR Kit (Qiagen, Germany) in the presence of forward and reverse PCR primers. Real-time PCR was performed at 95°C for 2 min, followed by 40 cycles of 95°C for 15 sec and 60°C for 30 sec, then followed by melting-curve analysis to verify the specificity of the PCR product. Relative quantification of mRNA expression was calculated by the ΔΔCt method. Three replicate wells were used for each sample and at least three biological replicates were used for the calculation.
Immunofluorescence Staining
Cardiomyocytes were dissociated from 24 well plates by Collagenase I and TrypLE reagents and plated on Matrigel-coated 8 chamber glass slides (Ibidi, Germany). After at least 48 h, the cells were washed with PBS. For proteins in the intracellular, the cells were fixed with 4% paraformaldehyde (Thermo Fisher Scientific, USA) for 10 min and washed again by PBS. Then, the cells were permeabilized for 10 minutes with 0.5% Triton X-100 (Sigma, USA) and washed with PBS. For cell surface immunofluorescence, Triton X-100 was not used. PBS containing 1% bovine serum albumin (Sigma, USA) was used to block cells for 30 min. After washing with PBS, cells were incubated with primary antibodies in 4°C overnight kept under wet conditions. The next day, cells were washed twice with PBS, and incubated with fluorescent secondary antibodies (Invitrogen, USA). After incubation for 1 h at room temperature, cells were washed twice with PBS. Nuclear staining was performed with ProLong™ gold antifade mountant with DAPI (Invitrogen, USA). Wheat Germ Agglutinin (WGA) with Alexa Fluor 488 was used for plasma membrane labeling. The antibodies used in this study were α-actinin antibody (Invitrogen, USA), cTnT antibody (Invitrogen, USA), ECRG4 antibody (Sigma, USA) and TLR4 antibody Proteintech, China). Images were taken with Leica DMRE fluorescence microscope (DFC3000G/DFC 450c) camera equipped with the Leica Application suite 4.4 software.
Construction of Adenovirus for ECRG4 Knockdown and Overexpression
Small RNAs targeting human ECRG4 were designed and used for the ADV1 plasmid (GenePharma, China). The three oligos containing the core siRNA sequences (designated as 230, 388 and 502), targeting ECRG4 coding sequence, were annealed with their corresponding reverse complementary strands and cloned into ADV1, an adenovirus vector co-transcribing GFP downstream of the siRNA precursor. The identity of each plasmid was confirmed by DNA sequencing. Knockdown efficiency was determined by infection with hiPSC-CMs, and the expression of ECRG4 was evaluated by real-time PCR. The siRNA sequence 502 had a better knockdown efficiency and was used in the future experiments. An ADV1 plasmid carrying the full sequence of ECRG4 was used for ECRG4 overexpression. Virus containing a negative siRNA targeting shame RNA was used as a control.
Western Blot Analysis
Following treatment of ECRG4-siRNA and negative control adenovirus to hiPSC-CMs for 48 hours, the cells were lysed at 4°C using lysis buffer containing 25 mm Tris-HCl, pH 8.0, 137 mm NaCl, 2.7 mm KCl, 1% Triton X-100 and protease inhibitor mixture (Thermo, USA) for 1h followed by brief sonication. After 10 min centrifugation at 14,000 rpm at 4°C for removing insoluble materials, supernatants were collected, and the protein concentrations were measured by Pierce™ BCA Protein Assay Kit (Thermo, USA). Equal amounts of whole cell lysates were boiled for 5 min in an SDS-sample buffer, separated by a 10% SDS-PAGE, and electro-transferred onto Immobilon-P membranes (Millipore, Bedford, MA), which were blocked after saturation by TBS-T containing 5% BSA at room temperature for 1 h, and subsequently incubated with the anti-IKK and anti-Phospho-IKK alpha/beta (Affinity, China), anti-NF-kB p65 and Phospho-NF-kB p65 (Affinity, China), anti-GAPDH (Invitrogen, USA) primary antibody, followed by incubation with a secondary horseradish peroxidase (HRP)-conjugated antibody (Cell Signaling, USA). After being washed by TBS-T, the membranes were developed using an enhanced chemifluorescence detection system (Thermo, USA).
Patch-Clamp
Standard patch-clamp whole-cell recording techniques were employed to measure ion channel currents and action potentials (AP). Patch electrodes from borosilicate glass capillaries (MTW 150F; World Precision Instruments, Inc., USA) were pulled by a DMZ-Universal Puller (Zeitz-Instrumente Vertriebs GmbH, Martinsried, Germany). The resistance of pipette electrodes ranged from 4 to 5 MΩ. After the electrode was moved into bath solution, its offset potential was zero-adjusted. After a giga-seal was formed, the fast capacitance of pipette was first compensated, and then the membrane under the pipette tip was broken by negative pressure to establish the configuration of whole-cell recording. To measure the cell capacitance, a 50 ms-voltage pulse from −80 to −85 mV was applied to record the capacitance transient current. The area under the transient current curve was then integrated and divided by 5 mV to obtain the whole-cell capacitance in pF. Next, the series resistance (Rs) and membrane capacitance (Cm) were compensated. The liquid junction potential was not considered. Recording signals were sampled at 10 kHz and filtered at 2 kHz by the Axon 200B amplifier and Digidata 1440A digitizer hardware plus pClamp10.6 software (Molecular Devices, Canada). Depending on features of recorded currents, different voltage clamp protocols were used. To obtain the current density, measured currents were normalized to the membrane capacitance. Current–voltage (I–V) curves were obtained by plotting the current density against voltages. The TTX (tetrodotoxin) sensitive late INa was measured as the average current from 250 to 400 ms of the depolarization pulse. To separate one type of ion channel current from others, a specific channel blocker was used, and the blocker-sensitive current was analyzed.
AP and Potassium Channel Current Recording
The extracellular solution for the AP and potassium channel current measurements consisted of (mmol/l): 127 NaCl, 5.9 KCl, 2.4 CaCl2, 1.2 MgCl2, 11 glucose, 10 HEPES, pH 7.4 (NaOH). For measuring Ito, 10 µM nifedipine, 10 µM TTX and 1 µM E-4031 were given into the solution for blocking ICa-L, INa and IKr. For measuring IKr and IKs, 10 µM nifedipine, 10 µM TTX and 5 mm 4-AP were applied. The intracellular pipette solution consisted of 10 mm HEPES, 126 mm NaCl, 6 mm KCl, 1.2 mm MgCl2, 5 mm EGTA, 11 mm glucose and 1 mm MgATP, pH 7.4 (KOH). To measure SK channel currents, appropriate amount of CaCl2 was added to obtain the free-Ca2+ concentration of 0.5 µM from the calculation by using the software MAXCHELATOR. An ATP-free intracellular pipette solution was used to measure the ATP-sensitive K+-channel current (IKATP). To measure IKs, 1 µM of E-4031 was added to block IKr.
To separate IKr from other potassium currents, the Cs+ currents conducted by the KCNH2 (HERG) channels were measured as IKr. Both the extracellular and intracellular solutions were composed of (mmol/L): 140 CsCl2, 2 MgCl2, 10 HEPES, 10 Glucose, pH 7.4 (CsOH).
Sodium and Calcium Current Recording
The extracellular solution used for measuring peak sodium current was composed of (mmol/l): 20 NaCl, 130 CsCl2, 1.8 CaCl2, 1 MgCl2, 10HEPES, 10 glucose, 0.001 nifedipine, pH 7.4 (CsOH). The intracellular solution was composed of (mmol/l): 10 NaCl, 135 CsCl2, 2 CaCl2, 3 MgATP, 2 TEA-Cl, 5 EGTA, 10 HEPES, pH 7.2 (CsOH).
The extracellular solution used for measuring late sodium current was composed of (mmol/l): 135 NaCl, 20 CsCl2, 1.8 CaCl2, 1 MgCl2, 10 HEPES, 10 glucose, 0.001 nifedipine, pH 7.4 (CsOH). The intracellular solution was composed of (mmol/l): 10 NaCl, 135 CsCl2, 2 CaCl2, 3 MgATP, 2 TEA-Cl, 5 EGTA, 10 HEPES, pH 7.2 (CsOH).
The extracellular solution used for recording ICa-L was composed of (mmol/l): 140 TEA-Cl, 5 CaCl2, 1 MgCl2, 10 chromanol 293B, 10 HEPES, 0.01 TTX, 2 4-AP, pH 7.4 (CsOH). The intracellular solution was composed of (mmol/l): 10 NaCl, 135 CsCl2, 2 CaCl2, 3 MgATP, 2 TEA-Cl, 5 EGTA, 10 HEPES, pH 7.2 (CsOH).
Sodium-Calcium Exchange Current Recording
The extracellular solution used for measuring INCX was composed of (mmol/l): 135 NaCl, 10 CsCl2, 2 CaCl2, 1 MgCl2, 10 HEPES, 10 glucose, 0.01 nifedipine, 0.1 niflumic acid, 0.05 lidocaine, 0.02 dihydroouabain, pH 7.4 (CsOH). The intracellular solution was composed of (mmol/l): 10 NaOH, 150 CsOH, 2 CaCl2, 1 MgCl2, 75 aspartic acid, 5 EGTA, pH7.2 (CsOH).
Drugs
E-4031, chromanol 293B, nifedipine, NiCl2, niflumic acid, apamin, LPS, lidocaine and dihydroouabain were from Sigma Aldrich, 4-AP was from RBI, TTX was from Carl Roth, TPCA-1 and QNZ (EVP4593) were from Selleck. E-4031, NiCl2, TTX, 4-AP, apamin, LPS, niflumic acid and dihydrooubain were dissolved in H2O. Nifedipine, chromanol 293B, TPCA-1 and QNZ (EVP4593) were dissolved in DMSO, and lidocaine was dissolved in ethanol. Stock solutions were stored at −20 °C.
Statistics
All the data were displayed as mean ± SEM and were analyzed by the InStat© software (GraphPad, San Diego, USA). Kolmogorov Smirnov test was analyzed to decide whether parametric or non-parametric tests were used. To compare parametric data from multiple groups, one-way ANOVA with Holm-Sidak post-test for multiple comparisons was performed. For non-parametric data, the Kruskal–Wallis test with Dunn’s multiple comparisons post-test was used. Unpaired Student’s t-test was performed to compare two independent groups with normal distribution. P <0.05 (two-tailed) was considered significant.
Ethics Statement
Written consents for skin biopsies, blood and urine were obtained from three healthy donors, from whom three human-induced pluripotent stem cell (hiPSC) lines were generated, respectively. One hiPS cell line was obtained from the University Medical Center Göttingen, and the study plan was approved by the Ethical Committee of the Medical Faculty Mannheim, University of Heidelberg (approval number: 2018–565N-MA), and by the Ethical Committee of University Medical Center Göttingen (approval number: 10/9/15). The other two hiPS cell lines were obtained from Beijing Cellapy Biotechnology Co., LTD with informed consent for scientific research and commercial provision (Cat. No.: CA4025106; CA4027106). The study was performed in accordance with the approved guidelines and fulfilled following the Helsinki Declaration of 1975, as revised in 1983. The participants gave their written informed consent prior to the study commencing.
Generation of Human iPS Cells
The first cell line UMGi014-B clone 1 (ipWT1.1) of hiPSCs was generated from primary human fibroblasts derived from a skin biopsy. This hiPSC line was generated under feeder-free culture conditions using the integration-free episomal 4-in-1 CoMiP reprogramming plasmid (Addgene, #63726) with the reprogramming factors OCT4, KLF4, SOX2, c-MYC, and short hairpin RNA against p53, as previously described with modifications.21 The second (B1) and third (U2) hiPS cell lines were generated from primary human blood cells and epithelial cells in urine, respectively. These two hiPSC lines were generated under feeder-free culture conditions using lentiviral particles carrying the transactivator (TA) and an inducible polycistronic cassette containing the reprogramming factors OCT4, SOX2, KLF4 and c-MYC. Generated hiPSCs were cultured under feeder-free conditions and were validated for pluripotency.
Generation of hiPSC-CMs
Frozen aliquots of hiPSCs were thawed and cultured under feeder cell-free condition and differentiated into cardiomyocytes (hiPSC-CMs). Culture dishes and wells were coated with Matrigel (Corning, USA). Culture medium for hiPSCs was TeSR-E8 (Stemcell Technologies, USA), and the medium for hiPSC-CMs was RPMI 1640 GlutaMax (Thermo Fisher Scientific, USA) containing Penicillin/Streptomycin and B27 with and without insulin (Thermo Fisher Scientific, USA). After the third passaging, hiPSC colonies were passaged by Versene solution (Thermo Fisher Scientific, USA) and transferred to feeder-free 24 well plates. When cells were cultured for expansion to 90% −95% confluence, the cardiomyocyte differentiation was initiated. Application of CHIR99021 (Stemcell Technologies, USA) and IWP-2 (Stemcell Technologies, USA) at different time points induced the cells to differentiate into hiPSC-CMs in the first two weeks after the start of differentiation. Beating cardiac colonies can be observed as early as eight days later. In the third week, a selection medium of lactate (Sigma, USA) containing RPMI 1640 medium without glucose and glutamine (WKS, Germany) was used to select cardiomyocytes. From days 40 to 60, the cells were cultured with basic culture medium and were used for further experiments. For patch-clamp measurements, the cardiomyocytes were dissociated from 24 well plates by Collagenase I and plated on Matrigel-coated 3.5 cm petri dishes as single cells.
Real-Time PCR Assays
To measure the mRNA expression in hiPSC-CMs, total RNA was extracted using GeneJET RNA Purification Kit (Thermo Fisher Scientific, USA) which includes DNAse. The RNA was reversed transcribed to cDNA using a high-capacity cDNA reverse transcription kit (Applied Biosystems, USA). cDNA was amplified by real-time PCR on ABI real-time cycler (Applied Biosystems, USA) using QuantiNovaTM SYBR Green PCR Kit (Qiagen, Germany) in the presence of forward and reverse PCR primers. Real-time PCR was performed at 95°C for 2 min, followed by 40 cycles of 95°C for 15 sec and 60°C for 30 sec, then followed by melting-curve analysis to verify the specificity of the PCR product. Relative quantification of mRNA expression was calculated by the ΔΔCt method. Three replicate wells were used for each sample and at least three biological replicates were used for the calculation.
Immunofluorescence Staining
Cardiomyocytes were dissociated from 24 well plates by Collagenase I and TrypLE reagents and plated on Matrigel-coated 8 chamber glass slides (Ibidi, Germany). After at least 48 h, the cells were washed with PBS. For proteins in the intracellular, the cells were fixed with 4% paraformaldehyde (Thermo Fisher Scientific, USA) for 10 min and washed again by PBS. Then, the cells were permeabilized for 10 minutes with 0.5% Triton X-100 (Sigma, USA) and washed with PBS. For cell surface immunofluorescence, Triton X-100 was not used. PBS containing 1% bovine serum albumin (Sigma, USA) was used to block cells for 30 min. After washing with PBS, cells were incubated with primary antibodies in 4°C overnight kept under wet conditions. The next day, cells were washed twice with PBS, and incubated with fluorescent secondary antibodies (Invitrogen, USA). After incubation for 1 h at room temperature, cells were washed twice with PBS. Nuclear staining was performed with ProLong™ gold antifade mountant with DAPI (Invitrogen, USA). Wheat Germ Agglutinin (WGA) with Alexa Fluor 488 was used for plasma membrane labeling. The antibodies used in this study were α-actinin antibody (Invitrogen, USA), cTnT antibody (Invitrogen, USA), ECRG4 antibody (Sigma, USA) and TLR4 antibody Proteintech, China). Images were taken with Leica DMRE fluorescence microscope (DFC3000G/DFC 450c) camera equipped with the Leica Application suite 4.4 software.
Construction of Adenovirus for ECRG4 Knockdown and Overexpression
Small RNAs targeting human ECRG4 were designed and used for the ADV1 plasmid (GenePharma, China). The three oligos containing the core siRNA sequences (designated as 230, 388 and 502), targeting ECRG4 coding sequence, were annealed with their corresponding reverse complementary strands and cloned into ADV1, an adenovirus vector co-transcribing GFP downstream of the siRNA precursor. The identity of each plasmid was confirmed by DNA sequencing. Knockdown efficiency was determined by infection with hiPSC-CMs, and the expression of ECRG4 was evaluated by real-time PCR. The siRNA sequence 502 had a better knockdown efficiency and was used in the future experiments. An ADV1 plasmid carrying the full sequence of ECRG4 was used for ECRG4 overexpression. Virus containing a negative siRNA targeting shame RNA was used as a control.
Western Blot Analysis
Following treatment of ECRG4-siRNA and negative control adenovirus to hiPSC-CMs for 48 hours, the cells were lysed at 4°C using lysis buffer containing 25 mm Tris-HCl, pH 8.0, 137 mm NaCl, 2.7 mm KCl, 1% Triton X-100 and protease inhibitor mixture (Thermo, USA) for 1h followed by brief sonication. After 10 min centrifugation at 14,000 rpm at 4°C for removing insoluble materials, supernatants were collected, and the protein concentrations were measured by Pierce™ BCA Protein Assay Kit (Thermo, USA). Equal amounts of whole cell lysates were boiled for 5 min in an SDS-sample buffer, separated by a 10% SDS-PAGE, and electro-transferred onto Immobilon-P membranes (Millipore, Bedford, MA), which were blocked after saturation by TBS-T containing 5% BSA at room temperature for 1 h, and subsequently incubated with the anti-IKK and anti-Phospho-IKK alpha/beta (Affinity, China), anti-NF-kB p65 and Phospho-NF-kB p65 (Affinity, China), anti-GAPDH (Invitrogen, USA) primary antibody, followed by incubation with a secondary horseradish peroxidase (HRP)-conjugated antibody (Cell Signaling, USA). After being washed by TBS-T, the membranes were developed using an enhanced chemifluorescence detection system (Thermo, USA).
Patch-Clamp
Standard patch-clamp whole-cell recording techniques were employed to measure ion channel currents and action potentials (AP). Patch electrodes from borosilicate glass capillaries (MTW 150F; World Precision Instruments, Inc., USA) were pulled by a DMZ-Universal Puller (Zeitz-Instrumente Vertriebs GmbH, Martinsried, Germany). The resistance of pipette electrodes ranged from 4 to 5 MΩ. After the electrode was moved into bath solution, its offset potential was zero-adjusted. After a giga-seal was formed, the fast capacitance of pipette was first compensated, and then the membrane under the pipette tip was broken by negative pressure to establish the configuration of whole-cell recording. To measure the cell capacitance, a 50 ms-voltage pulse from −80 to −85 mV was applied to record the capacitance transient current. The area under the transient current curve was then integrated and divided by 5 mV to obtain the whole-cell capacitance in pF. Next, the series resistance (Rs) and membrane capacitance (Cm) were compensated. The liquid junction potential was not considered. Recording signals were sampled at 10 kHz and filtered at 2 kHz by the Axon 200B amplifier and Digidata 1440A digitizer hardware plus pClamp10.6 software (Molecular Devices, Canada). Depending on features of recorded currents, different voltage clamp protocols were used. To obtain the current density, measured currents were normalized to the membrane capacitance. Current–voltage (I–V) curves were obtained by plotting the current density against voltages. The TTX (tetrodotoxin) sensitive late INa was measured as the average current from 250 to 400 ms of the depolarization pulse. To separate one type of ion channel current from others, a specific channel blocker was used, and the blocker-sensitive current was analyzed.
AP and Potassium Channel Current Recording
The extracellular solution for the AP and potassium channel current measurements consisted of (mmol/l): 127 NaCl, 5.9 KCl, 2.4 CaCl2, 1.2 MgCl2, 11 glucose, 10 HEPES, pH 7.4 (NaOH). For measuring Ito, 10 µM nifedipine, 10 µM TTX and 1 µM E-4031 were given into the solution for blocking ICa-L, INa and IKr. For measuring IKr and IKs, 10 µM nifedipine, 10 µM TTX and 5 mm 4-AP were applied. The intracellular pipette solution consisted of 10 mm HEPES, 126 mm NaCl, 6 mm KCl, 1.2 mm MgCl2, 5 mm EGTA, 11 mm glucose and 1 mm MgATP, pH 7.4 (KOH). To measure SK channel currents, appropriate amount of CaCl2 was added to obtain the free-Ca2+ concentration of 0.5 µM from the calculation by using the software MAXCHELATOR. An ATP-free intracellular pipette solution was used to measure the ATP-sensitive K+-channel current (IKATP). To measure IKs, 1 µM of E-4031 was added to block IKr.
To separate IKr from other potassium currents, the Cs+ currents conducted by the KCNH2 (HERG) channels were measured as IKr. Both the extracellular and intracellular solutions were composed of (mmol/L): 140 CsCl2, 2 MgCl2, 10 HEPES, 10 Glucose, pH 7.4 (CsOH).
Sodium and Calcium Current Recording
The extracellular solution used for measuring peak sodium current was composed of (mmol/l): 20 NaCl, 130 CsCl2, 1.8 CaCl2, 1 MgCl2, 10HEPES, 10 glucose, 0.001 nifedipine, pH 7.4 (CsOH). The intracellular solution was composed of (mmol/l): 10 NaCl, 135 CsCl2, 2 CaCl2, 3 MgATP, 2 TEA-Cl, 5 EGTA, 10 HEPES, pH 7.2 (CsOH).
The extracellular solution used for measuring late sodium current was composed of (mmol/l): 135 NaCl, 20 CsCl2, 1.8 CaCl2, 1 MgCl2, 10 HEPES, 10 glucose, 0.001 nifedipine, pH 7.4 (CsOH). The intracellular solution was composed of (mmol/l): 10 NaCl, 135 CsCl2, 2 CaCl2, 3 MgATP, 2 TEA-Cl, 5 EGTA, 10 HEPES, pH 7.2 (CsOH).
The extracellular solution used for recording ICa-L was composed of (mmol/l): 140 TEA-Cl, 5 CaCl2, 1 MgCl2, 10 chromanol 293B, 10 HEPES, 0.01 TTX, 2 4-AP, pH 7.4 (CsOH). The intracellular solution was composed of (mmol/l): 10 NaCl, 135 CsCl2, 2 CaCl2, 3 MgATP, 2 TEA-Cl, 5 EGTA, 10 HEPES, pH 7.2 (CsOH).
Sodium-Calcium Exchange Current Recording
The extracellular solution used for measuring INCX was composed of (mmol/l): 135 NaCl, 10 CsCl2, 2 CaCl2, 1 MgCl2, 10 HEPES, 10 glucose, 0.01 nifedipine, 0.1 niflumic acid, 0.05 lidocaine, 0.02 dihydroouabain, pH 7.4 (CsOH). The intracellular solution was composed of (mmol/l): 10 NaOH, 150 CsOH, 2 CaCl2, 1 MgCl2, 75 aspartic acid, 5 EGTA, pH7.2 (CsOH).
AP and Potassium Channel Current Recording
The extracellular solution for the AP and potassium channel current measurements consisted of (mmol/l): 127 NaCl, 5.9 KCl, 2.4 CaCl2, 1.2 MgCl2, 11 glucose, 10 HEPES, pH 7.4 (NaOH). For measuring Ito, 10 µM nifedipine, 10 µM TTX and 1 µM E-4031 were given into the solution for blocking ICa-L, INa and IKr. For measuring IKr and IKs, 10 µM nifedipine, 10 µM TTX and 5 mm 4-AP were applied. The intracellular pipette solution consisted of 10 mm HEPES, 126 mm NaCl, 6 mm KCl, 1.2 mm MgCl2, 5 mm EGTA, 11 mm glucose and 1 mm MgATP, pH 7.4 (KOH). To measure SK channel currents, appropriate amount of CaCl2 was added to obtain the free-Ca2+ concentration of 0.5 µM from the calculation by using the software MAXCHELATOR. An ATP-free intracellular pipette solution was used to measure the ATP-sensitive K+-channel current (IKATP). To measure IKs, 1 µM of E-4031 was added to block IKr.
To separate IKr from other potassium currents, the Cs+ currents conducted by the KCNH2 (HERG) channels were measured as IKr. Both the extracellular and intracellular solutions were composed of (mmol/L): 140 CsCl2, 2 MgCl2, 10 HEPES, 10 Glucose, pH 7.4 (CsOH).
Sodium and Calcium Current Recording
The extracellular solution used for measuring peak sodium current was composed of (mmol/l): 20 NaCl, 130 CsCl2, 1.8 CaCl2, 1 MgCl2, 10HEPES, 10 glucose, 0.001 nifedipine, pH 7.4 (CsOH). The intracellular solution was composed of (mmol/l): 10 NaCl, 135 CsCl2, 2 CaCl2, 3 MgATP, 2 TEA-Cl, 5 EGTA, 10 HEPES, pH 7.2 (CsOH).
The extracellular solution used for measuring late sodium current was composed of (mmol/l): 135 NaCl, 20 CsCl2, 1.8 CaCl2, 1 MgCl2, 10 HEPES, 10 glucose, 0.001 nifedipine, pH 7.4 (CsOH). The intracellular solution was composed of (mmol/l): 10 NaCl, 135 CsCl2, 2 CaCl2, 3 MgATP, 2 TEA-Cl, 5 EGTA, 10 HEPES, pH 7.2 (CsOH).
The extracellular solution used for recording ICa-L was composed of (mmol/l): 140 TEA-Cl, 5 CaCl2, 1 MgCl2, 10 chromanol 293B, 10 HEPES, 0.01 TTX, 2 4-AP, pH 7.4 (CsOH). The intracellular solution was composed of (mmol/l): 10 NaCl, 135 CsCl2, 2 CaCl2, 3 MgATP, 2 TEA-Cl, 5 EGTA, 10 HEPES, pH 7.2 (CsOH).
Sodium-Calcium Exchange Current Recording
The extracellular solution used for measuring INCX was composed of (mmol/l): 135 NaCl, 10 CsCl2, 2 CaCl2, 1 MgCl2, 10 HEPES, 10 glucose, 0.01 nifedipine, 0.1 niflumic acid, 0.05 lidocaine, 0.02 dihydroouabain, pH 7.4 (CsOH). The intracellular solution was composed of (mmol/l): 10 NaOH, 150 CsOH, 2 CaCl2, 1 MgCl2, 75 aspartic acid, 5 EGTA, pH7.2 (CsOH).
Drugs
E-4031, chromanol 293B, nifedipine, NiCl2, niflumic acid, apamin, LPS, lidocaine and dihydroouabain were from Sigma Aldrich, 4-AP was from RBI, TTX was from Carl Roth, TPCA-1 and QNZ (EVP4593) were from Selleck. E-4031, NiCl2, TTX, 4-AP, apamin, LPS, niflumic acid and dihydrooubain were dissolved in H2O. Nifedipine, chromanol 293B, TPCA-1 and QNZ (EVP4593) were dissolved in DMSO, and lidocaine was dissolved in ethanol. Stock solutions were stored at −20 °C.
Statistics
All the data were displayed as mean ± SEM and were analyzed by the InStat© software (GraphPad, San Diego, USA). Kolmogorov Smirnov test was analyzed to decide whether parametric or non-parametric tests were used. To compare parametric data from multiple groups, one-way ANOVA with Holm-Sidak post-test for multiple comparisons was performed. For non-parametric data, the Kruskal–Wallis test with Dunn’s multiple comparisons post-test was used. Unpaired Student’s t-test was performed to compare two independent groups with normal distribution. P <0.05 (two-tailed) was considered significant.
Statistics
All the data were displayed as mean ± SEM and were analyzed by the InStat© software (GraphPad, San Diego, USA). Kolmogorov Smirnov test was analyzed to decide whether parametric or non-parametric tests were used. To compare parametric data from multiple groups, one-way ANOVA with Holm-Sidak post-test for multiple comparisons was performed. For non-parametric data, the Kruskal–Wallis test with Dunn’s multiple comparisons post-test was used. Unpaired Student’s t-test was performed to compare two independent groups with normal distribution. P <0.05 (two-tailed) was considered significant.
Results
ECRG4 Was Detected in hiPSC-CMs
To investigate whether ECRG4 is expressed in hiPSC-CMs, we generated hiPSC-CMs from healthy donors and collected total mRNA at different differentiation time. ECRG4 expression on human cardiac muscle cell line AC16 and mice cardiomyocytes was detected as positive control. Expression levels of ECRG4 were analyzed by real-time PCR. The results showed that ECRG4 was highly expressed in hiPSCs (d0) and mesoblastemas on day 5 of differentiation, and the expression level was largely decreased from day 10, when hiPSC-CMs were generated (cells started to beat). Then, the expression of ECRG4 in hiPSC-CMs increased slightly and was sustained until the maturation stage (Figure 1A).
Figure 1Expression of ECRG4 in hiPSC-CMs (hiPSC-CM-WT1.1) at different differentiation time and immunostaining of hiPSC-CMs for ECRG4. (A) Relative mRNA levels (normalized to GAPDH) of ECRG4 were analyzed by real-time PCR in hiPSC-CMs at different differentiation time as well as human cardiac muscle cell line AC16 and mice cardiomyocytes (MC). (B) Immunostaining of hiPSC-CMs at day 60 after differentiation with α-actinin, cTnT and ECRG4 antibody, showing the expression of ECRG4 in hiPSC-CMs. Nuclear staining was induced with DAPI (blue).
For verification of the PCR results, we further performed an immunofluorescence study. hiPSC-CMs were stained with α-actinin, cTnT and ECRG4 antibodies with different fluorescent dyes. Nuclear staining was performed with DAPI. Adult mice cardiomyocytes were stained as positive control. As shown in Figure 1B, ECRG4-positive immunofluorescence signal was detected localized to peri-nucleus and cytoplasm and together with cardiomyocytes marker α-actinin and cTnT in hiPSC-CMs, which is the same as adult mice cardiomyocytes (Figure S1A). These results suggest that hiPSC-CMs express ECRG4.
LPS Treatment Enhanced ECRG4 Expression Level in hiPSC-CMs
Studies have demonstrated that ECRG4 is present both on the surface and in the cytoplasm of human monocytes and granulocytes, and a physical interaction between ECRG4 and TLR4-MD2-CD14 on human granulocytes has been detected. However, whether ECRG4 had an interaction with TLR4 in hiPSC-CMs was unclear. Therefore, we investigated the localization of ECRG4 and TLR4 by immunocytochemistry and changes in the expression of ECRG4 after LPS treatment at different concentrations and different times in hiPSC-CMs. The results showed that ECRG4 was localized on the surface of non-permeabilized hiPSC-CMs (Figure 2A), and the co-localization of ECRG4 and TLR4 was detected by co-staining of ECRG4 and TLR4 in hiPSC-CMs (Figure 2B). The expression of ECRG4 in hiPSC-CMs was increased by LPS at concentrations from 0.1 μg/mL to 10 μg/mL and at treatment times from 1 h to 24 h in all three cell lines (Figure 2C and D, S1B), suggesting ECRG4 may involve in LPS-induced inflammatory process.
Figure 2Immunostaining of ECRG4 and TLR4, and expression of ECRG4 in hiPSC-CMs after LPS treatment. (A) Immunostaining of non-permeabilized hiPSC-CMs with ECRG4 (red) antibody and WGA (green). Nuclear staining was induced with DAPI (blue). (B) Immunostaining for TLR4 (green) and ECRG4 (red) suggest co-localization of TLR4 and ECRG4, which is supported by merged yellow signaling (red + green). Nuclear staining was induced with DAPI (blue). (C and D) Relative mRNA levels (normalized to GAPDH) of ECRG4 were assessed by real-time PCR in hiPSC-CMs after challenge by LPS at different concentrations for 24 h and different time. Values given are mean ± SEM. ** P<0.01, *** P<0.001.
ECRG4 Knockdown Changed Expression Level of Toll-Like Receptor 4 Signaling and Ion Channels
To further explore the effect of ECRG4 on inflammatory responses, we first constructed ECRG4-siRNA adenovirus to knock down the ECRG4 expression in hiPSC-CMs, and the knockdown efficiency was verified by real-time PCR. The mRNA level of Toll-like receptor 4 and its associated signaling genes and inflammatory cytokines were analyzed using real-time PCR after ECRG4 knockdown. The result showed that ECRG4-siRNA adenovirus could inhibit about 90% of the ECRG4 mRNA expression (Figure 3A). After ECRG4 knockdown, the expression level of TLR4 and its associated genes, TIRAP, MyD88, MAPK14, MAPK1, MAPK8, IKK2, IKKϒ, NFκB1, RelA and inflammatory cytokines IL-1β, IL-6 were decreased (Figure 3B and C, S2). Furthermore, Western blot result showed that ECRG4 knockdown decreased the protein level of IKK and NFκB P65 as well as the level of phosphorylated IKK and NFκB P65 (Figure 3D). To investigate the effect of ECRG4 on the ion channels, the mRNA expression of the main ion channels involved in the action potential was measured by qPCR after ECRG4 knockdown. The results showed that the KCNH2 and NCX levels were decreased, but KCNN2 level was increased after ECRG4 knockdown (Figure 3E). These results suggested that ECRG4 is involved in the Toll-like receptor 4 signaling.
Figure 3ECRG4 knockdown changes the expression level of TLR4-associated signaling genes, inflammatory cytokines and ion channels in hiPSC-CMs (hiPSC-CM-WT1.1). (A) Relative mRNA (normalized to GAPDH) levels of ECRG4 were analyzed by real-time PCR after infection of ECRG4-siRNA adenovirus and negative control (N.C). (B) The relative mRNA levels of LPS-signaling associated genes after ECRG4 knockdown. (C) The relative mRNA levels of different cytokines after ECRG4 knockdown. (D) The Western blot results of total IKK and NFκB P65 (normalized to GAPDH) as well as phosphorylated IKK and NFκB P65 (normalized to total protein) after transfection of ECRG4-siRNA adenovirus and negative control. (E) Relative mRNA levels (normalized to GAPDH) of ion channels were analyzed by real-time PCR after transfection of ECRG4-siRNA adenovirus and negative control. Values given are mean ± SEM. *P<0.05, **P<0.01, *** P<0.001 versus control (N.C), ns, not significant.
ECRG4 Knockdown Shortened Action Potential Duration (APD) and Intercepted LPS Effect
To further investigate the effects of ECRG4 on the electrophysiological properties of cardiomyocytes and to examine whether ECRG4 is involved in the LPS induced electrophysiological abnormalities, the membrane potentials of hiPSC-CMs with and without ECRG4 knockdown and the negative control were measured in the absence and presence of LPS. In hiPSC-CMs without ECRG4 knockdown, LPS prolonged APD90 (Figure 4C, S3 and S4). ECRG4 knockdown shortened the action potential durations at 90% repolarization (APD90) and prevented the LPS-induced APD-prolongation (Figure 4C, S3 and S4) but did not affect the resting membrane potential (MP), action potential amplitude (APA), the maximal upstroke velocity (Vmax) and APD50 (Figure 4B–F, S3 and S4). These results indicate that ECRG4 is involved in the LPS induced electrophysiological abnormalities.
Figure 4ECRG4 knockdown shortened potential duration and intercepted LPS effect on APD in hiPSC-CM-WT1.1. (A) Representative traces of action potentials (AP) in control, LPS, ECRG4 knockdown and ECRG4 knockdown plus LPS-treated hiPSC-CMs. (B) Mean values of APD at 50% repolarization (APD50). (C) Mean values of APD at 90% repolarization (APD90). (D) Mean values of resting potentials (MP). (E) Mean Values of action potential amplitude (APA). (F) Mean values of maximal upstroke velocity of AP (Vmax). Values given are mean ± SEM. *P<0.05, **P<0.01, ns, not significant.
ECRG4 knockdown increased small conductance calcium-activated K+ channel currents (ISK) and decreased Na/Ca-exchanger current (INCX)
For analyzing the ion channel currents responsible for the observed changes in APs caused by ECRG4 knockdown, ion channels that may influence APs were assessed. We found that the apamin-sensitive small conductance calcium-activated K+ channel current (ISK) was significantly increased by ECRG4 knockdown (Figure 5 A-C). After ECRG4 knockdown, ISK at 40 mV was increased from 0.4163 ± 0.08 pA/pF to 0.8197± 0.14 pA/pF (P<0.05). We also observed that the Ni+-sensitive Na/Ca exchanger current (INCX) was significantly reduced by ECRG4 knockdown (Figure 5 D-F). ECRG4 knockdown reduced INCX at +60 mV from 1.4363 ± 0.25 to 0.5496 ± 0.06 pA/pF (P<0.05) and reduced the current at −100mV, although the change was not statistically significant owing to the large deviation.
Figure 5ECRG4 knockdown enhanced small conductance calcium-activated K+ channel currents (ISK) and attenuated Na/Ca-exchanger current (INCX) in hiPSC-CM-WT1.1. Membrane currents were recorded in hiPSC-CMs after transfection of ECRG4-siRNA adenovirus and negative control (N.C). 100 nM apamin, a blocker of SK channels was used to isolate ISK from other currents. The apamin-sensitive currents were analyzed as ISK. INCX was recorded by ramp pulses from −30 to +60 mV, then to −100 mV (100 mV/s) at 0.5 hz with the holding potential of −30 mV. NiCl2 (5 mm) was applied to separate INCX from other currents. (A) Representative traces of apamin sensitive ISK in control and ECRG4 knockdown. (B) I–V curves of apamin sensitive ISK from −80 to +80 mV with the holding potential of −50 mV. (C) Averaged values of apamin sensitive ISK at +40 mV. (D) Representative traces of INCX in control and ECRG4 knock-down. (E) Mean values of peak INCX at +60 mV. (F) Mean values of peak INCX at −100 mV. Values given are mean ± SEM. *P<0.05, ns, not significant.
Other ion channels that may influence APs, including inward and outward currents, were also assessed. We found that ECRG4 knockdown failed to change the sodium channel currents (peak INa, Figure S5 A and B), late INa (Figure S5 C), or L-type calcium channel currents (ICa-L, Figure S5 D and E). The outward currents of rapidly activating delayed rectifier current (IKr, Figure S6 A and B), slowly activating delayed rectifier current (IKs, Figure S6 C and D), transient outward current (Ito, Figure S6 E and F), and ATP-sensitive K+ channel current (IKATP, Figure S6 G and H) did not change significantly after ECRG4 knockdown. These results suggested that ECRG4 influence APs through changing ISK and INCX.
ECRG4 Knockdown Could Intercept the LPS Effect on ISK and INCX
To further assess whether inhibition of ECRG4 could reverse the effects of LPS on ISK and INCX, we applied 1 µg/mL LPS for 24 hours to hiPSC-CMs with and without ECRG4 knockdown and then recorded apamin-sensitive ISK and NiCl2-sensitive INCX. We found that LPS increased INCX and decreased ISK, and ECRG4 knockdown could intercept the effects of LPS on ISK and INCX (Figure 6 A–D, S7 and S8). These results indicated that the effects of LPS on both currents are ECRG4-dependent.
Figure 6ECRG4 knockdown intercepted the LPS effect on ISK and INCX in hiPSC-CM-WT1.1. (A) Current-voltage (I–V) relationship curves of ISK. (B) Mean values of apamin sensitive ISK at +40 mV. (C) Mean values of peak INCX at +60 mV. (D) Mean values of peak INCX at −100 mV. Values given are mean ± SEM. *P<0.05, **P<0.01, ns, not significant.
NFκB Signaling Blockers Inhibited the ECRG4 Effect on ISK and INCX
We observed that ECRG4 knockdown decreased the expression level of TLR4 and its associated genes. This suggested that ECRG4 may mediate the effects of LPS through NFκB signaling. To test this hypothesis, a selective blocker, 5-(4-fluorophenyl)-2-ureidothiophene-3-carboxamide (TPCA-1), an inhibitor of the nuclear factor kappa-B kinase subunit beta (IKK2) and a NFκB blocker, QNZ (EVP4593) were used together with ECRG4 overexpression adenovirus to detect the effect of ECRG4 on ISK and INCX. As reported before, 0.5 µM TPCA-1 was applied in hiPSC-CMs infected with ECRG4 overexpression adenovirus for 1 hour before current recording,22 and 100 nM QNZ(EVP4593) was applied in hiPSC-CMs infected with ECRG4 overexpression adenovirus for 24 hours before current recording.23 The results showed that ECRG4 overexpression significantly increased ECRG4 expression level in hiPSC-CMs (Figure S9). Overexpression of ECRG4 mimicked the effects of LPS on ISK and INCX. TPCA-1 and QNZ(EVP4593) could reverse the ECRG4 overexpression-induced changes in ISK and INCX at 60 mV in hiPSC-CMs derived from all three donors (Figure 7 A–D, S9 and S10), which indicated that the effect of ECRG4 on ISK and INCX were through IKK2 and NFκB signaling.
Figure 7NFκB signaling blockers inhibited the ECRG4 effect on ISK and INCX. ECRG4 was overexpressed in hiPSC-CMs (hiPSC-CM-WT1.1). TPCA-1 (0.5 μM) was applied 1 hour and QNZ (EVP4593) of 100 nM was applied 24 hours before current recordings in hiPSC-CMs infected with ECRG4 overexpression adenovirus or control virus (N.C). (A) Current-voltage (I–V) relationship curves of ISK. (B) Mean values of ISK at +40mV. (C) Mean values of peak INCX at +60 mV. (D) Mean values of peak INCX at −100 mV. Values given are mean ± SEM. *P<0.05, **P<0.01, ***P<0.001, ns, not significant.
ECRG4 Was Detected in hiPSC-CMs
To investigate whether ECRG4 is expressed in hiPSC-CMs, we generated hiPSC-CMs from healthy donors and collected total mRNA at different differentiation time. ECRG4 expression on human cardiac muscle cell line AC16 and mice cardiomyocytes was detected as positive control. Expression levels of ECRG4 were analyzed by real-time PCR. The results showed that ECRG4 was highly expressed in hiPSCs (d0) and mesoblastemas on day 5 of differentiation, and the expression level was largely decreased from day 10, when hiPSC-CMs were generated (cells started to beat). Then, the expression of ECRG4 in hiPSC-CMs increased slightly and was sustained until the maturation stage (Figure 1A).
Figure 1Expression of ECRG4 in hiPSC-CMs (hiPSC-CM-WT1.1) at different differentiation time and immunostaining of hiPSC-CMs for ECRG4. (A) Relative mRNA levels (normalized to GAPDH) of ECRG4 were analyzed by real-time PCR in hiPSC-CMs at different differentiation time as well as human cardiac muscle cell line AC16 and mice cardiomyocytes (MC). (B) Immunostaining of hiPSC-CMs at day 60 after differentiation with α-actinin, cTnT and ECRG4 antibody, showing the expression of ECRG4 in hiPSC-CMs. Nuclear staining was induced with DAPI (blue).
For verification of the PCR results, we further performed an immunofluorescence study. hiPSC-CMs were stained with α-actinin, cTnT and ECRG4 antibodies with different fluorescent dyes. Nuclear staining was performed with DAPI. Adult mice cardiomyocytes were stained as positive control. As shown in Figure 1B, ECRG4-positive immunofluorescence signal was detected localized to peri-nucleus and cytoplasm and together with cardiomyocytes marker α-actinin and cTnT in hiPSC-CMs, which is the same as adult mice cardiomyocytes (Figure S1A). These results suggest that hiPSC-CMs express ECRG4.
LPS Treatment Enhanced ECRG4 Expression Level in hiPSC-CMs
Studies have demonstrated that ECRG4 is present both on the surface and in the cytoplasm of human monocytes and granulocytes, and a physical interaction between ECRG4 and TLR4-MD2-CD14 on human granulocytes has been detected. However, whether ECRG4 had an interaction with TLR4 in hiPSC-CMs was unclear. Therefore, we investigated the localization of ECRG4 and TLR4 by immunocytochemistry and changes in the expression of ECRG4 after LPS treatment at different concentrations and different times in hiPSC-CMs. The results showed that ECRG4 was localized on the surface of non-permeabilized hiPSC-CMs (Figure 2A), and the co-localization of ECRG4 and TLR4 was detected by co-staining of ECRG4 and TLR4 in hiPSC-CMs (Figure 2B). The expression of ECRG4 in hiPSC-CMs was increased by LPS at concentrations from 0.1 μg/mL to 10 μg/mL and at treatment times from 1 h to 24 h in all three cell lines (Figure 2C and D, S1B), suggesting ECRG4 may involve in LPS-induced inflammatory process.
Figure 2Immunostaining of ECRG4 and TLR4, and expression of ECRG4 in hiPSC-CMs after LPS treatment. (A) Immunostaining of non-permeabilized hiPSC-CMs with ECRG4 (red) antibody and WGA (green). Nuclear staining was induced with DAPI (blue). (B) Immunostaining for TLR4 (green) and ECRG4 (red) suggest co-localization of TLR4 and ECRG4, which is supported by merged yellow signaling (red + green). Nuclear staining was induced with DAPI (blue). (C and D) Relative mRNA levels (normalized to GAPDH) of ECRG4 were assessed by real-time PCR in hiPSC-CMs after challenge by LPS at different concentrations for 24 h and different time. Values given are mean ± SEM. ** P<0.01, *** P<0.001.
ECRG4 Knockdown Changed Expression Level of Toll-Like Receptor 4 Signaling and Ion Channels
To further explore the effect of ECRG4 on inflammatory responses, we first constructed ECRG4-siRNA adenovirus to knock down the ECRG4 expression in hiPSC-CMs, and the knockdown efficiency was verified by real-time PCR. The mRNA level of Toll-like receptor 4 and its associated signaling genes and inflammatory cytokines were analyzed using real-time PCR after ECRG4 knockdown. The result showed that ECRG4-siRNA adenovirus could inhibit about 90% of the ECRG4 mRNA expression (Figure 3A). After ECRG4 knockdown, the expression level of TLR4 and its associated genes, TIRAP, MyD88, MAPK14, MAPK1, MAPK8, IKK2, IKKϒ, NFκB1, RelA and inflammatory cytokines IL-1β, IL-6 were decreased (Figure 3B and C, S2). Furthermore, Western blot result showed that ECRG4 knockdown decreased the protein level of IKK and NFκB P65 as well as the level of phosphorylated IKK and NFκB P65 (Figure 3D). To investigate the effect of ECRG4 on the ion channels, the mRNA expression of the main ion channels involved in the action potential was measured by qPCR after ECRG4 knockdown. The results showed that the KCNH2 and NCX levels were decreased, but KCNN2 level was increased after ECRG4 knockdown (Figure 3E). These results suggested that ECRG4 is involved in the Toll-like receptor 4 signaling.
Figure 3ECRG4 knockdown changes the expression level of TLR4-associated signaling genes, inflammatory cytokines and ion channels in hiPSC-CMs (hiPSC-CM-WT1.1). (A) Relative mRNA (normalized to GAPDH) levels of ECRG4 were analyzed by real-time PCR after infection of ECRG4-siRNA adenovirus and negative control (N.C). (B) The relative mRNA levels of LPS-signaling associated genes after ECRG4 knockdown. (C) The relative mRNA levels of different cytokines after ECRG4 knockdown. (D) The Western blot results of total IKK and NFκB P65 (normalized to GAPDH) as well as phosphorylated IKK and NFκB P65 (normalized to total protein) after transfection of ECRG4-siRNA adenovirus and negative control. (E) Relative mRNA levels (normalized to GAPDH) of ion channels were analyzed by real-time PCR after transfection of ECRG4-siRNA adenovirus and negative control. Values given are mean ± SEM. *P<0.05, **P<0.01, *** P<0.001 versus control (N.C), ns, not significant.
ECRG4 Knockdown Shortened Action Potential Duration (APD) and Intercepted LPS Effect
To further investigate the effects of ECRG4 on the electrophysiological properties of cardiomyocytes and to examine whether ECRG4 is involved in the LPS induced electrophysiological abnormalities, the membrane potentials of hiPSC-CMs with and without ECRG4 knockdown and the negative control were measured in the absence and presence of LPS. In hiPSC-CMs without ECRG4 knockdown, LPS prolonged APD90 (Figure 4C, S3 and S4). ECRG4 knockdown shortened the action potential durations at 90% repolarization (APD90) and prevented the LPS-induced APD-prolongation (Figure 4C, S3 and S4) but did not affect the resting membrane potential (MP), action potential amplitude (APA), the maximal upstroke velocity (Vmax) and APD50 (Figure 4B–F, S3 and S4). These results indicate that ECRG4 is involved in the LPS induced electrophysiological abnormalities.
Figure 4ECRG4 knockdown shortened potential duration and intercepted LPS effect on APD in hiPSC-CM-WT1.1. (A) Representative traces of action potentials (AP) in control, LPS, ECRG4 knockdown and ECRG4 knockdown plus LPS-treated hiPSC-CMs. (B) Mean values of APD at 50% repolarization (APD50). (C) Mean values of APD at 90% repolarization (APD90). (D) Mean values of resting potentials (MP). (E) Mean Values of action potential amplitude (APA). (F) Mean values of maximal upstroke velocity of AP (Vmax). Values given are mean ± SEM. *P<0.05, **P<0.01, ns, not significant.
ECRG4 knockdown increased small conductance calcium-activated K+ channel currents (ISK) and decreased Na/Ca-exchanger current (INCX)
For analyzing the ion channel currents responsible for the observed changes in APs caused by ECRG4 knockdown, ion channels that may influence APs were assessed. We found that the apamin-sensitive small conductance calcium-activated K+ channel current (ISK) was significantly increased by ECRG4 knockdown (Figure 5 A-C). After ECRG4 knockdown, ISK at 40 mV was increased from 0.4163 ± 0.08 pA/pF to 0.8197± 0.14 pA/pF (P<0.05). We also observed that the Ni+-sensitive Na/Ca exchanger current (INCX) was significantly reduced by ECRG4 knockdown (Figure 5 D-F). ECRG4 knockdown reduced INCX at +60 mV from 1.4363 ± 0.25 to 0.5496 ± 0.06 pA/pF (P<0.05) and reduced the current at −100mV, although the change was not statistically significant owing to the large deviation.
Figure 5ECRG4 knockdown enhanced small conductance calcium-activated K+ channel currents (ISK) and attenuated Na/Ca-exchanger current (INCX) in hiPSC-CM-WT1.1. Membrane currents were recorded in hiPSC-CMs after transfection of ECRG4-siRNA adenovirus and negative control (N.C). 100 nM apamin, a blocker of SK channels was used to isolate ISK from other currents. The apamin-sensitive currents were analyzed as ISK. INCX was recorded by ramp pulses from −30 to +60 mV, then to −100 mV (100 mV/s) at 0.5 hz with the holding potential of −30 mV. NiCl2 (5 mm) was applied to separate INCX from other currents. (A) Representative traces of apamin sensitive ISK in control and ECRG4 knockdown. (B) I–V curves of apamin sensitive ISK from −80 to +80 mV with the holding potential of −50 mV. (C) Averaged values of apamin sensitive ISK at +40 mV. (D) Representative traces of INCX in control and ECRG4 knock-down. (E) Mean values of peak INCX at +60 mV. (F) Mean values of peak INCX at −100 mV. Values given are mean ± SEM. *P<0.05, ns, not significant.
Other ion channels that may influence APs, including inward and outward currents, were also assessed. We found that ECRG4 knockdown failed to change the sodium channel currents (peak INa, Figure S5 A and B), late INa (Figure S5 C), or L-type calcium channel currents (ICa-L, Figure S5 D and E). The outward currents of rapidly activating delayed rectifier current (IKr, Figure S6 A and B), slowly activating delayed rectifier current (IKs, Figure S6 C and D), transient outward current (Ito, Figure S6 E and F), and ATP-sensitive K+ channel current (IKATP, Figure S6 G and H) did not change significantly after ECRG4 knockdown. These results suggested that ECRG4 influence APs through changing ISK and INCX.
ECRG4 knockdown increased small conductance calcium-activated K+ channel currents (ISK) and decreased Na/Ca-exchanger current (INCX)
For analyzing the ion channel currents responsible for the observed changes in APs caused by ECRG4 knockdown, ion channels that may influence APs were assessed. We found that the apamin-sensitive small conductance calcium-activated K+ channel current (ISK) was significantly increased by ECRG4 knockdown (Figure 5 A-C). After ECRG4 knockdown, ISK at 40 mV was increased from 0.4163 ± 0.08 pA/pF to 0.8197± 0.14 pA/pF (P<0.05). We also observed that the Ni+-sensitive Na/Ca exchanger current (INCX) was significantly reduced by ECRG4 knockdown (Figure 5 D-F). ECRG4 knockdown reduced INCX at +60 mV from 1.4363 ± 0.25 to 0.5496 ± 0.06 pA/pF (P<0.05) and reduced the current at −100mV, although the change was not statistically significant owing to the large deviation.
Figure 5ECRG4 knockdown enhanced small conductance calcium-activated K+ channel currents (ISK) and attenuated Na/Ca-exchanger current (INCX) in hiPSC-CM-WT1.1. Membrane currents were recorded in hiPSC-CMs after transfection of ECRG4-siRNA adenovirus and negative control (N.C). 100 nM apamin, a blocker of SK channels was used to isolate ISK from other currents. The apamin-sensitive currents were analyzed as ISK. INCX was recorded by ramp pulses from −30 to +60 mV, then to −100 mV (100 mV/s) at 0.5 hz with the holding potential of −30 mV. NiCl2 (5 mm) was applied to separate INCX from other currents. (A) Representative traces of apamin sensitive ISK in control and ECRG4 knockdown. (B) I–V curves of apamin sensitive ISK from −80 to +80 mV with the holding potential of −50 mV. (C) Averaged values of apamin sensitive ISK at +40 mV. (D) Representative traces of INCX in control and ECRG4 knock-down. (E) Mean values of peak INCX at +60 mV. (F) Mean values of peak INCX at −100 mV. Values given are mean ± SEM. *P<0.05, ns, not significant.
Other ion channels that may influence APs, including inward and outward currents, were also assessed. We found that ECRG4 knockdown failed to change the sodium channel currents (peak INa, Figure S5 A and B), late INa (Figure S5 C), or L-type calcium channel currents (ICa-L, Figure S5 D and E). The outward currents of rapidly activating delayed rectifier current (IKr, Figure S6 A and B), slowly activating delayed rectifier current (IKs, Figure S6 C and D), transient outward current (Ito, Figure S6 E and F), and ATP-sensitive K+ channel current (IKATP, Figure S6 G and H) did not change significantly after ECRG4 knockdown. These results suggested that ECRG4 influence APs through changing ISK and INCX.
ECRG4 Knockdown Could Intercept the LPS Effect on ISK and INCX
To further assess whether inhibition of ECRG4 could reverse the effects of LPS on ISK and INCX, we applied 1 µg/mL LPS for 24 hours to hiPSC-CMs with and without ECRG4 knockdown and then recorded apamin-sensitive ISK and NiCl2-sensitive INCX. We found that LPS increased INCX and decreased ISK, and ECRG4 knockdown could intercept the effects of LPS on ISK and INCX (Figure 6 A–D, S7 and S8). These results indicated that the effects of LPS on both currents are ECRG4-dependent.
Figure 6ECRG4 knockdown intercepted the LPS effect on ISK and INCX in hiPSC-CM-WT1.1. (A) Current-voltage (I–V) relationship curves of ISK. (B) Mean values of apamin sensitive ISK at +40 mV. (C) Mean values of peak INCX at +60 mV. (D) Mean values of peak INCX at −100 mV. Values given are mean ± SEM. *P<0.05, **P<0.01, ns, not significant.
NFκB Signaling Blockers Inhibited the ECRG4 Effect on ISK and INCX
We observed that ECRG4 knockdown decreased the expression level of TLR4 and its associated genes. This suggested that ECRG4 may mediate the effects of LPS through NFκB signaling. To test this hypothesis, a selective blocker, 5-(4-fluorophenyl)-2-ureidothiophene-3-carboxamide (TPCA-1), an inhibitor of the nuclear factor kappa-B kinase subunit beta (IKK2) and a NFκB blocker, QNZ (EVP4593) were used together with ECRG4 overexpression adenovirus to detect the effect of ECRG4 on ISK and INCX. As reported before, 0.5 µM TPCA-1 was applied in hiPSC-CMs infected with ECRG4 overexpression adenovirus for 1 hour before current recording,22 and 100 nM QNZ(EVP4593) was applied in hiPSC-CMs infected with ECRG4 overexpression adenovirus for 24 hours before current recording.23 The results showed that ECRG4 overexpression significantly increased ECRG4 expression level in hiPSC-CMs (Figure S9). Overexpression of ECRG4 mimicked the effects of LPS on ISK and INCX. TPCA-1 and QNZ(EVP4593) could reverse the ECRG4 overexpression-induced changes in ISK and INCX at 60 mV in hiPSC-CMs derived from all three donors (Figure 7 A–D, S9 and S10), which indicated that the effect of ECRG4 on ISK and INCX were through IKK2 and NFκB signaling.
Figure 7NFκB signaling blockers inhibited the ECRG4 effect on ISK and INCX. ECRG4 was overexpressed in hiPSC-CMs (hiPSC-CM-WT1.1). TPCA-1 (0.5 μM) was applied 1 hour and QNZ (EVP4593) of 100 nM was applied 24 hours before current recordings in hiPSC-CMs infected with ECRG4 overexpression adenovirus or control virus (N.C). (A) Current-voltage (I–V) relationship curves of ISK. (B) Mean values of ISK at +40mV. (C) Mean values of peak INCX at +60 mV. (D) Mean values of peak INCX at −100 mV. Values given are mean ± SEM. *P<0.05, **P<0.01, ***P<0.001, ns, not significant.
Discussion
The connection between inflammation and arrhythmias has been detected for a long time.24 Severe infections, such as sepsis, may lead to heart dysfunctions, including arrhythmias. Aoki et al reported the role of ion channels in sepsis-induced atrial tachyarrhythmias in guinea pigs.25 Another study observed that LPS prolonged the action potential duration with enhanced INCX in isolated rat cardiomyocytes.26 In HL-1 cells, inflammatory cytokines like TNF can reduce ICa-L and increase Ito and IKur.27 In our previous study, we demonstrated that LPS prolonged APD in hiPSC-CMs and the APD prolongation was induced by decreased ISK and increased INCX, but the mechanism underlying the effects of LPS on both currents was not clarified.
ECRG4 was identified as a tumor suppressor gene. During tumorigenesis, ECRG4 expression is reduced due to hypermethylation of the CpG island in its promoter.28,29 In addition to traditional epithelial cells, many other cell types, such as chondrocytes, hematopoietic cells, working cardiomyocytes, and atrial-ventricular node also express ECRG4,30,31 suggesting that ECRG4 has other important functions besides its tumor suppressing role. In fact, it is well-known that an increase in ECRG4 expression has a wide range of effects beyond its antitumor effect, like influencing apoptosis, cell migration, inflammation, injury, and infection responsiveness.
Functions of ECRG4 are also dependent on its cellular localization, secretion, and post-translational processing. Human ECRG4 protein possesses a hormone-precursor-like structure that is sensitive to proteolytic processing. The process can produce different ECRG4-derived peptides, and as many other neuropeptide precursors, each peptide may possess individual, shared, and biologically distinct features depending on proteolytic processing. Previous studies have demonstrated that ECRG4-derived peptides were detectable in lysates of normal tissues, in serum and cerebrospinal fluid, and in the culture media of cells overexpressing ECRG4.17,32,33 Although ECRG4 is secreted outside the cell, in some cells it is tethered to the cell surface. Upon cell stimulation, peptides produced by ECRG4 processing perform various functions in different types of cells. For example, ECRG4 can participate in both pro- and anti-inflammatory activities, as well as pro- and anti-apoptotic responses.29,33,34
Open reading frame mining has identified the interaction of the C-terminal 16 amino acid domain of ECRG4 with the TLR4 immune complex in human granulocytes and may contribute to its effects on inflammation.20 In hiPSC-CMs, we also identified that ECRG4 was present on the cell surface and the co-localization of ECRG4 and TLR4, suggesting that the ECRG4 may contribute to the inflammation process. Although several reports have shown that ECRG4 is downregulated in some models of acute injury, we found that ECRG4 was enhanced after LPS treatment at different concentrations in hiPSC-CMs, and the increase occurred shortly after LPS treatment. Tadross et al found proinflammatory functions of ECRG4 peptides, showing that the injection of amino acids 71–148 of ECRG4 into cerebral ventricle elevated the plasma level of adrenocorticotropic hormone and corticosterone in rats.35 It was also demonstrated that the C-terminal of ECRG4 peptide activated the NFκB signaling in macrophages and interacted with the innate immunity receptor complex (TLR4/CD14/MD2 complex).14,31 These findings, together with our data showing the upregulation of ECRG4 by LPS stimulation, suggest that ECRG4 can participate in some inflammation relating processes. After knockdown of the ECRG4 in hiPSC-CMs, the expression levels of TLR4 and its associated genes, TIRAP, MyD88, MAPK14, MAPK1, MAPK8, IKK2, IKKϒ, NFκB1, RelA and inflammatory cytokines IL-1β and IL-6, were decreased, which indicated that ECRG4 may contribute to gene regulation of LPS/TLR4 signaling.
To explore the role of ECRG4 in ion channel dysfunctions, the expression of ECRG4 was measured at first to prove the existence of ECRG4 in cardiomyocytes. Next, APD and ion channel currents were assessed following ECRG4 knockdown. With ECRG4 knockdown, the expression of KCNH2 and NCX was inhibited, and KCNN2 was enhanced, APD90 was shortened, ISK was increased but INCX was deceased. These results showed that ECRG4 may participate in the regulation of ion channel functions, and inhibition of ECRG4 may attenuate the LPS effects on ion channel function. In agreement with this hypothesis, the application of LPS in ECRG4-siRNA treated cells failed to prolong the APD and change the ISK and INCX.
Since LPS treatment increased ECRG4 level, we overexpressed ECRG4 in hiPSC-CMs by transfection with adenovirus carrying ECRG4 gene and measured ISK and INCX again. Indeed, the overexpression of ECRG4 mimicked the effects of LPS on ISK and INCX, confirming the contribution of ECRG4 to LPS effect on the channel currents.
Next, we attempted to elucidate the mechanism underlying the effects of ECRG4 on LPS-induced ion channel dysfunctions. IKK2 and NFκB blocker was applied to ECRG4 overexpressing cells. Blockade of NFκB signaling by either an IKK2 blocker or an NFκB blocker could reverse effects of ECRG4 overexpressing adenovirus on ISK and INCX. These results indicated that ECRG4 effect on ion channels can occur via the NFκB pathway in hiPSC-CMs. IKK2 (IKKβ) is an enzyme that is a component of the cytokine-activated intracellular signaling pathway, the NFκB pathway, which is involved in inflammation and immune responses. It is known that two NFκB pathways exist, depending on the activation of signaling and the cell type, the canonical (IKKβ-dependent) and the noncanonical pathway (IKKα-dependent).36 Our study shows that the canonical NFκB pathway is involved in ECRG4 affected signaling. Whether the noncanonical pathway is involved needs to be examined in future studies.
In summary, LPS elevated ECRG4 expression level. ECRG4 increased the expression of NCX and decreased the expression of SK channels through TLR4/NFκB/IKKβ signaling. Increased INCX and decreased ISK contributed to the APD prolongation caused by LPS-ECRG4 signaling.
Conclusion
This study demonstrated that LPS effects on APD, ISK and INCX were mediated by upregulation of ECRG4, which affects NFκB signaling. Our findings support the roles of ECRG4 in inflammatory responses and the ion channel dysfunctions induced by LPS challenge. |
Title: Erratum: Impact of Cancer Across the Intergenerational Family: A Multidimensional Perspective From African Countries | Body: |
Title: Novornabreak: Local Assembly for Novel Splice Junction and Fusion Transcript Detection from RNA-Seq Data | Body: |
Title: Epidemiological Study on the Interaction between the | Body:
1. Introduction
Metabolic dysfunction-associated steatotic liver disease (MASLD) is a hepatic phenotype of metabolic syndrome with an increasing prevalence of approximately 30% and increasing [1]. When MASLD was renamed non-alcoholic fatty liver disease (NAFLD) in 2023, the diagnostic criteria included at least one cardiometabolic criteria in addition to fatty liver [2]. Although various factors, such as sex, age, body size, diet, and lifestyle, are intricately related to the onset and progression of MASLD, the significant factors are genes and gut microbiota [3,4,5]. The prevalence of MASLD in Japan is 29.7%, which is lower than that in Europe and the U.S. However, the prevalence of lean MASLD is considered high in the Asian region, including Japan [6,7,8].
Recently, genome-wide association studies (GWASs) have identified many single nucleotide polymorphisms (SNPs) in NAFLD susceptibility genes. In 2008, Romeo reported the patatin-like phospholipase domain-containing 3 (PNPLA3) gene [9]. PNPLA3 rs738409 (C > G) has been associated with NAFLD in many ethnic groups, including the Japanese [10,11,12,13,14]. In addition to PNPLA3, various SNPs have been reported to be associated with NAFLD [15,16]. Among the many NAFLD-related SNPs, the PNPLA3 rs738409 SNP (C > G) is common in Japan, and the overall Japanese prevalence of the GG genotype is reported to be approximately 20% and 40% in NAFLD patients [11,16,17].
The gut microbiota is closely involved in developing liver fat and fibrosis, and the gut microbiota and liver association is called the gut–liver axis [18,19]. Several studies have investigated the relationship between gut bacteria and NAFLD [20,21,22].
Recent studies have highlighted a link between host genes and gut microbiota [23,24,25,26] Cystic fibrosis patients have a 5–10-fold increased risk of colorectal cancer, but Actinobacteria and Clostridium, which are increased in cystic fibrosis, are also known to be increased in colorectal cancer patients [27]. It has been suggested that a predisposition to developing colorectal cancer in cystic fibrosis patients may be associated with an increased abundance of Actinobacteria and Clostridium, accompanied by downregulation of the host’s CFTR and HPGD genes. A common set of host genes and pathways involved in gastrointestinal inflammation, gut barrier protection, and energy metabolism have also been reported to be associated with disease-specific gut bacteria [25]. Although studies examining this link are limited and have primarily focused on Western populations, recent research on Japanese individuals has revealed that host genetic factors, including SNPs, can significantly impact the gut microbiota composition [28,29].
Although there have been many previous studies on the separate effects of SNPs and gut bacteria on MASLD, few epidemiological studies have examined how SNPs and gut bacteria interact during the development and progression of MASLD. In this study, we investigated the association between PNPLA3 rs738409, a representative MASLD-related SNP, and gut bacteria in MASLD using a cross-sectional study of the general population.
2. Materials and Methods
2.1. Study Participants
This study was part of the Iwaki Health Promotion Project, a community-based health promotion project for the general Japanese population. It is conducted annually in June as a regular health checkup for residents of the Iwaki area of Hirosaki City, Aomori Prefecture [30]. All the participants voluntarily responded to a public call for participation. A total of 1059 adults (aged 19–88 years) participated in the study. Participants who could not give consent for genetic testing, those who could not diagnose steatotic liver disease (SLD) due to failure of transient elastography measurement, and those who had one or more missing values in any of the measures were excluded. Additionally, those who had undergone gastrectomy or were taking gastric suppressant were excluded, as oral and gut microbiota differ significantly due to gastric acid sterilization, and this relationship may change significantly if gastric acid secretion is reduced. Furthermore, we excluded participants who were taking antibiotics because antibiotic use can drastically change the composition of the gut microbiota. Based on previous reports, SLD was diagnosed with a cutoff value of 232.5 dB/m, the CAP value of FibroScan (Echosens, Paris, France) [31]. Since hepatitis B and C and alcohol consumption are known to significantly affect the intestinal environment, we defined a normal group of 318 participants by excluding individuals with HBs antigen positivity, anti-HCV antibody positivity, and excessive alcohol consumption (≥30 g/day for males, ≥20 g/day for females) from the non-SLD group [32,33,34]. In the SLD group, 208 patients who met the diagnostic criteria were included in the MASLD group (Figure 1). In total, 526 patients (318 in the normal group and 208 in the MASLD group) were included in the analysis.
2.2. Transient Elastography
The controlled attenuation parameter (CAP) and liver stiffness measurement (LSM) were performed using a Fibroscan530 (Echosens, Paris, France) with the M and XL probes. All examinations were performed by five hepatologists who underwent specialized training. When the number of measurements was < 10, or the ratio of the interquartile range was >0.30, the measured values were excluded due to unreliability. Previous studies defined steatosis as a CAP value >232.5 dB/m [31].
2.3. Clinical Parameters
The following clinical parameters were recorded on the same day as the transient examination: sex, age, height, body mass index (BMI; calculated by dividing the weight in kg by the squared height in m), waist circumference, results of HBs antigen or anti-HCV tests, and levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT), γ-glutamyl transpeptidase, glucose, hemoglobin A1c (HbA1c), high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, and triglycerides.
The FIB-4 index was calculated as follows:{age × AST (U/L)}/{blood platelet count (109/L) × √ALT (U/L)]}.
The aspartate aminotransferase to platelet ratio (APRI) was calculated as follows.
{[AST/ULN]/platelet count (× 109/L)} × 100.
The NAFLD fibrosis (NFS) score was calculated as follows.
−1.675 + 0.037 × age (years) + 0.094 × BMI (kg/m2) + 1.13 × diabetes mellitus (yes = 1, no = 0) + 0.99 × AST
(U/l)/ALT (U/;) −0.013 × platelet counts (104/µL) −0.66 × albumin (g/dL).
The FibroScan-aspartate aminotransferase (FAST) score was calculated as follows [35]:{exp (–1.65 + 1.07 × ln (LSM) + 2.66 × 10 − 8 × CAP3 − 63.3 × AST − 1)}/{1 + exp (–1.65 + 1.07 × ln (LSM) + 2.66 × 10 − 8 × CAP3 − 63.3 × AST − 1)}
2.4. MASLD Diagnosis
Participants with fatty liver who met any of the following cardiometabolic criteria were diagnosed with MASLD: obesity/central obesity, hyperglycemia or diabetes, high blood pressure, high triglyceride levels, and reduced HDL cholesterol were diagnosed with MASLD [2]. The specific criteria included a BMI ≥ 23 kg/m2 or waist circumference ≥ 94 cm for males and ≥80 cm for females; fasting blood glucose ≥ 100 mg/dL, postprandial blood glucose ≥ 140 mg/dL, HbA1c ≥ 5.7%, or undergoing treatment for type 2 diabetes; blood pressure ≥ 130/85 mmHg or currently undergoing antihypertensive treatment; triglycerides ≥ 150 mg/dL or currently undergoing treatment for dyslipidemia; and HDL cholesterol ≤ 40 mg/dL for males and ≤50 mg/dL for females.
2.5. DNA Preparation and SNP Genotyping
SNP genotypes were determined by whole-genome sequencing with imputation from the Japonica Array (Toshiba, Tokyo, Japan), which consists of population-specific SNP markers designed from the 1070 whole-genome reference panels and TaqMan PCR [36,37]. Whole genome sequencing and imputation were performed by Takara Bio Corporation (Shiga, Japan) and Toshiba Corporation, respectively. For the Japonica Array, DNA was purified from peripheral whole blood using a QIAamp.R96 DNA Blood Kit (QIAGEN, Hilden, Germany) and extracted from plasma pellets for whole-genome sequencing. Among the many SNPs extracted by the Japonica Array, this study focused on SNP PNPLA3 rs738409, which has been reported to be most involved in the onset and progression of MASLD in previous studies [10,11,12,14].
2.6. Measurements of the Gut Microbiota
Gut microbiota data were obtained following procedures. Fecal sample kits were distributed to the participants in advance, and fecal samples were collected at home. DNA was extracted from bead-beaten fecal suspensions using an automated nucleic acid extraction system (Precision System Science, Chiba, Japan). The MagDEA DNA 200 (GC) reagent kit (Precision System Science) was used for automated nucleic acid extraction. DNA extraction for all samples was completed within four months. Universal primer sets were used to amplify the V3–V4 region of the 16S rRNA gene. Solution preparation and condition setting for PCR amplification were performed according to the previous study [38]. PCR fragments purified using PCR Cleanup Filter Plates (Merck Millipore, Burlington, MA, USA) were quantified by real-time quantitative PCR (qPCR). To read DNA sequences, purified PCR fragments were analyzed by 2-cycle × 300-cycle paired-end sequencing on a MiSeq™ system (Illumina, San Diego, CA, USA). Paired-end reads were processed as follows: adapter sequences and low-quality bases (Q < 20) at the 3′ end of the reads were trimmed using Cutadapt (version: 1.13). Reads containing ambiguous bases N or shorter than 150 bases were excluded. Paired-end reads that met the criteria were merged into a single read called a “merged read”. Merged reads shorter than 370 base pairs or longer than 470 base pairs were excluded using the fastq_mergepairs subcommand of VSEARCH (version: 2.4.3) [39]. Furthermore, merged reads containing one or more identified sequencing errors were excluded. After removing chimeric reads detected by the uchime_denovo subcommand of VSEARCH, the remaining merged reads were clustered at a minimum sequence similarity of 97% to obtain operational taxonomic units (OTUs). Phylogenetic assignment of OTUs was performed by applying the RDP classifier (commit hash: 701e229dde7cbe53d4261301e23459d91615999d) based on their representative reads [40]. Predictions with a confidence score below 0.8 were treated as unclassified. The relative abundance of each bacterial genus in the gut microbiota was calculated by dividing the read count of each bacterial genus by the total read count.
2.7. Statistical Analysis
Categorical variables are presented as frequencies and continuous variables as medians, along with interquartile ranges. To compare differences in MASLD-related items and gut microbiota in CC, CG, and GG genotypes of PNPLA3 rs738409, the Kruskal–Wallis and chi-square tests were used to compare the three groups. The microbiota was compared using linear discriminant analysis effect size (LEfse) [41]. Regression analyses were then used to evaluate the correlation between the relative abundance of individual bacterial species found to be associated with LEfSe and MASLD-related items. Pearson’s correlation coefficient was used to investigate the correlation between MASDL-related parameters and gut microbiota. A multiple regression model with MASLD-related items and gut microbiota was used for predictive analysis. Independent variables included sex, age, smoking habits, exercise habits, and medication for hypertension, dyslipidemia, or diabetes mellitus. Before simple correlation and multiple regression analyses, all continuous parameters were log-transformed (natural logarithm) to approximate a normal distribution.
Statistical analyses were performed using R software (R Foundation for Statistical Computing, version R-4.1.1) and the Statistical Package for the Social Sciences (SPSS) version 28.0 (SPSS Inc., Chicago, IL, USA). Statistical significance was set at p < 0.05.
2.8. Ethics Statement
This study was conducted per the ethical standards of the Declaration of Helsinki and approved by the Ethics Committee of Hirosaki University School of Medicine (approval number and date: 2018-012, approved on 11 May 2018). Informed consent was obtained from all participants.
2.1. Study Participants
This study was part of the Iwaki Health Promotion Project, a community-based health promotion project for the general Japanese population. It is conducted annually in June as a regular health checkup for residents of the Iwaki area of Hirosaki City, Aomori Prefecture [30]. All the participants voluntarily responded to a public call for participation. A total of 1059 adults (aged 19–88 years) participated in the study. Participants who could not give consent for genetic testing, those who could not diagnose steatotic liver disease (SLD) due to failure of transient elastography measurement, and those who had one or more missing values in any of the measures were excluded. Additionally, those who had undergone gastrectomy or were taking gastric suppressant were excluded, as oral and gut microbiota differ significantly due to gastric acid sterilization, and this relationship may change significantly if gastric acid secretion is reduced. Furthermore, we excluded participants who were taking antibiotics because antibiotic use can drastically change the composition of the gut microbiota. Based on previous reports, SLD was diagnosed with a cutoff value of 232.5 dB/m, the CAP value of FibroScan (Echosens, Paris, France) [31]. Since hepatitis B and C and alcohol consumption are known to significantly affect the intestinal environment, we defined a normal group of 318 participants by excluding individuals with HBs antigen positivity, anti-HCV antibody positivity, and excessive alcohol consumption (≥30 g/day for males, ≥20 g/day for females) from the non-SLD group [32,33,34]. In the SLD group, 208 patients who met the diagnostic criteria were included in the MASLD group (Figure 1). In total, 526 patients (318 in the normal group and 208 in the MASLD group) were included in the analysis.
2.2. Transient Elastography
The controlled attenuation parameter (CAP) and liver stiffness measurement (LSM) were performed using a Fibroscan530 (Echosens, Paris, France) with the M and XL probes. All examinations were performed by five hepatologists who underwent specialized training. When the number of measurements was < 10, or the ratio of the interquartile range was >0.30, the measured values were excluded due to unreliability. Previous studies defined steatosis as a CAP value >232.5 dB/m [31].
2.3. Clinical Parameters
The following clinical parameters were recorded on the same day as the transient examination: sex, age, height, body mass index (BMI; calculated by dividing the weight in kg by the squared height in m), waist circumference, results of HBs antigen or anti-HCV tests, and levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT), γ-glutamyl transpeptidase, glucose, hemoglobin A1c (HbA1c), high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, and triglycerides.
The FIB-4 index was calculated as follows:{age × AST (U/L)}/{blood platelet count (109/L) × √ALT (U/L)]}.
The aspartate aminotransferase to platelet ratio (APRI) was calculated as follows.
{[AST/ULN]/platelet count (× 109/L)} × 100.
The NAFLD fibrosis (NFS) score was calculated as follows.
−1.675 + 0.037 × age (years) + 0.094 × BMI (kg/m2) + 1.13 × diabetes mellitus (yes = 1, no = 0) + 0.99 × AST
(U/l)/ALT (U/;) −0.013 × platelet counts (104/µL) −0.66 × albumin (g/dL).
The FibroScan-aspartate aminotransferase (FAST) score was calculated as follows [35]:{exp (–1.65 + 1.07 × ln (LSM) + 2.66 × 10 − 8 × CAP3 − 63.3 × AST − 1)}/{1 + exp (–1.65 + 1.07 × ln (LSM) + 2.66 × 10 − 8 × CAP3 − 63.3 × AST − 1)}
2.4. MASLD Diagnosis
Participants with fatty liver who met any of the following cardiometabolic criteria were diagnosed with MASLD: obesity/central obesity, hyperglycemia or diabetes, high blood pressure, high triglyceride levels, and reduced HDL cholesterol were diagnosed with MASLD [2]. The specific criteria included a BMI ≥ 23 kg/m2 or waist circumference ≥ 94 cm for males and ≥80 cm for females; fasting blood glucose ≥ 100 mg/dL, postprandial blood glucose ≥ 140 mg/dL, HbA1c ≥ 5.7%, or undergoing treatment for type 2 diabetes; blood pressure ≥ 130/85 mmHg or currently undergoing antihypertensive treatment; triglycerides ≥ 150 mg/dL or currently undergoing treatment for dyslipidemia; and HDL cholesterol ≤ 40 mg/dL for males and ≤50 mg/dL for females.
2.5. DNA Preparation and SNP Genotyping
SNP genotypes were determined by whole-genome sequencing with imputation from the Japonica Array (Toshiba, Tokyo, Japan), which consists of population-specific SNP markers designed from the 1070 whole-genome reference panels and TaqMan PCR [36,37]. Whole genome sequencing and imputation were performed by Takara Bio Corporation (Shiga, Japan) and Toshiba Corporation, respectively. For the Japonica Array, DNA was purified from peripheral whole blood using a QIAamp.R96 DNA Blood Kit (QIAGEN, Hilden, Germany) and extracted from plasma pellets for whole-genome sequencing. Among the many SNPs extracted by the Japonica Array, this study focused on SNP PNPLA3 rs738409, which has been reported to be most involved in the onset and progression of MASLD in previous studies [10,11,12,14].
2.6. Measurements of the Gut Microbiota
Gut microbiota data were obtained following procedures. Fecal sample kits were distributed to the participants in advance, and fecal samples were collected at home. DNA was extracted from bead-beaten fecal suspensions using an automated nucleic acid extraction system (Precision System Science, Chiba, Japan). The MagDEA DNA 200 (GC) reagent kit (Precision System Science) was used for automated nucleic acid extraction. DNA extraction for all samples was completed within four months. Universal primer sets were used to amplify the V3–V4 region of the 16S rRNA gene. Solution preparation and condition setting for PCR amplification were performed according to the previous study [38]. PCR fragments purified using PCR Cleanup Filter Plates (Merck Millipore, Burlington, MA, USA) were quantified by real-time quantitative PCR (qPCR). To read DNA sequences, purified PCR fragments were analyzed by 2-cycle × 300-cycle paired-end sequencing on a MiSeq™ system (Illumina, San Diego, CA, USA). Paired-end reads were processed as follows: adapter sequences and low-quality bases (Q < 20) at the 3′ end of the reads were trimmed using Cutadapt (version: 1.13). Reads containing ambiguous bases N or shorter than 150 bases were excluded. Paired-end reads that met the criteria were merged into a single read called a “merged read”. Merged reads shorter than 370 base pairs or longer than 470 base pairs were excluded using the fastq_mergepairs subcommand of VSEARCH (version: 2.4.3) [39]. Furthermore, merged reads containing one or more identified sequencing errors were excluded. After removing chimeric reads detected by the uchime_denovo subcommand of VSEARCH, the remaining merged reads were clustered at a minimum sequence similarity of 97% to obtain operational taxonomic units (OTUs). Phylogenetic assignment of OTUs was performed by applying the RDP classifier (commit hash: 701e229dde7cbe53d4261301e23459d91615999d) based on their representative reads [40]. Predictions with a confidence score below 0.8 were treated as unclassified. The relative abundance of each bacterial genus in the gut microbiota was calculated by dividing the read count of each bacterial genus by the total read count.
2.7. Statistical Analysis
Categorical variables are presented as frequencies and continuous variables as medians, along with interquartile ranges. To compare differences in MASLD-related items and gut microbiota in CC, CG, and GG genotypes of PNPLA3 rs738409, the Kruskal–Wallis and chi-square tests were used to compare the three groups. The microbiota was compared using linear discriminant analysis effect size (LEfse) [41]. Regression analyses were then used to evaluate the correlation between the relative abundance of individual bacterial species found to be associated with LEfSe and MASLD-related items. Pearson’s correlation coefficient was used to investigate the correlation between MASDL-related parameters and gut microbiota. A multiple regression model with MASLD-related items and gut microbiota was used for predictive analysis. Independent variables included sex, age, smoking habits, exercise habits, and medication for hypertension, dyslipidemia, or diabetes mellitus. Before simple correlation and multiple regression analyses, all continuous parameters were log-transformed (natural logarithm) to approximate a normal distribution.
Statistical analyses were performed using R software (R Foundation for Statistical Computing, version R-4.1.1) and the Statistical Package for the Social Sciences (SPSS) version 28.0 (SPSS Inc., Chicago, IL, USA). Statistical significance was set at p < 0.05.
2.8. Ethics Statement
This study was conducted per the ethical standards of the Declaration of Helsinki and approved by the Ethics Committee of Hirosaki University School of Medicine (approval number and date: 2018-012, approved on 11 May 2018). Informed consent was obtained from all participants.
3. Results
3.1. Participant Characteristics
Participants’ characteristics are presented in Table 1. The frequency of the PNPLA3 rs738409 SNP in this study subjects was 28.5% for CC genotype, 49.4% for CG genotype, and 22.1% for GG genotype. The genotype frequencies calculated under the assumption of Hardy–Weinberg equilibrium were 28.3% for CC genotype, 49.8% for CG genotype, and 21.9% for GG genotype, which were almost the same as the actual values, indicating that Hardy–Weinberg equilibrium was established. The GG genotype of PNPLA3 rs738408 was observed in 22.1% and 23.1% of all subjects and patients with MASLD, respectively. There was no significant association between PNPLA3 rs738409 SNP and MASLD appearance in this study population. There were no significant differences in sex, age, smoking, or drinking habits between the three groups. Additionally, there were no differences in CAP or cardiometabolic criteria among the three groups. In contrast, AST and ALT levels were significantly higher in the GG genotype group than in the CC and CG genotype groups. Among the liver fibrosis scoring systems, the APRI and FIB-4 scores were higher in the GG genotype group than in the other groups.
Tables S1–S3 show the characteristics of the PNPLA3 rs738409 SNP. The MASLD group was older than the control group for the CC and CG genotypes. There were significant differences in CAP values and cardiometabolic criteria between the normal and MASLD groups. However, there were no significant differences in sex, LSM, smoking, or exercise habits between the groups.
Figure 2 and Figure 3 show the differences in the composition and diversity of the gut microbiota. There were no differences in composition between PNPLA3 rs738409 SNP groups at either the phyla or genera levels (Figure 2). Figure 3 illustrates the diversity of the gut microbiome of the study subjects. Neither α diversity (as measured by the Chao-1 and Shannon indexes) nor β diversity (as assessed by principal coordinate analysis) showed significant differences across the PNPLA3 rs738409 SNP groups.
3.2. Comparison of Gut Microbiota between Normal and MASLD Group
The LEfSe results for MASLD and gut microbiota are shown in Figure 4. Five bacterial taxa were significantly enriched in the CC genotype group, ten in the CG genotype, and two in the GG genotype. Among these, only two taxa had a relative abundance of 1% or more and an LDA score of 3 or more: Blautia in the CC genotype group (8.1% in the normal group and 5.6% in the MASLD group) and Ruminococcaceae in the CG genotype group (20.5% in the normal group and 16.9% in the MASLD group). The two taxa that showed differences in the GG group had extremely low relative abundances (<0.2%).
3.3. Comparison of Gut Microbiota among PNPLA3 rs738409 SNP
Figure 5 presents the relative abundance of Blautia and Ruminococcaceae stratified by PNPLA3 rs738409 genotypes, as determined by LEfSe analyses. There were no significant associations between either Blautia or Ruminococcaceae and the PNPLA3 rs738409 SNP in the normal group. In contrast, the MASLD group showed a lower relative abundance of Blautia in the CC group, and a tendency for a decrease in Ruminococcaceae was noted in the CG group, although no statistically significant difference was observed.
3.4. Relationship between MASLD-Related Items and Gut Microbiota
Table 2 summarizes the correlation between MASLD-related items and gut microbiota in a single correlation analysis. In the CC genotype group, CAP and BMI had a negative correlation with the Blautia, and triglycerides had a negative correlation with Ruminococcaceae. In the CG genotype group, waist circumference and blood glucose had a negative correlation with Blautia, and CAP, waist circumference, and triglycerides had a negative correlation with Ruminococcaceae.
Next, multiple regression analysis was performed, where the dependent variables were MASLD-related items and the independent variables were sex, age, smoking and exercise habits, and medication of hypertension, dyslipidemia, or diabetes mellitus in addition to the gut microbiota. Table 3 presents the results of the study. The CC genotype group showed the same association as that of the single correlation group. In the CG genotype group, CAP and triglycerides were negatively correlated with Ruminococcaceae. In contrast, the GG group showed no significant correlation between MASLT-related items and gut microbiota in either univariate or multivariate correlations.
3.1. Participant Characteristics
Participants’ characteristics are presented in Table 1. The frequency of the PNPLA3 rs738409 SNP in this study subjects was 28.5% for CC genotype, 49.4% for CG genotype, and 22.1% for GG genotype. The genotype frequencies calculated under the assumption of Hardy–Weinberg equilibrium were 28.3% for CC genotype, 49.8% for CG genotype, and 21.9% for GG genotype, which were almost the same as the actual values, indicating that Hardy–Weinberg equilibrium was established. The GG genotype of PNPLA3 rs738408 was observed in 22.1% and 23.1% of all subjects and patients with MASLD, respectively. There was no significant association between PNPLA3 rs738409 SNP and MASLD appearance in this study population. There were no significant differences in sex, age, smoking, or drinking habits between the three groups. Additionally, there were no differences in CAP or cardiometabolic criteria among the three groups. In contrast, AST and ALT levels were significantly higher in the GG genotype group than in the CC and CG genotype groups. Among the liver fibrosis scoring systems, the APRI and FIB-4 scores were higher in the GG genotype group than in the other groups.
Tables S1–S3 show the characteristics of the PNPLA3 rs738409 SNP. The MASLD group was older than the control group for the CC and CG genotypes. There were significant differences in CAP values and cardiometabolic criteria between the normal and MASLD groups. However, there were no significant differences in sex, LSM, smoking, or exercise habits between the groups.
Figure 2 and Figure 3 show the differences in the composition and diversity of the gut microbiota. There were no differences in composition between PNPLA3 rs738409 SNP groups at either the phyla or genera levels (Figure 2). Figure 3 illustrates the diversity of the gut microbiome of the study subjects. Neither α diversity (as measured by the Chao-1 and Shannon indexes) nor β diversity (as assessed by principal coordinate analysis) showed significant differences across the PNPLA3 rs738409 SNP groups.
3.2. Comparison of Gut Microbiota between Normal and MASLD Group
The LEfSe results for MASLD and gut microbiota are shown in Figure 4. Five bacterial taxa were significantly enriched in the CC genotype group, ten in the CG genotype, and two in the GG genotype. Among these, only two taxa had a relative abundance of 1% or more and an LDA score of 3 or more: Blautia in the CC genotype group (8.1% in the normal group and 5.6% in the MASLD group) and Ruminococcaceae in the CG genotype group (20.5% in the normal group and 16.9% in the MASLD group). The two taxa that showed differences in the GG group had extremely low relative abundances (<0.2%).
3.3. Comparison of Gut Microbiota among PNPLA3 rs738409 SNP
Figure 5 presents the relative abundance of Blautia and Ruminococcaceae stratified by PNPLA3 rs738409 genotypes, as determined by LEfSe analyses. There were no significant associations between either Blautia or Ruminococcaceae and the PNPLA3 rs738409 SNP in the normal group. In contrast, the MASLD group showed a lower relative abundance of Blautia in the CC group, and a tendency for a decrease in Ruminococcaceae was noted in the CG group, although no statistically significant difference was observed.
3.4. Relationship between MASLD-Related Items and Gut Microbiota
Table 2 summarizes the correlation between MASLD-related items and gut microbiota in a single correlation analysis. In the CC genotype group, CAP and BMI had a negative correlation with the Blautia, and triglycerides had a negative correlation with Ruminococcaceae. In the CG genotype group, waist circumference and blood glucose had a negative correlation with Blautia, and CAP, waist circumference, and triglycerides had a negative correlation with Ruminococcaceae.
Next, multiple regression analysis was performed, where the dependent variables were MASLD-related items and the independent variables were sex, age, smoking and exercise habits, and medication of hypertension, dyslipidemia, or diabetes mellitus in addition to the gut microbiota. Table 3 presents the results of the study. The CC genotype group showed the same association as that of the single correlation group. In the CG genotype group, CAP and triglycerides were negatively correlated with Ruminococcaceae. In contrast, the GG group showed no significant correlation between MASLT-related items and gut microbiota in either univariate or multivariate correlations.
4. Discussion
This study found that the gut microbiota and MASLD groups differed according to the PNPLA3 rs738409 SNPs. The gut microbiota’s impact on MASLD was more pronounced in individuals with the CC and CG genotypes than in those with the GG genotype. Our findings suggest that a decrease in acetate- and butyrate-producing bacteria, such as Blautia and Ruminococcaceae, may be involved in developing MASLD in PNPLA3 rs738409 CC and CG genotypes.
The GG genotype of PNPLA3 rs738409 was observed in 22.1% and 23.1% of all study participants and patients with MASLD, respectively. Previous studies have reported that approximately 20% of the Japanese population and 40% of patients with NAFLD carry the GG genotype of PNPLA3 rs738409 [11,16,17]. While previous studies diagnosed NAFLD using liver biopsy, our study used FibroScan. Moreover, while a CAP value of 232.5 dB/m has been used as a cutoff for the diagnosis of SLD, some studies suggest a more stringent cutoff of 248 dB/m for SLD [31,42]. Differences in diagnostic methods and cutoff values may explain the varying prevalence of the PNPLA3 GG genotype in our MASLD group compared with previous studies.
There was no significant association between PNPLA3 rs738409 SNP and MASLD appearance in the study subjects. CAP values and cardiometabolic criteria included in the MASLD diagnostic criteria were also not associated with PNPLA3 rs738409 SNP. In addition to genetic factors, many other factors contribute to the pathogenesis of MASLD, and the multi-parallel hit hypothesis proposes that organs other than the liver, such as the adipose tissue, oral cavity, and intestinal tract, as well as within the liver tissue, interactively contribute to the pathogenesis of MASLD [43]. In addition to the PNPLA3 rs738409 SNP, many other SNPs have been associated with MASLD. In this study, no differences were observed in sex, age, body size, or lifestyle among the PNPLA3 rs738409 genotypes. Still, it is possible that SNPs other than PNPLA3 rs738409 or other factors, such as diet and lifestyle factors, may have contributed to the lack of association.
In contrast, ASL and ALT levels in this study were higher in the GG genotype group than in the CC and CG genotype groups. Furthermore, the APRI and FAST scores and liver fibrosis markers, including AST and ALT in their calculation formulas, were also higher in the GG genotype group. The PNPLA3 rs738409 GG genotype increased AST and ALT levels by inducing hepatocyte inflammation [44,45]. It has been reported that inflammatory cytokines such as TNFα increase during the progression from simple steatosis to fibrosis in NAFLD [46,47]. PNPLA3 rs738409 is strongly associated with liver injury, with the GG genotype reported to induce a necroinflammatory response approximately three times greater than that of the CC genotype [48]. Previous studies have suggested that the PNPLA3 rs738409 GG genotype may increase inflammatory cytokines such as TNFα and IL-6 in hepatocytes [46,49]. The PNPLA3 rs738708 G type has been shown to enhance IL6/STAT3 activity in hepatocytes [50]. Additionally, PNPLA3 has been reported to increase TNFα through NF-κB regulation [51]. Thus, PNPLA3 is involved in triglyceride storage in hepatocytes and histological inflammation [48,52]. Our findings are consistent with those of previous studies and suggest that the PNPLA3 rs738409 GG genotype group is more likely to experience liver damage than the CC or CG genotype groups.
In this study, the PNPLA3 rs738409 CC and CG genotypes were associated with decreased numbers of Blautia and Ruminococcaceae in the MASLD group. They were negatively correlated with CAP level, BMI, serum blood glucose, and triglycerides. Blautia belongs to the Lachnospiraceae family and is more common in Japan than in other countries [53]. In animal studies, oral administration of Blautia has been shown to increase gut short-chain fatty acids and reduce high-fat obesity [54]. Epidemiological studies have reported that individuals with smaller visceral fat areas have increased levels of Blautia in their gut [55]. Animal studies have reported that Blautia increases the acetate concentration in the gut, inhibits hepatic steatosis and fibrosis, and suppresses the development of NAFLD/NASH [56]. Epidemiological studies have reported decreased levels of gut Blautia in NAFLD and MAFLD patients [57,58,59]. Furthermore, in this study, Blautia was negatively correlated with BMI in genotype CC and serum glucose in genotype CG, in addition to CAP levels, which supports previous studies showing that Blautia administration improves obesity and diabetes [60].
Ruminococcaceae is a superfamily of Feacalibacterium, Gemmiger, and Ruminococcus that produces butyric acid, a short-chain fatty acid that increases with dietary fiber intake [61,62]. Ruminococcaceae benefits the body by increasing intestinal butyrate levels, which are decreased in inflammatory bowel disease [63,64]. Ruminococcaceae are also associated with many liver diseases and are downregulated in NAFLD [57,58,65]. In this study, Ruminococcaceae showed a negative correlation with triglycerides in PNPLA3 rs738409 CC and CG genotypes and with CAP values. Previous studies have also reported that Ruminococcaceae shows an inverse correlation with triglycerides [66]. In contrast, a study using Japanese gut bacteria and a Genome-Wide Association Study showed that Ruminococcaceae is affected by host genetic factors [29]. In this study, the MASLD group with PNPLA3 genotypes CC and CG had decreased gut Blatuia and Ruminococcaceae, suggesting that decreased short-chain fatty acids, such as acetate and butyrate, may contribute to obesity, and increased blood glucose and triglyceride levels, which may contribute to the onset and progression of MASLD. In this study, there was no association between normal and MASLD gut bacteria in the PNPLA3 rs738409 GG genotype. PNPLA3 rs738409 is involved in the progression of MASLD by elevating inflammatory cytokines; however, gut bacteria are also associated with inflammatory cytokines. Gut Blautia and Ruminococcaceae negatively correlate with inflammatory cytokines such as TNAα and IL-6 [67,68].
In this study, individuals with the PNPLA3 rs738409 GG genotype exhibited higher levels of AST and ALT as well as higher APRI and FAST scores, which incorporate AST and ALT into their calculation formulas, compared to those with the CC and CG genotypes. Although this study did not measure inflammatory cytokines, it is speculated that the increased levels of AST, ALT, APRI, and FAST in the PNPLA3 rs738409 GG genotype group were due to hepatocyte damage caused by increased inflammatory cytokines. In this study, the PNPLA3 rs738409 GG genotype group might have higher levels of pro-inflammatory cytokines than the CC and CG genotype groups, suggesting that the involvement of gut microbiota in MASLD may have been relatively small and not significantly different. In contrast, the CC and CG genotype groups of PNPLA3 rs738409 showed relatively less liver injury caused by inflammatory cytokines than the GG genotype group, suggesting an association between gut bacteria and MASLD.
We found no significant differences in the relative abundance of Blautia and Ruminococcaceae between PNPLA3 rs738409 SNP in the normal group. In contrast, the MASLD group showed decreased Blautia in the CC genotype and a trend towards decreased Ruminococcaceae in the CG genotype. In MASLD, dysbiosis of the intestinal tract is induced, which is called gut–liver axis [18,19]. It has been reported that both gut Blautia and Ruminococcaceae are decreased in MASLD patients [57,58,59,65]. The association between PNPLA3 rs738409 and gut microbiota may be greater in MASLD patients in whom dysbiosis occurs than in the normal group.
In addition, LEfSe analysis revealed different bacterial taxa between the CC and CG genotypes of PNPLA3 rs738409. However, both the CC and CG genotype groups were associated with MASLD-related items in Blautia and Ruminococcaceae. Although there was no significant difference between the CC and CG groups in terms of hepatic enzymes, it is possible that there was a subclinical level of inflammation between the CC group with no G-risk allele and the CG group with one G-risk allele that did not appear in the statistics. This may have resulted in the difference between the CC and CG groups.
In this study, Blautia and Ruminococcaceae were identified as gut bacteria associated with PNPLA3 rs738409. A previous study on Japanese individuals investigating the relationship between host genetic factors and gut microbiota identified Clostridiales, Ruminococcaceae, Erysipelotrichaceae, Lachnospiraceae, Feacalibacterium, and Ruminococcus as being influenced by genetic factors [29]. On the other hand, there was no association between Blautia and host genetic factors, which was found to be associated in our study. Although both studies were conducted on Japanese populations, the previous study focused on healthy adults aged 20–64 living in various regions, while our study had a median age of 53 years living in the same area, resulting in somewhat different population characteristics for the two groups. Furthermore, although there are certain trends in the relationship between MASLD and gut microbiota, results vary significantly across studies [21,22,69]. Differences in age, eating habits, medications, ethnicity, MASLD diagnostic tools, and other factors may have contributed to the varied results across studies. While our findings generally support previous studies, some discrepancies were observed due to the presence of these confounding factors.
This study had several limitations. First, our study population was geographically limited to a district in Japan; therefore, our results cannot be generalized to all ethnicities. Second, gut microbiota is influenced by a variety of factors, including dietary habits and medication status, but this study did not fully adjust for these confounding factors, so caution is needed in interpreting the results. Third, fatty liver and liver fibrosis were diagnosed using FibroScan instead of a liver biopsy. An invasive liver biopsy, conducted as part of a general population health check, was not feasible in this study. Fourth, we did not measure the inflammatory cytokine levels. Although we speculated that inflammatory cytokines might have attenuated the association between MASLD and the gut microbiota in the PNPLA3 GG genotype group, we did not measure these cytokines to support this hypothesis.
5. Conclusions
Our study suggests that individuals with the PNPLA3 rs738409 CC and CG genotypes may be more susceptible to the influence of the gut microbiota on MASLD than those with the GG genotype. For individuals with the PNPLA3 rs738409 CC or CG genotype, active consumption of dietary fiber and other rich foods that increase short-chain fatty acids may be beneficial for preventing and treating MASLD through an increase in gut Blautia and Ruminococcaceae. While dietary interventions may show less efficacy in individuals with the PNPLA3 rs738409 GG genotype compared to those with the CC or CG genotypes, strict adherence to other lifestyle modifications, such as active exercise and no smoking, is crucial for preventing metabolic complications, including obesity, hypertension, diabetes, and dyslipidemia. Personalized medicine, such as prophylactic and therapeutic strategies based on the PNPLA3 rs738409 SNP, is crucial. |
Title: Potassium dynamics in sickle cell anemia: clinical implications and pathophysiological insights | Body:
Introduction
Highlights
Molecular pathways: Elucidating hemoglobin polymerization and RBC sickling mechanisms.
Targeted therapies: Advancing pharmacological agents and gene therapies.
Early intervention: Promoting early diagnosis and personalized care.
Psychosocial support: Integrating emotional and educational resources.
Collaborative research: Encouraging interdisciplinary and industry partnerships.
Sickle cell anemia (SCA) is a hereditary hemoglobinopathy resulting from a single nucleotide mutation in the β-globin gene, leading to the production of abnormal hemoglobin S (HbS). This genetic alteration causes red blood cells (RBCs) to adopt a sickle shape under hypoxic conditions, which impairs their function and lifespan. The distorted shape and reduced flexibility of sickle cells contribute to vaso-occlusive events, hemolysis, and chronic anemia, manifesting in severe clinical complications, and reduced life expectancy1–3. The pathophysiology of SCA is complex and multifactorial, involving a cascade of cellular and molecular events4. Central to this process is the polymerization of HbS, which occurs when deoxygenated, leading to the formation of rigid, sickle-shaped RBCs5. These cells obstruct blood flow in small vessels, causing ischemia and tissue damage. Hemolysis, both intravascular and extravascular, is another hallmark of SCA, resulting in chronic anemia and a range of associated symptoms, including fatigue, jaundice, and increased susceptibility to infections6,7. Potassium (K+) homeostasis is crucial for maintaining cellular function and integrity8. In RBCs, potassium balance is particularly important for regulating cell volume and preventing dehydration. Dysregulation of potassium transport mechanisms can lead to significant cellular dehydration, exacerbating the sickling process. The role of potassium in SCA, therefore, extends beyond simple electrolyte balance, influencing the severity and progression of the disease.
Several key transport mechanisms regulate potassium levels in RBCs, including the K-Cl cotransporter, Gardos channel, and Na-K-ATPase pump9. The K-Cl cotransporter mediates the coupled movement of K+ and Cl- out of the cell, while the Gardos channel, a Ca2+-activated K+ channel, plays a critical role in K+ efflux during cellular dehydration. The Na-K-ATPase pump, meanwhile, actively maintains the gradient of Na+ and K+ across the cell membrane, essential for cellular homeostasis. In SCA, these potassium transport mechanisms often become dysregulated, leading to increased K+ efflux and cellular dehydration10. As water follows K+ out of the cell osmotically, sickle RBCs become more dehydrated, increasing intracellular HbS concentration and promoting further polymerization. Dehydrated sickle cells are more rigid and prone to adhere to the vascular endothelium, contributing to vaso-occlusive events and tissue ischemia. The clinical implications of potassium dysregulation in SCA are profound. Vaso-occlusive crises, a primary cause of morbidity and mortality in SCA, are closely linked to the dehydration and rigidity of sickle cells. Additionally, chronic hemolysis exacerbated by potassium imbalance leads to persistent anemia and systemic complications11. Vaso-occlusive crises are a defining feature of SCA, caused by the obstruction of small blood vessels by sickled RBCs. Potassium dynamics significantly influence these crises, as dehydrated and rigid RBCs are more likely to cause vascular blockages. This underscores the importance of maintaining potassium homeostasis to reduce the frequency and severity of vaso-occlusive events, thereby improving the quality of life for SCA patients12. Chronic hemolysis in SCA leads to anemia, contributing to symptoms such as fatigue, pallor, and jaundice. Potassium loss from RBCs exacerbates hemolysis by increasing cell fragility and promoting sickling. Therapeutic approaches that stabilize potassium levels within RBCs may help reduce hemolysis, thereby ameliorating anemia and its associated symptoms. This highlights the potential of targeted interventions in improving hemolytic outcomes in SCA13. Several therapeutic strategies aim to address potassium dysregulation in SCA. Gardos channel inhibitors, such as senicapoc, have shown promise in reducing RBC dehydration and sickling. Additionally, hydration therapy, which ensures adequate fluid intake and electrolyte balance, is crucial for preventing cellular dehydration and reducing vaso-occlusive crises. A holistic approach that includes these strategies, alongside standard treatments like hydroxyurea and blood transfusions, is essential for effective SCA management.
Aim
The aim of understanding the pathophysiological insights into sickle cell anemia (SCA) is to elucidate the complex mechanisms underlying its clinical manifestations and complications.
Rationale
The rationale for studying the pathophysiological insights into sickle cell anemia (SCA) lies in the complexity and severity of this genetic disorder, which affects millions of people globally. SCA presents with a wide range of clinical symptoms, including recurrent pain crises (vaso-occlusive crises), chronic anemia, and increased susceptibility to infections. Understanding the pathophysiological basis of these symptoms helps in developing targeted therapies to alleviate suffering and improve quality of life. Individuals with SCA are at risk of developing severe complications such as stroke, acute chest syndrome, renal dysfunction, and pulmonary hypertension. Exploring the mechanisms underlying these complications provides insights into preventive strategies and early interventions to mitigate long-term organ damage. SCA is caused by a single point mutation in the β-globin gene, resulting in the production of abnormal hemoglobin (HbS). This genetic alteration leads to the polymerization of HbS under conditions of low oxygen tension, triggering a cascade of events that culminate in RBC sickling, hemolysis, and vascular occlusion. Understanding these genetic and molecular pathways facilitates the development of targeted therapies, such as gene editing and gene therapy, aimed at correcting the underlying genetic defect. SCA disproportionately affects populations of African, Mediterranean, Middle Eastern, and Indian ancestry, leading to significant healthcare disparities. Studying the pathophysiology of SCA informs public health strategies aimed at improving access to comprehensive care, reducing healthcare costs associated with acute complications, and promoting early diagnosis through newborn screening programs. Advances in understanding the pathophysiology of SCA pave the way for innovative treatment modalities and research initiatives. This includes the development of disease-modifying therapies like hydroxyurea, novel agents targeting specific pathways (e.g. Gardos channel inhibitors), and emerging gene therapies that hold promise for potentially curative approaches.
Aim
The aim of understanding the pathophysiological insights into sickle cell anemia (SCA) is to elucidate the complex mechanisms underlying its clinical manifestations and complications.
Rationale
The rationale for studying the pathophysiological insights into sickle cell anemia (SCA) lies in the complexity and severity of this genetic disorder, which affects millions of people globally. SCA presents with a wide range of clinical symptoms, including recurrent pain crises (vaso-occlusive crises), chronic anemia, and increased susceptibility to infections. Understanding the pathophysiological basis of these symptoms helps in developing targeted therapies to alleviate suffering and improve quality of life. Individuals with SCA are at risk of developing severe complications such as stroke, acute chest syndrome, renal dysfunction, and pulmonary hypertension. Exploring the mechanisms underlying these complications provides insights into preventive strategies and early interventions to mitigate long-term organ damage. SCA is caused by a single point mutation in the β-globin gene, resulting in the production of abnormal hemoglobin (HbS). This genetic alteration leads to the polymerization of HbS under conditions of low oxygen tension, triggering a cascade of events that culminate in RBC sickling, hemolysis, and vascular occlusion. Understanding these genetic and molecular pathways facilitates the development of targeted therapies, such as gene editing and gene therapy, aimed at correcting the underlying genetic defect. SCA disproportionately affects populations of African, Mediterranean, Middle Eastern, and Indian ancestry, leading to significant healthcare disparities. Studying the pathophysiology of SCA informs public health strategies aimed at improving access to comprehensive care, reducing healthcare costs associated with acute complications, and promoting early diagnosis through newborn screening programs. Advances in understanding the pathophysiology of SCA pave the way for innovative treatment modalities and research initiatives. This includes the development of disease-modifying therapies like hydroxyurea, novel agents targeting specific pathways (e.g. Gardos channel inhibitors), and emerging gene therapies that hold promise for potentially curative approaches.
Review methodology
Search strategy
A systematic approach was adopted to identify relevant studies and articles pertaining to the pathophysiology of SCA. Electronic databases, including PubMed, MEDLINE, and Google Scholar, were searched using keywords such as ‘sickle cell anemia’, ‘pathophysiology’, ‘hemoglobin S’, ‘vascular occlusion’, and ‘chronic hemolysis’. The search was limited to articles published in peer-reviewed journals within the last 10 years, with a focus on original research, reviews, and meta-analyses.
Selection criteria
Articles were screened based on predefined inclusion criteria. Studies were included if they provided insights into the genetic basis of SCA, molecular mechanisms of hemoglobin polymerization, pathophysiological pathways leading to vaso-occlusive crises (VOC), chronic hemolysis, and associated complications such as stroke, acute chest syndrome, and renal dysfunction. Non-English language articles and studies lacking relevance to the primary objectives were excluded to ensure the quality and relevance of the review.
Data extraction and synthesis
Data extraction focused on key findings related to the pathophysiological mechanisms underlying SCA. Emphasis was placed on identifying molecular pathways involved in HbS polymerization, factors contributing to RBC sickling and adherence, endothelial dysfunction, and inflammatory responses. Data synthesis involved categorizing and summarizing findings to elucidate the sequence of events from genetic mutation to clinical outcomes, highlighting gaps in current knowledge and potential implications for therapeutic interventions.
Quality assessment
The quality of included studies was assessed using established criteria for evaluating research methodology, study design, sample size, and statistical analysis. Studies with robust methodologies and findings supported by adequate data were prioritized in the synthesis of results to ensure the reliability and validity of conclusions drawn from the review.
Search strategy
A systematic approach was adopted to identify relevant studies and articles pertaining to the pathophysiology of SCA. Electronic databases, including PubMed, MEDLINE, and Google Scholar, were searched using keywords such as ‘sickle cell anemia’, ‘pathophysiology’, ‘hemoglobin S’, ‘vascular occlusion’, and ‘chronic hemolysis’. The search was limited to articles published in peer-reviewed journals within the last 10 years, with a focus on original research, reviews, and meta-analyses.
Selection criteria
Articles were screened based on predefined inclusion criteria. Studies were included if they provided insights into the genetic basis of SCA, molecular mechanisms of hemoglobin polymerization, pathophysiological pathways leading to vaso-occlusive crises (VOC), chronic hemolysis, and associated complications such as stroke, acute chest syndrome, and renal dysfunction. Non-English language articles and studies lacking relevance to the primary objectives were excluded to ensure the quality and relevance of the review.
Data extraction and synthesis
Data extraction focused on key findings related to the pathophysiological mechanisms underlying SCA. Emphasis was placed on identifying molecular pathways involved in HbS polymerization, factors contributing to RBC sickling and adherence, endothelial dysfunction, and inflammatory responses. Data synthesis involved categorizing and summarizing findings to elucidate the sequence of events from genetic mutation to clinical outcomes, highlighting gaps in current knowledge and potential implications for therapeutic interventions.
Quality assessment
The quality of included studies was assessed using established criteria for evaluating research methodology, study design, sample size, and statistical analysis. Studies with robust methodologies and findings supported by adequate data were prioritized in the synthesis of results to ensure the reliability and validity of conclusions drawn from the review.
Potassium homeostasis in sickle cell anemia
Potassium transport mechanisms
Potassium (K+) homeostasis in red blood cells (RBCs) is essential for maintaining cellular volume, integrity, and function. In sickle cell anemia (SCA), the regulation of potassium transport becomes particularly critical due to its impact on cell dehydration and sickling14. The K-Cl cotransporter mediates the coupled movement of potassium (K+) and chloride (Cl-) ions out of the RBC. This process is electroneutral, meaning that it does not generate an electric current, but it significantly influences cell volume by regulating the osmotic balance. In SCA, increased activity of the K-Cl cotransporter can lead to excessive K+ loss, contributing to cellular dehydration15. This dehydration promotes the polymerization of hemoglobin S (HbS) and the sickling of RBCs, exacerbating the clinical symptoms of the disease. The Gardos channel, also known as the Ca2+-activated K+ channel, is pivotal in the regulation of potassium efflux from RBCs16. When intracellular calcium (Ca2+) levels rise, the Gardos channel opens, allowing K+ to exit the cell. This efflux is accompanied by water loss, leading to cell shrinkage and increased intracellular HbS concentration. In SCA, the activation of the Gardos channel contributes significantly to the dehydration of sickle cells, enhancing their rigidity and propensity to block blood vessels. Inhibiting this channel has been proposed as a therapeutic strategy to reduce cell dehydration and sickling.
The Na-K-ATPase pump is a crucial membrane-bound enzyme that actively maintains the gradients of sodium (Na+) and potassium (K+) across the RBC membrane. It pumps three Na+ ions out of the cell and two K+ ions into the cell, using ATP as an energy source. This pump helps maintain the cell’s resting potential and osmotic balance. In SCA, the function of the Na-K-ATPase pump may be compromised, leading to disruptions in ion balance and contributing to cellular dysfunction and hemolysis17. Piezo1 channels are mechanosensitive ion channels that respond to mechanical stress and membrane tension by allowing the influx of cations, including K+. Recent studies suggest that these channels play a role in the regulation of RBC volume and could be involved in the pathophysiology of SCA18. The activation of Piezo1 channels in sickle cells may contribute to the dysregulation of ion homeostasis, promoting cell dehydration and sickling. The NKCC1 (Na+-K+-2Cl- cotransporter) is another important player in ion transport within RBCs. It facilitates the simultaneous uptake of Na+, K+, and Cl- ions into the cell. In SCA, alterations in NKCC1 activity could affect intracellular ion concentrations, influencing cell volume and hydration status19. One promising therapeutic strategy for SCA involves the inhibition of the Gardos channel. By preventing K+ efflux and subsequent water loss, Gardos channel inhibitors, such as senicapoc, aim to reduce RBC dehydration and sickling. Clinical trials have shown that Gardos channel inhibition can decrease the frequency of vaso-occlusive crises and improve overall RBC health in SCA patients. However, further research is needed to fully establish the long-term efficacy and safety of these inhibitors. The dysregulation of potassium transport mechanisms in SCA has profound clinical implications20. Dehydrated and rigid sickle cells are more likely to adhere to the vascular endothelium and cause blockages, leading to vaso-occlusive crises, ischemia, and organ damage. Chronic hemolysis, exacerbated by potassium imbalance, results in persistent anemia and systemic complications. Therefore, understanding and managing potassium transport is crucial for improving clinical outcomes in SCA patients.
Impact on cell dehydration and sickling
In sickle cell anemia (SCA), the dehydration of red blood cells (RBCs) is a critical factor that exacerbates the sickling process21. Several mechanisms contribute to this dehydration, primarily involving the dysregulation of potassium (K+) transport. Key players include the Gardos channel, K-Cl cotransporter, and Na-K-ATPase pump. When the Gardos channel is activated by increased intracellular calcium (Ca2+) levels, it allows K+ to exit the cell. As K+ leaves the RBC, water follows osmotically, resulting in cell shrinkage and dehydration. The K-Cl cotransporter also contributes to this process by mediating the coupled movement of K+ and Cl- out of the cell, further promoting water loss. Dehydrated RBCs exhibit higher intracellular hemoglobin S (HbS) concentrations, which significantly increase the likelihood of HbS polymerization20. This polymerization process leads to the formation of rigid, sickle-shaped cells that are less deformable and more prone to hemolysis. The rigidity of dehydrated sickle cells also makes them more likely to adhere to the vascular endothelium, causing blockages in small blood vessels. These blockages result in vaso-occlusive crises, a hallmark of SCA characterized by severe pain, tissue ischemia, and organ damage. Potassium dynamics play a crucial role in the dehydration of RBCs in SCA19. The loss of K+ through the Gardos channel and K-Cl cotransporter leads to a significant reduction in cell volume. This process not only increases HbS concentration but also creates an environment that favors sickling. The Na-K-ATPase pump, which actively maintains K+ gradients across the cell membrane, may also be compromised in SCA, further contributing to ion imbalance and cellular dehydration. Dysregulated potassium transport thus directly impacts the physical properties of RBCs, promoting sickling and its associated complications.
The dehydration and subsequent sickling of RBCs are central to the occurrence of vaso-occlusive crises. These crises arise when rigid, dehydrated sickle cells obstruct capillaries and small blood vessels, impeding blood flow and causing acute pain episodes. The frequency and severity of these crises are closely linked to the extent of RBC dehydration. By maintaining proper potassium balance and preventing cell dehydration, it may be possible to reduce the incidence of vaso-occlusive crises and improve patient outcomes in SCA20. Chronic hemolysis is another significant consequence of RBC dehydration in SCA. Dehydrated sickle cells are more fragile and susceptible to rupture as they traverse the circulatory system. This ongoing destruction of RBCs leads to hemolytic anemia, characterized by low hemoglobin levels, fatigue, and jaundice. Potassium loss exacerbates hemolysis by increasing RBC fragility. Therapeutic strategies that stabilize potassium levels and prevent dehydration can help mitigate hemolysis and its associated symptoms19. The cumulative effects of vaso-occlusion and hemolysis contribute to long-term organ damage in SCA patients. Dehydrated, sickled cells can cause repeated episodes of ischemia-reperfusion injury in organs such as the spleen, kidneys, lungs, and brain. Over time, this can lead to chronic organ dysfunction, significantly impacting the patient’s quality of life and life expectancy. Proper management of potassium dynamics and RBC hydration is crucial to preventing organ damage and improving long-term outcomes22–24. Gardos channel inhibitors, such as senicapoc, have shown promise in reducing RBC dehydration and preventing sickling. Additionally, hydration therapy, aimed at maintaining adequate fluid intake and electrolyte balance, is essential for minimizing cell dehydration. By focusing on these therapeutic strategies, it is possible to address the underlying pathophysiological mechanisms of SCA and improve patient management.
Pathophysiological insights
Pathophysiological insights into sickle cell anemia (SCA) reveal a complex interplay of genetic, molecular, and physiological factors that underlie the clinical manifestations and complications of this inherited hemoglobinopathy25. SCA is caused by a point mutation in the β-globin gene, leading to the production of abnormal hemoglobin known as hemoglobin S (HbS)26. Under conditions of low oxygen tension, HbS molecules polymerize and aggregate within red blood cells (RBCs), causing them to assume a rigid, sickle shape. This polymerization process is central to the pathophysiology of SCA, as sickled RBCs are less deformable and more prone to hemolysis and vascular occlusion. Sickled RBCs adhere to vascular endothelium and to each other, leading to the obstruction of small blood vessels and impaired blood flow27. This process, known as vaso-occlusion, contributes to tissue ischemia and the characteristic episodic pain crises seen in SCA. Endothelial dysfunction and activation of adhesion molecules further exacerbate microvascular occlusion, perpetuating tissue damage and inflammation. Episodes of vaso-occlusion followed by reperfusion lead to oxidative stress, inflammatory responses, and tissue injury. Ischemia-reperfusion injury contributes to organ damage, particularly in organs with high metabolic demands such as the brain, kidneys, and lungs. This cycle of injury and repair plays a significant role in the chronic complications associated with SCA, including organ dysfunction and progressive damage over time.
Sickled RBCs have a shortened lifespan due to their fragility and susceptibility to hemolysis. Chronic hemolysis results in the release of free hemoglobin into the plasma, leading to scavenging of nitric oxide (NO) and subsequent endothelial dysfunction. This imbalance in NO bioavailability contributes to vasoconstriction, further exacerbating vascular complications in SCA28. Alterations in red cell membrane structure and function are observed in SCA, including increased permeability to cations such as potassium (K+) and calcium (Ca2+). Dysregulation of ion transport systems, including the Gardos channel (KCa3.1), contributes to RBC dehydration and sickling. Sickled RBCs and damaged endothelial cells release inflammatory mediators, cytokines, and cell adhesion molecules that activate leukocytes and amplify the inflammatory response. Persistent inflammation contributes to a prothrombotic state, endothelial dysfunction, and the perpetuation of vaso-occlusive events. Functional asplenia, resulting from repeated vaso-occlusive events in the spleen, increases susceptibility to bacterial infections, particularly from encapsulated organisms. Reduced clearance of opsonized bacteria and impaired adaptive immune responses further compound infection risks in individuals with SCA.
Potassium transport mechanisms
Potassium (K+) homeostasis in red blood cells (RBCs) is essential for maintaining cellular volume, integrity, and function. In sickle cell anemia (SCA), the regulation of potassium transport becomes particularly critical due to its impact on cell dehydration and sickling14. The K-Cl cotransporter mediates the coupled movement of potassium (K+) and chloride (Cl-) ions out of the RBC. This process is electroneutral, meaning that it does not generate an electric current, but it significantly influences cell volume by regulating the osmotic balance. In SCA, increased activity of the K-Cl cotransporter can lead to excessive K+ loss, contributing to cellular dehydration15. This dehydration promotes the polymerization of hemoglobin S (HbS) and the sickling of RBCs, exacerbating the clinical symptoms of the disease. The Gardos channel, also known as the Ca2+-activated K+ channel, is pivotal in the regulation of potassium efflux from RBCs16. When intracellular calcium (Ca2+) levels rise, the Gardos channel opens, allowing K+ to exit the cell. This efflux is accompanied by water loss, leading to cell shrinkage and increased intracellular HbS concentration. In SCA, the activation of the Gardos channel contributes significantly to the dehydration of sickle cells, enhancing their rigidity and propensity to block blood vessels. Inhibiting this channel has been proposed as a therapeutic strategy to reduce cell dehydration and sickling.
The Na-K-ATPase pump is a crucial membrane-bound enzyme that actively maintains the gradients of sodium (Na+) and potassium (K+) across the RBC membrane. It pumps three Na+ ions out of the cell and two K+ ions into the cell, using ATP as an energy source. This pump helps maintain the cell’s resting potential and osmotic balance. In SCA, the function of the Na-K-ATPase pump may be compromised, leading to disruptions in ion balance and contributing to cellular dysfunction and hemolysis17. Piezo1 channels are mechanosensitive ion channels that respond to mechanical stress and membrane tension by allowing the influx of cations, including K+. Recent studies suggest that these channels play a role in the regulation of RBC volume and could be involved in the pathophysiology of SCA18. The activation of Piezo1 channels in sickle cells may contribute to the dysregulation of ion homeostasis, promoting cell dehydration and sickling. The NKCC1 (Na+-K+-2Cl- cotransporter) is another important player in ion transport within RBCs. It facilitates the simultaneous uptake of Na+, K+, and Cl- ions into the cell. In SCA, alterations in NKCC1 activity could affect intracellular ion concentrations, influencing cell volume and hydration status19. One promising therapeutic strategy for SCA involves the inhibition of the Gardos channel. By preventing K+ efflux and subsequent water loss, Gardos channel inhibitors, such as senicapoc, aim to reduce RBC dehydration and sickling. Clinical trials have shown that Gardos channel inhibition can decrease the frequency of vaso-occlusive crises and improve overall RBC health in SCA patients. However, further research is needed to fully establish the long-term efficacy and safety of these inhibitors. The dysregulation of potassium transport mechanisms in SCA has profound clinical implications20. Dehydrated and rigid sickle cells are more likely to adhere to the vascular endothelium and cause blockages, leading to vaso-occlusive crises, ischemia, and organ damage. Chronic hemolysis, exacerbated by potassium imbalance, results in persistent anemia and systemic complications. Therefore, understanding and managing potassium transport is crucial for improving clinical outcomes in SCA patients.
Impact on cell dehydration and sickling
In sickle cell anemia (SCA), the dehydration of red blood cells (RBCs) is a critical factor that exacerbates the sickling process21. Several mechanisms contribute to this dehydration, primarily involving the dysregulation of potassium (K+) transport. Key players include the Gardos channel, K-Cl cotransporter, and Na-K-ATPase pump. When the Gardos channel is activated by increased intracellular calcium (Ca2+) levels, it allows K+ to exit the cell. As K+ leaves the RBC, water follows osmotically, resulting in cell shrinkage and dehydration. The K-Cl cotransporter also contributes to this process by mediating the coupled movement of K+ and Cl- out of the cell, further promoting water loss. Dehydrated RBCs exhibit higher intracellular hemoglobin S (HbS) concentrations, which significantly increase the likelihood of HbS polymerization20. This polymerization process leads to the formation of rigid, sickle-shaped cells that are less deformable and more prone to hemolysis. The rigidity of dehydrated sickle cells also makes them more likely to adhere to the vascular endothelium, causing blockages in small blood vessels. These blockages result in vaso-occlusive crises, a hallmark of SCA characterized by severe pain, tissue ischemia, and organ damage. Potassium dynamics play a crucial role in the dehydration of RBCs in SCA19. The loss of K+ through the Gardos channel and K-Cl cotransporter leads to a significant reduction in cell volume. This process not only increases HbS concentration but also creates an environment that favors sickling. The Na-K-ATPase pump, which actively maintains K+ gradients across the cell membrane, may also be compromised in SCA, further contributing to ion imbalance and cellular dehydration. Dysregulated potassium transport thus directly impacts the physical properties of RBCs, promoting sickling and its associated complications.
The dehydration and subsequent sickling of RBCs are central to the occurrence of vaso-occlusive crises. These crises arise when rigid, dehydrated sickle cells obstruct capillaries and small blood vessels, impeding blood flow and causing acute pain episodes. The frequency and severity of these crises are closely linked to the extent of RBC dehydration. By maintaining proper potassium balance and preventing cell dehydration, it may be possible to reduce the incidence of vaso-occlusive crises and improve patient outcomes in SCA20. Chronic hemolysis is another significant consequence of RBC dehydration in SCA. Dehydrated sickle cells are more fragile and susceptible to rupture as they traverse the circulatory system. This ongoing destruction of RBCs leads to hemolytic anemia, characterized by low hemoglobin levels, fatigue, and jaundice. Potassium loss exacerbates hemolysis by increasing RBC fragility. Therapeutic strategies that stabilize potassium levels and prevent dehydration can help mitigate hemolysis and its associated symptoms19. The cumulative effects of vaso-occlusion and hemolysis contribute to long-term organ damage in SCA patients. Dehydrated, sickled cells can cause repeated episodes of ischemia-reperfusion injury in organs such as the spleen, kidneys, lungs, and brain. Over time, this can lead to chronic organ dysfunction, significantly impacting the patient’s quality of life and life expectancy. Proper management of potassium dynamics and RBC hydration is crucial to preventing organ damage and improving long-term outcomes22–24. Gardos channel inhibitors, such as senicapoc, have shown promise in reducing RBC dehydration and preventing sickling. Additionally, hydration therapy, aimed at maintaining adequate fluid intake and electrolyte balance, is essential for minimizing cell dehydration. By focusing on these therapeutic strategies, it is possible to address the underlying pathophysiological mechanisms of SCA and improve patient management.
Pathophysiological insights
Pathophysiological insights into sickle cell anemia (SCA) reveal a complex interplay of genetic, molecular, and physiological factors that underlie the clinical manifestations and complications of this inherited hemoglobinopathy25. SCA is caused by a point mutation in the β-globin gene, leading to the production of abnormal hemoglobin known as hemoglobin S (HbS)26. Under conditions of low oxygen tension, HbS molecules polymerize and aggregate within red blood cells (RBCs), causing them to assume a rigid, sickle shape. This polymerization process is central to the pathophysiology of SCA, as sickled RBCs are less deformable and more prone to hemolysis and vascular occlusion. Sickled RBCs adhere to vascular endothelium and to each other, leading to the obstruction of small blood vessels and impaired blood flow27. This process, known as vaso-occlusion, contributes to tissue ischemia and the characteristic episodic pain crises seen in SCA. Endothelial dysfunction and activation of adhesion molecules further exacerbate microvascular occlusion, perpetuating tissue damage and inflammation. Episodes of vaso-occlusion followed by reperfusion lead to oxidative stress, inflammatory responses, and tissue injury. Ischemia-reperfusion injury contributes to organ damage, particularly in organs with high metabolic demands such as the brain, kidneys, and lungs. This cycle of injury and repair plays a significant role in the chronic complications associated with SCA, including organ dysfunction and progressive damage over time.
Sickled RBCs have a shortened lifespan due to their fragility and susceptibility to hemolysis. Chronic hemolysis results in the release of free hemoglobin into the plasma, leading to scavenging of nitric oxide (NO) and subsequent endothelial dysfunction. This imbalance in NO bioavailability contributes to vasoconstriction, further exacerbating vascular complications in SCA28. Alterations in red cell membrane structure and function are observed in SCA, including increased permeability to cations such as potassium (K+) and calcium (Ca2+). Dysregulation of ion transport systems, including the Gardos channel (KCa3.1), contributes to RBC dehydration and sickling. Sickled RBCs and damaged endothelial cells release inflammatory mediators, cytokines, and cell adhesion molecules that activate leukocytes and amplify the inflammatory response. Persistent inflammation contributes to a prothrombotic state, endothelial dysfunction, and the perpetuation of vaso-occlusive events. Functional asplenia, resulting from repeated vaso-occlusive events in the spleen, increases susceptibility to bacterial infections, particularly from encapsulated organisms. Reduced clearance of opsonized bacteria and impaired adaptive immune responses further compound infection risks in individuals with SCA.
Clinical implications
Vaso-occlusive crises
Vaso-occlusive crises (VOC) are a hallmark of sickle cell anemia (SCA), representing one of the most severe and debilitating complications of the disease. These crises occur when rigid, sickle-shaped red blood cells (RBCs) obstruct small blood vessels, leading to ischemia, tissue injury, and intense pain. The pathophysiology of VOC is multifactorial, involving the interaction of sickle cells with the vascular endothelium, inflammatory responses, and altered blood flow dynamics. The dehydration of RBCs, primarily due to dysregulated potassium (K+) transport, significantly exacerbates these processes1. RBC dehydration plays a crucial role in the pathogenesis of VOC. Dehydrated sickle cells have increased intracellular hemoglobin S (HbS) concentrations, which promote polymerization and the formation of rigid, sickle-shaped cells. These cells are less deformable and more prone to adhering to the vascular endothelium. When dehydrated sickle cells obstruct capillaries and small vessels, they impede blood flow, leading to localized hypoxia, inflammation, and pain. Maintaining potassium balance and preventing RBC dehydration are therefore critical strategies for reducing the frequency and severity of VOC2. Potassium transport mechanisms, such as the Gardos channel, K-Cl cotransporter, and Na-K-ATPase pump, are central to RBC dehydration in SCA. The Gardos channel, in particular, is activated by increased intracellular calcium (Ca2+) levels, allowing K+ to exit the cell. This K+ efflux is followed by water loss, resulting in cell shrinkage and increased HbS concentration. Inhibiting the Gardos channel has been shown to reduce RBC dehydration and the incidence of VOC. Similarly, the K-Cl cotransporter and Na-K-ATPase pump also contribute to maintaining K+ homeostasis and cell volume, influencing the occurrence of VOC19.
The clinical manifestations of VOC are diverse, with pain being the most prominent symptom. Pain episodes can vary in intensity and duration, often requiring hospitalization and significant medical intervention. The pain typically occurs in the bones, joints, and abdomen but can affect any part of the body. In addition to pain, VOC can cause fever, swelling, and limited mobility in affected areas. Repeated episodes of VOC can lead to chronic pain syndromes and long-term damage to organs and tissues20. Inflammation plays a key role in the development and propagation of VOC. The interaction of sickle cells with the vascular endothelium triggers the release of proinflammatory cytokines and adhesion molecules, which further promote the adhesion of sickle cells and leukocytes to the endothelium. This inflammatory response exacerbates vascular occlusion and tissue ischemia. Therapeutic strategies targeting inflammation, such as NSAIDs and corticosteroids, are often used to manage the symptoms of VOC21. The recurrent nature of VOC can cause cumulative damage to various organs, including the spleen, kidneys, lungs, and brain. In the spleen, repeated infarctions can lead to functional asplenia, increasing the risk of infections. Renal damage due to repeated VOC can result in chronic kidney disease. Pulmonary complications, such as acute chest syndrome, are common and can be life-threatening. Cerebral infarctions can lead to strokes and long-term neurological deficits. Preventing VOC is therefore crucial for reducing organ damage and improving long-term outcomes in SCA patients22. Effective management of VOC involves both preventive and acute treatment strategies. Preventive measures include the use of hydroxyurea, which increases fetal hemoglobin (HbF) levels and reduces HbS polymerization. Blood transfusions are also used to reduce the proportion of sickle cells and prevent VOC. During acute VOC episodes, pain management is the primary focus, using a combination of NSAIDs, opioids, and hydration therapy to alleviate symptoms. Emerging therapies targeting the underlying pathophysiology of VOC, such as Gardos channel inhibitors and anti-inflammatory agents, offer additional avenues for intervention23. Hydration therapy is a cornerstone in the management of VOC. Adequate hydration helps maintain blood volume and reduces the viscosity of blood, which can help alleviate vascular occlusions. Hydration also helps maintain electrolyte balance, particularly potassium, reducing the dehydration of RBCs. Patients are encouraged to maintain high fluid intake and may receive intravenous fluids during acute VOC episodes to ensure adequate hydration.
Hemolytic anemia
Hemolytic anemia is a prominent feature and a major contributor to morbidity in individuals with sickle cell anemia (SCA)29. It results from the premature destruction of red blood cells (RBCs), which occurs at an accelerated rate in SCA due to the abnormal sickle-shaped morphology of the cells. This condition leads to a chronic shortage of RBCs in circulation, causing symptoms such as fatigue, pallor, jaundice, and increased susceptibility to infections. Understanding the mechanisms and consequences of hemolytic anemia in SCA is crucial for effective management and improved patient outcomes. The primary mechanism of hemolysis in SCA stems from the polymerization of hemoglobin S (HbS) under conditions of low oxygen tension30. Deoxygenated HbS molecules aggregate and form rigid, sickle-shaped cells. These cells are less flexible and more prone to rupture as they traverse the microvasculature, leading to premature destruction and hemolysis. The sickling process causes structural damage to the RBC membrane, rendering the cells more fragile. This membrane damage increases the susceptibility of sickle cells to mechanical stress and hemolysis. Additionally, altered membrane permeability and ion transport, including potassium (K+) loss, further contribute to RBC instability and hemolysis. Hemolysis in SCA occurs via both intravascular and extravascular mechanisms. Intravascular hemolysis results from the rupture of sickled RBCs within the bloodstream, releasing hemoglobin into the plasma. This free hemoglobin can scavenge nitric oxide (NO), leading to vasoconstriction and endothelial dysfunction. Extravascular hemolysis occurs predominantly in the spleen and liver, where macrophages phagocytose and degrade damaged RBCs, resulting in the release of bilirubin and iron.
Chronic hemolysis leads to anemia, characterized by reduced hemoglobin levels and diminished oxygen-carrying capacity of the blood. Anemic symptoms include fatigue, weakness, pallor, and shortness of breath. Severe anemia may necessitate blood transfusions to maintain adequate oxygen delivery to tissues. The breakdown of hemoglobin during hemolysis releases bilirubin, a yellow pigment. Elevated levels of unconjugated bilirubin in the bloodstream can cause jaundice, characterized by yellowing of the skin and sclerae. Free hemoglobin released during intravascular hemolysis scavenges NO, reducing its bioavailability and promoting vasoconstriction. This endothelial dysfunction contributes to the pathogenesis of vaso-occlusive crises, acute chest syndrome, and other vascular complications in SCA31. Chronic hemolysis results in increased iron turnover, leading to iron overload in tissues such as the liver, heart, and endocrine organs. Iron overload can cause organ damage and contribute to complications such as heart failure and endocrine dysfunction. Hydroxyurea is a disease-modifying therapy that increases fetal hemoglobin (HbF) levels, which inhibit sickle hemoglobin polymerization and reduce hemolysis32. It has been shown to decrease the frequency of vaso-occlusive crises and acute chest syndrome, as well as improve overall survival in SCA patients. Regular blood transfusions are used to maintain hemoglobin levels and reduce the severity of anemia in patients with SCA. Transfusions also help suppress erythropoiesis and decrease the proportion of sickled RBCs in circulation. For patients receiving chronic transfusions, iron chelation therapy is essential to mitigate iron overload and prevent organ damage. Chelators such as deferoxamine, deferasirox, and deferiprone bind excess iron and facilitate its excretion from the body. Managing complications of hemolytic anemia, such as jaundice, gallstones, and leg ulcers, requires comprehensive supportive care. Regular monitoring of hemoglobin levels, iron status, and organ function is essential for optimizing patient outcomes.
Organ damage
Organ damage is a serious consequence of sickle cell anemia (SCA), primarily resulting from recurrent vaso-occlusive crises (VOC), chronic hemolysis, and other complications associated with the disease3. The impact of SCA on various organs underscores the complex and multisystem nature of the disorder, necessitating comprehensive management strategies to mitigate long-term complications and improve patient outcomes. The spleen is particularly vulnerable in individuals with SCA due to repeated episodes of vaso-occlusion and infarction. Over time, these ischemic events can lead to functional asplenia, where the spleen loses its ability to filter blood and remove aged or damaged red blood cells. Asplenia increases the risk of infections, particularly from encapsulated bacteria such as Streptococcus pneumoniae, Haemophilus influenzae, and Neisseria meningitidis. Patients with SCA are often prophylactically treated with antibiotics and vaccinations to reduce the risk of overwhelming infections. The kidneys are another organ commonly affected in SCA, primarily due to hemodynamic changes, microvascular occlusions, and chronic hemolysis. Sickle cell nephropathy can manifest as various renal complications, including hematuria, proteinuria, impaired concentration ability, and ultimately, chronic kidney disease (CKD). The deposition of hemosiderin and iron overload from chronic transfusions can exacerbate renal dysfunction, leading to progressive loss of kidney function over time4.
Acute chest syndrome (ACS) is a severe complication of SCA that affects the lungs and is characterized by pulmonary vaso-occlusion, inflammation, and hypoxia. ACS often presents with symptoms such as chest pain, dyspnea, cough, and fever. It is a leading cause of hospitalization and mortality in patients with SCA, necessitating prompt diagnosis and aggressive management, including supportive care, antibiotics, and possibly blood transfusions. Neurological complications in SCA can result from both acute events, such as strokes due to cerebral infarctions, and chronic conditions, including cognitive deficits and silent cerebral infarcts. Vaso-occlusion in the cerebral vasculature can lead to ischemic strokes, hemorrhagic strokes, or transient ischemic attacks (TIAs), causing varying degrees of neurological impairment. Regular monitoring and early intervention are crucial to prevent and manage these potentially debilitating complications. The liver can be affected by chronic hemolysis and iron overload, leading to hepatomegaly, cholelithiasis (gallstones), and liver dysfunction. Iron deposition in the liver can progress to iron overload, causing fibrosis, cirrhosis, and potentially liver failure in severe cases. Regular monitoring of liver function and iron status, coupled with iron chelation therapy when indicated, is essential to prevent irreversible liver damage. Cardiovascular complications, including cardiomyopathy, pulmonary hypertension, and heart failure, are increasingly recognized in patients with SCA. Chronic anemia and hemolysis contribute to increased cardiac output and vascular resistance, predisposing individuals to develop pulmonary hypertension. Furthermore, iron overload and myocardial iron deposition can lead to cardiomyopathy and congestive heart failure, necessitating comprehensive cardiac monitoring and management. Managing organ damage in SCA requires a multidisciplinary approach focused on preventing complications, optimizing organ function, and improving quality of life. Strategies include disease-modifying therapies such as hydroxyurea to reduce sickling, blood transfusions to alleviate anemia and prevent stroke, and supportive care to manage pain and other symptoms. Advances in gene therapy and curative treatments, such as hematopoietic stem cell transplantation, offer potential avenues for addressing the underlying genetic defect and preventing organ damage in the future5–8.
Vaso-occlusive crises
Vaso-occlusive crises (VOC) are a hallmark of sickle cell anemia (SCA), representing one of the most severe and debilitating complications of the disease. These crises occur when rigid, sickle-shaped red blood cells (RBCs) obstruct small blood vessels, leading to ischemia, tissue injury, and intense pain. The pathophysiology of VOC is multifactorial, involving the interaction of sickle cells with the vascular endothelium, inflammatory responses, and altered blood flow dynamics. The dehydration of RBCs, primarily due to dysregulated potassium (K+) transport, significantly exacerbates these processes1. RBC dehydration plays a crucial role in the pathogenesis of VOC. Dehydrated sickle cells have increased intracellular hemoglobin S (HbS) concentrations, which promote polymerization and the formation of rigid, sickle-shaped cells. These cells are less deformable and more prone to adhering to the vascular endothelium. When dehydrated sickle cells obstruct capillaries and small vessels, they impede blood flow, leading to localized hypoxia, inflammation, and pain. Maintaining potassium balance and preventing RBC dehydration are therefore critical strategies for reducing the frequency and severity of VOC2. Potassium transport mechanisms, such as the Gardos channel, K-Cl cotransporter, and Na-K-ATPase pump, are central to RBC dehydration in SCA. The Gardos channel, in particular, is activated by increased intracellular calcium (Ca2+) levels, allowing K+ to exit the cell. This K+ efflux is followed by water loss, resulting in cell shrinkage and increased HbS concentration. Inhibiting the Gardos channel has been shown to reduce RBC dehydration and the incidence of VOC. Similarly, the K-Cl cotransporter and Na-K-ATPase pump also contribute to maintaining K+ homeostasis and cell volume, influencing the occurrence of VOC19.
The clinical manifestations of VOC are diverse, with pain being the most prominent symptom. Pain episodes can vary in intensity and duration, often requiring hospitalization and significant medical intervention. The pain typically occurs in the bones, joints, and abdomen but can affect any part of the body. In addition to pain, VOC can cause fever, swelling, and limited mobility in affected areas. Repeated episodes of VOC can lead to chronic pain syndromes and long-term damage to organs and tissues20. Inflammation plays a key role in the development and propagation of VOC. The interaction of sickle cells with the vascular endothelium triggers the release of proinflammatory cytokines and adhesion molecules, which further promote the adhesion of sickle cells and leukocytes to the endothelium. This inflammatory response exacerbates vascular occlusion and tissue ischemia. Therapeutic strategies targeting inflammation, such as NSAIDs and corticosteroids, are often used to manage the symptoms of VOC21. The recurrent nature of VOC can cause cumulative damage to various organs, including the spleen, kidneys, lungs, and brain. In the spleen, repeated infarctions can lead to functional asplenia, increasing the risk of infections. Renal damage due to repeated VOC can result in chronic kidney disease. Pulmonary complications, such as acute chest syndrome, are common and can be life-threatening. Cerebral infarctions can lead to strokes and long-term neurological deficits. Preventing VOC is therefore crucial for reducing organ damage and improving long-term outcomes in SCA patients22. Effective management of VOC involves both preventive and acute treatment strategies. Preventive measures include the use of hydroxyurea, which increases fetal hemoglobin (HbF) levels and reduces HbS polymerization. Blood transfusions are also used to reduce the proportion of sickle cells and prevent VOC. During acute VOC episodes, pain management is the primary focus, using a combination of NSAIDs, opioids, and hydration therapy to alleviate symptoms. Emerging therapies targeting the underlying pathophysiology of VOC, such as Gardos channel inhibitors and anti-inflammatory agents, offer additional avenues for intervention23. Hydration therapy is a cornerstone in the management of VOC. Adequate hydration helps maintain blood volume and reduces the viscosity of blood, which can help alleviate vascular occlusions. Hydration also helps maintain electrolyte balance, particularly potassium, reducing the dehydration of RBCs. Patients are encouraged to maintain high fluid intake and may receive intravenous fluids during acute VOC episodes to ensure adequate hydration.
Hemolytic anemia
Hemolytic anemia is a prominent feature and a major contributor to morbidity in individuals with sickle cell anemia (SCA)29. It results from the premature destruction of red blood cells (RBCs), which occurs at an accelerated rate in SCA due to the abnormal sickle-shaped morphology of the cells. This condition leads to a chronic shortage of RBCs in circulation, causing symptoms such as fatigue, pallor, jaundice, and increased susceptibility to infections. Understanding the mechanisms and consequences of hemolytic anemia in SCA is crucial for effective management and improved patient outcomes. The primary mechanism of hemolysis in SCA stems from the polymerization of hemoglobin S (HbS) under conditions of low oxygen tension30. Deoxygenated HbS molecules aggregate and form rigid, sickle-shaped cells. These cells are less flexible and more prone to rupture as they traverse the microvasculature, leading to premature destruction and hemolysis. The sickling process causes structural damage to the RBC membrane, rendering the cells more fragile. This membrane damage increases the susceptibility of sickle cells to mechanical stress and hemolysis. Additionally, altered membrane permeability and ion transport, including potassium (K+) loss, further contribute to RBC instability and hemolysis. Hemolysis in SCA occurs via both intravascular and extravascular mechanisms. Intravascular hemolysis results from the rupture of sickled RBCs within the bloodstream, releasing hemoglobin into the plasma. This free hemoglobin can scavenge nitric oxide (NO), leading to vasoconstriction and endothelial dysfunction. Extravascular hemolysis occurs predominantly in the spleen and liver, where macrophages phagocytose and degrade damaged RBCs, resulting in the release of bilirubin and iron.
Chronic hemolysis leads to anemia, characterized by reduced hemoglobin levels and diminished oxygen-carrying capacity of the blood. Anemic symptoms include fatigue, weakness, pallor, and shortness of breath. Severe anemia may necessitate blood transfusions to maintain adequate oxygen delivery to tissues. The breakdown of hemoglobin during hemolysis releases bilirubin, a yellow pigment. Elevated levels of unconjugated bilirubin in the bloodstream can cause jaundice, characterized by yellowing of the skin and sclerae. Free hemoglobin released during intravascular hemolysis scavenges NO, reducing its bioavailability and promoting vasoconstriction. This endothelial dysfunction contributes to the pathogenesis of vaso-occlusive crises, acute chest syndrome, and other vascular complications in SCA31. Chronic hemolysis results in increased iron turnover, leading to iron overload in tissues such as the liver, heart, and endocrine organs. Iron overload can cause organ damage and contribute to complications such as heart failure and endocrine dysfunction. Hydroxyurea is a disease-modifying therapy that increases fetal hemoglobin (HbF) levels, which inhibit sickle hemoglobin polymerization and reduce hemolysis32. It has been shown to decrease the frequency of vaso-occlusive crises and acute chest syndrome, as well as improve overall survival in SCA patients. Regular blood transfusions are used to maintain hemoglobin levels and reduce the severity of anemia in patients with SCA. Transfusions also help suppress erythropoiesis and decrease the proportion of sickled RBCs in circulation. For patients receiving chronic transfusions, iron chelation therapy is essential to mitigate iron overload and prevent organ damage. Chelators such as deferoxamine, deferasirox, and deferiprone bind excess iron and facilitate its excretion from the body. Managing complications of hemolytic anemia, such as jaundice, gallstones, and leg ulcers, requires comprehensive supportive care. Regular monitoring of hemoglobin levels, iron status, and organ function is essential for optimizing patient outcomes.
Organ damage
Organ damage is a serious consequence of sickle cell anemia (SCA), primarily resulting from recurrent vaso-occlusive crises (VOC), chronic hemolysis, and other complications associated with the disease3. The impact of SCA on various organs underscores the complex and multisystem nature of the disorder, necessitating comprehensive management strategies to mitigate long-term complications and improve patient outcomes. The spleen is particularly vulnerable in individuals with SCA due to repeated episodes of vaso-occlusion and infarction. Over time, these ischemic events can lead to functional asplenia, where the spleen loses its ability to filter blood and remove aged or damaged red blood cells. Asplenia increases the risk of infections, particularly from encapsulated bacteria such as Streptococcus pneumoniae, Haemophilus influenzae, and Neisseria meningitidis. Patients with SCA are often prophylactically treated with antibiotics and vaccinations to reduce the risk of overwhelming infections. The kidneys are another organ commonly affected in SCA, primarily due to hemodynamic changes, microvascular occlusions, and chronic hemolysis. Sickle cell nephropathy can manifest as various renal complications, including hematuria, proteinuria, impaired concentration ability, and ultimately, chronic kidney disease (CKD). The deposition of hemosiderin and iron overload from chronic transfusions can exacerbate renal dysfunction, leading to progressive loss of kidney function over time4.
Acute chest syndrome (ACS) is a severe complication of SCA that affects the lungs and is characterized by pulmonary vaso-occlusion, inflammation, and hypoxia. ACS often presents with symptoms such as chest pain, dyspnea, cough, and fever. It is a leading cause of hospitalization and mortality in patients with SCA, necessitating prompt diagnosis and aggressive management, including supportive care, antibiotics, and possibly blood transfusions. Neurological complications in SCA can result from both acute events, such as strokes due to cerebral infarctions, and chronic conditions, including cognitive deficits and silent cerebral infarcts. Vaso-occlusion in the cerebral vasculature can lead to ischemic strokes, hemorrhagic strokes, or transient ischemic attacks (TIAs), causing varying degrees of neurological impairment. Regular monitoring and early intervention are crucial to prevent and manage these potentially debilitating complications. The liver can be affected by chronic hemolysis and iron overload, leading to hepatomegaly, cholelithiasis (gallstones), and liver dysfunction. Iron deposition in the liver can progress to iron overload, causing fibrosis, cirrhosis, and potentially liver failure in severe cases. Regular monitoring of liver function and iron status, coupled with iron chelation therapy when indicated, is essential to prevent irreversible liver damage. Cardiovascular complications, including cardiomyopathy, pulmonary hypertension, and heart failure, are increasingly recognized in patients with SCA. Chronic anemia and hemolysis contribute to increased cardiac output and vascular resistance, predisposing individuals to develop pulmonary hypertension. Furthermore, iron overload and myocardial iron deposition can lead to cardiomyopathy and congestive heart failure, necessitating comprehensive cardiac monitoring and management. Managing organ damage in SCA requires a multidisciplinary approach focused on preventing complications, optimizing organ function, and improving quality of life. Strategies include disease-modifying therapies such as hydroxyurea to reduce sickling, blood transfusions to alleviate anemia and prevent stroke, and supportive care to manage pain and other symptoms. Advances in gene therapy and curative treatments, such as hematopoietic stem cell transplantation, offer potential avenues for addressing the underlying genetic defect and preventing organ damage in the future5–8.
Therapeutic strategies
Gardos channel inhibitors
Gardos channel inhibitors represent a promising class of therapeutic agents for the management of sickle cell anemia (SCA)33. These inhibitors target the Gardos channel, also known as the calcium-activated potassium channel (KCa3.1), which plays a crucial role in regulating potassium efflux and cell dehydration in red blood cells (RBCs). Understanding the significance of Gardos channel inhibitors involves exploring their mechanism of action, clinical implications, and potential benefits for patients with SCA. The Gardos channel is activated by intracellular calcium (Ca2+) and facilitates the efflux of potassium ions (K+) from RBCs. This process is linked to the dehydration of RBCs, a critical factor in the pathophysiology of SCA. Increased potassium efflux leads to cell shrinkage and higher hemoglobin S (HbS) concentration, promoting the polymerization of HbS and the formation of sickle-shaped cells. By inhibiting the Gardos channel, potassium efflux is reduced, which helps maintain RBC hydration and decreases the propensity of cells to sickle under conditions of hypoxia or stress.
Gardos channel inhibitors, such as senicapoc (ICA-17043), have been studied for their ability to reduce RBC dehydration in individuals with SCA34. By inhibiting potassium efflux, these inhibitors help preserve RBC volume and reduce the concentration of intracellular HbS. This effect potentially lowers the incidence of vaso-occlusive crises (VOC) and other complications associated with SCA, improving overall patient outcomes. One of the primary benefits of Gardos channel inhibitors is their potential to mitigate the frequency and severity of VOC, which are characterized by intense pain episodes due to microvascular occlusions by sickled RBCs. By maintaining RBC hydration and reducing sickling, these inhibitors may decrease the need for hospitalizations and emergency treatments related to VOC, thereby enhancing the quality of life for patients with SCA. In addition to symptomatic relief, Gardos channel inhibitors hold promise as disease-modifying agents in SCA1. By targeting a key mechanism involved in the pathogenesis of the disease—RBC dehydration and sickling—these inhibitors may slow disease progression and prevent long-term complications, such as organ damage and chronic anemia. Long-term studies are needed to fully evaluate their impact on disease course and patient outcomes.
Hydration therapy
Hydration therapy plays a crucial role in the management of various medical conditions, including sickle cell anemia (SCA)35. In the context of SCA, hydration therapy focuses on maintaining adequate fluid intake to optimize red blood cell (RBC) hydration, reduce the viscosity of blood, and mitigate the complications associated with RBC sickling and dehydration. Understanding the principles, benefits, and clinical applications of hydration therapy in SCA is essential for effectively managing this complex genetic disorder. Individuals with SCA are prone to chronic dehydration due to increased fluid loss through various mechanisms, including increased urine output and insensible losses. Hydration therapy aims to counteract these losses by ensuring adequate fluid intake, typically through oral hydration with water and electrolyte-rich fluids. Dehydration in SCA leads to increased blood viscosity, exacerbating vaso-occlusive crises (VOC) and impairing blood flow to vital organs5. Adequate hydration helps lower blood viscosity, promoting smoother blood circulation and reducing the likelihood of RBC sickling and vascular occlusions. Dehydrated RBCs in SCA have higher concentrations of hemoglobin S (HbS), which increases the risk of polymerization and sickling under hypoxic conditions. Hydration therapy helps maintain RBC volume and reduces intracellular HbS concentration, thereby minimizing sickling and its associated complications.
Hydration therapy is integral to the acute management of VOC in SCA. During VOC episodes, increased fluid intake helps improve blood flow, alleviate pain, and prevent further sickling of RBCs. Intravenous hydration may be necessary for patients experiencing severe VOC to rapidly restore fluid balance and hydration status. Chronic dehydration in SCA contributes to various complications, including renal dysfunction, pulmonary complications (e.g. acute chest syndrome), and impaired cognitive function. Hydration therapy plays a preventive role by reducing the frequency and severity of these complications, enhancing overall health outcomes for individuals with SCA. In addition to acute crises, hydration therapy forms an essential component of the long-term management of SCA. Patients are encouraged to maintain adequate hydration throughout their daily routines to prevent dehydration-related complications and support overall well-being. Monitoring fluid intake and urine output, particularly during periods of illness or increased physical activity, helps optimize hydration therapy effectiveness. Encouraging individuals with SCA to drink sufficient fluids throughout the day, including water and electrolyte-containing beverages, is fundamental. Educating patients and caregivers about the importance of hydration and providing practical guidance on fluid intake goals can help maintain hydration status. In severe cases of VOC or when oral intake is insufficient, intravenous hydration with isotonic solutions (e.g. normal saline) may be administered under medical supervision. This approach rapidly restores fluid balance and electrolyte levels, supporting the resolution of acute crises13–15.
Comprehensive management
Comprehensive management of sickle cell anemia (SCA) involves a multifaceted approach aimed at addressing the diverse clinical manifestations and complications associated with this genetic disorder36. The goal is to improve quality of life, prevent acute complications, and mitigate long-term organ damage. Hydroxyurea is a cornerstone of disease-modifying therapy for SCA. It works by increasing fetal hemoglobin (HbF) production, which inhibits the polymerization of sickle hemoglobin (HbS) and reduces the frequency of vaso-occlusive crises (VOC). Hydroxyurea has been shown to decrease pain episodes, acute chest syndrome, and the need for blood transfusions in patients with SCA. Emerging therapies, such as gene therapy and gene editing techniques, hold promise for correcting the underlying genetic defect responsible for SCA. These approaches aim to provide a potential cure by restoring normal hemoglobin production and preventing sickle cell formation. Pain is a hallmark symptom of SCA, primarily due to VOC and chronic pain syndromes37. Effective pain management involves a combination of nonopioid analgesics, opioids for severe pain episodes, and non-pharmacological approaches such as heat therapy and relaxation techniques. Maintaining adequate hydration is critical for preventing RBC sickling and reducing the viscosity of blood. Hydration therapy includes encouraging oral fluid intake and, in severe cases, administering intravenous fluids during VOC episodes or periods of increased fluid needs. Due to functional asplenia in many patients with SCA, antibiotic prophylaxis (e.g. penicillin) and vaccinations against encapsulated bacteria (e.g. pneumococcus, Haemophilus influenzae type b, and meningococcus) are essential to prevent serious infections, particularly in children. Chronic transfusion therapy may be indicated for patients with severe SCA complications, such as stroke prevention in high-risk individuals or the management of severe anemia. Regular transfusions help dilute sickle cells and decrease the risk of VOC.
Acute chest syndrome (ACS) is a life-threatening complication characterized by pulmonary vaso-occlusion and inflammation. Management involves prompt recognition, supportive care with oxygen therapy and antibiotics, and sometimes transfusion therapy to improve oxygenation. Stroke prevention is crucial in SCA due to the increased risk of cerebral infarctions38. Transcranial Doppler (TCD) screening identifies children at high-risk for stroke, who may benefit from chronic transfusion therapy or other interventions to reduce stroke risk. Sickle cell nephropathy can lead to chronic kidney disease (CKD) due to microvascular occlusions and chronic hemolysis. Management includes monitoring renal function, controlling hypertension, and addressing iron overload to preserve kidney function. Cardiac complications, such as pulmonary hypertension and heart failure, require comprehensive cardiovascular monitoring and management. This includes regular echocardiography, medications to manage pulmonary hypertension, and iron chelation therapy to prevent iron overload cardiomyopathy. Educating patients and caregivers about SCA, including symptoms, complications, and the importance of adherence to treatment regimens, is essential39. Empowering patients with knowledge enhances self-management and improves treatment outcomes. Living with a chronic illness like SCA can impact mental health and quality of life. Psychosocial support, including counseling, support groups, and social services, helps patients and families cope with the emotional and social challenges associated with the disease. Regular monitoring of clinical and laboratory parameters is crucial in SCA management to assess treatment efficacy, detect complications early, and adjust therapies as needed. This includes monitoring hemoglobin levels, reticulocyte count, kidney function, iron status, and neurocognitive function.
Gardos channel inhibitors
Gardos channel inhibitors represent a promising class of therapeutic agents for the management of sickle cell anemia (SCA)33. These inhibitors target the Gardos channel, also known as the calcium-activated potassium channel (KCa3.1), which plays a crucial role in regulating potassium efflux and cell dehydration in red blood cells (RBCs). Understanding the significance of Gardos channel inhibitors involves exploring their mechanism of action, clinical implications, and potential benefits for patients with SCA. The Gardos channel is activated by intracellular calcium (Ca2+) and facilitates the efflux of potassium ions (K+) from RBCs. This process is linked to the dehydration of RBCs, a critical factor in the pathophysiology of SCA. Increased potassium efflux leads to cell shrinkage and higher hemoglobin S (HbS) concentration, promoting the polymerization of HbS and the formation of sickle-shaped cells. By inhibiting the Gardos channel, potassium efflux is reduced, which helps maintain RBC hydration and decreases the propensity of cells to sickle under conditions of hypoxia or stress.
Gardos channel inhibitors, such as senicapoc (ICA-17043), have been studied for their ability to reduce RBC dehydration in individuals with SCA34. By inhibiting potassium efflux, these inhibitors help preserve RBC volume and reduce the concentration of intracellular HbS. This effect potentially lowers the incidence of vaso-occlusive crises (VOC) and other complications associated with SCA, improving overall patient outcomes. One of the primary benefits of Gardos channel inhibitors is their potential to mitigate the frequency and severity of VOC, which are characterized by intense pain episodes due to microvascular occlusions by sickled RBCs. By maintaining RBC hydration and reducing sickling, these inhibitors may decrease the need for hospitalizations and emergency treatments related to VOC, thereby enhancing the quality of life for patients with SCA. In addition to symptomatic relief, Gardos channel inhibitors hold promise as disease-modifying agents in SCA1. By targeting a key mechanism involved in the pathogenesis of the disease—RBC dehydration and sickling—these inhibitors may slow disease progression and prevent long-term complications, such as organ damage and chronic anemia. Long-term studies are needed to fully evaluate their impact on disease course and patient outcomes.
Hydration therapy
Hydration therapy plays a crucial role in the management of various medical conditions, including sickle cell anemia (SCA)35. In the context of SCA, hydration therapy focuses on maintaining adequate fluid intake to optimize red blood cell (RBC) hydration, reduce the viscosity of blood, and mitigate the complications associated with RBC sickling and dehydration. Understanding the principles, benefits, and clinical applications of hydration therapy in SCA is essential for effectively managing this complex genetic disorder. Individuals with SCA are prone to chronic dehydration due to increased fluid loss through various mechanisms, including increased urine output and insensible losses. Hydration therapy aims to counteract these losses by ensuring adequate fluid intake, typically through oral hydration with water and electrolyte-rich fluids. Dehydration in SCA leads to increased blood viscosity, exacerbating vaso-occlusive crises (VOC) and impairing blood flow to vital organs5. Adequate hydration helps lower blood viscosity, promoting smoother blood circulation and reducing the likelihood of RBC sickling and vascular occlusions. Dehydrated RBCs in SCA have higher concentrations of hemoglobin S (HbS), which increases the risk of polymerization and sickling under hypoxic conditions. Hydration therapy helps maintain RBC volume and reduces intracellular HbS concentration, thereby minimizing sickling and its associated complications.
Hydration therapy is integral to the acute management of VOC in SCA. During VOC episodes, increased fluid intake helps improve blood flow, alleviate pain, and prevent further sickling of RBCs. Intravenous hydration may be necessary for patients experiencing severe VOC to rapidly restore fluid balance and hydration status. Chronic dehydration in SCA contributes to various complications, including renal dysfunction, pulmonary complications (e.g. acute chest syndrome), and impaired cognitive function. Hydration therapy plays a preventive role by reducing the frequency and severity of these complications, enhancing overall health outcomes for individuals with SCA. In addition to acute crises, hydration therapy forms an essential component of the long-term management of SCA. Patients are encouraged to maintain adequate hydration throughout their daily routines to prevent dehydration-related complications and support overall well-being. Monitoring fluid intake and urine output, particularly during periods of illness or increased physical activity, helps optimize hydration therapy effectiveness. Encouraging individuals with SCA to drink sufficient fluids throughout the day, including water and electrolyte-containing beverages, is fundamental. Educating patients and caregivers about the importance of hydration and providing practical guidance on fluid intake goals can help maintain hydration status. In severe cases of VOC or when oral intake is insufficient, intravenous hydration with isotonic solutions (e.g. normal saline) may be administered under medical supervision. This approach rapidly restores fluid balance and electrolyte levels, supporting the resolution of acute crises13–15.
Comprehensive management
Comprehensive management of sickle cell anemia (SCA) involves a multifaceted approach aimed at addressing the diverse clinical manifestations and complications associated with this genetic disorder36. The goal is to improve quality of life, prevent acute complications, and mitigate long-term organ damage. Hydroxyurea is a cornerstone of disease-modifying therapy for SCA. It works by increasing fetal hemoglobin (HbF) production, which inhibits the polymerization of sickle hemoglobin (HbS) and reduces the frequency of vaso-occlusive crises (VOC). Hydroxyurea has been shown to decrease pain episodes, acute chest syndrome, and the need for blood transfusions in patients with SCA. Emerging therapies, such as gene therapy and gene editing techniques, hold promise for correcting the underlying genetic defect responsible for SCA. These approaches aim to provide a potential cure by restoring normal hemoglobin production and preventing sickle cell formation. Pain is a hallmark symptom of SCA, primarily due to VOC and chronic pain syndromes37. Effective pain management involves a combination of nonopioid analgesics, opioids for severe pain episodes, and non-pharmacological approaches such as heat therapy and relaxation techniques. Maintaining adequate hydration is critical for preventing RBC sickling and reducing the viscosity of blood. Hydration therapy includes encouraging oral fluid intake and, in severe cases, administering intravenous fluids during VOC episodes or periods of increased fluid needs. Due to functional asplenia in many patients with SCA, antibiotic prophylaxis (e.g. penicillin) and vaccinations against encapsulated bacteria (e.g. pneumococcus, Haemophilus influenzae type b, and meningococcus) are essential to prevent serious infections, particularly in children. Chronic transfusion therapy may be indicated for patients with severe SCA complications, such as stroke prevention in high-risk individuals or the management of severe anemia. Regular transfusions help dilute sickle cells and decrease the risk of VOC.
Acute chest syndrome (ACS) is a life-threatening complication characterized by pulmonary vaso-occlusion and inflammation. Management involves prompt recognition, supportive care with oxygen therapy and antibiotics, and sometimes transfusion therapy to improve oxygenation. Stroke prevention is crucial in SCA due to the increased risk of cerebral infarctions38. Transcranial Doppler (TCD) screening identifies children at high-risk for stroke, who may benefit from chronic transfusion therapy or other interventions to reduce stroke risk. Sickle cell nephropathy can lead to chronic kidney disease (CKD) due to microvascular occlusions and chronic hemolysis. Management includes monitoring renal function, controlling hypertension, and addressing iron overload to preserve kidney function. Cardiac complications, such as pulmonary hypertension and heart failure, require comprehensive cardiovascular monitoring and management. This includes regular echocardiography, medications to manage pulmonary hypertension, and iron chelation therapy to prevent iron overload cardiomyopathy. Educating patients and caregivers about SCA, including symptoms, complications, and the importance of adherence to treatment regimens, is essential39. Empowering patients with knowledge enhances self-management and improves treatment outcomes. Living with a chronic illness like SCA can impact mental health and quality of life. Psychosocial support, including counseling, support groups, and social services, helps patients and families cope with the emotional and social challenges associated with the disease. Regular monitoring of clinical and laboratory parameters is crucial in SCA management to assess treatment efficacy, detect complications early, and adjust therapies as needed. This includes monitoring hemoglobin levels, reticulocyte count, kidney function, iron status, and neurocognitive function.
Results
SCA is caused by a point mutation in the β-globin gene, leading to the production of abnormal hemoglobin S (HbS). Under conditions of low oxygen tension, HbS molecules polymerize, causing red blood cells (RBCs) to assume a sickle shape. This polymerization process is central to the pathogenesis of SCA, contributing to RBC rigidity, reduced deformability, and increased susceptibility to hemolysis. The review highlighted that polymerized HbS promotes RBC adhesion to vascular endothelium, initiating vaso-occlusion in small blood vessels. This process obstructs blood flow, leading to tissue ischemia and acute pain crises characteristic of SCA. Endothelial dysfunction, inflammatory mediators, and adhesive interactions between sickled RBCs and endothelial cells further exacerbate Vaso-Occlusive Crises (VOC), perpetuating tissue damage and organ dysfunction. Chronic hemolysis in SCA results from the fragility of sickled RBCs and their shortened lifespan. Released hemoglobin leads to scavenging of nitric oxide (NO), impairing vasodilation and promoting vasoconstriction. Dysregulation of ion transport systems, including increased potassium efflux via the Gardos channel (KCa3.1), contributes to RBC dehydration and further promotes sickling. The review highlighted that chronic inflammation in SCA is driven by activated leukocytes, endothelial activation, and the release of proinflammatory cytokines and adhesion molecules. This inflammatory milieu contributes to a prothrombotic state, endothelial dysfunction, and tissue injury, exacerbating the pathophysiology of the disease.
Discussion
Targeting the molecular pathways involved in HbS polymerization, such as modifying the balance of HbF production or developing agents that inhibit polymer formation, represents promising therapeutic strategies40. Additionally, interventions aimed at reducing chronic hemolysis, preserving RBC hydration, and mitigating inflammatory responses may alleviate symptoms and prevent complications in patients with SCA. Insights into the pathophysiological mechanisms of SCA inform clinical management strategies aimed at preventing vaso-occlusive crises, managing chronic pain, and reducing organ damage. Therapeutic approaches may include hydroxyurea to increase HbF levels, blood transfusions to dilute sickled cells, and novel agents targeting specific molecular pathways involved in RBC sickling and adhesion. Advances in gene therapy and gene editing technologies offer potential curative approaches by correcting the genetic defect responsible for SCA41. Ongoing research focuses on optimizing these therapies, addressing challenges such as delivery methods and long-term efficacy, to pave the way for personalized medicine in the treatment of SCA. The review highlights the importance of multidisciplinary care and comprehensive management strategies for individuals with SCA. This includes genetic counseling, early detection of complications through regular monitoring, and addressing psychosocial needs to improve overall patient outcomes and quality of life.
Recommendations
Based on the findings and discussion of the pathophysiological insights into sickle cell anemia (SCA), several recommendations can be made to guide future research and clinical practice:Enhanced understanding of molecular pathways: Further research should focus on elucidating the intricate molecular pathways involved in hemoglobin polymerization, RBC sickling, and vaso-occlusive crises (VOC) in SCA. This includes investigating novel therapeutic targets that can interrupt these pathways to prevent or mitigate disease progression.Development of targeted therapies: There is a critical need to advance the development of targeted therapies for SCA based on the identified pathophysiological mechanisms. This includes optimizing existing treatments like hydroxyurea and exploring new pharmacological agents, gene therapies, and gene editing technologies aimed at correcting the underlying genetic defect or modulating disease severity.Early intervention and comprehensive management: Emphasize the importance of early intervention and comprehensive management strategies in SCA to prevent complications and improve patient outcomes. This involves regular monitoring of clinical and laboratory parameters, timely initiation of disease-modifying therapies, and personalized care plans tailored to individual patient needs.Integration of psychosocial support: Incorporate psychosocial support services into routine care for individuals with SCA to address the emotional, social, and educational challenges associated with chronic illness. This includes providing counseling, support groups, and educational resources to empower patients and caregivers in managing the disease effectively.Advancement in genetic counseling and screening programs: Expand genetic counseling services and promote broader implementation of newborn screening programs for SCA to facilitate early diagnosis, genetic education, and timely intervention. Early identification of at-risk individuals allows for proactive management and reduces the burden of acute complications.Healthcare provider education and training: Enhance education and training for healthcare providers to improve awareness, knowledge, and skills in managing SCA. This includes fostering interdisciplinary collaboration among hematologists, primary care physicians, genetic counselors, and allied health professionals to deliver coordinated and holistic care.Patient advocacy and community engagement: Foster patient advocacy efforts and community engagement initiatives to raise awareness about SCA, reduce stigma, and advocate for equitable access to healthcare resources and supportive services. Empowering patients and families through education and advocacy enhances disease management and promotes better health outcomes.Promotion of research collaborations: Encourage collaborative research efforts among academic institutions, healthcare providers, industry partners, and patient advocacy organizations to accelerate the translation of scientific discoveries into clinical practice. Collaborative initiatives can facilitate the development of innovative therapies and personalized treatment approaches for SCA.
Recommendations
Based on the findings and discussion of the pathophysiological insights into sickle cell anemia (SCA), several recommendations can be made to guide future research and clinical practice:Enhanced understanding of molecular pathways: Further research should focus on elucidating the intricate molecular pathways involved in hemoglobin polymerization, RBC sickling, and vaso-occlusive crises (VOC) in SCA. This includes investigating novel therapeutic targets that can interrupt these pathways to prevent or mitigate disease progression.Development of targeted therapies: There is a critical need to advance the development of targeted therapies for SCA based on the identified pathophysiological mechanisms. This includes optimizing existing treatments like hydroxyurea and exploring new pharmacological agents, gene therapies, and gene editing technologies aimed at correcting the underlying genetic defect or modulating disease severity.Early intervention and comprehensive management: Emphasize the importance of early intervention and comprehensive management strategies in SCA to prevent complications and improve patient outcomes. This involves regular monitoring of clinical and laboratory parameters, timely initiation of disease-modifying therapies, and personalized care plans tailored to individual patient needs.Integration of psychosocial support: Incorporate psychosocial support services into routine care for individuals with SCA to address the emotional, social, and educational challenges associated with chronic illness. This includes providing counseling, support groups, and educational resources to empower patients and caregivers in managing the disease effectively.Advancement in genetic counseling and screening programs: Expand genetic counseling services and promote broader implementation of newborn screening programs for SCA to facilitate early diagnosis, genetic education, and timely intervention. Early identification of at-risk individuals allows for proactive management and reduces the burden of acute complications.Healthcare provider education and training: Enhance education and training for healthcare providers to improve awareness, knowledge, and skills in managing SCA. This includes fostering interdisciplinary collaboration among hematologists, primary care physicians, genetic counselors, and allied health professionals to deliver coordinated and holistic care.Patient advocacy and community engagement: Foster patient advocacy efforts and community engagement initiatives to raise awareness about SCA, reduce stigma, and advocate for equitable access to healthcare resources and supportive services. Empowering patients and families through education and advocacy enhances disease management and promotes better health outcomes.Promotion of research collaborations: Encourage collaborative research efforts among academic institutions, healthcare providers, industry partners, and patient advocacy organizations to accelerate the translation of scientific discoveries into clinical practice. Collaborative initiatives can facilitate the development of innovative therapies and personalized treatment approaches for SCA.
Conclusion
The management of sickle cell anemia (SCA) is a multifaceted endeavor that encompasses a range of therapeutic strategies aimed at alleviating symptoms, preventing complications, and improving overall quality of life for individuals affected by this genetic disorder. Through a comprehensive approach that integrates disease-modifying therapies like hydroxyurea, supportive care measures such as pain management and hydration therapy, and proactive management of complications like vaso-occlusive crises and organ damage, healthcare providers can significantly enhance patient outcomes. The advent of novel therapies, including gene editing and gene therapy approaches, holds promise for potentially curing SCA by addressing the underlying genetic defect responsible for abnormal hemoglobin production. These advancements underscore ongoing efforts to transform the treatment landscape and offer hope for a future where individuals with SCA can live healthier lives, free from the burden of chronic pain and complications. Moreover, patient education and psychosocial support play pivotal roles in empowering individuals and families to manage SCA effectively. Educating patients about the disease, promoting adherence to treatment regimens, and providing emotional support are essential components of holistic care that promote self-management and improve overall well-being.
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Author contribution
Emmanuel performed the following roles: conceptualisation, methodology, supervision, draft writing, editing, and approval before submission.
Conflicts of interest disclosure
The author declares no conflict of interest.
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Emmanuel Ifeanyi Obeagu.
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Title: Metabolic Fingerprinting of Blood and Urine of Dairy Cows Affected by Bovine Leukemia Virus: A Mass Spectrometry Approach | Body:
1. Introduction
Bovine leukemia virus (BLV), the causative pathogen of bovine leukosis, exhibits a high prevalence in North American dairy herds, with studies reporting herd prevalence rates approaching 90% [1,2]. This retrovirus not only precipitates malignant lymphosarcoma but also incurs notable economic losses, akin to the genomic characteristics it shares with the human T-cell leukemia virus type 1 (HTLV-1). BLV instigates enzootic bovine leukosis (EBL), the foremost neoplastic disease afflicting cattle. While a majority of BLV-infected cattle remain asymptomatic, thus promoting high rates of viral shedding, approximately 30% manifest persistent lymphocytosis, and a minority of less than 5% exhibit overt clinical manifestations of EBL [3].
The implications of BLV infection extend to decreased milk yield, diminished longevity of dairy cattle, compromised vaccine efficacy, and the potential development of EBL. Owing to the retroviral nature of the BLV, which guarantees permanent viral persistence once infection occurs [4], managing its prevalence necessitates the strategic culling of infected cows from herds. A notable national survey within North America revealed that 40% of dairy and 31% of beef herds in Manitoba, Canada, harbored BLV-seropositive animals. Similarly, in Wisconsin, USA, the prevalence of herds with at least one BLV-positive blood sample stood at 83% [5]. Although over 20 countries, predominantly in Europe, have successfully eradicated EBL through rigorous control and eradication initiatives [6], the BLV continues to persist across several regions, with some countries reporting an escalation in infection rates [7]. This widespread prevalence underscores the need for comprehensive measures to mitigate BLV infection and its consequential impacts on the global cattle industry.
Transmission of the BLV primarily occurs through the exchange of infected lymphocytes, notably via blood-to-blood contact, from mother to offspring, and potentially through the ingestion of infected milk or the use of contaminated equipment. Recognized pathways for transmission include the use of contaminated needles, dehorning instruments, equipment used for rectal examinations, and vector transmission by insects. Additionally, direct contact with mucosal surfaces, consumption of unpasteurized waste milk, and proximate interactions among cattle serve as viable avenues for the BLV to spread. To mitigate the risk of BLV transmission, implementing rigorous biosecurity protocols to restrict the spread through communal equipment and close contact between animals is crucial [1,8,9].
Intriguingly, research by Buehring et al. [10] identified BLV DNA in the buffy coat of human blood samples in 38% of participants from a cohort of 95 individuals, suggesting potential zoonotic dimensions of the BLV. Similarly, Szczotka and Kuźmak [11] reported the presence of the BLV in the milk lymphocytes of infected bovines. These observations underscore the necessity for more comprehensive public health studies to ascertain the risk of BLV transmission to humans, especially through dairy consumption.
Despite prevalent infection rates within cattle populations, a mere 5% of BLV-infected cows manifest clinical signs such as lymphosarcoma, typically after an extensive asymptomatic latency period ranging from 2 to 8 years [12]. Although most infections remain subclinical, they nonetheless cause economic losses, including diminished milk output, heightened rates of culling, and the emergence of secondary health complications. Many carriers of the BLV may never exhibit overt signs of the disease within their productive lifespan, attributed to the delayed onset of leukemia post-infection. These subclinical carriers further facilitate the virus’s spread within herds. Presently, there are no available treatments to completely eliminate BLV infection. Therefore, prioritizing advancements in early detection methods and investigating the metabolic pathways influenced by EBL becomes imperative. Enhancing our understanding in these areas is essential for curbing transmission and devising effective management strategies for affected herds [12].
Traditional diagnostic approaches, such as ELISA, are adept at identifying antibodies subsequent to BLV infection, yet they are constrained by the latency of seroconversion, which may span several weeks [13]. Direct detection methods like PCR, capable of pinpointing proviral DNA within host cells, often falter in sensitivity, particularly among asymptomatic cattle harboring minimal viral loads [14]. This diagnostic gap underscores the imperative for pioneering non-invasive techniques capable of early BLV identification prior to seroconversion. “Omics” technologies, with a spotlight on metabolomics, are emerging as frontiers in this regard. Metabolomics, through the comprehensive analysis of small molecules in blood or urine, can unveil characteristic metabolic disruptions in BLV-infected animals, signaling disease presence through subtle molecular shifts. This approach signals a leap towards more prompt and precise detection methodologies, facilitating immediate intervention and effective disease management [15].
Given the absence of a viable vaccine against the BLV [3], the emphasis on detection and early diagnosis becomes paramount in curtailing its transmission and mitigating economic fallout. Metabolomics, by cataloging the intricate variety of small-molecule metabolites in biological specimens under distinct conditions, renders a detailed snapshot of an organism’s physiological state. Leveraging sophisticated analytical tools, such as mass spectrometry, metabolomics defines broad metabolic alterations indicative of disease emergence or progression [16]. Employing multivariate statistical methods to contrast metabolic signatures between unaffected and BLV-afflicted cohorts, metabolomics identifies unique biomarkers. These minor but significant shifts in metabolic pathways have the potential to forewarn forthcoming disease manifestations, thus enabling pre-emptive measures. With its profound capacity to reveal early biomarkers of disease before clinical signs surface, metabolomics helps in very early detection, leading towards precision medicine and targeted disease management strategies [15].
We hypothesized that metabolomic profiling of serum and urine samples from clinically asymptomatic BLV-infected cows will reveal distinct metabolic biomarkers and alterations that can facilitate the early diagnosis of BLV infection. Furthermore, these metabolic changes can elucidate the underlying pathomechanisms of leukemia in cattle, differentiating infected from healthy individuals based on their metabolic profiles.
Therefore, the objectives of our study were: (1) to determine the capability of metabolomic profiling through DI/LC-MS/MS to identify unique biomarkers in the serum and urine of BLV-infected, asymptomatic dairy cows compared to healthy controls; (2) to analyze the metabolic shifts occurring in asymptomatic BLV-infected cows to better understand the metabolic response of the host to the viral infection and the pathomechanisms leading to leukemia; and (3) to utilize multivariate statistical analysis to compare the metabolic profiles of BLV-infected cows with those of age-matched healthy controls, aiming to highlight the metabolic disturbances indicative of BLV infection.
2. Materials and Methods
2.1. Animals, Diets, and Blood and Urine Samples
In this study, we implemented a nested case-control design, involving 145 multiparous Holstein cows from a commercial dairy operation in Alberta, Canada, focusing on those beyond their first lactation to align with our research criteria, particularly targeting the dry-off period for sample collection. This decision to exclude primiparous cows was made to ensure the consistency and specificity of our data collection. We adhered to stringent animal care protocols, approved by the University of Alberta Animal Care and Use Committee for Livestock (AUP00003216), underscoring our commitment to ethical research practices.
The cows included in the study were selected based on their expected calving dates. Sampling was strictly timed to coincide with significant phases of the dry-off period, specifically at −8 wks (55–58 days) and −4 wks (27–30 days) before the anticipated parturition. To maintain the integrity of the study and focus on the metabolic effects associated with EBL, we excluded any cows presenting with conditions like mastitis, metritis, retained placenta, laminitis, displaced abomasum, milk fever, or ketosis, either prior to or during the calving period. This exclusion was important to clearly define the metabolic alterations attributable to EBL, effectively eliminating potential confounders. The health evaluations for these criteria were conducted by a veterinary professional and were further supported by detailed health and medical records from the dairy farm’s database, ensuring a rigorous and precise selection process for our study cohort.
From the initial cohort, 30 cows met our selection criteria and were subsequently divided into two groups. Fifteen cows, identified as clinically healthy and free of the BLV, formed the control group (CON), while the other 15, diagnosed with a BLV-positive status, comprised the leukosis (LEU) group. All cows with leukosis were confirmed BLV-positive through serological testing. Cows in the BLV-infected group exhibited visible tumors under the skin, particularly around the base of the tail, legs, and other joints. Notably, during the postpartum observation period, cows in the CON group exhibited no clinical signs, underscoring the asymptomatic nature of the disease in its early stages. This aspect of the study was particularly significant, highlighting the potential for early detection and management of EBL in dairy herds.
Blood samples were drawn from the coccygeal vein, a method chosen to minimize animal distress while adhering to animal welfare standards. To collect urine samples, we first ensured the cleanliness of the vulvar area with warm water and soap to remove any debris or fecal contamination. This was followed by an alcohol disinfection step to maintain aseptic conditions. The process of gentle perineal massaging was employed to encourage natural urination, aiding in the collection of uncontaminated urine samples.
Sampling was conducted during the early morning hours, specifically between 07:00 and 08:00 a.m., and prior to the morning feeding routine. For blood collection, we utilized 10 mL Vacutainer tubes (Becton Dickinson, Franklin Lakes, NJ, USA) equipped with a clot activator and a serum separator. Urine was collected in sterile 20 mL tubes to maintain sample integrity and sterility.
Immediately following collection, blood samples were placed on ice to promote coagulation, while urine samples were also transported to the laboratory on ice, using sterile 20 mL tubes to ensure sample preservation. Serum was then extracted by centrifuging the blood at 2090 RCF for 15 min using a Rotanta 460 R centrifuge (Hettich Zentrifugan, Tuttlingen, Germany). The resulting serum was carefully transferred into sterile tubes using a transfer pipette (Fisher Scientific, Toronto, ON, Canada).
To maintain the quality of the samples for accurate analysis, 200 μL aliquots of both serum and urine were stored at −80 °C until they were analyzed. This approach to sample collection and storage was critical to ensuring the reliability and validity of our subsequent analyses.
Before analysis, all samples were thawed on ice and then vortexed to ensure uniformity. The comprehensive metabolomic analyses were carried out by The Metabolomics Innovation Centre (TMIC) at the University of Alberta, employing liquid chromatography–tandem mass spectrometry (LC-MS/MS) techniques. This approach enabled precise and detailed metabolomic profiling.
For further context, the study includes Supplementary Tables S1 and S2, which provide a detailed breakdown of the feed ingredients given to the cows both pre- and post-parturition, quantified on a dry matter basis. These tables are instrumental in understanding the nutritional regimen of the cows throughout the duration of the study.
2.2. Metabolite Analysis Protocol
The analysis of biogenic amines (BAs), amino acids (AAs), lipids, acylcarnitines (ACs), and glucose was conducted using a designed protocol involving a 96-well filter plate. The process began with the addition of 10 μL of flow injection analysis (FIA) running buffer and liquid chromatography (LC) internal standards (ISTDs) to each well. The first well was designated as a double blank, receiving no additions. Wells two through fourteen were prepared with a mix of control and calibration samples. This included three phosphate-buffered saline (PBS) “zero-point” control samples, seven calibration curve standards, and three quality control (QC) samples.
The thawed serum and urine samples were then allocated to the subsequent wells, with each well receiving 10 μL of sample. Following this, the plate underwent an incubation period and was then subjected to drying under a nitrogen stream using a Zanntek Analytical Evaporator (Glas-Col, Terre Haute, IN, USA) for 30 min. After drying, 50 μL of a 5% phenylisothiocyanate (PITC) solution was added to each well, followed by a 20-minute incubation at room temperature. A final drying phase, lasting 90 min under nitrogen flow, completed the sample preparation process.
To extract metabolites, we added 300 μL of methanol containing 5 mM ammonium acetate to each well of the plate. The plate was then placed on a shaker and agitated at 330 rpm for 30 min. Following this, it was centrifuged at 50× g for 5 min using a Sorvall Evolution RC Superspeed Centrifuge (Fisher Scientific, Toronto, ON, Canada). This process facilitated the transfer of the contents into a lower 96-deep-well plate. For the analysis of amino acids (AAs) and biogenic amines (BAs), the extract was diluted in a 1:1 ratio with water, and 10 μL of this diluted sample was injected into the analytical column. In contrast, for the analysis of acylcarnitines (ACs), lipids, and glucose, 150 μL of the extract was mixed with 400 μL of FIA running buffer, and 20 μL of this mixture was used for column injection. Each step was executed with precision to maintain the accuracy and integrity of the metabolomic analysis.
2.3. Organic Acid Analysis Procedure
To analyze organic acids, we employed a protein precipitation method using 1.5 mL Eppendorf tubes. Each tube received a combination of an internal standard mixture (ISTD) solution (10 μL), the sample (50 μL), and ice-cold methanol (150 μL). For blanks, calibration standards, and quality control (QC) samples, we substituted methanol with a 3:1 methanol:water solution. The tubes were then vortexed thoroughly and stored at −20 °C overnight.
The following day, the samples were centrifuged at 21,000× g for 15 min. Post-centrifugation, 50 μL of the supernatant from each tube was carefully transferred into the wells of a 96-deep-well plate. To each well, we added 25 μL of three reagents: 3-nitrophenylhydrazine, 1-Ethyl-3-(3-dimethylaminopropyl) carbodiimide, and pyridine. This mixture was agitated at 450 rpm for 2 h at room temperature to complete the derivatization reaction.
After the reaction, we added 350 μL of double distilled water and 50 μL of methanol (MeOH) to each well, diluting and stabilizing the solution for subsequent analysis using liquid chromatography–tandem mass spectrometry (LC-MS/MS).
2.4. FIA/LC—MS/MS Analysis Method
In our study, the identification of metabolites in serum and urine samples was conducted using a targeted metabolomics approach, utilizing the TMIC Prime kit and flow injection analysis/liquid chromatography–tandem mass spectrometry. The analysis was performed with an Agilent 1100 series liquid chromatographic system (LC), equipped with an Agilent reversed phase Zorbax Eclipse XDB C18 column (3.0 × 100 mm, 3.5 μm particle size, 80 Å pore size) and a Phenomenex SecurityGuard C18 pre-column (4.0 × 3.0 mm). This LC system was integrated with an AB SCIEX QTRAP® 4000 mass spectrometer (Sciex Canada, Concord, ON, Canada), enhancing the detection and quantification of the metabolites.
The reagents employed in this analysis included LC/MS-grade formic acid and HPLC-grade water, both procured from Fisher Scientific (Ottawa, ON, Canada). Additionally, ammonium acetate, phenylisothiocyanate (PITC), and HPLC-grade acetonitrile (ACN) were sourced from Sigma-Aldrich (St. Louis, MO, USA). The LC-MS assay workflow was managed and controlled using Analyst® 1.6.2 software (Sciex Canada, Concord, ON, Canada), which was instrumental in ensuring precise and dependable data acquisition.
For the targeted analysis of amino acids (AAs) and biogenic amines (BAs), the LC system parameters were meticulously optimized. Mobile phase A was composed of 0.2% (v/v) formic acid in water, while mobile phase B contained the same concentration of formic acid in acetonitrile. A carefully designed gradient profile was implemented to enhance the separation and detection of AAs and BAs, with specific intervals set for adjusting the proportion of mobile phase B. The column oven temperature was consistently maintained at 50 °C to ensure analytical stability, and the flow rate was set at 500 μL/min. Each sample was introduced with an injection volume of 10 μL.
For the LC-MS/MS analysis of organic acids, the solvent system included 0.01% (v/v) formic acid in water (solvent A) and 0.01% (v/v) formic acid in methanol (solvent B). The operating conditions were finely tuned for optimal analysis of organic acids, setting the column oven temperature at 40 °C and regulating the flow rate at 300 μL/min. The injection volume for these samples was also maintained at 10 μL. The mass spectrometer operated in negative electrospray ionization mode, employing scheduled multiple reaction monitoring (MRM) scans to significantly enhance the detection’s specificity and sensitivity. These selected parameters and conditions were crucial in obtaining a comprehensive and accurate metabolite profile from the serum and urine samples.
2.5. Statistical Analysis
In this study, we conducted univariate statistical analyses using Python (v. 3.11.2, Python Software Foundation, Frederick, MA, USA, 1991) and R (v. 4.1.0, R Foundation for Statistical Computing, Vienna, Austria, 2008). We evaluated the differences in metabolite concentrations between the clinically healthy control group (CON) and the leukemia group (LEU) at two prepartum time points (−8 weeks and −4 weeks) utilizing the Mann–Whitney U test. Metabolites demonstrating a p-value of 0.05 or lower were deemed statistically significant. For an in-depth analysis of the metabolomics data, we employed MetaboAnalyst 4.0 (University of Alberta, Edmonton, Canada). Missing values were addressed by implementing a minimum value imputation strategy, and to achieve normalized data distributions, we applied log transformation.
Our multivariate analysis strategy encompassed principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) to elucidate the differences in metabolic profiles between the CON and LEU groups. The robustness of the PLS-DA model was verified via a permutation test comprising 10,000 iterations. Metabolites were prioritized based on their variable importance in projection (VIP) scores, highlighting their relevance in distinguishing between the CON and LEU groups. Additionally, receiver operating characteristic (ROC) curve analysis was performed for the top five metabolites with the highest VIP scores to assess their classification performance. The diagnostic accuracy was quantified using the area under the ROC curve (AUC).
To graphically illustrate the differences in metabolite concentrations between the CON and LEU groups, we utilized volcano plots, which displayed fold changes on the x-axis and statistical significance as −log10 p-value on the y-axis. Venn diagrams were employed to elucidate unique and shared metabolite alterations at each time point. We also conducted pathway enrichment analysis to identify significantly impacted metabolic pathways, taking into account both metabolite enrichment and topology. Furthermore, heatmaps were used to visually depict the relative abundance of metabolites within these pathways across the different groups, providing a clear and comprehensive representation of the metabolic alterations.
2.1. Animals, Diets, and Blood and Urine Samples
In this study, we implemented a nested case-control design, involving 145 multiparous Holstein cows from a commercial dairy operation in Alberta, Canada, focusing on those beyond their first lactation to align with our research criteria, particularly targeting the dry-off period for sample collection. This decision to exclude primiparous cows was made to ensure the consistency and specificity of our data collection. We adhered to stringent animal care protocols, approved by the University of Alberta Animal Care and Use Committee for Livestock (AUP00003216), underscoring our commitment to ethical research practices.
The cows included in the study were selected based on their expected calving dates. Sampling was strictly timed to coincide with significant phases of the dry-off period, specifically at −8 wks (55–58 days) and −4 wks (27–30 days) before the anticipated parturition. To maintain the integrity of the study and focus on the metabolic effects associated with EBL, we excluded any cows presenting with conditions like mastitis, metritis, retained placenta, laminitis, displaced abomasum, milk fever, or ketosis, either prior to or during the calving period. This exclusion was important to clearly define the metabolic alterations attributable to EBL, effectively eliminating potential confounders. The health evaluations for these criteria were conducted by a veterinary professional and were further supported by detailed health and medical records from the dairy farm’s database, ensuring a rigorous and precise selection process for our study cohort.
From the initial cohort, 30 cows met our selection criteria and were subsequently divided into two groups. Fifteen cows, identified as clinically healthy and free of the BLV, formed the control group (CON), while the other 15, diagnosed with a BLV-positive status, comprised the leukosis (LEU) group. All cows with leukosis were confirmed BLV-positive through serological testing. Cows in the BLV-infected group exhibited visible tumors under the skin, particularly around the base of the tail, legs, and other joints. Notably, during the postpartum observation period, cows in the CON group exhibited no clinical signs, underscoring the asymptomatic nature of the disease in its early stages. This aspect of the study was particularly significant, highlighting the potential for early detection and management of EBL in dairy herds.
Blood samples were drawn from the coccygeal vein, a method chosen to minimize animal distress while adhering to animal welfare standards. To collect urine samples, we first ensured the cleanliness of the vulvar area with warm water and soap to remove any debris or fecal contamination. This was followed by an alcohol disinfection step to maintain aseptic conditions. The process of gentle perineal massaging was employed to encourage natural urination, aiding in the collection of uncontaminated urine samples.
Sampling was conducted during the early morning hours, specifically between 07:00 and 08:00 a.m., and prior to the morning feeding routine. For blood collection, we utilized 10 mL Vacutainer tubes (Becton Dickinson, Franklin Lakes, NJ, USA) equipped with a clot activator and a serum separator. Urine was collected in sterile 20 mL tubes to maintain sample integrity and sterility.
Immediately following collection, blood samples were placed on ice to promote coagulation, while urine samples were also transported to the laboratory on ice, using sterile 20 mL tubes to ensure sample preservation. Serum was then extracted by centrifuging the blood at 2090 RCF for 15 min using a Rotanta 460 R centrifuge (Hettich Zentrifugan, Tuttlingen, Germany). The resulting serum was carefully transferred into sterile tubes using a transfer pipette (Fisher Scientific, Toronto, ON, Canada).
To maintain the quality of the samples for accurate analysis, 200 μL aliquots of both serum and urine were stored at −80 °C until they were analyzed. This approach to sample collection and storage was critical to ensuring the reliability and validity of our subsequent analyses.
Before analysis, all samples were thawed on ice and then vortexed to ensure uniformity. The comprehensive metabolomic analyses were carried out by The Metabolomics Innovation Centre (TMIC) at the University of Alberta, employing liquid chromatography–tandem mass spectrometry (LC-MS/MS) techniques. This approach enabled precise and detailed metabolomic profiling.
For further context, the study includes Supplementary Tables S1 and S2, which provide a detailed breakdown of the feed ingredients given to the cows both pre- and post-parturition, quantified on a dry matter basis. These tables are instrumental in understanding the nutritional regimen of the cows throughout the duration of the study.
2.2. Metabolite Analysis Protocol
The analysis of biogenic amines (BAs), amino acids (AAs), lipids, acylcarnitines (ACs), and glucose was conducted using a designed protocol involving a 96-well filter plate. The process began with the addition of 10 μL of flow injection analysis (FIA) running buffer and liquid chromatography (LC) internal standards (ISTDs) to each well. The first well was designated as a double blank, receiving no additions. Wells two through fourteen were prepared with a mix of control and calibration samples. This included three phosphate-buffered saline (PBS) “zero-point” control samples, seven calibration curve standards, and three quality control (QC) samples.
The thawed serum and urine samples were then allocated to the subsequent wells, with each well receiving 10 μL of sample. Following this, the plate underwent an incubation period and was then subjected to drying under a nitrogen stream using a Zanntek Analytical Evaporator (Glas-Col, Terre Haute, IN, USA) for 30 min. After drying, 50 μL of a 5% phenylisothiocyanate (PITC) solution was added to each well, followed by a 20-minute incubation at room temperature. A final drying phase, lasting 90 min under nitrogen flow, completed the sample preparation process.
To extract metabolites, we added 300 μL of methanol containing 5 mM ammonium acetate to each well of the plate. The plate was then placed on a shaker and agitated at 330 rpm for 30 min. Following this, it was centrifuged at 50× g for 5 min using a Sorvall Evolution RC Superspeed Centrifuge (Fisher Scientific, Toronto, ON, Canada). This process facilitated the transfer of the contents into a lower 96-deep-well plate. For the analysis of amino acids (AAs) and biogenic amines (BAs), the extract was diluted in a 1:1 ratio with water, and 10 μL of this diluted sample was injected into the analytical column. In contrast, for the analysis of acylcarnitines (ACs), lipids, and glucose, 150 μL of the extract was mixed with 400 μL of FIA running buffer, and 20 μL of this mixture was used for column injection. Each step was executed with precision to maintain the accuracy and integrity of the metabolomic analysis.
2.3. Organic Acid Analysis Procedure
To analyze organic acids, we employed a protein precipitation method using 1.5 mL Eppendorf tubes. Each tube received a combination of an internal standard mixture (ISTD) solution (10 μL), the sample (50 μL), and ice-cold methanol (150 μL). For blanks, calibration standards, and quality control (QC) samples, we substituted methanol with a 3:1 methanol:water solution. The tubes were then vortexed thoroughly and stored at −20 °C overnight.
The following day, the samples were centrifuged at 21,000× g for 15 min. Post-centrifugation, 50 μL of the supernatant from each tube was carefully transferred into the wells of a 96-deep-well plate. To each well, we added 25 μL of three reagents: 3-nitrophenylhydrazine, 1-Ethyl-3-(3-dimethylaminopropyl) carbodiimide, and pyridine. This mixture was agitated at 450 rpm for 2 h at room temperature to complete the derivatization reaction.
After the reaction, we added 350 μL of double distilled water and 50 μL of methanol (MeOH) to each well, diluting and stabilizing the solution for subsequent analysis using liquid chromatography–tandem mass spectrometry (LC-MS/MS).
2.4. FIA/LC—MS/MS Analysis Method
In our study, the identification of metabolites in serum and urine samples was conducted using a targeted metabolomics approach, utilizing the TMIC Prime kit and flow injection analysis/liquid chromatography–tandem mass spectrometry. The analysis was performed with an Agilent 1100 series liquid chromatographic system (LC), equipped with an Agilent reversed phase Zorbax Eclipse XDB C18 column (3.0 × 100 mm, 3.5 μm particle size, 80 Å pore size) and a Phenomenex SecurityGuard C18 pre-column (4.0 × 3.0 mm). This LC system was integrated with an AB SCIEX QTRAP® 4000 mass spectrometer (Sciex Canada, Concord, ON, Canada), enhancing the detection and quantification of the metabolites.
The reagents employed in this analysis included LC/MS-grade formic acid and HPLC-grade water, both procured from Fisher Scientific (Ottawa, ON, Canada). Additionally, ammonium acetate, phenylisothiocyanate (PITC), and HPLC-grade acetonitrile (ACN) were sourced from Sigma-Aldrich (St. Louis, MO, USA). The LC-MS assay workflow was managed and controlled using Analyst® 1.6.2 software (Sciex Canada, Concord, ON, Canada), which was instrumental in ensuring precise and dependable data acquisition.
For the targeted analysis of amino acids (AAs) and biogenic amines (BAs), the LC system parameters were meticulously optimized. Mobile phase A was composed of 0.2% (v/v) formic acid in water, while mobile phase B contained the same concentration of formic acid in acetonitrile. A carefully designed gradient profile was implemented to enhance the separation and detection of AAs and BAs, with specific intervals set for adjusting the proportion of mobile phase B. The column oven temperature was consistently maintained at 50 °C to ensure analytical stability, and the flow rate was set at 500 μL/min. Each sample was introduced with an injection volume of 10 μL.
For the LC-MS/MS analysis of organic acids, the solvent system included 0.01% (v/v) formic acid in water (solvent A) and 0.01% (v/v) formic acid in methanol (solvent B). The operating conditions were finely tuned for optimal analysis of organic acids, setting the column oven temperature at 40 °C and regulating the flow rate at 300 μL/min. The injection volume for these samples was also maintained at 10 μL. The mass spectrometer operated in negative electrospray ionization mode, employing scheduled multiple reaction monitoring (MRM) scans to significantly enhance the detection’s specificity and sensitivity. These selected parameters and conditions were crucial in obtaining a comprehensive and accurate metabolite profile from the serum and urine samples.
2.5. Statistical Analysis
In this study, we conducted univariate statistical analyses using Python (v. 3.11.2, Python Software Foundation, Frederick, MA, USA, 1991) and R (v. 4.1.0, R Foundation for Statistical Computing, Vienna, Austria, 2008). We evaluated the differences in metabolite concentrations between the clinically healthy control group (CON) and the leukemia group (LEU) at two prepartum time points (−8 weeks and −4 weeks) utilizing the Mann–Whitney U test. Metabolites demonstrating a p-value of 0.05 or lower were deemed statistically significant. For an in-depth analysis of the metabolomics data, we employed MetaboAnalyst 4.0 (University of Alberta, Edmonton, Canada). Missing values were addressed by implementing a minimum value imputation strategy, and to achieve normalized data distributions, we applied log transformation.
Our multivariate analysis strategy encompassed principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) to elucidate the differences in metabolic profiles between the CON and LEU groups. The robustness of the PLS-DA model was verified via a permutation test comprising 10,000 iterations. Metabolites were prioritized based on their variable importance in projection (VIP) scores, highlighting their relevance in distinguishing between the CON and LEU groups. Additionally, receiver operating characteristic (ROC) curve analysis was performed for the top five metabolites with the highest VIP scores to assess their classification performance. The diagnostic accuracy was quantified using the area under the ROC curve (AUC).
To graphically illustrate the differences in metabolite concentrations between the CON and LEU groups, we utilized volcano plots, which displayed fold changes on the x-axis and statistical significance as −log10 p-value on the y-axis. Venn diagrams were employed to elucidate unique and shared metabolite alterations at each time point. We also conducted pathway enrichment analysis to identify significantly impacted metabolic pathways, taking into account both metabolite enrichment and topology. Furthermore, heatmaps were used to visually depict the relative abundance of metabolites within these pathways across the different groups, providing a clear and comprehensive representation of the metabolic alterations.
3. Results
In this investigation of 145 dairy cows, 42 (28.9%) were serologically identified as infected by BLV. Within this cohort of BLV-infected cows, 16 (38%) of the infected population were observed to have elevated somatic cell counts (SCCs) exceeding 200,000 cells/mL of milk, indicative of subclinical mastitis. Additionally, among the BLV-positive cows, 15 exhibited further health complications: 5 presented with ketosis, 4 with milk fever, and 3 were diagnosed with lameness. Notably, a single cow with leukosis was diagnosed with two concurrent conditions, ketosis and displaced abomasum. The study’s findings did not indicate significant differences in milk yield and composition (for the first 56 d of lactation) between the cows diagnosed with leukosis (LEU) and the control group (CON), suggesting the need for further detailed analysis to ascertain the broader impacts of BLV infection on dairy production parameters.
3.1. Blood Metabolomic Alterations in Leukemic Cows During Dry-Off Period
Utilizing univariate analysis with the Mann–Whitney U test, we observed significant differences in metabolite concentrations between the LEU and CON groups within serum samples. Specifically, at −8 wks before the expected parturition, differential analysis revealed distinct variations in 30 metabolites (p < 0.05) in the serum, effectively differentiating the two groups. This difference decreased to 17 metabolites (p < 0.05) at −4 wks prepartum, illustrating the dynamic nature of metabolic shifts as the calving period approached (Figure 1, Table 1 and Table 2).
This figure presents two Venn diagrams showing the distribution of down-regulated (LEFT) and up-regulated (RIGHT) metabolites in BLV-infected cows versus healthy controls. The diagrams compare metabolite changes in serum and urine at two critical time points: 8 and 4 weeks before expected calving. Each circle represents a specific sample type and timepoint, with numbers indicating unique or shared metabolites. Key altered metabolites are listed beside their respective circles.
Further exploration through volcano plot analysis revealed crucial metabolic alterations in the serum of LEU cows during the prepartum phase. At the −8 wks mark, significant reductions in specific sphingomyelins (SMs) and phosphatidylcholines (PCs), such as 14:1SMOH and 16:1SM, were recorded, alongside elevations in metabolites such as arginine and phenylalanine (p < 0.05) (Figure 2A). Approaching parturition, at −4 wks prepartum, the volcano plot depicted a reduced array of significant metabolic differences between the LEU and CON groups. Noteworthy changes included decreased concentrations of metabolites such as carnosine and methionine-sulfoxide, coupled with increases in C5MDC and C5OH (p < 0.05), highlighting the ongoing metabolic adaptation and response in cows affected by leukosis (Figure 3A).
In our multivariate analysis of serum samples, a pronounced differentiation in the metabolic profiles of the LEU and CON groups was evident, as depicted in Figure 2A–E. At −8 wks prior to parturition, the application of PLS-DA yielded a high degree of predictive accuracy. Critical metabolites, namely choline, aspartic acid, phenylalanine (Phe), and arginine (Arg), emerged as significant discriminators, marked by their heightened variable importance in projection (VIP) scores, effectively differentiating the LEU group from the CON group (as shown in Figure 2D).
The receiver operating characteristic (ROC) curve analysis for serum, zeroing in on the five metabolites with the highest VIP scores—choline, aspartic acid, C5DC, C16, and LYSOC28:0—demonstrated superior classification capability. The area under the curve (AUC) for this assessment reached 0.95 (95% confidence interval: 0.90–1.0, p < 0.001), highlighting the reliability of these metabolites in accurately distinguishing between the LEU and CON cows (illustrated in Figure 2E).
Progressing to the −4 week prepartum interval, our multivariate analysis persisted in defining a clear division between the metabolic signatures of the LEU and CON cows, as illustrated in Figure 3A–E. The PLS-DA retained its elevated predictive accuracy, with VIP analysis pinpointing C3OH, methionine-sulfoxide, C5MDC, PC40:2AA, and proline (Pro) as the foremost discriminative metabolites for this stage, affirming the robustness of metabolomic profiling in discerning between health statuses among dairy cows.
The ROC curve analysis conducted at −4 wks prepartum revealed commendable predictive accuracy, with an AUC value of 0.83, employing these principal metabolites for differentiation between the LEU and CON groups, as shown in Figure 3E. The variations in these metabolites signal underlying metabolic disturbances in cows affected by leukosis, highlighting their potential as biomarkers for early detection and diagnosis.
3.2. Urinary Metabolomic Alterations in Leukosis Cows During the Dry-Off Period
The analysis of urine samples revealed significant metabolic changes in cows affected by leukemia, identifying eight metabolites with significant differences at −8 wks before parturition (p < 0.05) and a single metabolite at −4 wks before parturition (p < 0.05). These findings underscore the metabolic impact of leukosis on these animals, as detailed in Table 3 and Table 4, alongside Figure 1.
Specifically, in the urine samples from cows diagnosed with leukosis, our volcano plot analysis highlighted significant shifts in metabolites before parturition. Eight weeks prior to parturition, we detected significant increases in concentrations of specific metabolites, including glucose, asymmetric dimethylarginine, and citrulline, as well as short- and medium-chain fatty acids (C4, C10, C10:1, C12, and C12:1) (p < 0.05). Closer to parturition, at −4 wks before, a significant rise in pyruvic acid (p < 0.05) was the only notable change, pointing to distinct metabolic disruptions as calving approaches (Figure 1).
Furthermore, our multivariate analysis of urinary metabolites revealed a clear differentiation between the metabolic profiles of cows with leukosis compared to those in the control group (CON), as depicted in Figure 4 and Figure 5. This differentiation was particularly evident at −8 wks prepartum, with the PLS-DA demonstrating a high level of predictive accuracy (Figure 4C). Notably, metabolites such as glucose and C10 were identified as key factors in distinguishing between the LEU-affected cows and the CON group, evidenced by their high variable importance in projection (VIP) scores (Figure 4D). This highlights their significance in understanding the metabolic impact of leukosis on cows.
The ROC curve analysis of urine samples collected −8 wks before parturition, focusing on metabolites such as glucose, C10, C10:1, C12:1, and C2—those with the highest VIP scores—showcased excellent classification performance. The AUC for this analysis reached an impressive accuracy of 0.92, indicating a high level of precision in using these urinary metabolites to differentiate between cows affected by leukosis (LEU) and those in the healthy control group, as detailed in Figure 4E.
In the analysis performed −4 wks before parturition, the metabolic profiles in urine samples further delineated the distinction between the LEU and CON groups, as evidenced in Figure 5A–E. At this stage, metabolites like pyruvic acid and isobutyric acid were identified as significant contributors to the metabolic discrepancies observed, as highlighted in Figure 5D.
The ROC analysis at −4 wks before parturition presented an AUC of 0.74. Although this figure represents a somewhat lesser degree of classification accuracy compared to the analysis at eight weeks prepartum, it still reflects a noteworthy capability for discerning between the groups, as depicted in Figure 5E. The consistent detection of metabolic changes at both the −8-week and −4-week marks before parturition underscores the utility of urine metabolomics as a potential diagnostic approach for the early identification of leukosis in cattle. This method promises a non-invasive and efficient way to identify animals at risk, enabling early intervention and informed management decisions.
3.1. Blood Metabolomic Alterations in Leukemic Cows During Dry-Off Period
Utilizing univariate analysis with the Mann–Whitney U test, we observed significant differences in metabolite concentrations between the LEU and CON groups within serum samples. Specifically, at −8 wks before the expected parturition, differential analysis revealed distinct variations in 30 metabolites (p < 0.05) in the serum, effectively differentiating the two groups. This difference decreased to 17 metabolites (p < 0.05) at −4 wks prepartum, illustrating the dynamic nature of metabolic shifts as the calving period approached (Figure 1, Table 1 and Table 2).
This figure presents two Venn diagrams showing the distribution of down-regulated (LEFT) and up-regulated (RIGHT) metabolites in BLV-infected cows versus healthy controls. The diagrams compare metabolite changes in serum and urine at two critical time points: 8 and 4 weeks before expected calving. Each circle represents a specific sample type and timepoint, with numbers indicating unique or shared metabolites. Key altered metabolites are listed beside their respective circles.
Further exploration through volcano plot analysis revealed crucial metabolic alterations in the serum of LEU cows during the prepartum phase. At the −8 wks mark, significant reductions in specific sphingomyelins (SMs) and phosphatidylcholines (PCs), such as 14:1SMOH and 16:1SM, were recorded, alongside elevations in metabolites such as arginine and phenylalanine (p < 0.05) (Figure 2A). Approaching parturition, at −4 wks prepartum, the volcano plot depicted a reduced array of significant metabolic differences between the LEU and CON groups. Noteworthy changes included decreased concentrations of metabolites such as carnosine and methionine-sulfoxide, coupled with increases in C5MDC and C5OH (p < 0.05), highlighting the ongoing metabolic adaptation and response in cows affected by leukosis (Figure 3A).
In our multivariate analysis of serum samples, a pronounced differentiation in the metabolic profiles of the LEU and CON groups was evident, as depicted in Figure 2A–E. At −8 wks prior to parturition, the application of PLS-DA yielded a high degree of predictive accuracy. Critical metabolites, namely choline, aspartic acid, phenylalanine (Phe), and arginine (Arg), emerged as significant discriminators, marked by their heightened variable importance in projection (VIP) scores, effectively differentiating the LEU group from the CON group (as shown in Figure 2D).
The receiver operating characteristic (ROC) curve analysis for serum, zeroing in on the five metabolites with the highest VIP scores—choline, aspartic acid, C5DC, C16, and LYSOC28:0—demonstrated superior classification capability. The area under the curve (AUC) for this assessment reached 0.95 (95% confidence interval: 0.90–1.0, p < 0.001), highlighting the reliability of these metabolites in accurately distinguishing between the LEU and CON cows (illustrated in Figure 2E).
Progressing to the −4 week prepartum interval, our multivariate analysis persisted in defining a clear division between the metabolic signatures of the LEU and CON cows, as illustrated in Figure 3A–E. The PLS-DA retained its elevated predictive accuracy, with VIP analysis pinpointing C3OH, methionine-sulfoxide, C5MDC, PC40:2AA, and proline (Pro) as the foremost discriminative metabolites for this stage, affirming the robustness of metabolomic profiling in discerning between health statuses among dairy cows.
The ROC curve analysis conducted at −4 wks prepartum revealed commendable predictive accuracy, with an AUC value of 0.83, employing these principal metabolites for differentiation between the LEU and CON groups, as shown in Figure 3E. The variations in these metabolites signal underlying metabolic disturbances in cows affected by leukosis, highlighting their potential as biomarkers for early detection and diagnosis.
3.2. Urinary Metabolomic Alterations in Leukosis Cows During the Dry-Off Period
The analysis of urine samples revealed significant metabolic changes in cows affected by leukemia, identifying eight metabolites with significant differences at −8 wks before parturition (p < 0.05) and a single metabolite at −4 wks before parturition (p < 0.05). These findings underscore the metabolic impact of leukosis on these animals, as detailed in Table 3 and Table 4, alongside Figure 1.
Specifically, in the urine samples from cows diagnosed with leukosis, our volcano plot analysis highlighted significant shifts in metabolites before parturition. Eight weeks prior to parturition, we detected significant increases in concentrations of specific metabolites, including glucose, asymmetric dimethylarginine, and citrulline, as well as short- and medium-chain fatty acids (C4, C10, C10:1, C12, and C12:1) (p < 0.05). Closer to parturition, at −4 wks before, a significant rise in pyruvic acid (p < 0.05) was the only notable change, pointing to distinct metabolic disruptions as calving approaches (Figure 1).
Furthermore, our multivariate analysis of urinary metabolites revealed a clear differentiation between the metabolic profiles of cows with leukosis compared to those in the control group (CON), as depicted in Figure 4 and Figure 5. This differentiation was particularly evident at −8 wks prepartum, with the PLS-DA demonstrating a high level of predictive accuracy (Figure 4C). Notably, metabolites such as glucose and C10 were identified as key factors in distinguishing between the LEU-affected cows and the CON group, evidenced by their high variable importance in projection (VIP) scores (Figure 4D). This highlights their significance in understanding the metabolic impact of leukosis on cows.
The ROC curve analysis of urine samples collected −8 wks before parturition, focusing on metabolites such as glucose, C10, C10:1, C12:1, and C2—those with the highest VIP scores—showcased excellent classification performance. The AUC for this analysis reached an impressive accuracy of 0.92, indicating a high level of precision in using these urinary metabolites to differentiate between cows affected by leukosis (LEU) and those in the healthy control group, as detailed in Figure 4E.
In the analysis performed −4 wks before parturition, the metabolic profiles in urine samples further delineated the distinction between the LEU and CON groups, as evidenced in Figure 5A–E. At this stage, metabolites like pyruvic acid and isobutyric acid were identified as significant contributors to the metabolic discrepancies observed, as highlighted in Figure 5D.
The ROC analysis at −4 wks before parturition presented an AUC of 0.74. Although this figure represents a somewhat lesser degree of classification accuracy compared to the analysis at eight weeks prepartum, it still reflects a noteworthy capability for discerning between the groups, as depicted in Figure 5E. The consistent detection of metabolic changes at both the −8-week and −4-week marks before parturition underscores the utility of urine metabolomics as a potential diagnostic approach for the early identification of leukosis in cattle. This method promises a non-invasive and efficient way to identify animals at risk, enabling early intervention and informed management decisions.
4. Discussion
To our knowledge, this study represents the first comprehensive analysis of the metabolomic fingerprint associated with bovine leukemia virus (BLV) infection in dairy cows. Metabolic fingerprinting provides a precise snapshot of the host’s metabolic state at the moment of sampling. This research is pioneering in its examination of the metabolic alterations observed in the blood and urine of dairy cows that have been serologically confirmed to carry BLV infection. The sampling was intentionally performed at two critical time points: two months and one month prior to parturition. These time points were selected to gain insights into the impact of BLV infection on the physiological processes of the host during late pregnancy, a critical phase for the well-being of the calf and the initiation of lactation.
There is evidence that BLV infection negatively affects the milk yield and overall health of cows, making them more susceptible to other diseases around parturition. For instance, previous studies have identified an increased risk of both subclinical and clinical mastitis in dairy cows with a high proviral load [17,18]. Indeed, the analysis revealed that 38% of the cows (n = 42) diagnosed with leukosis also had subclinical mastitis. Additionally, 35.7% of the leukotic cows (15 out of the 42 cows with leukosis, from the total study group of 145) presented with one or multiple periparturient diseases, such as ketosis, milk fever, lameness, or displaced abomasum, in contrast to the healthy control group cows, which exhibited no such diseases.
Our study aims to further elucidate the metabolic repercussions of BLV infection by providing a detailed discussion of the key metabolic findings, focusing on the specific alterations induced by the infection in the serum and urine of dairy cows at two significant points during the dry-off period.
4.1. Blood Alterations in BLV-Infected Cows During the Dry-Off Period
One of the noteworthy observations from our study was a significant change in lipid metabolism, highlighted by a noticeable decrease in lipid molecules: eight molecular species of LysoPCs, two species of PCs, and five species of SMs in cows infected with BLV observed −8 weeks prior to parturition. Furthermore, −4 weeks before parturition, we detected a decrease in seven molecular species of PCs and one LysoPC in BLV-infected cows. These findings align with research in human acute myeloid leukemia (AML) patients, which documented lowered concentrations of LysoPCs, PCs, and SMs in individuals with the leukemia virus [19].
In the context of tumor cells, including those infected by BLV, there is an increased demand for PCs, driven by rapid cell growth and the necessity for membrane biosynthesis. The synthesis of PCs occurs via the cytidine-diphosphate (CDP)-choline pathway, where choline is converted into phosphocholine and then combined with diacylglycerol [20]. Conversely, LysoPCs are generated from the hydrolysis of PCs, a reaction catalyzed by phospholipase A2 (PLA2) [21]. These molecules play a crucial role in maintaining membrane structure and fluidity, fundamental for a range of cellular functions. Additionally, they act as signaling molecules, potentially involved in processes such as inflammation, cell migration, and proliferation.
BLV infection specifically targets B cells in dairy cows, leading to leukosis characterized by the abnormal proliferation of these infected B cells as well as immune cells [22]. This increased proliferation necessitates additional resources, particularly for membrane synthesis, to support the growing population of cells [23]. The resulting demand for increased membrane synthesis in BLV-infected B cells contributes to the altered metabolism of LysoPCs and PCs. As LysoPCs are crucial components of cell membranes, their metabolism is significantly affected by the increased need for new cell membranes.
In the case of sphingomyelins, which are synthesized from ceramide and phosphocholine, their turnover is vital in tumor cells for maintaining membrane structure and facilitating the production of bioactive lipids like ceramide [24]. Sphingomyelins are integral to signal transduction mechanisms and have a role in regulating processes such as apoptosis, often disrupted in cancerous cells [25]. This underscores the intricate relationship between lipid metabolism and the biological functions of B cells in the progression of BLV-induced leukosis in dairy cows.
In B cells infected by BLV, as in other types of leukemic cells, there is a distinctive shift in lipid metabolism towards increased uptake and utilization of lipids such as PCs, LysoPCs, and SMs. This shift is necessary to accommodate rapid cell division and the synthesis of new cellular membranes [26]. Such an escalated lipid demand may result in diminished levels of these lipids in circulation. The immune system’s response to BLV infection, characterized by the activation and proliferation of various immune cells, further amplifies this lipid consumption for membrane synthesis and signaling, exacerbating the decline in their serum concentrations [27].
The study observed a decrease in choline levels in serum among BLV-infected dairy cows. Choline is a vital element of phosphatidylcholine, and its depletion might reflect intensified demands for membrane synthesis and repair, driven by elevated cell turnover or immune activation [28]. Beyond its structural role, choline acts as a methyl donor in several biochemical pathways, including those involved in DNA and histone methylation, essential for regulating gene expression [29]. Additionally, choline plays a role in lipid transport and metabolism, notably in the formation of lipoproteins [30]. A decrease in choline could signify adjustments in lipid management, likely due to the metabolic challenges posed by BLV infection or the host’s immune reaction.
Our study identified significant changes in the amino acid profiles of dairy cows, distinguishing the control group from those infected with BLV. Specifically, there was an increase in the concentrations of arginine, aspartic acid (aspartate), phenylalanine, and choline in the serum of BLV-infected cows at −8 wks before parturition, followed by a decrease in alanine, glycine, proline, methionine-sulfoxide, and carnosine at −4 wks prepartum.
Arginine and its metabolic pathways have been linked to tumorigenesis [31]. Some tumors depend on external sources of arginine for growth, classifying them as either completely or partially arginine-dependent (arguably auxotrophic or semi-auxotrophic) [32]. The presence of enzymes involved in arginine biosynthesis, like argininosuccinate synthase (ASS1) and argininosuccinate lyase (ASL), plays a crucial role in determining a tumor’s arginine auxotrophy [33]. In the context of our research, inducing arginine deficiency through NEI-01 led to the death of ASS1-deficient acute myeloid leukemia (AML) cells by triggering sub-G1 cell cycle arrest and apoptosis. Beyond promoting apoptosis, arginine deprivation is known to exhibit anti-angiogenic properties and inhibit de novo protein synthesis [34], highlighting the complex role of amino acid metabolism in the pathophysiology of BLV infection and its associated cellular changes.
Aspartate is integral to the biosynthesis of pyrimidines, one of the two categories of nitrogenous bases found in nucleic acids, with the other category being purines [35]. Pyrimidines constitute essential elements of both DNA and RNA, playing vital roles in numerous cellular operations such as cell division and protein synthesis. Research by Van Gastel et al. [36] highlighted that aspartate levels in the bone marrow plasma of mice with acute myeloid leukemia (AML) were significantly elevated—up to 70 times higher than in peripheral blood. This finding underscores the importance of aspartate for leukemic cells, given its critical role in pyrimidine synthesis. Aspartate also participates in transamination reactions that are fundamental for both the creation and degradation of amino acids. The observed increase in aspartate levels in the serum of BLV-infected cows within our study could reflect an up-regulation of amino acid metabolism, likely driven by heightened protein turnover as a response to the infection [37]. This suggests that alterations in aspartate metabolism could be a marker of underlying metabolic shifts associated with BLV infection and its impact on cellular functions.
Phenylalanine, identified as elevated in the serum of cows with a BLV infection, serves as a foundational protein building block, essential for synthesizing new proteins. Beyond its role in protein construction, phenylalanine is a precursor to several critical biomolecules, including tyrosine. Tyrosine, in turn, is vital for synthesizing neurotransmitters such as dopamine, norepinephrine, and epinephrine, as well as the pigment melanin [35]. Although not a direct energy source, phenylalanine can contribute to glucose production through gluconeogenesis under specific conditions, such as prolonged fasting or in certain metabolic disorders [38].
Research has shown that dietary phenylalanine restriction to 0.08% or lower can significantly reduce the growth of intraperitoneal leukemia tumors in mice, an effect not observed with restrictions on other amino acids like isoleucine, leucine, cysteine-methionine, or overall protein [39]. Furthermore, the lifespan of mice with L1210 leukemia or P288 leukemia was extended when phenylalanine intake was restricted to 0.08% of their diet. This suggests that dietary intervention might enhance the host’s defense mechanisms rather than directly inhibiting tumor growth [40].
The increased serum concentrations of phenylalanine observed in cows affected by BLV could have adverse implications, potentially favoring the proliferation of BLV-infected cells. This highlights the complex interplay between diet, metabolism, and disease progression, underscoring the potential of nutritional strategies in influencing the course of leukosis conditions.
The lowering of serum concentrations of glycine, proline, trans-hydroxyproline, alanine, and methionine-sulfoxide observed in dairy cows during the prepartum period (−8 and −4 wks) suggests potential metabolic and physiological adaptations, particularly within the milieu of leukemic cell activity. Glycine, known for its role in glutathione synthesis—a critical antioxidant—and in purine production, necessary for nucleic acid synthesis, has far-reaching implications [41]. Decreased glycine levels may result in diminished antioxidant defense mechanisms, elevating oxidative stress, which could compromise health and immune functionality. In the context of leukemic cells, characterized by high rates of proliferation and purine demand, a glycine shortfall could restrict their growth capacity [42].
In contrast, recent research by Verstraete et al. [43] highlights that, while the majority of cells derive serine and glycine from extracellular sources, about 30% of cancers, including T-cell acute leukemia and acute myeloid leukemia, engage the de novo synthesis pathways for serine and glycine. This adaptation supports their proliferation and survival by enabling the production of serine/glycine independently [44]. Through a branch of the glycolytic pathway, cancer cells can generate serine and glycine, which not only serve as antioxidants but also provide essential components for purine synthesis. This dual role underscores the complexity of metabolic regulation in cancer cells, where alterations in amino acid levels reflect both the demands of rapid cell division and the strategies employed by cells to meet these demands.
Proline and its derivative, trans-hydroxyproline, play important roles in collagen synthesis and maintaining connective tissue integrity [45]. Decreased levels may impair tissue repair and compromise the structural integrity of the cow, including mammary tissue health, indirectly affecting the environment for leukosis cell proliferation. Furthermore, proline-rich antimicrobial peptides, essential for the innate immune response and containing high levels of L-proline residues (up to 50%), may be compromised with decreased serum proline, potentially weakening the cow’s initial defense against infections [46,47].
Alanine is crucial for glucose metabolism via gluconeogenesis, serving as an important energy source for tissues. Lowered alanine levels suggest a shift in energy metabolism, possibly due to increased energy consumption by leukemic cells or compromised liver function, affecting the overall metabolic balance [48]. Methionine sulfoxide, the oxidized form of the essential amino acid methionine, is key for methylation and antioxidant defenses, leading to cysteine and glutathione synthesis. A decline in its levels may reflect changes in redox balance and methylation activity, influencing DNA regulation and potentially impacting epigenetic control in cancer cells [49,50].
The observed reduction in blood amino acids suggests a notable metabolic reorganization in the cow, possibly driven by the leukemic cells’ heightened demands for energy and biosynthetic precursors. Leukemic cells are characterized by a distinctive metabolic adaptation, the Warburg effect, which involves elevated glucose consumption and lactate secretion, even under aerobic conditions, potentially diverting resources from the cow’s normal physiological functions [51]. This shift may hint at disrupted protein synthesis and degradation rates. During the prepartum phase, cows experience profound physiological adjustments in preparation for lactation, a process that could be further complicated by leukosis [4]. Depleted essential amino acids like glycine, important for immune system efficacy, compromise the cow’s defensive capacity against leukosis and other infections [52]. The diminished presence of amino acids vital for antioxidant defense points to elevated oxidative stress, a hallmark of cancer that influences both tumor development and host well-being [53]. Thus, the decrease in these specific amino acids during the prepartum period could reflect a redirection of metabolic resources in favor of leukosis cell proliferation, accompanied by increased oxidative stress and alterations in protein metabolism, with significant consequences for the health of the host and the dynamics of the leukosis cells [54].
Our findings indicate a reduction in serum carnosine levels in cows diagnosed with leukosis at −8 and −4 weeks prepartum. Carnosine, a dipeptide made of β-alanine and L-histidine, is predominantly found in skeletal muscles and brain tissue, playing multiple roles, including maintaining pH balance in muscles and exhibiting anti-inflammatory, antioxidant, anti-glycation, and metal ion chelating effects [55]. Research by Nagai and Suda [56] revealed that carnosine, along with beta-alanine, has immunoregulatory functions, activating both T and B cells. Notably, Prakash et al. [57] identified carnosine’s anticancer properties, particularly against U937 cells, a promonocytic human myeloid leukemia cell line. Carnosine was shown to inhibit leukemic cell proliferation, enhancing the secretion of immunomodulatory cytokines IL-10, GM-CSF, and TNF, reducing IL-8 secretion, and up-regulating gene expression of IL-8, IL-1b, TNF, as well as the expression of immune cell surface markers CD11b, CD11c, CD86, and MHCII, underscoring its anti-proliferative effects. The observed decrease in serum carnosine in prepartum cows with leukosis could, therefore, potentially contribute to the enhanced proliferation of leukotic cells.
Our research also uncovered a marked decline in serum acylcarnitines, specifically C10, C10:1, and C12:1. Acylcarnitines, carnitine, and fatty acid derivatives play an essential role in transporting fatty acids into mitochondria for β-oxidation, a critical energy production process [58]. BLV infection may disrupt cellular metabolism, as viruses typically modify the metabolism of host cells to enhance their own replication and survival, including rerouting energy sources, modifying oxidative stress mechanisms, and impairing mitochondrial functionality [59]. These viral-induced alterations could modify fatty acid metabolism, as evidenced by the observed changes in acylcarnitine levels. Such changes may stem from a shift in energy substrate preference (from lipids to carbohydrates), variations in the expression of β-oxidation enzymes, or disruptions in fatty acid transport into mitochondria [60]. In the broader context of BLV-induced systemic infection, factors including the animal’s nutritional status, hormonal fluctuations, and the energetic requirements of mounting an immune response can further affect mitochondrial efficiency and acylcarnitine oxidation [61].
4.2. Urinary Metabolite Alterations in BLV-Infected Cows During the Dry-Off Period
This study revealed an increase in various acylcarnitines, amino acid derivatives, and glucose in the urine of dairy cows serologically diagnosed with BLV at −4 wks prepartum. Specifically, pyruvate and propionylcarnitine were elevated at −8 wks prepartum, while nine metabolites—including C4, C8, C10, C10:1, C12, C12:1, glucose, citrulline, and ADMA—were elevated at −4 wks prepartum. Our previous research indicated that cows with subclinical mastitis post-calving exhibited increased urinary acylcarnitine excretion at −4 wks prepartum, suggesting a link between pre-disease states and acylcarnitine excretion [62,63]. Changes in acylcarnitine urinary excretion in cows with subclinical mastitis were observed not only before disease diagnosis at −8 and −4 wks prepartum but also during and after the diagnosis week, up to +8 wks postpartum [62].
Acylcarnitines, which are fatty acids bonded to carnitine, play a crucial role in transporting long-chain fatty acids into mitochondria for β-oxidation, a key energy production process [58]. Within the mitochondria, these fatty acids are degraded to acetyl-CoA, which then participates in the citric acid (TCA) cycle to produce ATP. In conditions such as leukosis, significant metabolic alterations occur, including changes in lipid metabolism [64]. Cancer cells may increase lipolysis and fatty acid oxidation as alternate pathways for energy, potentially leading to acylcarnitine accumulation if the rate of fatty acid release exceeds their mitochondrial utilization [65].
Acylcarnitines, while not toxic by themselves, signify a metabolic disequilibrium when they accumulate [66]. Typically, the body ensures a balance between acylcarnitine production and use. However, in disease states, their buildup in tissues may serve as a protective mechanism against the detrimental effects of excess fatty acids and their partially oxidized derivatives. The excretion of acylcarnitines via urine can then act as a regulatory measure to preserve metabolic equilibrium and safeguard cells from damage by these intermediates [58]. Cancer cells often exhibit mitochondrial dysfunction, aggravating the situation by impairing the mitochondria’s capacity to efficiently carry out β-oxidation, thereby leading to acylcarnitine accumulation [64].
In the context of leukosis, despite acylcarnitines being potential energy substrates, their utilization is hampered due to metabolic alterations, such as a shift towards glycolysis (the Warburg effect) and mitochondrial inefficiency [67]. These disruptions hinder the normal processing of acylcarnitines through β-oxidation and the TCA cycle, preventing their effective conversion into energy. Consequently, this leads to their buildup and subsequent excretion, reflecting a diversion from typical energy metabolism pathways in cancer cells [68].
An analysis of urine samples from kidney cancer patients and corresponding mouse models revealed that urinary acylcarnitines increase in correlation with tumor grade. It is posited that these compounds originate directly from the tumor tissue, possessing cytotoxic and immunomodulatory properties that could favor tumor growth and persistence within the host [69]. Mass spectrometry imaging analysis in a human breast tumor xenograft model identified two acylcarnitines, palmitoylcarnitine and stearoylcarnitine, with significant overlap in hypoxic tumor regions. This finding indicates a potential impairment of the fatty acid β-oxidation process within mitochondria [70]. Given the critical role of acylcarnitines in various diseases, they hold promise as valuable biomarkers for clinical diagnosis. Investigating acylcarnitines’ function could enhance our understanding of disease mechanisms and advance the development of diagnostic and therapeutic approaches.
Our research showed that cows afflicted with BLV infection exhibited a marked rise in urinary pyruvic acid −8 wks before parturition and increased concentrations of glucose, citrulline, and ADMA −4 wks before calving. Remarkably, glucose levels in the urine of infected cows were nearly twice those of uninfected ones. These metabolic alterations hint at various underlying biological mechanisms. The surge in pyruvic acid, a crucial product of glycolysis, suggests an increase in glycolytic activity, likely due to the altered metabolic needs of BLV-infected cells. This may reflect a shift toward anaerobic metabolism, similar to the Warburg effect observed in cancer cells, to meet the heightened energy requirements of proliferating leukotic cells [71,72]. The significant glucosuria indicates a disturbance in glucose metabolism, which could signal insulin resistance or impaired glucose uptake by cells, a condition possibly exacerbated by the metabolic strain of BLV infection [73]. The elevation in citrulline levels suggests an upsurge in amino acid metabolism, likely due to increased protein synthesis and turnover in response to infection [74]. Moreover, the rise in ADMA, an inhibitor of nitric oxide synthase, points to potential vascular dysfunction, perhaps due to systemic inflammation or altered hemodynamics induced by BLV infection [75]. Collectively, these findings illustrate a profound metabolic disruption in BLV-infected dairy cows, characterized by changes in pyruvic acid, glucose, citrulline, and ADMA levels. This intricate mosaic of glucose dysregulation, altered amino acid metabolism, and potential vascular modifications offers crucial insights into BLV infection’s pathophysiology, underscoring the importance of targeted strategies for managing the health and productivity of affected cows.
4.1. Blood Alterations in BLV-Infected Cows During the Dry-Off Period
One of the noteworthy observations from our study was a significant change in lipid metabolism, highlighted by a noticeable decrease in lipid molecules: eight molecular species of LysoPCs, two species of PCs, and five species of SMs in cows infected with BLV observed −8 weeks prior to parturition. Furthermore, −4 weeks before parturition, we detected a decrease in seven molecular species of PCs and one LysoPC in BLV-infected cows. These findings align with research in human acute myeloid leukemia (AML) patients, which documented lowered concentrations of LysoPCs, PCs, and SMs in individuals with the leukemia virus [19].
In the context of tumor cells, including those infected by BLV, there is an increased demand for PCs, driven by rapid cell growth and the necessity for membrane biosynthesis. The synthesis of PCs occurs via the cytidine-diphosphate (CDP)-choline pathway, where choline is converted into phosphocholine and then combined with diacylglycerol [20]. Conversely, LysoPCs are generated from the hydrolysis of PCs, a reaction catalyzed by phospholipase A2 (PLA2) [21]. These molecules play a crucial role in maintaining membrane structure and fluidity, fundamental for a range of cellular functions. Additionally, they act as signaling molecules, potentially involved in processes such as inflammation, cell migration, and proliferation.
BLV infection specifically targets B cells in dairy cows, leading to leukosis characterized by the abnormal proliferation of these infected B cells as well as immune cells [22]. This increased proliferation necessitates additional resources, particularly for membrane synthesis, to support the growing population of cells [23]. The resulting demand for increased membrane synthesis in BLV-infected B cells contributes to the altered metabolism of LysoPCs and PCs. As LysoPCs are crucial components of cell membranes, their metabolism is significantly affected by the increased need for new cell membranes.
In the case of sphingomyelins, which are synthesized from ceramide and phosphocholine, their turnover is vital in tumor cells for maintaining membrane structure and facilitating the production of bioactive lipids like ceramide [24]. Sphingomyelins are integral to signal transduction mechanisms and have a role in regulating processes such as apoptosis, often disrupted in cancerous cells [25]. This underscores the intricate relationship between lipid metabolism and the biological functions of B cells in the progression of BLV-induced leukosis in dairy cows.
In B cells infected by BLV, as in other types of leukemic cells, there is a distinctive shift in lipid metabolism towards increased uptake and utilization of lipids such as PCs, LysoPCs, and SMs. This shift is necessary to accommodate rapid cell division and the synthesis of new cellular membranes [26]. Such an escalated lipid demand may result in diminished levels of these lipids in circulation. The immune system’s response to BLV infection, characterized by the activation and proliferation of various immune cells, further amplifies this lipid consumption for membrane synthesis and signaling, exacerbating the decline in their serum concentrations [27].
The study observed a decrease in choline levels in serum among BLV-infected dairy cows. Choline is a vital element of phosphatidylcholine, and its depletion might reflect intensified demands for membrane synthesis and repair, driven by elevated cell turnover or immune activation [28]. Beyond its structural role, choline acts as a methyl donor in several biochemical pathways, including those involved in DNA and histone methylation, essential for regulating gene expression [29]. Additionally, choline plays a role in lipid transport and metabolism, notably in the formation of lipoproteins [30]. A decrease in choline could signify adjustments in lipid management, likely due to the metabolic challenges posed by BLV infection or the host’s immune reaction.
Our study identified significant changes in the amino acid profiles of dairy cows, distinguishing the control group from those infected with BLV. Specifically, there was an increase in the concentrations of arginine, aspartic acid (aspartate), phenylalanine, and choline in the serum of BLV-infected cows at −8 wks before parturition, followed by a decrease in alanine, glycine, proline, methionine-sulfoxide, and carnosine at −4 wks prepartum.
Arginine and its metabolic pathways have been linked to tumorigenesis [31]. Some tumors depend on external sources of arginine for growth, classifying them as either completely or partially arginine-dependent (arguably auxotrophic or semi-auxotrophic) [32]. The presence of enzymes involved in arginine biosynthesis, like argininosuccinate synthase (ASS1) and argininosuccinate lyase (ASL), plays a crucial role in determining a tumor’s arginine auxotrophy [33]. In the context of our research, inducing arginine deficiency through NEI-01 led to the death of ASS1-deficient acute myeloid leukemia (AML) cells by triggering sub-G1 cell cycle arrest and apoptosis. Beyond promoting apoptosis, arginine deprivation is known to exhibit anti-angiogenic properties and inhibit de novo protein synthesis [34], highlighting the complex role of amino acid metabolism in the pathophysiology of BLV infection and its associated cellular changes.
Aspartate is integral to the biosynthesis of pyrimidines, one of the two categories of nitrogenous bases found in nucleic acids, with the other category being purines [35]. Pyrimidines constitute essential elements of both DNA and RNA, playing vital roles in numerous cellular operations such as cell division and protein synthesis. Research by Van Gastel et al. [36] highlighted that aspartate levels in the bone marrow plasma of mice with acute myeloid leukemia (AML) were significantly elevated—up to 70 times higher than in peripheral blood. This finding underscores the importance of aspartate for leukemic cells, given its critical role in pyrimidine synthesis. Aspartate also participates in transamination reactions that are fundamental for both the creation and degradation of amino acids. The observed increase in aspartate levels in the serum of BLV-infected cows within our study could reflect an up-regulation of amino acid metabolism, likely driven by heightened protein turnover as a response to the infection [37]. This suggests that alterations in aspartate metabolism could be a marker of underlying metabolic shifts associated with BLV infection and its impact on cellular functions.
Phenylalanine, identified as elevated in the serum of cows with a BLV infection, serves as a foundational protein building block, essential for synthesizing new proteins. Beyond its role in protein construction, phenylalanine is a precursor to several critical biomolecules, including tyrosine. Tyrosine, in turn, is vital for synthesizing neurotransmitters such as dopamine, norepinephrine, and epinephrine, as well as the pigment melanin [35]. Although not a direct energy source, phenylalanine can contribute to glucose production through gluconeogenesis under specific conditions, such as prolonged fasting or in certain metabolic disorders [38].
Research has shown that dietary phenylalanine restriction to 0.08% or lower can significantly reduce the growth of intraperitoneal leukemia tumors in mice, an effect not observed with restrictions on other amino acids like isoleucine, leucine, cysteine-methionine, or overall protein [39]. Furthermore, the lifespan of mice with L1210 leukemia or P288 leukemia was extended when phenylalanine intake was restricted to 0.08% of their diet. This suggests that dietary intervention might enhance the host’s defense mechanisms rather than directly inhibiting tumor growth [40].
The increased serum concentrations of phenylalanine observed in cows affected by BLV could have adverse implications, potentially favoring the proliferation of BLV-infected cells. This highlights the complex interplay between diet, metabolism, and disease progression, underscoring the potential of nutritional strategies in influencing the course of leukosis conditions.
The lowering of serum concentrations of glycine, proline, trans-hydroxyproline, alanine, and methionine-sulfoxide observed in dairy cows during the prepartum period (−8 and −4 wks) suggests potential metabolic and physiological adaptations, particularly within the milieu of leukemic cell activity. Glycine, known for its role in glutathione synthesis—a critical antioxidant—and in purine production, necessary for nucleic acid synthesis, has far-reaching implications [41]. Decreased glycine levels may result in diminished antioxidant defense mechanisms, elevating oxidative stress, which could compromise health and immune functionality. In the context of leukemic cells, characterized by high rates of proliferation and purine demand, a glycine shortfall could restrict their growth capacity [42].
In contrast, recent research by Verstraete et al. [43] highlights that, while the majority of cells derive serine and glycine from extracellular sources, about 30% of cancers, including T-cell acute leukemia and acute myeloid leukemia, engage the de novo synthesis pathways for serine and glycine. This adaptation supports their proliferation and survival by enabling the production of serine/glycine independently [44]. Through a branch of the glycolytic pathway, cancer cells can generate serine and glycine, which not only serve as antioxidants but also provide essential components for purine synthesis. This dual role underscores the complexity of metabolic regulation in cancer cells, where alterations in amino acid levels reflect both the demands of rapid cell division and the strategies employed by cells to meet these demands.
Proline and its derivative, trans-hydroxyproline, play important roles in collagen synthesis and maintaining connective tissue integrity [45]. Decreased levels may impair tissue repair and compromise the structural integrity of the cow, including mammary tissue health, indirectly affecting the environment for leukosis cell proliferation. Furthermore, proline-rich antimicrobial peptides, essential for the innate immune response and containing high levels of L-proline residues (up to 50%), may be compromised with decreased serum proline, potentially weakening the cow’s initial defense against infections [46,47].
Alanine is crucial for glucose metabolism via gluconeogenesis, serving as an important energy source for tissues. Lowered alanine levels suggest a shift in energy metabolism, possibly due to increased energy consumption by leukemic cells or compromised liver function, affecting the overall metabolic balance [48]. Methionine sulfoxide, the oxidized form of the essential amino acid methionine, is key for methylation and antioxidant defenses, leading to cysteine and glutathione synthesis. A decline in its levels may reflect changes in redox balance and methylation activity, influencing DNA regulation and potentially impacting epigenetic control in cancer cells [49,50].
The observed reduction in blood amino acids suggests a notable metabolic reorganization in the cow, possibly driven by the leukemic cells’ heightened demands for energy and biosynthetic precursors. Leukemic cells are characterized by a distinctive metabolic adaptation, the Warburg effect, which involves elevated glucose consumption and lactate secretion, even under aerobic conditions, potentially diverting resources from the cow’s normal physiological functions [51]. This shift may hint at disrupted protein synthesis and degradation rates. During the prepartum phase, cows experience profound physiological adjustments in preparation for lactation, a process that could be further complicated by leukosis [4]. Depleted essential amino acids like glycine, important for immune system efficacy, compromise the cow’s defensive capacity against leukosis and other infections [52]. The diminished presence of amino acids vital for antioxidant defense points to elevated oxidative stress, a hallmark of cancer that influences both tumor development and host well-being [53]. Thus, the decrease in these specific amino acids during the prepartum period could reflect a redirection of metabolic resources in favor of leukosis cell proliferation, accompanied by increased oxidative stress and alterations in protein metabolism, with significant consequences for the health of the host and the dynamics of the leukosis cells [54].
Our findings indicate a reduction in serum carnosine levels in cows diagnosed with leukosis at −8 and −4 weeks prepartum. Carnosine, a dipeptide made of β-alanine and L-histidine, is predominantly found in skeletal muscles and brain tissue, playing multiple roles, including maintaining pH balance in muscles and exhibiting anti-inflammatory, antioxidant, anti-glycation, and metal ion chelating effects [55]. Research by Nagai and Suda [56] revealed that carnosine, along with beta-alanine, has immunoregulatory functions, activating both T and B cells. Notably, Prakash et al. [57] identified carnosine’s anticancer properties, particularly against U937 cells, a promonocytic human myeloid leukemia cell line. Carnosine was shown to inhibit leukemic cell proliferation, enhancing the secretion of immunomodulatory cytokines IL-10, GM-CSF, and TNF, reducing IL-8 secretion, and up-regulating gene expression of IL-8, IL-1b, TNF, as well as the expression of immune cell surface markers CD11b, CD11c, CD86, and MHCII, underscoring its anti-proliferative effects. The observed decrease in serum carnosine in prepartum cows with leukosis could, therefore, potentially contribute to the enhanced proliferation of leukotic cells.
Our research also uncovered a marked decline in serum acylcarnitines, specifically C10, C10:1, and C12:1. Acylcarnitines, carnitine, and fatty acid derivatives play an essential role in transporting fatty acids into mitochondria for β-oxidation, a critical energy production process [58]. BLV infection may disrupt cellular metabolism, as viruses typically modify the metabolism of host cells to enhance their own replication and survival, including rerouting energy sources, modifying oxidative stress mechanisms, and impairing mitochondrial functionality [59]. These viral-induced alterations could modify fatty acid metabolism, as evidenced by the observed changes in acylcarnitine levels. Such changes may stem from a shift in energy substrate preference (from lipids to carbohydrates), variations in the expression of β-oxidation enzymes, or disruptions in fatty acid transport into mitochondria [60]. In the broader context of BLV-induced systemic infection, factors including the animal’s nutritional status, hormonal fluctuations, and the energetic requirements of mounting an immune response can further affect mitochondrial efficiency and acylcarnitine oxidation [61].
4.2. Urinary Metabolite Alterations in BLV-Infected Cows During the Dry-Off Period
This study revealed an increase in various acylcarnitines, amino acid derivatives, and glucose in the urine of dairy cows serologically diagnosed with BLV at −4 wks prepartum. Specifically, pyruvate and propionylcarnitine were elevated at −8 wks prepartum, while nine metabolites—including C4, C8, C10, C10:1, C12, C12:1, glucose, citrulline, and ADMA—were elevated at −4 wks prepartum. Our previous research indicated that cows with subclinical mastitis post-calving exhibited increased urinary acylcarnitine excretion at −4 wks prepartum, suggesting a link between pre-disease states and acylcarnitine excretion [62,63]. Changes in acylcarnitine urinary excretion in cows with subclinical mastitis were observed not only before disease diagnosis at −8 and −4 wks prepartum but also during and after the diagnosis week, up to +8 wks postpartum [62].
Acylcarnitines, which are fatty acids bonded to carnitine, play a crucial role in transporting long-chain fatty acids into mitochondria for β-oxidation, a key energy production process [58]. Within the mitochondria, these fatty acids are degraded to acetyl-CoA, which then participates in the citric acid (TCA) cycle to produce ATP. In conditions such as leukosis, significant metabolic alterations occur, including changes in lipid metabolism [64]. Cancer cells may increase lipolysis and fatty acid oxidation as alternate pathways for energy, potentially leading to acylcarnitine accumulation if the rate of fatty acid release exceeds their mitochondrial utilization [65].
Acylcarnitines, while not toxic by themselves, signify a metabolic disequilibrium when they accumulate [66]. Typically, the body ensures a balance between acylcarnitine production and use. However, in disease states, their buildup in tissues may serve as a protective mechanism against the detrimental effects of excess fatty acids and their partially oxidized derivatives. The excretion of acylcarnitines via urine can then act as a regulatory measure to preserve metabolic equilibrium and safeguard cells from damage by these intermediates [58]. Cancer cells often exhibit mitochondrial dysfunction, aggravating the situation by impairing the mitochondria’s capacity to efficiently carry out β-oxidation, thereby leading to acylcarnitine accumulation [64].
In the context of leukosis, despite acylcarnitines being potential energy substrates, their utilization is hampered due to metabolic alterations, such as a shift towards glycolysis (the Warburg effect) and mitochondrial inefficiency [67]. These disruptions hinder the normal processing of acylcarnitines through β-oxidation and the TCA cycle, preventing their effective conversion into energy. Consequently, this leads to their buildup and subsequent excretion, reflecting a diversion from typical energy metabolism pathways in cancer cells [68].
An analysis of urine samples from kidney cancer patients and corresponding mouse models revealed that urinary acylcarnitines increase in correlation with tumor grade. It is posited that these compounds originate directly from the tumor tissue, possessing cytotoxic and immunomodulatory properties that could favor tumor growth and persistence within the host [69]. Mass spectrometry imaging analysis in a human breast tumor xenograft model identified two acylcarnitines, palmitoylcarnitine and stearoylcarnitine, with significant overlap in hypoxic tumor regions. This finding indicates a potential impairment of the fatty acid β-oxidation process within mitochondria [70]. Given the critical role of acylcarnitines in various diseases, they hold promise as valuable biomarkers for clinical diagnosis. Investigating acylcarnitines’ function could enhance our understanding of disease mechanisms and advance the development of diagnostic and therapeutic approaches.
Our research showed that cows afflicted with BLV infection exhibited a marked rise in urinary pyruvic acid −8 wks before parturition and increased concentrations of glucose, citrulline, and ADMA −4 wks before calving. Remarkably, glucose levels in the urine of infected cows were nearly twice those of uninfected ones. These metabolic alterations hint at various underlying biological mechanisms. The surge in pyruvic acid, a crucial product of glycolysis, suggests an increase in glycolytic activity, likely due to the altered metabolic needs of BLV-infected cells. This may reflect a shift toward anaerobic metabolism, similar to the Warburg effect observed in cancer cells, to meet the heightened energy requirements of proliferating leukotic cells [71,72]. The significant glucosuria indicates a disturbance in glucose metabolism, which could signal insulin resistance or impaired glucose uptake by cells, a condition possibly exacerbated by the metabolic strain of BLV infection [73]. The elevation in citrulline levels suggests an upsurge in amino acid metabolism, likely due to increased protein synthesis and turnover in response to infection [74]. Moreover, the rise in ADMA, an inhibitor of nitric oxide synthase, points to potential vascular dysfunction, perhaps due to systemic inflammation or altered hemodynamics induced by BLV infection [75]. Collectively, these findings illustrate a profound metabolic disruption in BLV-infected dairy cows, characterized by changes in pyruvic acid, glucose, citrulline, and ADMA levels. This intricate mosaic of glucose dysregulation, altered amino acid metabolism, and potential vascular modifications offers crucial insights into BLV infection’s pathophysiology, underscoring the importance of targeted strategies for managing the health and productivity of affected cows.
5. Conclusions
Overall, this study investigated the metabolomic profile associated with BLV infection in dairy cows prior to parturition, with a focus on changes in blood and urine metabolites at −8 and −4 wks prepartum. Data revealed notable disruptions in lipid metabolism, specifically in LysoPCs, PCs, and SMs, mirroring patterns observed in human acute myeloid leukemia. Such metabolite alterations suggest an increased need for membrane biosynthesis in BLV-infected cells, particularly during the critical phases of late pregnancy, thereby influencing outcomes related to pregnancy and lactation. The research also highlights significant shifts in amino acid profiles, including changes in levels of arginine, aspartate, phenylalanine, and carnosine, which are crucial for processes like cell division, protein synthesis, and immune function.
Moreover, the study documented notable changes in urinary metabolites during the dry-off period in cows with BLV. A marked increase in pyruvate levels at −8 wks pre-parturition and a rise in acylcarnitines, glucose, citrulline, and ADMA at −4 wks before calving were observed. Elevated urinary pyruvate suggests heightened glycolytic activity, a likely result of BLV-induced metabolic shifts. The increase in acylcarnitines reflects a metabolic imbalance, possibly due to increased lipolysis and fatty acid oxidation, a characteristic of pathological conditions like cancer, leading to acylcarnitine buildup when fatty acid release exceeds mitochondrial utilization. Furthermore, the significant rise in urinary glucose levels, nearly double those of uninfected cows, indicates a disturbance in glucose metabolism, hinting at insulin resistance or reduced glucose uptake. These findings reveal extensive metabolic reprogramming in BLV-infected dairy cows, characterized by disrupted carbohydrate and lipid metabolism, underscoring the importance of targeted strategies for BLV management. Finally, we observed a high incidence of subclinical mastitis in 38% of the cows diagnosed with leukosis and found that 35.7% of these cows were diagnosed with other periparturient diseases such as ketosis, milk fever, lameness, or displaced abomasum. These findings underline the significant health challenges BLV-infected cows face, emphasizing the necessity for effective management and intervention strategies to improve the health and productivity of dairy herds affected by BLV. |
Title: p14 | Body:
Introduction
Arf (Alternative Reading Frame; p14ARF in human, p19Arf in mouse) is an intrinsically disordered protein and key tumor suppressor that is lost or silenced in most human cancers. Arf is induced in response to oncogene activation, e.g., Myc and Ras signaling, and binds MDM2, an E3 ubiquitin ligase for p53, leading to MDM2 inhibition, p53 stabilization and cell cycle arrest1. In proliferating cells, Arf is maintained at low levels and localizes to the granular component (GC) of the nucleolus through its interaction with Nucleophosmin (NPM1)2,3. Interactions with NPM1 in the nucleolus are critical for regulating Arf stability and function. By binding Arf in the nucleolus, NPM1 protects Arf from proteasomal degradation in the nucleus4–8. Release from NPM1 facilitates Arf targeting of MDM2 in the nucleus9 and occurs in response to various stressors, including DNA damage7,10 and nucleolar disruption11,12. Furthermore, induced p19Arf expression induces NPM1 degradation13 and redistribution of HDM2 into nucleoli14.
Nucleoli are liquid-like membrane-less organelles (MLOs) assembled in part through liquid-liquid phase separation (LLPS)15,16. NPM1 forms pentamers and mediates the assembly of the GC in part through multivalent interactions of acidic tracts (A-tracts) within its central intrinsically disordered region (IDR) with multivalent arginine-rich motifs (R-motifs) in nucleolar proteins, e.g., ribosomal proteins and non-ribosomal proteins17,18. Accordingly, purified NPM1 undergoes phase separation with R-motif proteins in vitro, forming condensates that mimic the liquid-like features of the nucleolus16 and interactions with NPM1 facilitate the localization of R-motif proteins to nucleoli17,19. Importantly, p14ARF contains several multivalent R-motifs, which are required for nucleolar localization and when mutated, as in certain cancers, cause redistribution of p14ARF throughout the cell20,21.
Many intrinsically disordered proteins, or intrinsically disordered protein regions, adopt compact conformations in isolation under physiological conditions but some assume more expanded conformations after a phase transition22,23. Conformational expansion exposes the so-called sticker residues within polypeptide chains to make multivalent interactions that underly intermolecular network formation and phase separation23. Crosslinks may also be mediated by folded segments within stretches of otherwise disordered regions. For example, FG nucleoporin hydrogels are scaffolded by intermolecular β-sheet interactions24, and TDP43-CTD phase separation requires transient contacts between a conserved α-helix25. We previously showed that p14ARF undergoes phase separation with NPM1 in vitro and that p14ARF restricts NPM1 mobility within condensates26.
Here we characterize the structure and dynamics of p14ARF and NPM1 within condensates using solution- and solid-state nuclear magnetic resonance (NMR) spectroscopy and small-angle neutron scattering (SANS). We show that p14ARF forms meso-scale assemblies within condensates with NPM1, mediated by intermolecular hydrophobic interactions between residues within a partially folded N-terminal region. Further, we show that substitution mutagenesis to abrogate p14ARF hydrophobic interactions restores p14ARF and NPM1 mobility in condensates while reducing the propensity for phase separation. In nucleoli, p14ARF and endogenous NPM1 exhibit reduced diffusion and mobility. Furthermore, p14ARF expression reduces cell viability. These phenotypes all correlate with p14ARF expression levels and are dependent upon hydrophobic residues within the p14ARF N-terminus. These results demonstrate that although the R-motifs are sufficient to induce phase separation with NPM1, the hydrophobicity of p14ARF potentiates phase separation and is required for restricting p14ARF and NPM1 dynamics within the nucleolus.
Results
p14ARF forms local and long-range order in NPM1 condensates
Pentameric NPM1 engages its binding partners in part through multivalent electrostatic interactions of its disordered A2 and A3 acidic tracts (Fig. 1A, Supplementary Table 1) and R-motifs in partner proteins17,18,27–29. p14ARF contains several multivalent R-motifs, termed R1-3 (Fig. 1B, Supplementary Table 1). p14ARF also displays three well-conserved N-terminal clusters of hydrophobic residues, termed H1-H3 (Fig. 1B, Supplementary Fig. 1), which are predicted by PSI-PRED430 to form α-helical and β-sheet secondary structures (Fig. 1B; top). Furthermore, the second β-sheet and α-helix are predicted by ZipperBD31 to contain amyloidogenic hexapeptide motifs (Fig. 1B; bottom). To gain insight into the structural organization within phase-separated p14ARF-NPM1 complexes, we applied contrast variation small-angle neutron scattering (CV-SANS). This approach leverages the differences in the neutron scattering length densities of protons and deuterons to isolate the scattering contributions from select biomolecules in complex mixtures through protein perdeuteration (replacement of H-atoms with D-atoms) and adjustment of the H2O/D2O ratio within buffers32. Fitting the CV-SANS curve of p14ARF-NPM1 condensates under p14ARF-matched conditions (only scattering from NPM1 detected) to a correlation length model (Fig. 1C, green trace; Supplementary Table 2, see Methods for fitting procedure), yielded a correlation length, ξ = 61 Å, which is close to the radius of gyration measured in solution, Rg = 65 Å (Supplementary Fig 2). Within this characteristic length scale, a scaling exponent describing the NPM1 conformational ensemble was extracted. This factor, from polymer theory, ranges from 0-1, with small values (< 0.5) indicative of collapsed or folded conformations, and large values (> 0.5) indicative of expanded or unfolded conformations33. NPM1 exhibited a scaling exponent, \documentclass[12pt]{minimal}
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\begin{document}$${{{\rm{\upsilon }}}}$$\end{document}υ = 0.65, suggesting that the IDRs of pentameric NPM117,18 are in extended conformations in condensates (Fig. 1D). Strikingly, the CV-SANS curves for p14ARF-NPM1 condensates under full-scattering conditions (scattering from both NPM1 and p14ARF detected) and NPM1-matched conditions (only scattering from p14ARF detected) exhibited prominent Bragg peaks (Fig. 1C; gray and blue traces, respectively). The CV-SANS curve from NPM1-matched conditions was fit to a broad peak model, which revealed that p14ARF molecules also assume extended conformations (\documentclass[12pt]{minimal}
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\begin{document}$${{{\rm{\upsilon }}}}$$\end{document}υ = 0.66) and form a meso-scale (100–1000 Å) assembly with a characteristic intermolecular spacing, d ≈ 180 Å, within the condensed phase with NPM1 (Fig. 1C, E). This assembly appears branched at the longest length scales measured (\documentclass[12pt]{minimal}
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\begin{document}$${{{\rm{\upsilon }}}}$$\end{document}υ = 0.35) with an inter-contact distance34, Ξ ≈ 160 Å (Fig. 1C, E; magenta contacts). Meso-scale ordering of this type is common within phase separated materials, e.g., polymer gels, and is caused by physical crosslinks32.Fig. 1p14ARF exhibits local and long-range ordering within condensates with NPM1.A NPM1 structural features, including the secondary structure calculated from the oligomerization domain (OD) PDB 4N8M and the nucleic acid binding domain (NBD) PDB 2LLH, using DSSP (2o Struc.; β-strands are indicated with arrows and α-helicies are indicated with cylinders). The CIDER linear net charge per residue (LNCPR) and linear hydropathy (Hydro.) are shown for the IDR. B p14ARF structural features, including PSI-PRED secondary structure prediction (2o Struc.), CIDER linear net charge per residue (LNCPR), and linear hydropathy (Hydro.). The amino acid sequence conservation (Cons.) is based on a multiple sequence alignment using MUSCLE. The bottom panel shows the Rosetta steric zipper propensity energy (R. Energy) calculated using ZipperDB. C CV-SANS curves for p14ARF-NPM1 condensates, in 100% D2O buffer for full scattering intensity (NPM1 and p14ARF detected; gray trace), in 45% D2O buffer where p14ARF is contrast matched ([2H]-NPM1 detected; green trace), and in 85% D2O buffer where [2H]-NPM1 is contrast matched (p14ARF detected; blue trace). Correlation peaks at ~200 Å and ~400 Å correspond to meso-scale organization of p14ARF. All curves are offset for clarity. Scatter points represent the average, the error bars represent the uncertainty derived from the counting statistics of the SANS instrument, as described and cited in the Methods. D Schematic describing NPM1 with extended IDR conformations. E Schematic describing the spatial organization of p14ARF within p14ARF-NPM1 condensates. F 2D CC-DARR spectrum of [13C,15N]-p14ARF within the condensed phase. Select resonance assignments are labeled. G Secondary 13C chemical shifts for [13C,15N]-p14ARF within the condensed phase. Assigned residues are highlighted in gray. The secondary structure prediction from panel B is shown for reference (2o Struc.; top).
We next characterized the residue-level structure of p14ARF within condensates with NPM1 and identified sites of intra- and intermolecular p14ARF contacts using NMR spectroscopy. The two-dimensional transverse relaxation-optimized spectroscopy, heteronuclear single-quantum 1H-15N correlation (2D 1H-15N TROSY-HSQC) spectrum of [13C,15N]-p14ARF within condensates with unlabeled NPM1 revealed resonances for a subset of residues (Supplementary Fig. 3). Using triple-resonance NMR methods (see Methods), these were assigned to residues in the C-terminal region of p14ARF, following R-motif R3 (Supplementary Fig. 4, Supplementary Table 3, Supplementary Table 4), indicating that this region of p14ARF is disordered in condensates with NPM1. In contrast, N-terminal p14ARF residues showed extensive resonance broadening and could not be analyzed using solution-state NMR methods.
We reasoned that resonance broadening resulted from limited mobility of p14ARF within its phase separated meso-scale assemblies, as indicated by previous fluorescence recovery after photobleaching (FRAP) results26. Therefore, we applied cross-polarization magic-angle spinning solid-state NMR (CP-MAS ssNMR) (see Methods; Supplementary Table 5), which can detect resonances for both mobile (Supplementary Fig. 5) and immobile (Fig. 1F) segments of proteins within condensates26. Analysis of multiple two- and three-dimensional ssNMR spectra enabled resonance assignments for residues within the p14ARF N-terminus (Supplementary Figs. 6, 7; Supplementary Table 6). Analysis of secondary 13C chemical shifts, which report on secondary structure, revealed that the N-terminal portion of p14ARF adopts α-helical and β-strand secondary structure in condensates with NPM1 (Fig. 1G).
Consistent with the findings from CV-SANS, in 2D 13C-13C dipolar assisted rotational resonance (CC-DARR) spectra, we observed only one long-range intramolecular contact in p14ARF, between T8 and H26, which was most evident at DARR mixing times above 200 ms (Fig. 2A, B; Supplementary Fig. 6C, D). These findings suggest that compact conformations are not highly populated or form only transiently. To probe for intermolecular p14ARF–p14ARF contacts, we recorded NHHC spectra35 for a p14ARF-NPM1 condensate comprised of a 1∶1 mixture of independently 15N- or 13C-labeled p14ARF molecules, to ensure that only intermolecular 15N − 13C correlations were detected36. The resulting spectrum showed a high degree of similarity to 2D HNCA and 2D HNCACX spectra, demonstrating that structured regions within the p14ARF N-terminus engage in intermolecular contacts (Fig. 2C, D). Furthermore, based on the low signal-to-noise ratio observed, persistent p14ARF contacts either constitute a minor state or occur over long distances.Fig. 2p14ARF engages in intra- and intermolecular contacts within the condensed phase with NPM1.A A CC-DARR spectrum acquired for [13C,15N]-p14ARF with 20 ms DARR mixing time shows resonances for T8 in two states, including in an expanded p14ARF conformation (top), and in a collapsed p14ARF conformation (bottom). B A CC-DARR spectrum acquired with 400 ms DARR mixing time shows additional cross-peaks indicating intramolecular contacts between T8 and H26. C The 2D-NHHC spectrum (gray) of p14ARF (equal mixture of [13C]-p14ARF and [15N]-p14ARF) shows that sidechains within the p14ARF N-terminus make intermolecular contacts within the condensed phase with NPM1. HNCA (magenta) and 2D-HNCACX (blue) spectra for [13C,15N]-p14ARF are shown for reference. D Schematic describing possible modes of p14ARF intra- and intermolecular interaction.
NPM1’s IDR is disordered in the condensed phase with p14ARF
We previously applied CP-MAS ssNMR to show that the N-terminal NPM1 oligomerization domain (OD) retains secondary structure in condensates with p14ARF and experiences limited mobility26. However, we detected no resonances corresponding to residues in the NPM1 central IDR or the C-terminal, nucleic acid binding domain (NBD), suggesting that these structural elements remain dynamic. Therefore, we applied solution-state NMR to probe the structure and dynamics of the NPM1 IDR within p14ARF-NPM1 condensates. The 2D 1H-15N TROSY-HSQC spectrum for condensed [13C, 15N]-NPM1 showed resonances for residues in the IDR, although resonance broadening was apparent (Fig. 3A). This stemmed from an enhancement in 15N R2 relaxation, as detected through measurements of different types of nuclear spin relaxation (Fig. 3B–E). This was most pronounced for residues closest to the A3 acidic tract (residues 161–188), which mediates interactions with R-motif-containing proteins19, including Arf2. Interestingly, a portion of the R2 enhancement was caused by chemical exchange as measured by 15N Carr-Purcell-Meiboom-Gill (15N-CPMG) relaxation dispersion (Fig. 3F, G). Fitting to a 2-state exchange model showed that interconversion of NPM1 IDR conformations occurred on the 100 s of µs timescale (Fig. 3G, Supplementary Fig. 8, Supplementary Table 7, see Methods). This suggests that the condensate environment restrains the conformational dynamics of the NPM1 IDR (Fig. 3H).Fig. 3The NPM1 IDR retains disorder and experiences attenuated backbone motions within the condensed phase with p14ARF.A The 2D 1H-15N TROSY-HSQC spectrum of [13C,15N]-NPM1 within the p14ARF-NPM1 condensed phase displays resonances from the NPM1 IDR. B Linear net charge per residue (LNCPR) for the NPM1 IDR. Nuclear spin relaxation for [2H,15N]-NPM1 in solution (blue scatter points) and condensed phase [13C,15N]-NPM1 (red scatter points), including C
1H-15N heteronuclear NOEs, D R1, and E R2 transverse relaxation, which shows a restriction of NPM1 IDR backbone motions on the ps-ns timescale. The error bars for R1 and R2 transverse relaxation plots represent the standard errors from curve fitting, as described in Methods. F The contributions from exchange broadening, Rex. G
15N-CPMG relaxation dispersion profiles for condensed [13C,15N]-NPM1 measured at 800 MHz, including A186, T199, and A201, fit to a two-state model. Scatter points represent the decay rates, and the error bars represent the estimated systematic error, as described in Methods. H Schematic describing NPM1 IDR conformational exchange within condensates with p14ARF.
p14ARF hydrophobic residues limit condensed NPM1’s mobility
We hypothesized that the hydrophobic interfaces in the p14ARF N-terminal region are involved in interactions that drive phase separation and reduce NPM1 mobility within condensates. To test this, we substituted multiple aliphatic residues (Ile, Leu, and Val) within the p14ARF N-terminus with Gly and Ser (termed p14ARFΔH1-3) (Fig. 4A, Supplementary Table 1). We then performed titrations of p14ARF and p14ARFΔH1-3 into solutions of Alexa Fluor 488 conjugated NPM1 (NPM1-AF488) and determined their respective thresholds for heterotypic phase separation (termed saturation concentration values, Csat) using confocal fluorescence microscopy (Fig. 4B–D). As expected, the Csat value for phase separation of p14ARFΔH1-3 with NPM1-AF488 was greater than that for p14ARF (Fig. 4C, D). The free energy of transfer for NPM1, ΔGtrNPM1, which reports on the thermodynamics of partitioning from the light to the dense phase37, were markedly lower across the p14ARF titration as compared to the p14ARFΔH1-3 titration, demonstrating that p14ARF hydrophobic residues enhance the propensity for NPM1 phase separation (Fig. 4D). Interestingly, for titration points above a 1:1 molar ratio, p14ARF increased the ΔGtrNPM1, demonstrating that NPM1 becomes destabilized within the condensate network at saturating p14ARF concentrations (Fig. 4C, D). This transition mirrored the reentrant phase behavior observed for other Arg-rich proteins in condensates with NPM119.Fig. 4Substitution of p14ARF hydrophobic residues blocks p14ARF meso-scale ordering and restores NPM1 mobility within condensates.A p14ARF structural features, including PSI-PRED4.0 secondary structure prediction (2o Struc.), CIDER linear net charge per residue (LNCPR) and linear hydropathy (Hydro.). The CIDER analysis for p14ARFΔH1-3 is shown below. B Zoomed in regions from confocal fluorescence micrographs of NPM1-AF488 in condensates with p14ARF (top) and p14ARFΔH1-3 (bottom). Scale bars = 10 µm. C Index of dispersion for NPM1 in condensates with p14ARF (gray boxes, whiskers and trace; derived from n = 6, 6, 6, 7, 6, 6, 7, 6, 6, 6, 7 images) and p14ARFΔH1-3 (blue boxes and whiskers and trace, where n = 5, 4, 6, 6, 8, 6, 6, 6, 6, 6, 6 images). Whiskers extend from the box to the furthest point within 1.5x the inter-quartile range. The black arrow highlights the increased NPM1 saturation concentration, ΔCsat, upon substitution of p14ARF hydrophobic residues to Gly/Ser spacer residues. The gray arrow highlights the reentrant phase transition, which occurs at elevated p14ARF concentrations. D ΔGtr for NPM1 in condensates with p14ARF (gray boxes, whiskers and trace, where n = 696, 42, 61, 159, 225, 285, 333, 276, 306, 227, 773 condensates) and p14ARFΔH1-3 (blue boxes, whiskers and trace, where n = 2561, 1787, 29, 31, 82, 92, 134, 153, 166, 145, 162 condensates). Whiskers extend from the box to the furthest point within 1.5x the inter-quartile range. The Csat for NPM1 increases when p14ARF hydrophobic residues are substituted. The gray arrow highlights the destabilization of NPM1 during the reentrant phase transition. E CV-SANS curves for p14ARFΔH1-3-[2H]-NPM1 condensates collected at 50% D2O, where p14ARFΔH1-3 is contrast matched ([2H]-NPM1 detected; green trace), at 85% D2O where [2H]-NPM1 is contrast matched (p14ARFΔH1-3 detected; blue trace), and p14ARFΔH1-3-NPM1 condensate at 100% D2O for full scattering intensity (NPM1 and p14ARFΔH1-3 detected; gray trace). All curves are offset for clarity. Scatter points represent the average, the error bars represent the uncertainty derived from the counting statistics of the SANS instrument, as described and cited in the Methods. F Schematic describing condensed NPM1 with extended IDR conformations. G Schematic describing condensed p14ARFΔH1-3 in an extended conformation. H FRAP of NPM1-AF488 within condensates shows that substitution of p14ARF hydrophobic residues to Gly/Ser spacer residues restores NPM1 mobility. Scale bars = 1 µm. I FRAP recovery curves for p14ARF-NPM1 and p14ARFΔH1-3-NPM1 condensates with fits, as described in Methods (statistical significance was assessed by two-sided Wilcoxon rank-sum test, n = 10 curves for each condition, the p-value is shown in the figure). J NPM1-AF488 DApp values extracted from the FRAP recovery curves in panel I (statistical significance was assessed by two-sided Wilcoxon rank-sum test, n = 10 DApp values for each condition, the p-value is shown in the figure).
CV-SANS analysis of p14ARFΔH1-3-NPM1 condensates showed that substitution of the hydrophobic residues in p14ARF abrogates meso-scale ordering of p14ARF molecules within the condensed phase with NPM1 (Fig. 4E). The NPM1 IDRs were in extended conformations, as in condensates with p14ARF (Fig. 4F). However, p14ARFΔH1-3 molecules appeared slightly more expanded as compared to p14ARF, with a scaling exponent, \documentclass[12pt]{minimal}
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\begin{document}$${{{\rm{\upsilon }}}}$$\end{document}υ = 0.82 (Fig. 4G), likely due to an increase in structural disorder.
To test whether the elimination of p14ARF hydrophobic interfaces would enhance NPM1 mobility within condensates, we performed FRAP assays on p14ARF-NPM1-AF488 and p14ARFΔH1-3-NPM1-AF488 condensates (Fig. 4H–J; Supplementary Fig. 9,10). Within condensates containing p14ARFΔH1-3, NPM1-AF488 exhibited significantly greater mobility (Fig. 4I) and faster diffusion (based on apparent diffusion rates, DApp) (Fig. 4J) as compared to condensates containing p14ARF. Taken together, these results show that hydrophobic residues within the p14ARF N-terminus act as stickers23 that mediate self-association, enhance multivalent heterotypic interactions that drive phase separation with NPM1, and promote the meso-scale assembly of p14ARF molecules, thus restricting NPM1 translational diffusion.
p14ARF reduces nucleolar NPM1 mobility and cell proliferation
NPM1 localizes p14ARF in nucleoli to inhibit it from engaging other binding partners and activating anti-proliferative pathways4. Given that NPM1 usually forms dynamic, liquid-like condensates27 and in the absence of denaturants, purified p14ARF rapidly precipitates from solution38, we reasoned that p14ARF and NPM1 form condensates that block p14ARF aggregation by capturing it within the gel-like interaction network of the meso-scale assembly. This is akin to NPM1’s role as a chaperone for misfolded proteins in the nucleolus during cellular stress39. On the other hand, overexpression of p19Arf promotes NPM1 degradation13 and the assembly of high molecular weight p19Arf-containing complexes2. Based on these observations, we reasoned that an abundance of NPM1 is needed to form stable p14ARF-NPM1 complexes in nucleoli and limit the potential for p14ARF homo- and hetero-oligomerization with other nucleolar biomacromolecules. Therefore, we next asked whether expression of p14ARF alters the dynamics of NPM1 in nucleoli.
We addressed this question using the human DLD-1 colorectal adenocarcinoma cell line, which harbors transcriptionally inactive, mutant p53 (p53S241F)40,41 and is effectively p14ARF-null due to promotor hypermethylation42. We performed CRISPR-Cas9 editing to insert the gene for monomeric, enhanced green fluorescent protein (mEGFP)43 at the 3’-end of both alleles of the NPM1 gene, leading to expression of C-terminally mEGFP-tagged NPM1 at endogenous levels (NPM1-GFP; Supplementary Table 1), termed DLD-1NPM1-G cells. Next, we performed lentiviral transduction of DLD-1NPM1-G cells to enable doxycycline-inducible expression of p14ARF fused at the C-terminus to the monomeric, near-infrared fluorescent protein, miRFP670 (p14ARF-iRFP; Supplementary Table 1)44. As expected, following doxycycline induction, p14ARF-iRFP localized to nucleoli with NPM1-GFP (Fig. 5A). High-throughput imaging of DLD-1NPM1-G nucleoli showed that nucleolar NPM1-GFP and p14ARF-iRFP levels were anti-correlated (Fig. 5B; Supplementary Figs. 11 and 12A, B).Fig. 5p14ARF reduces nucleolar NPM1 diffusion in a concentration dependent manner.A Zoomed in regions from fluorescence microscopy images of live DLD-1NPM1-G (clone B11) cells, before and after 48 h of doxycycline induced p14ARF-iRFP expression. Scale bars = 2 µm. B Z-score analysis of NPM1-GFP and p14ARF-iRFP levels in DLD-1NPM1-G cells, showing that p14ARF and NPM1 levels are anti-correlated (statistical significance was assessed by two-sided Mann–Whitney U-test, n = 2272, 122, 54 cells, p-values are shown in the figure) C FRAP curves with fits, as described in Methods, for cells sorted from the DLD-1NPM1-G population shown in B. The curves on the left are from a cell expressing a high level of nucleolar NPM1 (clone F6; green trace) and a low level of nucleolar p14ARF (clone F6; blue trace). The curves on the right are from a cell expressing a low level of nucleolar NPM1 (clone G2; green trace) and a high level of nucleolar p14ARF (clone G2; blue traces). In unsorted DLD-1NPM1-G cells, D The DApp and E mobility for nucleolar NPM1-GFP and p14ARF-iRFP (small green and blue transparent markers, respectively, n = 45 cells) are reduced as nucleolar p14ARF-iRFP levels increase. Reductions also occur as the duration of p14ARF-iRFP expression is extended (large opaque markers; scatter points represent the mean and error bars represent the standard deviation, where n = 20 cells). F A schematic describing the correlated reductions in p14ARF and NPM1 dynamics and their assembly into large molecular weight complexes within the granular component (GC) of the nucleolus.
We next performed FRAP of p14ARF-iRFP and NPM1-GFP in DLD-1NPM1-G cells (Supplementary Figs. 9, and 12C–E). Consistent with our in vitro results, we observed a substantial reduction in DApp and mobility values for NPM1-GFP with increasing p14ARF-iRFP levels (Supplementary Fig. 13A–G). Similarly, DApp and mobility values for p14ARF-iRFP itself decreased as its levels increased (Supplementary Fig. 13H,I). To assess the dependence of this effect on the level of p14ARF-iRFP expression, we first used flow cytometry to isolate DLD-1NPM1-G clones that expressed p14ARF-iRFP at different levels (Supplementary Fig. 14A). Consistent with our observations with unsorted DLD-1NPM1-G cells, expression of p14ARF-iRFP in the isolated DLD-1NPM1-G clones caused dose-dependent reductions in DApp and mobility for NPM1-GFP, which was correlated with values for p14ARF-iRFP (Fig. 5C–E, Supplementary Fig. 14B,C). We then monitored p14ARF-iRFP and NPM1-GFP diffusion for two clones (termed G2 and B11) before, 24 h, and 48 h after doxycycline induction of p14ARF-iRFP expression. Both DLD-1NPM1-G clones showed significant reductions in the DApp value for p14ARF-iRFP and NPM1-GFP within 24 h, which persisted after 48 h of p14ARF-iRFP expression (Supplementary Fig. 14D,E). Furthermore, NPM1-GFP mobility was reduced in both DLD-1NPM1-G cell clones at the 48-hour time point (Supplementary Fig. 14F,G). Consistent with previous reports of p14ARF expression in p53-null cell lines6,45, expression of p14ARF-iRFP correlated with reduced viability of DLD-1NPM1-G cells in a dose- and time-dependent manner (Supplementary Fig. 13H,I).
We hypothesized that the reductions in diffusion for p14ARF-iRFP and NPM1-GFP are dependent on hydrophobic residues within p14ARF’s N-terminal β-strands and α-helix and tested this by expressing miRFP670-tagged p14ARFΔH1-3 (p14ARFΔH1-3-iRFP) in DLD-1NPM1-G cells (Supplementary Fig. 15A). Using high-throughput imaging, we did not observe a reduction in nucleolar NPM1 levels with increasing p14ARFΔH1-3-iRFP levels (Supplementary Fig. 15B,C). We next performed FRAP of p14ARFΔH1-3-iRFP and NPM1-GFP in DLD-1NPM1-G cells and observed no apparent reduction in diffusion rate or mobility for p14ARFΔH1-3-iRFP or NPM1-GFP with increasing p14ARF ΔH1-3-iRFP levels (Supplementary Fig. 15D,E). We further used single-cell sorting to identify DLD-1NPM1-G clones that expressed p14ARFΔH1-3-iRFP at varied levels (Supplementary Fig. 15F). We next performed FRAP assays to monitor p14ARFΔH1-3-iRFP and NPM1-GFP diffusion over the course of two days, for clones C10 and H5, which expressed p14ARFΔH1-3-iRFP levels comparable to p14ARF-iRFP in clones G2 and B11. In contrast to results with wild-type p14ARF-iRFP, for clones C10 and H5, DApp values for NPM1-GFP remained relatively constant after induced expression of p14ARFΔH1-3-iRFP (Supplementary Fig. 15G,H), reduced NPM1-GFP mobility was not observed (Supplementary Fig. 14I,J), and importantly, cell proliferation was not significantly reduced (Supplementary Fig. 15K,L). Indeed, during enforced expression of p14ARFΔH1-3-iRFP, DLD-1NPM1-G cells proliferated to the same extent as during expression of miRFP670 (iRFP) alone (Supplementary Fig. 15M). However, nucleolar partitioning of p14ARFΔH1-3-iRFP was reduced relative to that of p14ARF-iRFP (Supplementary Fig. 15N). Thus, in agreement with our observations of in vitro p14ARF-NPM1 condensates, hydrophobic residues within the p14ARF N-terminus enhance nucleolar partitioning and mediate interactions in nucleoli that restrain p14ARF-iRFP and NPM1-GFP dynamics (Fig. 5F).
p14ARF forms local and long-range order in NPM1 condensates
Pentameric NPM1 engages its binding partners in part through multivalent electrostatic interactions of its disordered A2 and A3 acidic tracts (Fig. 1A, Supplementary Table 1) and R-motifs in partner proteins17,18,27–29. p14ARF contains several multivalent R-motifs, termed R1-3 (Fig. 1B, Supplementary Table 1). p14ARF also displays three well-conserved N-terminal clusters of hydrophobic residues, termed H1-H3 (Fig. 1B, Supplementary Fig. 1), which are predicted by PSI-PRED430 to form α-helical and β-sheet secondary structures (Fig. 1B; top). Furthermore, the second β-sheet and α-helix are predicted by ZipperBD31 to contain amyloidogenic hexapeptide motifs (Fig. 1B; bottom). To gain insight into the structural organization within phase-separated p14ARF-NPM1 complexes, we applied contrast variation small-angle neutron scattering (CV-SANS). This approach leverages the differences in the neutron scattering length densities of protons and deuterons to isolate the scattering contributions from select biomolecules in complex mixtures through protein perdeuteration (replacement of H-atoms with D-atoms) and adjustment of the H2O/D2O ratio within buffers32. Fitting the CV-SANS curve of p14ARF-NPM1 condensates under p14ARF-matched conditions (only scattering from NPM1 detected) to a correlation length model (Fig. 1C, green trace; Supplementary Table 2, see Methods for fitting procedure), yielded a correlation length, ξ = 61 Å, which is close to the radius of gyration measured in solution, Rg = 65 Å (Supplementary Fig 2). Within this characteristic length scale, a scaling exponent describing the NPM1 conformational ensemble was extracted. This factor, from polymer theory, ranges from 0-1, with small values (< 0.5) indicative of collapsed or folded conformations, and large values (> 0.5) indicative of expanded or unfolded conformations33. NPM1 exhibited a scaling exponent, \documentclass[12pt]{minimal}
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\begin{document}$${{{\rm{\upsilon }}}}$$\end{document}υ = 0.65, suggesting that the IDRs of pentameric NPM117,18 are in extended conformations in condensates (Fig. 1D). Strikingly, the CV-SANS curves for p14ARF-NPM1 condensates under full-scattering conditions (scattering from both NPM1 and p14ARF detected) and NPM1-matched conditions (only scattering from p14ARF detected) exhibited prominent Bragg peaks (Fig. 1C; gray and blue traces, respectively). The CV-SANS curve from NPM1-matched conditions was fit to a broad peak model, which revealed that p14ARF molecules also assume extended conformations (\documentclass[12pt]{minimal}
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\begin{document}$${{{\rm{\upsilon }}}}$$\end{document}υ = 0.66) and form a meso-scale (100–1000 Å) assembly with a characteristic intermolecular spacing, d ≈ 180 Å, within the condensed phase with NPM1 (Fig. 1C, E). This assembly appears branched at the longest length scales measured (\documentclass[12pt]{minimal}
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\begin{document}$${{{\rm{\upsilon }}}}$$\end{document}υ = 0.35) with an inter-contact distance34, Ξ ≈ 160 Å (Fig. 1C, E; magenta contacts). Meso-scale ordering of this type is common within phase separated materials, e.g., polymer gels, and is caused by physical crosslinks32.Fig. 1p14ARF exhibits local and long-range ordering within condensates with NPM1.A NPM1 structural features, including the secondary structure calculated from the oligomerization domain (OD) PDB 4N8M and the nucleic acid binding domain (NBD) PDB 2LLH, using DSSP (2o Struc.; β-strands are indicated with arrows and α-helicies are indicated with cylinders). The CIDER linear net charge per residue (LNCPR) and linear hydropathy (Hydro.) are shown for the IDR. B p14ARF structural features, including PSI-PRED secondary structure prediction (2o Struc.), CIDER linear net charge per residue (LNCPR), and linear hydropathy (Hydro.). The amino acid sequence conservation (Cons.) is based on a multiple sequence alignment using MUSCLE. The bottom panel shows the Rosetta steric zipper propensity energy (R. Energy) calculated using ZipperDB. C CV-SANS curves for p14ARF-NPM1 condensates, in 100% D2O buffer for full scattering intensity (NPM1 and p14ARF detected; gray trace), in 45% D2O buffer where p14ARF is contrast matched ([2H]-NPM1 detected; green trace), and in 85% D2O buffer where [2H]-NPM1 is contrast matched (p14ARF detected; blue trace). Correlation peaks at ~200 Å and ~400 Å correspond to meso-scale organization of p14ARF. All curves are offset for clarity. Scatter points represent the average, the error bars represent the uncertainty derived from the counting statistics of the SANS instrument, as described and cited in the Methods. D Schematic describing NPM1 with extended IDR conformations. E Schematic describing the spatial organization of p14ARF within p14ARF-NPM1 condensates. F 2D CC-DARR spectrum of [13C,15N]-p14ARF within the condensed phase. Select resonance assignments are labeled. G Secondary 13C chemical shifts for [13C,15N]-p14ARF within the condensed phase. Assigned residues are highlighted in gray. The secondary structure prediction from panel B is shown for reference (2o Struc.; top).
We next characterized the residue-level structure of p14ARF within condensates with NPM1 and identified sites of intra- and intermolecular p14ARF contacts using NMR spectroscopy. The two-dimensional transverse relaxation-optimized spectroscopy, heteronuclear single-quantum 1H-15N correlation (2D 1H-15N TROSY-HSQC) spectrum of [13C,15N]-p14ARF within condensates with unlabeled NPM1 revealed resonances for a subset of residues (Supplementary Fig. 3). Using triple-resonance NMR methods (see Methods), these were assigned to residues in the C-terminal region of p14ARF, following R-motif R3 (Supplementary Fig. 4, Supplementary Table 3, Supplementary Table 4), indicating that this region of p14ARF is disordered in condensates with NPM1. In contrast, N-terminal p14ARF residues showed extensive resonance broadening and could not be analyzed using solution-state NMR methods.
We reasoned that resonance broadening resulted from limited mobility of p14ARF within its phase separated meso-scale assemblies, as indicated by previous fluorescence recovery after photobleaching (FRAP) results26. Therefore, we applied cross-polarization magic-angle spinning solid-state NMR (CP-MAS ssNMR) (see Methods; Supplementary Table 5), which can detect resonances for both mobile (Supplementary Fig. 5) and immobile (Fig. 1F) segments of proteins within condensates26. Analysis of multiple two- and three-dimensional ssNMR spectra enabled resonance assignments for residues within the p14ARF N-terminus (Supplementary Figs. 6, 7; Supplementary Table 6). Analysis of secondary 13C chemical shifts, which report on secondary structure, revealed that the N-terminal portion of p14ARF adopts α-helical and β-strand secondary structure in condensates with NPM1 (Fig. 1G).
Consistent with the findings from CV-SANS, in 2D 13C-13C dipolar assisted rotational resonance (CC-DARR) spectra, we observed only one long-range intramolecular contact in p14ARF, between T8 and H26, which was most evident at DARR mixing times above 200 ms (Fig. 2A, B; Supplementary Fig. 6C, D). These findings suggest that compact conformations are not highly populated or form only transiently. To probe for intermolecular p14ARF–p14ARF contacts, we recorded NHHC spectra35 for a p14ARF-NPM1 condensate comprised of a 1∶1 mixture of independently 15N- or 13C-labeled p14ARF molecules, to ensure that only intermolecular 15N − 13C correlations were detected36. The resulting spectrum showed a high degree of similarity to 2D HNCA and 2D HNCACX spectra, demonstrating that structured regions within the p14ARF N-terminus engage in intermolecular contacts (Fig. 2C, D). Furthermore, based on the low signal-to-noise ratio observed, persistent p14ARF contacts either constitute a minor state or occur over long distances.Fig. 2p14ARF engages in intra- and intermolecular contacts within the condensed phase with NPM1.A A CC-DARR spectrum acquired for [13C,15N]-p14ARF with 20 ms DARR mixing time shows resonances for T8 in two states, including in an expanded p14ARF conformation (top), and in a collapsed p14ARF conformation (bottom). B A CC-DARR spectrum acquired with 400 ms DARR mixing time shows additional cross-peaks indicating intramolecular contacts between T8 and H26. C The 2D-NHHC spectrum (gray) of p14ARF (equal mixture of [13C]-p14ARF and [15N]-p14ARF) shows that sidechains within the p14ARF N-terminus make intermolecular contacts within the condensed phase with NPM1. HNCA (magenta) and 2D-HNCACX (blue) spectra for [13C,15N]-p14ARF are shown for reference. D Schematic describing possible modes of p14ARF intra- and intermolecular interaction.
NPM1’s IDR is disordered in the condensed phase with p14ARF
We previously applied CP-MAS ssNMR to show that the N-terminal NPM1 oligomerization domain (OD) retains secondary structure in condensates with p14ARF and experiences limited mobility26. However, we detected no resonances corresponding to residues in the NPM1 central IDR or the C-terminal, nucleic acid binding domain (NBD), suggesting that these structural elements remain dynamic. Therefore, we applied solution-state NMR to probe the structure and dynamics of the NPM1 IDR within p14ARF-NPM1 condensates. The 2D 1H-15N TROSY-HSQC spectrum for condensed [13C, 15N]-NPM1 showed resonances for residues in the IDR, although resonance broadening was apparent (Fig. 3A). This stemmed from an enhancement in 15N R2 relaxation, as detected through measurements of different types of nuclear spin relaxation (Fig. 3B–E). This was most pronounced for residues closest to the A3 acidic tract (residues 161–188), which mediates interactions with R-motif-containing proteins19, including Arf2. Interestingly, a portion of the R2 enhancement was caused by chemical exchange as measured by 15N Carr-Purcell-Meiboom-Gill (15N-CPMG) relaxation dispersion (Fig. 3F, G). Fitting to a 2-state exchange model showed that interconversion of NPM1 IDR conformations occurred on the 100 s of µs timescale (Fig. 3G, Supplementary Fig. 8, Supplementary Table 7, see Methods). This suggests that the condensate environment restrains the conformational dynamics of the NPM1 IDR (Fig. 3H).Fig. 3The NPM1 IDR retains disorder and experiences attenuated backbone motions within the condensed phase with p14ARF.A The 2D 1H-15N TROSY-HSQC spectrum of [13C,15N]-NPM1 within the p14ARF-NPM1 condensed phase displays resonances from the NPM1 IDR. B Linear net charge per residue (LNCPR) for the NPM1 IDR. Nuclear spin relaxation for [2H,15N]-NPM1 in solution (blue scatter points) and condensed phase [13C,15N]-NPM1 (red scatter points), including C
1H-15N heteronuclear NOEs, D R1, and E R2 transverse relaxation, which shows a restriction of NPM1 IDR backbone motions on the ps-ns timescale. The error bars for R1 and R2 transverse relaxation plots represent the standard errors from curve fitting, as described in Methods. F The contributions from exchange broadening, Rex. G
15N-CPMG relaxation dispersion profiles for condensed [13C,15N]-NPM1 measured at 800 MHz, including A186, T199, and A201, fit to a two-state model. Scatter points represent the decay rates, and the error bars represent the estimated systematic error, as described in Methods. H Schematic describing NPM1 IDR conformational exchange within condensates with p14ARF.
p14ARF hydrophobic residues limit condensed NPM1’s mobility
We hypothesized that the hydrophobic interfaces in the p14ARF N-terminal region are involved in interactions that drive phase separation and reduce NPM1 mobility within condensates. To test this, we substituted multiple aliphatic residues (Ile, Leu, and Val) within the p14ARF N-terminus with Gly and Ser (termed p14ARFΔH1-3) (Fig. 4A, Supplementary Table 1). We then performed titrations of p14ARF and p14ARFΔH1-3 into solutions of Alexa Fluor 488 conjugated NPM1 (NPM1-AF488) and determined their respective thresholds for heterotypic phase separation (termed saturation concentration values, Csat) using confocal fluorescence microscopy (Fig. 4B–D). As expected, the Csat value for phase separation of p14ARFΔH1-3 with NPM1-AF488 was greater than that for p14ARF (Fig. 4C, D). The free energy of transfer for NPM1, ΔGtrNPM1, which reports on the thermodynamics of partitioning from the light to the dense phase37, were markedly lower across the p14ARF titration as compared to the p14ARFΔH1-3 titration, demonstrating that p14ARF hydrophobic residues enhance the propensity for NPM1 phase separation (Fig. 4D). Interestingly, for titration points above a 1:1 molar ratio, p14ARF increased the ΔGtrNPM1, demonstrating that NPM1 becomes destabilized within the condensate network at saturating p14ARF concentrations (Fig. 4C, D). This transition mirrored the reentrant phase behavior observed for other Arg-rich proteins in condensates with NPM119.Fig. 4Substitution of p14ARF hydrophobic residues blocks p14ARF meso-scale ordering and restores NPM1 mobility within condensates.A p14ARF structural features, including PSI-PRED4.0 secondary structure prediction (2o Struc.), CIDER linear net charge per residue (LNCPR) and linear hydropathy (Hydro.). The CIDER analysis for p14ARFΔH1-3 is shown below. B Zoomed in regions from confocal fluorescence micrographs of NPM1-AF488 in condensates with p14ARF (top) and p14ARFΔH1-3 (bottom). Scale bars = 10 µm. C Index of dispersion for NPM1 in condensates with p14ARF (gray boxes, whiskers and trace; derived from n = 6, 6, 6, 7, 6, 6, 7, 6, 6, 6, 7 images) and p14ARFΔH1-3 (blue boxes and whiskers and trace, where n = 5, 4, 6, 6, 8, 6, 6, 6, 6, 6, 6 images). Whiskers extend from the box to the furthest point within 1.5x the inter-quartile range. The black arrow highlights the increased NPM1 saturation concentration, ΔCsat, upon substitution of p14ARF hydrophobic residues to Gly/Ser spacer residues. The gray arrow highlights the reentrant phase transition, which occurs at elevated p14ARF concentrations. D ΔGtr for NPM1 in condensates with p14ARF (gray boxes, whiskers and trace, where n = 696, 42, 61, 159, 225, 285, 333, 276, 306, 227, 773 condensates) and p14ARFΔH1-3 (blue boxes, whiskers and trace, where n = 2561, 1787, 29, 31, 82, 92, 134, 153, 166, 145, 162 condensates). Whiskers extend from the box to the furthest point within 1.5x the inter-quartile range. The Csat for NPM1 increases when p14ARF hydrophobic residues are substituted. The gray arrow highlights the destabilization of NPM1 during the reentrant phase transition. E CV-SANS curves for p14ARFΔH1-3-[2H]-NPM1 condensates collected at 50% D2O, where p14ARFΔH1-3 is contrast matched ([2H]-NPM1 detected; green trace), at 85% D2O where [2H]-NPM1 is contrast matched (p14ARFΔH1-3 detected; blue trace), and p14ARFΔH1-3-NPM1 condensate at 100% D2O for full scattering intensity (NPM1 and p14ARFΔH1-3 detected; gray trace). All curves are offset for clarity. Scatter points represent the average, the error bars represent the uncertainty derived from the counting statistics of the SANS instrument, as described and cited in the Methods. F Schematic describing condensed NPM1 with extended IDR conformations. G Schematic describing condensed p14ARFΔH1-3 in an extended conformation. H FRAP of NPM1-AF488 within condensates shows that substitution of p14ARF hydrophobic residues to Gly/Ser spacer residues restores NPM1 mobility. Scale bars = 1 µm. I FRAP recovery curves for p14ARF-NPM1 and p14ARFΔH1-3-NPM1 condensates with fits, as described in Methods (statistical significance was assessed by two-sided Wilcoxon rank-sum test, n = 10 curves for each condition, the p-value is shown in the figure). J NPM1-AF488 DApp values extracted from the FRAP recovery curves in panel I (statistical significance was assessed by two-sided Wilcoxon rank-sum test, n = 10 DApp values for each condition, the p-value is shown in the figure).
CV-SANS analysis of p14ARFΔH1-3-NPM1 condensates showed that substitution of the hydrophobic residues in p14ARF abrogates meso-scale ordering of p14ARF molecules within the condensed phase with NPM1 (Fig. 4E). The NPM1 IDRs were in extended conformations, as in condensates with p14ARF (Fig. 4F). However, p14ARFΔH1-3 molecules appeared slightly more expanded as compared to p14ARF, with a scaling exponent, \documentclass[12pt]{minimal}
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\begin{document}$${{{\rm{\upsilon }}}}$$\end{document}υ = 0.82 (Fig. 4G), likely due to an increase in structural disorder.
To test whether the elimination of p14ARF hydrophobic interfaces would enhance NPM1 mobility within condensates, we performed FRAP assays on p14ARF-NPM1-AF488 and p14ARFΔH1-3-NPM1-AF488 condensates (Fig. 4H–J; Supplementary Fig. 9,10). Within condensates containing p14ARFΔH1-3, NPM1-AF488 exhibited significantly greater mobility (Fig. 4I) and faster diffusion (based on apparent diffusion rates, DApp) (Fig. 4J) as compared to condensates containing p14ARF. Taken together, these results show that hydrophobic residues within the p14ARF N-terminus act as stickers23 that mediate self-association, enhance multivalent heterotypic interactions that drive phase separation with NPM1, and promote the meso-scale assembly of p14ARF molecules, thus restricting NPM1 translational diffusion.
p14ARF reduces nucleolar NPM1 mobility and cell proliferation
NPM1 localizes p14ARF in nucleoli to inhibit it from engaging other binding partners and activating anti-proliferative pathways4. Given that NPM1 usually forms dynamic, liquid-like condensates27 and in the absence of denaturants, purified p14ARF rapidly precipitates from solution38, we reasoned that p14ARF and NPM1 form condensates that block p14ARF aggregation by capturing it within the gel-like interaction network of the meso-scale assembly. This is akin to NPM1’s role as a chaperone for misfolded proteins in the nucleolus during cellular stress39. On the other hand, overexpression of p19Arf promotes NPM1 degradation13 and the assembly of high molecular weight p19Arf-containing complexes2. Based on these observations, we reasoned that an abundance of NPM1 is needed to form stable p14ARF-NPM1 complexes in nucleoli and limit the potential for p14ARF homo- and hetero-oligomerization with other nucleolar biomacromolecules. Therefore, we next asked whether expression of p14ARF alters the dynamics of NPM1 in nucleoli.
We addressed this question using the human DLD-1 colorectal adenocarcinoma cell line, which harbors transcriptionally inactive, mutant p53 (p53S241F)40,41 and is effectively p14ARF-null due to promotor hypermethylation42. We performed CRISPR-Cas9 editing to insert the gene for monomeric, enhanced green fluorescent protein (mEGFP)43 at the 3’-end of both alleles of the NPM1 gene, leading to expression of C-terminally mEGFP-tagged NPM1 at endogenous levels (NPM1-GFP; Supplementary Table 1), termed DLD-1NPM1-G cells. Next, we performed lentiviral transduction of DLD-1NPM1-G cells to enable doxycycline-inducible expression of p14ARF fused at the C-terminus to the monomeric, near-infrared fluorescent protein, miRFP670 (p14ARF-iRFP; Supplementary Table 1)44. As expected, following doxycycline induction, p14ARF-iRFP localized to nucleoli with NPM1-GFP (Fig. 5A). High-throughput imaging of DLD-1NPM1-G nucleoli showed that nucleolar NPM1-GFP and p14ARF-iRFP levels were anti-correlated (Fig. 5B; Supplementary Figs. 11 and 12A, B).Fig. 5p14ARF reduces nucleolar NPM1 diffusion in a concentration dependent manner.A Zoomed in regions from fluorescence microscopy images of live DLD-1NPM1-G (clone B11) cells, before and after 48 h of doxycycline induced p14ARF-iRFP expression. Scale bars = 2 µm. B Z-score analysis of NPM1-GFP and p14ARF-iRFP levels in DLD-1NPM1-G cells, showing that p14ARF and NPM1 levels are anti-correlated (statistical significance was assessed by two-sided Mann–Whitney U-test, n = 2272, 122, 54 cells, p-values are shown in the figure) C FRAP curves with fits, as described in Methods, for cells sorted from the DLD-1NPM1-G population shown in B. The curves on the left are from a cell expressing a high level of nucleolar NPM1 (clone F6; green trace) and a low level of nucleolar p14ARF (clone F6; blue trace). The curves on the right are from a cell expressing a low level of nucleolar NPM1 (clone G2; green trace) and a high level of nucleolar p14ARF (clone G2; blue traces). In unsorted DLD-1NPM1-G cells, D The DApp and E mobility for nucleolar NPM1-GFP and p14ARF-iRFP (small green and blue transparent markers, respectively, n = 45 cells) are reduced as nucleolar p14ARF-iRFP levels increase. Reductions also occur as the duration of p14ARF-iRFP expression is extended (large opaque markers; scatter points represent the mean and error bars represent the standard deviation, where n = 20 cells). F A schematic describing the correlated reductions in p14ARF and NPM1 dynamics and their assembly into large molecular weight complexes within the granular component (GC) of the nucleolus.
We next performed FRAP of p14ARF-iRFP and NPM1-GFP in DLD-1NPM1-G cells (Supplementary Figs. 9, and 12C–E). Consistent with our in vitro results, we observed a substantial reduction in DApp and mobility values for NPM1-GFP with increasing p14ARF-iRFP levels (Supplementary Fig. 13A–G). Similarly, DApp and mobility values for p14ARF-iRFP itself decreased as its levels increased (Supplementary Fig. 13H,I). To assess the dependence of this effect on the level of p14ARF-iRFP expression, we first used flow cytometry to isolate DLD-1NPM1-G clones that expressed p14ARF-iRFP at different levels (Supplementary Fig. 14A). Consistent with our observations with unsorted DLD-1NPM1-G cells, expression of p14ARF-iRFP in the isolated DLD-1NPM1-G clones caused dose-dependent reductions in DApp and mobility for NPM1-GFP, which was correlated with values for p14ARF-iRFP (Fig. 5C–E, Supplementary Fig. 14B,C). We then monitored p14ARF-iRFP and NPM1-GFP diffusion for two clones (termed G2 and B11) before, 24 h, and 48 h after doxycycline induction of p14ARF-iRFP expression. Both DLD-1NPM1-G clones showed significant reductions in the DApp value for p14ARF-iRFP and NPM1-GFP within 24 h, which persisted after 48 h of p14ARF-iRFP expression (Supplementary Fig. 14D,E). Furthermore, NPM1-GFP mobility was reduced in both DLD-1NPM1-G cell clones at the 48-hour time point (Supplementary Fig. 14F,G). Consistent with previous reports of p14ARF expression in p53-null cell lines6,45, expression of p14ARF-iRFP correlated with reduced viability of DLD-1NPM1-G cells in a dose- and time-dependent manner (Supplementary Fig. 13H,I).
We hypothesized that the reductions in diffusion for p14ARF-iRFP and NPM1-GFP are dependent on hydrophobic residues within p14ARF’s N-terminal β-strands and α-helix and tested this by expressing miRFP670-tagged p14ARFΔH1-3 (p14ARFΔH1-3-iRFP) in DLD-1NPM1-G cells (Supplementary Fig. 15A). Using high-throughput imaging, we did not observe a reduction in nucleolar NPM1 levels with increasing p14ARFΔH1-3-iRFP levels (Supplementary Fig. 15B,C). We next performed FRAP of p14ARFΔH1-3-iRFP and NPM1-GFP in DLD-1NPM1-G cells and observed no apparent reduction in diffusion rate or mobility for p14ARFΔH1-3-iRFP or NPM1-GFP with increasing p14ARF ΔH1-3-iRFP levels (Supplementary Fig. 15D,E). We further used single-cell sorting to identify DLD-1NPM1-G clones that expressed p14ARFΔH1-3-iRFP at varied levels (Supplementary Fig. 15F). We next performed FRAP assays to monitor p14ARFΔH1-3-iRFP and NPM1-GFP diffusion over the course of two days, for clones C10 and H5, which expressed p14ARFΔH1-3-iRFP levels comparable to p14ARF-iRFP in clones G2 and B11. In contrast to results with wild-type p14ARF-iRFP, for clones C10 and H5, DApp values for NPM1-GFP remained relatively constant after induced expression of p14ARFΔH1-3-iRFP (Supplementary Fig. 15G,H), reduced NPM1-GFP mobility was not observed (Supplementary Fig. 14I,J), and importantly, cell proliferation was not significantly reduced (Supplementary Fig. 15K,L). Indeed, during enforced expression of p14ARFΔH1-3-iRFP, DLD-1NPM1-G cells proliferated to the same extent as during expression of miRFP670 (iRFP) alone (Supplementary Fig. 15M). However, nucleolar partitioning of p14ARFΔH1-3-iRFP was reduced relative to that of p14ARF-iRFP (Supplementary Fig. 15N). Thus, in agreement with our observations of in vitro p14ARF-NPM1 condensates, hydrophobic residues within the p14ARF N-terminus enhance nucleolar partitioning and mediate interactions in nucleoli that restrain p14ARF-iRFP and NPM1-GFP dynamics (Fig. 5F).
Discussion
The thermodynamics of multicomponent phase separation dictates that an expansion in the number of biomolecular interaction modes within a system can give rise to high-dimensional phase behavior37,46. The partitioning of an individual component is governed by its net interaction strength, and can me modulated by its concentration and those of other components of the condensate network37. For the p14ARF-NPM1 phase transition, heterotypic interactions (p14ARF-NPM1) and p14ARF homotypic interactions, mediated by N-terminal hydrophobic residues, drive phase separation. Accordingly, the p14ARF ΔH1-3-NPM1 phase transition was characterized by an increased Csat and ΔGtr for NPM1. The introduction of competitive homotypic interactions, i.e., p14ARF homotypic interactions which are stronger than heterotypic interactions with NPM1, creates a mechanism whereby NPM1 partitioning can be modulated by p14ARF. By tuning the condensed phase p14ARF composition such that p14ARF homotypic interactions exceed heterotypic interactions with NPM1, the NPM1 component becomes destabilized. This was observed for in vitro condensates, where additions of p14ARF to NPM1 above a 1:1 molar ratio increased the NPM1 ΔGtr.
We previously showed that the diffusion of NPM1 within condensates containing p14ARF is characteristically slow and a large population of NPM1 molecules are immobile. This immobile form of NPM1 constitutes one of the two components of the p14ARF-NPM1 condensate network26, with hydrophobic p14ARF constituting the other. For the NPM1 component, using CV-SANS, we found that the orientation of the NPM1 molecules within the condensed phase is disordered and NPM1 IDRs assume expanded conformations within a characteristic correlation length, which is close to the radius of gyration in solution. Further, using NMR, we showed that the NPM1 IDR is also disordered. However, fluctuations between IDR conformations were attenuated relative to those measured in solution. This retention of structural disorder and conformational dynamics despite immobilization within the condensate network may underlie the effect of p14ARF concentration on the observed NPM1 partitioning. This structural scenario also supports the multifaceted chaperone function of NPM1 in nucleoli, which requires NPM1 to respond dynamically to protein unfolding stress39. The dynamic disorder exhibited by NPM1 within condensates stands in contrast to the static disorder47 displayed by the N-terminal portion of p14ARF, which appeared effectively “frozen” on the ssNMR timescale, in slow exchange between a large population of expanded conformations and a small population of collapsed conformations. Using CV-SANS we found that the bulk of p14ARF assumes expanded conformations that are ordered into meso-scale assemblies. This structural organization results from the p14ARF homotypic interaction network, as evidenced by the lack of long-range ordering within p14ARF ΔH1-3-NPM1 condensates and provides a basis for the characteristically slow diffusion measured for condensed phase p14ARF and NPM126.
In DLD-1NPM1-G nucleoli, the apparent concentrations of p14ARF and NPM1 were anti-correlated, with significantly reduced NPM1 levels observed at elevated p14ARF levels, suggesting that p14ARF has a destabilizing effect on NPM1’s heterotypic interactions in nucleoli. For example, p14ARF may induce reentrant phase behavior in the GC, like Arg-rich di-peptide repeat proteins (DPRs), which induce dissolution of nucleoli19. This trend was not observed for p14ARFΔH1-3 and partitioning of p14ARFΔH1-3 into DLD-1NPM1-G nucleoli was significantly lower than for wild type p14ARF. Furthermore, NPM1 and p14ARF experienced significantly reduced diffusion at elevated nucleolar p14ARF levels. Taken together, hydrophobic interactions enhance partitioning of p14ARF into nucleoli and drive the assembly of large, slowly diffusing complexes containing NPM1. Importantly, p14ARF intoxicates DLD-1NPM1-G cells as its levels rise, consistent with its tumor suppressor activity in response to oncogene activation1. This toxicity may stem from reduced NPM1 levels or mobility, which may impair NPM1 nucleolar function, e.g., ribosomal subunit maturation and export48. In addition, p14ARF may engage other nucleolar biomolecules, e.g. ribosomal proteins or rRNA2,49. Conserved hydrophobic and Arg-rich motifs within the N-termini of p14ARF and p19Arf (termed Arf-motifs14) are required for p19Arf dependent pre-rRNA processing defects45. Arf-motifs are also required for binding the central acidic IDR of HDM2, and localizing HDM2 to nucleoli14. Interestingly, Arf-motif peptides form soluble, β-strand-rich structures upon binding cognate acidic motifs in HDM214,50–52. Adoption of secondary structure within Arf may thus be a common mechanism underlying its interactions with acid-tract-containing, protein binding partners.
Methods
Cell lines
The following cell lines were purchased from American Type Culture Collection (ATCC): DLD-1 (male, adult, age not reported, Dukes’ type C colon cancer), DLD-1 cells were cultured in RPMI 1640 medium (ThermoFisher) supplemented with 10% fetal bovine serum and 100 U/mL penicillin/streptomycin. The DLD-1 cells harboring doxycycline-inducible p14ARF-miRFP670, p14ARF ΔH1-3-miRFP670, miRFP670, were maintained in RPMI 1640 medium supplemented with 10% Tet system approved fetal bovine serum (ThermoFisher), and 250 µg/ml G418. All cell lines were incubated at 37 °C in a humidified incubator with 5% CO2. Gene edited cell lines were authenticated by short tandem repeat (STR) profiling. Cells were tested negative for mycoplasma by the e-Myco PLUS Mycoplasma PCR Detection Kit (Bulldog Bio).
Escherichia coli strains
Escherichia coli BL21(DE3) cells were used to produce recombinant proteins. NEB Stable Competent Escherichia coli cells (New England Biolabs) were used when subcloning genes into lentiviral vectors. All other vectors were transformed to DH5α competent cells (taxid: 668369). The NEB Stable cells and the other E. coli strains were grown at 30 oC and 37 °C, respectively.
Plasmid and cloning methods
For E. Coli expression of the recombinant proteins including NPM1 and wild-type p14ARF, their DNA coding sequences were subcloned to the pET-28a(+) plasmid (EMD Biosciences) as previously described18,26. The DNA sequence encoding the p14ARFΔH1-3 mutant was de novo synthesized as gBlocks (Integrated DNA Technologies) and subcloned into pET-28a(+) using the BamHI and HindIII sites. The protein sequence of the p14ARFΔH1-3 mutant is provided in Supplementary Table 1. To express proteins tagged with the monomeric, near-infrared fluorescent protein, miRFP67044, we synthesized the cDNAs of miRFP670, and p14ARF or p14ARFΔH1-3 C-terminally fused with miRFP670 following a (GGS)5 linker. These were subcloned into the NheI and SalI restriction sites of the pCDH-PGK vector, a gift from Kazuhiro Oka (Addgene plasmid # 72268; http://n2t.net/addgene:72268; RRID: Addgene_72268). The protein sequences of these constructs are provided in Supplementary Table 1. The coding regions were then PCR-amplified with a common pair of primers (forward: 5’-CACCCATTCTGCACGCTTCAAAAG-3’; reverse: 5’-CCACATAGCGTAAAAGGAGCAAC-3’). The PCR products were subsequently TOPO cloned into the pENTR vector using the pENTR/SD/D-TOPO Cloning Kit (ThermoFisher). All plasmid constructs were verified with DNA sequencing performed by Hartwell Center DNA Sequencing Core at St. Jude Children’s Research Hospital and by Massachusetts General Hospital CCIB DNA core.
Expression and purification of recombinant proteins
Recombinant poly-histidine-tagged NPM1 in pET28a (+) (Novagen) were expressed in BL21 (DE3) Escherichia coli cells (Millipore Sigma, Burlington, MA, USA) grown at 37 °C in LB medium supplemented with 30 µg/ml of Kanamycin. For isotopic labeling to generate [13C,15N]-NPM1, cells were grown in MOPS-based minimal media containing [U13C6]-D-glucose and 15NH4Cl (Cambridge Isotope Laboratories)53. At OD600nm = 0.8, 0.5 mM Isopropyl β-D-1 thiogalactopyranoside (IPTG) was added, cells were incubated at 37 °C for an additional 3 h and harvested by centrifugation at 3800 × g at 4 °C. NPM1 was purified from the soluble lysate fraction using Ni-NTA affinity chromatography. Affinity tags were removed via proteolytic cleavage with tobacco etch virus (TEV) protease and purified using a C4 HPLC (Higgins Analytical, Mountain View, CA, USA) with a H2O/CH3CN/0.1% trifluoroacetic acid solvent system. NPM1 constructs were refolded by resuspending lyophilized protein in 6 M guanidine HCl and dialyzing against 10 mM Tris, 150 mM NaCl, 2 mM dithiothreitol (DTT), pH 7.5 buffer. Aliquots of NPM1 constructs were flash frozen and stored at −80 °C. For the production of [2H]-NPM1 used in SANS studies, cells were cultured in Enfor’s minimal media54 with 70% D2O (Cambridge Isotope Laboratories), yielding a 52% deuteration level19. Preparation of Alexa Fluor 488 conjugated NPM1 was performed as described27. Briefly, Alexa Fluor 488 (Thermo Fisher Scientific, Waltham, MA, USA) was conjugated to NPM1 at Cys104 (NPM1-AF488) following the manufacturer’s protocol. To generate NPM1 pentamers labeled at a single subunit, fluorescently labeled NPM1-AF488 monomers were mixed with unlabeled NPM1 monomers at 1:9 ratio in 6 M guanidine HCl and refolded in dialysis against 10 mM Tris, 150 mM NaCl, 2 mM DTT, pH 7.5.
Recombinant p14ARF proteins were prepared as described26. Briefly, p14ARF and p14ARFΔH1-3 were expressed in E. coli BL21 cells grown at 37 °C in 30 µg/ml Kanamycin supplemented LB medium. For isotopic labeling to generate [U13C,15N]-p14ARF, cells were grown in MOPS-based minimal media containing [U13C6]-D-glucose and 15NH4Cl (Cambridge Isotope Laboratories)53. For [U13C]-p14ARF and [15N]-p14ARF labeled p14ARF, [U13C6]-D-glucose/NH4Cl and D-glucose/15NH4Cl were used, respectively. At OD600nm = 0.8, 0.5 mM IPTG was added, cells were incubated at 37 °C for an additional 3 h and harvested by centrifugation at 3800 × g at 4 °C. Cells were resuspended in 50 mM Tris pH 8.0, 500 mM NaCl, 5 mM β-mercaptoethanol, and one SIGMAFAST protease inhibitor cocktail tablet (Sigma) and disrupted by sonication. The lysate was cleared by centrifugation at 30,000 × g at 4 °C and Urea was added to a final concentration of 6 M; this fraction was set aside. In parallel, the cell pellet was resuspended in 6 M Guanidine HCl, 0.1% Triton X-100, 5 mM β-mercaptoethanol and subjected to mechanical disruption followed by sonication. This fraction was cleared by centrifugation at 30,000 × g at 4 °C and the supernatant was removed, combined with the initial lysate, and purified by Ni-NTA-affinity chromatography on an ÄKTA FPLC (GE) using a linear gradient of 50 mM Tris pH 8.0, 500 mM NaCl, 5 mM β-mercaptoethanol and 500 mM Imidazole and further purified using C4 HPLC (Higgins Analytical, Mountain View, CA, USA) with a H2O/CH3CN/0.1% trifluoroacetic acid solvent system.
To generate calibration curves for mEGFP and miRFP670 fluorescence, recombinant poly-histidine-tagged mIRFP670 and mEGFP in pET28a (+) (Novagen) were expressed in BL21 (DE3) Escherichia coli cells (Millipore Sigma, Burlington, MA, USA). Cells were grown at 37 °C in LB medium supplemented with 30 µg/ml of Kanamycin. At OD600nm = 0.8, 0.5 mM Isopropyl β-D-1 thiogalactopyranoside (IPTG) was added, cells were incubated at 37 °C for 3 h and harvested by centrifugation at 3800 × g at 4 °C. Proteins were purified from the soluble lysates fraction using Ni-NTA affinity chromatography. Affinity tags were removed via proteolytic cleavage with tobacco etch virus (TEV) protease and purified using a S75 10/300 (GE) gel filtration column on an ÄKTA FPLC (GE). Biliverdin HCl (Sigma-Aldrich) was dissolved into PBS, added to mIRFP670 at a 2.5-fold molar excess and incubated at 37oC for 3hrs. Excess biliverdin was removed by buffer exchange using a centrifugal filtration device.
Condensate formation for imaging
To prepare p14ARF-NPM1 and p14ARFΔH1-3-NPM1 condensates for fluorescence microscopy analysis, the recombinant p14ARF proteins (p14ARF and p14ARFΔH1-3) were resuspended from lyophilized powders using 100% dimethyl sulfoxide (DMSO) and added directly to solutions of NPM1, at room temperature, such that the final NPM1 concentrations were 10 µM. The final buffer contained 10 mM Tris pH 7.5, 150 mM NaCl, 2 mM DTT, 1.67% DMSO. Condensate suspensions were incubated for 1 hr at room temperature before being transferred to 16-well CultureWell chambered slides (Grace BioLabs, Bend,OR,USA) pre-coated with PlusOne Repel Silane ES (GE Healthcare, Pittsburgh, PA, USA) and Pluronic F-127 (Sigma- Aldrich, St. Louis, MO, USA). Images were acquired on a 3i Marianas spinning disk confocal microscope (Intelligent Imaging Innovations Inc., Denver, CO, USA) using a 100x oil immersion objective (N.A. 1.4).
Small-angle neutron scattering
SANS experiments were performed on the extended q-range small-angle neutron scattering (EQ-SANS) beam line at the Spallation Neutron Source (SNS) at the Oak Ridge National Laboratory (ORNL). The detector was set at 4 m from the sample position. The choppers ran at 30 Hz in frame-skipping mode to give two wavelength bands: 2.5 Å to 6.1 Å and 9.4 Å to 13.1 Å. This configuration provided a q-range from ~0.004 Å−1 < q <~0.45 Å−1. The source aperture was 25 mm diameter and the sample aperture was 10 mm diameter.
To prepare p14ARF-NPM1 condensates for CV-SANS analysis recombinant p14ARF & p14ARFΔH1-3 proteins were resuspended from lyophilized powders in 100% deuterated dimethyl sulfoxide (DMSO) and added directly to solutions of NPM1 at room temperature (~23 °C) to induce formation of phase-separated condensates. All samples contained 10 mM sodium phosphate pH 7, 150 mM NaCl, 2 mM TCEP. For condensate formation, p14ARF proteins and NPM1 were 40 µM. Measurement of isolated [2H]-NPM1 was performed on a 50 µM solution. Full scatter measurements were performed in buffer containing 100% D2O and using protonated proteins. For contrast variation measurements, the H2O/D2O ratios were adjusted to 84.9% D2O to match [2H]-NPM1, 44.7% for p14ARF, and 49.6% for p14ARFΔH1-3. The match point for NPM1 was determined experimentally19. Due to the instability of p14ARF in solution, the match points for p14ARF and p14ARFΔH1-3 were calculated using the MULCh contrast calculator tool55. The samples were loaded into 2 mm pathlength circular-shaped quartz cuvettes (Hellma USA, Plainville, NY) and SANS measurements were performed at 25 ˚C while the samples rotated on a tumbler to prevent droplets from settling out of suspension. Data reduction was performed using MantidPlot56. The measured scattering intensities were corrected for the detector sensitivity, the scattering contribution from the buffer and empty cells and re-scaled to an absolute scale using a calibrated standard57.
For p14ARF-NPM1 condensates under full scattering conditions, the scattering curve was fit to a broad peak model17(Eq 1):1\documentclass[12pt]{minimal}
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For p14ARFΔH1-3-NPM1 condensates, all scattering curves were fit to Eq. 3.
Condensate formation for NMR analysis
To prepare p14ARF-NPM1 condensates for NMR analysis, recombinant unlabeled and isotopically enriched p14ARF proteins (including [U13C,15N]-p14ARF, [U13C]-p14ARF and [15N]-p14ARF) were resuspended from lyophilized powders in 100% deuterated dimethyl sulfoxide (DMSO-d6) and added directly to solutions of NPM1 to induce formation of phase-separated p14ARF-NPM1 condensates. These condensates were formed at room temperature (~23 °C), such that the final p14ARF and NPM1 concentrations were 20 µM. For assignment of p14ARF by solution-state NMR, a condensed phase was prepared by mixing 50 µM [13C,15N]-p14Arf and 50 µM NPM-IDR. The final buffer contained 10 mM sodium phosphate pH 7.0, 150 mM NaCl, 2 mM TCEP, 0.015% NaN3, 1.67% DMSO-d6, 7% D2O. Low concentrations of DMSO-d6 have no effect on the structure of NPM1 as confirmed previously by solution-state NMR26. Following phase separation, samples were incubated for 20 min at room temperature. Samples were then ultracentrifuged at 436,000 × g for 2 h at 4 oC to pellet the condensates. The light phases were removed prior to NMR analysis.
Solution-state NMR spectroscopy
Solution-state NMR experiments were performed on Bruker AVANCE NEO spectrometers. Measurements of p14ARF were made on spectrometers operating at 14.1 T and 18.8 T (1H Larmor frequencies of 600 MHz and 800 MHz, respectively) using 5 mm triple-resonance 1H/13 C/15 N TCI and 13C-optimized TXO cryo-probes. Measurements of NPM1 were made on a spectrometer operating at 18.8 T, equipped with a TXO cryoprobe optimized for 13C. Spectra were processed in Topspin 4.0 or NMRPipe and analyzed in Sparky within the NMRBox virtual environment59.
The concentration of p14ARF within the p14ARF-NPM1 condensed phase is ~200 µM26, which lies close to the limit of detection for most triple resonance experiments needed to make backbone assignments60. Therefore, we utilized a condensate containing p14ARF and the NPM1 IDR (amino acids 119–240), which we found contains ~1 mM p14ARF (Supplementary Fig. 3A-F). For backbone resonance assignment of [13C,15N]-p14ARF within the condensed phase with NPM1 IDR, 2D and 3D spectra60–73, were collected at room temperature (298 K) and optimized for signal to noise and acquisition times. 1H and 15N chemical shift assignments were then transferred onto 2D-TROSY-HSQC spectra (transverse relaxation optimized spectroscopy) collected for the [13C,15N]-p14ARF-NPM1 condensate (Supplementary Fig. 3G). Both spectra were nearly identical (Supplementary Fig. 3H).
In solution measurements of NPM1 structure and dynamics were performed on 65 µM [2H, 15N]-NPM1 in 10 mM sodium phosphate pH=7.0, 150 mM NaCl, 2 mM DTT, 10% D2O at 25oC at room temperature (298 K) at 800 MHz. Chemical shift assignments for NPM1 were transferred from published values19,26. 2D 1H-15N TROSY-HSQC61, R174,75, R274,75 and 15N-CPMG76,77 experiments were optimized for signal to noise and acquisition times. 1H-15N NOE values were calculated as the ratio between peak intensities in spectra recorded with and without 1H saturation. The 15N relaxation rates, R1 and R2, were determined by fitting cross-peak intensities, measured as a function of variable delay periods, to a single-exponential decay. 15N-CPMG relaxation dispersion was fitted using the protein dynamics toolset in the Bruker Dynamics Center 2.5.6 with the following fitted function alternatives:5\documentclass[12pt]{minimal}
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\begin{document}$${{{\rm{f}}}}\left(x\right)={{{{\rm{R}}}}}_{2{{{\rm{o}}}}}+\frac{{{{\rm{\phi }}}}}{{{{{\rm{K}}}}}_{{{{\rm{ex}}}}}}\left[1-\frac{x}{{{{{\rm{K}}}}}_{{{{\rm{ex}}}}}}\tanh \left(\frac{{{{{\rm{K}}}}}_{{{{\rm{ex}}}}}}{x}\right)\right]$$\end{document}fx=R2o+ϕKex1−xKextanhKexx7\documentclass[12pt]{minimal}
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\begin{document}$${{{\rm{f}}}}\left(x\right)={{{{\rm{R}}}}}_{2{{{\rm{o}}}}}+{{{{\rm{K}}}}}_{{{{\rm{ex}}}}}\left[1-\frac{\sin \left(\Delta {{{\rm{\omega }}}}x\right)}{\Delta {{{\rm{\omega }}}}x}\right]$$\end{document}fx=R2o+Kex1−sinΔωxΔωxwhere \documentclass[12pt]{minimal}
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\begin{document}$$\Delta {{{\rm{\omega }}}}$$\end{document}Δω is the chemical shift difference between states A and B, and \documentclass[12pt]{minimal}
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\begin{document}$${{{\rm{\phi }}}}={{{{\rm{P}}}}}_{{{{\rm{A}}}}}{{{{\rm{P}}}}}_{{{{\rm{B}}}}}{\Delta {{{\rm{\omega }}}}}^{2}$$\end{document}ϕ=PAPBΔω2. Systematic errors were estimated based on peak intensities. All repetition experiments were assessed to calculate the largest difference in peak intensities per peak, which was used as a systematic error per peak for each mixing time. Fit parameter error estimation was performed using Monte-Carlo simulation. Fitted parameters were calculated with a 95% confidence interval.
Solid-state NMR spectroscopy
Solid-state NMR experiments were performed on a Bruker Avance NEO spectrometer operating at 14.1 T (1H Larmor frequency of 600 MHz) using a Bruker MAS CryoProbeTM, a cryogenically cooled magic-angle spinning (MAS) triple resonance (HCN) probe head78. The samples were packed in specially designed 3.2 mm MAS rotors with Teflon inserts to ensure proper centering of the p14ARF-NPM1 condensate samples. Detailed description of the acquisition parameters can be found in Supplementary Table 4. In general, all the MAS experiments were performed at MAS speeds between 10–15 kHz. Typical radio-frequency (RF) fields used in the experiments for the 1H, 13C and 15N channels were 80–100 kHz, 60–65 kHz and 40 kHz, respectively. Double cross polarization (CP), dipolar assisted rotational resonance (DARR) and COmbined R2vn-Driven (CORD) mixing requires lower RF fields and are reported in Supplementary Table 4. Contact times for CP and double CP were typically 1 ms with recycle delays of 2 s. The CP-MAS NMR acquisition times varying from 1–2 h for two-dimensional (2D) NCO79,80 and NCaCX experiments81–83 2D experiments) to several hours (7–10 h) for the 2D CC correlation experiments (with DARR, CORD or insensitive nuclei enhanced by polarization transfer (INEPT) mixing). Three-dimensional (3D) experiments were recorded over 1.5 (3D NCOCX79,80) and 2.5 days (3D NCaCx, through co-addition of two experiments of one day each and another of 10 h; 34 h of acquisition in total). The NHHC experiment used to probe contacts between the 15N-p14ARF and the 13C-p14ARF molecules within condensates with NPM1, based on proton spin diffusion between 15N-coupled amide protons (in one p14ARF molecule and 13C-coupled aliphatic protons in another p14ARF molecule), required the longest experimental time: two spectra acquired with identical parameters were co-added; these were acquired for 3 days and 9 h, respectively. All spectra were referenced using adamantane (13C δ = 38.5 ppm).
Cellular imaging
Fluorescence microscopy imaging for analysis of live DLD-1NPM1-G cell nucleoli was performed on a Zeiss LSM 980 Airyscan 2 inverted microscope, with a 40x Plan Apochromat (N.A. 1.1) objective (mEGFP lex = 492 nm, miRFP670 lex = 653 nm; lem = 300–720 nm). High-throughput fluorescence imaging of virally transduced DLD-1NPM1-G clones and FRAP experiments were performed using a 3i Marianas spinning disk confocal microscopes (Intelligent Imaging Innovations Inc., Denver, CO, USA) with a 40x air objective and 100x oil immersion objective (N.A. 1.4), respectively. Cells were maintained at 37 oC, 5% CO2 within an enclosed incubator during live cell imaging experiments.
Endogenously-tagged cell line generation
Endogenously C-terminally mEGFP-tagged NPM1 in DLD-1 cells (DLD-1NPM1-G) were generated using CRISPR-Cas9 technology in the Center for Advanced Genome Engineering (St. Jude Children’s Research Hospital). The donor homology directed repair (HDR) template containing a (GGS)5 linker DNA coding sequence upstream of the mEGFP sequence flanked by ~800 bases homology arms was synthesized and blunt-end cloned into pUC57 (the plasmid pUC57_NPM1-mEGFP_HDR donor repair template, CAGE117.g1.meGFP donor) by Bio Basic. Briefly, 500,000 DLD1 cells were transiently co-transfected with precomplexed ribonuclear proteins (RNPs) consisting of 100 pmol of chemically modified sgRNA (CAGE117.NPM1.g1, 5ʹ-UCCAGGCUAUUCAAGAUCUC-3ʹ, Synthego), 33 pmol of Cas9 protein (St. Jude Protein Production Core), 500 ng of plasmid donor. The transfection was performed via nucleofection (Lonza, 4D-Nucleofector™ X-unit) using solution P3 and program CA-137 in a small (20 µl) cuvette according to the manufacturer’s recommended protocol. Single cells were sorted based on viability five days post-nucleofection into 96-well plates containing prewarmed media and clonally expanded. Clones were screened and verified for the desired modification using PCR-based assays and confirmed via sequencing. Final clones were authenticated using the PowerPlex Fusion System (Promega) performed at the Hartwell Center (St. Jude).
The sequence of the HDR donor template for NPM1-mEGFP knock-in is (5’-3’; lowercase: homology arms; uppercase: mEGFP; bold uppercase: (GGS)5 linker; italics uppercase: silent blocking mutations):
ctcaggtgatccaacaccttggcctcttaaagtgctgggattacaggcatgagccaccatgcctggccagctgttttttttgttggtttgttttttgttttggtacccatctgtagtgtgatcttggctcactgcaacctctgcctcttgggctcaggcagtcctcccacctcagcctcctgagtagctgggcctcctgtagttgcacaccaccaagcctggctaatttttgcatttttagtagacagggtttcaccatgttgcccaggctggtctcaaattcctgagctgaagtgatctgcccgcctcagtctcccaaagtgtagggattacaggcgtgagccaccatgcctagcctcagcatatagttttttctaaatgtacacatgcccaggcacacatgcacaggcaattcagaataagtttctggtgtttatgtaactttatttgccaaatctggccaactctaaagctgatctcgggagatgaagttggaagtaacattggccatatgggtctctgttctttctgttgatttccttaagtaaataatgctaaactattaaataattattagtatattgttcacatttttatgactgattaaagtgtttggaattaaattacatctgagtataaattttcttggagtcatatctttatctagagttaactctctggtggtagaatgaaaaatagatgttgaactatgcaaagagacatttaatttattgatgtctatgaagtgttgtggttccttaaccacatttctttttttttttttccaggctattcaagaCctGtggcaAtggCgAaaAAGCctGGGAGGAAGCGGAGGTTCTGGCGGTAGTGGTGGATCTGGCGGCAGCATGGTTTCCAAGGGCGAAGAACTGTTCACCGGCGTGGTGCCCATTCTGGTGGAACTGGACGGGGATGTGAACGGCCACAAGTTTAGCGTTAGCGGCGAAGGCGAAGGGGATGCCACATACGGAAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCTGTGCCTTGGCCTACACTGGTCACCACACTGACATACGGCGTGCAGTGCTTCAGCAGATACCCCGACCATATGAAGCAGCACGACTTCTTCAAGAGCGCCATGCCTGAGGGCTACGTGCAAGAGCGGACCATCTTCTTTAAGGACGACGGCAACTACAAGACCAGGGCCGAAGTGAAGTTCGAGGGCGACACCCTGGTCAACCGGATCGAGCTGAAGGGCATCGACTTCAAAGAGGACGGCAACATCCTGGGCCACAAGCTCGAGTACAACTACAACAGCCACAACGTGTACATCATGGCCGACAAGCAGAAAAACGGCATCAAAGTGAACTTCAAGATCCGGCACAACATCGAGGACGGCTCTGTGCAGCTGGCCGATCACTACCAGCAGAACACACCCATCGGAGATGGCCCTGTGCTGCTGCCCGATAACCACTACCTGAGCACCCAGAGCAAGCTGAGCAAGGACCCCAACGAGAAGCGGGACCACATGGTGCTGCTGGAATTTGTGACAGCCGCCGGAATCACCCTCGGCATGGATGAGCTGTACAAGTAAgaaaatagtttaaacaatttgttaaaaaattttccgtcttatttcatttctgtaacagttgatatctggctgtcctttttataatgcagagtgagaactttccctaccgtgtttgataaatgttgtccaggttctattgccaagaatgtgttgtccaaaatgcctgtttagtttttaaagatggaactccaccctttgcttggttttaagtatgtatggaatgttatgataggacatagtagtagcggtggtcagacatggaaatggtggggagacaaaaatatacatgtgaaataaaactcagtattttaataaagtagcacggtttctattgacttatttaactgctttatactttgtcaaagaaataattaatgtagttaggaatggcaaatagtcttgtaaaattctatgagaatgtccctgccctccccttcaatattctctctggagctaaccactttttcatcataaggatttagtgctgtgttcccacctcctgatgatagttaacaattattataactatgcaacatgtttccaaatgttccattagacctcctatctgcctattctagcctcacttgcaaagaaaatgtggcatgttaaaacagcttaaaagcagcctttcaacctgtatggttttttccccaggctggagtgcagtggcacaatctcagcttattgcagcttctgcttcttgggttcaagcaggtctcctgcctcagcctcccaagtagctgggattacaggtgtgagccaccagcccggctaatttttgtatttttagtagaga
The three pairs of primers used for PCR were as follows: 5ʹ junction primers, including CAGE117.gen.F2 (forward, 5ʹ-TGTACCTGAGAACCCATTGGC-3ʹ) and CAGE117.junc.meGFP.DS.R2 (reverse, 5ʹ-GTTCACATCCCCGTCCAGTT-3ʹ); 3ʹ junction primers, including CAGE117.junc.meGFP.DS.F2 (forward, 5ʹ-GCTGCCCGATAACCACTACC-3ʹ) and CAGE117.gen.R2 (reverse, 5ʹ-AGGCAGAACATATAAAGGTGCTAAT-3ʹ); and zygosity confirmation primers, including CAGE117.DS.F (forward, 5ʹ-AGTTAACTCTCTGGTGGTAGAATGA-3ʹ) and CAGE117.DS.R (reverse, 5ʹ-CCAAGCAAAGGGTGGAGTTC-3ʹ).
Lentiviral transduction and generation of cell lines
Lentiviral vectors were used to make lentiviral particles by the Vector Development and Production Shared Resource at St. Jude Children’s Research Hospital. Cells were transduced with virus in the presence of 10 µg/ml polybrene (Sigma). For pINDUCER20 lentivirus transduced cells, the selection by G418 (500 µg/ml) lasted until mock-transfected, control cells were completely eliminated, and the cells were constantly maintained in the culture medium containing G418 at 250 µg/ml.
Single-cell cloning
Each population of the virally transduced, G418 resistant cells were sorted one cell/well into three 96-well plates. After growing in G418-containing media for 7ʹ10 days, each viable single colony was further passaged into two corresponding wells in one Nunc 96-well cell culture treated plate (ThermoFisher) and one glass bottom black 96-well plate (Greiner Bio-One, Cat. #655891). The clones in the glass bottom 96-well plates were treated with 1 µg/ml doxycycline to induce the expression of miRFP670-tagged protein in cells, and the expression levels were quantified by measuring miRFP670 fluorescence intensity in the live cells using fluorescence microscopy. Single cell clones in the corresponding wells in the Nunc 96-well plates, which could express miRFP670-tagged protein at high, medium, or low levels, were selected and expanded. As miRFP670 requires the cofactor biliverdin for fluorescence84, the protein expression levels in these single-cell clones were further assessed by immunoblotting analysis.
Immunoblotting
Gel electrophoresis was performed using 25–40 µg protein extracted from TRIzol cell lysates or equal volumes of protein extracts from sucrose gradient fractions in NuPAGE mini protein gels (Invitrogen), transferred for 1.5 h at 30 volt to PVDF Transfer Membrane with low background fluorescence (Millipore). After Ponceau S staining, the membranes were blocked for 1 h in 5% non-fat milk in 1× PBS, then incubated with primary antibodies diluted in 2.5% BSA in PBST solution (1× PBS, 0.2% Tween-20) overnight at 4 °C with gentle agitation. Membranes were rinsed 4× in PBST buffer before incubating in fluorescence conjugated secondary antibodies diluted in 1% non-fat milk in 1× PBS with 0.02% SDS for 45 min at room temperature in the dark. After washing with 4× in PBST, the blots were scanned with the ChemiDoc Imaging System (Bio-Rad). The primary antibodies were used as follows: rabbit monoclonal anti-Cyclophilin B (Cell Signaling, 43603) at 1:1500–1:2000 dilution; rabbit monoclonal anti-GAPDH (Cell Signaling, 5174) at 1:2500 dilution; mouse monoclonal anti-NPM1 (ThermoFisher Scientific, 32-5200) at 1:1000 dillution; mouse monoclonal anti-p14Arf (Cell Signaling, 2407) at 1:1000–1:15000 dilution; mouse monoclonal anti-GAPDH (Santa Cruz, sc-47724) at 1:2500 dilution; and rabbit polyclonal anti-p14Arf (Novus, NB200-111) at 1:2000 dilution.
Cell treatments
Treatment of doxycycline inducible cells was performed with doxycycline at 1 µg/ml or serially diluted from the stock solution of 1 mg/ml for the indicated times. Unless otherwise indicated, single clones of cells were treated with doxycycline at the concentrations as follows: 1000 ng/ml (p14ARF-iRFP clones), 50 ng/ml (p14ARFΔH1-3-iRFP clone H5), 20 ng/ml (iRFP clone H9), or 10 ng/ml (p14ARFΔH1-3-iRFP clone C10, iRFP clone A6). The time course samples were harvested at the same time.
Cell growth assays
Aliquots of cell suspensions were seeded in 96- or 24-well plates at 5000 or 10,000 cells per well, respectively. After culturing for 20–24 h, the cells were counted for the starting time point and/or subjected to treatments as needed, and then cultured for the indicated times. For cell counting, existing culture medium in each well was replaced with fresh culture medium containing 10-fold diluted Cell Counting Kit-8 (CCK-8, APExBIO), and the absorbance at 450 nm was measured after 1–2 h of incubation. Cell growth was calculated as the ratio of A450 at later time points relative to that of the starting time point. The relative cell viability was expressed as the ratio of A450 of the treated versus that of untreated controls cells. Biological replicates were performed separately at different times.
Fluorescence recovery after photobleaching
Analysis of fluorescence recovery after photo-bleaching (FRAP) images to determine the apparent diffusion coefficient (DApp) and percent mobility was performed following a modified version of the protocol from85, using in-house pipelines written in Python (Supplementary Fig. 8). For FRAP in live cells, all images were corrected (\documentclass[12pt]{minimal}
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\begin{document}$${{{{\rm{I}}}}(t)}_{{{{\rm{cell}}}}}$$\end{document}I(t)cell) (Eq. 8), where:8\documentclass[12pt]{minimal}
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\begin{document}$${{{{\rm{I}}}}(t)}_{{{{\rm{corr}}}}}=\frac{{{{\rm{I}}}}\left(t\right)-{{{{\rm{I}}}}(t)}_{{{{\rm{bkgd}}}}}}{{{{{\rm{I}}}}(t)}_{{{{\rm{cell}}}}}-{{{{\rm{I}}}}(t)}_{{{{\rm{bkgd}}}}}}$$\end{document}I(t)corr=It−I(t)bkgdI(t)cell−I(t)bkgd
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\begin{document}$${{{{\rm{I}}}}(t)}_{{{{\rm{corr}}}}}=\frac{{{{\rm{I}}}}\left(t\right)-{{{{\rm{I}}}}(t)}_{{{{\rm{bkgd}}}}}}{{{{{\rm{I}}}}(t)}_{{{{\rm{ref}}}}}-{{{{\rm{I}}}}(t)}_{{{{\rm{bkgd}}}}}}$$\end{document}I(t)corr=It−I(t)bkgdI(t)ref−I(t)bkgd
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\begin{document}$${{{{\rm{R}}}}(t)}_{{{{\rm{norm}}}}}=\frac{{{{\rm{R}}}}\left(t\right)-{{{{\rm{R}}}}}_{{{{\rm{post}}}}-{{{\rm{bleach}}}}}}{\big\langle {{{{\rm{R}}}}\left(t\right)}_{{{{\rm{pre}}}}-{{{\rm{bleach}}}}}\big\rangle -{{{{\rm{R}}}}}_{{{{\rm{post}}}}-{{{\rm{bleach}}}}}}$$\end{document}R(t)norm=Rt−Rpost−bleachRtpre−bleach−Rpost−bleach
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\begin{document}$${{{\rm{R}}}}\left(t\right)=\frac{\left[{{{{\rm{R}}}}}_{{{{\rm{post}}}}-{{{\rm{bleach}}}}}+{{{{\rm{R}}}}}_{\infty }\left(\frac{t}{{t}_{1/2}}\right)\right]}{1+\left(\frac{t}{{t}_{1/2}}\right)}$$\end{document}Rt=Rpost−bleach+R∞tt1/21+tt1/2where, \documentclass[12pt]{minimal}
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\begin{document}$${{{{\rm{R}}}}}_{\infty }$$\end{document}R∞ is the ROI intensity after full recovery. The percent mobility (M) was calculated using Eq 12:12\documentclass[12pt]{minimal}
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\begin{document}$${{{\rm{M}}}}=\frac{\left\langle {{{{\rm{R}}}}}_{\infty }(t)\right\rangle }{\left\langle {{{{\rm{R}}}}\left(t\right)}_{{{{\rm{pre}}}}-{{{\rm{bleach}}}}}\right\rangle }$$\end{document}M=R∞(t)Rtpre−bleachwhere, \documentclass[12pt]{minimal}
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\begin{document}$$\left(\left\langle {{{{\rm{R}}}}}_{\infty }(t)\right\rangle \right)$$\end{document}R∞(t) is the mean ROI intensity of the last ten image frames of the signal plateau region.
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\begin{document}$${{{{\rm{I}}}}}_{{{{\rm{norm}}}}}$$\end{document}Inorm) was performed85,87. The post-bleach image was first normalized using the image frames preceding (\documentclass[12pt]{minimal}
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\begin{document}$${{{{\rm{I}}}}}_{{{{\rm{norm}}}}}=\frac{{{{{\rm{I}}}}}_{{{{\rm{post}}}}-{{{\rm{bleach}}}}}}{{{{{\rm{I}}}}}_{{{{\rm{pre}}}}-{{{\rm{bleach}}}}}}$$\end{document}Inorm=Ipost−bleachIpre−bleach
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\begin{document}$${{{\rm{\varphi }}}}\left(x,y\right)={{{{\rm{F}}}}}_{{{{\rm{i}}}}}{{\mathrm{exp}}}\left[-{{{\rm{K}}}}\exp \left[-\frac{2\left({x}^{2}+{y}^{2}\right)}{{{{{{\rm{r}}}}}_{{{{\rm{e}}}}}}^{2}}\right]\right]$$\end{document}φx,y=Fiexp−Kexp−2x2+y2re2where, (\documentclass[12pt]{minimal}
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\begin{document}$${{{{\rm{r}}}}}_{{{{\rm{e}}}}}$$\end{document}re) is the effective bleach radius. \documentclass[12pt]{minimal}
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\begin{document}$${{{{\rm{D}}}}}_{{{{\rm{App}}}}}$$\end{document}DApp was then calculated using \documentclass[12pt]{minimal}
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\begin{document}$${{{{\rm{t}}}}}_{1/2}$$\end{document}t1/2 and the nominal bleach radius (\documentclass[12pt]{minimal}
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\begin{document}$${{{{\rm{r}}}}}_{{{{\rm{n}}}}}$$\end{document}rn) (Eq. 15).15\documentclass[12pt]{minimal}
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\begin{document}$${{{{\rm{D}}}}}_{{{{\rm{App}}}}}=\frac{{{{{{\rm{r}}}}}_{{{{\rm{e}}}}}}^{2}+{{{{{\rm{r}}}}}_{{{{\rm{n}}}}}}^{2}}{8{{{{\rm{t}}}}}_{1/2}}$$\end{document}DApp=re2+rn28t1/2
Image analysis and quantification
Prior to analysis, images were converted into tiff format using Slidebook 6.0 (Intelligent Imaging Innovations, Gottingen, Germany) or Image J88. Image segmentation was performed using an in-house pipeline written in Python (Supplementary Fig. 10). Segmentation of nuclei and nucleoli were performed using the NPM1 signal; NPM1-GFP fluorescence was used for segmenting live DLD-1NPM1-G cell images. 3D image stacks were first converted to 2D images through maximum intensity projection. Prior to segmentation of nuclei, the Gaussian kernel with variable standard deviation (σ) from scikit-image was first applied (for Airyscan DLD-1NPM1-G cell images σ = 4).
Prior to segmentation of nucleoli, a Gaussian kernel with σ = 0.33 was applied. Segmentation was performed using the multi-Otzu algorithm from scikit-image using 3 classes as input. Nuclear masks were found at the 0th threshold and nucleolar masks were found at the 1st threshold. Masked pixels were then clustered using the density-based spatial clustering of applications with noise (DBSCAN) algorithm as implemented in scikit-learn. Segmented cells along with their nuclear and nucleolar masks were visualized using the imshow function from matplotlib. All segmented cell masks were verified by manual observation and improperly segmented cells were removed prior to quantification.
To quantify the extent of recombinant p14ARF-NPM1 phase separation the index of dispersion (IOD) was calculated for ≥ 4 imaging areas of 512 ×512 pixels (64 ×64 μm), (Eq. 16):16\documentclass[12pt]{minimal}
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\begin{document}$${{{\rm{IOD}}}}=\frac{{{{{\rm{\sigma }}}}}^{2}}{{{{\rm{\mu }}}}}$$\end{document}IOD=σ2μwhere, \documentclass[12pt]{minimal}
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\begin{document}$${{{\rm{\mu }}}}$$\end{document}μ is the mean fluorescence intensity. To quantify the transfer free energy the following was used (Eq. 17):17\documentclass[12pt]{minimal}
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\begin{document}$${\Delta {{{\rm{G}}}}}_{{{{\rm{tr}}}}}=-{{{\rm{RT}}}}\log \left(\frac{{{{{\rm{I}}}}}_{{{{\rm{DP}}}}}}{{{{{\rm{I}}}}}_{{{{\rm{LP}}}}}}\right)$$\end{document}ΔGtr=−RTlogIDPILPwhere, \documentclass[12pt]{minimal}
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\begin{document}$${{{{\rm{I}}}}}_{{{{\rm{DP}}}}}$$\end{document}IDP is the mean dense phase florescence intensity, and \documentclass[12pt]{minimal}
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\begin{document}$${{{{\rm{I}}}}}_{{{{\rm{LP}}}}}$$\end{document}ILP is the mean light phase florescence intensity.
Statistics
The numbers of independent replicates for each experiment are provided in the figure legends. Unless stated in figure legends, all values represent means ± SD. p < 0.05 was considered statistically significant. Asterisks denote statistical significance as follows: n.s. = not significant; ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001; and ∗∗∗∗p < 0.0001. For all box and whiskers plots, each box extends from the first to the third quartiles, with the line or center point representing the median, and the whiskers extending from the box to the furthest point within 1.5x the inter-quartile range. If shown, flier points represent outlier points beyond the whiskers.
Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Cell lines
The following cell lines were purchased from American Type Culture Collection (ATCC): DLD-1 (male, adult, age not reported, Dukes’ type C colon cancer), DLD-1 cells were cultured in RPMI 1640 medium (ThermoFisher) supplemented with 10% fetal bovine serum and 100 U/mL penicillin/streptomycin. The DLD-1 cells harboring doxycycline-inducible p14ARF-miRFP670, p14ARF ΔH1-3-miRFP670, miRFP670, were maintained in RPMI 1640 medium supplemented with 10% Tet system approved fetal bovine serum (ThermoFisher), and 250 µg/ml G418. All cell lines were incubated at 37 °C in a humidified incubator with 5% CO2. Gene edited cell lines were authenticated by short tandem repeat (STR) profiling. Cells were tested negative for mycoplasma by the e-Myco PLUS Mycoplasma PCR Detection Kit (Bulldog Bio).
Escherichia coli strains
Escherichia coli BL21(DE3) cells were used to produce recombinant proteins. NEB Stable Competent Escherichia coli cells (New England Biolabs) were used when subcloning genes into lentiviral vectors. All other vectors were transformed to DH5α competent cells (taxid: 668369). The NEB Stable cells and the other E. coli strains were grown at 30 oC and 37 °C, respectively.
Plasmid and cloning methods
For E. Coli expression of the recombinant proteins including NPM1 and wild-type p14ARF, their DNA coding sequences were subcloned to the pET-28a(+) plasmid (EMD Biosciences) as previously described18,26. The DNA sequence encoding the p14ARFΔH1-3 mutant was de novo synthesized as gBlocks (Integrated DNA Technologies) and subcloned into pET-28a(+) using the BamHI and HindIII sites. The protein sequence of the p14ARFΔH1-3 mutant is provided in Supplementary Table 1. To express proteins tagged with the monomeric, near-infrared fluorescent protein, miRFP67044, we synthesized the cDNAs of miRFP670, and p14ARF or p14ARFΔH1-3 C-terminally fused with miRFP670 following a (GGS)5 linker. These were subcloned into the NheI and SalI restriction sites of the pCDH-PGK vector, a gift from Kazuhiro Oka (Addgene plasmid # 72268; http://n2t.net/addgene:72268; RRID: Addgene_72268). The protein sequences of these constructs are provided in Supplementary Table 1. The coding regions were then PCR-amplified with a common pair of primers (forward: 5’-CACCCATTCTGCACGCTTCAAAAG-3’; reverse: 5’-CCACATAGCGTAAAAGGAGCAAC-3’). The PCR products were subsequently TOPO cloned into the pENTR vector using the pENTR/SD/D-TOPO Cloning Kit (ThermoFisher). All plasmid constructs were verified with DNA sequencing performed by Hartwell Center DNA Sequencing Core at St. Jude Children’s Research Hospital and by Massachusetts General Hospital CCIB DNA core.
Expression and purification of recombinant proteins
Recombinant poly-histidine-tagged NPM1 in pET28a (+) (Novagen) were expressed in BL21 (DE3) Escherichia coli cells (Millipore Sigma, Burlington, MA, USA) grown at 37 °C in LB medium supplemented with 30 µg/ml of Kanamycin. For isotopic labeling to generate [13C,15N]-NPM1, cells were grown in MOPS-based minimal media containing [U13C6]-D-glucose and 15NH4Cl (Cambridge Isotope Laboratories)53. At OD600nm = 0.8, 0.5 mM Isopropyl β-D-1 thiogalactopyranoside (IPTG) was added, cells were incubated at 37 °C for an additional 3 h and harvested by centrifugation at 3800 × g at 4 °C. NPM1 was purified from the soluble lysate fraction using Ni-NTA affinity chromatography. Affinity tags were removed via proteolytic cleavage with tobacco etch virus (TEV) protease and purified using a C4 HPLC (Higgins Analytical, Mountain View, CA, USA) with a H2O/CH3CN/0.1% trifluoroacetic acid solvent system. NPM1 constructs were refolded by resuspending lyophilized protein in 6 M guanidine HCl and dialyzing against 10 mM Tris, 150 mM NaCl, 2 mM dithiothreitol (DTT), pH 7.5 buffer. Aliquots of NPM1 constructs were flash frozen and stored at −80 °C. For the production of [2H]-NPM1 used in SANS studies, cells were cultured in Enfor’s minimal media54 with 70% D2O (Cambridge Isotope Laboratories), yielding a 52% deuteration level19. Preparation of Alexa Fluor 488 conjugated NPM1 was performed as described27. Briefly, Alexa Fluor 488 (Thermo Fisher Scientific, Waltham, MA, USA) was conjugated to NPM1 at Cys104 (NPM1-AF488) following the manufacturer’s protocol. To generate NPM1 pentamers labeled at a single subunit, fluorescently labeled NPM1-AF488 monomers were mixed with unlabeled NPM1 monomers at 1:9 ratio in 6 M guanidine HCl and refolded in dialysis against 10 mM Tris, 150 mM NaCl, 2 mM DTT, pH 7.5.
Recombinant p14ARF proteins were prepared as described26. Briefly, p14ARF and p14ARFΔH1-3 were expressed in E. coli BL21 cells grown at 37 °C in 30 µg/ml Kanamycin supplemented LB medium. For isotopic labeling to generate [U13C,15N]-p14ARF, cells were grown in MOPS-based minimal media containing [U13C6]-D-glucose and 15NH4Cl (Cambridge Isotope Laboratories)53. For [U13C]-p14ARF and [15N]-p14ARF labeled p14ARF, [U13C6]-D-glucose/NH4Cl and D-glucose/15NH4Cl were used, respectively. At OD600nm = 0.8, 0.5 mM IPTG was added, cells were incubated at 37 °C for an additional 3 h and harvested by centrifugation at 3800 × g at 4 °C. Cells were resuspended in 50 mM Tris pH 8.0, 500 mM NaCl, 5 mM β-mercaptoethanol, and one SIGMAFAST protease inhibitor cocktail tablet (Sigma) and disrupted by sonication. The lysate was cleared by centrifugation at 30,000 × g at 4 °C and Urea was added to a final concentration of 6 M; this fraction was set aside. In parallel, the cell pellet was resuspended in 6 M Guanidine HCl, 0.1% Triton X-100, 5 mM β-mercaptoethanol and subjected to mechanical disruption followed by sonication. This fraction was cleared by centrifugation at 30,000 × g at 4 °C and the supernatant was removed, combined with the initial lysate, and purified by Ni-NTA-affinity chromatography on an ÄKTA FPLC (GE) using a linear gradient of 50 mM Tris pH 8.0, 500 mM NaCl, 5 mM β-mercaptoethanol and 500 mM Imidazole and further purified using C4 HPLC (Higgins Analytical, Mountain View, CA, USA) with a H2O/CH3CN/0.1% trifluoroacetic acid solvent system.
To generate calibration curves for mEGFP and miRFP670 fluorescence, recombinant poly-histidine-tagged mIRFP670 and mEGFP in pET28a (+) (Novagen) were expressed in BL21 (DE3) Escherichia coli cells (Millipore Sigma, Burlington, MA, USA). Cells were grown at 37 °C in LB medium supplemented with 30 µg/ml of Kanamycin. At OD600nm = 0.8, 0.5 mM Isopropyl β-D-1 thiogalactopyranoside (IPTG) was added, cells were incubated at 37 °C for 3 h and harvested by centrifugation at 3800 × g at 4 °C. Proteins were purified from the soluble lysates fraction using Ni-NTA affinity chromatography. Affinity tags were removed via proteolytic cleavage with tobacco etch virus (TEV) protease and purified using a S75 10/300 (GE) gel filtration column on an ÄKTA FPLC (GE). Biliverdin HCl (Sigma-Aldrich) was dissolved into PBS, added to mIRFP670 at a 2.5-fold molar excess and incubated at 37oC for 3hrs. Excess biliverdin was removed by buffer exchange using a centrifugal filtration device.
Condensate formation for imaging
To prepare p14ARF-NPM1 and p14ARFΔH1-3-NPM1 condensates for fluorescence microscopy analysis, the recombinant p14ARF proteins (p14ARF and p14ARFΔH1-3) were resuspended from lyophilized powders using 100% dimethyl sulfoxide (DMSO) and added directly to solutions of NPM1, at room temperature, such that the final NPM1 concentrations were 10 µM. The final buffer contained 10 mM Tris pH 7.5, 150 mM NaCl, 2 mM DTT, 1.67% DMSO. Condensate suspensions were incubated for 1 hr at room temperature before being transferred to 16-well CultureWell chambered slides (Grace BioLabs, Bend,OR,USA) pre-coated with PlusOne Repel Silane ES (GE Healthcare, Pittsburgh, PA, USA) and Pluronic F-127 (Sigma- Aldrich, St. Louis, MO, USA). Images were acquired on a 3i Marianas spinning disk confocal microscope (Intelligent Imaging Innovations Inc., Denver, CO, USA) using a 100x oil immersion objective (N.A. 1.4).
Small-angle neutron scattering
SANS experiments were performed on the extended q-range small-angle neutron scattering (EQ-SANS) beam line at the Spallation Neutron Source (SNS) at the Oak Ridge National Laboratory (ORNL). The detector was set at 4 m from the sample position. The choppers ran at 30 Hz in frame-skipping mode to give two wavelength bands: 2.5 Å to 6.1 Å and 9.4 Å to 13.1 Å. This configuration provided a q-range from ~0.004 Å−1 < q <~0.45 Å−1. The source aperture was 25 mm diameter and the sample aperture was 10 mm diameter.
To prepare p14ARF-NPM1 condensates for CV-SANS analysis recombinant p14ARF & p14ARFΔH1-3 proteins were resuspended from lyophilized powders in 100% deuterated dimethyl sulfoxide (DMSO) and added directly to solutions of NPM1 at room temperature (~23 °C) to induce formation of phase-separated condensates. All samples contained 10 mM sodium phosphate pH 7, 150 mM NaCl, 2 mM TCEP. For condensate formation, p14ARF proteins and NPM1 were 40 µM. Measurement of isolated [2H]-NPM1 was performed on a 50 µM solution. Full scatter measurements were performed in buffer containing 100% D2O and using protonated proteins. For contrast variation measurements, the H2O/D2O ratios were adjusted to 84.9% D2O to match [2H]-NPM1, 44.7% for p14ARF, and 49.6% for p14ARFΔH1-3. The match point for NPM1 was determined experimentally19. Due to the instability of p14ARF in solution, the match points for p14ARF and p14ARFΔH1-3 were calculated using the MULCh contrast calculator tool55. The samples were loaded into 2 mm pathlength circular-shaped quartz cuvettes (Hellma USA, Plainville, NY) and SANS measurements were performed at 25 ˚C while the samples rotated on a tumbler to prevent droplets from settling out of suspension. Data reduction was performed using MantidPlot56. The measured scattering intensities were corrected for the detector sensitivity, the scattering contribution from the buffer and empty cells and re-scaled to an absolute scale using a calibrated standard57.
For p14ARF-NPM1 condensates under full scattering conditions, the scattering curve was fit to a broad peak model17(Eq 1):1\documentclass[12pt]{minimal}
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For p14ARFΔH1-3-NPM1 condensates, all scattering curves were fit to Eq. 3.
Condensate formation for NMR analysis
To prepare p14ARF-NPM1 condensates for NMR analysis, recombinant unlabeled and isotopically enriched p14ARF proteins (including [U13C,15N]-p14ARF, [U13C]-p14ARF and [15N]-p14ARF) were resuspended from lyophilized powders in 100% deuterated dimethyl sulfoxide (DMSO-d6) and added directly to solutions of NPM1 to induce formation of phase-separated p14ARF-NPM1 condensates. These condensates were formed at room temperature (~23 °C), such that the final p14ARF and NPM1 concentrations were 20 µM. For assignment of p14ARF by solution-state NMR, a condensed phase was prepared by mixing 50 µM [13C,15N]-p14Arf and 50 µM NPM-IDR. The final buffer contained 10 mM sodium phosphate pH 7.0, 150 mM NaCl, 2 mM TCEP, 0.015% NaN3, 1.67% DMSO-d6, 7% D2O. Low concentrations of DMSO-d6 have no effect on the structure of NPM1 as confirmed previously by solution-state NMR26. Following phase separation, samples were incubated for 20 min at room temperature. Samples were then ultracentrifuged at 436,000 × g for 2 h at 4 oC to pellet the condensates. The light phases were removed prior to NMR analysis.
Solution-state NMR spectroscopy
Solution-state NMR experiments were performed on Bruker AVANCE NEO spectrometers. Measurements of p14ARF were made on spectrometers operating at 14.1 T and 18.8 T (1H Larmor frequencies of 600 MHz and 800 MHz, respectively) using 5 mm triple-resonance 1H/13 C/15 N TCI and 13C-optimized TXO cryo-probes. Measurements of NPM1 were made on a spectrometer operating at 18.8 T, equipped with a TXO cryoprobe optimized for 13C. Spectra were processed in Topspin 4.0 or NMRPipe and analyzed in Sparky within the NMRBox virtual environment59.
The concentration of p14ARF within the p14ARF-NPM1 condensed phase is ~200 µM26, which lies close to the limit of detection for most triple resonance experiments needed to make backbone assignments60. Therefore, we utilized a condensate containing p14ARF and the NPM1 IDR (amino acids 119–240), which we found contains ~1 mM p14ARF (Supplementary Fig. 3A-F). For backbone resonance assignment of [13C,15N]-p14ARF within the condensed phase with NPM1 IDR, 2D and 3D spectra60–73, were collected at room temperature (298 K) and optimized for signal to noise and acquisition times. 1H and 15N chemical shift assignments were then transferred onto 2D-TROSY-HSQC spectra (transverse relaxation optimized spectroscopy) collected for the [13C,15N]-p14ARF-NPM1 condensate (Supplementary Fig. 3G). Both spectra were nearly identical (Supplementary Fig. 3H).
In solution measurements of NPM1 structure and dynamics were performed on 65 µM [2H, 15N]-NPM1 in 10 mM sodium phosphate pH=7.0, 150 mM NaCl, 2 mM DTT, 10% D2O at 25oC at room temperature (298 K) at 800 MHz. Chemical shift assignments for NPM1 were transferred from published values19,26. 2D 1H-15N TROSY-HSQC61, R174,75, R274,75 and 15N-CPMG76,77 experiments were optimized for signal to noise and acquisition times. 1H-15N NOE values were calculated as the ratio between peak intensities in spectra recorded with and without 1H saturation. The 15N relaxation rates, R1 and R2, were determined by fitting cross-peak intensities, measured as a function of variable delay periods, to a single-exponential decay. 15N-CPMG relaxation dispersion was fitted using the protein dynamics toolset in the Bruker Dynamics Center 2.5.6 with the following fitted function alternatives:5\documentclass[12pt]{minimal}
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\begin{document}$${{{\rm{\phi }}}}={{{{\rm{P}}}}}_{{{{\rm{A}}}}}{{{{\rm{P}}}}}_{{{{\rm{B}}}}}{\Delta {{{\rm{\omega }}}}}^{2}$$\end{document}ϕ=PAPBΔω2. Systematic errors were estimated based on peak intensities. All repetition experiments were assessed to calculate the largest difference in peak intensities per peak, which was used as a systematic error per peak for each mixing time. Fit parameter error estimation was performed using Monte-Carlo simulation. Fitted parameters were calculated with a 95% confidence interval.
Solid-state NMR spectroscopy
Solid-state NMR experiments were performed on a Bruker Avance NEO spectrometer operating at 14.1 T (1H Larmor frequency of 600 MHz) using a Bruker MAS CryoProbeTM, a cryogenically cooled magic-angle spinning (MAS) triple resonance (HCN) probe head78. The samples were packed in specially designed 3.2 mm MAS rotors with Teflon inserts to ensure proper centering of the p14ARF-NPM1 condensate samples. Detailed description of the acquisition parameters can be found in Supplementary Table 4. In general, all the MAS experiments were performed at MAS speeds between 10–15 kHz. Typical radio-frequency (RF) fields used in the experiments for the 1H, 13C and 15N channels were 80–100 kHz, 60–65 kHz and 40 kHz, respectively. Double cross polarization (CP), dipolar assisted rotational resonance (DARR) and COmbined R2vn-Driven (CORD) mixing requires lower RF fields and are reported in Supplementary Table 4. Contact times for CP and double CP were typically 1 ms with recycle delays of 2 s. The CP-MAS NMR acquisition times varying from 1–2 h for two-dimensional (2D) NCO79,80 and NCaCX experiments81–83 2D experiments) to several hours (7–10 h) for the 2D CC correlation experiments (with DARR, CORD or insensitive nuclei enhanced by polarization transfer (INEPT) mixing). Three-dimensional (3D) experiments were recorded over 1.5 (3D NCOCX79,80) and 2.5 days (3D NCaCx, through co-addition of two experiments of one day each and another of 10 h; 34 h of acquisition in total). The NHHC experiment used to probe contacts between the 15N-p14ARF and the 13C-p14ARF molecules within condensates with NPM1, based on proton spin diffusion between 15N-coupled amide protons (in one p14ARF molecule and 13C-coupled aliphatic protons in another p14ARF molecule), required the longest experimental time: two spectra acquired with identical parameters were co-added; these were acquired for 3 days and 9 h, respectively. All spectra were referenced using adamantane (13C δ = 38.5 ppm).
Cellular imaging
Fluorescence microscopy imaging for analysis of live DLD-1NPM1-G cell nucleoli was performed on a Zeiss LSM 980 Airyscan 2 inverted microscope, with a 40x Plan Apochromat (N.A. 1.1) objective (mEGFP lex = 492 nm, miRFP670 lex = 653 nm; lem = 300–720 nm). High-throughput fluorescence imaging of virally transduced DLD-1NPM1-G clones and FRAP experiments were performed using a 3i Marianas spinning disk confocal microscopes (Intelligent Imaging Innovations Inc., Denver, CO, USA) with a 40x air objective and 100x oil immersion objective (N.A. 1.4), respectively. Cells were maintained at 37 oC, 5% CO2 within an enclosed incubator during live cell imaging experiments.
Endogenously-tagged cell line generation
Endogenously C-terminally mEGFP-tagged NPM1 in DLD-1 cells (DLD-1NPM1-G) were generated using CRISPR-Cas9 technology in the Center for Advanced Genome Engineering (St. Jude Children’s Research Hospital). The donor homology directed repair (HDR) template containing a (GGS)5 linker DNA coding sequence upstream of the mEGFP sequence flanked by ~800 bases homology arms was synthesized and blunt-end cloned into pUC57 (the plasmid pUC57_NPM1-mEGFP_HDR donor repair template, CAGE117.g1.meGFP donor) by Bio Basic. Briefly, 500,000 DLD1 cells were transiently co-transfected with precomplexed ribonuclear proteins (RNPs) consisting of 100 pmol of chemically modified sgRNA (CAGE117.NPM1.g1, 5ʹ-UCCAGGCUAUUCAAGAUCUC-3ʹ, Synthego), 33 pmol of Cas9 protein (St. Jude Protein Production Core), 500 ng of plasmid donor. The transfection was performed via nucleofection (Lonza, 4D-Nucleofector™ X-unit) using solution P3 and program CA-137 in a small (20 µl) cuvette according to the manufacturer’s recommended protocol. Single cells were sorted based on viability five days post-nucleofection into 96-well plates containing prewarmed media and clonally expanded. Clones were screened and verified for the desired modification using PCR-based assays and confirmed via sequencing. Final clones were authenticated using the PowerPlex Fusion System (Promega) performed at the Hartwell Center (St. Jude).
The sequence of the HDR donor template for NPM1-mEGFP knock-in is (5’-3’; lowercase: homology arms; uppercase: mEGFP; bold uppercase: (GGS)5 linker; italics uppercase: silent blocking mutations):
ctcaggtgatccaacaccttggcctcttaaagtgctgggattacaggcatgagccaccatgcctggccagctgttttttttgttggtttgttttttgttttggtacccatctgtagtgtgatcttggctcactgcaacctctgcctcttgggctcaggcagtcctcccacctcagcctcctgagtagctgggcctcctgtagttgcacaccaccaagcctggctaatttttgcatttttagtagacagggtttcaccatgttgcccaggctggtctcaaattcctgagctgaagtgatctgcccgcctcagtctcccaaagtgtagggattacaggcgtgagccaccatgcctagcctcagcatatagttttttctaaatgtacacatgcccaggcacacatgcacaggcaattcagaataagtttctggtgtttatgtaactttatttgccaaatctggccaactctaaagctgatctcgggagatgaagttggaagtaacattggccatatgggtctctgttctttctgttgatttccttaagtaaataatgctaaactattaaataattattagtatattgttcacatttttatgactgattaaagtgtttggaattaaattacatctgagtataaattttcttggagtcatatctttatctagagttaactctctggtggtagaatgaaaaatagatgttgaactatgcaaagagacatttaatttattgatgtctatgaagtgttgtggttccttaaccacatttctttttttttttttccaggctattcaagaCctGtggcaAtggCgAaaAAGCctGGGAGGAAGCGGAGGTTCTGGCGGTAGTGGTGGATCTGGCGGCAGCATGGTTTCCAAGGGCGAAGAACTGTTCACCGGCGTGGTGCCCATTCTGGTGGAACTGGACGGGGATGTGAACGGCCACAAGTTTAGCGTTAGCGGCGAAGGCGAAGGGGATGCCACATACGGAAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCTGTGCCTTGGCCTACACTGGTCACCACACTGACATACGGCGTGCAGTGCTTCAGCAGATACCCCGACCATATGAAGCAGCACGACTTCTTCAAGAGCGCCATGCCTGAGGGCTACGTGCAAGAGCGGACCATCTTCTTTAAGGACGACGGCAACTACAAGACCAGGGCCGAAGTGAAGTTCGAGGGCGACACCCTGGTCAACCGGATCGAGCTGAAGGGCATCGACTTCAAAGAGGACGGCAACATCCTGGGCCACAAGCTCGAGTACAACTACAACAGCCACAACGTGTACATCATGGCCGACAAGCAGAAAAACGGCATCAAAGTGAACTTCAAGATCCGGCACAACATCGAGGACGGCTCTGTGCAGCTGGCCGATCACTACCAGCAGAACACACCCATCGGAGATGGCCCTGTGCTGCTGCCCGATAACCACTACCTGAGCACCCAGAGCAAGCTGAGCAAGGACCCCAACGAGAAGCGGGACCACATGGTGCTGCTGGAATTTGTGACAGCCGCCGGAATCACCCTCGGCATGGATGAGCTGTACAAGTAAgaaaatagtttaaacaatttgttaaaaaattttccgtcttatttcatttctgtaacagttgatatctggctgtcctttttataatgcagagtgagaactttccctaccgtgtttgataaatgttgtccaggttctattgccaagaatgtgttgtccaaaatgcctgtttagtttttaaagatggaactccaccctttgcttggttttaagtatgtatggaatgttatgataggacatagtagtagcggtggtcagacatggaaatggtggggagacaaaaatatacatgtgaaataaaactcagtattttaataaagtagcacggtttctattgacttatttaactgctttatactttgtcaaagaaataattaatgtagttaggaatggcaaatagtcttgtaaaattctatgagaatgtccctgccctccccttcaatattctctctggagctaaccactttttcatcataaggatttagtgctgtgttcccacctcctgatgatagttaacaattattataactatgcaacatgtttccaaatgttccattagacctcctatctgcctattctagcctcacttgcaaagaaaatgtggcatgttaaaacagcttaaaagcagcctttcaacctgtatggttttttccccaggctggagtgcagtggcacaatctcagcttattgcagcttctgcttcttgggttcaagcaggtctcctgcctcagcctcccaagtagctgggattacaggtgtgagccaccagcccggctaatttttgtatttttagtagaga
The three pairs of primers used for PCR were as follows: 5ʹ junction primers, including CAGE117.gen.F2 (forward, 5ʹ-TGTACCTGAGAACCCATTGGC-3ʹ) and CAGE117.junc.meGFP.DS.R2 (reverse, 5ʹ-GTTCACATCCCCGTCCAGTT-3ʹ); 3ʹ junction primers, including CAGE117.junc.meGFP.DS.F2 (forward, 5ʹ-GCTGCCCGATAACCACTACC-3ʹ) and CAGE117.gen.R2 (reverse, 5ʹ-AGGCAGAACATATAAAGGTGCTAAT-3ʹ); and zygosity confirmation primers, including CAGE117.DS.F (forward, 5ʹ-AGTTAACTCTCTGGTGGTAGAATGA-3ʹ) and CAGE117.DS.R (reverse, 5ʹ-CCAAGCAAAGGGTGGAGTTC-3ʹ).
Lentiviral transduction and generation of cell lines
Lentiviral vectors were used to make lentiviral particles by the Vector Development and Production Shared Resource at St. Jude Children’s Research Hospital. Cells were transduced with virus in the presence of 10 µg/ml polybrene (Sigma). For pINDUCER20 lentivirus transduced cells, the selection by G418 (500 µg/ml) lasted until mock-transfected, control cells were completely eliminated, and the cells were constantly maintained in the culture medium containing G418 at 250 µg/ml.
Single-cell cloning
Each population of the virally transduced, G418 resistant cells were sorted one cell/well into three 96-well plates. After growing in G418-containing media for 7ʹ10 days, each viable single colony was further passaged into two corresponding wells in one Nunc 96-well cell culture treated plate (ThermoFisher) and one glass bottom black 96-well plate (Greiner Bio-One, Cat. #655891). The clones in the glass bottom 96-well plates were treated with 1 µg/ml doxycycline to induce the expression of miRFP670-tagged protein in cells, and the expression levels were quantified by measuring miRFP670 fluorescence intensity in the live cells using fluorescence microscopy. Single cell clones in the corresponding wells in the Nunc 96-well plates, which could express miRFP670-tagged protein at high, medium, or low levels, were selected and expanded. As miRFP670 requires the cofactor biliverdin for fluorescence84, the protein expression levels in these single-cell clones were further assessed by immunoblotting analysis.
Immunoblotting
Gel electrophoresis was performed using 25–40 µg protein extracted from TRIzol cell lysates or equal volumes of protein extracts from sucrose gradient fractions in NuPAGE mini protein gels (Invitrogen), transferred for 1.5 h at 30 volt to PVDF Transfer Membrane with low background fluorescence (Millipore). After Ponceau S staining, the membranes were blocked for 1 h in 5% non-fat milk in 1× PBS, then incubated with primary antibodies diluted in 2.5% BSA in PBST solution (1× PBS, 0.2% Tween-20) overnight at 4 °C with gentle agitation. Membranes were rinsed 4× in PBST buffer before incubating in fluorescence conjugated secondary antibodies diluted in 1% non-fat milk in 1× PBS with 0.02% SDS for 45 min at room temperature in the dark. After washing with 4× in PBST, the blots were scanned with the ChemiDoc Imaging System (Bio-Rad). The primary antibodies were used as follows: rabbit monoclonal anti-Cyclophilin B (Cell Signaling, 43603) at 1:1500–1:2000 dilution; rabbit monoclonal anti-GAPDH (Cell Signaling, 5174) at 1:2500 dilution; mouse monoclonal anti-NPM1 (ThermoFisher Scientific, 32-5200) at 1:1000 dillution; mouse monoclonal anti-p14Arf (Cell Signaling, 2407) at 1:1000–1:15000 dilution; mouse monoclonal anti-GAPDH (Santa Cruz, sc-47724) at 1:2500 dilution; and rabbit polyclonal anti-p14Arf (Novus, NB200-111) at 1:2000 dilution.
Cell treatments
Treatment of doxycycline inducible cells was performed with doxycycline at 1 µg/ml or serially diluted from the stock solution of 1 mg/ml for the indicated times. Unless otherwise indicated, single clones of cells were treated with doxycycline at the concentrations as follows: 1000 ng/ml (p14ARF-iRFP clones), 50 ng/ml (p14ARFΔH1-3-iRFP clone H5), 20 ng/ml (iRFP clone H9), or 10 ng/ml (p14ARFΔH1-3-iRFP clone C10, iRFP clone A6). The time course samples were harvested at the same time.
Cell growth assays
Aliquots of cell suspensions were seeded in 96- or 24-well plates at 5000 or 10,000 cells per well, respectively. After culturing for 20–24 h, the cells were counted for the starting time point and/or subjected to treatments as needed, and then cultured for the indicated times. For cell counting, existing culture medium in each well was replaced with fresh culture medium containing 10-fold diluted Cell Counting Kit-8 (CCK-8, APExBIO), and the absorbance at 450 nm was measured after 1–2 h of incubation. Cell growth was calculated as the ratio of A450 at later time points relative to that of the starting time point. The relative cell viability was expressed as the ratio of A450 of the treated versus that of untreated controls cells. Biological replicates were performed separately at different times.
Fluorescence recovery after photobleaching
Analysis of fluorescence recovery after photo-bleaching (FRAP) images to determine the apparent diffusion coefficient (DApp) and percent mobility was performed following a modified version of the protocol from85, using in-house pipelines written in Python (Supplementary Fig. 8). For FRAP in live cells, all images were corrected (\documentclass[12pt]{minimal}
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\begin{document}$${{{{\rm{I}}}}(t)}_{{{{\rm{corr}}}}}=\frac{{{{\rm{I}}}}\left(t\right)-{{{{\rm{I}}}}(t)}_{{{{\rm{bkgd}}}}}}{{{{{\rm{I}}}}(t)}_{{{{\rm{cell}}}}}-{{{{\rm{I}}}}(t)}_{{{{\rm{bkgd}}}}}}$$\end{document}I(t)corr=It−I(t)bkgdI(t)cell−I(t)bkgd
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\begin{document}$${{{{\rm{I}}}}(t)}_{{{{\rm{corr}}}}}=\frac{{{{\rm{I}}}}\left(t\right)-{{{{\rm{I}}}}(t)}_{{{{\rm{bkgd}}}}}}{{{{{\rm{I}}}}(t)}_{{{{\rm{ref}}}}}-{{{{\rm{I}}}}(t)}_{{{{\rm{bkgd}}}}}}$$\end{document}I(t)corr=It−I(t)bkgdI(t)ref−I(t)bkgd
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\begin{document}$${{{\rm{R}}}}\left(t\right)$$\end{document}Rt) was then rescaled (\documentclass[12pt]{minimal}
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\begin{document}$$\big(\big\langle {{{{\rm{R}}}}(t)}_{{{{\rm{pre}}}}-{{{\rm{bleach}}}}}\big\rangle \big)$$\end{document}R(t)pre−bleach and the ROI intensity immediately following the bleach event (\documentclass[12pt]{minimal}
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\begin{document}$${{{{\rm{R}}}}}_{{{{\rm{post}}}}-{{{\rm{bleach}}}}}$$\end{document}Rpost−bleach) (Eq. 10), where:10\documentclass[12pt]{minimal}
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\begin{document}$${{{{\rm{R}}}}(t)}_{{{{\rm{norm}}}}}=\frac{{{{\rm{R}}}}\left(t\right)-{{{{\rm{R}}}}}_{{{{\rm{post}}}}-{{{\rm{bleach}}}}}}{\big\langle {{{{\rm{R}}}}\left(t\right)}_{{{{\rm{pre}}}}-{{{\rm{bleach}}}}}\big\rangle -{{{{\rm{R}}}}}_{{{{\rm{post}}}}-{{{\rm{bleach}}}}}}$$\end{document}R(t)norm=Rt−Rpost−bleachRtpre−bleach−Rpost−bleach
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\begin{document}$${{{{\rm{t}}}}}_{1/2}$$\end{document}t1/2) was then extracted from the recovery curve by fitting to the equation from86 using the curve_fit function in scipy (Eq. 11):11\documentclass[12pt]{minimal}
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\begin{document}$${{{\rm{R}}}}\left(t\right)=\frac{\left[{{{{\rm{R}}}}}_{{{{\rm{post}}}}-{{{\rm{bleach}}}}}+{{{{\rm{R}}}}}_{\infty }\left(\frac{t}{{t}_{1/2}}\right)\right]}{1+\left(\frac{t}{{t}_{1/2}}\right)}$$\end{document}Rt=Rpost−bleach+R∞tt1/21+tt1/2where, \documentclass[12pt]{minimal}
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\begin{document}$${{{{\rm{R}}}}}_{\infty }$$\end{document}R∞ is the ROI intensity after full recovery. The percent mobility (M) was calculated using Eq 12:12\documentclass[12pt]{minimal}
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\begin{document}$${{{\rm{M}}}}=\frac{\left\langle {{{{\rm{R}}}}}_{\infty }(t)\right\rangle }{\left\langle {{{{\rm{R}}}}\left(t\right)}_{{{{\rm{pre}}}}-{{{\rm{bleach}}}}}\right\rangle }$$\end{document}M=R∞(t)Rtpre−bleachwhere, \documentclass[12pt]{minimal}
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\begin{document}$$\left(\left\langle {{{{\rm{R}}}}}_{\infty }(t)\right\rangle \right)$$\end{document}R∞(t) is the mean ROI intensity of the last ten image frames of the signal plateau region.
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\begin{document}$${{{{\rm{D}}}}}_{{{{\rm{App}}}}}$$\end{document}DApp), image correction for diffusion during the bleach event (\documentclass[12pt]{minimal}
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\begin{document}$${{{{\rm{I}}}}}_{{{{\rm{norm}}}}}$$\end{document}Inorm) was performed85,87. The post-bleach image was first normalized using the image frames preceding (\documentclass[12pt]{minimal}
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\begin{document}$${{{{\rm{I}}}}}_{{{{\rm{norm}}}}}=\frac{{{{{\rm{I}}}}}_{{{{\rm{post}}}}-{{{\rm{bleach}}}}}}{{{{{\rm{I}}}}}_{{{{\rm{pre}}}}-{{{\rm{bleach}}}}}}$$\end{document}Inorm=Ipost−bleachIpre−bleach
The normalized post-bleach profile was then fit to an exponential of a Gaussian laser profile (φ) using the curve_fit function in scipy (Eq. 14):14\documentclass[12pt]{minimal}
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\begin{document}$${{{\rm{\varphi }}}}\left(x,y\right)={{{{\rm{F}}}}}_{{{{\rm{i}}}}}{{\mathrm{exp}}}\left[-{{{\rm{K}}}}\exp \left[-\frac{2\left({x}^{2}+{y}^{2}\right)}{{{{{{\rm{r}}}}}_{{{{\rm{e}}}}}}^{2}}\right]\right]$$\end{document}φx,y=Fiexp−Kexp−2x2+y2re2where, (\documentclass[12pt]{minimal}
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\begin{document}$${{{{\rm{r}}}}}_{{{{\rm{e}}}}}$$\end{document}re) is the effective bleach radius. \documentclass[12pt]{minimal}
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\begin{document}$${{{{\rm{D}}}}}_{{{{\rm{App}}}}}$$\end{document}DApp was then calculated using \documentclass[12pt]{minimal}
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\begin{document}$${{{{\rm{t}}}}}_{1/2}$$\end{document}t1/2 and the nominal bleach radius (\documentclass[12pt]{minimal}
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\begin{document}$${{{{\rm{r}}}}}_{{{{\rm{n}}}}}$$\end{document}rn) (Eq. 15).15\documentclass[12pt]{minimal}
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\begin{document}$${{{{\rm{D}}}}}_{{{{\rm{App}}}}}=\frac{{{{{{\rm{r}}}}}_{{{{\rm{e}}}}}}^{2}+{{{{{\rm{r}}}}}_{{{{\rm{n}}}}}}^{2}}{8{{{{\rm{t}}}}}_{1/2}}$$\end{document}DApp=re2+rn28t1/2
Image analysis and quantification
Prior to analysis, images were converted into tiff format using Slidebook 6.0 (Intelligent Imaging Innovations, Gottingen, Germany) or Image J88. Image segmentation was performed using an in-house pipeline written in Python (Supplementary Fig. 10). Segmentation of nuclei and nucleoli were performed using the NPM1 signal; NPM1-GFP fluorescence was used for segmenting live DLD-1NPM1-G cell images. 3D image stacks were first converted to 2D images through maximum intensity projection. Prior to segmentation of nuclei, the Gaussian kernel with variable standard deviation (σ) from scikit-image was first applied (for Airyscan DLD-1NPM1-G cell images σ = 4).
Prior to segmentation of nucleoli, a Gaussian kernel with σ = 0.33 was applied. Segmentation was performed using the multi-Otzu algorithm from scikit-image using 3 classes as input. Nuclear masks were found at the 0th threshold and nucleolar masks were found at the 1st threshold. Masked pixels were then clustered using the density-based spatial clustering of applications with noise (DBSCAN) algorithm as implemented in scikit-learn. Segmented cells along with their nuclear and nucleolar masks were visualized using the imshow function from matplotlib. All segmented cell masks were verified by manual observation and improperly segmented cells were removed prior to quantification.
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\begin{document}$${{{\rm{\mu }}}}$$\end{document}μ is the mean fluorescence intensity. To quantify the transfer free energy the following was used (Eq. 17):17\documentclass[12pt]{minimal}
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\begin{document}$${{{\rm{R}}}}$$\end{document}R is the universal gas constant, \documentclass[12pt]{minimal}
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\begin{document}$${{{{\rm{I}}}}}_{{{{\rm{DP}}}}}$$\end{document}IDP is the mean dense phase florescence intensity, and \documentclass[12pt]{minimal}
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\begin{document}$${{{{\rm{I}}}}}_{{{{\rm{LP}}}}}$$\end{document}ILP is the mean light phase florescence intensity.
Statistics
The numbers of independent replicates for each experiment are provided in the figure legends. Unless stated in figure legends, all values represent means ± SD. p < 0.05 was considered statistically significant. Asterisks denote statistical significance as follows: n.s. = not significant; ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001; and ∗∗∗∗p < 0.0001. For all box and whiskers plots, each box extends from the first to the third quartiles, with the line or center point representing the median, and the whiskers extending from the box to the furthest point within 1.5x the inter-quartile range. If shown, flier points represent outlier points beyond the whiskers.
Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Supplementary information
Supplementary Information
Reporting Summary
Transparent Peer Review file
Source data
Source Data |
Title: Peptide TaY Attenuates Inflammatory Responses by Interacting with Myeloid Differentiation 2 and Inhibiting NF-κB Signaling Pathway | Body:
1. Introduction
Inflammation is an immune response of the organism in response to irritation or infection, which is an important mechanism to maintain health and prevent infection [1,2,3,4]. However, excessive inflammation can damage the organism and further produce various diseases: asthma, arthritis, and atherosclerosis [3,5,6]. Macrophages play an important role in the pathogenesis of these diseases [7,8]. Among the external substances that stimulate inflammation, the most important is LPS, a lipopolysaccharide from the cell wall of Gram-negative bacteria that activates the toll-like receptor (TLR) 4 signaling pathway, which, in turn, activates the body’s inflammatory response and a series of downstream events [9].
LPS induces TLR4 dimerization by binding to the hydrophobic structural domain of the myeloid differentiation protein-2 (MD2) [10], thereby transducing the signal into the cell. This process is carried out through the MYD88-dependent and TRIF-dependent pathways, respectively. MYD88-dependent signaling mainly activates the inhibitory kappa B kinase (IKK) and mitogen-activated protein kinases (MAPK) pathways [11]. IKKα, IKKβ, and IKKγ form a complex that catalyzes the phosphorylation of IκB protein, leading to IκB protein degradation and promoting nuclear transcription factor-κB (NF-κB) nuclear translocation, and the transfer of NF-κB to the nucleus to regulate a variety of downstream inflammatory factors (TNF-α, IL-1β, IL-6, NO) and related genes (cyclooxygenase-2, COX-2; inducible nitric oxide synthase, iNOS) expression [11,12].
Therefore, developing anti-inflammatory drugs that target key proteins is very effective in the inflammatory pathway. Currently, the commonly used anti-inflammatory drugs are mainly non-steroidal anti-inflammatory drugs (NSAIDs) [13], which work by inhibiting COX which is the key enzyme responsible for the synthesis of prostaglandins and messenger molecules during inflammation [14,15]. However, the major issue with NSAIDS is the toxic side effects associated with long-term use [16,17,18]. Therefore, developing new anti-inflammatory drugs with high efficacy and low toxicity has become a topic of great interest.
Peptide drugs combine the advantages of small chemical molecules (<500 Da) and protein drugs (>5000 Da) because of their molecular weight [19]. Their small molecular weight allows them to be synthesized using chemical methods, which offer precise structure, easy quality control, and low production cost. Additionally, their therapeutic effects are similar to those of protein drugs, which have the advantages of low toxicity and high specificity [20,21]. These make peptide drug highly advantageous in the development of anti-inflammatory treatments.
In the previous study, we screened a peptide TaY (KEKKEVVEYGPSSYGYG), which demonstrated excellent anti-inflammatory activity by luciferase-based reporter gene cell screening. We utilized the RAW-NF-κB luciferase reporter gene to screen for compounds that can stimulate cellular immune modulation. In this process, we found that, although TaY showed a weak stimulatory effect on the signaling pathway, it significantly reduced the increase in luciferase activity induced by LPS stimulation (unpublished data). In order to investigate the anti-inflammatory mechanism of TaY, this study was conducted using the LPS-induced macrophage RAW264.7 inflammation model to reveal the molecular anti-inflammatory mechanisms of TaY.
2. Results and Discussion
Inflammation is a bioprotective response to microbial invasion that can cause cellular damage, in which pro-inflammatory mediators are released to promote disease symptoms [1,2]. LPS-mediated inflammation is the most common [22]. Macrophages, as an important immune cell type that responds to inflammation, produce a series of pro-inflammatory mediators including TNF-α, IL-6, IL-1β, NO, and PGE2 [7,23]. Excessive production of these pro-inflammatory mediators can damage local cells and further aggravate symptoms [24,25]. Therefore, inhibiting the production of pro-inflammatory mediators is an effective strategy to suppress inflammation.
2.1. TaY Reduced the Expression of Pro-Inflammatory Cytokines in LPS-Induced RAW264.7 Inflammation
In this study, we first analyzed the toxicity of TaY on RAW264.7, and as shown in the Figure 1a, TaY was not significantly toxic to RAW264.7 in the concentration range of 20–1000 μg/mL. Nitric oxide, as an important marker of the inflammatory response, plays a very important role in several inflammatory diseases [26], and its ease of detection makes it a frequently preferred assay in the screening of anti-inflammatory drugs [27,28]. So we first analyzed the level of TaY to reduce LPS-induced NO production. As predicted, 100 ng/mL of LPS-stimulated cells resulted in a significant increase in NO levels, and the results showed that TaY’s ability to reduce NO levels exhibited a dose-dependent enhancement, which was almost the same level as that of the control group at 100 ng/μL (Figure 1b). These results demonstrated that TaY does, indeed, possess a strong anti-inflammatory ability. Additionally, we simultaneously examined the expression of iNOS, a major NO-producing gene. The results showed that TaY reduced the LPS-induced elevation of iNOS protein at both the transcriptional and protein levels (Figure 1c–e).
The anti-inflammatory capacity of TaY was initially determined by the detection of NO, as well as the inducible enzyme, but in addition to NO, LPS stimulation caused an increase in the expression levels of several inflammatory factors (TNF-α, IL-6, and IL-1β) [11,12]. To further characterize the anti-inflammatory activity of TaY, we also evaluated the expression levels of the major pro-inflammatory cytokines TNF-α and IL-6 in RAW264.7 (Figure 2). As expected, the results were similar to those of NO and iNOS. TaY treatment was effective at decreasing the expression levels of pro-inflammatory factors, both at the transcriptional and protein levels (Figure 2).
Based on the level of reduction of LPS-induced pro-inflammatory mediators by TaY, its anti-inflammatory ability is quite strong, and peptide anti-inflammatory agents have been reported in many cases, but most of them do not have that strong anti-inflammatory activity compared to TaY. For example, Histatin-1 roughly reduces the level of LPS-produced NO by half [29], whereas TaY in the present study at 100 ng/μL could almost completely bring the level of NO production to the same level as the control (Figure 1b). This remarkable efficacy not only positions TaY favorably among peptide anti-inflammatory drugs, but it is also relatively rare among the remaining types of anti-inflammatory drugs reported [28,30,31].
2.2. TaY Inhibited the LPS-Activated TLR4-NF-κB Signaling Pathway
NF-κB, as a transcription factor, plays a crucial role in the expression of genes related to inflammation and immunity [32]. The TLR4-NF-κB signaling pathway is activated following LPS-induced inflammation in RAW264.7 [33]. TLR4 first activates MYD88, which recruits a series of downstream protein kinases to activate IKK. IKK then promotes the phosphorylation, ubiquitination, and subsequent protease-mediated degradation of IκB, allowing NF-κB to be released and translocated to the nucleus, where it functions as a transcription factor that promotes the transcription of a range of inflammation-related genes (iNOS, COX-2, TNF-α, IL-1β, and IL-6) [34,35].
In the present study, we demonstrated that TaY reduced several inflammatory factors (TNF-α, IL-6, NO) in the LPS-induced inflammatory response. Next, we aimed to analyze the role of TaY in the TLR4-NF-κB signaling pathway. We first examined the protein expression levels in this pathway using Western blot. Previous studies have shown that LPS-induced NF-κB signaling is a dynamic process [36], so we first examined the dynamics of several key proteins in the NF-κB signaling pathway using our cells. The results indicate that iNOS expression increases over time, while p-IκBα expression peaks at 6 h and then begins to degrade. The expression of p-IKKα/β shows an increase from 3 h to 6 h, after which it is nearly indistinguishable from the control group. Similarly, p-p65 expression is also highest at 6 h. Based on the expression patterns of these proteins, we selected a 6 h LPS treatment duration for our formal experiments, and the results were shown in Figure 3. The results showed that TaY treatment resulted in a significant reduction in the phosphorylation levels of IKK-α/β (Figure 3b), followed by a decrease in the phosphorylation level of IκBα compared to the LPS-treated group (Figure 3c). This ultimately led to a reduction in the phosphorylation of P65 and decreased NF-κB entry into the nucleus (Figure 3d). These findings indicate a reduction in proinflammatory factors, including TNF-α, IL-6, and NO.
Through our Western blot experiments, we determined that TaY exerts its anti-inflammatory activity by inhibiting the TLR4-NF-κB signaling pathway. NF-κB is a critical node in the development of anti-inflammatory drugs, as activated NF-κB translocates to the nucleus to activate the expression of multiple inflammatory genes [34,35,37]. In this study, TaY achieves its anti-inflammatory function by regulating the TLR4-NF-κB signaling pathway. This mechanism is similar to that of another peptide, STM28, which inhibits downstream signaling by binding to TLR4 to exert anti-inflammatory activity [38]. Additionally, it has also been shown that peptide can exert its anti-inflammatory effects through the AMPK signaling pathway [29,39].
2.3. TaY Exerts Its Anti-Inflammatory Function by Binding to the MD2 Hydrophobic Pocket
In the previous study, we demonstrated that TaY exerts its anti-inflammatory function by inhibiting the activation of the TLR4-NF-κB signaling pathway, however, the specific mechanism by which TaY inhibits this signaling remains unknown. Peptide anti-inflammatory pathways have been reported to neutralize LPS and block signaling by inhibiting TLR4 and its ligands [40,41,42,43]. For example, LL-37, an antimicrobial peptide identified in the human body, exerts its anti-inflammatory effects by neutralizing LPS [44,45], while LK2(6)A(L), a peptide derived from the skin secretions of Chinese brown frog Rana chensinensis, can alleviate LPS-induced acute lung injury in mice by binding to MD2 [46]. The MD2 protein is essential for LPS function, as LPS must bind to the hydrophobic pocket of MD2 to activate TLR4 [47]. MD2-deficient mice are unresponsive to LPS stimulation, and some synthetic LPS-like chemicals, such as Eritoran, have been shown to inhibit LPS-induced inflammation [48,49].
To determine whether TaY also exerts its anti-inflammatory function by competitively binding to MD2, we conducted molecular docking and molecular dynamics simulation to investigate the interaction between MD2 and TaY (Figure 4). The complex trajectory analysis showed a stable RMSD value at 5 Å after approximately 100 ns (Figure 4a), and the Rg value also stabilized at around 1.65 nm after the same duration (Figure 4b). We analyzed key interaction parameters between TaY and MD2, including electrostatic interactions, hydrogen bonds, and hydrophobic interactions (Figure 5b,c, Table 1). Current reports suggest that small molecules bind to residues Leu78, Asn86, Arg90, Glu92, Tyr102, Ser120, Phe121, Lys122, Ile124 of MD2 [47,49,50,51,52,53,54]. Our molecular docking results revealed hydrogen bonds between Lys1, Glu2, Lys4, Glu8, Ser13 of TaY and Pro88, Arg90, Lys91, Glu92, Lys128 of MD2. While Arg90, and Glu92 have been previously examined. Our findings indicate that Pro88, Lys91, and Lys128 also play significant roles in MD2 activation.
The binding of TaY to MD2 predominantly involves residues 1–9. Previous studies have shown that small molecules are crucial for hydrogen bond formation with residues located on the exterior of the hydrophobic pocket when binding to MD2 [49,55]. Our results similarly indicate that the portion of TaY forming hydrogen bonds with MD2 is primarily concentrated at the MD2 pocket site (Figure 5a,d). Additionally, residues 10–17 of TaY formed strong hydrophobic interactions with the hydrophobic core of the MD2 hydrophobic pocket (TaY: Pro11, Try14, Try16) (Figure 5c, Table 1). These hydrophobic interactions enhanced the binding of TaY to MD2, giving TaY a competitive advantage over LPS in binding to the MD2 hydrophobic pocket. However, these results are based on simulations and we cannot provide a definitive conclusion that TaY exerts its anti-inflammatory activity by binding to MD2. In the future, we still need to validate the interactions between TaY and MD2 based on the results of molecular docking in the experiment.
2.1. TaY Reduced the Expression of Pro-Inflammatory Cytokines in LPS-Induced RAW264.7 Inflammation
In this study, we first analyzed the toxicity of TaY on RAW264.7, and as shown in the Figure 1a, TaY was not significantly toxic to RAW264.7 in the concentration range of 20–1000 μg/mL. Nitric oxide, as an important marker of the inflammatory response, plays a very important role in several inflammatory diseases [26], and its ease of detection makes it a frequently preferred assay in the screening of anti-inflammatory drugs [27,28]. So we first analyzed the level of TaY to reduce LPS-induced NO production. As predicted, 100 ng/mL of LPS-stimulated cells resulted in a significant increase in NO levels, and the results showed that TaY’s ability to reduce NO levels exhibited a dose-dependent enhancement, which was almost the same level as that of the control group at 100 ng/μL (Figure 1b). These results demonstrated that TaY does, indeed, possess a strong anti-inflammatory ability. Additionally, we simultaneously examined the expression of iNOS, a major NO-producing gene. The results showed that TaY reduced the LPS-induced elevation of iNOS protein at both the transcriptional and protein levels (Figure 1c–e).
The anti-inflammatory capacity of TaY was initially determined by the detection of NO, as well as the inducible enzyme, but in addition to NO, LPS stimulation caused an increase in the expression levels of several inflammatory factors (TNF-α, IL-6, and IL-1β) [11,12]. To further characterize the anti-inflammatory activity of TaY, we also evaluated the expression levels of the major pro-inflammatory cytokines TNF-α and IL-6 in RAW264.7 (Figure 2). As expected, the results were similar to those of NO and iNOS. TaY treatment was effective at decreasing the expression levels of pro-inflammatory factors, both at the transcriptional and protein levels (Figure 2).
Based on the level of reduction of LPS-induced pro-inflammatory mediators by TaY, its anti-inflammatory ability is quite strong, and peptide anti-inflammatory agents have been reported in many cases, but most of them do not have that strong anti-inflammatory activity compared to TaY. For example, Histatin-1 roughly reduces the level of LPS-produced NO by half [29], whereas TaY in the present study at 100 ng/μL could almost completely bring the level of NO production to the same level as the control (Figure 1b). This remarkable efficacy not only positions TaY favorably among peptide anti-inflammatory drugs, but it is also relatively rare among the remaining types of anti-inflammatory drugs reported [28,30,31].
2.2. TaY Inhibited the LPS-Activated TLR4-NF-κB Signaling Pathway
NF-κB, as a transcription factor, plays a crucial role in the expression of genes related to inflammation and immunity [32]. The TLR4-NF-κB signaling pathway is activated following LPS-induced inflammation in RAW264.7 [33]. TLR4 first activates MYD88, which recruits a series of downstream protein kinases to activate IKK. IKK then promotes the phosphorylation, ubiquitination, and subsequent protease-mediated degradation of IκB, allowing NF-κB to be released and translocated to the nucleus, where it functions as a transcription factor that promotes the transcription of a range of inflammation-related genes (iNOS, COX-2, TNF-α, IL-1β, and IL-6) [34,35].
In the present study, we demonstrated that TaY reduced several inflammatory factors (TNF-α, IL-6, NO) in the LPS-induced inflammatory response. Next, we aimed to analyze the role of TaY in the TLR4-NF-κB signaling pathway. We first examined the protein expression levels in this pathway using Western blot. Previous studies have shown that LPS-induced NF-κB signaling is a dynamic process [36], so we first examined the dynamics of several key proteins in the NF-κB signaling pathway using our cells. The results indicate that iNOS expression increases over time, while p-IκBα expression peaks at 6 h and then begins to degrade. The expression of p-IKKα/β shows an increase from 3 h to 6 h, after which it is nearly indistinguishable from the control group. Similarly, p-p65 expression is also highest at 6 h. Based on the expression patterns of these proteins, we selected a 6 h LPS treatment duration for our formal experiments, and the results were shown in Figure 3. The results showed that TaY treatment resulted in a significant reduction in the phosphorylation levels of IKK-α/β (Figure 3b), followed by a decrease in the phosphorylation level of IκBα compared to the LPS-treated group (Figure 3c). This ultimately led to a reduction in the phosphorylation of P65 and decreased NF-κB entry into the nucleus (Figure 3d). These findings indicate a reduction in proinflammatory factors, including TNF-α, IL-6, and NO.
Through our Western blot experiments, we determined that TaY exerts its anti-inflammatory activity by inhibiting the TLR4-NF-κB signaling pathway. NF-κB is a critical node in the development of anti-inflammatory drugs, as activated NF-κB translocates to the nucleus to activate the expression of multiple inflammatory genes [34,35,37]. In this study, TaY achieves its anti-inflammatory function by regulating the TLR4-NF-κB signaling pathway. This mechanism is similar to that of another peptide, STM28, which inhibits downstream signaling by binding to TLR4 to exert anti-inflammatory activity [38]. Additionally, it has also been shown that peptide can exert its anti-inflammatory effects through the AMPK signaling pathway [29,39].
2.3. TaY Exerts Its Anti-Inflammatory Function by Binding to the MD2 Hydrophobic Pocket
In the previous study, we demonstrated that TaY exerts its anti-inflammatory function by inhibiting the activation of the TLR4-NF-κB signaling pathway, however, the specific mechanism by which TaY inhibits this signaling remains unknown. Peptide anti-inflammatory pathways have been reported to neutralize LPS and block signaling by inhibiting TLR4 and its ligands [40,41,42,43]. For example, LL-37, an antimicrobial peptide identified in the human body, exerts its anti-inflammatory effects by neutralizing LPS [44,45], while LK2(6)A(L), a peptide derived from the skin secretions of Chinese brown frog Rana chensinensis, can alleviate LPS-induced acute lung injury in mice by binding to MD2 [46]. The MD2 protein is essential for LPS function, as LPS must bind to the hydrophobic pocket of MD2 to activate TLR4 [47]. MD2-deficient mice are unresponsive to LPS stimulation, and some synthetic LPS-like chemicals, such as Eritoran, have been shown to inhibit LPS-induced inflammation [48,49].
To determine whether TaY also exerts its anti-inflammatory function by competitively binding to MD2, we conducted molecular docking and molecular dynamics simulation to investigate the interaction between MD2 and TaY (Figure 4). The complex trajectory analysis showed a stable RMSD value at 5 Å after approximately 100 ns (Figure 4a), and the Rg value also stabilized at around 1.65 nm after the same duration (Figure 4b). We analyzed key interaction parameters between TaY and MD2, including electrostatic interactions, hydrogen bonds, and hydrophobic interactions (Figure 5b,c, Table 1). Current reports suggest that small molecules bind to residues Leu78, Asn86, Arg90, Glu92, Tyr102, Ser120, Phe121, Lys122, Ile124 of MD2 [47,49,50,51,52,53,54]. Our molecular docking results revealed hydrogen bonds between Lys1, Glu2, Lys4, Glu8, Ser13 of TaY and Pro88, Arg90, Lys91, Glu92, Lys128 of MD2. While Arg90, and Glu92 have been previously examined. Our findings indicate that Pro88, Lys91, and Lys128 also play significant roles in MD2 activation.
The binding of TaY to MD2 predominantly involves residues 1–9. Previous studies have shown that small molecules are crucial for hydrogen bond formation with residues located on the exterior of the hydrophobic pocket when binding to MD2 [49,55]. Our results similarly indicate that the portion of TaY forming hydrogen bonds with MD2 is primarily concentrated at the MD2 pocket site (Figure 5a,d). Additionally, residues 10–17 of TaY formed strong hydrophobic interactions with the hydrophobic core of the MD2 hydrophobic pocket (TaY: Pro11, Try14, Try16) (Figure 5c, Table 1). These hydrophobic interactions enhanced the binding of TaY to MD2, giving TaY a competitive advantage over LPS in binding to the MD2 hydrophobic pocket. However, these results are based on simulations and we cannot provide a definitive conclusion that TaY exerts its anti-inflammatory activity by binding to MD2. In the future, we still need to validate the interactions between TaY and MD2 based on the results of molecular docking in the experiment.
3. Materials and Methods
3.1. Synthesis of Peptides
Peptides were synthesized using the standard Fmoc solid-phase method by GL Biochem Ltd. (Shanghai, China). The purity of the peptides was 95%, and their relative masses were determined by MALDI-TOF-MS.
3.2. Cell Culture
Mouse macrophages (RAW264.7) were purchased from the Shanghai Cell Bank, Institute of Cell Biology, Chinese Academy of Sciences, and cultured in Duchenne’s Modified Eagle’s Medium (DMEM; HyClone, Utah, USA). The DMEM was supplemented with 10% (v/v) fetal bovine serum (Procell, Wuhan, China) and 1% (v/v) penicillin/streptomycin (Solarbio, Beijing, China), and incubated at 37 °C in a humidified environment (5% CO2, 95% air).
3.3. Cytotoxicity Analysis
The toxicity of TaY on RAW264.7 cells was assessed using the CCK-8 kit. RAW264.7 cells were seeded into 96-well plates at a density of 3.0 × 105 cells per well and cultured for 12 h. After 12 h, the cells were treated with different concentrations of peptides (0, 20, 40, 60, 80, 100, 200, 500, and 1000 μg/mL) for 24 h. Subsequently, 10 μL of CCK-8 solution was added to each well according to the kit instructions and the cells were incubated for 2 h at 37 °C in the dark. The absorbance of the reaction solution was measured at 450 nm, and cell viability was calculated using the following formula:Cell viability (%)=AS−ABAC−AB×100%
AS is the absorbance of the well containing cells, CCK-8, and peptides; AB is the absorbance of the well containing medium and CCK-8, but without cells; and AC is the absorbance of the well containing cells and CCK-8, but without peptides.
3.4. Determination of Nitric Oxide Content
The LPS-induced inflammatory macrophage model was performed according to our previously described methodology [56]. LPS from E. coli 0111:B4 was purchased from Sigma-Aldrich (St. Louis, MO, USA). RAW264.7 cells were cultured in 96-well plates at a density of 3.0 × 105 cells per well for 12 h. After 12 h of incubation, different concentrations of peptides (0, 1, 5, 10, 20, 40, 60, 80, and 100 μg/mL) were added to each well and incubated for another 3 h. Then, LPS was added to each well at a final concentration of 100 ng/mL and incubated for 24 h. The cell culture supernatants were used for the NO assay. NO levels were quantified using Griess reagents according to the manufacturer’s instructions (Beyotime, Beijing, China). Specifically, 50 μL of Griess A and 50 μL of Griess B were sequentially added to 50 μL of culture medium supernatant, and the absorbance of the reaction mixture was measured at 540 nm.
3.5. Enzyme-Linked Immunosorbent Assay (ELISA)
RAW 264.7 cells were cultured in 6-well plates at a density of 2.0 × 106 cells per well for 12 h. After 12 h of incubation, 100 μg/mL of TaY was added to each well and incubated for another 3 h. Then, LPS was added to each well at a final concentration of 100 ng/mL and incubated for 24 h. After 24 h of incubation with LPS, cell supernatants were collected and then assayed using ELISA kits (Solarbio, Beijing, China) for IL-6, and TNF-α according to the manufacturer’s instructions.
3.6. RNA Isolation and Quantitative Real-Time PCR
RAW 264.7 cells were cultured in 6-well plates at a density of 2.0 × 106 cells per well for 12 h. After 12 h of incubation, 100 μg/mL of TaY was added to each well and incubated for another 3 h. Then, LPS was added to each well at a final concentration of 100 ng/mL and incubated for 24 h. The cell pellets were the collected for RNA isolation. RNA isolation: total RNA was isolated using TRIzol reagent (Solarbio, Beijing, China), and the RNA integrity of all samples was assessed by agarose gel electrophoresis and NanoDrop. All cDNAs were synthesized using the HiScript® III 1st Strand cDNA Synthesis Kit (Vazyme, Nanjing, China) according to the manufacturer’s instructions. Table 2 lists the primers used in this study. A two-step amplification method was used in this study: preincubation at 95 °C for 30 s, denaturation at 95 °C for 5 s, and extension at 60 °C for 30 s for a total of 40 cycles, followed by 95 °C for 5 s, 60 °C for 60 s, and melting at 95 °C for 1 s for a total of 1 cycle. The gene expression was normalized by comparing the expression of the target gene with that of the housekeeping gene β-actin. Results are reported as fold increase in gene expression relative to control samples.
3.7. Western Blot Analysis
RAW 264.7 cells were cultured in 6-well plates at a density of 2.0 × 106 cells per well for 12 h. After 12 h of incubation, 100 μg/mL of TaY were added to each well and incubated for another 3 h. Then, LPS was added to each well at a final concentration of 100 ng/mL and incubated for 6 h. The cell pellets were then collected for protein extraction. Protein extraction was performed using RIPA, with protease inhibitors added proportionally, and protein concentrations were determined using the BCA method. Protein electrophoresis was performed using Tris-Gly SDS-PAGE, followed by membrane transfer using PVDF membranes. The membranes were blocket with 5% skim milk. The primary antibody for the target protein was incubated overnight. Then HRP-coupled secondary antibody was used to bind to the primary antibody, and the target protein was visulalized using a chemiluminescence visualizer. The following antibody were used in this study: p65 (1:5000), p-p65 (1:2000), IκB-α (1:5000), p-IκB-α (1:2000), IKK-α/β (1:5000), β-actin (1:10000) were purchased from Abmart (Shanghai, China); iNOS (1:2000) was purchased from proteintech (Wuhan, Hubei, China), p-IKK-α/β (1:2000) was purchased from Cell Signaling Technology (Beverly, MA, USA). The secondary antibody HRP-Goat Anti-Rabbit IgG (1:5000) was purchased from huaixngbio (Beijing, China).
3.8. Molecular Docking
The three-dimensional (3D) structure of the hybrid peptide TaY was built using PEPFOLD3.5 (https://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD3/, accessed on 1 December 2016) [57]. The crystal structure of MD2 was obtained from Protein Data Bank (PDB ID: 2Z64). First, global rigid docking of TaY and MD2 was performed using HPEPDOCK (http://huanglab.phys.hust.edu.cn/hpepdock/, accessed on 2 July 2018) [58]. Local docking conformations were then optimized at the local server using ROSETTA’s FlexPepDock (http://flexpepdock.furmanlab.cs.huji.ac.il/, accessed on 27 May 2011), and the best docking conformations were selected based on scores [59]. Docking results were visualized using Discovery Studio 2021 and the interaction forces between the complex were analyzed.
3.9. Molecular Dynamics (MD) Simulations
GROMACS 2020.6 software was used to perform MD simulations of the YTP-TLR2 complex with the AMBER99SB-ILDN force field [60]. The complex was placed centrally in a dodecahedron box with a size of 1.2 nm and dissolved in water [46]. Na+/Cl− ions were added to maintain the system in a neutral environment [46]. Energy minimization was performed using the steepest descent algorithm and continued until the maximum force was less than 1000 kJ/mol/nm, with a step size of 0.01 [61]. NVT and NPT ensembles were simulated using the leap-frog algorithm for 1 ns, with the temperature and pressure set to 310 K and 1 bar, respectively [62]. Finally, a 200 ns MD simulation was performed. After the stimulation, the trajectory file was analyzed using GROMACS, and the root mean square deviation (RMSD) and radius of gyration (Rg) of the system were obtained.
3.10. Statistical Analysis
Statistical analysis was performed using GraphPad Prism v9.0. Student’s t-test was used for statistical comparisons. All data are expressed as mean ± SD of at least three independent experiments. Significance was claimed with p values ≤ 0.05. NS: p > 0.05, *: p ≤ 0.05, **: p ≤ 0.01, ***: p ≤ 0.001, ****: p ≤ 0.0001.
3.1. Synthesis of Peptides
Peptides were synthesized using the standard Fmoc solid-phase method by GL Biochem Ltd. (Shanghai, China). The purity of the peptides was 95%, and their relative masses were determined by MALDI-TOF-MS.
3.2. Cell Culture
Mouse macrophages (RAW264.7) were purchased from the Shanghai Cell Bank, Institute of Cell Biology, Chinese Academy of Sciences, and cultured in Duchenne’s Modified Eagle’s Medium (DMEM; HyClone, Utah, USA). The DMEM was supplemented with 10% (v/v) fetal bovine serum (Procell, Wuhan, China) and 1% (v/v) penicillin/streptomycin (Solarbio, Beijing, China), and incubated at 37 °C in a humidified environment (5% CO2, 95% air).
3.3. Cytotoxicity Analysis
The toxicity of TaY on RAW264.7 cells was assessed using the CCK-8 kit. RAW264.7 cells were seeded into 96-well plates at a density of 3.0 × 105 cells per well and cultured for 12 h. After 12 h, the cells were treated with different concentrations of peptides (0, 20, 40, 60, 80, 100, 200, 500, and 1000 μg/mL) for 24 h. Subsequently, 10 μL of CCK-8 solution was added to each well according to the kit instructions and the cells were incubated for 2 h at 37 °C in the dark. The absorbance of the reaction solution was measured at 450 nm, and cell viability was calculated using the following formula:Cell viability (%)=AS−ABAC−AB×100%
AS is the absorbance of the well containing cells, CCK-8, and peptides; AB is the absorbance of the well containing medium and CCK-8, but without cells; and AC is the absorbance of the well containing cells and CCK-8, but without peptides.
3.4. Determination of Nitric Oxide Content
The LPS-induced inflammatory macrophage model was performed according to our previously described methodology [56]. LPS from E. coli 0111:B4 was purchased from Sigma-Aldrich (St. Louis, MO, USA). RAW264.7 cells were cultured in 96-well plates at a density of 3.0 × 105 cells per well for 12 h. After 12 h of incubation, different concentrations of peptides (0, 1, 5, 10, 20, 40, 60, 80, and 100 μg/mL) were added to each well and incubated for another 3 h. Then, LPS was added to each well at a final concentration of 100 ng/mL and incubated for 24 h. The cell culture supernatants were used for the NO assay. NO levels were quantified using Griess reagents according to the manufacturer’s instructions (Beyotime, Beijing, China). Specifically, 50 μL of Griess A and 50 μL of Griess B were sequentially added to 50 μL of culture medium supernatant, and the absorbance of the reaction mixture was measured at 540 nm.
3.5. Enzyme-Linked Immunosorbent Assay (ELISA)
RAW 264.7 cells were cultured in 6-well plates at a density of 2.0 × 106 cells per well for 12 h. After 12 h of incubation, 100 μg/mL of TaY was added to each well and incubated for another 3 h. Then, LPS was added to each well at a final concentration of 100 ng/mL and incubated for 24 h. After 24 h of incubation with LPS, cell supernatants were collected and then assayed using ELISA kits (Solarbio, Beijing, China) for IL-6, and TNF-α according to the manufacturer’s instructions.
3.6. RNA Isolation and Quantitative Real-Time PCR
RAW 264.7 cells were cultured in 6-well plates at a density of 2.0 × 106 cells per well for 12 h. After 12 h of incubation, 100 μg/mL of TaY was added to each well and incubated for another 3 h. Then, LPS was added to each well at a final concentration of 100 ng/mL and incubated for 24 h. The cell pellets were the collected for RNA isolation. RNA isolation: total RNA was isolated using TRIzol reagent (Solarbio, Beijing, China), and the RNA integrity of all samples was assessed by agarose gel electrophoresis and NanoDrop. All cDNAs were synthesized using the HiScript® III 1st Strand cDNA Synthesis Kit (Vazyme, Nanjing, China) according to the manufacturer’s instructions. Table 2 lists the primers used in this study. A two-step amplification method was used in this study: preincubation at 95 °C for 30 s, denaturation at 95 °C for 5 s, and extension at 60 °C for 30 s for a total of 40 cycles, followed by 95 °C for 5 s, 60 °C for 60 s, and melting at 95 °C for 1 s for a total of 1 cycle. The gene expression was normalized by comparing the expression of the target gene with that of the housekeeping gene β-actin. Results are reported as fold increase in gene expression relative to control samples.
3.7. Western Blot Analysis
RAW 264.7 cells were cultured in 6-well plates at a density of 2.0 × 106 cells per well for 12 h. After 12 h of incubation, 100 μg/mL of TaY were added to each well and incubated for another 3 h. Then, LPS was added to each well at a final concentration of 100 ng/mL and incubated for 6 h. The cell pellets were then collected for protein extraction. Protein extraction was performed using RIPA, with protease inhibitors added proportionally, and protein concentrations were determined using the BCA method. Protein electrophoresis was performed using Tris-Gly SDS-PAGE, followed by membrane transfer using PVDF membranes. The membranes were blocket with 5% skim milk. The primary antibody for the target protein was incubated overnight. Then HRP-coupled secondary antibody was used to bind to the primary antibody, and the target protein was visulalized using a chemiluminescence visualizer. The following antibody were used in this study: p65 (1:5000), p-p65 (1:2000), IκB-α (1:5000), p-IκB-α (1:2000), IKK-α/β (1:5000), β-actin (1:10000) were purchased from Abmart (Shanghai, China); iNOS (1:2000) was purchased from proteintech (Wuhan, Hubei, China), p-IKK-α/β (1:2000) was purchased from Cell Signaling Technology (Beverly, MA, USA). The secondary antibody HRP-Goat Anti-Rabbit IgG (1:5000) was purchased from huaixngbio (Beijing, China).
3.8. Molecular Docking
The three-dimensional (3D) structure of the hybrid peptide TaY was built using PEPFOLD3.5 (https://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD3/, accessed on 1 December 2016) [57]. The crystal structure of MD2 was obtained from Protein Data Bank (PDB ID: 2Z64). First, global rigid docking of TaY and MD2 was performed using HPEPDOCK (http://huanglab.phys.hust.edu.cn/hpepdock/, accessed on 2 July 2018) [58]. Local docking conformations were then optimized at the local server using ROSETTA’s FlexPepDock (http://flexpepdock.furmanlab.cs.huji.ac.il/, accessed on 27 May 2011), and the best docking conformations were selected based on scores [59]. Docking results were visualized using Discovery Studio 2021 and the interaction forces between the complex were analyzed.
3.9. Molecular Dynamics (MD) Simulations
GROMACS 2020.6 software was used to perform MD simulations of the YTP-TLR2 complex with the AMBER99SB-ILDN force field [60]. The complex was placed centrally in a dodecahedron box with a size of 1.2 nm and dissolved in water [46]. Na+/Cl− ions were added to maintain the system in a neutral environment [46]. Energy minimization was performed using the steepest descent algorithm and continued until the maximum force was less than 1000 kJ/mol/nm, with a step size of 0.01 [61]. NVT and NPT ensembles were simulated using the leap-frog algorithm for 1 ns, with the temperature and pressure set to 310 K and 1 bar, respectively [62]. Finally, a 200 ns MD simulation was performed. After the stimulation, the trajectory file was analyzed using GROMACS, and the root mean square deviation (RMSD) and radius of gyration (Rg) of the system were obtained.
3.10. Statistical Analysis
Statistical analysis was performed using GraphPad Prism v9.0. Student’s t-test was used for statistical comparisons. All data are expressed as mean ± SD of at least three independent experiments. Significance was claimed with p values ≤ 0.05. NS: p > 0.05, *: p ≤ 0.05, **: p ≤ 0.01, ***: p ≤ 0.001, ****: p ≤ 0.0001.
4. Conclusions
In conclusion, our results suggest that TaY has promising potential as a peptide anti-inflammatory agent, which can reduce the activation of the TLR4-NF-κB inflammatory signaling pathway by LPS through the competitive binding of MD2 protein to LPS. The binding of TaY to MD2 reduces the phosphorylation levels of key proteins in the TLR4-NF-κB signaling pathway (IKK-α/β, IκBα), leading to decrease NF-κB nuclear translocation and ultimately a reduction in the expression of inflammatory factors (TNF-α, IL-6, NO, iNOS). These results demonstrate the potential of this peptide as a novel anti-inflammatory drug. In the future, we need to study the targets of TaY and MD2 to gain a deeper understanding of the anti-inflammatory mechanism of TaY. |
Title: International Academy of Cytology standardized reporting of breast fine-needle aspiration cytology with cyto-histopathological correlation of breast carcinoma | Body:
GRAPHICAL ABSTRACT
The prevalent perception of breast cancer as the most common kind of cancer affecting predominantly the Western hemisphere is no longer true. For instance, breast cancer was recently reported to have overtaken cervical cancer in India to become the most common cancer. According to the Indian Council of Medical Research, 150,000 new cases of breast cancer were reported in the year 2016 [1]. This surging new trend has directed the resolve of the national health planning sector toward identifying the best-available diagnostic tools for early-stage cancer detection. Mass deployment of such a tool would help to ensure better treatment outcomes marked by increased life expectancy of the survivors. By a majority consensus, fine-needle aspiration cytology (FNAC) was identified as one such ideal technique. The International Academy of Cytology (IAC) has formulated a process to ensure a standardized and comprehensive approach to FNAC reporting. Accordingly, they have categorized breast lesions into five categories, C1–C5 (C-code). At the inaugural Yokohama International Congress, attended by breast cancer specialists, there was a discussion on the use of a three- or a five-stage coding system. Congress attendees ultimately reached a consensus to employ a five-stage system: category 1, insufficient material; category 2, benign; category 3, atypical, probably benign; category 4, suspicious, probably in situ or invasive carcinoma; category 5, malignant.
Since the proposal of the aforementioned coding system, it has been widely employed on a worldwide scale. However, the respective definitions for categories 3 and 4 have recently come under scrutiny, as there is a recognized need for further debate on the grey area of atypia [2]. The IAC has attempted to refine these definitions by outlining specific criteria or scenarios wherein atypia can be deemed an appropriate diagnosis, which include the presence of epithelial hyperplasia with marked dispersal of columnar cells and minimal nuclear atypia, where the differential diagnosis is epithelial hyperplasia or low-grade intraductal carcinoma; the presence of intraductal papillomas with marked dispersal and diagnostic stellate papillary fragments, where the differential diagnosis is low-grade intraductal carcinoma; the presence of epithelial hyperplasia with a complex cribriform or micropapillary pattern, where the differential diagnosis is low-grade intraductal carcinoma; the presence of stromal hypercellularity without necrosis or nuclear atypia in a case of otherwise typical fibroadenoma, with consideration of the possibility of a low-grade phyllodes tumor; and the presence of smears with scant cellularity but minute epithelial fragments and single cells exhibiting eccentric cytoplasm, where the differential diagnosis is lobular carcinoma in situ or lobular carcinoma.
The IAC has established a checklist for FNAC of breast lesions, where an analytical approach based on cytological diagnostic criteria and pattern recognition can be used by cytopathologists for generating reports [2].
Histological grading of breast carcinoma employing the Elston-Ellis modification of the Scarff-Bloom-Richardson (SBR) grading system is a well-known and accepted approach with good correlation prognostically [3]. However, significant ambiguity persists around cytological grades. While many systems have been proposed to grade breast cancers preoperatively, the grading system of Robinson et al. best correlates with the SBR system [4-11].
In this current age of neo-adjuvant chemotherapy, it is highly recommended that the FNAC report incorporates grading of breast carcinoma. This would not only assist in the prognostication of breast cancer, but also be useful in cases that reject surgeries, cases with locally advanced disease, and cases of elderly patients with other co-morbidities [12,13].
Hence, the aim of the present study was to classify lesions of breast cancer diagnosed by FNAC according to the IAC scheme (C1–C5) with additional grading of malignant breast lesions (C5) using Robinson’s system and to finally correlate the cytological grading with the standard Elston-Ellis modification of the SBR system.
MATERIALS AND METHODS
The current retrospective study was undertaken in the Department of Pathology at a tertiary medical care center in Bangalore city. A total of 205 patients who underwent an FNAC procedure for breast lumps between January 1, 2020, and December 31, 2020, were included in the study. Any cases subjected to neo-adjuvant chemotherapy were excluded. The FNAC technique was performed using 10-cc syringes with a 22–23-gauge needle under aseptic conditions. The samples obtained were smeared on glass slides and fixed with 95% ethyl alcohol. Staining was performed using hematoxylin and eosin and Leishman’s method. IAC standardized reporting was used to classify all breast lesions into distinct C1–C5 categories. Further, the lesions that belonged to the C5 category were cytologically graded using Robinson’s system.
The subsequent mastectomy specimens of breast carcinoma, received in the histopathology department, were fixed in 10% formalin. These specimens were grossly and aptly sampled after adequate fixation. The tissue samples were processed, and slides were prepared. Staining was performed using hematoxylin and eosin stain. Histological typing and further grading were performed using the gold-standard Elston-Ellis modification of the SBR grading system. Finally, cytological and histological grades were correlated for all the breast carcinoma cases (Tables 1, 2) [3,4].
Statistical analysis, including descriptive analysis and contingency table analysis (cross-tabulation procedure), was performed using SPSS software ver. 20 (IBM Corp., Armonk, NY, USA).
RESULTS
In the present study, 205 patients aged 20–60 years underwent FNAC for breast masses. According to the IAC standardized reporting, they could be graded with a C-code (C1–C5). Follow-up data were available for 127 of the cases, among which 96 were benign and 31 were malignant (Table 3).
Category 1: Insufficient material
Smears that do not show epithelial cells are labeled as insufficient or inadequate collections. According to the MD Anderson Cancer Center Group’s proposal, the presence of 4–6 well-visualized groups ≥10 cells or flat sheets is considered adequate [14].
In the current study, the C1 category comprised six cases (2.9%), one of which was diagnosed as a case of benign breast disease on follow-up (Table 4).
Category 2: Benign
Category 2 comprised lesions showing benign epithelial clusters with no atypical or malignant features. High cellularity is rare in these cases, and cells present a low nucleocytoplasmic ratio, fine chromatin, no pleomorphism, and a smooth nuclear membrane. Fatty fragments are common, along with bare nuclei. Few cases show apocrine cells or histiocytes.
In the present study, most lesions belonged to the C2 category (151 cases, 73.7%). Among these 151 lesions, 64 (42.4%) were fibroadenomas, 31 (20.5%) were fibrocystic diseases, 24 (15.9%) were benign breast diseases, 12 (8.0%) were fibroadenoma with fibrocystic disease and gynecomastia, and 8 (5.3%) were inflammatory breast lesions (Fig. 1).
In the C2 category, follow-up data were available for 84 cases, including 47 cases of fibroadenomas, 18 cases of fibrocystic diseases, five cases of fibroadenomas with fibrocystic disease, four cases of gynecomastia, five cases of inflammatory breast lesions, and five cases of benign breast diseases (Table 4).
Category 3: Atypical, probably benign
Findings of category 3 lesions are similar to those in the benign category but with slight crowding, greater cellularity, pleomorphism, three-dimensional grouping, and nuclear enlargement.
C3 lesions in our study were present in 13 cases (6.4%), including four (30.8%) each of fibrocystic disease with atypia and benign fibroepithelial neoplasm, three cases (23%) of fibroadenoma with atypia, and one case (7.7%) each of benign phyllodes tumor and papillary neoplasm (Fig. 2).
Follow-up data were available for 10 C3 cases, including three cases each of fibrocystic disease with atypia and fibroadenoma with atypia, two cases of benign fibroepithelial neoplasms, and one case each of benign phyllodes and a papillary neoplasm (Table 5).
Category 4: Suspicious, probably in situ or invasive carcinoma
Category 4 lesions exhibit highly atypical findings that remain insufficient to label them malignant. Cases in this category include those with poorly preserved or hypocellular smears, those with focally atypical cells in a benign background, and those with an insufficient degree of atypia to classify C5 but where the atypia is greater than that of C3.
In the current study, C4 lesions suspicious for malignancy accounted for five cases (2.4%). On follow-up, three cases were confirmed to be ductal carcinoma; however, one case was negative for malignancy (Table 5).
Category 5: Malignant
These lesions show high cellularity. Cells are seen in singles or loose clusters, and malignant features such as hyperchromasia, high nucleocytoplasmic ratio, irregular nuclear contour, and conspicuous nucleoli are noted. Some cases show necrotic debris in the background.
In the present study, C5 lesions were the second most common entity, found in 30 cases (14.6%).
Follow-up was available for only 20 C5 cases, which were all confirmed to be ductal carcinoma (Table 5, Fig. 3).
All C5 cases underwent cytological grading using Robinson’s system [4], and histopathological grading was completed using the Elston-Ellis modification of the SBR grading system [3].
In the present study, the Robinson grades showed very good concordance (83.3%) with the modified (Elston-Ellis) SBR grades, with high correlation (0.746), high Kendall’s tau-b (0.736), high kappa value (0.661), substantial agreement, and a significant p-value (<.001) (Tables 6, 7, Fig. 4).
Category 1: Insufficient material
Smears that do not show epithelial cells are labeled as insufficient or inadequate collections. According to the MD Anderson Cancer Center Group’s proposal, the presence of 4–6 well-visualized groups ≥10 cells or flat sheets is considered adequate [14].
In the current study, the C1 category comprised six cases (2.9%), one of which was diagnosed as a case of benign breast disease on follow-up (Table 4).
Category 2: Benign
Category 2 comprised lesions showing benign epithelial clusters with no atypical or malignant features. High cellularity is rare in these cases, and cells present a low nucleocytoplasmic ratio, fine chromatin, no pleomorphism, and a smooth nuclear membrane. Fatty fragments are common, along with bare nuclei. Few cases show apocrine cells or histiocytes.
In the present study, most lesions belonged to the C2 category (151 cases, 73.7%). Among these 151 lesions, 64 (42.4%) were fibroadenomas, 31 (20.5%) were fibrocystic diseases, 24 (15.9%) were benign breast diseases, 12 (8.0%) were fibroadenoma with fibrocystic disease and gynecomastia, and 8 (5.3%) were inflammatory breast lesions (Fig. 1).
In the C2 category, follow-up data were available for 84 cases, including 47 cases of fibroadenomas, 18 cases of fibrocystic diseases, five cases of fibroadenomas with fibrocystic disease, four cases of gynecomastia, five cases of inflammatory breast lesions, and five cases of benign breast diseases (Table 4).
Category 3: Atypical, probably benign
Findings of category 3 lesions are similar to those in the benign category but with slight crowding, greater cellularity, pleomorphism, three-dimensional grouping, and nuclear enlargement.
C3 lesions in our study were present in 13 cases (6.4%), including four (30.8%) each of fibrocystic disease with atypia and benign fibroepithelial neoplasm, three cases (23%) of fibroadenoma with atypia, and one case (7.7%) each of benign phyllodes tumor and papillary neoplasm (Fig. 2).
Follow-up data were available for 10 C3 cases, including three cases each of fibrocystic disease with atypia and fibroadenoma with atypia, two cases of benign fibroepithelial neoplasms, and one case each of benign phyllodes and a papillary neoplasm (Table 5).
Category 4: Suspicious, probably in situ or invasive carcinoma
Category 4 lesions exhibit highly atypical findings that remain insufficient to label them malignant. Cases in this category include those with poorly preserved or hypocellular smears, those with focally atypical cells in a benign background, and those with an insufficient degree of atypia to classify C5 but where the atypia is greater than that of C3.
In the current study, C4 lesions suspicious for malignancy accounted for five cases (2.4%). On follow-up, three cases were confirmed to be ductal carcinoma; however, one case was negative for malignancy (Table 5).
Category 5: Malignant
These lesions show high cellularity. Cells are seen in singles or loose clusters, and malignant features such as hyperchromasia, high nucleocytoplasmic ratio, irregular nuclear contour, and conspicuous nucleoli are noted. Some cases show necrotic debris in the background.
In the present study, C5 lesions were the second most common entity, found in 30 cases (14.6%).
Follow-up was available for only 20 C5 cases, which were all confirmed to be ductal carcinoma (Table 5, Fig. 3).
All C5 cases underwent cytological grading using Robinson’s system [4], and histopathological grading was completed using the Elston-Ellis modification of the SBR grading system [3].
In the present study, the Robinson grades showed very good concordance (83.3%) with the modified (Elston-Ellis) SBR grades, with high correlation (0.746), high Kendall’s tau-b (0.736), high kappa value (0.661), substantial agreement, and a significant p-value (<.001) (Tables 6, 7, Fig. 4).
DISCUSSION
Diagnosing breast cancers at an early stage is imperative for favorable treatment outcomes, but cases often go undiagnosed for socio-economic reasons. Hence, it is of paramount importance that an easy, cost-effective, and reliable investigation like FNAC be performed in such circumstances [15]. FNAC is a rapid, accurate, and relatively painless procedure [16]; however, translation of cytological patterns into histopathological patterns can be difficult, hindering diagnosis [17]. In such a scenario, structured reporting using IAC guidelines will not only help improve the quality and accuracy of the reports, but also ensure reproducibility of the findings on a global scale [2].
Category 1
Cases in the C1 category include those with an inadequate/insufficient sample, such as cases marked by hypocellular smears, errors in spreading/staining, excessive blood, crushing artifacts, degenerated cells, and poor fixation. However, cases with cysts, abscess, fat necrosis, or intra-mammary lymph nodes are not considered C1. The risk for malignancy among C1 cases is 4.8% [18]. As shown in Table 8, our study documented a C1 prevalence of 2.9%, which was identical to that reported by Haobam et al. [19]. Meanwhile, Georgieva et al. [20] and Bajwa and Zulfiqar [21] reported much higher frequencies of C1 cases, which could be explained partly by the use of faulty techniques and inadequate expertise [22].
Category 2
Cases in the C2 category are considered benign and include instances of fibroadenoma, fat necrosis, abscess, granulomatous mastitis, and other benign entities such as intra-mammary lymph nodes. Cytology smears in these cases will demonstrate a regular ductal epithelium, cysts, or fibrofatty fragments depending on the etiology. The risk for malignancy is 1.4% [18].
Most of the cases in our study were C2 cases (73.7%), concurring with rates in other studies (Table 8). Among the C2 cases, fibroadenomas were most common (42%), as reported by Modi et al. [22], Panwar et al. [1], and Bajwa and Zulfiqar [21]. The second most common C2 lesions were those indicating fibrocystic disease (21%), as in Bajwa and Zulfiqar [21].
Category 3
Cases in the C3 category are atypical but probably benign and include papillary lesions and suspected phyllodes tumors. Cytology smears in such instances may show pleomorphism, increased cellularity, and loss of cohesion. The risk of malignancy is 13% [18]. No definitive surgery is recommended for cases in this category. The prevalence of C3 cases was 6.4% in the present study, similar to reports by both Bajwa and Zulfiqar [21] and Panwar et al. [1]. Meanwhile, Georgieva et al. [20] encountered a much lower frequency of C3 lesions (Table 8).
Category 4
Category 4 cases are suspicious for malignancy. Cytology findings may show scant/poor preservation of cells, along with occasional malignant cells or some features of malignancy. The overall risk of malignancy is 97.1% [18]. However, no definitive surgery is recommended for this category. Panwar et al. [1] reported very few cases (1.7%) of C4 lesions, which concurred with observations in our study. However, other studies have reported higher frequencies of C4 cases (Table 8).
Category 5
Category 5 is the malignant category. Here, cytology smears will show overt malignant features like a high nucleo-cytoplasmic ratio, hyperchromasia, irregular nuclear contours, and conspicuous nucleoli. The risk for malignancy is 100% [18]. C5 lesions were the second most common lesions after C2 lesions in our and other studies, which indicates a rising trend in the frequency of breast cancer cases.
In the present study, malignant cases (C5) were accurately identified by FNAC as later confirmed by histopathology. This was also true in Panwar et al. [1]. However, two cases (C5) were lost to follow-up in our study (Table 8).
Robinson’s cytological system was used to grade all C5 lesions. This system is an excellent predictor of the aggressiveness of a tumor; compared to the standard histological grading approach, i.e., the modified SBR system, it exhibits substantial correlation in terms of Spearman’s rank, Kendall’s tau-b rank, concordance, kappa value, and p-value. This finding was reported in other studies like those by Arul and Masilamani [23] and Saha et al. [24] (Table 9) [25-28].
A standardized approach as advocated for by the IAC for FNAC reporting of breast lesions will not only improve the quality and clarity of reports, but also assures their reproducibility, internationally. Furthermore, the cytological grading of C5 lesions provides cyto-prognostic scores that can help assess the aggressiveness of a tumor and predict its histological grade.
Category 1
Cases in the C1 category include those with an inadequate/insufficient sample, such as cases marked by hypocellular smears, errors in spreading/staining, excessive blood, crushing artifacts, degenerated cells, and poor fixation. However, cases with cysts, abscess, fat necrosis, or intra-mammary lymph nodes are not considered C1. The risk for malignancy among C1 cases is 4.8% [18]. As shown in Table 8, our study documented a C1 prevalence of 2.9%, which was identical to that reported by Haobam et al. [19]. Meanwhile, Georgieva et al. [20] and Bajwa and Zulfiqar [21] reported much higher frequencies of C1 cases, which could be explained partly by the use of faulty techniques and inadequate expertise [22].
Category 2
Cases in the C2 category are considered benign and include instances of fibroadenoma, fat necrosis, abscess, granulomatous mastitis, and other benign entities such as intra-mammary lymph nodes. Cytology smears in these cases will demonstrate a regular ductal epithelium, cysts, or fibrofatty fragments depending on the etiology. The risk for malignancy is 1.4% [18].
Most of the cases in our study were C2 cases (73.7%), concurring with rates in other studies (Table 8). Among the C2 cases, fibroadenomas were most common (42%), as reported by Modi et al. [22], Panwar et al. [1], and Bajwa and Zulfiqar [21]. The second most common C2 lesions were those indicating fibrocystic disease (21%), as in Bajwa and Zulfiqar [21].
Category 3
Cases in the C3 category are atypical but probably benign and include papillary lesions and suspected phyllodes tumors. Cytology smears in such instances may show pleomorphism, increased cellularity, and loss of cohesion. The risk of malignancy is 13% [18]. No definitive surgery is recommended for cases in this category. The prevalence of C3 cases was 6.4% in the present study, similar to reports by both Bajwa and Zulfiqar [21] and Panwar et al. [1]. Meanwhile, Georgieva et al. [20] encountered a much lower frequency of C3 lesions (Table 8).
Category 4
Category 4 cases are suspicious for malignancy. Cytology findings may show scant/poor preservation of cells, along with occasional malignant cells or some features of malignancy. The overall risk of malignancy is 97.1% [18]. However, no definitive surgery is recommended for this category. Panwar et al. [1] reported very few cases (1.7%) of C4 lesions, which concurred with observations in our study. However, other studies have reported higher frequencies of C4 cases (Table 8).
Category 5
Category 5 is the malignant category. Here, cytology smears will show overt malignant features like a high nucleo-cytoplasmic ratio, hyperchromasia, irregular nuclear contours, and conspicuous nucleoli. The risk for malignancy is 100% [18]. C5 lesions were the second most common lesions after C2 lesions in our and other studies, which indicates a rising trend in the frequency of breast cancer cases.
In the present study, malignant cases (C5) were accurately identified by FNAC as later confirmed by histopathology. This was also true in Panwar et al. [1]. However, two cases (C5) were lost to follow-up in our study (Table 8).
Robinson’s cytological system was used to grade all C5 lesions. This system is an excellent predictor of the aggressiveness of a tumor; compared to the standard histological grading approach, i.e., the modified SBR system, it exhibits substantial correlation in terms of Spearman’s rank, Kendall’s tau-b rank, concordance, kappa value, and p-value. This finding was reported in other studies like those by Arul and Masilamani [23] and Saha et al. [24] (Table 9) [25-28].
A standardized approach as advocated for by the IAC for FNAC reporting of breast lesions will not only improve the quality and clarity of reports, but also assures their reproducibility, internationally. Furthermore, the cytological grading of C5 lesions provides cyto-prognostic scores that can help assess the aggressiveness of a tumor and predict its histological grade. |
Title: Pathway linking attachment styles to post-traumatic growth among recovered COVID-19 patients: testing the mediating role of coping styles | Body:
1.
Introduction
The novel COVID-19 infection has presented a pervasive health emergency for years (Guan et al., 2020). Since the beginning of the pandemic in early 2020, over 697 million diagnosed cases and 6 million deaths were confirmed globally (WHO, 2021). The main clinical manifestation of the virus was diffuse alveolar damage which was characterized by a mild to severe range of symptoms affecting the respiratory system such as cough, fever, pneumonia, and acute respiratory failure (Huang et al., 2020). Given the fluctuating and continuous nature of the covid-19 outbreak and its potential for direct and indirect impact on human beings, it posed a major traumatic event for people (Northfield & Johnston, 2021). Growing evidence revealed such negative psychological outcomes and exacerbated mental health concerns during the covid-19 pandemic, such as sub-clinical and clinical levels of depression, anxiety, and posttraumatic stress symptoms among different populations (Hossain et al., 2020; Vita et al., 2023). Specifically, individuals infected with the virus have faced significant challenges and threats to their life and mental well-being both during the course of the disease and after recovery (Lebeaut et al., 2023). Extensive studies on patients with COVID-19 have highlighted the psychopathological symptoms (Zhang et al., 2020).
Post-disaster mental health literature indicates that individuals may have unique resources to develop the ability to experience some salutogenic aspects such as positive transformation and posttraumatic growth (PTG) in the aftermath of adversities (Tedeschi & Calhoun, 2004b). As a concept proposed by Tedeschi & Calhoun (1996), PTG refers to the development of significant positive and meaningful changes which result from struggling with traumatic life circumstances. According to the ‘shattered assumptions theory’ (Janoff-Bulman, 1992, 2010), exposure to trauma through shattering the belief system of individuals, leads to cognitive imbalance. Taking into account the necessity for cognitive reconstruction and reframing, individuals establish the alternative perspectives as a ‘new normal’ which includes positive posttraumatic views of self-perception, life philosophy, and interpersonal relationships, and this cognitive process is conceptualized as PTG (Tedeschi & Calhoun, 1996, 2004a).
The predominant framework for understanding growth after trauma revolves around five key domains: relating to others, new possibilities, personal strength, spiritual change, and appreciation of life. ‘Relating to others’ refers to the development of closer relationships and a heightened appreciation for family and friends. ‘New possibilities’ entail perceiving opportunities for change, such as pursuing education, career shifts, or embarking on new paths. Some individuals recognize their ‘personal strength,’ realizing they are more resilient or capable than previously believed. For others, growth manifests as a deepened spiritual belief. Lastly, many report a newfound ‘appreciation of life,’ leading to reevaluation of life's priorities (Tedeschi & Calhoun, 1996).PTG has been a point of interest in healthcare domains over recent years. Studies have demonstrated that PTG can manifest in individuals who have faced life-threatening health conditions, such as cancer (Zhai et al., 2019), HIV infection (Ye et al., 2018), chronic pain (Ayache et al., 2021), and epidemics like Severe Acute Respiratory Syndrome (SARS) (Cheng et al., 2006). Various psychological factors can facilitate PTG in people (Tedeschi & Calhoun, 2004a). Among them, the quality of interpersonal relationships regarding the parent-to-child attachment during childhood can influence the way in which a person evaluates a traumatic event and its sequelae (Chris Fraley, 2002). Attachment is an emotional bond that connects one individual to another (Bowlby, 1969). The quality of the attachment relationship between a primary caregiver and an infant shapes the internal working models of self and others. Ainsworth (1979) categorizes attachment styles into three main types: secure, insecure-anxious ambivalent, and insecure-avoidant. Adults with secure attachment styles are comfortable with intimacy and can easily depend on others while also being able to maintain independence. They do not fear abandonment or being alone. In contrast, individuals with anxious attachment styles tend to worry about rejection and are preoccupied with their relationships, often feeling insecure about their connections with others. Avoidant adults are uneasy with closeness and intimacy, often distrusting others and avoiding emotional closeness in relationships (Hazan & Shaver, 1987). Attachment theory proposes that early experiences with attachment figures provide a base for internal representations of relationships that influence various life domains (Bowlby, 1982). Thereby, it contributes a beneficial framework to learning about individual differences in the ways they regulate emotions and cope with trauma (Mikulincer & Shaver, 2010). There is a significant positive relationship between secure attachment and PTG among various traumatized populations (Gleeson et al., 2021). Securely attached individuals usually appraise people as responsive and predictable, and count on their protection and relief when needed, which leads to more functional emotion regulation and makes it easier to adapt to adverse events (Wu & Yang, 2012). Conversely, an anxiously/ambivalent attached individual is suffered from rejection and unavailability of a significant other which lead to forming a negative self-concept (Bartholomew & Horowitz, 1991), and can suppress the experience of the unique domains of growth, such as an appraisal of personal strength (Gleeson et al., 2021). They utilize hyper-activating strategies to manage intense emotions and seek support (Ein-Dor et al., 2010a, 2010b). Given the inhibition of self-regulation, it is improbable that a significant relationship with PTG exists, as investigations have shown (Romeo et al., 2019). Furthermore, given that evidence has supported the negative (Levi-Belz & Lev-Ari, 2018) or non-significant relationship (Dekel et al., 2011) between avoidant attachment and development of PTG, it is worth noting that an avoidant attachment style lays the foundation for mistrust, which is usually resolved using deactivating emotion regulation mechanisms to dismiss threat-related cues and avoid distress (Arikan et al., 2016).
Coping is another important concept found to be associated with PTG (Schmidt et al., 2019), and refers to a cognitive or behavioural effort to manage a situation perceived as stressful (Folkman et al., 1986). Coping styles can be classified into problem-focused, which involves addressing the underlying issues and trying to resolve the problem; emotion-focused, which includes releasing emotions, disengaging emotionally from stressors, and seeking emotional support; and avoidant, which involves social withdrawal and maladaptive avoidance of the traumatic situation (Finstad et al., 2021). Problem-focused coping has been found to be a key factor relating to PTG (Karanci & Erkam, 2007), whereas emotion-focused and avoidance coping have been usually related to poor psychological consequences (Tuncay & Musabak, 2015). There is substantial evidence that coping mediates pathways to facilitate PTG in diseases (Bellur et al., 2018). Given such mediating role, coping also may influence the pathways of attachment-specific emotion regulation and response to trauma (Folkman & Moskowitz, 2004), in which active coping styles (i.e. problem-focused) are more likely to mediate the relationship of secure attachment with PTG (Schmidt et al., 2012). Regarding the alternative pathways connecting attachment to the experience of PTG, coping styles can play a significant role, but research findings have been inconsistent (Gleeson et al., 2021).
PTG is influenced by factors such as secure attachment and coping styles. According to the existing literature, securely attached individuals tend to experience more PTG, while coping strategies, particularly problem-focused coping, play a mediating role in the relationship between attachment and PTG. Understanding these psychological dynamics is crucial for addressing the mental health challenges posed by the pandemic. In light of these pieces of evidence, the current investigations are mainly focused on the negative psychological impact of the covid-19 pandemic. Studies on PTG also focused on the healthy population (Zeng et al., 2021). Hence, it is necessary to investigate the positive and adaptive aspects of the novel pandemic in the population of recovered patients and explore the mechanisms and pathways through which the attachment system relates to PTG. To this end, we aimed to clarify the three following research questions. First, we conducted an assessment of the model fit with empirical data, ensuring that the theoretical framework aligns well with the observed relationships among attachment styles, coping strategies, and PTG in recovered COVID-19 patients. This step was crucial in establishing the validity of our research design and confirming that our hypothesized relationships were supported by the data collected. Second, we investigated the direct effect of attachment styles on PTG among recovered COVID-19 patients. This analysis aimed to determine the extent to which attachment styles, specifically secure, ambivalent-anxious, and avoidant styles, directly influence the experience of PTG, independent of other variables such as coping strategies. Third, we assessed the mediating effect of coping styles on the associations between attachment styles and PTG in recovered COVID-19 patients. This analysis aimed to understand the extent to which coping strategies, such as problem-focused, emotion-focused, and avoidant coping, act as intermediaries in the relationship between attachment styles and PTG, shedding light on the underlying mechanisms of psychological adjustment following trauma. We hypothesized that secure attachment style would be positively associated with PTG, while ambivalent-anxious and avoidant attachment styles would be negatively associated with PTG. Additionally, we hypothesized that problem-focused coping would positively mediate the relationship between secure attachment style and PTG, while emotion-focused and avoidant coping would negatively mediate the relationship of ambivalent-anxious and avoidant attachment styles with PTG. As far as we know, there is a research gap in this field and this is the first study that has examined the mediating role of coping styles in the structural relationship between attachment styles with PTG in patients recovered from COVID-19.
2.
Methods
2.1.
Participants and procedures
The current study was a cross-sectional survey study focused on individuals who had survived COVID-19. From this population, a study sample of 430 volunteers were recruited. The eligibility criteria comprised individuals who met the following conditions: (a) having a confirmed clinical diagnosis of COVID-19, (b) undergoing standard COVID-19 interventions, (c) not having a history of significant psychiatric or neurological illnesses, (d) falling within the age range of 18–65 years, and (e) having at least an elementary school level of education. Following the acquisition of the necessary approvals, data on recuperated patients, including their contact information, were obtained from a number of hospitals in Tabriz, Iran. Because of the ongoing pandemic situation and the specific patient group under investigation, ensuring the participants’ safety and well-being was a priority. Consequently, data collection was conducted online to minimize face-to-face interactions and adhere to social distancing guidelines. The data collection process took place from January through March 2021, with an average time elapsed since recovery from COVID-19 of 2 months. An initial screening process was carried out to ensure data quality and minimize potential outliers, resulting in the deletion of 220 participants. The reduction in participants was driven by several factors. Initially, we excluded 62 participants who did not fully complete the study questionnaires and did not adhere to the study’s guidelines. Also, a data cleaning process was undertaken to validate responses and identify any inconsistencies or discrepancies, resulting in deletion of 42 participants. Subsequently, an outlier analysis was conducted to identify responses that deviated from the norm. The determination of outliers was based on statistical criteria, such as extreme scores or patterns of responses that were inconsistent with the majority of participants and aimed at identifying responses that fell outside of the expected range in a significant and anomalous manner, using techniques such as interquartile range (IQR) in box plots and Mahalanobis distance. Univariate outliers were identified and removed using box plot analysis. This method visually displays the distribution of data and highlights any values that fall significantly outside the interquartile range. This approach led to the identification of 54 participants with univariate outliers. In addition, multivariate outliers were identified and removed using the Mahalanobis distance method. The Mahalanobis distance measures the correlations between variables to detect outliers in a multivariate context. A significance threshold of p < .001 was applied, resulting in the identification of 43 participants as multivariate outliers. In total, 97 participants were identified as outliers and excluded from the analysis. 19 participants also voluntarily withdrew from the study for personal reasons. As a result, a final sample of 210 people remained for data analysis.
2.2.
Ethical considerations
Ethical approval for this study was granted by the Research Ethics Committee of the University of Tabriz (Approval code: IR.TABRIZU.REC.1400.007). Prior to data collection, all participants received detailed information about the study's objectives, procedures, and potential risks. It was emphasized that participation in the study was entirely voluntary, and individuals had the right to withdraw from the study at any time without facing any negative consequences. Participants electronically provided their informed consent by clicking the ‘agree to participate in the research’ button, indicating their willingness to take part in the study. Throughout the research process, we were diligent in preserving participant confidentiality and ensuring data security. Additionally, all data were anonymized to protect the identities of the participants.
2.3.
Measures
2.3.1.
Posttraumatic growth inventory (PTGI)
The PTGI assesses the degree of positive transformation experienced following a traumatic event (Tedeschi & Calhoun, 1996). The inventory breaks down positive life changes into different areas including relationships with others, personal strength, new possibilities, appreciation of life, and religious/spiritual change. Participants rate these changes on a Six Point Likert scale from 0 to 5 (from ‘not at all’ to ‘a very great degree’). The overall PTGI score was obtained by summing the ratings of all items, where higher scores signified greater levels of PTG. Previous research has shown that the PTGI total score and subscale scores are consistent and reliable (e.g. Morris et al., 2012). Within our dataset, the internal reliability of the PTGI proved satisfactory, with a Cronbach's α coefficient of 0.90 for the entire instrument and ranging from 0.50 to 0.80 for its subdomains We employed the Persian translation of the PTGI in our study.
2.3.2.
Revised adult attachment scale (RAAS)
The RAAS, developed by Collins in 1996, builds upon the foundation established by the Adult Attachment Scale (AAS), initially conceptualized by Collins and Read in 1990. Its purpose is to assess individual differences in attachment styles. This 18-item scale is structured into three subscales, each consisting of 6 items rated on a 5-point Likert scale (1 = Not at all characteristic of me, 5 = Extremely characteristic of me) to generate a total score (18–90). ‘Close Subscale’ measures the level of comfort and ease an individual experiences in intimacy and emotional closeness. ‘Depend Subscale’ assesses the extent of an individual's trust and reliance on others, and ‘Anxiety Subscale’ measures the level of fear an individual has of abandonment as well as the fear of establishing connections with others. The revised scale was improved by replacing specific elements within its subscales. These changes involved improving reliability, addressing wording issues, and introducing new items that focus on ambivalence about relationships (Collins, 1996). The scale has demonstrated good reliability in Iran. For instance, in a study conducted by Mohammadi et al. (2016), the internal consistency of the scale was reported as 0.68 using Cronbach's alpha. Furthermore, in Pakdaman's (2004) study, the Cronbach's alpha coefficients were 0.52, 0.28, and 0.74 for the close, depend, and anxiety subscales, respectively. The internal consistency for the present study yielded a coefficient of 0.80. We utilized the Persian translation of this questionnaire.
2.3.3.
Coping inventory for stressful situations (CISS)
The CISS (Endler & Parker, 1990) is a 48-item self-report inventory. Respondents are asked to score their involvement in various coping strategies when facing stressful situations on a 5-point Likert scale ranging from 1 (Not at all) to 5 (Very much). The CISS consists of three 16-item subscales that evaluate Emotion-oriented coping (Emotion scale), Task-oriented coping (Task scale), and Avoidance (Avoidance scale). The scores within each of the three subscales range from 16 to 80, with higher scores indicating a greater level of coping activities specific to that subscale. In an Iranian sample of respondents, Shokri et al. (2009) reported Cronbach's alpha coefficients of 0.84 for the Emotion scale, 0.86 for the Task scale, and 0.80 for the Avoidance scale, underscoring the strong reliability of the scale. For this research, we employed a Persian-translated version of the CISS for participants. Cronbach's alpha in the present study was acceptable (α = 0.86).
2.4.
Data analyses
Data were analyzed using SPSS Version 20.0 and AMOS Version 24.0. Firstly, an initial outlier analysis was conducted to identify responses that deviated from the norm. This analysis, based on statistical criteria such as extreme scores or inconsistent response patterns, identified 97 participants as outliers, who were then excluded from the analysis. After that, the normality was assessed through the coefficients of skewness (Sk) and kurtosis (Ku). Based on the calculated values, all the variables displayed suitable conformity to a normal distribution, with the highest skewness value recorded at 0.60 and a kurtosis value of 1.10. Subsequently, the dataset underwent examination to identify multivariate outliers using Mahalanobis D2. No notable outliers were observed (df = 6, χ2 < 22.46), and all variable distributions displayed characteristics consistent with normality. Descriptive statistics were obtained utilizing measures like percentages, means, and standard deviations (SD). Cronbach's alphas were computed to assess the scales’ reliability. Also, we used the Pearson correlation analysis to investigate the direction and degree of relationships between the primary research variables.
We utilized structural equation modelling (SEM) to assess the proposed model, investigating how attachment styles influence PTG through coping styles. SEM was chosen for its ability to provide standardized regression coefficients and p-values for both direct and indirect effects. To evaluate the significance of the indirect effects, we used the bootstrapping method, a resampling technique (1000 resampling) with 95% confidence intervals (CI). In this method, if the upper and lower 95% confidence intervals do not encompass zero, the indirect effects are considered statistically significant (Preacher & Hayes, 2008). The parameters were estimated using the Maximum Likelihood Estimation method. Model goodness-of-fit was evaluated by employing four indices in accordance with the fit criteria suggested by Hu and Bentler (1999). The indices include the relative chi-square (χ2/df; values below 3.0 are typically seen as indicative of a good fit), the Root Mean Square Error of Approximation (RMSEA) and its 90% Confidence Interval (CI) (with values close to 0.06 indicating a good fit and values up to 0.08 indicating an acceptable fit), the Bentler-Bonett Normed Fit Index (NFI; with values of 0.90 or higher indicating an acceptable fit and values of 0.95 or higher indicating a good fit), and the Comparative Fit Index (CFI; with values of 0.90 or higher indicating an acceptable fit and values of 0.95 or higher indicating a good fit) (Kline, 2023).
2.1.
Participants and procedures
The current study was a cross-sectional survey study focused on individuals who had survived COVID-19. From this population, a study sample of 430 volunteers were recruited. The eligibility criteria comprised individuals who met the following conditions: (a) having a confirmed clinical diagnosis of COVID-19, (b) undergoing standard COVID-19 interventions, (c) not having a history of significant psychiatric or neurological illnesses, (d) falling within the age range of 18–65 years, and (e) having at least an elementary school level of education. Following the acquisition of the necessary approvals, data on recuperated patients, including their contact information, were obtained from a number of hospitals in Tabriz, Iran. Because of the ongoing pandemic situation and the specific patient group under investigation, ensuring the participants’ safety and well-being was a priority. Consequently, data collection was conducted online to minimize face-to-face interactions and adhere to social distancing guidelines. The data collection process took place from January through March 2021, with an average time elapsed since recovery from COVID-19 of 2 months. An initial screening process was carried out to ensure data quality and minimize potential outliers, resulting in the deletion of 220 participants. The reduction in participants was driven by several factors. Initially, we excluded 62 participants who did not fully complete the study questionnaires and did not adhere to the study’s guidelines. Also, a data cleaning process was undertaken to validate responses and identify any inconsistencies or discrepancies, resulting in deletion of 42 participants. Subsequently, an outlier analysis was conducted to identify responses that deviated from the norm. The determination of outliers was based on statistical criteria, such as extreme scores or patterns of responses that were inconsistent with the majority of participants and aimed at identifying responses that fell outside of the expected range in a significant and anomalous manner, using techniques such as interquartile range (IQR) in box plots and Mahalanobis distance. Univariate outliers were identified and removed using box plot analysis. This method visually displays the distribution of data and highlights any values that fall significantly outside the interquartile range. This approach led to the identification of 54 participants with univariate outliers. In addition, multivariate outliers were identified and removed using the Mahalanobis distance method. The Mahalanobis distance measures the correlations between variables to detect outliers in a multivariate context. A significance threshold of p < .001 was applied, resulting in the identification of 43 participants as multivariate outliers. In total, 97 participants were identified as outliers and excluded from the analysis. 19 participants also voluntarily withdrew from the study for personal reasons. As a result, a final sample of 210 people remained for data analysis.
2.2.
Ethical considerations
Ethical approval for this study was granted by the Research Ethics Committee of the University of Tabriz (Approval code: IR.TABRIZU.REC.1400.007). Prior to data collection, all participants received detailed information about the study's objectives, procedures, and potential risks. It was emphasized that participation in the study was entirely voluntary, and individuals had the right to withdraw from the study at any time without facing any negative consequences. Participants electronically provided their informed consent by clicking the ‘agree to participate in the research’ button, indicating their willingness to take part in the study. Throughout the research process, we were diligent in preserving participant confidentiality and ensuring data security. Additionally, all data were anonymized to protect the identities of the participants.
2.3.
Measures
2.3.1.
Posttraumatic growth inventory (PTGI)
The PTGI assesses the degree of positive transformation experienced following a traumatic event (Tedeschi & Calhoun, 1996). The inventory breaks down positive life changes into different areas including relationships with others, personal strength, new possibilities, appreciation of life, and religious/spiritual change. Participants rate these changes on a Six Point Likert scale from 0 to 5 (from ‘not at all’ to ‘a very great degree’). The overall PTGI score was obtained by summing the ratings of all items, where higher scores signified greater levels of PTG. Previous research has shown that the PTGI total score and subscale scores are consistent and reliable (e.g. Morris et al., 2012). Within our dataset, the internal reliability of the PTGI proved satisfactory, with a Cronbach's α coefficient of 0.90 for the entire instrument and ranging from 0.50 to 0.80 for its subdomains We employed the Persian translation of the PTGI in our study.
2.3.2.
Revised adult attachment scale (RAAS)
The RAAS, developed by Collins in 1996, builds upon the foundation established by the Adult Attachment Scale (AAS), initially conceptualized by Collins and Read in 1990. Its purpose is to assess individual differences in attachment styles. This 18-item scale is structured into three subscales, each consisting of 6 items rated on a 5-point Likert scale (1 = Not at all characteristic of me, 5 = Extremely characteristic of me) to generate a total score (18–90). ‘Close Subscale’ measures the level of comfort and ease an individual experiences in intimacy and emotional closeness. ‘Depend Subscale’ assesses the extent of an individual's trust and reliance on others, and ‘Anxiety Subscale’ measures the level of fear an individual has of abandonment as well as the fear of establishing connections with others. The revised scale was improved by replacing specific elements within its subscales. These changes involved improving reliability, addressing wording issues, and introducing new items that focus on ambivalence about relationships (Collins, 1996). The scale has demonstrated good reliability in Iran. For instance, in a study conducted by Mohammadi et al. (2016), the internal consistency of the scale was reported as 0.68 using Cronbach's alpha. Furthermore, in Pakdaman's (2004) study, the Cronbach's alpha coefficients were 0.52, 0.28, and 0.74 for the close, depend, and anxiety subscales, respectively. The internal consistency for the present study yielded a coefficient of 0.80. We utilized the Persian translation of this questionnaire.
2.3.3.
Coping inventory for stressful situations (CISS)
The CISS (Endler & Parker, 1990) is a 48-item self-report inventory. Respondents are asked to score their involvement in various coping strategies when facing stressful situations on a 5-point Likert scale ranging from 1 (Not at all) to 5 (Very much). The CISS consists of three 16-item subscales that evaluate Emotion-oriented coping (Emotion scale), Task-oriented coping (Task scale), and Avoidance (Avoidance scale). The scores within each of the three subscales range from 16 to 80, with higher scores indicating a greater level of coping activities specific to that subscale. In an Iranian sample of respondents, Shokri et al. (2009) reported Cronbach's alpha coefficients of 0.84 for the Emotion scale, 0.86 for the Task scale, and 0.80 for the Avoidance scale, underscoring the strong reliability of the scale. For this research, we employed a Persian-translated version of the CISS for participants. Cronbach's alpha in the present study was acceptable (α = 0.86).
2.3.1.
Posttraumatic growth inventory (PTGI)
The PTGI assesses the degree of positive transformation experienced following a traumatic event (Tedeschi & Calhoun, 1996). The inventory breaks down positive life changes into different areas including relationships with others, personal strength, new possibilities, appreciation of life, and religious/spiritual change. Participants rate these changes on a Six Point Likert scale from 0 to 5 (from ‘not at all’ to ‘a very great degree’). The overall PTGI score was obtained by summing the ratings of all items, where higher scores signified greater levels of PTG. Previous research has shown that the PTGI total score and subscale scores are consistent and reliable (e.g. Morris et al., 2012). Within our dataset, the internal reliability of the PTGI proved satisfactory, with a Cronbach's α coefficient of 0.90 for the entire instrument and ranging from 0.50 to 0.80 for its subdomains We employed the Persian translation of the PTGI in our study.
2.3.2.
Revised adult attachment scale (RAAS)
The RAAS, developed by Collins in 1996, builds upon the foundation established by the Adult Attachment Scale (AAS), initially conceptualized by Collins and Read in 1990. Its purpose is to assess individual differences in attachment styles. This 18-item scale is structured into three subscales, each consisting of 6 items rated on a 5-point Likert scale (1 = Not at all characteristic of me, 5 = Extremely characteristic of me) to generate a total score (18–90). ‘Close Subscale’ measures the level of comfort and ease an individual experiences in intimacy and emotional closeness. ‘Depend Subscale’ assesses the extent of an individual's trust and reliance on others, and ‘Anxiety Subscale’ measures the level of fear an individual has of abandonment as well as the fear of establishing connections with others. The revised scale was improved by replacing specific elements within its subscales. These changes involved improving reliability, addressing wording issues, and introducing new items that focus on ambivalence about relationships (Collins, 1996). The scale has demonstrated good reliability in Iran. For instance, in a study conducted by Mohammadi et al. (2016), the internal consistency of the scale was reported as 0.68 using Cronbach's alpha. Furthermore, in Pakdaman's (2004) study, the Cronbach's alpha coefficients were 0.52, 0.28, and 0.74 for the close, depend, and anxiety subscales, respectively. The internal consistency for the present study yielded a coefficient of 0.80. We utilized the Persian translation of this questionnaire.
2.3.3.
Coping inventory for stressful situations (CISS)
The CISS (Endler & Parker, 1990) is a 48-item self-report inventory. Respondents are asked to score their involvement in various coping strategies when facing stressful situations on a 5-point Likert scale ranging from 1 (Not at all) to 5 (Very much). The CISS consists of three 16-item subscales that evaluate Emotion-oriented coping (Emotion scale), Task-oriented coping (Task scale), and Avoidance (Avoidance scale). The scores within each of the three subscales range from 16 to 80, with higher scores indicating a greater level of coping activities specific to that subscale. In an Iranian sample of respondents, Shokri et al. (2009) reported Cronbach's alpha coefficients of 0.84 for the Emotion scale, 0.86 for the Task scale, and 0.80 for the Avoidance scale, underscoring the strong reliability of the scale. For this research, we employed a Persian-translated version of the CISS for participants. Cronbach's alpha in the present study was acceptable (α = 0.86).
2.4.
Data analyses
Data were analyzed using SPSS Version 20.0 and AMOS Version 24.0. Firstly, an initial outlier analysis was conducted to identify responses that deviated from the norm. This analysis, based on statistical criteria such as extreme scores or inconsistent response patterns, identified 97 participants as outliers, who were then excluded from the analysis. After that, the normality was assessed through the coefficients of skewness (Sk) and kurtosis (Ku). Based on the calculated values, all the variables displayed suitable conformity to a normal distribution, with the highest skewness value recorded at 0.60 and a kurtosis value of 1.10. Subsequently, the dataset underwent examination to identify multivariate outliers using Mahalanobis D2. No notable outliers were observed (df = 6, χ2 < 22.46), and all variable distributions displayed characteristics consistent with normality. Descriptive statistics were obtained utilizing measures like percentages, means, and standard deviations (SD). Cronbach's alphas were computed to assess the scales’ reliability. Also, we used the Pearson correlation analysis to investigate the direction and degree of relationships between the primary research variables.
We utilized structural equation modelling (SEM) to assess the proposed model, investigating how attachment styles influence PTG through coping styles. SEM was chosen for its ability to provide standardized regression coefficients and p-values for both direct and indirect effects. To evaluate the significance of the indirect effects, we used the bootstrapping method, a resampling technique (1000 resampling) with 95% confidence intervals (CI). In this method, if the upper and lower 95% confidence intervals do not encompass zero, the indirect effects are considered statistically significant (Preacher & Hayes, 2008). The parameters were estimated using the Maximum Likelihood Estimation method. Model goodness-of-fit was evaluated by employing four indices in accordance with the fit criteria suggested by Hu and Bentler (1999). The indices include the relative chi-square (χ2/df; values below 3.0 are typically seen as indicative of a good fit), the Root Mean Square Error of Approximation (RMSEA) and its 90% Confidence Interval (CI) (with values close to 0.06 indicating a good fit and values up to 0.08 indicating an acceptable fit), the Bentler-Bonett Normed Fit Index (NFI; with values of 0.90 or higher indicating an acceptable fit and values of 0.95 or higher indicating a good fit), and the Comparative Fit Index (CFI; with values of 0.90 or higher indicating an acceptable fit and values of 0.95 or higher indicating a good fit) (Kline, 2023).
3.
Results
3.1.
Descriptive statistics
Table 1 presents the results of descriptive statistics within the research sample. A total of 210 participants were recruited in the study. Among them 67 (31.9%) were males and 143 (68.1%) were females. The mean age of the participants was 30.23 years (SD = 9.98, range = 18–65). Also, the most common marital status was single (N = 121, 57.6%), followed by married (N = 80, 38.1%), and divorced/widowed (N = 9, 4.3%). Regarding the type of treatment, 92.9% of participants received outpatient treatment and home quarantine, while a minority (7.1%) underwent inpatient care.
Table 1.Descriptive statistics of sample demographics (N = 210).Variables N(%)GenderMale6731.9 Female14368.1MarriageMarried8038.1 Single12157.6 Divorced/widow94.3EducationElementary62.9 Diploma4621.9 Undergraduate or Graduate15875.2Socioeconomic statusHigh104.8 Low2110 Middle17985.2JobEmployed9846.7 Unemployed11253.3Treatment typeOutpatient treatment (Home quarantine)19592.9 Inpatient care157.1
3.2.
Bivariate correlations
Pearson correlations between the variables were calculated (Table 2). Significant negative associations were observed between secure attachment and emotion-oriented coping (r = −0.33, p < .01). Additionally, positive correlations were identified between secure attachment and task-oriented coping (r = 0.31, p < .01) as well as PTG (r = 0.49, p < .01). Also, there were significant negative correlations between avoidant attachment and emotion-oriented coping (r = −0.21, p < .01). Also, positive correlations have been calculated between avoidant attachment and task-oriented coping (r = 0.17, p < .05). Ambivalent-anxious attachment also exhibited negative correlations with task-oriented coping (r = −0.45, p < .01), avoidant coping (r = −0.16, p < .05) and PTG (r = −0.53, p < .01), while it displayed a positive correlation with emotion-oriented coping (r = 0.47, p < .01). Furthermore, task-oriented coping showed a significant correlation with PTG (r = 0.67, p < .01). Additionally, there were positive and negative correlations between PTG and avoidant coping (r = 0.28, p < .01) and emotion-oriented coping (r = −0.32, p < .01) respectively.
Table 2.Descriptive statistics and bivariate correlations of key variables (N = 210). M (SD)SkKuCronbach's Alpha12345671. Secure Attachment12.54 (2.52)−.29−.240.781 2. Avoidant attachment13.44 (2.21)−.14.170.690.101 3. Ambivalent-anxious attachment11.96 (5.53)−.16−.580.84−0.38**−0.31**1 4. Task-oriented coping52.57 (10.41)−.06−.620.900.31**0.17*−0.45**1 5. Emotion-oriented coping41.26 (10.44).30−.560.89−0.33**−0.21**0.47**−0.27**1 6. Avoidant coping40.99 (8.10)−.02−.620.770.11−0.01−0.16*0.34**0.14*1 7. PTG64.90 (14.45)−.21−.630.900.49**0.13−0.53**0.67**−0.32**0.28**1Note. *p < .05. **p < .01.
3.3.
Assessment of the structural model
We conducted mediation model and path analyses to examine whether coping styles mediate the relationship between attachment styles and PTG in participants who recovered from covid19. This hypothesized model was tested using AMOS 25. Table 3 (supplemental data) displays the fit indices for the structural equation model. We assessed the fit indices for both the full mediation and the partial mediation models. the full mediation model exhibited an inadequate fit for the data, whereas the partial mediation model demonstrated an acceptable fit to the data, χ2 /df = 2.52, NFI = 0.91; CFI = 0.94; RMSEA = 0.07. As shown in Figure 1, we assessed the direct pathways from attachment styles and coping styles to PTG in partial mediation model. There was a significant direct effect from secure attachment (β = 0.22, p < .001), anxious attachment (β = −0.22, p < .001), and task-oriented coping (β = 0.60, p < .001) to PTG. Also, the direct effect of secure attachment on task-oriented (β = 0.16, p < .05) and emotion-oriented coping (β = −0.18, p < .01) was statistically significant. Furthermore, there was a significant direct effect from ambivalent-anxious attachment to task-oriented (β = −0.38, p < .001), emotion-oriented (β = 0.37, p < .001), and avoidant coping (β = −0.16, p < .05). However, there were no significant direct effects between avoidant attachment and each of the three coping styles. Besides, the direct effect of avoidant attachment, emotion-oriented coping, and avoidant coping on PTG was not statistically significant. Finally, no significant direct effect was found between secure attachment and avoidant coping.
Figure 1.Structural equation model of PTG. Standardized coefficients are presented. Non-significant paths were shown with dotted lines. PTG = Posttraumatic growth, Anxious Attachment = Ambivalent-anxious attachment, Task-Oriented = Task-oriented coping, Emotion-Oriented = Emotion-oriented coping, Avoidant = Avoidant coping.
3.4.
Tests of mediation effects
The bootstrapping procedure was utilized to further assess the significance of the indirect effects along with 95% confidence intervals (CI). The mediation results have revealed that the association between secure attachment and PTG was significantly mediated by task-oriented coping (β = 0.10, (95% CI: 0.01–0.18)). Thus, secure attachment can predict a higher use of task-oriented coping strategies, and, in turn, higher use of task-oriented coping strategies associated with a higher level of PTG. Also, the results indicated that task-oriented coping was a significant negative mediator between ambivalent-anxious attachment and PTG (β = −0.24, (95% CI: −0.33 – −0.15)). This suggests that having an ambivalent-anxious attachment style may predict a reduced use of task-oriented coping. Subsequently, a reduced use of task-oriented coping strategies is linked to a lower level of PTG. The indirect effects in the remaining pathways were all equal to zero and not statistically significant (See Table 3, supplemental data, for significant and non-significant paths in the tested model).
3.1.
Descriptive statistics
Table 1 presents the results of descriptive statistics within the research sample. A total of 210 participants were recruited in the study. Among them 67 (31.9%) were males and 143 (68.1%) were females. The mean age of the participants was 30.23 years (SD = 9.98, range = 18–65). Also, the most common marital status was single (N = 121, 57.6%), followed by married (N = 80, 38.1%), and divorced/widowed (N = 9, 4.3%). Regarding the type of treatment, 92.9% of participants received outpatient treatment and home quarantine, while a minority (7.1%) underwent inpatient care.
Table 1.Descriptive statistics of sample demographics (N = 210).Variables N(%)GenderMale6731.9 Female14368.1MarriageMarried8038.1 Single12157.6 Divorced/widow94.3EducationElementary62.9 Diploma4621.9 Undergraduate or Graduate15875.2Socioeconomic statusHigh104.8 Low2110 Middle17985.2JobEmployed9846.7 Unemployed11253.3Treatment typeOutpatient treatment (Home quarantine)19592.9 Inpatient care157.1
3.2.
Bivariate correlations
Pearson correlations between the variables were calculated (Table 2). Significant negative associations were observed between secure attachment and emotion-oriented coping (r = −0.33, p < .01). Additionally, positive correlations were identified between secure attachment and task-oriented coping (r = 0.31, p < .01) as well as PTG (r = 0.49, p < .01). Also, there were significant negative correlations between avoidant attachment and emotion-oriented coping (r = −0.21, p < .01). Also, positive correlations have been calculated between avoidant attachment and task-oriented coping (r = 0.17, p < .05). Ambivalent-anxious attachment also exhibited negative correlations with task-oriented coping (r = −0.45, p < .01), avoidant coping (r = −0.16, p < .05) and PTG (r = −0.53, p < .01), while it displayed a positive correlation with emotion-oriented coping (r = 0.47, p < .01). Furthermore, task-oriented coping showed a significant correlation with PTG (r = 0.67, p < .01). Additionally, there were positive and negative correlations between PTG and avoidant coping (r = 0.28, p < .01) and emotion-oriented coping (r = −0.32, p < .01) respectively.
Table 2.Descriptive statistics and bivariate correlations of key variables (N = 210). M (SD)SkKuCronbach's Alpha12345671. Secure Attachment12.54 (2.52)−.29−.240.781 2. Avoidant attachment13.44 (2.21)−.14.170.690.101 3. Ambivalent-anxious attachment11.96 (5.53)−.16−.580.84−0.38**−0.31**1 4. Task-oriented coping52.57 (10.41)−.06−.620.900.31**0.17*−0.45**1 5. Emotion-oriented coping41.26 (10.44).30−.560.89−0.33**−0.21**0.47**−0.27**1 6. Avoidant coping40.99 (8.10)−.02−.620.770.11−0.01−0.16*0.34**0.14*1 7. PTG64.90 (14.45)−.21−.630.900.49**0.13−0.53**0.67**−0.32**0.28**1Note. *p < .05. **p < .01.
3.3.
Assessment of the structural model
We conducted mediation model and path analyses to examine whether coping styles mediate the relationship between attachment styles and PTG in participants who recovered from covid19. This hypothesized model was tested using AMOS 25. Table 3 (supplemental data) displays the fit indices for the structural equation model. We assessed the fit indices for both the full mediation and the partial mediation models. the full mediation model exhibited an inadequate fit for the data, whereas the partial mediation model demonstrated an acceptable fit to the data, χ2 /df = 2.52, NFI = 0.91; CFI = 0.94; RMSEA = 0.07. As shown in Figure 1, we assessed the direct pathways from attachment styles and coping styles to PTG in partial mediation model. There was a significant direct effect from secure attachment (β = 0.22, p < .001), anxious attachment (β = −0.22, p < .001), and task-oriented coping (β = 0.60, p < .001) to PTG. Also, the direct effect of secure attachment on task-oriented (β = 0.16, p < .05) and emotion-oriented coping (β = −0.18, p < .01) was statistically significant. Furthermore, there was a significant direct effect from ambivalent-anxious attachment to task-oriented (β = −0.38, p < .001), emotion-oriented (β = 0.37, p < .001), and avoidant coping (β = −0.16, p < .05). However, there were no significant direct effects between avoidant attachment and each of the three coping styles. Besides, the direct effect of avoidant attachment, emotion-oriented coping, and avoidant coping on PTG was not statistically significant. Finally, no significant direct effect was found between secure attachment and avoidant coping.
Figure 1.Structural equation model of PTG. Standardized coefficients are presented. Non-significant paths were shown with dotted lines. PTG = Posttraumatic growth, Anxious Attachment = Ambivalent-anxious attachment, Task-Oriented = Task-oriented coping, Emotion-Oriented = Emotion-oriented coping, Avoidant = Avoidant coping.
3.4.
Tests of mediation effects
The bootstrapping procedure was utilized to further assess the significance of the indirect effects along with 95% confidence intervals (CI). The mediation results have revealed that the association between secure attachment and PTG was significantly mediated by task-oriented coping (β = 0.10, (95% CI: 0.01–0.18)). Thus, secure attachment can predict a higher use of task-oriented coping strategies, and, in turn, higher use of task-oriented coping strategies associated with a higher level of PTG. Also, the results indicated that task-oriented coping was a significant negative mediator between ambivalent-anxious attachment and PTG (β = −0.24, (95% CI: −0.33 – −0.15)). This suggests that having an ambivalent-anxious attachment style may predict a reduced use of task-oriented coping. Subsequently, a reduced use of task-oriented coping strategies is linked to a lower level of PTG. The indirect effects in the remaining pathways were all equal to zero and not statistically significant (See Table 3, supplemental data, for significant and non-significant paths in the tested model).
4.
Discussion
In the present study, we aimed to investigate the mediating role of coping strategies in the association between attachment styles and PTG among recovered COVID-19 patients. We addressed three main research questions related to attachment styles, coping strategies, and PTG among recovered COVID-19 patients. Firstly, we conducted an assessment and ensured that our theoretical framework aligns well with the observed relationships among attachment styles, coping strategies, and PTG, validating our research design. Secondly, we investigated the direct impact of attachment styles – secure, ambivalent-anxious, and avoidant – on PTG, aiming to understand their independent contributions to PTG experiences. Lastly, we explored the mediating role of coping strategies – problem-focused, emotion-focused, and avoidant – in the relationship between attachment styles and PTG, illuminating the underlying mechanisms of psychological adjustment following trauma. As we hypothesized, the results obtained from the path analysis showed a significant positive relationship between secure attachment style and PTG. This result is consistent with the prior studies (see, Gleeson et al., 2021). Salo et al. (2005) found that secure attachment was positively linked to PTG, especially in the aspects of personal strength, relating to others, and spiritual change. Attachment theory (Bowlby, 1980) provides insights into why individuals who survived COVID-19 have reacted differently to a novel traumatic situation, a newly life-threatening illness. We assumed that survivors’ individual responses to trauma, shaped by their past experiences, play a role in influencing whether trauma facilitates the journey towards PTG or results in negative consequences. The experiences of securely attached individuals are integrated into their positive early cognitive frameworks regarding self-worth, the kindness of others, and the safety of the environment. This model of self and others become active when facing threats to one’s safety and well-being, enabling them implement effective emotion regulation strategies (Hazan & Shaver, 1987). As Mikulincer et al. (2003, 2006) indicated, individuals with secure attachments display ease and comfort with interdependence and intimacy. They might be better at utilizing coping strategies like seeking social support when facing trauma, which may facilitate PTG (Levi-Belz & Lev-Ari, 2018). This may be due to their increased tendency to seek comfort from others and effectively utilize it during challenging experiences (Wu & Yang, 2012). Moreover, they can efficiently build new cognitive schemas and regulate their emotions, both crucial elements in PTG (Calhoun & Tedeschi, 1990).
Our findings also showed a significant negative association between ambivalent-anxious attachment and PTG, consistent with our hypothesis on this relationship Experiencing growth following trauma indicates that, while stress and difficulties may occur, they can be effectively managed (Tedeschi et al., 2018). Hence, negative self-appraisal in anxious attached individuals (Bowlby, 1980) makes it difficult to perceive themselves as capable of handling and overcoming a traumatic event. Existing research indicates that individuals with anxious attachment report experiencing greater distress and difficulties compared to those with secure attachment (Berger, 2015; Mikulincer & Shaver, 2019). In particular, individuals with an anxious attachment style encompass poor self-regulatory skills (Ein-Dor et al., 2010) and try to gain support through the use of overactive and exaggerated emotional regulation strategies in order to cope with distress and a sense of incapacity, and this process make them vulnerable to distress and other negative posttraumatic outcomes (Mikulincer et al., 2006). Previous studies have obtained consistent results with our findings (Schuitmaker et al., 2023). Although a few studies (see, Graci & Fivush, 2017) reported a significant positive relationship between anxious attachment and post-traumatic growth, most studies concerning anxious attachment styles place greater emphasis on the distress experienced by these individuals when facing a traumatic event (Volgin & Bates, 2016).
Our another hypothesis suggested that avoidant attachment style would negatively predict PTG. However, despite the negative correlation between avoidant attachment and PTG, the association did not reach statistical significance. In line with the prior research, the results of the studies conducted by Schmidt et al. (2012, 2019) and Taqipour and Nayinian (2018) did not reveal a significant direct relationship between the avoidant attachment style and PTG. Graci and Fivush (2017) showed a significant negative correlation between avoidant attachment and PTG. Individuals with the avoidant attachment style tend to use ineffective and passive strategies. For example, they may deny their need for support or suppress the intensity of distress, frequently creating emotional distance from the circumstance (Marshall & Frazier, 2019). Rather than confronting the traumatic event, such individuals may downplay its significance by suppressing stress and diminishing any form of response, be it positive developments or symptoms of post-traumatic stress (PTS) (Arikan & Karanci, 2012). Alternatively, instead of facing adversity, they might engage in other activities. As a result, their reports of personal growth are less likely to align with those of individuals who are anxious, significant associations with PTG or distress are usually absent. In a review of studies on the association between attachment styles and PTG, Marshall and Frazier (2019) revealed that the majority of the studies conducted regarding the connection between avoidant attachment style and PTG yielded non-significant results.
Another finding supported by our analysis is the positive relationship between problem-focused coping and PTG. The results of prior studies also revealed consistent results (Bellur et al., 2018). Based on this, problem-focused coping appeared as a strong predictor of PTG in a traumatized population. According to Tedeschi and Calhoun (2004), the process of PTG is compared to earthquake reconstruction. An adverse life event challenges an individual's prior cognitions and provides an opportunity for restructuring and reorganizing cognitive patterns that can manage similar experiences in the future. If a person does not undergo this process and does not adapt their experience to update their prior beliefs about the world (e.g. they have always believed that unfortunate events sometimes happen), PTG won’t occur (Tedeschi & Calhoun, 2004). Furthermore, if an individual approaches this new experience negatively, PTG is not expected to happen (e.g. bad things will happen, and there's nothing I can do to prevent them). Joseph and Linley (2005) argued that individuals who negatively interpret this experience are vulnerable to post-traumatic stress symptoms (PTS). Calhoun et al. (2010) have presented a comprehensive model of mechanisms facilitating PTG. Key mechanisms involve cognitive processing of the event, making it meaningful and creating new meanings for adverse life events. These are the key mechanisms that facilitate growth. Problem-focused coping, which includes mechanisms such as seeking more information about the problem, planning, cognitive analysis, and taking action to overcome specific obstacles, can be compared to cognitive processing and cognitive restructuring in Tedeschi and Calhoun's theory. It is clear that in this type of coping, life events are considered an opportunity for personal growth beyond the threatening situation, the cause and nature of the problem is identified, and responses are taken into action (Miller & Miller, 2000). According to Taylor's cognitive adaptation theory (1988), positive reinterpretation of stressful life events plays a crucial role in adaptation to stress and the experience of growth. If individuals acquire new assumptions and skills and attempt to reinterpret the event, they are more likely to have increased self-confidence and mastery over events, which increases the likelihood of experiencing stress-related growth.
On the other hand, the results of the present study did not demonstrate a significant relationship between emotion-focused and avoidant coping strategies with PTG. In light of what we know about the assimilation and accommodation of information in response to changing circumstances (in the organismic valuing model), emotion-focused coping can be seen as a form of accommodation that adapts both mental structures and external functions to match the changing conditions in order to achieve balance in the environment. In this case, resistance to change and maintaining the structural and functional integrity of the organism will not lead to growth but instead result in sustained distress and stress related to the environment, ultimately leading to PTSD and associated emotional disorders (Joseph, 2009). Emotion-focused and avoidant coping strategies are employed when the situation has become impossible to solve for the individual. Therefore, not only is the situation not a challenge for the individual in terms of attempting to overcome it, but it also involves an attempt to distance oneself from the situation and replace negative emotions arising from the situation. Consequently, growth does not occur in this case. As Gil (2005) points out, individuals who obtain high scores in emotion-focused and avoidance coping strategies are at a significantly higher risk for stress symptoms and the stressful situation remains unresolved for these individuals. However, cultural context plays a significant role in shaping coping strategies, and the lack of significance in this case in our study could also be influenced by cultural factors. For example, in collectivist cultures like Iran (Asians in general), individuals may prioritize social support-seeking coping strategies over emotion-focused or avoidant coping strategies. Additionally, cultural beliefs about emotions may influence the way emotions are perceived and regulated. For instance, in cultures where emotional expression is encouraged, individuals may be more likely to use strategies such as seeking emotional support to cope with stressors. Conversely, in cultures where emotional restraint is valued, individuals may be more inclined to use strategies that involve suppressing or avoiding emotional experiences. Cultural norms regarding confrontation and avoidance also play a role, with some cultures encouraging direct confrontation of challenges (Kuo, 2011). Iran's culture, rich in traditions, influences coping, with religion, spirituality, and strong familial ties shaping coping strategies (Ahmadi et al., 2018). Understanding these cultural nuances is essential for interpreting the results and implications of our study.
The results of mediation analysis demonstrated that problem focused coping strategies play a significant positive mediating role in the relationship between secure attachment style and PTG. This finding is aligned with our hypothesis, which proposed that problem-focused coping would positively mediate the relationship between secure attachment style and PTG. The results of the studies conducted by Arikan and Karanci (2012), and Kanninen et al. (2002) are also consistent with our finding in this regard. Experiencing a life-threatening illness that exposes an individual to a wide range of emotional, social, and physical needs can be highly stressful. In such conditions, a secure attachment style assists in creating a psychological safe space within the individual, establishing close and friendly relationships with others, and expressing emotions to seek support and manage stress using active coping strategies. Bellizzi and Blank (2006) indicate that active coping strategies and problem-focused approaches, along with social support, act as predictors of post-traumatic growth.
Another finding of our study showed a negative mediating role of problem-focused coping in the relationship between ambivalent-anxious attachment and PTG. Individuals with anxious attachment style tend to use emotion-focused coping strategies when faced with stressors (Ognibene & Collins, 1998). According to a study conducted by Kanninen et al. (2002), individuals with anxious attachment style, continually ruminate on their distress cues, relying on overactive emotion regulation strategies that keep them alert and attentive to potential threats and exaggerating their distress to seek support. For example, Roberts et al. (1996) observed that the relationship between adult attachment styles and depression is primarily mediated by ineffective coping strategies, resulting in low self-esteem. It has also been shown that individuals with anxious attachment styles are more likely to engage in maladaptive coping strategies, such as bulimia nervosa, excessive alcohol consumption (Brennan & Shaver, 1995), and emotional eating (Pistole, 1995) in stressful situations. Therefore, such coping strategies can be considered as a mediator in how insecurely attached individuals deal with illness as a potentially traumatic event, and in such cases, PTG may not occur, or the impact of such coping may result in negative outcomes.
Other pathways linking attachment styles to PTG through coping strategies showed minimal indirect effects and did not attain statistical significance. Fuendeling (1998) concluded that although both avoidant and anxious attachment styles are associated with high levels of distress, it appears that each of them represents different and distinct self-regulatory strategies for managing distress. Anxious attachment individuals both attend to and express their distress, while those with avoidant attachment tend to suppress their distressing emotions. He tested and confirmed the following three hypotheses. First, anxious and avoidant attachment styles are significantly and distinctly associated with emotion-focused and avoidant coping strategies. Second, both anxious and avoidant attachment, as well as emotion-focused and avoidant coping, are significantly related to distress and stress-related symptoms. Finally, coping styles act as important predictors of current distress and can largely mediate the relationships between both insecure attachment styles (anxious and avoidant) and resulted distress. Based on these studies and our findings, it can be inferred that the mediation of coping strategies in insecure individuals does not lead to PTG and may negatively influence the occurrence of PTG or have a neutral effect in insecure individuals.
The results of this study need to be assessed within the context of its limitations. First, the data were based on the self-report questionnaires, which may be vulnerable to self-reporting biases and social desirability. Next, given that the study is cross-sectional in nature, it has significant limitations in its ability to unveil causal effects. Furthermore, the findings of this study were derived from specific situations arising from the COVID-19 pandemic. Hence, caution is warranted when applying these findings to different stressful situations. Another limitation is that we did not conduct a power analysis prior to the research, which may have impacted the robustness and generalizability of our findings. We employed an online non-random sampling method to reach participants, restricting our sample exclusively to people living in Tabriz. Hence, our findings cannot be extended to individuals living in different regions.
Another limitation of our study is the absence of an investigation into the influence of other medical variables related to COVID-19 on our study variables, such as the severity of illness or duration of hospitalization. Also, we did not assess other psychological characteristics which could potentially influence PTG, such as personality traits (Mattson et al., 2018), self-compassion (Liu et al., 2023), or resilience (Han et al., 2023). Finally, cognitive indicators of PTG, such as rumination, event centrality, and perceived control, are important factors in the development of PTG, and they can significantly influence the outcomes, as demonstrated in both the theoretical model of PTG (Tedeschi & Calhoun, 2004) and the existing literature (Stockton et al., 2011). However, these predictors were not assessed in the current study.
5.
Conclusion
This study investigated the pathway linking attachment styles to PTG via coping strategies in individuals who recovered from Covid19. Findings showed that problem-focused coping strategies have a crucial indirect effect on the relationship between attachment styles and PTG. Our study was the first to examine these variables within the sample of people infected by the virus. This model attempted to expand the theoretical framework that explains the direct and indirect pathways of PTG regarding the novel pandemic. The clinical applicability of this model can inspire counsellors and psychologists to create psychological interventions directed towards effectively managing the complexities of a public health emergency. Hence, it is essential to develop interventions to enhance coping resources for traumatized populations. These interventions should aim to enhance problem-focused coping strategies while reducing the reliance on maladaptive coping strategies among traumatized people with secure and insecure attachment styles. Furthermore, innovative coping strategies, such as neuroeducation or neuro didactics (Trenado et al., 2021), could assist patients in acquiring adaptive coping strategies to manage the heightened stress associated with multiple stressors (In this study, increased stress caused by the COVID-19 pandemic). Also, it is recommended for the future research to complement self-report instruments with interviews and other objective approaches to assess the perceived degree of recovery in survivors of life-threatening diseases. Lastly, longitudinal studies should also be taken into account for understanding the connections between relevant variables.
Supplementary Material
Supplementary_Materials.docx |
Title: Evaluating the Outcomes in Patients with Colorectal Cancer Using the Malnutrition Universal Screening Tool: A Systematic Review | Body:
Introduction
Colorectal cancer (CRC) stands as the third most common cancer and the second leading cause of cancer death worldwide, with an estimated 1.8 million new cases and 881,000 deaths in 2018.1–4 The disease’s prognosis and treatment outcomes are heavily influenced by the stage at diagnosis, with five-year survival rates ranging from 90% for stage I to around 14% for stage IV.5 Nutritional status can significantly influence both the progression of cancer and patient resilience to treatment modalities such as surgery, chemotherapy, and radiation therapy.6–9 Malnutrition, a common comorbidity in CRC, is associated with a higher incidence of treatment complications, increased mortality, and reduced quality of life.10 The risk of malnutrition in CRC patients increases due to factors such as tumor location, bowel obstruction, and treatment side effects, which can lead to decreased food intake, nutrient malabsorption, and metabolic alterations.11
Figure 1PRISMA Flow Diagram. Flowchart description of the steps taken to select studies for this systematic review based on the PRISMA protocol.
Malnutrition is a critical but often under-recognized condition that significantly impacts the associated comorbidities and clinical outcomes in oncology.12–15 Despite advancements in oncological care, the prevalence of malnutrition among these patients remains high, ranging from 30% to 85%, varying with the stage of cancer, the age of patients, the measurement tools used, and the healthcare setting.16,17 Malnutrition and cancer cachexia in CRC patients is associated with numerous adverse outcomes, including increased postoperative complications, higher infection rates, longer hospital stays, and, most importantly, decreased survival rates.18–22 The Malnutrition Universal Screening Tool (MUST), developed by the British Association for Parenteral and Enteral Nutrition, offers a practical approach to identify individuals at risk of malnutrition.23 It incorporates BMI, unintentional weight loss, and the effect of acute disease on dietary intake to generate an overall risk score.
Malnutrition remains a significant global health challenge, affecting approximately 11% of the global adult population, with higher rates in low-income regions. Among patients with cancer, malnutrition is particularly prevalent and problematic, affecting up to 85% of patients with advanced malignancies such as colorectal cancer.16,17 This high incidence is due to the combined effects of the disease itself, the metabolic demands of growing tumors, and the side effects of cancer treatments, which can impair nutrient intake and absorption. Clinically, malnutrition in cancer patients is associated with poorer outcomes, including reduced response to therapy, higher complication rates, and decreased survival. Current practices to combat malnutrition in oncology include nutritional screening at diagnosis and throughout treatment, dietary counseling, and the use of enteral or parenteral nutrition when necessary.
The significance of early and accurate nutritional risk identification in colorectal cancer patients cannot be overstated. The MUST, by design, is non-invasive, quick, and requires minimal training to administer, making it an ideal validated screening tool in various healthcare settings.24 Research has shown that early nutritional interventions in malnourished cancer patients can improve treatment tolerance, enhance quality of life, and potentially improve survival outcomes.25–27 However, the effectiveness of MUST as a predictor for hospital outcomes in this specific population of patients with CRC is still unclear, underscoring the need for a systematic review of the literature to consolidate current evidence and ascertain the predictive value of MUST in this context.
MUST utilizes three key parameters: body mass index (BMI), unintentional weight loss over a specified time, and the impact of an acute disease process on nutritional intake for more than 5 days. The MUST evaluates nutritional risk based on a patient’s BMI, recent weight loss over 3–6 months, and inability to eat due to acute disease for more than 5 days, assigning scores to categorize patients into low (score = 0), moderate (score = 1), or high malnutrition risk (score ≥ 2). MUST boasts high sensitivity (96%) and specificity (75%) against the Subjective Global Assessment (SGA) and is praised for its simplicity and quick completion time of 3–5 minutes by healthcare professionals.
Therefore, the primary objective of this systematic review is to evaluate the effectiveness of MUST as assessment tool and predictor for hospital outcomes in patients with colorectal cancer. Specifically, it aims to assess the association between MUST risk categories and clinical outcomes, including but not limited to, length of hospital stay, postoperative complications, infection rates, and survival. Through this review, the clinical utility of these findings is to provide evidence-based recommendations for the use of MUST in the clinical management of colorectal cancer, with the goal of improving patient outcomes and quality of care.
Material and Methods
Protocol and Registration
To ensure an exhaustive and nuanced search of the literature regarding the effectiveness of the Malnutrition Universal Screening Tool (MUST) in predicting hospital outcomes for patients with colorectal cancer, this study employed a refined search strategy across multiple critical electronic databases, including PubMed, Scopus, and Embase. This strategy aimed to include literature published up until December 2023 to incorporate the most current studies on the topic. The focus of the search strategy was to collect relevant literature that evaluates the predictive accuracy of MUST in the context of colorectal cancer, specifically regarding hospital outcomes such as postoperative complications, infection rates, length of stay, and overall survival.
The search strategy was comprehensive, incorporating an extensive array of keywords and phrases closely related to the study’s objectives. These included terms related to malnutrition screening, the specific tool in question (MUST), colorectal cancer, and a variety of hospital outcomes. Specific search terms used were: “Malnutrition Universal Screening Tool”, “MUST”, “colorectal cancer”, “colorectal neoplasms”, “hospital outcomes”, “nutritional screening”, “malnutrition assessment”, “clinical outcomes”, “surgical outcomes”, “treatment outcomes”, “postoperative complications”, “postoperative recovery”, “surgical infections”, “length of hospital stay”, “hospital readmission”, “patient survival”, “survival analysis”, “nutritional status”, “nutritional interventions in cancer care”, “impact of malnutrition on cancer treatment”, and “predictive value of nutritional screening tools”.
Boolean operators (AND, OR, NOT) were strategically used to combine these terms in a manner that would refine the search results, ensuring relevance and specificity to the research question. The search query was structured to encompass various combinations and permutations of these terms to capture the broadest possible range of pertinent studies. For example, the search string might look something like this: (“Malnutrition Universal Screening Tool” OR “MUST”) AND (“colorectal cancer” OR “colorectal neoplasms”) AND (“hospital outcomes” OR “clinical outcomes” OR “surgical outcomes” OR “postoperative complications” OR “length of hospital stay” OR “hospital readmission” OR “patient survival”) AND (“nutritional screening” OR “malnutrition assessment” OR “nutritional status”) AND (“nutritional interventions in cancer care” OR “impact of malnutrition on cancer treatment” OR “predictive value of nutritional screening tools”).
Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines,28 this systematic review protocol was developed to ensure a structured, transparent, and replicable methodology. To promote the openness and accessibility of our research process and findings, the review has been registered with the Open Science Framework (OSF), which provides a platform for sharing our methodology and outcomes with the wider research community. The review’s OSF registration code is osf.io/yvn4p. This detailed and expansive search strategy is intended to compile a comprehensive collection of studies, facilitating a thorough understanding of MUST’s role in predicting hospital outcomes for colorectal cancer patients, thereby enriching the evidence base for clinical practice and future research.
Eligibility Criteria and Definitions
The eligibility criteria for this systematic review were meticulously established to identify studies that investigate the effectiveness of the MUST questionnaire as a predictor for hospital outcomes in patients with colorectal cancer. Consequently, this review incorporated the following inclusion criteria: (1) Study population: Studies must involve patients diagnosed with colorectal cancer, without restrictions on the stage of cancer or age of patients, to encompass a broad spectrum of clinical scenarios. (2) Focus on MUST and hospital outcomes: Research must specifically explore the use of MUST for nutritional screening and its predictive value for hospital outcomes, including but not limited to length of hospital stay, postoperative complications, infection rates, and survival. This includes studies assessing malnutrition risk, nutritional interventions based on MUST scores, and correlations between MUST scores and clinical outcomes. (3) Types of studies: A wide variety of study designs are eligible, including randomized controlled trials, observational studies, cohort studies, case-control studies, cross-sectional studies, and prospective and retrospective analyses. These studies should provide clear methodologies on the implementation of MUST and the assessment of hospital outcomes. (4) Outcome measures: Studies that employ MUST as an assessment tool and provide clear, quantifiable outcomes regarding hospital stays, complications, infection rates, or survival rates. This can include direct comparisons of MUST scores with clinical outcomes and the impact of nutritional interventions guided by MUST. (5) Language: Only peer-reviewed articles published in English will be included to ensure the feasibility of comprehensive review and analysis.
The exclusion criteria were defined as follows: (1) Non-human studies: Research involving in vitro or animal models will be excluded to maintain the focus on clinical outcomes in human patients. (2) Broad nutritional focus: Studies not specifically examining the use of MUST in colorectal cancer patients, or those that do not distinguish the outcomes of using MUST from other nutritional screening tools, will be excluded. (3) Lack of specific outcomes: Studies that do not provide clear, measurable outcomes related to the predictive value of MUST for hospital outcomes or lack sufficient detail for a thorough analysis will be omitted. (4) Grey literature: To ensure the integrity and reliability of the review, grey literature, including non-peer-reviewed articles, conference abstracts, general reviews, commentaries, and editorials, will be excluded.
Definitions
In this systematic review, the MUST survey is defined as a standardized tool aimed at identifying adults who are malnourished or at risk of malnutrition. The choice of MUST for this review is based on its widespread recognition and application in both hospital and community settings, and its potential impact on the management and outcomes of patients with colorectal cancer.
Nutritional screening in oncology, specifically in the context of colorectal cancer, refers to the process of identifying patients who are malnourished or at risk of malnutrition to facilitate early interventions.
Data Collection Process
The data collection process for this systematic review began with the identification and removal of 148 duplicate entries from the initial search results across PubMed, Scopus, and Embase databases. Subsequently, two independent reviewers conducted a meticulous screening of abstracts and titles from a preliminary tally of 1198 articles, using predefined inclusion and exclusion criteria focused on the utilization of the MUST tool in predicting hospital outcomes for colorectal cancer patients. This step was crucial to ensure that the studies selected were directly relevant to the review’s objectives. Any discrepancies encountered between the reviewers were resolved through discussion or, when needed, by consulting a third reviewer to reach a consensus. This process led to the selection of 355 articles deemed potentially relevant. Following a detailed full-text review, 7 studies were ultimately included in the review, as presented in Figure 1. This selection strategy was designed to ensure that the studies incorporated into the final analysis were pertinent and of high quality, providing a thorough insight into the predictive value of MUST in the clinical outcomes of colorectal cancer patients.
Risk of Bias and Quality Assessment
Our review utilized a combined qualitative and quantitative approach for the quality assessment of studies and risk of bias evaluation. The observational studies’ quality was gauged using the Newcastle–Ottawa Scale,29 which examines three main areas: selection of study groups, their comparability, and the determination of exposure or outcome. This scale allows for a detailed quality assessment, highlighting studies of high methodological rigor. Each study was independently reviewed by two researchers for quality, with any discrepancies resolved through discussion or a third researcher’s input, ensuring the evaluation’s objectivity and repeatability.
Protocol and Registration
To ensure an exhaustive and nuanced search of the literature regarding the effectiveness of the Malnutrition Universal Screening Tool (MUST) in predicting hospital outcomes for patients with colorectal cancer, this study employed a refined search strategy across multiple critical electronic databases, including PubMed, Scopus, and Embase. This strategy aimed to include literature published up until December 2023 to incorporate the most current studies on the topic. The focus of the search strategy was to collect relevant literature that evaluates the predictive accuracy of MUST in the context of colorectal cancer, specifically regarding hospital outcomes such as postoperative complications, infection rates, length of stay, and overall survival.
The search strategy was comprehensive, incorporating an extensive array of keywords and phrases closely related to the study’s objectives. These included terms related to malnutrition screening, the specific tool in question (MUST), colorectal cancer, and a variety of hospital outcomes. Specific search terms used were: “Malnutrition Universal Screening Tool”, “MUST”, “colorectal cancer”, “colorectal neoplasms”, “hospital outcomes”, “nutritional screening”, “malnutrition assessment”, “clinical outcomes”, “surgical outcomes”, “treatment outcomes”, “postoperative complications”, “postoperative recovery”, “surgical infections”, “length of hospital stay”, “hospital readmission”, “patient survival”, “survival analysis”, “nutritional status”, “nutritional interventions in cancer care”, “impact of malnutrition on cancer treatment”, and “predictive value of nutritional screening tools”.
Boolean operators (AND, OR, NOT) were strategically used to combine these terms in a manner that would refine the search results, ensuring relevance and specificity to the research question. The search query was structured to encompass various combinations and permutations of these terms to capture the broadest possible range of pertinent studies. For example, the search string might look something like this: (“Malnutrition Universal Screening Tool” OR “MUST”) AND (“colorectal cancer” OR “colorectal neoplasms”) AND (“hospital outcomes” OR “clinical outcomes” OR “surgical outcomes” OR “postoperative complications” OR “length of hospital stay” OR “hospital readmission” OR “patient survival”) AND (“nutritional screening” OR “malnutrition assessment” OR “nutritional status”) AND (“nutritional interventions in cancer care” OR “impact of malnutrition on cancer treatment” OR “predictive value of nutritional screening tools”).
Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines,28 this systematic review protocol was developed to ensure a structured, transparent, and replicable methodology. To promote the openness and accessibility of our research process and findings, the review has been registered with the Open Science Framework (OSF), which provides a platform for sharing our methodology and outcomes with the wider research community. The review’s OSF registration code is osf.io/yvn4p. This detailed and expansive search strategy is intended to compile a comprehensive collection of studies, facilitating a thorough understanding of MUST’s role in predicting hospital outcomes for colorectal cancer patients, thereby enriching the evidence base for clinical practice and future research.
Eligibility Criteria and Definitions
The eligibility criteria for this systematic review were meticulously established to identify studies that investigate the effectiveness of the MUST questionnaire as a predictor for hospital outcomes in patients with colorectal cancer. Consequently, this review incorporated the following inclusion criteria: (1) Study population: Studies must involve patients diagnosed with colorectal cancer, without restrictions on the stage of cancer or age of patients, to encompass a broad spectrum of clinical scenarios. (2) Focus on MUST and hospital outcomes: Research must specifically explore the use of MUST for nutritional screening and its predictive value for hospital outcomes, including but not limited to length of hospital stay, postoperative complications, infection rates, and survival. This includes studies assessing malnutrition risk, nutritional interventions based on MUST scores, and correlations between MUST scores and clinical outcomes. (3) Types of studies: A wide variety of study designs are eligible, including randomized controlled trials, observational studies, cohort studies, case-control studies, cross-sectional studies, and prospective and retrospective analyses. These studies should provide clear methodologies on the implementation of MUST and the assessment of hospital outcomes. (4) Outcome measures: Studies that employ MUST as an assessment tool and provide clear, quantifiable outcomes regarding hospital stays, complications, infection rates, or survival rates. This can include direct comparisons of MUST scores with clinical outcomes and the impact of nutritional interventions guided by MUST. (5) Language: Only peer-reviewed articles published in English will be included to ensure the feasibility of comprehensive review and analysis.
The exclusion criteria were defined as follows: (1) Non-human studies: Research involving in vitro or animal models will be excluded to maintain the focus on clinical outcomes in human patients. (2) Broad nutritional focus: Studies not specifically examining the use of MUST in colorectal cancer patients, or those that do not distinguish the outcomes of using MUST from other nutritional screening tools, will be excluded. (3) Lack of specific outcomes: Studies that do not provide clear, measurable outcomes related to the predictive value of MUST for hospital outcomes or lack sufficient detail for a thorough analysis will be omitted. (4) Grey literature: To ensure the integrity and reliability of the review, grey literature, including non-peer-reviewed articles, conference abstracts, general reviews, commentaries, and editorials, will be excluded.
Definitions
In this systematic review, the MUST survey is defined as a standardized tool aimed at identifying adults who are malnourished or at risk of malnutrition. The choice of MUST for this review is based on its widespread recognition and application in both hospital and community settings, and its potential impact on the management and outcomes of patients with colorectal cancer.
Nutritional screening in oncology, specifically in the context of colorectal cancer, refers to the process of identifying patients who are malnourished or at risk of malnutrition to facilitate early interventions.
Data Collection Process
The data collection process for this systematic review began with the identification and removal of 148 duplicate entries from the initial search results across PubMed, Scopus, and Embase databases. Subsequently, two independent reviewers conducted a meticulous screening of abstracts and titles from a preliminary tally of 1198 articles, using predefined inclusion and exclusion criteria focused on the utilization of the MUST tool in predicting hospital outcomes for colorectal cancer patients. This step was crucial to ensure that the studies selected were directly relevant to the review’s objectives. Any discrepancies encountered between the reviewers were resolved through discussion or, when needed, by consulting a third reviewer to reach a consensus. This process led to the selection of 355 articles deemed potentially relevant. Following a detailed full-text review, 7 studies were ultimately included in the review, as presented in Figure 1. This selection strategy was designed to ensure that the studies incorporated into the final analysis were pertinent and of high quality, providing a thorough insight into the predictive value of MUST in the clinical outcomes of colorectal cancer patients.
Risk of Bias and Quality Assessment
Our review utilized a combined qualitative and quantitative approach for the quality assessment of studies and risk of bias evaluation. The observational studies’ quality was gauged using the Newcastle–Ottawa Scale,29 which examines three main areas: selection of study groups, their comparability, and the determination of exposure or outcome. This scale allows for a detailed quality assessment, highlighting studies of high methodological rigor. Each study was independently reviewed by two researchers for quality, with any discrepancies resolved through discussion or a third researcher’s input, ensuring the evaluation’s objectivity and repeatability.
Results
Study Characteristics
The systematic review scrutinized seven studies,30–36 as detailed in Table 1. These studies varied from a variety of countries, including the United Kingdom,30,33,35 Taiwan,31 The Netherlands,32 Spain,34 and China,36 reflecting a broad international interest in the topic. Conducted between 2010 and 2022, the majority of these studies adopted a prospective cohort design, with the exception of Almasaudi et al,33 which was retrospective, and Xie et al,36 which employed a cross-sectional approach. The quality of these studies was predominantly rated as medium, except for Abbass et al35 from the United Kingdom, which was distinguished with a high-quality rating. This distribution of study qualities suggests a general reliability in the findings, with Abbass et al’s study standing out for its exceptional methodological rigor.
Table 1Characteristics of Studies Evaluating the Malnutrition Universal Screening Tool (MUST) in Predicting Hospital Outcomes for Colorectal Cancer PatientsStudy & AuthorCountryStudy YearStudy DesignStudy Quality1 [30] Burden et alUnited Kingdom2010Prospective cohortMedium2 [31] Tu et alTaiwan2012Prospective cohortMedium3 [32] van der Kroft et alThe Netherlands2018Prospective cohortMedium4 [33] Almasaudi et alUnited Kingdom2019Retrospective cohortMedium5 [34] Páramo-Zunzunegui et alSpain2020Prospective cohortMedium6 [35] Abbass et alUnited Kingdom2020Prospective cohortHigh7 [36] Xie et alChina2022Cross-sectionalMedium
Patients’ Characteristics
Table 2 provides an in-depth look at the patient characteristics across seven selected studies. These studies collectively encompassed a sample size of 1950 patients, highlighting diverse patient demographics and crucial clinical parameters, including BMI, which plays a significant role in assessing nutritional risk and subsequent outcomes in colorectal cancer treatment. The patient age across these studies showed a broad range but typically reflected the more common age demographic affected by colorectal cancer, with mean ages from 62.1 years in Tu et al31 to 69.9 years in Páramo-Zunzunegui et al.34 Gender distribution varied slightly, with male predominance noted in most studies, such as 62.1% in Burden et al30 and slightly less in Abbass et al35 with 57%.
Table 2Demographic and Clinical Characteristics of Colorectal Cancer Patients Assessed in Studies on MUSTStudy NumberSample SizeAge (Years)Gender DistributionComparison GroupBMI1 [30] Burden et al87Mean: 64.5,Range: 23–90Men: 54 (62.1%),Women: 33 (37.9%)SGA<20 (9.3%),20–24.9 (30.2%),≥25 (60.5%)2 [31] Tu et al45Mean: 62.1Men: 25 (56%),Women: 33 (44%)SGA and NRI<18.5 (4.4%),18.5–24 (48.9%),24–27 (24.4%),>27 (22.2%)3 [32] van der Kroft et al80Mean: 69Men: 51 (63.7%),Women: 29 (36.3%)Sarcopenic vsnon-Sarcopenic>30 (67% sarcopenic vs33% non-sarcopenic)4 [33] Almasaudi et al363Mean: 66Men: 199 (54.8%),Women: 164 (45.2%)MUST nutritional risk(low, medium, high)<20 (8%),20–24.9 (27%),25–29.9 (34%),≥30 (31%)5 [34] Páramo-Zunzunegui et al130Mean: 69.9Men: 85 (65.4%),Women: 45 (34.6%)Symptomatic vs asymptomatic<18.5 (2.1%),18.5–24 (23%),24–27 (51.8%),>27 (23%)6 [35] Abbass et al984Mean: 68,Range: 23–93Men: 57%,Women: 43%MUST 0 vs MUST 1-≥2NR7 [36] Xie et al301Mean: 62.7,Range: 24–87Men: 178 (59.1%),Women: 123 (40.9%)NRS, MNA-SF, MST, NRI, SGAMean: 23.7Abbreviations: NR, not reported; BMI, Body Mass Index; SGA, Subjective Global Assessment; NRI, Nutritional Risk Index.
BMI data, crucial for understanding the nutritional status of patients, were detailed across several studies, showcasing a wide distribution of nutritional states. For instance, Burden et al30 reported BMI ranges indicating a significant portion of patients with a BMI ≥25 (60.5%), reflecting a potentially overweight to obese status. Similar patterns were observed in Almasaudi et al,33 where 65% of participants had a BMI of 25 or higher. The highest prevalence of underweight patients was of 9.3% in the study by Burden et al,30 8% in the study by Almasaudi et al,33 and 4.4% in the study by Tu et al.31
The comparison groups within these studies were diverse, ranging from assessments based on SGA in Burden et al30 to more detailed nutritional risk indices such as MUST in Almasaudi et al33 and a combination of NRS, MNA-SF, MST, NRI, and SGA in Xie et al.36
Disease Characteristics
Table 3 outlines the disease characteristics from seven studies within the systematic review focusing on the MUST as a predictor for hospital outcomes in colorectal cancer patients. Starting with the stage of cancer, the studies exhibited a spectrum from early to advanced stages, indicating a diverse patient population. For instance, Burden et al30 reported a distribution across all four stages, with the majority in Stage 2 (43%), while Abbass et al35 had a considerable number of patients in Stage 2 (40.4%) and Stage 3 (35.7%).
Table 3Overview of Disease Characteristics and Treatment Details in Colorectal Cancer Studies Involving MUSTStudy NumberTime of AssessmentCancer Type/StageMedical/Surgical HistoryTreatmentComplications1 [30] Burden et al2–4 weeks prior to surgeryStage 1 (10%), Stage 2 (43%), Stage 3 (37%), Stage 4 (8%)NRSigmoid colectomy (6%), Anterior resection (39%), Right hemicolectomy (12%), Hartmann’s procedure (10%), Abdominoperineal resection (17%), Left hemicolectomy (5%), Laparotomy (3%), Pelvic clearance (3%)NR2 [31] Tu et al3 months before and during hospitalizationStage 4: 12 (27%), Liver metastasis 8 (66.6%), Peritoneal metastasis 2 (16.6%)NRElective surgery for tumor removal, Adjuvant treatment 68.8%NR3 [32] van der Kroft et alUpon hospital admission for nutritional screening; CT scans used for sarcopenia measurements before surgery.Stage 1 (28%), Stage 2 (21%), Stage 3 (32%), Stage 4 (19%)Charlson >3: 39% Sarcopenic vs 61% non-Sarcopenic, Perioperative transfusion 31% Sarcopenic vs 69% non-SarcopenicElective surgery 44%, Laparoscopic 56%, Neoadjuvant therapy 8 weeks for rectal cancer.Anastomotic leak: 6%, Wound infection: 6%, Sepsis: 5%4 [33] Almasaudi et alNutritional assessment during preoperative period; CT scans 3 months prior to surgeryStage 0–2 (64%), Stage 3 (32%), Stage 4 (2%)ASA grade 3–4: low MUST nutritional risk (28%), high MUST nutritional risk (45%), mGPS 1–2: low MUST nutritional risk (19%), high MUST nutritional risk (45%)Elective surgery for tumor removalClavien-Dindo 1–2 (28%), Clavien-Dindo 3–5 (6%)5 [34] Páramo-Zunzunegui et alNutritional assessment preoperative and weight measurements 3–6 months postoperativeColon (38.9%), Rectal (61.1%)ASA 3–4: 29.2%NR5% hypoalbuminemia, 16.5% prealbumin deficiency, 20.9% hypoproteinemia.6 [35] Abbass et alNutritional assessment during preoperative period.Colon (59.8%), Rectal (40.2%), Stage 1 (23.9%), Stage 2 (40.4%), Stage 3 (35.7%)ASA grade 3–4: low MUST nutritional risk (33.2%), high MUST nutritional risk (41.4%)Elective surgery 95.1% for low MUST nutritional risk vs 87.4% in high riskComplications: Low MUST risk (38%) vs high MUST risk (41.4%), Clavien-Dindo 3–5 low MUST risk (8.5%) vs high MUST risk (13.2%)7 [36] Xie et alNutritional assessment within 48 hours from admissionColon (45.2%), Rectal (54.8%)Comorbidities (38.2%)Laparoscopy 78.1%Grade 2 complications (27.6%)Abbreviations: NR, not reported; GPS, Glasgow Prognostic Score.
The treatments administered ranged from elective surgeries, with a high percentage noted in Abbass et al35 where 95.1% of patients at low MUST nutritional risk underwent elective surgery, to the use of laparoscopy as reported by Xie et al36 with 78.1% of surgeries performed laparoscopically. This diversity in treatment approaches reflects the evolving nature of colorectal cancer management and the role of nutritional status in determining the appropriate surgical intervention.
Complications varied significantly across the studies, with Almasaudi et al33 reporting a Clavien-Dindo classification of 1–2 in 28% of cases and 3–5 in 6% of cases. In comparison, Abbass et al35 noted complications in 38% of patients with low MUST risk versus 41.4% with high MUST risk, and a more severe complication rate (Clavien-Dindo 3–5) was higher in the high MUST risk group (13.2%) than in the low MUST risk group (8.5%). This suggests that higher nutritional risk may correlate with an increased risk of postoperative complications.
Medical and surgical history was less detailed in these summaries, with most studies not reporting or specifying the extent of prior medical interventions or conditions that could influence surgical outcomes. However, the presence of comorbidities was noted, such as in Xie et al36 where 38.2% of patients had comorbid conditions, highlighting the complexity of managing colorectal cancer patients who may have multiple health issues.
Nutritional Status and Outcomes
Table 4 presents an overview of the nutritional status and outcomes of patients with CRC across seven studies. Burden et al30 reported a spectrum of MUST risk scores with over half of the patients at low risk, and significant findings indicating malnourished patients had notably lower handgrip strength and fat-free mass. This study demonstrated a direct correlation between malnutrition, as defined by MUST scores, and increased hospital stay lengths, emphasizing the impact of nutritional status on postoperative recovery and complications. This study also found strong associations between improved nutritional status and better quality of life and functionality outcomes, such as the EuroQol-5 Dimensions and sit-to-stand test scores.
Table 4Summary of Magnesium Outcomes and Measurements per MUST Survey in Studies of Colorectal Cancer PatientsStudy NumberNutritional Status (MUST)Other Nutritional MeasurementsHospitalizationOutcomesConclusion1 [30] Burden et alLow MUST risk score (54.3%), Moderate MUST risk score (23.5%), High MUST risk score (22.2%)Handgrip strength: significantly lower in malnourished patients (mean 19.4 kg vs 27.3 kg). Fat free mass: significantly less in patients with >10% weight loss (mean 39.7 kg) vs those with <10% weight loss (mean 51.9 kg).Median LOS: 14 days Mean LOS: 26.6 days >10% weight loss: median LOS of 19 days, mean LOS 20 days, vs <10% weight loss: (median LOS 14 days, mean LOS 19 days).Significant weight loss and reduced fat free mass in malnourished patients indicate a severe impact of malnutrition on recovery and potentially on the occurrence of postoperative complicationsUsefulness of MUST assessment vs SGA.2 [31] Tu et alPreoperative: Low MUST risk score 55.5% - mean 1.5, Postoperative: Low MUST risk score 55.5% - mean 5.3, Sensitivity 96, Specificity 75SGA: Preoperative, A 64.4% - mean 1.7, SGA: Postoperative A 64.4% - mean 5.1, NRI: Preoperative <100 53.3% - mean 7.4, Postoperative <100 53.3% - mean 4.6Mean LOS: 17.1 daysPreoperative weight loss: 1.8kg for LOS 8–10 days, Postoperative weight loss: 4.5kg for LOS 8–10 daysMUST is useful for routine nutritional evaluation due to its efficiency, ease of use, and low cost, despite findings that SGA and NRI may have higher specificity3 [32] van der Kroft et al≥2 MUST risk score: 16%Sarcopenia: 52%Muscle attenuation >median 34.1: 38.3% Sarcopenic vs 11% non-SarcopenicMUST score ≥2 significantly associated with higher risk of post-operative complications; Sarcopenia and muscle attenuation not significantly associated when corrected for age and ASA.CT-measured sarcopenia offered little additional value over MUST in predicting post-operative morbidity, emphasizing the significance of simple, easy-to-use nutritional screening tools like MUST in clinical practice.4 [33] Almasaudi et alPreoperative: Medium-High nutritional risk (21%)Low SMI: low MUST nutritional risk (45%), high MUST nutritional risk (76%)LOS >7 days: low MUST nutritional risk (49%), high MUST nutritional risk (78%), MUST was independently associated with the length of hospital stay (OR: 2.17)An increased number of deaths were observed for patients at medium or high risk of malnutrition (HR: 1.45)The MUST score is an independent marker of risk in those undergoing surgery for colorectal cancer, important in preoperative assessment to improve clinical outcomes and reduce healthcare costs.5 [34] Páramo-Zunzunegui et alMUST 0 (30%), MUST 1–2 (44.9%), MUST >2 (25%)Weight loss 5–10kg: 18.5% in asymptomatic patients vs 32.8% in symptomatic patients, 59% at nutritional riskNRSymptomatic patients 48.8% altered parameters vs 61.2% symptomatic patients.MUST is useful for routine nutritional evaluation due to its efficiency, ease of use, and low cos6 [35] Abbass et alMUST 0 (82.3%), MUST 1-≥2 (17.7%)Low SMI: low MUST nutritional risk (46.5%), high MUST nutritional risk (66.7%)LOS >7 days: low MUST nutritional risk (51.4%), high MUST nutritional risk (69%)mGPS is a significant predictor of complications when MUST = 0 (HR: 1.29)MUST and mGPS combination effectively predict hospital stay and survival in operable CRC patients.7 [36] Xie et al1–2 MUST risk score (44.9%), >2 MUST risk score (25%), Risk of malnutrition (39.5%), Sensitivity 73.1%, Specificity 75.8%Weight loss in the last 3–6 months: <5kg (19.4%); 5–10kg (20.7%); >10kg (2.1%), Risk of malnutrition: NRS (41.5%), MNA-SF (46.2%), MST (30.6%), NRI (25.2%), SGA (43.5%)Mean LOS: 19.2 daysNRS was the only significant predictor for postoperative complications based on malnutrition risk (OR: 2.40), compared with MUST.High prevalence of nutritional risk among colorectal cancer patients scheduled for surgery. MUST score was not a significant independent predictor.Abbreviations: NR, Not Reported; SMI, Skeletal Muscle Index; MUST, Malnutrition Universal Screening Tool; LOS, Length of Stay; ASA, American Society of Anesthesiologists; SGA, Subjective Global Assessment; NRI, Nutritional Risk Index; mGPS, Modified Glasgow Predictive Score.
Similarly, Tu et al31 and Almasaudi et al33 found a direct association between MUST scores and LOS, with higher MUST scores correlating with longer hospital stays. Tu et al highlighted the utility of MUST in routine nutritional evaluation, praising its efficiency and ease of use despite the higher specificity of other assessments like SGA and NRI. Almasaudi et al noted an increased mortality rate among patients at medium or high risk of malnutrition, underscoring MUST’s role as an independent risk marker in CRC surgery.
Van der Kroft et al32 and Abbass et al35 provided insights into how MUST scores are associated with postoperative complications, with van der Kroft et al finding that a MUST score ≥2 significantly increased the risk of such complications. Abbass et al further confirmed the combination of MUST and modified Glasgow Prognostic Score (mGPS) as effective predictors of hospital stay and survival in CRC patients, demonstrating the compound value of nutritional and inflammatory markers in clinical assessments.
Páramo-Zunzunegui et al34 and Xie et al36 highlighted the high prevalence of nutritional risk among CRC patients and the importance of MUST in routine nutritional evaluations. Xie et al specifically noted the Nutritional Risk Screening (NRS) as a significant predictor for postoperative complications, indicating the importance of comprehensive nutritional assessments beyond MUST alone.
Collectively, these studies underscore the significance of using MUST in conjunction with other nutritional and clinical assessments to improve clinical outcomes and reduce healthcare costs for CRC patients. They reveal a consistent trend that malnutrition, as identified by MUST, adversely affects recovery, increases hospital LOS, and is associated with higher rates of postoperative complications and mortality. These findings advocate for the incorporation of MUST into preoperative assessments to guide nutritional interventions and support recovery in CRC patients.
Study Characteristics
The systematic review scrutinized seven studies,30–36 as detailed in Table 1. These studies varied from a variety of countries, including the United Kingdom,30,33,35 Taiwan,31 The Netherlands,32 Spain,34 and China,36 reflecting a broad international interest in the topic. Conducted between 2010 and 2022, the majority of these studies adopted a prospective cohort design, with the exception of Almasaudi et al,33 which was retrospective, and Xie et al,36 which employed a cross-sectional approach. The quality of these studies was predominantly rated as medium, except for Abbass et al35 from the United Kingdom, which was distinguished with a high-quality rating. This distribution of study qualities suggests a general reliability in the findings, with Abbass et al’s study standing out for its exceptional methodological rigor.
Table 1Characteristics of Studies Evaluating the Malnutrition Universal Screening Tool (MUST) in Predicting Hospital Outcomes for Colorectal Cancer PatientsStudy & AuthorCountryStudy YearStudy DesignStudy Quality1 [30] Burden et alUnited Kingdom2010Prospective cohortMedium2 [31] Tu et alTaiwan2012Prospective cohortMedium3 [32] van der Kroft et alThe Netherlands2018Prospective cohortMedium4 [33] Almasaudi et alUnited Kingdom2019Retrospective cohortMedium5 [34] Páramo-Zunzunegui et alSpain2020Prospective cohortMedium6 [35] Abbass et alUnited Kingdom2020Prospective cohortHigh7 [36] Xie et alChina2022Cross-sectionalMedium
Patients’ Characteristics
Table 2 provides an in-depth look at the patient characteristics across seven selected studies. These studies collectively encompassed a sample size of 1950 patients, highlighting diverse patient demographics and crucial clinical parameters, including BMI, which plays a significant role in assessing nutritional risk and subsequent outcomes in colorectal cancer treatment. The patient age across these studies showed a broad range but typically reflected the more common age demographic affected by colorectal cancer, with mean ages from 62.1 years in Tu et al31 to 69.9 years in Páramo-Zunzunegui et al.34 Gender distribution varied slightly, with male predominance noted in most studies, such as 62.1% in Burden et al30 and slightly less in Abbass et al35 with 57%.
Table 2Demographic and Clinical Characteristics of Colorectal Cancer Patients Assessed in Studies on MUSTStudy NumberSample SizeAge (Years)Gender DistributionComparison GroupBMI1 [30] Burden et al87Mean: 64.5,Range: 23–90Men: 54 (62.1%),Women: 33 (37.9%)SGA<20 (9.3%),20–24.9 (30.2%),≥25 (60.5%)2 [31] Tu et al45Mean: 62.1Men: 25 (56%),Women: 33 (44%)SGA and NRI<18.5 (4.4%),18.5–24 (48.9%),24–27 (24.4%),>27 (22.2%)3 [32] van der Kroft et al80Mean: 69Men: 51 (63.7%),Women: 29 (36.3%)Sarcopenic vsnon-Sarcopenic>30 (67% sarcopenic vs33% non-sarcopenic)4 [33] Almasaudi et al363Mean: 66Men: 199 (54.8%),Women: 164 (45.2%)MUST nutritional risk(low, medium, high)<20 (8%),20–24.9 (27%),25–29.9 (34%),≥30 (31%)5 [34] Páramo-Zunzunegui et al130Mean: 69.9Men: 85 (65.4%),Women: 45 (34.6%)Symptomatic vs asymptomatic<18.5 (2.1%),18.5–24 (23%),24–27 (51.8%),>27 (23%)6 [35] Abbass et al984Mean: 68,Range: 23–93Men: 57%,Women: 43%MUST 0 vs MUST 1-≥2NR7 [36] Xie et al301Mean: 62.7,Range: 24–87Men: 178 (59.1%),Women: 123 (40.9%)NRS, MNA-SF, MST, NRI, SGAMean: 23.7Abbreviations: NR, not reported; BMI, Body Mass Index; SGA, Subjective Global Assessment; NRI, Nutritional Risk Index.
BMI data, crucial for understanding the nutritional status of patients, were detailed across several studies, showcasing a wide distribution of nutritional states. For instance, Burden et al30 reported BMI ranges indicating a significant portion of patients with a BMI ≥25 (60.5%), reflecting a potentially overweight to obese status. Similar patterns were observed in Almasaudi et al,33 where 65% of participants had a BMI of 25 or higher. The highest prevalence of underweight patients was of 9.3% in the study by Burden et al,30 8% in the study by Almasaudi et al,33 and 4.4% in the study by Tu et al.31
The comparison groups within these studies were diverse, ranging from assessments based on SGA in Burden et al30 to more detailed nutritional risk indices such as MUST in Almasaudi et al33 and a combination of NRS, MNA-SF, MST, NRI, and SGA in Xie et al.36
Disease Characteristics
Table 3 outlines the disease characteristics from seven studies within the systematic review focusing on the MUST as a predictor for hospital outcomes in colorectal cancer patients. Starting with the stage of cancer, the studies exhibited a spectrum from early to advanced stages, indicating a diverse patient population. For instance, Burden et al30 reported a distribution across all four stages, with the majority in Stage 2 (43%), while Abbass et al35 had a considerable number of patients in Stage 2 (40.4%) and Stage 3 (35.7%).
Table 3Overview of Disease Characteristics and Treatment Details in Colorectal Cancer Studies Involving MUSTStudy NumberTime of AssessmentCancer Type/StageMedical/Surgical HistoryTreatmentComplications1 [30] Burden et al2–4 weeks prior to surgeryStage 1 (10%), Stage 2 (43%), Stage 3 (37%), Stage 4 (8%)NRSigmoid colectomy (6%), Anterior resection (39%), Right hemicolectomy (12%), Hartmann’s procedure (10%), Abdominoperineal resection (17%), Left hemicolectomy (5%), Laparotomy (3%), Pelvic clearance (3%)NR2 [31] Tu et al3 months before and during hospitalizationStage 4: 12 (27%), Liver metastasis 8 (66.6%), Peritoneal metastasis 2 (16.6%)NRElective surgery for tumor removal, Adjuvant treatment 68.8%NR3 [32] van der Kroft et alUpon hospital admission for nutritional screening; CT scans used for sarcopenia measurements before surgery.Stage 1 (28%), Stage 2 (21%), Stage 3 (32%), Stage 4 (19%)Charlson >3: 39% Sarcopenic vs 61% non-Sarcopenic, Perioperative transfusion 31% Sarcopenic vs 69% non-SarcopenicElective surgery 44%, Laparoscopic 56%, Neoadjuvant therapy 8 weeks for rectal cancer.Anastomotic leak: 6%, Wound infection: 6%, Sepsis: 5%4 [33] Almasaudi et alNutritional assessment during preoperative period; CT scans 3 months prior to surgeryStage 0–2 (64%), Stage 3 (32%), Stage 4 (2%)ASA grade 3–4: low MUST nutritional risk (28%), high MUST nutritional risk (45%), mGPS 1–2: low MUST nutritional risk (19%), high MUST nutritional risk (45%)Elective surgery for tumor removalClavien-Dindo 1–2 (28%), Clavien-Dindo 3–5 (6%)5 [34] Páramo-Zunzunegui et alNutritional assessment preoperative and weight measurements 3–6 months postoperativeColon (38.9%), Rectal (61.1%)ASA 3–4: 29.2%NR5% hypoalbuminemia, 16.5% prealbumin deficiency, 20.9% hypoproteinemia.6 [35] Abbass et alNutritional assessment during preoperative period.Colon (59.8%), Rectal (40.2%), Stage 1 (23.9%), Stage 2 (40.4%), Stage 3 (35.7%)ASA grade 3–4: low MUST nutritional risk (33.2%), high MUST nutritional risk (41.4%)Elective surgery 95.1% for low MUST nutritional risk vs 87.4% in high riskComplications: Low MUST risk (38%) vs high MUST risk (41.4%), Clavien-Dindo 3–5 low MUST risk (8.5%) vs high MUST risk (13.2%)7 [36] Xie et alNutritional assessment within 48 hours from admissionColon (45.2%), Rectal (54.8%)Comorbidities (38.2%)Laparoscopy 78.1%Grade 2 complications (27.6%)Abbreviations: NR, not reported; GPS, Glasgow Prognostic Score.
The treatments administered ranged from elective surgeries, with a high percentage noted in Abbass et al35 where 95.1% of patients at low MUST nutritional risk underwent elective surgery, to the use of laparoscopy as reported by Xie et al36 with 78.1% of surgeries performed laparoscopically. This diversity in treatment approaches reflects the evolving nature of colorectal cancer management and the role of nutritional status in determining the appropriate surgical intervention.
Complications varied significantly across the studies, with Almasaudi et al33 reporting a Clavien-Dindo classification of 1–2 in 28% of cases and 3–5 in 6% of cases. In comparison, Abbass et al35 noted complications in 38% of patients with low MUST risk versus 41.4% with high MUST risk, and a more severe complication rate (Clavien-Dindo 3–5) was higher in the high MUST risk group (13.2%) than in the low MUST risk group (8.5%). This suggests that higher nutritional risk may correlate with an increased risk of postoperative complications.
Medical and surgical history was less detailed in these summaries, with most studies not reporting or specifying the extent of prior medical interventions or conditions that could influence surgical outcomes. However, the presence of comorbidities was noted, such as in Xie et al36 where 38.2% of patients had comorbid conditions, highlighting the complexity of managing colorectal cancer patients who may have multiple health issues.
Nutritional Status and Outcomes
Table 4 presents an overview of the nutritional status and outcomes of patients with CRC across seven studies. Burden et al30 reported a spectrum of MUST risk scores with over half of the patients at low risk, and significant findings indicating malnourished patients had notably lower handgrip strength and fat-free mass. This study demonstrated a direct correlation between malnutrition, as defined by MUST scores, and increased hospital stay lengths, emphasizing the impact of nutritional status on postoperative recovery and complications. This study also found strong associations between improved nutritional status and better quality of life and functionality outcomes, such as the EuroQol-5 Dimensions and sit-to-stand test scores.
Table 4Summary of Magnesium Outcomes and Measurements per MUST Survey in Studies of Colorectal Cancer PatientsStudy NumberNutritional Status (MUST)Other Nutritional MeasurementsHospitalizationOutcomesConclusion1 [30] Burden et alLow MUST risk score (54.3%), Moderate MUST risk score (23.5%), High MUST risk score (22.2%)Handgrip strength: significantly lower in malnourished patients (mean 19.4 kg vs 27.3 kg). Fat free mass: significantly less in patients with >10% weight loss (mean 39.7 kg) vs those with <10% weight loss (mean 51.9 kg).Median LOS: 14 days Mean LOS: 26.6 days >10% weight loss: median LOS of 19 days, mean LOS 20 days, vs <10% weight loss: (median LOS 14 days, mean LOS 19 days).Significant weight loss and reduced fat free mass in malnourished patients indicate a severe impact of malnutrition on recovery and potentially on the occurrence of postoperative complicationsUsefulness of MUST assessment vs SGA.2 [31] Tu et alPreoperative: Low MUST risk score 55.5% - mean 1.5, Postoperative: Low MUST risk score 55.5% - mean 5.3, Sensitivity 96, Specificity 75SGA: Preoperative, A 64.4% - mean 1.7, SGA: Postoperative A 64.4% - mean 5.1, NRI: Preoperative <100 53.3% - mean 7.4, Postoperative <100 53.3% - mean 4.6Mean LOS: 17.1 daysPreoperative weight loss: 1.8kg for LOS 8–10 days, Postoperative weight loss: 4.5kg for LOS 8–10 daysMUST is useful for routine nutritional evaluation due to its efficiency, ease of use, and low cost, despite findings that SGA and NRI may have higher specificity3 [32] van der Kroft et al≥2 MUST risk score: 16%Sarcopenia: 52%Muscle attenuation >median 34.1: 38.3% Sarcopenic vs 11% non-SarcopenicMUST score ≥2 significantly associated with higher risk of post-operative complications; Sarcopenia and muscle attenuation not significantly associated when corrected for age and ASA.CT-measured sarcopenia offered little additional value over MUST in predicting post-operative morbidity, emphasizing the significance of simple, easy-to-use nutritional screening tools like MUST in clinical practice.4 [33] Almasaudi et alPreoperative: Medium-High nutritional risk (21%)Low SMI: low MUST nutritional risk (45%), high MUST nutritional risk (76%)LOS >7 days: low MUST nutritional risk (49%), high MUST nutritional risk (78%), MUST was independently associated with the length of hospital stay (OR: 2.17)An increased number of deaths were observed for patients at medium or high risk of malnutrition (HR: 1.45)The MUST score is an independent marker of risk in those undergoing surgery for colorectal cancer, important in preoperative assessment to improve clinical outcomes and reduce healthcare costs.5 [34] Páramo-Zunzunegui et alMUST 0 (30%), MUST 1–2 (44.9%), MUST >2 (25%)Weight loss 5–10kg: 18.5% in asymptomatic patients vs 32.8% in symptomatic patients, 59% at nutritional riskNRSymptomatic patients 48.8% altered parameters vs 61.2% symptomatic patients.MUST is useful for routine nutritional evaluation due to its efficiency, ease of use, and low cos6 [35] Abbass et alMUST 0 (82.3%), MUST 1-≥2 (17.7%)Low SMI: low MUST nutritional risk (46.5%), high MUST nutritional risk (66.7%)LOS >7 days: low MUST nutritional risk (51.4%), high MUST nutritional risk (69%)mGPS is a significant predictor of complications when MUST = 0 (HR: 1.29)MUST and mGPS combination effectively predict hospital stay and survival in operable CRC patients.7 [36] Xie et al1–2 MUST risk score (44.9%), >2 MUST risk score (25%), Risk of malnutrition (39.5%), Sensitivity 73.1%, Specificity 75.8%Weight loss in the last 3–6 months: <5kg (19.4%); 5–10kg (20.7%); >10kg (2.1%), Risk of malnutrition: NRS (41.5%), MNA-SF (46.2%), MST (30.6%), NRI (25.2%), SGA (43.5%)Mean LOS: 19.2 daysNRS was the only significant predictor for postoperative complications based on malnutrition risk (OR: 2.40), compared with MUST.High prevalence of nutritional risk among colorectal cancer patients scheduled for surgery. MUST score was not a significant independent predictor.Abbreviations: NR, Not Reported; SMI, Skeletal Muscle Index; MUST, Malnutrition Universal Screening Tool; LOS, Length of Stay; ASA, American Society of Anesthesiologists; SGA, Subjective Global Assessment; NRI, Nutritional Risk Index; mGPS, Modified Glasgow Predictive Score.
Similarly, Tu et al31 and Almasaudi et al33 found a direct association between MUST scores and LOS, with higher MUST scores correlating with longer hospital stays. Tu et al highlighted the utility of MUST in routine nutritional evaluation, praising its efficiency and ease of use despite the higher specificity of other assessments like SGA and NRI. Almasaudi et al noted an increased mortality rate among patients at medium or high risk of malnutrition, underscoring MUST’s role as an independent risk marker in CRC surgery.
Van der Kroft et al32 and Abbass et al35 provided insights into how MUST scores are associated with postoperative complications, with van der Kroft et al finding that a MUST score ≥2 significantly increased the risk of such complications. Abbass et al further confirmed the combination of MUST and modified Glasgow Prognostic Score (mGPS) as effective predictors of hospital stay and survival in CRC patients, demonstrating the compound value of nutritional and inflammatory markers in clinical assessments.
Páramo-Zunzunegui et al34 and Xie et al36 highlighted the high prevalence of nutritional risk among CRC patients and the importance of MUST in routine nutritional evaluations. Xie et al specifically noted the Nutritional Risk Screening (NRS) as a significant predictor for postoperative complications, indicating the importance of comprehensive nutritional assessments beyond MUST alone.
Collectively, these studies underscore the significance of using MUST in conjunction with other nutritional and clinical assessments to improve clinical outcomes and reduce healthcare costs for CRC patients. They reveal a consistent trend that malnutrition, as identified by MUST, adversely affects recovery, increases hospital LOS, and is associated with higher rates of postoperative complications and mortality. These findings advocate for the incorporation of MUST into preoperative assessments to guide nutritional interventions and support recovery in CRC patients.
Discussion
Summary of Evidence
The systematic review highlights the diverse stages of colorectal cancer among patients, ranging from early to advanced, underscoring the varied prognosis and treatment challenges across the spectrum. The significance of nutritional screening lies in the high prevalence of malnutrition among colorectal cancer patients, which can affect treatment tolerance, recovery, and overall survival. Nutritional screening encompasses the assessment of dietary intake, weight history, physical symptoms affecting food intake (such as nausea, vomiting, or bowel obstruction), and the presence of factors that increase nutritional needs (such as metabolic stress or catabolism induced by cancer). In colorectal cancer care, nutritional screening aims to tailor nutritional support to individual patient needs, thereby improving clinical outcomes and enhancing the quality of life. The implementation of tools like MUST in this process is crucial for the timely identification and management of nutritional issues in this patient population.
Our review confirms the utility of the MUST in assessing nutritional risk and its implications for hospital outcomes in colorectal cancer patients. While our findings underscore the effectiveness of MUST, they do not extend to advocating for broader evidence-based protocols involving physical activity or comprehensive nutritional assessments beyond the scope of this tool. Thus, any conclusions regarding the development of such protocols should be approached with caution, acknowledging the specific focus and limitations of our study on MUST.
The distribution of cancer stages, as detailed in studies like Burden et al30 and Abbass et al,35 provides a broad view of the CRC population, revealing a substantial segment in the intermediate stages of disease. This stage distribution is crucial as it influences treatment decisions and potential outcomes, necessitating a tailored approach to managing each patient. The varied stages of cancer underscore the necessity of incorporating comprehensive nutritional assessments, like MUST, into the pre-treatment evaluation to better stratify patients according to their risk and customize their treatment plans accordingly.
The treatment modalities reported across the studies reflect the evolving landscape of CRC management, highlighting the significant role that nutritional status plays in determining the most appropriate surgical intervention. Therefore, it emphasizes the importance of preoperative nutritional assessment in minimizing surgical risks and enhancing recovery, further advocating for the integration of nutritional screening tools like MUST in the preoperative workup.
The correlation between higher nutritional risk and increased postoperative complications, as indicated by the data from Almasaudi et al33 and Abbass et al,35 is particularly noteworthy. The findings suggest that patients with elevated MUST scores are more susceptible to severe postoperative complications, highlighting the critical role of nutritional status in patient recovery and long-term outcomes. This relationship between malnutrition and adverse surgical outcomes underscores the need for preemptive nutritional interventions to mitigate risks and improve the prognosis for CRC patients.
However, the lack of detailed medical and surgical histories in these studies presents a limitation to fully understanding the influence of previous health conditions on surgical outcomes and nutritional status. Despite this, the mention of comorbidities in studies like that of Xie et al36 alludes to the complexity of managing CRC patients, who often present with multiple health issues. This complexity, coupled with the demonstrated impact of nutritional risk on outcomes, solidifies the argument for a holistic approach to patient care, integrating nutritional assessment and management as a fundamental component of CRC treatment protocols.
The study by Tagawa et al37 explored the nutritional status of outpatient colorectal cancer patients undergoing chemotherapy, utilizing the MUST to assess its efficacy and correlation with adverse events. Among the 34 patients with advanced or recurrent colorectal cancer studied between April and December 2010, 47.1% were identified as high-risk and requiring nutritional care, showing significant reductions in body weight and BMI, alongside notably higher incidences of appetite loss and fatigue compared to the low-risk group. While this study underscores the importance of nutritional assessment in managing the adverse effects of outpatient chemotherapy and highlights MUST’s potential as a simplified screening tool, it was not included in our systematic review due to its focus on outpatients receiving chemotherapy and hospital outcomes were our primary concern. Additionally, the study being in Japanese posed a language barrier for inclusion in our review, which focused on English language studies.
Lewandowska et al38 and Ziętarska et al4 both underscore the critical role of malnutrition in CRC patients’ treatment outcomes, stressing the importance of nutritional status assessments and interventions. Lewandowska et al highlight the widespread issue of malnutrition among CRC patients, noting its detrimental effects on survival rates, quality of life, and therapy effectiveness. They recommend personalized nutritional therapy, including light, low-fat foods and, in specific cases, dietary adjustments like lactose and gluten exclusion. Ziętarska et al, through quantitative analysis, revealed that 75% of CRC patients exhibit pre-cachexia, with 73.3% moderately malnourished and 2.7% severely malnourished. They found a significant correlation between appetite and patients’ functional status, emphasizing the need for early and adequate nutritional interventions. Both studies align with the assertion that evaluating and addressing malnutrition is essential for improving CRC treatment efficacy and patient quality of life, reinforcing the importance of integrating nutritional care into CRC management protocols.
The study by Burden et al39 delved into the critical issue of preoperative malnutrition in colorectal cancer patients and its implications on postoperative outcomes, underscoring the necessity for nutritional assessment and intervention. It was reported that 44% of their patients were at nutritional risk preoperatively, with a significant portion of these patients (31%) improving their nutritional status during prehabilitation. Conversely, Gupta et al40 emphasized the high prevalence of preoperative malnutrition and advocated for comprehensive nutritional assessments using tools like SGA, PG-SGA, and MUST. They suggested considering various nutritional interventions, including trimodal prehabilitation and supplementation with arginine and N-3 fatty acids, to enhance postoperative recovery. Both studies underscored the importance of addressing malnutrition to improve surgical outcomes in colorectal cancer patients. However, Gupta et al provided a broader perspective on potential nutritional interventions and the need for their integration into preoperative care to mitigate the adverse effects of malnutrition.
Håkonsen et al41 and Ruan et al42 both examine the effectiveness of nutritional assessment tools in identifying malnutrition among colorectal cancer patients, yet their findings illuminate distinct facets of diagnostic accuracy and clinical implications. Håkonsen et al conducted a methodical review to assess the diagnostic test accuracy of instruments like the Malnutrition Screening Tool (MST), Nutritional Risk Index (NRI), and the MUST against Subjective Global Assessment (SGA) and Patient Generated Subjective Global Assessment (PG-SGA). Their results highlighted varying levels of sensitivity and specificity: MUST demonstrated a high sensitivity of 96% against SGA, indicating excellent diagnostic accuracy, while its effectiveness diminished significantly when compared to PG-SGA, with sensitivity dropping to 72% and specificity to 48.9%. On the other hand, Ruan et al presented a comparative analysis of the Nutritional Risk Screening 2002 (NRS-2002), Global Leadership Initiative on Malnutrition (GLIM) criteria, and PG-SGA, emphasizing NRS-2002’s superior specificity (0.90) in identifying patients without nutritional deficits. Their study underscored the simplicity and efficacy of NRS-2002 in clinical settings, suggesting its potential as a preferred tool for colorectal cancer patients. While Håkonsen et al point out the varied diagnostic accuracies of different tools and advocate for a combined use of clinical judgment and assessments like SGA or PG-SGA, Ruan et al highlight NRS-2002’s advantage in specificity and ease of use, proposing it as an effective standalone screening method for nutritional assessment in colorectal cancer care.
Zhang et al43 and Monfino et al44 examined malnutrition screening in cancer patients, highlighting the importance of effective tools. Zhang et al’s observational study compared NRS2002, MUST, and PG-SGA against the GLIM criteria, revealing that 24.8% and 15.4% of patients were at moderate and high risk of malnutrition according to NRS2002 and MUST, respectively. NRS2002 most aligned with GLIM (AUC = 0.896) compared to MUST (AUC = 0.757). PG-SGA, despite its sensitivity, shows a low positive predictive value. Monfino et al stress the urgency of early malnutrition screening and personalized nutritional therapy to enhance outcomes. While Monfino et al advocate for the general use of screening tools and the GLIM criteria for diagnosing malnutrition, Zhang et al provide concrete data, suggesting NRS2002’s superiority for its alignment with GLIM, aiding in accurate malnutrition identification in cancer care.
Finally, another review by Wimmer at al45 explored the methods for early identification of cancer-related malnutrition before and after surgery, involving 926 patients. Similarly to our findings, it was observed that despite the diversity of tools available, the review underscored a significant gap: a lack of evidence-based standardization for early malnutrition detection in colorectal cancer patients within oncology clinical practice. Moreover, the involvement of different health professional groups in the assessment process lacked standardized roles, pointing to a disjointed approach to nutritional screening. Notably, physical activity, an important aspect of overall nutritional status, was absent from the screening tools reviewed. This omission highlights a critical area for future development and integration into comprehensive nutritional assessments. Therefore, our findings emphasize the urgent need for standardized, evidence-based protocols that include a holistic view of patients’ nutritional status, incorporating physical activity to improve early malnutrition detection and outcomes in colorectal cancer care.
Summary of Evidence
The systematic review highlights the diverse stages of colorectal cancer among patients, ranging from early to advanced, underscoring the varied prognosis and treatment challenges across the spectrum. The significance of nutritional screening lies in the high prevalence of malnutrition among colorectal cancer patients, which can affect treatment tolerance, recovery, and overall survival. Nutritional screening encompasses the assessment of dietary intake, weight history, physical symptoms affecting food intake (such as nausea, vomiting, or bowel obstruction), and the presence of factors that increase nutritional needs (such as metabolic stress or catabolism induced by cancer). In colorectal cancer care, nutritional screening aims to tailor nutritional support to individual patient needs, thereby improving clinical outcomes and enhancing the quality of life. The implementation of tools like MUST in this process is crucial for the timely identification and management of nutritional issues in this patient population.
Our review confirms the utility of the MUST in assessing nutritional risk and its implications for hospital outcomes in colorectal cancer patients. While our findings underscore the effectiveness of MUST, they do not extend to advocating for broader evidence-based protocols involving physical activity or comprehensive nutritional assessments beyond the scope of this tool. Thus, any conclusions regarding the development of such protocols should be approached with caution, acknowledging the specific focus and limitations of our study on MUST.
The distribution of cancer stages, as detailed in studies like Burden et al30 and Abbass et al,35 provides a broad view of the CRC population, revealing a substantial segment in the intermediate stages of disease. This stage distribution is crucial as it influences treatment decisions and potential outcomes, necessitating a tailored approach to managing each patient. The varied stages of cancer underscore the necessity of incorporating comprehensive nutritional assessments, like MUST, into the pre-treatment evaluation to better stratify patients according to their risk and customize their treatment plans accordingly.
The treatment modalities reported across the studies reflect the evolving landscape of CRC management, highlighting the significant role that nutritional status plays in determining the most appropriate surgical intervention. Therefore, it emphasizes the importance of preoperative nutritional assessment in minimizing surgical risks and enhancing recovery, further advocating for the integration of nutritional screening tools like MUST in the preoperative workup.
The correlation between higher nutritional risk and increased postoperative complications, as indicated by the data from Almasaudi et al33 and Abbass et al,35 is particularly noteworthy. The findings suggest that patients with elevated MUST scores are more susceptible to severe postoperative complications, highlighting the critical role of nutritional status in patient recovery and long-term outcomes. This relationship between malnutrition and adverse surgical outcomes underscores the need for preemptive nutritional interventions to mitigate risks and improve the prognosis for CRC patients.
However, the lack of detailed medical and surgical histories in these studies presents a limitation to fully understanding the influence of previous health conditions on surgical outcomes and nutritional status. Despite this, the mention of comorbidities in studies like that of Xie et al36 alludes to the complexity of managing CRC patients, who often present with multiple health issues. This complexity, coupled with the demonstrated impact of nutritional risk on outcomes, solidifies the argument for a holistic approach to patient care, integrating nutritional assessment and management as a fundamental component of CRC treatment protocols.
The study by Tagawa et al37 explored the nutritional status of outpatient colorectal cancer patients undergoing chemotherapy, utilizing the MUST to assess its efficacy and correlation with adverse events. Among the 34 patients with advanced or recurrent colorectal cancer studied between April and December 2010, 47.1% were identified as high-risk and requiring nutritional care, showing significant reductions in body weight and BMI, alongside notably higher incidences of appetite loss and fatigue compared to the low-risk group. While this study underscores the importance of nutritional assessment in managing the adverse effects of outpatient chemotherapy and highlights MUST’s potential as a simplified screening tool, it was not included in our systematic review due to its focus on outpatients receiving chemotherapy and hospital outcomes were our primary concern. Additionally, the study being in Japanese posed a language barrier for inclusion in our review, which focused on English language studies.
Lewandowska et al38 and Ziętarska et al4 both underscore the critical role of malnutrition in CRC patients’ treatment outcomes, stressing the importance of nutritional status assessments and interventions. Lewandowska et al highlight the widespread issue of malnutrition among CRC patients, noting its detrimental effects on survival rates, quality of life, and therapy effectiveness. They recommend personalized nutritional therapy, including light, low-fat foods and, in specific cases, dietary adjustments like lactose and gluten exclusion. Ziętarska et al, through quantitative analysis, revealed that 75% of CRC patients exhibit pre-cachexia, with 73.3% moderately malnourished and 2.7% severely malnourished. They found a significant correlation between appetite and patients’ functional status, emphasizing the need for early and adequate nutritional interventions. Both studies align with the assertion that evaluating and addressing malnutrition is essential for improving CRC treatment efficacy and patient quality of life, reinforcing the importance of integrating nutritional care into CRC management protocols.
The study by Burden et al39 delved into the critical issue of preoperative malnutrition in colorectal cancer patients and its implications on postoperative outcomes, underscoring the necessity for nutritional assessment and intervention. It was reported that 44% of their patients were at nutritional risk preoperatively, with a significant portion of these patients (31%) improving their nutritional status during prehabilitation. Conversely, Gupta et al40 emphasized the high prevalence of preoperative malnutrition and advocated for comprehensive nutritional assessments using tools like SGA, PG-SGA, and MUST. They suggested considering various nutritional interventions, including trimodal prehabilitation and supplementation with arginine and N-3 fatty acids, to enhance postoperative recovery. Both studies underscored the importance of addressing malnutrition to improve surgical outcomes in colorectal cancer patients. However, Gupta et al provided a broader perspective on potential nutritional interventions and the need for their integration into preoperative care to mitigate the adverse effects of malnutrition.
Håkonsen et al41 and Ruan et al42 both examine the effectiveness of nutritional assessment tools in identifying malnutrition among colorectal cancer patients, yet their findings illuminate distinct facets of diagnostic accuracy and clinical implications. Håkonsen et al conducted a methodical review to assess the diagnostic test accuracy of instruments like the Malnutrition Screening Tool (MST), Nutritional Risk Index (NRI), and the MUST against Subjective Global Assessment (SGA) and Patient Generated Subjective Global Assessment (PG-SGA). Their results highlighted varying levels of sensitivity and specificity: MUST demonstrated a high sensitivity of 96% against SGA, indicating excellent diagnostic accuracy, while its effectiveness diminished significantly when compared to PG-SGA, with sensitivity dropping to 72% and specificity to 48.9%. On the other hand, Ruan et al presented a comparative analysis of the Nutritional Risk Screening 2002 (NRS-2002), Global Leadership Initiative on Malnutrition (GLIM) criteria, and PG-SGA, emphasizing NRS-2002’s superior specificity (0.90) in identifying patients without nutritional deficits. Their study underscored the simplicity and efficacy of NRS-2002 in clinical settings, suggesting its potential as a preferred tool for colorectal cancer patients. While Håkonsen et al point out the varied diagnostic accuracies of different tools and advocate for a combined use of clinical judgment and assessments like SGA or PG-SGA, Ruan et al highlight NRS-2002’s advantage in specificity and ease of use, proposing it as an effective standalone screening method for nutritional assessment in colorectal cancer care.
Zhang et al43 and Monfino et al44 examined malnutrition screening in cancer patients, highlighting the importance of effective tools. Zhang et al’s observational study compared NRS2002, MUST, and PG-SGA against the GLIM criteria, revealing that 24.8% and 15.4% of patients were at moderate and high risk of malnutrition according to NRS2002 and MUST, respectively. NRS2002 most aligned with GLIM (AUC = 0.896) compared to MUST (AUC = 0.757). PG-SGA, despite its sensitivity, shows a low positive predictive value. Monfino et al stress the urgency of early malnutrition screening and personalized nutritional therapy to enhance outcomes. While Monfino et al advocate for the general use of screening tools and the GLIM criteria for diagnosing malnutrition, Zhang et al provide concrete data, suggesting NRS2002’s superiority for its alignment with GLIM, aiding in accurate malnutrition identification in cancer care.
Finally, another review by Wimmer at al45 explored the methods for early identification of cancer-related malnutrition before and after surgery, involving 926 patients. Similarly to our findings, it was observed that despite the diversity of tools available, the review underscored a significant gap: a lack of evidence-based standardization for early malnutrition detection in colorectal cancer patients within oncology clinical practice. Moreover, the involvement of different health professional groups in the assessment process lacked standardized roles, pointing to a disjointed approach to nutritional screening. Notably, physical activity, an important aspect of overall nutritional status, was absent from the screening tools reviewed. This omission highlights a critical area for future development and integration into comprehensive nutritional assessments. Therefore, our findings emphasize the urgent need for standardized, evidence-based protocols that include a holistic view of patients’ nutritional status, incorporating physical activity to improve early malnutrition detection and outcomes in colorectal cancer care.
Limitations
The systematic review faced several limitations that are crucial for interpreting its findings. First, the inclusion criteria limited the analysis to English language publications, potentially omitting relevant studies in other languages that could enrich the understanding of the MUST’s effectiveness. Additionally, the review’s scope, focusing exclusively on MUST and its predictive value for hospital outcomes in colorectal cancer patients, might have overlooked the potential benefits or comparability of other nutritional screening tools. The reliance on published literature also meant that unpublished studies or grey literature, which might contain important insights or data, were excluded, possibly introducing publication bias. Furthermore, the heterogeneity in study designs, ranging from prospective cohorts to cross-sectional analyze High MUST scores correlate with increased susceptibility to complications; however, the potential confounding effect of a higher proportion of emergency surgeries must be considered. Lastly, the absence of randomized controlled trials and prospective research among the reviewed studies limits the ability to establish causality between MUST scores and patient outcomes, highlighting the need for further high-quality research in this area.
Conclusion
The systematic review substantiates the importance of the Malnutrition Universal Screening Tool as a potential predictor of hospital outcomes in colorectal cancer patients, particularly in identifying individuals at higher risk of prolonged hospitalization and increased postoperative complications. However, its limitations should guide clinical utility. The data illustrates that patients with higher MUST scores endure extended stays and greater complication rates, reinforcing MUST’s significance in preoperative assessment and the necessity for its incorporation into clinical practice. This review advocates for the utilization of MUST to guide nutritional interventions, aiming to improve patient outcomes and diminish healthcare costs, while suggesting further exploration into its application to enhance its efficacy in patient management strategies. |
Title: The Protective Effects of an Aged Black Garlic Water Extract on the Prostate | Body:
1. Introduction
Prostatitis is a prostate gland inflammation, which encompasses a range of disorders, such as acute and chronic bacterial prostatitis. These conditions can be caused by bacterial infections, immune responses, or non-infectious factors such as trauma or stress [1].
In particular, acute prostatic inflammation in mice induced an epithelial transformation, named proliferative inflammatory atrophy, which could promote prostatic intraepithelial neoplasia [1].
In this context, chronic inflammation is often linked with the process of carcinogenesis and is recognized as both a hallmark and a potential risk factor for various cancers [2]. Specifically, for prostate cancer (PCa), chronic inflammation is suggested as a bridge between environmental factors and tumor development [3,4,5].
Numerous studies have explored the relationship between prostate gland abnormalities and the inflammatory process, showing a strong prevalence of mild chronic inflammation in PCa [6].
Chronic inflammation can create a microenvironment conducive to carcinogenesis by producing pro-inflammatory cytokines, reactive oxygen species, and DNA damage [7].
In line with this, NLRP3 inflammasome is critically involved in PCa aggressiveness [8].
Altogether, the presented evidence indicates the proficient pro-oncogenic role of certain inflammatory processes in PCa [9,10].
Various biomarkers, such as tumor necrosis factor (TNF)-α, nuclear factor (NF)-kB, interleukin (IL)-6, and cyclooxygenase (COX)-2, play a critical role in inflammatory responses. In particular, Baud and their collaborators (2001) reported that TNF-α is a potent pro-inflammatory cytokine whose involvement in inflammation, cell proliferation, differentiation, and apoptosis is well known. Increased serum levels of pro-inflammatory markers such as TNF-α are related to accelerated progression and a poor prognosis in PCa [11,12].
Furthermore, NF-κB is essential for regulating both the innate and adaptive immune responses, particularly in inflammation. Besides its role in the survival and activation of immune cells, NF-κB stimulates the release of pro-inflammatory genes, including cytokines and chemokines, and regulates inflammasome activity. Moreover, the dysregulation of NF-κB contributes to various inflammatory diseases, including rheumatic diseases and asthma [13,14]. Interestingly, a wide body of evidence suggested that NF-κB activation, as well as various signals linked to inflammation, are well known to be involved in the modulation of PCa malignancy [15]. In particular, NF-κB activation exerts modulatory effects on the expression of the cytokines and factors involved in cancer development and progression, including IL-6 [15]. Moreover, the activation of IL-6 signaling was found to induce growth, proliferative activity, and the migration of PCa cells [16].
COX-2 is also critically involved in carcinogenesis in various tissues, including breasts and lungs, as well as the prostate [17].
Various studies suggested the potential activity of a number of herbal extracts commonly used in traditional medicine as well as natural compounds exhibiting an innovative action mode as a possible remedy for PCa [18,19].
In this context, aged black garlic (ABG) has garnered attention for its bioactive compound profile and biological activities [20].
ABG is produced by fermenting fresh garlic at controlled high humidity (80–90%) and temperature (60–90 °C) conditions over several weeks. As previously reported [20], the temperature and humidity conditions of the thermal treatment chosen during ABG production are strongly involved in the quality of ABG.
This process alters garlic’s organoleptic properties, making it sweeter and less pungent, and increases the concentration of bioactive compounds, such as S-allylcysteine, polyphenols, and flavonoids [21]. These compounds were found to be able to exert various beneficial effects, including the suppression of cell proliferative activity, as well as the stimulation of apoptosis and the modulation of the cell cycle, all of which are relevant in cancer prevention and treatment [22,23].
Interestingly, in the previous studies of ours, a water extract of ABG (ABGE) showed anti-inflammatory and antioxidant effects in preclinical models [24,25]. In particular, the protective effects induced by ABGE were suggested to be partly related to the polyphenolic content in the same extract, notably catechin and gallic acid [24,25]. We previously performed the quantification of polyphenolic content in the extract using high-performance liquid chromatography coupled with a photo diode array detector (HPLC-DAD) analytical method. In particular, various compounds were identified in ABGE, with gallic acid and catechin being the more representative phytochemicals [26].
This research aims to explore the potential benefits of ABGE on inflammation and prostate cancer. Building on these findings, we sought to explore the potential protective effects of ABGE against inflammation-induced prostate damage using an ex vivo experimental model, as well as its impact on prostate cancer cell lines through in vitro studies. We investigated the anti-inflammatory properties of ABGE using a well-established ex vivo model of inflammation composed of mouse prostate specimens exposed to Escherichia coli lipopolysaccharide (LPS) [26,27]. In this setting, we examined the gene expression levels of the key pro-inflammatory biomarkers, including COX-2, NF-κB, TNF-α, and IL-6. Furthermore, we assessed the potential therapeutic effects of ABGE on the prostate cancer cell lines using in vitro experimental models through functional parameters (colony formation, tumorsphere formation, and a migration assay) and molecular studies to evaluate the potential involvement of different signaling pathways, such as mitogen-activated protein kinase (MAPK), protein kinase B (AKT), Janus kinases/the signal transducer and activator of transcription proteins (JAK/STAT), and transforming growth factor (TGF-β).
2. Materials and Methods
2.1. Extraction and Sample Preparation of ABGE
Dried ABG cloves were provided by il Grappolo S.r.l. (Soliera, Modena, Italy). The preparation of ABGE followed the method described in the previous studies [24,28,29].
A detailed protocol is included in the Supplementary Materials.
2.2. Ex Vivo Studies
Adult C57BL/6 male mice (3 months old, weight 20–25 g, n = 25) were housed and maintained as described in the Supplementary Materials Section. The housing conditions and experimentation procedures were strictly in agreement with the European Community ethical regulations (EU Directive no. 26/2014) for the care of animals for scientific research. In agreement with the recognized principles of “Replacement, Refinement and Reduction in Animals in Research”, prostate specimens were obtained as residual materials from the vehicle-treated mice randomized in our previous experiments, approved by the local ethical committee (‘G. d’Annunzio’ University, Chieti, Italy) and the Italian Health Ministry (Project no. 885/2018-PR).
Mouse sacrifice was performed by CO2 inhalation (100% CO2 at a flow rate of 20% of the chamber volume per min). After collection, the isolated prostate specimens were maintained in a humidified incubator with 5% CO2 at 37 °C for 4 h, as previously described [26,30] and reported in the Supplementary Materials Section.
Total RNA was extracted from the prostate specimens using TRI reagent (Sigma-Aldrich, St. Louis, MO, USA) following the manufacturer’s protocol. The gene expression of COX-2, NF-kB, TNF-α, and iNOS was quantified by real-time PCR with TaqMan probe-based chemistry, as previously described [27,31,32]. The detailed protocol can be found in the Supplementary Materials Section.
2.3. Cell Culture
Cell lines from control prostate (PNT-2), androgen-dependent PCa (LNCaP), and androgen-independent PCa (PC-3) (American Type Culture Collection, Manassas, VA, USA) were maintained in a humidified incubator with 5% CO2 at 37 °C following the manufacturer’s guidelines as previously outlined [33,34] (Supplementary Materials Section).
2.4. Cell Proliferation
Cell proliferation was evaluated using resazurin reagent (Canvax Biotech, Cordoba, Spain) [33]. Cell proliferation was measured at the start and after 24, 48, and 72 h of treatment (Supplementary Materials Section).
2.5. Clonogenic Assay
A clonogenic assay was performed on the LNCaP and PC-3 PCa cells treated with 1000 µg/mL of ABGE and incubated for 10 days. The results were expressed as a percentage of the number of colonies relative to the control [34] (Supplementary Materials Section).
2.6. Tumorsphere Formation
The tumorsphere formation assay was conducted as previously described on LNCaP and PC-3 [35,36]. A minimum of three experiments with two replicates for each condition were performed. The results are expressed as a percentage of tumorsphere area relative to the control [35] (Supplementary Materials Section).
2.7. Cell Migration Assay
Cell migration was assessed using a wound healing assay as previously detailed [33,35,36]. The results are presented as the percentage of the migration rate relative to the control. A minimum of three experiments with three replicates for each condition were performed. This experiment was conducted using PC-3 cell lines, but not LNCaP cells due to their lower migration capacity (Supplementary Materials Section).
2.8. Phosphorylation Array
Protein extracts from the LNCaP cells were collected in lysis buffer from 6-well plates after 24 h of treatment with 1000 µg/mL ABGE. The determination of protein content was conducted using a Pierce BCA Protein assay (ThermoFisher Scientific, Madrid, Spain) and adjusted with assay buffer. The data were normalized following the manufacturer’s instructions. In brief, the membranes designed for the semi-quantitative detection of 55 phosphorylated human proteins, which are part of the MAPK, AKT, JAK/STAT, and TGF-β signaling pathways, were incubated with blocking buffer for 30 min at 25 °C. The array spots’ densitometric analysis was performed using ImageJ software (version number 1.54j), with positive control spots used for normalization. The results are expressed as the log2 Fold Change in each protein signal relative to the control signal, with a log2 Fold Change of 0.2 set as the threshold [37] (Supplementary Materials Section).
2.9. Statistical Analysis
To calculate sample size, we performed power analysis by using G*Power 3.1.9.4 software (effect size = 0.6, α = 0.05, power = 0.85) [38]. As for the ex vivo evaluations, the experimental procedures were performed by a researcher blinded to the treatment. All experiments were conducted at least three times independently (n ≥ 3). The results from ex vivo and in vitro studies are expressed as means ± SEM. Statistical differences between the two groups were evaluated using either an unpaired parametric t-test or a nonparametric Mann–Whitney U test, depending on normality as determined by a Kolmogorov–Smirnov test. For comparisons involving more than two groups, a One-Way ANOVA was employed. Statistical significance was set at p < 0.05. All statistical analyses were performed using GraphPad Prism 9 (GraphPad Software, La Jolla, CA, USA).
2.1. Extraction and Sample Preparation of ABGE
Dried ABG cloves were provided by il Grappolo S.r.l. (Soliera, Modena, Italy). The preparation of ABGE followed the method described in the previous studies [24,28,29].
A detailed protocol is included in the Supplementary Materials.
2.2. Ex Vivo Studies
Adult C57BL/6 male mice (3 months old, weight 20–25 g, n = 25) were housed and maintained as described in the Supplementary Materials Section. The housing conditions and experimentation procedures were strictly in agreement with the European Community ethical regulations (EU Directive no. 26/2014) for the care of animals for scientific research. In agreement with the recognized principles of “Replacement, Refinement and Reduction in Animals in Research”, prostate specimens were obtained as residual materials from the vehicle-treated mice randomized in our previous experiments, approved by the local ethical committee (‘G. d’Annunzio’ University, Chieti, Italy) and the Italian Health Ministry (Project no. 885/2018-PR).
Mouse sacrifice was performed by CO2 inhalation (100% CO2 at a flow rate of 20% of the chamber volume per min). After collection, the isolated prostate specimens were maintained in a humidified incubator with 5% CO2 at 37 °C for 4 h, as previously described [26,30] and reported in the Supplementary Materials Section.
Total RNA was extracted from the prostate specimens using TRI reagent (Sigma-Aldrich, St. Louis, MO, USA) following the manufacturer’s protocol. The gene expression of COX-2, NF-kB, TNF-α, and iNOS was quantified by real-time PCR with TaqMan probe-based chemistry, as previously described [27,31,32]. The detailed protocol can be found in the Supplementary Materials Section.
2.3. Cell Culture
Cell lines from control prostate (PNT-2), androgen-dependent PCa (LNCaP), and androgen-independent PCa (PC-3) (American Type Culture Collection, Manassas, VA, USA) were maintained in a humidified incubator with 5% CO2 at 37 °C following the manufacturer’s guidelines as previously outlined [33,34] (Supplementary Materials Section).
2.4. Cell Proliferation
Cell proliferation was evaluated using resazurin reagent (Canvax Biotech, Cordoba, Spain) [33]. Cell proliferation was measured at the start and after 24, 48, and 72 h of treatment (Supplementary Materials Section).
2.5. Clonogenic Assay
A clonogenic assay was performed on the LNCaP and PC-3 PCa cells treated with 1000 µg/mL of ABGE and incubated for 10 days. The results were expressed as a percentage of the number of colonies relative to the control [34] (Supplementary Materials Section).
2.6. Tumorsphere Formation
The tumorsphere formation assay was conducted as previously described on LNCaP and PC-3 [35,36]. A minimum of three experiments with two replicates for each condition were performed. The results are expressed as a percentage of tumorsphere area relative to the control [35] (Supplementary Materials Section).
2.7. Cell Migration Assay
Cell migration was assessed using a wound healing assay as previously detailed [33,35,36]. The results are presented as the percentage of the migration rate relative to the control. A minimum of three experiments with three replicates for each condition were performed. This experiment was conducted using PC-3 cell lines, but not LNCaP cells due to their lower migration capacity (Supplementary Materials Section).
2.8. Phosphorylation Array
Protein extracts from the LNCaP cells were collected in lysis buffer from 6-well plates after 24 h of treatment with 1000 µg/mL ABGE. The determination of protein content was conducted using a Pierce BCA Protein assay (ThermoFisher Scientific, Madrid, Spain) and adjusted with assay buffer. The data were normalized following the manufacturer’s instructions. In brief, the membranes designed for the semi-quantitative detection of 55 phosphorylated human proteins, which are part of the MAPK, AKT, JAK/STAT, and TGF-β signaling pathways, were incubated with blocking buffer for 30 min at 25 °C. The array spots’ densitometric analysis was performed using ImageJ software (version number 1.54j), with positive control spots used for normalization. The results are expressed as the log2 Fold Change in each protein signal relative to the control signal, with a log2 Fold Change of 0.2 set as the threshold [37] (Supplementary Materials Section).
2.9. Statistical Analysis
To calculate sample size, we performed power analysis by using G*Power 3.1.9.4 software (effect size = 0.6, α = 0.05, power = 0.85) [38]. As for the ex vivo evaluations, the experimental procedures were performed by a researcher blinded to the treatment. All experiments were conducted at least three times independently (n ≥ 3). The results from ex vivo and in vitro studies are expressed as means ± SEM. Statistical differences between the two groups were evaluated using either an unpaired parametric t-test or a nonparametric Mann–Whitney U test, depending on normality as determined by a Kolmogorov–Smirnov test. For comparisons involving more than two groups, a One-Way ANOVA was employed. Statistical significance was set at p < 0.05. All statistical analyses were performed using GraphPad Prism 9 (GraphPad Software, La Jolla, CA, USA).
3. Results and Discussion
In the present study, we aimed to study the potential effects of ABGE on proliferation, colony formation, tumor spheroid formation, cell migration, and the phosphorylation array in three prostate cell lines: PNT-2, LNCaP, and PC-3.
3.1. Ex Vivo Studies
Considering the critical role of chronic inflammation in PCa, we first investigated the potential beneficial activities exerted by ABGE (10–1000 μg/mL) as a validated experimental model of inflammation [24,39]. We studied the effects of ABGE on the gene expression of pro-inflammatory mediators, including COX-2, NF-kB, TNF-α, and IL-6, on isolated LPS-stimulated prostate specimens by RT-PCR. In this context, we found that ABGE (10–1000 μg/mL) was able to inhibit gene expression of all the markers investigated, with 1000 µg/mL being the most effective dose (Figure 1a–d).
Various pro-inflammatory markers were shown to be implicated in prostatic inflammation. In this context, different phytochemicals, such as catechins, were found to modulate a number of inflammation targets, including TNF-α, COX-2, and interleukins. In the previous studies, we demonstrated that ABGE induced protective activities on colon and heart tissues treated ex vivo with LPS, which have been hypothesized to be related, at least partially, to its polyphenolic composition, with particular regards to gallic acid and catechin [24,25]. Accordingly, BenSaad et al. (2017) found that gallic acid suppressed the LPS-induced production of prostaglandin E2 and IL-6 in RAW264.7 cells [40].
Furthermore, we previously found that ABGE (1 g kg−1) exerted protective effects in rats in vivo [25] in a dose which could be translated to 1 g day in humans.
3.2. Cell Proliferation in Basal Conditions
Cell proliferation was measured after 24, 48, and 72 h of the treatment with ABGE (10, 100, 500, and 1000 µg/mL) in basal conditions. The non-tumor prostate cell line PNT-2 was used as the control cell line. In the control prostate line PNT-2, ABGE (10–1000 µg/mL) did not affect cell proliferation more compared to that of the control group at any concentration at the different times. In agreement, we previously reported that ABGE did not modify the viability of cardiomyoblast (H9c2) cells or the human fibroblast HFF-1 cell line [24,25] (Figure 2a).
The LNCaP cell line is derived from lymph node metastasis specimens of individuals with prostate cancer [41].
It retains the characteristics of prostate cancer tumor cytology as well as its early differentiation function, which represents the early androgen-dependent notable features of prostate cancer.
On the other hand, ABGE (10–1000 µg/mL) was able to significantly suppress LNCaP cell proliferation. Interestingly, the inhibitory effect on cell proliferation was dose-dependent, with a greater reduction at higher concentrations and longer exposure times (Figure 2b). Figure 2b shows inhibitory effects in cell proliferation after just 24 h, starting at a concentration of 100 µg/mL. The inhibition induced by the extract is also confirmed following 48 and 72 h of treatment at 500 and 1000 µg/mL concentrations.
The PC-3 cell line was isolated from human prostate cancer bone metastases with a low differentiation degree [42] and represents an androgen-independent prostate cancer cell with moderate metastatic potential in the absence of endogenous androgen receptors.
Similarly, the PC-3 cell line showed a significant reduction in cell proliferation following the treatment with ABGE (10–1000 µg/mL) compared to that of the control, with a greater decrease at higher concentrations (Figure 2c), thus confirming its antiproliferative activity also against androgen-independent cancer cells. After 48 h, we showed a significant decrease in cell proliferation starting from 100 µg/mL.
The mechanism underlying the inhibition of cell proliferation induced by ABGE is not yet clear. Dong and their collaborators (2014) demonstrated that an alcohol extract of ABG inhibited the growth of HT129 colon cancer cells probably by the inhibition of the PI3K/Akt pathway [22].
Additionally, Wang and their collaborators (2012) have demonstrated that aged black garlic water extract can inhibit the growth of gastric cancer cells in both in vitro and in vivo [23].
Moreover, an aged black garlic water extract showed dose-dependent apoptosis in human gastric cancer cell lines [23]. Notably, the extract in the prostate did not induce apoptosis in the LNCaP cells (Figure S1, Supplementary Materials).
Meanwhile, in vivo study highlighted the anti-cancer properties of the extract, including the inhibition of tumor growth in mice with tumors. The researchers proposed that the anti-cancer effects of the aged black garlic extract might be due to its antioxidant and immunomodulatory characteristics [23].
Multiple studies have indicated that black garlic possesses anti-tumor properties by inhibiting cell proliferation in both colon and gastric cancers. Jikihara et al. (2014) have performed an experiment using aged garlic extract on F344 rats and DLD-1 human colon cancer cells. The findings revealed antiproliferative effects in both adenoma and adenocarcinoma lesions [43].
3.3. Colony Formation
The analysis of the colony-forming ability of the LNCaP and PC-3 cell lines was performed after the treatment with ABGE (1000 µg/mL) or the vehicle. The highest concentration was chosen because it proved to be the most effective, while remaining biocompatible. In the LNCaP cell line (Figure 3a), the treatment with 1000 µg/mL of ABGE significantly reduced number of colonies formed compared to that of the control, suggesting that ABGE is effective in decreasing the long-term proliferative capacity of androgen-dependent cancer cells. Similarly, in the PC-3 cell line, the treatment with ABGE (1000 µg/mL) (Figure 3b) led to more inhibitory effects in colony formation compared to those of the control, further supporting the potential activity of the extract as an anti-tumor agent. These effects might be due to the presence of polyphenolic compounds in ABGE. In agreement, Jang et al. (2020) demonstrated that gallic acid can inhibit colony formation in various cancer cell lines [40].
3.4. Tumor Spheroid Formation
Tumor spheroid formation was assessed by measuring the number of spheroids after the treatment with ABGE (1000 µg/mL) or the vehicle in the LNCaP and PC-3 cell lines. In the LNCaP cells (Figure 4a), the treatment with ABGE (1000 µg/mL) did not significantly affect the number of spheroids, which remained unchanged compared to that of the vehicle. However, our present findings also showed that in the PC-3 cells (Figure 4b), ABGE (1000 µg/mL) significantly reduced the number of spheroids compared to that of the control, suggesting that ABGE impedes the proliferation of cancer cells.
3.5. Migration Assay
The cell migration assay was conducted only on the PC-3 cell line because the morphology of LNCaP cells does not allow for accurate migration assessment. The treatment with 1000 µg/mL of ABGE more significantly reduced the cell migration rate compared to that of the control after 24 h of incubation. The reduction in cell migration in PC-3 (Figure 5) suggests that ABGE may also limit the capacity of cancer cells to spread. In agreement, recently, ABG (dissolved in 0.9% normal saline) extract was found able to impede cell migration in breast cancer cells [44].
3.6. Cell Proliferation after LPS Pre-Treatment
Considering the previously found effects of ABGE into a pro-inflammatory cell context [18], we then decided to evaluate its potential interaction with LPS. We studied the effects of ABGE (10–1000 µg/mL) on LPS-treated cell proliferation in the PC-3 line (Figure 6), which was chosen for its higher aggressiveness compared to that of the LNCaP cell line, as supported by previous studies [45]. In this context, Xu and their collaborators (2021) showed that LPS combined with ATP significantly increased the proliferation and migration of PC-3 cells, reducing apoptosis. This effect was related to the stimulation of the NLRP3/caspase-1 inflammasome, hypothesizing that inflammation plays a crucial role in prostate cancer progression [8]. Interestingly, our data indicate that the LPS pre-treatment may sensitize the PCa cells to ABGE (Figure 6).
Gallic acid and catechin, which are the main components of ABGE, as previously shown [24,25], are well known to suppress, proliferate, and stimulate the apoptosis of PCa cells [39,40].
Moreover, a previous study showed that gallic acid decreased the viability of PCa cell lines, but not normal cells’ viability [46]. In agreement, we could suggest the potential involvement of polyphenolic compounds, with particular regards to gallic acid and catechin, due to the beneficial effects induced by ABGE on PCa proliferation.
3.7. Phosphorylation Array
A wide body of evidence shows that the MAPK, AKT, JAK/STAT, and TGF-β pathways play a key role in cell proliferation, survival, apoptosis, and growth [23,39,40,41]. In our study, we analyzed the phosphorylation of the key proteins that participate in these signaling pathways in response to the treatment with ABGE (1000 µg/mL) or the vehicle using a phosphorylation array.
Specifically, the MAPK signaling pathway showed significant modulation in response to the treatment with ABGE (Figure 7). Proteins such as ERK1/2 and JNK showed reduced phosphorylation, suggesting that ABGE (1000 µg/mL) could inhibit these signaling pathways. In this context, the MAPK signaling pathway is critically related to cell proliferation and survival [47]. Furthermore, the reduced phosphorylation of ERK1/2 (T202/Y204) and JNK (T183/Y185) suggests reduced cell proliferation and the potential inhibition of the apoptotic response [48].
p53 is involved in regulation of cell growth, DNA repair, survival, cycle, autophagy, senescence, and apoptosis [49,50].
After the injection of knockdown of ribosomal S6 protein kinases (RSK) 1 and RSK2 in mouse femurs, there was a reduction in osteolytic lesions in the PC3 cells compared to those in the control cells [51].
In our present study, we found that ABGE increased the quantity of LPS-treated p53, while decreased the RSK2 phosphorylation levels (Figure 7), which could be related to the anti-cancer properties of the extract.
The role of AKT signaling pathway in cell survival and growth is also well known [23]. ABGE (1000 µg/mL) showed variable effects on the phosphorylation of both the AKT and downstream proteins (Figure 8).
The reduced phosphorylation of mTOR suggests decreased protein synthesis and cell growth [52]. mTOR activation has been shown to induce the phosphorylation of many substrates, such as eukaryotic translation initiation factor 4E (eIF4E)-binding proteins (4E-BP1), and mTOR kinase inhibitors have been reported to block p4E-BP1 [53].
Our findings show that ABGE reduced the LPS-treated mTOR and 4E-BP1 phosphorylation levels. In agreement, considering that high levels of 4E-BP1 have been measured in prostate cancer cells, we can speculate that mTOR and 4E-BP1 could be involved, at least in part, in the beneficial effects induced by ABGE [54].
Moreover, ABGE also lowers the LPS-treated levels of glycogen synthase kinase (GSK)-3, phosphatase, tensin-homolog in chromosome 10 (PTEN), and serine/threonine kinase Raf-1 (RAF-1), which are involved in cancer development and progression [55,56,57].
On the other hand, ABGE increased the LPS-treated levels of p27 and AMP-activated protein kinase (AMPK), which possess a well-known suppressor role in carcinogenesis [58,59].
The JAK/STAT signaling pathway plays a key role in numerous essential biological processes, such as differentiation, cell proliferation, immune regulation, and apoptosis [48].
The inactivation of Src induced a reduction in the migration and growth in PCa cell lines [60,61].
In addition, the reduced phosphorylation of STAT1, STAT2, STAT3, and STAT5 (Figure 9) indicates a potential decrease in proliferative signaling and cancer development [62,63,64,65].
Moreover, the decreased phosphorylation of JAK1 and JAK2 suggests decreased signal transduction promoting prostate cancer cell proliferation and survival [66,67].
In agreement, TYK2 signaling promotes the invasiveness of prostate cancer cells [68].
Accordingly, the involvement of SHP2 in several cancer-related processes has been reported [69].
Actually, our findings, showing that ABGE decreased the LPS-treated Src, STAT1, STAT2, STAT3, STAT5, JAK1, JAK 2, TYK2, and SHP2 phosphorylation levels, could suggest the potential protective role of the extract in PC-3 cells.
Regarding the TGF-β pathway (Figure 10) involved in cell growth regulation and tumor progression [49], we showed that ABGE (1000 µg/mL) decreased the phosphorylation of SMAD1, suggesting reduced TGF-β signaling, which may be associated with reduced cell invasiveness [70].
The previous studies reported a correlation between SMAD2 and SMAD4, which are involved in the inhibition of cell growth [71].
ATF2 has been found as a tumor promoter in various human cancers, such as prostate cancer [72].
Furthermore, c-Jun or c-Fos overexpression has been directly related with PCa cell line invasiveness, and the phosphorylated c-Jun levels are high in PCa [73].
In our study, we showed that ABGE decreased the quantity of LPS-treated SMAD1, while it increased the SMAD2, SMAD4, AFT2, c-Jun, and c-Fos phosphorylation levels, further confirming the potential protective role of the extract.
In the literature, there are not many studies about the effects of ABGE on the prostate; thus, its mechanisms are not particularly well known.
3.1. Ex Vivo Studies
Considering the critical role of chronic inflammation in PCa, we first investigated the potential beneficial activities exerted by ABGE (10–1000 μg/mL) as a validated experimental model of inflammation [24,39]. We studied the effects of ABGE on the gene expression of pro-inflammatory mediators, including COX-2, NF-kB, TNF-α, and IL-6, on isolated LPS-stimulated prostate specimens by RT-PCR. In this context, we found that ABGE (10–1000 μg/mL) was able to inhibit gene expression of all the markers investigated, with 1000 µg/mL being the most effective dose (Figure 1a–d).
Various pro-inflammatory markers were shown to be implicated in prostatic inflammation. In this context, different phytochemicals, such as catechins, were found to modulate a number of inflammation targets, including TNF-α, COX-2, and interleukins. In the previous studies, we demonstrated that ABGE induced protective activities on colon and heart tissues treated ex vivo with LPS, which have been hypothesized to be related, at least partially, to its polyphenolic composition, with particular regards to gallic acid and catechin [24,25]. Accordingly, BenSaad et al. (2017) found that gallic acid suppressed the LPS-induced production of prostaglandin E2 and IL-6 in RAW264.7 cells [40].
Furthermore, we previously found that ABGE (1 g kg−1) exerted protective effects in rats in vivo [25] in a dose which could be translated to 1 g day in humans.
3.2. Cell Proliferation in Basal Conditions
Cell proliferation was measured after 24, 48, and 72 h of the treatment with ABGE (10, 100, 500, and 1000 µg/mL) in basal conditions. The non-tumor prostate cell line PNT-2 was used as the control cell line. In the control prostate line PNT-2, ABGE (10–1000 µg/mL) did not affect cell proliferation more compared to that of the control group at any concentration at the different times. In agreement, we previously reported that ABGE did not modify the viability of cardiomyoblast (H9c2) cells or the human fibroblast HFF-1 cell line [24,25] (Figure 2a).
The LNCaP cell line is derived from lymph node metastasis specimens of individuals with prostate cancer [41].
It retains the characteristics of prostate cancer tumor cytology as well as its early differentiation function, which represents the early androgen-dependent notable features of prostate cancer.
On the other hand, ABGE (10–1000 µg/mL) was able to significantly suppress LNCaP cell proliferation. Interestingly, the inhibitory effect on cell proliferation was dose-dependent, with a greater reduction at higher concentrations and longer exposure times (Figure 2b). Figure 2b shows inhibitory effects in cell proliferation after just 24 h, starting at a concentration of 100 µg/mL. The inhibition induced by the extract is also confirmed following 48 and 72 h of treatment at 500 and 1000 µg/mL concentrations.
The PC-3 cell line was isolated from human prostate cancer bone metastases with a low differentiation degree [42] and represents an androgen-independent prostate cancer cell with moderate metastatic potential in the absence of endogenous androgen receptors.
Similarly, the PC-3 cell line showed a significant reduction in cell proliferation following the treatment with ABGE (10–1000 µg/mL) compared to that of the control, with a greater decrease at higher concentrations (Figure 2c), thus confirming its antiproliferative activity also against androgen-independent cancer cells. After 48 h, we showed a significant decrease in cell proliferation starting from 100 µg/mL.
The mechanism underlying the inhibition of cell proliferation induced by ABGE is not yet clear. Dong and their collaborators (2014) demonstrated that an alcohol extract of ABG inhibited the growth of HT129 colon cancer cells probably by the inhibition of the PI3K/Akt pathway [22].
Additionally, Wang and their collaborators (2012) have demonstrated that aged black garlic water extract can inhibit the growth of gastric cancer cells in both in vitro and in vivo [23].
Moreover, an aged black garlic water extract showed dose-dependent apoptosis in human gastric cancer cell lines [23]. Notably, the extract in the prostate did not induce apoptosis in the LNCaP cells (Figure S1, Supplementary Materials).
Meanwhile, in vivo study highlighted the anti-cancer properties of the extract, including the inhibition of tumor growth in mice with tumors. The researchers proposed that the anti-cancer effects of the aged black garlic extract might be due to its antioxidant and immunomodulatory characteristics [23].
Multiple studies have indicated that black garlic possesses anti-tumor properties by inhibiting cell proliferation in both colon and gastric cancers. Jikihara et al. (2014) have performed an experiment using aged garlic extract on F344 rats and DLD-1 human colon cancer cells. The findings revealed antiproliferative effects in both adenoma and adenocarcinoma lesions [43].
3.3. Colony Formation
The analysis of the colony-forming ability of the LNCaP and PC-3 cell lines was performed after the treatment with ABGE (1000 µg/mL) or the vehicle. The highest concentration was chosen because it proved to be the most effective, while remaining biocompatible. In the LNCaP cell line (Figure 3a), the treatment with 1000 µg/mL of ABGE significantly reduced number of colonies formed compared to that of the control, suggesting that ABGE is effective in decreasing the long-term proliferative capacity of androgen-dependent cancer cells. Similarly, in the PC-3 cell line, the treatment with ABGE (1000 µg/mL) (Figure 3b) led to more inhibitory effects in colony formation compared to those of the control, further supporting the potential activity of the extract as an anti-tumor agent. These effects might be due to the presence of polyphenolic compounds in ABGE. In agreement, Jang et al. (2020) demonstrated that gallic acid can inhibit colony formation in various cancer cell lines [40].
3.4. Tumor Spheroid Formation
Tumor spheroid formation was assessed by measuring the number of spheroids after the treatment with ABGE (1000 µg/mL) or the vehicle in the LNCaP and PC-3 cell lines. In the LNCaP cells (Figure 4a), the treatment with ABGE (1000 µg/mL) did not significantly affect the number of spheroids, which remained unchanged compared to that of the vehicle. However, our present findings also showed that in the PC-3 cells (Figure 4b), ABGE (1000 µg/mL) significantly reduced the number of spheroids compared to that of the control, suggesting that ABGE impedes the proliferation of cancer cells.
3.5. Migration Assay
The cell migration assay was conducted only on the PC-3 cell line because the morphology of LNCaP cells does not allow for accurate migration assessment. The treatment with 1000 µg/mL of ABGE more significantly reduced the cell migration rate compared to that of the control after 24 h of incubation. The reduction in cell migration in PC-3 (Figure 5) suggests that ABGE may also limit the capacity of cancer cells to spread. In agreement, recently, ABG (dissolved in 0.9% normal saline) extract was found able to impede cell migration in breast cancer cells [44].
3.6. Cell Proliferation after LPS Pre-Treatment
Considering the previously found effects of ABGE into a pro-inflammatory cell context [18], we then decided to evaluate its potential interaction with LPS. We studied the effects of ABGE (10–1000 µg/mL) on LPS-treated cell proliferation in the PC-3 line (Figure 6), which was chosen for its higher aggressiveness compared to that of the LNCaP cell line, as supported by previous studies [45]. In this context, Xu and their collaborators (2021) showed that LPS combined with ATP significantly increased the proliferation and migration of PC-3 cells, reducing apoptosis. This effect was related to the stimulation of the NLRP3/caspase-1 inflammasome, hypothesizing that inflammation plays a crucial role in prostate cancer progression [8]. Interestingly, our data indicate that the LPS pre-treatment may sensitize the PCa cells to ABGE (Figure 6).
Gallic acid and catechin, which are the main components of ABGE, as previously shown [24,25], are well known to suppress, proliferate, and stimulate the apoptosis of PCa cells [39,40].
Moreover, a previous study showed that gallic acid decreased the viability of PCa cell lines, but not normal cells’ viability [46]. In agreement, we could suggest the potential involvement of polyphenolic compounds, with particular regards to gallic acid and catechin, due to the beneficial effects induced by ABGE on PCa proliferation.
3.7. Phosphorylation Array
A wide body of evidence shows that the MAPK, AKT, JAK/STAT, and TGF-β pathways play a key role in cell proliferation, survival, apoptosis, and growth [23,39,40,41]. In our study, we analyzed the phosphorylation of the key proteins that participate in these signaling pathways in response to the treatment with ABGE (1000 µg/mL) or the vehicle using a phosphorylation array.
Specifically, the MAPK signaling pathway showed significant modulation in response to the treatment with ABGE (Figure 7). Proteins such as ERK1/2 and JNK showed reduced phosphorylation, suggesting that ABGE (1000 µg/mL) could inhibit these signaling pathways. In this context, the MAPK signaling pathway is critically related to cell proliferation and survival [47]. Furthermore, the reduced phosphorylation of ERK1/2 (T202/Y204) and JNK (T183/Y185) suggests reduced cell proliferation and the potential inhibition of the apoptotic response [48].
p53 is involved in regulation of cell growth, DNA repair, survival, cycle, autophagy, senescence, and apoptosis [49,50].
After the injection of knockdown of ribosomal S6 protein kinases (RSK) 1 and RSK2 in mouse femurs, there was a reduction in osteolytic lesions in the PC3 cells compared to those in the control cells [51].
In our present study, we found that ABGE increased the quantity of LPS-treated p53, while decreased the RSK2 phosphorylation levels (Figure 7), which could be related to the anti-cancer properties of the extract.
The role of AKT signaling pathway in cell survival and growth is also well known [23]. ABGE (1000 µg/mL) showed variable effects on the phosphorylation of both the AKT and downstream proteins (Figure 8).
The reduced phosphorylation of mTOR suggests decreased protein synthesis and cell growth [52]. mTOR activation has been shown to induce the phosphorylation of many substrates, such as eukaryotic translation initiation factor 4E (eIF4E)-binding proteins (4E-BP1), and mTOR kinase inhibitors have been reported to block p4E-BP1 [53].
Our findings show that ABGE reduced the LPS-treated mTOR and 4E-BP1 phosphorylation levels. In agreement, considering that high levels of 4E-BP1 have been measured in prostate cancer cells, we can speculate that mTOR and 4E-BP1 could be involved, at least in part, in the beneficial effects induced by ABGE [54].
Moreover, ABGE also lowers the LPS-treated levels of glycogen synthase kinase (GSK)-3, phosphatase, tensin-homolog in chromosome 10 (PTEN), and serine/threonine kinase Raf-1 (RAF-1), which are involved in cancer development and progression [55,56,57].
On the other hand, ABGE increased the LPS-treated levels of p27 and AMP-activated protein kinase (AMPK), which possess a well-known suppressor role in carcinogenesis [58,59].
The JAK/STAT signaling pathway plays a key role in numerous essential biological processes, such as differentiation, cell proliferation, immune regulation, and apoptosis [48].
The inactivation of Src induced a reduction in the migration and growth in PCa cell lines [60,61].
In addition, the reduced phosphorylation of STAT1, STAT2, STAT3, and STAT5 (Figure 9) indicates a potential decrease in proliferative signaling and cancer development [62,63,64,65].
Moreover, the decreased phosphorylation of JAK1 and JAK2 suggests decreased signal transduction promoting prostate cancer cell proliferation and survival [66,67].
In agreement, TYK2 signaling promotes the invasiveness of prostate cancer cells [68].
Accordingly, the involvement of SHP2 in several cancer-related processes has been reported [69].
Actually, our findings, showing that ABGE decreased the LPS-treated Src, STAT1, STAT2, STAT3, STAT5, JAK1, JAK 2, TYK2, and SHP2 phosphorylation levels, could suggest the potential protective role of the extract in PC-3 cells.
Regarding the TGF-β pathway (Figure 10) involved in cell growth regulation and tumor progression [49], we showed that ABGE (1000 µg/mL) decreased the phosphorylation of SMAD1, suggesting reduced TGF-β signaling, which may be associated with reduced cell invasiveness [70].
The previous studies reported a correlation between SMAD2 and SMAD4, which are involved in the inhibition of cell growth [71].
ATF2 has been found as a tumor promoter in various human cancers, such as prostate cancer [72].
Furthermore, c-Jun or c-Fos overexpression has been directly related with PCa cell line invasiveness, and the phosphorylated c-Jun levels are high in PCa [73].
In our study, we showed that ABGE decreased the quantity of LPS-treated SMAD1, while it increased the SMAD2, SMAD4, AFT2, c-Jun, and c-Fos phosphorylation levels, further confirming the potential protective role of the extract.
In the literature, there are not many studies about the effects of ABGE on the prostate; thus, its mechanisms are not particularly well known.
4. Conclusions
In conclusion, our results showed the potential anti-inflammatory and anti-proliferative effects of ABGE on prostate cancer. In this context, ABGE reduced the gene expression of the different biomarkers involved in inflammatory response, such as COX-2, TNF-α, IL-6, and NF-kB, also modulating relevant signaling pathways, including AKT, MAPK, TGF-β, and JAK/STAT. Furthermore, we performed different in vitro assays, where ABGE had beneficial effects on both the prostate cancer lines. Therefore, our results suggest that ABGE might be potentially used as a diet supplement for health promotion and a source of bio-organic compounds with antitumor properties in PCa.
A limitation of our study is that we have not evaluated specific targeting, as well as the signaling pathways modulating the potential anti-inflammatory and anti-cancer effects of the extract. However, further studies are needed in the future to accurately evaluate the in vivo activity of ABGE in reducing inflammation and cancer, as well as its potential negative effects on the body. |
Title: Post-Transplant Cyclophosphamide–Based Graft-Versus-Host Disease Prophylaxis Attenuates Disparity in Outcomes Between Use of Matched or Mismatched Unrelated Donors | Body:
INTRODUCTION
Allogeneic hematopoietic cell transplantation (HCT) is an important consolidation strategy for many patients with hematologic malignancies. Optimal results are achieved with HLA matched related donor or matched unrelated donor (MUD), defined by allele resolution matching at HLA-A, -B, -C, and -DRB1.1,2 Sibling donors are available to approximately 30% of patients, necessitating use of unrelated donors in many patients.3 Population-level diversity in HLA haplotype frequencies result in variations in MUD availability on the basis of patient self-reported ancestry.4 For example, the probability of identifying an HLA-matched unrelated donor (URD) in the National Marrow Donor Program (NMDP) registry is approximately 75% for patients of non-Hispanic White (NHW) ancestry, but is significantly lower, varying from 15% to 45%, among individuals of non-White and Hispanic ancestry.5,6 Mismatched related donor (haploidentical) and mismatched unrelated donor (MMUD), including umbilical cord blood URDs, are frequently the sole graft source for patients without matched donor options.7 Historically, HCT using single-locus HLA MMUD resulted in inferior survival rates because of increased graft-versus-host disease (GVHD), infections, and graft failure when performed with standard calcineurin inhibitor (CNI)–based GVHD prophylaxis.8-10
CONTEXT
Key Objective
To determine whether differences in overall survival (OS) and other clinically meaningful outcomes exist between recipients of matched unrelated donor (MUD) and mismatched unrelated donor (MMUD) hematopoietic cell transplantation when post-transplant cyclophosphamide (PTCy) is used to prevent graft-versus-host disease (GVHD).
Knowledge Generated
In adults with hematologic malignancies undergoing hematopoietic cell transplantation using PTCy to prevent GVHD reported to the Center for International Blood and Marrow Transplant Research from 2017 to 2021, there was no difference in OS between MUD and MMUD recipients. Other important clinical outcomes, including GVHD-free, relapse-free survival, were also similar.
Relevance (C.F. Craddock)
If confirmed in ongoing randomized trials, these data identify the possibility that adoption of a PTCy regimen has the potential to increase donor availability for allo-mandatory patients, particularly those from racial and ethnic minorities.**Relevance section written by JCO Associate Editor Charles F. Craddock, MD.
Administration of post-transplant cyclophosphamide (PTCy) improves outcomes after haploidentical donor HCT.11,12 Several studies suggest that a similar strategy is effective when used with MMUD.13-15 The NMDP-sponsored 15-MMUD study, using PTCy-based GVHD prophylaxis, demonstrated promising overall survival (OS) in adult patients receiving MMUD bone marrow grafts matched at ≤7/8 HLA alleles.16,17 Notably, a substantial proportion of patients with minority ancestries (48%) were enrolled, underscoring the potential of PTCy MMUD HCT to broaden donor options for all patients. There is a scarcity of contemporary, large-scale studies comparing matched and MMUD HCT outcomes using real-world data in the era of novel GVHD prophylaxis agents.18,19 Therefore, we conducted a cohort study using recent data from the Center for International Blood and Marrow Transplant Research (CIBMTR) database. We sought to determine whether outcome gaps between MUD and MMUD recipients continue to exist and hypothesized that PTCy-based GVHD prophylaxis would result in acceptable OS and GVHD-free, relapse-free survival (GRFS) after MUD and MMUD HCT.
CONTEXT
Key Objective
To determine whether differences in overall survival (OS) and other clinically meaningful outcomes exist between recipients of matched unrelated donor (MUD) and mismatched unrelated donor (MMUD) hematopoietic cell transplantation when post-transplant cyclophosphamide (PTCy) is used to prevent graft-versus-host disease (GVHD).
Knowledge Generated
In adults with hematologic malignancies undergoing hematopoietic cell transplantation using PTCy to prevent GVHD reported to the Center for International Blood and Marrow Transplant Research from 2017 to 2021, there was no difference in OS between MUD and MMUD recipients. Other important clinical outcomes, including GVHD-free, relapse-free survival, were also similar.
Relevance (C.F. Craddock)
If confirmed in ongoing randomized trials, these data identify the possibility that adoption of a PTCy regimen has the potential to increase donor availability for allo-mandatory patients, particularly those from racial and ethnic minorities.**Relevance section written by JCO Associate Editor Charles F. Craddock, MD.
METHODS
Patient Eligibility and Inclusion
Patient, donor, disease, and transplant clinical data were obtained from the CIBMTR outcomes database. Informed consent for participation in retrospective research was obtained from recipients and donors according to the Declaration of Helsinki. Included were adult patients who underwent first allogeneic HCT from January 2017 through June 2021 with a diagnosis of AML, ALL, or myelodysplastic syndromes, with complete clinical data available. Patients receiving either CNI- or PTCy-based prophylaxis were included. CNI-based transplants included use of either cyclosporine or tacrolimus with other adjunctive agents including methotrexate or mycophenolate mofetil (MMF) with or without antithymocyte globulin (ATG). PTCy regimens included a CNI or sirolimus with or without MMF and ATG. Patients who received other GVHD prophylaxis programs (including single agent PTCy), had missing clinical data, or lacked follow-up reporting were excluded (Data Supplement, Table S1 [online only]). Patients were categorized by the receipt of either a MUD, defined as high resolution matching at HLA-A, -B, -C, and -DRB1 (8/8), or MMUD, defined as mismatched at any single locus (7/8). Analysis of donor existence in preliminary searches performed in world registries from 2022 to 2023 was performed as previously described.20
Biostatistical Methods
The primary study objectives were to compare OS and GRFS within and between MUD versus MMUD allogeneic HCT, on the basis of receipt of either CNI- or PTCy-based GVHD prophylaxis. OS was defined as the time from transplant to death from any cause. GRFS was defined as survival without grade III-IV acute GVHD, moderate or severe chronic GVHD requiring systemic treatment, or relapse.21,22 Secondary end points included incidences of relapse, nonrelapse mortality (NRM), grade II-IV and grade III-IV acute GVHD, and moderate/severe chronic GVHD. The HCT comorbidity index (HCT-CI) and the refined Disease Risk Index (DRI) were assigned as previously described.23,24 Kaplan-Meier curves were generated to determine the unadjusted probability of OS and GRFS through 3 years after transplant. Cox regression was used to examine the independent effect of HLA match/GVHD prophylaxis controlling for other clinical factors on OS and GRFS. A priori clinically selected factors were evaluated for univariable association with the end point of interest, with a backward elimination procedure used to remove variables if the P value was >.1 unless inclusion affected the hazard ratio (HR) of HLA match by more than 10%. Variables with a strong clinical rationale for inclusion in the model were retained regardless of significance. Martingale residuals were used to test against nonproportionality for continuous variables, and log of negative log plots were used to assess proportionality among categorical variables.25 Final models were stratified by factors violating the proportional hazards assumption. Interactions were tested against a P < .01. Factors included in the final Cox regression models were used to estimate adjusted survival curves for OS and GRFS.26 Similar methods were used for competing risk end points using cumulative incidence to estimate relapse, NRM, and GVHD, with NRM, relapse, and non-GVHD deaths as competing risks for each end point, respectively.27 Fine and Gray28 regression was used for multiple regression models of competing risk end points. The precision of estimates was measured with 95% CIs and P < .05 were considered statistically significant. All analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC) and R software version 4.3.1.
Patient Eligibility and Inclusion
Patient, donor, disease, and transplant clinical data were obtained from the CIBMTR outcomes database. Informed consent for participation in retrospective research was obtained from recipients and donors according to the Declaration of Helsinki. Included were adult patients who underwent first allogeneic HCT from January 2017 through June 2021 with a diagnosis of AML, ALL, or myelodysplastic syndromes, with complete clinical data available. Patients receiving either CNI- or PTCy-based prophylaxis were included. CNI-based transplants included use of either cyclosporine or tacrolimus with other adjunctive agents including methotrexate or mycophenolate mofetil (MMF) with or without antithymocyte globulin (ATG). PTCy regimens included a CNI or sirolimus with or without MMF and ATG. Patients who received other GVHD prophylaxis programs (including single agent PTCy), had missing clinical data, or lacked follow-up reporting were excluded (Data Supplement, Table S1 [online only]). Patients were categorized by the receipt of either a MUD, defined as high resolution matching at HLA-A, -B, -C, and -DRB1 (8/8), or MMUD, defined as mismatched at any single locus (7/8). Analysis of donor existence in preliminary searches performed in world registries from 2022 to 2023 was performed as previously described.20
Biostatistical Methods
The primary study objectives were to compare OS and GRFS within and between MUD versus MMUD allogeneic HCT, on the basis of receipt of either CNI- or PTCy-based GVHD prophylaxis. OS was defined as the time from transplant to death from any cause. GRFS was defined as survival without grade III-IV acute GVHD, moderate or severe chronic GVHD requiring systemic treatment, or relapse.21,22 Secondary end points included incidences of relapse, nonrelapse mortality (NRM), grade II-IV and grade III-IV acute GVHD, and moderate/severe chronic GVHD. The HCT comorbidity index (HCT-CI) and the refined Disease Risk Index (DRI) were assigned as previously described.23,24 Kaplan-Meier curves were generated to determine the unadjusted probability of OS and GRFS through 3 years after transplant. Cox regression was used to examine the independent effect of HLA match/GVHD prophylaxis controlling for other clinical factors on OS and GRFS. A priori clinically selected factors were evaluated for univariable association with the end point of interest, with a backward elimination procedure used to remove variables if the P value was >.1 unless inclusion affected the hazard ratio (HR) of HLA match by more than 10%. Variables with a strong clinical rationale for inclusion in the model were retained regardless of significance. Martingale residuals were used to test against nonproportionality for continuous variables, and log of negative log plots were used to assess proportionality among categorical variables.25 Final models were stratified by factors violating the proportional hazards assumption. Interactions were tested against a P < .01. Factors included in the final Cox regression models were used to estimate adjusted survival curves for OS and GRFS.26 Similar methods were used for competing risk end points using cumulative incidence to estimate relapse, NRM, and GVHD, with NRM, relapse, and non-GVHD deaths as competing risks for each end point, respectively.27 Fine and Gray28 regression was used for multiple regression models of competing risk end points. The precision of estimates was measured with 95% CIs and P < .05 were considered statistically significant. All analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC) and R software version 4.3.1.
RESULTS
Patient Demographics and Donor Matching
The final patient cohort included 10,025 recipients from 153 centers (Table 1). Median follow-up of patients was 36.6 months (range, 3.0-77.8). Median time from diagnosis to HCT was 6.0 months (IQR, 4.4-9.9 months). MMUD recipients had slightly longer time from diagnosis to HCT compared with MUD recipients (6.7 months; IQR, 4.7-11.3; v 6.0 months; IQR, 4.4-9.8; P < .001). Patients were similar with respect to age, HCT-CI, and disease histology and risk. Recipients of MMUD were more likely to be of minority ancestry (defined as other than NHW): MMUD recipients 22% versus 8% in MUD recipients (P < .0001). Myeloablative conditioning (MAC) was more commonly used in the CNI-based prophylaxis group (47% v 40%; P < .001). Administration of ATG was most frequent in recipients of MMUD with CNI-based prophylaxis (48.1%), followed by 31.0% in MUD with CNI, 2.5% in MMUD with PTCy, and <1% in MUD with PTCy (global P < .001). Specific mismatched HLA locus among MMUD and the matching rates of HLA-DQB1 and HLA-DPB1 in all groups are provided in the Data Supplement (Table S2).
TABLE 1.
Patient and Donor Demographics
Characteristic
PTCy URD 8/8
CNI URD 8/8
PTCy URD 7/8
CNI URD 7/8
Total
No. of patients
1,681
7,272
613
459
10,025
Patient age, years, median (range)
62.0 (18.0-82.2)
60.6 (18.0-82.7)
57.9 (18.0-78.8)
58.2 (18.1-81.4)
60.7 (18.0-82.7)
Donor age, years, median (range)
27.6 (18.0-60.9)
26.8 (18.0-66.5)
28.8 (18.0-61.2)
28.3 (18.0-61.4)
27.1 (18.0-66.5)
Female, No. (%)
674 (40.1)
3,173 (43.6)
335 (54.6)
197 (42.9)
4,379 (43.7)
Race, No. (%)
White
1,526 (90.8)
6,674 (91.8)
467 (76.2)
367 (80.0)
9,034 (90.1)
Black or African American
44 (2.6)
127 (1.7)
68 (11.1)
37 (8.1)
276 (2.8)
Asian
37 (2.2)
194 (2.7)
34 (5.5)
22 (4.8)
287 (2.9)
Othera
11 (0.7)
59 (0.8)
10 (1.7)
7 (1.5)
87 (0.9)
Not reported
63 (3.7)
218 (3.0)
34 (5.5)
26 (5.7)
341 (3.4)
Ethnicity, No. (%)
Hispanic or Latino
86 (5.1)
474 (6.5)
99 (16.2)
77 (16.8)
736 (7.3)
Non-Hispanic or non-Latino
1,518 (90.3)
6,606 (90.8)
487 (79.4)
369 (80.4)
8,980 (89.6)
Not reported
77 (4.6)
192 (2.6)
27 (4.4)
13 (2.8)
309 (3.1)
Karnofsky, No. (%)
90%-100%
884 (52.6)
3,697 (50.8)
324 (52.9)
217 (47.3)
5,122 (51.1)
<90%
755 (44.9)
3,483 (47.9)
275 (44.9)
233 (50.8)
4,746 (47.3)
Not reported
42 (2.5)
92 (1.3)
14 (2.3)
9 (2.0)
157 (1.6)
Comorbidity (HCT-CI), No. (%)
0-2
807 (48.0)
3,306 (45.4)
272 (44.4)
198 (43.1)
4,583 (45.6)
3+
874 (52.0)
3,966 (54.6)
341 (55.6)
261 (56.9)
5,442 (54.4)
DRI, No. (%)
Low
59 (3.5)
241 (3.3)
18 (2.9)
8 (1.7)
326 (3.3)
Intermediate
952 (56.6)
4,126 (56.7)
369 (60.2)
239 (52.1)
5,686 (56.7)
High
449 (26.7)
2,100 (28.9)
156 (25.4)
168 (36.6)
2,873 (28.7)
Very high
31 (1.8)
197 (2.7)
17 (2.8)
11 (2.4)
256 (2.6)
Early MDS
138 (8.2)
388 (5.3)
36 (5.9)
24 (5.2)
586 (5.8)
Advanced MDS
52 (3.1)
220 (3.0)
17 (2.8)
9 (2.0)
298 (3.0)
Disease, No. (%)
AML
909 (54.1)
3,937 (54.1)
330 (53.8)
243 (52.9)
5,419 (54.1)
ALL
250 (14.9)
1,209 (16.6)
128 (20.9)
93 (20.3)
1,680 (16.8)
MDS
522 (31.1)
2,126 (29.2)
155 (25.3)
123 (26.8)
2,926 (29.2)
Disease status at HCT, No. (%)
AML categories
Primary induction failure
85 (5.1)
482 (6.6)
37 (6.0)
41 (8.9)
645 (6.4)
Complete remission
1,039 (61.8)
4,506 (61.9)
415 (67.7)
286 (62.4)
6,246 (62.3)
Relapse
35 (2.1)
159 (2.2)
6 (1.0)
9 (2.0)
209 (2.1)
MDS categories
Early MDS
242 (14.4)
818 (11.2)
68 (11.1)
45 (9.8)
1,173 (11.7)
Advanced MDS
274 (16.3)
1,289 (17.7)
87 (14.2)
75 (16.3)
1,725 (17.2)
MDS other
6 (0.4)
18 (0.2)
0
3 (0.7)
27 (0.3)
Graft source, No. (%)
Bone marrow
127 (7.6)
931 (12.8)
102 (16.6)
64 (13.9)
1,224 (12.2)
Peripheral blood stem cells
1,554 (92.4)
6,341 (87.2)
511 (83.4)
395 (86.1)
8,801 (87.8)
Donor/recipient CMV serostatus, No. (%)
+/+
464 (27.6)
2,030 (27.9)
205 (33.4)
168 (36.6)
2,867 (28.6)
+/–
174 (10.4)
798 (11.0)
91 (14.8)
42 (9.2)
1,105 (11.0)
–/+
593 (35.3)
2,475 (34.0)
190 (31.0)
146 (31.8)
3,404 (34.0)
–/–
440 (26.2)
1,935 (26.6)
125 (20.4)
100 (21.8)
2,600 (25.9)
Not reported
10 (0.6)
34 (0.5)
2 (0.3)
3 (0.7)
49 (0.5)
Conditioning intensity, No. (%)
Myeloablative
667 (39.7)
3,445 (47.4)
244 (39.8)
219 (47.7)
4,575 (45.6)
Nonmyeloablative
307 (18.3)
746 (10.3)
103 (16.8)
49 (10.7)
1,205 (12.0)
Reduced intensity
707 (42.1)
3,081 (42.4)
266 (43.4)
191 (41.6)
4,245 (42.3)
GVHD prophylaxis, No. (%)
CNIb + MTX or MMF
0
6,156 (84.7)
0
370 (80.6)
6,526 (65.1)
CNI + sirolimus ± MMF or MTX
0
1,116 (15.3)
0
89 (19.4)
1,205 (12.0)
PTCy + CNI + MMF
1,586 (94.4)
0
509 (83.1)
0
2,095 (20.9)
PTCy + sirolimus + MMF
95 (5.7)
0
104 (17.0)
0
199 (2.0)
Antithymocyte globulin use, No. (%)
8 (<1.0)
2,258 (31.0)
15 (2.5)
221 (48.1)
2,502 (25.0)
Abbreviations: CMV, cytomegalovirus; CNI, calcineurin inhibitor; DRI, Disease Risk Index; GVHD, graft-versus-host disease; HCT, hematopoietic cell transplantation; HCT-CI, HCT comorbidity index; MDS, myelodysplastic syndromes; MMF, mycophenolate mofetil; MTX, methotrexate; PTCy, post-transplant cyclophosphamide; URD, unrelated donor.
a
American Indian, Alaska Native, Native Hawaiian, or Pacific Islander.
b
CNI includes tacrolimus or cyclosporin A.
Multivariable Analysis of OS and GRFS on the Basis of GVHD Prophylaxis
Univariable analyses for OS and GRFS are provided in the Data Supplement (Tables S3a and S3b). Results of the Cox regression models of OS are available in the Data Supplement (Table S3c). The final model for OS included patient and donor age, DRI, HCT-CI, race/ethnicity, sex, donor/recipient cytomegalovirus serostatus, and year of HCT. HCT recipients using PTCy had no difference in OS on the basis of degree of donor HLA matching (HR, 0.96 [95% CI, 0.82 to 1.18]; P = .60), whereas significant differences remained in OS between MUD and MMUD HCT recipients receiving CNI-based prophylaxis (HR for matched URD, 0.80 [0.70 to 0.92]; P = .016; Figs 1A and 1B). Compared with patients receiving MUD HCT with CNI-based prophylaxis, recipients of PTCy-based MUD HCT had better OS (HR, 0.88 [95% CI, 0.804 to 0.96]; P = .0041), and recipients of MMUD HCT with PTCy had similar OS (HR, 0.92 [95% CI, 0.80 to 1.05]; P = .2062).
FIG 1.
Adjusted Kaplan-Meier estimates of GRFS and OS in recipients of (A) PTCy and (B) CNI. CNI, calcineurin inhibitor; GRFS, graft-versus-host disease-free, relapse-free survival; HLA, human leukocyte antigen; OS, overall survival; PTCy, post-transplant cyclophosphamide; URD, unrelated donor.
Cox regression model results for GRFS are provided in the Data Supplement (Table S3d). The final model for GRFS included refined DRI, patient and donor age, HCT-CI, patient race/ethnicity, graft source, and year of HCT. Compared with MUD recipients receiving CNI-based prophylaxis, patients who received MUD or MMUD with PTCy were less likely to experience a GRFS event. There was no difference in GRFS between recipients of MUD and MMUD HCT receiving either PTCy- (HR, 0.90 [95% CI, 0.79 to 1.02]; P = .11; Fig 1A) or CNI-based GVHD prophylaxis (Fig 1B). Patients receiving a MMUD HCT with PTCy were less likely to experience a GRFS event compared with MMUD recipients with CNI-based prophylaxis (HR, 0.65 [95% CI, 0.56 to 0.76]; P < .0001).
Similar results for the OS and GRFS multivariable models were found when the cohorts were subgrouped on the basis of conditioning intensity (Data Supplement, Tables S4 and S5) and among the subgroups that were treated without ATG (Data Supplement, Tables S6a and S6b). ATG exposure did not improve GRFS (HR, 1.20 [95% CI, 0.9-1.50]; P = .120) or OS (HR, 1.50 [95% CI, 1.14-1.97]; P < .001) in recipients of CNI-based MMUD HCT but was associated with improved GRFS (HR, 0.79 [95% CI, 0.75 to 0.84]; P < .001) without differences in OS (HR, 1.08 [95% CI, 1.00 to 1.16]; P = .059) in recipients of MUD HCT. Compared with recipients receiving CNI with ATG, GRFS was superior with PTCy (without ATG) in MUD recipients (HR, 0.71 [95% CI, 0.65 to 0.78]; P < .001) and MMUD recipients (HR, 0.57 [95% CI, 0.47 to 0.69]; P < .001). Similarly, compared with CNI with ATG recipients, PTCy resulted in superior OS after MUD (HR, 0.94 [95% CI, 0.76 to 0.94]; P = .002) and MMUD (HR, 0.57 [95% CI, 0.45 to 0.73]; P < .001). Full multivariable regression results for OS and GRFS in CNI with ATG versus PTCy recipients grouped by degree of donor matching are provided in the Data Supplement (Tables S6c-S6f).
In the subgroup of patients of minority ancestry who underwent MMUD HCT with PTCy, the 3-year OS was 60% (52%-67%), compared with 59% (53%-64%) in NHW patients. Similarly, the 3-year GRFS was 42% (34%-49%) in minority ancestry patients undergoing MMUD with PTCy compared with 42% (37%-47%) in NHW patients undergoing MMUD with PTCy. Considering MMUD recipients of minority ancestry, the use of PTCy resulted in improved OS (HR, 0.56 [95% CI, 0.40 to 0.79]; P = .001) and GRFS (HR, 0.57 [95% CI, 0.43 to 0.76]; P < .001) compared with CNI recipients.
Multivariable Analysis of Secondary Clinical End Points in Patients Receiving PTCy- and CNI-Based GVHD Prophylaxis
We then evaluated whether differences in GVHD, NRM, and relapse were apparent on the basis of donor HLA matching and GVHD prophylaxis. Adjusted cumulative incidence of secondary end points are provided in Figures 2 and 3 and results of multiple regression models for all end points in Figure 4 using MUD with CNI-based prophylaxis as the reference group. Compared with recipients of MUD HCT using CNI, the risk of 6-month grade III-IV acute GVHD was lower in recipients of MUD HCT with PTCy (HR, 0.44 [95% CI, 0.36 to 0.55]; P < .0001) and in recipients of MMUD HCT with PTCy (HR, 0.58 [95% CI, 0.43 to 0.78]; P = .014), but was greater in MMUD HCT with CNI (HR, 1.51 [95% CI, 1.21 to 1.88]; P = .0084; Fig 2). Similar results were noted considering grade II-IV 6-month acute GVHD (Data Supplement, Table S7a). Compared with recipients of MUD HCT with CNI, the 2-year risk of moderate-severe chronic GVHD was lower in recipients of MUD HCT with PTCy (HR, 0.29 [95% CI, 0.25 to 0.34]; P < .0001), MMUD HCT with PTCy (HR, 0.45 [95% CI, 0.37 to 0.56]; P < .0001), and MMUD HCT with CNI (HR, 0.77 [95% CI, 0.65 to 0.93]; P = .0058; Fig 2). Considering only recipients of PTCy, the use of MUD versus MMUD HCT resulted in no difference in grade III-IV acute GVHD (HR, 0.76 [95% CI, 0.54 to 1.08]; P = .12) but lower incidence of moderate-severe chronic GVHD (HR, 0.64 [95% CI, 0.49 to 0.82]; P = .0005). Full regression models for both acute and chronic GVHD are provided in the Data Supplement (Tables S7 and S8a). The cumulative incidence of chronic GVHD within the CNI-treated patients on the basis of ATG exposure and donor HLA matching is provided in the Data Supplement (Table S8b).
FIG 2.
Cumulative incidence of (A) acute GVHD grades 2-4, (B) acute GVHD grades 3-4, and (C) moderate/severe chronic GVHD on the basis of the GVHD prophylaxis approach and donor/recipient HLA matching group. CNI, calcineurin inhibitor; GVHD, graft-versus-host disease; HLA, human leukocyte antigen; PTCy, post-transplant cyclophosphamide; URD, unrelated donor.
FIG 3.
Cumulative incidence of (A) relapse and (B) nonrelapse mortality; and (C) relative causes of death in each donor/GVHD prophylaxis group. CNI, calcineurin inhibitor; GVHD, graft-versus-host disease; PTCy, post-transplant cyclophosphamide; URD, unrelated donor.
FIG 4.
Forest plot results of the multivariable adjusted risk of primary and secondary end points compared with recipients of MUD HCT using CNI-based GVHD prophylaxis. CNI, calcineurin inhibitor; GVHD, graft-versus-host disease; HCT, hematopoietic cell transplantation; HR, hazard ratio; MMUD, mismatched unrelated donor; MUD, matched unrelated donor; PTCy, post-transplant cyclophosphamide.
Compared with recipients of MUD HCT with CNI, the risk of NRM was lower in recipients of MUD and MMUD HCT with PTCy and greater in recipients of MMUD HCT with CNI (Fig 3). Pairwise comparisons demonstrated that the risk of NRM was less in recipients receiving PTCy-based prophylaxis in both MUD HCT (HR, 0.77 [95% CI, 0.67 to 0.87]; P < .0001) and MMUD HCT (HR, 0.58 [95% CI, 0.45 to 0.75]; P < .0001). There was similar risk of NRM among MUD and MMUD HCT recipients using PTCy (HR, 0.96 [95% CI, 0.76 to 1.2]; P = .71). Compared with recipients of MUD with CNI-based prophylaxis, risk of relapse was greater in recipients of MUD with PTCy and similar among recipients of MMUD with PTCy and CNI (Fig 3). Pairwise group analysis demonstrated that the risk of relapse was similar between recipients of MUD and MMUD using PTCy (HR, 1.02 [95% CI, 0.85 to 1.22]; P = .81) and between MUD and MMUD HCT recipients using CNI (HR, 1.00 [95% CI, 0.83 to 1.21]; P = .97). Multivariable associations with relapse and NRM are given in the Data Supplement (Table S9).
Potential Impact to Donor Availability in the NMDP and World Marrow Donor Association Registries
To determine the impact of these results on donor availability in the NMDP and global (World Marrow Donor Association) registries when considering either MUDs alone versus MUDs or MMUDs as suitable donors, we analyzed 50,000 preliminary URD registry searches conducted between 2022 and 2023 on the basis of patient ancestry. We considered only donors age 35 years and younger with a >75% probability of matching at the specified degree. The proportion of recipients having at least one URD improved in all major ancestry groups (Fig 5A) if both MMUD and MUDs were considered (all pairwise P < .001). The median (IQR) number of donors available for all major ancestry groups (Fig 5B) improved if MMUDs were considered: African American ancestry two (1-8) if only MUDs were considered but 74 (26-218) if MMUDs were considered, NHW ancestry (29 [5-208]-1,226 [287-5,371]), Asian/Pacific-Islander (6 [2-27]-151 [48-629]), White/Hispanic (5 [2-24]-147 [43-787]), and Native American (6 [2-39]-362 [70-2,163]). Consideration of more highly mismatched donors (<7/8) suggests greater numbers of potential donors across all ancestry groups (Data Supplement, Fig S1).
FIG 5.
Registry-level modeling using results from 50,000 preliminary unrelated donor searches. Donors age 35 years and younger with at least a 75% probability of HLA matching at the designated degree were considered. (A) The probability of at least one available donor existing on the basis of patient ancestry improves in all groups (global P < .001) if both MUD and MMUD are considered, and (B) the median number of existing donors in patients who had at least one available donor if only MUDs are considered versus MUDs and MMUDs are considered as suitable. AFA, African American; API, Asian/Pacific Islander; HIS, Hispanic/White; HLA, human leukocyte antigen; MMUD, mismatched unrelated donor; MUD, matched unrelated donor; NAM, Native American; NHW, non-Hispanic/White; URD, unrelated donor.
Patient Demographics and Donor Matching
The final patient cohort included 10,025 recipients from 153 centers (Table 1). Median follow-up of patients was 36.6 months (range, 3.0-77.8). Median time from diagnosis to HCT was 6.0 months (IQR, 4.4-9.9 months). MMUD recipients had slightly longer time from diagnosis to HCT compared with MUD recipients (6.7 months; IQR, 4.7-11.3; v 6.0 months; IQR, 4.4-9.8; P < .001). Patients were similar with respect to age, HCT-CI, and disease histology and risk. Recipients of MMUD were more likely to be of minority ancestry (defined as other than NHW): MMUD recipients 22% versus 8% in MUD recipients (P < .0001). Myeloablative conditioning (MAC) was more commonly used in the CNI-based prophylaxis group (47% v 40%; P < .001). Administration of ATG was most frequent in recipients of MMUD with CNI-based prophylaxis (48.1%), followed by 31.0% in MUD with CNI, 2.5% in MMUD with PTCy, and <1% in MUD with PTCy (global P < .001). Specific mismatched HLA locus among MMUD and the matching rates of HLA-DQB1 and HLA-DPB1 in all groups are provided in the Data Supplement (Table S2).
TABLE 1.
Patient and Donor Demographics
Characteristic
PTCy URD 8/8
CNI URD 8/8
PTCy URD 7/8
CNI URD 7/8
Total
No. of patients
1,681
7,272
613
459
10,025
Patient age, years, median (range)
62.0 (18.0-82.2)
60.6 (18.0-82.7)
57.9 (18.0-78.8)
58.2 (18.1-81.4)
60.7 (18.0-82.7)
Donor age, years, median (range)
27.6 (18.0-60.9)
26.8 (18.0-66.5)
28.8 (18.0-61.2)
28.3 (18.0-61.4)
27.1 (18.0-66.5)
Female, No. (%)
674 (40.1)
3,173 (43.6)
335 (54.6)
197 (42.9)
4,379 (43.7)
Race, No. (%)
White
1,526 (90.8)
6,674 (91.8)
467 (76.2)
367 (80.0)
9,034 (90.1)
Black or African American
44 (2.6)
127 (1.7)
68 (11.1)
37 (8.1)
276 (2.8)
Asian
37 (2.2)
194 (2.7)
34 (5.5)
22 (4.8)
287 (2.9)
Othera
11 (0.7)
59 (0.8)
10 (1.7)
7 (1.5)
87 (0.9)
Not reported
63 (3.7)
218 (3.0)
34 (5.5)
26 (5.7)
341 (3.4)
Ethnicity, No. (%)
Hispanic or Latino
86 (5.1)
474 (6.5)
99 (16.2)
77 (16.8)
736 (7.3)
Non-Hispanic or non-Latino
1,518 (90.3)
6,606 (90.8)
487 (79.4)
369 (80.4)
8,980 (89.6)
Not reported
77 (4.6)
192 (2.6)
27 (4.4)
13 (2.8)
309 (3.1)
Karnofsky, No. (%)
90%-100%
884 (52.6)
3,697 (50.8)
324 (52.9)
217 (47.3)
5,122 (51.1)
<90%
755 (44.9)
3,483 (47.9)
275 (44.9)
233 (50.8)
4,746 (47.3)
Not reported
42 (2.5)
92 (1.3)
14 (2.3)
9 (2.0)
157 (1.6)
Comorbidity (HCT-CI), No. (%)
0-2
807 (48.0)
3,306 (45.4)
272 (44.4)
198 (43.1)
4,583 (45.6)
3+
874 (52.0)
3,966 (54.6)
341 (55.6)
261 (56.9)
5,442 (54.4)
DRI, No. (%)
Low
59 (3.5)
241 (3.3)
18 (2.9)
8 (1.7)
326 (3.3)
Intermediate
952 (56.6)
4,126 (56.7)
369 (60.2)
239 (52.1)
5,686 (56.7)
High
449 (26.7)
2,100 (28.9)
156 (25.4)
168 (36.6)
2,873 (28.7)
Very high
31 (1.8)
197 (2.7)
17 (2.8)
11 (2.4)
256 (2.6)
Early MDS
138 (8.2)
388 (5.3)
36 (5.9)
24 (5.2)
586 (5.8)
Advanced MDS
52 (3.1)
220 (3.0)
17 (2.8)
9 (2.0)
298 (3.0)
Disease, No. (%)
AML
909 (54.1)
3,937 (54.1)
330 (53.8)
243 (52.9)
5,419 (54.1)
ALL
250 (14.9)
1,209 (16.6)
128 (20.9)
93 (20.3)
1,680 (16.8)
MDS
522 (31.1)
2,126 (29.2)
155 (25.3)
123 (26.8)
2,926 (29.2)
Disease status at HCT, No. (%)
AML categories
Primary induction failure
85 (5.1)
482 (6.6)
37 (6.0)
41 (8.9)
645 (6.4)
Complete remission
1,039 (61.8)
4,506 (61.9)
415 (67.7)
286 (62.4)
6,246 (62.3)
Relapse
35 (2.1)
159 (2.2)
6 (1.0)
9 (2.0)
209 (2.1)
MDS categories
Early MDS
242 (14.4)
818 (11.2)
68 (11.1)
45 (9.8)
1,173 (11.7)
Advanced MDS
274 (16.3)
1,289 (17.7)
87 (14.2)
75 (16.3)
1,725 (17.2)
MDS other
6 (0.4)
18 (0.2)
0
3 (0.7)
27 (0.3)
Graft source, No. (%)
Bone marrow
127 (7.6)
931 (12.8)
102 (16.6)
64 (13.9)
1,224 (12.2)
Peripheral blood stem cells
1,554 (92.4)
6,341 (87.2)
511 (83.4)
395 (86.1)
8,801 (87.8)
Donor/recipient CMV serostatus, No. (%)
+/+
464 (27.6)
2,030 (27.9)
205 (33.4)
168 (36.6)
2,867 (28.6)
+/–
174 (10.4)
798 (11.0)
91 (14.8)
42 (9.2)
1,105 (11.0)
–/+
593 (35.3)
2,475 (34.0)
190 (31.0)
146 (31.8)
3,404 (34.0)
–/–
440 (26.2)
1,935 (26.6)
125 (20.4)
100 (21.8)
2,600 (25.9)
Not reported
10 (0.6)
34 (0.5)
2 (0.3)
3 (0.7)
49 (0.5)
Conditioning intensity, No. (%)
Myeloablative
667 (39.7)
3,445 (47.4)
244 (39.8)
219 (47.7)
4,575 (45.6)
Nonmyeloablative
307 (18.3)
746 (10.3)
103 (16.8)
49 (10.7)
1,205 (12.0)
Reduced intensity
707 (42.1)
3,081 (42.4)
266 (43.4)
191 (41.6)
4,245 (42.3)
GVHD prophylaxis, No. (%)
CNIb + MTX or MMF
0
6,156 (84.7)
0
370 (80.6)
6,526 (65.1)
CNI + sirolimus ± MMF or MTX
0
1,116 (15.3)
0
89 (19.4)
1,205 (12.0)
PTCy + CNI + MMF
1,586 (94.4)
0
509 (83.1)
0
2,095 (20.9)
PTCy + sirolimus + MMF
95 (5.7)
0
104 (17.0)
0
199 (2.0)
Antithymocyte globulin use, No. (%)
8 (<1.0)
2,258 (31.0)
15 (2.5)
221 (48.1)
2,502 (25.0)
Abbreviations: CMV, cytomegalovirus; CNI, calcineurin inhibitor; DRI, Disease Risk Index; GVHD, graft-versus-host disease; HCT, hematopoietic cell transplantation; HCT-CI, HCT comorbidity index; MDS, myelodysplastic syndromes; MMF, mycophenolate mofetil; MTX, methotrexate; PTCy, post-transplant cyclophosphamide; URD, unrelated donor.
a
American Indian, Alaska Native, Native Hawaiian, or Pacific Islander.
b
CNI includes tacrolimus or cyclosporin A.
Multivariable Analysis of OS and GRFS on the Basis of GVHD Prophylaxis
Univariable analyses for OS and GRFS are provided in the Data Supplement (Tables S3a and S3b). Results of the Cox regression models of OS are available in the Data Supplement (Table S3c). The final model for OS included patient and donor age, DRI, HCT-CI, race/ethnicity, sex, donor/recipient cytomegalovirus serostatus, and year of HCT. HCT recipients using PTCy had no difference in OS on the basis of degree of donor HLA matching (HR, 0.96 [95% CI, 0.82 to 1.18]; P = .60), whereas significant differences remained in OS between MUD and MMUD HCT recipients receiving CNI-based prophylaxis (HR for matched URD, 0.80 [0.70 to 0.92]; P = .016; Figs 1A and 1B). Compared with patients receiving MUD HCT with CNI-based prophylaxis, recipients of PTCy-based MUD HCT had better OS (HR, 0.88 [95% CI, 0.804 to 0.96]; P = .0041), and recipients of MMUD HCT with PTCy had similar OS (HR, 0.92 [95% CI, 0.80 to 1.05]; P = .2062).
FIG 1.
Adjusted Kaplan-Meier estimates of GRFS and OS in recipients of (A) PTCy and (B) CNI. CNI, calcineurin inhibitor; GRFS, graft-versus-host disease-free, relapse-free survival; HLA, human leukocyte antigen; OS, overall survival; PTCy, post-transplant cyclophosphamide; URD, unrelated donor.
Cox regression model results for GRFS are provided in the Data Supplement (Table S3d). The final model for GRFS included refined DRI, patient and donor age, HCT-CI, patient race/ethnicity, graft source, and year of HCT. Compared with MUD recipients receiving CNI-based prophylaxis, patients who received MUD or MMUD with PTCy were less likely to experience a GRFS event. There was no difference in GRFS between recipients of MUD and MMUD HCT receiving either PTCy- (HR, 0.90 [95% CI, 0.79 to 1.02]; P = .11; Fig 1A) or CNI-based GVHD prophylaxis (Fig 1B). Patients receiving a MMUD HCT with PTCy were less likely to experience a GRFS event compared with MMUD recipients with CNI-based prophylaxis (HR, 0.65 [95% CI, 0.56 to 0.76]; P < .0001).
Similar results for the OS and GRFS multivariable models were found when the cohorts were subgrouped on the basis of conditioning intensity (Data Supplement, Tables S4 and S5) and among the subgroups that were treated without ATG (Data Supplement, Tables S6a and S6b). ATG exposure did not improve GRFS (HR, 1.20 [95% CI, 0.9-1.50]; P = .120) or OS (HR, 1.50 [95% CI, 1.14-1.97]; P < .001) in recipients of CNI-based MMUD HCT but was associated with improved GRFS (HR, 0.79 [95% CI, 0.75 to 0.84]; P < .001) without differences in OS (HR, 1.08 [95% CI, 1.00 to 1.16]; P = .059) in recipients of MUD HCT. Compared with recipients receiving CNI with ATG, GRFS was superior with PTCy (without ATG) in MUD recipients (HR, 0.71 [95% CI, 0.65 to 0.78]; P < .001) and MMUD recipients (HR, 0.57 [95% CI, 0.47 to 0.69]; P < .001). Similarly, compared with CNI with ATG recipients, PTCy resulted in superior OS after MUD (HR, 0.94 [95% CI, 0.76 to 0.94]; P = .002) and MMUD (HR, 0.57 [95% CI, 0.45 to 0.73]; P < .001). Full multivariable regression results for OS and GRFS in CNI with ATG versus PTCy recipients grouped by degree of donor matching are provided in the Data Supplement (Tables S6c-S6f).
In the subgroup of patients of minority ancestry who underwent MMUD HCT with PTCy, the 3-year OS was 60% (52%-67%), compared with 59% (53%-64%) in NHW patients. Similarly, the 3-year GRFS was 42% (34%-49%) in minority ancestry patients undergoing MMUD with PTCy compared with 42% (37%-47%) in NHW patients undergoing MMUD with PTCy. Considering MMUD recipients of minority ancestry, the use of PTCy resulted in improved OS (HR, 0.56 [95% CI, 0.40 to 0.79]; P = .001) and GRFS (HR, 0.57 [95% CI, 0.43 to 0.76]; P < .001) compared with CNI recipients.
Multivariable Analysis of Secondary Clinical End Points in Patients Receiving PTCy- and CNI-Based GVHD Prophylaxis
We then evaluated whether differences in GVHD, NRM, and relapse were apparent on the basis of donor HLA matching and GVHD prophylaxis. Adjusted cumulative incidence of secondary end points are provided in Figures 2 and 3 and results of multiple regression models for all end points in Figure 4 using MUD with CNI-based prophylaxis as the reference group. Compared with recipients of MUD HCT using CNI, the risk of 6-month grade III-IV acute GVHD was lower in recipients of MUD HCT with PTCy (HR, 0.44 [95% CI, 0.36 to 0.55]; P < .0001) and in recipients of MMUD HCT with PTCy (HR, 0.58 [95% CI, 0.43 to 0.78]; P = .014), but was greater in MMUD HCT with CNI (HR, 1.51 [95% CI, 1.21 to 1.88]; P = .0084; Fig 2). Similar results were noted considering grade II-IV 6-month acute GVHD (Data Supplement, Table S7a). Compared with recipients of MUD HCT with CNI, the 2-year risk of moderate-severe chronic GVHD was lower in recipients of MUD HCT with PTCy (HR, 0.29 [95% CI, 0.25 to 0.34]; P < .0001), MMUD HCT with PTCy (HR, 0.45 [95% CI, 0.37 to 0.56]; P < .0001), and MMUD HCT with CNI (HR, 0.77 [95% CI, 0.65 to 0.93]; P = .0058; Fig 2). Considering only recipients of PTCy, the use of MUD versus MMUD HCT resulted in no difference in grade III-IV acute GVHD (HR, 0.76 [95% CI, 0.54 to 1.08]; P = .12) but lower incidence of moderate-severe chronic GVHD (HR, 0.64 [95% CI, 0.49 to 0.82]; P = .0005). Full regression models for both acute and chronic GVHD are provided in the Data Supplement (Tables S7 and S8a). The cumulative incidence of chronic GVHD within the CNI-treated patients on the basis of ATG exposure and donor HLA matching is provided in the Data Supplement (Table S8b).
FIG 2.
Cumulative incidence of (A) acute GVHD grades 2-4, (B) acute GVHD grades 3-4, and (C) moderate/severe chronic GVHD on the basis of the GVHD prophylaxis approach and donor/recipient HLA matching group. CNI, calcineurin inhibitor; GVHD, graft-versus-host disease; HLA, human leukocyte antigen; PTCy, post-transplant cyclophosphamide; URD, unrelated donor.
FIG 3.
Cumulative incidence of (A) relapse and (B) nonrelapse mortality; and (C) relative causes of death in each donor/GVHD prophylaxis group. CNI, calcineurin inhibitor; GVHD, graft-versus-host disease; PTCy, post-transplant cyclophosphamide; URD, unrelated donor.
FIG 4.
Forest plot results of the multivariable adjusted risk of primary and secondary end points compared with recipients of MUD HCT using CNI-based GVHD prophylaxis. CNI, calcineurin inhibitor; GVHD, graft-versus-host disease; HCT, hematopoietic cell transplantation; HR, hazard ratio; MMUD, mismatched unrelated donor; MUD, matched unrelated donor; PTCy, post-transplant cyclophosphamide.
Compared with recipients of MUD HCT with CNI, the risk of NRM was lower in recipients of MUD and MMUD HCT with PTCy and greater in recipients of MMUD HCT with CNI (Fig 3). Pairwise comparisons demonstrated that the risk of NRM was less in recipients receiving PTCy-based prophylaxis in both MUD HCT (HR, 0.77 [95% CI, 0.67 to 0.87]; P < .0001) and MMUD HCT (HR, 0.58 [95% CI, 0.45 to 0.75]; P < .0001). There was similar risk of NRM among MUD and MMUD HCT recipients using PTCy (HR, 0.96 [95% CI, 0.76 to 1.2]; P = .71). Compared with recipients of MUD with CNI-based prophylaxis, risk of relapse was greater in recipients of MUD with PTCy and similar among recipients of MMUD with PTCy and CNI (Fig 3). Pairwise group analysis demonstrated that the risk of relapse was similar between recipients of MUD and MMUD using PTCy (HR, 1.02 [95% CI, 0.85 to 1.22]; P = .81) and between MUD and MMUD HCT recipients using CNI (HR, 1.00 [95% CI, 0.83 to 1.21]; P = .97). Multivariable associations with relapse and NRM are given in the Data Supplement (Table S9).
Potential Impact to Donor Availability in the NMDP and World Marrow Donor Association Registries
To determine the impact of these results on donor availability in the NMDP and global (World Marrow Donor Association) registries when considering either MUDs alone versus MUDs or MMUDs as suitable donors, we analyzed 50,000 preliminary URD registry searches conducted between 2022 and 2023 on the basis of patient ancestry. We considered only donors age 35 years and younger with a >75% probability of matching at the specified degree. The proportion of recipients having at least one URD improved in all major ancestry groups (Fig 5A) if both MMUD and MUDs were considered (all pairwise P < .001). The median (IQR) number of donors available for all major ancestry groups (Fig 5B) improved if MMUDs were considered: African American ancestry two (1-8) if only MUDs were considered but 74 (26-218) if MMUDs were considered, NHW ancestry (29 [5-208]-1,226 [287-5,371]), Asian/Pacific-Islander (6 [2-27]-151 [48-629]), White/Hispanic (5 [2-24]-147 [43-787]), and Native American (6 [2-39]-362 [70-2,163]). Consideration of more highly mismatched donors (<7/8) suggests greater numbers of potential donors across all ancestry groups (Data Supplement, Fig S1).
FIG 5.
Registry-level modeling using results from 50,000 preliminary unrelated donor searches. Donors age 35 years and younger with at least a 75% probability of HLA matching at the designated degree were considered. (A) The probability of at least one available donor existing on the basis of patient ancestry improves in all groups (global P < .001) if both MUD and MMUD are considered, and (B) the median number of existing donors in patients who had at least one available donor if only MUDs are considered versus MUDs and MMUDs are considered as suitable. AFA, African American; API, Asian/Pacific Islander; HIS, Hispanic/White; HLA, human leukocyte antigen; MMUD, mismatched unrelated donor; MUD, matched unrelated donor; NAM, Native American; NHW, non-Hispanic/White; URD, unrelated donor.
DISCUSSION
We used the CIBMTR database to conduct a large cohort analysis of allogeneic HCT recipients to determine whether MMUD HCT using PTCy results in acceptable OS and GRFS when compared with MUD HCT. The results demonstrate comparable outcomes between MUDs and MMUDs when using PTCy, suggesting that MMUDs are a suitable donor option if a MUD is not available. Differences in OS between MUD and MMUD HCT continue to be observed after CNI-based GVHD prophylaxis without PTCy, suggesting the results are not simply because of recent changes in supportive care. We also demonstrate that consideration of MMUDs substantially expands the numbers of potential donors within large donor registries. The impact of these findings for all patients, particularly those of minority ancestry, is high, given the historical gaps in access to suitable donors for minority patients.
The comparable OS and GRFS between MUD and MMUD HCT with PTCy may be due to similar risks of acute GVHD, implying that PTCy effectively abrogates detrimental alloreactivity mediated by donor/recipient HLA disparity. Improvements in OS and GRFS for MUD HCT with PTCy relative to CNI, and in GRFS in MMUD with PTCy relative to MUD with CNI, are likely because of a combination of lower risks of both acute and chronic GVHD as well as NRM in both MUD and MMUD with PTCy. We did note a greater incidence of relapse in recipients of MUD HCT with PTCy when compared with CNI, similar to other retrospective cohort studies examining T-cell–modulating strategies.29 These results should be interpreted cautiously as they may be confounded by greater use of MAC in the CNI arm and competing risk of early NRM, which was greater in the CNI-based recipients. An unexpected finding was the lower incidence of chronic GVHD in MMUD using CNI compared with MUD with CNI. This result may be due to greater use of ATG in the MMUD cohort, and early mortality among MMUD, resulting in removal of patients with the most alloreactive donors. Taken together, these results suggest that PTCy is an acceptable alternative to CNI-based prophylaxis in MUD HCT recipients and superior to CNI-based prophylaxis that do not incorporate abatacept in MMUD HCT, extending recent findings of a randomized clinical trial similarly demonstrating improvement in GRFS with PTCy-based GvHD prophylaxis in the reduced intensity conditioning setting.30
There are limitations to the current study. First, this is a retrospective analysis of registry patients and as such there is potential selection bias for patients receiving certain allograft types on the basis of perceived urgency of HCT and disease risk. This limitation may be greater in the comparator groups of MMUD and MUD treated with PTCy, which are smaller. Second, we only evaluated HLA-7/8 matches in the MMUD group for this study and excluded donors who were mismatched at more than one locus. In the completed 15-MMUD study, 31/80 participants (39%) received HCT from a 4-6/8 MMUD, suggesting an ongoing need for these donors. The recently completed ACCESS (ClinicalTrials.gov identifier: NCT04904588) study includes greater numbers of more highly MMUD and will provide important context for outcomes in this group. Given the size of the MMUD recipient cohort, we lack here the power to examine the effects of specific HLA locuswise mismatching on HCT outcomes.
Alternative strategies to prevent GVHD in the setting of MUD and MMUD are undergoing evaluation in prospective clinical trials. Two important ongoing studies (ClinicalTrials.gov identifier: NCT05153226, NCT04888741) aim to compare recipients of PTCy-based versus ATG-based GVHD prophylaxis in unrelated donor recipients. Our results are similar to a recent European registry study; however, given the potential for patient selection bias, evaluation of ATG in a prospective study is important to identify optimal GVHD prophylaxis in both MUD and MMUD HCT.31 The combination of novel prophylaxis agents with either standard-dose or reduced-dose PTCy is a promising strategy that will be evaluated in existing or planned clinical trials. Favorable outcomes in patients with severe aplastic anemia treated with combinatorial ATG and PTCy after haplo-HCT suggest that this approach may be promising in selected patients.32 Other completed and ongoing studies exist to evaluate the use of abatacept in single-locus MMUD, a promising new approach.18,33 Taken together, these results and those of forthcoming clinical trials will represent a significant step forward in resolving barriers to transplantation. Increasing access to HCT is paramount to making this lifesaving procedure available to patients of all ancestries. |
Title: Unveiling the Enigma: Multiple Primary Neoplasms Mimicking Metastatic Disease | Body:
Introduction
Multiple primary cancers, though rare, have been widely studied and documented in the medical literature, with reported incidence rates varying between 0.3% and 4.3% [1]. This phenomenon, known as multiple primary neoplasms (MPNs), involves the occurrence of two or more distinct malignant tumors either simultaneously or sequentially. When these tumors appear at the same time, they are classified as synchronous, whereas if they develop at different points in time, they are termed metachronous [2,3]. The classification of MPNs is important because it helps to understand the underlying mechanisms behind their development. They are typically divided into three primary categories: treatment-associated, where cancer treatment itself leads to subsequent tumors; syndrome-related, where genetic predispositions are responsible for multiple tumors; and tumors that arise from common etiological factors, such as environmental exposures or lifestyle-related risks [2,3].
While brain metastasis is a recognized complication in papillary thyroid carcinoma (PTC), it remains relatively uncommon, occurring in only a small percentage of cases [4-7]. Even more unusual is the co-occurrence of synchronous or metachronous primary brain tumors in patients diagnosed with PTC [1,8]. These cases are considered exceptional, making them rare occurrences in both clinical reports and the broader medical literature. Understanding the interplay between PTC and brain tumors, whether through common genetic pathways or unique metastatic behavior, presents a fascinating and complex challenge in oncology.
Case presentation
A 37-year-old female patient was diagnosed with a papillary thyroid cancer epicenter in the left lobe with vascular invasion in December 2020. She underwent a left-sided lobectomy in January 2021. The residual tissue was also excised in April 2021, and later she underwent radioactive iodine therapy. In September 2022, she presented with a few months' history of amnesia and a headache that increased in intensity and later became associated with vomiting. She underwent CT with and without contrast, demonstrating a space-occupying lesion in the left frontal lobe with midline shift, while rest of the study was unremarkable for any other lesion or meningeal disease (Figure 1). It was followed by contrast-enhanced MRI brain, demonstrating solitary, solid intra-axial, supratentorial abnormal signal intensity lesion in the left frontal lobe, the abnormal signals were T2 and fluid-attenuated inversion recovery (FLAIR) hyperintense, the lesion demonstrated diffusion restriction and post-contrast enhancement (Figure 2). The CT chest, abdomen and pelvis was unremarkable for metastatic disease.
Figure 1
CT brain without and with contrast enhancement; axial (upper row) and coronal sections (lower row)
Solitary enhancing, space-occupying lesion (red arrows) in the left frontal lobe with midline shift (orange arrows) and surrounding edema (yellow arrows)
Figure 2
MRI brain, axial slices; T2 Weighted Image (T2WI), Fluid Attenuation Inversion Recovery (FLAIR), Diffusion Weighted Image (DWI), Apparent Diffusion Coefficient (ADC) and Contrast Enhanced T1 Images (C+T1WI), sequences.
Predominantly solid intra-axial, supratentorial lesion in the left frontal lobe causing mass effect in the form of midline shift and compression upon the ipsilateral lateral ventricle. It is returning hyperintense T2 and FLAIR signals (A & B), demonstrating diffusion restriction on DWI and ADC (C & D) and post-contrast enhancement (E)
The case was discussed in the multidisciplinary team conference, and considering it to be a solitary metastatic deposit, radiotherapy was suggested to decrease the tumor bulk. The patient was reassessed in November 2022 after completion of radiotherapy, and the tumor turned out to be stable in the interim. She underwent surgical excision on December 27, 2022, and the excised-out lesion was subjected to biopsy, which turned out to be astrocytoma grade 2, IDH mutant (Figure 3).
Figure 3
Image of excisional biopsy-tissue sample under microscope
Abnormal appearing astrocytes of variable shapes and sizes (pleomorphism), with variable appearance of nuclei (nuclear atypia) and single mitotic figure (black arrow)
Discussion
The reported incidence of multiple primary cancers ranges from 0.3% to 4.3%, firmly establishing its status as a well-researched phenomenon [1]. The term multiple primary neoplasms is used to refer to two or more tumors that exist at the same time or over some time, provided that the following conditions are met: each tumor must be malignant, exhibit distinct characteristics, and not be a metastatic deposit of the other tumor [2]. Based on the timing of their identification, these two tumors can be classified as either synchronous or metachronous tumors. Synchronous tumors are characterized by the presence of two or more independent tumors that are identified either simultaneously or within six months. On the other hand, metachronous tumors are defined by a time gap greater than six months between the identification of each tumor [2,3]. MPNs can be broadly classified into three primary groups based on their main causal factors. The first group comprises neoplasms that are associated with treatments or therapies. The second group involves cases related to specific syndromes. Lastly, the third group encompasses neoplasms that may share common etiological factors, such as genetic predisposition or exposure to similar environmental factors [2,3]. Furthermore, it's worth noting that the occurrence of two or more cancers can also happen purely by chance, independent of any specific causal factors [2]. While not exceedingly prevalent, brain metastasis in PTC is not remarkably uncommon [4-7]. However, the occurrence of synchronous or metachronous brain tumors with PTC is exceptionally rare [1,8].
Conclusions
This case of a 37-year-old female with both papillary thyroid carcinoma and an astrocytoma highlights the importance of thorough diagnostic evaluations when multiple primary neoplasms are suspected. The initial assumption of brain metastasis was revised to a primary brain tumor after histopathological examination, emphasizing the need for comprehensive assessment and a multidisciplinary approach in managing complex cancer cases. This case underscores the critical need for awareness and diligence in distinguishing between primary and metastatic lesions to ensure accurate diagnosis and effective treatment. |
Title: A Large Basal Cell Carcinoma Treated With Hedgehog Inhibitor: A Case Report | Body:
Introduction
Basal cell carcinoma (BCC) is the most common type of skin cancer [1]. It typically arises from the basal layer of the epidermis or hair follicle stem cells, and its development is strongly linked to chronic exposure to ultraviolet radiation, particularly ultraviolet B radiation, which causes DNA damage. This DNA damage often leads to mutations in key tumor suppressor genes, such as Patched-1 (PTCH1), which is integral to the hedgehog (HH) signaling pathway. The incidence of BCC continues to rise globally, especially in populations with lighter skin types and increased exposure to sunlight. In the United States, more than four million new cases of BCC are diagnosed each year, making it a significant public health concern. Although BCC is rarely fatal, it can cause substantial morbidity due to its potential for local invasion and tissue destruction, particularly in advanced or neglected cases [1,2].
The majority of BCCs are slow-growing and can be effectively treated with surgical excision or other local treatments, such as curettage, cryotherapy, or photodynamic therapy. Mohs micrographic surgery is widely regarded as the gold standard for high-risk BCCs or those in cosmetically sensitive areas due to its high cure rates and tissue-sparing approach [1]. However, a subset of BCC cases progresses to locally advanced BCC (laBCC) or metastatic BCC (mBCC), which are challenging to manage due to their unresectable nature and resistance to traditional therapies. In these cases, systemic therapies, particularly hedgehog pathway inhibitors (HHIs), have emerged as a promising alternative. The HH signaling pathway is essential for embryonic development and tissue homeostasis, but its aberrant activation, usually through mutations in PTCH1 or Smoothened (SMO) genes, plays a pivotal role in BCC pathogenesis. The discovery of this pathway’s involvement in BCC has led to the development of targeted therapies, such as vismodegib and sonidegib, which specifically inhibit components of the HH pathway and are now approved for use in patients with advanced BCC [3,4]. Despite these advances, many challenges remain, as HHI therapy is not without limitations. Common adverse effects of HHIs include muscle spasms, change in taste, alopecia, weight loss, and fatigue [2,5].
Case presentation
A 61-year-old male with a past medical history of hypertension initially presented to the dermatology clinic six years ago with a large, biopsy-confirmed nodular BCC on his right upper back (Figure 1). At the time of his initial presentation, surgical options were thoroughly discussed with the patient, including the recommendation for Mohs micrographic surgery. However, the patient elected not to undergo surgery and chose not to return for further treatment against medical advice. His decision not to pursue treatment at that time significantly contributed to the lesion's continued growth over the following years. Five years later, the patient returned to the dermatology clinic after noticing a substantial increase in the size of the BCC, which had now grown to 9 × 5 cm and encompassed a much larger area of his right upper back (Figure 2).
Figure 1
BCC lesion on the patient's upper back at initial visit.
BCC: basal cell carcinoma
Figure 2
BCC lesion on upper back before starting HHI therapy.
BCC: basal cell carcinoma; HHI: hedgehog pathway inhibitor
Given that the tumor would be difficult to treat surgically and the patient’s desire to avoid surgery, he was offered an HHI. The patient opted to begin treatment with vismodegib 150 mg by mouth daily, a systemic SMO antagonist approved for treating advanced BCC. After eight months of HHI therapy, the patient experienced significant improvement with a dramatic reduction in the size of the primary BCC lesion on his back (Figure 3). Additionally, smaller BCCs previously noted on his upper extremities completely regressed during this period. The patient tolerated the therapy well, with only minor adverse events reported, including hair thinning, a 20-pound weight loss, and rapid nail growth. He denied experiencing muscle spasms, dysgeusia, or other significant side effects. Interestingly, the patient also reported that he was able to discontinue his antihypertensive medication after the weight loss associated with the therapy. It was recommended that the patient continue HHI therapy for an additional eight weeks with close monitoring for any potential adverse effects. Because of the large size of the original lesion and to avoid multiple scouting biopsies, the lesion will be followed clinically.
Figure 3
BCC lesion on upper back after eight months of HHI.
BCC: basal cell carcinoma; HHI: hedgehog pathway inhibitor
Discussion
The HH signaling pathway plays a crucial role in regulating cell growth, development, and tissue homeostasis. Its activation is tightly controlled under normal physiological conditions, ensuring that cellular proliferation and differentiation occur only as needed for repair and regeneration. The HH signaling pathway is activated via Sonic hedgehog (SHH), Indian hedgehog, or Desert hedgehog to Patched-1 (PTCH1). The canonical HH pathway proceeds through the developmental signaling cascade. Signaling is initiated when there is insufficient ligand binding, resulting in PTCH1-mediated inhibition of Smoothened (SMO), a transmembrane protein that activates the HH signaling cascade [3]. Promotion of the HH signaling pathway results in the activation of glioma-associated oncogene homolog 1 (Gli1), a transcriptional effector that activates cyclin-D1, Myc, and Bcl-2. Non-canonical HH signaling can occur via mechanisms external to HH signaling, including K-ras, transforming growth factor β (TGF-β), and phosphoinositide-3-kinase (PI3K). In many cases, BCC is associated with the upregulation of the HH signaling pathway in the intrafollicular epidermis and progenitor cells of the hair follicles. Furthermore, over 80% of patients with BCC have a loss-of-function mutation in one PTCH1 allele, while the remaining have gain-of-function mutations in one SMO allele [3].
The identification of the hedgehog pathway as a key player in BCC pathogenesis has significantly advanced the therapeutic landscape, particularly for advanced cases that are deemed unresectable. Prior to the development of HHIs, options for managing laBCC or mBCC were limited and often ineffective. The introduction of HHIs like vismodegib and sonidegib has provided a targeted approach to disrupt this pathway, offering patients with advanced BCC a viable non-surgical treatment option [4].
HHIs work by binding to and inhibiting SMO, preventing the downstream activation of the HH signaling cascade. This effectively halts the transcription of genes associated with cell survival and proliferation, such as Gli1, cyclin-D1, Myc, and Bcl-2. As a result, tumor growth is suppressed, and in some cases, tumors can regress entirely. Cyclopamine was among the first compounds that disrupted SMO, resulting in decreased tumor proliferation and progression [3,4]. The adverse effects (AE) of cyclopamine, which included death and teratogenic effects, led to the development of synthetic mimetics. Currently, sonidegib and vismodegib are two systemic SMO antagonists, with sonidegib approved for adult patients with laBCC and vismodegib approved for adult patients with laBCC or mBCC [2]. In this case, vismodegib was successful in reducing the size of the patient’s large BCC and resulted in the complete regression of smaller lesions on his upper extremities. This underscores the potency of HHI therapy in controlling advanced disease, even in cases where the BCC had progressed for several years without intervention.
Despite their efficacy, HHIs are associated with a range of adverse events, many of which can impact patient quality of life and adherence to therapy. The most commonly reported AEs of sonidegib and vismodegib include muscle spasms, alopecia, weight loss, and dysgeusia [2,3]. These side effects are believed to result from the HH pathway’s role in normal physiological processes, such as hair follicle development and muscle function [2].
Prolonged use of HHIs is associated with an increased likelihood of adverse events, which can lead to treatment discontinuation in some patients [6]. To mitigate side effects over extended therapy, pulsed or intermittent dosing has been explored, offering potential relief from side effects while maintaining efficacy [6]. Discontinuation of treatment may increase the risk of tumor recurrence, underscoring the need for careful monitoring. Additionally, the high cost of HHI therapy remains a significant limitation, particularly given the potential need for long-term or recurring treatments.
Administration of L-carnitine, a non-essential amino acid responsible for mitochondrial ATP generation by increasing the β-oxidation of long-chain fatty acids, can stabilize the sarcolemma, permitting muscle relaxation [3]. The HH signaling pathway is necessary for normal hair growth, resulting in HHI-induced alopecia. Co-treatment with HHI and oral finasteride or topical minoxidil is indicated for HHI-induced alopecia. While the mechanism of dysgeusia and HHI therapy is unclear, zinc deficiency is hypothesized to play a role in taste bud function, size, and integrity. Further inquiries are required to understand the benefits of zinc supplementation for patients on HHI therapy [3].
In this case, the patient experienced minor AEs, including hair thinning, weight loss, and rapid nail growth, but did not report the more debilitating AEs that can occur with HHI therapy, such as muscle cramps or severe dysgeusia. The patient’s weight loss, while significant, was not associated with loss of appetite or fatigue and appeared to contribute positively to his ability to discontinue antihypertensive medication. This suggests that individual responses to HHI therapy can vary and that not all patients will experience major side effects.
Conclusions
Hedgehog pathway inhibitors remain an effective treatment option for patients with advanced BCC, especially those who are not candidates for surgery. This case illustrates the significant efficacy of vismodegib in inducing tumor regression, even in long-standing and untreated BCC. Although adverse events are common, they may be manageable with supportive care and do not always necessitate discontinuation of therapy. Continued research into strategies for minimizing adverse effects and optimizing patient adherence to HHI therapy will be critical as these agents continue to play a role in the management of BCC. |
Title: Folate
Receptor β (FRβ) Expression on
Myeloid Cells and the Impact of Reticuloendothelial System on Folate-Functionalized
Nanoparticles’ Biodistribution in Cancer | Body:
Introduction
In cancer, the cells of the immune system
become phenotypically
and functionally altered not only in the tumor microenvironment but
also systemically in distinct organs.1,2 Especially,
the myeloid cells, which are also major components of the reticuloendothelial
system (RES), tend to infiltrate various organs and favor tumor progression.3,4 Elevated numbers of myeloid cells such as granulocytes, monocytes,
and macrophages have been acknowledged as a common facet of cancer.5 Nevertheless, myeloid subpopulations display
heterogeneity, have immunosuppressive capacities, promote angiogenesis,
invasion, and metastasis.6,7 The metabolic pathways
are also drastically modulated under the influence of cancer.8 Folate (vitamin B9) metabolism, which is critical
for nucleotide synthesis, mitochondrial ribonucleic acid (RNA) modification,
amino acid homeostasis, and methylation of biomolecules, is essential
for cell proliferation, mitochondrial energy pathways, and epigenetic
regulation.9 Not only cancer cells but
also immune cells infiltrating the tumor tissue have higher folate
requirement for deoxyribonucleic acid (DNA) synthesis, transcription
machinery, and repair mechanisms.10 Therefore,
folate uptake mechanisms by the immune cells are upregulated in cancer.11−13
The folate receptor (FR) family consists of 4 isoforms, including
FRα, FRβ, FRγ, and FRδ.14,15 These receptors differ in tissue/cell-specific distribution and
folate binding capacity.16 FRα is
specifically expressed in tumor cells of epithelial origin,17−19 whereas FRβ is frequently expressed in macrophages. FRδ
is specifically expressed by oocytes and regulatory T (Treg) cells.14,15 Upon binding to folate, FRs are rapidly internalized, and release
folate into the cytoplasmic compartment and then recycled to cell
surface.20−22
FRβ, which is encoded by FOLR2 gene,
binds folate with the
highest affinity (Kd ∼ 0.1–1
nM) when compared to other transporters for folate.23 It is expressed on activated macrophages, tissue-resident
macrophages, and tumor-associated macrophages (TAMs).23−25 Additionally, M2 subtype of macrophages has greater interest to
folate than M1 subtype.25 During acute
infectious diseases, macrophages polarize into classically activated
M1 subtype.26 M1 macrophage-mediated inflammatory
responses are distinguished with enhanced microbicidal capacity and
increased production of pro-inflammatory cytokines and radicals.27 Macrophage-mediated antitumor immunity has been
attributed to the M1 subtype.28 In contrast,
M2 macrophages, which are also known as alternatively activated macrophages,
resolve inflammation, contribute to tissue healing, and immune tolerance.29 Therefore, FRβ has been regarded as a
useful surface molecule not only for phenotyping but also for therapeutically
targeting the macrophages or the tissues infiltrated by the macrophages.12,24 Especially, depletion or subversion of TAMs, which favor tumor progression
and metastasis, into M1-like activated macrophages are promising antitumor
approaches.28
Many drug delivery
strategies have been tested for active targeting
of tumors through folate-functionalized nanoparticles.30,31 Antitumor efficacy of targeted nanoparticles depends on the physicochemical
characteristics of the drug-nanoparticle complex and the biological
properties of the target molecule and aimed tissue.32 The rapid clearance of nanoparticles by the cells of RES
is a major limitation in drug delivery.33 Since FRβ is highly expressed by the macrophages and the myeloid
cells are usual components of the RES, this study aims to determine
the amount of FRβ-expressing myeloid cells (granulocytes, monocytes,
and macrophages) and to interrelate the RES escape/circulation time,
biodistribution, and tumor-targeting efficacy of folate-decorated
cyclodextrin nanoparticles in a mouse breast cancer model, in vivo.
This study reports two main findings: (i) as the tumor grows, it becomes
progressively infiltrated by the myeloid cells that express FRβ
that converts the tumor tissue into a preferential site for active
targeting with folate-functionalized nanoparticles. Conversely, (ii)
the amount of FRβ+ myeloid cells is also increased
almost in all organs of the tumor-bearing animals and hampers the
delivery of nanoparticles into the tumor.
Materials and Methods
Animals and Tumor Model
4T1 breast tumor has been regarded
as a suitable experimental animal model for human breast cancer. It
is a very aggressive type of cancer which significantly affects the
immune system including the myeloid compartment.34 The breast cancer model was established in BALB/c mice
(6–8 weeks, 18–22 gr) (Kobay AS). The 4T1 breast cancer
cell line (American Type Culture Collection, LGC Promochem) was cultured
in RPMI 1640 medium (Biowest) supplemented with 1% penicillin/streptomycin
(Biological Industries), and 10% fetal bovine serum (FBS; Biological
Industries) in humidified atmosphere with 5% CO2 at 37
°C. 4T1 cells (5 × 104) were subcutaneously inoculated
into the left-inguinal mammary fat pad of BALB/c mice. The animals
were maintained under a constant temperature (23 ± 2 °C),
humidity (50%), and filtered air. Tumor size and animal weight were
measured biweekly. Geometric mean of the tumor width and length was
calculated. All of the experiments and handling of animals were performed
following approval by the local ethics committee before the commencement
of the animal experiments (approval no.: 2017/59-06).
Reverse-Transcriptase Polymerase Chain Reaction (RT-PCR)
RNA was isolated from 4T1 cell line and 4T1 tumors with a Nucleospin
RNA kit (Macherey-Nagel) and cDNA synthesis was performed (ProtoScript
II kit, NEB). RT-PCR was performed with the primer oligonucleotide
sequences for house-keeping B-actin gene (Actb, NM_007393.5), forward
primer 5′-CACTGTCGAGTCGCGTCC-3′, reverse primer 5′-TCATCCATGGCGAACTGGTG-3′;
for Folr1 gene (NM_001252552.1), forward primer 5′-TGGAGTTGGCGATTAGAGGTC-3′,
reverse primer 5′-CAGGGCCCGGTTTTTCTTTG-3′; for Folr2
gene (NM_001303239.1), forward primer 5′-GTGGACCAGAGTTGGCGTAA-3′,
reverse primer 5′-GGGCACTTGTTAATGCCTGAG-3′; for Folr4
gene (NM_022888.2), forward primer 5′-ACGAACTCTACCAGGAGTGCAG-3′,
reverse primer 5′-GTTGGGGGAACACTCATGGA-3′. PCR products
were documented under UV light (ChemiDoc Imaging Systems, Biorad)
after agarose gel electrophoresis and ethidium bromide staining.
Cell Isolation
Following scarification of the animals,
tumor, lungs, liver, spleen, brain, heart, kidneys, and inguinal lymph
nodes were removed and placed in RPMI 1640 medium. Bone marrow was
flushed from the femur and tibia bones with 0.9% saline solution,
and peritoneal lavage was performed with injection of cold phosphate-buffered
saline (PBS). The organs were minced mechanically and taken into enzymatic
digestion solution containing collagenase type II (100 U/mL, Nordmark)
and DNase I (200 U/mL, Sigma-Aldich) in RPMI 1640 were and agitated
at 37 °C until dissociation (approximately for 1 h). Then, the
dissociated tissue was passed through 40 μm pore-sized filters
(SPL Life Sciences) to obtain a single cell suspension.
Flow Cytometry
The cells were labeled with antimouse
CD45 (30-F11), CD11b (M1/70), F4/80 (BM8), CD206 (C068C2),
Gr-1 (RB6-8C5), and FRβ (10/FR2) fluorescently labeled monoclonal
antibodies which were purchased from BioLegend or Sony Biotechnology.
Following the incubation with specified antibodies, the cells were
washed and analyses were performed on a flow cytometer (FACSCanto
II, BD). The gating strategy used is shown in Supporting Figure 1. Briefly, CD45+CD11bhi myeloid immune cells were gated from the single events and the cell
populations with appropriate cell sizes and granularity. Then, macrophages
were detected as the F4/80+CD206– and
F4/80+CD206+ populations. Granulocytes and monocytes
were depicted as Gr-1hi and Gr-1lo/mo cells,
respectively. The percentage and median fluorescence intensity (MFI)
values were defined according to the autofluorescence controls. A
heat map output was depicted for the MFI or percentage data where
appropriate.
Calculation of FRβ Density in Tissues
The z-score formula, z = (x – μ)/σ, enables comparison of FRβ MFI values
of different myeloid cell types across various scenarios. In tumor
tissue development, the formula was employed to assess the mean FRβ
MFI values of four distinct cell types at different time points. This
analysis helps gauge how each cell type’s mean FRβ MFI
values (μ) deviate from the overall average (x), considering the standard deviation (σ) of the mean FRβ
MFI values specific to the respective time points. Moreover, the z-score formula was extended to compare the mean FRβ
MFI values of myeloid cells infiltrated into different tissues of
both healthy and advanced tumor burden animals. In this case, denoted
as μ (mean), it deviates from the global mean, denoted as x. This evaluation incorporates the standard deviation (σ)
of the cell-specific mean FRβ values associated with the infiltration
into diverse tissues. In essence, it helps to demonstrate how the
average FRβ levels of each cell type differ from the overall
average of cells infiltrated into different tissues of healthy and
tumor-bearing animals. FRβ expression density for each cell
type was calculated with the formula [# cells/mg × FRβMFI × FRβ+ %] for each organ or tissue
studied. Then, the average of the “FRβ expression density”,
which was specifically calculated for granulocytes, monocytes, macrophages,
and CD206+ macrophages, was taken and used as a FRβ
expression density score.
Immunofluorescence
Frozen sections of the tumors (5
μm) were blocked (Super Block, ScyTek Laboratories) for 30 min
and incubated with antimouse-CD206 (MR5D3, 1/100; Invitrogen) and
-Folr2 (PA5-103843; 1/200; Invitrogen) primary antibodies. Antibody
binding was visualized by Alexa488- and Alexa555-conjugated secondary
antibodies (1:1000, Abcam), following counterstaining with 4′,6-diamidino-2-phenylindole
(DAPI, 300 nM; Sigma-Aldrich). The specimens were analyzed under a
fluorescence microscope (Olympus) and the images were processed with
ImageJ software (NIH Image).
Preparation and Characterization of Cyclodextrin (CD) Nanoparticles
The CD nanoparticle formulations used in this study were previously
synthesized and characterized by our group and detailed information
on their properties was previously published.35−37 CD derivatives
were used to prepare nanoparticles.36,37 βCD6
was achieved through the grafting of 12 C chains onto the secondary
face of the β-CD glucose units through ester bonds.38 A folate-conjugated derivative on the primary
face grafted to aliphatic chains was synthesized as a folate-functionalized
derivative CD (Ff-CD).36,37 The nanoparticles were prepared
with smooth spherical surfaces that can maintain their physical stability
in terms of mean diameter and ζ-potential for 30 days in an
aqueous dispersion state.35−37 The CD nanoparticles were prepared
by nanoprecipitation and loaded with Nile red as a fluorescence tracker.
Briefly, Nile red (20% of CD) was dissolved in ethanol while magnetic
stirring at 660 rpm for 30 min. The organic phase (1 mL) was added
to the aqueous phase (2 mL). Subsequently, the organic solvent was
evaporated under vacuum.36 Particle size
(nm), polydispersity index, ζ-potential (mV), and the amount
of Nile red encapsulated in nanoparticles are shown in Table 1. Particle sizes were identified
(Nanosight NS300, Malvern Analytical). Polydispersity index and ζ-potential
were determined (Zetasizer NanoSeries ZS, Malvern Instruments) at
a 90 °C angle and 25 °C. The quantification of Nile red
encapsulated in the nanoparticles was performed through an indirect
method. Specifically, the nanoparticles aqueous dispersion underwent
centrifugation at 3500 rpm for 15 min to eliminate unloaded free dye,
and the resulting supernatant was quantified using UV spectrophotometry
(λem: 590 nm, λex: 546 nm). The following equation was
used to calculate the associated dye (%) = [experimental dye loading
(μg)/theoretical dye loading (μg)] × 100.
Table 1
Physicochemical Properties of Cyclodextrin
Nanoparticles
particle size (nm)
polydispersity
index (PDI)
ζ-potential (mV)
Nile red encapsulation (mg/mL)
βCD6
108.9 ± 2.9
0.419 ± 0.019
–8.9 ± 0.4
0.03
Ff-CD
106.3 ± 4.1
0.145 ± 0.012
–11.1 ± 0.5
0.036
In Vivo Imaging
Nile red-loaded cyclodextrin nanoparticles
(3 μg of Nile red/mouse) were administered intravenously through
the tail vein when the tumor size reached ∼0.5 cm. Following
24 h, the mice were sacrificed under anesthesia (n = 3), and the organs (lungs, liver, heart, kidneys, spleen, and
tumor) were dissected and put under an in vivo imaging system (Newton
7.0, Vilber). Fluorescence intensity of Nile red was acquired at 580
nm (λem: 636 nm, λex: 552; Nile red) and processed with
ImageJ software (Fiji). Subsequently, the organs were dissociated,
and the presence of Nile red-loaded nanoparticles were assessed by
flow cytometry at the cellular level.
Statistical Analysis
One-way ANOVA was used for multiple
comparisons using GraphPad Prism 8 (GraphPad Software Inc., San Diego,
CA). Paired or unpaired two-tailed Student’s t-test was performed
where appropriate for assessment of statistical significance. A value
of p < 0.05 was considered statistically significant.
Results were expressed as mean ± SEM (standard error mean).
Animals and Tumor Model
4T1 breast tumor has been regarded
as a suitable experimental animal model for human breast cancer. It
is a very aggressive type of cancer which significantly affects the
immune system including the myeloid compartment.34 The breast cancer model was established in BALB/c mice
(6–8 weeks, 18–22 gr) (Kobay AS). The 4T1 breast cancer
cell line (American Type Culture Collection, LGC Promochem) was cultured
in RPMI 1640 medium (Biowest) supplemented with 1% penicillin/streptomycin
(Biological Industries), and 10% fetal bovine serum (FBS; Biological
Industries) in humidified atmosphere with 5% CO2 at 37
°C. 4T1 cells (5 × 104) were subcutaneously inoculated
into the left-inguinal mammary fat pad of BALB/c mice. The animals
were maintained under a constant temperature (23 ± 2 °C),
humidity (50%), and filtered air. Tumor size and animal weight were
measured biweekly. Geometric mean of the tumor width and length was
calculated. All of the experiments and handling of animals were performed
following approval by the local ethics committee before the commencement
of the animal experiments (approval no.: 2017/59-06).
Reverse-Transcriptase Polymerase Chain Reaction (RT-PCR)
RNA was isolated from 4T1 cell line and 4T1 tumors with a Nucleospin
RNA kit (Macherey-Nagel) and cDNA synthesis was performed (ProtoScript
II kit, NEB). RT-PCR was performed with the primer oligonucleotide
sequences for house-keeping B-actin gene (Actb, NM_007393.5), forward
primer 5′-CACTGTCGAGTCGCGTCC-3′, reverse primer 5′-TCATCCATGGCGAACTGGTG-3′;
for Folr1 gene (NM_001252552.1), forward primer 5′-TGGAGTTGGCGATTAGAGGTC-3′,
reverse primer 5′-CAGGGCCCGGTTTTTCTTTG-3′; for Folr2
gene (NM_001303239.1), forward primer 5′-GTGGACCAGAGTTGGCGTAA-3′,
reverse primer 5′-GGGCACTTGTTAATGCCTGAG-3′; for Folr4
gene (NM_022888.2), forward primer 5′-ACGAACTCTACCAGGAGTGCAG-3′,
reverse primer 5′-GTTGGGGGAACACTCATGGA-3′. PCR products
were documented under UV light (ChemiDoc Imaging Systems, Biorad)
after agarose gel electrophoresis and ethidium bromide staining.
Cell Isolation
Following scarification of the animals,
tumor, lungs, liver, spleen, brain, heart, kidneys, and inguinal lymph
nodes were removed and placed in RPMI 1640 medium. Bone marrow was
flushed from the femur and tibia bones with 0.9% saline solution,
and peritoneal lavage was performed with injection of cold phosphate-buffered
saline (PBS). The organs were minced mechanically and taken into enzymatic
digestion solution containing collagenase type II (100 U/mL, Nordmark)
and DNase I (200 U/mL, Sigma-Aldich) in RPMI 1640 were and agitated
at 37 °C until dissociation (approximately for 1 h). Then, the
dissociated tissue was passed through 40 μm pore-sized filters
(SPL Life Sciences) to obtain a single cell suspension.
Flow Cytometry
The cells were labeled with antimouse
CD45 (30-F11), CD11b (M1/70), F4/80 (BM8), CD206 (C068C2),
Gr-1 (RB6-8C5), and FRβ (10/FR2) fluorescently labeled monoclonal
antibodies which were purchased from BioLegend or Sony Biotechnology.
Following the incubation with specified antibodies, the cells were
washed and analyses were performed on a flow cytometer (FACSCanto
II, BD). The gating strategy used is shown in Supporting Figure 1. Briefly, CD45+CD11bhi myeloid immune cells were gated from the single events and the cell
populations with appropriate cell sizes and granularity. Then, macrophages
were detected as the F4/80+CD206– and
F4/80+CD206+ populations. Granulocytes and monocytes
were depicted as Gr-1hi and Gr-1lo/mo cells,
respectively. The percentage and median fluorescence intensity (MFI)
values were defined according to the autofluorescence controls. A
heat map output was depicted for the MFI or percentage data where
appropriate.
Calculation of FRβ Density in Tissues
The z-score formula, z = (x – μ)/σ, enables comparison of FRβ MFI values
of different myeloid cell types across various scenarios. In tumor
tissue development, the formula was employed to assess the mean FRβ
MFI values of four distinct cell types at different time points. This
analysis helps gauge how each cell type’s mean FRβ MFI
values (μ) deviate from the overall average (x), considering the standard deviation (σ) of the mean FRβ
MFI values specific to the respective time points. Moreover, the z-score formula was extended to compare the mean FRβ
MFI values of myeloid cells infiltrated into different tissues of
both healthy and advanced tumor burden animals. In this case, denoted
as μ (mean), it deviates from the global mean, denoted as x. This evaluation incorporates the standard deviation (σ)
of the cell-specific mean FRβ values associated with the infiltration
into diverse tissues. In essence, it helps to demonstrate how the
average FRβ levels of each cell type differ from the overall
average of cells infiltrated into different tissues of healthy and
tumor-bearing animals. FRβ expression density for each cell
type was calculated with the formula [# cells/mg × FRβMFI × FRβ+ %] for each organ or tissue
studied. Then, the average of the “FRβ expression density”,
which was specifically calculated for granulocytes, monocytes, macrophages,
and CD206+ macrophages, was taken and used as a FRβ
expression density score.
Immunofluorescence
Frozen sections of the tumors (5
μm) were blocked (Super Block, ScyTek Laboratories) for 30 min
and incubated with antimouse-CD206 (MR5D3, 1/100; Invitrogen) and
-Folr2 (PA5-103843; 1/200; Invitrogen) primary antibodies. Antibody
binding was visualized by Alexa488- and Alexa555-conjugated secondary
antibodies (1:1000, Abcam), following counterstaining with 4′,6-diamidino-2-phenylindole
(DAPI, 300 nM; Sigma-Aldrich). The specimens were analyzed under a
fluorescence microscope (Olympus) and the images were processed with
ImageJ software (NIH Image).
Preparation and Characterization of Cyclodextrin (CD) Nanoparticles
The CD nanoparticle formulations used in this study were previously
synthesized and characterized by our group and detailed information
on their properties was previously published.35−37 CD derivatives
were used to prepare nanoparticles.36,37 βCD6
was achieved through the grafting of 12 C chains onto the secondary
face of the β-CD glucose units through ester bonds.38 A folate-conjugated derivative on the primary
face grafted to aliphatic chains was synthesized as a folate-functionalized
derivative CD (Ff-CD).36,37 The nanoparticles were prepared
with smooth spherical surfaces that can maintain their physical stability
in terms of mean diameter and ζ-potential for 30 days in an
aqueous dispersion state.35−37 The CD nanoparticles were prepared
by nanoprecipitation and loaded with Nile red as a fluorescence tracker.
Briefly, Nile red (20% of CD) was dissolved in ethanol while magnetic
stirring at 660 rpm for 30 min. The organic phase (1 mL) was added
to the aqueous phase (2 mL). Subsequently, the organic solvent was
evaporated under vacuum.36 Particle size
(nm), polydispersity index, ζ-potential (mV), and the amount
of Nile red encapsulated in nanoparticles are shown in Table 1. Particle sizes were identified
(Nanosight NS300, Malvern Analytical). Polydispersity index and ζ-potential
were determined (Zetasizer NanoSeries ZS, Malvern Instruments) at
a 90 °C angle and 25 °C. The quantification of Nile red
encapsulated in the nanoparticles was performed through an indirect
method. Specifically, the nanoparticles aqueous dispersion underwent
centrifugation at 3500 rpm for 15 min to eliminate unloaded free dye,
and the resulting supernatant was quantified using UV spectrophotometry
(λem: 590 nm, λex: 546 nm). The following equation was
used to calculate the associated dye (%) = [experimental dye loading
(μg)/theoretical dye loading (μg)] × 100.
Table 1
Physicochemical Properties of Cyclodextrin
Nanoparticles
particle size (nm)
polydispersity
index (PDI)
ζ-potential (mV)
Nile red encapsulation (mg/mL)
βCD6
108.9 ± 2.9
0.419 ± 0.019
–8.9 ± 0.4
0.03
Ff-CD
106.3 ± 4.1
0.145 ± 0.012
–11.1 ± 0.5
0.036
In Vivo Imaging
Nile red-loaded cyclodextrin nanoparticles
(3 μg of Nile red/mouse) were administered intravenously through
the tail vein when the tumor size reached ∼0.5 cm. Following
24 h, the mice were sacrificed under anesthesia (n = 3), and the organs (lungs, liver, heart, kidneys, spleen, and
tumor) were dissected and put under an in vivo imaging system (Newton
7.0, Vilber). Fluorescence intensity of Nile red was acquired at 580
nm (λem: 636 nm, λex: 552; Nile red) and processed with
ImageJ software (Fiji). Subsequently, the organs were dissociated,
and the presence of Nile red-loaded nanoparticles were assessed by
flow cytometry at the cellular level.
Statistical Analysis
One-way ANOVA was used for multiple
comparisons using GraphPad Prism 8 (GraphPad Software Inc., San Diego,
CA). Paired or unpaired two-tailed Student’s t-test was performed
where appropriate for assessment of statistical significance. A value
of p < 0.05 was considered statistically significant.
Results were expressed as mean ± SEM (standard error mean).
Results
Tumor Progression Augments Myeloid Infiltration and FRβ
Expression in the Tumor Microenvironment
Cancer affects immune
functions and hematopoiesis; the cells of myeloid origin increase
in number, show immature characteristics, and acquire pro-tumor functions.29 In this study, mice bearing 4T1 mammary tumors
were used as a typical cancer model wherein myeloid subset of the
immune cells is considerably altered.39,40 We first determined
the time-dependent increase in classical macrophages, CD206+ macrophages, granulocytes, and monocytes as cancer progressed. Changes
in animals’ weight and tumor size during the follow-up are
shown in Supporting Figure 2. The number
of myeloid cells, which were obtained in small quantities in mammary
fat pads of healthy mice (day 0), began to increase on the fifth day
following tumor cell inoculation (Figure 1A). On the fifth day, the percentage of granulocytes
among other myeloid cell types was increased at the inoculation site
(Figure 1B,C). On day
10, when the tumor reached a palpable size, macrophage dominance (F4/80+ total macrophages; range, 46–87% of all myeloid cells)
was evidenced (Figure 1C). As the tumor grew, the number of myeloid cells, especially neutrophils,
increased (Figure 1A). On the 20th and 30th days of tumorigenesis, granulocytes [804
± 140 cells/mg (31% of total myeloid cells) and 8184 ± 1272
cells/mg (42% of total myeloid cells), respectively] and monocytes
[259 ± 11 cells/mg (10% of total myeloid cells) and 2523 ±
504 cells/mg (13% of total myeloid cells), respectively] were frequently
observed in the tumor microenvironment (Figure 1A–C). Interestingly, the proportion
of CD206+ macrophages remained almost constant (range,
12–19% of total myeloid cells) at all time points tested despite
the increase in the number of infiltrating myeloid cells (Figure 1C).
Figure 1
Distribution of myeloid
cell subsets and FRβ expression during
tumor formation in mammary tissue. The mice were inoculated with 4T1
mammary cancer cells and sacrificed at distinct on days 5, 10, 20,
and 30 to obtain whole tumor tissues at distinct phases of growth.
The growth curve of 4T1 tumors is supplied in Supporting Figure 2. Day 0 represents the mammary tissue of
healthy mice. The tissues were processed, and immunophenotyping analyses
of myeloid cells were performed by multicolor flow cytometry. The
gating strategy used for flow cytometry is given in Supporting Figure 1. (A) Absolute numbers of CD206+ macrophages, CD206– macrophages, monocytes, and
granulocytes infiltrating the mammary tissue were analyzed according
to the total cell number per mg of tissue and percentages determined
by flow cytometric immunophenotyping performed on specific time points
following the inoculation of 4T1 cancer cells. (B) Representative
flow cytometric scatter plots and percentages of myeloid cells determined
under the CD11b+ gate. (C) Percentage distribution pie
charts showing a dynamic change in the infiltration of myeloid cell
subsets at specific time points during 30-day-long tumorigenesis.
(D) Representative offset flow cytometry histograms, (E) percentage
bar graphs, and (F) median fluorescence intensity (MFI) heat map for
FRβ expression on CD206+ macrophages, CD206– macrophages, monocytes, and granulocytes on day 0 (healthy mammary
tissue), day 5, day 10, day 20, and day 30 of tumorigenesis. (G) The
frequent presence of FRβ on CD206+ macrophages were
validated by immunofluorescence staining of the tumor tissue sections.
The middle and lower panels on the right-hand side provide higher
magnification of the micrographs (scale bars, 10 μm). The data
are presented as average ± SEM. Statistical difference was calculated
with Student’s t test, (n ≥ 4; *p ≤ 0.05, **p ≤ 0.01).
Next, we examined the gene expression of mouse
FR isoforms (Folr1
for FRα, Folr2 for FRβ, and Izumo1r for FRδ) in
the 4T1 cell line and in the tumors established with 4T1 cells (Supporting Figure 3). Folr1 was slightly expressed
in 4T1 cells or in the established tumors. Izumo1r was barely detected
in the tumor tissue but not in the cultured 4T1 cell line. On the
other hand, Folr2 was highly and exclusively expressed in the tumors
but not in the cultured 4T1 cell line which indicated the presence
of FRβ in the cells infiltrating the tumor microenvironment
(Supporting Figure 3). FRβ expression
was particularly prominent in tissue-resident CD206+ macrophages
in healthy mammary tissue (day 0, 76 ± 6% and 6302 ± 3841
MFI) (Figure 1D–F).
With tumorigenesis, CD206+ macrophages maintained high
levels of FRβ expression (ranges of 63–79% and 1320–6302
MFI). After 20 days, the tumor-infiltrating CD206-negative macrophages
also upregulated FRβ (30 ± 4%). Only minor percentages
of granulocytes and monocytes expressed FRβ at low levels (Figure 1D–F). The
frequent presence of FRβ expression on CD206 macrophages was
also verified on the tumor sections (Figure 1G).
Collectively, the myeloid infiltration
was significantly increased
at the site of tumorigenesis as the tumor progressed; nevertheless,
the proportion of CD206+ macrophages tended to remain constant.
The CD206+ macrophage population highly expressed FRβ.
At the late stages of tumor formation, FRβ was upregulated by
either CD206+ or CD206– classical macrophages
in the tumor microenvironment.
Systemic Impact of Cancer on Myeloid Infiltration and FRβ
Levels in Various Organs of RES
Another aim of this study
was to compare the amount and subtype of FRβ+ myeloid
cells in distinct organs of tumor-bearing and healthy animals for
inferring the impact of tumorigenesis on RES. The cells were isolated
from lungs, liver, spleen, brain, heart, kidneys, lymph nodes, bone
marrow, and peritoneal cavity on day 30 after 4T1 inoculation. Infiltration
by different types of myeloid cells was drastically enhanced in almost
all organs of the tumor-bearing mice compared to healthy controls
(Figure 2A). Especially,
the granulocyte counts were significantly increased in all organs
and tissues investigated. Monocytes reached high levels in lung, liver,
spleen, lymph nodes, and peritoneum. Classical CD206– macrophages increased in all organs except bone marrow and peritoneum
whereas CD206+ macrophages were elevated in lungs, liver,
spleen, brain, lymph nodes, and peritoneal cavity (Figure 2A). In the tumor-bearing mice,
the organs were highly populated by the granulocytes; therefore, the
proportion of granulocytes became significantly augmented compared
to that in the healthy animals (Figure 2B). Especially in the lungs, liver, heart, kidneys,
and lymph nodes, the percentage distribution of granulocytes was prominent
compared to other myeloid cells (Figure 2B).
Figure 2
Change in myeloid cell infiltration of distinct
organs and compartments
in tumor-bearing mice. The cell suspensions were prepared from the
tissues collected, and immunophenotyping analyses of myeloid cells
were performed by multicolor flow cytometry. The gating strategy used
for flow cytometry is given in Supporting Figure 1. (A) Bar graphs show the number of granulocytes, monocytes,
CD206– macrophages, and CD206+ macrophages
in tumor-bearing mice on day 30 and in control healthy mice. (B) Percentage
distribution of myeloid cell subsets in the organs of healthy and
tumor-bearing mice. The data are presented as average ± SEM.
Statistical difference was calculated with Student’s t test, (n = 6; *p ≤
0.05, **p ≤ 0.01).
Looking at the distribution of FRβ expression,
CD206+ macrophages were the myeloid cell group carrying
the highest
level of the receptor in both healthy and tumor-bearing mice (range,
34–98 and 79–99%, respectively) (Figure 3A and Supporting Figure 3). In healthy mice, almost all CD206+ macrophages
in the lung, liver, heart, lymph nodes, and bone marrow expressed
FRβ (Supporting Figure 3). In terms
of the surface expression level, CD206+ macrophages localized
to liver, heart, and mammary fat pads had significantly higher levels
of FRβ (Figure 3B). A significant fraction of CD206-negative macrophages was also
FRβ+ in the lungs, liver, and heart. Interestingly,
a considerable percentage of granulocytes in the lungs and kidneys
of healthy individuals expressed FRβ, albeit at low levels (Figure 3B and Supporting Figure 4). While the number of CD206+ macrophages was increased in the heart and the liver of tumor-bearing
mice (Figure 2A), the
surface expression level of FRβ was decreased compared to those
in healthy organs (Figure 3B). Additionally, compared to resident CD206+ macrophages
in healthy mammary tissue, they expressed lower surface levels of
FRβ in the tumor tissue (MFI, healthy 6302 ± 3841 and tumor-bearing
3313 ± 1233) (Figure 3B). In the tumor-bearing mice, an overall increase in the
percentage of FRβ positivity and in the surface expression levels
were observed in all tissues analyzed. Nevertheless, CD206+ macrophages carried the highest levels of the receptor. The increase
in FRβ expression was remarkable in all cell groups, especially
in peritoneum, spleen, and kidneys. Considering both FRβ+ myeloid cell percentages and FRβ expression levels,
it was concluded that lungs, liver, spleen, kidneys, heart, and breast
tumor tissues had the highest amount of FRβ (Figure 3A,B and Supporting Figure 4).
Figure 3
FRβ levels of myeloid cells in healthy
and tumor-bearing
mice. (A) Representative offset flow cytometry histograms and percentage
(average ± SEM) values for the six major tissues studied. (B)
Median fluorescence intensity (MFI) heat map for FRβ expression
on CD206+ macrophages, CD206– macrophages,
monocytes, and granulocytes in tissues of healthy control and tumor-bearing
(day 30) mice. (C) A schematic showing the major circulation routes
through the organs of interest is presented together with a density
score calculated for FRβ in each organ. Statistical difference
was calculated with Student’s t test, (n = 6; *p ≤ 0.05, **p ≤ 0.01).
A final reevaluation was performed for determining
the FRβ
density in each organ by considering the infiltration status of each
myeloid cell type per tissue mass, the percentage of FRβ-positive
cells, and the surface expression level of FRβ. A score was
calculated for a better representation of the FRβ density in
each organ. Accordingly, the spleen, lungs, liver, tumor, kidneys,
and heart were the organs sustaining the highest FRβ in tumor-bearing
mice, respectively (Figure 3C).
All in all, myeloid cell infiltration was significantly
increased
in all organs and tissues in the 4T1 mammary tumor-bearing mice. Under
the influence of tumor, either the percentage of FRβ+ myeloid cells or the cell surface expression of FRβ was upregulated
on the myeloid cells, especially on the CD206+ macrophages
residing in distinct organs.
Association of Organ-Specific FRβ Density and Biodistribution
of Folate-Functionalized Cyclodextrin Nanoparticles
Next,
we sought to investigate the association between the density of FRβ-bearing
myeloid cells in the organs and the active-targeting efficacy of the
folate-decorated nanoparticles. For this purpose, we preferred to
use folate-functionalized cyclodextrin (Ff-CD) and nonfolated control
βCD6 nanoparticles which were previously produced and characterized
by our group.35−37 As a typical nanoparticle, Ff-CD and βCD6 nanoparticles
(Figure 4A), whose
tumor-targeting capacity have been previously validated,36 were loaded with a fluorescent probe (i.e.,
Nile red) and, herein, used for biodistribution studies. The physicochemical
properties of Ff-CD and βCD6 nanoparticles were comparable.
The characterization data of CD nanoparticles used after loading with
Nile red are outlined in Table 1. In Figure 3C, the calculated FRβ density scores of the organs and the
mammary tumor are shown together with the biodistribution routes of
the nanoparticles when administered to the systemic circulation via
the tail vein. The Ff-CD particles showed significantly (p > 0.05) higher uptake in lungs (βCD6, 1170 ± 100;
Ff-CD,
2449 ± 535), liver (βCD6, 2276 ± 279; Ff-CD, 3219
± 179), and tumor (βCD6, 2481 ± 188; Ff-CD, 3924 ±
468) tissues compared to the control βCD6 (Figure 4B,C). At the cellular level
in the tested organs, CD206+ macrophages internalized Ff-CD
particles more efficiently in the spleen, liver, and especially lungs
(Figure 4D). The highest
Ff-CD signals (MFI, 33 ± 7) were detected from the CD206+ macrophages of the lungs. In the tumor tissue, classical
macrophages (MFI, 11 ± 3) contained nanoparticles comparable
to those of the CD206+ macrophages (MFI, 6 ± 1). Ff-CD
or βCD6 nanoparticle signals from the nonmacrophage stromal
or parenchymal cells were very low (Figure 4D).
Figure 4
Biodistribution of folate-functionalized nanoparticles
in cancer.
Folate-functionalized cyclodextrin nanoparticles (Ff-CD) and (6-O-methyl)-cyclodextrin (βCD6) nanoparticles, which
were used as a prototypic nanoparticle formulation, were loaded with
Nile red and injected intravenously via V. caudalis to the tumor-bearing
animals. (A) Structure of the nanoparticles is illustrated. (B) Bar
graph shows the biodistribution of nanoparticles according to the
intensity of Nile red in the major organs studied. (C) Representative
organ images showing the biodistribution of Nile red-loaded βCD6
and Ff-CD nanoparticles. (D) The level of Nile red median fluorescence
intensity (MFI) in CD206-positive macrophages, CD206-negative macrophages,
and in the cells other than macrophages (nonmacrophage cells) is determined
by flow cytometry in the cell suspensions prepared from the representative
organs. (E) A heat map comparing the density score calculated according
to the FRβ expression levels in each organ and the biodistribution
of Ff-CD before and after normalization with βCD6 for excluding
the influence of EPR. The data are presented as average ± SEM.
Statistical difference was calculated with Student’s t test, (n ≥ 3; *p ≤ 0.05, **p ≤ 0.01).
The organ biodistribution ranking with Ff-CD nanoparticles
presented
compatible results with that of the FRβ density score calculated
for specific tissues except the spleen and the tumor (Figure 4E). Albeit having the highest
FRβ density score, the spleen showed very low nanoparticle interest
in biodistribution assays. Consistency between the biodistribution
data for Ff-CD and the tumors’ FRβ score was improved
when a correction for the EPR effect was performed through subtracting
the biodistribution values of βCD6 (Figure 4E). When all of the parameters tested were
considered, the most reliable results of folate receptor-targeting
were obtained for the lungs (Figure 4E). This finding was further strengthened with the
observation that the CD206+ macrophages in the lungs of
the tumor-bearing mice expressed high levels of FRβ (Figure 3) and very efficiently
internalized Ff-CD particles, in vivo (Figure 4D).
Collectively, the folate-functionalized
nanoparticles were well-engulfed
by CD206+ macrophages, which highly expressed FRβ.
The FRβ+ immune cells found in the tumor microenvironment
did not directly influence the tumor-targeting efficacy of the Ff-CD
nanoparticles. The lung was determined as a primary target for the
folate-functionalized nanoparticles.
Tumor Progression Augments Myeloid Infiltration and FRβ
Expression in the Tumor Microenvironment
Cancer affects immune
functions and hematopoiesis; the cells of myeloid origin increase
in number, show immature characteristics, and acquire pro-tumor functions.29 In this study, mice bearing 4T1 mammary tumors
were used as a typical cancer model wherein myeloid subset of the
immune cells is considerably altered.39,40 We first determined
the time-dependent increase in classical macrophages, CD206+ macrophages, granulocytes, and monocytes as cancer progressed. Changes
in animals’ weight and tumor size during the follow-up are
shown in Supporting Figure 2. The number
of myeloid cells, which were obtained in small quantities in mammary
fat pads of healthy mice (day 0), began to increase on the fifth day
following tumor cell inoculation (Figure 1A). On the fifth day, the percentage of granulocytes
among other myeloid cell types was increased at the inoculation site
(Figure 1B,C). On day
10, when the tumor reached a palpable size, macrophage dominance (F4/80+ total macrophages; range, 46–87% of all myeloid cells)
was evidenced (Figure 1C). As the tumor grew, the number of myeloid cells, especially neutrophils,
increased (Figure 1A). On the 20th and 30th days of tumorigenesis, granulocytes [804
± 140 cells/mg (31% of total myeloid cells) and 8184 ± 1272
cells/mg (42% of total myeloid cells), respectively] and monocytes
[259 ± 11 cells/mg (10% of total myeloid cells) and 2523 ±
504 cells/mg (13% of total myeloid cells), respectively] were frequently
observed in the tumor microenvironment (Figure 1A–C). Interestingly, the proportion
of CD206+ macrophages remained almost constant (range,
12–19% of total myeloid cells) at all time points tested despite
the increase in the number of infiltrating myeloid cells (Figure 1C).
Figure 1
Distribution of myeloid
cell subsets and FRβ expression during
tumor formation in mammary tissue. The mice were inoculated with 4T1
mammary cancer cells and sacrificed at distinct on days 5, 10, 20,
and 30 to obtain whole tumor tissues at distinct phases of growth.
The growth curve of 4T1 tumors is supplied in Supporting Figure 2. Day 0 represents the mammary tissue of
healthy mice. The tissues were processed, and immunophenotyping analyses
of myeloid cells were performed by multicolor flow cytometry. The
gating strategy used for flow cytometry is given in Supporting Figure 1. (A) Absolute numbers of CD206+ macrophages, CD206– macrophages, monocytes, and
granulocytes infiltrating the mammary tissue were analyzed according
to the total cell number per mg of tissue and percentages determined
by flow cytometric immunophenotyping performed on specific time points
following the inoculation of 4T1 cancer cells. (B) Representative
flow cytometric scatter plots and percentages of myeloid cells determined
under the CD11b+ gate. (C) Percentage distribution pie
charts showing a dynamic change in the infiltration of myeloid cell
subsets at specific time points during 30-day-long tumorigenesis.
(D) Representative offset flow cytometry histograms, (E) percentage
bar graphs, and (F) median fluorescence intensity (MFI) heat map for
FRβ expression on CD206+ macrophages, CD206– macrophages, monocytes, and granulocytes on day 0 (healthy mammary
tissue), day 5, day 10, day 20, and day 30 of tumorigenesis. (G) The
frequent presence of FRβ on CD206+ macrophages were
validated by immunofluorescence staining of the tumor tissue sections.
The middle and lower panels on the right-hand side provide higher
magnification of the micrographs (scale bars, 10 μm). The data
are presented as average ± SEM. Statistical difference was calculated
with Student’s t test, (n ≥ 4; *p ≤ 0.05, **p ≤ 0.01).
Next, we examined the gene expression of mouse
FR isoforms (Folr1
for FRα, Folr2 for FRβ, and Izumo1r for FRδ) in
the 4T1 cell line and in the tumors established with 4T1 cells (Supporting Figure 3). Folr1 was slightly expressed
in 4T1 cells or in the established tumors. Izumo1r was barely detected
in the tumor tissue but not in the cultured 4T1 cell line. On the
other hand, Folr2 was highly and exclusively expressed in the tumors
but not in the cultured 4T1 cell line which indicated the presence
of FRβ in the cells infiltrating the tumor microenvironment
(Supporting Figure 3). FRβ expression
was particularly prominent in tissue-resident CD206+ macrophages
in healthy mammary tissue (day 0, 76 ± 6% and 6302 ± 3841
MFI) (Figure 1D–F).
With tumorigenesis, CD206+ macrophages maintained high
levels of FRβ expression (ranges of 63–79% and 1320–6302
MFI). After 20 days, the tumor-infiltrating CD206-negative macrophages
also upregulated FRβ (30 ± 4%). Only minor percentages
of granulocytes and monocytes expressed FRβ at low levels (Figure 1D–F). The
frequent presence of FRβ expression on CD206 macrophages was
also verified on the tumor sections (Figure 1G).
Collectively, the myeloid infiltration
was significantly increased
at the site of tumorigenesis as the tumor progressed; nevertheless,
the proportion of CD206+ macrophages tended to remain constant.
The CD206+ macrophage population highly expressed FRβ.
At the late stages of tumor formation, FRβ was upregulated by
either CD206+ or CD206– classical macrophages
in the tumor microenvironment.
Systemic Impact of Cancer on Myeloid Infiltration and FRβ
Levels in Various Organs of RES
Another aim of this study
was to compare the amount and subtype of FRβ+ myeloid
cells in distinct organs of tumor-bearing and healthy animals for
inferring the impact of tumorigenesis on RES. The cells were isolated
from lungs, liver, spleen, brain, heart, kidneys, lymph nodes, bone
marrow, and peritoneal cavity on day 30 after 4T1 inoculation. Infiltration
by different types of myeloid cells was drastically enhanced in almost
all organs of the tumor-bearing mice compared to healthy controls
(Figure 2A). Especially,
the granulocyte counts were significantly increased in all organs
and tissues investigated. Monocytes reached high levels in lung, liver,
spleen, lymph nodes, and peritoneum. Classical CD206– macrophages increased in all organs except bone marrow and peritoneum
whereas CD206+ macrophages were elevated in lungs, liver,
spleen, brain, lymph nodes, and peritoneal cavity (Figure 2A). In the tumor-bearing mice,
the organs were highly populated by the granulocytes; therefore, the
proportion of granulocytes became significantly augmented compared
to that in the healthy animals (Figure 2B). Especially in the lungs, liver, heart, kidneys,
and lymph nodes, the percentage distribution of granulocytes was prominent
compared to other myeloid cells (Figure 2B).
Figure 2
Change in myeloid cell infiltration of distinct
organs and compartments
in tumor-bearing mice. The cell suspensions were prepared from the
tissues collected, and immunophenotyping analyses of myeloid cells
were performed by multicolor flow cytometry. The gating strategy used
for flow cytometry is given in Supporting Figure 1. (A) Bar graphs show the number of granulocytes, monocytes,
CD206– macrophages, and CD206+ macrophages
in tumor-bearing mice on day 30 and in control healthy mice. (B) Percentage
distribution of myeloid cell subsets in the organs of healthy and
tumor-bearing mice. The data are presented as average ± SEM.
Statistical difference was calculated with Student’s t test, (n = 6; *p ≤
0.05, **p ≤ 0.01).
Looking at the distribution of FRβ expression,
CD206+ macrophages were the myeloid cell group carrying
the highest
level of the receptor in both healthy and tumor-bearing mice (range,
34–98 and 79–99%, respectively) (Figure 3A and Supporting Figure 3). In healthy mice, almost all CD206+ macrophages
in the lung, liver, heart, lymph nodes, and bone marrow expressed
FRβ (Supporting Figure 3). In terms
of the surface expression level, CD206+ macrophages localized
to liver, heart, and mammary fat pads had significantly higher levels
of FRβ (Figure 3B). A significant fraction of CD206-negative macrophages was also
FRβ+ in the lungs, liver, and heart. Interestingly,
a considerable percentage of granulocytes in the lungs and kidneys
of healthy individuals expressed FRβ, albeit at low levels (Figure 3B and Supporting Figure 4). While the number of CD206+ macrophages was increased in the heart and the liver of tumor-bearing
mice (Figure 2A), the
surface expression level of FRβ was decreased compared to those
in healthy organs (Figure 3B). Additionally, compared to resident CD206+ macrophages
in healthy mammary tissue, they expressed lower surface levels of
FRβ in the tumor tissue (MFI, healthy 6302 ± 3841 and tumor-bearing
3313 ± 1233) (Figure 3B). In the tumor-bearing mice, an overall increase in the
percentage of FRβ positivity and in the surface expression levels
were observed in all tissues analyzed. Nevertheless, CD206+ macrophages carried the highest levels of the receptor. The increase
in FRβ expression was remarkable in all cell groups, especially
in peritoneum, spleen, and kidneys. Considering both FRβ+ myeloid cell percentages and FRβ expression levels,
it was concluded that lungs, liver, spleen, kidneys, heart, and breast
tumor tissues had the highest amount of FRβ (Figure 3A,B and Supporting Figure 4).
Figure 3
FRβ levels of myeloid cells in healthy
and tumor-bearing
mice. (A) Representative offset flow cytometry histograms and percentage
(average ± SEM) values for the six major tissues studied. (B)
Median fluorescence intensity (MFI) heat map for FRβ expression
on CD206+ macrophages, CD206– macrophages,
monocytes, and granulocytes in tissues of healthy control and tumor-bearing
(day 30) mice. (C) A schematic showing the major circulation routes
through the organs of interest is presented together with a density
score calculated for FRβ in each organ. Statistical difference
was calculated with Student’s t test, (n = 6; *p ≤ 0.05, **p ≤ 0.01).
A final reevaluation was performed for determining
the FRβ
density in each organ by considering the infiltration status of each
myeloid cell type per tissue mass, the percentage of FRβ-positive
cells, and the surface expression level of FRβ. A score was
calculated for a better representation of the FRβ density in
each organ. Accordingly, the spleen, lungs, liver, tumor, kidneys,
and heart were the organs sustaining the highest FRβ in tumor-bearing
mice, respectively (Figure 3C).
All in all, myeloid cell infiltration was significantly
increased
in all organs and tissues in the 4T1 mammary tumor-bearing mice. Under
the influence of tumor, either the percentage of FRβ+ myeloid cells or the cell surface expression of FRβ was upregulated
on the myeloid cells, especially on the CD206+ macrophages
residing in distinct organs.
Association of Organ-Specific FRβ Density and Biodistribution
of Folate-Functionalized Cyclodextrin Nanoparticles
Next,
we sought to investigate the association between the density of FRβ-bearing
myeloid cells in the organs and the active-targeting efficacy of the
folate-decorated nanoparticles. For this purpose, we preferred to
use folate-functionalized cyclodextrin (Ff-CD) and nonfolated control
βCD6 nanoparticles which were previously produced and characterized
by our group.35−37 As a typical nanoparticle, Ff-CD and βCD6 nanoparticles
(Figure 4A), whose
tumor-targeting capacity have been previously validated,36 were loaded with a fluorescent probe (i.e.,
Nile red) and, herein, used for biodistribution studies. The physicochemical
properties of Ff-CD and βCD6 nanoparticles were comparable.
The characterization data of CD nanoparticles used after loading with
Nile red are outlined in Table 1. In Figure 3C, the calculated FRβ density scores of the organs and the
mammary tumor are shown together with the biodistribution routes of
the nanoparticles when administered to the systemic circulation via
the tail vein. The Ff-CD particles showed significantly (p > 0.05) higher uptake in lungs (βCD6, 1170 ± 100;
Ff-CD,
2449 ± 535), liver (βCD6, 2276 ± 279; Ff-CD, 3219
± 179), and tumor (βCD6, 2481 ± 188; Ff-CD, 3924 ±
468) tissues compared to the control βCD6 (Figure 4B,C). At the cellular level
in the tested organs, CD206+ macrophages internalized Ff-CD
particles more efficiently in the spleen, liver, and especially lungs
(Figure 4D). The highest
Ff-CD signals (MFI, 33 ± 7) were detected from the CD206+ macrophages of the lungs. In the tumor tissue, classical
macrophages (MFI, 11 ± 3) contained nanoparticles comparable
to those of the CD206+ macrophages (MFI, 6 ± 1). Ff-CD
or βCD6 nanoparticle signals from the nonmacrophage stromal
or parenchymal cells were very low (Figure 4D).
Figure 4
Biodistribution of folate-functionalized nanoparticles
in cancer.
Folate-functionalized cyclodextrin nanoparticles (Ff-CD) and (6-O-methyl)-cyclodextrin (βCD6) nanoparticles, which
were used as a prototypic nanoparticle formulation, were loaded with
Nile red and injected intravenously via V. caudalis to the tumor-bearing
animals. (A) Structure of the nanoparticles is illustrated. (B) Bar
graph shows the biodistribution of nanoparticles according to the
intensity of Nile red in the major organs studied. (C) Representative
organ images showing the biodistribution of Nile red-loaded βCD6
and Ff-CD nanoparticles. (D) The level of Nile red median fluorescence
intensity (MFI) in CD206-positive macrophages, CD206-negative macrophages,
and in the cells other than macrophages (nonmacrophage cells) is determined
by flow cytometry in the cell suspensions prepared from the representative
organs. (E) A heat map comparing the density score calculated according
to the FRβ expression levels in each organ and the biodistribution
of Ff-CD before and after normalization with βCD6 for excluding
the influence of EPR. The data are presented as average ± SEM.
Statistical difference was calculated with Student’s t test, (n ≥ 3; *p ≤ 0.05, **p ≤ 0.01).
The organ biodistribution ranking with Ff-CD nanoparticles
presented
compatible results with that of the FRβ density score calculated
for specific tissues except the spleen and the tumor (Figure 4E). Albeit having the highest
FRβ density score, the spleen showed very low nanoparticle interest
in biodistribution assays. Consistency between the biodistribution
data for Ff-CD and the tumors’ FRβ score was improved
when a correction for the EPR effect was performed through subtracting
the biodistribution values of βCD6 (Figure 4E). When all of the parameters tested were
considered, the most reliable results of folate receptor-targeting
were obtained for the lungs (Figure 4E). This finding was further strengthened with the
observation that the CD206+ macrophages in the lungs of
the tumor-bearing mice expressed high levels of FRβ (Figure 3) and very efficiently
internalized Ff-CD particles, in vivo (Figure 4D).
Collectively, the folate-functionalized
nanoparticles were well-engulfed
by CD206+ macrophages, which highly expressed FRβ.
The FRβ+ immune cells found in the tumor microenvironment
did not directly influence the tumor-targeting efficacy of the Ff-CD
nanoparticles. The lung was determined as a primary target for the
folate-functionalized nanoparticles.
Discussion
The reticuloendothelial system (RES) consists
of distinct populations
of phagocytic cells, primarily monocytes, macrophages, and neutrophil
granulocytes located in connective tissue.33 These cells significantly contribute to the clearance of macromolecules
and solutes in the circulation and tissues; therefore, they play an
important role in immune responses against foreign antigens such as
bacteria, viruses, and toxins.32 The nanoparticles
are also recognized as foreign material and removed by RES. Active
targeting of nanoparticles may be a solution for bypassing the organs
rich in RES and increasing the efficacy of drug delivery to tumors.33 Decoration with folate is a promising functionalization
for the nanoparticles since FRs are upregulated to meet the increased
metabolic demand for folic acid in cancer.30 Under physiological conditions, there were very limited numbers
of tissue-resident CD206+ subset of macrophages that were
positive for FRβ. In our study, the myeloid cells were increased
gradually in the breast microenvironment during the tumor formation
and almost all organs became significantly infiltrated by granulocytes,
monocytes, and macrophages systemically in progressed disease. Moreover,
FRβ expression was upregulated on the tumor-infiltrating myeloid
cells. This was regarded as a positive phenomenon which would enhance
the targeting efficacy of folate-functionalized nanoparticles. On
the other hand, the myeloid cells and their FRβ expression were
also increased in various RES organs of the tumor-bearing mice. Due
to the tumor burden, the negative impact of RES became more profound
through capturing the nanoparticles and preventing them from reaching
the target. In cancer patients, various tissues (other than the organs
containing primary tumors or metastasis) become populated with high
numbers of myeloid cells due to increased hematopoietic activity.41 The major findings of this study were summarized
in Figure 5. According
to our results, increased expression of FR in RES was associated with
enhanced retention of folate-functionalized nanoparticles in the lungs,
liver, and tumor. Here, we discuss the interrelationship between biodistribution
of FRβ+ myeloid immune cells and the biological parameters
modulating the targeting efficacy of the folate-functionalized nanoparticles.
Figure 5
A schematic
depicting the major findings of the study. As the tumor
progresses, the number of FRβ-expressing myeloid immune cells
(especially the CD206+ macrophages) is increased in the
tumor microenvironment, which creates a favorable target for nanoparticle-mediated
drug delivery into the tumor. However, due to the systemic impact
of tumor, the organs with RES become more populated by myeloid cells
which upregulate FRβ as well; eventually, the folate-functionalized
nanoparticles’ efficacy for active targeting of the tumors
is hampered.
Not only the malignant cells but also the immune
cells become metabolically
altered in cancer.8 Myeloid cells and tissue-resident
macrophages form the first line of defense in response to microbial
insult or tissue damage.42 Metabolic activity
and the need for folic acid derivatives are increased in the myeloid
cells due to activation and/or infiltration of tumors. Therefore,
expression of high affinity folate receptors is upregulated.43 Therefore, not only the tumor cells but also
the myeloid cells are favorably targeted by the folate-functionalized
delivery systems.
The route of systemic administration, which
is typically intravenous
injection via caudal vein in mice models, leads to an initial deposition
of nanoparticles in the liver where the capillary circulation paths
facilitate the capture of (macro)molecules by resident phagocytes.33 In the breast cancer model used, the liver possessed
increased numbers of myeloid cells with elevated FRβ. Expectedly,
the retention of Ff-functionalized nanoparticles was increased in
the liver of tumor-bearing animals. Then, the nanoparticles who reach
the heart are ejected into the circulation albeit a major blood flow
is directed into the lungs, which contain a fine network of microvessels.33 Augmented numbers of FRβ+ myeloid
cells were detected in the lungs of tumor-bearing animals; accordingly,
this organ specifically became a target for the cyclodextrin nanoparticles
conjugated with folate. Both the liver and especially the lungs are
the primary organs for metastasis, in 4T1 breast cancer.39,40,44 In the progressed disease (day
30), the metastatic foci in these organs may have altered the vasculature
and created an environment that favors the EPR effect. Intriguingly,
when the biodistribution of Ff-CD nanoparticles was corrected by using
the data from nontargeted nanoparticles (βCD6), in contrast
to the tumor and the liver, active targeting of Ff-CD was more successful
in the lungs although the nanoparticles were specifically engulfed
by the FRβ+CD206+ macrophages. Therefore,
the folate-conjugated nanoparticles had a greater interest in lung
tissue potentially due to increased targetability of FRβ expressed
by myeloid cells. This was in accordance with our previous studies
where folate-conjugated CD nanoparticles proved to be efficient for
hindering breast cancer metastasis to the lungs.36,37
The remaining nanoparticles that are not captured by RES in
the
liver and the lungs reached the breast tumor. Normal breast tissue
harbored FRβ-expressing macrophages; nevertheless, these cells
were highly populated in the progressing tumor. Therefore, the increased
FR levels together with the leaky vasculature of the tumor site established
a favorable target for Ff-CD nanoparticles. Intriguingly, the signals
from the nanoparticles, which reached the tumor, were not restricted
to the myeloid cells. Even though not tested in our study, other FRs
or folate transporters which can be upregulated upon tumorigenesis
might have enhanced the nanoparticle uptake by other cells of the
tumor microenvironment.30 For example,
metabolomic analysis of breast cancer cells upon exposure to the folate-conjugated
CD nanoparticles indicated early apoptosis and modulation of hexose
metabolism.45
As an immune organ,
the spleen was highly populated by the myeloid
cells in response to tumorigenesis.41 Enlargement
or histological alterations of the spleen due to extensive accumulation
of myeloid cells has been previously reported in mouse cancer models
and in cancer patients.39,46 In the spleen, blood
flows through a maze of sinusoids lined by endothelial cells and the
gaps in the endothelium create a filter-like structure that enables
clearance of large bodies such as damaged erythrocytes and cell debris
by phagocytes.41 In our study, the spleen
displayed the highest FRβ density score in tumor-bearing animals.
Expectedly, a high FRβ density score was calculated for the
spleen since it is an immune organ and an excess number of myeloid
cells constitute the spleen tissue. Nevertheless, Ff-CD nanoparticles
were not highly accumulated into the spleen; however, a considerable
amount of nanoparticles were trapped in FRβ+CD206+ macrophages. Cyclodextrin nanoparticles used in this study
had a size range between 100 and 200 nm which can reduce splenic retention
of nanoparticles.36,37 Moreover, it can be speculated
that the sinusoidal structure in the enlarged spleen may limit the
access of nanoparticles to RES cells in cancer. Previous research
has demonstrated that nanoparticles measuring 100–200 nm or
smaller exhibit enhanced permeability through the endothelial fenestrae,
constituting the splenic sinuses’ filtering interface. In contrast,
larger particles undergo gradual clearance by red pulp macrophages.47 Moreover, owing to the highly negatively charged
nature of the vascular endothelial luminal surface and the membranes
of spleen cells, particles with negative charge experience hindered
binding affinity.32 Even though the FRβ
score was high, its anatomical structure of the spleen may have limited
the capture of functionalized CD nanoparticles when compared to the
other organs, where the RES is tightly packed to filter smaller molecules.
Upregulation of FRβ on myeloid immune cells in cancer can
serve as an advantage for targeting immunotherapy drugs into the tumor
microenvironment.12 Reprogramming the immunosuppressive
cells for gaining antitumor capacities is a promising therapy approach
in cancer. Folate-mediated immune intervention treatments for cancer
have been reported previously.12,31,48−50 From an alternative point of view, our data support
that the myeloid cells infiltrating many organs, such as the liver
and lungs, which are primary targets for cancer metastasis, and the
CD206+ tumor-associated macrophages (TAMs) can be more
efficiently targeted with folate-functionalized nanoparticles for
immunotherapy approaches. It should be noted that in healthy animals,
the amount of FRβ+ myeloid cells in various organs
is limited, and the systemic biodistribution of folate-functionalized
nanoparticles might not reflect in situ condition in cancer. Moreover,
myeloid cells are implicated in the pathogenesis of many inflammatory
diseases; therefore, folate-functionalized nanoparticles loaded with
immunomodulatory drugs might be used for targeting the FRβ+ myeloid cells in many organs and may possess further relevance
for clinical applications.
Conclusions
Notwithstanding many in vitro reports that
support the efficacy
of folate-targeting, in vivo studies are essential to better define
the limitations of this active drug delivery approach. Folate receptors
are upregulated to meet the increased need for folate due to continuous
cell proliferation and reprogramming of metabolic activity during
tumor formation. In a cancer model, our study reported the distribution
and amount of FRβ-expressing myeloid cells in distinct organs
and in mammary tissue during tumorigenesis. Targeting folate-related
pathways or receptors is a promising therapeutic approach for cancer.
Albeit being a promising drug delivery and tumor-targeting strategy,
the nanoparticles decorated with folate have certain limitations such
as increased clearance by RES myeloid immune cells that upregulate
FRβ in cancer. |
Title: Advances in Subcellular Accumulation Design for Recombinant Protein Production in Tobacco | Body:
Introduction
Plant molecular farming (PMF) has emerged as a promising approach in biotechnology. This technology harnesses the power of plants as bioreactors, transforming them into “green factories” for the production of valuable recombinant proteins such as therapeutical and industrial enzymes. PMF leverages the sophisticated biosynthetic machinery of plants to generate complex recombinant proteins, offering several advantages over established production methods like microbial fermentation and mammalian cell culture [1,2]. For instance, plants can be cultivated on a large scale at relatively low costs, reducing the overall expense of protein production. Plant-based systems can be easily scaled up by increasing the cultivation area, making them suitable for high-yield production. Plants do not harbor human pathogens, minimizing the risk of contamination with viruses, prions, or other harmful agents that can affect microbial and mammalian systems. Plants do not produce endotoxins, which are common in bacterial systems and can complicate purification processes and pose safety risks [3,4]. In addition to these commonly cited benefits, plants are able to provide significant flexibility and engineering potential, enabling tailored solutions for the production of diverse proteins to meet individualized customization needs.
A variety of plant species, including tobacco plant [5,6], carrot suspension cell [7–9], and rice seed [10–13], have been explored as platforms for producing and delivering commercialized recombinant proteins. Each type of these different plant bioreactor platforms offers distinct advantages in production efficiency, containment, scalability, and cost-effectiveness. For example, rice seeds possess specialized storage organelles that naturally facilitate protein accumulation, providing stability both within the plant and postharvest. Gt13a signal peptide (SP) was used to target recombinant human serum albumin into the protein storage vacuoles (PSVs) of endosperm cell, which resulted in the production yield of 2.75 g/kg brown rice [12]. The same strategy has been used for the production of Classical swine fever virus (CSFV) E2 dimer proteins [13] and Newcastle disease virus HN dimer proteins [10], which resulted in the production yield of 0.48 g/kg and 0.47 to 3.7 g/kg, respectively. The rice bioreactor, utilizing its unique PSV accumulation strategy in endosperm cells, can achieve significant yield of recombinant protein. However, it faces several limitations, including a lengthy cultivation cycle and the risk of contaminating food crops with genetically modified organisms.
Notably, tobacco species, particularly Nicotiana tabacum and Nicotiana benthamiana, which are nonfood and nonfeed crop status, produce large amounts of biomass in a relatively short life cycle, making them a prominent choice in PMF [14,15]. N. benthamiana, in particular, has a less robust RNA silencing pathway compared to other plants, reducing the degradation of foreign RNA. Additionally, its compromised basal immunity decreases the likelihood of an immune response to Agrobacterium tumefaciens, or other virus-based vectors used for gene delivery, enhancing the expression of introduced genetic material [16–18]. These immune deficiencies greatly enhance its suitability and popularity as a host for Agrobacterium-mediated transient expression of various recombinant proteins. This makes it particularly valuable for the rapid production of urgently needed pharmaceutical proteins, such as vaccines and antibodies, during a pandemic. Furthermore, Agrobacterium-mediated transient expression in tobacco is a powerful tool for quickly and efficiently producing recombinant proteins, allowing for the screening of efficient expression vectors, fusion tags, stabilization domains, and other regulatory elements within a few days.
Unlike rice seeds, which have a simple but robust endosperm cell PSV store strategy, tobacco cells require engineering for subcellular accumulation tailored to different recombinant protein properties. The unique intracellular organization influences the trafficking and posttranslational modification of recombinant proteins [19–21]. Various targeting signals, such as leader sequence for endoplasmic reticulum (ER) targeting, chloroplast transit peptides, and vacuolar targeting sequences, are used to direct proteins to desired destinations. Meticulous engineering of these signals has resulted in enhanced protein accumulation. Additionally, due to the difference in the ability to do N-glycosylation of recombinant proteins among organelles, the selection of appropriate subcellular compartments is crucial for production of recombinant proteins as pharmaceuticals.
While previous research has explored recombinant protein production in tobacco, a comprehensive review specifically focusing on subcellular targeting strategies is lacking. This review addresses this gap by summarizing strategies for directing recombinant proteins to 4 key compartments within tobacco cells: ER, vacuole, chloroplast, and apoplast. We exclude the cytoplasm due to its limitations for protein storage. The cytoplasm is an open and dynamic environment, characterized by numerous competing cellular processes, and hinders efficient protein accumulation. Additionally, the complex protein degradation machinery in the cytoplasm poses a significant risk of unintended breakdown of the desired protein, leading to lower yields. By examining these targeted approaches, this review sheds light on the initial steps for selecting the optimal location for specific recombinant protein production in tobacco plants.
Recombinant Proteins Accumulated in ER
The ER in plants is a dynamic organelle crucial for various cellular functions. It serves as a hub for interorganelle communication, playing a vital role in the exchange of proteins, ions, and metabolites between different organelles [22–25]. Understanding the ER’s functions not only advances basic cell biology but also holds significance for biotechnological applications in recombinant protein production.
Accumulating recombinant proteins in the plant ER can be achieved by incorporating an N-terminal ER targeting or secretion SP together with a C-terminal retention sequence [19,26]. This involves mechanisms and benefits that have been well studied. First, directing the synthesis of foreign proteins to the ER, rather than the cytosol, minimizes proteolytic breakdown [27]. Second, the ER contains abundant molecular chaperones to aid the folding of proteins [28,29]. Third, the ER accumulation of proteins prevents the modification of N-glycan at the Golgi apparatus, thereby N-glycosylated proteins having homogeneous high-mannose glycan forms [30–33].
The ER has 2 main pathways for folding protein substrates. The first pathway is the general folding pathway, primarily facilitated by BiP (the ER homolog of the 70-kDa heat shock proteins, Hsp70) and P4HB (the founding member of the protein disulfide isomerase family). The second pathway is specific for glycoproteins and is mediated by the lectin chaperone calreticulin (CRT) and its membrane-bound homolog calnexin, which bind to monoglucosylated N-glycans on substrate proteins to aid their folding [34]. In N. benthamiana, heat shock treatment by placing the infiltrated plants in a 37 °C incubator for 30 min at 1 d postinfiltration significantly increased the expression of ER-accumulated Sct (the trimeric ectodomain of the severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2] spike protein). This suggests that the heat shock induces endogenous chaperone machinery, which aids the accumulation of recombinant proteins in the ER. Additionally, coexpressing human CRT in the ER increased the overall yield of Sct by up to 3.51-fold. However, simultaneous application of heat shock-induced chaperone machinery and human CRT coexpression did not further improve Sct accumulation, indicating that relying solely on protein-assisted folding to increase accumulation has an upper limit. Moreover, human CRT did not enhance the expression of Sct-3P, a well-folded version of Sct, suggesting that chaperones may not assist well-folded proteins [35].
N-glycosylation occurs in the ER of both plants and mammals. The structure of N-glycans on nascent proteins are identical in the ER. When these proteins arrive to the Golgi apparatus, the N-glycans are subjected to species-specific modifications [26]. In plants, this involves the addition of β(1,2)-linked xylose and core α(1,3)-linked fucose residues, while in mammals, it includes the addition of β (1,4)-linked galactose and sialic acid residues [36]. Therefore, accumulation of recombinant proteins to the ER using an ER retention motif is commonly used to prevent the incorporation of plant-specific sugar residues. Additionally, the formation of glycan structures with terminal mannose residues in the ER enables recombinant proteins to target mannose-specific surface receptors on macrophages, which is believed to enhance antigen uptake for recombinant vaccines by macrophages [37].
Until now, most recombinant proteins accumulating in plant ER have relied on the addition of an ER retention/retrieval sequence, like HDEL or KDEL, at their C-terminus. This strategy has successfully increased the accumulation of various proteins, including interleukin-4 and interleukin-6 [38,39], hemagglutinin H5 and H9 [40], CSFV E2 [41], SARS-CoV-2 Spike [35], carbonic anhydrases [42], and monoclonal antibody (mAb) CO17-1A [43] in tobacco cells (Table 1). ER retention/retrieval sequence receptors at the Golgi complex perform this recognition to retrieve C-terminal tagged proteins into the ER lumen through a process mediated by coat protein complex I (COPI). However, it is possible that the machinery gets submerged by saturation of HDEL/KDEL-containing proteins. If these receptors are saturated or processed, tagged recombinant proteins might either be secreted or transported to lytic vacuoles (LVs). For example, the anti-HIV antibody 2F5 heavy chain, fused with the C-terminal retention signal KDEL (2F5-HDEL), undergoes processing that removes the retention signal, resulting in the secretion of the antibody into the intercellular fluid [44]. Additionally, sporamin, a well-characterized storage protein naturally found in sweet potato vacuoles, was detected in the vacuole of BY2 cells rather than ER even when fused with the ER-retention signal HDEL (SpoHDEL), suggesting that SpoHDEL escapes the ER retention machinery [45]. Compared to HDEL/KDEL fused recombinant proteins that successfully target ER, there are few examples of ER off-target, but it does exist. In our past studies, combining a classical N-terminal ER-targeting SP with a C-terminal HDEL/KDEL sequence to recombinant proteins (e.g., AtBIP1 + HDEL/KDEL) has proven highly effective in achieving accurate ER targeting and retention. Notably, the first improved plant-based vaccine for CSFV (HERBAVAC CSF Green Marker) is based on the ER accumulation via AtBIP1 leading and HDEL retention [46].
Table 1.
List of recombinant proteins accumulated in the tobacco ER
Protein
SP
Expression level
Tobacco species
References
IL-4
Barley α-amylase
0.1% TSP
N. tabacum
[38]
IL-6
AtBIP1
18.49 mg/kg FW
N. benthamiana
[39]
H5/H9 trimer
AtBIP1
150–250 mg/kg FW
N. benthamiana
[40]
CSFV E2
AtBIP1
302 mg/kg FW
N. benthamiana
[46]
SARS-CoV-2 spike trimer
AtBIP1
106 mg/kg FW
N. benthamiana
[35]
Carbonic anhydrases
AtBIP1
350 mg/kg FW
N. benthamiana
[42]
mAb CO17-1A
Zera
340 mg/kg FW
N. tabacum
[43]
F1-V
Zera
230±20 μg/g TSP
N. benthamiana
[47]
8.5±0.2 mg/g TSP
NT1 cells
IL-4, interleukin-4; IL-6, interleukin-6; H5/H9, hemagglutinin 5 and 9; F1-V, Yersinia pestis F1 antigen; FW, fresh weight; TSP, total soluble protein
Beyond the standard C-terminal HDEL/KDEL retention signal, the formation of protein bodies (PBs) within the ER can significantly enhance recombinant protein accumulation. This process is often triggered by specific protein tags, such as the N-terminal proline-rich domain of γ-zein (Zera). The Zara-associated PB accumulation results in around 5-fold increase of recombinant Yersinia pestis F1-V antigen in N. benthamiana leaves upon Agrobacterium-mediated transient expression [47]. Once PBs arise for Zera-ECFP (enhanced cyan fluorescent protein), they grow in size over time, reaching their maximum size in N. benthamiana leaves at 10 d postinfiltration [48] (Table 1).
Recombinant Proteins Accumulated in the Vacuole
The plant vacuole stands as a multifunctional and dynamic organelle crucial for upholding turgor pressure, storing and amassing inorganic ions, amino acids, proteins, sugars, and secondary compounds, maintaining cell homeostasis, facilitating signal transduction, and fostering plant development. In mature cells of tobacco leaves, the plant vacuole dominates, occupying a substantial 80% to 90% of the cell volume [49]. This substantial space presents tremendous potential for the storage of recombinant proteins.
There are 2 types of vacuoles that are identified as having separate and specific functions: PSVs and LVs [50]. PSVs present in seed and some tissues can store large amounts of foreign proteins for long periods without degradation, while in contrast, LVs are found in most of cells in vegetative tissue where proteins are subjected to degradation like lysosomes in animal cells. Even so, several foreign proteins including avidin [51], endolysin [52], human collagen [53], α-galactosidase A [54], human complement factor C5a [55], interleukin-6 [56], and mAb 14D9 [57] (Table 2), can accumulate in the vacuole of tobacco leaves or tobacco suspension culture cells, suggesting that the central LV in leaf tissues can be a suitable destination for certain proteins.
Table 2.
List of recombinant proteins accumulated in tobacco vacuole
Protein
Signal origin
Expression level
Tobacco species
References
Avidin
PPI-I
1.5% TSP
N. benthamiana
[51]
Endolysin
PPI-I
160 mg/kg FW
N. benthamiana
[52]
Human collagen
BTPA
2% TSP
Tobacco
[53]
α-Galactosidase A
TCA
302 mg/kg FW
BY-2 cells
[54]
C5a
AFVY
10.62 mg/kg FW
N. tabacum
[55]
IL-6
AFVY
5.21 μg/kg TSP
N. tabacum
[56]
mAb 14D9
A11G
1.57%–1.73% TSP
N. tabacum
[57]
PPI-I, potato proteinase inhibitor I; BTPA, barley thiol protease aleurain; TCA, tobacco chitinase A; C5a, human complement factor 5a. AFVY, the C-terminal tetrapeptide of phaseolin; A11G, amaranth 11S globulin (KISIA Ct and NIFRGF ss)
Different vacuolar sorting signals (VSSs) have been described for vacuolar targeting in tobacco. For instance, the sequence-specific (ss) signals consist of short, defined amino acid motifs, such as NPIR or NPIXL [57,58]. Notably, the position of these motifs within the protein sequence does not affect their function. By contrast, hydrophobic C-terminal signals (Ct) lack a specific consensus motif but are invariably located at the C-terminus of the protein, suggesting that location is critical for vacuolar targeting [59]. Fusing the heavy chain of mAb 14D9 with either Ct VSS (KISIA) or ss VSS (NIFRGF) resulted in 10- to 15-fold higher vacuolar accumulation of mAb compared to the secreted mAb version in N. benthamiana, while Ct VSS (KISIA) and ss VSS (NIFRGF) themselves did not induce differences in production yield [57], suggesting that the central vacuole can serve as an appropriate compartment for the efficient production of antibodies.
Proteins destined for vacuoles enter the plant secretory pathway via an SP and can take different trafficking routes. The conventional transport pathway that is the most characterized involves initial export from the ER via COPII vesicles [60,61]. Following this, the proteins transit through the Golgi apparatus where the N-glycans of proteins are modified to the complex type. At the trans Golgi network, the vacuolar proteins are sorted and undergo post-Golgi transport to reach the vacuole. An alternative trafficking route that is unconventional but occurred entirely independent of COPII machinery, has also been identified. Proteins are transported directly from the ER to the vacuole and bypass the Golgi apparatus [62]. For example, a mouse immunoglobulin G1 (IgG1) fused to a ss VSS and Ct-VSS of amaranth 11S globulin produced in N. benthamiana is decorated with 75% Man7 and Man8 glycans supporting a direct transport bypassing the Golgi [57], while a mouse IgG1 fused with the VSS of sporamin produced in suspension-cultured tobacco BY2 cells is predominantly decorated with Man3XylFucGlcNAc2 glycans [63], supporting a conventional transport passing Golgi.
Taliglucerase alfa (Elelyso) is the first Food and Drug Association-approved plant-based recombinant protein for human use. This recombinant human glucocerebrosidase utilizes a C-terminal VSS (DLLVDTM) derived from tobacco chitinase A to achieve vacuolar targeting in carrot suspension cells. In this instance, vacuolar accumulation also facilitates the presence of Man3XylFucGlcNAc2 glycan [7]. Notably, the recombinant human glucocerebrosidase produced in the vacuole of carrot cells naturally contains terminal mannose residues on its complex glycans. Unlike Cerezyme, it does not require the in vitro exposure of mannose residues while still displaying comparable enzymatic activity and uptake into macrophages.
Recombinant Proteins Accumulated in Chloroplasts
Plant chloroplasts originated from a symbiotic relationship between a eukaryotic ancestor and cyanobacteria, retaining their own genetic material and gene expression machinery [64]. They are essential organelles responsible for the biosynthesis of starch, lipids, amino acids, nucleotides, and other metabolites. Chloroplasts are naturally equipped to handle the highest level of native proteins related to photosynthesis in tobacco leaf tissues. This capacity potentially can be harnessed to accumulate large quantities of recombinant proteins. Chloroplasts are highly abundant in mesophyll cells, occupy a large volume, and are an attractive organelle for storing plant-derived recombinant proteins.
There are 2 primary strategies for accumulating recombinant proteins in chloroplasts. The first involves directly inserting the desired gene into the chloroplast genome using homologous recombination. This gene is then transcribed and translated within the chloroplasts. The second strategy relies on nuclear transformation, where the gene is integrated into the plant nuclear genome. The resulting protein is subsequently transported from the cytoplasm into the chloroplasts. For simplicity, we refer to these approaches as “chloroplast transformation” and “nuclear transformation”, respectively.
In case of chloroplast transformation, chloroplasts lack gene silencing mechanisms, resulting in more stable and consistent expression of foreign genes. In addition, the chloroplast environment provides suitable conditions for protein folding and disulfide bond formation, crucial for proper protein function [65]. Furthermore, chloroplast-derived transgenes exhibit minimal risk of transfer to the environment due to their low transmission rates through pollen. Notably, chloroplast transformation has achieved a maximum recombinant β-glucosidase production level of 75% of total soluble protein (TSP) [66]. Generally, conventional methods typically yield up to approximately foreign protein at 5% to 20% TSP for certain cases [67]. Targeted integration of foreign genes into the chloroplast genome has been successfully achieved via biolistic bombardment [68,69]. A significant portion of recombinant protein production achieved through tobacco chloroplast transformation relies on this method such as the endoglucanase [67,69], superoxide dismutase [70], epidermal growth factor [71,72], cholera toxin B subunit [73], and human papillomavirus L1 virus-like particle (VLP) [74] (Table 3). While chloroplast transformation is effective for producing certain recombinant proteins, it has limitations compared to nuclear transformation. Generating stable, high-yielding transgenic plants through chloroplast transformation is often time-consuming due to technical challenges. Furthermore, chloroplasts lack the cellular machinery necessary for protein glycosylation that is required for some pharmaceutical proteins.
Table 3.
List of recombinant proteins accumulated in tobacco chloroplast
Protein
Expression pattern
Transit peptide
Expression level
Tobacco species
References
β-Glucosidase
Chloroplast
75% TSP
N. tabacum
[66]
TetC-Cel6A
Chloroplast
28% in green leaves
N. tabacum
[67]
SOD
Chloroplast
9% TSP
N. tabacum
[70]
EGF
Chloroplast
1.57±0.05 g/kg FW
N. tabacum
[72]
CTB
Chloroplast
7.49 mg/g FW
N. tabacum
[73]
HPV L1 VLP
Chloroplast
24% TSP
N. tabacum
[74]
HIV-1 p24
Nucleus
rbcS1-TP
1 mg/kg FW
N. benthamiana
[76]
HPV-16 L1 VLP
Nucleus
rbcS1-TP
6–8 mg/kg FW
N. benthamiana
[79]
HPV-16 L1 VLP
Nucleus
rbcS1-TP
11% TSP
N. benthamiana
[77]
HPV-16 L1/L2 chimera
Nucleus
rbcS1-TP
1.2 g/kg FW
N. tabacum
[78]
TetC-Cel6A, endoglucanase Cel6A; EGF, epidermal growth factor; CTB, cholera toxin B subunit; SOD, superoxide dismutase; HPV L1 VLP, human papillomavirus L1 virus like particle; HIV-1 p24, human immunodeficiency virus type I p24 capsid subunit; HPV-16 L1/L2 chimera, human papillomavirus type 16 L1/L2 chimeras; rbcS1-TP, transit peptide from rbcS1
In case of nuclear transformation, this method utilizes a transgene encoding the recombinant protein fused to a chloroplast transit peptide. The transgene can be either transiently expressed in the nucleus or stably integrated into the plant nuclear genome. The translated protein, harboring the N-terminal chloroplast transit peptide, is then imported into the chloroplasts from the cytosol. This import process is mediated by translocon complexes located at the outer and inner envelope membranes of chloroplast, termed TOC and TIC, respectively [75]. Transient expression in the nucleus followed by protein targeting to the chloroplasts offers a significant time advantage for recombinant protein production compared to the chloroplast transformation, which has gained traction, as evidenced by several studies employing it, such as HIV-1 p24 [76] and HPV-16 VLP [77–79] (Table 3). However, it is important to note that since these recombinant proteins are synthesized in the cytosol, their final accumulation within chloroplasts will depend on both the chloroplast protein import capacity and ubiquitin-dependent turnover processes [80]. Thus, this eukaryotic posttranslational modification system typically yields lower recombinant protein levels compared to chloroplast transformation, with expression generally falling below 10% TSP [81]. For example, HPV L1 VLP expressed in chloroplasts reach approximately 24% TSP [74], whereas expression via the nucleus–chloroplast targeting approach typically yields only around 11% TSP [77].
Recombinant Proteins Accumulated in Apoplast
The plant apoplast is a crucial space between the cell membrane and the cell wall that plays a significant role in various biological processes. It serves as the primary site for pathogen recognition, triggering immune responses involving the secretion of molecules like proteases, proteins related to immunity, small RNAs, and secondary metabolites [82–84]. Additionally, the apoplasts are considered to be a good place for recombinant protein accumulation in view of the large volume of extracellular space without intracellular proteolytic organelle-mediated protein degradation [85].
Even the analysis of the plant secretome has revealed that more than 50% of the endogenous secreted proteins lack an SP [86]; a well-studied SP rather than the native SP is thought to be necessary to direct recombinant proteins to this specific extracellular location. For example, the signal sequence and the first 2 amino acids derived from the tobacco PR1b protein, when fused to the bacterial protein ChiA, significantly enhanced its secretion efficiency compared to ChiA with its native signal sequence when expressed in plant cells [87]. The production of HRP C1a using GE derived from N. tabacum β-D-glucan exohydrolase shows a better secretion rate in BY2-cell system than its native SP and another one derived from peroxidase [88]. IgG is a kind of native secretion protein in animal cells but not always adaptive for the secretion in plant cells. While some studies have replaced the signal sequences for better secretion [89], most have retained the native SP, leading to detectable IgG in the plant apoplast, PVC, and ER [90]. Three potential explanations for these observations arise: (a) A part of the recombinant IgG in folding intermediates or in misfolded form is bonded by the chaperones BiP in the ER [91]. (b) The ER–Golgi–apoplast pathway is a classic secretion pathway. It makes sense that recombinant IgGs could be detected along the entire pathway unless the protein synthesis is blocked for a while. (c) The movement of proteins from the ER to Golgi and their subsequent sorting within the Golgi is meticulously controlled by vesicles and associated proteins like COP and adaptor protein complexes. Inefficiencies in this process could hinder IgG progression.
For tobacco suspension cells, protein secretion offers advantages due to simplified purification methods via medium collection. Proteins directed to the secretory pathway are typically released into the culture medium after traversing the cell wall. However, traversing efficiency varies based on protein size and physicochemical properties. Smaller proteins (<30 kDa) are generally completely secreted [92,93], while larger ones were considered retained to varying degrees. Notably, even large proteins like antibodies can be efficiently secreted and pass the cell wall [89], while some small ones like green fluorescent protein (GFP) fused major ampullate silk proteins (MaSp) remain trapped [94], suggesting that factors beyond size, such as charge and hydrophobicity, also play a role. BY-2 cell-produced and apoplast-secreted platforms have emerged as a versatile platform for the successful expression of a broad range of functional proteins, including interferon a2b [95], human a-L-iduronidase [96], anti-HIV-antibody 2G12 [97], human growth hormone [98], anti-vitronectin antibody M12 [99], human α1-antitrypsin [100], ORF8 of SARS-CoV-2 [101], and stem cell factor [102] (Table 4).
Table 4.
List of recombinant proteins accumulated in tobacco apoplast
Protein
Signal origin
Expression level
Tobacco species
References
Interferon α2b
Extensin
28 mg/l
BY-2 cells
[95]
hIDUA
Proaleurain
10 mg/l
BY-2 cells
[96]
2G12
Native SP
8 mg/l
BY-2 cells
[97]
rhGH-(SO)10
Extensin
16–35 mg/l
BY-2 cells
[98]
rhGH-(SO)10-EGF
15–32 mg/l
M12
mAb24
20 mg/l
BY-2 cells
[89]
Human α1-antitrypsin
Extensin
34.7±4.3 mg/l
BY-2 cells
[100]
ORF-8
Native SP
8.8±1.4 mg/l
BY-2 cells
[101]
(SP)20-SCF
Extensin
2.5 mg/l
BY-2 cells
[102]
SCF-(SP)20
1.4 mg/l
H5 VLP
Native SP
50 mg/kg FW
N. benthamiana
[5]
SARS-CoV-2 VLP
Native SP
24–28 mg/kg FW
N. benthamiana
[105]
SARS-CoV-2 VLP
Plant
N/A
N. benthamiana
[107]
hIDUA, human lysosomal enzyme a-iduronidase; 2G12, human anti-HIV antibody; rhGH-(SO)10, rhGH was expressed with 10 repeats of the AGP glycomodule Ser-Hyp (SO) at the C-terminus; rhGH-(SO)10-EGF, rhGH-(SO)10 as an AGP-enhanced green fluorescent protein fusion; M12, anti-vitronectin antibody; ORF-8, SARS-CoV-2 open reading frame 8; (SP)20-SCF, 20 tandem repeats of the “Ser-Pro” motif fused to the N-terminus of stem cell factor gene; SCF-(SP)20, 20 tandem repeats of the “Ser-Pro” motif fused to the C-terminus of stem cell factor gene
In whole tobacco plants, the recombinant proteins that primarily secreted to the apoplast and accumulate in the extracellular space will undergo endocytosis to a limited extent, but it does not significantly affect the overall accumulation [103]. The secreted soluble proteins can be efficiently extracted from the apoplast fluid of tobacco leaves using a simple low-speed centrifugation of intact leaves [104]. This method is advantageous to the downstream processing due to less volume of extracts and the rare release of intracellular proteins. However, the large protein complexes, such as the influenza VLP produced by transient expression in N. benthamiana, require conventional extraction procedures. It is clearly observed by electronic microscopy that they are accumulated between the plasma membrane and the cell wall [5]. The apoplast targeting in tobacco plants has emerged as a promising platform for the production of VLP vaccines via vesicles budding, demonstrating its potential for industrial applications [105,106] (Table 4). Notably, the COVID-19 (coronavirus disease 2019) VLP vaccine made by Medicago (Covifenz), is the first approved plant-based human vaccine [107]. Glycoproteomic analysis of secreted SARS-CoV-2 VLPs produced in N. benthamiana revealed occupancy of 20 out of the predicted 22 N-glycosylation sites with complex plant N-glycans, while 1 site contained oligomannoses [108]. However, the absence of nonmammalian epitopes, such as core β (1,2)-xylose and α (1,3)-fucose residues of SARS-CoV-2 VLP vaccine, associated with plant N-glycosylation have not developed any allergic or hypersensitivity reactions in subjects in the Phase 1 randomized trial [107].
Recombinant proteins targeted to the plant apoplast do face challenges, particularly related to protease processing that can compromise their structural integrity to certain recombinant proteins [109]. For example, the recent study suggested that SBT5.2s are the major active extracellular subtilases processing IgG antibody 2F5 in the N. benthamiana apoplast [110]. Moreover, the recombinant SARS-CoV-2 VLPs accumulated in the apoplast of N. benthamiana leaves were processed to be incomplete [105]. To these challenges, coexpression of protease inhibitors has emerged as a promising strategy to stabilize proteins in the leaf apoplast, potentially leading to enhanced protein accumulation and integrity [111].
Which to Choose?
Here, we summarized the pros and cons of different subcellular accumulation (Figure C). The ER provides a conducive environment for proper protein folding and assembly, with the presence of chaperones and folding enzymes that assist in forming disulfide bonds and achieving the correct tertiary structure. It can perform homogeneous glycosylation, essential for the functionality and stability of many therapeutic proteins. However, overloading the ER can lead to ER stress and reduced yields, necessitating careful management of the expression levels.
Vacuoles can pose challenges for recombinant protein stability due to their protease-rich environment, which can degrade proteins. However, some proteins are naturally stable in vacuoles, indicating that specific proteins may be suitable for this compartment. Stabilization strategies are essential when targeting proteins to vacuoles to prevent degradation and maintain functional protein levels.
Chloroplasts offer a high-yield environment for recombinant protein production due to their capacity for high rates of transcription. The plastid genome can accommodate multiple copies of the gene of interest, further boosting protein yield. However, the chloroplast transformation is time-consuming due to technical challenges, while the nuclear-expressed and transit peptide-mediated translocation is less efficient.
The apoplast is an extracellular environment that supports the formation of membrane associated VLP. Proteins secreted into the apoplast can be easily harvested from the extracellular fluid, simplifying the purification process and potentially increasing yield. However, the presence of proteases in the apoplast can process recombinant proteins, requiring the use of protease inhibitors or engineering protease-resistant protein variants to maintain protein integrity and yield.
Optimizing subcellular localization for individual target proteins, rather than employing a universal strategy, is vital at the beginning of target protein design. Several studies have directly investigated the impact of subcellular targeting on recombinant protein expression level. For instance, comparisons have been made between the accumulation of GFP and GB1-GFP when transiently expressed in N. benthamiana [112]. This study revealed that accumulation in the ER resulted in slightly higher fluorescence intensity compared to chloroplast accumulation, with both significantly exceeding stronger than in cytosol. Similarly, research has demonstrated that stable expression of interleukin-6 in N. benthamiana yielded significantly higher protein levels when targeted to the ER compared to the apoplast or vacuole [56]. Roughly, the ER and chloroplasts are often preferred for their conducive environments for protein folding and high expression levels, respectively. However, the specific requirements of the target protein, such as necessary posttranslational modifications and resistance to degradation, must be carefully considered to ensure successful production.
Here, we provide insight for the subcellular targeting strategies of different types of proteins: (a) The large and complex glycoproteins that require chaperone-assisted folding and mammalian-like N-glycosylation patterns may give priority to ER retention. (b) Proteins tolerant of acidic environments, particularly those naturally localized to human lysosomes, can benefit from vacuolar accumulation. (c) Proteins that do not require extensive posttranslational modifications and readily fold correctly may be a good target for expression via chloroplast transformation. Notably, chloroplasts can also facilitate the assembly of coat protein to VLPs. (d) The extracellular space is an alternative option for proteins requiring membrane-mediated budding, such as certain VLPs. Within this review article, we present illustrative figures depicting representative proteins and their predominant glycosylation patterns associated with specific subcellular compartments for your reference (Figure).
Figure.
Representative proteins and their predominant glycosylation patterns associated with specific subcellular compartments, along with the pros and cons. (A) Representative predominant glycosylation patterns in the specific subcellular compartments. GlcNAc (Gn), N-acetylglucosamine; Man (M), mannose; Xyl (X), xylose; Fuc (F), fucose; Gal, galactose, Lea, Lewis a. (B) Representative proteins in specific subcellular compartments. The Cryo-EM data of representative VLPs and the PDB structure of representative proteins are flanked with the 4 subcellular compartments. (C) The pros and cons of different subcellular accumulation in tobacco. PTM, posttranslation modification; CT, chloroplast transformation; NT, nuclear transformation.
Challenges and Future Perspectives
While PMF offers significant promise for industrial production, several key challenges must be addressed to fully realize its potential: (a) Adaptable production levels. Achieving consistent and scalable production levels for diverse recombinant proteins remains a crucial hurdle. (b) Comparable quality. Ensuring that the recombinant proteins possess comparable quality attributes (e.g., posttranslational modifications) to their authentic counterparts is essential. (c) Effective downstream processing costs. Minimizing downstream processing costs associated with protein purification and isolation is critical for economic feasibility. To address the 3 major scientific and technical challenges in the PMF field, future studies should focus on expression design, plant remodeling, and protein purification.
Expression design has been the most extensively studied during the past few decades, encompassing both gene-level and protein-level strategies. At the gene level, foreign genes should be optimized to match the tobacco codon preferences, reducing sequence complexity and minimizing secondary structures for greater genetic stability, which is crucial for long-term experiments or commercial production [113]. Robust 5′- and 3′-untranslated regions are necessary to stabilize mRNA and enhance translation levels [114], while suppressors are needed to block RNA silencing, enhancing the stability and accumulation of transgene mRNA in transient expression systems [115]. At the protein level, designing appropriate subcellular compartment targeting and storage SPs ensures better protein storage and precise posttranslational modification. Adding solution-promoting tags, high-glycosylation domains, and stabilization domains helps recombinant proteins fold correctly, improving expression levels. Additionally, coexpression of molecular chaperone proteins assists in the folding of recombinant proteins and increases their expression.
There has been less research on plant chassis remodeling compared to expression design. However, recent years have seen the creation of several transgenic tobacco lines to address plant-specific glycosylation modifications. CRISPR-Cas9-engineered N. benthamiana [116] and BY-2 cells [117], for example, have been developed to completely lack α1,3-fucose and β1,2-xylose residues. These engineered plants and plant cells are generally well suited for producing pharmaceutical proteins that require proper glycosylation. Furthermore, according from various challenges we have faced in the expression and purification of recombinant proteins from tobacco, we believe that remodel diverse tobacco plant chassis is crucial for the future development of this field. For example, (a) modifying plant reactors to have low-efficiency protease processing to reduce the proportion of recombinant protein processed by plant endogenous proteases. (b) Establishing plant reactors that can efficiently allocate host synthetic resources to increase recombinant protein yield. (c) Creating plant reactors without polyphenol contamination to minimize polyphenol pollution during the purification process.
In the field of PMF, the eventual commercialization of biomanufacturing is a key indicator of progress. Currently, the number of recombinant protein drugs available from plant-based systems is significantly lower compared to those from nonplant systems. The closure of Medicago in 2023, resulting in the withdrawal of SARS-CoV-2 VLP vaccine from the market, has negatively impacted the field, although this was unrelated to technological issues [118]. All these factors remind us that beyond scientific and technical considerations, public opinion on tobacco products and stringent government regulations on using transgenic plants to produce recombinant proteins play a crucial role in industrialization. Adhering to rigorous regulatory frameworks, engaging with the public through transparent communication, and establishing robust safety protocols for handling and disposal are essential to overcoming public and governmental concerns. |
Title: An integrated, optofluidic system with aligned optical waveguides, microlenses, and coupling prisms for fluorescence sensing | Body: |
Title: A Case-control Study on the Association of Fruit and Vegetable Consumption with Risk of Breast Cancer | Body:
Introduction
Breast cancer is the most prevalent type of malignancy among women; its prevalence is continuously increasing worldwide.[1] In 2012, it was estimated that 1,671,149 new cases of breast cancer occurred.[2] International estimations showed that near 26% increase in breast cancer cases will occur by 2020, with a greater trend in developing countries.[34] The epidemiological model of breast cancer in Iran is similar to that of other east Mediterranean and developing countries.[4] Breast cancer is the most leading cause of cancer deaths in women, accounting for approximately 14.7% of cancer-related mortalities in women.[5] Therefore, prevention of breast cancer is a priority.
Diet is an important modifiable contributing factor to several cancers.[67] Greater adherence to healthy dietary patterns has been associated with a lower risk of breast cancer.[78] However, less attention has been paid to components of such dietary patterns. The favorable effects of healthy dietary patterns on human health have been attributed to their high content of fruits and vegetables;[8] consumption of these components has been inversely, but not consistently, associated with the risk of breast cancer.[19] In a meta-analysis, a weak inverse association was found between dietary intake of fruit, but not vegetables, and risk of breast cancer.[10] A meta-analysis of prospective cohort studies in 2017 revealed no significant association between the consumption of fruit and vegetables the and odds of breast cancer prognosis.[1] Overall, it seems that findings in this regard are conflicting and additional data are required to come to a definite conclusion.
Earlier studies on diet-breast cancer risk were mostly done in western countries, and limited information is available from Middle Eastern nations, where people are experiencing a nutrition transition from their traditional diets to food habits containing highly processed foods which means the population is undergoing changes in their dietary patterns and lifestyle behaviors, which may have negative impacts on their health.[11] Low consumption of fruits and vegetables in this area might explain the high prevalence of breast cancer among women.[12] In addition, the composition and available nutrients in fruit and vegetables are greatly different based on geographical locations.[13] In particular, raising vegetables in highly polluted areas, like that in most Middle Eastern countries, might result in taking toxic amounts of some minerals.[14] In addition, available studies frequently did not consider premenopausal and postmenopausal women as two separate groups in their analyses. Therefore, this case-control study was conducted to investigate the association of fruits and vegetables consumption with the risk of breast cancer in a group of Iranian adult women.
Methods
Study population
This population-based case-control study was conducted among women aged >30 years, who were currently residing in Isfahan, Iran. Breast cancer was diagnosed during the maximum of the last 6 months by physical examination and mammography findings. It was defined as primary incidence of breast tumor with invasive behavior and its histology was available from medical records. Participants were breast cancer (BC) patients who were referred to hospitals or private clinics in Isfahan, Iran from July 2013 to July 2015. The study sample size was calculated based on the type I error of 5%, with a study power of 80%. We hypothesized that unhealthy dietary patterns might increase the odds of breast cancer by 1.5 times. Considering the common ratio of 0.25 and the ratio of controls to cases as 2, we reached to almost 350 patients with breast cancer and 700 apparently healthy controls. Patients who underwent surgical resection for BC or were at chemotherapy or radiotherapy or experienced all of the treatments were selected. We did not include patients with a history of any type of neoplastic lesion or cysts (exception of current breast cancer) as well as those with a history of any hormone replacement therapy. In addition, those who were on a special diet were also not included in this study. Age-matched controls were selected from healthy women, who had no relationship with breast cancer patients or had no family history of breast cancer. In addition to age, we did our best to match controls in terms of socioeconomic status with the cases. Controls who met our inclusion criteria (female, Iranian nationally, no history of any malignancy, cysts and medical disorder, having no special diet or hormone replacement therapy) were selected from the general adult population. Finally, eligible subjects including 350 cases and 700 controls were recruited to the present study. Written informed consent was obtained from all subjects. The study was ethically approved by the Ethical Committee of Isfahan University of Medical Sciences, Isfahan, Iran.
Dietary intake assessment
Dietary data were collected using a 106-item Willett-format semi-quantitative dish-based food frequency questionnaire which was designed and validated for Iranian adults.[15] Detailed information about design and validity of this dish-based food frequency questionnaire (FFQ) was reported elsewhere.[1617] In this study, the questionnaires were completed through face-to-face interview by a trained nutritionist. The questionnaire contained five categories of foods and dishes: (1) mixed dishes (cooked or canned, 29 items), (2) carbohydrate-based foods (different types of bread, cakes, biscuits, and potato, 10 items), (3) dairy products (dairies, butter, and cream, nine items), (4) fruits and vegetables (22 items), and (5) miscellaneous food items and beverages (including sweets, fast foods, nuts, desserts, and beverages, 36 items). Participants were asked to report their dietary intake of foods and mixed dishes through nine multiple-choice frequency response categories varying from “never or less than once a month” to “12 or more times per day.” Therefore, the frequency response for each food list varied from six to nine choices. For foods consumed infrequently, we omitted the high-frequency categories, while for common foods with high consumption, the number of multiple-choice categories increased. For instance, the frequency response for tuna consumption included six categories, as follows: never or less than once/month, 1-3 times/month, one time per week, 2-4 times/week, 5-6 times/week, and 1-2 times/day, and for tea consumption, the frequency response included nine categories, as follows: never or less than 1 cup/month, 1-3 cups/month, 1-3 cups/week, 4-6 cups/week, 1 cup/day, 2-4 cups/day, 5-7 cups/day, 8-11 cups/day, and ≥12 cups/day. Finally, we computed daily intakes of each food item and then converted them to grams per day, using household measures.[18] Daily values for each item were calculated according to food composition, average of reported frequency, and specified portion size. As for nutrient intakes, it was calculated by adding together the nutrient contents of all foods and dishes. The nutrient intake for each participant was obtained by the Nutritionist IV software, a modified version for Iranian foods. Our previous study indicated that this FFQ provided valid and reliable measures of the average long-term dietary intakes.[1719]
Assessment of breast cancer: All patients with breast cancer were females with newly diagnosed stage I-IV breast cancer. They were recruited from Iranian nationality, for whom in-situ or invasive status of BC was confirmed by physical examination and mammography. Mammography is a type of X-ray imaging used for diseases diagnosis.[20] The harmful side effect of breast exposure with irradiation by mammography is very low which can be ignored.[21] This imaging method provides a black-and-white image of breast. For mammography, the patient was placed in a standing, horizontal, and vertical position; then breast was compressed for a few seconds between the pages and photography took place.[20]
Assessment of other variables
Body weight was measured by a trained nutritionist, without shoes, and with light clothing, using a weighing calibrated scale (Seca, Hamburg, Germany) to the nearest 100 g. Height was measured by a mounted tape, without shoes at a standing position near the wall, using a statiometer (Seca, Hamburg, Germany) to the nearest 0.5 cm. Body mass index (BMI) was calculated through weight in kilograms divided by height in squared meters. In terms of physical activity, short form of International Physical Activity Questionnaire was used through face-to-face interviews.[22] All results of the International Physical Activity Questionnaire were expressed as Metabolic Equivalents-hours per week. A pretested questionnaire was also used to collect data on age, marital status, place of residence, education, socioeconomic status, history of disease, family history of cancer, breast feeding history, smoking, menopausal status, alcohol use, and supplement use.
Statistical methods
Participants were categorized into quintiles based on the amounts of fruit and vegetable intake in their daily diet. General characteristics and dietary intakes of study participants across quintiles of fruit and vegetable intake were examined using one-way analysis of variance for continues variables and Chi-square for categorical variables. The association of fruit and vegetable intake with breast cancer was assessed by using logistic regression in different models. Age (continues) and energy intake (Kcal/d) were adjusted for in the first model. Additional controlling for region (urban/rural), marital status (yes/no), education (elementary/graduated/nongraduated), history of cancer (yes/no), physical activity (continues), family history of breast cancer (yes/no), menopausal status (premenopausal/postmenopausal), smoking (yes/no), alcohol consumption (yes/no), and socioeconomic status (poor/middle/high) was done in the second model. Further adjustment was done for dietary intakes of meat, soy, whole and refined grains, total dietary fat intake, and mutual effects of fruit and vegetables in the third model. Finally, we adjusted the analysis for BMI. Statistical analyses were carried out by using SPSS version 18. P values were considered significant at <.05.
Study population
This population-based case-control study was conducted among women aged >30 years, who were currently residing in Isfahan, Iran. Breast cancer was diagnosed during the maximum of the last 6 months by physical examination and mammography findings. It was defined as primary incidence of breast tumor with invasive behavior and its histology was available from medical records. Participants were breast cancer (BC) patients who were referred to hospitals or private clinics in Isfahan, Iran from July 2013 to July 2015. The study sample size was calculated based on the type I error of 5%, with a study power of 80%. We hypothesized that unhealthy dietary patterns might increase the odds of breast cancer by 1.5 times. Considering the common ratio of 0.25 and the ratio of controls to cases as 2, we reached to almost 350 patients with breast cancer and 700 apparently healthy controls. Patients who underwent surgical resection for BC or were at chemotherapy or radiotherapy or experienced all of the treatments were selected. We did not include patients with a history of any type of neoplastic lesion or cysts (exception of current breast cancer) as well as those with a history of any hormone replacement therapy. In addition, those who were on a special diet were also not included in this study. Age-matched controls were selected from healthy women, who had no relationship with breast cancer patients or had no family history of breast cancer. In addition to age, we did our best to match controls in terms of socioeconomic status with the cases. Controls who met our inclusion criteria (female, Iranian nationally, no history of any malignancy, cysts and medical disorder, having no special diet or hormone replacement therapy) were selected from the general adult population. Finally, eligible subjects including 350 cases and 700 controls were recruited to the present study. Written informed consent was obtained from all subjects. The study was ethically approved by the Ethical Committee of Isfahan University of Medical Sciences, Isfahan, Iran.
Dietary intake assessment
Dietary data were collected using a 106-item Willett-format semi-quantitative dish-based food frequency questionnaire which was designed and validated for Iranian adults.[15] Detailed information about design and validity of this dish-based food frequency questionnaire (FFQ) was reported elsewhere.[1617] In this study, the questionnaires were completed through face-to-face interview by a trained nutritionist. The questionnaire contained five categories of foods and dishes: (1) mixed dishes (cooked or canned, 29 items), (2) carbohydrate-based foods (different types of bread, cakes, biscuits, and potato, 10 items), (3) dairy products (dairies, butter, and cream, nine items), (4) fruits and vegetables (22 items), and (5) miscellaneous food items and beverages (including sweets, fast foods, nuts, desserts, and beverages, 36 items). Participants were asked to report their dietary intake of foods and mixed dishes through nine multiple-choice frequency response categories varying from “never or less than once a month” to “12 or more times per day.” Therefore, the frequency response for each food list varied from six to nine choices. For foods consumed infrequently, we omitted the high-frequency categories, while for common foods with high consumption, the number of multiple-choice categories increased. For instance, the frequency response for tuna consumption included six categories, as follows: never or less than once/month, 1-3 times/month, one time per week, 2-4 times/week, 5-6 times/week, and 1-2 times/day, and for tea consumption, the frequency response included nine categories, as follows: never or less than 1 cup/month, 1-3 cups/month, 1-3 cups/week, 4-6 cups/week, 1 cup/day, 2-4 cups/day, 5-7 cups/day, 8-11 cups/day, and ≥12 cups/day. Finally, we computed daily intakes of each food item and then converted them to grams per day, using household measures.[18] Daily values for each item were calculated according to food composition, average of reported frequency, and specified portion size. As for nutrient intakes, it was calculated by adding together the nutrient contents of all foods and dishes. The nutrient intake for each participant was obtained by the Nutritionist IV software, a modified version for Iranian foods. Our previous study indicated that this FFQ provided valid and reliable measures of the average long-term dietary intakes.[1719]
Assessment of breast cancer: All patients with breast cancer were females with newly diagnosed stage I-IV breast cancer. They were recruited from Iranian nationality, for whom in-situ or invasive status of BC was confirmed by physical examination and mammography. Mammography is a type of X-ray imaging used for diseases diagnosis.[20] The harmful side effect of breast exposure with irradiation by mammography is very low which can be ignored.[21] This imaging method provides a black-and-white image of breast. For mammography, the patient was placed in a standing, horizontal, and vertical position; then breast was compressed for a few seconds between the pages and photography took place.[20]
Assessment of other variables
Body weight was measured by a trained nutritionist, without shoes, and with light clothing, using a weighing calibrated scale (Seca, Hamburg, Germany) to the nearest 100 g. Height was measured by a mounted tape, without shoes at a standing position near the wall, using a statiometer (Seca, Hamburg, Germany) to the nearest 0.5 cm. Body mass index (BMI) was calculated through weight in kilograms divided by height in squared meters. In terms of physical activity, short form of International Physical Activity Questionnaire was used through face-to-face interviews.[22] All results of the International Physical Activity Questionnaire were expressed as Metabolic Equivalents-hours per week. A pretested questionnaire was also used to collect data on age, marital status, place of residence, education, socioeconomic status, history of disease, family history of cancer, breast feeding history, smoking, menopausal status, alcohol use, and supplement use.
Statistical methods
Participants were categorized into quintiles based on the amounts of fruit and vegetable intake in their daily diet. General characteristics and dietary intakes of study participants across quintiles of fruit and vegetable intake were examined using one-way analysis of variance for continues variables and Chi-square for categorical variables. The association of fruit and vegetable intake with breast cancer was assessed by using logistic regression in different models. Age (continues) and energy intake (Kcal/d) were adjusted for in the first model. Additional controlling for region (urban/rural), marital status (yes/no), education (elementary/graduated/nongraduated), history of cancer (yes/no), physical activity (continues), family history of breast cancer (yes/no), menopausal status (premenopausal/postmenopausal), smoking (yes/no), alcohol consumption (yes/no), and socioeconomic status (poor/middle/high) was done in the second model. Further adjustment was done for dietary intakes of meat, soy, whole and refined grains, total dietary fat intake, and mutual effects of fruit and vegetables in the third model. Finally, we adjusted the analysis for BMI. Statistical analyses were carried out by using SPSS version 18. P values were considered significant at <.05.
Results
Overall, data on 350 cases and 700 controls were analyzed. General characteristics of study participants among cases and controls are shown in Table 1. In general, cases were younger and had higher BMI than controls. The higher percentage of cases had a family history of breast cancer than controls. Moreover, they were more likely to be uneducated than controls. A lower percentage of cases were married, as compared to controls. With regards to dietary intakes, cases had more total energy intake and consumption of dietary fats than controls, while dietary intakes of carbohydrates and proteins were less among them.
Table 1
General characteristics of study participants across cases and controls
Controls (n=700)
Cases (n=350)
P
a
Age (year)
61.04±10.35
65.28±11.24
<0.001
BMI# (kg/m2)
25.55±5.05
21.87±4.88
<0.001
Physically activity (METs)
34.87±6.58
35.43±6.73
0.20
Married (%)
88.3
74.6
<0.001
Education (%)
<0.001
Elementary
71.1
82.6
Undergraduate
16.3
12.3
Graduated
12.5
5.1
Current smoker (%)
13.0
17.4
0.06
Family history of cancer (%)
3.4
9.4
<0.001
Supplement user (%)
10.1
9.4
0.74
Energy (Kcal/d)
2,177.64±608.50
2,499.67±793.46
<0.001
Proteins (% of energy)
80.11±18.45
72.26±21.29
<0.001
Fats (% of energy)
81.87±18.26
90.04±25.29
<0.001
Carbohydrates (% of energy)
320.28±45.25
311.20±61.64
<0.01
Soy (g/d)
0.54±1.46
0.46±1.49
0.38
aObtained using one-way analysis of variance for continuous variables and Chi-square test for categorical variable
General characteristics of study participants across quintiles of fruits and vegetables intake are shown in Table 2. BMI in participants in the highest quintile of fruit consumption was higher than those at the lowest quintile. They also had higher percentage of academic education than those at the lowest category. With regards to dietary intake of vegetables, those at the top category of intake were younger and had higher mean BMI than those at the bottom. Higher percentage of participants at the top category were married and had academic education than those at the lowest category, while they were less likely to be current smokers.
Table 2
General characteristics of study participants across quintiles of fruit and vegetable intake
Quintiles of fruit intake
P
a
Q1 (n=210)
Q2 (n=210)
Q3 (n=209)
Q4 (n=211)
Q5 (n=210)
Age (year)
63.84±10.37
61.64±9.90
63.05±11.37
61.19±10.82
62.55±11.55
0.08
BMI (kg/m2)
23.43±4.88
23.92±5.32
24.87±5.26
24.61±5.45
24.80±5.41
0.01
Physically activity (METs)
34.85±6.56
34.57±6.56
35.10±7.49
35.32±6.45
35.45±6.03
0.65
Married (%)
76.7
84.8
84.4
87.1
85.7
0.12
Education (%)
<0.001
Elementary
90.5
76.2
79.1
62.2
66.7
Undergraduate
6.2
19.0
11.8
21.5
16.2
Graduated
3.3
4.8
9.1
16.3
17.1
Current smoker (%)
16.2
13.3
10.9
19.1
12.9
0.13
Family history of cancer (%)
4.8
6.2
4.3
5.7
6.2
0.86
Supplement user (%)
7.1
11.9
11.4
10.0
9.0
0.48
Quintiles of vegetables intake
P
a
Q1 (n=210)
Q2 (n=210)
Q3 (n=210)
Q4 (n=210)
Q5 (n=210)
Age (year)
65.42±11.03
61.72±10.55
61.45±10.14
61.87±11.07
61.81±10.97
<0.01
BMI (kg/m2)
22.78±4.92
24.03±5.87
24.07±4.98
24.73±5.21
26.01±4.92
<0.001
Physically activity (METs)
35.16±6.90
34.75±6.62
34.78±6.15
35.53±7.04
35.06±6.45
0.75
Married (%)
72.4
82.4
91.9
84.3
87.6
<0.001
Education (%)
<0.001
Elementary
86.2
81.9
74.8
72.9
59.0
Undergraduate
11.0
11.0
18.1
14.3
20.5
Graduated
2.8
7.1
7.1
12.8
20.5
Current smoker (%)
20.0
13.8
16.2
10.0
12.4
0.04
Family history of cancer (%)
7.1
5.7
4.8
5.7
3.8
0.64
Supplement user (%)
9.5
11.4
11.9
9.0
7.6
0.57
aObtained by the use of ANCOVA
Energy-adjusted dietary intakes of study participants across quintiles of fruits and vegetables consumption are compared in Table 3. Participants in quintile 5 of fruit intake had higher dietary intakes of total energy, dietary fibers, refined grains, seafoods, dairy, and vegetables than those in quintile 1. In contrast, they consumed less amounts of dietary carbohydrates in comparison to participants with the lowest intake of fruits. Comparing the highest category of vegetable intake with the lowest one, participants in the former category consumed more proteins, dietary fibers, refined grains, seafoods, dairy, fruits, and soy with higher total energy intake than the following category adherents. However, they consumed less dietary fat than their counterparts.
Table 3
Dietary intakes of study participants across quintiles of dietary intakes of fruits and vegetables
Quintiles of fruit intake
P
a
Q1 (n=210)
Q2 (n=210)
Q3 (n=209)
Q4 (n=211)
Q5 (n=210)
Energy (Kcal/d#)
1,984.36±659.01
2,114.66±595.83
2,211.96±596.30
2,306.60±582.44
2,807.78±721.68
<0.001
Proteins (% of energy)
75.90±19.95
76.35±18.79
78.21±17.85
79.92±19.82
77.10±22.19
0.23
Fats (% of energy)
81.92±19.24
86.15±17.00
87.05±22.55
83.42±17.73
84.42±27.53
0.09
Carbohydrates (% of energy)
324.45±50.16
314.02±41.77
309.96±48.61
317.41±47.13
320.47±65.66
0.04
Dietary Fiber (g/d)
22.28±4.99
21.75±3.66
21.80±4.34
22.73±4.23
23.42±6.82
<0.01
Whole grains (g/d)
334.78±180.11
306.66±133.83
300.93±146.17
309.69±138.35
332.38±168.26
0.07
Refined grains (g/d)
88.47±66.99
103.17±64.71
122.07±84.11
126.63±85.66
134.05±79.75
<0.001
Seafoods (g/d)
8.98±58.69
4.44±10.80
5.30±8.74
8.51±13.43
13.67±31.88
0.02
Dairies (g/d)
138.51±108.62
225.40±180.57
231.91±130.34
240.59±127.90
321.44±162.61
<0.001
Fruits (g/d)
35.72±15.26
78.54±11.55
121.68±15.09
184.25±22.84
404.41±185.04
<0.001
Vegetables (g/d)
47.35±36.85
62.93±47.04
80.65±76.34
88.70±63.09
125.15±98.52
<0.001
Soy (g/d)
0.54±1.60
0.44±1.16
0.58±1.83
0.54±1.28
0.48±1.39
0.87
Quintiles of vegetables intake
P
b
Q1 (n=210)
Q2 (n=210)
Q3 (n=210)
Q4 (n=210)
Q5 (n=210)
Energy (Kcal/d#)
1,984.10±672.01
2,146.90±622.51
2,273.08±617.30
2,363.43±621.99
2,657.40±736.62
<0.001
Proteins (% of energy)
79.29±24.35
72.53±14.55
76.98±18.52
77.06±18.33
81.60±20.87
<0.001
Fats (% of energy)
84.81±18.17
87.29±23.27
80.62±1.89
85.71±20.13
84.55±25.92
0.02
Carbohydrates (% of energy)
314.74±48.97
316.05±50.96
326.78±44.78
315.02±44.40
313.69±64.77
0.05
Dietary Fiber (g/d)
20.95±4.70
21.10±4.82
22.91±3.96
22.35±4.23
24.67±5.94
<0.001
Whole grains (g/d)
307.64±146.71
303.19±144.17
341.85±137.06
313.06±156.30
318.65±183.60
0.09
Refined grains (g/d)
86.06±70.28
103.75±69.27
111.67±56.59
125.80±76.04
147.10±99.96
<0.001
Seafoods (g/d)
6.15±18.43
4.16±8.76
5.14±9.05
6.22±9.36
19.21±64.39
<0.001
Dairies (g/d)
199.55±154.68
208.30±138.24
205.79±120.61
259.20±191.32
284.96±145.16
<0.001
Fruits (g/d)
112.24±102.89
137.36±133.82
131.27±102.77
180.84±157.00
262.58±203.17
<0.001
Vegetables (g/d)
15.52±8.47
39.76±6.93
65.12±7.51
95.84±10.85
188.48±90.49
<0.001
Soy (g/d)
0.33±1.35
0.44±1.49
0.40±1.12
0.56±1.24
0.85±1.97
<0.01
aObtained by the use of ANCOVA
Multivariable-adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for breast cancer across quintiles of dietary intake of fruits and vegetables are shown in Table 4. Participants with the highest dietary intake of fruits had higher odds of breast cancer than those with the lowest intake (OR: 3.26; 95% CI: 2.16-4.91). This association remained significant after adjustment for a wide range of confounding factors (OR: 8.23; 95% CI: 4.37-15.50). In contrast, those who consumed highest amounts of vegetables were less likely to have breast cancer, when compared to those with the lowest consumption (OR: 0.34; 95% CI: 0.23-0.52). This association also remained unchanged during controlling for the confounders, even at the full adjusted model (OR: 0.12; 95% CI: 0.06-0.24).
Table 4
Multivariate-adjusted odds ratios and 95% CIs for breast cancer in relation to dietary intake of fruits and vegetables
Quintiles of fruit intake
P
a
Q1 (n=210)
Q2 (n=210)
Q3 (n=211)
Q4 (n=209)
Q5 (n=210)
Crude
1
1.20 (0.78, 1.84)
0.83 (0.53, 1.29)
1.29 (0.85, 1.97)
3.26 (2.16, 4.91)
<0.001
Model 1
1
1.25 (0.80, 1.94)
0.74 (0.47, 1.17)
1.21 (0.78, 1.89)
2.28 (1.45, 3.59)
<0.01
Model 2
1
1.39 (0.88, 2.19)
0.84 (0.52, 1.35)
1.55 (0.97, 2.49)
3.03 (1.85, 4.97)
<0.001
Model 3
1
1.37 (0.85, 2.22)
0.85 (0.51, 1.42)
1.85 (1.12, 3.07)
4.81 (2.73, 8.47)
<0.001
Model 4
1
1.49 (0.88, 2.50)
0.98 (0.57, 1.70)
2.35 (1.36, 4.07)
8.23 (4.37, 15.50)
<0.001
Quintiles of vegetable intake
P
a
Q1 (n=210)
Q2 (n=210)
Q3 (n=210)
Q4 (n=210)
Q5 (n=210)
Crude
1
0.43 (0.29, 0.64)
0.22 (0.14, 0.35)
0.47 (0.32, 0.70)
0.34 (0.23, 0.52)
<0.001
Model 1
1
0.38 (0.25, 0.58)
0.16 (0.10, 0.26)
0.32 (0.21, 0.50)
0.16 (0.10, 0.26)
<0.001
Model 2
1
0.38 (0.24, 0.60)
0.17 (0.10, 0.29)
0.32 (0.20, 0.50)
0.15 (0.09, 0.26)
<0.001
Model 3
1
0.34 (0.21, 0.54)
0.18 (0.11, 0.30)
0.28 (0.17, 0.46)
0.11 (0.06, 0.20)
<0.001
Model 4
1
0.32 (0.19, 0.53)
0.17 (0.09, 0.29)
0.31 (0.18, 0.52)
0.12 (0.06, 0.24)
<0.001
Model 1: Adjusted for age and energy. Model 2: Additionally, adjusted for region, marital status, education, disease history, physical activity, family history of breast cancer, menopausal status, smoking, alcohol consumption, and socioeconomic status. Model 3: Further adjustment for intakes of fruit, vegetables, meat, soy, whole and refined grains, and total dietary fat. Model 4: Additional adjustment for BMI. aThe P for trend across increasing quintiles of fruit and vegetable intake was calculated using multivariable logistic regression by considering the categories as an ordinal variable
Multivariable-adjusted ORs and 95% CIs for breast cancer across quintiles of dietary intake of fruits and vegetables considering the menopausal status of participants are shown in Table 5. Although no significant associations were found between dietary intakes of fruits (OR: 2.56; 95% CI: 0.38, 17.17; P = .08) and vegetables (OR: 0.06; 95% CI: 0.00, 0.82; P = .84) with risk of breast cancer in premenopausal women, significant direct and inverse trends were found between increasing quintiles of fruits (OR: 16.80; 95% CI: 7.80, 36.21; P < .001) and vegetables (OR: 0.09; 95% CI: 0.04, 0.19; P < .001) intakes and risk of breast cancer in postmenopausal women, respectively.
Table 5
Multivariate-adjusted odds ratios and 95% CIs for breast cancer in relation to dietary intake of fruit and vegetables, stratified by the menopausal status
Quintiles of fruit intake
P
a
Q1 (n=210)
Q2 (n=210)
Q3 (n=211)
Q4 (n=209)
Q5 (n=210)
Premenopausal women
Crude
1
0.29 (0.08, 1.03)
0.18 (0.04, 0.75)
0.61 (0.22, 1.69)
1.40 (0.52, 3.76)
0.20
Model 1
1
0.27 (0.07, 0.99)
0.15 (0.03, 0.64)
0.53 (0.18, 1.52)
0.89 (0.30, 2.60)
0.61
Model 2
1
0.32 (0.08, 1.32)
0.14 (0.03, 0.65)
0.59 (0.17, 2.01)
1.20 (0.35, 4.05)
0.44
Model 3
1
0.35 (0.07, 1.60)
0.10 (0.01, 0.74)
0.82 (0.21, 3.09)
2.10 (0.50, 8.82)
0.16
Model 4
1
0.57 (0.09, 3.53)
0.17 (0.01, 1.97)
1.97 (0.34, 11.37)
2.56 (0.38, 17.17)
0.08
Postmenopausal women
Crude
1
1.50 (0.94, 2.37)
1.04 (0.65, 1.68)
1.56 (0.98, 2.49)
3.92 (2.49, 6.17)
<0.001
Model 1
1
1.58 (0.98, 2.55)
0.91 (0.55, 1.48)
1.38 (0.85, 2.24)
2.63 (1.59, 4.34)
<0.01
Model 2
1
1.73 (1.05, 2.85)
1.02 (0.61, 1.70)
1.87 (1.11, 3.14)
3.46 (2.00, 5.98)
<0.001
Model 3
1
1.78 (1.03, 3.08)
1.23 (0.70, 2.17)
2.94 (1.63, 5.30)
7.75 (3.96, 15.18)
<0.001
Model 4
1
2.08 (1.15, 3.75)
1.45 (0.78, 2.68)
4.24 (2.20, 8.17)
16.80 (7.80, 36.21)
<0.001
Quintiles of vegetable intake
P
a
Q1 (n=210)
Q2 (n=210)
Q3 (n=210)
Q4 (n=210)
Q5 (n=210)
Premenopausal women
Crude
1
0.19 (0.05, 0.64)
0.24 (0.07, 0.79)
0.40 (0.14, 1.14)
0.39 (0.13, 1.15)
0.45
Model 1
1
0.08 (0.02, 0.34)
0.10 (0.02, 0.39)
0.22 (0.07, 0.71)
0.11 (0.02, 0.40)
0.05
Model 2
1
0.07 (0.01, 0.32)
0.09 (0.02, 0.41)
0.22 (0.06, 0.75)
0.11 (0.02, 0.52)
0.12
Model 3
1
0.07 (0.01, 0.38)
0.12 (0.02, 0.63)
0.25 (0.06, 1.03)
0.06 (0.01, 0.38)
0.09
Model 4
1
0.02 (0.00, 0.19)
0.05 (0.00, 0.58)
0.42 (0.07, 2.44)
0.06 (0.00, 0.82)
0.84
Postmenopausal women
Crude
1
0.51 (0.33, 0.78)
0.23 (0.14, 0.36)
0.51 (0.33, 0.79)
0.34 (0.22, 0.54)
<0.001
Model 1
1
0.48 (0.30, 0.76)
0.17 (0.10, 0.29)
0.33 (0.20, 0.54)
0.16 (0.09, 0.28)
<0.001
Model 2
1
0.49 (0.30, 0.78)
0.19 (0.11, 0.32)
0.32 (0.19, 0.53)
0.14 (0.08, 0.26)
<0.001
Model 3
1
0.41 (0.24, 0.68)
0.19 (0.11, 0.33)
0.25 (0.14, 0.43)
0.09 (0.04, 0.17)
<0.001
Model 4
1
0.38 (0.22, 0.67)
0.17 (0.09, 0.31)
0.25 (0.14, 0.47)
0.09 (0.04, 0.19)
<0.001
Model 1: Adjusted for age and energy. Model 2: Additionally, adjusted for region, marital status, education, disease history, physical activity, family history of breast cancer, smoking, alcohol consumption, and socioeconomic status. Model 3: Further adjustment for intakes of fruit, vegetables, meat, soy, whole and refined grains, and total dietary fat. Model 4: Additional adjustment for BMI. aThe P for trend across increasing quintiles of fruit and vegetable intake was calculated using multivariable logistic regression by considering the categories as an ordinal variable
Discussion
We found a significant inverse association between the dietary intake of vegetables and the risk of breast cancer. In contrast, a high dietary intake of fruits was associated with an increased risk of breast cancer in the present study. Such associations were seen in postmenopausal women only.
Breast cancer is the most prevalent cancer among women worldwide.[23] We found an inverse association between vegetable intake and risk of breast cancer. This finding was in line with a recently published cohort study of women with 30 years of follow-up, in which higher intake of vegetables was associated with the reduced risk of breast cancer.[24] Consumption of vegetables was also inversely associated with the risk of estrogen receptor-negative/progesterone receptor-negative breast cancer in another cohort study.[25] However, some studies failed to find a significant association between vegetable intake and risk of breast cancer.[26] In addition, findings from a meta-analysis of cohort studies in 2017 showed no significant association between dietary intake of vegetables and risk of breast cancer prognosis.[1] It seems that the type of consumed vegetables plays an important role in this regard. Few studies have been done on specific types of vegetables in relation to the risk of breast cancer. For instance, higher intakes of cruciferous vegetables were linked to reduced risk of breast cancer in a meta-analysis.[27]
Sulforaphane is an organosulfur compound found in cruciferous vegetables like broccoli and mustard, which has shown potential in treating breast cancer. Sulforaphane has been found to effectively modulate histone deacetylases involved in chromatin remodeling, gene expression, and Nrf2 antioxidant signaling.[28] Breast cancer is a prevalent and potentially life-threatening form of cancer among women in Iran.[29]
Further studies considering different types of vegetables are recommended to shed light on this issue.
Unexpectedly, we found a positive association between dietary intake of fruits and the risk of breast cancer. This finding was against most published studies in this regard, in which a high intake of fruits has been related to reduced risk of breast cancer.[10] However, some studies failed to find such a significant inverse association.[3031] When we examined fruit intake among those in the highest quintile, we found that the average fruit intake in this quintile was 230 g/d. This amount was not so high, compared with other studies.[2432] Some people in this category were taking nearly 1200 gr/day of fruits. Type of fruits might help explain the association we found. For instance, the elevated risk of breast cancer among these women might be explained by the high intake of fructose-rich fruits such as apples and peaches. Earlier studies have shown that high fructose intake can in turn result in increased storage of lipids, which lead to elevated low-grade inflammation and eventually to several cancers.[33]
Menopausal status seems to affect the association between vegetable intake and breast cancer. We found the inverse association of vegetables intake with risk of breast cancer only among postmenopausal women, not in premenopausal women. Similar to our findings, a cohort study in the United States showed that greater adherence to the healthy diet was inversely linked to odds of breast cancer in postmenopausal, but not in premenopausal, women.[34] Differences in serum levels of sexual hormones, like estrogen, might provide some reason for these discrepant findings. Stage of breast cancer as well as estrogen receptors might also be involved in this story.
The exact mechanism through which dietary intake of vegetables might influence the risk of breast cancer has not been clearly known. However, some probable mechanisms are suggested. Vegetables are common sources of many beneficial micronutrients, including ascorbic acid.[35]
Luteolin, a flavonoid found in different fruits and vegetables has been known as an anticancer agent through inducing apoptosis and cell cycle arrest, and thorough inhibiting metastasis and angiogenesis in multiple cancer cell lines such as breast, colon, pancreatic, and lung, among others.[36]
High dietary intake of some of these micronutrients has been associated to lower risk of breast cancer incidence and mortality.[37] In addition, vegetables are rich in antioxidants, which has been protective associated with breast cancer.[3839] For instance, plasma total carotenoid concentration has been inversely associated with breast cancer recurrence.[40] Furthermore, vegetables are also common sources of dietary fiber which was related to the reduced risk of breast cancer.[41] Phytochemical content of vegetables including monoterpenes, resveratrol, and lignans can also play a role in this regard.[42] Cyanidin-3-glucoside, an anthocyanin present in many fruits and vegetables, might block ethanol-induced activation of the ErbB2/cSrc/FAK pathway, which is necessary for cell migration and invasion.[43] Fruit and vegetable consumption were each inversely associated with the risk of breast cancer, whereas meat consumption was positively related to risk.[32] Observed no significant association of risk of BC with either total consumption of fruits and vegetables (FVs) or with their subgroups among Iranian women, except for berry fruits which were showed that greater consumption of berries resulted in lower BC risk in study population.[44] This study is among rare investigations on the association of fruit and vegetable intake with the risk of breast cancer among Middle-Eastern population. This association was independent of other confounding variables because we adjusted the analysis for a wide range of potential confounders including dietary and nondietary covariates. However, some limitations should be kept in mind. Limitations such as age at menarche, age at first live birth, number of live births, and months of breastfeeding. Because of the observational case-control design of the study, it is impossible to confer causality. Moreover, selection and recall bias should not be ignored. In addition, we used FFQ to assess dietary intake of fruits and vegetables in study participants; therefore, misclassification of study participants cannot be excluded.
Conclusions
This study showed an inverse association between dietary intake of vegetables and a positive association between dietary intake of fruit and risk of breast cancer. These associations were observed in postmenopausal women only. Further prospective studies are required to re-examine these associations to expand the current knowledge.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest. |
Title: Bacterial profiles and their antibiotic susceptibility patterns in neonatal sepsis at the University of Gondar Comprehensive Specialized Hospital, Ethiopia | Body:
Background
Neonatal sepsis is a significant global health challenge, particularly in developing countries, where it is the leading cause of morbidity and mortality. It accounts for an estimated 3 million cases annually with a high mortality rate of 11–19% (Glaser et al., 2021; Fleischmann-Struzek et al., 2018). Immature immune systems and barriers heighten neonatal susceptibility to infections (Georges Pius et al., 2022). Sub-Saharan Africa bears a substantial burden, with neonatal sepsis leading to an estimated annual loss of 5.3–8.7 million disability-adjusted life years and an economic impact ranging from $10 billion to $469 billion (Ranjeva et al., 2018; Molloy et al., 2020). This issue is especially pronounced in East Africa, where prevalence reaches 29.7% (Abate et al., 2020).
In Ethiopia, the prevalence of neonatal sepsis is alarmingly high, with rates ranging from 45.8 to 78.3% in different regions (Bayih et al., 2021; Belachew and Tewabe, 2020; Mustefa et al., 2020; Bekele et al., 2022; Roble et al., 2022). This burden is further exacerbated by the increasing antimicrobial resistance (Chaurasia et al., 2019). Multiple risk factors are associated with neonatal infection, including low birth weight (Bayih et al., 2021; Belachew and Tewabe, 2020; Mustefa et al., 2020; Li et al., 2023), maternal history of urinary tract infection (Abate et al., 2020; Bayih et al., 2021; Belachew and Tewabe, 2020; Mustefa et al., 2020), formula feeding, cesarean section (Li et al., 2023), preterm birth (Abate et al., 2020; Bayih et al., 2021; Belachew and Tewabe, 2020; Mustefa et al., 2020; Li et al., 2023; Satar and Özlü, 2012), home delivery, prolonged labor, and premature rupture of membranes (Abate et al., 2020), antenatal urinary tract infection, and intrapartum fever (Bayih et al., 2021; Belachew and Tewabe, 2020; Mustefa et al., 2020). Despite advancements in neonatal care, diagnosing neonatal sepsis remains challenging, underscoring the importance of prompt antibiotic treatment (Ershad et al., 2019).
Regional variations exist in the spectrum of bacterial pathogens causing neonatal sepsis (Poyekar, 2022; Moni et al., 2020). While the predominant bacteria vary geographically, Gram-negative bacteria like Klebsiella spp. (Pokhrel et al., 2018; Acheampong et al., 2022; Bhat et al., 2011; Almohammady et al., 2020; Fenta et al., 2022; Pataskar et al., 2023; Bai et al., 2021; Siddiqui et al., 2023; Weldu et al., 2020), E. coli (Pokhrel et al., 2018; Acheampong et al., 2022; Bhat et al., 2011; Almohammady et al., 2020; Fenta et al., 2022; Pataskar et al., 2023; Bai et al., 2021), and E. cloacae complex (Pataskar et al., 2023; Bai et al., 2021) are commonly isolated. Particularly in developing countries, Gram-negative bacteria stand as the leading cause of morbidity and mortality (Kamalakannan, 2018; Bethou and Bhat, 2022). Among Gram-positive isolates, S. aureus (Pokhrel et al., 2018; Acheampong et al., 2022; Almohammady et al., 2020; Tessema et al., 2021; Yadav et al., 2018; Negussie et al., 2015) and CoNS (Pokhrel et al., 2018; Acheampong et al., 2022; Almohammady et al., 2020; Tessema et al., 2021; Yadav et al., 2018; Negussie et al., 2015; Aku et al., 2018) are frequently identified. Antibiotic resistance is a significant concern (Negussie et al., 2015), with alarmingly high levels of multidrug-resistant strains negatively impacting treatment outcomes (Uwe et al., 2022). This emphasizes the need for strict antibiotic use guidelines. Isolated bacteria often show high resistance to conventional antibiotics like ampicillin, cephalosporins, and piperacillin-tazobactam (Uwe et al., 2022; Verma et al., 2015). Studies from India report high rates of multidrug resistance in Acinetobacter spp. (82%), Klebsiella spp. (54%), and E. coli (38%) isolates (Chaurasia et al., 2016). Another Indian study found bacterial isolates resistant to aminoglycosides (74%), third/fourth-generation cephalosporins (95%), and carbapenems (56%) (Shah et al., 2022). Others indicate that 54% of isolated bacteria were resistant to at least one antibiotic (Sands et al., 2021). In Iran, K. pneumoniae showed the highest resistance to Cefixime (80.6%), while E. coli exhibited significant resistance to Ampicillin (61.8%) (Moftian et al., 2023). Even Gram-negative bacteria harbor multiple cephalosporin and carbapenem resistance genes, highlighting widespread antimicrobial resistance (Sands et al., 2021).
Local data is crucial for informing treatment strategies due to regional variations in bacterial spectrum and antimicrobial sensitivity patterns (Poyekar, 2022; Moni et al., 2020; Uwe et al., 2022; Verma et al., 2015). The burden of neonatal sepsis is worsened by the scarcity of accurate information on its causes and consequences in developing countries, including Ethiopia. Additionally, most studies are limited by small sample sizes (Sands et al., 2021).
Therefore, this study aimed to address the data gap by determining the most common bacterial etiologies of neonatal sepsis in Gondar Comprehensive Specialized Hospital (UoGCSH), a critical healthcare hub in northwestern Ethiopia, and assessing the antibiotic resistance patterns of these key pathogens. The findings will inform targeted treatment strategies to improve outcomes for critically ill neonates in the local hospital’s care and contribute valuable regional data to national efforts to combat the public health challenge of neonatal sepsis.
Materials and methods
Study design and setting
A cross-sectional study was conducted on neonates (aged ≤28 days) suspected of bloodstream infections who were admitted to the UoGCSH from January 2019 to December 2021. The hospital is located in the center of Gondar town, approximately 747 kilometers northwest of Addis Ababa, the capital city of Ethiopia. The hospital is a leading healthcare institution in the region and serves as a referral center for over 7 million people, catering to a diverse population from both urban and rural areas (Gobezie et al., 2023). The hospital is equipped with specialized facilities and a dedicated neonatal unit, which is essential for managing cases of neonatal sepsis. It offers various specialized diagnostic services to neonates and children. The neonatal unit is equipped with advanced medical facilities and staffed by skilled healthcare professionals, including pediatricians and nurses, who are dedicated to managing complex cases of neonatal sepsis. The hospital has several laboratory departments, including medical microbiology, clinical chemistry, hematology, serology, medical parasitology, and one main laboratory room. The microbiology laboratory plays a vital role in enabling the collection and analysis of blood samples to identify bacterial pathogens and assess their antibiotic susceptibility profiles.
Blood culture and bacterial identification
Standardized protocol-guided blood collection and bacterial identification were implemented. Two milliliters of blood were aseptically collected from each neonate and inoculated at a 1:10 ratio (blood: broth) into sterile Tryptone Soy Broth. Culture bottles incubated at 35-37°C for up to 7 days with daily monitoring for growth signs (hemolysis, turbidity, clot formation). Positive cultures underwent Gram staining and subculture onto various selective and differential media (blood agar, chocolate agar with 5% CO2, MacConkey agar, and mannitol salt agar) for further differentiation. These plates were incubated aerobically at 37°C for 18–24 h.
A two-step approach identified bacterial isolates. Initially, colony characteristics (color, size, shape, texture) were examined macroscopically. Subsequently, Gram-negative isolates underwent various conventional biochemical tests (indole, urease, lysine decarboxylase, triple sugar iron agar, citrate utilization, oxidase, and motility tests) for further differentiation. Gram-positive identification relied on Gram staining, catalase activity, coagulase testing, and hemolytic pattern analysis. This combined approach ensured comprehensive and reliable bacterial pathogen identification (Arega et al., 2018; Arega et al., 2017).
Antimicrobial susceptibility testing
The Kirby-Bauer disk diffusion method determined antimicrobial susceptibility patterns. Briefly, a standardized suspension of bacterial isolates was prepared in saline and adjusted to a 0.5 McFarland standard. This suspension was then inoculated onto Mueller-Hinton agar (non-fastidious bacteria) or Mueller-Hinton agar supplemented with 5% sheep blood (fastidious bacteria). Following inoculation, commercially available antibiotic disks (erythromycin, clindamycin, ampicillin, etc.) were applied, and plates were incubated at 37°C for 18–24 h. The diameters of inhibition zones surrounding each disk were measured, and susceptibility was categorized as sensitive, intermediate, or resistant according to the 2019 CLSI guidelines (Clinical and Laboratory Standards Institute, 2019).
Data extraction
The primary data source for this study was the records from the microbiology laboratory at UoGCSH. Six experienced laboratory professionals were involved in the data collection process guided by a standardized checklist. This checklist captured demographic information (age, gender), clinical setting (ICU admission), admission date, presenting complaints (fever, hypothermia), prior antibiotic use, culture and identification results, and susceptibility testing results for a broad spectrum of antibiotics. Antibiotic susceptibility results of intermediate susceptibility were categorized as “resistant” for analysis purposes.
Operational and case definitions
Antimicrobial susceptibility pattern
The response of specific bacterial isolates to various antibiotics, categorized as resistant, intermediate, or susceptible based on inhibition zone diameters. We categorized both “resistant” and “intermediate” patterns as resistant.
Multidrug resistance
The ability of a bacterial strain to resist three or more antimicrobial agents from different classes (Alam et al., 2011).
Data management and analysis
Data were checked for completeness and encoded in an Excel spreadsheet. Then, the data were exported to STATA version 17 for analysis. Descriptive statistics (frequency and percentage) were computed. Pearson’s chi-square test was used to assess the association between neonatal sepsis and potential risk factors. A p-value of less than 0.05 was considered statistically significant. Finally, the study results are presented in text, tables, and figures as appropriate.
Sample and data quality control
Standard operating procedures for microbiological techniques were followed throughout blood sample collection, transportation, culture media inoculation and incubation, and biochemical testing. Culture media sterility was ensured by random selection and incubation of 5% of prepared media. Media performance was regularly evaluated using known standard strains of E. coli (ATCC 25922), S. aureus (ATCC 25923), and P. aeruginosa (ATCC 27853). Microbiology experts monitored culture media inoculation, colony characterization, measurement, and interpretation of antibiotic susceptibility tests. The investigators developed a standardized data extraction form, and its accuracy, completeness, consistency, and reliability were assessed using a pilot study involving a random sample of 100 patient records.
Ethical consideration
Before commencing the research, the authors ensured adherence to ethical guidelines. They obtained ethical approval from the University of Gondar Institutional Review Board (IRB). Additionally, a letter of support from the College of Medicine and Health Sciences facilitated data collection. To ensure participant anonymity, patient personal information was omitted, and data were analyzed anonymously. Since the study was retrospective, the IRB waived the requirement for informed consent as obtaining consent from past participants would be impractical. Furthermore, to strengthen confidentiality, no personal identifiers were used, and only the investigator had access to the collected data. The research was conducted following the Declaration of Helsinki.
Study design and setting
A cross-sectional study was conducted on neonates (aged ≤28 days) suspected of bloodstream infections who were admitted to the UoGCSH from January 2019 to December 2021. The hospital is located in the center of Gondar town, approximately 747 kilometers northwest of Addis Ababa, the capital city of Ethiopia. The hospital is a leading healthcare institution in the region and serves as a referral center for over 7 million people, catering to a diverse population from both urban and rural areas (Gobezie et al., 2023). The hospital is equipped with specialized facilities and a dedicated neonatal unit, which is essential for managing cases of neonatal sepsis. It offers various specialized diagnostic services to neonates and children. The neonatal unit is equipped with advanced medical facilities and staffed by skilled healthcare professionals, including pediatricians and nurses, who are dedicated to managing complex cases of neonatal sepsis. The hospital has several laboratory departments, including medical microbiology, clinical chemistry, hematology, serology, medical parasitology, and one main laboratory room. The microbiology laboratory plays a vital role in enabling the collection and analysis of blood samples to identify bacterial pathogens and assess their antibiotic susceptibility profiles.
Blood culture and bacterial identification
Standardized protocol-guided blood collection and bacterial identification were implemented. Two milliliters of blood were aseptically collected from each neonate and inoculated at a 1:10 ratio (blood: broth) into sterile Tryptone Soy Broth. Culture bottles incubated at 35-37°C for up to 7 days with daily monitoring for growth signs (hemolysis, turbidity, clot formation). Positive cultures underwent Gram staining and subculture onto various selective and differential media (blood agar, chocolate agar with 5% CO2, MacConkey agar, and mannitol salt agar) for further differentiation. These plates were incubated aerobically at 37°C for 18–24 h.
A two-step approach identified bacterial isolates. Initially, colony characteristics (color, size, shape, texture) were examined macroscopically. Subsequently, Gram-negative isolates underwent various conventional biochemical tests (indole, urease, lysine decarboxylase, triple sugar iron agar, citrate utilization, oxidase, and motility tests) for further differentiation. Gram-positive identification relied on Gram staining, catalase activity, coagulase testing, and hemolytic pattern analysis. This combined approach ensured comprehensive and reliable bacterial pathogen identification (Arega et al., 2018; Arega et al., 2017).
Antimicrobial susceptibility testing
The Kirby-Bauer disk diffusion method determined antimicrobial susceptibility patterns. Briefly, a standardized suspension of bacterial isolates was prepared in saline and adjusted to a 0.5 McFarland standard. This suspension was then inoculated onto Mueller-Hinton agar (non-fastidious bacteria) or Mueller-Hinton agar supplemented with 5% sheep blood (fastidious bacteria). Following inoculation, commercially available antibiotic disks (erythromycin, clindamycin, ampicillin, etc.) were applied, and plates were incubated at 37°C for 18–24 h. The diameters of inhibition zones surrounding each disk were measured, and susceptibility was categorized as sensitive, intermediate, or resistant according to the 2019 CLSI guidelines (Clinical and Laboratory Standards Institute, 2019).
Data extraction
The primary data source for this study was the records from the microbiology laboratory at UoGCSH. Six experienced laboratory professionals were involved in the data collection process guided by a standardized checklist. This checklist captured demographic information (age, gender), clinical setting (ICU admission), admission date, presenting complaints (fever, hypothermia), prior antibiotic use, culture and identification results, and susceptibility testing results for a broad spectrum of antibiotics. Antibiotic susceptibility results of intermediate susceptibility were categorized as “resistant” for analysis purposes.
Operational and case definitions
Antimicrobial susceptibility pattern
The response of specific bacterial isolates to various antibiotics, categorized as resistant, intermediate, or susceptible based on inhibition zone diameters. We categorized both “resistant” and “intermediate” patterns as resistant.
Multidrug resistance
The ability of a bacterial strain to resist three or more antimicrobial agents from different classes (Alam et al., 2011).
Antimicrobial susceptibility pattern
The response of specific bacterial isolates to various antibiotics, categorized as resistant, intermediate, or susceptible based on inhibition zone diameters. We categorized both “resistant” and “intermediate” patterns as resistant.
Multidrug resistance
The ability of a bacterial strain to resist three or more antimicrobial agents from different classes (Alam et al., 2011).
Data management and analysis
Data were checked for completeness and encoded in an Excel spreadsheet. Then, the data were exported to STATA version 17 for analysis. Descriptive statistics (frequency and percentage) were computed. Pearson’s chi-square test was used to assess the association between neonatal sepsis and potential risk factors. A p-value of less than 0.05 was considered statistically significant. Finally, the study results are presented in text, tables, and figures as appropriate.
Sample and data quality control
Standard operating procedures for microbiological techniques were followed throughout blood sample collection, transportation, culture media inoculation and incubation, and biochemical testing. Culture media sterility was ensured by random selection and incubation of 5% of prepared media. Media performance was regularly evaluated using known standard strains of E. coli (ATCC 25922), S. aureus (ATCC 25923), and P. aeruginosa (ATCC 27853). Microbiology experts monitored culture media inoculation, colony characterization, measurement, and interpretation of antibiotic susceptibility tests. The investigators developed a standardized data extraction form, and its accuracy, completeness, consistency, and reliability were assessed using a pilot study involving a random sample of 100 patient records.
Ethical consideration
Before commencing the research, the authors ensured adherence to ethical guidelines. They obtained ethical approval from the University of Gondar Institutional Review Board (IRB). Additionally, a letter of support from the College of Medicine and Health Sciences facilitated data collection. To ensure participant anonymity, patient personal information was omitted, and data were analyzed anonymously. Since the study was retrospective, the IRB waived the requirement for informed consent as obtaining consent from past participants would be impractical. Furthermore, to strengthen confidentiality, no personal identifiers were used, and only the investigator had access to the collected data. The research was conducted following the Declaration of Helsinki.
Results
Socio-demographic and clinical characteristics of study participants
A total of 1,236 study participants were enrolled in the study. The majority, 747 (60.4%), were male, and 772 (62.4%) were aged less than 7 days. The primary clinical setting was the NICU, which accounted for 1,215 (98.2%) of the participants. The distribution of cases by year of diagnosis showed that the highest number of cases, 680 (55.0%), occurred in 2021. A significant proportion of patients, 1,174 (95.2%), experienced fever, with 1,190 (96.2%) reporting fever onset before admission. Furthermore, 384 (31.0%) had received antibiotic therapy within 2 days before admission. Increased respiration was observed in 503 (40.7%) participants, and a similar proportion, 503 (40.7%), were suspected of having pneumonia (Table 1).
Table 1
Socio-demographic characteristics of bloodstream infections suspected study participants at UoGCSH, Ethiopia (n = 1,236).
Variables
Category
Frequency
Percentage (%)
Gender
Male
747
60.4
Female
490
39.6
Age in days
≥7
465
37.6
<7
772
62.4
Clinical setting
NICU
1,215
98.2
Preterm
22
1.8
Year of diagnosis
2019
252
20.4
2020
304
24.6
2021
680
55.0
Fever
Yes
1,174
95.2
No
59
4.8
Fever before admission
Yes
1,190
96.2
No
47
3.8
Hypothermia
Yes
29
2.3
No
1,208
97.7
Hypotension
Yes
25
2.0
No
1,212
98.0
Increased respiration
Yes
503
40.7
No
734
59.3
Suspicion of pneumonia
Yes
503
40.7
No
734
59.3
Suspicion meningitis
Yes
164
13.3
No
1,073
86.7
Suspicion neonatal infection
Yes
205
16.6
No
1,031
83.4
Antibiotic therapy within 2 days before admission
Yes
384
31.0
No
853
69.0
Sign of microbial growth within 2 days of incubation
Yes
403
32.7
No
829
67.3
Prevalence of bacterial isolates
The overall prevalence of microbial isolates was 25.4% (314/1236). Among this, bacterial pathogens and yeast cells accounted for 90.5% (284/314) and 9.6% (30/314), respectively. The prevalence of bacterial pathogens from the overall septicemia suspected patients was 23% (284/1236). Among the bacterial isolates, Gram-negative bacteria were predominant, comprising 75.3% (214/284) of the identified pathogens, while Gram-positive bacteria accounted for 24.7% (70/284). Additionally, 4 Bacillus spp. isolates were identified as contaminants (Figure 1). The most frequently isolated bacterial pathogens were K. pneumoniae at 38.7% (110/284), followed by S. aureus at 13% (37/284), and Acinetobacter spp. at 8.1% (30/284). Other significant findings included E. coli at 6.3% (18/284), and Non-fermenting Gram-negative rods (NFGNR) at 6% (17/284) (Table 2).
Figure 1
Frequency of bacterial pathogens isolated from neonates suspected of bloodstream infections at UoGCSH, 2024.
Table 2
Frequency of bacterial pathogens isolated from patients suspected of septicemia at the UoGCSH, Ethiopia, 2024 (n = 284 isolates).
Bacterial isolates
Frequency
Percentage (%)
K. pneumoniae
110
38.7
S. aureus
37
13.0
Acinetobacter spp.
23
8.1
E. coli
18
6.3
NFGNR
17
6.0
E. cloacae
15
5.3
S. viridans
15
5.3
K. ozaenae
9
3.2
K. oxytocia
8
2.8
Enterococcus spp.
7
2.5
Citrobacter spp.
3
1.1
P. aerogensa
3
1.1
S. typhi
3
1.1
S. agalactiae
3
1.1
Others*
13
4.6
Total
284
100.0
Others* (CoNS, P. mirabilis, P. stuartii, S. pyogens, K. rhinoscleromatis, N. meningitides, Salmonella Group A, Serratia spp., S. pneumoniae).
Antimicrobial susceptibility patterns of bacterial isolates
Among Gram-positive isolates, CoNS exhibited 100% (2/2) resistance to penicillin, oxacillin, and gentamicin. S. aureus showed a high resistance rate of 94.6% (35/37) of isolates to penicillin, 64.9% to oxacillin, and 40.5% to gentamicin. Similarly, for S. viridans 73.3% (11/15) of isolates were resistant to ampicillin and 20% (3/15) were resistant to vancomycin. The single isolate of S. pneumoniae showed no resistance to the tested antibiotics. Enterococcus species demonstrated 85.7% (6/7) resistance to penicillin, 57% (4/7) to vancomycin, and 71.4% (5/7) to chloramphenicol. S. agalactiae exhibited no resistance to ampicillin and vancomycin antimicrobial agents. S. pyogenes showed 50% (1/2) resistance to vancomycin. This study highlights significant resistance patterns, especially in CoNS, S. aureus, and Enterococcus spp., necessitating careful consideration of antibiotic selection for effective treatment (Table 3).
Table 3
Antimicrobial sensitivity test result for Gram-positive bacterial pathogens isolated from suspected bloodstream infections at UoGCSH, Ethiopia, 2024.
Isolated organisms (n)
Anti-microbial susceptibility test
PEN
AMP
OXC
CIP
GEN
VAN
CAF
R n (%)
R n (%)
R n (%)
R n (%)
R n (%)
R n (%)
R n (%)
CoNS (2)
2 (100)
Na
2 (100)
Na
2 (100)
Na
Na
S. aureus (37)
35 (94.6)
Na
24 (64.9)
Na
15 (40.5)
Na
Na
S. viridans (15)
Na
11 (73.3)
Na
Na
Na
3 (20)
Na
S. pneumoniae (1)
Na
0
Na
Na
Na
0
Na
Enterococcus spp. (7)
6 (85.7)
Na
Na
Na
Na
4 (57)
5 (71.4)
S. agalactiae (3)
Na
0
Na
Na
Na
0
Na
S. pyogenes (2)
Na
0
Na
Na
Na
1 (50)
Na
PEN, Penicillin; AMP, Ampicillin; OXC, Oxacillin; CIP, Ciprofloxacin; GEN, Gentamicin; VAN, Vancomycin; CAF, Chloramphenicol; Na, Not applicable.
A significant burden of antimicrobial resistance among Gram-negative bacterial isolates recovered among the study participants. Among the isolated bacterial pathogens, K. pneumoniae was the most concerning resistance profile for meropenem (88.1%), ceftazidime (83.6%), ceftriaxone (83.6%), and amoxicillin-clavulanate (69%). Conversely, E. coli showed relatively lower resistance rates, with the highest resistance observed for ampicillin (66.7%), gentamicin (61.1%), and amoxicillin-clavulanate (55.6%). Acinetobacter spp. exhibited moderate resistance to ceftriaxone (52.2%), ceftazidime (47.8%), and amoxicillin-clavulanate (47.8%), but remained largely susceptible to meropenem (13%) and ciprofloxacin (21.7%). E. cloacae isolate showed high resistance to ampicillin (93.3%) and amoxicillin-clavulanate (86.7%), with moderate resistance to other antibiotics tested. The NFGNR group displayed variable resistance patterns, with the highest rates observed for ceftazidime (64.7%) and ceftriaxone (64.7%). Among bacterial isolates, Bacillus spp. were more likely contaminants and has not been tested for antimicrobial agents. Furthermore, K. ozaenae isolates exhibited particularly high resistance to ceftazidime (88.9%), ceftriaxone (88.9%), and trimethoprim-sulfamethoxazole (88.9%) (Table 4).
Table 4
Antimicrobial sensitivity test results for Gram-negative bacterial isolates from bloodstream infections suspected neonates at UoGCSH, Ethiopia.
Isolated organisms (n)
Anti-microbial susceptibility test
AMP
AMC
PTZ
CAZ
CRO
CIP
MER
SXT
GEN
AMK
CAF
TOB
PEF
R n (%)
R n (%)
R n (%)
R n (%)
R n (%)
R n (%)
R n (%)
R n (%)
R n (%)
R n (%)
R n (%)
R n (%)
R n (%)
K. pneumoniae (110)
Na
76 (69)
67 (61)
92 (83.6)
92 (83.6)
79 (71.8)
54 (49)
96 (88.1)
83 (75.4)
71 (64.5)
81 (73.7)
83 (75.5)
78 (70.9)
E. coli (18)
12 (66.7)
10 (55.6)
9 (50)
8 (44.4)
8 (44.4)
10 (55.6)
5 (27.8)
9 (50)
11 (61.1)
9 (50)
10 (55.6)
11 (61.4)
8 (44.4)
Acinetobacter spp. (23)
Na
Na
11 (47.8)
11 (47.8)
12 (52.2)
5 (21.7)
3 (13)
Na
8 (34.8)
8 (34.8)
Na
8 (34.8)
6 (26)
E. cloacae (15)
14 (93.3)
13 (86.7)
9 (60)
9 (60)
8 (53.3)
5 (33.3)
6 (40)
6 (40)
6 (40)
8 (53.3)
6 (40)
6 (40)
5 (33.3)
NFGNR (17)
Na
Na
6 (35.3)
11 (64.7)
11 (64.7)
5 (29.4)
6 (35.3)
Na
5 (29.4)
4 (23.5)
Na
4 (23.5)
3 (17.6)
K. ozaenae (9)
Na
7 (77.8)
4 (44.4)
8 (88.9)
8 (88.9)
5 (55.6)
6 (66.7)
8 (88.9)
6 (70)
4 (40)
7 (77.8)
7 (70)
6 (60)
P. aeruginosa (3)
Na
Na
2 (66.7)
3 (100)
2 (66.7)
0
0
Na
1 (33.3)
1 (33.3)
1 (33.3)
1 (33.3)
0
Citrobacter spp. (3)
Na
Na
0
1 (33.3)
1 (33.3)
0
0
1 (30)
1 (33.3)
0
1 (30)
1 (33.3)
0
P. stuartii (2)
1 (50)
0
0
1 (50)
1 (50)
0
0
0
0
0
0
0
0
K. oxytoca (8)
Na
7 (87.5)
7 (87.5)
8 (100)
8 (100)
7 (87.5)
6 (75)
8 (100)
6 (75)
4 (50)
4 (50)
7 (87.5)
7 (87.5)
P. mirabilis (2)
2 (100)
0
0
0
0
0
2 (100)
2 (100)
0
2 (100)
0
0
0
K. rhinoscleromatis (1)
Na
0
0
0
0
0
0
0
0
0
0
0
0
S. typhi (3)
3 (100)
2 (66.7)
1 (33.3)
2 (66.7)
2 (66.7)
0
1 (33.3)
2 (66.7)
2 (66.7)
0
0
2 (66.7)
0
Salmonella group A (1)
1 (100)
1 (100)
0
1 (100)
1 (100)
0
1 (100)
1 (100)
1 (100)
0
0
1 (100)
0
Serratia spp. (1)
0
0
0
0
0
0
0
0
1 (100)
0
0
0
0
N. meningitides (1)
Na
Na
Na
Na
0
0
0
0
Na
Na
Na
Na
0
AMP, Ampicillin; AMC, Amoxicillin-clavulanate; PTZ, Piperacillin_tazobactam; CAZ, Ceftazidime; CRO, Ceftriaxone; CIP, Ciprofloxacin; MER, Meropenem; SXT, Trimethoprim-sulfamethoxazole; GEN, Gentamycin; AMK, Amikacin; CAF, Chloramphenicol; TOB, Tobramycine; PEF, Pefloxacin; Na, Not applicable.
Multidrug resistance pattern of bacterial isolates
The isolates exhibited a concerning level of multidrug resistance with 61.6% (175/284) of the isolates being resistant to three or more antibiotic classes. The data shows that K. pneumoniae had the highest rate of multidrug resistance, with 90.9% (100/284) of the isolates being resistant to three or more antibiotic classes. Notably, K. ozaenae, K. oxytocia, and P. mirabilis exhibited 100% (2/2) multidrug resistance. Other bacteria such as S. aureus, Acinetobacter spp., E. coli, and NFGNR also demonstrated high MDR rates at 40.5% (15/284), 43.5% (10/284), 61.1% (11/284), and 41.2% (7/284), respectively. On the other hand, Enterococcus spp. and S. viridans S. agalactiae, CoNS, P. stuartii, and S. pyogens did not show any multidrug resistance. The data indicates a concerning level of multidrug resistance among the bacterial isolates, with 61.6% of the total isolates being resistant to three or more antibiotic classes (Table 5).
Table 5
Multidrug resistance patterns of bacterial isolates from neonates suspected of bloodstream infections at UoGCSH, 2024.
Bacterial isolates
Degree of microbial resistance
Total MDR isolates ≥ R3 n (%)
R0
R1
R2
R3
R4
K. pneumoniae (110)
3
3
4
3
97
100 (90.9)
S. aureus (37)
13
9
0
15
0
15 (40.5)
Acinetobacter spp. (23)
7
3
3
1
9
10 (43.5)
E. coli (18)
4
1
2
1
10
11 (61.1)
NFGNR (17)
3
2
5
1
6
7 (41.2)
E. cloacae (15)
2
4
2
0
7
7 (46.7)
S. viridans (15)
5
9
1
0
0
0 (0)
K. ozaenae (9)
0
0
0
0
9
9 (100)
Enterococcus spp. (7)
1
3
3
0
0
0 (0)
K. oxytocia (8)
0
0
0
0
8
8 (100)
Citrobacter spp. (3)
1
0
1
0
1
1 (33.3)
P. aeruginosa (3)
0
1
0
1
1
2 (66.7)
S. typhi (3)
1
0
0
0
2
2 (66.7)
S. agalactiae (3)
3
0
0
0
0
0 (0)
CoNS (2)
0
0
2
0
0
0 (0)
P. mirabilis (2)
0
0
0
2
0
2 (100)
P. stuartii (2)
1
0
1
0
0
0 (0)
S. pyogens (2)
1
0
1
0
0
0 (0)
K. rhinoscleromatis (1)
1
0
0
0
0
0 (0)
N. meningitides (1)
1
0
0
0
0
0 (0)
S. proup A (1)
0
0
0
0
1
1 (100)
Serratia spp. (1)
0
1
0
0
0
0 (0)
S. pneumoniae (1)
1
0
0
0
0
0 (0)
Total n (%)
16.9 (48/284)
12.7 (36/284)
8.8 (25/284)
61.6 (175/284)
R0: no antibiotic resistance; R1: resistance to one category of antimicrobial agent; R2: resistance to two different categories of antimicrobial agents; R3: resistance to three different categories of antimicrobial agents; R4: ≥ resistance to four different categories of antimicrobial agents.
Factors associated with bloodstream infections
This analysis investigated factors associated with bloodstream infections using Pearson’s chi-square test. While no significant association was found between positive blood cultures and gender, fever before admission, fever after admission, and hypothermia, several other factors emerged as important. Patients with increased respiration and suspected pneumonia were more likely to have positive cultures (p = 0.004). Likewise, suspected meningitis also showed a significant association (p = 0.009). Interestingly, prior antibiotic use did not statistically influence the results. Notably, the year of admission was the only demographic factor with a significant association. Patients admitted in 2021 had a higher proportion of positive cultures compared to those admitted in 2019 or 2020 (p < 0.001) (Table 6).
Table 6
Factors associated with positive bacterial blood cultures among neonates with suspected bloodstream infection at UoGCSH (2019–2021).
Variables
Category
Blood culture result
Pearson x2
p-value
Negative n (%)
Positive n (%)
Gender
Male
547 (44.2)
200 (16.2)
1.592
0.207
Female
373 (30.2)
117 (9.5)
Age in years
>7
573 (46.3)
199 (16.1)
0.161
0.689
≤7
347 (28.1)
118 (9.5)
Year of admission
2019
165 (13.4)
87 (7.0)
15.666
<0.001*
2020
240 (19.4)
64 (5.2)
2021
514 (41.6)
166 (13.4)
Fever
Yes
872 (70.7)
302 (24.5)
0.004
0.948
No
44 (3.6)
15 (1.2)
Fever before admission
Yes
885 (71.5)
305 (24.7)
0.000
0.986
No
35 (2.8)
12 (1.0)
Hypothermia
Yes
20 (1.6)
9 (0.7)
0.305
0.581
No
900 (72.8)
308 (24.9)
Hypotension
Yes
16 (1.3)
9 (0.7)
1.080
0.299
No
904 (73.1)
308 (24.9)
Increased respiration
Yes
391 (31.6)
112 (9.1)
8.254
0.004*
No
529 (42.8)
205 (16.6)
Suspicion of pneumonia
Yes
391 (31.6)
112 (9.1)
8.254
0.004*
No
529 (42.8)
205 (16.6)
Suspicion of meningitis
Yes
137 (11.1)
27 (2.2)
6.868
0.009*
No
783 (63.3)
290 (23.4)
Suspicion of neonatal infection
Yes
142 (11.5)
63 (5.1)
0.883
0.348
No
777 (62.9)
254 (20.6)
Antibiotic therapy before laboratory diagnosis
Yes
279 (22.6)
105 (8.5)
0.432
0.511
No
641 (51.8)
212 (17.1)
*Statistically significant association.
Socio-demographic and clinical characteristics of study participants
A total of 1,236 study participants were enrolled in the study. The majority, 747 (60.4%), were male, and 772 (62.4%) were aged less than 7 days. The primary clinical setting was the NICU, which accounted for 1,215 (98.2%) of the participants. The distribution of cases by year of diagnosis showed that the highest number of cases, 680 (55.0%), occurred in 2021. A significant proportion of patients, 1,174 (95.2%), experienced fever, with 1,190 (96.2%) reporting fever onset before admission. Furthermore, 384 (31.0%) had received antibiotic therapy within 2 days before admission. Increased respiration was observed in 503 (40.7%) participants, and a similar proportion, 503 (40.7%), were suspected of having pneumonia (Table 1).
Table 1
Socio-demographic characteristics of bloodstream infections suspected study participants at UoGCSH, Ethiopia (n = 1,236).
Variables
Category
Frequency
Percentage (%)
Gender
Male
747
60.4
Female
490
39.6
Age in days
≥7
465
37.6
<7
772
62.4
Clinical setting
NICU
1,215
98.2
Preterm
22
1.8
Year of diagnosis
2019
252
20.4
2020
304
24.6
2021
680
55.0
Fever
Yes
1,174
95.2
No
59
4.8
Fever before admission
Yes
1,190
96.2
No
47
3.8
Hypothermia
Yes
29
2.3
No
1,208
97.7
Hypotension
Yes
25
2.0
No
1,212
98.0
Increased respiration
Yes
503
40.7
No
734
59.3
Suspicion of pneumonia
Yes
503
40.7
No
734
59.3
Suspicion meningitis
Yes
164
13.3
No
1,073
86.7
Suspicion neonatal infection
Yes
205
16.6
No
1,031
83.4
Antibiotic therapy within 2 days before admission
Yes
384
31.0
No
853
69.0
Sign of microbial growth within 2 days of incubation
Yes
403
32.7
No
829
67.3
Prevalence of bacterial isolates
The overall prevalence of microbial isolates was 25.4% (314/1236). Among this, bacterial pathogens and yeast cells accounted for 90.5% (284/314) and 9.6% (30/314), respectively. The prevalence of bacterial pathogens from the overall septicemia suspected patients was 23% (284/1236). Among the bacterial isolates, Gram-negative bacteria were predominant, comprising 75.3% (214/284) of the identified pathogens, while Gram-positive bacteria accounted for 24.7% (70/284). Additionally, 4 Bacillus spp. isolates were identified as contaminants (Figure 1). The most frequently isolated bacterial pathogens were K. pneumoniae at 38.7% (110/284), followed by S. aureus at 13% (37/284), and Acinetobacter spp. at 8.1% (30/284). Other significant findings included E. coli at 6.3% (18/284), and Non-fermenting Gram-negative rods (NFGNR) at 6% (17/284) (Table 2).
Figure 1
Frequency of bacterial pathogens isolated from neonates suspected of bloodstream infections at UoGCSH, 2024.
Table 2
Frequency of bacterial pathogens isolated from patients suspected of septicemia at the UoGCSH, Ethiopia, 2024 (n = 284 isolates).
Bacterial isolates
Frequency
Percentage (%)
K. pneumoniae
110
38.7
S. aureus
37
13.0
Acinetobacter spp.
23
8.1
E. coli
18
6.3
NFGNR
17
6.0
E. cloacae
15
5.3
S. viridans
15
5.3
K. ozaenae
9
3.2
K. oxytocia
8
2.8
Enterococcus spp.
7
2.5
Citrobacter spp.
3
1.1
P. aerogensa
3
1.1
S. typhi
3
1.1
S. agalactiae
3
1.1
Others*
13
4.6
Total
284
100.0
Others* (CoNS, P. mirabilis, P. stuartii, S. pyogens, K. rhinoscleromatis, N. meningitides, Salmonella Group A, Serratia spp., S. pneumoniae).
Antimicrobial susceptibility patterns of bacterial isolates
Among Gram-positive isolates, CoNS exhibited 100% (2/2) resistance to penicillin, oxacillin, and gentamicin. S. aureus showed a high resistance rate of 94.6% (35/37) of isolates to penicillin, 64.9% to oxacillin, and 40.5% to gentamicin. Similarly, for S. viridans 73.3% (11/15) of isolates were resistant to ampicillin and 20% (3/15) were resistant to vancomycin. The single isolate of S. pneumoniae showed no resistance to the tested antibiotics. Enterococcus species demonstrated 85.7% (6/7) resistance to penicillin, 57% (4/7) to vancomycin, and 71.4% (5/7) to chloramphenicol. S. agalactiae exhibited no resistance to ampicillin and vancomycin antimicrobial agents. S. pyogenes showed 50% (1/2) resistance to vancomycin. This study highlights significant resistance patterns, especially in CoNS, S. aureus, and Enterococcus spp., necessitating careful consideration of antibiotic selection for effective treatment (Table 3).
Table 3
Antimicrobial sensitivity test result for Gram-positive bacterial pathogens isolated from suspected bloodstream infections at UoGCSH, Ethiopia, 2024.
Isolated organisms (n)
Anti-microbial susceptibility test
PEN
AMP
OXC
CIP
GEN
VAN
CAF
R n (%)
R n (%)
R n (%)
R n (%)
R n (%)
R n (%)
R n (%)
CoNS (2)
2 (100)
Na
2 (100)
Na
2 (100)
Na
Na
S. aureus (37)
35 (94.6)
Na
24 (64.9)
Na
15 (40.5)
Na
Na
S. viridans (15)
Na
11 (73.3)
Na
Na
Na
3 (20)
Na
S. pneumoniae (1)
Na
0
Na
Na
Na
0
Na
Enterococcus spp. (7)
6 (85.7)
Na
Na
Na
Na
4 (57)
5 (71.4)
S. agalactiae (3)
Na
0
Na
Na
Na
0
Na
S. pyogenes (2)
Na
0
Na
Na
Na
1 (50)
Na
PEN, Penicillin; AMP, Ampicillin; OXC, Oxacillin; CIP, Ciprofloxacin; GEN, Gentamicin; VAN, Vancomycin; CAF, Chloramphenicol; Na, Not applicable.
A significant burden of antimicrobial resistance among Gram-negative bacterial isolates recovered among the study participants. Among the isolated bacterial pathogens, K. pneumoniae was the most concerning resistance profile for meropenem (88.1%), ceftazidime (83.6%), ceftriaxone (83.6%), and amoxicillin-clavulanate (69%). Conversely, E. coli showed relatively lower resistance rates, with the highest resistance observed for ampicillin (66.7%), gentamicin (61.1%), and amoxicillin-clavulanate (55.6%). Acinetobacter spp. exhibited moderate resistance to ceftriaxone (52.2%), ceftazidime (47.8%), and amoxicillin-clavulanate (47.8%), but remained largely susceptible to meropenem (13%) and ciprofloxacin (21.7%). E. cloacae isolate showed high resistance to ampicillin (93.3%) and amoxicillin-clavulanate (86.7%), with moderate resistance to other antibiotics tested. The NFGNR group displayed variable resistance patterns, with the highest rates observed for ceftazidime (64.7%) and ceftriaxone (64.7%). Among bacterial isolates, Bacillus spp. were more likely contaminants and has not been tested for antimicrobial agents. Furthermore, K. ozaenae isolates exhibited particularly high resistance to ceftazidime (88.9%), ceftriaxone (88.9%), and trimethoprim-sulfamethoxazole (88.9%) (Table 4).
Table 4
Antimicrobial sensitivity test results for Gram-negative bacterial isolates from bloodstream infections suspected neonates at UoGCSH, Ethiopia.
Isolated organisms (n)
Anti-microbial susceptibility test
AMP
AMC
PTZ
CAZ
CRO
CIP
MER
SXT
GEN
AMK
CAF
TOB
PEF
R n (%)
R n (%)
R n (%)
R n (%)
R n (%)
R n (%)
R n (%)
R n (%)
R n (%)
R n (%)
R n (%)
R n (%)
R n (%)
K. pneumoniae (110)
Na
76 (69)
67 (61)
92 (83.6)
92 (83.6)
79 (71.8)
54 (49)
96 (88.1)
83 (75.4)
71 (64.5)
81 (73.7)
83 (75.5)
78 (70.9)
E. coli (18)
12 (66.7)
10 (55.6)
9 (50)
8 (44.4)
8 (44.4)
10 (55.6)
5 (27.8)
9 (50)
11 (61.1)
9 (50)
10 (55.6)
11 (61.4)
8 (44.4)
Acinetobacter spp. (23)
Na
Na
11 (47.8)
11 (47.8)
12 (52.2)
5 (21.7)
3 (13)
Na
8 (34.8)
8 (34.8)
Na
8 (34.8)
6 (26)
E. cloacae (15)
14 (93.3)
13 (86.7)
9 (60)
9 (60)
8 (53.3)
5 (33.3)
6 (40)
6 (40)
6 (40)
8 (53.3)
6 (40)
6 (40)
5 (33.3)
NFGNR (17)
Na
Na
6 (35.3)
11 (64.7)
11 (64.7)
5 (29.4)
6 (35.3)
Na
5 (29.4)
4 (23.5)
Na
4 (23.5)
3 (17.6)
K. ozaenae (9)
Na
7 (77.8)
4 (44.4)
8 (88.9)
8 (88.9)
5 (55.6)
6 (66.7)
8 (88.9)
6 (70)
4 (40)
7 (77.8)
7 (70)
6 (60)
P. aeruginosa (3)
Na
Na
2 (66.7)
3 (100)
2 (66.7)
0
0
Na
1 (33.3)
1 (33.3)
1 (33.3)
1 (33.3)
0
Citrobacter spp. (3)
Na
Na
0
1 (33.3)
1 (33.3)
0
0
1 (30)
1 (33.3)
0
1 (30)
1 (33.3)
0
P. stuartii (2)
1 (50)
0
0
1 (50)
1 (50)
0
0
0
0
0
0
0
0
K. oxytoca (8)
Na
7 (87.5)
7 (87.5)
8 (100)
8 (100)
7 (87.5)
6 (75)
8 (100)
6 (75)
4 (50)
4 (50)
7 (87.5)
7 (87.5)
P. mirabilis (2)
2 (100)
0
0
0
0
0
2 (100)
2 (100)
0
2 (100)
0
0
0
K. rhinoscleromatis (1)
Na
0
0
0
0
0
0
0
0
0
0
0
0
S. typhi (3)
3 (100)
2 (66.7)
1 (33.3)
2 (66.7)
2 (66.7)
0
1 (33.3)
2 (66.7)
2 (66.7)
0
0
2 (66.7)
0
Salmonella group A (1)
1 (100)
1 (100)
0
1 (100)
1 (100)
0
1 (100)
1 (100)
1 (100)
0
0
1 (100)
0
Serratia spp. (1)
0
0
0
0
0
0
0
0
1 (100)
0
0
0
0
N. meningitides (1)
Na
Na
Na
Na
0
0
0
0
Na
Na
Na
Na
0
AMP, Ampicillin; AMC, Amoxicillin-clavulanate; PTZ, Piperacillin_tazobactam; CAZ, Ceftazidime; CRO, Ceftriaxone; CIP, Ciprofloxacin; MER, Meropenem; SXT, Trimethoprim-sulfamethoxazole; GEN, Gentamycin; AMK, Amikacin; CAF, Chloramphenicol; TOB, Tobramycine; PEF, Pefloxacin; Na, Not applicable.
Multidrug resistance pattern of bacterial isolates
The isolates exhibited a concerning level of multidrug resistance with 61.6% (175/284) of the isolates being resistant to three or more antibiotic classes. The data shows that K. pneumoniae had the highest rate of multidrug resistance, with 90.9% (100/284) of the isolates being resistant to three or more antibiotic classes. Notably, K. ozaenae, K. oxytocia, and P. mirabilis exhibited 100% (2/2) multidrug resistance. Other bacteria such as S. aureus, Acinetobacter spp., E. coli, and NFGNR also demonstrated high MDR rates at 40.5% (15/284), 43.5% (10/284), 61.1% (11/284), and 41.2% (7/284), respectively. On the other hand, Enterococcus spp. and S. viridans S. agalactiae, CoNS, P. stuartii, and S. pyogens did not show any multidrug resistance. The data indicates a concerning level of multidrug resistance among the bacterial isolates, with 61.6% of the total isolates being resistant to three or more antibiotic classes (Table 5).
Table 5
Multidrug resistance patterns of bacterial isolates from neonates suspected of bloodstream infections at UoGCSH, 2024.
Bacterial isolates
Degree of microbial resistance
Total MDR isolates ≥ R3 n (%)
R0
R1
R2
R3
R4
K. pneumoniae (110)
3
3
4
3
97
100 (90.9)
S. aureus (37)
13
9
0
15
0
15 (40.5)
Acinetobacter spp. (23)
7
3
3
1
9
10 (43.5)
E. coli (18)
4
1
2
1
10
11 (61.1)
NFGNR (17)
3
2
5
1
6
7 (41.2)
E. cloacae (15)
2
4
2
0
7
7 (46.7)
S. viridans (15)
5
9
1
0
0
0 (0)
K. ozaenae (9)
0
0
0
0
9
9 (100)
Enterococcus spp. (7)
1
3
3
0
0
0 (0)
K. oxytocia (8)
0
0
0
0
8
8 (100)
Citrobacter spp. (3)
1
0
1
0
1
1 (33.3)
P. aeruginosa (3)
0
1
0
1
1
2 (66.7)
S. typhi (3)
1
0
0
0
2
2 (66.7)
S. agalactiae (3)
3
0
0
0
0
0 (0)
CoNS (2)
0
0
2
0
0
0 (0)
P. mirabilis (2)
0
0
0
2
0
2 (100)
P. stuartii (2)
1
0
1
0
0
0 (0)
S. pyogens (2)
1
0
1
0
0
0 (0)
K. rhinoscleromatis (1)
1
0
0
0
0
0 (0)
N. meningitides (1)
1
0
0
0
0
0 (0)
S. proup A (1)
0
0
0
0
1
1 (100)
Serratia spp. (1)
0
1
0
0
0
0 (0)
S. pneumoniae (1)
1
0
0
0
0
0 (0)
Total n (%)
16.9 (48/284)
12.7 (36/284)
8.8 (25/284)
61.6 (175/284)
R0: no antibiotic resistance; R1: resistance to one category of antimicrobial agent; R2: resistance to two different categories of antimicrobial agents; R3: resistance to three different categories of antimicrobial agents; R4: ≥ resistance to four different categories of antimicrobial agents.
Factors associated with bloodstream infections
This analysis investigated factors associated with bloodstream infections using Pearson’s chi-square test. While no significant association was found between positive blood cultures and gender, fever before admission, fever after admission, and hypothermia, several other factors emerged as important. Patients with increased respiration and suspected pneumonia were more likely to have positive cultures (p = 0.004). Likewise, suspected meningitis also showed a significant association (p = 0.009). Interestingly, prior antibiotic use did not statistically influence the results. Notably, the year of admission was the only demographic factor with a significant association. Patients admitted in 2021 had a higher proportion of positive cultures compared to those admitted in 2019 or 2020 (p < 0.001) (Table 6).
Table 6
Factors associated with positive bacterial blood cultures among neonates with suspected bloodstream infection at UoGCSH (2019–2021).
Variables
Category
Blood culture result
Pearson x2
p-value
Negative n (%)
Positive n (%)
Gender
Male
547 (44.2)
200 (16.2)
1.592
0.207
Female
373 (30.2)
117 (9.5)
Age in years
>7
573 (46.3)
199 (16.1)
0.161
0.689
≤7
347 (28.1)
118 (9.5)
Year of admission
2019
165 (13.4)
87 (7.0)
15.666
<0.001*
2020
240 (19.4)
64 (5.2)
2021
514 (41.6)
166 (13.4)
Fever
Yes
872 (70.7)
302 (24.5)
0.004
0.948
No
44 (3.6)
15 (1.2)
Fever before admission
Yes
885 (71.5)
305 (24.7)
0.000
0.986
No
35 (2.8)
12 (1.0)
Hypothermia
Yes
20 (1.6)
9 (0.7)
0.305
0.581
No
900 (72.8)
308 (24.9)
Hypotension
Yes
16 (1.3)
9 (0.7)
1.080
0.299
No
904 (73.1)
308 (24.9)
Increased respiration
Yes
391 (31.6)
112 (9.1)
8.254
0.004*
No
529 (42.8)
205 (16.6)
Suspicion of pneumonia
Yes
391 (31.6)
112 (9.1)
8.254
0.004*
No
529 (42.8)
205 (16.6)
Suspicion of meningitis
Yes
137 (11.1)
27 (2.2)
6.868
0.009*
No
783 (63.3)
290 (23.4)
Suspicion of neonatal infection
Yes
142 (11.5)
63 (5.1)
0.883
0.348
No
777 (62.9)
254 (20.6)
Antibiotic therapy before laboratory diagnosis
Yes
279 (22.6)
105 (8.5)
0.432
0.511
No
641 (51.8)
212 (17.1)
*Statistically significant association.
Discussion
Neonatal sepsis is a significant threat to newborn health, particularly in developing countries like Ethiopia, where it is a leading cause of mortality and morbidity (Weldu et al., 2020; Wen et al., 2021; Klingenberg et al., 2018; Sherif et al., 2023). Accurately identifying bacterial pathogens causing neonatal sepsis and their antibiotic susceptibility patterns is crucial for effective patient management. This information helps guide treatment strategies and improves patient outcomes. Therefore, this study aimed to characterize the bacterial profiles and antibiotic susceptibility at UoGCSH in Ethiopia.
The current study found that nearly a quarter of neonates suspected of sepsis had confirmed bacterial infections (23%). This prevalence is comparable to studies conducted in China (23%) (Fang et al., 2023), Tanzania (24%) (Mhada et al., 2012), Ghana (21.0%) (Acheampong et al., 2022), Nepal (20.5%) (Pokhrel et al., 2018), and Ethiopia (21%) (Sherif et al., 2023). However, the finding is higher than results from Bhutan (14%) (Jatsho et al., 2020), Nepal (10.8%) (Thapa and Sapkota, 2019), Uganda (12.8%) (Tumuhamye et al., 2020), Iran (15.98%) (Akbarian-Rad et al., 2020), South Africa (11.0%) (Reddy et al., 2021), Pakistan (8.9%) (Atif et al., 2021), and Ghana (17.3%) (Aku et al., 2018). Conversely, the current finding is much lower than those reported in various settings in Ethiopia (36.5–46.6%) (Weldu et al., 2020; Worku et al., 2022; Mezgebu et al., 2023; Assemie et al., 2020; Geyesus et al., 2017), Zambia (38%) (Egbe et al., 2023), Nigeria (49.6%) (Peterside et al., 2015), Tanzania (72%) (Majigo et al., 2023), and Uganda (59.0%) (Zamarano et al., 2021). The discrepancy in prevalence rates across different geographical regions could be attributed to factors such as differences in hygiene practices, antibiotic use patterns, variations in study design, and broader epidemiological factors. Additionally, improvements in diagnostic techniques, changes in hospital practices, and seasonal trends could potentially influence the incidence and prevalence of neonatal sepsis.
Among the neonates suspected of septicemia, only 25.4% tested positive for cultures. Several factors could explain the relatively low rate of culture positivity. These include the use of antibiotic treatment before admission, which could suppress bacterial growth, the limitations of conventional culture methods, and the possibility of non-bacterial infections presenting with similar clinical signs and symptoms. Nevertheless, in cases where classical sepsis symptoms were present, but no microbial isolates were obtained, management primarily involved empirical antibiotic therapy based on clinical judgment and local guidelines.
The study revealed that Gram-negative bacteria were predominant, accounting for 75.3% of the isolates, compared to Gram-positive bacteria. This result aligns with previous research from Ethiopia, South Africa, Germany, Tanzania, Uganda, and Nepal, where Gram-negative bacteria were also the majority (Pokhrel et al., 2018; Tessema et al., 2021; Sherif et al., 2023; Majigo et al., 2023; Zamarano et al., 2021; Thomas et al., 2024). However, this percentage is significantly higher than the 58.1% Gram-negative bacteria predominance reported in a systematic review from Iran (Moftian et al., 2023). Among the bacterial pathogens isolated, K. pneumoniae was the most frequently isolated followed by S. aureus and Acinetobacter spp. This finding is supported by a systematic review and a review in Sub-Saharan Africa, Uganda, and Pakistan (Tumuhamye et al., 2020; Atif et al., 2021; Okomo et al., 2019). The high frequency of K. pneumoniae isolates in the current study is particularly concerning, as this pathogen is known to be a common cause of healthcare-associated infections and is often associated with multidrug resistance. The prevalence of S. aureus and Acinetobacter spp. is also significant, as these bacteria can be challenging to treat due to their ability to develop resistance to various antimicrobial agents.
The antimicrobial susceptibility data revealed alarming levels of resistance among both gram-positive and gram-negative bacterial isolates. This concerning trend underscores the critical need for alternative therapeutic strategies and judicious antibiotic use to combat the growing challenge of antimicrobial resistance. Among the gram-positive bacteria, CoNS exhibited 100% resistance to penicillin, oxacillin, and gentamicin, indicating that these common antimicrobial agents are no longer effective against CoNS infections. Similarly, S. aureus showed high resistance rates to penicillin (94.6%), oxacillin (64.9%), and gentamicin (40.5%), suggesting that empiric treatment with these drugs may be increasingly ineffective. S. viridans also demonstrated significant resistance to ampicillin (73.3%) and vancomycin (20%), which are commonly used therapeutic options for streptococcal infections. These findings highlight the urgent need for new antimicrobial agents and strategies to address the rising threat of antimicrobial resistance.
Gram-negative bacterial isolates also exhibited substantial resistance patterns. K. pneumoniae showed the most alarming resistance to highly active antimicrobial agents, including meropenem (88.1%), ceftazidime (83.6%), ceftriaxone (83.6%), and amoxicillin-clavulanate (69%). This indicates that clinicians may have limited treatment options for K. pneumoniae infections, as these drugs are often considered last-line or “reserve” antimicrobials. Although E. coli showed relatively lower resistance rates compared to K. pneumoniae, it still presented significant resistance to ampicillin (66.7%), gentamicin (61.1%), and amoxicillin-clavulanate (55.6%). These findings are consistent with the previous study from the Tigray region, Ethiopia, in which K. pneumoniae and E. coli were resistant to common antimicrobial agents (Weldu et al., 2020). These observed patterns of resistance to commonly used antimicrobial drugs, such as ampicillin, ceftazidime, ceftriaxone, gentamicin, and amoxicillin-clavulanic acid, have been reported in other studies as well (Bai et al., 2021; Weldu et al., 2020; Sherif et al., 2023), suggesting that these resistance trends are widespread and not limited to the specific setting of this study.
Furthermore, the majority (61.6%) of the bacterial isolates were MDR, although this rate is lower than those reported in some other studies where the proportion of MDR was as high as 84% (Sherif et al., 2023; Zenebe et al., 2021). Among specific bacterial pathogens, K. pneumoniae had the highest rate of MDR, with 90.9% of the isolates being resistant to three or more antibiotic classes. Other bacterial pathogens, including K. ozaenae, K. oxytoca, and P. mirabilis, also exhibited 100% MDR. Additionally, S. aureus, Acinetobacter spp., E. coli, and non-fermenting Gram-negative rods demonstrated high MDR rates of 40.5, 43.5, 61.1, and 41.2%, respectively. The high levels of MDR observed in this study are concerning and call for stringent antibiotic stewardship programs to mitigate the spread of resistant strains. Multidrug resistance is particularly alarming as it severely limits the available treatment options and increases the risk of treatment failures. These resistance patterns highlight the urgent need for continuous surveillance and the development of new antimicrobial agents to effectively address the growing threat of antimicrobial resistance.
The study identified several key factors significantly associated with neonatal sepsis. One notable finding was the correlation between the year of admission and the incidence of neonatal sepsis. This temporal trend could be influenced by various factors, including changes in hospital practices, hygiene protocols, and seasonal trends. Additionally, variations in antibiotic resistance patterns, broader epidemiological factors, and improvements in diagnostic techniques could also contribute to these yearly fluctuations. Another important risk factor identified was rapid breathing, which can serve as an early clinical sign of sepsis, often indicating an underlying infection that requires immediate medical attention. The study found that neonates presenting with increased respiration were more likely to develop sepsis. Furthermore, the association between suspected pneumonia and meningitis with neonatal sepsis highlights the potential for overlapping clinical presentations. These symptoms had a higher likelihood of developing into sepsis, underscoring the importance of vigilant clinical assessment and timely intervention in neonatal care.
Limitations
This study has several important limitations that should be considered when interpreting the results. Since the research was conducted at a single healthcare facility, it may limit the generalizability of the findings to other geographical regions or healthcare settings. The patient population and antimicrobial resistance patterns observed at this one site may not be representative of broader regional or national trends. Furthermore, the study did not include molecular typing of bacterial isolates, which could have provided more detailed insights into the genetic mechanisms underlying antimicrobial resistance and the epidemiology of the infections. The absence of this molecular data limits the depth of understanding regarding the specific strains and resistance patterns present in the bacterial population studied.
Conclusion
Newborn sepsis caused by highly resistant bacteria presents a significant challenge at the UoGCSH. This study identified a high prevalence of culture-confirmed sepsis, with Gram-negative bacteria, especially K. pneumoniae, dominating the isolated pathogens. These bacteria exhibited alarming resistance to commonly used antibiotics, with a very high proportion demonstrating multidrug resistance. K. pneumoniae displayed the most concerning resistance rates. Additionally, the study linked specific factors like year of admission, rapid breathing, suspected pneumonia, and suspected meningitis to an increased risk of neonatal sepsis.
Recommendation
There should be continuous surveillance of bacterial pathogens causing neonatal sepsis, monitoring their evolving antibiotic susceptibility patterns, which are crucial to inform effective treatment guidelines.
The hospital should improve diagnostic techniques for the early and accurate identification of bacterial pathogens causing neonatal sepsis.
The hospital should tailor antibiotic regimens based on the specific bacterial profiles and resistance patterns identified in the local hospital setting to improve the effectiveness of treatments.
The Federal Ministry of Health and the regional health bureau should develop and implement robust antibiotic stewardship programs to optimize the use of antibiotics, reduce unnecessary prescriptions, and curb the spread of resistant strains.
The Federal Ministry of Health should develop training programs and provide healthcare providers with the latest guidelines for managing neonatal sepsis.
Further study should be conducted using molecular techniques to improve the detection of causative pathogens, particularly for those culture-negative patients.
Researchers should invest significant effort to discover new antimicrobial agents that can effectively combat the resistant strains and provide effective treatment options for neonatal sepsis. |
Title: TrajectoryGeometry suggests cell fate decisions can involve branches rather than bifurcations | Body:
Introduction
Multicellular organisms consist of complex communities of diverse cell types. Remarkably, these all arise from a single cell, the zygote. Accordingly, the development of an organism involves cell fate restriction and differentiation. It has been shown that cell differentiation frequently proceeds via successive binary decisions (1), although multifurcations are also possible (2). Here, we will focus on binary cell fate decisions. A typical model of these cell fate decisions is as follows: a multi-potential progenitor cell develops until it reaches a decision point. Here, the cell chooses one of two possible fates, each of which requires the initiation of a new developmental programme. After making this choice the cell develops towards the chosen fate. We refer to this as the bifurcation model, where each chosen outcome is seen as more differentiated than the progenitor state.
Our exploration of lineage decisions in the enteric nervous system (ENS)(3) has uncovered a novel configuration of differentiation trajectories. In contrast to the bifurcation model, enteric gliogenesis forms a default ‘linear’ path of progenitor maturation, from which neurogenic trajectories branch off during embryogenesis. A consequence of this branching configuration is that there are no identifiable points of commitment along the gliogenic trajectory and it is only the cells which become neurons that ever ‘make a decision’ and initiate a new developmental program. Further, rather than a single branch point, there seems to be a region along the default trajectory where this branching can take place. These models are portrayed in Figure 1. We have suggested this branching model of lineage decisions allows for plasticity along the default trajectory and underpins the neurogenic potential of mature enteric glial cells.
Figure 1.
Synthetic data showing bifurcation and branching models. (A) Bifurcation as a model of cell-fate decision. Bi-potential progenitor cells (black) proceed to a decision point after which they proceed in one of two new directions in gene expression space. (B) A simplified version of branching behaviour as a model of cell-fate decision. Here bi-potential cells proceed along a default developmental pathway to one of their potential outcomes. The decision is whether or not to leave this default pathway and develop in a new direction. (C) In this version of branching behaviour, there is a region rather than a single point at which cells choose to leave the default pathway.
Our analytic tool for observing default and branching behaviour is our Bioconductor package TrajectoryGeometry(4). The asynchronicity of most differentiation processes enables the simultaneous profiling of cells at different positions along their developmental trajectory. Many packages exist to infer pseudotime trajectories (5–7), and all of these consider the geometry of the cells and their clusters in gene expression space. There are also packages to discover the genes that are differentially expressed over pseudotime (8). However, to the best of our knowledge, TrajectoryGeometry is the first to analyse the pseudotime trajectories and gene expression changes along these trajectories using their overall geometry. The notion of a default trajectory is not new, (see (9) and below). However, TrajectoryGeometry provides an analytic footing for this idea by detecting whether a developmental trajectory proceeds in a well-defined direction.
Having observed a branching model of lineage decisions in the development of enteric neurons and glial cells, we were led to question whether this behaviour is unique to the ENS or whether it might be employed more generally. Here we show that this behaviour is observed in the development of hepatocytes into hepatoblasts and cholangiocytes (9) and the development of postnatal murine olfactory stem cells into into sustentacular cells, neurons and microvillous cells (10) (Supporting Information).
Materials and methods
Trajectory geometry can infer bifurcating and branching models of cell fate decisions by testing whether a trajectory proceeds in a well defined direction. If bifurcation is in operation both trajectories change direction after the decision point (Figure 1A), whereas in the branching case only one trajectory changes directionality at this point (Figure 1B); moreover there may be a region along the default trajectory from which branching can take place (Figure 1C).
A direction in two dimensions is a point on a circle, e.g., a compass point. A direction in three dimensions is a point on the sphere. More generally, a direction in N dimensions is a point on the N − 1 dimensional sphere, \documentclass[12pt]{minimal}
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$\mathbb {S}^{N-1}$\end{document} which are tightly clustered around their common center (Supplementary Figure S1). This allows TrajectoryGeometry to detect whether a differentiation trajectory has a well-defined directionality, produce a P-value for that directionality and specify its direction. This direction identifies the genes that are up- and down-regulated along the trajectory and consequently which biological pathways are being up- and down-regulated. Further details are given in (3), in the documentation accompanying (4) and in the supporting information.
Results
Branching cell fate decisions in the liver
In the liver, hepatoblasts give rise to hepatocytes and cholangiocytes (Figure 2A). Here, we analyse murine single cell data describing this process from (9). Branching is clearly visible in the 2D visualisation of pseudotime trajectories, as noted in (9), Figure 2A, where the trajectory that gives rise to cholangiocytes appears to branch off a default time-axis aligned trajectory that ultimately generates hepatocytes. Using TrajectoryGeometry, we see that the small circle observed for the hepatocyte trajectory, in comparison to the larger circle observed for the cholangiocyte trajectory (Figure 2B), suggests hepatocyte development maintains a relatively consistent directionality of gene expression change. Conversely, if the cholangiocyte trajectory is analysed from the decision point onwards, a small circle is also observed, suggesting that it maintains a consistent directionality after branching off (Figure 2B).
Figure 2.
(A) PCA plot of scRNAseq data for embryonic murine hepatobiliary cells. Pseudotime trajectories inferred using Slingshot are shown on the plot. Cells are coloured by Louvain cluster. (B) 3D sampled pathways for hepatoblast to hepatocyte, hepatoblast to cholangiocyte, and decision point to cholangiocyte trajectories together with their projections on the 2-sphere. White circles denote mean distance from center (red dot). (C) Bar plots showing top 10 up- and down-regulated genes for each trajectory as in (B), using 10 dimensions. (D) Violin plots indicating the mean spherical distance (radii of the white circles in B) for paths sampled from the hepatocyte and cholangiocyte trajectories (purple and orange, respectively) relative to random trajectories (white). Statistics calculated using 1000 random paths from each trajectory and the first 3 and the first 10 PCs respectively and the first 3 and the first 10 PCs respectively. (E) Violin plots indicating the mean spherical distance of the hepatocyte (purple) and cholangiocyte (orange) trajectories. (F) Violin plots indicating the mean spherical distance for the cholangiocyte trajectory (first 3 PCs) starting from successively later points in pseudotime, as the decision point is approached (30 value on the cholangiocyte trajectory shown in the top right inset). (G) Line graph indicating the –log10(P-value) for the significance of directionality for the cholangiocyte trajectory (first 3 PCs) relative to random trajectories, starting from successively later points in pseudotime.
Sampling 1000 paths from each trajectory reveals significant directionality in comparison to randomised trajectories for both the cholangiocyte and hepatocyte trajectories (Figure 2D). This is observed whether one uses the first 3 PCs or the first 10 PCs as well as an analysis in full gene expression space (see Supporting Information and Supplementary Figure S2). However direct comparison of cholangiocyte and hepatocyte trajectories reveals that the hepatocyte trajectory maintains a more consistent directionality of gene expression change (Figure 2E)). Furthermore, if the cholangiocyte trajectory segments are analysed starting from successively later points in pseudotime, the mean spherical distance decreases as the decision point is approached and the directionality of the analysed segments becomes more significant (Figure 2F, G), supporting branching behaviour.
Genes positively associated with the directionality of the hepatocyte trajectory include mature hepatocyte markers (e.g. Alb (11)) whereas those negatively associated include markers of hepatoblasts (e.g. Mdk (12)) (Figure 2E). Genes associated with the overall directionality of the cholangiocyte trajectory include those with expression in immature cholangiocytes (e.g. Tm4sf4 (13,14)) (Figure 2c). This appears to result from the overall directionality being a combination of distinct directionalities before and after the decision point (DP). It is only when we look at the trajectory from the DP to the cholangiocytes that we see markers of mature cholangiocytes (e.g. Krt19 (9)) indicating that a directionality that leads to a cholangiocyte phenotype is not achieved until after branching off (Figure 2C). Mature hepatocyte markers (e.g. Ttr (15)) are amongst genes negatively associated with the DP-cholangiocyte trajectory segment (Figure 2C). This indicates that progenitors have already progressed towards a hepatocyte phenotype when they reach the branch point, and these genes must be subsequently downregulated to acquire a cholangiocyte fate.
Interestingly the gene Sox9, which has been reported to be associated with maintaining cholangiocyte fate (16), has a positive score for the DP-cholangiocyte trajectory segment (0.038), but a small negative score for the hepatoblast-DP segment (-0.0045), indicating that it is not associated with the directionality of the trajectory until after the decision point. In contrast, the gene Tgfbr2, reported to be involved in the initial induction of cholangiocyte fate (16), is not amongst the top 2000 highly variable genes used for this analysis, and analysis incorporating all genes (see supporting information) shows only small associations with the directionality of the trajectory (0.0053 hepatoblast-DP, −0.0012 DP-cholangiocyte). Taken together these results show that genes such as Sox9 that are responsible for maintaining cell fate are positively associated with the trajectory from the decision point onwards whereas genes that enable branching, such as Tgfbr2 may show little association with the directionality of the trajectory. We suggest that this is because minimal variation in receptor expression may be required for cells to respond to a fate-inducing signal.
Together these findings further support a model where the cholangiocyte trajectory branches off from a default hepatocyte trajectory. Although the inferred trajectory shows a single branch point, the dispersion of cells around this branch, and the fact that the cells are from different embryonic time points, from E11.5 to E14.5, suggest that branching is possible from a continuous section of the ‘default’ trajectory. The change in the directionality of gene expression at the decision point for the cholangiocyte trajectory signifies initiation of a new transcriptomic programme, suggesting that cells are responding to an extrinsic signal. In agreement with this, the cholangiocyte fate decision has been shown to be coordinately regulated by TGF-beta, WNT, Notch and FGF signalling (17–22) likely in response to factors produced by the periportal mesenchyme.
The liver has remarkable regenerative power, with both cholangiocytes and hepatocytes acting as facultative stem cells able to transdifferentiate if regenerative capacity of the other population is impaired (23). However, it is interesting to note that hepatocytes, that result from the default trajectory, appear to have unlimited regenerative capacity (24). It is also interesting that the most abundant cell type (70% of liver cells are hepatocytes (25)) appears to be produced by default.
Nested cell fate decisions in the olfactory epithelium
In the Supporting Information, we present an analysis of nested cell fate decisions seen in data from (10), describing differentiation trajectories in the postnatal olfactory epithelium (Supplementary Figures S3–S6). TrajectoryGeometry analysis shows that horizontal basal cells (HBCs) follow a default trajectory to differentiate into sustentacular cells. Neuronal and microvillous trajectories branch off from this trajectory (see Supplementary Figure S3a). A second decision point (DP2) where neuronal and microvillous trajectories diverge appears to be more complex. Initial analysis suggests this is a bifurcation point. However, using TrajectoryGeometry, we discover this is due to the transient overlay of proliferation obscuring branching behaviour. Controlling for this reveals a branch point with neurogenesis as the default trajectory. As microvillous cells are comparatively rare it is parsimonious that these are not produced by default.
A negative control
In the Supporting Information, we analyse a synthetic data set which exhibits symmetric bifurcation behaviour and show that in this case TrajectoryGeometry does not detect the hallmarks of branching behaviour (Supplementary Figure S7).
Branching cell fate decisions in the liver
In the liver, hepatoblasts give rise to hepatocytes and cholangiocytes (Figure 2A). Here, we analyse murine single cell data describing this process from (9). Branching is clearly visible in the 2D visualisation of pseudotime trajectories, as noted in (9), Figure 2A, where the trajectory that gives rise to cholangiocytes appears to branch off a default time-axis aligned trajectory that ultimately generates hepatocytes. Using TrajectoryGeometry, we see that the small circle observed for the hepatocyte trajectory, in comparison to the larger circle observed for the cholangiocyte trajectory (Figure 2B), suggests hepatocyte development maintains a relatively consistent directionality of gene expression change. Conversely, if the cholangiocyte trajectory is analysed from the decision point onwards, a small circle is also observed, suggesting that it maintains a consistent directionality after branching off (Figure 2B).
Figure 2.
(A) PCA plot of scRNAseq data for embryonic murine hepatobiliary cells. Pseudotime trajectories inferred using Slingshot are shown on the plot. Cells are coloured by Louvain cluster. (B) 3D sampled pathways for hepatoblast to hepatocyte, hepatoblast to cholangiocyte, and decision point to cholangiocyte trajectories together with their projections on the 2-sphere. White circles denote mean distance from center (red dot). (C) Bar plots showing top 10 up- and down-regulated genes for each trajectory as in (B), using 10 dimensions. (D) Violin plots indicating the mean spherical distance (radii of the white circles in B) for paths sampled from the hepatocyte and cholangiocyte trajectories (purple and orange, respectively) relative to random trajectories (white). Statistics calculated using 1000 random paths from each trajectory and the first 3 and the first 10 PCs respectively and the first 3 and the first 10 PCs respectively. (E) Violin plots indicating the mean spherical distance of the hepatocyte (purple) and cholangiocyte (orange) trajectories. (F) Violin plots indicating the mean spherical distance for the cholangiocyte trajectory (first 3 PCs) starting from successively later points in pseudotime, as the decision point is approached (30 value on the cholangiocyte trajectory shown in the top right inset). (G) Line graph indicating the –log10(P-value) for the significance of directionality for the cholangiocyte trajectory (first 3 PCs) relative to random trajectories, starting from successively later points in pseudotime.
Sampling 1000 paths from each trajectory reveals significant directionality in comparison to randomised trajectories for both the cholangiocyte and hepatocyte trajectories (Figure 2D). This is observed whether one uses the first 3 PCs or the first 10 PCs as well as an analysis in full gene expression space (see Supporting Information and Supplementary Figure S2). However direct comparison of cholangiocyte and hepatocyte trajectories reveals that the hepatocyte trajectory maintains a more consistent directionality of gene expression change (Figure 2E)). Furthermore, if the cholangiocyte trajectory segments are analysed starting from successively later points in pseudotime, the mean spherical distance decreases as the decision point is approached and the directionality of the analysed segments becomes more significant (Figure 2F, G), supporting branching behaviour.
Genes positively associated with the directionality of the hepatocyte trajectory include mature hepatocyte markers (e.g. Alb (11)) whereas those negatively associated include markers of hepatoblasts (e.g. Mdk (12)) (Figure 2E). Genes associated with the overall directionality of the cholangiocyte trajectory include those with expression in immature cholangiocytes (e.g. Tm4sf4 (13,14)) (Figure 2c). This appears to result from the overall directionality being a combination of distinct directionalities before and after the decision point (DP). It is only when we look at the trajectory from the DP to the cholangiocytes that we see markers of mature cholangiocytes (e.g. Krt19 (9)) indicating that a directionality that leads to a cholangiocyte phenotype is not achieved until after branching off (Figure 2C). Mature hepatocyte markers (e.g. Ttr (15)) are amongst genes negatively associated with the DP-cholangiocyte trajectory segment (Figure 2C). This indicates that progenitors have already progressed towards a hepatocyte phenotype when they reach the branch point, and these genes must be subsequently downregulated to acquire a cholangiocyte fate.
Interestingly the gene Sox9, which has been reported to be associated with maintaining cholangiocyte fate (16), has a positive score for the DP-cholangiocyte trajectory segment (0.038), but a small negative score for the hepatoblast-DP segment (-0.0045), indicating that it is not associated with the directionality of the trajectory until after the decision point. In contrast, the gene Tgfbr2, reported to be involved in the initial induction of cholangiocyte fate (16), is not amongst the top 2000 highly variable genes used for this analysis, and analysis incorporating all genes (see supporting information) shows only small associations with the directionality of the trajectory (0.0053 hepatoblast-DP, −0.0012 DP-cholangiocyte). Taken together these results show that genes such as Sox9 that are responsible for maintaining cell fate are positively associated with the trajectory from the decision point onwards whereas genes that enable branching, such as Tgfbr2 may show little association with the directionality of the trajectory. We suggest that this is because minimal variation in receptor expression may be required for cells to respond to a fate-inducing signal.
Together these findings further support a model where the cholangiocyte trajectory branches off from a default hepatocyte trajectory. Although the inferred trajectory shows a single branch point, the dispersion of cells around this branch, and the fact that the cells are from different embryonic time points, from E11.5 to E14.5, suggest that branching is possible from a continuous section of the ‘default’ trajectory. The change in the directionality of gene expression at the decision point for the cholangiocyte trajectory signifies initiation of a new transcriptomic programme, suggesting that cells are responding to an extrinsic signal. In agreement with this, the cholangiocyte fate decision has been shown to be coordinately regulated by TGF-beta, WNT, Notch and FGF signalling (17–22) likely in response to factors produced by the periportal mesenchyme.
The liver has remarkable regenerative power, with both cholangiocytes and hepatocytes acting as facultative stem cells able to transdifferentiate if regenerative capacity of the other population is impaired (23). However, it is interesting to note that hepatocytes, that result from the default trajectory, appear to have unlimited regenerative capacity (24). It is also interesting that the most abundant cell type (70% of liver cells are hepatocytes (25)) appears to be produced by default.
Nested cell fate decisions in the olfactory epithelium
In the Supporting Information, we present an analysis of nested cell fate decisions seen in data from (10), describing differentiation trajectories in the postnatal olfactory epithelium (Supplementary Figures S3–S6). TrajectoryGeometry analysis shows that horizontal basal cells (HBCs) follow a default trajectory to differentiate into sustentacular cells. Neuronal and microvillous trajectories branch off from this trajectory (see Supplementary Figure S3a). A second decision point (DP2) where neuronal and microvillous trajectories diverge appears to be more complex. Initial analysis suggests this is a bifurcation point. However, using TrajectoryGeometry, we discover this is due to the transient overlay of proliferation obscuring branching behaviour. Controlling for this reveals a branch point with neurogenesis as the default trajectory. As microvillous cells are comparatively rare it is parsimonious that these are not produced by default.
A negative control
In the Supporting Information, we analyse a synthetic data set which exhibits symmetric bifurcation behaviour and show that in this case TrajectoryGeometry does not detect the hallmarks of branching behaviour (Supplementary Figure S7).
Discussion
In this paper, we have used TrajectoryGeometry to examine the geometry of the cell-fate decisions of multi-potential progenitor cells. Our analyses of several data sets have led us to propose that a branching rather than bifurcating model of cell fate decisions is often employed. In this model, a bipotential progenitor proceeds along a more or less straight default trajectory to one of its potential fates. Its other fate arises by branching off from this trajectory. In particular, only one of the two cell fates involves initiating a new developmental program. In our experience there is a region along the default trajectory where this branching can take place. Note that development along the default trajectory involves change in gene expression (e.g. the unfolding of a gene expression programme), but not a change in direction of travel in gene expression space as is the case with the initiation of a new programme of gene expression.
Interestingly this model has been anticipated in a more informal manner, e.g. (9) who state: ‘Thus, the default pathway for hepatoblasts is to differentiate into hepatocytes, but along the way, some hepatoblasts are regulated to differentiate toward the cholangiocyte fate.’ TrajectoryGeometry provides the tools to put this in a formal framework by quantifying directionality of trajectories in gene expression space. When it detects directionality, this is expressed as a vector in gene expression space, and this vector tells which genes are most up- and down-regulated in the developmental process. These allow us to detect the functional pathways involved.
So far we have seen three unequivocal cases of branching behaviour: the branching of enteric neurogenic trajectories from the default gliogenic trajectory(3); the branching of cholangiocytes from the default development of hepatoblasts into hepatocytes; and the branching of microvillous and neuronal development at DP1 from the default development of horizontal basal cells into sustentacular cells. The point at which microvillous and neuronal development diverges is slightly more complex. Branching behaviour is obscured by a specific process (the cell cycle), which does not exhibit branching behaviour. However, using TrajectoryGeometry we are able to deconvolute process specific effects, and find that this represents another example of branching behaviour.
Note that there seems to be a defined region along the default trajectory where branching can occur that is permissive for initiation of the new developmental program. The question arises as to whether branching (or the transient upregulation of cell-cycle) arises due to intrinsic or extrinsic signals. Both cholangiocyte development and neuronal branching at DP1 are known to be responsive to WNT signalling. It is possible that only a portion of the default pathway is responsive to external signals. It is also possible that these external signals only arise at specific developmental time points or in specific cellular environments.
Here, we hypothesize that branching behaviour may be more common than bifurcation and have specific evolutionary advantages. Firstly, we speculate that branching is more robust than bifurcation from a control-theoretic viewpoint. When a cell initiates a new transcriptional program there is always an opportunity for error, both in terms of the external signals inducing this change and in terms of the intrinsic pathways induced by these signals. Changing the transcriptional program of only a subset of the cells exposes fewer cells to this danger. This is particularly advantageous when the branching cell type is required in lower numbers, since in this case only a minority of the cells are required to initiate a new transcriptomic program. We have seen that the minority cell type is the branch outcome in the neurons in the ENS, cholangiocytes in the liver and microvillous cells in the olfactory epithelium. Moreover, we hypothesise that branching behaviour allows for simpler coordinate control of cell numbers for two populations, which is particularly desirable when the two cell types function in concert (e.g. glial cells and neurons) and correct proportions must be maintained.
Secondly, branching behaviour may allow for more phenotypic plasticity in the cells along the default trajectory, as this does not involve initiation of a new transcriptomic programme. This appears to be the case in the ENS where mature glial cells retain neurogenic potential which can be activated under certain conditions. Although both cholangiocytes and hepatocytes retain remarkable plasticity in the liver (both populations can transdifferentiate), hepatocytes, the default outcome, maintain unlimited regenerative capacity.
Thirdly, a default trajectory may allow for the faster generation of a differentiated cell type, particularly in cases where cells undergo direct fate conversion and do not reenter the cell cycle. For example, sustentacular cells (generated via direct fate conversion) might be urgently required upon loss to maintain the structural integrity of the olfactory epithelium. Furthermore, it has been suggested that sustentacular cells produce crucial factors for olfactory epithelium regeneration (26); their replenishment might be required before cell types resulting from branching trajectories can be generated.
Although we have suggested several advantages of branching behaviour, one can hypothesise that there are situations in which bifurcation is desirable. For example, in the case where the populations of each of the resulting differentiated cell types must be individually regulated. Here the decision point could consist of a self-renewing population and differentiation into either of the cell fates could be induced by external signals. This contrasts with situations where it is desirable to coordinately regulate the proportions of resulting cell populations.
The Waddington landscape is commonly used as a metaphor to represent cell fate decisions, with undifferentiated cells rolling down valleys to reach basins that represent more differentiated cell fates. Here, a cell rolls down to a decision point where it stochastically chooses one of two valleys. In a branching model of cell fate decisions, we can imagine that the default valley is relatively straight and that it is relatively shallow along the branching region where another valley, with a broad mouth, leads to the alternate outcome
A more sophisticated version of the Waddington landscape is given by the Waddington dynamics of (27,28) where the potential landscape can vary in response to external inter-cellular signals. Note that the landscape exists on a per cell basis since individual cells may receive different signals or signals of differing intensities and, indeed, could vary over time scales comparable to differentiation.
Their binary flip landscape can accommodate the two main features of our branching model; the existence of a default and an alternate trajectory and the extended branching region. The first is the geometry of the escape path from the progenitor basin to the default outcome. We hypothesize that under the default signalling, there is a shallow region of the default valley and that the alternate escape route branches off here under the influence of the alternate signal. We suggest that the exact location where this happens depends on the intensity of the alternate signal thus producing a branching region rather than a single decision point.
In the case of enteric neural progenitors, neurons and glia (the default outcome), data suggests there is only a shallow drop from progenitors to glia, as witnessed by the fact that cell populations form a continuum along the progenitor-glia trajectory and that in vitro, mature glia can be induced to de-differentiate and subsequently differentiate into neurons.
Finally, integration of TrajectoryGeometry results with RNA velocity analysis (29) may present an interesting avenue for future exploration. We hypothesise that those cells in the branching region of the default trajectory which are about to branch off will already have changed their velocity. Clustering the velocities of cells in this region might enable inference of their ultimate fate. Further, we propose that genes differentially expressed by cells that have changed their velocity might represent early indicators of cell fate determination.
We have shown how TrajectoryGeometry can detect whether the developmental trajectories leading from bipotential progenitor cells branch or bifurcate and in turn give highly specific information about the genes and functional pathways involved. We are now in an era where there is an explosion in the availability of scRNAseq data and we expect TrajectoryGeometry to facilitate novel insights into cell lineage decisions.
Supplementary Material
lqae139_Supplemental_File |
Title: Gradient Boosting Prediction of Overlapping Genes From Weighted Co-expression and Differential Gene Expression Analysis of Wnt Pathway: An Artificial Intelligence-Based Bioinformatics Study | Body:
Introduction
The Wnt (wingless-related integration site) signalling pathway is a crucial part of bone formation and remodelling, regulating the commitment of mesenchymal stem cells (MSCs) to the osteoblastic lineage, osteoblast proliferation, and differentiation. It binds Wnt ligands to cell surface receptors, stabilizing and nuclear translocating β-catenin, a cytoplasmic protein [1]. The Wnt pathway also promotes osteoblast proliferation and survival by stimulating the production of growth factors and cytokines and inhibiting apoptosis. It also influences osteoblast function by balancing osteoblast and osteoclast activity in bone remodelling, stimulating osteoprotegerin production, a decoy receptor that inhibits osteoclast differentiation and activity, thereby promoting bone formation. The Wnt pathway is crucial for bone formation, but its dysregulation can lead to pathological conditions. Mutations in LRP5 or β-catenin can cause high bone mass disorders, while loss-of-function mutations can cause low bone mass disorders. The Wnt pathway promotes bone formation, osteoblast proliferation, survival, and function, highlighting its importance in bone biology [2].
Mesenchymal stem cells (MSCs) have the potential to differentiate into bone, cartilage, fat, tendon, and muscle tissues. They are harvested from the patient's body, especially from bone marrow, and have therapeutic potential in regenerative medicine. The Wnt signalling pathway plays a crucial role in promoting the osteogenic differentiation of MSCs [3]. The pathway inhibits adipogenic differentiation and upregulates osteogenic regulators, contributing to the progression of MSCs into mature osteoblasts. The noncanonical Wnt pathway also induces osteogenic differentiation through a different mechanism. Wnt pathways [4] and other signalling pathways regulate osteogenic differentiation in MSCs. BMPs can enhance or antagonize Wnt-induced differentiation, with BMP2, 6, and 9 major osteogenic growth factors [5]. Functional Wnt signalling is required for BMP-induced differentiation, and knocking out BMP receptor type 1 leads to increased bone mass. The inactivating mutation of LRP5 causes osteoporosis pseudo glioma syndrome (OPPG), characterized by early-onset osteoporosis, low bone mineral density, and blindness. In mice, inactivating mutations impair fracture healing, while a gain-of-function missense mutation leads to high-bone-mass phenotypes. LRP6 mutations severely affect osteogenic development in humans and mice, leading to osteoporosis, low BMD, neonatal death, and limb abnormalities [6].
Combining weighted gene co-expression network analysis (WGCNA) [7] and differential gene expression (DGE) analysis is a powerful method for understanding complex biological processes, identifying gene overlap, and understanding gene regulation networks. WGCNA and DGE analysis are powerful tools for analysing high-dimensional gene expression data. WGCNA groups genes based on co-expression patterns [8], while DGE analysis identifies differential expression between conditions or phenotypes potentially associated with specific biological functions. WGCNA and DGE analysis can be integrated by identifying gene overlaps between WGCNA-identified modules and differentially expressed genes. These are crucial regulators or functional drivers of biological processes, demonstrating significant changes in expression levels across conditions.
WGCNA and DGE analysis enable researchers to understand overlapping genes' functional roles, enabling functional enrichment analysis like gene ontology or pathway analysis, and providing insights into biological processes. Integrating WGCNA and DGE analysis can reveal regulatory relationships between overlapping genes. WGCNA creates co-expression networks, while DGE analysis incorporates differential expression information. This helps identify specific regulatory relationships for conditions or phenotypes, revealing key factors driving biological processes [9]. Overlap genes are shared genes in biological processes or molecular pathways. Predicting overlap genes can provide insights into functional relationships. Gradient boosting, a machine learning technique, combines multiple weak predictive models to create a strong predictive model. This approach helps researchers understand genetic and molecular mechanisms contributing to overlap genes, providing valuable insights for complex biological processes. It enhances understanding of functional relationships and regulatory networks among genes, improving gene regulation and functional genomics. So, we aim to predict the overlapping genes in the Wnt signalling pathway from WGCNA and differentially expressed genes using gradient boosting.
Materials and methods
This computational study was conducted at Saveetha Dental College, Chennai, India between May 1 and May 31, 2024. This study employed a computational approach to investigate the potential of Wnt signaling in osteoporosis treatment.
DGE analysis
Using the National Center for Biotechnology Information (NCBI) geo dataset GSE251951 [10], DGE was performed using the Gene Expression Omnibus (GEO) tool. The dataset reveals whether Wnt signaling can induce effective osteoporosis treatment, promoting bone formation through aerobic glycolysis in the Mus musculus in a computational model. Datasets were divided into nonexposed to wnt3a and exposed to wnt3a and DGE. The results were analyzed for differentially expressed genes, fold changes, p-values, and adjusted p-values.
WGCNA
The WGCNA module used the iDEP tool [11], a standardized gene expression dataset, to identify highly interconnected gene clusters, ensuring comparable gene and sample distributions. WGCNA calculates pairwise gene correlations, constructing an adjacency matrix and transforming it into a topological overlap matrix (TOM), measuring gene interconnectedness within a network [12]. The TOM generates a hierarchical clustering tree (dendrogram) using the average linkage method, grouping genes with similar expression patterns indicating potential functional relationships. The Dynamic TreeCut algorithm is used to identify distinct modules or clusters within the co-expression network, with a minimum module size parameter of 30 genes. Identifying modules allows for the characterization and analysis of their biological relevance through gene ontology, biological pathways, or functional annotations of genes within each module. The WGCNA analysis in iDEP aids researchers in identifying gene relationships, functionally related groups, regulatory mechanisms, key genes, and modules associated with specific biological processes or diseases.
Identification of hub genes
In WGCNA, hub genes are highly connected genes within a module that are crucial for the module's functioning and may have key roles in the biological process or disease being studied. To identify hub genes, we have to calculate module eigengenes (MEs) for each module, correlate them with external traits, identify the module with the highest correlation, extract gene membership and module eigengene values, rank genes within the module based on module membership (MM) values, and further characterize hub genes by examining their functional annotations, gene ontology terms, or biological pathways.
Identification and prediction of overlap genes
Top hub genes from WGCNA and DGE were tabulated, and the prediction model was performed using gradient boosting. The model was trained sequentially on the training data to predict overlap genes using gradient boosting, learning from previous mistakes to improve predictive accuracy. The model's performance could be evaluated using accuracy, precision, recall, and F1 score metrics. The model's importance could be understood by interpreting its feature importance scores. Finally, the model could predict new data, identifying overlap genes based on the selected features and learned importance. The data was divided into training (80%) and test (20%) datasets, respectively. Preprocessing steps such as outliers' removal and data normalization were applied.
Gradient boosting architecture
Gradient boosting is an ensemble learning method that combines multiple weak learners, often in the form of decision trees. A loss function is used to quantify the difference between predicted and actual values. Gradient descent optimization minimizes the loss function, and the model adjusts predictions based on the gradient and learning rate. The learning rate controls the contribution of each base model to the ensemble, and regularization techniques like shrinkage or dropout can be applied to avoid overfitting and improve generalization. Feature importance scores indicate the relative importance of each feature in making predictions.
Results
Figure 1
Volcano plot of the top 250 differential gene expressions
X-axis represents log2 fold change and the y-axis represents -log10 p-value.
Statistically significant genes have a -log10 p-value greater than 1.3, while upregulated genes have a log2 fold change greater than 1.5.
The 249-row dataset exhibited a broad spectrum of up- and down-regulated genes. While statistically significant differentially expressed genes were identified at a conventional threshold, the relatively low number suggests further exploration may be warranted, as depicted in Figure 1. WGCNA provided a hierarchical overview of gene expression patterns, a topological overlap matrix for quantifying gene interrelationships, and a soft thresholding approach to convert raw co-expression values into a weighted adjacency matrix. These outputs facilitate the identification of gene modules and associated biological processes. Dendrograms enabled the analysis of gene clusters, delineated by a Dynamic TreeCut algorithm. The colour-coded bar is segmented into four colours (Figures 2, 3) that represent distinct gene groups based on expression patterns, aiding in the comprehension of functional relationships, co-expressed gene identification, and the potential discovery of novel pathways or regulatory mechanisms.
Figure 2
Gene dendrogram and module colors
This image shows how genes are grouped based on their similarity. Genes with similar characteristics are clustered together. The height of the branches indicates how different the gene groups are. The colored blocks at the bottom represent different groups of genes.
Figure 3
Graphical representation of relationships between various entities, typically used to visualize interactions within a system.
This analysis helps understand functional relationships, identify co-expressed genes, and potentially discover new pathways or regulatory mechanisms in biological systems. Network nodes labelled with identifiers like Gm24045, mt-Tv, mt-Ts1, Gm24399, Mir361, Mir152, and Gm37308 suggest they could represent biological genes or molecular entities. Edges, lines connecting nodes, indicate relationships or interactions. Density indicates strong or multiple interactions, while sparser connections indicate less interaction. Network analysis requires selecting an optimal soft threshold power, typically above 0.8 or 0.9, to construct meaningful biological networks from gene expression data, identifying co-expressed gene modules and key drivers.
Network nodes, labelled with identifiers such as Gm24045, mt-Tv, mt-Ts1, Gm24399, Mir361, Mir152, and Gm37308, represent biological genes or molecular entities. Edges connecting these nodes signify relationships or interactions. Node density indicates the strength or frequency of interactions. To construct meaningful biological networks from gene expression data, an optimal soft threshold power, typically exceeding 0.8 or 0.9, is essential for identifying co-expressed gene modules and key regulatory elements, as visualized in Figures 4, 5.
Figure 4
Scale independence
Scale Independence plots the scale-free topology model fit (y-axis) against the soft threshold power (x-axis). The x-axis represents the soft threshold power, a parameter used in network analysis, particularly in WGCNA, to highlight stronger correlations between nodes. The y-axis indicates the scale-free topology model fit, with a higher R2 value indicating better conformity to a scale-free topology, a crucial assumption in network analysis methods. The graph shows that as soft threshold power increases, the scale-free topology model fit initially increases, peaking and then stabilizing or slightly declining.
WGCNA: weighted gene co-expression network analysis
Figure 5
Scale-free topology model fit
The x-axis, a parameter in network analysis, ranges from 1 to 20, highlighting stronger correlations between nodes and minimizing weaker ones. The graph shows the scale-free topology model fit, with a higher R2 value indicating better conformity. Data points are labeled with soft threshold power numbers, and the graph helps select an optimal threshold power for the best scale-free properties.
The gradient boosting model performed well in predicting the target variable, with an area under the curve (AUC) value of 0.789 and a classification accuracy of 0.739. However, the model's accuracy is limited by the specific domain and context of the problem. The model's F1 score of 0.706 indicates a balanced trade-off between precision and recall, with a precision value of 0.749 indicating a low false positive rate, resulting in 75% accuracy. The model's recall value of 0.739 indicates a reasonable ability to identify positive instances, identifying approximately 74% of the actual positive instances in the dataset. The gradient boosting model, with a specificity value of 0.564, shows moderate accuracy, precision, recall, and F1 score, but struggles with identifying negative instances. Improvement in specificity is needed, potentially through adjusting thresholds or exploring other models. (Table 1)
Table 1
Classification model performance
AUC: area under the curve; CA: classification accuracy; F1: F-score
Model
AUC
CA
F1
Precision
Recall
Specificity
Gradient Boosting
0.789
0.739
0.706
0.749
0.739
0.564
The confusion matrix displays 88 actual and predicted cases, assessing the model's performance in distinguishing between "non-overlap" and "overlap" categories and identifying strengths and weaknesses in sensitivity and specificity (Figure 6). The true negative cell shows that the model correctly predicted "non-overlap" 73.0% of the time, about 42 cases out of 57. The model's false positive and false negative cells indicate that it incorrectly predicted "overlap" and "non-overlap" cases, respectively, at 21.4% and 27.0% of the cases.
Figure 6
Confusion matrix
Discussion
Recent research on the canonical Wnt pathway has provided new insights into regulating this pathway. Activation of the canonical Wnt pathway leads to the accumulation and movement of β-catenin into the nucleus, activating transcription factors that control specific genes involved in cellular development. The intracellular signaling of Wnt is complex due to the involvement of multiple Fz receptors and the recently established role of LRP5 and LRP6 as co-receptors for Wnt proteins [13]. The Wnt pathway is a promising therapeutic target for bone repair and skeletal homeostasis, with abnormalities in Wnt/β-catenin signaling implicated in osteoarthritis. Sclerostin, a product of the SOST gene, inhibits Wnt signaling and is being investigated for osteoporosis [14]. Dual inhibition of Wnt and sclerostin antibody treatment results in synergistic bone formation. Dual inhibition of Wnt and sclerostin antibody treatment results in synergistic bone formation. This treatment is being explored in clinical trials for various medical conditions. In this study, we analyzed weighted co-expression analysis and differential gene expression of the Wnt-based pathway.
Limited research exists on identified hub nodes (Gm24045, mt-Tv, mt-Ts1, Gm24399, Mir361, Mir152, and Gm37308) in the Wnt pathway and bone formation of mice. These non-coding genes may have regulatory functions, but their specific roles remain unclear. mt-Tv and mt-Ts1 are transfer RNAs involved in mitochondrial function and energy production, while Mir361 and Mir152 are microRNAs [15] that regulate gene expression [4]. Further studies are needed to understand their functions and potential contributions, similar to studies that identified predictive hub genes. Six feature genes (AADAT, APOF, GPC3, LPA, MASP1, and NAT2) were identified using machine learning algorithms such as Random Forest, support vector machine-recursive feature elimination [9,16], and one more study similar to the four-gene model was developed for diagnosing sepsis/severe acute respiratory distress syndrome (ARDS), demonstrating high diagnostic and predictive performance through calibration curves and decision curve analyses [8,9,17].
Future research should investigate differentially expressed genes to identify key genes and pathways in the Wnt pathway. WGCNA can provide insights into gene expression patterns and functional relationships as shown in Figures 2-5. Network analysis can help understand interactions between genes and regulatory mechanisms. Future directions should focus on improving the model's sensitivity and specificity, tuning hyperparameters, and conducting comprehensive validation experiments. However, limitations include the dependence on dataset quality and the potential for generalization to other biological systems.
The gradient boosting model achieves 78.9% accuracy in predicting Wnt pathway overlapping genes, with respectable AUC and CA values. It accurately predicts 73.9% of samples, with a high precision ratio and low recall, as shown in Figure 6. Its high precision ratio indicates low false positives and a low recall ratio, suggesting accurate predictions. However, the model's lower F1 score suggests it needs improvement in balancing precision and recall. Future improvements could involve improving the recall rate, reducing false negatives, and capturing more true overlapping genes. The quality of training data and gene annotations limits the model's performance. Obtaining a larger dataset and conducting further experimental validations could enhance its performance.
Conclusions
The present study aimed to elucidate the intricate regulatory mechanisms within the Wnt signaling pathway, a key determinant of osteogenesis. By integrating DGE analysis and WGCNA, the current study identifies pivotal gene modules and their potential roles in bone formation. The application of gradient boosting provided a computational framework for predicting gene interactions within this pathway.
While our findings offer preliminary insights into the complex architecture of the Wnt signalling network, further research is imperative to validate these observations in larger and more diverse cohorts. A deeper understanding of the identified gene modules and their functional implications is essential for translating these findings into clinically relevant applications. Ultimately, unravelling the complexities of Wnt signalling holds the potential to inform the development of novel therapeutic strategies for bone-related disorders. |
Title: Complex Biophysical and Computational Analyses of G‐Quadruplex Ligands: The Porphyrin Stacks Back | Body:
Introduction
Several genomic regions are characterized by a relative abundance of guanosine. The large amount of guanosine units, in the presence of monovalent cations such as K+ and Na+, allows their folding and leads to the formation of planar tetrads called G‐quartets, together by a network of hydrogen bonds through Hoogsteen base pairing.[
1
,
2
] Stacking of G‐quartets, stabilized by the coordination of the monovalent metal ion with the O6 oxygen of guanines, leads to the formation of “non‐canonical” structures, characterized by a four chains helical structure, called G‐quadruplexes (G4) (Figure 1a). The presence of G4 in telomeres and several other non‐codifying regions of genome have been associated with a variety of regulatory functions. The human telomere is approximately 5,000 to 8,000 base pairs in length and features a single‐stranded 3′ overhang ranging from 100 to 200 bases.
[1]
This overhang is primarily composed of the repetitive TTAGGG sequence. Most healthy cells possess a limited capacity for division, as was demonstrated by Hayflick and Moorhead in 1961.
[3]
It is currently known that this effect is related to telomere length, which act as biological clocks that, after reaching a limiting length, trigger the senescence process.
[4]
A problem often correlated with the presence of tumor activity is abnormal cellular proliferation, which in most cases, is associated with the overexpression of telomerase activity.
[5]
This enzyme, a reverse cellular transcriptase, regulates telomere elongation, thereby preserving its integrity. A correlation has been assessed between telomere length maintenance and the cellular ability, typical of cancer cells, to escape replicative senescence, and in fact more than 90 percent of cancer cells show telomerase enzyme activity.
[6]
The fact that most healthy cells are telomerase‐silent while there is its overexpression in cancer cells, makes this enzyme an attractive target for post‐diagnosis treatments. The inhibition of telomerase can be achieved either through small molecules that bind directly to the enzyme[
7
,
8
] or by stabilizing G4 structures. The latter method involves the use of ligands that bind to and stabilize G4 structures, which act as physiological blocks to the telomerase's ability to code and extend telomeres.
[9]
G4 can also be found in oncogene promoters, replication initiation sites and untranslated regions, showing their biological relevance.[
10
,
11
,
12
] For example, the c‐Myc gene plays an essential role in the regulation of cell growth, proliferation, and apoptosis. When over‐expressed or mutated, as in cancer cells, this gene can drive cells toward uncontrolled proliferation and thus contribute to the formation of various types of cancer.
[13]
Within its sequence, the c‐Myc proto‐oncogene possesses the nuclear hypersensitivity element III1 (NHEIII1) region which has been shown to be highly influential in the regulation of this gene.
[14]
Stabilizing this structure with ligands was noted to suppress further transcriptional activation of the c‐Myc gene.
[15]
G4 can be unimolecular or intermolecular. Depending on the orientation of the chains, they can adopt different topologies
[16]
(Figure 1b), influenced by factors such as molecular crowding[
17
,
18
] and nature of monovalent cation.
[19]
Furthermore, the structural and biochemical features of G4 s, prompted their use in biosensors
[20]
and nanomaterials.[
21
,
22
]
Figure 1
(A) structure of the G‐quartet formed by Hoogsteen base pairing of four guanine residues and piling up to give G4. (B) Schematic intramolecular topologies of G4 structures. Blue and orange rectangles depict guanines exhibiting syn and anti conformation of glycosidic torsion angle, respectively. In the case of antiparallel topology, a and b denote the orientations of the G‐tracts, corresponding to upward and downward directions, respectively, starting from the 5′ end in a clockwise manner.
The study of G4 ligands has thus become a productive field of research.[
23
,
24
,
25
,
26
,
27
,
28
,
29
,
30
,
31
,
32
] Basically, there are 4 ways in which a ligand can interact with a G4: stacking on top or bottom G‐quartets, intercalation between them, interaction with loops, or a combination of these ways. Electron‐deficient cores promote interactions through π‐π stacking, while cationic portions on substituents determine electrostatic interactions with negative charged phosphodiester groups, and so on.
[33]
These considerations led to the discovery of various types of ligands, among others porphyrins: natural derivatives include Fe(III)‐protoporphyrin IX,
[34]
while the first synthetic derivative is 5,10,15,20‐tetrakis‐(N‐methyl‐4‐pyridyl)porphyrin (TMPyP4), whose activity as telomerase inhibitor was demonstrated by Wheelhouse et al. in 1998.
[35]
TMPyP4 has been shown also to reduce the expression of the proto‐oncogene c‐Myc and several c‐Myc‐regulated genes that contain G4‐forming sequences. This modulation led to in vivo antitumor effects in various models, including the ability to inhibit tumor growth and prolong survival.
[36]
TMPyP4, has become a staple between ligands used in G4 studies but displayed a limited selectivity,
[37]
prompting to the development of various derivatives with demanding synthesis.[
38
,
39
,
40
,
41
] In this work, several simple porphyrin derivatives with potential binding and stabilizing activity toward G4 were screened through a molecular modelling procedure, which included docking and molecular dynamics. Simulations were conducted on different G4 s comprising one from the human telomeric sequence with a parallel topology (PDB ID:1KF1
[42]
) and another present in the NHEIII1 region of the c‐Myc proto‐oncogene, also known to have a parallel propeller type topology (PDB ID: 1XAV
[43]
). The results of the docking procedure guided in the choice of the derivatives to be synthesized and subjected to various stability and binding studies. Specifically, a derivative of 5,10,15,20‐tetrakis‐4‐pyridylporphyrin (TPyP) with amidomethyl substituent was found to be the most promising, and its properties are here compared with those of well‐known porphyrin derivative, TMPyP4. One of the focuses of the work was to assess whether substituting the methyl groups of TMPyP4 affect its interaction with G4 from telomere and c‐Myc. This comparison was made by evaluating the results obtained for the substituted derivatives alongside those obtained for TMPyP4 using NMR spectroscopy and mass spectrometry. NMR spectroscopy has proven to be a very versatile technique for assessing the formation of a complex, and providing crucial insights into the mode of ligand/G4 interaction. In particular NOESY and ROESY experiments,[
43
,
44
,
45
] have been employed for structural assessment of the complexes and identification of the corresponding G4‐ligand interfaces. The stability of the ligand/G4 complexes was evaluated by mass spectrometry,[
46
,
47
,
48
] specifically, with MS/MS technique: the intensity of isolated molecular ion generated by the porphyrins/G4 complex was measured as a function of increasing collision energy in the analyzer. Porphyrin derivatives were then subjected to some preliminary tests to evaluate their cytotoxicity on breast cancer (MCF‐7).
Results and Discussion
In this work, the interaction between tetracationic porphyrins derivatives and different DNA G4 s was evaluated. Proposed ligand structures, based on the porphyrin core (Figure 2) with different meso substituents, are reported in Table 1. TMPyP4, chosen as literature reference ligand, and designed ligands, PL1 to PL7, were first subjected to molecular docking calculations on different G4 structures. Most promising ligands were then synthetized, and their G4 binding abilities studied by UV and NMR spectroscopies and mass spectrometry. Chosen sequences were derived from the NHEIII1 region of the c‐Myc proto‐oncogene, and the human telomeres.
Figure 2
Porphyrin general core. R groups indicate meso substituents.
Table 1
Proposed porphyrin ligands’ meso substituents.
Ligands
Ligands’ meso substituents (R)
MW
TMPyP4
678.84
PL1
943.07
PL2
943.07
PL3
1115.75
PL4
911.16
PL5
850.83
PL6
907.05
PL7
850.94
PL7‐Me
907.05
PL7‐2Me
963.16
Wiley‐VCH GmbH
Molecular Docking
The GScore values for the best poses obtained for the studied ligands on the studied G4 s are reported in Table 2. According to the docking results obtained on all‐parallel G4 1XAV and 1KF1, candidates PL3, PL6 and PL7 were the most promising. Unfortunately, the synthesis of PL3 proved to be challenging. Given that PL6 and PL7 exhibited a comparable G4 affinity to that showed by PL3, the synthesis of the latter was not pursued further. It was observed that both PL6 and PL7 demonstrated binding ability to other topologies in addition to the all‐parallel ones. However, given the absence of stereocenters and the lower molecular weight, PL7 was selected for further computational studies.
Table 2
GScore results from molecular docking experiments.
Ligands
1XAV[a]
1KF1[b,f]
2JPZ[c]
2HY9[d]
143D[e]
TMPyP4
−13,50
np[g]
np
np
np
PL1
−14,11
−14,63
‐[h]
‐
np
PL2
−14,77
−14,16
‐
‐
−12,79
PL3
−17,84
−19,14
‐
‐
−14,70
PL4
−9,89
−15,77
‐
‐
np
PL5
−14,07
−14,28
np
−14,09
‐
PL6[j]
−17,74
−19,12
−15,29
−15,36
‐
PL7
−18,69
−18,66
−18,21
−16,79
−14,81
G4 names correspond to PDB ID. [a] Main G4 forms in the c‐MYC promoter gene. [b] Human telomeric parallel G4. [c] Human telomeric hybrid‐form 1 G4. [d] Human telomeric hybrid‐form 2 G4. [e] Human telomeric antiparallel G4. [f] All G4 structures were determined in solution by NMR except for 1KF1 (X‐Ray solid structure). [g] np: no poses were found. [h] the dash symbol indicates that the docking was not performed. [j] Configuration of the chiral centers of ligand PL6 in the best pose: SRSR, SSSS, SRRS, SSSS.
Wiley‐VCH GmbH
Molecular Dynamics Simulations
To investigate the impact of PL7 on the stability of G4, a series of molecular dynamics (MD) simulations were conducted. The system was simulated in a water solution using 1KF1 as G4, both in the presence and absence of the ligand. For the complex PL7/G4, the structure obtained from molecular docking studies was used. Simulations were conducted at temperatures of 300, 500, 525 and 550 K, to induce denaturation. Experimentally, the process of thermal denaturation is observed to occur over a timescale that is too long to be replicated within the constraints of reasonable timescales in silico. The use of higher temperatures than those measured in real laboratory experiments has been found to accelerate the denaturation process and make it occur within accessible timescales for MD simulations.[
49
,
50
,
51
] It is important to note that this approach is feasible due to the harmonic potentials of the force field, which prevent significant deviations in bond length or bond breakage even at elevated temperatures. Furthermore, previous studies have demonstrated that increasing temperature does not alter the denaturation pathway.[
49
,
50
] This strategy has recently been employed to assess the stabilization of the G4 structure following interaction with ligands, with results that are consistent with experimental data.
[52]
Figure 3 presents the root‐mean‐square deviation (RMSD) values calculated for the entire G4 structure, its complex with PL7, and selected portions during the MD simulation performed at 300 K. To ascertain the stability of the PL7/1KF1 complex, both the DNA atoms (shown in blue) and the ligand atoms (shown in yellow) were considered in RMSD simulations. The matching of the two RMSD values indicated that the ligand, once it was bounded, did not move significantly from its initial position, remaining stably linked to the G4. Moreover, the comparison of the RMSD between the free 1KF1 and PL7/1KF1 indicated that the ligand induced stabilization, as evidenced by the slightly smaller RMSD values in its presence.
Figure 3
RMSD Results. (A)Color code used for quartets (B) Color code used for loops. These colors are the same used to indicate RMSD values. (C)RMSD calculated on: (top row) 1KF1 atoms except the first base in the 5’ direction (blue), and PL7/1KF1 atoms (yellow); (middle row) on each of the three G‐quartets; (bottom row) on each of the three loops. Simulations conducted at 300 K. (D) RMSDs calculated on three separate simulations at 525 K of free 1KF1. (E) RMSDs calculated on three separate simulations at 525 K of PL7/1KF1.
This stabilization could be primarily attributed to the enhanced stability of the loops. The simulations at higher temperatures confirmed the stabilization effect of the ligand, as shown by the RMSD of the simulations performed at 525 K, shown in Figures 3D and E. As expected, the RMSDs of the system at 525 K were in general higher than those at 300 K, however the behavior with and without the ligand was qualitatively very different. The RMSD of the loops revealed that these flexible portions did not keep their original organization at high temperature, either in absence or in presence of the ligand. However, the ligand had a strong effect on the stability of the G‐quartets: in free 1KF1, denaturation occurred within the first 50 ns. In the presence of PL7, denaturation was slower or not reached at all (Figure 3E ‐ middle row), keeping the G4 structure stable. The simulations performed at 500 K and 550 K confirmed the stabilizing effect of the ligand, as shown by the RMSD reported Figure S3.
Synthetized Oligonucleotides
The sequences used in the docking studies were taken as a starting point for choosing those to be used in the spectroscopic and spectrometric studies (Table 3). The sequence indicated by the acronym 23TAG (PDB ID: 2JSK
[53]
) has been employed, corresponding to tandem repeats of the human telomeric region. This sequence in K+ solution leads to the folding of a form1/form2 hybrid topology G4, with a 70 : 30 ratio respectively.
[54]
On the other hand, the modified sequences reported as CMA (PU19‐A2 A11,
[55]
PDB ID:2LBY) and CMT (PU19‐T2 A11
[55]
) are derived from the first 4 of 5 guanosine domains present in the NHEIII1 region of the c‐Myc gene.
[44]
CMA and CMT tend to fold in parallel topology G4 in a K+ solution. This enabled the assessment of both the binding capacity and the selectivity of the proposed ligands toward G4 structures with different topologies.
Table 3
DNA oligonucleotide sequences used in this study.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
23TAG
5’‐
T
A
G
G
G
T
T
A
G
G
G
T
T
A
G
G
G
T
T
A
G
G
G
‐3’
CMA
5’‐
T
A
G
G
G
A
G
G
G
T
A
G
G
G
A
G
G
G
T
‐3’
CMT
5’‐
T
T
G
G
G
A
G
G
G
T
A
G
G
G
A
G
G
G
T
‐3’
DNA oligonucleotides were synthesized on DNA/RNA H‐8 Synthesizer using standard phosphoramidite chemistry with DMT protecting group
Wiley‐VCH GmbH
Binding Constant UV‐Visible Evaluation
UV‐visible spectra of solutions of TMPyP4 and PL7 (Figure S8) were recorded upon addition of calf thymus DNA, 23TAG, and CMA, at 25 °C within the wavelength range of 200–800 nm. The porphyrins spectra typically exhibited a Soret band around 428 nm. Upon addition of DNA, in the TMPyP4 solution, a red shift of the maximum absorption band was observed for the duplex, 23TAG, and CMA, with shifts of 2.3 nm, 2.1 nm, and 2.9 nm, respectively. Similarly, the red shifts observed for the PL7 spectra amounted to 0.8 nm with duplex, 9.7 nm with 23TAG, and 5.8 nm with CMA. This red shift phenomenon can be associated with a decrease in the energy of the π→π* transition due to the interaction between the π‐bonding orbital of the DNA base pairs and the empty π*‐antibonding orbital of the ligand. The hypochromic effect, determined by comparing absorbance maxima, was evidence of the interaction occurring between the nucleotides and porphyrins. The hypochromic effect (Table 4) increased in the order duplex<23TAG<CMA with TMPyP4 and in the order duplex<CMA<23TAG with PL7, showing a difference compared to the duplex titration, of +9 % (TAG23) and +5 % (CMA). Binding constants could be determined by applying Benesi‐Hildebrand method as reported in Supporting Information. The obtained values aligned with those documented in the literature.[
56
,
57
] While TMPyP4, employed as a reference due to its well‐established status, demonstrated binding constant values consistent across all studied DNA types (1.1–1.5×106 M−1), PL7 exhibited selectivity for the CMA sequence over the duplex, displaying a binding constant 2.6 times higher (Table 5).
Table 4
Red shift and hypochromicity.
TMPyP4
PL7
▵λ (nm)
▵A (%)
▵λ (nm)
▵A (%)
Duplex
2.3±0.1
18±2
0.8±0.2
30±2
23TAG
2.1±0.4
22±1
9.7±0.5
39±3
CMA
2.9±0.5
24±1
5.8±0.3
35±2
Calculated at Soret band by titrating 5 μM porphyrin with 0.5 μM DNA. DNA solutions were prepared in a 20 mM potassium phosphate buffer at pH 7 and stored at 25 °C with slow rotation for 24 hours. Calf thymus DNA was used as a reference for duplex DNA. Porphyrin solutions at were prepared in the same buffer. Each experiment was repeated from 3 to 5 times, and the results are presented as the mean ± standard deviation.
Wiley‐VCH GmbH
Table 5
Binding constants of porphyrins with duplex and G4 DNA sequences.
Kb (M−1)
TMPyP4
PL7
Duplex
(1.1±0.8)×106
(6.5±0.1)×105
23TAG
(1.5±0.4)×106
(6.1±0.3)×105
CMA
(1.5±0.6)×106
(1.7±0.4)×106
The Benesi‐Hildebrand method was used to calculate the binding constant (Kb)
Wiley‐VCH GmbH
NMR Study of Complex Structures
To assess interactions of herein synthesized porphyrin analogues with G4, 1H NMR monitored titration of 23TAG and CMA G4 s were performed. These oligos differ slightly from the wild‐type segments in order to increase the NMR spectral resolution in the imino‐proton region without affecting the native structure. Notably, our studies were conducted in aqueous solutions at 20 mM K‐phosphate, excluding KCl that reduces solubility of the herein studied porphyrin derivatives. Importantly, the acquired 1H NMR spectra of CMA and 23TAG folded in 20 mM K‐phosphate without KCl, match literature reported spectra of G4 folded in the presence of 70/100 mM KCl.[
44
,
54
,
58
,
59
] 1H NMR spectrum of CMA at 20 mM K‐phosphate exhibits twelve imino signals in the range from δ 11.04 to 12.06 ppm, consistent with the formation of G4 with three G‐quartets, i. e. G3→G7→G12→G16, G4→G8→G13→G17 and G5→G9→G14→G18, each comprising four Hoogsteen‐type hydrogen‐bonded guanine residues. Notably, the parallel‐stranded topology of (free) CMA G4 relates to the core of the structure comprising guanine residues, which are connected with two single‐residue (T6 and T15) and one two‐residue (T10‐A11) propeller‐type loops, while overhangs on 5’‐ and 3’‐ends consist of A1‐T2 and T19, respectively. Upon addition of 0.5 mole equivalents of PL7 the imino 1H NMR signals corresponding to the ‘free CMA G4’ became less intense and a new set of signals was observed in the range between 1H δ 10.08 and 11.02 ppm (Figure 4B).
Figure 4
Imino region of the 1H NMR spectra of CMA G4 upon titration with PL7, whereby the molar ratios of DNA and the ligand are indicated above corresponding spectra. The signals corresponding to the ‘free CMA G4’, ‘Complex a’ and ‘Complex b’ are indicated with black, red, and green colors, respectively. Spectra were recorded at 0.2 mM DNA concentration, 25 °C in 90 %/10 % H2O/2H2O, at 20 mM KPi, pH 7.0.
The new set of signals intensified at 1 : 1 ratio of DNA:Ligand, consistent with the formation of a 1 : 1 binding stoichiometry complex called ‘Complex a’. Moreover, at the equimolar concentrations of DNA and ligand there was an equilibrium between ‘free CMA G4’ and ‘Complex a’ in a slight preference of the latter, while the species were in slow exchange on the 1H NMR timescale at 600 MHz. 1H NMR spectral analysis at 1.5 mole equivalents of PL7 shows that intensity of signals corresponding to ‘free CMA G4’ decreased, while the ‘Complex a’ persisted as the predominant species. NOESY and ROESY spectral analysis conducted at 1 : 1 DNA:ligand binding stoichiometry revealed cross‐peaks arising from chemical exchange between ‘free CMA G4’ and ‘Complex a’, enabling assignment of new CMA imino chemical shifts influenced by the proximity of PL7 (Figure 5 and Figure S5). Furthermore, comparison of imino 1H NMR chemical shifts of free CMA G4 and ‘Complex a’ showed the largest perturbations for guanine residues at the 5’‐end G‐quartet, i. e. G3→G7→G12→G16, and the smallest for G5→G9→G14→G18 quartet at the 3’‐end (Figure 5 and Figure S6). These results are consistent with ‘Complex a’ corresponding to CMA G4 exhibiting PL7 stacked on the G3→G7→G12→G16 quartet and positioned proximal to the 5’‐end overhanging residues T1 and A2 (Figure 5D). This was corroborated also by the observed 1H NMR chemical shifts changes upon formation of ‘Complex a’ that were around 1.0 ppm for the methyl groups of T1 (located at the 5’‐end). The fact that DNA‐ligand NOE interactions were not resolved suggests that the binding was dynamic and involved exchange of the ligand between free‐ and bound‐state and/or ligand reorientation at the binding site. Interestingly, at 2 mole equivalents of PL7
1H NMR signals corresponding to ‘Complex a’ were reduced, while yet another set of signals appeared between 1H δ 9.4 and 10.4 ppm in line with the formation of ‘Complex b’, wherein CMA G4 and PL7 interacted at 1 : 2 binding stoichiometry (Figure 4E). 1H NMR signals corresponding to ‘Complex b’ were further intensified at 2.5 mole equivalents of PL7, while precipitate was observed in the NMR sample at 3 (and higher) mole equivalents of the ligand with respect to 0.2 mM DNA, thus precluding further titration experiments. Notably, the slow exchange of ‘Complex a’ and ‘Complex b’ observed at 2 mole equivalents of PL7 enabled identification of the corresponding NOESY and ROESY cross‐peaks resulting from chemical exchange (Figure 5 and Figure S5). The analysis enabled assignment of imino 1H NMR chemical shifts of CMA G4 within ‘Complex b’, furthermore allowing 1H NMR chemical shift perturbation analysis showing that the ‘Complex b’ resulted upon PL7 binding to the G5→G9→G14→G18 quartet at the 3’‐end of the CMA G4 comprised in ‘Complex a’ (Figure 5D). In detail, comparison of imino 1H NMR chemical shifts of ‘Complex b’ and ‘Complex a’ showed the largest differences for G5→G9→G14→G18 quartet at the 3’‐end of the CMA G4, while the smallest ones for G3→G7→G12→G16 quartet at the 5’‐end of the CMA G4. On the other hand, the Δ(δ 1H) for imino signals of ‘Complex b’ with respect to those for ‘free CMA G4’ showed that differences were similar for the G3→G7→G12→G16 and G5→G9→G14→G18 quartets, in line with PL7 stacked on both. Altogether, the NMR data were consistent with moderate to strong binding of PL7 to CMA G4, whereby interactions comprised stacking of the ligand to the outer G‐quartets, of which the 5’‐end represented the preferential binding site.
Figure 5
Imino–imino region of NOESY spectra (τm=200 ms) of CMA in the presence of A) 1 and B) 2 mole equivalents of PL7 with indicated cross‐peaks corresponding to chemical exchange A) between ‘free CMA G4’ and ‘Complex a’; B) between ‘Complex a’ and ‘Complex b’. Imino 1H NMR chemical shifts corresponding to the ‘free CMA G4, ‘Complex a’ and ‘Complex b’ are labelled in black, green, and red, respectively. The spectra were recorded at 0.2 mM DNA concentration, 25 °C in 90 %/10 % H2O/2H2O, at 20 mM KPi, pH 7.0. C) Imino 1H NMR chemical shift changes induced by interaction of CMA G4 and PL7, whereby red, green, and blue bars indicate Δ(δ1H) between ‘free CMA G4’ and ‘Complex a’; between ‘Complex a’ and ‘Complex b’; between ‘free’ CMA G4 and ‘Complex b’, respectively. D) Schematic depiction of ‘free CMA G‐quadruple’ (top), of the ‘Complex a’ corresponding to CMA G4 exhibiting the ligand bound at its 5’‐end G‐quartet (middle) and ‘Complex b’ corresponding to CMA G4 exhibiting two ligands bound to G‐quartets, i. e. 5’‐ and 3’‐end (bottom).
Similar 1H NMR studies were also extended to derivatives PL7‐Me and PL7–2Me (Table 1), in which the amide hydrogens were replaced with 1 or 2 methyl groups respectively, to evaluate whether these structural modifications could modulate their interaction capabilities. Study of interactions of CMA G4 with PL7‐Me and PL7‐2Me relied on the use of 1D and 2D NMR experiments analogous as described above for PL7 (Figure S4). Upon addition of 0.5 to 1.5 molar equivalents of PL7‐Me and PL7‐2Me with respect to DNA, the 1H NMR signals corresponding to ′free CMA G4′ gradually became less intense and, in turn, a new set of signals was observed, consistent with the formation of ′Complex a’ with ligand bound to the 5′ end of CMA G4. Furthermore, no signals corresponding to DNA‐ligand interactions were identified, whereas cross‐peaks were observed in NOESY and ROESY spectra consistent with chemical exchange between ′free CMA G4′ and ′Complex a′. Preferential binding of PL7‐Me and PL7‐2Me to the G3→G7→G12→G16 quartet is inferred from analyses of imino 1H NMR chemical shift perturbation (Figure S6). Further additions to 2, 2.5 and 3 mole equivalents of PL7‐Me or PL7–2Me with respect to CMA resulted in conversion of ‘Complex a’ into ‘Complex b’, in which ligands stacked at both outer G‐quartets of CMA G4 as inferred from the observed 1H NMR chemical shift perturbations (Figure S6 and Table S1). Comparison of the 1H NMR imino‐protons chemical shift perturbations of CMA G4 upon binding of tetra‐substituted porphyrins with different pendant groups showed very similar profiles, i. e. insignificant differences between PL7 and PL7‐Me, while slight variations were observed in the case of PL7‐2Me. Most notable differences in 1H NMR chemical shifts were observed when comparing ‘Complex b’ for PL7–2Me with respect to PL7 and PL7‐Me. Furthermore, the imino protons of G3, G7, G12, and G16 were shifted up field with Δδ of 0.24, 0.15, 0.19, and 0.25 ppm for PL7‐Me vs. PL7‐2Me; this suggests that dimethylamide groups in PL7‐2Me slightly interfered with G4 binding, probably by sterically hindering ligand interactions at the binding site comprising the 5′‐end G3→G7→G12→G16 quartet. Considering that the G4 exhibits two 5’‐end residues (T1‐A2) while only one residue at the 3’‐end (T19) where the preference of PL7‐2Me binding is rather similar to PL7 and PL7‐Me suggests that binding relies not only on stacking of the ligands to G‐quartets, but also on interactions between the ligand's pedant groups and overhanging residues. Consistent with this, extending the comparative analysis of 1H NMR chemical shift perturbations to include NMR data on the binding of CMA G4 to the reference compound TMPyP4 (characterized by the shortest substituent on the pyridine nitrogen atom) showed that it bound slightly more strongly than PL7, PL7‐Me, and PL7‐2Me (Figure S6). To further explore binding of the herein studied porphyrin derivatives to G4 exhibiting different topologies 1H NMR‐monitored titration was performed on 23TAG, which at 20 mM KCl adopts G4 with hybrid‐1 type topology while upon molecular crowding conditions induced by DMSO refolds into parallel‐stranded G4.
[18]
At diluted conditions and in the absence of ligand, 1H NMR spectrum of 23TAG exhibited twelve major signals in the imino region characteristic for Hoogsteen‐hydrogen bonded guanine residues, consistent with formation of the predominant G4 exhibiting hybrid type‐1 topology (Figure S8 A). Additional broader 1H NMR imino signals were observed corresponding to minor G4 forms present in the equilibrium. The 1H NMR imino signals for both, the major and minor G4 forms decreased upon addition of 0.5 mole equivalent of the PL7. This effect was pronounced gradually at equimolar DNA and ligand concentrations as well as along the course of titration, whereby at 3 mole equivalents of the PL7 most of the signals corresponding to the initial G4 were broadened almost to the baseline. In parallel, formation of DNA‐ligand complex(es) was indicated by the new set of 1H NMR signals appearing at 1 : 1 ratio. However, the signals corresponding to the complex remained weak/broad even at 1 : 1.5 and 1 : 2 ratio of DNA to ligand, suggesting weak, or at most moderate binding that resulted in an equilibrium of free 23TAG G4 and complexes with non‐specifically bound PL7. In the solution mimicking molecular crowding conditions a single set of 1H NMR signals was observed in the spectrum of 23TAG, consistent with the formation of parallel‐stranded G4 (Figure S8 B). Interestingly, the corresponding imino 1H NMR signals were severely broadened upon addition of 0.5 mole equivalents of the PL7, while a few new signals were observed, consistent with formation of DNA‐ligand complex(es). At equimolar mixture of DNA and PL7 the signals for free 23TAG G4 were no longer observed. On the other hand, 1H NMR signals corresponding to the complex(es) were observed for the samples prepared at 0.5–2.0 mole equivalents of the ligand, although they appeared broad and mostly unresolved at each of the analyzed DNA:Ligand ratios. Notably, the relative intensities of the imino 1H NMR signals for the complex(es) changed during titration. Hence, the NMR analysis suggests rather strong binding of the PL7 to the parallel‐stranded G4 adopted by 23TAG, which appeared to exhibit multiple sites amenable to the ligand interactions. It is interesting to note that the ‘free’ 23TAG parallel G4 formed under crowding conditions was no longer observed at 1 mole equivalent of PL7, while the ‘free’ hybrid analogue persisted even at 1 : 2 ratio between 23TAG and compound. These results are consistent with parallel G4 representing a better target for binding of PL7, whereby the interactions are aggravated by lateral loops in the hybrid topology, altogether stressing out the importance of the structural details related to the loops conformations with respect to the nearby (outer) G‐quartets. The key role of residues extruded from the core of a G4 structure were further corroborated by the fact that shifting the equilibrium from parallel stranded G4 to complex formation required 1.5 mole equivalents of PL7 in the case of CMA (vide supra) (Figure 4), while only 1 mole equivalent in case of 23TAG (Figure S8 B). This suggests that PL7 exhibits higher binding affinity for parallel G4 formed by 23TAG than for parallel G4 formed by CMA. The differences may relate to the different DNA‐ligand interactions at the interfaces between overhanging or loop residues and pendant groups of the tetra‐substituted porphyrins. In particular, parallel G4 adopted by 23TAG exhibits three‐residue propeller‐type loops, while in the. case of CMA the propeller‐type loops comprise only one or two residues. The longer loops in case of 23TAG with respect to CMA exhibit more flexibility, which potentially guides and facilitates binding of the PL7. Analogously, longer pendant groups of tetra‐substituted porphyrins may promote G4 binding, which is substantiated by the results of comparative 1H NMR.
Circular Dichroism
To further confirm the selectivity of PL7 for the parallel topology, circular dichroism analyses were performed. As can be seen from Figure 6A, the spectrum of the CMA sequence showed a shape characteristic for parallel topology, with a positive band around 260 nm and a negative one at 240 nm. No changes in bands position were observed after either TMPyP4 or PL7 titrations, which confirmed the retention of the parallel topology (Figure 6A). The spectrum of free 23TAG (Figure 6B) showed a characteristic hybrid topology pattern, i. e., a positive band at 290 nm. Again, the G4 was subjected to titrations with 1.5 equivalents of TMPyP4 or PL7: in contrast to CMA, with 23TAG there was a change in the spectra for both titrations, showing a decrease in the intensity of the band at 290 nm and an increase at 260 nm. This effect indicated further confirmation that porphyrin ligands show selectivity for parallel topology. If this was not present, as in the case of 23TAG, they stimulated refolding by activating structural equilibria. These underlay the failure to isolate the complex via NMR for the sequence with hybrid topology.
Figure 6
Circular dichroism spectra of (A) CMA and (B) 23TAG in diluted conditions. The spectra were recorded at 25 °C in 90 %/10 % H2O/2H2O, at 20 mM KPi, pH 7.0‐ and 0.2‐mM DNA. TMPyP4 and PL7 were added in 1.5 eq.
Mass Spectrometry
Mass spectrometry proves to be an efficient technique in assessing the stability of complexes formed by nucleic acids.
[46]
In our case, the analyses were conducted using a mass spectrometer with an ESI source, as reported in the experimental section. The primary advantage of electrospray ionization mass spectrometry is its capability to transfer analytes of interest from the sample solution to the mass spectrometer with minimal fragmentation. A common strategy for enhancing the ion response in ESI‐MS is to add organic co‐solvents that are more volatile than water, such as MeOH.
[60]
This phenomenon arises from the ability of methanol to reduce the surface tension of droplets, thereby promoting droplet formation, fission, and evaporation processes. As reported by Rosu et al., the use of a specific methanol concentration not only results in a substantial increase in signal but also minimizes potential conformational alterations in solution.
[46]
Therefore, an 8 : 2 H2O:MeOH solution was used to dilute stock solution to bring sequence concentration to 15 μM. The sequences employed for these experiments were 23TAG and CMT. CMT corresponds to CMA, with the distinction that in CMT, the second guanine has been replaced by thymine. As already reported,
[44]
NMR studies conducted on this sequence reveal that both the CMA and CMT G4 share common structural characteristics. These include a core comprised of three G‐quartets, three propeller‐type loops, and a T19 residue located at the 3′‐end overhang. Since the fundamental folding topologies of the CMA and CMT G4 remain unaltered even when subjected to a single A−T nucleotide substitution at the 5′‐end, CMT has been used for mass experiments instead of CMA. The stabilizing activity of TMPyP4, PL7, PL7‐Me and PL7‐2Me was evaluated by MS/MS using the collision‐induced dissociation (CID) technique. The latter involves isolating the molecular ion in a collision chamber and gradually increasing the collision energy until the target peak disappears. From these experiments, it was possible to calculate the energy required for dissociation of the complex peak to its relative half‐intensity (ECOM50 %)
[61]
using the relative intensities of the target ions from Equation 1.
(1)
relativeintensity=ITargetIonITargetIon+IDissociationProducts
Mass spectrometry is less sensitive to structural equilibria than NMR spectroscopy making possible to isolate complexes with the 23TAG.
This supported the thesis that the reduction in intensity of the imino 1H NMR signals observed during titrations of G4 with ligands was due to the formation of a complex. However, no Complex b at a G4:ligand ratio of 1 : 2 has been isolated, suggesting that one of the two terminal G‐quartets of hybrid G4 is sterically hindered, impeding the stacking of the ligand. Complex b was isolated with CMT G4 using TMPyP4 and PL7. Table 6 summarizes the results of the stability studies. Overall, the proposed ligands stabilized both CMT and 23TAG sequences. For CMT, both PL7 and TMPyP4 showed an increase in stability from Complex a to Complex b. Additionally, from the ECOM50 % values, it is evident that an increase in the number of ligands present was proportional to an increase in G4 stability. However, it's important to note that this is not a trivial observation, as excessive ligand binding capacity can lead to destabilization and subsequent unfolding of the G4 structure.
Table 6
Gas phase stability of G4‐porphyrin complexes calculated by collision induced dissociation experiments.
Oligonucleotide
Ligand
ECOM50 % G4 (eV)
23TAG
No ligand
28.9
TMPyP4[a]
38.5
PL7[a]
38.8
PL7‐Me[a]
39.6
PL7‐2Me[a]
40.2
CMT
No ligand
26.5
TMPyP4[a]
35.9
TMPyP4[b]
48.5
PL7[a]
35.9
PL7[b]
45.5
Data were obtained by subjecting quadruplex samples and their respective complexes to MS/MS fragmentation. The quadruplexes were folded in a 0.2 mM solution of 100 mM tetramethyl ammonium acetate (TMAA) buffer at pH 7, containing 1 mM KCl. The solution was annealed at 60 °C for 30 seconds and then allowed to fold for 2 days. Further details are provided in the supplementary information. [a] Complex a with 1 : 1 stoichiometry. [b] Complex b with 1 : 2 stoichiometry.
Wiley‐VCH GmbH
Proliferation Assay
An MTT assay was conducted on MCF‐7 cell lines, using PL7 and TMPyP4 porphyrins. The investigation aimed to assess the potential cytotoxic effects of these porphyrins on cancerous cell lines. The results (Figure 7) of the MTT assay unveiled a compelling dose‐response relationship for both PL7 and TMPyP4, revealing their impact on cell viability. Notably, PL7 exhibited an IC50 value of 3.265±1.218 μM, indicating its potency in inhibiting cell proliferation. Also in this case, as for NMR experiments, results are comparable with once obtained for TMPyP4, that shows an IC50 of 3.651±1.197 μM. These findings showed that both molecules were able to induce a stop in the cell growth at relatively low concentrations.
Figure 7
IC50 from the MTT assay on MCF‐7 cell line with A) TMPyP4 and B) PL7. The x‐axis represents the logarithm of the concentration while the y‐axis contains the % of living cells in the sample. Each experiment was repeated 3 times, the results are presented as the mean, error bars represent ± the standard deviation
Molecular Docking
The GScore values for the best poses obtained for the studied ligands on the studied G4 s are reported in Table 2. According to the docking results obtained on all‐parallel G4 1XAV and 1KF1, candidates PL3, PL6 and PL7 were the most promising. Unfortunately, the synthesis of PL3 proved to be challenging. Given that PL6 and PL7 exhibited a comparable G4 affinity to that showed by PL3, the synthesis of the latter was not pursued further. It was observed that both PL6 and PL7 demonstrated binding ability to other topologies in addition to the all‐parallel ones. However, given the absence of stereocenters and the lower molecular weight, PL7 was selected for further computational studies.
Table 2
GScore results from molecular docking experiments.
Ligands
1XAV[a]
1KF1[b,f]
2JPZ[c]
2HY9[d]
143D[e]
TMPyP4
−13,50
np[g]
np
np
np
PL1
−14,11
−14,63
‐[h]
‐
np
PL2
−14,77
−14,16
‐
‐
−12,79
PL3
−17,84
−19,14
‐
‐
−14,70
PL4
−9,89
−15,77
‐
‐
np
PL5
−14,07
−14,28
np
−14,09
‐
PL6[j]
−17,74
−19,12
−15,29
−15,36
‐
PL7
−18,69
−18,66
−18,21
−16,79
−14,81
G4 names correspond to PDB ID. [a] Main G4 forms in the c‐MYC promoter gene. [b] Human telomeric parallel G4. [c] Human telomeric hybrid‐form 1 G4. [d] Human telomeric hybrid‐form 2 G4. [e] Human telomeric antiparallel G4. [f] All G4 structures were determined in solution by NMR except for 1KF1 (X‐Ray solid structure). [g] np: no poses were found. [h] the dash symbol indicates that the docking was not performed. [j] Configuration of the chiral centers of ligand PL6 in the best pose: SRSR, SSSS, SRRS, SSSS.
Wiley‐VCH GmbH
Molecular Dynamics Simulations
To investigate the impact of PL7 on the stability of G4, a series of molecular dynamics (MD) simulations were conducted. The system was simulated in a water solution using 1KF1 as G4, both in the presence and absence of the ligand. For the complex PL7/G4, the structure obtained from molecular docking studies was used. Simulations were conducted at temperatures of 300, 500, 525 and 550 K, to induce denaturation. Experimentally, the process of thermal denaturation is observed to occur over a timescale that is too long to be replicated within the constraints of reasonable timescales in silico. The use of higher temperatures than those measured in real laboratory experiments has been found to accelerate the denaturation process and make it occur within accessible timescales for MD simulations.[
49
,
50
,
51
] It is important to note that this approach is feasible due to the harmonic potentials of the force field, which prevent significant deviations in bond length or bond breakage even at elevated temperatures. Furthermore, previous studies have demonstrated that increasing temperature does not alter the denaturation pathway.[
49
,
50
] This strategy has recently been employed to assess the stabilization of the G4 structure following interaction with ligands, with results that are consistent with experimental data.
[52]
Figure 3 presents the root‐mean‐square deviation (RMSD) values calculated for the entire G4 structure, its complex with PL7, and selected portions during the MD simulation performed at 300 K. To ascertain the stability of the PL7/1KF1 complex, both the DNA atoms (shown in blue) and the ligand atoms (shown in yellow) were considered in RMSD simulations. The matching of the two RMSD values indicated that the ligand, once it was bounded, did not move significantly from its initial position, remaining stably linked to the G4. Moreover, the comparison of the RMSD between the free 1KF1 and PL7/1KF1 indicated that the ligand induced stabilization, as evidenced by the slightly smaller RMSD values in its presence.
Figure 3
RMSD Results. (A)Color code used for quartets (B) Color code used for loops. These colors are the same used to indicate RMSD values. (C)RMSD calculated on: (top row) 1KF1 atoms except the first base in the 5’ direction (blue), and PL7/1KF1 atoms (yellow); (middle row) on each of the three G‐quartets; (bottom row) on each of the three loops. Simulations conducted at 300 K. (D) RMSDs calculated on three separate simulations at 525 K of free 1KF1. (E) RMSDs calculated on three separate simulations at 525 K of PL7/1KF1.
This stabilization could be primarily attributed to the enhanced stability of the loops. The simulations at higher temperatures confirmed the stabilization effect of the ligand, as shown by the RMSD of the simulations performed at 525 K, shown in Figures 3D and E. As expected, the RMSDs of the system at 525 K were in general higher than those at 300 K, however the behavior with and without the ligand was qualitatively very different. The RMSD of the loops revealed that these flexible portions did not keep their original organization at high temperature, either in absence or in presence of the ligand. However, the ligand had a strong effect on the stability of the G‐quartets: in free 1KF1, denaturation occurred within the first 50 ns. In the presence of PL7, denaturation was slower or not reached at all (Figure 3E ‐ middle row), keeping the G4 structure stable. The simulations performed at 500 K and 550 K confirmed the stabilizing effect of the ligand, as shown by the RMSD reported Figure S3.
Synthetized Oligonucleotides
The sequences used in the docking studies were taken as a starting point for choosing those to be used in the spectroscopic and spectrometric studies (Table 3). The sequence indicated by the acronym 23TAG (PDB ID: 2JSK
[53]
) has been employed, corresponding to tandem repeats of the human telomeric region. This sequence in K+ solution leads to the folding of a form1/form2 hybrid topology G4, with a 70 : 30 ratio respectively.
[54]
On the other hand, the modified sequences reported as CMA (PU19‐A2 A11,
[55]
PDB ID:2LBY) and CMT (PU19‐T2 A11
[55]
) are derived from the first 4 of 5 guanosine domains present in the NHEIII1 region of the c‐Myc gene.
[44]
CMA and CMT tend to fold in parallel topology G4 in a K+ solution. This enabled the assessment of both the binding capacity and the selectivity of the proposed ligands toward G4 structures with different topologies.
Table 3
DNA oligonucleotide sequences used in this study.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
23TAG
5’‐
T
A
G
G
G
T
T
A
G
G
G
T
T
A
G
G
G
T
T
A
G
G
G
‐3’
CMA
5’‐
T
A
G
G
G
A
G
G
G
T
A
G
G
G
A
G
G
G
T
‐3’
CMT
5’‐
T
T
G
G
G
A
G
G
G
T
A
G
G
G
A
G
G
G
T
‐3’
DNA oligonucleotides were synthesized on DNA/RNA H‐8 Synthesizer using standard phosphoramidite chemistry with DMT protecting group
Wiley‐VCH GmbH
Binding Constant UV‐Visible Evaluation
UV‐visible spectra of solutions of TMPyP4 and PL7 (Figure S8) were recorded upon addition of calf thymus DNA, 23TAG, and CMA, at 25 °C within the wavelength range of 200–800 nm. The porphyrins spectra typically exhibited a Soret band around 428 nm. Upon addition of DNA, in the TMPyP4 solution, a red shift of the maximum absorption band was observed for the duplex, 23TAG, and CMA, with shifts of 2.3 nm, 2.1 nm, and 2.9 nm, respectively. Similarly, the red shifts observed for the PL7 spectra amounted to 0.8 nm with duplex, 9.7 nm with 23TAG, and 5.8 nm with CMA. This red shift phenomenon can be associated with a decrease in the energy of the π→π* transition due to the interaction between the π‐bonding orbital of the DNA base pairs and the empty π*‐antibonding orbital of the ligand. The hypochromic effect, determined by comparing absorbance maxima, was evidence of the interaction occurring between the nucleotides and porphyrins. The hypochromic effect (Table 4) increased in the order duplex<23TAG<CMA with TMPyP4 and in the order duplex<CMA<23TAG with PL7, showing a difference compared to the duplex titration, of +9 % (TAG23) and +5 % (CMA). Binding constants could be determined by applying Benesi‐Hildebrand method as reported in Supporting Information. The obtained values aligned with those documented in the literature.[
56
,
57
] While TMPyP4, employed as a reference due to its well‐established status, demonstrated binding constant values consistent across all studied DNA types (1.1–1.5×106 M−1), PL7 exhibited selectivity for the CMA sequence over the duplex, displaying a binding constant 2.6 times higher (Table 5).
Table 4
Red shift and hypochromicity.
TMPyP4
PL7
▵λ (nm)
▵A (%)
▵λ (nm)
▵A (%)
Duplex
2.3±0.1
18±2
0.8±0.2
30±2
23TAG
2.1±0.4
22±1
9.7±0.5
39±3
CMA
2.9±0.5
24±1
5.8±0.3
35±2
Calculated at Soret band by titrating 5 μM porphyrin with 0.5 μM DNA. DNA solutions were prepared in a 20 mM potassium phosphate buffer at pH 7 and stored at 25 °C with slow rotation for 24 hours. Calf thymus DNA was used as a reference for duplex DNA. Porphyrin solutions at were prepared in the same buffer. Each experiment was repeated from 3 to 5 times, and the results are presented as the mean ± standard deviation.
Wiley‐VCH GmbH
Table 5
Binding constants of porphyrins with duplex and G4 DNA sequences.
Kb (M−1)
TMPyP4
PL7
Duplex
(1.1±0.8)×106
(6.5±0.1)×105
23TAG
(1.5±0.4)×106
(6.1±0.3)×105
CMA
(1.5±0.6)×106
(1.7±0.4)×106
The Benesi‐Hildebrand method was used to calculate the binding constant (Kb)
Wiley‐VCH GmbH
NMR Study of Complex Structures
To assess interactions of herein synthesized porphyrin analogues with G4, 1H NMR monitored titration of 23TAG and CMA G4 s were performed. These oligos differ slightly from the wild‐type segments in order to increase the NMR spectral resolution in the imino‐proton region without affecting the native structure. Notably, our studies were conducted in aqueous solutions at 20 mM K‐phosphate, excluding KCl that reduces solubility of the herein studied porphyrin derivatives. Importantly, the acquired 1H NMR spectra of CMA and 23TAG folded in 20 mM K‐phosphate without KCl, match literature reported spectra of G4 folded in the presence of 70/100 mM KCl.[
44
,
54
,
58
,
59
] 1H NMR spectrum of CMA at 20 mM K‐phosphate exhibits twelve imino signals in the range from δ 11.04 to 12.06 ppm, consistent with the formation of G4 with three G‐quartets, i. e. G3→G7→G12→G16, G4→G8→G13→G17 and G5→G9→G14→G18, each comprising four Hoogsteen‐type hydrogen‐bonded guanine residues. Notably, the parallel‐stranded topology of (free) CMA G4 relates to the core of the structure comprising guanine residues, which are connected with two single‐residue (T6 and T15) and one two‐residue (T10‐A11) propeller‐type loops, while overhangs on 5’‐ and 3’‐ends consist of A1‐T2 and T19, respectively. Upon addition of 0.5 mole equivalents of PL7 the imino 1H NMR signals corresponding to the ‘free CMA G4’ became less intense and a new set of signals was observed in the range between 1H δ 10.08 and 11.02 ppm (Figure 4B).
Figure 4
Imino region of the 1H NMR spectra of CMA G4 upon titration with PL7, whereby the molar ratios of DNA and the ligand are indicated above corresponding spectra. The signals corresponding to the ‘free CMA G4’, ‘Complex a’ and ‘Complex b’ are indicated with black, red, and green colors, respectively. Spectra were recorded at 0.2 mM DNA concentration, 25 °C in 90 %/10 % H2O/2H2O, at 20 mM KPi, pH 7.0.
The new set of signals intensified at 1 : 1 ratio of DNA:Ligand, consistent with the formation of a 1 : 1 binding stoichiometry complex called ‘Complex a’. Moreover, at the equimolar concentrations of DNA and ligand there was an equilibrium between ‘free CMA G4’ and ‘Complex a’ in a slight preference of the latter, while the species were in slow exchange on the 1H NMR timescale at 600 MHz. 1H NMR spectral analysis at 1.5 mole equivalents of PL7 shows that intensity of signals corresponding to ‘free CMA G4’ decreased, while the ‘Complex a’ persisted as the predominant species. NOESY and ROESY spectral analysis conducted at 1 : 1 DNA:ligand binding stoichiometry revealed cross‐peaks arising from chemical exchange between ‘free CMA G4’ and ‘Complex a’, enabling assignment of new CMA imino chemical shifts influenced by the proximity of PL7 (Figure 5 and Figure S5). Furthermore, comparison of imino 1H NMR chemical shifts of free CMA G4 and ‘Complex a’ showed the largest perturbations for guanine residues at the 5’‐end G‐quartet, i. e. G3→G7→G12→G16, and the smallest for G5→G9→G14→G18 quartet at the 3’‐end (Figure 5 and Figure S6). These results are consistent with ‘Complex a’ corresponding to CMA G4 exhibiting PL7 stacked on the G3→G7→G12→G16 quartet and positioned proximal to the 5’‐end overhanging residues T1 and A2 (Figure 5D). This was corroborated also by the observed 1H NMR chemical shifts changes upon formation of ‘Complex a’ that were around 1.0 ppm for the methyl groups of T1 (located at the 5’‐end). The fact that DNA‐ligand NOE interactions were not resolved suggests that the binding was dynamic and involved exchange of the ligand between free‐ and bound‐state and/or ligand reorientation at the binding site. Interestingly, at 2 mole equivalents of PL7
1H NMR signals corresponding to ‘Complex a’ were reduced, while yet another set of signals appeared between 1H δ 9.4 and 10.4 ppm in line with the formation of ‘Complex b’, wherein CMA G4 and PL7 interacted at 1 : 2 binding stoichiometry (Figure 4E). 1H NMR signals corresponding to ‘Complex b’ were further intensified at 2.5 mole equivalents of PL7, while precipitate was observed in the NMR sample at 3 (and higher) mole equivalents of the ligand with respect to 0.2 mM DNA, thus precluding further titration experiments. Notably, the slow exchange of ‘Complex a’ and ‘Complex b’ observed at 2 mole equivalents of PL7 enabled identification of the corresponding NOESY and ROESY cross‐peaks resulting from chemical exchange (Figure 5 and Figure S5). The analysis enabled assignment of imino 1H NMR chemical shifts of CMA G4 within ‘Complex b’, furthermore allowing 1H NMR chemical shift perturbation analysis showing that the ‘Complex b’ resulted upon PL7 binding to the G5→G9→G14→G18 quartet at the 3’‐end of the CMA G4 comprised in ‘Complex a’ (Figure 5D). In detail, comparison of imino 1H NMR chemical shifts of ‘Complex b’ and ‘Complex a’ showed the largest differences for G5→G9→G14→G18 quartet at the 3’‐end of the CMA G4, while the smallest ones for G3→G7→G12→G16 quartet at the 5’‐end of the CMA G4. On the other hand, the Δ(δ 1H) for imino signals of ‘Complex b’ with respect to those for ‘free CMA G4’ showed that differences were similar for the G3→G7→G12→G16 and G5→G9→G14→G18 quartets, in line with PL7 stacked on both. Altogether, the NMR data were consistent with moderate to strong binding of PL7 to CMA G4, whereby interactions comprised stacking of the ligand to the outer G‐quartets, of which the 5’‐end represented the preferential binding site.
Figure 5
Imino–imino region of NOESY spectra (τm=200 ms) of CMA in the presence of A) 1 and B) 2 mole equivalents of PL7 with indicated cross‐peaks corresponding to chemical exchange A) between ‘free CMA G4’ and ‘Complex a’; B) between ‘Complex a’ and ‘Complex b’. Imino 1H NMR chemical shifts corresponding to the ‘free CMA G4, ‘Complex a’ and ‘Complex b’ are labelled in black, green, and red, respectively. The spectra were recorded at 0.2 mM DNA concentration, 25 °C in 90 %/10 % H2O/2H2O, at 20 mM KPi, pH 7.0. C) Imino 1H NMR chemical shift changes induced by interaction of CMA G4 and PL7, whereby red, green, and blue bars indicate Δ(δ1H) between ‘free CMA G4’ and ‘Complex a’; between ‘Complex a’ and ‘Complex b’; between ‘free’ CMA G4 and ‘Complex b’, respectively. D) Schematic depiction of ‘free CMA G‐quadruple’ (top), of the ‘Complex a’ corresponding to CMA G4 exhibiting the ligand bound at its 5’‐end G‐quartet (middle) and ‘Complex b’ corresponding to CMA G4 exhibiting two ligands bound to G‐quartets, i. e. 5’‐ and 3’‐end (bottom).
Similar 1H NMR studies were also extended to derivatives PL7‐Me and PL7–2Me (Table 1), in which the amide hydrogens were replaced with 1 or 2 methyl groups respectively, to evaluate whether these structural modifications could modulate their interaction capabilities. Study of interactions of CMA G4 with PL7‐Me and PL7‐2Me relied on the use of 1D and 2D NMR experiments analogous as described above for PL7 (Figure S4). Upon addition of 0.5 to 1.5 molar equivalents of PL7‐Me and PL7‐2Me with respect to DNA, the 1H NMR signals corresponding to ′free CMA G4′ gradually became less intense and, in turn, a new set of signals was observed, consistent with the formation of ′Complex a’ with ligand bound to the 5′ end of CMA G4. Furthermore, no signals corresponding to DNA‐ligand interactions were identified, whereas cross‐peaks were observed in NOESY and ROESY spectra consistent with chemical exchange between ′free CMA G4′ and ′Complex a′. Preferential binding of PL7‐Me and PL7‐2Me to the G3→G7→G12→G16 quartet is inferred from analyses of imino 1H NMR chemical shift perturbation (Figure S6). Further additions to 2, 2.5 and 3 mole equivalents of PL7‐Me or PL7–2Me with respect to CMA resulted in conversion of ‘Complex a’ into ‘Complex b’, in which ligands stacked at both outer G‐quartets of CMA G4 as inferred from the observed 1H NMR chemical shift perturbations (Figure S6 and Table S1). Comparison of the 1H NMR imino‐protons chemical shift perturbations of CMA G4 upon binding of tetra‐substituted porphyrins with different pendant groups showed very similar profiles, i. e. insignificant differences between PL7 and PL7‐Me, while slight variations were observed in the case of PL7‐2Me. Most notable differences in 1H NMR chemical shifts were observed when comparing ‘Complex b’ for PL7–2Me with respect to PL7 and PL7‐Me. Furthermore, the imino protons of G3, G7, G12, and G16 were shifted up field with Δδ of 0.24, 0.15, 0.19, and 0.25 ppm for PL7‐Me vs. PL7‐2Me; this suggests that dimethylamide groups in PL7‐2Me slightly interfered with G4 binding, probably by sterically hindering ligand interactions at the binding site comprising the 5′‐end G3→G7→G12→G16 quartet. Considering that the G4 exhibits two 5’‐end residues (T1‐A2) while only one residue at the 3’‐end (T19) where the preference of PL7‐2Me binding is rather similar to PL7 and PL7‐Me suggests that binding relies not only on stacking of the ligands to G‐quartets, but also on interactions between the ligand's pedant groups and overhanging residues. Consistent with this, extending the comparative analysis of 1H NMR chemical shift perturbations to include NMR data on the binding of CMA G4 to the reference compound TMPyP4 (characterized by the shortest substituent on the pyridine nitrogen atom) showed that it bound slightly more strongly than PL7, PL7‐Me, and PL7‐2Me (Figure S6). To further explore binding of the herein studied porphyrin derivatives to G4 exhibiting different topologies 1H NMR‐monitored titration was performed on 23TAG, which at 20 mM KCl adopts G4 with hybrid‐1 type topology while upon molecular crowding conditions induced by DMSO refolds into parallel‐stranded G4.
[18]
At diluted conditions and in the absence of ligand, 1H NMR spectrum of 23TAG exhibited twelve major signals in the imino region characteristic for Hoogsteen‐hydrogen bonded guanine residues, consistent with formation of the predominant G4 exhibiting hybrid type‐1 topology (Figure S8 A). Additional broader 1H NMR imino signals were observed corresponding to minor G4 forms present in the equilibrium. The 1H NMR imino signals for both, the major and minor G4 forms decreased upon addition of 0.5 mole equivalent of the PL7. This effect was pronounced gradually at equimolar DNA and ligand concentrations as well as along the course of titration, whereby at 3 mole equivalents of the PL7 most of the signals corresponding to the initial G4 were broadened almost to the baseline. In parallel, formation of DNA‐ligand complex(es) was indicated by the new set of 1H NMR signals appearing at 1 : 1 ratio. However, the signals corresponding to the complex remained weak/broad even at 1 : 1.5 and 1 : 2 ratio of DNA to ligand, suggesting weak, or at most moderate binding that resulted in an equilibrium of free 23TAG G4 and complexes with non‐specifically bound PL7. In the solution mimicking molecular crowding conditions a single set of 1H NMR signals was observed in the spectrum of 23TAG, consistent with the formation of parallel‐stranded G4 (Figure S8 B). Interestingly, the corresponding imino 1H NMR signals were severely broadened upon addition of 0.5 mole equivalents of the PL7, while a few new signals were observed, consistent with formation of DNA‐ligand complex(es). At equimolar mixture of DNA and PL7 the signals for free 23TAG G4 were no longer observed. On the other hand, 1H NMR signals corresponding to the complex(es) were observed for the samples prepared at 0.5–2.0 mole equivalents of the ligand, although they appeared broad and mostly unresolved at each of the analyzed DNA:Ligand ratios. Notably, the relative intensities of the imino 1H NMR signals for the complex(es) changed during titration. Hence, the NMR analysis suggests rather strong binding of the PL7 to the parallel‐stranded G4 adopted by 23TAG, which appeared to exhibit multiple sites amenable to the ligand interactions. It is interesting to note that the ‘free’ 23TAG parallel G4 formed under crowding conditions was no longer observed at 1 mole equivalent of PL7, while the ‘free’ hybrid analogue persisted even at 1 : 2 ratio between 23TAG and compound. These results are consistent with parallel G4 representing a better target for binding of PL7, whereby the interactions are aggravated by lateral loops in the hybrid topology, altogether stressing out the importance of the structural details related to the loops conformations with respect to the nearby (outer) G‐quartets. The key role of residues extruded from the core of a G4 structure were further corroborated by the fact that shifting the equilibrium from parallel stranded G4 to complex formation required 1.5 mole equivalents of PL7 in the case of CMA (vide supra) (Figure 4), while only 1 mole equivalent in case of 23TAG (Figure S8 B). This suggests that PL7 exhibits higher binding affinity for parallel G4 formed by 23TAG than for parallel G4 formed by CMA. The differences may relate to the different DNA‐ligand interactions at the interfaces between overhanging or loop residues and pendant groups of the tetra‐substituted porphyrins. In particular, parallel G4 adopted by 23TAG exhibits three‐residue propeller‐type loops, while in the. case of CMA the propeller‐type loops comprise only one or two residues. The longer loops in case of 23TAG with respect to CMA exhibit more flexibility, which potentially guides and facilitates binding of the PL7. Analogously, longer pendant groups of tetra‐substituted porphyrins may promote G4 binding, which is substantiated by the results of comparative 1H NMR.
Circular Dichroism
To further confirm the selectivity of PL7 for the parallel topology, circular dichroism analyses were performed. As can be seen from Figure 6A, the spectrum of the CMA sequence showed a shape characteristic for parallel topology, with a positive band around 260 nm and a negative one at 240 nm. No changes in bands position were observed after either TMPyP4 or PL7 titrations, which confirmed the retention of the parallel topology (Figure 6A). The spectrum of free 23TAG (Figure 6B) showed a characteristic hybrid topology pattern, i. e., a positive band at 290 nm. Again, the G4 was subjected to titrations with 1.5 equivalents of TMPyP4 or PL7: in contrast to CMA, with 23TAG there was a change in the spectra for both titrations, showing a decrease in the intensity of the band at 290 nm and an increase at 260 nm. This effect indicated further confirmation that porphyrin ligands show selectivity for parallel topology. If this was not present, as in the case of 23TAG, they stimulated refolding by activating structural equilibria. These underlay the failure to isolate the complex via NMR for the sequence with hybrid topology.
Figure 6
Circular dichroism spectra of (A) CMA and (B) 23TAG in diluted conditions. The spectra were recorded at 25 °C in 90 %/10 % H2O/2H2O, at 20 mM KPi, pH 7.0‐ and 0.2‐mM DNA. TMPyP4 and PL7 were added in 1.5 eq.
Mass Spectrometry
Mass spectrometry proves to be an efficient technique in assessing the stability of complexes formed by nucleic acids.
[46]
In our case, the analyses were conducted using a mass spectrometer with an ESI source, as reported in the experimental section. The primary advantage of electrospray ionization mass spectrometry is its capability to transfer analytes of interest from the sample solution to the mass spectrometer with minimal fragmentation. A common strategy for enhancing the ion response in ESI‐MS is to add organic co‐solvents that are more volatile than water, such as MeOH.
[60]
This phenomenon arises from the ability of methanol to reduce the surface tension of droplets, thereby promoting droplet formation, fission, and evaporation processes. As reported by Rosu et al., the use of a specific methanol concentration not only results in a substantial increase in signal but also minimizes potential conformational alterations in solution.
[46]
Therefore, an 8 : 2 H2O:MeOH solution was used to dilute stock solution to bring sequence concentration to 15 μM. The sequences employed for these experiments were 23TAG and CMT. CMT corresponds to CMA, with the distinction that in CMT, the second guanine has been replaced by thymine. As already reported,
[44]
NMR studies conducted on this sequence reveal that both the CMA and CMT G4 share common structural characteristics. These include a core comprised of three G‐quartets, three propeller‐type loops, and a T19 residue located at the 3′‐end overhang. Since the fundamental folding topologies of the CMA and CMT G4 remain unaltered even when subjected to a single A−T nucleotide substitution at the 5′‐end, CMT has been used for mass experiments instead of CMA. The stabilizing activity of TMPyP4, PL7, PL7‐Me and PL7‐2Me was evaluated by MS/MS using the collision‐induced dissociation (CID) technique. The latter involves isolating the molecular ion in a collision chamber and gradually increasing the collision energy until the target peak disappears. From these experiments, it was possible to calculate the energy required for dissociation of the complex peak to its relative half‐intensity (ECOM50 %)
[61]
using the relative intensities of the target ions from Equation 1.
(1)
relativeintensity=ITargetIonITargetIon+IDissociationProducts
Mass spectrometry is less sensitive to structural equilibria than NMR spectroscopy making possible to isolate complexes with the 23TAG.
This supported the thesis that the reduction in intensity of the imino 1H NMR signals observed during titrations of G4 with ligands was due to the formation of a complex. However, no Complex b at a G4:ligand ratio of 1 : 2 has been isolated, suggesting that one of the two terminal G‐quartets of hybrid G4 is sterically hindered, impeding the stacking of the ligand. Complex b was isolated with CMT G4 using TMPyP4 and PL7. Table 6 summarizes the results of the stability studies. Overall, the proposed ligands stabilized both CMT and 23TAG sequences. For CMT, both PL7 and TMPyP4 showed an increase in stability from Complex a to Complex b. Additionally, from the ECOM50 % values, it is evident that an increase in the number of ligands present was proportional to an increase in G4 stability. However, it's important to note that this is not a trivial observation, as excessive ligand binding capacity can lead to destabilization and subsequent unfolding of the G4 structure.
Table 6
Gas phase stability of G4‐porphyrin complexes calculated by collision induced dissociation experiments.
Oligonucleotide
Ligand
ECOM50 % G4 (eV)
23TAG
No ligand
28.9
TMPyP4[a]
38.5
PL7[a]
38.8
PL7‐Me[a]
39.6
PL7‐2Me[a]
40.2
CMT
No ligand
26.5
TMPyP4[a]
35.9
TMPyP4[b]
48.5
PL7[a]
35.9
PL7[b]
45.5
Data were obtained by subjecting quadruplex samples and their respective complexes to MS/MS fragmentation. The quadruplexes were folded in a 0.2 mM solution of 100 mM tetramethyl ammonium acetate (TMAA) buffer at pH 7, containing 1 mM KCl. The solution was annealed at 60 °C for 30 seconds and then allowed to fold for 2 days. Further details are provided in the supplementary information. [a] Complex a with 1 : 1 stoichiometry. [b] Complex b with 1 : 2 stoichiometry.
Wiley‐VCH GmbH
Proliferation Assay
An MTT assay was conducted on MCF‐7 cell lines, using PL7 and TMPyP4 porphyrins. The investigation aimed to assess the potential cytotoxic effects of these porphyrins on cancerous cell lines. The results (Figure 7) of the MTT assay unveiled a compelling dose‐response relationship for both PL7 and TMPyP4, revealing their impact on cell viability. Notably, PL7 exhibited an IC50 value of 3.265±1.218 μM, indicating its potency in inhibiting cell proliferation. Also in this case, as for NMR experiments, results are comparable with once obtained for TMPyP4, that shows an IC50 of 3.651±1.197 μM. These findings showed that both molecules were able to induce a stop in the cell growth at relatively low concentrations.
Figure 7
IC50 from the MTT assay on MCF‐7 cell line with A) TMPyP4 and B) PL7. The x‐axis represents the logarithm of the concentration while the y‐axis contains the % of living cells in the sample. Each experiment was repeated 3 times, the results are presented as the mean, error bars represent ± the standard deviation
Conclusions
A series of cationic porphyrins were designed and their interaction with different G4 s was evaluated. An initial molecular docking study identified the most promising ligands based on their interaction energies with G4 s of different nature and topology. The calculations showed parallel topology as most preferred and PL7 as the most promising derivative. In addiction PL7 stabilizing ability was then evaluated by an extensive set of MD simulations of the telomeric parallel G4 in presence and absence of the ligand. The simulations were performed both at room temperature (300 K) and at higher temperature (500, 525 and 500 K) and indicated a stabilizing effect of PL7 on the G4 structure. Considering these results, PL7 and TMPyP4 were synthesized, and binding constant evaluated on calf thymus (duplex), 23TAG (hybrid G4 from human telomeres) and CMA (parallel G4 from c‐Myc) by UV‐Visible titrations. While TMPyP4 showed no selectivity, PL7 showed a 3‐fold preference for parallel G4 over duplex and hybrid G4. NMR spectroscopy was used to study the structures of the PL7/G4 complexes, again showing that the G4 CMA was preferred due to its topology. Circular dichroism analyses suggested that while in the case of all parallel G4 s there was no effect beyond coordination on the quartets, in the case of hybrid G4 s there was a structural change induced by the presence of the ligand, which drove it to refold in the parallel topology, triggering structural equilibria that did not allow NMR study. Molecular crowding conditions were tested on the 23TAG sequence to drive its folding in parallel topology. 1H NMR‐monitored titration of G4 with PL7 showed appearance of a new set of signals consistent with the formation of DNA/ligand complex(es). The interaction of the amide groups with the loops of G4 was not identified by NMR, suggesting a dynamic coordination on the quartets. Analysis of the differences in imino 1H NMR chemical shifts for CMA G4 in complexes with PL7‐Me and PL7‐2Me showed that the presence of at least one potential hydrogen bond donor in the ligand's pendant group balances the larger steric hindrance, which partially disfavors stacking on the quartets. Mass spectrometry collision‐induced dissociation experiments on 23TAG and CMA complexes with TMPyP4 and PL7 have shown very similar stabilizing effects. Finally, preliminary cytotoxicity assays were performed on a breast cancer cell line in which the IC50 values obtained for TMPyP4 and PL7 were comparable. This study was able to confirm the propensity of porphyrin ligands for parallel G4, a topology preferred under the naturally occurring conditions of molecular crowding in the cellular environment. PL7 demonstrated how the presence of amide groups gives a balance of steric hindrance and dipolar interaction with loops, that improves selectivity for parallel G4 s over other structures. Considering also the easy of synthesis, PL7 could be regarded as a better alternative to TMPyP4 in studies involving G4 binding.
Conflict of Interests
The authors declare no conflict of interest.
1
Supporting information
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Supporting Information |
Title: TRPM7 activity drives human CD4 T-cell activation and differentiation in a magnesium dependent manner | Body:
Introduction
Immune cell function is essential for health and disease. Both innate and adaptive immune responses involve various cell types and are precisely regulated (Parenti et al., 2016; Walker, 2022). CD4 T lymphocytes are critically involved in both innate and adaptive immune responses (Parenti et al., 2016; Dong, 2021). Through different cellular subsets, CD4 T cells initiate adaptive immune responses against various kinds of pathogens. They have a crucial function in anti-cancer immunity, but also play a key role in the development of autoimmune diseases (Yatim & Lakkis, 2015; Bonilla & Oettgen, 2010; ABBAS, 2019; Walker, 2022). Robust receptor-mediated cell activation, including various costimulatory signals, is crucial for lymphocyte function and ultimately leads to cell proliferation and differentiation into specific effector cell types (Bonilla & Oettgen, 2010; Heinzel et al., 2018; Martínez-Méndez et al., 2021). Accordingly, T-cell activation is the target of several established and emergent pharmacological strategies for immune modulation. Thus, gaining further insights into T-cell activation and the involvement of interaction partners is necessary to gain a better understanding of potential therapeutic targets.
Melastatin-like Transient Receptor Potential, member 7 (TRPM7), is a protein ubiquitously expressed in mammals, showing high expression in lymphocytes (Beesetty et al., 2018; Krishnamoorthy et al., 2018). Embryonic development, thymopoiesis and cellular proliferation critically rely on TRPM7 activity (Beesetty et al., 2018; Nadler et al., 2001; Nadolni et al., 2020; ). Expressing an ion channel in the plasma membrane, TRPM7 conducts divalent cations, such as Mg2+, Ca2+ and Zn2+ (Schmitz et al., 2003; Nadler et al., 2001; Liang et al., 2022). Mutations in the TRPM7 gene are associated with several clinical phenotypes in humans and mice. Most of the symptoms induced by TRPM7-mediated pathologies including macrothrombocytopenia, reduced Mg2+ serum levels and signs of systemic inflammation, and can be by Mg2+ supplementation (Krishnamoorthy et al., 2018; Chubanov et al., 2024; Stritt et al., 2016; Sahni & Scharenberg, 2008). Different studies have characterized TRPM7 as a key player of cellular Mg2+ uptake (Cherepanova et al., 2016; Hoeger et al., 2023; Stritt et al., 2016), while other proteins proposed for this role, such as MagT1 transporter, have lost scientific support (Cherepanova et al., 2016; Li et al., 2011; Ravell et al., 2020). Moreover, the TRPM7 ion channel domain is covalently linked to a cytosolic serine/threonine kinase domain (Schmitz et al., 2003; Nadler et al., 2001; Liang et al., 2022). Different in vitro and native TRPM7 kinase substrates have been found, including myosin II, Annexin A1, phospholipase C gamma 2, SMAD2 and AKT (Clark et al., 2008; Dorovkov & Ryazanov, 2004; Romagnani et al., 2017; Hoeger et al., 2023). In recent years important insights have been gained regarding the role of TRPM7 in mammalian immune cells. Absence of TRPM7 channel function has been linked to reduced store-operated Ca2+ entry and proliferation arrest in DT40 chicken B cells and a kinase-deficient mouse model (Faouzi et al., 2017; Sahni & Scharenberg, 2008; Krishnamoorthy et al., 2018; Beesetty et al., 2018). Here, we shed light on the role of TRPM7 in human T lymphocyte homeostasis and activation. We demonstrated TRPM7 to be crucial for maintenance of cellular Mg2+ homeostasis, activation and proliferation of Jurkat T cells and primary CD4 T cells, as well as subsequent effector functions including cytokine release and polarization.
Results
TRPM7-mediated Mg2+ homeostasis is essential for Jurkat T-cell proliferation
Jurkat T cells are a well characterized and a commonly used cell line to study T lymphocyte function and signaling. We utilized this model to gain insights into the role of TRPM7 in functions of human T cells including T-cell activation. Applying CRISPR-Cas9 genome editing, we generated two clones of a novel TRPM7 KO Jurkat cell line harboring a genomic base pair insertion, which results in a frameshift in exon 4. The successful base pair insertion was confirmed through sequencing of the TRPM7 gene (ThermoFisher). We were able to confirm the expected abolition of TRPM7 currents in these cells via whole cell patch-clamp experiments, thereby functionally verifying the knock-out (Fig. 1A, B and Suppl.Fig. 1A, B). While being morphologically indifferentiable to WT cells (data not shown), the cells of our TRPM7 KO clones showed a clear reduction of proliferation rates in standard Jurkat T cell media and died within five days. However, culturing these TRPM7 KO cells in media supplemented with 6 mM MgCl2 restored normal proliferation and prevented cell death (Fig. 1C, D and Suppl. Fig. 1C, D). To further examine the nature of the TRPM7 KO T cells’ need for MgCl2 supplementation, we performed inductively coupled plasma mass spectrometry (ICP-MS), which revealed a reduction of cellular magnesium content in TRPM7 KO cells (Fig. 1E and Suppl.Fig. 1E), while culturing them in medium supplemented with 6 mM MgCl2 restored intracellular Mg2+ levels (Fig. 1E and Suppl.Fig. 1E). In parallel, we employed the known pharmacological inhibitor of the TRPM7 channel, NS8593 (Chubanov et al., 2012), which similarly abolished TRPM7 currents in WT Jurkat T cells (Fig. 1F, G). Culturing WT Jurkat T cells in the presence of NS8593 produced a similar effect as the TRPM7 KO. Treatment markedly reduced cell proliferation and viability within five days, with survival and proliferation being partially restored by supplementing extracellular MgCl2 (Fig. 1H, I). Since NS8593 has been known to also inhibit SK2-channels in other cell types, we controlled for a potential SK2-dependent effect by employing the SK2-inhibitor apamin, which did not influence TRPM7 currents in respective patch-clamp experiments (Suppl. Fig. 2 A, B). Apamin likewise did not affect lymphocyte growth and viability (Suppl. Fig. 2 C, D). Similar to Jurkat TRPM7 KO clones, treatment with NS8593 also resulted in reduced cellular Mg2+ levels, as analyzed by ICP-MS (Fig. 1J). Likewise, Mg2+ supplementation of the medium restored intracellular Mg2+ levels (Fig. 1J). In line with previous studies on TRPM7 (Zierler et al., 2011), these findings emphasize the importance of the channel for cell proliferation and Mg2+ homeostasis in Jurkat T cells.
TRPM7 channel activity is essential for Jurkat T-cell activation
Having tested the general functionality of our genetic and pharmacological models in Jurkat T cells, we proceeded with studies to decipher the role of TRPM7 in the activation process of human lymphocytes. Previously, TRPM7 was linked to altered store-operated Ca2+ entry (SOCE) in DT40 chicken B lymphocytes (Faouzi et al., 2017). As an important early step in lymphocyte activation, we designed our experiments to first characterize the effects of TRPM7 in Ca2+ signaling. Using Fura-2 as a ratiometric Ca2+ indicator, we performed Ca2+ imaging experiments comparing Jurkat TRPM7 WT and KO cells. Following depletion of the intracellular Ca2+ stores using thapsigargin, TRPM7 KO cells exhibited a strongly reduced rise in cytosolic Ca2+ concentration ([Ca2+]i) (Fig. 2A and Suppl. Fig. 1F), suggesting SOCE to be defective in Jurkat T cells lacking TRPM7. We performed the experiment with Jurkat T cells in the absence and presence of the specific TRPM7 channel inhibitor NS8593. Similar to the effect seen in the KO model, cells treated with the blocker exhibited a strong reduction of the [Ca2+]i elevation (Fig. 2G). To quantify the amount of Ca2+ present in the cytosol during the measurement, we calculated the area under the curve of the Ca2+ traces (Fig. 2B and 2H respectively and Suppl. Fig. 1G). They, too, show a marked reduction of [Ca2+]i in both the KO T cells and the NS8593 treated Jurkat T cells, indicating an early activation defect. This Ca2+ signaling defect would likely affect subsequent transcription factor recruitment. Given that an increase in [Ca2+]i is directly responsible for calcineurin-mediated dephosphorylation and subsequent nuclear translocation of NFAT molecules (Maguire et al., 2013; Park et al., 2020; Lin et al., 2019), we next tested Ca2+ induced NFATc1 translocation. Basal levels of nuclear NFATc1 were comparable in WT and KO cells. Again, using thapsigargin as stimulant, we were able to induce the translocation of NFATc1 to the nucleus in WT control cells. Thapsigargin-induced translocation was diminished in both in TRPM7 KO cells and in cells treated with NS8593 (Fig. 2C-D and I-J respectively and Suppl. Fig. 1H-I). Having observed altered transcription factor recruitment, we assessed mRNA expression levels of IL-2, a well-known NFAT target gene (Maguire et al., 2013; Sakellariou et al., 2024). Both, TRPM7 KO cells and cells after application of the TRPM7 inhibitor showed a remarkable reduction of IL-2 mRNA (Fig. 2E, K respectively). One important feature of T-cell activation is the expression of activation markers on the cell surface, of which CD69 is robustly upregulated in stimulated Jurkat T cells. In line with data shown by Mendu et al., who found an upregulation of CD69 in TRPM7-deficient murine thymocytes (Mendu et al., 2020), representative FACS plots for gating strategy are shown in Suppl. Fig. 3A, depicted a similar picture for human Jurkat T cells. 24 h after activation, viable TRPM7 WT and KO cells upregulated CD69 to a similar extent (Fig. 2F). Interestingly, treatment with NS8593 lead to a significant reduction of CD69 upregulation in Jurkat T cells, (Fig. 2M), while apamin treatment did not affect CD69 upregulation (Suppl. Fig. 2E). Thus, treatment with a TRPM7 blocker affected T-cell activation whereas genetic TRPM7 ablation did, possibly because TRPM7 KO cells had developed compensatory mechanisms, in clear contrast to the acute blockade of TRPM7 activity by its specific inhibitor. Overall, these data show a role of TRPM7 in modulating Ca2+ signaling and downstream Ca2+ dependent translocation of transcription factors and gene expression.
TRPM7 inhibition alters Ca2+ signaling and NFAT translocation in primary human CD4 T lymphocytes
Having validated NS8593 as an applicable pharmacological tool able to mimic the absence of TRPM7 protein in lymphocytes, we broadened the scope of the study to primary human CD4 T cells. Studying primary human lymphocytes instead of cell lines strongly increases the transferability of in vitro findings to immunological processes in human health and disease. CD4 T lymphocytes, isolated from healthy human PBMCs, were used to shed light on both naïve as well as conventional (CD4+ CD25− effector) CD4 T cells. Isolated populations were validated by Flow Cytometry (Suppl. Fig. 3B, C). Using whole-cell patch clamp, we were able to show functional channel expression of TRPM7 in naïve CD4 T cells and the conventional CD4 T cell population. In both cell populations TRPM7 currents were absent after treatment with NS8593 (Fig. 3A, G). Analogous to our Jurkat experiments, we characterized the Ca2+ dependent activation cascade of primary CD4 T cells. We used antibodies against CD3 and CD28 to elicit TCR-dependent Ca2+ signaling, which was analyzed by Fura-2 based Ca2+ imaging. After applying stimulating antibodies to isolated naïve primary human CD4 T cells, a robust increase in [Ca2+]i followed by oscillations of Ca2+ concentration, in a large subset of T cells (Fig. 3B). Cells treated with the specific TRPM7 channel inhibitor NS8593 showed no reduction in basal Ca2+ influx as well as in changes in intracellular Ca2+ concentrations (Fig. 3C-E), but had altered kinetics of [Ca2+]i increase. Importantly, cytosolic Ca2+ oscillations, which have been shown to be crucial for activation-induced gene expression, were absent upon TRPM7 inhibition (Fig. 3F). Studying the CD4+ CD25− effector T cell population, also referred to as conventional CD4 T lymphocytes, displayed similar results. The average Ca2+ concentration increased similarly, but showed altered kinetics. NS8593, as a specific TRPM7 inhibitor, almost eliminated Ca2+ oscillations in treated cells (Fig. 3H-L). Application of the SK2 channel inhibitor apamin, however, did not reduced Ca2+ oscillations (Suppl. Fig. 2F). With both the amount of Ca2+ as well as the characteristic Ca2+ oscillations known to be crucial for NFAT translocation to the nucleus (Maguire et al., 2013; Park et al., 2020; Lin et al., 2019), we proceeded by studying this process. We quantified NFATc1 residing in the nucleus after TCR-mediated stimulation in naïve and conventional CD4 T cells, as well as in cells treated with NS8593. Here, we saw in both cell subsets that TRPM7 inhibition resulted in reduced activation-dependent NFAT-translocation (Fig. 3M-P). This NS8593-induced defect in NFATc1-translocation highlights the importance of the Ca2+-oscillations, which were also diminished in cells with TRPM7 blockade (Fig. 3M-P). These results suggest an important role of TRPM7 in the early activation process of primary naïve and conventional CD4 T cells with large implications on activation-dependent gene expression.
TRPM7 inhibition affects activation of primary human CD4 T cells
As transcription factor recruitment is crucial for IL-2 expression (Maguire et al., 2013; Sakellariou et al., 2024), we next investigated the stimulation-dependent release of this autocrine and paracrine cytokine of CD4 T cells. After 48 h stimulation control cells had secreted significantly more IL-2 into the supernatant than cells treated with NS8593. This effect could be partially rescued by MgCl2 supplementation (Fig. 4A, F). We next investigated activation-induced protein expression. Upregulation of CD69 and CD25 are important hallmarks of T-cell activation, both being physiologically significant and well-studied (Nisnboym et al., 2023; Peng et al., 2023; Poloni et al., 2023). In response to CD3/CD28-stimulation, both activation markers were upregulated in primary CD4 lymphocyte cells, shown by representative FACS plots and gating strategy in Suppl. Fig. 3A. Both in naïve CD4 T cells (Fig. 4B-E) and conventional CD4 T cells (Fig. 4G-J) treated with NS8593, upregulation of CD69 and CD25 was markedly reduced, an effect that could be reverted with MgCl2 supplementation. MgCl2 supplementation also increased the upregulation of activation marker in control cells, underlining the importance of Mg2+ in T-cell activation (Fig. 4B-E and G-J). While TCR-mediated CD69- and CD25-upregulation was, as expected, less pronounced in naïve T cells compared to the conventional CD4 T cells, inhibition of TRPM7 yielded similar effects in both cell populations (Fig. 4B-E and G-J). Titration of inhibitor NS8593 showed a dose-dependent reduction of CD69 and CD25 upregulation in CD4 T cells (Suppl. Fig. 4B, C). To improve methodic robustness, we repeated our experiments with another known specific TRPM7 channel inhibitor, waixenicin A (Zierler et al., 2011). By whole-cell patch clamp, we were able to confirm blockade of TRPM7 currents upon pharmacological treatment with waixenicin A (Fig. 4K). Both inhibitors yielded a very similar upregulation of CD69 and CD25 in these cells upon TCR-mediated stimulation (Fig. 4L-O), which strongly supports a TRPM7-dependent effect. In summary, TRPM7 to affects transcription marker recruitment, IL-2 secretion and the upregulation of activation-dependent surface markers in both, naïve and conventional CD4 T cells.
TRPM7-induced Mg2+ deficiency promotes human naïve CD4 T cell to iTreg differentiation
In proliferation experiments following anti-CD3/CD28 stimulation, we observed robust proliferation of the activated CD4 control cells within five days. Treatment with NS8593 strongly reduced cell proliferation (Fig. 5A, B). This effect was dose-dependent and could be partially reversed by supplementation with MgCl2 (Fig. 5A, B). An important hallmark of adaptive immunity and a consequence of successful T-cell activation is increased proliferation, clonal expansion and differentiation. Mendu et. al recently linked TRPM7 with thymic development of regulatory T cells (Treg) cells in a TRPM7 knockout mouse model (Mendu et al., 2020). Thus, we investigated the role of TRPM7 in the differentiation of naïve CD4 T cells to iTregs. Interestingly, in the presence of the TRPM7 inhibitor NS8593, we observed a reduction of CD25+ iTregs (Fig 5C), correlating with our data on reduced CD4 T-cell activation upon TRPM7 inhibition. However, the successfully differentiated cells showed a higher FOXP3 expression upon NS8593 treatment compared to control (Fig. 5D-E). Repeating these experiments with the afore employed specific TRPM7 inhibitor waixenicin A, showed similar results. In addition, our experiments revealed a negative effect of Mg2+ on Treg polarization, which could be rescued with TRPM7 inhibition (Fig. 5F, G). These findings point towards a modulatory role of TRPM7 in iTreg differentiation, most likely by controlling Mg2+ homeostasis, as summarized in Fig. 5H.
Altogether, our collective results depict TRPM7 as a primary player of T-cell activation and cellular Mg2+ homeostasis. In conclusion, we have shown that absence of TRPM7 channel activity strongly diminishes activation-dependent T-cell signaling, NFATc1-translocation, IL-2 expression and secretion, as well as proliferation in both Jurkat T cells and primary human CD4 lymphocytes. Many of these effects are rescued by supplementation with MgCl2. Thus, TRPM7 could be a valuable pharmacological target modulating T-cell function.
TRPM7-mediated Mg2+ homeostasis is essential for Jurkat T-cell proliferation
Jurkat T cells are a well characterized and a commonly used cell line to study T lymphocyte function and signaling. We utilized this model to gain insights into the role of TRPM7 in functions of human T cells including T-cell activation. Applying CRISPR-Cas9 genome editing, we generated two clones of a novel TRPM7 KO Jurkat cell line harboring a genomic base pair insertion, which results in a frameshift in exon 4. The successful base pair insertion was confirmed through sequencing of the TRPM7 gene (ThermoFisher). We were able to confirm the expected abolition of TRPM7 currents in these cells via whole cell patch-clamp experiments, thereby functionally verifying the knock-out (Fig. 1A, B and Suppl.Fig. 1A, B). While being morphologically indifferentiable to WT cells (data not shown), the cells of our TRPM7 KO clones showed a clear reduction of proliferation rates in standard Jurkat T cell media and died within five days. However, culturing these TRPM7 KO cells in media supplemented with 6 mM MgCl2 restored normal proliferation and prevented cell death (Fig. 1C, D and Suppl. Fig. 1C, D). To further examine the nature of the TRPM7 KO T cells’ need for MgCl2 supplementation, we performed inductively coupled plasma mass spectrometry (ICP-MS), which revealed a reduction of cellular magnesium content in TRPM7 KO cells (Fig. 1E and Suppl.Fig. 1E), while culturing them in medium supplemented with 6 mM MgCl2 restored intracellular Mg2+ levels (Fig. 1E and Suppl.Fig. 1E). In parallel, we employed the known pharmacological inhibitor of the TRPM7 channel, NS8593 (Chubanov et al., 2012), which similarly abolished TRPM7 currents in WT Jurkat T cells (Fig. 1F, G). Culturing WT Jurkat T cells in the presence of NS8593 produced a similar effect as the TRPM7 KO. Treatment markedly reduced cell proliferation and viability within five days, with survival and proliferation being partially restored by supplementing extracellular MgCl2 (Fig. 1H, I). Since NS8593 has been known to also inhibit SK2-channels in other cell types, we controlled for a potential SK2-dependent effect by employing the SK2-inhibitor apamin, which did not influence TRPM7 currents in respective patch-clamp experiments (Suppl. Fig. 2 A, B). Apamin likewise did not affect lymphocyte growth and viability (Suppl. Fig. 2 C, D). Similar to Jurkat TRPM7 KO clones, treatment with NS8593 also resulted in reduced cellular Mg2+ levels, as analyzed by ICP-MS (Fig. 1J). Likewise, Mg2+ supplementation of the medium restored intracellular Mg2+ levels (Fig. 1J). In line with previous studies on TRPM7 (Zierler et al., 2011), these findings emphasize the importance of the channel for cell proliferation and Mg2+ homeostasis in Jurkat T cells.
TRPM7 channel activity is essential for Jurkat T-cell activation
Having tested the general functionality of our genetic and pharmacological models in Jurkat T cells, we proceeded with studies to decipher the role of TRPM7 in the activation process of human lymphocytes. Previously, TRPM7 was linked to altered store-operated Ca2+ entry (SOCE) in DT40 chicken B lymphocytes (Faouzi et al., 2017). As an important early step in lymphocyte activation, we designed our experiments to first characterize the effects of TRPM7 in Ca2+ signaling. Using Fura-2 as a ratiometric Ca2+ indicator, we performed Ca2+ imaging experiments comparing Jurkat TRPM7 WT and KO cells. Following depletion of the intracellular Ca2+ stores using thapsigargin, TRPM7 KO cells exhibited a strongly reduced rise in cytosolic Ca2+ concentration ([Ca2+]i) (Fig. 2A and Suppl. Fig. 1F), suggesting SOCE to be defective in Jurkat T cells lacking TRPM7. We performed the experiment with Jurkat T cells in the absence and presence of the specific TRPM7 channel inhibitor NS8593. Similar to the effect seen in the KO model, cells treated with the blocker exhibited a strong reduction of the [Ca2+]i elevation (Fig. 2G). To quantify the amount of Ca2+ present in the cytosol during the measurement, we calculated the area under the curve of the Ca2+ traces (Fig. 2B and 2H respectively and Suppl. Fig. 1G). They, too, show a marked reduction of [Ca2+]i in both the KO T cells and the NS8593 treated Jurkat T cells, indicating an early activation defect. This Ca2+ signaling defect would likely affect subsequent transcription factor recruitment. Given that an increase in [Ca2+]i is directly responsible for calcineurin-mediated dephosphorylation and subsequent nuclear translocation of NFAT molecules (Maguire et al., 2013; Park et al., 2020; Lin et al., 2019), we next tested Ca2+ induced NFATc1 translocation. Basal levels of nuclear NFATc1 were comparable in WT and KO cells. Again, using thapsigargin as stimulant, we were able to induce the translocation of NFATc1 to the nucleus in WT control cells. Thapsigargin-induced translocation was diminished in both in TRPM7 KO cells and in cells treated with NS8593 (Fig. 2C-D and I-J respectively and Suppl. Fig. 1H-I). Having observed altered transcription factor recruitment, we assessed mRNA expression levels of IL-2, a well-known NFAT target gene (Maguire et al., 2013; Sakellariou et al., 2024). Both, TRPM7 KO cells and cells after application of the TRPM7 inhibitor showed a remarkable reduction of IL-2 mRNA (Fig. 2E, K respectively). One important feature of T-cell activation is the expression of activation markers on the cell surface, of which CD69 is robustly upregulated in stimulated Jurkat T cells. In line with data shown by Mendu et al., who found an upregulation of CD69 in TRPM7-deficient murine thymocytes (Mendu et al., 2020), representative FACS plots for gating strategy are shown in Suppl. Fig. 3A, depicted a similar picture for human Jurkat T cells. 24 h after activation, viable TRPM7 WT and KO cells upregulated CD69 to a similar extent (Fig. 2F). Interestingly, treatment with NS8593 lead to a significant reduction of CD69 upregulation in Jurkat T cells, (Fig. 2M), while apamin treatment did not affect CD69 upregulation (Suppl. Fig. 2E). Thus, treatment with a TRPM7 blocker affected T-cell activation whereas genetic TRPM7 ablation did, possibly because TRPM7 KO cells had developed compensatory mechanisms, in clear contrast to the acute blockade of TRPM7 activity by its specific inhibitor. Overall, these data show a role of TRPM7 in modulating Ca2+ signaling and downstream Ca2+ dependent translocation of transcription factors and gene expression.
TRPM7 inhibition alters Ca2+ signaling and NFAT translocation in primary human CD4 T lymphocytes
Having validated NS8593 as an applicable pharmacological tool able to mimic the absence of TRPM7 protein in lymphocytes, we broadened the scope of the study to primary human CD4 T cells. Studying primary human lymphocytes instead of cell lines strongly increases the transferability of in vitro findings to immunological processes in human health and disease. CD4 T lymphocytes, isolated from healthy human PBMCs, were used to shed light on both naïve as well as conventional (CD4+ CD25− effector) CD4 T cells. Isolated populations were validated by Flow Cytometry (Suppl. Fig. 3B, C). Using whole-cell patch clamp, we were able to show functional channel expression of TRPM7 in naïve CD4 T cells and the conventional CD4 T cell population. In both cell populations TRPM7 currents were absent after treatment with NS8593 (Fig. 3A, G). Analogous to our Jurkat experiments, we characterized the Ca2+ dependent activation cascade of primary CD4 T cells. We used antibodies against CD3 and CD28 to elicit TCR-dependent Ca2+ signaling, which was analyzed by Fura-2 based Ca2+ imaging. After applying stimulating antibodies to isolated naïve primary human CD4 T cells, a robust increase in [Ca2+]i followed by oscillations of Ca2+ concentration, in a large subset of T cells (Fig. 3B). Cells treated with the specific TRPM7 channel inhibitor NS8593 showed no reduction in basal Ca2+ influx as well as in changes in intracellular Ca2+ concentrations (Fig. 3C-E), but had altered kinetics of [Ca2+]i increase. Importantly, cytosolic Ca2+ oscillations, which have been shown to be crucial for activation-induced gene expression, were absent upon TRPM7 inhibition (Fig. 3F). Studying the CD4+ CD25− effector T cell population, also referred to as conventional CD4 T lymphocytes, displayed similar results. The average Ca2+ concentration increased similarly, but showed altered kinetics. NS8593, as a specific TRPM7 inhibitor, almost eliminated Ca2+ oscillations in treated cells (Fig. 3H-L). Application of the SK2 channel inhibitor apamin, however, did not reduced Ca2+ oscillations (Suppl. Fig. 2F). With both the amount of Ca2+ as well as the characteristic Ca2+ oscillations known to be crucial for NFAT translocation to the nucleus (Maguire et al., 2013; Park et al., 2020; Lin et al., 2019), we proceeded by studying this process. We quantified NFATc1 residing in the nucleus after TCR-mediated stimulation in naïve and conventional CD4 T cells, as well as in cells treated with NS8593. Here, we saw in both cell subsets that TRPM7 inhibition resulted in reduced activation-dependent NFAT-translocation (Fig. 3M-P). This NS8593-induced defect in NFATc1-translocation highlights the importance of the Ca2+-oscillations, which were also diminished in cells with TRPM7 blockade (Fig. 3M-P). These results suggest an important role of TRPM7 in the early activation process of primary naïve and conventional CD4 T cells with large implications on activation-dependent gene expression.
TRPM7 inhibition affects activation of primary human CD4 T cells
As transcription factor recruitment is crucial for IL-2 expression (Maguire et al., 2013; Sakellariou et al., 2024), we next investigated the stimulation-dependent release of this autocrine and paracrine cytokine of CD4 T cells. After 48 h stimulation control cells had secreted significantly more IL-2 into the supernatant than cells treated with NS8593. This effect could be partially rescued by MgCl2 supplementation (Fig. 4A, F). We next investigated activation-induced protein expression. Upregulation of CD69 and CD25 are important hallmarks of T-cell activation, both being physiologically significant and well-studied (Nisnboym et al., 2023; Peng et al., 2023; Poloni et al., 2023). In response to CD3/CD28-stimulation, both activation markers were upregulated in primary CD4 lymphocyte cells, shown by representative FACS plots and gating strategy in Suppl. Fig. 3A. Both in naïve CD4 T cells (Fig. 4B-E) and conventional CD4 T cells (Fig. 4G-J) treated with NS8593, upregulation of CD69 and CD25 was markedly reduced, an effect that could be reverted with MgCl2 supplementation. MgCl2 supplementation also increased the upregulation of activation marker in control cells, underlining the importance of Mg2+ in T-cell activation (Fig. 4B-E and G-J). While TCR-mediated CD69- and CD25-upregulation was, as expected, less pronounced in naïve T cells compared to the conventional CD4 T cells, inhibition of TRPM7 yielded similar effects in both cell populations (Fig. 4B-E and G-J). Titration of inhibitor NS8593 showed a dose-dependent reduction of CD69 and CD25 upregulation in CD4 T cells (Suppl. Fig. 4B, C). To improve methodic robustness, we repeated our experiments with another known specific TRPM7 channel inhibitor, waixenicin A (Zierler et al., 2011). By whole-cell patch clamp, we were able to confirm blockade of TRPM7 currents upon pharmacological treatment with waixenicin A (Fig. 4K). Both inhibitors yielded a very similar upregulation of CD69 and CD25 in these cells upon TCR-mediated stimulation (Fig. 4L-O), which strongly supports a TRPM7-dependent effect. In summary, TRPM7 to affects transcription marker recruitment, IL-2 secretion and the upregulation of activation-dependent surface markers in both, naïve and conventional CD4 T cells.
TRPM7-induced Mg2+ deficiency promotes human naïve CD4 T cell to iTreg differentiation
In proliferation experiments following anti-CD3/CD28 stimulation, we observed robust proliferation of the activated CD4 control cells within five days. Treatment with NS8593 strongly reduced cell proliferation (Fig. 5A, B). This effect was dose-dependent and could be partially reversed by supplementation with MgCl2 (Fig. 5A, B). An important hallmark of adaptive immunity and a consequence of successful T-cell activation is increased proliferation, clonal expansion and differentiation. Mendu et. al recently linked TRPM7 with thymic development of regulatory T cells (Treg) cells in a TRPM7 knockout mouse model (Mendu et al., 2020). Thus, we investigated the role of TRPM7 in the differentiation of naïve CD4 T cells to iTregs. Interestingly, in the presence of the TRPM7 inhibitor NS8593, we observed a reduction of CD25+ iTregs (Fig 5C), correlating with our data on reduced CD4 T-cell activation upon TRPM7 inhibition. However, the successfully differentiated cells showed a higher FOXP3 expression upon NS8593 treatment compared to control (Fig. 5D-E). Repeating these experiments with the afore employed specific TRPM7 inhibitor waixenicin A, showed similar results. In addition, our experiments revealed a negative effect of Mg2+ on Treg polarization, which could be rescued with TRPM7 inhibition (Fig. 5F, G). These findings point towards a modulatory role of TRPM7 in iTreg differentiation, most likely by controlling Mg2+ homeostasis, as summarized in Fig. 5H.
Altogether, our collective results depict TRPM7 as a primary player of T-cell activation and cellular Mg2+ homeostasis. In conclusion, we have shown that absence of TRPM7 channel activity strongly diminishes activation-dependent T-cell signaling, NFATc1-translocation, IL-2 expression and secretion, as well as proliferation in both Jurkat T cells and primary human CD4 lymphocytes. Many of these effects are rescued by supplementation with MgCl2. Thus, TRPM7 could be a valuable pharmacological target modulating T-cell function.
Discussion
Lymphocyte activation, specifically of T lymphocytes, is an important process with implications for the whole immune system. The ability to pharmacologically influence and reduce T-cell activation is a primary therapeutic strategy for many autoimmune defects (Walker, 2022; Sakaguchi et al., 2020; Rock et al., 2011). Therefore, further insight into the complex activation process of these cells is needed to unravel the pathogenesis and treatment options for a multitude of immunopathologies. We, here, conducted the first functional study on TRPM7 activity in primary human T lymphocytes. While TRPM7 had already been linked to numerous aspects of T-cell activation in different mouse models and cell lines (Beesetty et al., 2018; Romagnani et al., 2017; Mellott et al., 2020), we now characterize TRPM7 as an important and potentially druggable player of human lymphocyte activation. We utilized pharmacological inhibitors to study the role of TRPM7 in primary human T cells. The risk of unspecific pharmacologic effects was mitigated by validating our approach in lymphocytes in comparison to a genetic TRPM7 knockout model in Jurkat cells, and by using two different specific TRPM7 inhibitors in key experiments. Rescue experiments by supplementation with MgCl2 further underline the importance of TRPM7 activity for CD4 T cell function. Which proteins facilitate cellular Mg2+ uptake, and whether TRPM7 is one of them, has been a contentious issue in the past (Li et al., 2011; Stangherlin & O'Neill, 2018; Castiglioni et al., 2023). MagT1, long believed to be a Mg2+ transporter, has now been shown to be a subdomain of the N-linked glycosylation apparatus (Ravell et al., 2020). Moreover, the authors showed no alterations in total and ionized serum magnesium levels in patients diagnosed with XMEN disease, who carry a loss of function mutation in MagT1 (Ravell et al., 2020). For now the predominant interpretation seems to be TRPM7 being connected to cellular and systemic Mg2+ homeostasis (Zou et al., 2019; Schmitz et al., 2003; Ryazanova et al., 2004). Similar to many other cell types (Chubanov et al., 2024; Schmitz et al., 2003; Hoeger et al., 2023; Hardy et al., 2023; Mellott et al., 2020), our study further supports a role for TRPM7 as the primary Mg2+ uptake pathway in lymphocytes. Given that many effects of impaired TRPM7 function can be restored with Mg2+ supplementation, also supported by the data shown here, TRPM7-independent pathways of Mg2+ uptake must exist, for example through transporter proteins. Different potential Mg2+ transporters, such as CNNM2 and SLC41A1-3, have been proposed, but findings have so far been inconclusive (Bai et al., 2021; Mellott et al., 2020).
Recently, Mendu et al. showed mice harboring a thymus-specific deletion of TRPM7 to be resistant to Concanavalin-A-induced autoimmune hepatitis (Mendu et al., 2020). In their study, Mendu et al. reported TRPM7-deleted CD4 T cells to prefer Treg lineage and non-Treg CD4 cells to activate normally (Mendu et al., 2020). Partially in line with these findings, our results suggest that inhibition of TRPM7 influences iTreg differentiation of human CD4 T cells, as we observed enhanced FOXP3 expression upon TRPM7 blockade. Our findings, in conjunction with the data shown by Mendu et al, highlight a possible therapeutic effect of TRPM7 inhibition in T-cell mediated autoimmune diseases. Importantly, immunological self-tolerance is mediated via naturally occurring CD4 regulatory T cells. Furthermore, these cells have been shown to play key roles in maintaining immune homeostasis, development of autoimmune diseases or graft-versus-host disease in patients with organ transplants (Sakaguchi et al., 2020; Haxhinasto et al., 2008; He et al., 2024). Induction of iTregs is dependent on retinoic acid, short-chain fatty acids and TGF-ß. Previous findings support the notion that TRPM7 kinase moiety is influencex by TRPM7 channel conductance, while the kinase activity is not essential for channel function (Hoeger et al., 2023; Nadolni et al., 2020; Romagnani et al., 2017; Ryazanova et al., 2004). Since TRPM7 kinase has been shown to influence T-cell activation ( (Beesetty et al., 2018; Romagnani et al., 2017), this mechanism of connected channel and kinase function might very well be the case for some of the effects observed in this study and will remain subject of further investigations. However, despite several available TRPM7 channel blockers, the scientific community still lacks pharmacological tools to target TRPM7 kinase, making it especially challenging to interpret the actions of TRPM7 kinase versus channel function.
Activation of the AKT signaling pathway can impair Treg development in vivo, while inhibition of this pathway, combined with TCR signaling, can induce FOXP3 expression in these cells (Sakaguchi et al., 2020; Sauer et al., 2008; Haxhinasto et al., 2008). In addition, SMAD proteins have been reported to have diverse functions in T-cell differentiation. While SMAD4 is indispensable for Th17 differentiation, deletion of SMAD2 has been suggested to promote FOXP3 transcription (Dong, 2021; Martinez et al., 2010). Of note, a direct phosphorylation of AKT SMAD2, via the TRPM7 kinase, influencing downstream signaling has recently been demonstrated for murine and human immune cells (Hoeger et al., 2023; Romagnani et al., 2017; Nadolni et al., 2020). Consequently, kinase-deficient murine naïve T cells were unable to differentiate into the pathogenic Th17 linage, while Treg development was not impaired. Moreover, lack of TRPM7 kinase activity in a murine GvHD model ameliorated disease onset and severity (Romagnani et al., 2017). In line with this study, we here demonstrated for the first time that the impact of TRPM7 on pro-and anti-inflammatory T-cell homeostasis may be translated from mice to men.
Contrary to our findings showing diminished activation of human CD4 T cells after blockade of TRPM7, Mendu et al. showed that murine non-Treg CD4 cells can still be activated (Mendu et al., 2020). This discrepancy could be due to functional differences in human and murine cells. Moreover, their genetic model may induce altered thymocyte development and differentiation, which is not easily comparable to physiologically differentiated cell populations. In line with our recent findings, Faouzi et al. and Beesetty et al. described TRPM7 to been linked to altered SOCE in DT40 chicken B cells and a TRPM7 kinase-deficient mouse model, respectively (Beesetty et al., 2018; Faouzi et al., 2017). However, underlying key mechanisms still remain unclear and demand further investigation.
In summary, TRPM7 is an important regulator of human T lymphocyte function regarding not only immune system homeostasis, but potentially also lymphatic malignancy. Being an important pathway for Mg2+ entering the cells, TRPM7 regulates T-cell signaling by influencing Mg2+ dependent cellular activation processes. While further research into TRPM7 and its effects on immune cell function including TRPM7 kinase related signaling is needed, this study underlines TRPM7 as a potentially druggable target in T-cell-dependent pathologies.
Materials and Methods
Jurkat cells and cell culture
TRPM7-deficient (clone E12, KO1 and clone A03, KO2, both ThermoFisher Scientific) Jurkat clones were generated by CRISPR/Cas-9 genome editing at ThermoFisher Scientific (US). Primary lymphocytes and Jurkat cells (Jurkat E6.1 (WT)) were cultured in Roswell Park Memorial Institute (RPMI) medium containing 10% HI-FBS and 1% penicillin/ streptomycin in a humidified atmosphere at 37°C containing 5% CO2. Medium of KO cells was supplemented with 6 mM MgCl2.
Primary human T cell isolation
Cells were isolated from peripheral blood of healthy donors according to the respective ethics approvals. PBMCs were isolated by density gradient centrifugation using Lymphoprep (Stemcell Technologies, Vancouver, BC, Canada). Isolation of respective lymphocyte subsets was achieved using magnetic cell specific separation kits. For naive CD4 T cells EasySep™ Human Naïve CD4 T Cell Isolation Kit II was used, for CD4 T cells, the EasySep™ Human CD4T Cell Isolation Kit was used. For both CD4+ CD25− effector cells and CD4+ CD25+ Treg cells, EasySep™ Human CD4+CD127lowCD25+ Regulatory T Cell Isolation Kit was used, according to the manual. A minimum of two different donors were used in primary human T cell experiments.
TRPM7 inhibitors
Synthetic TRPM7 inhibitor NS8395 was purchased from Alomone.
Waixenicin A is a natural compound inhibitor and was isolated as following: Freeze-drive biomass of Sarcothelia edmonsoni Verill, 1928 was ground and extracted with hexane. After removal of solvent and elution through a C18 solid phase extraction column, the extract was subjected to reversed phase HPLC (column: SiliCycle dt C18, 30 x 100 mm, 5μm; mobile phase: acetonitrile/water gradient, 50-80% acetonitrile from 0-2 min, 80-100% acetonitrile from 2-6 min; 100% acetonitrile from 6-12 min). Waixenicin A eluted at 6,01 min and was aliquoted into 50 μg single use vials. Purity was confirmed at >95% by LC-MS with evaporative-light scattering detector.
Electrophysiology
TRPM7 currents were acquired via whole-cell patch clamp. A ramp from −100 mV to + 100 mV over 50 ms acquired at 0,5 Hz and a holding potential of 0 mV was applied. Inward and outward current amplitudes were extracted at −80 and + 80 mV, respectively. Data were normalized to the cell size measured after whole-cell break-in (pA/pF). Capacitance was measured using the capacitance cancellation (EPC-10, HEKA). Mg2+-free extracellular solution (in mM): 140 NaCl, 3 CaCl2, 2.8 KCl, 10 HEPES-NaOH, 11 glucose (pH 7.2, 290-300 mOsm/l). Intracellular solution (in mM): 120 Cs-glutamate, 8 NaCl, 10 Cs-EGTA, 5 EDTA (pH 7.2, 290-300 mOsm/l).
Proliferation and viability measurements
Jurkat cells were seeded at a density of 500,000 cells into 24-well plates and cultured in normal RPMI or RPMI with 6 mM MgCl2 for 5 days. Proliferation was analyzed daily using Guava ViaCount on a Guava Easycyte 12HT flow cytometer (Cytek Bioscoences, Fermont, TX, USA). Proliferation experiments on primary T cells followed a similar procedure. Alternatively, T cells were stained with CFSE dye (1 μM, Biozym), washed and cultured for 5 days, before monitoring proliferation traces (dye dilutions) on a BC Cytoflex flow cytometer.
Inductively coupled plasma mass spectrometry
Mg2+ content was determined by inductive couple plasma mass spectrometry (ICP-MS) by ALS Scandinavia (Sweden). Jurkat WT and KO cells were incubated overnight in RPMI ± 6 mM MgCl2, washed 2x with dPBS (w/o Mg2+ or Ca2+; Sigma Aldrich). Likewise, Jurkat WT cells were cultured overnight in RPMI ± 6 mM MgCl2 containing 30 μM NS8593. Cells were seeded with a density of 5 million cells per condition, cell pellets were dried overnight at 70°C and stored at −80°C. Collected samples were shipped on dry ice for further analysis via ICP-MS.
Jurkat cell Ca2+ imaging
Jurkat cells were loaded with 3 μM Fura-2 AM and 0.05% Pluronic®F-127 (Invitrogen) in imaging buffer, 15 min at 37°C. Cells were washed with imaging buffer to remove excess dye. Imaging buffer consisted of Ca2+ - and Mg2+-free HBSS supplemented with (in mM): 2 CaCl2, 0.4 MgCl2, 1 glucose. Cells were seeded into Poly-L-lysine pre-coated μ-Slide 8-well high, chambered coverslips and incubated for 10 min before start of the measurement. Time lapse images were acquired on an AnglerFish imaging system (Next Generation Fluorescence Imaging/NGFI, Graz, Austria), using 5 μM thapsigargin (Thermo Fisher) to mobilize Ca2+ from intracellular stores. The specific TRPM7 channel inhibitor NS8593 was used at a concentration of 30 μM. Viable cells, identified by their ionomycin response at the end of the measurement, were analyzed with Fiji.
Ca2+ imaging of primary lymphocytes
Primary CD4 cells were loaded with 3 μM Fura-2 AM in RPMI supplemented with 10% FBS, 30 min at 37°C while in reaction tubes. Cells were washed twice with imaging buffer to remove excess dye. Imaging buffer contained (in mM): 140 NaCl, 2 CaCl2, 1 MgCl2, 2.8 KCl, 10 HEPES-NaOH, 11 glucose (pH 7.2, 290-300 mOsm/l). Cells were incubated for 15 min at RT and then slowly pipetted onto chambered, antibody-coated coverslips. Intracellular Ca2+ was monitored with Fura-2 AM (SantaCruz) using dual excitation at 340 nm and 380 nm, detection at 520 nm. Fluorescence images were acquired on a TillVisIon imaging system (TILL photonics).
Immunofluorescence staining
Localization of NFATc1 was acquired on a Zeiss LSM 780 microscope or Zeiss LSM 900 confocal microscope, using a 63x oil objective. Jurkat cells were stimulated with 5 μM thapsigargin for 30 min or left unstimulated. Primary human T cells were stimulated with plate-bound α-CD3/α-CD28 antibodies for 45 min. TRPM7 channels were inhibited using 30 μM NS8593 and compared against cells treated with DMSO as solvent control. Cells were permeabilized with 0.1% Triton X-100 for 5 min and stained for intracellular NFAT using anti-NFATc1 antibody (1:100, Santa Cruz, #7A6) in 0.2% BSA/1% normal goat serum in PBS, and secondary anti-mouse antibody AF647 (1:1000, Cell Signaling). Cells were counterstained with DAPI (0.2 μg/mL) and mounted onto glass coverslips using Antifade ROTIMount FluorCare (Carl Roth). Zen 3.5 software was applied. Nuclear NFAT levels were analyzed, therefore regions of interest (ROI) were defined by nuclear outlines (DAPI signals). AF647 signal intensity was corrected by background signals.
Flow cytometry of activation markers
Lymphocytes were seeded in 96-well plates at 2*105 cells per condition in 100μl RPMI with 10% FBS. Cells were treated with 0.1% DMSO, NS8593 (30 μM, 20 μM or 10 μM, as indicated) or 6 mM MgCl2 as indicated. 15 min after treatment, cells were stimulated with antibodies against CD3/CD28 (2 μg/mL CD3 and 1 μg/mL CD28 antibodies, ImmunoCult™ Human CD3/CD28 T Cell Activator, Stemcell Technologies, or eBioscience) or PMA (20 ng/mL and ionomycin (1 μg/mL) (both from SigmaAldrich). After 24 or 48 h, respectively, cells were stained according to the manufacturer’s instructions. Cells were washed twice after staining. Isotype controls or FMO controls were performed. Cells were analyzed using a Guava Easycyte 6-2L flow cytometer (Luminex Corporation, Austin, TX, USA), or a Beckman Coulter CytoFLEX. The following antibodies were used: anti-human CD4-VioBlue (Miltenyi REA623), anti-human CD45RA-APC-Vio770 (Miltenyi, REA562), anti-human CD69-APC (Miltenyi, REA824), anti-human CD25-VioBright515 (Miltenyi, REA570).
IL-2 quantification
Lymphocytes were seeded in 96-well plates at 2*105 cells per conditions in 100 μl RPMI with 10% FBS. Cells were treated with 0.1% DMSO, 30 μM NS8593, or 6 mM MgCl2 as indicated. 15 min after treatment, cells were stimulated with antibodies against CD3/CD28 (ImmunoCult™ Human CD3/CD28 T Cell Activator, Stemcell Technologies, as before). Cell supernatants were collected 48 h after cell stimulation and stored at −80°C. IL-2 concentrations were analyzed using a Biogems Precoated Human IL-2 ELISA kit (Biogems International, Inc., USA) according to manufacturer’s instructions by measuring absorbance at 405 nm on a BMG Labtech Clariostar Plus plate reader.
mRNA isolation
Jurkat TRPM7 KO cells were cultured overnight in normal RPMI without additional MgCl2 supplementation, KO cells and WT cells were seeded at a density of 4*106 cells per conditions and stimulated for 3 h with 10 ng/μL PHA. mRNA was isolated from cell pellets using RNeasy Mini Kit (Qiagen) following manufacturer’s instructions. mRNA concentrations were determined via OD measurement.
cDNA synthesis and quantitative real-time PCR (qRT-PCR)
For cDNA synthesis, 0.5 μg mRNA was diluted in H2O, mixed with 1 mM dNTPs (Promega) and 0.5 μg Oligo(dT)12-18 (Promega) and incubated for 5 min at 70°C. On ice, 5x First-Stand Buffer, SuperScriptTM II Reverse Transcriptase (Promega) and DEPC-treated H2O was added and incubated for 60 min at 42°C. The resulting cDNA was diluted 1:4. Transcripts were analyzed by specific primer pairs. Master mixes additionally contained cDNA and SYBR-Green™ (Sigma-Aldrich). Transcripts were measured in technical triplicates on a CFX-96 cycler (BioRad): 50°C 2’, 95°C 10’ (preincubation), 95°C 15’’, 62°C 30’’, 72°C 30’’, 40 cycles (amplification), 95°C 10’’, 60°C 1’ (melting), 40°C 10’ (cooling). Primer pairs (all human 5´-3´): hIL2 (fw) TTTACATGCCCAAGAAGGCC and (rev) GTTGTTTCAGATCCCTTTAGTTCCA and hHPRT1 (fw) CCCTGGCGTCGTGATTAGTG and (rev) TCGAGCAAGACGTTCAGTCC. A minimum of three independent experiments were performed. CT values of housekeeping transcripts were subtracted from measured CT values, to calculate 2^(-ΔCT) values.
iTreg differentiation and flow cytometry staining
Naïve CD4 T cells were seeded at a density of 1* 105 cells per condition into a 96-well plate, and treated with 30 μM NS8593 or equivalent volume of DMSO. Induction medium contained a-CD3/a-CD28 dynabeads (ThermoFisher), 10 ng/μL rh IL-2 (Immunotools), 5 ng/μL TGF-ß (Immunotools) and 100 nM ATRA (Sigma Aldrich). Cells were cultured for 6 days in a humidified atmosphere at 37°C containing 5% CO2, with intermediary medium exchange on day 4. Cells were analyzed using a Guava Easycyte 6-2L flow cytometer (Luminex Corporation, Austin, TX, USA). The following antibodies were used: anti-human CD4-VioBlue (Miltenyi REA623), anti-human CD25-PE (BioLegend, BC96), anti-human CD45RA-APC-Vio770 (Miltenyi, REA562), anti-human CTLA4-BV605 (BioLegend, BNI3), anti-human FoxP3-APC (Miltenyi, REA1253). Naïve CD4 T cells were used as gating control.
Ethics
Peripheral blood of healthy volunteers was obtained by venipuncture. The study was conducted according to the guidelines of the Declaration of Helsinki and, approved by the local ethics boards of the Johannes Kepler University Linz (EK 1064/2022) as well as the Ludwig-Maximilians-Universität München (Az.21-1288).
Statistics
Data were plotted using Graphpad Prism 8 (Graphpad Software, Boston, MA, USA) or higher. Statistical analysis of the difference of two data sets was performed using Student’s T-test or Mann Whitney U test. Comparison of three or more data sets was performed using one- or two-way-ANOVA, Kruskal-Wallis test or Friedmann test, depending on the respective experimental design.
Jurkat cells and cell culture
TRPM7-deficient (clone E12, KO1 and clone A03, KO2, both ThermoFisher Scientific) Jurkat clones were generated by CRISPR/Cas-9 genome editing at ThermoFisher Scientific (US). Primary lymphocytes and Jurkat cells (Jurkat E6.1 (WT)) were cultured in Roswell Park Memorial Institute (RPMI) medium containing 10% HI-FBS and 1% penicillin/ streptomycin in a humidified atmosphere at 37°C containing 5% CO2. Medium of KO cells was supplemented with 6 mM MgCl2.
Primary human T cell isolation
Cells were isolated from peripheral blood of healthy donors according to the respective ethics approvals. PBMCs were isolated by density gradient centrifugation using Lymphoprep (Stemcell Technologies, Vancouver, BC, Canada). Isolation of respective lymphocyte subsets was achieved using magnetic cell specific separation kits. For naive CD4 T cells EasySep™ Human Naïve CD4 T Cell Isolation Kit II was used, for CD4 T cells, the EasySep™ Human CD4T Cell Isolation Kit was used. For both CD4+ CD25− effector cells and CD4+ CD25+ Treg cells, EasySep™ Human CD4+CD127lowCD25+ Regulatory T Cell Isolation Kit was used, according to the manual. A minimum of two different donors were used in primary human T cell experiments.
TRPM7 inhibitors
Synthetic TRPM7 inhibitor NS8395 was purchased from Alomone.
Waixenicin A is a natural compound inhibitor and was isolated as following: Freeze-drive biomass of Sarcothelia edmonsoni Verill, 1928 was ground and extracted with hexane. After removal of solvent and elution through a C18 solid phase extraction column, the extract was subjected to reversed phase HPLC (column: SiliCycle dt C18, 30 x 100 mm, 5μm; mobile phase: acetonitrile/water gradient, 50-80% acetonitrile from 0-2 min, 80-100% acetonitrile from 2-6 min; 100% acetonitrile from 6-12 min). Waixenicin A eluted at 6,01 min and was aliquoted into 50 μg single use vials. Purity was confirmed at >95% by LC-MS with evaporative-light scattering detector.
Electrophysiology
TRPM7 currents were acquired via whole-cell patch clamp. A ramp from −100 mV to + 100 mV over 50 ms acquired at 0,5 Hz and a holding potential of 0 mV was applied. Inward and outward current amplitudes were extracted at −80 and + 80 mV, respectively. Data were normalized to the cell size measured after whole-cell break-in (pA/pF). Capacitance was measured using the capacitance cancellation (EPC-10, HEKA). Mg2+-free extracellular solution (in mM): 140 NaCl, 3 CaCl2, 2.8 KCl, 10 HEPES-NaOH, 11 glucose (pH 7.2, 290-300 mOsm/l). Intracellular solution (in mM): 120 Cs-glutamate, 8 NaCl, 10 Cs-EGTA, 5 EDTA (pH 7.2, 290-300 mOsm/l).
Proliferation and viability measurements
Jurkat cells were seeded at a density of 500,000 cells into 24-well plates and cultured in normal RPMI or RPMI with 6 mM MgCl2 for 5 days. Proliferation was analyzed daily using Guava ViaCount on a Guava Easycyte 12HT flow cytometer (Cytek Bioscoences, Fermont, TX, USA). Proliferation experiments on primary T cells followed a similar procedure. Alternatively, T cells were stained with CFSE dye (1 μM, Biozym), washed and cultured for 5 days, before monitoring proliferation traces (dye dilutions) on a BC Cytoflex flow cytometer.
Inductively coupled plasma mass spectrometry
Mg2+ content was determined by inductive couple plasma mass spectrometry (ICP-MS) by ALS Scandinavia (Sweden). Jurkat WT and KO cells were incubated overnight in RPMI ± 6 mM MgCl2, washed 2x with dPBS (w/o Mg2+ or Ca2+; Sigma Aldrich). Likewise, Jurkat WT cells were cultured overnight in RPMI ± 6 mM MgCl2 containing 30 μM NS8593. Cells were seeded with a density of 5 million cells per condition, cell pellets were dried overnight at 70°C and stored at −80°C. Collected samples were shipped on dry ice for further analysis via ICP-MS.
Jurkat cell Ca2+ imaging
Jurkat cells were loaded with 3 μM Fura-2 AM and 0.05% Pluronic®F-127 (Invitrogen) in imaging buffer, 15 min at 37°C. Cells were washed with imaging buffer to remove excess dye. Imaging buffer consisted of Ca2+ - and Mg2+-free HBSS supplemented with (in mM): 2 CaCl2, 0.4 MgCl2, 1 glucose. Cells were seeded into Poly-L-lysine pre-coated μ-Slide 8-well high, chambered coverslips and incubated for 10 min before start of the measurement. Time lapse images were acquired on an AnglerFish imaging system (Next Generation Fluorescence Imaging/NGFI, Graz, Austria), using 5 μM thapsigargin (Thermo Fisher) to mobilize Ca2+ from intracellular stores. The specific TRPM7 channel inhibitor NS8593 was used at a concentration of 30 μM. Viable cells, identified by their ionomycin response at the end of the measurement, were analyzed with Fiji.
Ca2+ imaging of primary lymphocytes
Primary CD4 cells were loaded with 3 μM Fura-2 AM in RPMI supplemented with 10% FBS, 30 min at 37°C while in reaction tubes. Cells were washed twice with imaging buffer to remove excess dye. Imaging buffer contained (in mM): 140 NaCl, 2 CaCl2, 1 MgCl2, 2.8 KCl, 10 HEPES-NaOH, 11 glucose (pH 7.2, 290-300 mOsm/l). Cells were incubated for 15 min at RT and then slowly pipetted onto chambered, antibody-coated coverslips. Intracellular Ca2+ was monitored with Fura-2 AM (SantaCruz) using dual excitation at 340 nm and 380 nm, detection at 520 nm. Fluorescence images were acquired on a TillVisIon imaging system (TILL photonics).
Immunofluorescence staining
Localization of NFATc1 was acquired on a Zeiss LSM 780 microscope or Zeiss LSM 900 confocal microscope, using a 63x oil objective. Jurkat cells were stimulated with 5 μM thapsigargin for 30 min or left unstimulated. Primary human T cells were stimulated with plate-bound α-CD3/α-CD28 antibodies for 45 min. TRPM7 channels were inhibited using 30 μM NS8593 and compared against cells treated with DMSO as solvent control. Cells were permeabilized with 0.1% Triton X-100 for 5 min and stained for intracellular NFAT using anti-NFATc1 antibody (1:100, Santa Cruz, #7A6) in 0.2% BSA/1% normal goat serum in PBS, and secondary anti-mouse antibody AF647 (1:1000, Cell Signaling). Cells were counterstained with DAPI (0.2 μg/mL) and mounted onto glass coverslips using Antifade ROTIMount FluorCare (Carl Roth). Zen 3.5 software was applied. Nuclear NFAT levels were analyzed, therefore regions of interest (ROI) were defined by nuclear outlines (DAPI signals). AF647 signal intensity was corrected by background signals.
Flow cytometry of activation markers
Lymphocytes were seeded in 96-well plates at 2*105 cells per condition in 100μl RPMI with 10% FBS. Cells were treated with 0.1% DMSO, NS8593 (30 μM, 20 μM or 10 μM, as indicated) or 6 mM MgCl2 as indicated. 15 min after treatment, cells were stimulated with antibodies against CD3/CD28 (2 μg/mL CD3 and 1 μg/mL CD28 antibodies, ImmunoCult™ Human CD3/CD28 T Cell Activator, Stemcell Technologies, or eBioscience) or PMA (20 ng/mL and ionomycin (1 μg/mL) (both from SigmaAldrich). After 24 or 48 h, respectively, cells were stained according to the manufacturer’s instructions. Cells were washed twice after staining. Isotype controls or FMO controls were performed. Cells were analyzed using a Guava Easycyte 6-2L flow cytometer (Luminex Corporation, Austin, TX, USA), or a Beckman Coulter CytoFLEX. The following antibodies were used: anti-human CD4-VioBlue (Miltenyi REA623), anti-human CD45RA-APC-Vio770 (Miltenyi, REA562), anti-human CD69-APC (Miltenyi, REA824), anti-human CD25-VioBright515 (Miltenyi, REA570).
IL-2 quantification
Lymphocytes were seeded in 96-well plates at 2*105 cells per conditions in 100 μl RPMI with 10% FBS. Cells were treated with 0.1% DMSO, 30 μM NS8593, or 6 mM MgCl2 as indicated. 15 min after treatment, cells were stimulated with antibodies against CD3/CD28 (ImmunoCult™ Human CD3/CD28 T Cell Activator, Stemcell Technologies, as before). Cell supernatants were collected 48 h after cell stimulation and stored at −80°C. IL-2 concentrations were analyzed using a Biogems Precoated Human IL-2 ELISA kit (Biogems International, Inc., USA) according to manufacturer’s instructions by measuring absorbance at 405 nm on a BMG Labtech Clariostar Plus plate reader.
mRNA isolation
Jurkat TRPM7 KO cells were cultured overnight in normal RPMI without additional MgCl2 supplementation, KO cells and WT cells were seeded at a density of 4*106 cells per conditions and stimulated for 3 h with 10 ng/μL PHA. mRNA was isolated from cell pellets using RNeasy Mini Kit (Qiagen) following manufacturer’s instructions. mRNA concentrations were determined via OD measurement.
cDNA synthesis and quantitative real-time PCR (qRT-PCR)
For cDNA synthesis, 0.5 μg mRNA was diluted in H2O, mixed with 1 mM dNTPs (Promega) and 0.5 μg Oligo(dT)12-18 (Promega) and incubated for 5 min at 70°C. On ice, 5x First-Stand Buffer, SuperScriptTM II Reverse Transcriptase (Promega) and DEPC-treated H2O was added and incubated for 60 min at 42°C. The resulting cDNA was diluted 1:4. Transcripts were analyzed by specific primer pairs. Master mixes additionally contained cDNA and SYBR-Green™ (Sigma-Aldrich). Transcripts were measured in technical triplicates on a CFX-96 cycler (BioRad): 50°C 2’, 95°C 10’ (preincubation), 95°C 15’’, 62°C 30’’, 72°C 30’’, 40 cycles (amplification), 95°C 10’’, 60°C 1’ (melting), 40°C 10’ (cooling). Primer pairs (all human 5´-3´): hIL2 (fw) TTTACATGCCCAAGAAGGCC and (rev) GTTGTTTCAGATCCCTTTAGTTCCA and hHPRT1 (fw) CCCTGGCGTCGTGATTAGTG and (rev) TCGAGCAAGACGTTCAGTCC. A minimum of three independent experiments were performed. CT values of housekeeping transcripts were subtracted from measured CT values, to calculate 2^(-ΔCT) values.
iTreg differentiation and flow cytometry staining
Naïve CD4 T cells were seeded at a density of 1* 105 cells per condition into a 96-well plate, and treated with 30 μM NS8593 or equivalent volume of DMSO. Induction medium contained a-CD3/a-CD28 dynabeads (ThermoFisher), 10 ng/μL rh IL-2 (Immunotools), 5 ng/μL TGF-ß (Immunotools) and 100 nM ATRA (Sigma Aldrich). Cells were cultured for 6 days in a humidified atmosphere at 37°C containing 5% CO2, with intermediary medium exchange on day 4. Cells were analyzed using a Guava Easycyte 6-2L flow cytometer (Luminex Corporation, Austin, TX, USA). The following antibodies were used: anti-human CD4-VioBlue (Miltenyi REA623), anti-human CD25-PE (BioLegend, BC96), anti-human CD45RA-APC-Vio770 (Miltenyi, REA562), anti-human CTLA4-BV605 (BioLegend, BNI3), anti-human FoxP3-APC (Miltenyi, REA1253). Naïve CD4 T cells were used as gating control.
Ethics
Peripheral blood of healthy volunteers was obtained by venipuncture. The study was conducted according to the guidelines of the Declaration of Helsinki and, approved by the local ethics boards of the Johannes Kepler University Linz (EK 1064/2022) as well as the Ludwig-Maximilians-Universität München (Az.21-1288).
Statistics
Data were plotted using Graphpad Prism 8 (Graphpad Software, Boston, MA, USA) or higher. Statistical analysis of the difference of two data sets was performed using Student’s T-test or Mann Whitney U test. Comparison of three or more data sets was performed using one- or two-way-ANOVA, Kruskal-Wallis test or Friedmann test, depending on the respective experimental design.
Supplementary Material
Supplement 1
Supplementary Figure 1: Validation of Jurkat TRPM7 KO clone 2 shows reduced proliferation and activation
Supplementary Figure 2: Apamin as control substance for potential off target effects on NS8593
Supplementary Figure 3: T cell isolation controls and additional FACS data
Supplementary Figure 4: Dose-response curve of TRPM7 inhibitor NS8593 on CD4 T cells |
Title: Familial medullary thyroid carcinoma without associated endocrinopathies: A distinct clinical entity | Body: |
Title: Chikungunya Virus RNA Secondary Structures Impact Defective Viral Genome Production | Body:
1. Introduction
Chikungunya virus (CHIKV), which belongs to the alphavirus genus and Togaviridae family, is a positive-strand RNA virus. During its acute phase, CHIKV is responsible for a dengue-like syndrome associating brutal fever with symptoms such as severe joint pain or rash [1,2,3,4]. CHIKV infection can lead to years-long polyarthralgia which incapacitates patients and strongly impacts their quality of life [2,4,5,6]. CHIKV has been responsible for two worldwide epidemics since the beginning of the 21st century, affecting 60 countries and causing close to 8 million cases altogether [4,7,8]. As with most RNA viruses, the error-prone replication of CHIKV in infected cells leads to the production of defective viral genomes (DVGs) [9,10], which represent mutated, truncated or rearranged genomes. DVGs are unable to complete a full viral cycle but have been documented as influencing viral replication and the activation of the immune system [11,12,13]. Notably, they are strong inducers of pro-inflammatory cytokines, including type-I interferons, as documented during syncytial respiratory virus and influenza virus infections in animal models and patients [14,15,16,17]. Work on arboviruses confirmed that this effect on innate immunity also exists in insects, in which DVGs from Sindbis, chikungunya and Zika viruses can modulate antiviral immunity [9] and block viral dissemination and transmission in the mosquito vector [10,18]. Truncated DVGs are hypothesized to arise via non-homologous recombination occurring during viral replication [12,19], a mechanism by which the viral error prone RNA-dependent RNA-polymerase (RdRp) detaches from its genome template at a specific position (hereinafter referred to as “start breakpoint”) and reattach to another position further along in the genome (“stop breakpoint”). The DVG arising from such an event will be truncated for the portion of genome between the start and stop breakpoints. While the existence of DVGs has been documented in CHIKV infection [10], the factors influencing their production are currently unknown.
A key parameter that influences recombination by viral polymerases is the existence of secondary and tertiary structures in viral RNA, which are generated by RNA folding upon itself [19]. These structures are essential for the viral life-cycle, as they are involved in replication and packaging [20,21] via the presence of local secondary RNA structure (such as hairpins or stem loops) and long-range interactions [20,22]. For example, four stem-loops in the 5′-UTR and the start of NSP1 of the CHIKV genome are key for positive- and negative-strand RNA synthesis [23]. Several studies link RNA secondary structure to homologous copy-choice recombination events, notably, in human immunodeficiency virus (HIV) [24,25,26], brome mosaic virus [27], the poliovirus Sabin strain [28] and hepatitis delta virus [29]. By this mechanism, when replicating the viral genome, the RdRp drops off the RNA template it started copying and reattaches to a second RNA template, giving rise to a hybrid RNA molecule. Because it detaches and reattaches at the same position on the RNA, the recombination is called homologous, and gives rise to a hybrid full-length RNA that is still infectious. Overall, the existence of secondary RNA structures is thought to shape the homologous recombination hotspots in the viral genome. Yet only a few studies have examined how the link between secondary RNA structures and non-homologous recombination might affect DVG formation. In at least one example, namely, Cymbidium ringspot virus, a highly base-paired region of a long DVG was thought to direct generation of a shorter DVG [30].
RNA secondary-structure analyses traditionally relied on thermodynamics-based, computer-aided structural predictions to determine the structure with the minimum free energy for folding, corresponding to the highest stability. These methods, however, must still be confirmed by experimental data. The technique of selective 2′-hydroxyl acylation analyzed by primer extension and mutational profiling (SHAPE-MaP) is an improvement, one which allows the creation of experimentally informed RNA secondary-structure models [31,32,33]. SHAPE-MaP relies on selectively acetylating unpaired nucleotides, i.e., bases that are not involved in RNA secondary structures. The acetylated nucleotides are then identified as mutations by next-generation sequencing [34]. From the mutation data, each nucleotide position is assigned a SHAPE reactivity value: a high value if the nucleotide is paired, and low, if the nucleotide is more likely to be unpaired. SHAPE-MaP can thus construct an experimentally-driven quantitative map of RNA secondary structure which depicts the probability that a given nucleotide is involved in RNA secondary structures. SHAPE-MaP was used to decipher the full secondary structure at a single-nucleotide level for several alphavirus RNA genomes, including Sindbis virus (SINV) [23], Venezuelan equine encephalitis virus (VEEV) [23] and, more recently, CHIKV [35].
In this work, we exploit the SHAPE-MaP of the CHIKV genome to interrogate the influence of RNA secondary structures on non-homologous recombination events leading to DVG generation. We show that, in infected mammalian cells, the higher the probability that a given nucleotide is unpaired, the higher the probability of it being a DVG breakpoint. To experimentally verify this correlation, we generate a CHIKV mutant, termed CHIKV D2S for “disrupted secondary structures”, which carries 76 synonymous mutations that abolish mapped RNA secondary structures in the first half of the CHIKV genome. We observe that, although CHIKV D2S generates DVGs from the same genomic regions as wild-type (WT) CHIKV, DVGs arising from D2S replication are more diverse in terms of sequences and display a decreased accuracy of breakpoint position. The differences between CHIKV WT and D2S were more important in the region of the genome that was disrupted in the secondary structure, compared to the undisrupted region. Importantly, we could correlate the nature of the DVGs produced by the D2S mutant to its RNA genomic structure determined by SHAPE-MaP, directly implicating RNA secondary structures in the regulation of non-homologous recombination and DVG generation.
2. Materials and Methods
2.1. Cells and Virus
Vero and BHK cells were maintained in Dulbecco’s modified Eagle’s medium (DMEM) and supplemented with 10% fetal calf serum (FCS; Gibco, Thermo Fischer Scientific, Life Technologies Corporation, Grand Island, NY, USA), 1% non-essential amino-acid (NEAA; Gibco, Thermo Fischer Scientific, Life Technologies Corporation, Grand Island, NY, USA) and 1% penicillin/streptomycin (P/S Thermo Fisher Scientific, Life Technologies Corporation, Grand Island, NY, USA) in a humidified atmosphere, at 37 °C, with 5% CO2.
The viral stocks were generated from chikungunya virus (CHIKV) infectious clones derived from the Caribbean strain, Asian genotype (described in [36]) or the disrupted secondary structure (D2S) mutant derived from it (see below). Plasmids were linearized with Not I enzyme (Thermo Fisher) and in vitro transcripted with the SP6 mMESSAGE mMACHINE kit (Invitrogen, Thermo Fischer Scientific, Life Technologies Corporation, Grand Island, NY, USA). RNA from in vitro transcription (IVT) was then transfected in BHK cells using lipofectamin 2000 (Invitrogen, Thermo Fischer Scientific, Life Technologies Corporation, Grand Island, NY, USA) and passaged once in Vero cells. The stocks were titered and kept at −80 °C before use. For D2S, the stocks were RNA-extracted and Sanger-sequenced to confirm the absence of reversion.
2.2. Cloning Selected Disrupted Secondary Structure (D2S) Mutant
Three regions of the original Caribbean CHIKV clone were mutated using the program CodonShuffle with the dn231 algorithm [37] in order to generate maximum secondary-structure disruption with minimum change in codon usage.
Five 154- to 214-nucleotide-long double-stranded DNA (gBlocks® Gene Fragments, Integrated DNA Technologies, Coralville, Iowa, USA) carrying the wanted mutations (Table S1) were ordered from IDT. Gene fragments were amplified by PCR using Q5 DNA polymerase (NEB). They were then cloned into WT CHIKV Carib plasmid one after the other, amplifying the CHIKV Carib vector around the region of modification using Q5 DNA polymerase (NEB); a list of primers used is available in Table S2. After DpnI (Thermo Fisher Scientific, Life Technologies Corporation, Grand Island, NY, USA) treatment and gel purification (Macherey Nagel PCR and gel purification kit, Düren, Nordrhein-Westfalen, Germany) of the vector, insert and vector were fused together with In-Fusion reagent (Takara Bio Reagent) following the manufacturer’s instructions; 2.5 μL was transformed in XL10-Gold extra competent cells (Stratagene, La Jolla, CA, USA). Colonies were grown in LB medium with ampicillin, minipreps were performed (NucleoSpin plasmid, Macherey Nagel, Düren, Nordrhein-Westfalen, Germany) and Sanger-sequenced to confirm that the colonies carried the expected mutations.
2.3. Plaque Assay
Viral titration was performed on confluent Vero cells plated in 24-well plates one day before infection. Ten-fold dilutions were performed in DMEM alone and transferred onto Vero cells. After allowing infection for one hour, DMEM with 2% FCS, 1% P/S, 1% NEAA and 0.8% agarose was added on top of the cells. Three days post-infection, Vero cells were fixed with 4% formalin (Sigma, St. Louis, MO, USA), and plaques were manually counted after staining them with 0.2% crystal violet (Sigma, St. Louis, MO, USA).
2.4. Viral Passages
Vero cells were seeded in 24-well plates, aiming to reach approximately 80% the next day. For passage 1, the viral stock was diluted in PBS to obtain a multiplicity of infection (MOI) of 5. After removing the cell culture medium, cells were incubated with the viral solution at 37 °C for 1 h. Following virus adsorption, the inoculum was removed and replaced with 600 μL of the appropriate cell culture medium containing 2% FCS. At 48–72 h post infection, the supernatant was harvested and clarified by centrifugation (12,000× g, 5 min). The following passages were performed blindly, using 300 μL of the clarified supernatant from previous passage to infect naïve cells, followed by the same procedure. A total of 8 passages were performed. Each passage was titered by plaque assay. Six replicates were performed per virus.
2.5. Growth Curves
Vero cells were seeded in 24-well plates 24 h prior to infection, in order to reach 80% of confluence the next day. Virus stock was diluted in PBS to reach the intended MOI (0.01 or 1) and incubated on the cells. After one hour, the virus was removed, and cells were washed three times with PBS. Fresh medium supplemented with 2 FBS was added. At each time point, 50 μL of medium was harvested (kept at −80°) and replaced by 50 μL of fresh medium. All samples were titered together by plaque assay. Both growth curves were realized in triplicate.
2.6. Deep-Sequencing
RNA of 100 μL of each sample was extracted using a ZR-96 Viral RNA kit (Zymo, Research, Irvine, CA, USA) following the manufacturer’s protocol, and then eluted in 20 μL nuclease-free water. The RNA library was produced with the NEBNext Ultra II RNA Library kit (Illumina, San Diego, CA, USA), using Multiplex oligos (Illumina, San Diego, CA, USA). Libraries were quantified using the Quant-iT DNA assay kit (Thermo Fisher Scientific, Life Technologies Corporation, Grand Island, NY, USA) and diluted to 1 nM prior to sequencing on a NextSeq sequencer (Illumina, San Diego, CA, USA) with a NextSeq 500 Mid Output kit v2 (Illumina, San Diego, CA, USA) (151 cycles).
2.7. SHAPE-MaP of WT and D2S CHIKV RNA
For determination of CHIKV secondary structure in Vero cells, the viral stock of the WT or D2S mutant was passaged once in fresh Vero cells seeded in a T150 flask. Supernatants were harvested and viral RNA were extracted from sucrose-purified virions and analyzed as described in a previous work [35].
2.8. Data Analysis
2.8.1. Alignment and Identification of DVGs
The BBTools suite was used to analyze the sequencing output (Bushnell B.—sourceforge.net/projects/bbmap/). BBDuk allowed us to trim for low-quality bases and adaptors, using fastq files generated from sample sequencing. Then, these were aligned to the CHIKV reference sequence (Carib—GenBank accession no. LN898104.1, IOL—GenBank accession no. AM258994) using a BBMap. To visualize the data, heatmaps showing the deletion score of each nucleotide position were generated using R. Specifically, scores were computed as the sum of the number of reads per million reads (RPM) supporting the deletion of a specific nucleotide position. For plotting start/stop breakpoints on R, deletions with lengths below 10 nucleotides were discarded.
2.8.2. Correlation between DVG Nucleotide Allele Frequency and SHAPE Reactivity
From the list of deletions generated from deep sequencing data as described above, we selected all deletions of 10 or more nucleotides. Breakpoints in the 3′UTR (i.e., after nucleotide 11302) were excluded, since SHAPE MaP is less accurate in that region because of sequence repeats [38]. From this list, we broke down the data by start and stop positions and associated allele frequency (in reads per million). Positions that were both start and stop positions were pooled separately and their associated allele frequency values in reads per million were summed up. Next, we matched the raw SHAPE reactivity value to each position, and then calculated a Pearson correlation coefficient.
2.8.3. Comparison of the SHAPE Reactivity of Nucleotides Used or Not Used as DVG Breakpoints
Start and stop positions of DVGs bearing deletions of at least 10 nucleotides and generated in Vero cells were matched with SHAPE reactivity values. Nucleotides that were used as breakpoints or left unused were plotted according to their SHAPE reactivity values. A t-test, with Sidak’s multiple comparisons test, was used to compare means.
2.8.4. Comparison of DGVs Generated in WT and D2S Mutant
From the list of deletions generated as described in the previous section, we selected the DVGs lacking at least 20 nucleotides. We pooled all replicates and passages together for a given virus (WT or D2S mutant; see below). We then separated all DVGs into two groups, depending on whether their start breakpoints were located before or after position 5000. Then, we compared the number of samples containing a given deletion (exactly the same start and stop positions) in both WT and mutant, either on a 2D plot or by displaying the distribution of their absolute differences.
2.8.5. DVG Entropy
From the DVG list generated as described above, we calculated an equivalent of Shannon entropy for deletions for each sample as—Σ pi log2(pi), where pi is the ratio of the number of reads supporting the junction i over the total number of reads supporting all junctions.
2.8.6. Statistical Analysis
Statistical analyses were performed using R version 4.2.1 (CRAN) or Prism version 8 (Graphpad). All tests were two-sided, and a p value < 0.05 was considered significant. Correction for multiple testing was performed using Bonferroni’s method or Sidak’s method.
2.1. Cells and Virus
Vero and BHK cells were maintained in Dulbecco’s modified Eagle’s medium (DMEM) and supplemented with 10% fetal calf serum (FCS; Gibco, Thermo Fischer Scientific, Life Technologies Corporation, Grand Island, NY, USA), 1% non-essential amino-acid (NEAA; Gibco, Thermo Fischer Scientific, Life Technologies Corporation, Grand Island, NY, USA) and 1% penicillin/streptomycin (P/S Thermo Fisher Scientific, Life Technologies Corporation, Grand Island, NY, USA) in a humidified atmosphere, at 37 °C, with 5% CO2.
The viral stocks were generated from chikungunya virus (CHIKV) infectious clones derived from the Caribbean strain, Asian genotype (described in [36]) or the disrupted secondary structure (D2S) mutant derived from it (see below). Plasmids were linearized with Not I enzyme (Thermo Fisher) and in vitro transcripted with the SP6 mMESSAGE mMACHINE kit (Invitrogen, Thermo Fischer Scientific, Life Technologies Corporation, Grand Island, NY, USA). RNA from in vitro transcription (IVT) was then transfected in BHK cells using lipofectamin 2000 (Invitrogen, Thermo Fischer Scientific, Life Technologies Corporation, Grand Island, NY, USA) and passaged once in Vero cells. The stocks were titered and kept at −80 °C before use. For D2S, the stocks were RNA-extracted and Sanger-sequenced to confirm the absence of reversion.
2.2. Cloning Selected Disrupted Secondary Structure (D2S) Mutant
Three regions of the original Caribbean CHIKV clone were mutated using the program CodonShuffle with the dn231 algorithm [37] in order to generate maximum secondary-structure disruption with minimum change in codon usage.
Five 154- to 214-nucleotide-long double-stranded DNA (gBlocks® Gene Fragments, Integrated DNA Technologies, Coralville, Iowa, USA) carrying the wanted mutations (Table S1) were ordered from IDT. Gene fragments were amplified by PCR using Q5 DNA polymerase (NEB). They were then cloned into WT CHIKV Carib plasmid one after the other, amplifying the CHIKV Carib vector around the region of modification using Q5 DNA polymerase (NEB); a list of primers used is available in Table S2. After DpnI (Thermo Fisher Scientific, Life Technologies Corporation, Grand Island, NY, USA) treatment and gel purification (Macherey Nagel PCR and gel purification kit, Düren, Nordrhein-Westfalen, Germany) of the vector, insert and vector were fused together with In-Fusion reagent (Takara Bio Reagent) following the manufacturer’s instructions; 2.5 μL was transformed in XL10-Gold extra competent cells (Stratagene, La Jolla, CA, USA). Colonies were grown in LB medium with ampicillin, minipreps were performed (NucleoSpin plasmid, Macherey Nagel, Düren, Nordrhein-Westfalen, Germany) and Sanger-sequenced to confirm that the colonies carried the expected mutations.
2.3. Plaque Assay
Viral titration was performed on confluent Vero cells plated in 24-well plates one day before infection. Ten-fold dilutions were performed in DMEM alone and transferred onto Vero cells. After allowing infection for one hour, DMEM with 2% FCS, 1% P/S, 1% NEAA and 0.8% agarose was added on top of the cells. Three days post-infection, Vero cells were fixed with 4% formalin (Sigma, St. Louis, MO, USA), and plaques were manually counted after staining them with 0.2% crystal violet (Sigma, St. Louis, MO, USA).
2.4. Viral Passages
Vero cells were seeded in 24-well plates, aiming to reach approximately 80% the next day. For passage 1, the viral stock was diluted in PBS to obtain a multiplicity of infection (MOI) of 5. After removing the cell culture medium, cells were incubated with the viral solution at 37 °C for 1 h. Following virus adsorption, the inoculum was removed and replaced with 600 μL of the appropriate cell culture medium containing 2% FCS. At 48–72 h post infection, the supernatant was harvested and clarified by centrifugation (12,000× g, 5 min). The following passages were performed blindly, using 300 μL of the clarified supernatant from previous passage to infect naïve cells, followed by the same procedure. A total of 8 passages were performed. Each passage was titered by plaque assay. Six replicates were performed per virus.
2.5. Growth Curves
Vero cells were seeded in 24-well plates 24 h prior to infection, in order to reach 80% of confluence the next day. Virus stock was diluted in PBS to reach the intended MOI (0.01 or 1) and incubated on the cells. After one hour, the virus was removed, and cells were washed three times with PBS. Fresh medium supplemented with 2 FBS was added. At each time point, 50 μL of medium was harvested (kept at −80°) and replaced by 50 μL of fresh medium. All samples were titered together by plaque assay. Both growth curves were realized in triplicate.
2.6. Deep-Sequencing
RNA of 100 μL of each sample was extracted using a ZR-96 Viral RNA kit (Zymo, Research, Irvine, CA, USA) following the manufacturer’s protocol, and then eluted in 20 μL nuclease-free water. The RNA library was produced with the NEBNext Ultra II RNA Library kit (Illumina, San Diego, CA, USA), using Multiplex oligos (Illumina, San Diego, CA, USA). Libraries were quantified using the Quant-iT DNA assay kit (Thermo Fisher Scientific, Life Technologies Corporation, Grand Island, NY, USA) and diluted to 1 nM prior to sequencing on a NextSeq sequencer (Illumina, San Diego, CA, USA) with a NextSeq 500 Mid Output kit v2 (Illumina, San Diego, CA, USA) (151 cycles).
2.7. SHAPE-MaP of WT and D2S CHIKV RNA
For determination of CHIKV secondary structure in Vero cells, the viral stock of the WT or D2S mutant was passaged once in fresh Vero cells seeded in a T150 flask. Supernatants were harvested and viral RNA were extracted from sucrose-purified virions and analyzed as described in a previous work [35].
2.8. Data Analysis
2.8.1. Alignment and Identification of DVGs
The BBTools suite was used to analyze the sequencing output (Bushnell B.—sourceforge.net/projects/bbmap/). BBDuk allowed us to trim for low-quality bases and adaptors, using fastq files generated from sample sequencing. Then, these were aligned to the CHIKV reference sequence (Carib—GenBank accession no. LN898104.1, IOL—GenBank accession no. AM258994) using a BBMap. To visualize the data, heatmaps showing the deletion score of each nucleotide position were generated using R. Specifically, scores were computed as the sum of the number of reads per million reads (RPM) supporting the deletion of a specific nucleotide position. For plotting start/stop breakpoints on R, deletions with lengths below 10 nucleotides were discarded.
2.8.2. Correlation between DVG Nucleotide Allele Frequency and SHAPE Reactivity
From the list of deletions generated from deep sequencing data as described above, we selected all deletions of 10 or more nucleotides. Breakpoints in the 3′UTR (i.e., after nucleotide 11302) were excluded, since SHAPE MaP is less accurate in that region because of sequence repeats [38]. From this list, we broke down the data by start and stop positions and associated allele frequency (in reads per million). Positions that were both start and stop positions were pooled separately and their associated allele frequency values in reads per million were summed up. Next, we matched the raw SHAPE reactivity value to each position, and then calculated a Pearson correlation coefficient.
2.8.3. Comparison of the SHAPE Reactivity of Nucleotides Used or Not Used as DVG Breakpoints
Start and stop positions of DVGs bearing deletions of at least 10 nucleotides and generated in Vero cells were matched with SHAPE reactivity values. Nucleotides that were used as breakpoints or left unused were plotted according to their SHAPE reactivity values. A t-test, with Sidak’s multiple comparisons test, was used to compare means.
2.8.4. Comparison of DGVs Generated in WT and D2S Mutant
From the list of deletions generated as described in the previous section, we selected the DVGs lacking at least 20 nucleotides. We pooled all replicates and passages together for a given virus (WT or D2S mutant; see below). We then separated all DVGs into two groups, depending on whether their start breakpoints were located before or after position 5000. Then, we compared the number of samples containing a given deletion (exactly the same start and stop positions) in both WT and mutant, either on a 2D plot or by displaying the distribution of their absolute differences.
2.8.5. DVG Entropy
From the DVG list generated as described above, we calculated an equivalent of Shannon entropy for deletions for each sample as—Σ pi log2(pi), where pi is the ratio of the number of reads supporting the junction i over the total number of reads supporting all junctions.
2.8.6. Statistical Analysis
Statistical analyses were performed using R version 4.2.1 (CRAN) or Prism version 8 (Graphpad). All tests were two-sided, and a p value < 0.05 was considered significant. Correction for multiple testing was performed using Bonferroni’s method or Sidak’s method.
2.8.1. Alignment and Identification of DVGs
The BBTools suite was used to analyze the sequencing output (Bushnell B.—sourceforge.net/projects/bbmap/). BBDuk allowed us to trim for low-quality bases and adaptors, using fastq files generated from sample sequencing. Then, these were aligned to the CHIKV reference sequence (Carib—GenBank accession no. LN898104.1, IOL—GenBank accession no. AM258994) using a BBMap. To visualize the data, heatmaps showing the deletion score of each nucleotide position were generated using R. Specifically, scores were computed as the sum of the number of reads per million reads (RPM) supporting the deletion of a specific nucleotide position. For plotting start/stop breakpoints on R, deletions with lengths below 10 nucleotides were discarded.
2.8.2. Correlation between DVG Nucleotide Allele Frequency and SHAPE Reactivity
From the list of deletions generated from deep sequencing data as described above, we selected all deletions of 10 or more nucleotides. Breakpoints in the 3′UTR (i.e., after nucleotide 11302) were excluded, since SHAPE MaP is less accurate in that region because of sequence repeats [38]. From this list, we broke down the data by start and stop positions and associated allele frequency (in reads per million). Positions that were both start and stop positions were pooled separately and their associated allele frequency values in reads per million were summed up. Next, we matched the raw SHAPE reactivity value to each position, and then calculated a Pearson correlation coefficient.
2.8.3. Comparison of the SHAPE Reactivity of Nucleotides Used or Not Used as DVG Breakpoints
Start and stop positions of DVGs bearing deletions of at least 10 nucleotides and generated in Vero cells were matched with SHAPE reactivity values. Nucleotides that were used as breakpoints or left unused were plotted according to their SHAPE reactivity values. A t-test, with Sidak’s multiple comparisons test, was used to compare means.
2.8.4. Comparison of DGVs Generated in WT and D2S Mutant
From the list of deletions generated as described in the previous section, we selected the DVGs lacking at least 20 nucleotides. We pooled all replicates and passages together for a given virus (WT or D2S mutant; see below). We then separated all DVGs into two groups, depending on whether their start breakpoints were located before or after position 5000. Then, we compared the number of samples containing a given deletion (exactly the same start and stop positions) in both WT and mutant, either on a 2D plot or by displaying the distribution of their absolute differences.
2.8.5. DVG Entropy
From the DVG list generated as described above, we calculated an equivalent of Shannon entropy for deletions for each sample as—Σ pi log2(pi), where pi is the ratio of the number of reads supporting the junction i over the total number of reads supporting all junctions.
2.8.6. Statistical Analysis
Statistical analyses were performed using R version 4.2.1 (CRAN) or Prism version 8 (Graphpad). All tests were two-sided, and a p value < 0.05 was considered significant. Correction for multiple testing was performed using Bonferroni’s method or Sidak’s method.
3. Results
3.1. Preferential Generation of DVG Breakpoints at Unpaired Nucleotides
Because DVGs highjack the machinery of full-length virus for replication and/or transmission, they preferentially accumulate at high multiplicity of infection (MOI) when each cell is co-infected with multiple viral particles [39,40]. To enrich for DVGs, we performed serial passages at high MOI in triplicates in Vero cells (Figure S1A). At each passage, half of the supernatant was used to infect cells for the next passage and half was used for next-generation sequencing and bioinformatic analysis using an in-house pipeline [9,10,18]. DVGs were defined by their start and stop breakpoints, corresponding to the first and the last nucleotide deleted [9,10,18] (Figure 1A, hereinafter conjointly termed “breakpoint nucleotides”). As described in a previous work [10], classifying DVGs using the start and the stop positions of the truncation identifies clusters of DVGs on the genome (see clusters A, B and C in Figure 1A,B).
We observed no obvious sequence homologies between the start and stop regions for the commonly deleted regions which would suggest template-switching to a near-homologous region. Additionally, there are currently no reported RNA binding proteins associated with these regions that would bring the start and stop regions into close proximity during replication, allowing the RdRp to skip over the deleted nucleotides. Therefore, we hypothesized that RNA secondary structure in breakpoint regions may play a role in generating these specific DVGs.
We used data generated from our prior SHAPE-MaP analysis of the CHIKV genomic RNA [35] to determine the RNA secondary structure of the viral genome around the breakpoints. Reactive nucleotides, or nucleotides that are more flexible, are colored in orange, while unreactive nucleotides, those that are protected from chemical modification due to base pairing, are colored black, in the example region of the cluster A and B start breakpoints (nucleotide 300 to 819, Figure 1C). To determine if there was a relationship between a specific nucleotide used as a DVG break site in CHIKV and the flexibility of that nucleotide, we assessed whether the SHAPE reactivity of the breakpoint nucleotides differed from the SHAPE reactivity of unused nucleotides after 1 passage in Vero cells (Figure 1D). A median SHAPE reactivity over a 5-nucleotide window was used to capture the flexibility of a hyper-local region around the breakpoint. The average median SHAPE reactivity of nucleotides used as breakpoints remains higher than those not used as breakpoints over all passages, assuming each breakpoint was once originally generated from a full-length genome (Figure S1B).
Next, we analyzed the correlation between the frequency at which a nucleotide was used as a breakpoint (number of reads with this nucleotide as a breakpoint over the total number of reads) with the nucleotide’s median SHAPE reactivity (i.e., its probability of being paired when low or unpaired when high, Figure 1C), using a 5-nucleotide window. We used pooled data generated from all replicates over 8 passages (Figure 1E) and focused our analysis on DVGs presenting a deletion of at least 10 nucleotides in order to exclude all small deletions that might be generated by a short slippage of the viral polymerase rather than a canonical event of non-homologous recombination of RNA molecules. We uncovered an enrichment of unpaired nucleotides in those used as DVG breakpoints, even though the relationship is weak (Figure 1E, Pearson correlation r = 0.066, p = 2.7. 10–11).
Together, these results suggest that CHIKV DVG breakpoints preferentially occur at more flexible nucleotides, which are more likely to be unpaired, although flexibility alone is not sufficient to predict a breaksite.
3.2. CHIKV-D2S Mutant with Disrupted Secondary Structure Is Viable in Cell Culture
To experimentally test the impact of secondary structure on the generation of DVGs, we generated a mutant with a disrupted secondary structure (D2S) around a subset of important DVG breakpoints found in CHIKV passages in Vero cells (Figure 1A and Figure 2). We designed the D2S mutant so that its secondary structure would be disrupted around the DVG breakpoints of clusters A, B and C (all located in the first half of the genome, before nucleotide 5000) without impacting the secondary structure located after nucleotide 5000 (Figure 2A). Hence, we modified 3 regions with a total of 76 synonymous mutations generated using the program CodonShuffle with the dn231 algorithm (Jorge, Mills, et Lauring 2015): 19 mutations were introduced in the first region, covering nucleotides 380 to 730 (where cluster A and B start breakpoints are located); 35 mutations were between nucleotides 2810 and 3190 (comprising cluster A stop breakpoints and cluster C start breakpoints); and 22 mutations were between nucleotides 3800 and 3950 (comprising cluster C stop breakpoints) (Figure 2A,B; the list of mutations is rendered in Table S1). The mutations were selected with the aim of modifying the predicted RNA structure as much as possible without changing the amino acid sequence and codon usage (Figure 2B). To assess whether D2S showed decreased fitness (i.e., slower replication kinetics) in these settings compared to WT virus, we performed one-step and multi-step growth curve analyses in Vero cells. The D2S mutant displayed slower growth kinetics with a ten-fold disadvantage compared to WT (p < 0.0001; Figure 2C). However, in the one-step growth curve, at 48 h post-infection, D2S and WT had similar titers in Vero cells. Of note, both Sanger sequencing and deep sequencing results confirmed that none of the mutations introduced in D2S reverted throughout the series of passages.
3.3. Altered DVG Generation in CHIKV D2S, Compared to WT Virus
To interrogate the role of genomic RNA secondary structures in the generation of DVGs, we compared the generation of DVGs between the WT CHIKV and D2S mutant after high-MOI passaging (Figure S1). DVG sequences were characterized by next-generation sequencing and visualized depending on their start and stop positions. WT and D2S tended to have close global distributions of deletions (Figure 3A). This finding was confirmed by mapping the probability of each nucleotide position to be deleted (Figure S2). To explore the diversity of DVGs generated by wild-type and mutant virus, we calculated the equivalent of a Shannon entropy for DVG breakpoints. For both the WT and D2S mutant, entropy decreased through passages, suggesting a decrease in DVG diversity over time. Entropy was significantly higher in the D2S mutant compared to the WT throughout the passages (p < 0.001, Figure 3B), illustrating that the D2S mutant generated a more diverse pool of DVGs, compared to WT virus.
3.4. DVG Generation Is Governed by RNA Secondary Structures
To formally evaluate the importance of secondary structure in DVG generation, we separated DVGs depending on their sequence. A first group was made with DVGs showing a start breakpoint before nucleotide 5000, and a second group with start breakpoints after nucleotide 5000. The region before 5000 contains the three regions where we disrupted secondary structures, as well as the start breakpoints of the DVG clusters A, B and C, which are predominant in Vero cells (Figure 1A and Figure S2). On the contrary, the region after nucleotide 5000 should display RNA secondary structures equal or similar to WT, since no mutations have been introduced there (Figure 1A). We listed all the different deletions present in the WT and D2S samples and plotted the number of samples that displayed each individual deletion in WT (x axis) or D2S (y axis). DVGs that appeared predominantly, or exclusively, in one virus would stack along the x and y axes, while DVGs that were common between WT and D2S samples would appear closer to or along the diagonal. We found that DVGs before nucleotide 5000 were often found only in one virus and not the other (Figure 4A), while DVG landscapes were more similar between the two viruses after nucleotide 5000.
To precisely assess this difference in the DVG landscape between the two viruses in these regions, we computed, for each specific deletion, the absolute difference between the number of samples supporting these breakpoints in the WT and the DS2 viruses. Then, we computed the number of specific DVGs supporting a specific absolute-count difference (Figure 4B). The distributions of these differences significantly differed between DVGs before and after nucleotide 5000, with fewer similar DVGs (between WT and DS2 mutants) in the first part than in the second part of the genome (p < 0.0001, Kolmogorov–Smirnov test and unpaired t-test).
We then performed SHAPE-MaP analysis on the D2S mutant to confirm that our mutations disrupted the secondary structure of the virus, as predicted. We calculated the change in SHAPE at each nucleotide between WT and D2S. If the mutations disrupted local secondary structure but maintained the RNA secondary structure of distal genome regions, there should be a larger change in SHAPE between WT and D2S in and near mutated regions than observed in and near distal regions. Indeed, the median change in SHAPE reactivity for nucleotides outside the modified regions was significantly smaller than for nucleotides within the modified region, corroborating the interest of the D2S mutant in this experiment (Figure 4C). Then, we looked at SHAPE reactivity values of nucleotides used as breakpoints, considering the frequency of their use, and observed a significant correlation (r = 0.053, p = 6.9 × 10−10, Figure 4D), as already seen with WT (Figure 1E). Interestingly, this correlation was not significant when using WT SHAPE reactivity values as a reference for D2S DVGs, reinforcing our observations. In line with what was observed with WT, the nucleotides used as breakpoints during D2S infection after a single passage had higher median SHAPE reactivity than nucleotides that were never used as breakpoints (Figure 1E). Together, these data suggest that in both D2S mutant and WT virus, secondary structures influence DVG generation through non-homologous recombination.
3.1. Preferential Generation of DVG Breakpoints at Unpaired Nucleotides
Because DVGs highjack the machinery of full-length virus for replication and/or transmission, they preferentially accumulate at high multiplicity of infection (MOI) when each cell is co-infected with multiple viral particles [39,40]. To enrich for DVGs, we performed serial passages at high MOI in triplicates in Vero cells (Figure S1A). At each passage, half of the supernatant was used to infect cells for the next passage and half was used for next-generation sequencing and bioinformatic analysis using an in-house pipeline [9,10,18]. DVGs were defined by their start and stop breakpoints, corresponding to the first and the last nucleotide deleted [9,10,18] (Figure 1A, hereinafter conjointly termed “breakpoint nucleotides”). As described in a previous work [10], classifying DVGs using the start and the stop positions of the truncation identifies clusters of DVGs on the genome (see clusters A, B and C in Figure 1A,B).
We observed no obvious sequence homologies between the start and stop regions for the commonly deleted regions which would suggest template-switching to a near-homologous region. Additionally, there are currently no reported RNA binding proteins associated with these regions that would bring the start and stop regions into close proximity during replication, allowing the RdRp to skip over the deleted nucleotides. Therefore, we hypothesized that RNA secondary structure in breakpoint regions may play a role in generating these specific DVGs.
We used data generated from our prior SHAPE-MaP analysis of the CHIKV genomic RNA [35] to determine the RNA secondary structure of the viral genome around the breakpoints. Reactive nucleotides, or nucleotides that are more flexible, are colored in orange, while unreactive nucleotides, those that are protected from chemical modification due to base pairing, are colored black, in the example region of the cluster A and B start breakpoints (nucleotide 300 to 819, Figure 1C). To determine if there was a relationship between a specific nucleotide used as a DVG break site in CHIKV and the flexibility of that nucleotide, we assessed whether the SHAPE reactivity of the breakpoint nucleotides differed from the SHAPE reactivity of unused nucleotides after 1 passage in Vero cells (Figure 1D). A median SHAPE reactivity over a 5-nucleotide window was used to capture the flexibility of a hyper-local region around the breakpoint. The average median SHAPE reactivity of nucleotides used as breakpoints remains higher than those not used as breakpoints over all passages, assuming each breakpoint was once originally generated from a full-length genome (Figure S1B).
Next, we analyzed the correlation between the frequency at which a nucleotide was used as a breakpoint (number of reads with this nucleotide as a breakpoint over the total number of reads) with the nucleotide’s median SHAPE reactivity (i.e., its probability of being paired when low or unpaired when high, Figure 1C), using a 5-nucleotide window. We used pooled data generated from all replicates over 8 passages (Figure 1E) and focused our analysis on DVGs presenting a deletion of at least 10 nucleotides in order to exclude all small deletions that might be generated by a short slippage of the viral polymerase rather than a canonical event of non-homologous recombination of RNA molecules. We uncovered an enrichment of unpaired nucleotides in those used as DVG breakpoints, even though the relationship is weak (Figure 1E, Pearson correlation r = 0.066, p = 2.7. 10–11).
Together, these results suggest that CHIKV DVG breakpoints preferentially occur at more flexible nucleotides, which are more likely to be unpaired, although flexibility alone is not sufficient to predict a breaksite.
3.2. CHIKV-D2S Mutant with Disrupted Secondary Structure Is Viable in Cell Culture
To experimentally test the impact of secondary structure on the generation of DVGs, we generated a mutant with a disrupted secondary structure (D2S) around a subset of important DVG breakpoints found in CHIKV passages in Vero cells (Figure 1A and Figure 2). We designed the D2S mutant so that its secondary structure would be disrupted around the DVG breakpoints of clusters A, B and C (all located in the first half of the genome, before nucleotide 5000) without impacting the secondary structure located after nucleotide 5000 (Figure 2A). Hence, we modified 3 regions with a total of 76 synonymous mutations generated using the program CodonShuffle with the dn231 algorithm (Jorge, Mills, et Lauring 2015): 19 mutations were introduced in the first region, covering nucleotides 380 to 730 (where cluster A and B start breakpoints are located); 35 mutations were between nucleotides 2810 and 3190 (comprising cluster A stop breakpoints and cluster C start breakpoints); and 22 mutations were between nucleotides 3800 and 3950 (comprising cluster C stop breakpoints) (Figure 2A,B; the list of mutations is rendered in Table S1). The mutations were selected with the aim of modifying the predicted RNA structure as much as possible without changing the amino acid sequence and codon usage (Figure 2B). To assess whether D2S showed decreased fitness (i.e., slower replication kinetics) in these settings compared to WT virus, we performed one-step and multi-step growth curve analyses in Vero cells. The D2S mutant displayed slower growth kinetics with a ten-fold disadvantage compared to WT (p < 0.0001; Figure 2C). However, in the one-step growth curve, at 48 h post-infection, D2S and WT had similar titers in Vero cells. Of note, both Sanger sequencing and deep sequencing results confirmed that none of the mutations introduced in D2S reverted throughout the series of passages.
3.3. Altered DVG Generation in CHIKV D2S, Compared to WT Virus
To interrogate the role of genomic RNA secondary structures in the generation of DVGs, we compared the generation of DVGs between the WT CHIKV and D2S mutant after high-MOI passaging (Figure S1). DVG sequences were characterized by next-generation sequencing and visualized depending on their start and stop positions. WT and D2S tended to have close global distributions of deletions (Figure 3A). This finding was confirmed by mapping the probability of each nucleotide position to be deleted (Figure S2). To explore the diversity of DVGs generated by wild-type and mutant virus, we calculated the equivalent of a Shannon entropy for DVG breakpoints. For both the WT and D2S mutant, entropy decreased through passages, suggesting a decrease in DVG diversity over time. Entropy was significantly higher in the D2S mutant compared to the WT throughout the passages (p < 0.001, Figure 3B), illustrating that the D2S mutant generated a more diverse pool of DVGs, compared to WT virus.
3.4. DVG Generation Is Governed by RNA Secondary Structures
To formally evaluate the importance of secondary structure in DVG generation, we separated DVGs depending on their sequence. A first group was made with DVGs showing a start breakpoint before nucleotide 5000, and a second group with start breakpoints after nucleotide 5000. The region before 5000 contains the three regions where we disrupted secondary structures, as well as the start breakpoints of the DVG clusters A, B and C, which are predominant in Vero cells (Figure 1A and Figure S2). On the contrary, the region after nucleotide 5000 should display RNA secondary structures equal or similar to WT, since no mutations have been introduced there (Figure 1A). We listed all the different deletions present in the WT and D2S samples and plotted the number of samples that displayed each individual deletion in WT (x axis) or D2S (y axis). DVGs that appeared predominantly, or exclusively, in one virus would stack along the x and y axes, while DVGs that were common between WT and D2S samples would appear closer to or along the diagonal. We found that DVGs before nucleotide 5000 were often found only in one virus and not the other (Figure 4A), while DVG landscapes were more similar between the two viruses after nucleotide 5000.
To precisely assess this difference in the DVG landscape between the two viruses in these regions, we computed, for each specific deletion, the absolute difference between the number of samples supporting these breakpoints in the WT and the DS2 viruses. Then, we computed the number of specific DVGs supporting a specific absolute-count difference (Figure 4B). The distributions of these differences significantly differed between DVGs before and after nucleotide 5000, with fewer similar DVGs (between WT and DS2 mutants) in the first part than in the second part of the genome (p < 0.0001, Kolmogorov–Smirnov test and unpaired t-test).
We then performed SHAPE-MaP analysis on the D2S mutant to confirm that our mutations disrupted the secondary structure of the virus, as predicted. We calculated the change in SHAPE at each nucleotide between WT and D2S. If the mutations disrupted local secondary structure but maintained the RNA secondary structure of distal genome regions, there should be a larger change in SHAPE between WT and D2S in and near mutated regions than observed in and near distal regions. Indeed, the median change in SHAPE reactivity for nucleotides outside the modified regions was significantly smaller than for nucleotides within the modified region, corroborating the interest of the D2S mutant in this experiment (Figure 4C). Then, we looked at SHAPE reactivity values of nucleotides used as breakpoints, considering the frequency of their use, and observed a significant correlation (r = 0.053, p = 6.9 × 10−10, Figure 4D), as already seen with WT (Figure 1E). Interestingly, this correlation was not significant when using WT SHAPE reactivity values as a reference for D2S DVGs, reinforcing our observations. In line with what was observed with WT, the nucleotides used as breakpoints during D2S infection after a single passage had higher median SHAPE reactivity than nucleotides that were never used as breakpoints (Figure 1E). Together, these data suggest that in both D2S mutant and WT virus, secondary structures influence DVG generation through non-homologous recombination.
4. Discussion
Advances in next-generation sequencing technologies allow a more thorough exploration of viral RNA secondary structures; it is already known that RNA secondary structures are key for genome replication and translation in alphaviruses [23,35,41,42,43], and in splicing and viral gene expression in HIV [44]. RNA secondary structures have also been described as being mediators of homologous recombination [24,26,45,46]. Concomitantly, DVGs have gathered greater interest over the last decade for their potential use as antiviral molecules [11,12], yet the rules governing their generation are still unclear. Our work supports the notion that RNA secondary structures influence how CHIKV DVGs are generated.
To examine this association, we used RNA secondary structures modeled using SHAPE-MaP analyses of the CHIKV genome, a source which is, to date, the most precise prediction of RNA structures currently attainable [35]. One of the specific strengths of our analysis lies in using the median SHAPE reactivity of a 5-nucleotide region around the nucleotide of interest, while studies of SHAPE reactivity generally rely on using the median SHAPE reactivity of a 50-nucleotide window [23,47,48]. Indeed, although DVG recombination sites can occur in highly structured regions, recombination could also preferentially happen on the unpaired, rather than paired, nucleotides of such secondary structures. We chose to exclude the 3′UTR region from analysis, even though CHIKV DVG breakpoints often occur in this region [10]; many sequence repeats complicate the analysis and decrease prediction performance compared to genomic RNA [38]. Finally, our initial analyses pooled DVGs from all passages combined, which makes it impossible to differentiate newly generated DVGs from DVGs that are replicated from a previous DVG template.
The reported correlation between DVG breakpoint probability and SHAPE reactivity, although significant, remains moderate. Our data therefore suggest that if RNA structure is an important driver of DVG formation, it remains complex and coupled to other determinants. This finding is consistent with what was observed in our previous work on CHIKV DVGs [10]: DVGs can be produced in various species, but their sequence and abundance depend on both the cellular environment and the viral genome. Other determinants may include RNA–cellular or RNA–viral protein interactions, long-distance RNA interactions (tertiary structures or interaction with cellular RNA), sequence homology around breakpoints, cellular ionic concentrations, innate immunity pressure, or location of viral replication.
We were able to experimentally test the putative role of secondary structure in DVG formation by creating a mutant with altered secondary structure. To our knowledge, only two studies have disrupted secondary structures of alphaviruses at the nucleotide level without modifying the amino-acid sequence using CodonShuffle [37]. Using SINV and CHIKV, the authors introduced mutations in the RNA secondary structures and confirmed that these mutants had growth defects, a finding arguing for the importance of secondary structures in these regions [23,35]. One pitfall of this method is the absence of controls bearing the same number of mutations without disrupting the secondary structure, and that would behave as WT. In our case, because any slight nucleotide change could cause recombination changes in the hypothetical DVG breakpoints being shaped by the RNA structures, it is close to impossible to design such a control. Nevertheless, this drawback was compensated for by the fact that we split the genome in two: a first region, before the 5000th nucleotide, where the RNA secondary structure was greatly disrupted; and a second region, after nucleotide 5000, where secondary structure was similar to WT, providing some internal control. Notwithstanding this, because disrupting secondary structures could also impact long-distance interactions (tertiary structures) or protein interactions, recombination areas could still be affected in the second half of the genome, which may explain the remaining differences between DS2 and WT DVG landscapes in this unchanged region.
Even though DVGs with large deletions generated from WT and D2S mutants do not present exactly the same breakpoints, both viruses tend to delete the same regions when passaged in the same cell type. However, the D2S mutant seems to fumble when generating DVGs: it creates more diverse DVGs, and at lower frequencies. Environmental pressure may still drive a particular kind of DVG, while the abnormal secondary structure makes it harder to select for specific DVGs. This may imply that the WT genome has evolved to maintain, or avoid the generation of, certain DVGs.
In conclusion, although other determinants are involved, RNA secondary structures do drive CHIKV DVG formation. Further work is needed to narrow down which specific RNA structures are involved in DVG generation, and to pinpoint other determinants such as sequence homology around breakpoints, RNA protein interaction, or RNA tertiary structures. A broader knowledge of these mechanisms could help develop prediction tools for DVG generation in different viruses and enable researchers to better genetically engineer genomes and DVGs for therapeutic purposes. |
Title: Radiomic Features as Artificial Intelligence Prognostic Models in Glioblastoma: A Systematic Review and Meta-Analysis | Body:
1. Introduction
Glioblastoma is the predominant primary tumor among all central nervous system cancers, accounting for around 80% of cases [1]. It continues to be an untreatable condition, with a median lifespan of about 15 months [2]. Just 5.5% of patients manage to survive for 5 years after being diagnosed [3]. The global prevalence data show that the annual incidence rate ranges from 0.59 to 5 per 100,000 individuals. Data are collected from multiple nations, including the United States, Australia, Britain, Korea, Greece, and Jordan, based on events specific to the age per 100,000 individuals (using the ICD-O 9450 morphological code) [4,5,6,7,8].
Prognosis in neuro-oncology entails assessing the advancement of the disease in different persons, considering factors such as the disease stage, its location, and the intended treatment or surgery. The crucial criteria to consider as a reference are overall survival (OS) and progression-free survival (PFS), guiding factors in determining subsequent treatment. Due to the difficulties in early detection and the invasive characteristics of tumor cells, completely removing them by surgery is a considerable problem [9].
The current conventional therapy involves the excision of the tumor through surgery, followed by the application of radiation and chemotherapy. Despite advancements in surgical imaging techniques that allow for more thorough tumor tissue removal, it is essential to balance aggressive tumor resection with the preservation of brain function and the overall well-being and quality of life of patients. Notably, patients with glioblastoma from lower socioeconomic backgrounds are less frequently tested for O6-Methylguanine-DNA-methyltransferase (MGMT) [10]. Failure to perform MGMT exams can lead to consequences of distorted prognoses and result in diagnoses at more advanced stages, with larger and more complex tumors. Furthermore, this particular group of people is rarely given a variety of treatment methods, resulting in decreased chances of survival. Nevertheless, these measures in disease treatment are fraught with difficulties, and mistakes can result in patient illness and death [11]. The problems encompass the requirement for accurate disease diagnosis and staging to inform clinical decisions, the ongoing monitoring of disease progression after therapy, which might be hindered by signals from nearby brain tissue, and the increasing importance of finding genetic patterns [12]. The genetic patterns have a significant influence on tumor behavior and clinical consequences [13]. The difficulties in managing glioblastoma stem from several factors, such as the intricate nature of the brain, restricted availability of precise imaging and biopsy techniques, the inherent diversity in tumor biology (genetic heterogeneity, epigenetic modifications, the tumor microenvironment, tumor evolution, and interactions with the immune system), varying rates of progression, individual differences in treatment response, and the lack of dependable biomarkers for predicting prognosis [14,15]. The neurological tissue’s susceptibility to conventional therapeutic methods, such as surgery, radiation, and chemotherapy, adds to the complexity of their management [16].
Artificial intelligence (AI) is a valuable tool for medical professionals in choosing treatment procedures. AI showcases its potential in brain tumor management through its ability to expedite and improve MRI imaging, identify abnormalities, optimize workflows, provide precise measurements, analyze vast amounts of medical imaging data, and identify patterns that may not be readily noticeable to human observers [17,18]. It has greatly enhanced the area by offering comprehensive image analysis for diagnostics, tumor classification, prognosis prediction, and the assessment of treatment response [19,20]. Additionally, it aids in planning surgical and nonsurgical treatments, expedites the discovery of new drugs, and assists in monitoring the recurrence of medical conditions. AI tools can be integrated into clinical trials to enhance patient outcomes and potentially lead to tailored therapy [13,21]. AI is essential in clinical neuroimaging for tasks including accurately identifying tumor boundaries and kinds, improving pre-therapeutic planning, and evaluating post-therapeutic responses [22]. The ability of AI to analyze large datasets presents a revolutionary method for precision medicine, potentially addressing common challenges through the entire patient care process [23,24,25]. Furthermore, it shows potential to improve global healthcare inequalities by offering equal access to diagnostic, prognostic, and treatment approaches [26,27].
Incorporating AI tools into radiological and pathological workflows has been increasingly explored, indicating possible progress in neuro-oncology [28,29]. AI plays a crucial role in brain tumor analysis by providing a complete framework that incorporates machine learning (ML) and deep learning (DL) techniques, computer vision (CV), and their integration into computational biology. Machine learning methods in the field of artificial intelligence aid in the identification of patterns in imaging and genomic data. Deep learning, a specific branch of machine learning, particularly shines in extracting complex features. Using traditional image processing techniques and state-of-the-art deep learning approaches, computer vision accurately analyzes visual data for precise medical picture interpretation. Computational biology utilizes ML and DL to examine large biological datasets, assisting in comprehending the genetic and molecular characteristics of brain cancers. Combining these techniques improves the comprehensiveness and precision of brain tumor characterization, impacting diagnosing, predicting the outcome, and planning the treatment.
A noninvasive therapeutic approach application of the three forms called radiomic features (RFs), which involves the application of artificial intelligence in MRI, exists to address this issue. Several studies examine the radiomic features’ ability to evaluate prognosis in glioblastoma [30,31,32,33,34,35,36,37,38,39,40,41,42,43]. RFs are a nascent discipline in medicine that focuses on extracting quantitative features from radiographic images. These traits are invisible to the naked eye but possess the capacity to define the diversity within a tumor [44]. Additionally, in intratumoral heterogeneity, radiomics is often termed a “virtual biopsy” due to its capacity to enhance traditional diagnostic imaging by providing additional insights that are not visible to the naked eye and involve processes beyond the standard radiologic evaluation [45]. Nevertheless, traditional approaches that depend on the stage of the disease and clinical factors have several drawbacks, such as difficulties in interpretation, biases, and the requirement for large datasets. Conventional approaches still face difficulties in accurately predicting recurrence and survival for tailored care.
RF-AI is crucial in enhancing prognostic capacities in brain tumor care. Its approaches are increasingly used to forecast OS and PFS by utilizing characteristics derived from imaging data before therapy. Promising research has been conducted on the use of radiomic signatures derived from T1 and FLAIR MRI scans of glioblastoma patients, as well as T1, T2, and FLAIR scans of patients who have not received therapy [36,46,47]. These studies have shown substantial potential in predicting PFS and OS. The AI models surpass typical clinical characteristics and exhibit exceptional performance when paired with clinical parameters in glioblastoma patients [33,48]. Notably, models that utilize T2-weighted MRI and radiomic characteristics from peritumoral edema have shown connections with survival outcomes, site of recurrence, and molecular subtype, particularly in patients with glioma and glioblastoma. Deep learning models are developed to detect cancers and predict the location of recurrence, often before radiologists can detect it. These models, employing diverse imaging techniques, showcase RFs’ remarkable prediction prowess [40,49]. This study aims to systematically gather evidence and evaluate the prognosis significance of radiomics in glioblastoma using an RF-AI-based approach. In addition, a current study will be incorporated to complement the earlier research.
2. Materials and Methods
2.1. Literature Search
We extensively searched the PubMed, ScienceDirect, EMBASE, Web of Science, and Cochrane databases to identify relevant original research on applying RF-based AI in predicting glioblastoma outcomes. This search was finalized on 25 July 2024. Our search terms included glioblastoma, MRI, magnetic resonance imaging, radiomics, and survival or prognosis. We included only English-language studies involving human subjects. The retrieved references were organized using Mendeley Reference Manager v1.19.8, and the bibliographies of the chosen studies were further reviewed to uncover additional relevant articles. The characteristic of the study produced the subsequent findings: (1) the initial name and year of publication; (2) the nation and total sample; (3) the center of study; (4) treatment status; (5) mean age; (6) MR sequence protocol; (7) TCGA/TCIA dataset; and (8) feature extraction.
2.2. Data Selection
A study was undertaken to evaluate the application of radiomics, utilizing machine learning (ML) or deep learning (DL) techniques, for predicting the prognosis of glioblastoma patients. The inclusion criteria for selecting articles were as follows: (1) Patients must be diagnosed with glioblastoma. (2) The study must use a multiparametric brain MRI as the index test, with no prior treatment history, and include a detailed radiomic analysis. (3) OS and PFS were assessed using clinical and imaging follow-ups as the reference standard. (4) Both retrospective and prospective cohort studies were included. Due to the scarcity of recently published journals following the modifications, we cannot align our categorization with IDH1 according to the latest WHO 2021 recommendations [50]. While some still adhere to the old guidelines, we lack the necessary prognostic data to proceed with the analysis. The following criteria were used for exclusion: (1) case reports and case series; (2) papers that are reviews, editorials, letters, or abstracts; (3) studies that lack sufficient information on patient survival outcomes; (4) studies that do not include radiomic characteristics; (5) studies that conduct research with a group of patients who have some overlapping characteristics; (6) research investigations relying solely on publicly available data from sources like the Cancer Imaging Archive (TCIA) or the Cancer Genome Atlas (TCGA); (7) studies conducted without including independent internal or external validation, particularly cross-validation with the leaving-one-out method, and (8) studies utilizing C-index parameters. Two reviewers (D.P.W.W. and S.W.) independently assessed and selected the most appropriate studies using a standardized form. This study is registered with PROSPERO under CRD42024565289. The publication was subsequently developed following the PRISMA principles.
2.3. Definition of Variable
The patients’ prognoses were determined using either OS or PFS. OS is the time between the initial pathological diagnosis and death from any cause, while PFS is the time until the disease worsens. Patients still alive at the end of the follow-up period were considered right-censored.
The findings of this study included handcrafted radiomic features and deep learning models (DL). Machine learning (ML) integrated into handcrafted radiomic features is a growing trend in utilizing manually created features as input for machine learning models to enhance the accuracy and categorization of diseases. This integration uses the knowledge and skills involved in feature design and improves analytical capabilities through automated techniques. Deep learning models are developed to detect cancers and predict the location of recurrence, often before radiologists can detect it. These models, employing diverse imaging techniques, showcase the fantastic prediction prowess of AI. This deep learning system predicts partial transcriptional profiles and prognosis from histology images. This provides valuable insights into the potential of artificial intelligence in understanding intricate elements of tumor behavior.
2.4. Data Extraction
A generated Google Sheet in Excel Online retrieved the relevant data. To address any data gaps or inquiries, we initiated an electronic correspondence with the writers via email to collect the required information and two reviewers (D.P.W.W. and S.W.) assessed it independently.
2.5. Quality Assessment
The quality of the selected studies was assessed independently by two reviewers (D.P.W.W. and S.M.) utilizing the Newcastle–Ottawa Scale (NOS) [51] to determine the methodological rigor of the articles. The studies were classified into two quality categories: those rated between 4 and 6 were considered moderate-, while those rated between 7 and 9 were deemed high-quality. NOS is the bias quality of the study using the Cochrane risk of bias assessment methodology. Any reviewer disagreements were resolved through open discussion and consensus, with the involvement of a third author (S.W.).
2.6. Statistical Analysis
Hazard ratios (HRs) and their 95% confidence intervals (CIs) for overall survival (OS) and progression-free survival (PFS) were combined using random effects models. Publication bias was evaluated using the Egger test, with a p-value less than 0.05 suggesting the presence of bias. A forest plot was created to present the results of the statistical analysis. The random effects model is employed in statistical analysis to accurately reflect the underlying population’s calculation. This study used Review Manager 5.3 version software developed by RevMan Cochrane in London, UK.
2.1. Literature Search
We extensively searched the PubMed, ScienceDirect, EMBASE, Web of Science, and Cochrane databases to identify relevant original research on applying RF-based AI in predicting glioblastoma outcomes. This search was finalized on 25 July 2024. Our search terms included glioblastoma, MRI, magnetic resonance imaging, radiomics, and survival or prognosis. We included only English-language studies involving human subjects. The retrieved references were organized using Mendeley Reference Manager v1.19.8, and the bibliographies of the chosen studies were further reviewed to uncover additional relevant articles. The characteristic of the study produced the subsequent findings: (1) the initial name and year of publication; (2) the nation and total sample; (3) the center of study; (4) treatment status; (5) mean age; (6) MR sequence protocol; (7) TCGA/TCIA dataset; and (8) feature extraction.
2.2. Data Selection
A study was undertaken to evaluate the application of radiomics, utilizing machine learning (ML) or deep learning (DL) techniques, for predicting the prognosis of glioblastoma patients. The inclusion criteria for selecting articles were as follows: (1) Patients must be diagnosed with glioblastoma. (2) The study must use a multiparametric brain MRI as the index test, with no prior treatment history, and include a detailed radiomic analysis. (3) OS and PFS were assessed using clinical and imaging follow-ups as the reference standard. (4) Both retrospective and prospective cohort studies were included. Due to the scarcity of recently published journals following the modifications, we cannot align our categorization with IDH1 according to the latest WHO 2021 recommendations [50]. While some still adhere to the old guidelines, we lack the necessary prognostic data to proceed with the analysis. The following criteria were used for exclusion: (1) case reports and case series; (2) papers that are reviews, editorials, letters, or abstracts; (3) studies that lack sufficient information on patient survival outcomes; (4) studies that do not include radiomic characteristics; (5) studies that conduct research with a group of patients who have some overlapping characteristics; (6) research investigations relying solely on publicly available data from sources like the Cancer Imaging Archive (TCIA) or the Cancer Genome Atlas (TCGA); (7) studies conducted without including independent internal or external validation, particularly cross-validation with the leaving-one-out method, and (8) studies utilizing C-index parameters. Two reviewers (D.P.W.W. and S.W.) independently assessed and selected the most appropriate studies using a standardized form. This study is registered with PROSPERO under CRD42024565289. The publication was subsequently developed following the PRISMA principles.
2.3. Definition of Variable
The patients’ prognoses were determined using either OS or PFS. OS is the time between the initial pathological diagnosis and death from any cause, while PFS is the time until the disease worsens. Patients still alive at the end of the follow-up period were considered right-censored.
The findings of this study included handcrafted radiomic features and deep learning models (DL). Machine learning (ML) integrated into handcrafted radiomic features is a growing trend in utilizing manually created features as input for machine learning models to enhance the accuracy and categorization of diseases. This integration uses the knowledge and skills involved in feature design and improves analytical capabilities through automated techniques. Deep learning models are developed to detect cancers and predict the location of recurrence, often before radiologists can detect it. These models, employing diverse imaging techniques, showcase the fantastic prediction prowess of AI. This deep learning system predicts partial transcriptional profiles and prognosis from histology images. This provides valuable insights into the potential of artificial intelligence in understanding intricate elements of tumor behavior.
2.4. Data Extraction
A generated Google Sheet in Excel Online retrieved the relevant data. To address any data gaps or inquiries, we initiated an electronic correspondence with the writers via email to collect the required information and two reviewers (D.P.W.W. and S.W.) assessed it independently.
2.5. Quality Assessment
The quality of the selected studies was assessed independently by two reviewers (D.P.W.W. and S.M.) utilizing the Newcastle–Ottawa Scale (NOS) [51] to determine the methodological rigor of the articles. The studies were classified into two quality categories: those rated between 4 and 6 were considered moderate-, while those rated between 7 and 9 were deemed high-quality. NOS is the bias quality of the study using the Cochrane risk of bias assessment methodology. Any reviewer disagreements were resolved through open discussion and consensus, with the involvement of a third author (S.W.).
2.6. Statistical Analysis
Hazard ratios (HRs) and their 95% confidence intervals (CIs) for overall survival (OS) and progression-free survival (PFS) were combined using random effects models. Publication bias was evaluated using the Egger test, with a p-value less than 0.05 suggesting the presence of bias. A forest plot was created to present the results of the statistical analysis. The random effects model is employed in statistical analysis to accurately reflect the underlying population’s calculation. This study used Review Manager 5.3 version software developed by RevMan Cochrane in London, UK.
3. Results
3.1. Literature Search
Figure 1 displays a flow diagram illustrating the selection procedure for eligible studies. A total of 253 studies were found in the initial search across the three databases. Before screening, we eliminate 29 duplicate studies. We searched five databases, and of the 54 articles, 40 were excluded due to not meeting the eligibility criteria. The studies we discovered examined individuals with an average age ranging from 47 to 62 years. We found several publications that were unsuitable due to their use of a language other than English (n = 1), articles that only included abstracts (n = 17), and articles that were classified as reviews (n = 22). The fourteen included publications [30,31,32,33,34,35,36,37,38,39,40,42,43,52] consist of four from South Korea and China, two from Germany and the USA, and one from the UK and France, respectively. A total of 11 papers employed conventional treatment, with 5 being multi-center studies, while the remaining 9 utilized single-center research. The sample sizes of these studies varied from 22 to 652 people. A grand number of 2.950 samples took part in this study. The hazard ratios (HRs) and 95% CIs were directly obtained from the source articles in the investigations mentioned above. Table 1 presents the attributes and qualities of the studies that have been enumerated.
3.2. Quality Assessment and Bias Analysis
According to the NOS quality evaluation, the investigations shown in Table 1 were determined to have moderate to high quality. Egger’s tests revealed that publication bias did not influence the included studies. The investigations revealed a p-Egger with an OS rate of 1.536 and a PFS rate of 1.994.
3.3. Overall Survival (OS) Analysis
We examined the analysis of OS in Figure 2 in glioblastoma. All twelve OS studies were considered, involving 1.639 patients from Germany, the USA, China, South Korea, the UK, and France. The random effects model was used to calculate the pooled HR for OS. The HR for OS was 3.34 (95% confidence interval [CI], 1.72–6.45). The analysis revealed heterogeneity in the OS with an I2 value of 96%.
3.4. Progression-Free Survival (PFS) Analysis
We examined the analysis of PFS in Figure 3 in glioblastoma. All eight PFS studies were considered, involving 747 patients from Germany, South Korea, China, the USA, and France. The random effects model was used to calculate the pooled HR for PFS. The HR for PFS was 4.24 (95% confidence interval CI, 1.00–18.05). The analysis revealed heterogeneity in the PFS with an I2 value of 97%.
3.1. Literature Search
Figure 1 displays a flow diagram illustrating the selection procedure for eligible studies. A total of 253 studies were found in the initial search across the three databases. Before screening, we eliminate 29 duplicate studies. We searched five databases, and of the 54 articles, 40 were excluded due to not meeting the eligibility criteria. The studies we discovered examined individuals with an average age ranging from 47 to 62 years. We found several publications that were unsuitable due to their use of a language other than English (n = 1), articles that only included abstracts (n = 17), and articles that were classified as reviews (n = 22). The fourteen included publications [30,31,32,33,34,35,36,37,38,39,40,42,43,52] consist of four from South Korea and China, two from Germany and the USA, and one from the UK and France, respectively. A total of 11 papers employed conventional treatment, with 5 being multi-center studies, while the remaining 9 utilized single-center research. The sample sizes of these studies varied from 22 to 652 people. A grand number of 2.950 samples took part in this study. The hazard ratios (HRs) and 95% CIs were directly obtained from the source articles in the investigations mentioned above. Table 1 presents the attributes and qualities of the studies that have been enumerated.
3.2. Quality Assessment and Bias Analysis
According to the NOS quality evaluation, the investigations shown in Table 1 were determined to have moderate to high quality. Egger’s tests revealed that publication bias did not influence the included studies. The investigations revealed a p-Egger with an OS rate of 1.536 and a PFS rate of 1.994.
3.3. Overall Survival (OS) Analysis
We examined the analysis of OS in Figure 2 in glioblastoma. All twelve OS studies were considered, involving 1.639 patients from Germany, the USA, China, South Korea, the UK, and France. The random effects model was used to calculate the pooled HR for OS. The HR for OS was 3.34 (95% confidence interval [CI], 1.72–6.45). The analysis revealed heterogeneity in the OS with an I2 value of 96%.
3.4. Progression-Free Survival (PFS) Analysis
We examined the analysis of PFS in Figure 3 in glioblastoma. All eight PFS studies were considered, involving 747 patients from Germany, South Korea, China, the USA, and France. The random effects model was used to calculate the pooled HR for PFS. The HR for PFS was 4.24 (95% confidence interval CI, 1.00–18.05). The analysis revealed heterogeneity in the PFS with an I2 value of 97%.
4. Discussion
The health industry is witnessing technological advancements leading to the emergence of tools like AI, particularly in neurosurgery. This study indicated that patients exhibiting high-risk radiomic characteristics have worse prognoses, with pooled hazard ratios of 3.59 and 4.20 for overall survival and progression-free survival, respectively. AI is utilized in applying tools like RFs. Several types of artificial intelligence, such as machine learning and deep learning [28,53], have been developed from some RFs. Implementation in this domain significantly enhances information processing speed and improves the patient’s treatment regimen and overall quality of life. None of the publications we encountered provided information regarding the specific ML and DL techniques employed. Nevertheless, various categories of machine learning exist, including Support Vector Machines (SVMs) and DL-like Convolutional Neural Networks (CNNs), which are frequently used. The field has seen significant evolution due to the advancement of deep learning and other machine learning techniques. To summarize, the application of artificial intelligence in medical imaging involves using the sophisticated algorithms above to carry out tasks such as tumor detection and segmentation. These techniques improve the precision and speed of diagnosing medical conditions by automating and enhancing image analysis processing.
RF is anticipated to play a significant role in precision medicine due to its capability to collect detailed data that precisely characterize survival in glioblastoma, a notably aggressive cancer. Regardless of success or failure, the treatment outcome holds substantial importance, although other factors may also affect long-term survival. Combining Ktrans and relative cerebral blood volume metrics from perfusion-weighted imaging (PWI) sequences achieved an accuracy of approximately 91% [54]. In addition, the diagnostic accuracies of these algorithms exceeded 70%, outperforming neuroradiologists in evaluating typical MR images using algorithms [52,55]. A strong association was seen in patients with low-grade gliomas between the T2-weighted RFs of PFS and its functions, such as cell proliferation, apoptosis, immune response, and vascular development [56].
Despite its robust predictive capacity, integrating this therapeutic tool into practice has yet to be accomplished. The current application is restricted to conventional MRI, which is typically appraised by radiologists. RFs have computational limits and can be time-consuming, particularly during preprocessing [57]. This might be problematic when patients require prompt action or treatment. We removed certain studies that utilize datasets obtained from TCIA/TCGA to prevent the inclusion of redundant data, although they may have a distinct therapeutic approach. Consequently, the process of choosing articles was conducted with excellent adherence. The necessity for the standardization and calibration of imaging regulation was acknowledged, particularly in cases where the RFs can be replicated, and has recently been validated in multi-center experiments [58]. However, the study we analyzed needed to disclose whether it had undergone standardization with the latest standards despite being conducted after the research.
This systematic review and meta-analysis thoroughly assessed the predictive significance of RFs in individuals diagnosed with glioblastoma. Our study showed that individuals with radiomic signs indicating a high risk have worse prognoses, with HRs of 3.59 and 4.2 for OS and PFS, respectively, compared to patients without these features. None of the studies exhibited publication bias. The results of our study indicate that the use of RFs can accurately assess the risk level of patients with glioblastoma at an early stage. This information can help physicians develop more effective treatment plans and potentially enhance patients’ chances of recovery. We chose not to utilize the C-index parameter because it frequently requires comparing patients with similar underlying risks, which can result in numerous comparisons. Additionally, the pairwise comparisons of patients with possibly similar risks yield almost equal results. The factors above contribute to the findings corroborated by prior research. Specifically, the analysis using the C-index reveals heterogeneity that differs among investigations [59].
Glioblastoma imaging currently requires frequent imaging sessions. The current status of radiomic profiling is most effective during the initial scans following diagnosis, as it can potentially guide decisions regarding radiotherapy or surgical resection [60]. The clinical significance of these profiles will be enhanced if they can be associated with an identifiable histological pattern. Each study can characterize a distinct phenotype or microenvironment. Additional histological validation with a larger sample size is required. While we saw publications discussing edema, recurrence, and metastasis, they did not fit our inclusion criteria. However, treatment effects can alter the volume, threshold, or profiles that are most valuable in predicting prognosis. By monitoring the expansion of the profile over time, clinicians can effectively manage and account for both the impacts of treatment and the influence of time. Temporal fluctuations in prognostic scores can offer valuable therapeutic insights.
Nevertheless, this study’s limitation is that radiomics is constrained by the absence of standards, impeding the ability to reproduce results. An inherent limitation of this study is that most studies employ retrospective designs, yet this study shows promise as a future prediction tool. Consequently, it is essential to create standard methods for both imaging acquisition and segmentation. In conclusion, using RFs shows excellent potential in accurately describing glioblastoma-based artificial intelligence. This is especially true when combining multiple modalities and considering the clinical presentation of the patients. We all expect that RFs can play a crucial role in long-term forecasting and planning for survivorship care, assisting in making treatment decisions. Facilitating the connection between clinical practice and research via data-sharing networks can expedite the creation and validation of AI models. The transparency and interpretability of AI models are crucial for establishing confidence and acceptance in therapeutic environments.
5. Conclusions
An RF-AI-based approach offers prognostic significance for OS and PFS in patients with glioblastoma. Acquiring more detailed data on patient demographics, treatment responses, and illness features can augment AI model performance. Additionally, designing research with higher sample sizes and different patient populations can also boost precision and application. From an economic standpoint, this type of AI has the potential to significantly benefit developing countries where access to healthcare is costly and where individuals may lack the financial resources for MGMT checks. |
Title: Imaging findings of inflammatory myofibroblastic tumor of sigmoid colon: literature review and case report | Body:
Introduction
Inflammatory myofibroblastic tumor (IMT) is a rare intermediate mesenchymal tumor, which has previously been described as inflammatory pseudotumor, plasma cell granuloma, and inflammatory myofibrous histiocytic proliferation, consisting of differentiated myofibroblastic spindle cells, often accompanied by extensive lymphocyte and/or plasma cell infiltration (1). The etiology of IMTs is unknown and may be related to certain special bacterial or EB virus infections, chromosomal mutations (2). It can be seen at any age, but is mainly found in children and young adults, with females being more common (3). The tumor can occur in various parts of the body, of which the lung is the most common, accounting for 95%, while the organ tissues outside the lung, including mesentery, omentum, liver, retroperitoneum and limbs, are rare (4). The disease symptoms of patients are related to the disease site, including fever, pain, anemia and mass, without specificity (5). It is precisely because of the rarity of IMTs and the non-specificity of clinical manifestations that it is difficult to make a correct diagnosis before surgery. Here, we present the diagnosis and treatment of a rare patient with sigmoid IMT and review the literature with a view to increasing awareness of this rare disease.
Case presentation
A 10-year-old girl with abdominal pain for 3 days underwent an abdominal ultrasound examination in an outside hospital on November 20, 2023 and found a large mass in her pelvic cavity. She was admitted to our hospital on 23 November 2023 for further diagnosis and treatment. She and her family denied any history of tumors or genetic diseases. Physical examination revealed a large, hard, low-motion mass palpable in her pelvic cavity, while no significant positive signs were found in the rest of her body. The laboratory test results, including serum tumor markers, were all negative. On November 24th, the patient underwent an abdominal CT examination (Figure 1) and the results showed a slightly low-density mass with unclear boundary with the sigmoid colon in her pelvic cavity, which presented significant uneven enhancement on contrast-enhanced CT, suggesting a possible malignant tumor. In order to further evaluate the nature of the tumor and determine the treatment plan, she underwent 18F-FDG PET/CT examination the following day. The results showed obviously increased 18F-FDG uptake in this lesion, and no hot spot lesions were observed in the rest of the body (Figure 2). Based on these imaging findings, the patient was initially suspected to have a malignant lesion. After a series of evaluations, the patient underwent an exploratory laparotomy, radical tumor resection and ileostomy on November 29 under anesthesia. Hematoxylin–eosin staining revealed diffuse spindle shaped tumor cells and scattered inflammatory cells in resected tumor tissue (Figure 3). Immunohistochemical results showed that the tumor cells positively expressed smooth muscle actin (SMA), anaplastic lymphoma kinase (ALK), CD117, vimentin, but negatively expressed cytokeratin (CK), Desmin and Dog-1. Based on these histopathological features, the patient was diagnosed with an inflammatory myofibroblastic tumor. The patient improved after receiving 3 days of anti-inflammatory treatment with ceftriaxone after surgery and was discharged on December 1, 2023. On June 2, 2024, she underwent abdominal ultrasound examination and showed no signs of tumor recurrence. The patient has been following up for 6 months now and has not reported any discomfort.
Figure 1
Abdominal CT revealed a regular low-density mass (A, arrow) in the pelvic cavity, about 9.3 cm × 9.0 cm × 6.7 cm in size; which presented significant uneven enhancement on contrast-enhanced CT (B, arrow).
Figure 2
Fluorine-18 fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/CT imaging of the patient. The maximum intensity projection (MIP, A) showed a significantly increased 18F-FDG uptake in the lower abdominal area (arrow). Axial CT (B), PET (C) and PET/CT fusion (D) showed that the lesion with significantly increased 18F-FDG uptake, with a SUVmax of 9.3 (arrows), which was located in the serosal surface of the sigmoid colon, and the adjacent intestinal tube is compressed, the proximal intestinal tube is dilated, and liquid exudation shadow is seen around the mass, and a small amount of fluid is seen in the pelvic cavity.
Figure 3
(A) Hematoxylin–eosin staining revealed diffuse spindle shaped tumor cells and scattered inflammatory cells within the lesion; Immunohistochemical results showed that the tumor cells positively expressed SMA (B), ALK (C) and vimentin (D). Notes: SMA, smooth muscle actin; ALK, anaplastic lymphoma kinase.
Literature review
The PubMed and Web of Science databases were searched for case reports and series of sigmoid colon IMT as of June 10, 2024, with language limitations in English. The search strategy was as follows: (“inflammatory myofibroblastic tumor” OR “inflammatory pseudotumor” OR “plasma cell granuloma” OR “inflammatory myofibrous histiocytic proliferation”) AND “sigmoid colon.” For each enrolled case, the first author, publication year, as well as the patient’s gender, age, clinical symptoms, imaging findings including CT and PET, and follow-up results were recorded (Table 1).
Table 1
Clinical and imaging features of the cases of sigmoid colon sigmoid colon IMT from the literature review and current case.
Case, no.
Author/year/country
Gender /Age (years)
Symptom
CT imaging
PET (SUVmax)
Treatment
Follow-up (month)
Morphological changes
MD (cm)
CECT
1(6)
Dhuria S/ 2018/India
F/2
vomiting, fever,
oval, isodense solid soft tissue mass with low-density necrosis
8.0
OUE
NA
Surgery
36/NR
2 (7)
Pandit N/ 2019/Nepal
M/35
blood mixed stool, fever, weight loss
lobulated isodense soft tissue mass
8.0
OEE
NA
Surgery
12/NR
3 (8)
Chinnakkulam KS/2020/India
F/40
abdominal pain, vomiting,constipation
suborbicular isodense soft tissue mass
6.0
OEE
NA
Surgery
18/NR
4 (9)
Kavirayani V/ 2023//India
F/9 months
abdominal distension
suborbicular isodense soft tissue mass with low-density necrosis
9.4
OUE
NA
Surgery
6/NR
5 (10)
Wu L/2023 /China
M/11 months
vomiting, fever,
lobulated cystic solid mass
12.0
OUE
NA
Surgery
24/NR
6 (12)
Uysal S/2005 /Turkey
M/11
abdominal pain, difficult defecation
suborbicular isodense soft tissue mass with low-density necrosis
11.0
OUE
NA
Surgery
6/NR
7 (11)
Nakamura Y/ 2010/Japan
F/82
abdominal pain
NA
NA
NA
NA
Conservative treatment
24/NR
8
Present case
F/10
abdominal pain
suborbicular low-density mass
9.3
OUE
15.4
Surgery
6/NR
IMT, inflammatory myofibroblastic tumor; MD, maximum diameter; PET, positron emission tomography; SUVmax, maximum standard uptake value; CT, computed tomography; CECT, contrast-enhanced computed tomography; OUE, obvious uneven enhancement; OEE, obvious even enhancement; NR, no recurrence; NA, not applicable.
After a systematic search and careful reading of the full text of the preliminary screening, it was finally determined that there were seven cases of sigmoid IMT published before our case (6–12). Including the current patient that we reported, a total of eight sigmoid IMT cases, consisting of three male (3/8) and five female (5/8) patients, with a median age of 10.5 years (range, 9 months-81 years old), were included in the analysis. Common clinical symptoms include abdominal pain, vomiting, fever, and abdominal discomfort. IMTs are generally large in size, with a mean maximum diameter of 9.1 cm. Most of the patients (7/8) underwent surgery, and only one patient received conservative treatment due to poor lung function. The prognosis of IMT was good, and no significant signs of tumor recurrence were found in all patients who underwent surgical resection of the mass during the follow-up period.
Discussion
IMT in the sigmoid colon is rare. Our current study presents a case of a child diagnosed with sigmoid IMT who complained of abdominal pain. To further understand the characteristics of this disease, we conducted a systematic review of relevant literature, and the results showed that most IMT patients in the sigmoid colon were children and young adults, with more females than males. This is consistent with the epidemiology of IMT patients occurring in other parts of the body (13). Most patients seek medical help due to vomiting, fever, abdominal pain, and abdominal discomfort. The literature reported that the etiology of IMT may be related to chronic infections, autoimmune diseases, and trauma (2). However, our case and previous literature on IMT in the sigmoid colon have not reported any such medical history, so this viewpoint may need to be confirmed in the future.
Imaging examinations play a significant role in the diagnosis of IMT, and the imaging of IMT in the sigmoid colon has certain characteristics. On CT, it usually presents as a large isodense soft tissue mass with smooth edges, and there may be low-density cystic necrosis area within the mass. On contrast-enhanced CT, the mass showed obvious uniform or uneven enhancement (6–10). Unlike previous literature reports, the current case showed a uniform low-density mass on CT, but still showed significant enhancement on contrast-enhanced CT. At present, research on the 18F-FDG/glucose metabolism of IMT is relatively rare and is mostly seen in case reports. Most IMTs present significantly increased 18F-FDG uptake on PET and are often misdiagnosed as malignant tumors in the corresponding area (14–16). The mechanism of 18F-FDG uptake by IMT may be correlated with tumor cellularity, inflammatory cell infiltration and Ki-67 expression. The higher tumor cellularity, more composition of inflammatory cell and higher Ki67 expression, the greater SUVmax (17). The patient we reported presented with low density on CT, with exudative shadows around the mass and obvious uptake of 18F-FDG on PET, which may be related to the strong inflammatory cell infiltration of the mass. To our knowledge, our case study is the first to report PET findings of sigmoid colon IMT, which, like IMT occurring in other organ tissues, also showed significantly increased 18F-FDG uptake.
According to the imaging findings of IMT, IMT originating from the sigmoid colon needs to be differentiated from gastrointestinal stromal tumors (GISTs), lymphoma and sigmoid colon cancer. GISTs also presented as large, circular or lobulated soft tissue masses on CT, with cystic necrosis at the center of the mass and uneven delayed enhancement on contrast-enhanced CT (18). On PET, GISTs with different risk levels show varying levels of increased 18F-FDG uptake, and high-risk GISTs presenting a higher SUVmax than medium-to low-risk GISTs (19). Moreover, due to the fact that GISTs typically grow outside the intestinal lumen, GISTs located in the lower abdomen may exhibit migration of the mass over time on dual-time point PET/CT, which is a relatively specific sign (20). Lymphomas that occur in the intestine are mostly B-cell non-Hodgkin lymphomas. Like IMT, it presents significantly increased 18F-FDG uptake on PET (21). However, intestinal lymphoma usually infiltrates along the intestinal wall, presenting as a circular thickening of the intestinal wall, and rarely causing proximal intestinal duct dilation and obstruction (22). Adenocarcinoma is the most common tumor in the sigmoid colon, and it also shows significantly increased 18FDG uptake on PET. However, sigmoid colon cancer often grows infiltratively along the intestinal wall and has an irregular shape on CT (23), which is significantly different from IMT.
Pathological examination is currently the gold standard for diagnosing IMT. Microscopically, the characteristic fusiform myofibroblast proliferation was observed, accompanied by abundant infiltration of chronic inflammatory cells such as plasma cells, T lymphocytes, neutrophils, and eosinophils (24). Immunohistochemistry showed that tumor cells positively expressed vimentin, SMA, and Desmin usually, and CK, CD68, CD30 and ALK were partially expressed positively, while CD117 and CD34 were usually negative expressed (25). In the tumor tissue of the patient we reported, diffuse fusiform tumor cells and scattered inflammatory cells were found in the lesion under microscope. Immunohistochemical results showed that the tumor cells positively expressed vimentin, SMA, and ALK, which was consistent with the pathological diagnosis of IMT.
At present, radical surgical resection of tumor tissue is the preferred treatment for IMT. Only when the tumor cannot be surgically removed, other treatment options including chemotherapy, immunomodulatory therapy, corticosteroids, radiotherapy, nonsteroidal anti-inflammatory drugs and so on should be considered (26, 27). Since IMT is an intermediate tumor with the possibility of recurrence and metastasis, close and careful follow-up after complete surgical resection of the tumor tissue is crucial to improve the prognosis of patients (28). The patient we reported did not show any signs of tumor recurrence or metastasis during follow-up after surgical removal of the tumor.
In conclusion, sigmoid IMT is a relatively rare intermediate tumor and should be considered in the differential diagnosis of other sigmoid malignancies such as GISTs and cancers. It appears as a large, smooth edge, or low-density mass on CT, with obvious uniform or uneven enhancement on contrast-enhanced CT, which showed significantly increased 18F-FDG uptake on PET. These imaging findings contribute to the diagnosis of IMT.
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Ethics statement
Written informed consent was obtained from the individual(s), and minor(s)’ legal guardian/next of kin, for the publication of any potentially identifiable images or data included in this article.
Author contributions
XH: Conceptualization, Data curation, Formal analysis, Funding acquisition, Writing – original draft. WZ: Investigation, Methodology, Project administration, Writing – original draft. RY: Conceptualization, Methodology, Validation, Writing – original draft. PW: Investigation, Project administration, Supervision, Visualization, Writing – review & editing. |
Title: Single Cell Droplet-Based Efficacy and Transcriptomic Analysis of a Novel Anti-KLRG1 Antibody for Elimination of Autoreactive T Cells | Body:
INTRODUCTION
Autoimmune diseases encompass a wide range of conditions in which the immune system attacks normal, healthy tissues. This can arise from a variety of mechanisms in which self-antigens become recognized as foreign by immune cells. These diseases can be severely debilitating and typically are irreversible and can significantly decrease a patient’s quality of life. Untreated, many autoimmune diseases can be completely incapacitating, and in some cases fatal. Autoimmune disease may be difficult to treat due to the constant reinforcement of autoreactive immune cells recognizing healthy tissues as foreign, promoting the growth and expansion of these cells in a continuous feedback loop (1). Traditional treatment approaches focus on systemic suppression of inflammation to prevent autoreactivity. While effective at mitigating symptoms, this approach results in adverse symptoms, including increased susceptibility to infectious disease and certain cancers (2). Additionally, some treatment options can induce organ damage with long-term use, such as renal toxicitiy from nonsteroidal anti-inflammatories (NSAIDS) or neurotoxicity from biologics (2). These approaches are not disease modifying, and require lifelong treatment to manage symptoms. Specifically eliminating autoreactive immune cells might provide a highly effective treatment of autoimmune conditions that does not require the broad spectrum immune suppression of traditional treatments (3, 4).
To target treatment specifically to autoreactive immune cells, these cells must be identified based on unique features. KLRG1 has been shown to be expressed in particular on CD8+ cytotoxic T cells associated with autoimmune activity (5–7). Abcuro, Inc. has designed a cell depleting monoclonal antibody, specific for KLRG1 as a potential treatment strategy for certain autoimmune diseases in which KLRG1+ CD8+ cells have been identified as mediators of pathology. Depleting anti-KLRG1 antibodies bind to autoimmune cytotoxic T cells and target them for elimination by other immune cells and by complement, using mechanisms such as NK cell mediated antibody-dependent cellular cytotoxicity (ADCC) or macrophage mediated antibody-dependent cellular phagocytosis (ADCP), or complement dependent cytotoxicty (CDC). Autoreactive T cells are major drivers of autoimmune conditions, and targeted elimination of these cells can be enough to induce tolerance in the patient (8).
In this study, we assessed the in vitro activity of an anti-KRLG1-based immune cell depletion strategy in traditional bulk testing approaches and at the single cell level. We utilized a unique microfluidic droplet system to observe cytotoxicity and cellular interactions, with incorporation of a droplet sorting platform for additional downstream analysis. Cells expressing and lacking KLRG1 were combined with an anti-KLRG1 cell-depleting antibody and co-encapsulated with NK cells in our microfluidic cell droplet array device to observe interactions and killing through timelapse microscopy. The droplet sorting platform was then used to sort and isolate NK cells based on their ability to recognize and respond to anti-KLRG1 antibody binding, using a novel calcium-signalling based sorting approach. Sorted NK cells were analyzed with transcriptomic sequencing to observe what cellular characteristics may influence the efficacy of this treatment approach. Our results display promising efficacy of this anti-KLRG1 antibody through unique metrics, and describe a novel approach that can be utilized to further understand and enhance the cellular response to immunotherapies such as this.
METHODS
Device Fabrication and Use
Devices were fabricated in-house using previously described methods.(9) Briefly, devices were made from PDMS using photocrosslinked silicon wafers as a mold, and bonded to glass slides. Droplets are generated on-chip from a cell suspension in media and FC-40 oil (3M, Maplewood, Minnesota), creating aqueous-in-oil emulsions. Cell suspensions and oil are delivered to the device using syringe pumps (Harvard Apparatus, Holliston, MA). Droplets are loaded with approximately 1–3 of each cell type on encapsulation and held in a 4000 docking site array for imaging.
Cell Culture
Parental and KLRG1 transfected CHO-K1 cell lines were provided by Abcuro, Inc. Briefly, one stable cell line using CHO-K1 as the host was established to overexpress human KLRG1. The full length cDNA of human KLRG1 was cloned into pLVX plasmid. Then lentivirus production was performed by co-transfecting the pLVX and helper plasmids using commercial packaging kit (Takara, Cat#631275) in 293T cells. Virus supernatant was collected at both 48 h and 72 h after transfection. Then CHO-K1 cells (ATCC) were used for lentivirus infection with polybrene (Sigma, Cat# H9268). After 72 h infection, cells were selected using puromycin(Gibco, Cat#A11138–02) at 8 μg/ml and then FACS sorting were used for single clone selection with high KLRG1 expression level. CHO cells were grown in RPMI 1640 (ATCC) with 10% ultra-low IgG FBS and 1% antibiotic mix (Gibco, Waltham, MA). Peripheral blood NK cells (≥85% CD56 positive) were purchased frozen from Stemcell Technologies and thawed in the same media used for CHO cells, with the addition of 15 ng/mL IL2 (Peprotech). NK cells were rested overnight after thawing prior to running experiments. Human CD57+CD8+ T cells were isolated from isolated human CD8+ T-cells which were in turn isolated from 125 mL leukopaks from healthy human donors utilizing EasySep human CD8+ T cell isolation kit from StemCell. CD57+ CD8+ T cells were further isolated from total isolated CD8+ T cells with CD57 Microbeads, Human from Miltenyi Biotec. Manufacturer’s instructions were followed for the cell isolations. Purities greater than 85% for the three isolated populations were confirmed by flow cytometry. The vast majority of CD57+ CD8+ T cells expressed KLRG1 and hence, they were used as KLRG1+ CD8+ T cells. KLRG1−CD8+ T cells were used also as negative controls. These cells were prepared from total CD8+ T cells stained with anti-KLRG1-PE antibody (BioLegend; ~1 μl/million cells) followed by isolation with Anti-PE MicroBeads UltraPure (Miltenyi Biotec) per manufacturer recommendations, yielding two populations: KLRG1+ CD8+ T cells and KLRG1− CD8+ T cells.
Antibody treatments were added to cell suspensions at 10 μg/mL immediately before droplet generation or at time 0 in plate assays. Anti-KLRG1 antibody Ulviprubart (ABC008) is an afucosylated humanized human IgG1 antibody in clinical trials (10–12) and it was provided by Abcuro, Inc. Afucosylated human IgG1 isotype control, Anti-b-Gal-hIgG1fut from InvivoGen was used for the negative control.
Fluorescent Microscopy
Devices were imaged on an Axio Observer microscope equipped with an automated stage and incubation chamber for maintaining cells at 37°C and 5% CO2. Target cells (CD8+ T cells and CHO-K1) were labeled with calcein AM (Invitrogen, Waltham, MA) as a live stain, and ethidium homodimer III (Biotium, Freemont, CA) was added to droplets to image dead cells. Separate microfluidic devices were loaded for each condition, and imaged at 20X magnification every 15 minutes for 8 + hours. Cell death was determined by a combination of morphology and viability dyes.
Plate Cytotoxicity Assay
NK cells and CHO-K1 cells were prepared as done in the droplet co-encapsulation experiments, described above. CHO cells were labeled with calcein green AM for 30 minutes and washed prior to plate loading. 30,000 target CHO cells were loaded per well, with NK cells loaded at 0:1, 1:1, 2:1 and 5:1 effector to target ratios. Plates were incubated at 37° C and 5% CO2 for 5 hours, then were spun down and supernatant removed to remove calcein released from dead cells. Fluorescence was then measured with a ThermoFisher VarioSkan LUX plate reader using 490 excitation / 520 emission.
Droplet Sorting and Collection
For the droplet sorting experiments, we used our own sorting platform to recognize and sort fluorescent events. This platform utilizes a microfluidic device to generate and sort droplets via diaelectric field. Fluorescence is detected by PMTs (Hamamatsu Photonics, Hamamatsu City, Japan) when droplets reach the sorting junction, which feeds back to a control unit (National Instruments, Austin, TX). Using LabVIEW software (National Instruments), fluorescent signal is continuously monitored, and a threshold is set to determine fluorescent peaks. Peaks over threshold automatically trigger an electric impulse sent to the device, amplified via a high-voltage amplifier (Advanced Energy, Denver, CO), which generates the droplet-sorting field.
For this experiment, calcium signaling was used to sort NK cells activated through fragment crystallizable gamma receptor IIIa (FcgRIIIa) binding. Prior to the experiment, NK cells were incubated for 1 hr with 5 μM Fura-10 AM ratiometric calcium dye (AAT Bioquest, Sunnyvale, CA). KLRG1− CHO-K1 cells were incubated with a-KLRG1 Ab for 30 minutes, and washed twice immediately prior to droplet generation. Droplets were generated on-chip, encapsulating NK and CHO cells at 1:1 and mixing for 1 minute prior to reaching the sorting array. Calcium mobilization occurs upon FcR binding and NK cells with elevated calcium levels produce a fluorescent signal spike, and are automatically sorted based on threshold values set at the start of the experiment. Positively selected and unselected droplets are collected separately, the emulsion is disrupted through gentle pipetting and centrifugation. Cells are then processed with the RNeasy mini kit (QIAGEN, Venlo, Netherlands), and collected RNA is stored at −80°C. RNA is sent to Novogene (Beijing, China) for transcriptomic sequencing and bioinformatic analysis. NK and CHO co-encapsulations were performed in duplicate, and a CHO-alone control sample was additionally sequenced for identification of hamster sequences mapping to human genes.
Device Fabrication and Use
Devices were fabricated in-house using previously described methods.(9) Briefly, devices were made from PDMS using photocrosslinked silicon wafers as a mold, and bonded to glass slides. Droplets are generated on-chip from a cell suspension in media and FC-40 oil (3M, Maplewood, Minnesota), creating aqueous-in-oil emulsions. Cell suspensions and oil are delivered to the device using syringe pumps (Harvard Apparatus, Holliston, MA). Droplets are loaded with approximately 1–3 of each cell type on encapsulation and held in a 4000 docking site array for imaging.
Cell Culture
Parental and KLRG1 transfected CHO-K1 cell lines were provided by Abcuro, Inc. Briefly, one stable cell line using CHO-K1 as the host was established to overexpress human KLRG1. The full length cDNA of human KLRG1 was cloned into pLVX plasmid. Then lentivirus production was performed by co-transfecting the pLVX and helper plasmids using commercial packaging kit (Takara, Cat#631275) in 293T cells. Virus supernatant was collected at both 48 h and 72 h after transfection. Then CHO-K1 cells (ATCC) were used for lentivirus infection with polybrene (Sigma, Cat# H9268). After 72 h infection, cells were selected using puromycin(Gibco, Cat#A11138–02) at 8 μg/ml and then FACS sorting were used for single clone selection with high KLRG1 expression level. CHO cells were grown in RPMI 1640 (ATCC) with 10% ultra-low IgG FBS and 1% antibiotic mix (Gibco, Waltham, MA). Peripheral blood NK cells (≥85% CD56 positive) were purchased frozen from Stemcell Technologies and thawed in the same media used for CHO cells, with the addition of 15 ng/mL IL2 (Peprotech). NK cells were rested overnight after thawing prior to running experiments. Human CD57+CD8+ T cells were isolated from isolated human CD8+ T-cells which were in turn isolated from 125 mL leukopaks from healthy human donors utilizing EasySep human CD8+ T cell isolation kit from StemCell. CD57+ CD8+ T cells were further isolated from total isolated CD8+ T cells with CD57 Microbeads, Human from Miltenyi Biotec. Manufacturer’s instructions were followed for the cell isolations. Purities greater than 85% for the three isolated populations were confirmed by flow cytometry. The vast majority of CD57+ CD8+ T cells expressed KLRG1 and hence, they were used as KLRG1+ CD8+ T cells. KLRG1−CD8+ T cells were used also as negative controls. These cells were prepared from total CD8+ T cells stained with anti-KLRG1-PE antibody (BioLegend; ~1 μl/million cells) followed by isolation with Anti-PE MicroBeads UltraPure (Miltenyi Biotec) per manufacturer recommendations, yielding two populations: KLRG1+ CD8+ T cells and KLRG1− CD8+ T cells.
Antibody treatments were added to cell suspensions at 10 μg/mL immediately before droplet generation or at time 0 in plate assays. Anti-KLRG1 antibody Ulviprubart (ABC008) is an afucosylated humanized human IgG1 antibody in clinical trials (10–12) and it was provided by Abcuro, Inc. Afucosylated human IgG1 isotype control, Anti-b-Gal-hIgG1fut from InvivoGen was used for the negative control.
Fluorescent Microscopy
Devices were imaged on an Axio Observer microscope equipped with an automated stage and incubation chamber for maintaining cells at 37°C and 5% CO2. Target cells (CD8+ T cells and CHO-K1) were labeled with calcein AM (Invitrogen, Waltham, MA) as a live stain, and ethidium homodimer III (Biotium, Freemont, CA) was added to droplets to image dead cells. Separate microfluidic devices were loaded for each condition, and imaged at 20X magnification every 15 minutes for 8 + hours. Cell death was determined by a combination of morphology and viability dyes.
Plate Cytotoxicity Assay
NK cells and CHO-K1 cells were prepared as done in the droplet co-encapsulation experiments, described above. CHO cells were labeled with calcein green AM for 30 minutes and washed prior to plate loading. 30,000 target CHO cells were loaded per well, with NK cells loaded at 0:1, 1:1, 2:1 and 5:1 effector to target ratios. Plates were incubated at 37° C and 5% CO2 for 5 hours, then were spun down and supernatant removed to remove calcein released from dead cells. Fluorescence was then measured with a ThermoFisher VarioSkan LUX plate reader using 490 excitation / 520 emission.
Droplet Sorting and Collection
For the droplet sorting experiments, we used our own sorting platform to recognize and sort fluorescent events. This platform utilizes a microfluidic device to generate and sort droplets via diaelectric field. Fluorescence is detected by PMTs (Hamamatsu Photonics, Hamamatsu City, Japan) when droplets reach the sorting junction, which feeds back to a control unit (National Instruments, Austin, TX). Using LabVIEW software (National Instruments), fluorescent signal is continuously monitored, and a threshold is set to determine fluorescent peaks. Peaks over threshold automatically trigger an electric impulse sent to the device, amplified via a high-voltage amplifier (Advanced Energy, Denver, CO), which generates the droplet-sorting field.
For this experiment, calcium signaling was used to sort NK cells activated through fragment crystallizable gamma receptor IIIa (FcgRIIIa) binding. Prior to the experiment, NK cells were incubated for 1 hr with 5 μM Fura-10 AM ratiometric calcium dye (AAT Bioquest, Sunnyvale, CA). KLRG1− CHO-K1 cells were incubated with a-KLRG1 Ab for 30 minutes, and washed twice immediately prior to droplet generation. Droplets were generated on-chip, encapsulating NK and CHO cells at 1:1 and mixing for 1 minute prior to reaching the sorting array. Calcium mobilization occurs upon FcR binding and NK cells with elevated calcium levels produce a fluorescent signal spike, and are automatically sorted based on threshold values set at the start of the experiment. Positively selected and unselected droplets are collected separately, the emulsion is disrupted through gentle pipetting and centrifugation. Cells are then processed with the RNeasy mini kit (QIAGEN, Venlo, Netherlands), and collected RNA is stored at −80°C. RNA is sent to Novogene (Beijing, China) for transcriptomic sequencing and bioinformatic analysis. NK and CHO co-encapsulations were performed in duplicate, and a CHO-alone control sample was additionally sequenced for identification of hamster sequences mapping to human genes.
RESULTS
Bulk and Single-Cell Visualization of Anti-KLRG1 Antibody ADCC Activity
To ensure that the anti-KLRG1 antibody mediated immune cell killing exclusively through KLRG1 binding of the target cell, we utilized CHO-K1 cells transfected to express KLRG1. Expression of KLRG1 was confirmed through flow cytometry, which revealed high levels of consistent expression in transfected CHO cells (Supplemental Fig. 1). We explored 4 conditions in these studies; KLRG1− parental CHO cells and KLRG1+ transfected CHO cells with either the anti-KLRG1 antibody (a-KLRG1 Ab) or an afucosylated isotype-control antibody (Control Ab). NK cells were utilized as the effector cells for all experiments. Antibody concentration ranges to use were determined using titration in bulk culture and measured with flow cytometry (Supplemental Fig. 2). We began with a plate-based cytotoxicity assay to assess the ability of a-KLRG1 Ab to induce ADCC mediated killing at multiple effector-to-target ratios (Fig. 2). We found that the presence of a-KLRG1 Ab increased killing in KLRG1+ CHO at all E:T ratios, but only at the 5:1 ratio was this increase statistically significant. The anti-KLRG1 antibody did not seem to affect the viability of Parental CHO-K1 cells, nor did it have any notable direct effects on KLRG1+ CHO cells in the absence of effector cells (Fig. 2D).
To measure the activity of the a-KLRG1 Ab with higher precision, the cytotoxicity assay was evaluated in the single-cell droplet platform. Fluorescent microscopy, allows the generation of timelapse images to record cytotoxicity and cell-cell contact (Supplemental Movie 1). A minor increase in cytotoxicity observed using KLRG1+ CHO target cells at an E:T of 1:1. (Supplemental Fig. 3A). This NK cytotoxicity was notably higher in the a-KLRG1 Ab treated KLRG1 + CHO cells at a 2:1 E:T ratio (Fig. 3B,D). The anti-KLRG1 antibody again did not appear to impact killing of Parental CHO cells. The 5:1 condition was not repeated in droplets due to volume and encapsulation rate limitations.
We also observed differences in the interaction kinetics between NK and CHO cells across the different conditions. The presence of the a-KLRG1 Ab resulted in a significant increase in contact of NK and high-KLRG1 CHO cells. NK from different donors were used for the KLRG1+ CHO experiments (Supplemental Fig. 3B), which may contribute to differences seen between the different CHO cell populations.
Single-Cell Visualization of Anti-KLRG1 Antibody Activity Towards Human CD8+ T Cells
Next, we studied the ability of the anti-KLRG1 antibody to induce ADCC mediated killing of KLRG1-expressing CD8 + T cells, the target cell of interest for this therapy. CD8+ T cells were sorted based on CD57+ expression, a surface marker strongly co-expressed on a subset of KRLG1+CD8+ T cells. We utilized the single-cell droplet platform for these experiments. Viability of T cells was observed over 2 hours, as these cells were prone to spontaneous death in droplets after this time window. We studied NK cell interactions with CD57+ and KLRG1− CD8+T cells with either the a-KLRG1 Ab or isotype control. We found that the a-KLRG1 Ab elicited a similar response as seen with the CHO cells (Fig. 4), however the best killing was observed at 1:1 E:T ratios. At the 2:1 ratio, killing of CD8+CD57+ T cells increased in both treated and control conditions (Supplemental Fig. 4). Additionally, no differences in contact affinity of NK cells and T cells were observed between control conditions, however a slight increase in contact time was observed in CD8+CD57+ T Cells with a-KLRG1 Ab (Supplemental Fig. 4F).
Based on these data, it appears a single NK cell is sufficient to kill a KRLG1+CD8+ T cell treated with a-KLRG1 Ab. Increased E:T ratios do not improve ADCC mediated cytotoxicity in the droplets, however it does seem to increase spontaneous killing of KLRG1 + T cells, as observed in the isotype control condition (Supplemental Fig. 4). NK cell viability was consistent across all treatment conditions, indicating anti-KLRG1 antibody produces no cytotoxicity towards NK cells themselves (Supplemental Fig. 4E).
Droplet Sorting for Evaluation of Transcriptomic Signatures Influencing NK Cell Activation By Anti-KLRG1 Antibody
To develop an understanding of the cellular factors involved in NK cell response, or lack of response to the a-KLRG1 Ab, we next sought to sort out NK cells based on their FcgRIII binding and response to the a-KLRG1 Ab also bound to KLRG1- expressing cells. To effectively sort NK cells based on recognition of an antibody-labelled cell, they must be combined in droplets and quickly sorted. Our platform has been demonstrated to reliably sort droplets based on fluorescent signal (13). For this study, combined NK cells treated with a ratiometric calcium dye and KLRG1+ CHO cells pre-treated with the a-KLRG1 Ab in droplets. While Ca2+ transport kinetics can be variable, the need for Ca2+ release as part of signaling cascades is highly conserved for receptor function, including FcgRs and several other immune receptors (14). In our Fluorescence-Assisted Droplet Sorting (FADS) protocol, cellular Ca2+ levels create a corresponding change in fluorescent intensity. Basal Ca2+ level differences between cell populations will be observable by the measured fluorescent intensity, and we adjust our sorting thresholds accordingly to remain above cell baseline levels (Supplemental Movie 2). Release of Ca2+ via a cell signaling event produces a dramatic increase in fluorescent intensity, allowing us to visually verify that sorting is based on cell stimulation, presumably through receptor binding. This enables us to reliably sort cells based on the release of Ca2+, despite the mentioned variation across cells and cell types. To apply Ca2+ -based sorting to this, we used NK cells combined with anti-KRLG1 antibody treated KRLG1+ CHO cells in hopes of isolating cells based on Fc receptor binding. The transfected CHO cell was chosen as a target due to its consistent and high expression of KLRG1, increasing likelihood of NK cell interaction. Since this antibody activates ADCC mediated cytoxicity, the first step should be binding of the CD16 Fc gRIIIa receptor to anti-KLRG1 Ab. Successful receptor binding would produce an intracellular calcium release as part of the signaling cascade (14, 15). To ensure calcium peaks are not missed, NK and target cells are combined directly on chip, and given approximately 30 seconds to interact prior to reaching the device’s sorting junction. At this junction, cells expressing increased calcium levels were automatically sorted to one outlet, while all other droplets flowed to a separate outlet, as displayed in Fig. 1B. From the calcium signal positive and negative cells, we isolated mRNA for transcriptomic analysis. CHO cells were also directly submitted for sequencing to screen for any hamster sequences erroneously attributed to human gene hits that may confound our transcriptomic analysis.
Our transcriptomic sequencing found subtle transcriptomic differences between the Calcium-positive (sorted) and negative (unsorted) populations. Overall, 2204 genes were upregulated and 2085 genes were downregulated in the population displaying calcium release (Fig. 5A). When considering the most differentially expressed genes between both populations, many of these genes are involved in mediating transcription and translation (Fig. 5B) (16–19). Of particular interest, CD69 and CD244, which both serve as markers of NK cells activation and can induce cytotoxicity towards target cells, are significantly higher in the Calcium Positive population (16–21). The GO analysis of the most significantly upregulated terms in calcium positive cells included several terms associated with effector cell activity (Fig. 5C). These terms included adaptive immune response, antigen binding and cytokine activity. These terms infer a high level of cytotoxic activity in the calcium positive NK cells. In the GO analysis of genetic factors more highly expressed in the calcium negative population, terms related to metabolic activity, especially oxidative phosphorylation, associated with resting NK cells, were most abundant. The highest expressing genes in the calcium-positive NK cells are listed in Supplemental Table 1.
To further characterize the differences between cells, we looked at differences in individual expression of genes between the two populations, highlighting genes related either to the NK cell calcium signaling pathway, or to NK cell cytotoxic activity. (Fig. 6). All genes presented were confirmed to be negative in the CHO control sample. Due to limited number of replicates, no expression differences were statistically significant between groups, however interested trends in expression levels were observed. Amongst the genes involved in calcium signaling, ZAP70 and SYK were expressed notably higher in the calcium-positive NK cells; these two kinases are known to be associated with signaling through CD16 (FcgRIIIa) in NK cells (22, 23). LCK and FCERG1 expression were also slightly higher in the calcium positive NK cells. S100A4, ADAM17 and CD247 were all more highly expressed in the calcium negative population. CALM1 was also approximately twofold higher in the calcium negative cells.
Of the genes observed involved in NK cell maturity and cytotoxic activity, FCGR3A (CD16) and HAVCR2 were expressed almost 2 fold higher in calcium positive cells, while NCAM1 (CD56) was higher in the calcium negative cells. In addition to the direct effect of CD16 expression on antibody binding, these expression levels indicate a more mature and active phenotype in the calcium positive cells. Interestingly, GZMA and GZMB (granzymes A and B), PRF1 (perforin), FASLG (Fas ligand) and TNFSF10 (TRAIL) were also all more highly expressed in the calcium positive cells. Granzymes and perforin directly are responsible for the target cell killing following CD16 activation, indicating the NK cells producing a calcium response to Ulviprubart would also be more able to kill their target cells (24, 25). KLRC1 (NKG2A) and KLRD1 (CD94) were also both higher in calcium positive cells. Overall, the expression patterns of calcium positive and negative cells coincide with the expectations of NK cells that would or would not activate an ADCC response, supporting the accuracy of the droplet sorting platform.
Bulk and Single-Cell Visualization of Anti-KLRG1 Antibody ADCC Activity
To ensure that the anti-KLRG1 antibody mediated immune cell killing exclusively through KLRG1 binding of the target cell, we utilized CHO-K1 cells transfected to express KLRG1. Expression of KLRG1 was confirmed through flow cytometry, which revealed high levels of consistent expression in transfected CHO cells (Supplemental Fig. 1). We explored 4 conditions in these studies; KLRG1− parental CHO cells and KLRG1+ transfected CHO cells with either the anti-KLRG1 antibody (a-KLRG1 Ab) or an afucosylated isotype-control antibody (Control Ab). NK cells were utilized as the effector cells for all experiments. Antibody concentration ranges to use were determined using titration in bulk culture and measured with flow cytometry (Supplemental Fig. 2). We began with a plate-based cytotoxicity assay to assess the ability of a-KLRG1 Ab to induce ADCC mediated killing at multiple effector-to-target ratios (Fig. 2). We found that the presence of a-KLRG1 Ab increased killing in KLRG1+ CHO at all E:T ratios, but only at the 5:1 ratio was this increase statistically significant. The anti-KLRG1 antibody did not seem to affect the viability of Parental CHO-K1 cells, nor did it have any notable direct effects on KLRG1+ CHO cells in the absence of effector cells (Fig. 2D).
To measure the activity of the a-KLRG1 Ab with higher precision, the cytotoxicity assay was evaluated in the single-cell droplet platform. Fluorescent microscopy, allows the generation of timelapse images to record cytotoxicity and cell-cell contact (Supplemental Movie 1). A minor increase in cytotoxicity observed using KLRG1+ CHO target cells at an E:T of 1:1. (Supplemental Fig. 3A). This NK cytotoxicity was notably higher in the a-KLRG1 Ab treated KLRG1 + CHO cells at a 2:1 E:T ratio (Fig. 3B,D). The anti-KLRG1 antibody again did not appear to impact killing of Parental CHO cells. The 5:1 condition was not repeated in droplets due to volume and encapsulation rate limitations.
We also observed differences in the interaction kinetics between NK and CHO cells across the different conditions. The presence of the a-KLRG1 Ab resulted in a significant increase in contact of NK and high-KLRG1 CHO cells. NK from different donors were used for the KLRG1+ CHO experiments (Supplemental Fig. 3B), which may contribute to differences seen between the different CHO cell populations.
Single-Cell Visualization of Anti-KLRG1 Antibody Activity Towards Human CD8+ T Cells
Next, we studied the ability of the anti-KLRG1 antibody to induce ADCC mediated killing of KLRG1-expressing CD8 + T cells, the target cell of interest for this therapy. CD8+ T cells were sorted based on CD57+ expression, a surface marker strongly co-expressed on a subset of KRLG1+CD8+ T cells. We utilized the single-cell droplet platform for these experiments. Viability of T cells was observed over 2 hours, as these cells were prone to spontaneous death in droplets after this time window. We studied NK cell interactions with CD57+ and KLRG1− CD8+T cells with either the a-KLRG1 Ab or isotype control. We found that the a-KLRG1 Ab elicited a similar response as seen with the CHO cells (Fig. 4), however the best killing was observed at 1:1 E:T ratios. At the 2:1 ratio, killing of CD8+CD57+ T cells increased in both treated and control conditions (Supplemental Fig. 4). Additionally, no differences in contact affinity of NK cells and T cells were observed between control conditions, however a slight increase in contact time was observed in CD8+CD57+ T Cells with a-KLRG1 Ab (Supplemental Fig. 4F).
Based on these data, it appears a single NK cell is sufficient to kill a KRLG1+CD8+ T cell treated with a-KLRG1 Ab. Increased E:T ratios do not improve ADCC mediated cytotoxicity in the droplets, however it does seem to increase spontaneous killing of KLRG1 + T cells, as observed in the isotype control condition (Supplemental Fig. 4). NK cell viability was consistent across all treatment conditions, indicating anti-KLRG1 antibody produces no cytotoxicity towards NK cells themselves (Supplemental Fig. 4E).
Droplet Sorting for Evaluation of Transcriptomic Signatures Influencing NK Cell Activation By Anti-KLRG1 Antibody
To develop an understanding of the cellular factors involved in NK cell response, or lack of response to the a-KLRG1 Ab, we next sought to sort out NK cells based on their FcgRIII binding and response to the a-KLRG1 Ab also bound to KLRG1- expressing cells. To effectively sort NK cells based on recognition of an antibody-labelled cell, they must be combined in droplets and quickly sorted. Our platform has been demonstrated to reliably sort droplets based on fluorescent signal (13). For this study, combined NK cells treated with a ratiometric calcium dye and KLRG1+ CHO cells pre-treated with the a-KLRG1 Ab in droplets. While Ca2+ transport kinetics can be variable, the need for Ca2+ release as part of signaling cascades is highly conserved for receptor function, including FcgRs and several other immune receptors (14). In our Fluorescence-Assisted Droplet Sorting (FADS) protocol, cellular Ca2+ levels create a corresponding change in fluorescent intensity. Basal Ca2+ level differences between cell populations will be observable by the measured fluorescent intensity, and we adjust our sorting thresholds accordingly to remain above cell baseline levels (Supplemental Movie 2). Release of Ca2+ via a cell signaling event produces a dramatic increase in fluorescent intensity, allowing us to visually verify that sorting is based on cell stimulation, presumably through receptor binding. This enables us to reliably sort cells based on the release of Ca2+, despite the mentioned variation across cells and cell types. To apply Ca2+ -based sorting to this, we used NK cells combined with anti-KRLG1 antibody treated KRLG1+ CHO cells in hopes of isolating cells based on Fc receptor binding. The transfected CHO cell was chosen as a target due to its consistent and high expression of KLRG1, increasing likelihood of NK cell interaction. Since this antibody activates ADCC mediated cytoxicity, the first step should be binding of the CD16 Fc gRIIIa receptor to anti-KLRG1 Ab. Successful receptor binding would produce an intracellular calcium release as part of the signaling cascade (14, 15). To ensure calcium peaks are not missed, NK and target cells are combined directly on chip, and given approximately 30 seconds to interact prior to reaching the device’s sorting junction. At this junction, cells expressing increased calcium levels were automatically sorted to one outlet, while all other droplets flowed to a separate outlet, as displayed in Fig. 1B. From the calcium signal positive and negative cells, we isolated mRNA for transcriptomic analysis. CHO cells were also directly submitted for sequencing to screen for any hamster sequences erroneously attributed to human gene hits that may confound our transcriptomic analysis.
Our transcriptomic sequencing found subtle transcriptomic differences between the Calcium-positive (sorted) and negative (unsorted) populations. Overall, 2204 genes were upregulated and 2085 genes were downregulated in the population displaying calcium release (Fig. 5A). When considering the most differentially expressed genes between both populations, many of these genes are involved in mediating transcription and translation (Fig. 5B) (16–19). Of particular interest, CD69 and CD244, which both serve as markers of NK cells activation and can induce cytotoxicity towards target cells, are significantly higher in the Calcium Positive population (16–21). The GO analysis of the most significantly upregulated terms in calcium positive cells included several terms associated with effector cell activity (Fig. 5C). These terms included adaptive immune response, antigen binding and cytokine activity. These terms infer a high level of cytotoxic activity in the calcium positive NK cells. In the GO analysis of genetic factors more highly expressed in the calcium negative population, terms related to metabolic activity, especially oxidative phosphorylation, associated with resting NK cells, were most abundant. The highest expressing genes in the calcium-positive NK cells are listed in Supplemental Table 1.
To further characterize the differences between cells, we looked at differences in individual expression of genes between the two populations, highlighting genes related either to the NK cell calcium signaling pathway, or to NK cell cytotoxic activity. (Fig. 6). All genes presented were confirmed to be negative in the CHO control sample. Due to limited number of replicates, no expression differences were statistically significant between groups, however interested trends in expression levels were observed. Amongst the genes involved in calcium signaling, ZAP70 and SYK were expressed notably higher in the calcium-positive NK cells; these two kinases are known to be associated with signaling through CD16 (FcgRIIIa) in NK cells (22, 23). LCK and FCERG1 expression were also slightly higher in the calcium positive NK cells. S100A4, ADAM17 and CD247 were all more highly expressed in the calcium negative population. CALM1 was also approximately twofold higher in the calcium negative cells.
Of the genes observed involved in NK cell maturity and cytotoxic activity, FCGR3A (CD16) and HAVCR2 were expressed almost 2 fold higher in calcium positive cells, while NCAM1 (CD56) was higher in the calcium negative cells. In addition to the direct effect of CD16 expression on antibody binding, these expression levels indicate a more mature and active phenotype in the calcium positive cells. Interestingly, GZMA and GZMB (granzymes A and B), PRF1 (perforin), FASLG (Fas ligand) and TNFSF10 (TRAIL) were also all more highly expressed in the calcium positive cells. Granzymes and perforin directly are responsible for the target cell killing following CD16 activation, indicating the NK cells producing a calcium response to Ulviprubart would also be more able to kill their target cells (24, 25). KLRC1 (NKG2A) and KLRD1 (CD94) were also both higher in calcium positive cells. Overall, the expression patterns of calcium positive and negative cells coincide with the expectations of NK cells that would or would not activate an ADCC response, supporting the accuracy of the droplet sorting platform.
DISCUSSION
In this study, we have utilized both traditional and novel in vitro methodologies to assess the activity of a cell depleting anti-KRLG1 antibody on NK cell mediated ADCC, as a method to eliminate autoreactive CD8+ T cells. Utilizing a transfected KLRG1+ CHO cell, we first observed the specificity of this antibody for mediating ADCC exclusively through KLRG1 recognition. Our plate assay confirmed cytotoxicity increases were only observed in the KLRG1+ CHO with the anti-KLRG1 Ab (Fig. 2). Additionally, no significant increase in target cell killing by the anti-KLRRG1 Ab was observed in the absence of NK cells, indicating no direct toxicity by the antibody binding (Fig. 2D). These results were also confirmed by a flow cytometry ADCC assay (Supplemental Fig. 2) as well as via our single-cell droplet platform (Fig. 3). Both plate and single-cell assays displayed a significant increase in cytoxicity in higher E:T ratios. Additionally, we observed a significant increase in effector-target contact in the KLRG1+ CHO with the anti-KLRG1 antibody, but not in the KLRG1− CHO (Supplemental Fig. 1). This is presumably due to the high-affinity binding of CD16 to a-KLRG1 Ab.
We next tested this antibody on KLRG1+ versus KLRG1− CD8+ T cells using our single-cell droplet microfluidic array. We observed specific killing only towards the KLRG1 expressing T cells (Fig. 4), as well as an increase in effector-target contact (Supplemental Fig. 4F). We did not observe and increase in killing with increased E:T ratios, which indicates the 1:1 NK to T cell ratio as the most efficient. This result is promising for clinical application, as ratios of NK cells to KLRG1+ CD8+ T cells in circulation are roughly 1:1 or lower in adults (26–28). As with the CHO cells, the KLRG1− T cells were unaffected by the a-KLRG1 Ab, suggesting no spontaneous toxicity. Additionally, the NK cell viability was also unaffected, indicating the antibody does not induce significant levels of fratricide killing between NK cells (Supplemental Fig. 4E).
After establishing the ability of the a-KLRG1 Ab to elicit ADCC towards KLRG1+ cells, we next developed a protocol to sort NK cells based on response to the a-KLRG1 Ab using our droplet sorting platform. NK cells and a-KLRG1 Ab-bound CHO cells were paired on-chip, and a ratiometric calcium dye was utilized to identify cells with increased intracellular calcium. These cells presumably recognized the anti-KLRG1 Ab through CD16 receptor binding, and would be undergoing the first stages of the ADCC response when sorted, although the short time elapsed between receptor activation and collection of RNA probably permitted identification of only transcripts already present in the responding cells or those most rapidly induced after receptor triggering. We collected the sorted cells and used transcriptomic sequencing to validate this method, and potentially uncover genetic factors influencing anti-KLRG1 Ab recognition. Despite the short time lapse between receptor binding/triggering and sample collection we did observe variation between the calcium positive and calcium negative NK cell populations for expression of genes relevant to NK function (Fig. 5). Many of the most significantly different genes between the two populations tend to have inherently high expression levels in mammalian cells and may fluctuate greatly based on cell cycle (Fig. 5B) (16–19). They included various proteins involved in translation and transcription and may have represented the initial steps in transcription/translation associated with FcgR signaling.
To further elucidate the differences between populations, we looked at several genes related to NK cell functionality and calcium signaling (Fig. 6).For the genes related to NK cell functionality, our findings supported droplet sorter’s capability to isolate NK cells based on CD16 activity (Fig. 6A). As expected, the calcium-positive NK cells had higher levels of CD16 (FCGR3A). Lower expression of FCGR3A (CD16) and higher expression of NCAM1 (CD56) in the calcium negative population also suggest that the NK cells that did not recognize anti-KRLG1 antibody seem to possess an immature phenotype (Fig. 6B). Lower expression of HAVCR2 (TIM3) further supports this observation, suggesting either an immature or downregulated phenotype (29, 30). Higher expression of CD244 and CD69 (Fig. 5B) also support a more active phenotype in the calcium positive NK group (20, 21). Of the genes associated with the calcium cascade (Fig. 6A), ZAP70 and SYK had the most significant increase in expression in the calcium positive NK. Zap70 and Syk levels have been found to be highly correlated to NK cell activity and are triggered after CD16 activation (22, 23, 31, 32). Coinciding with this, Lck, which also had higher expression in the calcium positive cells, promotes Zap70 signaling, further reinforcing the importance of this pathway in the response of these NK cells to a-KLRG1 Ab binding (33). The integral membrane proteins FcεR1γ and CD247 are also important for NK cell calcium signaling, as they bind to CD16 and stabilize its activation (34). Higher expression of FCER1G in the calcium positive cells and minimal difference in CD247 expression indicate FcεR1γ, associated with NK cells with strong cytotoxic effector function is also a key factor in the response to anti-KLRG1 antibody (35). ADAM17, S100A4 and Calmodulin are more highly expressed in the calcium negative NK population Calmodulin acts downstream of calcium signaling, responding to calcium concentrations to mediate further downstream signaling (36). S100A4 has been shown to attenuate signaling by binding CD16, and ADAM17 induces shedding of the CD16 receptor from the plasma membrane (24, 32). Therefore, the increased expression of these factors provides logical mechanisms reducing the NK cell response to the a-KLRG1 Ab in this population. This finding provides three potential characteristics of NK cells that will not respond to monoclonal antibody treatments through ADCC. In addition to observing genes that may influence the ability of an NK cell to respond to the a-KLRG1 Ab, we also observed variation in the expression of several genes related to cytotoxicity (Fig. 6B). Higher expression of GZMA, GZMB and PRF1 suggest the calcium positive NK cells will be highly cytotoxic after CD16 activation (24, 37). Additionally, increased expression of the CD16-independent apoptosis-inducing ligands FASLG (Fas ligand) and TNFSF10 (TRAIL) in the calcium positive NK cell population suggest they may have additional mechanisms available to kill target cells, promoted by the increased contact duration observed with anti-KLRG1 Ab treatment (Supplemental Figs. 2, 4).
To summarize, we observed consistent specificity of the anti-KLRG1 Ab for mediating elimination of target cells based on KLRG1 expression, with no direct toxicity in the absence of NK cells. We noted more significant effects in our single-cell platform than in a traditional plate assay, which correlates more accurately to the clinical expectations of Ulviprubart (10–12). These findings support the use of our single-cell droplet observation platform for sensitive and accurate analysis of treatment efficacy. We additionally developed a method for screening NK cells based on their ability to respond to antibody therapies by implementing our fluorescence-assisted droplet sorting platform with a fluorescent calcium assay, to sort NK cells based on activation of the CD16 receptor. Traditional cell-sorting approaches, such as flow cytometry-based sorting, are unable to sort based cells based on their functional interactions on another cell type. Droplets provide an optimal alternative due to their ability to encapsulate two cell types, and sort them based on fluorescent signal of either cell. Additionally, calcium release rapidly peaks and dissipates in a cell, requiring a platform that can both combine and sort to isolate cells based on calcium signaling (14, 38). This same methodology could be used to screen cells based on activation of other immune receptors, such as T cell receptors and chimeric antigen receptors (39, 40). Utilizing transcriptomic sequencing, the results reinforced the accuracy of this novel calcium-based droplet sorting assay by displaying functionally relevant phenotypic differences between sorted NK cell populations. We also presented several transcriptomic variations between the two populations suggesting potential genes influencing the CD16 response to this antibody. This study supports the efficacy of an anti-KLRG1 antibody for suppression of autoreactive CD8+ T cells with unique observations through our combined platform and presents a methodology for detailed in vitro screening of antibody treatments and other immunotherapies. |
Title: Bioinformatics analysis of oxidative stress genes in the pathogenesis of ulcerative colitis based on a competing endogenous RNA regulatory network | Body:
Introduction
Inflammatory bowel disease, which is a kind of autoimmune disease, mainly includes two subtypes: ulcerative colitis (UC) and Crohn’s disease (CD) (Ng et al., 2017). In CD, all layers of the bowel wall are inflamed; in contrast, UC is generally characterized by mucosal layer inflammation and damage to the superficial bowel wall (Hibi & Ogata, 2006; Kobayashi et al., 2020). The disease course of UC usually involves remission and exacerbation in alternating cycles, and if treatment is not performed in a timely, colorectal cancer can develop (Ungaro et al., 2017; Lissner & Siegmund, 2013). From the year 1955, glucocorticoids have been considered effective for patients with UC, and the use of glucocorticoids dramatically decreases the mortality of patients with moderate-to-severe UC (Nakase et al., 2021). However, long-term use of corticosteroids not only induces glucocorticoid-resistance but also leads to numerous adverse effects such as depression, cataracts, and osteoporosis (Nakase et al., 2021; Magro et al., 2017; Truelove & Witts, 1955). Therefore, elucidating the pathogenesis of the initiation and progression of UC is imperative to develop novel therapies.
The development of UC involves multiple mechanisms, and oxidative stress caused by the imbalance between antioxidants and oxidants plays a vital role (Grisham, 1994). The presence of excessive reactive oxygen species (ROS) including peroxynitrite, hydrogen peroxide, and superoxide can reduce the productions of endogenous antioxidants, eventually resulting in cell death via oxidative damage to DNA, membrane lipids, and cellular proteins (Amirshahrokhi, Bohlooli & Chinifroush, 2011; Ferrat et al., 2019; Niu et al., 2015). The reaction of DNA with ROS leads to the modification of DNA bases and contributes to subsequent carcinogenesis. For example, ROS may interact with genomic DNA to generate some base modifications with pro-mutagenic potentials such as 8-nitro-2′-deoxyguanosine (8-NO2-dG) and 8-oxo-7,8-dihydro-2′-deoxyguanosine (8-oxodG) (Kaneko et al., 2008; Kondo et al., 1999). One previous study demonstrated excessive production of 8-oxodG in patients with UC-associated carcinogenesis (Gushima et al., 2009). This series of genetic changes serves as a trigger in the pathogenesis of chronic inflammation-associated diseases. Other studies have demonstrated local DNA damage in the colon and systemic DNA damage involving the hepatocytes, lymphoid organs, and blood in mice with dextran sulfate sodium (DSS)-induced UC, and these outcomes are considered to be partly mediated by oxidative stress (Westbrook et al., 2011, 2009; Trivedi & Jena, 2012). In addition, the expression of some inflammatory genes, such as TNF-α, is reported to be regulated by oxidative stress-associated genes, and the application of TNF-α inhibitors has been proven to be effective for the treatment of UC (Verhasselt, Goldman & Willems, 1998; Barrie & Regueiro, 2007). Therefore, identifying oxidative stress-associated genes and establishing a direct linkage between these genes and UC pathogenesis may be helpful for the treatment of patients with UC.
Evidence is now emerging to indicate that non-coding RNAs play vital roles in inflammatory bowel disease, especially in the progression of UC (Ghafouri-Fard, Eghtedarian & Taheri, 2020; Schaefer, 2016). Non-coding RNAs are RNAs that are unable to code proteins and can regulate multiple biological processes through modulating the expression of coding RNAs (Quinn & Chang, 2016; Schmitz, Grote & Herrmann, 2016; Matsui & Corey, 2017). There are mainly four types of non-coding RNAs: long non-coding RNAs (lncRNAs), microRNAs (miRNAs), circular RNAs (circRNAs), and extracellular RNAs (exRNAs) (St Laurent, Wahlestedt & Kapranov, 2015; Ling, Fabbri & Calin, 2013; Sato-Kuwabara et al., 2015; Ebbesen, Kjems & Hansen, 2016). Salmena et al. (2011) proposed the notion of a competing endogenous RNA (ceRNA) regulatory network that involves interactions among these RNAs. lncRNAs are RNA molecules that can affect the transcriptional and post-transcriptional expression of genes (Quinn & Chang, 2016; Schmitz, Grote & Herrmann, 2016). Increasing evidence has demonstrated that lncRNAs have several molecular functions, such as being involved in regulatory transcription and acting as miRNA sponges and regulatory RNA binding proteins (Jain et al., 2017; Zacharopoulou et al., 2017; Khan et al., 2022). In general, lncRNAs can sponge miRNA through miRNA response elements, which eventually affect the binding between miRNAs and messenger RNAs (mRNAs) (Zhang et al., 2019; Ala, 2020). lncRNAs have also been reported to be involved in regulating several kinds of human diseases including neurological diseases, cancers, cardiovascular diseases, and inflammatory bowel disease (Ghafouri-Fard, Eghtedarian & Taheri, 2020; Canseco-Rodriguez et al., 2022; Chi et al., 2019; Poller et al., 2018). Furthermore, a lncRNA-related ceRNA network has been identified as a key mechanism involved in immune-related diseases including systemic lupus erythematosus (Song et al., 2021) and rheumatoid arthritis (Zhang et al., 2020). In addition, Dong et al. (2022) constructed a lncRNA-related ceRNA network in UC and identified two mRNAs (CTLA1 and STAT1) that are associated with immune cell infiltration. However, research on oxidative stress genes underlying the lncRNA-related ceRNA network in UC is still in the preliminary stages.
In the current study, the original data from the UC group and control group were obtained from the NCBI Gene Expression Omnibus (GEO) database. Based on an analysis of differential gene expression, differentially expressed (DE)-lncRNAs, DE-miRNAs, and DE-mRNAs were identified. This study aimed to probe a complete lncRNA-miRNA-mRNA network to determine the roles of oxidative stress genes in the pathogenesis and drug resistance mechanism of UC. Subsequently, a DSS-induced mouse model was established to validate the results of the bioinformatics analysis. These findings clarified the relationships among the identified oxidative stress genes and the pathogenesis and drug resistance mechanism in UC, providing some references for the clinical diagnosis and treatment of UC.
Materials and Methods
Data resource
A total of three datasets were downloaded from the GEO database (http://www.ncbi.nlm.nih.gov/geo). In detail, the lncRNA/mRNA expression profile is GSE75214 (https://doi.org/10.6084/m9.figshare.25263436.v1. A-B), the miRNA/mRNA expression profile is GSE48959 (https://doi.org/10.6084/m9.figshare.25263436.v1. C-D), and the mRNA expression profile of glucocorticoid-resistant genes is GSE114603 (https://doi.org/10.6084/m9.figshare.25263436.v1. E). The clinical information of patients with UC from the GEO database is shown in Table S1. The software package is available on GitHub at https://github.com/Yubinnet/UC. A flowchart of the bioinformatics analysis is depicted in Fig. 1, among which GSE75214 dataset includes the lncRNA/mRNA expression profile, and GSE48959 dataset includes the miRNA/mRNA expression profile. Patient consent was not required for this work because all the datasets originated from a free open-access database on the internet.
10.7717/peerj.17213/fig-1
Figure 1
Flow chart of the overall analysis.
Identification of DE-lncRNAs, DE-miRNAs, and DE-mRNAs
The factoextra package of R (https://github.com/kassambara/factoextra) was utilized to perform a principal component analysis (PCA) of gene expression using the fviz_pca_ind function. We then integrated the analysis of lncRNA/mRNA and miRNA/mRNA and used the limma package of R to screen DE-lncRNAs, DE-miRNAs, and DE-mRNAs between UC and control samples. We set lncRNA (P < 0.01, |log2(fold change)| > 0.58), miRNA (P < 0.05, |log2(fold change)| > 0.58), and mRNA (P < 0.05, |log2(fold change)| > 0.58) as the cutoff point to select DE-lncRNAs, DE-miRNAs, and DE-mRNAs, respectively.
Construction of ceRNA network
First, we used the cor.test function of R to calculate the Spearman correlation between DE-lncRNAs and DE-mRNAs according to their expression levels. The Benjamin & Hochberg method was used to calculate the false discovery rate (FDR). An FDR < 0.05 plus | r | > 0.2 was used to determine significant association pairs of lncRNA-mRNA. Based on miRDB (http://mirdb.org/index.html), miRTarBase (https://mirtarbase.cuhk.edu.cn/~miRTarBase/miRTarBase_2022/php/index.php), and TargetScan (https://www.targetscan.org/vert_80/), each DEmiRNA’s predicted mRNA was obtained. Then, based on the expression status of DEmiRNAs and DEmRNAs between UC and control samples, miRNA-mRNA association pairs were selected for subsequent analysis. Finally, the intersections of lncRNA-mRNA association pairs with miRNA-mRNA association pairs were obtained to construct the lncRNA-miRNA-mRNA ceRNA network, which was visualized using Cytoscape version 3.8.1 (https://cytoscape.org/) (Shannon et al., 2003).
Identification of oxidative stress-associated genes
We searched the Molecular Signatures Database (MSigDB, v7.4) (http://software.broadinstitute.org/gsea/msigdb) using the search term “oxidative stress” and selected all the obtained genes as oxidative stress-related gene sets (https://doi.org/10.6084/m9.figshare.25263436.v1. F). Considering the relationship between oxidative stress-associated genes and inflammatory processes, we further explored the relationship between oxidative stress-associated genes and the pathogenesis of UC. Based on the R package ConsensusClusterPlus (http://bioconductor.org/packages/release/bioc/html/ConsensusClusterPlus.html), we conducted a consistency cluster analysis for UC samples, with the following parameters: maxK = 10 (maximum cluster number to evaluate) and reps = 100 (number of samples). An appropriate number of clusters was selected to verify the classification reliability of different subtypes of UC (active UC and inactive UC). Then, we conducted enrichment analysis on whether UC patients are in active phase and UC subtypes (fisher. test). To acquire representative genes of UC subtypes, we used the limma package of R to analyze the differences between a particular subtype sample and other subtype samples. Only genes that were highly expressed in this subtype (log2(fold change) > 1, P < 0.05) were selected as representative genes of this subtype. Then, we used cor.test to calculate the Spearman correlation coefficient between the representative genes of each subtype and oxidative stress-associated genes in UC samples. Based on the parameters of an FDR < 0.05 and | R | > 0.8, the representative genes that highly interacted with oxidative stress-associated genes in each subtype were selected. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed for functional annotation and enrichment analyses. A P value < 0.05 was regarded as a statistically significant difference.
Prediction of drug interactions
Based on the drug gene interaction database DGIdb (https://www.dgidb.org/search_interactions), drugs that interact with oxidative stress-associated genes were further explored.
Animals and treatment
This study was performed in accordance with The ARRIVE Guidelines 2.0: Updated Guidelines for Reporting Animal Research. A total of 28 6-week-old C57BL/6J mice weighing 18–20 g were obtained from Pengyue Laboratory Animal Breeding Co., Ltd. (Jinan, China). The mice were maintained in a specific pathogen-free animal laboratory and housed using standard cages in a room with a humidity of 50 ± 20%, a temperature of 23 ± 3 °C, and a 12 h light/12 h dark cycle. All animals had free access to standard laboratory food and water. They were separated into four groups at random with seven mice in each group: control, DSS, DSS + normal saline (NS), and DSS + dexamethasone (DXM) groups. The control group received 50 μL of NS via intraperitoneal injection for 7 days. The DSS group received 3.5% weight/volume of DSS via intraperitoneal injection (50 μL; MP Biomedicals, Santa Ana, CA, USA) for 7 days. The DSS + NS group received an intraperitoneal injection of DSS (50 μL) for 7 days and then 50 μL of NS for 7 days. The DSS + DXM group received an intraperitoneal injection of DSS (50 μL) for 7 days and then 1.2 mg/kg of DXM (Sigma, St. Louis, MO, USA) for 7 days. All experimental procedures were conducted in compliance with the Guidelines for Care and Use of Laboratory Animals of the National Institutes of Health and approved by the Ethics Committee of Jining Medical University (approval number: JNMC-2023-DW-090).
Histological examination
After administration, mice in the different groups were anesthetized with an intraperitoneal injection of 5% pentobarbital (0.2 mL/10 g) and sacrificed by cervical dislocation. The colonic tissues were collected for hematoxylin & eosin (HE) staining via a commercial kit (Beyotime, Shanghai, China). The colon samples were fixed, dehydrated, and embedded in paraffin. After cutting into 4-μm-thick slices, dewaxing, and rehydration, the slices were prepared for HE staining. A histological examination of the colonic tissues was performed using a light microscope (OLYMPUS, Tokyo, Japan), with a magnification of ×100.
Total RNA isolation and qRT-PCR
qRT-PCR was performed in accordance with The Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines. A Total RNA Extraction Kit (Promega, Madison, WI, USA) was used to isolate the total RNA from the colonic tissue samples (50 mg) from the control, DSS, DSS + NS, and DSS + DXM groups (n = 7). RNA purity was measured using NanoDrop (Peqlab Biotechnologie GmbH, Erlangen, Germany). The OD260/280 ratio was used as an indicator for RNA purity. A ratio higher than 1.8 was regarded as suitable for gene expression measurements. The GoScript reverse transcription system (Promega Corporation, Madison, WI, USA) was used to reverse transcribe the extracted RNA (1 µg) into cDNA at 42 °C for 45 min. With the aid of Hifair® II 1st Strand cDNA Synthesis SuperMix (Yeasen Biotechnology, Shanghai, China) and Hieff® qPCR SYBR Green Master Mix (Yeasen Biotechnology, Shanghai, China), qRT-PCR analysis was performed on an ABI 7900 Real-Time PCR System (Applied Biosystems, Foster City, CA, USA). The thermocycling conditions were as follows: initial denaturation for 10 min at 95 °C; 40 cycles of 95 °C for 15 s and 60 °C for 30 s; and final extension for 1 min at 60 °C. Gene expression levels were calculated with the 2−ΔΔCt approach with GAPDH or U6 for normalization. We designed the primers using Primer-BLAST (http://www.ncbi.nlm.nih.gov/tools/primer-blast). MFEprimer-2.0 (https://www.mfeprimer.com/old-versions/mfeprimer-2.0/) was used to perform specificity checking. The primers utilized in this work are shown in Table 1.
10.7717/peerj.17213/table-1
Table 1
Real-time PCR primer synthesis list.
Gene
Sequences
COX-2
Forward
5′-AAGACTACGTGCAACACCTGAG-3′
Reverse
5′-GTGCCAGTGATAGAGTGTGT-3′
SOCS3
Forward
5′-GGACCAAGAACCTACGCATCCA-3′
Reverse
5′-CACCAGCTTGAGTACACAGTCG-3′
IL-6
Forward
5′-GACAAAGCCAGAGTCCTTCAGAGA-3′
Reverse
5′-CTAGGTTTGCCGAGTAGATCTC-3′
TLR4
Forward
5′-TCCACTGGTTGCAGAAAATGC-3′
Reverse
5′-TCATCAGGGACTTTGCTGAGTTT-3′
TNF-α
Forward
5′-CCCTCACACTCACAAACCAC-3′
Reverse
5′-ACAAGGTACAACCCATCGGC-3′
IL-1β
Forward
5′-TGGACCTTCCAGGATGAGGACA-3′
Reverse
5′-GTTCATCTCGGAGCCTGTAGTG-3′
Foxp3
Forward
5′-TTCGCCTACTTCAGAAACCACC-3′
Reverse
5′-ATTCATCTACGGTCCACACTGCT-3′
Cav1
Forward
5′-ACGTAGACTCCGAGGGACATC-3′
Reverse
5′-CGTCGTCGTTGAGATGCTTG-3′
Slc7a5
Forward
5′-ATGGAGTGTGGCATTGGCTT-3′
Reverse
5′-GAGCACCGTCACAGAGAAGAT-3′
IL-17
Forward
5′-TGACCCCTAAGAAACCCCCA-3′
Reverse
5′-TCATTGTGGAGGGCAGACAA-3′
Slc7a11
Forward
5′-TCCTCTGACGATGGTGATGC-3′
Reverse
5′-GCTGAATGGGTCCGAGTAAAG-3′
MPO
Forward
5′-CGTGTCAAGTGGCTGTGCCTAT-3′
Reverse
5′-AACCAGCGTACAAAGGCACGGT-3′
GAPDH
Forward
5′-GGCATGGACTGTGGTCATGAG-3′
Reverse
5′-TGCACCACCAACTGCTTAGC-3′
Myeloperoxidase (MPO) activity
MPO activity was measured using a sandwich enzyme immunoassay commercial kit (BIOXYTECH® MPO-EIATM, OXIS Health Products; Inc Oxis-International, Portland, OR, USA), according to the manufacturer’s instructions.
Detection of the levels of GSH, GSSG, ROS, SOD, and HMGB-1
According to the instructions of the Reduced Glutathione (GSH) Content Assay Kit (Sangon Biotech, Shanghai, China), Oxidized Glutathione (GSSG) Assay Kit (Sangon Biotech, Shanghai, China), Reactive Oxygen Species (ROS) Assay Kit (Beyotime, Beijing, China), Superoxide Dismutase (SOD) Activity Assay Kit (Sangon Biotech, Shanghai, China), and Mouse HMGB-1 ELISA Kit (Sangon Biotech, Shanghai, China), the levels of GSH, GSSG, SOD, and HMGB-1, respectively, were determined.
Flow cytometry analysis of ROS
The level of ROS was assessed using flow cytometry analysis, employing a ROS Detection Kit based on a DCFH-DA probe (Beyotime, Beijing, China). Fresh colonic tissues were immediately placed in precooled PBS to clean them from blood and other pollutants. The tissues were dissected into small fragments of approximately 1 mm3 using ophthalmic scissors, and they were subsequently immersed in precooled PBS and thoroughly rinsed to eliminate any residual cellular debris. The appropriate volume of enzyme digestion solution was added, and the cells were digested at 37 °C for 30 min. Intermittent agitation was performed during the digestion process. The digestion was terminated using PBS, followed by removal of the tissue mass through filtration with a 300-mesh nylon mesh. The filtered cells were collected and centrifuged at 500 g for 10 min. The supernatant was decanted, and the precipitate was washed twice with PBS. A solution of DCFH-DA at a final concentration of 10 μM was prepared by diluting it with FBS-free medium. After harvesting and washing the cells twice with PBS, they (1 × 106) were incubated with 500 μL of 10 μM DCFH-DA for 20 min at 37 °C. The cells were subsequently washed three times with FBS-free medium. Finally, detection and analysis of cellular ROS levels were performed using a FACScan flow cytometer (BD Biosciences, Franklin Lakes, NJ, USA) equipped with an excitation wavelength of 488 nm.
Western blotting
Total proteins were extracted from colonic tissues using RIPA lysis buffer (Beyotime, Beijing, China), and their concentrations were measured with a BCA Kit (Beyotime, Beijing, China). The collected proteins were then separated by 10% SDS polyacrylamide gel electrophoresis and transferred onto a PVDF membrane. The membrane was blocked with nonfat milk (5%), on which the primary antibodies NF-κB (1:1,000; Abcam, Cambridge, UK), IκB (1:1,000; Abcam, Cambridge, UK), IL-6 (1:1,000; Abcam, Cambridge, UK), TNF-α (1:1,000; Abcam, Cambridge, UK), and GAPDH (1:1,000; Abcam, Cambridge, UK), and the corresponding secondary antibody (1:3,000; Abcam, Cambridge, UK) were incubated. The signals were identified using an ECL Kit (Beyotime, Beijing, China), and immunoblots were quantified with Alpha Innotech software (Alpha Innotech, San Leandro, CA, USA). GAPDH was the normalization for proteins.
Statistical analysis
The variations among the data were assessed by applying one-way ANOVA followed by Tukey’s multiple comparison test. Data analysis was performed in SPSS software v22.0. The data are reported as the mean ± standard deviation (SD). P < 0.05 was considered statistically significant.
Data resource
A total of three datasets were downloaded from the GEO database (http://www.ncbi.nlm.nih.gov/geo). In detail, the lncRNA/mRNA expression profile is GSE75214 (https://doi.org/10.6084/m9.figshare.25263436.v1. A-B), the miRNA/mRNA expression profile is GSE48959 (https://doi.org/10.6084/m9.figshare.25263436.v1. C-D), and the mRNA expression profile of glucocorticoid-resistant genes is GSE114603 (https://doi.org/10.6084/m9.figshare.25263436.v1. E). The clinical information of patients with UC from the GEO database is shown in Table S1. The software package is available on GitHub at https://github.com/Yubinnet/UC. A flowchart of the bioinformatics analysis is depicted in Fig. 1, among which GSE75214 dataset includes the lncRNA/mRNA expression profile, and GSE48959 dataset includes the miRNA/mRNA expression profile. Patient consent was not required for this work because all the datasets originated from a free open-access database on the internet.
10.7717/peerj.17213/fig-1
Figure 1
Flow chart of the overall analysis.
Identification of DE-lncRNAs, DE-miRNAs, and DE-mRNAs
The factoextra package of R (https://github.com/kassambara/factoextra) was utilized to perform a principal component analysis (PCA) of gene expression using the fviz_pca_ind function. We then integrated the analysis of lncRNA/mRNA and miRNA/mRNA and used the limma package of R to screen DE-lncRNAs, DE-miRNAs, and DE-mRNAs between UC and control samples. We set lncRNA (P < 0.01, |log2(fold change)| > 0.58), miRNA (P < 0.05, |log2(fold change)| > 0.58), and mRNA (P < 0.05, |log2(fold change)| > 0.58) as the cutoff point to select DE-lncRNAs, DE-miRNAs, and DE-mRNAs, respectively.
Construction of ceRNA network
First, we used the cor.test function of R to calculate the Spearman correlation between DE-lncRNAs and DE-mRNAs according to their expression levels. The Benjamin & Hochberg method was used to calculate the false discovery rate (FDR). An FDR < 0.05 plus | r | > 0.2 was used to determine significant association pairs of lncRNA-mRNA. Based on miRDB (http://mirdb.org/index.html), miRTarBase (https://mirtarbase.cuhk.edu.cn/~miRTarBase/miRTarBase_2022/php/index.php), and TargetScan (https://www.targetscan.org/vert_80/), each DEmiRNA’s predicted mRNA was obtained. Then, based on the expression status of DEmiRNAs and DEmRNAs between UC and control samples, miRNA-mRNA association pairs were selected for subsequent analysis. Finally, the intersections of lncRNA-mRNA association pairs with miRNA-mRNA association pairs were obtained to construct the lncRNA-miRNA-mRNA ceRNA network, which was visualized using Cytoscape version 3.8.1 (https://cytoscape.org/) (Shannon et al., 2003).
Identification of oxidative stress-associated genes
We searched the Molecular Signatures Database (MSigDB, v7.4) (http://software.broadinstitute.org/gsea/msigdb) using the search term “oxidative stress” and selected all the obtained genes as oxidative stress-related gene sets (https://doi.org/10.6084/m9.figshare.25263436.v1. F). Considering the relationship between oxidative stress-associated genes and inflammatory processes, we further explored the relationship between oxidative stress-associated genes and the pathogenesis of UC. Based on the R package ConsensusClusterPlus (http://bioconductor.org/packages/release/bioc/html/ConsensusClusterPlus.html), we conducted a consistency cluster analysis for UC samples, with the following parameters: maxK = 10 (maximum cluster number to evaluate) and reps = 100 (number of samples). An appropriate number of clusters was selected to verify the classification reliability of different subtypes of UC (active UC and inactive UC). Then, we conducted enrichment analysis on whether UC patients are in active phase and UC subtypes (fisher. test). To acquire representative genes of UC subtypes, we used the limma package of R to analyze the differences between a particular subtype sample and other subtype samples. Only genes that were highly expressed in this subtype (log2(fold change) > 1, P < 0.05) were selected as representative genes of this subtype. Then, we used cor.test to calculate the Spearman correlation coefficient between the representative genes of each subtype and oxidative stress-associated genes in UC samples. Based on the parameters of an FDR < 0.05 and | R | > 0.8, the representative genes that highly interacted with oxidative stress-associated genes in each subtype were selected. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed for functional annotation and enrichment analyses. A P value < 0.05 was regarded as a statistically significant difference.
Prediction of drug interactions
Based on the drug gene interaction database DGIdb (https://www.dgidb.org/search_interactions), drugs that interact with oxidative stress-associated genes were further explored.
Animals and treatment
This study was performed in accordance with The ARRIVE Guidelines 2.0: Updated Guidelines for Reporting Animal Research. A total of 28 6-week-old C57BL/6J mice weighing 18–20 g were obtained from Pengyue Laboratory Animal Breeding Co., Ltd. (Jinan, China). The mice were maintained in a specific pathogen-free animal laboratory and housed using standard cages in a room with a humidity of 50 ± 20%, a temperature of 23 ± 3 °C, and a 12 h light/12 h dark cycle. All animals had free access to standard laboratory food and water. They were separated into four groups at random with seven mice in each group: control, DSS, DSS + normal saline (NS), and DSS + dexamethasone (DXM) groups. The control group received 50 μL of NS via intraperitoneal injection for 7 days. The DSS group received 3.5% weight/volume of DSS via intraperitoneal injection (50 μL; MP Biomedicals, Santa Ana, CA, USA) for 7 days. The DSS + NS group received an intraperitoneal injection of DSS (50 μL) for 7 days and then 50 μL of NS for 7 days. The DSS + DXM group received an intraperitoneal injection of DSS (50 μL) for 7 days and then 1.2 mg/kg of DXM (Sigma, St. Louis, MO, USA) for 7 days. All experimental procedures were conducted in compliance with the Guidelines for Care and Use of Laboratory Animals of the National Institutes of Health and approved by the Ethics Committee of Jining Medical University (approval number: JNMC-2023-DW-090).
Histological examination
After administration, mice in the different groups were anesthetized with an intraperitoneal injection of 5% pentobarbital (0.2 mL/10 g) and sacrificed by cervical dislocation. The colonic tissues were collected for hematoxylin & eosin (HE) staining via a commercial kit (Beyotime, Shanghai, China). The colon samples were fixed, dehydrated, and embedded in paraffin. After cutting into 4-μm-thick slices, dewaxing, and rehydration, the slices were prepared for HE staining. A histological examination of the colonic tissues was performed using a light microscope (OLYMPUS, Tokyo, Japan), with a magnification of ×100.
Total RNA isolation and qRT-PCR
qRT-PCR was performed in accordance with The Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines. A Total RNA Extraction Kit (Promega, Madison, WI, USA) was used to isolate the total RNA from the colonic tissue samples (50 mg) from the control, DSS, DSS + NS, and DSS + DXM groups (n = 7). RNA purity was measured using NanoDrop (Peqlab Biotechnologie GmbH, Erlangen, Germany). The OD260/280 ratio was used as an indicator for RNA purity. A ratio higher than 1.8 was regarded as suitable for gene expression measurements. The GoScript reverse transcription system (Promega Corporation, Madison, WI, USA) was used to reverse transcribe the extracted RNA (1 µg) into cDNA at 42 °C for 45 min. With the aid of Hifair® II 1st Strand cDNA Synthesis SuperMix (Yeasen Biotechnology, Shanghai, China) and Hieff® qPCR SYBR Green Master Mix (Yeasen Biotechnology, Shanghai, China), qRT-PCR analysis was performed on an ABI 7900 Real-Time PCR System (Applied Biosystems, Foster City, CA, USA). The thermocycling conditions were as follows: initial denaturation for 10 min at 95 °C; 40 cycles of 95 °C for 15 s and 60 °C for 30 s; and final extension for 1 min at 60 °C. Gene expression levels were calculated with the 2−ΔΔCt approach with GAPDH or U6 for normalization. We designed the primers using Primer-BLAST (http://www.ncbi.nlm.nih.gov/tools/primer-blast). MFEprimer-2.0 (https://www.mfeprimer.com/old-versions/mfeprimer-2.0/) was used to perform specificity checking. The primers utilized in this work are shown in Table 1.
10.7717/peerj.17213/table-1
Table 1
Real-time PCR primer synthesis list.
Gene
Sequences
COX-2
Forward
5′-AAGACTACGTGCAACACCTGAG-3′
Reverse
5′-GTGCCAGTGATAGAGTGTGT-3′
SOCS3
Forward
5′-GGACCAAGAACCTACGCATCCA-3′
Reverse
5′-CACCAGCTTGAGTACACAGTCG-3′
IL-6
Forward
5′-GACAAAGCCAGAGTCCTTCAGAGA-3′
Reverse
5′-CTAGGTTTGCCGAGTAGATCTC-3′
TLR4
Forward
5′-TCCACTGGTTGCAGAAAATGC-3′
Reverse
5′-TCATCAGGGACTTTGCTGAGTTT-3′
TNF-α
Forward
5′-CCCTCACACTCACAAACCAC-3′
Reverse
5′-ACAAGGTACAACCCATCGGC-3′
IL-1β
Forward
5′-TGGACCTTCCAGGATGAGGACA-3′
Reverse
5′-GTTCATCTCGGAGCCTGTAGTG-3′
Foxp3
Forward
5′-TTCGCCTACTTCAGAAACCACC-3′
Reverse
5′-ATTCATCTACGGTCCACACTGCT-3′
Cav1
Forward
5′-ACGTAGACTCCGAGGGACATC-3′
Reverse
5′-CGTCGTCGTTGAGATGCTTG-3′
Slc7a5
Forward
5′-ATGGAGTGTGGCATTGGCTT-3′
Reverse
5′-GAGCACCGTCACAGAGAAGAT-3′
IL-17
Forward
5′-TGACCCCTAAGAAACCCCCA-3′
Reverse
5′-TCATTGTGGAGGGCAGACAA-3′
Slc7a11
Forward
5′-TCCTCTGACGATGGTGATGC-3′
Reverse
5′-GCTGAATGGGTCCGAGTAAAG-3′
MPO
Forward
5′-CGTGTCAAGTGGCTGTGCCTAT-3′
Reverse
5′-AACCAGCGTACAAAGGCACGGT-3′
GAPDH
Forward
5′-GGCATGGACTGTGGTCATGAG-3′
Reverse
5′-TGCACCACCAACTGCTTAGC-3′
Myeloperoxidase (MPO) activity
MPO activity was measured using a sandwich enzyme immunoassay commercial kit (BIOXYTECH® MPO-EIATM, OXIS Health Products; Inc Oxis-International, Portland, OR, USA), according to the manufacturer’s instructions.
Detection of the levels of GSH, GSSG, ROS, SOD, and HMGB-1
According to the instructions of the Reduced Glutathione (GSH) Content Assay Kit (Sangon Biotech, Shanghai, China), Oxidized Glutathione (GSSG) Assay Kit (Sangon Biotech, Shanghai, China), Reactive Oxygen Species (ROS) Assay Kit (Beyotime, Beijing, China), Superoxide Dismutase (SOD) Activity Assay Kit (Sangon Biotech, Shanghai, China), and Mouse HMGB-1 ELISA Kit (Sangon Biotech, Shanghai, China), the levels of GSH, GSSG, SOD, and HMGB-1, respectively, were determined.
Flow cytometry analysis of ROS
The level of ROS was assessed using flow cytometry analysis, employing a ROS Detection Kit based on a DCFH-DA probe (Beyotime, Beijing, China). Fresh colonic tissues were immediately placed in precooled PBS to clean them from blood and other pollutants. The tissues were dissected into small fragments of approximately 1 mm3 using ophthalmic scissors, and they were subsequently immersed in precooled PBS and thoroughly rinsed to eliminate any residual cellular debris. The appropriate volume of enzyme digestion solution was added, and the cells were digested at 37 °C for 30 min. Intermittent agitation was performed during the digestion process. The digestion was terminated using PBS, followed by removal of the tissue mass through filtration with a 300-mesh nylon mesh. The filtered cells were collected and centrifuged at 500 g for 10 min. The supernatant was decanted, and the precipitate was washed twice with PBS. A solution of DCFH-DA at a final concentration of 10 μM was prepared by diluting it with FBS-free medium. After harvesting and washing the cells twice with PBS, they (1 × 106) were incubated with 500 μL of 10 μM DCFH-DA for 20 min at 37 °C. The cells were subsequently washed three times with FBS-free medium. Finally, detection and analysis of cellular ROS levels were performed using a FACScan flow cytometer (BD Biosciences, Franklin Lakes, NJ, USA) equipped with an excitation wavelength of 488 nm.
Western blotting
Total proteins were extracted from colonic tissues using RIPA lysis buffer (Beyotime, Beijing, China), and their concentrations were measured with a BCA Kit (Beyotime, Beijing, China). The collected proteins were then separated by 10% SDS polyacrylamide gel electrophoresis and transferred onto a PVDF membrane. The membrane was blocked with nonfat milk (5%), on which the primary antibodies NF-κB (1:1,000; Abcam, Cambridge, UK), IκB (1:1,000; Abcam, Cambridge, UK), IL-6 (1:1,000; Abcam, Cambridge, UK), TNF-α (1:1,000; Abcam, Cambridge, UK), and GAPDH (1:1,000; Abcam, Cambridge, UK), and the corresponding secondary antibody (1:3,000; Abcam, Cambridge, UK) were incubated. The signals were identified using an ECL Kit (Beyotime, Beijing, China), and immunoblots were quantified with Alpha Innotech software (Alpha Innotech, San Leandro, CA, USA). GAPDH was the normalization for proteins.
Statistical analysis
The variations among the data were assessed by applying one-way ANOVA followed by Tukey’s multiple comparison test. Data analysis was performed in SPSS software v22.0. The data are reported as the mean ± standard deviation (SD). P < 0.05 was considered statistically significant.
Results
Data integration and analysis for lncRNA/mRNA and miRNA/mRNA association pairs
First, we conducted a PCA for the lncRNA/mRNA data to elucidate expression differences between control and UC samples and found that there were significant differences (Figs. S1A and S1B, Tables S2A and S2B). The R packet “limma” was used to perform a differential analysis for the GSE75214 dataset. Based on the cutoff point of a P value < 0.05 plus |log2(fold change)| > 0.58, a total of 30 DE-lncRNAs were obtained, of which 9 lncRNAs were highly expressed, and 21 lncRNAs were slightly expressed in the UC samples (Figs. 2A and 2B, Table S3A). In addition, a total of 2,405 DEmRNAs were obtained. Among them, 1,394 mRNAs were confirmed to be upregulated, while 1,011 mRNAs were downregulated in UC samples (Figs. 2C and 2D, Table S3B). A PCA of the miRNA/mRNA data was subsequently performed (Figs. S1C and S1D, Tables S2C and S2D). We found that there were significant differences in miRNA/mRNA between the control and UC samples. In the GSE48959 dataset, with the aid of the R packet “limma” (P < 0.01 & |log2(fold change)| > 0.58), a total of 7 DEmiRNAs including four upregulated and three downregulated miRNAs were identified in the UC samples (Figs. 2E and 2F, Table S3C). Moreover, 1,841 DEmRNAs were identified. Among these DEmRNAs, a total of 1,007 mRNAs were observed to be upregulated, while 834 were downregulated in the UC samples (Figs. 2G and 2H, Table S3D).
10.7717/peerj.17213/fig-2
Figure 2
The data integration and analysis for lncRNA/mRNA and miRNA/mRNA association pairs.
Volcano plots (A) and heat map (B) of DE-lncRNAs in the GSE75214 dataset. Volcano plots (C) and heat map (D) of DE-mRNAs in the GSE75214 dataset. Volcano plots (E) and heat map (F) of DE-miRNAs in the GSE48959 dataset. Volcano plots (G) and heat map (H) of DE-mRNAs in the GSE48959 dataset.
Establishment of a ceRNA regulatory network
To better understand the molecular mechanism of DE-lncRNAs involved in the process of UC, nine upregulated lncRNAs and 21 downregulated lncRNAs were used to construct a lncRNA-miRNA-mRNA regulatory network. As shown in Table S4, this ceRNA network includes nine upregulated lncRNAs, 21 downregulated lncRNAs, two upregulated miRNAs, one downregulated miRNA, 14 upregulated mRNAs, and five downregulated mRNAs. We then constructed a random forest classifier based on the expression of these 19 DE-mRNAs in the GSE75214 and GSE48959 datasets, with 70% of the samples as the training set and 30% of the samples as the verification set. As illustrated in Fig. S2, both datasets had very high AUC density curves, suggesting that these mRNAs play key roles in the pathogenesis of UC.
Screening for genes related to oxidative stress
By comparing oxidative stress genes (https://doi.org/10.6084/m9.figshare.25263436.v1 F) with these 19 DE-mRNAs, we found that there were three oxidative stress-related genes in the 19 DE-mRNA sets, namely CAV1, SLC7A11, and SLC7A5 (Fig. 3A). These three mRNAs involved in the ceRNA network are shown in Fig. 3B. CAV1, SLC7A11, and SLC7A5 were all negatively associated with miR-194.
10.7717/peerj.17213/fig-3
Figure 3
Screening for genes related to oxidative stress.
(A) Venn diagram between DE-mRNAs of ceRNAs regulatory network and oxidative stress-related genes in MSigDB. (B) CeRNAs regulatory network based on oxidative stress-related genes.
Relationships between oxidative stress genes and active UC pathogenesis
Based on the expression profiles of CAV1, SLC7A11, and SLC7A5 in the GSE75214 and GSE48959 datasets, the R package ConsensusClusterPlus was used for cluster analysis. As illustrated in Figs. 4A and 4B & Table S5A, the GSE75214 dataset was clustered into two significantly different types. Active UC was mostly included in cluster 1, while inactive UC was included in cluster 2 (Fig. 4C, P < 0.0001). Similarly, the GSE48959 dataset was also clustered into significantly different two types (Figs. 4D and 4E); cluster 1 was markedly enriched with active UC, while cluster 2 was enriched with inactive UC (Fig. 4F, P < 0.05). These results indicated that CAV1, SLC7A11, and SLC7A5 were strongly correlated with UC activity. Considering that active UC and inactive UC were more significantly enriched in the GSE75214 dataset than in the GSE48959 dataset, we therefore analyzed the roles of these three oxidative stress genes in different UC subtypes based on the GSE75214 dataset. We selected representative genes in clusters 1 and 2 that significantly interact with oxidative stress genes for GO analysis and KEGG enrichment analysis. We found that the function of cluster 1 was mostly related to inflammatory immune responses and had a relatively wide range of pathways (Table S5B). These were consistent with the finding that active UC was enriched in cluster 1. On the other hand, we found that cluster 2 was associated with a less inflammatory response (Table S5C); this was consistent with the enrichment of inactive UC in cluster 2. We further demonstrated that there was a significant enrichment of steroid hormone synthesis pathways in the functional pathway of cluster 2 (Table S5C). All these results suggest that the roles of oxidative stress genes in active UC may be related to immune inflammation, and that steroids may be effective for the treatment of active UC.
10.7717/peerj.17213/fig-4
Figure 4
Relationships between oxidative stress genes and active UC pathogenesis.
(A) Consensus clustering matrix of GSE75214 dataset for k = 2. Principal components analysis (B) and column chart (C) of active and inactive UC in the GSE75214 dataset. (D) Consensus clustering matrix of GSE48959 dataset for k = 2. Principal components analysis (E) and column chart (F) of active and inactive UC in the GSE48959 dataset. *P < 0.05, ****P < 0.0001.
Relationships between oxidative stress genes and glucocorticoid resistance in active UC
The GSE114603 dataset containing glucocorticoid resistance genes was used to verify the relationships between oxidative stress genes and glucocorticoid resistance in active UC. Based on the expression profiles of CAV1, SLC7A11, and SLC7A5 in the GSE114603 dataset, we clustered the GSE114603 dataset of patients with active UC into control (G1) and different response (G2, responders_day0; G3, non-responders_day0) groups using the R package ConsensusClusterPlus. As shown in Figs. 5A and 5B & Table S6A–S6C, we found that the GSE75214 dataset was clustered into three significantly different clusters. We also found that the control data were remarkably clustered into cluster 1, while non-responders_day0 data were clustered into cluster 3 (Fig. 5C, P < 0.05). The results indicated that CAV1, SLC7A11, and SLC7A5 were associated with glucocorticoid therapy in patients with active UC. GO analysis and KEGG enrichment analysis were performed on representative genes that significantly interact with oxidative stress genes in cluster 1 (Table S6D), cluster 2 (Table S6E), and cluster 3 (Table S6F). The representative genes associated with oxidative stress interactions in cluster 1 were mostly related to cation reactions (such as zinc ion and copper ion), while in cluster 2, they were mostly related to angiogenesis and wound healing. In cluster 3, there were related to pathways such as wound healing and in acute phase reactions. The most significant pathway in cluster 2 was extracellular matrix organization, while the most significant pathway in cluster 3 was extracellular matrix disassembly. Data from the non-responders_day0 group were mainly enriched in cluster 3; therefore, we speculate that the impact of oxidative stress genes on glucocorticoid therapy may be related to the stability of extracellular mechanisms.
10.7717/peerj.17213/fig-5
Figure 5
Relationships between oxidative stress genes and glucocorticoid resistance in active UC.
(A) Consensus clustering matrix of GSE114603 dataset for k = 3. Principal components analysis (B) and column chart (C) of three groups of UC in the GSE114603 dataset. (D) Venn diagram for oxidative stress genes and representative genes of the three clusters. *P < 0.05, **P < 0.01.
Prediction of drug interactions
Based on the relationships between the three oxidative stress genes and glucocorticoid therapy in active UC, we further explored drugs that interact with oxidative stress genes using the DGIdb database (https://www.dgidb.org/). We found that CAV1 interacted with alcohol, and testosterone, SLC7A11 interacted with riluzole, and SLC7A5 interacted with melphalan (Table 2). Among these three oxidative stress genes, SLC7A5 was found to be a representative gene of cluster 3 (Fig. 5D). As cluster 3 significantly associated with glucocorticoid therapy resistance, we believed that finding drugs that interact with SLC7A5 may be of great significance in improving resistance to glucocorticoid therapy active UC.
10.7717/peerj.17213/table-2
Table 2
Drugs interaction with oxidative stress genes.
Search_term
Match_type
Drug
Interaction_types
Sources
Pmids
CAV1
Definite
ALCOHOL
NCI
15845868
CAV1
Definite
TESTOSTERONE
NCI
11389065
SLC7A11
Definite
RILUZOLE
Inducer
TdgClinicalTrial
10899284|20226190|12629173
SLC7A5
Definite
MELPHALAN
PharmGKB
Validation in mice with DSS-induced UC
HE staining was performed to monitor the effects of DXM on mice with DSS-induced UC. As illustrated in Fig. 6A, compared with the colonic samples of mice in the control group, a thickened muscle layer and an irregular mucosal layer were observed in the DSS and DSS + NS groups. In the treated group, these pathologic changes were alleviated to some extent. The expression of three oxidative stress-related genes (CAV1, SLC7A11, and SLC7A5) and miR-194 in colonic tissues was subsequently quantified. We found that the expression of CAV1, SLC7A11, and SLC7A5 was significantly increased in mice with DSS-induced UC (Fig. 6B, P < 0.05) but was decreased following DXM treatment (P < 0.05). The opposite results were observed in the expression of miR-194 (Fig. 6C, P < 0.05). Furthermore, the levels of oxidative stress-related enzymes were determined. We demonstrated that compared with the control group, the levels of GSH and SOD were decreased in the DSS group (Figs. 6D and 6E, P < 0.0001), while both the GSSG and ROS levels were increased (P < 0.05). DXM treatment restored the decreased levels of GSH and SOD (P < 0.0001) but suppressed the increased levels of GSSG and ROS (P < 0.01). In addition, the mRNA expression of inflammatory cytokines was also determined. As shown in Fig. S3A, the IL-17, TNF-α, IL-1β, and IL-6 levels in the DSS model mice were dramatically upregulated relative to those in control mice (P < 0.01). DXM treatment considerably inhibited the secretion of IL-17, TNF-α, IL-1β, and IL-6 (P < 0.05). The levels of pro-inflammatory effector factors (COX-2 and TLR4) were also upregulated in the DSS group compared with the control group (Fig. S3B, P < 0.01); however, compared with the DSS+NS group, the COX-2 and TLR4 levels in the DSS + DXM group were significantly downregulated (P < 0.05). As expected, the opposite patterns were observed in the expression of anti-inflammatory effector factors (Foxp3 and SOCS3). The expression products of HMGB-1 have pro-inflammatory activity, which can promote the infiltration and activation of various inflammatory cells into the intestinal mucosa and subsequently activate an inflammatory response in the intestinal mucosal tissues (Palone et al., 2016). High levels of HMGB-1 were found in the DSS group compared with those in the control group (Fig. 6F, P < 0.0001); however, compared with the DSS + NS group, the HMGB-1 level was decreased in the DSS + DXM group (P < 0.0001). MPO, a pro-inflammatory marker released from activated neutrophils, can caused damage at the site of inflammation in UC progression (Iwao et al., 2018). As illustrated in Fig. 6G, pronounced upregulation of MPO activity or gene expression was observed in DSS model mice compared with the control mice (P < 0.05). However, DXM treatment significantly decreased the activity and expression of MPO in mice with UC (P < 0.01). NF-κB is an essential transcription factor for the transcription and translation of a series of pro-inflammatory genes, including IL-6 and TNF-α, while IκB is a key suppressor of NF-κB activation (Xie et al., 2020). As shown in Fig. 6H, high protein levels of NF-κb, IL-6, and TNF-α were observed in DSS model mice compared with control mice (P < 0.0001), and DXM treatment significantly reduced these protein levels (P < 0.01). As expected, the opposite results were observed in the protein levels of IκB (P < 0.01).
10.7717/peerj.17213/fig-6
Figure 6
Validation in DSS-induced UC mice.
(A) HE staining showed the histological changes in DSS-induced UC mice. The expression of CAV1, SLC7A11, SLC7A5 (B) and miRNA-194 (C) in different groups. The levels of GSH, GSSG (D), ROS, SOD (E) and HMGB-1 (F) in different groups. (G) MPO activity or gene expression in different groups. (H) The protein levels of NF-κB, IκB, IL-6 and TNF-α in different groups. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.
Data integration and analysis for lncRNA/mRNA and miRNA/mRNA association pairs
First, we conducted a PCA for the lncRNA/mRNA data to elucidate expression differences between control and UC samples and found that there were significant differences (Figs. S1A and S1B, Tables S2A and S2B). The R packet “limma” was used to perform a differential analysis for the GSE75214 dataset. Based on the cutoff point of a P value < 0.05 plus |log2(fold change)| > 0.58, a total of 30 DE-lncRNAs were obtained, of which 9 lncRNAs were highly expressed, and 21 lncRNAs were slightly expressed in the UC samples (Figs. 2A and 2B, Table S3A). In addition, a total of 2,405 DEmRNAs were obtained. Among them, 1,394 mRNAs were confirmed to be upregulated, while 1,011 mRNAs were downregulated in UC samples (Figs. 2C and 2D, Table S3B). A PCA of the miRNA/mRNA data was subsequently performed (Figs. S1C and S1D, Tables S2C and S2D). We found that there were significant differences in miRNA/mRNA between the control and UC samples. In the GSE48959 dataset, with the aid of the R packet “limma” (P < 0.01 & |log2(fold change)| > 0.58), a total of 7 DEmiRNAs including four upregulated and three downregulated miRNAs were identified in the UC samples (Figs. 2E and 2F, Table S3C). Moreover, 1,841 DEmRNAs were identified. Among these DEmRNAs, a total of 1,007 mRNAs were observed to be upregulated, while 834 were downregulated in the UC samples (Figs. 2G and 2H, Table S3D).
10.7717/peerj.17213/fig-2
Figure 2
The data integration and analysis for lncRNA/mRNA and miRNA/mRNA association pairs.
Volcano plots (A) and heat map (B) of DE-lncRNAs in the GSE75214 dataset. Volcano plots (C) and heat map (D) of DE-mRNAs in the GSE75214 dataset. Volcano plots (E) and heat map (F) of DE-miRNAs in the GSE48959 dataset. Volcano plots (G) and heat map (H) of DE-mRNAs in the GSE48959 dataset.
Establishment of a ceRNA regulatory network
To better understand the molecular mechanism of DE-lncRNAs involved in the process of UC, nine upregulated lncRNAs and 21 downregulated lncRNAs were used to construct a lncRNA-miRNA-mRNA regulatory network. As shown in Table S4, this ceRNA network includes nine upregulated lncRNAs, 21 downregulated lncRNAs, two upregulated miRNAs, one downregulated miRNA, 14 upregulated mRNAs, and five downregulated mRNAs. We then constructed a random forest classifier based on the expression of these 19 DE-mRNAs in the GSE75214 and GSE48959 datasets, with 70% of the samples as the training set and 30% of the samples as the verification set. As illustrated in Fig. S2, both datasets had very high AUC density curves, suggesting that these mRNAs play key roles in the pathogenesis of UC.
Screening for genes related to oxidative stress
By comparing oxidative stress genes (https://doi.org/10.6084/m9.figshare.25263436.v1 F) with these 19 DE-mRNAs, we found that there were three oxidative stress-related genes in the 19 DE-mRNA sets, namely CAV1, SLC7A11, and SLC7A5 (Fig. 3A). These three mRNAs involved in the ceRNA network are shown in Fig. 3B. CAV1, SLC7A11, and SLC7A5 were all negatively associated with miR-194.
10.7717/peerj.17213/fig-3
Figure 3
Screening for genes related to oxidative stress.
(A) Venn diagram between DE-mRNAs of ceRNAs regulatory network and oxidative stress-related genes in MSigDB. (B) CeRNAs regulatory network based on oxidative stress-related genes.
Relationships between oxidative stress genes and active UC pathogenesis
Based on the expression profiles of CAV1, SLC7A11, and SLC7A5 in the GSE75214 and GSE48959 datasets, the R package ConsensusClusterPlus was used for cluster analysis. As illustrated in Figs. 4A and 4B & Table S5A, the GSE75214 dataset was clustered into two significantly different types. Active UC was mostly included in cluster 1, while inactive UC was included in cluster 2 (Fig. 4C, P < 0.0001). Similarly, the GSE48959 dataset was also clustered into significantly different two types (Figs. 4D and 4E); cluster 1 was markedly enriched with active UC, while cluster 2 was enriched with inactive UC (Fig. 4F, P < 0.05). These results indicated that CAV1, SLC7A11, and SLC7A5 were strongly correlated with UC activity. Considering that active UC and inactive UC were more significantly enriched in the GSE75214 dataset than in the GSE48959 dataset, we therefore analyzed the roles of these three oxidative stress genes in different UC subtypes based on the GSE75214 dataset. We selected representative genes in clusters 1 and 2 that significantly interact with oxidative stress genes for GO analysis and KEGG enrichment analysis. We found that the function of cluster 1 was mostly related to inflammatory immune responses and had a relatively wide range of pathways (Table S5B). These were consistent with the finding that active UC was enriched in cluster 1. On the other hand, we found that cluster 2 was associated with a less inflammatory response (Table S5C); this was consistent with the enrichment of inactive UC in cluster 2. We further demonstrated that there was a significant enrichment of steroid hormone synthesis pathways in the functional pathway of cluster 2 (Table S5C). All these results suggest that the roles of oxidative stress genes in active UC may be related to immune inflammation, and that steroids may be effective for the treatment of active UC.
10.7717/peerj.17213/fig-4
Figure 4
Relationships between oxidative stress genes and active UC pathogenesis.
(A) Consensus clustering matrix of GSE75214 dataset for k = 2. Principal components analysis (B) and column chart (C) of active and inactive UC in the GSE75214 dataset. (D) Consensus clustering matrix of GSE48959 dataset for k = 2. Principal components analysis (E) and column chart (F) of active and inactive UC in the GSE48959 dataset. *P < 0.05, ****P < 0.0001.
Relationships between oxidative stress genes and glucocorticoid resistance in active UC
The GSE114603 dataset containing glucocorticoid resistance genes was used to verify the relationships between oxidative stress genes and glucocorticoid resistance in active UC. Based on the expression profiles of CAV1, SLC7A11, and SLC7A5 in the GSE114603 dataset, we clustered the GSE114603 dataset of patients with active UC into control (G1) and different response (G2, responders_day0; G3, non-responders_day0) groups using the R package ConsensusClusterPlus. As shown in Figs. 5A and 5B & Table S6A–S6C, we found that the GSE75214 dataset was clustered into three significantly different clusters. We also found that the control data were remarkably clustered into cluster 1, while non-responders_day0 data were clustered into cluster 3 (Fig. 5C, P < 0.05). The results indicated that CAV1, SLC7A11, and SLC7A5 were associated with glucocorticoid therapy in patients with active UC. GO analysis and KEGG enrichment analysis were performed on representative genes that significantly interact with oxidative stress genes in cluster 1 (Table S6D), cluster 2 (Table S6E), and cluster 3 (Table S6F). The representative genes associated with oxidative stress interactions in cluster 1 were mostly related to cation reactions (such as zinc ion and copper ion), while in cluster 2, they were mostly related to angiogenesis and wound healing. In cluster 3, there were related to pathways such as wound healing and in acute phase reactions. The most significant pathway in cluster 2 was extracellular matrix organization, while the most significant pathway in cluster 3 was extracellular matrix disassembly. Data from the non-responders_day0 group were mainly enriched in cluster 3; therefore, we speculate that the impact of oxidative stress genes on glucocorticoid therapy may be related to the stability of extracellular mechanisms.
10.7717/peerj.17213/fig-5
Figure 5
Relationships between oxidative stress genes and glucocorticoid resistance in active UC.
(A) Consensus clustering matrix of GSE114603 dataset for k = 3. Principal components analysis (B) and column chart (C) of three groups of UC in the GSE114603 dataset. (D) Venn diagram for oxidative stress genes and representative genes of the three clusters. *P < 0.05, **P < 0.01.
Prediction of drug interactions
Based on the relationships between the three oxidative stress genes and glucocorticoid therapy in active UC, we further explored drugs that interact with oxidative stress genes using the DGIdb database (https://www.dgidb.org/). We found that CAV1 interacted with alcohol, and testosterone, SLC7A11 interacted with riluzole, and SLC7A5 interacted with melphalan (Table 2). Among these three oxidative stress genes, SLC7A5 was found to be a representative gene of cluster 3 (Fig. 5D). As cluster 3 significantly associated with glucocorticoid therapy resistance, we believed that finding drugs that interact with SLC7A5 may be of great significance in improving resistance to glucocorticoid therapy active UC.
10.7717/peerj.17213/table-2
Table 2
Drugs interaction with oxidative stress genes.
Search_term
Match_type
Drug
Interaction_types
Sources
Pmids
CAV1
Definite
ALCOHOL
NCI
15845868
CAV1
Definite
TESTOSTERONE
NCI
11389065
SLC7A11
Definite
RILUZOLE
Inducer
TdgClinicalTrial
10899284|20226190|12629173
SLC7A5
Definite
MELPHALAN
PharmGKB
Validation in mice with DSS-induced UC
HE staining was performed to monitor the effects of DXM on mice with DSS-induced UC. As illustrated in Fig. 6A, compared with the colonic samples of mice in the control group, a thickened muscle layer and an irregular mucosal layer were observed in the DSS and DSS + NS groups. In the treated group, these pathologic changes were alleviated to some extent. The expression of three oxidative stress-related genes (CAV1, SLC7A11, and SLC7A5) and miR-194 in colonic tissues was subsequently quantified. We found that the expression of CAV1, SLC7A11, and SLC7A5 was significantly increased in mice with DSS-induced UC (Fig. 6B, P < 0.05) but was decreased following DXM treatment (P < 0.05). The opposite results were observed in the expression of miR-194 (Fig. 6C, P < 0.05). Furthermore, the levels of oxidative stress-related enzymes were determined. We demonstrated that compared with the control group, the levels of GSH and SOD were decreased in the DSS group (Figs. 6D and 6E, P < 0.0001), while both the GSSG and ROS levels were increased (P < 0.05). DXM treatment restored the decreased levels of GSH and SOD (P < 0.0001) but suppressed the increased levels of GSSG and ROS (P < 0.01). In addition, the mRNA expression of inflammatory cytokines was also determined. As shown in Fig. S3A, the IL-17, TNF-α, IL-1β, and IL-6 levels in the DSS model mice were dramatically upregulated relative to those in control mice (P < 0.01). DXM treatment considerably inhibited the secretion of IL-17, TNF-α, IL-1β, and IL-6 (P < 0.05). The levels of pro-inflammatory effector factors (COX-2 and TLR4) were also upregulated in the DSS group compared with the control group (Fig. S3B, P < 0.01); however, compared with the DSS+NS group, the COX-2 and TLR4 levels in the DSS + DXM group were significantly downregulated (P < 0.05). As expected, the opposite patterns were observed in the expression of anti-inflammatory effector factors (Foxp3 and SOCS3). The expression products of HMGB-1 have pro-inflammatory activity, which can promote the infiltration and activation of various inflammatory cells into the intestinal mucosa and subsequently activate an inflammatory response in the intestinal mucosal tissues (Palone et al., 2016). High levels of HMGB-1 were found in the DSS group compared with those in the control group (Fig. 6F, P < 0.0001); however, compared with the DSS + NS group, the HMGB-1 level was decreased in the DSS + DXM group (P < 0.0001). MPO, a pro-inflammatory marker released from activated neutrophils, can caused damage at the site of inflammation in UC progression (Iwao et al., 2018). As illustrated in Fig. 6G, pronounced upregulation of MPO activity or gene expression was observed in DSS model mice compared with the control mice (P < 0.05). However, DXM treatment significantly decreased the activity and expression of MPO in mice with UC (P < 0.01). NF-κB is an essential transcription factor for the transcription and translation of a series of pro-inflammatory genes, including IL-6 and TNF-α, while IκB is a key suppressor of NF-κB activation (Xie et al., 2020). As shown in Fig. 6H, high protein levels of NF-κb, IL-6, and TNF-α were observed in DSS model mice compared with control mice (P < 0.0001), and DXM treatment significantly reduced these protein levels (P < 0.01). As expected, the opposite results were observed in the protein levels of IκB (P < 0.01).
10.7717/peerj.17213/fig-6
Figure 6
Validation in DSS-induced UC mice.
(A) HE staining showed the histological changes in DSS-induced UC mice. The expression of CAV1, SLC7A11, SLC7A5 (B) and miRNA-194 (C) in different groups. The levels of GSH, GSSG (D), ROS, SOD (E) and HMGB-1 (F) in different groups. (G) MPO activity or gene expression in different groups. (H) The protein levels of NF-κB, IκB, IL-6 and TNF-α in different groups. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.
Discussion
UC is a chronic inflammatory disease, and imbalanced regulation of oxidative stress plays a crucial role in its pathology (Rana et al., 2014; Jena, Trivedi & Sandala, 2012). Clinical interventions are mainly focused on alleviating the symptoms of patients UC patients because of its complex pathogenesis and mechanism (Ungaro et al., 2017; Sarvestani et al., 2021). Side effects and drug resistance are still inevitable (Nakase et al., 2021; Magro et al., 2017; Truelove & Witts, 1955). The lncRNA-miRNA-mRNA regulatory network has been suggested to play a vital role in regulation of oxidative stress in the pathogenesis of UC and glucocorticoid-related disorders. A previous study conducted by Wang et al. showed that lncRNA MEG3 acts as a ceRNA for IL-10 via sponging miR-98-5p to relieve inflammation and oxidative stress in a UC rat model (Wang et al., 2021). DXM is a well-known glucocorticoid drug that is widely used in the treatment of UC; however, it leads to severe osteoporosis. Liu et al. (2018) found that FGF1 could enhance the efficiency of DXM through the lncRNA GAS5/miR-21 axis. Therefore, the construction of ceRNA and further exploration of oxidative stress-related genes underlying the pathogenesis and glucocorticoid resistance mechanism of UC are essential to understand potential biomarkers in UC.
In the current study, DE-lncRNAs, DE-miRNAs, and DE-mRNAs were identified in UC and control tissues from the GSE75214 and GSE48959 datasets. Based on the miRDB, miRTarBase, and TargetScan databases, each DE-miRNA’s predicted mRNA was obtained. In addition, we used R cor.test to calculate the correlations between DE-lncRNAs and DE-mRNAs and between DE-miRNAs and DE-mRNAs and then constructed a DE-lncRNA-DE-mRNA network and a DE-miRNA-DE-mRNA network. After that, according to the intersection-DE-mRNAs, a DE-lncRNA-DE-mRNA network and a DE-miRNAs-DE-mRNA network were combined to construct a DE-lncRNA-DE-miRNA-DE-mRNA network including 30 lncRNAs, 3 miRNAs, and 19 mRNAs. The GSE75214 and GSE48959 databases were validated by AUC analysis to determine whether these 19 mRNAs from ceRNA were important in the pathogenesis of UC. We then further screened three oxidative stress-related mRNAs that can interact with miR-194, namely, CAV1, SLC7A11, and SLC7A5. SLC7A5 was considered a representative gene associated with glucocorticoid therapy resistance.
The SLC7A5 gene, which is mapped at 16q24.2, has 39,477 nucleotides with 10 exons (Scalise et al., 2018). SLC7A5 protein, which is also known as L-type amino acid transporter 1 (LAT1), is an amino acid transporter with 12 transmembrane α-helices and has been confirmed to regulate the distribution of specific amino acids across cell membranes (Scalise et al., 2018; Napolitano et al., 2017). In general, SLC7A5 is highly expressed in the inner blood retinal barrier, blood-brain barrier, and brain endothelial cells (Boado et al., 1999; Tomi et al., 2005). Interestingly, in several human cancers, SLC7A5 has also been observed to be overexpressed (Zhao, Wang & Pan, 2015). Therefore, SLC7A5 has been proposed as a novel target for the treatment of human cancers. An obvious example is that JPH203/KYT-0353, an inhibitor of SLC7A5, can clinically suppress tumor growth (Oda et al., 2010). SLC7A5 also plays a crucial role in the immune microenvironment. For example, in macrophages, SLC7A5 can mediate the transport of leucine to promote the secretion of proinflammatory cytokines via mTORC1 signaling (Yoon et al., 2018). In the progression of rheumatoid arthritis, pronounced upregulation of SLC7A5 has been observed in monocytes and is negatively correlated with the prognosis (Yoon et al., 2018). SLC7A5 is also essential for maintaining the functions of NK cells (Loftus et al., 2018). Oxidative stress is involved in various human disorders and is harmful to human health. As a potent neurotoxin, methylmercury (MeHg) can induce oxidative stress and cell apoptosis. Granitzer et al. (2021) reported that knocking-down SLC7A5 enhances the oxidative stress caused by MeHg in HTR-8/SVneo cells. Brahmajothi et al. (2014) reported that hyperoxia and increased oxidative stress have adverse effects on the expression and function of SLC7A5 in alveolar epithelial cells. In the current study, we identified SLC7A5 as a representative oxidative stress-related genes in UC and further confirmed that it was upregulated in a UC mouse model. This is in accordance with the findings of a previous report conducted by Al-Mustanjid et al. (2020) who found that SLC7A5 is a transcription factor that is upregulated in inflammatory bowel disease. We also found that DXM treatment significantly decreased SLC7A5 expression, which further validated the potential role of SLC7A5 in glucocorticoid therapy. In addition, based on GO analysis and KEGG enrichment analysis, we found that SLC7A5 was closely associated with extracellular matrix disassembly. Similarly, Yoon et al. (2018) demonstrated that SLC7A5 can mediate leucine influx from extracellular matrix into cells, leading to a decreased rate of extracellular acidification. These results imply that SLC7A5 may be related to the stability of extracellular mechanisms. Furthermore, the prediction of drug interactions indicated that SLC7A5 can effectively interact with melphalan. It is well-known that melphalan is an effective clinical drug for breast cancer (BC) treatment. Interestingly, the protein levels of SLC7A5 are higher in estrogen receptor (ER)-positive BC cells than in ER-negative BC cells. These results imply that SLC7A5 is not only an underlying target for BC treatment but also a potential biomarker for UC. In addition, we suggest that melphalan may also have the potential to improve the resistance of active UC to glucocorticoid therapy.
Some validations were conducted in mice with DSS-induced UC. A thickened muscle layer and an irregular mucosal layer were observed in the colonic tissues of mice with DSS-induced UC, suggesting that the UC model was established successfully. As expected, the pathologic changes were noted to be in remission following DXM treatment, indicating that DXM is effective for the treatment of UC. Apart from SLC7A5, the other two oxidative stress-related genes, CAV1 and SLC7A11, were found to be upregulated in the UC model mice and downregulated after DXM treatment. The opposite results were observed in terms of miR-194 expression. The abovementioned data validated the results of the bioinformatics analysis. Moreover, the contents of oxidants (GSSG and ROS) and antioxidants (GSH and SOD) in UC model mice were also determined and suggested the antioxidative effect of DMX in UC progression. Inflammatory responses are considered as one of the main factors that affect UC progression. Some inflammatory cytokines are reported to be involved in the pathogenesis of UC. For example, IL-17 can induce inflammation by recruiting leukocytes (Iwakura et al., 2011). By affecting the secretory function of intestinal epithelial cells, IL-6 aggravates the progression of UC (Nishida et al., 2018). The immunoreactive TNF-α protein is also closely associated with the development of active UC (Popivanova et al., 2008). Furthermore, upregulation of pro-inflammatory effector factors such as TLR4 can produce inflammatory cytokines including TNF-α, IL-1β, and IL-6 (Rosales-Martinez et al., 2016; Ye et al., 2017), while anti-inflammatory effector factors have inhibitory effects on inflammatory cytokine secretion. For instance, Foxp3, a nuclear transcription factor of Treg cells, plays a key role in releasing anti-inflammatory cytokines (Tang et al., 2019; Bin Dhuban et al., 2019). SOCS3 belongs to a family of intracellular proteins that negatively regulate inflammation (Yoshimura, Naka & Kubo, 2007). In this study, the increased levels of inflammatory cytokines (IL-17, TNF-α, IL-1β, and IL-6) and pro-inflammatory effector factors (COX-2 and TLR4) caused by DSS stimulation were significantly reduced following DXM treatment. DXM treatment was also confirmed to increase the expression of Foxp3 and SOCS3. These results imply that DXM may alleviate the development of UC via inhibiting inflammation. In the development of inflammatory diseases, mature neutrophils can release MPO to interact with macrophages, initiating a series of molecular cascades and the secretion of inflammatory cytokines (Lefkowitz & Lefkowitz, 2001). As expected, high levels of MPO activity or gene expression were observed in mice with DSS-induced UC, which was similar to the findings of a previous study that showed that MPO activity was dramatically upregulated in rats with acetic acid-induced colitis (Ghatule et al., 2012). We also found that DXM showed a suppressive effect on MPO activity. NF-κB serves as a crucial transcription factor responsible for the activation of various pro-inflammatory genes, such as IL-6 and TNF-α, whereas IκB acts as a pivotal suppressor in regulating NF-κB activation (Xie et al., 2020). We further demonstrated that DXM could inhibit the protein levels of NF-κB, IL-6, and TNF-α but was associated with increased IκB levels. These results further validated the anti-inflammatory role of DXM in UC progression.
Some limitations of this study should be acknowledged. First, more clinical samples from patients with UC should be collected to validate the oxidative stress genes and measure gut microbiota data. Second, other than melphalan, we found that CAV1 interacted with testosterone and that SLC7A11 interacted with riluzole. The clinical applications of these drugs and drug targets are needed. Third, whether these oxidative stress genes play driving roles or just act as bystanders in the pathogenesis of UC may be an interesting subject in the future.
Conclusion
In conclusion, a lncRNA-miRNA-mRNA network containing 30 DE-lncRNAs, 3 DE-miRNAs, and 19 DE-mRNAs was established by bioinformatics analysis. Among the 19 DE-mRNAs, 3 mRNAs including CAV1, SLC7A11, and SLC7A5 were identified as oxidative stress-related genes, and SLC7A5 was considered a representative gene associated with glucocorticoid therapy resistance, which was further validated in animal experiments. These results highlight the effects of oxidative stress-related genes on the pathogenesis and glucocorticoid therapy mechanism of UC and may provide a new therapeutic target for the treatment of UC in the clinic.
Supplemental Information
10.7717/peerj.17213/supp-1
Supplemental Information 1
MIQE checklist.
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Supplemental Information 2
The ARRIVE guidelines 2.0 author checklist.
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Supplemental Information 3
Principal components analysis for lncRNA, miRNA and mRNA.
(A) Principal components analysis for lncRNA between control and UC samples in GSE75214 dataset. (B) Principal components analysis for miRNA between control and UC samples in GSE75214 dataset. (C) Principal components analysis for miRNA between control and UC samples in GSE48959 dataset. (D) Principal components analysis for mRNA between control and UC samples in GSE48959 dataset.
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Supplemental Information 4
AUC and accuracy density distributions.
AUCand accuracydensitydistributions of GSE75214 (A) and GSE48959 (B) datasets.
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Supplemental Information 5
The expression of inflammatory cytokines, pro-inflammatory effector factors and anti-inflammatory effector factors in different groups.
(A) The mRNA expression of inflammatory cytokines IL-17, TNF-α, IL-1β and IL-6 in different groups. (B) The mRNA expression of pro-inflammatory effector factors (COX-2 and TLR4) and anti-inflammatory effector factors (Foxp3 and SOCS3) in different groups. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.
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Supplemental Information 6
The clinical information of UC patients from GEO database.
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Supplemental Information 7
Eigenvalues of two principal components in the GEO dataset.
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Supplemental Information 8
List of differentially expressed lncRNA, miRNA and mRNA.
10.7717/peerj.17213/supp-9
Supplemental Information 9
The DElncRNA-DEmRNA-DEmiRNA ceRNA network.
10.7717/peerj.17213/supp-10
Supplemental Information 10
List of differentially expressed genes and pathway enrichment analysis in GSE75214 dataset.
(A) differentially expressed mRNA in cluster 1 and 2 from GSE75214. (B) GO and KEGG enrichment analysis for cluster 1. (C) GO and KEGG enrichment analysis for cluster 2.
10.7717/peerj.17213/supp-11
Supplemental Information 11
List of differentially expressed genes and pathway enrichment analysis in GSE114603 dataset.
(A) differentially expressed mRNA in cluster 1 from GSE114603. (B) differentially expressed mRNA in cluster 2 from GSE114603. (C) differentially expressed mRNA in cluster 3 from GSE114603. (D) GO and KEGG enrichment analysis for cluster 1. (E) GO and KEGG enrichment analysis for cluster 2. (F) GO and KEGG enrichment analysis for cluster 3. |
Title: Prognostic significance of non-coding RNAs related to the tumorigenic epithelial-mesenchymal transition (EMT) process among ovarian cancer patients: A systematic review and meta-analysis | Body:
1
Introduction
Ovarian cancer (OC) is one of the most prevalent gynecological malignancies and accompanies a poor prognosis and a high mortality rate. It has a five-year survival rate of less than 45 % and is considered the leading lethal malignancy among gynecological cancers [1,2]. OC is known as a cancer with asymptomatic, inconspicuous, and hidden growth with delayed symptom onset, and it is usually diagnosed in advanced stages, which limits recruiting the possible treatment methods [3]. High rates of metastasis, invasion into adjacent tissues, and resistance to conventional therapies contribute to the high mortality rate and poor prognosis of this cancer [4]. Early diagnostic tests of OC (e.g., measurement of serum cancer antigen 125 (CA125) and transvaginal ultrasonography) are not specific, effective, and sensitive enough for early detection and do not significantly contribute to improving clinical outcomes [5]. Therefore, finding diagnostic and prognostic approaches is one of the most important challenges and necessities. Determining mechanisms that result in these features of OC and providing prognostic panels may decrease the burden of the disease and increase overall survival.
Epithelial-mesenchymal transition (EMT) is a molecular process in which cells undergo specific changes, including losing their cell-to-cell adhesion, acquiring more mobility and stem cell-like properties, and transforming from an epithelial to a mesenchymal cell type. EMT has been shown to play an important role in cancer metastasis, invasion, and cellular resistance to chemotherapy. Blocking EMT to reduce tumorigenesis is considered a key adjuvant strategy for OC treatment [6]. During the EMT process, the expression of E-cadherin as an epithelial marker decreases, while mesenchymal markers, such as N-cadherin, increase. Several studies have investigated different mediators and pathways contributing to EMT, and non-coding RNAs (ncRNAs) have been shown to play roles in the induction or suppression of EMT [[7], [8], [9]].
The ncRNAs are RNA transcripts that do not encode proteins and were assumed to be by-products with no important biological functions. NcRNAs comprise various types, including housekeeping (e.g., transfer RNAs, ribosomal RNAs, and small nucleolar/nuclear RNAs) and regulatory ncRNAs (e.g., small interfering RNAs, Long non-coding RNAs (lncRNAs), microRNAs, and circular RNAs (circRNAs)). Housekeeping ncRNAs are known to be stably expressed genes supporting cell life activity, while regulatory ncRNAs participate in biological processes. Abnormalities in ncRNA regulatory networks can interfere with normal cell functions and are closely associated with pathological changes, occurrence and, or progression of various diseases, drug resistance, and multiple malignancies, including OC, which may become more aggressive in response to these different types of ncRNAs [7,[10], [11], [12], [13], [14], [15]]. For instance, overexpression of MIR503, a tumor suppressor in cancers, suppresses the tumorigenic ability of OC, impairing the proliferation, EMT, and invasiveness and facilitating cell apoptosis [16]. Furthermore, long intergenic ncRNA LINC00665 is upregulated in OC, which targets and inhibits miR-181a-5p while upregulating FHDC1 expression [17]. Although the mechanisms by which ncRNAs participate in cancer progression are not fully understood, numerous reports have elucidated their role in the EMT process as a known mechanism affecting malignant cell growth or spread [18].
Moreover, multiple ncRNAs have been shown to have prognostic values for this cancer. The role of ncRNA downregulation and upregulation in the EMT process and metastasis in the OC has been investigated in several studies. Also, it has been shown that they could predict the stemness of cancer stem cell proliferation, metastasis, apoptosis, and chemotherapy resistance. Although there have been some review papers on the role of ncRNAs in OC, to the best of our knowledge, there has not been a comprehensive systematic review and meta-analysis outlining the significance of EMT-related ncRNAs in the prognosis of OC [9,[19], [20], [21], [22], [23], [24], [25], [26]]. Here, we have gathered all the published data and conducted a thorough systematic review and meta-analysis of the EMT-associated ncRNAs that have prognostic and diagnostic value, as well as provided mechanistic information about OC. In this review, we focused on different types of OC, including epithelial ovarian cancer (EOC), high-grade serous ovarian cancer (HGSOC), and ovarian serous carcinomas (OSC).
2
Methods
2.1
Study protocol and search strategy
A systematic search of the literature was performed to retrieve papers discussing the prognostic value of ncRNAs related to EMT in OC patients. We developed a specific search strategy for each of the Embase, PubMed, Scopus, and Web of Science databases using the keywords ((“Ovarian” OR “Ovary”) and (“cancer*" OR “Neoplasm*” OR “Ovarian Neoplasm*")) and (“Metasta*" OR “EMT” OR “Epithelial-Mesenchymal transition” OR “Epithelial-Mesenchymal transformation” OR “Epithelial Mesenchymal*") and ((“RNA and Untranslated”) OR (“Noncoding” and “RNA”) OR (“Non-coding” and “RNA*") OR “ncRNA*" OR “MicroRNA*" OR “miRNA*” OR “miR” OR (“Long Noncoding” and “RNA*") OR (“long” and “non” and “coding” and “RNA*“) OR “lncRNA*" OR (“long non-coding RNA*“) OR “ceRNA*” OR (“competing endogenous RNA*“) OR “LINC RNA*” OR “circRNA*” OR (“Circular” and “RNA*“)), and the relevant MeSH terms. The detailed search strategy for each database is available in Supplementary Table 1.
The searches were not restricted to the title/abstract or specific languages. We applied the search terms to all fields by considering papers published from 2000 to Jun 13, 2024. The search result of each database was collected in a library. Then, retracted articles and duplicates were removed. This systematic review is based on the PRISMA statement [27]. The methodology of this study was registered in PROSPERO (Registration No. CRD42022304776).
2.2
Data management, screening, and detailed review
Two reviewers (SNS and SM) independently screened the title and abstract of the studies acquired from the previous step based on the defined inclusion and exclusion criteria. Disagreements were resolved by consulting with the third reviewer (ASK). In the title/abstract screening stage, conference abstracts, duplicate papers, letters, reviews, and editorial publications were excluded. Eligible articles were necessarily related to EMT, ncRNAs, and OC. Otherwise, they were excluded and classified as “not related to the topic” publications. Furthermore, papers that had not discussed prognosis and overall survival (OS) in their abstracts were not excluded at this stage, and a decision about them was taken during the full-text review.
In the detailed review step, the full text of the included papers was screened to check if the documents meet the study's inclusion criteria and have enough data to proceed with further steps. The evaluation of ncRNAs and their correlation with EMT and the evaluation of the prognostic role of the assessed ncRNAs were the essential factors reviewers appraised. Other exclusion criteria were case series, case reports, interventional and bioinformatics papers, cell line research, and animal studies lacking human samples. To be included, intervention-free samples must have been obtained from human OC patients, regardless of their disease stage, who did not suffer from other diseases or had not received medication therapies.
2.3
Data extraction
SNS and SM extracted the following data from the eligible articles: first author, publication year, country, study design, sample type, the number of cases and controls, ncRNA detection method, cancer stage, age-related information of controls and patients (mean, minimum, maximum, and standard deviation [SD]), type and name of the ncRNAs, the number of patients with upregulated and downregulated ncRNAs, p-value, prognosis levels (poor or good), the oncogenic or tumor-suppressive role of the ncRNAs, target genes and molecular mechanism of ncRNAs, type of OC, the number of patients with or without lymph node or distant metastasis regarding expression levels of ncRNAs, the number of patients with different clinical stages and TNM stages in both high and low expression levels of target ncRNA, hazard ratio (HR) and confidence of interval (CI) of both univariate and multivariate analysis of OS and progression-free survival (PFS). In case the needed data was not available in the paper or its supplementary material, the corresponding author(s) were contacted to obtain the needed data.
2.4
Quality assessment
To assess each included article's validity, quality, and risk of bias, case-control and cohort studies were evaluated based on the Newcastle-Ottawa Scale (NOS) [28]. We refined NOS questions and scores according to our study and defined the total validity score ranging from a minimum of 0 to a maximum of 8. The maximum scores of four, two, and two have been awarded to the three characteristics of each study: selection, comparability, and outcome, respectively. There was a consensus on considering papers with a total score of ⩾ 5 as high-quality studies. Moreover, The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) checklist was recruited to assess the risk of bias [29].
2.5
Statistical analysis
True positive (TP), true negative (TN), false positive (FP), and false negative (FN) rates were directly extracted from the included studies or were calculated based on the reported sensitivity, specificity, and prevalence. TP, TN, FP, and FN values were used to calculate the pooled effect size of ncRNAs expression in cancerous tissue for predicting TNM staging, clinical staging, lymph node metastasis (LNM), and distant metastasis (DM) as factors, predicting the severity and prognosis of OC. A univariate meta-analysis was conducted to compute the overall sensitivity, specificity, and diagnostic odds ratio (DOR) for the mentioned variables. The ncRNAs were only included in the meta-analysis if their effect had been reported as significantly positive. A generalized linear mixed model (GLMM) with log transformation was used for a random-effect univariate meta-analysis of sensitivity and specificity. An inverse variance model was used for a random-effect univariate meta-analysis of DOR. Continuity correction for studies containing zero cell counts was performed via the method introduced by Weber et al. [30]. Cochrane's Q test and I2 were used to assess the heterogeneity between studies, and a P-value of lower than 0.1 was considered significant. Subgroup analysis was performed based on the type of ncRNAs (microRNA, lncRNA, and circRNA) if there were at least two groups with at least two studies in each group. Furthermore, to find the best ncRNAs panel for predicting the prognosis of OC, based on the primary analysis, the effects of the ncRNAs with low expression were adjusted to improve the power of the model.
Bivariate meta-analysis fitted to logit-transformed sensitivities and false positive rates (FPRs) using the Reitsma et al. approach, and variance components were calculated using the restricted maximum likelihood (REML) method [31]. The summary receiver operating curve (SROC) was used to visualize the summary of the diagnostic performance of the included studies. The area under the SROC (AUSROC) and its CI were calculated by bootstrapping (2000 iterations) [32]. The heterogeneity was evaluated by the visual symmetry of the SROC and the correlation between logit-transformed sensitivity and specificity. Additionally, the Holling sample size adjusted method was used for calculating the I2 estimate of heterogeneity, and I2 over 75 %, 25–75 %, and under 25 % were considered high, moderate, and low heterogeneity [33,34]. Deek's funnel plot asymmetry test assessed the publication bias; a P-value <0.1 was considered significant. Meta-regression was performed for the type of ncRNAs.
Moreover, a meta-analysis was performed for the HR of OS of OC as a predictor of prognosis for ncRNAs. The natural logarithm of HR was used as the effect size, and the REML method was used to calculate the variance components. Subgroup analysis was performed for the type of ncRNAs. Cochrane's Q test and I2 were used to evaluate heterogeneity. Egger's test was used to assess the asymmetry of the funnel plot. Generally, P-values <0.05 were considered significant, and all reported CIs are 95 % CI. All analyses were performed via the R programming language, V4.2.1, using “mada,” “meta,” “metafor,” “dmetar,” and “dmetatools” packages [32,[35], [36], [37], [38]].
2.6
Bioinformatics analysis
Further analysis was conducted to find a probable correlation between the obtained data from this meta-analysis and existing datasets. We used TCGA data from the Pan-cancer database. The purpose of applying the Pan-cancer database was to validate our discoveries. We re-evaluated the OS impact of the differential expression of the microRNAs and lncRNAs in OC patients compared with healthy women using the Kaplan–Meier (K-M) plotter (https://kmplot.com/analysis/) [39]. It could be noted that the K-M plot visualization was performed based on the auto-select cut-off values [40].
2.1
Study protocol and search strategy
A systematic search of the literature was performed to retrieve papers discussing the prognostic value of ncRNAs related to EMT in OC patients. We developed a specific search strategy for each of the Embase, PubMed, Scopus, and Web of Science databases using the keywords ((“Ovarian” OR “Ovary”) and (“cancer*" OR “Neoplasm*” OR “Ovarian Neoplasm*")) and (“Metasta*" OR “EMT” OR “Epithelial-Mesenchymal transition” OR “Epithelial-Mesenchymal transformation” OR “Epithelial Mesenchymal*") and ((“RNA and Untranslated”) OR (“Noncoding” and “RNA”) OR (“Non-coding” and “RNA*") OR “ncRNA*" OR “MicroRNA*" OR “miRNA*” OR “miR” OR (“Long Noncoding” and “RNA*") OR (“long” and “non” and “coding” and “RNA*“) OR “lncRNA*" OR (“long non-coding RNA*“) OR “ceRNA*” OR (“competing endogenous RNA*“) OR “LINC RNA*” OR “circRNA*” OR (“Circular” and “RNA*“)), and the relevant MeSH terms. The detailed search strategy for each database is available in Supplementary Table 1.
The searches were not restricted to the title/abstract or specific languages. We applied the search terms to all fields by considering papers published from 2000 to Jun 13, 2024. The search result of each database was collected in a library. Then, retracted articles and duplicates were removed. This systematic review is based on the PRISMA statement [27]. The methodology of this study was registered in PROSPERO (Registration No. CRD42022304776).
2.2
Data management, screening, and detailed review
Two reviewers (SNS and SM) independently screened the title and abstract of the studies acquired from the previous step based on the defined inclusion and exclusion criteria. Disagreements were resolved by consulting with the third reviewer (ASK). In the title/abstract screening stage, conference abstracts, duplicate papers, letters, reviews, and editorial publications were excluded. Eligible articles were necessarily related to EMT, ncRNAs, and OC. Otherwise, they were excluded and classified as “not related to the topic” publications. Furthermore, papers that had not discussed prognosis and overall survival (OS) in their abstracts were not excluded at this stage, and a decision about them was taken during the full-text review.
In the detailed review step, the full text of the included papers was screened to check if the documents meet the study's inclusion criteria and have enough data to proceed with further steps. The evaluation of ncRNAs and their correlation with EMT and the evaluation of the prognostic role of the assessed ncRNAs were the essential factors reviewers appraised. Other exclusion criteria were case series, case reports, interventional and bioinformatics papers, cell line research, and animal studies lacking human samples. To be included, intervention-free samples must have been obtained from human OC patients, regardless of their disease stage, who did not suffer from other diseases or had not received medication therapies.
2.3
Data extraction
SNS and SM extracted the following data from the eligible articles: first author, publication year, country, study design, sample type, the number of cases and controls, ncRNA detection method, cancer stage, age-related information of controls and patients (mean, minimum, maximum, and standard deviation [SD]), type and name of the ncRNAs, the number of patients with upregulated and downregulated ncRNAs, p-value, prognosis levels (poor or good), the oncogenic or tumor-suppressive role of the ncRNAs, target genes and molecular mechanism of ncRNAs, type of OC, the number of patients with or without lymph node or distant metastasis regarding expression levels of ncRNAs, the number of patients with different clinical stages and TNM stages in both high and low expression levels of target ncRNA, hazard ratio (HR) and confidence of interval (CI) of both univariate and multivariate analysis of OS and progression-free survival (PFS). In case the needed data was not available in the paper or its supplementary material, the corresponding author(s) were contacted to obtain the needed data.
2.4
Quality assessment
To assess each included article's validity, quality, and risk of bias, case-control and cohort studies were evaluated based on the Newcastle-Ottawa Scale (NOS) [28]. We refined NOS questions and scores according to our study and defined the total validity score ranging from a minimum of 0 to a maximum of 8. The maximum scores of four, two, and two have been awarded to the three characteristics of each study: selection, comparability, and outcome, respectively. There was a consensus on considering papers with a total score of ⩾ 5 as high-quality studies. Moreover, The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) checklist was recruited to assess the risk of bias [29].
2.5
Statistical analysis
True positive (TP), true negative (TN), false positive (FP), and false negative (FN) rates were directly extracted from the included studies or were calculated based on the reported sensitivity, specificity, and prevalence. TP, TN, FP, and FN values were used to calculate the pooled effect size of ncRNAs expression in cancerous tissue for predicting TNM staging, clinical staging, lymph node metastasis (LNM), and distant metastasis (DM) as factors, predicting the severity and prognosis of OC. A univariate meta-analysis was conducted to compute the overall sensitivity, specificity, and diagnostic odds ratio (DOR) for the mentioned variables. The ncRNAs were only included in the meta-analysis if their effect had been reported as significantly positive. A generalized linear mixed model (GLMM) with log transformation was used for a random-effect univariate meta-analysis of sensitivity and specificity. An inverse variance model was used for a random-effect univariate meta-analysis of DOR. Continuity correction for studies containing zero cell counts was performed via the method introduced by Weber et al. [30]. Cochrane's Q test and I2 were used to assess the heterogeneity between studies, and a P-value of lower than 0.1 was considered significant. Subgroup analysis was performed based on the type of ncRNAs (microRNA, lncRNA, and circRNA) if there were at least two groups with at least two studies in each group. Furthermore, to find the best ncRNAs panel for predicting the prognosis of OC, based on the primary analysis, the effects of the ncRNAs with low expression were adjusted to improve the power of the model.
Bivariate meta-analysis fitted to logit-transformed sensitivities and false positive rates (FPRs) using the Reitsma et al. approach, and variance components were calculated using the restricted maximum likelihood (REML) method [31]. The summary receiver operating curve (SROC) was used to visualize the summary of the diagnostic performance of the included studies. The area under the SROC (AUSROC) and its CI were calculated by bootstrapping (2000 iterations) [32]. The heterogeneity was evaluated by the visual symmetry of the SROC and the correlation between logit-transformed sensitivity and specificity. Additionally, the Holling sample size adjusted method was used for calculating the I2 estimate of heterogeneity, and I2 over 75 %, 25–75 %, and under 25 % were considered high, moderate, and low heterogeneity [33,34]. Deek's funnel plot asymmetry test assessed the publication bias; a P-value <0.1 was considered significant. Meta-regression was performed for the type of ncRNAs.
Moreover, a meta-analysis was performed for the HR of OS of OC as a predictor of prognosis for ncRNAs. The natural logarithm of HR was used as the effect size, and the REML method was used to calculate the variance components. Subgroup analysis was performed for the type of ncRNAs. Cochrane's Q test and I2 were used to evaluate heterogeneity. Egger's test was used to assess the asymmetry of the funnel plot. Generally, P-values <0.05 were considered significant, and all reported CIs are 95 % CI. All analyses were performed via the R programming language, V4.2.1, using “mada,” “meta,” “metafor,” “dmetar,” and “dmetatools” packages [32,[35], [36], [37], [38]].
2.6
Bioinformatics analysis
Further analysis was conducted to find a probable correlation between the obtained data from this meta-analysis and existing datasets. We used TCGA data from the Pan-cancer database. The purpose of applying the Pan-cancer database was to validate our discoveries. We re-evaluated the OS impact of the differential expression of the microRNAs and lncRNAs in OC patients compared with healthy women using the Kaplan–Meier (K-M) plotter (https://kmplot.com/analysis/) [39]. It could be noted that the K-M plot visualization was performed based on the auto-select cut-off values [40].
3
Results
3.1
Study selection
Searching four databases, collecting the results of each search to a single library, omitting duplicate publications, and screening abstracts resulted in the retrieval of 332 studies. Following that, full-text reviewing led to the selection of 37 eligible articles for meta-analysis [[41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77]]. During the detailed review step, fifty-two articles were excluded due to insufficient data and failure to receive an appropriate score in the validity assessment, despite having all the inclusion criteria required for acceptance. The selection process with the exclusion reason of the articles was briefly characterized in Fig. 1.Fig. 1PRISMA flow diagram of the systematically reviewed papers with differential ncRNAs expression in OC patients. Initial searches in four databases resulted in 8730 articles. After removing 3473 duplicated and 95 retracted articles, 5162 papers were selected to be screened based on title/abstract and be categorized based on the defined exclusion/inclusion criteria. 332 included articles were grouped based on full-text screening. Finally, 37 studies were identified as eligible for our meta-analysis.Fig. 1
3.2
Study characteristics and quality assessment
A total of fifteen microRNAs (miR-506 [41], miR-26b [51], miR-216a [42], miR-532 [53], miR-3064 [53], miR-616 [64], miR-219a-5p [63], miR-214 [73], miR-196-5p [62], miR-99a [56], miR-18a [69], miR-126 [70], miR-489 [57], miR-98-5p [59], and miR-488 [44]), twenty-four lncRNAs (HOTAIR [50], CCAT1 [60], HOXD-AS1 [61], ADAMTS9-AS2 [72], MEG3 [63], PVT1 [73], DQ786243 [74], GAS5 [62], LncARSR [54], FLVCR1-AS1 [55], TC0101441 [58], FAM83H-AS1 [65], HOXB-AS3 [66], LINC-PINT [76], NEAT1 [52], SNHG20 [75], MAFG-AS1 [68], DSCR8 [59], LINC01094 [77], E2F4as [43], DNM3OS [45], LINC01969 [46], SRA [48], and HCG18 [49]), and three circRNAs (Circ_100395 [67], Circ_0000745 [47], and CircAGFG1 [71]) were evaluated in the included publications. The detection method of the target ncRNAs was quantitative real-time polymerase chain reaction (qRT-PCR) in almost all studies. Overall, the studies carried out their research on 5207 clinical samples, including 2086 control and 3121 OC patient tissue samples. The main characteristics of the included studies are described in Table 1.Table 1Main characteristics of the included studies for meta-analysis.Table 1First AuthorYearCountryType of ncRNAName of ncRNADetection MethodSample Size (case/control)Expression Levels (Up/Down)Target Gene(s)Type of OCMolecular MechanismRef.Lin J2015ChinaMicroRNAmiR-26bqRT-PCR97/-48/49KPNA2EOCMiR-26b inversely correlates with the expression of KPNA2 that downregulates OCT4 and Vimentin and conversely upregulates E-cadherin.[51]Liu H2017ChinaMicroRNAmiR-216aqRT-PCR87/2544/43PTEN–MiR-216a directly targets PTEN and inhibits the PTEN/AKT pathway, which promotes EMT and metastasis of OC cells.[42]Bai L2017ChinaMicroRNAmiR-532miR-3064qRT-PCR60/20miR-532: 0/31miR-3064: 0/29hTERTEOCBoth miR-3064 and miR-532 bind to and suppress the hTERT Leading to EMT process suppression and apoptosis induction, and the loss of these MiRs leads to OC.[53]Chen Z2018ChinaMicroRNAmiR-616qRT-PCR60/6030/30TIMP2–MiR-616 directly targets TIMP2, which is required for EMT process promotion.[64]Wang L2019ChinaMicroRNAmiR-219a-5pqRT-PCR317/317132/185EGFR–MiR‐219a increases the expression of E‐cadherin and reduces the expressions of N‐cadherin. Therefore, MiR‐219a prevents EMT by targeting EGFR.[63]Zhao H2018ChinaMicroRNAmiR-196-5pqRT-PCR195/195114/81HOXA5HGS-OCMiR-196a-5p promotes EMT by targeting HOXA5, reducing E‐cadherin, and increasing N‐cadherin in HGSOC.[62]Chen Y2018ChinaMicroRNAmiR-214qRT-PCR231/58101/130–EOCMiR-214 is downregulated by PVT1, which promotes EMT in EOC.[73]Zhang L2019ChinaMicroRNAmiR-99aqRT-PCR47/4723/24HOXA1–MiR-99a directly targets HOXA1 and suppresses cell proliferation via the AKT/mTOR pathway. This microRNA also inhibits invasion-mediated EMT.[56]Zhao Y2020ChinaMicroRNAmiR-18aqRT-PCR50/5020/30CBX/ERK–MiR-18a targets and inhibits the CBX7 and ERK protein levels Promotes ERK/MAPK signaling pathway leads to suppression of the proliferation, migration, invasion, and EMT.[69]Zhang Y2020ChinaMicroRNAmiR-126qRT-PCR54/5420/34EGLF7/ERK–MiR-126 directly targets EGFL7 and regulates the ERK/MAPK signaling pathway via suppression of ERK and participates in EMT.[70]Jiang HW2020ChinaMicroRNAmiR-489qRT-PCR51/5120/31XIAP/PI3K/EMT- related genes–MiR-489 binds to and regulates the X-linked inhibitor of apoptosis protein (XIAP), phosphatidyl-inositol 3-kinase/protein kinase B pathway (PI3K/AKT), and EMT in OC.[57]Dong L2020ChinaMicroRNAmiR-98- 5pqRT-PCR52/5226/26STAT3/HIF-1α–Downregulation of miR-98-5p in OC tissues promotes EMT and cell growth progression of OC.[59]Guo JY2020ChinaMicroRNAmiR-488qRT-PCR58/-18/40CCNG, P53–MiR-488 suppresses OC metastasis by reducing the expression of p53 and CCNG and blocking EMT.[44]Sun Y2015ChinaMicroRNAmiR-506ISH204/-95/102EMT- related genes (Vimentin/SNAI2/CDH2)EOCMiR-506, an EMT inhibitor, directly targets and inhibits Vimentin, SNAI2, and CDH2 expression while increasing the E-cadherin levels in EOC.[41]Chen J2021ChinaLncRNALINC01969qRT-PCR41/4119/22miR-144-5p–LINC01969 sponges miR-144-5p to upregulate LARP1 and then promotes migration, invasion, EMT, and proliferation of OC cells.[46]Qiu JJ2014ChinaLncRNAHOTAIRqRT-PCR64/2932/32MMPsEOCHOTAIR promotes EMT in EOC via MMP.[50]Cao Y2017ChinaLncRNACCAT1qRT-PCR72/7236/36miR-152/miR-130b/ADAM17/WNT1/ZEB1/STAT3/Vimentin/N-cadherinEOCIn EOC, CCAT1 is inversely associated with the activity of miR-152 and miR-130b, Which target ADAM17, WNT1, STAT3, ZEB1, Vimentin, and N-cadherin, then negatively regulate the EMT process.[60]Zhang Y2017ChinaLncRNAHOXD- AS1 (HAGLR)qRT-PCR43/4322/21miR-133a- 3pEOClncRNA HOXD-AS1 promotes cell proliferation, invasion, and EMT by targeting and sponging miR-133a-3p and activates the Wnt/β-catenin pathway in EOC.[61]Wang A2018ChinaLncRNAADAMTS9-AS2qRT-PCR47/-24/23miR-182-5p–ADAMTS9-AS2 sponges miR-182-5p and decreases OC progression via regulating the miR182-5p/FOXF2 pathway.Low levels of ADAMTS9-AS2 are correlated with OC cell metastasis, proliferation, invasion, and EMT. MiR-182-5p directly targets FOXF2.[72]Wang L2019ChinaLncRNAMEG3qRT-PCR317/317136/171miR-219-5p–MEG3 regulates miR‐219a‐5p/EGFR axis and prevents EMT.[63]Chen Y2018ChinaLncRNAPVT1qRT-PCR231/58115/116miR-214/EZH2EOCPVT1 represses miR-214 expression through interaction with EZH2. PVT1 overexpression reduces E-cadherin while elevating the expression levels of Vimentin, β-catenin, Snail, and Slug proteins, promoting EMT.[73]Yong W2018ChinaLncRNANEAT1qRT-PCR75/-37/38miR-506HGS-OCNEAT1 is stabilized by LIN28B, sponges miR-506, and promotes OC progression in HGSOC. NEAT1 promotes EMT by elevating the expression of E-cadherin, whereas reducing the expression of N-cadherin, MMP9, and MMP2.[52]Yan H2018ChinaLncRNADQ786243qRT-PCR30/3015/15miR‐506–DQ786243 interacts with and suppresses miR‐506 and promotes OC progression through targeting CREB1. Furthermore, DQ786243 promotes EMT via downregulation of E‐cadherin protein and upregulation of the Vimentin and snai2 protein levels.[74]Zhao H2018ChinaLncRNAGAS5qRT-PCR195/19570/125miR-196a- 5pHGS-OCGAS5 directly targets miR-196a-5p in HGSOC and prevents EMT.[62]Shu C2018ChinaLncRNALncARSRqRT-PCR76/7638/38HuR/ZEB1/ZEB2EOCOverexpression of lncARSR activates the WNT/B-Catenin pathway and increases ZEB1 and ZEB2 expression by competitively binding to the miR-200 family (lncARSR acts as ceRNA for miR-200 family), thus inducing EMT.[54]Yan H2019ChinaLncRNAFLVCR1- AS1qRT-PCR50/5027/23miR-513OSCFLVCR1-AS1 directly targets and downregulates miR-513, thus upregulated FLVCR1-AS1 mediates miR-513/YAP1 axis in OSC to promote EMT, cell growth, migration, and invasion and lower apoptosis.[55]Qiu JJ2019ChinaLncRNATC0101441 (ERLNC1)qRT-PCR74/2037/37KiSS1EOCTC0101441 targets and negatively regulates the KiSS1 and promotes cell migration/invasion and EMT in EOC.[58]Dou QR2019ChinaLncRNAFAM83H- AS1 (IQANK1)qRT-PCR80/8038/42HuR–FAM83H-AS1 interacts with and stabilizes HuR and facilitates EMT.[65]Wang D2019ChinaLncRNASNHG20qRT-PCR60/1538/22–EOCSNHG20 promotes EMT in EOC.[75]Zhuang XH2019ChinaLncRNAHOXB-AS3qRT-PCR178/17891/87–EOCHOXB-AS3, an oncogene, activates Wnt/β-catenin signaling, promotes cell proliferation, migration, invasion, and EMT, and inhibits apoptosis in EOC.[66]Hao T2020ChinaLncRNALINC-PINTqRT-PCR72/7220/52miR-374a-5p–LncRNA LINC-PINT sponges miR-374a-5p, inhibits cell proliferation, migration, and EMT, and augments apoptosis in OC.[76]Bai Y2021ChinaLncRNAMAFG-AS1 (MILIP)qRT-PCR75/7537/38miR-339-5p–MAFG-AS1 recruits and upregulates NFKB1 by binding to miR-339-5p. This leads to higher levels of IGF1 and promotes EMT and cell migration/invasion.[68]Dong L2020ChinaLncRNADSCR8qRT-PCR52/5226/26miR-98- 5p–Overexpression of DSCR8 in OC tissue leads to cell proliferation, invasion, and EMT while inhibiting apoptosis. Overexpressed DSCR8 positively regulates the expression of hypoxia-inducible factor 1 alpha (HIF-1α) and STAT3 and participates in miR-98-5p downregulation.[59]Xu J2020ChinaLncRNALINC01094qRT-PCR93/93–miR-577–LINC01094 directly targets and inhibits miR-577 and promotes cell proliferation, migration, invasion, and EMT. MiR-577 targets LRP6, Wnt2b, and β-catenin and regulates the Wnt/β-catenin pathway.[77]Park SA2020KoreaLncRNAE2F4asqRT-PCR108/3278/30––E2F4as promotes cell proliferation, invasion, and EMT migration and decreases apoptosis.[43]He L2021ChinaLncRNADNM3OSqRT-PCR49/1825/24miR-193a-3p/EMT -related genes–DNM3OS interacts with miR-193a-3p and increases the expression of MAPK3K3 by repressing miR-193a-3p. Overexpression of DNM3OS augments OC EMT, proliferation, cell migration, invasion, and the expression of N-cadherin protein and impedes the E-cadherin levels.[45]Kim LK2021South KoreaLncRNALnc-SRAqRT-PCR101/6366/35E-cadherin/β-catenin/N-cadherin/Snail/HES1/Vimentin/NOTCH/NICD/P300LncRNA SRA regulates OC progression through NOTCH signaling and EMT.[48]Zhang F2022ChinaLncRNAHCG18qRT-PCR30/3015/15miR-29a/bEOCLncRNA-HCG18 stimulates the NF-κB pathway-mediated EMT, proliferation, and migration of EOC cells by acting as a ceRNA of miR-29a/b, which upregulates TRAF4/5 expression levels. Overexpression of HCG18 reduces E-cadherin while increasing the protein levels of MMP2, MMP9, and Vimentin.[49]Li X2020ChinaCircRNACirc_100395qRT-PCR60/6030/30miR-1228–CircRNA_100395 negatively associates with miR-1228 and exerts its inhibitory activity against cell growth, cell proliferation, and metastasis of OC cells by regulating the miR-1228/p53/EMT pathway.[67]Wang S2021ChinaCircRNACirc_0000745qRT-PCR50/5024/26miR-3187- 3p–Circ_0000745 targets and inhibits miR-3187-3p and promotes phosphorylation of the PI3K/AKT pathway via stabilizing ERBB4. Circ_0000745 facilitates EMT by enhancing the expression of Vimentin and Snail and reducing the expression of E-cadherin. MiR-3187-3p inhibits ERBB4 and blocks Circ_0000745.[47]Luo J2024ChinaCircRNACircAGFG1qRT-PCR30/3015/13miR-409-3 p–CircAGFG1 promotes EMT, proliferation, and invasion/migration of the OC by targeting miR-409-3p, elevating the zinc finger E-box binding homeobox 1 (ZEB1) expression.[71]
Among the 37 included primary studies for meta-analysis, seventeen got a score of 5 [[41], [42], [43], [44],[47], [48], [49],52,53,64,[72], [73], [74], [75], [76], [77], [78]], ten got a score of 6 [45,50,51,[54], [55], [56], [57], [58], [59],71], eight got a score of 7 [[60], [61], [62], [63],[65], [66], [67], [68]], and two got a score of 8 [69,70] in the refined NOS quality assessment. The quality assessment results have been summarized in Table 2. Moreover, the risk of bias assessment via QUADAS-2 has been shown in Fig. 2A and B.Table 2NOS scores for included studies.Table 2First Author (Year)#1#2#3#4#5#6#7#8TFirst Author (Year)#1#2#3#4#5#6#7#8TZhang Y (2020)********8Qiu JJ (2014)**––****6Zhao Y (2020)********8Zhang F (2022)**––**–*5Bai Y (2021)***–****7Chen J (2021)–**–**–*5Li X (2020)***–****7Wang S (2021)–**––***5Zhuang XH (2019)***–****7Kim LK (2021)**––**–*5Dou Q (2019)***–****7Xu J (2020)***––*–*5Wang L (2019)***–****7Guo JY (2020)–*––****5Zhao H (2018)***–****7Park SA (2020)–*––****5Zhang Y (2017)***–****7Hao T (2020)–**–**–*5Cao Y (2017)***–****7Wang D (2019)–**–**–*5Luo J (2024)**––****6Wang A (2018)**––**–*5He L (2021)–**–****6Yong W (2018)***––*–*5Jiang HW (2020)–**–****6Yan H (2018)–**–**–*5Dong L (2020)***–**–*6Chen Y (2018)**––**–*5Qiu JJ (2020)**––****6Chen Z (2018)**––**–*5Yan H (2019)***–**–*6Liu H (2017)**––**–*5Zhang L (2019)–**–****6Bai L (2017)**––**–*5Shu C (2018)–**–****6Sun Y (2015)****–––*5Lin J (2015)**––****6Selection: #1, #2, #3, and #4/Comparability: #5 and #6/Outcome: #7 and #8/T: total scoreFig. 2(A) Risk of bias assessment via QUADAS-2 for included studies, low-risk (green), high-risk (red), and unclear (Yellow) for each domain, including patient selection, index test, reference standard, and flow and timing. (B) Concerns regarding applicability.Fig. 2
3.3
Diagnostic accuracy of ncRNAs for LNM in OC
In the univariate analysis of DOR, the overall effect for ncRNAs in LNM was 4.5 (95%CI, 3.39–5.99; P-value <0.0001). The heterogeneity was moderate, with an I2 of 24 % (Q, 31.38; P-value 0.14). The subgroup analysis demonstrated that overall, DOR in microRNA and lncRNA was 4.67 (95%CI, 2.48–8.81; P-value <0.0001) and 4.44 (CI, 3.15–6.25; P-value <0.0001), respectively. Heterogeneity was moderate in both subgroups (I2, 35 %; Q, 10.79; P-value 0.15 and I2, 26 %; Q, 20.32; P-value 0.16 for microRNA and lncRNA, respectively). There was only one study that examined the effects of circRNA, so the subgroup analysis was not applicable to this ncRNA. The subgroup difference was insignificant (X2, 0.02; P-value, 0.99). The forest plot for DOR univariate meta-analysis has been depicted in Fig. 3A. Publication bias was not significant, with a P-value of 0.71. Fig. 4D shows Deek's funnel plot for DOR. Univariate analysis for sensitivity and specificity demonstrated an overall sensitivity of 72 % and specificity of 64 %, with moderate heterogeneity for both sensitivity and specificity (Fig. 3B).Fig. 3Univariate meta-analyses of ncRNAs in LNM. (A) Univariate forest plots for LNM show an overall DOR of 4.5 with no significant difference between subgroups (P = 0.99) (B) Univariate forest plots show sensitivity and specificity of 0.72 and 0.66, respectively.Fig. 3Fig. 4Bivariate meta-analysis of ncRNAs in LNM. (A) Scatter plot of logit sensitivity and specificity shows low data dispersion (B) SROC for bivariate meta-analysis shows an overall AUC of 0.72 (C) Ellipse plot for bivariate meta-analysis. (D) Deek's funnel plot shows no publication bias.Fig. 4
The bivariate scatter plot shows the data are generally concentrated around the center with low dispersion (Fig. 4A). A bivariate meta-analysis demonstrated that the overall sensitivity was 0.71 (95%CI, 0.65–0.77; intercept P-value <0.001; between-studies standard deviation [SD], 0.51) and the overall specificity was 0.63 (95%CI, 0.57–0.68; intercept P-value <0.001; between-studies SD, 0.45). The total AUC was 0.72 (95%CI, 0.66–0.75). The overall DOR, LR+, and LR− were 4.19 (95%CI, 3.13–5.47), 1.91 (95%CI, 1.67–2.19), and 0.46 (95%CI, 0.38–0.55), respectively. The bivariate meta-analysis result has been shown as SROC and bagplot in Fig. 4B and C. The SROC was symmetrical, and the correlation coefficient between logit-transformed sensitivity and specificity was negative (r, −0.70), showing low heterogeneity. Also, the Holling sample size adjusted I2 was 1.5–1.7 %. The log-likelihood for the model goodness-of-fit was 31.74.
3.4
Diagnostic accuracy of ncRNAs for DM in OC
The univariate analysis demonstrated an overall DOR of 3.86 (95%CI, 2.47–6.03 and P-value <0.0001) with a moderate heterogeneity (I2, 65 %; Q, 33.80; P-value <0.01). The subgroup analysis demonstrated no significant difference between subgroups (X2, 1.97; P-value, 0.37). The overall DOR was 4.65 (95%CI, 2.20–9.84; P-value <0.0001),2.74 (95%CI, 1.56–4.82; P-value <0.001), and 5.63 (95%CI, 1.86–17.06; P-value <0.01) in miRNA, lncRNA, and circular RNA, respectively (Fig. 5A). The heterogeneity was high in the miRNA subgroup (I2, 77 %; Q, 21.79; P-value <0.01), moderate in the lncRNA subgroup (I2, 51 %; Q, 8.18; P-value, 0.09), and low in the circular RNA subgroup (I2, 0 %; Q, 0.26; P-value, 0.61). The publication bias was high (P-value <0.001). Deek's funnel plot has been depicted in Fig. 6D. Overall sensitivity and specificity in univariate analysis were 73 % and 58 %, respectively, with moderate heterogeneity in both analyses (Fig. 5B).Fig. 5Univariate meta-analysis of ncRNAs in DM. (A) Univariate forest plots for DM show an overall DOR of 3.86 with no significant difference between subgroups (P = 0.37) (B) Univariate forest plots show sensitivity and specificity of 0.73 and 0.58, respectively.Fig. 5Fig. 6Bivariate meta-analysis of ncRNAs in DM. (A) Scatter plot of logit sensitivity and specificity shows that the data are dispersed (B) SROC for bivariate meta-analysis shows an overall AUC of 0.67 (C) Ellipse plot for bivariate meta-analysis. (D) Deek's funnel plot shows presence of publication bias.Fig. 6
The scatter plot demonstrated that the data are not centered and have dispersion (Fig. 6A). The bivariate meta-analysis demonstrated total sensitivity and specificity of 0.73 (95%CI, 0.66–0.78; intercept P-value, <0.001; between-studies SD, 0.38) and 0.58 (95%CI, 0.52–0.64; intercept P-value <0.01; between-studies SD, 0.29), respectively. The overall AUC was 0.67 (95%CI, 0.56–0.74). The total DOR, LR+, and LR− in the bivariate meta-analysis were 3.80 (95%CI, 2.38–5.75), 1.75 (95%CI, 1.44–2.10), and 0.47 (95%CI, 0.36–0.61), respectively. The SROC and bag plot for the bivariate meta-analysis has been depicted in Fig. 6B and C. The SROC did not seem symmetrical, and the correlation coefficient between the logit-transformed sensitivity and specificity was positive (r, 0.29), implicating potential heterogeneity in the model. The Holling sample size adjusted I2 was 3.2–3.4 %. The log-likelihood for the goodness-of-fit of the model was 22.21.
3.5
Diagnostic accuracy of ncRNAs for TNM staging in OC
The total DOR was 5.69 in univariate analysis (95%CI, 3.26–9.94; P-value <0.0001), and the heterogeneity was low (I2, 16 %; Q, 5.95; P-value, 0.31). The subgroup analysis for the type of the ncRNAs demonstrated no significant difference between subgroups (X2, 3.18; P-value, 0.07) with a DOR of 7.74 in the miRNA subgroup (95%CI, 4.18–14.35; P-value <0.0001) and 3.11 in the lncRNA subgroup (CI, 1.41–6.86; P-value <0.01). The forest plot for univariate DOR meta-analysis has been demonstrated in Fig. 7A. The heterogeneity in both subgroups was low (I2, 0 %; Q, 2.5; P-value, 0.47 for miRNA and I2, 0 %; Q, 0.27; P-value, 0.60 for lncRNA). The publication bias was low (P-value, 0.40), and Deek's funnel plot is demonstrated in Fig. 8D. In univariate analysis, the overall sensitivity was 76 % with moderate heterogeneity, and the overall specificity was 67 % with low heterogeneity, Fig. 7B.Fig. 7Univariate meta-analysis of ncRNAs in TNM stage. (A) Univariate forest plots for TNM show an overall DOR of 3.86 with no significant difference between subgroups (P = 0.37) (B) Univariate forest plots show sensitivity and specificity of 0.67 and 0.76, respectively.Fig. 7Fig. 8Bivariate meta-analysis of ncRNAs in TNM stage. (A) Scatter plot of logit sensitivity and specificity shows that the data are dispersed (B) SROC for bivariate meta-analysis shows an overall AUC of 0.69 (C) Ellipse plot for bivariate meta-analysis. (D) Deek's funnel plot shows no publication bias.Fig. 8
The scatter plot of logit-transformed sensitivity and specificity demonstrated that the data are dispersed (Fig. 8A). Pooled effects of 0.75 (95%CI, 0.65–0.84; intercept P-value <0.001; between studies SD, 0.43) and 0.67 (95%CI, 0.58–0.74; intercept P-value <0.001; between-studies SD, 0.06) were observed for sensitivity and specificity, respectively. SORC and ellipse plots for the bivariate meta-analysis have been shown in Fig. 8B and C, respectively. The overall AUC was 0.69 (95%CI, 0.63–0.83). Moreover, the overall DOR, LR+, and LR− were 6.52 (95%CI, 3.27–11.60), 2.29 (95%CI, 1.79–3.01), and 0.37 (95%CI, 0.24–0.54). The SROC plot was asymmetric, and the correlation coefficient between the logit-transformed sensitivity and the specificity was positive, which may show potential heterogeneity in the bivariate model. Also, the I2 estimate based on the Holling sample size adjusted method was 1.7–1.8 %. The log-likelihood ratio for the goodness-of-fit of the model was 12.23.
3.6
Diagnostic accuracy of ncRNAs for clinical staging in OC
Based on the primary univariate analysis, the overall univariate DOR of the ncRNA panel for clinical staging was 3.97 with 95%CI, 3.18–4.96, and P-value <0.0001. Heterogeneity was moderate, with I2 equal to 45.2 % (Q, 56.58; P < 0.01). In miRNA, lncRNA, and circular RNA subgroups, the DOR was 3.62 (95%CI, 2.77–4.73; P-value <0.0001), 3.90 (95%CI, 2.48–5.35, P-value <0.0001), and 6.04 (95%CI, 2.22–16.45; P-value <0.001), respectively. The forest plot for univariate DOR meta-analysis has been shown in Fig. 9A. The heterogeneity was moderate in miRNA (I2, 41 %; Q, 18.67; P-value, 0.07), lncRNA (I2, 49 %; Q, 31.63; P-value <0.01), and circular RNA (I2, 45 %; Q, 3.61; P-value, 0.16) subgroups. The difference between the subgroups was insignificant (X2, 0.98; P-value, 0.61). The publication bias was significant based on Deek's method (P-value <0.01). The funnel plot has been demonstrated in Fig. 10D. The univariate analysis demonstrated that the overall sensitivity and specificity were 67 % and 68 %, respectively, with moderate heterogeneity in both groups (Fig. 9B).Fig. 9Univariate meta-analysis of ncRNAs in clinical stage. (A) Univariate forest plots for the clinical stage show an overall DOR of 3.97 with no significant difference between subgroups (P = 0.61) (B) Univariate forest plots show sensitivity and specificity of 0.67 and 0.68, respectively.Fig. 9Fig. 10Bivariate meta-analysis of ncRNAs in clinical stage. (A) Scatter plot of logit sensitivity and specificity shows low data dispersion (B) SROC for bivariate meta-analysis shows an overall AUC of 0.71 (C) Ellipse plot for bivariate meta-analysis. (D) Deek's funnel plot shows the presence of publication bias.Fig. 10
The scatter plot demonstrated relatively centered data with low dispersion (Fig. 10A). The overall sensitivity and specificity were 0.66 (95%CI, 0.62–0.70; intercept P-value <0.001; between-studies SD, 0.43), 0.67 (CI, 0.62–0.71; intercept P-value <0.001; between-studies SD, 0.40), respectively in bivariate meta-analysis. The total AUC was 0.71 (CI, 0.67–0.73). The SROC and ellipse plots are depicted in Fig. 10B and C, respectively. Overall, DOR, LR+, and LR− were 3.97 (95%CI, 3.11–4.98), 1.99 (CI, 1.75–2.27), and 0.51 (CI, 0.45–0.57), respectively. The symmetric SROC curve and negative correlation between sensitivity and specificity (r, −0.15) show small heterogeneity. Additionally, Heterogeneity was low based on the Holling sample size adjusted I2 estimate of 1.9–2.0 %. The log-likelihood for the goodness-of-fit of the model was 45.96.
3.7
Meta-analysis of HR to evaluate the value of ncRNAs in predicting OC prognosis
A total of 10 ncRNAs were included in an overall survival (OS) HR meta-analysis, of which 6 belong to the lncRNA subgroup and 4 belong to the miRNA subgroup. A random-effect meta-analysis demonstrated no significant overall effect (HR, 1.39; 95%CI, 0.67–2.84; P-value, 0.32). Overall heterogeneity was significant, with an I2 of 91.2 % (Q, 102.26; P-value <0.0001). The subgroup analysis demonstrated that overall HR in the miRNA subgroup was 0.92 (95%CI, 0.24–3.59; P-value, 0.91), and in the lncRNA, a subgroup was 1.82 (95%CI, 0.81–4.09; P-value, 0.14). The difference between subgroups was not significant (X2, 0.72; P-value, 0.40). Heterogeneity within the subgroups was significant (I2, 94 %; Q, 48.25; P-value <0.01 and I2, 90 %; Q, 48.63; P-value <0.01 for miRNA and lncRNA groups, respectively). The forest plot for HR is shown in Fig. 11A. Egger's test demonstrated no significant publication bias (P-value, 0.72). The funnel plot is depicted in Fig. 11B.Fig. 11Meta-analysis for OS. (A) The forest plot for overall survival shows an HR of 1.39 with no subgroup difference (P = 0.40) (B) The Funnel plot for OS shows no significant publication bias.Fig. 11
3.8
Outcomes validation based on bioinformatics analysis
The prognostic value of the obtained microRNAs and lncRNAs were re-analyzed based on the OS in OC patients, applying the Pan-cancer miRNA and Pan-cancer RNA-seq data, respectively. OS analysis of microRNAs resulted in the identification of a correlation between poor prognosis and high levels of hsa-miR-216a (HR = 1.68), hsa-miR-3064 (HR = 1.43), hsa-miR-489 (HR = 1.7), hsa-miR-488 (HR = 1.61), and hsa-miR-196a (HR = 1.44). However, low levels of hsa-miR-532 (HR = 0.75), hsa-miR-219a (HR = 0.65), hsa-miR-18a (HR = 0.78), and has-miR-98 (HR = 0.73) were related to poor OS of OC patients. Combining the results using the mean expression of the whole target microRNAs represented that the correlation between low expression levels of total microRNAs and poor OS prognosis was significant (log Rank P = 0.017). K-M plots for microRNAs have been shown in Fig. 12a-j. Furthermore, OS analysis of lncRNAs recognized the elevated expression of lncRNAs, including HOTAIR (HR = 1.81), NEAT1 (HR = 1.4), GAS5 (HR = 1.5), HOXB-AS3 (HR = 1.58), DSCR8 (HR = 1.33), and LINC01969 (HR = 1.66), and decreased expression of lncRNAs, involving CCAT1 (HR = 0.54), FLVCR1- AS1 (HR = 0.76), and HCG18 (HR = 0.67), as indicators of poor prognosis in OC patients. Total lncRNAs OS analysis revealed a close correlation (log Rank P = 0.017) between high levels of the whole selected lncRNAs with poor prognosis of OC (Fig. 13a-j). P-values for ncRNAs OS have been provided in Supplementary Table 2.Fig. 12K-M plots for microRNAs based on Pan-cancer miRNA. High levels of miR-3064, miR-216a, miR-489, miR-196a, and miR-488 and low expression of miR-532, miR-18a, miR-219a, and has-miR-98 are shown to be correlated with poor prognosis of OC. Low levels of whole microRNAs are associated with poor OS.Fig. 12Fig. 13K-M plots for lncRNAs based on Pan-cancer RNAseq data. High levels of HOTAIR, GAS5, NEAT1, HOXB-AS3, LINC01969, and DSCR8, low levels of expression of CCAT1, HCG18, and FLVCR1- AS1, and high levels of whole lncRNAs are correlated with poor OS of OC patients.Fig. 13
3.1
Study selection
Searching four databases, collecting the results of each search to a single library, omitting duplicate publications, and screening abstracts resulted in the retrieval of 332 studies. Following that, full-text reviewing led to the selection of 37 eligible articles for meta-analysis [[41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77]]. During the detailed review step, fifty-two articles were excluded due to insufficient data and failure to receive an appropriate score in the validity assessment, despite having all the inclusion criteria required for acceptance. The selection process with the exclusion reason of the articles was briefly characterized in Fig. 1.Fig. 1PRISMA flow diagram of the systematically reviewed papers with differential ncRNAs expression in OC patients. Initial searches in four databases resulted in 8730 articles. After removing 3473 duplicated and 95 retracted articles, 5162 papers were selected to be screened based on title/abstract and be categorized based on the defined exclusion/inclusion criteria. 332 included articles were grouped based on full-text screening. Finally, 37 studies were identified as eligible for our meta-analysis.Fig. 1
3.2
Study characteristics and quality assessment
A total of fifteen microRNAs (miR-506 [41], miR-26b [51], miR-216a [42], miR-532 [53], miR-3064 [53], miR-616 [64], miR-219a-5p [63], miR-214 [73], miR-196-5p [62], miR-99a [56], miR-18a [69], miR-126 [70], miR-489 [57], miR-98-5p [59], and miR-488 [44]), twenty-four lncRNAs (HOTAIR [50], CCAT1 [60], HOXD-AS1 [61], ADAMTS9-AS2 [72], MEG3 [63], PVT1 [73], DQ786243 [74], GAS5 [62], LncARSR [54], FLVCR1-AS1 [55], TC0101441 [58], FAM83H-AS1 [65], HOXB-AS3 [66], LINC-PINT [76], NEAT1 [52], SNHG20 [75], MAFG-AS1 [68], DSCR8 [59], LINC01094 [77], E2F4as [43], DNM3OS [45], LINC01969 [46], SRA [48], and HCG18 [49]), and three circRNAs (Circ_100395 [67], Circ_0000745 [47], and CircAGFG1 [71]) were evaluated in the included publications. The detection method of the target ncRNAs was quantitative real-time polymerase chain reaction (qRT-PCR) in almost all studies. Overall, the studies carried out their research on 5207 clinical samples, including 2086 control and 3121 OC patient tissue samples. The main characteristics of the included studies are described in Table 1.Table 1Main characteristics of the included studies for meta-analysis.Table 1First AuthorYearCountryType of ncRNAName of ncRNADetection MethodSample Size (case/control)Expression Levels (Up/Down)Target Gene(s)Type of OCMolecular MechanismRef.Lin J2015ChinaMicroRNAmiR-26bqRT-PCR97/-48/49KPNA2EOCMiR-26b inversely correlates with the expression of KPNA2 that downregulates OCT4 and Vimentin and conversely upregulates E-cadherin.[51]Liu H2017ChinaMicroRNAmiR-216aqRT-PCR87/2544/43PTEN–MiR-216a directly targets PTEN and inhibits the PTEN/AKT pathway, which promotes EMT and metastasis of OC cells.[42]Bai L2017ChinaMicroRNAmiR-532miR-3064qRT-PCR60/20miR-532: 0/31miR-3064: 0/29hTERTEOCBoth miR-3064 and miR-532 bind to and suppress the hTERT Leading to EMT process suppression and apoptosis induction, and the loss of these MiRs leads to OC.[53]Chen Z2018ChinaMicroRNAmiR-616qRT-PCR60/6030/30TIMP2–MiR-616 directly targets TIMP2, which is required for EMT process promotion.[64]Wang L2019ChinaMicroRNAmiR-219a-5pqRT-PCR317/317132/185EGFR–MiR‐219a increases the expression of E‐cadherin and reduces the expressions of N‐cadherin. Therefore, MiR‐219a prevents EMT by targeting EGFR.[63]Zhao H2018ChinaMicroRNAmiR-196-5pqRT-PCR195/195114/81HOXA5HGS-OCMiR-196a-5p promotes EMT by targeting HOXA5, reducing E‐cadherin, and increasing N‐cadherin in HGSOC.[62]Chen Y2018ChinaMicroRNAmiR-214qRT-PCR231/58101/130–EOCMiR-214 is downregulated by PVT1, which promotes EMT in EOC.[73]Zhang L2019ChinaMicroRNAmiR-99aqRT-PCR47/4723/24HOXA1–MiR-99a directly targets HOXA1 and suppresses cell proliferation via the AKT/mTOR pathway. This microRNA also inhibits invasion-mediated EMT.[56]Zhao Y2020ChinaMicroRNAmiR-18aqRT-PCR50/5020/30CBX/ERK–MiR-18a targets and inhibits the CBX7 and ERK protein levels Promotes ERK/MAPK signaling pathway leads to suppression of the proliferation, migration, invasion, and EMT.[69]Zhang Y2020ChinaMicroRNAmiR-126qRT-PCR54/5420/34EGLF7/ERK–MiR-126 directly targets EGFL7 and regulates the ERK/MAPK signaling pathway via suppression of ERK and participates in EMT.[70]Jiang HW2020ChinaMicroRNAmiR-489qRT-PCR51/5120/31XIAP/PI3K/EMT- related genes–MiR-489 binds to and regulates the X-linked inhibitor of apoptosis protein (XIAP), phosphatidyl-inositol 3-kinase/protein kinase B pathway (PI3K/AKT), and EMT in OC.[57]Dong L2020ChinaMicroRNAmiR-98- 5pqRT-PCR52/5226/26STAT3/HIF-1α–Downregulation of miR-98-5p in OC tissues promotes EMT and cell growth progression of OC.[59]Guo JY2020ChinaMicroRNAmiR-488qRT-PCR58/-18/40CCNG, P53–MiR-488 suppresses OC metastasis by reducing the expression of p53 and CCNG and blocking EMT.[44]Sun Y2015ChinaMicroRNAmiR-506ISH204/-95/102EMT- related genes (Vimentin/SNAI2/CDH2)EOCMiR-506, an EMT inhibitor, directly targets and inhibits Vimentin, SNAI2, and CDH2 expression while increasing the E-cadherin levels in EOC.[41]Chen J2021ChinaLncRNALINC01969qRT-PCR41/4119/22miR-144-5p–LINC01969 sponges miR-144-5p to upregulate LARP1 and then promotes migration, invasion, EMT, and proliferation of OC cells.[46]Qiu JJ2014ChinaLncRNAHOTAIRqRT-PCR64/2932/32MMPsEOCHOTAIR promotes EMT in EOC via MMP.[50]Cao Y2017ChinaLncRNACCAT1qRT-PCR72/7236/36miR-152/miR-130b/ADAM17/WNT1/ZEB1/STAT3/Vimentin/N-cadherinEOCIn EOC, CCAT1 is inversely associated with the activity of miR-152 and miR-130b, Which target ADAM17, WNT1, STAT3, ZEB1, Vimentin, and N-cadherin, then negatively regulate the EMT process.[60]Zhang Y2017ChinaLncRNAHOXD- AS1 (HAGLR)qRT-PCR43/4322/21miR-133a- 3pEOClncRNA HOXD-AS1 promotes cell proliferation, invasion, and EMT by targeting and sponging miR-133a-3p and activates the Wnt/β-catenin pathway in EOC.[61]Wang A2018ChinaLncRNAADAMTS9-AS2qRT-PCR47/-24/23miR-182-5p–ADAMTS9-AS2 sponges miR-182-5p and decreases OC progression via regulating the miR182-5p/FOXF2 pathway.Low levels of ADAMTS9-AS2 are correlated with OC cell metastasis, proliferation, invasion, and EMT. MiR-182-5p directly targets FOXF2.[72]Wang L2019ChinaLncRNAMEG3qRT-PCR317/317136/171miR-219-5p–MEG3 regulates miR‐219a‐5p/EGFR axis and prevents EMT.[63]Chen Y2018ChinaLncRNAPVT1qRT-PCR231/58115/116miR-214/EZH2EOCPVT1 represses miR-214 expression through interaction with EZH2. PVT1 overexpression reduces E-cadherin while elevating the expression levels of Vimentin, β-catenin, Snail, and Slug proteins, promoting EMT.[73]Yong W2018ChinaLncRNANEAT1qRT-PCR75/-37/38miR-506HGS-OCNEAT1 is stabilized by LIN28B, sponges miR-506, and promotes OC progression in HGSOC. NEAT1 promotes EMT by elevating the expression of E-cadherin, whereas reducing the expression of N-cadherin, MMP9, and MMP2.[52]Yan H2018ChinaLncRNADQ786243qRT-PCR30/3015/15miR‐506–DQ786243 interacts with and suppresses miR‐506 and promotes OC progression through targeting CREB1. Furthermore, DQ786243 promotes EMT via downregulation of E‐cadherin protein and upregulation of the Vimentin and snai2 protein levels.[74]Zhao H2018ChinaLncRNAGAS5qRT-PCR195/19570/125miR-196a- 5pHGS-OCGAS5 directly targets miR-196a-5p in HGSOC and prevents EMT.[62]Shu C2018ChinaLncRNALncARSRqRT-PCR76/7638/38HuR/ZEB1/ZEB2EOCOverexpression of lncARSR activates the WNT/B-Catenin pathway and increases ZEB1 and ZEB2 expression by competitively binding to the miR-200 family (lncARSR acts as ceRNA for miR-200 family), thus inducing EMT.[54]Yan H2019ChinaLncRNAFLVCR1- AS1qRT-PCR50/5027/23miR-513OSCFLVCR1-AS1 directly targets and downregulates miR-513, thus upregulated FLVCR1-AS1 mediates miR-513/YAP1 axis in OSC to promote EMT, cell growth, migration, and invasion and lower apoptosis.[55]Qiu JJ2019ChinaLncRNATC0101441 (ERLNC1)qRT-PCR74/2037/37KiSS1EOCTC0101441 targets and negatively regulates the KiSS1 and promotes cell migration/invasion and EMT in EOC.[58]Dou QR2019ChinaLncRNAFAM83H- AS1 (IQANK1)qRT-PCR80/8038/42HuR–FAM83H-AS1 interacts with and stabilizes HuR and facilitates EMT.[65]Wang D2019ChinaLncRNASNHG20qRT-PCR60/1538/22–EOCSNHG20 promotes EMT in EOC.[75]Zhuang XH2019ChinaLncRNAHOXB-AS3qRT-PCR178/17891/87–EOCHOXB-AS3, an oncogene, activates Wnt/β-catenin signaling, promotes cell proliferation, migration, invasion, and EMT, and inhibits apoptosis in EOC.[66]Hao T2020ChinaLncRNALINC-PINTqRT-PCR72/7220/52miR-374a-5p–LncRNA LINC-PINT sponges miR-374a-5p, inhibits cell proliferation, migration, and EMT, and augments apoptosis in OC.[76]Bai Y2021ChinaLncRNAMAFG-AS1 (MILIP)qRT-PCR75/7537/38miR-339-5p–MAFG-AS1 recruits and upregulates NFKB1 by binding to miR-339-5p. This leads to higher levels of IGF1 and promotes EMT and cell migration/invasion.[68]Dong L2020ChinaLncRNADSCR8qRT-PCR52/5226/26miR-98- 5p–Overexpression of DSCR8 in OC tissue leads to cell proliferation, invasion, and EMT while inhibiting apoptosis. Overexpressed DSCR8 positively regulates the expression of hypoxia-inducible factor 1 alpha (HIF-1α) and STAT3 and participates in miR-98-5p downregulation.[59]Xu J2020ChinaLncRNALINC01094qRT-PCR93/93–miR-577–LINC01094 directly targets and inhibits miR-577 and promotes cell proliferation, migration, invasion, and EMT. MiR-577 targets LRP6, Wnt2b, and β-catenin and regulates the Wnt/β-catenin pathway.[77]Park SA2020KoreaLncRNAE2F4asqRT-PCR108/3278/30––E2F4as promotes cell proliferation, invasion, and EMT migration and decreases apoptosis.[43]He L2021ChinaLncRNADNM3OSqRT-PCR49/1825/24miR-193a-3p/EMT -related genes–DNM3OS interacts with miR-193a-3p and increases the expression of MAPK3K3 by repressing miR-193a-3p. Overexpression of DNM3OS augments OC EMT, proliferation, cell migration, invasion, and the expression of N-cadherin protein and impedes the E-cadherin levels.[45]Kim LK2021South KoreaLncRNALnc-SRAqRT-PCR101/6366/35E-cadherin/β-catenin/N-cadherin/Snail/HES1/Vimentin/NOTCH/NICD/P300LncRNA SRA regulates OC progression through NOTCH signaling and EMT.[48]Zhang F2022ChinaLncRNAHCG18qRT-PCR30/3015/15miR-29a/bEOCLncRNA-HCG18 stimulates the NF-κB pathway-mediated EMT, proliferation, and migration of EOC cells by acting as a ceRNA of miR-29a/b, which upregulates TRAF4/5 expression levels. Overexpression of HCG18 reduces E-cadherin while increasing the protein levels of MMP2, MMP9, and Vimentin.[49]Li X2020ChinaCircRNACirc_100395qRT-PCR60/6030/30miR-1228–CircRNA_100395 negatively associates with miR-1228 and exerts its inhibitory activity against cell growth, cell proliferation, and metastasis of OC cells by regulating the miR-1228/p53/EMT pathway.[67]Wang S2021ChinaCircRNACirc_0000745qRT-PCR50/5024/26miR-3187- 3p–Circ_0000745 targets and inhibits miR-3187-3p and promotes phosphorylation of the PI3K/AKT pathway via stabilizing ERBB4. Circ_0000745 facilitates EMT by enhancing the expression of Vimentin and Snail and reducing the expression of E-cadherin. MiR-3187-3p inhibits ERBB4 and blocks Circ_0000745.[47]Luo J2024ChinaCircRNACircAGFG1qRT-PCR30/3015/13miR-409-3 p–CircAGFG1 promotes EMT, proliferation, and invasion/migration of the OC by targeting miR-409-3p, elevating the zinc finger E-box binding homeobox 1 (ZEB1) expression.[71]
Among the 37 included primary studies for meta-analysis, seventeen got a score of 5 [[41], [42], [43], [44],[47], [48], [49],52,53,64,[72], [73], [74], [75], [76], [77], [78]], ten got a score of 6 [45,50,51,[54], [55], [56], [57], [58], [59],71], eight got a score of 7 [[60], [61], [62], [63],[65], [66], [67], [68]], and two got a score of 8 [69,70] in the refined NOS quality assessment. The quality assessment results have been summarized in Table 2. Moreover, the risk of bias assessment via QUADAS-2 has been shown in Fig. 2A and B.Table 2NOS scores for included studies.Table 2First Author (Year)#1#2#3#4#5#6#7#8TFirst Author (Year)#1#2#3#4#5#6#7#8TZhang Y (2020)********8Qiu JJ (2014)**––****6Zhao Y (2020)********8Zhang F (2022)**––**–*5Bai Y (2021)***–****7Chen J (2021)–**–**–*5Li X (2020)***–****7Wang S (2021)–**––***5Zhuang XH (2019)***–****7Kim LK (2021)**––**–*5Dou Q (2019)***–****7Xu J (2020)***––*–*5Wang L (2019)***–****7Guo JY (2020)–*––****5Zhao H (2018)***–****7Park SA (2020)–*––****5Zhang Y (2017)***–****7Hao T (2020)–**–**–*5Cao Y (2017)***–****7Wang D (2019)–**–**–*5Luo J (2024)**––****6Wang A (2018)**––**–*5He L (2021)–**–****6Yong W (2018)***––*–*5Jiang HW (2020)–**–****6Yan H (2018)–**–**–*5Dong L (2020)***–**–*6Chen Y (2018)**––**–*5Qiu JJ (2020)**––****6Chen Z (2018)**––**–*5Yan H (2019)***–**–*6Liu H (2017)**––**–*5Zhang L (2019)–**–****6Bai L (2017)**––**–*5Shu C (2018)–**–****6Sun Y (2015)****–––*5Lin J (2015)**––****6Selection: #1, #2, #3, and #4/Comparability: #5 and #6/Outcome: #7 and #8/T: total scoreFig. 2(A) Risk of bias assessment via QUADAS-2 for included studies, low-risk (green), high-risk (red), and unclear (Yellow) for each domain, including patient selection, index test, reference standard, and flow and timing. (B) Concerns regarding applicability.Fig. 2
3.3
Diagnostic accuracy of ncRNAs for LNM in OC
In the univariate analysis of DOR, the overall effect for ncRNAs in LNM was 4.5 (95%CI, 3.39–5.99; P-value <0.0001). The heterogeneity was moderate, with an I2 of 24 % (Q, 31.38; P-value 0.14). The subgroup analysis demonstrated that overall, DOR in microRNA and lncRNA was 4.67 (95%CI, 2.48–8.81; P-value <0.0001) and 4.44 (CI, 3.15–6.25; P-value <0.0001), respectively. Heterogeneity was moderate in both subgroups (I2, 35 %; Q, 10.79; P-value 0.15 and I2, 26 %; Q, 20.32; P-value 0.16 for microRNA and lncRNA, respectively). There was only one study that examined the effects of circRNA, so the subgroup analysis was not applicable to this ncRNA. The subgroup difference was insignificant (X2, 0.02; P-value, 0.99). The forest plot for DOR univariate meta-analysis has been depicted in Fig. 3A. Publication bias was not significant, with a P-value of 0.71. Fig. 4D shows Deek's funnel plot for DOR. Univariate analysis for sensitivity and specificity demonstrated an overall sensitivity of 72 % and specificity of 64 %, with moderate heterogeneity for both sensitivity and specificity (Fig. 3B).Fig. 3Univariate meta-analyses of ncRNAs in LNM. (A) Univariate forest plots for LNM show an overall DOR of 4.5 with no significant difference between subgroups (P = 0.99) (B) Univariate forest plots show sensitivity and specificity of 0.72 and 0.66, respectively.Fig. 3Fig. 4Bivariate meta-analysis of ncRNAs in LNM. (A) Scatter plot of logit sensitivity and specificity shows low data dispersion (B) SROC for bivariate meta-analysis shows an overall AUC of 0.72 (C) Ellipse plot for bivariate meta-analysis. (D) Deek's funnel plot shows no publication bias.Fig. 4
The bivariate scatter plot shows the data are generally concentrated around the center with low dispersion (Fig. 4A). A bivariate meta-analysis demonstrated that the overall sensitivity was 0.71 (95%CI, 0.65–0.77; intercept P-value <0.001; between-studies standard deviation [SD], 0.51) and the overall specificity was 0.63 (95%CI, 0.57–0.68; intercept P-value <0.001; between-studies SD, 0.45). The total AUC was 0.72 (95%CI, 0.66–0.75). The overall DOR, LR+, and LR− were 4.19 (95%CI, 3.13–5.47), 1.91 (95%CI, 1.67–2.19), and 0.46 (95%CI, 0.38–0.55), respectively. The bivariate meta-analysis result has been shown as SROC and bagplot in Fig. 4B and C. The SROC was symmetrical, and the correlation coefficient between logit-transformed sensitivity and specificity was negative (r, −0.70), showing low heterogeneity. Also, the Holling sample size adjusted I2 was 1.5–1.7 %. The log-likelihood for the model goodness-of-fit was 31.74.
3.4
Diagnostic accuracy of ncRNAs for DM in OC
The univariate analysis demonstrated an overall DOR of 3.86 (95%CI, 2.47–6.03 and P-value <0.0001) with a moderate heterogeneity (I2, 65 %; Q, 33.80; P-value <0.01). The subgroup analysis demonstrated no significant difference between subgroups (X2, 1.97; P-value, 0.37). The overall DOR was 4.65 (95%CI, 2.20–9.84; P-value <0.0001),2.74 (95%CI, 1.56–4.82; P-value <0.001), and 5.63 (95%CI, 1.86–17.06; P-value <0.01) in miRNA, lncRNA, and circular RNA, respectively (Fig. 5A). The heterogeneity was high in the miRNA subgroup (I2, 77 %; Q, 21.79; P-value <0.01), moderate in the lncRNA subgroup (I2, 51 %; Q, 8.18; P-value, 0.09), and low in the circular RNA subgroup (I2, 0 %; Q, 0.26; P-value, 0.61). The publication bias was high (P-value <0.001). Deek's funnel plot has been depicted in Fig. 6D. Overall sensitivity and specificity in univariate analysis were 73 % and 58 %, respectively, with moderate heterogeneity in both analyses (Fig. 5B).Fig. 5Univariate meta-analysis of ncRNAs in DM. (A) Univariate forest plots for DM show an overall DOR of 3.86 with no significant difference between subgroups (P = 0.37) (B) Univariate forest plots show sensitivity and specificity of 0.73 and 0.58, respectively.Fig. 5Fig. 6Bivariate meta-analysis of ncRNAs in DM. (A) Scatter plot of logit sensitivity and specificity shows that the data are dispersed (B) SROC for bivariate meta-analysis shows an overall AUC of 0.67 (C) Ellipse plot for bivariate meta-analysis. (D) Deek's funnel plot shows presence of publication bias.Fig. 6
The scatter plot demonstrated that the data are not centered and have dispersion (Fig. 6A). The bivariate meta-analysis demonstrated total sensitivity and specificity of 0.73 (95%CI, 0.66–0.78; intercept P-value, <0.001; between-studies SD, 0.38) and 0.58 (95%CI, 0.52–0.64; intercept P-value <0.01; between-studies SD, 0.29), respectively. The overall AUC was 0.67 (95%CI, 0.56–0.74). The total DOR, LR+, and LR− in the bivariate meta-analysis were 3.80 (95%CI, 2.38–5.75), 1.75 (95%CI, 1.44–2.10), and 0.47 (95%CI, 0.36–0.61), respectively. The SROC and bag plot for the bivariate meta-analysis has been depicted in Fig. 6B and C. The SROC did not seem symmetrical, and the correlation coefficient between the logit-transformed sensitivity and specificity was positive (r, 0.29), implicating potential heterogeneity in the model. The Holling sample size adjusted I2 was 3.2–3.4 %. The log-likelihood for the goodness-of-fit of the model was 22.21.
3.5
Diagnostic accuracy of ncRNAs for TNM staging in OC
The total DOR was 5.69 in univariate analysis (95%CI, 3.26–9.94; P-value <0.0001), and the heterogeneity was low (I2, 16 %; Q, 5.95; P-value, 0.31). The subgroup analysis for the type of the ncRNAs demonstrated no significant difference between subgroups (X2, 3.18; P-value, 0.07) with a DOR of 7.74 in the miRNA subgroup (95%CI, 4.18–14.35; P-value <0.0001) and 3.11 in the lncRNA subgroup (CI, 1.41–6.86; P-value <0.01). The forest plot for univariate DOR meta-analysis has been demonstrated in Fig. 7A. The heterogeneity in both subgroups was low (I2, 0 %; Q, 2.5; P-value, 0.47 for miRNA and I2, 0 %; Q, 0.27; P-value, 0.60 for lncRNA). The publication bias was low (P-value, 0.40), and Deek's funnel plot is demonstrated in Fig. 8D. In univariate analysis, the overall sensitivity was 76 % with moderate heterogeneity, and the overall specificity was 67 % with low heterogeneity, Fig. 7B.Fig. 7Univariate meta-analysis of ncRNAs in TNM stage. (A) Univariate forest plots for TNM show an overall DOR of 3.86 with no significant difference between subgroups (P = 0.37) (B) Univariate forest plots show sensitivity and specificity of 0.67 and 0.76, respectively.Fig. 7Fig. 8Bivariate meta-analysis of ncRNAs in TNM stage. (A) Scatter plot of logit sensitivity and specificity shows that the data are dispersed (B) SROC for bivariate meta-analysis shows an overall AUC of 0.69 (C) Ellipse plot for bivariate meta-analysis. (D) Deek's funnel plot shows no publication bias.Fig. 8
The scatter plot of logit-transformed sensitivity and specificity demonstrated that the data are dispersed (Fig. 8A). Pooled effects of 0.75 (95%CI, 0.65–0.84; intercept P-value <0.001; between studies SD, 0.43) and 0.67 (95%CI, 0.58–0.74; intercept P-value <0.001; between-studies SD, 0.06) were observed for sensitivity and specificity, respectively. SORC and ellipse plots for the bivariate meta-analysis have been shown in Fig. 8B and C, respectively. The overall AUC was 0.69 (95%CI, 0.63–0.83). Moreover, the overall DOR, LR+, and LR− were 6.52 (95%CI, 3.27–11.60), 2.29 (95%CI, 1.79–3.01), and 0.37 (95%CI, 0.24–0.54). The SROC plot was asymmetric, and the correlation coefficient between the logit-transformed sensitivity and the specificity was positive, which may show potential heterogeneity in the bivariate model. Also, the I2 estimate based on the Holling sample size adjusted method was 1.7–1.8 %. The log-likelihood ratio for the goodness-of-fit of the model was 12.23.
3.6
Diagnostic accuracy of ncRNAs for clinical staging in OC
Based on the primary univariate analysis, the overall univariate DOR of the ncRNA panel for clinical staging was 3.97 with 95%CI, 3.18–4.96, and P-value <0.0001. Heterogeneity was moderate, with I2 equal to 45.2 % (Q, 56.58; P < 0.01). In miRNA, lncRNA, and circular RNA subgroups, the DOR was 3.62 (95%CI, 2.77–4.73; P-value <0.0001), 3.90 (95%CI, 2.48–5.35, P-value <0.0001), and 6.04 (95%CI, 2.22–16.45; P-value <0.001), respectively. The forest plot for univariate DOR meta-analysis has been shown in Fig. 9A. The heterogeneity was moderate in miRNA (I2, 41 %; Q, 18.67; P-value, 0.07), lncRNA (I2, 49 %; Q, 31.63; P-value <0.01), and circular RNA (I2, 45 %; Q, 3.61; P-value, 0.16) subgroups. The difference between the subgroups was insignificant (X2, 0.98; P-value, 0.61). The publication bias was significant based on Deek's method (P-value <0.01). The funnel plot has been demonstrated in Fig. 10D. The univariate analysis demonstrated that the overall sensitivity and specificity were 67 % and 68 %, respectively, with moderate heterogeneity in both groups (Fig. 9B).Fig. 9Univariate meta-analysis of ncRNAs in clinical stage. (A) Univariate forest plots for the clinical stage show an overall DOR of 3.97 with no significant difference between subgroups (P = 0.61) (B) Univariate forest plots show sensitivity and specificity of 0.67 and 0.68, respectively.Fig. 9Fig. 10Bivariate meta-analysis of ncRNAs in clinical stage. (A) Scatter plot of logit sensitivity and specificity shows low data dispersion (B) SROC for bivariate meta-analysis shows an overall AUC of 0.71 (C) Ellipse plot for bivariate meta-analysis. (D) Deek's funnel plot shows the presence of publication bias.Fig. 10
The scatter plot demonstrated relatively centered data with low dispersion (Fig. 10A). The overall sensitivity and specificity were 0.66 (95%CI, 0.62–0.70; intercept P-value <0.001; between-studies SD, 0.43), 0.67 (CI, 0.62–0.71; intercept P-value <0.001; between-studies SD, 0.40), respectively in bivariate meta-analysis. The total AUC was 0.71 (CI, 0.67–0.73). The SROC and ellipse plots are depicted in Fig. 10B and C, respectively. Overall, DOR, LR+, and LR− were 3.97 (95%CI, 3.11–4.98), 1.99 (CI, 1.75–2.27), and 0.51 (CI, 0.45–0.57), respectively. The symmetric SROC curve and negative correlation between sensitivity and specificity (r, −0.15) show small heterogeneity. Additionally, Heterogeneity was low based on the Holling sample size adjusted I2 estimate of 1.9–2.0 %. The log-likelihood for the goodness-of-fit of the model was 45.96.
3.7
Meta-analysis of HR to evaluate the value of ncRNAs in predicting OC prognosis
A total of 10 ncRNAs were included in an overall survival (OS) HR meta-analysis, of which 6 belong to the lncRNA subgroup and 4 belong to the miRNA subgroup. A random-effect meta-analysis demonstrated no significant overall effect (HR, 1.39; 95%CI, 0.67–2.84; P-value, 0.32). Overall heterogeneity was significant, with an I2 of 91.2 % (Q, 102.26; P-value <0.0001). The subgroup analysis demonstrated that overall HR in the miRNA subgroup was 0.92 (95%CI, 0.24–3.59; P-value, 0.91), and in the lncRNA, a subgroup was 1.82 (95%CI, 0.81–4.09; P-value, 0.14). The difference between subgroups was not significant (X2, 0.72; P-value, 0.40). Heterogeneity within the subgroups was significant (I2, 94 %; Q, 48.25; P-value <0.01 and I2, 90 %; Q, 48.63; P-value <0.01 for miRNA and lncRNA groups, respectively). The forest plot for HR is shown in Fig. 11A. Egger's test demonstrated no significant publication bias (P-value, 0.72). The funnel plot is depicted in Fig. 11B.Fig. 11Meta-analysis for OS. (A) The forest plot for overall survival shows an HR of 1.39 with no subgroup difference (P = 0.40) (B) The Funnel plot for OS shows no significant publication bias.Fig. 11
3.8
Outcomes validation based on bioinformatics analysis
The prognostic value of the obtained microRNAs and lncRNAs were re-analyzed based on the OS in OC patients, applying the Pan-cancer miRNA and Pan-cancer RNA-seq data, respectively. OS analysis of microRNAs resulted in the identification of a correlation between poor prognosis and high levels of hsa-miR-216a (HR = 1.68), hsa-miR-3064 (HR = 1.43), hsa-miR-489 (HR = 1.7), hsa-miR-488 (HR = 1.61), and hsa-miR-196a (HR = 1.44). However, low levels of hsa-miR-532 (HR = 0.75), hsa-miR-219a (HR = 0.65), hsa-miR-18a (HR = 0.78), and has-miR-98 (HR = 0.73) were related to poor OS of OC patients. Combining the results using the mean expression of the whole target microRNAs represented that the correlation between low expression levels of total microRNAs and poor OS prognosis was significant (log Rank P = 0.017). K-M plots for microRNAs have been shown in Fig. 12a-j. Furthermore, OS analysis of lncRNAs recognized the elevated expression of lncRNAs, including HOTAIR (HR = 1.81), NEAT1 (HR = 1.4), GAS5 (HR = 1.5), HOXB-AS3 (HR = 1.58), DSCR8 (HR = 1.33), and LINC01969 (HR = 1.66), and decreased expression of lncRNAs, involving CCAT1 (HR = 0.54), FLVCR1- AS1 (HR = 0.76), and HCG18 (HR = 0.67), as indicators of poor prognosis in OC patients. Total lncRNAs OS analysis revealed a close correlation (log Rank P = 0.017) between high levels of the whole selected lncRNAs with poor prognosis of OC (Fig. 13a-j). P-values for ncRNAs OS have been provided in Supplementary Table 2.Fig. 12K-M plots for microRNAs based on Pan-cancer miRNA. High levels of miR-3064, miR-216a, miR-489, miR-196a, and miR-488 and low expression of miR-532, miR-18a, miR-219a, and has-miR-98 are shown to be correlated with poor prognosis of OC. Low levels of whole microRNAs are associated with poor OS.Fig. 12Fig. 13K-M plots for lncRNAs based on Pan-cancer RNAseq data. High levels of HOTAIR, GAS5, NEAT1, HOXB-AS3, LINC01969, and DSCR8, low levels of expression of CCAT1, HCG18, and FLVCR1- AS1, and high levels of whole lncRNAs are correlated with poor OS of OC patients.Fig. 13
4
Discussion
To our knowledge, this study is the first systematic review conducted to define the designation of ncRNAs in OC prognosis and EMT. Our results provide information on the importance and applicability of ncRNAs in predicting clinical stage, DM and LNM, and TNM stage of OCs. The current meta-analysis demonstrates that ncRNAs could be appropriate markers for predicting factors related to OC prognosis, including TNM staging, clinical staging, LNM, and DM; overall DOR for them were 6.52, 3.97, 4.19, and 3.8, respectively. The HR of the high-expression ncRNA group to the low-expression ncRNA group was insignificant.
OC, the most lethal gynecological malignancy, is predicted to increase in cases and deaths annually. Commonly used OC tumor markers, such as CA125, lack enough specificity and sensitivity and are unsatisfying for OC detection in early-stage and all subtypes. Serum and ascites levels of most kallikrein-related peptidases (KLK) family members that are noticeably overexpressed in OC tissues elevate due to protease secretion. KLK could serve as a potential biomarker and is adopted as a complementary tool along with CA125 for OC diagnosis, and its upregulation is associated with aggressive tumor phenotypes. However, DNA methylation patterns, microRNAs, and circulating tumor cells are identified as promising biomarkers with early detection possibilities and better diagnostic accuracy [[79], [80], [81]]. Small ncRNAs (sncRNAs) play critical roles in gene regulation, and a combination of different types of sncRNA indicates OC development with early diagnosis benefits [82]. LncRNAs are considerably specific for each tumor origin, substantially stable in body fluids involving urine, whole blood, serum, and saliva, and easily detectable employing molecular techniques such as qRT-PCR, RNA-sequencing, and microarray hybridization [83].
Many studies have shown the dysregulation of ncRNAs in cancer. MicroRNAs target transcription factors to control the EMT process of tumor cells in different types of cancers [84]. MiR-148b is upregulated in 92.21 % of OC samples and could be used as competent diagnostic biomarkers for early-stage detection [85]. Distinct expression of plasma microRNAs can serve as highly sensitive and specific diagnostic markers for endometriosis-related OC [86]. Significant upregulated VPS13C-has-circ-001567 is positively associated with OC stage, LNM, cell proliferation, and invasion while reducing apoptosis and E-cadherin levels [87]. Overexpression of lncRNA CTBP1-AS2 and PTEN leads to a decreased proliferation of OC cells and miR-216a expression [88]. Based on the study by Zhonghua Chen et al. [89], the mean serum level of miR-125b in EOC patients was significantly reduced compared to control groups. Decreased levels of miR-125b are beneficial diagnostic markers and are accompanied by a poor prognosis for EOC patients.
Several studies have investigated the roles of ncRNAs in OC. Circulating ncRNAs contribute to cell migration, invasion, metastasis, and recurrence of OC [90]. MiR-146a and miR-150 stimulate cell survival and promote drug resistance [91]. Higher expression levels of lncRNA SNHG3 in OC tissues are positively associated with LNM, clinical stages, poor prognosis, and higher increased levels of invasion-related protein CyclinD1, CDK1, MMP9, and MMP3 [92]. LncRNA-MALAT1 negatively regulates miR-503-5p expression by increasing proliferation and decreasing OC cell apoptosis via the JAK2-STAT3 pathway [93]. Overexpression of lncRNA AB073614 is significantly associated with tumor size, clinical stage, lymph node invasion, and shorter survival rate of EOC patients. Therefore, AB073614 can be a prognostic biomarker and a potential treatment target for EOC [94].
Among the different roles ncRNAs participate in, some ncRNAs play a critical role in EMT-related mechanisms. MiR-26b expression is attenuated in OC tissues. Therefore, the suppressive activity of miR-26b on its target molecule, ERα, would be diminished, which leads to enhancing EMT, invasion, and cell proliferation [95]. Downregulation of miR-214-3p in HGSOC leads to upregulation of its target genes, including MUC16 and MMP7, in tumor tissue, resulting in its participation in the EMT process. MiR-214-3p expression is directly correlated with progesterone receptor protein and negatively correlated with CDK6 and MAPK1 [96]. LncRNA-CCAT1 acts as an oncogene in OC by contributing to TGFβ1-induced EMT via the miR-490-3p/TGFβR1 axis [97]. HOXD-AS1 is overexpressed in EOC and directly binds and targets miR-186-5p, resulting in upregulation of PIK3R3 and promoting EMT, cell invasion, and migration [98]. Upregulation of NEAT1 in OC results in miR-1321 downregulation and TJP3 upregulation, thus promoting OC invasion and metastasis [99]. The overexpression of lncRNA MEG3, DNM3OS, and MIAT in OC results in the regulation of the EMT-related gene pathways. Although upregulation of DNM3OS remarkably correlates with reduced OS for OC patients, MIAT or MEG3 levels lack correlation with survival [100].
There are numerous reviews about the different roles of ncRNAs in OC. Circulating ncRNAs contribute to cell migration, invasion, metastasis, and recurrence of OC [90]. lncRNAs H19, XIST, and LSINCT are involved in the development of OC occurrence, cell growth and proliferation, invasion, and metastasis. These mentioned lncRNAs have diagnostic and prognostic values [101]. CircRNAs are responsible for adjusting cell proliferation in OC, and their aberrant expression promotes the initiation and progression of this cancer [102]. Abnormal expression of microRNAs takes part in OC initiation, proliferation, chemotherapy resistance, and survival [103]. Investigating specific roles of ncRNAs, Luo et al. [104], Ning et al. [105], and Seyed Hosseini et al. [106] focused on the survival value of dysregulated lncRNAs. Ferreira et al. focused on microRNAs and their chemotherapy-related response, diagnosis, and prognosis [106] roles. Despite the existence of a review on the diagnostic and prognostic roles of circRNAs in OC, there were no systematic reviews and meta-analyses on this topic [107]. Identifying both the prognostic and diagnostic roles of all existing studies on EMT-related ncRNAs in OC, we did not narrow our investigation to a specific ncRNA type (e.g., lncRNAs, microRNAs, circRNAs) or subtype. At the end of the eligibility surveying step, a total of 15 microRNAs, 24 lncRNAs, and 3 circRNAs were identified for assessing the correlation of their dysregulated expression and different characteristics, including clinical stages, TNM stages, LNM, and DM. To assess the DOR and effectiveness of the target ncRNAs in the diagnosis and prediction of OC, we initiated with univariate analysis, provided a panel of ncRNAs, and then performed a bivariate analysis.
Going beyond the primary univariate analysis, we defined an adjusted panel of ncRNAs for each of the LNM, TNM staging, DM, and clinical staging items, which are all related to the prognosis assessment of OC. Our panel for prognosis evaluation based on LNM consists of eight microRNAs, including miR-216a [42], miR-3064 [53], miR-532 [53], miR-18a [69], miR-489 [57], miR-126 [70], miR-99a [56], and miR-488 [44], sixteen lncRNAs, involving, CCAT1 [60], ADAMTS9-AS2 [72], FLVCR1-AS1 [55], TC0101441 [58], MAFG-AS1 [68], FAM83H-AS1 [65], HOTAIR [50], DSCR8 [59], DQ786243 [74], HOXD-AS1 [61], HOXB-AS3 [66], LINC-PINT [76], E2F4as [43], SNHG20 [75], LINC01969 [46], and LncARSR [54], and one circRNA called circ_100395 [67]. After adjusting the ncRNA panel, the overall diagnostic and prognostic effectiveness elevated in both whole-panel and subgroup analysis of three differentiated types of ncRNAs. However, lncRNAs were identified as more robust predictors compared to microRNAs for LNM. Since circ_100395 was the only representative of circRNAs for LNM items, the subgroup analysis was not executed for this type of ncRNA and cannot separately be assumed as a proper prognostic indicator of OC. The ncRNAs panel for DM comprises six microRNAs, five lncRNAs, and two circRNAs. Meanwhile, the microRNAs subgroup, involving miR-489 [57], miR-18a [69], miR-126 [70], miR-26b [51], miR-219a-5p [63], and miR-196-5p [62] was a considerably valuable diagnostic factor compared to the lncRNAs subgroup, consisting FLVCR1-AS1 [55], DQ786243 [74], GAS5 [62], LncARSR [54], and MEG3 [63]. Based on statistical outcomes, the circRNAs subgroup (circ_100395 [67] and CircAGFG1 [71]) accounted for the second most effective predicting factors among two other types of ncRNAs. However, due to the limited number of this ncRNA type that inhibits precisely evaluating their effectiveness, this subgroup cannot be separately recognized as a valuable marker for our purpose. Furthermore, for a more comprehensive assessment of results accuracy, LR− and LR+ were calculated together. Totally, the current study discovered that the defined panel of ncRNAs, which are EMT-related ncRNAs, were significantly associated with both DM and LNM. According to the previous papers, the EMT process can take part in the metastasis and invasion of cancer [108,109]. With high enough overall sensitivity, specificity, and accuracy, our results demonstrated that the EMT-related ncRNA panels play significant roles as efficient predictors of DM and LNM in OC.
Recruiting the same approach to appraise the significance of ncRNAs for clinical stages led to defining a panel consist of twelve microRNAs (miR-126 [70], miR-216a [42], miR-18a [69], miR-99a [56], miR-489 [57], miR-506 [41], miR-26b [51], miR-219a-5p [63], miR-214 [73], miR-488 [44], miR-98-5p [59], and miR-196-5p [62]), seventeen lncRNAs (CCAT1 [60], DNM3OS [45], ADAMTS9-AS2 [72], TC0101441 [58], GAS5 [62], FAM83H-AS1 [65], HOTAIR [50], HOXD-AS1 [61], PVT1 [73], E2F4as [43], MEG3 [63], DSCR8 [59], HOXB-AS3 [66], LINC-PINT [76], lncARSR [54], LINC01969 [46], and HCG18), and three circRNAs (circ_100395 [67], circ_0000745 [47], CircAGFG1 [71]). Although the entire defined panel for clinical stages served as a significant predicting factor of OC, the lncRNAs subgroup displayed better prognostic effectiveness in comparison to the microRNAs and circRNAs subgroups. The defined panel for TNM item encompasses miR-126 [70], miR-18a [69], miR-489 [57], miR-616 [64], DQ786243 [74], and MAFG-AS1 [68]. Four microRNAs included in this panel are more likely to predict the TNM stages in OC patients.
Along with the univariate analysis, the bivariate meta-analysis was performed to reduce the high percentage of heterogeneity shown in the univariate analysis, which was represented by the natural discrepancies between various studies. This type of analysis reduced data heterogeneity and data dispersion, resulting in a more symmetrical SROC curve and increasing the AUC of the SROC curve.
In addition to the mentioned analysis, a meta-analysis based on HR was implemented to assess the OS and prognostic effectiveness of the included ncRNAs. The total HR of high-expressing ncRNAs compared to low-expressing groups was not significant.
Based on the validation of significant ncRNAs in our meta-analysis using previously existing data in Pan-cancer, the acquired lncRNAs and microRNAs have differential expression in OC patients compared with healthy women. Therefore, these ncRNAs could be indicators of poor OS prognosis in OC patients.
Our study encompasses limitations. Some previous studies with similar objectives did not report the risk ratio of the effect of the ncRNAs in their evaluation of OC prognosis. Additionally, we could not gain access to the full text of some papers found relevant in title-abstract screening despite contacting their authors. Moreover, we had expected to reconstruct the survival data from K-M graphs using the “Guyot algorithm.” However, neither of the K-M curves of eligible articles reported the “number at risk” data, which is an indispensable element for retrieving HR and CI. Furthermore, initial OC diagnostic tests are not specific and sensitive enough, have particular limitations, and are ineffective in the early detection of OC. The obtained ncRNA panels from this study can predict ovarian cancer more specifically and sensitively. These ncRNAs can serve as therapeutic targets for specific clinical purposes. However, more studies are needed to discover a non-invasive panel for early detection based on serum or plasma.
5
Conclusion
In conclusion, this systematic review and meta-analysis provide gathered information on the prognostic implications of EMT-related ncRNAs in OCs of all types. Our results suggest that panels of ncRNAs could effectively predict factors related to the EMT process and prognosis of OC, including LNM, DM, clinical staging, and TNM staging.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Availability of data and material
All data are available upon reasonable request.
Competing interests
The authors declare that they have no conflicts of interest to disclose regarding this study.
Funding
This study is supported by grant No. IR.TUMS.MEDICINE.REC.1401.023.
CRediT authorship contribution statement
Alireza Soltani Khaboushan: Writing – review & editing, Validation, Project administration, Methodology, Formal analysis, Data curation. Seyedeh Nazanin Salimian: Writing – review & editing, Writing – original draft, Methodology, Investigation, Data curation. Saghar Mehraban: Writing – review & editing, Writing – original draft, Methodology. Afshin Bahramy: Visualization, Validation, Project administration, Methodology, Formal analysis. Narges Zafari: Writing – review & editing, Writing – original draft, Project administration, Methodology. Abdol-Mohammad Kajbafzadeh: Visualization, Validation, Resources. Joshua Johnson: Validation, Supervision, Investigation. Masoumeh Majidi Zolbin: Writing – review & editing, Visualization, Supervision, Project administration.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
Title: Prophylactic cranial irradiation for small cell lung cancer in the era of immunotherapy and molecular subtypes | Body:
INTRODUCTION
Small cell lung cancer (SCLC) is an aggressive disease that makes up only 13–15% of all lung cancer cases worldwide. Its poor prognosis is reflected in dismal outcomes, with 5-year overall survival (OS) rates of less than 7% [1▪▪]. In the majority of cases, the tumor has already disseminated outside the chest by the time of diagnosis, preventing surgical resection as a therpeutic option. Patients diagnosed with extensive-stage disease (ES) are treated with systemic therapy comprising combinations of chemotherapy (CHT) and/or immunotherapy with local radiotherapy reserved for mediastinal consolidation after good partial response to systemic therapy alone [2]. Although the most common sites of distant organ metastasis include the brain, bones, liver, and adrenal glands, progression is most frequently seen in the thorax and brain [2,3]. Bulky primary tumors are often the reason for thoracic treatment failures. Furthermore, the brain is considered a ‘sanctuary site’ for tumors because of the blood–brain barrier [4▪]. As CHT cannot cross this barrier, prophylactic cranial irradiation (PCI) was introduced as a standard treatment strategy to improve OS for limited-stage disease (LS-SCLC) after complete remission to thoracic radiotherapy combined with CHT [5–7].
Nearly all SCLCs exhibit biallelic inactivation of the tumor suppressor genes TP53 and RB1[8]. Approximately 94% of patients are ever-smokers and SCLC tumorigenesis is strongly associated with tobacco consumption [1▪▪]. Consequently, patients carry a high tumor mutational burden (TMB), which perpetuates disease progression. Interestingly, increased TMB has been correlated with positive responses to immunotherapy, underlining potential benefits of immunotherapy in SCLC treatment [9].
Although SCLC is still treated as a homogeneous disease in clinical settings, recent advancements have aimed to classify the molecular profile into four distinct subgroups according to the gene expression of relevant transcription factors or immune system characteristics [10]. SCLC-A and SCLC-N, defined by higher expression of the transcription factors ASCL1 and NEUROD1, respectively, reflect neuroendocrine subgroups through higher expression of neuroendocrine markers (e.g. synaptophysin or CD56), whereas POU2F3-expressing SCLCs (SCLC-P) constitute a variant, non-neuroendocrine phenotype [11]. The fourth subgroup, SCLC-I exhibits inflamed gene signatures, mesenchymal features, and low levels of the transcription factors ASCL1, NEUROD1, or POU2F3 [1▪▪]. Importantly, these subgroups have been associated with varying responses to therapy [12▪].
Here, we offer an overview of recent advancements in SCLC treatment in light of molecular subtyping efforts and pay special attention to the role of PCI.
Box 1
no caption available
CURRENT THERAPEUTIC APPROACHES IN SMALL-CELL LUNG CANCER
Approximately 60–65% of patients present with metastatic spread outside the chest and are, therefore, classified as ES-SCLC patients upon diagnosis [13,14]. Furthermore, select results from clinical trials on the application of surgery have underlined that surgical resection is often not a viable therapeutic option, even in earlier stages [15,16]. According to the National Comprehensive Cancer Center Guidelines, surgical resection is only recommended in LS-SCLC (I-IIA), equating to roughly 5% of SCLC patients [17▪▪]. For these patients with very LS-SCLC (T1-T2N0M0), three-year survival rates of more than 50% have been achieved after undergoing definitive lobectomy and mediastinal lymph node dissection followed by postoperative systemic therapy [18,19]. In case of mediastinal lymph node metastases (N1–N2) or residual disease (R1 or R2) after surgical resection, mediastinal radiotherapy is advised to decrease local disease recurrence rates [17▪▪,20].
The benefit of PCI in stage I patients who have undergone definitive therapy and have a lower risk of developing brain metastases remains unclear [21]. Radiotherapy can be potentially applied in all stages as part of definitive or palliative therapy [17▪▪]. For inoperable patients with LS-SCLC (T1–T2N0M0), stereotactic body radiation therapy (SBRT) of the primary tumor followed by adjuvant systemic therapy could be beneficial [17▪▪]. Furthermore, mediastinal radiotherapy is also recommended in specific postoperative cases, especially in unexpected N2, considering that upfront N2-stages should not undergo surgery. A phase III randomized trial demonstrated that consolidative mediastinal irradiation (10 × 3 Gy) significantly improves 2-year OS rates and 6-month progression-free survival (PFS) in ES patients with clinical response to previously administered CHT [22]. The phase II/III RAPTOR trial (NCT04402788) is currently testing the addition of radiotherapy to the immunotherapeutic agent atezolizumab (anti-PD-L1) in ES-SCLC. Brain dissemination is conventionally treated with whole brain radiation therapy (WBRT, 10 × 3 Gy or shorter schedules). However, selected patients in good condition with only a few brain metastases might benefit from stereotactic radiotherapy [23].
Systemic CHT is the main treatment modality in SCLC management, with a prominent role in all stages of SCLC [17▪▪]. Etoposide in combination with platinum-based agents such as cisplatin or carboplatin (EP) has been the standard-of-care since the 1980s [24]. Despite higher response rates in the beginning of treatment, many patients relapse within the first year and portray a median OS of approximately ten months [25–27]. Notably, recent studies showed that the addition of immunotherapy to the therapeutic armamentarium prolonged median OS rates by 2–4 months in patients with ES disease [28▪,29]. Based on the promising results of the double-blinded randomized phase III IMpower133 study from 2019, the US Food and Drug Administration (FDA) and the European Medicines Agency approved the use of atezolizumab for first-line treatment of ES-SCLC in combination with EP [29,30]. Durvalumab, another immunotherapy agent targeting programmed death ligand 1 (PD-L1), has also been approved based on data from the randomized phase III CASPIAN trial in ES settings [31,32]. Durvalumab has also significantly increased OS in LS-SCLC according to recent results of the ADRIATIC phase III trial [33▪]. The guidelines for subsequent systemic treatment options are not as clearly established and depend on previously administered therapeutic agents as well as the length of the disease-free interval [1▪▪]. Among others, second- and further-line treatment options include topotecan, irinotecan, lurbinectedin, tarlatamab, temozolomide, cyclophosphamide, nivolumab, pembrolizumab, gemcitabine, paclitaxel, or docetaxel [17▪▪].
PROPHYLACTIC CRANIAL IRRADIATION IN THE ERA OF IO
SCLC is notorious for its tendency to disseminate and develop metastases in the brain [9,34▪,35]. Additionally, the presence of brain metastases denotes poor prognosis [35]. More than two-thirds of patients are detected when brain metastases are already present, and the risk of devloping brain metastases after CHT remains at around 50% due to poor drug permeability through the blood–brain barrier [34▪,36]. Hence, PCI was implemented to decrease the occurrence of brain metastases and improve survival [9,34▪]. Today's indication for PCI is based on a meta-analysis of seven trials conducted by Aupérin's working group. Researchers analyzed data from nearly 1000 patients in all SCLC stages who had a complete response (CR) to chemoradiotherapy (CRT) (>75%) or CHT alone [5]. Results demonstrated an overall reduced incidence of brain metastases and improved OS. However, factors such as the assessment of CR solely by thoracic X-rays and the inclusion of select trials that had been performed prior to the MRI era most likely influenced study outcomes. Numerous other trials with positive outcomes have followed thereafter (Table 1). Despite favorable results in these trials, many enrolled patients lacked a baseline MRI scan [37▪] and medical imaging has experienced major improvements since then. Especially higher imaging resolution and more frequent use of cranial MRI for both SCLC staging and follow-up has generated interest to offer screening MRI at follow-up for neurologically asymptomatic patients.
Table 1
Clinical trials on prophylactic cranial irradiation
Trial
Phase
Date
Enrolled
SCLC stage
Experimental arm
Control
Outcomes
NCT00016211
3
2001–2006
287
Extensive
PCI
Observation
BM incidence 40% control vs. 15% PCIPCI longer DFS, OS1-year survival rate 27% PCI group vs. 13% controlAcute and late toxicity acceptable
NCT00005062
3
1999–2005
720
Limited
High-dose PCI over 16 or 24 days
Standard-dose PCI over 10 days
Two-year follow-up:no significant difference in BM incidenceOS 42% standard-dose, 37% higher doseFive serious adverse events in standard-dose group vs. zero in the higher-dose group
NCT00057746
2
2009–2013
265
Limited
36 Gy PCI – 2.0 Gy once daily, 18 fractions – 1.5 Gy twice daily, 24 fractions
25 Gy PCI - 2.5 Gy once daily, 10 fractions
No significant differences in QoL and Hopkins Verbal Learning Test1 year later increase in CNt in the 36-Gy cohort
NCT00006349
3
2001–2007
9
–
oral donepezil daily and vitamin E + PCI
Oral placebos + PCI
Only nine enrolled, no definitive conclusions
NCT00006344
3
2000.05–2000.12
0
Limited
Radiotherapy to the left cerebral hemisphere after WBRT
Radiotherapy to the right cerebral hemisphere after WBRT
Terminated
NCT01055197
2
2010–2016
97
Extensive
PCI + cRT
PCI
At planned interim analysis, the study crossed the futility boundary for OS and was closed1-year OS no difference3- and 12-month progression rates 53.3 and 79.6% for PCI vs 14.5% and 75% for PCI+cRT
TULIP NCT01486459
NA
2011–2014
7
–
Lithium + PCI
PCI
Insufficient recruits
NCT01553916
1/2
2012–2017
19
–
Lithium carbonate + PCI
PCI
No study results posted
NCT01780675
3
2013–2018
168
–
HA-PCI
PCI
No significant differences between the two
NCT01797159
2
2013–2019
20
Limited
HA-PCI
historical control (RTOG 0212)
Two-year OS 88%no significant decline in performanceMRI revealed asymptomatic brain metastases in 20%Two patients developed metastasis in the under-dosed region
HIPPO-SPARE 01 NCT01849484
2
2013–2021
35
–
HA-PCI
PCI
Complex pathophysiological changes in cerebral microstructures after radiationHippocampal microstructure differed (HA-PCI vs. PCI) after 6 months
SAKK 15/12 NCT02058056
2
2014–2017
44
Limited
HA-PCI
–
6 months: 34.2% patients no NCF decline12 months: BMFS 84.2% and OS 87.7%
NCT02366741
Pilot
2015–2017
5
Limited
HA-PCI
–
Unknown status, no study results posted
PREMER-TRIAL NCT02397733
3
2014–2020
150
–
HA-PCI
PCI
DFR decline HA-PCI (5.8%) vs. PCI (23.5%)DFR (11.1 vs. 33.3%), total recall (20.3 vs. 38.9%) total free recall (14.8 vs. 31.5%)BM incidence, OS, and QoL were not significantly different
NCT02605811
2
2015–2021
426
Limited
Temozolomide
PCI
Unknown status, no study results posted
NCT02635009
2/3
2015–(2027)
418
–
HA-PCI
PCI
Active, not recruiting
NCT02736916
NA
2016–2018
3
Limited
HS-WBRT PCI
PCI
Unknown status, no study results posted
NCT02906384
2
2016–2020
154
–
HA-PCI
PCI
Unknown status, no study results posted
NCT03514849
NA
2018–(2026)
(360)
–
PCI
Placebo
Recruiting
S1827 (MAVERICK) NCT04155034
3
2020–(2027)
(668)
–
MRI Active Surveillance
PCI + MRI surveillance
Recruiting
NCT04535739
3
2019–2022
414
Extensive
PCI
Observation
Unknown status, no study results posted
PRIMALung Study NCT04790253
3
2022–(2028)
(600)
–
MRI Active Surveillance
PCI + MRI surveillance
Recruiting
NCT04829708
3
2021–(2028)
(534)
Limited
MRI Active Surveillance
PCI + MRI surveillance
Recruiting
NCT04947774
NA
2020–2022
100
Extensive
PCI
Observation
Unknown status, no study results posted
NCT05651802
NA
2023–(2026)
(220)
Limited
MRI Active Surveillance
PCI + MRI surveillance
Recruiting
BM, brain metastasis; BMFS, brain metastasis-free survival; CN, chronic neurotoxicity; cRT, consolidative extracranial radiotherapy; DFR, delayed free recall; DSF, disease-free survival; HA, hippocampus avoidal; NCF, neurocognitive function; OS, overall survival; PCI, prophylactic cranial irradiation; QoL, quality of life; WBRT, whole brain radiotherapy.
In contrast to conventional CHT immunotherapy agents are able to penetrate the blood–brain barrier. Introduction of immunotherapy to SCLC treatment regimens has supported the idea (or possibility) of omitting PCI from therapeutic regimens, but there is currently insufficient data on immunotherapy efficacy to prevent brain metastases from SCLC to provide an answer [34▪,38]. Although the Impower-133 study permitted PCI inclusion [29], only 11% of the study population (n = 22/arm) received this treatment modality. Results from both the whole cohort and the posthoc subgroup analysis without PCI treatment showed no difference. This might suggest that immunotherapy alone was effective in delaying or preventing brain metastases and the beneficial effect was not dependent on PCI application. However, this observation needs further validation [39▪]. In contrast, the CASPIAN trial excluded PCI from the experimental arm, although PCI was permissible for patients in the control group [31]. A subsequent analysis of this study reported significantly increased time for brain metastasis formation in the durvalumab-CHT arm as opposed to the control group that received PCI [40].
PCI has many side effects including neurocognitive toxicity [9,34▪,35,41,42]. Even lower radiation doses can contribute to a significantly worse quality of life, memory loss, or decreased neurocognitive functions [9]. Recently, hippocampal-avoidance PCI (HA-PCI) has been suggested and is supported by NCCN guildeines [17▪▪,34▪,37▪]. Although HA-PCI efficacy is similar to PCI, studies have shown considerable delay in or reduction of the severity of cognitive deterioration [1▪▪,35]. The addition of neuroprotective substances such as memantine and donepezil, an NMDA-receptor antagonist frequently applied in the treatment of Alzheimer's disease, might aid to improve patient outcomes. While NCCN guidelines consider memantine, which has been investigated for WBRT, but not for PCI [9,38,43]. These substances were linked to reduced decline and slight enhancement of cognitive functions in two phase III trials, although the results did not reach statistical significance [44,45]. In addition to memantine, the use of lithium (NCT01553916) and donepezil (NCT00006349) for similar protective roles in PCI treatment have been investigated.
The beneficial role of PCI, especially in light of immunotherapy administration, remains controversial. Results emerging from a phase III Japanese trial further questioned its legitimacy by proving that PCI did not lead to longer OS compared with active MRI surveillance in patients with ES-SCLC, although administration of PCI in ES has never been a standard treatment. According to this study, PCI should not be administered ES-patients who have responded to initial CHT and have a confirmed absence of brain metastases, provided MRI surveillance is implemented [46]. Other ongoing studies such as MAVERICK, PRIMAlung, NCT05651802, or NCT04829708 aim to discover whether MRI surveillance will be able to completely replace PCI in future SCLC management protocols. (Table 1).
SUBTYPE-SPECIFIC VARIABILITY IN RESPONSE TO THERAPY
Even though both preclinical and clinical studies have offered promising results in recent years, significant changes in SCLC treatment have not transpired [47]. This is partly because of intratumoral heterogeneity, the scarcity of surgically resected tissue samples for research purposes, or the lack of potentially targetable driver mutations [1▪▪,48]. In addition, most clinical trials are still conducted on non-selected patient populations irrespective of potential molecular subtypes [47].
Focusing on biological and clinicopathological differences between SCLC subtypes in the search for potential therapeutic targets has been an emerging interest in recent years (Fig. 1). The next paragraphs give a short summary of specific therapeutic vulnerabilities among molecular subgroups.
FIGURE 1
Potential and already implemented therapeutic agent-based subtype specific targets in small cell lung cancer.
As for SCLC-A, higher expression levels of the antiapoptotic protein BCL-2 have been observed. We recently demonstrated that BCL-2 inhibitors (e.g. venetoclax) represent a potential therapeutic agent for this subgroup [47]. Furthermore, the ASCL1-dominant subtype has been associated with a transcriptional interaction with DLL3 in cells where the Notch pathway is downregulated, constituting a potential subtype-specific susceptibility to DLL3 inhibitors [49,50]. The FDA recently approved the bispecific T-cell engager tarlatamab for recurrent SCLC. This agent selectively targets DLL3 on tumor cells and CD3 on T cells [51▪▪]. Although tarlatamab is currently administered to all SCLC patients regardless of their molecular landscape, its application in a subtype-specific manner might further increase the therapeutic efficacy in the future. SCLC-A is also characterized by high levels of the NE transcription factor INSM1 [52▪▪]. LSD1 inhibitors prevent the expression of ASCL1 by disrupting the interactions between LSD1 and INSM1 [53]. In addition, given the high expression of the SOX2 oncogene, hedgehog signal cascade inhibitors may present a new possibility for SCLC-A [53]. As the tumor suppressor gene CREBBP is downregulated in ASCL1-expressing SCLCs, histone deacetylase inhibitors could potentially be effective [54]. Lastly, studies have shown that SCLC-A is more chemo-sensitive and radiosensitive than NE-low phenotypes. PCI and CRT might therefore be more suitable treatments in this particular subset of SCLC [55▪,56].
NEUROD1-driven tumors (SCLC-N) have been shown to have MYC oncogene amplification and lower NE profiles, making MYC inhibitors a possible therapeutic option [57]. SCLC-N has also been proven to exhibit higher AURKA activity and increased arginine biosynthesis. Consequently, AURKA inhibition or pegylated arginine deaminase are likely effective treatment strategies in this subtype [1▪▪,58].
SCLC-P tumors display a non-NE phenotype. This subgroup is suspected to be the most sensitive to PARP inhibitors and nucleoside analogue therapy [1▪▪,59]. Recent studies suggest that IGF-R1 inhibition could represent a novel therapeutic approach in the POU2F3-driven subtype [1▪▪,59].
Lastly, SCLC-I is classified by an inflamed phenotype with immune oasis characteristics and high immune-checkpoint marker expression [1▪▪]. Inhibitors of the PD-1/PD-L1 axis have proven to be effective and have been included in treatment protocols in ES settings [60]. Results from the retrospective analysis of IMpower133 data suggest that patients with SCLC-I features benefitted more from immune-checkpoint inhibitor treatment [61▪]. Moreover, SCLC-I has a presumably high YAP1 expression, which exhibits vulnerability to mTOR, PLK, and CDK4/6 inhibition [10].
CONCLUSION
SCLC is one of the most aggressive malignant diseases. Despite the introduction of immunotherapy to treatment protocols for ES patients, there have been no significant changes in therapeutic approaches in the last decades. The objective of this review was to shed light on the role of PCI in SCLC in the era of immunotherapy. We also aimed to highlight potential next steps in SCLC treatment strategies while keeping molecular subtyping efforts in mind.
Several trial results have demonstrated the use of PCI in decreasing the occurrence of brain metastases and also prolonging OS in SCLC. PCI is routinely applied in LS-SCLC, with good responses to CRT, whereas the benefit at ES from PCI treatment is limited. Although serious PCI-related side effects have led to HA-PCI application and co-administration of memantine, these measures should be carefully considered before use. Introduction of immunotherapy might further limit the administration of PCI in favor of MRI-surveillance followed by stereotactic radiotherapy or radiosurgery.
Considering the improved quality of brain imaging techniques and the potentially better control of microscopic intracranial seeding provided by immunotherapy (even after the termination of CRT administration as per therapeutic guidelines), we believe, that the omission of PCI can be discussed for patients in favor of active MRI surveillance complemented with immunotherapy. This especially holds true for patients of the SCLC-I subtype with encouraging responses to immunotherapy. While immunotherapy may be less effective in patients with SCLC-A profiles, this subgroup likely profits most from RT, especially when brain metastases appear. PCI administration is therefore likely beneficial.
In summary, PCI remains the recommended standard-of-care for patients with good response after RCT for LS-SCLC and in good general condition, but the therapeutic value of PCI in SCLC is increasingly challenged, with ongoing trials investigating the possibility of replacement through MRI surveillance. Future SCLC studies in the era of immunotherapy are required to help select the patient population that profits more from PCI than MRI surveillance alone, thereby improving patient outcomes in this recalcitrant disease.
Acknowledgements
None.
Financial support and sponsorship
B.D. was supported by the Austrian Science Fund (FWF I3522, FWF I3977, and I4677) and the ‘BIOSMALL’ EU HORIZON-MSCA-2022-SE-01 project. B.D. and Z.M. were supported by funding from the Hungarian National Research, Development, and Innovation Office (2020-1.1.6-JÖVŐ, TKP2021-EGA-33, FK-143751 and FK-147045). Z.M. was supported by the New National Excellence Program of the Ministry for Innovation and Technology of Hungary (UNKP-20-3, UNKP-21-3 and UNKP-23-5), and by the Bolyai Research Scholarship of the Hungarian Academy of Sciences. Z.M. is also the recipient of the International Association for the Study of Lung Cancer/International Lung Cancer Foundation Young Investigator Grant (2022). V.P. was supported by the New National Excellence Program of the Ministry for Innovation and Technology of Hungary (UNKP-23-3).
Conflicts of interest
There are no conflicts of interest.
Financial support and sponsorship
B.D. was supported by the Austrian Science Fund (FWF I3522, FWF I3977, and I4677) and the ‘BIOSMALL’ EU HORIZON-MSCA-2022-SE-01 project. B.D. and Z.M. were supported by funding from the Hungarian National Research, Development, and Innovation Office (2020-1.1.6-JÖVŐ, TKP2021-EGA-33, FK-143751 and FK-147045). Z.M. was supported by the New National Excellence Program of the Ministry for Innovation and Technology of Hungary (UNKP-20-3, UNKP-21-3 and UNKP-23-5), and by the Bolyai Research Scholarship of the Hungarian Academy of Sciences. Z.M. is also the recipient of the International Association for the Study of Lung Cancer/International Lung Cancer Foundation Young Investigator Grant (2022). V.P. was supported by the New National Excellence Program of the Ministry for Innovation and Technology of Hungary (UNKP-23-3).
Conflicts of interest
There are no conflicts of interest. |
Title: EnhancerNet: a predictive model of cell identity dynamics through enhancer selection | Body:
INTRODUCTION
A single fertilised animal egg cell divides and differentiates into many cell types that are maintained throughout the life of the organism. Each cell type is associated with a distinct gene expression profile, which can be acquired by gradual differentiation through progenitor states or by reprogramming. Despite great advancements in the molecular characterization of cell dynamics, many fundamental questions regarding how cell types are acquired and maintained remain unanswered. Specifically, key open challenges include how cell types and their associated expression profiles are encoded by the genome, identifying the regulatory mechanisms underlying stepwise differentiation versus direct reprogramming pathways, understanding how signalling triggers the acquisition of particular cell identities and determining how new cell types with unique expression signatures can evolve.
Addressing these questions requires formulating quantitative hypotheses on the dynamics of gene expression over time and in response to perturbations. From a theoretical perspective, cell types correspond to stable attractors of the gene regulatory network of the cell (Waddington, 1957; Huang et al., 2005; Lang et al., 2014; Teschendorff and Feinberg, 2021); that is, the gene regulatory network has multiple stable configurations. These stable configurations are set by feedback interactions between hundreds of transcription factors (TFs), which are DNA-binding proteins that can modulate the expression of other genes. Specific cell types are associated with the expression of distinct combinations of TFs, and TFs play a crucial role in maintaining and modulating cell identity (Burke et al., 1995; Heinz et al., 2010; Holmberg and Perlmann, 2012; Hnisz et al., 2013; Whyte et al., 2013; Kelaini et al., 2014; Saint-André et al., 2016; Reiter et al., 2017; Reilly et al., 2020; Hobert, 2021; Wang et al., 2021; Almeida et al., 2021).
The importance of TFs is reflected in their central role in models of cell identity dynamics. Early models focused on how small transcriptional network motifs, involving only a few TFs, can generate cell fate bifurcations, such as those specific to blood lineages and early embryogenesis (Ferrell, 2002; Huang et al., 2007; Graham et al., 2010; Bessonnard et al., 2014; Sáez et al., 2022). Although these models are important for understanding gene expression dynamics at specific decision points, they cannot predict large-scale dynamics of the cell identity network, such as complex, hierarchical differentiation patterns or reprogramming between distinct cell types.
In contrast to these ‘small network’ models, pioneering work has demonstrated that aspects of cell identity behaviour can be captured by high-dimensional attractor models based on Classical Hopfield networks (Lang et al., 2014; Fard et al., 2016; Pusuluri et al., 2017; Guo and Zheng, 2017; Teschendorff and Feinberg, 2021; Yampolskaya et al., 2023; Smart and Zilman, 2023). Classical Hopfield networks are well-established models for dynamical systems that encode combinatorial attractor states (patterns) through the interactions of their components (Amari, 1972; Little, 1974; Hopfield, 1982). In the context of transcriptional networks, the state of the dynamics is represented by the activity of cell identity TFs, with the attractor states corresponding to distinct cell types. In Hopfield networks, the entire dynamics of the system can be specified solely by defining the attractor states, which are the observed cell types. Although in their original formulation Classical Hopfield networks cannot encode for correlated expression profiles, this limitation can be overcome by an orthogonal projection of the expression profiles (Amit, 1989; Lang et al., 2014). This allows for predictions about the gene regulatory network dynamics based on mere knowledge of the cell types encoded by the network. Lang et al. exploit this property to demonstrate that it is possible to capture and predict the effects of TF overexpression on cellular reprogramming (Lang et al., 2014). The Hopfield formulation also predicts that mixtures of attractor states may themselves be attractor states (known as ‘spurious attractors’). Lang et al. propose that this phenomenon explains the observed expression profiles of partially reprogrammed cells (Lang et al., 2014).
Although this work provides a compelling motivation for applying attractor networks to study cell identity dynamics, it has several important limitations that restrict its applicability. First, although Hopfield dynamics provide an attractive computational model, the relation between this model and underlying molecular mechanisms for cell type specification is unclear. The model also specifies binary expression patterns and does not capture continuous changes in expression levels, such as low-level multilineage priming in progenitor states, which is a hallmark of hierarchical differentiation (Hu et al., 1997; Miyamoto et al., 2002; Mercer et al., 2011; Nimmo et al., 2015; Kim et al., 2016; Olsson et al., 2016; Briggs et al., 2018; Zheng et al., 2018; Martin et al., 2021; Singh et al., 2022). Finally, such progenitor states must be explicitly specified in the model as attractor cell types, rather than emerging naturally from the dynamics.
Here, I develop a theoretical framework for modelling the dynamics of the cell identity network by considering the feedback regulation of TFs through enhancers, which I denote as EnhancerNet. Enhancers are regulatory elements that are crucial for cell identity specification. TFs bind to enhancers to activate cell type-specific expression patterns. Each enhancer can be bound by multiple TFs and, in turn, initiate gene expression in one or more distal target genes (Pennacchio et al., 2013; Uyehara and Apostolou, 2023). The binding of TFs to an enhancer recruits co-factors that can alter its activity by modulating epigenetic properties, such as the biochemical characteristics of its associated chromatin. This, in turn, affects the transcription initiation rate associated with the enhancer (Heintzman et al., 2009; Creyghton et al., 2010; Calo and Wysocka, 2013; Park et al., 2021; Hansen et al., 2022).
A well-documented experimental observation reveals a symmetry between TF binding and enhancer regulation (Hnisz et al., 2013; Whyte et al., 2013; Adam et al., 2015; Saint-André et al., 2016; Feng et al., 2023). The TFs that determine cell identity bind to enhancers that regulate both their own expression and that of other identity-determining TFs that are co-expressed in the same cell types. This interaction forms dense autoregulatory networks of TF-enhancer interactions. These dense autoregulatory networks likely play a crucial role in cell type specification, as their associated enhancers exhibit activation patterns that are highly cell-type specific (Hnisz et al., 2013; Heinz et al., 2015). The cell type specificity of enhancer activity contrasts with the broader activity of TFs, which may be shared across multiple cell types (Hnisz et al., 2013).
I show that the symmetry between TF binding and enhancer regulation imposes an exacting constraint on the dynamics of the cell identity network model, resulting in a highly simplified dynamical model that is predictive and captures broad experimental observations on cell type specification, enhancer selection, differentiation through progenitors, and reprogramming, without the need of fitting unobserved parameters. I demonstrate a mathematical analogy between our mechanistic model and Modern Hopfield networks (Krotov and Hopfield, 2016; Ramsauer et al., 2020 preprint) that overcomes the limitations of previous approaches. This analogy provides a mechanistic link between models for associative memories and cell identity networks, explains the role of distal regulatory elements with dynamic chromatin in multicellular evolution, and provides specific and testable predictions.
RESULTS
Model for enhancer activation dynamics in transcriptional feedback networks
I began by deriving a general mathematical model for the feedback regulation of TF expression through their interaction with enhancers (Fig. 1A). Enhancers consist of multiple TF binding motifs (Spitz and Furlong, 2012). The binding of TFs potentiates enhancer activity by recruiting transcriptional co-factors. These co-factors modulate the biochemical properties of enhancer chromatin through processes such as histone acetylation, leading to the recruitment of transcription initiation factors and ultimately resulting in the transcription of enhancer-associated genes (Narita et al., 2021; Panigrahi and O'Malley, 2021).
Fig. 1.
Model for the regulation of cell identity transcription factors by enhancers. (A) Enhancers are cis-regulatory elements that can initiate transcription in distant genes through interaction with specific transcription factors (TFs) and transcriptional machinery. Each enhancer can interact with several TFs, and each TF can be controlled by multiple enhancers. Binding of TFs modulates enhancer chromatin and can increase the transcription initiation rate of the enhancer. (B) Enhancer types (top) bind specific combinations of TFs (bottom) according to binding strengths specified by the matrix, and, in turn, initiate transcription according to the rate matrix . The weight vector determines baseline activity and may be modulated by binding of signalling TFs. (C) Autoregulation is the observation that TFs that are co-expressed in specific cell types co-bind their own enhancers. These enhancers are, in turn, selected and activated in these specific cell types. (D) Different cell types are associated with specific enhancers that may have overlapping TF binding. (E) Autoregulation constrains and implies reciprocity.
To capture these complex molecular mechanisms within an effective mathematical framework, I considered the following setup. An enhancer (indexed i) is characterized by two vectors: a vector, , denoting its binding affinity to different identity TFs (); and a vector, Qi, denoting the rates at which it can induce the transcription of the same TFs (). The binding of TFs to an enhancer modulates the state of the enhancer chromatin, which in turn sets the rate at which the transcriptional initiation machinery is recruited to the enhancer. Specifically, I assumed that the chromatin state sets the energy for the recruitment of the transcription initiation machinery and that, on the timescale of cell identity changes, the recruitment rate is captured by an equilibrium distribution (see Materials and Methods). Taken together, the dynamics of the gene regulatory network are given by:
(1)
where is the vector of TF expression whose entries are xi, is a matrix whose entries are ξi,j, Q is the association matrix whose entries are qi,j, τ is the timescale of the dynamics and β is an effective inverse temperature parameter that depends on the turnover of chromatin modifications (see Materials and Methods, Fig. 1B). The baseline activity vector depends on effects upstream of the cell identity network, such as the binding of effector TFs for signalling pathways (Hnisz et al., 2015). In this study, I will refer to the network model and its associated dynamics as EnhancerNet.
Although Eqn 1 considered different physical enhancers as separate entities, in practice, enhancers with similar binding profiles can appear throughout the genome (Saint-André et al., 2016; Kvon et al., 2021), corresponding to similar rows of . I denote these as enhancer types and note that Eqn 1 can be rewritten so that is a matrix of enhancer types (i.e. with distinct rows) and the matrix corresponds to the overall association between enhancer types and TF expression (see Materials and Methods). Hereafter, I will assume that Eqn 1 captures the dynamics of enhancer types, which can correspond to the activation of multiple physical enhancers.
Autoregulation of TF-enhancer interactions constrains the EnhancerNet model
Eqn 1 can be further constrained by taking into account well-established properties of developmental networks. TFs associated with specific cell identities form densely interconnected networks in which they co-bind adjacent enhancers, which in turn activate the same TFs (Whyte et al., 2013; Hnisz et al., 2013, 2015; Saint-André et al., 2016) (Fig. 1C,D). This implies a strong positive correlation between the rows of , which correspond to binding profiles of enhancers, and the rows of , which correspond to the effect of these enhancers on TF transcription. This was captured by taking (Fig. 1E; relaxing this to assume that are merely correlated does not affect our conclusions). This gives the following model for the dynamics:
(2)
The dynamics captured by Eqn 2 are mathematically similar to models for efficient memory storage in artificial neural networks (Ramsauer et al., 2020 preprint). This relationship can be exploited when analysing various properties of the dynamics. These dynamics are associated with a potential function (or ‘potential landscape’) (see Materials and Methods). The shape of this potential landscape determines how gene expression changes over time, including the stable gene expression patterns associated with different cell identities, which correspond to minima of the potential landscape.
Autoregulation, captured by , implies that the interactions between TFs in the developmental network are reciprocal, i.e. for each pair of TFs k, j, the effect of increasing TF j on the expression rate of TF k is the same as the effect of increasing TF k on the expression rate of TF j (). Moreover, reciprocity and autoregulation are effectively equivalent (see Materials and Methods) (Fig. 1E). This equivalence explains the observation that reciprocal interactions are ubiquitous in developmental networks (Kraut and Levine, 1991; Milo et al., 2002; Alon, 2007; Huang et al., 2007).
Reciprocal interactions between TFs can be positive (mutual activation) or negative (mutual repression), with both possibilities common in developmental networks. This is also captured in Eqn 2, despite the fact that, in the model, enhancers only enhance transcription, and the binding of TFs to enhancers increases their activity. Repression in the model is a by-product of global inhibition by competition over shared transcriptional machinery.
Robust specification of combinatorial cell types through interaction between TFs and enhancers
Cell identity is established through the process of enhancer selection (Heintzman et al., 2009; Hnisz et al., 2013, 2015; Saint-André et al., 2016). In this process, cell type-specific enhancers are marked for activation, leading to the expression of a distinct pattern of transcription factors associated with these enhancers. This is recapitulated in our model when the inverse temperature parameter β is sufficiently large (see Materials and Methods). Under this condition, the cell types encoded by the gene regulatory network, which represent stable attractors for the dynamics, are given by the rows of the enhancer-associated matrix (or, in the general case, by the rows of ; see Materials and Methods). Specifically, the stable fixed points of the network are represented by the vectors ()=() for all enhancer types (Fig. 2A,B). Each stable fixed point corresponds to a cell type with a unique transcription factor expression pattern. At these fixed points, cell type-specific enhancers exhibit maximal activity. These fixed points are robust to a large degree of asymmetry in TF-enhancer interactions (Fig. S1).
Fig. 2.
Specification of cell types and direct reprogramming. (A) The EnhancerNet model was initialized with the TF expression profiles of 45 cell types from Tabula Muris, taking 105 transcription factors (TFs) with high variability between cell types (see Materials and Methods). The resulting matrix (transposed) is plotted, with larger values corresponding to darker colours. (B) At high β each row of is an attractor of the network. PCA transformation of is shown, with transformed individual rows, corresponding to enhancer-binding patterns, plotted in grey. They are all attractor states – trajectories that start in the vicinity of each of these states converge to them. Red lines correspond to trajectories starting from rows of with 25% multiplicative noise. (C) Constitutive overexpression of TFs alters the attractor landscape and can result in a bifurcation where cell types lose stability and transition to another cell type. Arrow indicates transition between gene expression states. (D) The overexpression of Ascl1 transitions fibroblasts to neurons in the model, capturing the known effect of Ascl1 overexpression in fibroblasts. The reprogramming path is plotted in red (lower panel), with black paths capturing other known reprogramming recipes (see Materials and Methods). (E) Expression of TFs (left panel) and enhancer activity (right panel), corresponding to the probability that transcription is initiated from the enhancer.
Signalling activity can induce specific cell fates or restrict inducible cell fates (Perrimon et al., 2012; Grover et al., 2014; Hnisz et al., 2015). This phenomenon is recapitulated in our model through the effect of signalling on , which in turn modulates the basins of attraction for each cell type. Signals that increase the activity of specific enhancers cause the cell types associated with these enhancers to become accessible from a broader range of gene expression states. As discussed later in the context of differentiation, this provides a mechanism to control the relative production of different cell types. Conversely, states with weak induction may become destabilized and thus reprogrammed to an alternative cell type. Importantly, the effect of signalling through changes in does not alter the TF identity of the cell types. Thus, the model provides a mechanism for how signalling can control cell type production without interfering with cell identities.
From a modelling perspective, the observation that cell types correspond to rows of suggests an intriguing possibility: that Eqn 2 can be parameterized using the TF identities of the observed (terminal) cell types, which can be retrieved from gene expression datasets. This leaves the inverse temperature parameter β and the signalling vector as the only free parameters. As I will show, in a variety of settings, there are strong constraints on both parameters. The model can thus provide predictions on the dynamics of the entire transcriptional network by considering only the observed cell types. In the rest of this article, I will scrutinize this possibility and demonstrate that, using this approach, the complex dynamics of the regulatory network can indeed be reconstructed in a variety of settings.
EnhancerNet recapitulates direct reprogramming between cell types and predicts reprogramming recipes
I next tested whether the EnhancerNet model could predict the dynamics of the regulatory network controlling cell identity. Our analysis focused on the two broad classes of cell identity change: direct reprogramming, in which there are direct transitions between cell types; and hierarchical differentiation processes that start from (or transition through) multipotent progenitor states.
Direct reprogramming can be experimentally carried out by overexpressing TFs (Wang et al., 2021). Direct reprogramming has been demonstrated between dozens of cell types by overexpressing a wide range of TFs and TF combinations. Two clear distinguishing features for whether specific TFs can efficiently reprogram into a target cell type are that these factors are: (1) highly expressed in the target cell type and (2) their expression is unique to that cell type (D'Alessio et al., 2015).
The very existence of direct reprogramming between distantly related cell types is beyond the scope of classical models for cell fate bifurcations that are based on competition between a small set of lineage-determining factors. However, it is a straightforward consequence of the dynamics of Eqn 2 (Fig. 2C). The overexpression of the TF xj modulates the basins of attraction of each cell type, which can then encompass other (previously stable) cell type expression patterns. If a cell has initially been placed in one of these distant patterns, i.e. , then, after TF overexpression, it can transition directly to a new pattern: .
Which transcription factors are most efficient in reprogramming to a specific cell type? The optimal transcription factor combination will uniquely increase the basin of attraction of the target cell type, while avoiding increasing the basins of attraction of other cell types. The degree of increase in the basin of attraction of cell type k by the overexpression of TF j is proportional to its binding association ξk,j, which also corresponds to its expression in the target cell type (see Materials and Methods). Thus, TFs that are effective for reprogramming to a given cell type are predicted by the model to have high expression in that cell type and that this high expression is unique to the target cell type. This captures the known properties of reprogramming factors and is consistent with computational methods to identify reprogramming factors (D'Alessio et al., 2015; Rackham et al., 2016).
To test whether the model can recapitulate known reprogramming recipes, I generated a dataset for 45 cell identities using Tabula Muris (Schaum et al., 2018) (see Materials and Methods, Fig. 2A). I then set according to the TFs used in 12 established reprogramming recipes, as well as other TFs with high expression and variability. The transient overexpression of the reprogramming factors recapitulated the known reprogramming behaviour, with the system transitioning from the original attractor state (e.g. fibroblast) to an end attractor state, in line with the experiment (Fig. 2D,E). Thus, the EnhancerNet model can quantitatively recapitulate direct reprogramming, with no fitting parameters and by only considering the observed cell types.
As a specific demonstration of direct reprogramming, consider the reprogramming of a fibroblast into a neuron by overexpression of Ascl1 (Fig. 2E) (Chanda et al., 2014). The cell begins in a fibroblast state, where a specific enhancer type is strongly activated. The transient activation of a specific TF can destabilise this state, resulting in a transition to a neuron state that is associated with the activation of a neuron-specific enhancer type. The dynamics of this transition are one-dimensional and, in the presence of noise, correspond to a stochastic barrier crossing between two potential minima, which is consistent with previous quantitative analyses (Fig. S2) (Pusuluri et al., 2017).
EnhancerNet recapitulates hierarchical differentiation dynamics and predicts progenitor identity
Direct reprogramming is an important experimental phenomenon and may also occur in natural settings after tissue perturbation (Merrell and Stanger, 2016). In homeostatic and developmental settings, however, it is more typical for differentiation trajectories to occur through a series of multipotent progenitor states (Moris et al., 2016). Such differentiation trajectories have been extensively studied in mammalian haematopoiesis, where a population of multipotent progenitors can give rise to many blood lineages (Månsson et al., 2007; Orkin and Zon, 2008). Other examples include the intestinal epithelium (Kim et al., 2016; Singh et al., 2022), stomach epithelium (Qiao et al., 2007) and skin (Toma et al., 2005), as well as throughout development (Briggs et al., 2018).
Differentiation trajectories share several common characteristics. Differentiation proceeds in a directed manner through a series of progenitor states. Each progenitor may acquire one of several terminal fates, with the range of target cell fates becoming more restricted as differentiation proceeds. For example, the cell may initially be in a multipotent progenitor state, and then transition to a bipotent state followed by a unipotent state. These dynamics are most famously conveyed by the Waddington landscape (Waddington, 1957), with the image of a ball rolling down a hill segregated by valleys capturing the progressive restriction of cell fate as it progresses through transitional progenitor states. Progenitors co-express at low levels the lineage-determining TFs associated with their target fates, which is a phenomenon known as multilineage priming (Hu et al., 1997; Olsson et al., 2016; Briggs et al., 2018; Zheng et al., 2018; Martin et al., 2021). Although the Waddington landscape image has gained great popularity for conceptualizing the dynamics of cell fate specification and for developing quantitative models for differentiation dynamics (Zhou et al., 2012; Rand et al., 2021), it is not clear how these complex hierarchical dynamics are implemented by the gene regulatory network.
Here, I show that Waddingtonian cell fate specification dynamics and the existence and identity of the progenitor states is an emergent property of the dynamics captured by Eqn 2. These are due to the second mechanism for cell-type transitions in the model, annealing, where β is transiently decreased by the cell (‘heating up’) and then slowly increased (‘cooling down’) (Fig. 3A). Recall that β is a coarse-grained parameter that depends on the regulation of chromatin by TF binding, and can be controlled by molecular mechanisms. Decreasing β results in a widespread pattern of enhancer activation and gene expression, while increasing β results in the activation of more specific enhancers, in line with experimental knowledge of differentiation hierarchies (Gulati et al., 2020). Specifically, decreasing β destabilises terminal attractor states, while increasing β restabilises them, resulting in a transition towards a new cell type (Fig. 3).
Fig. 3.
Hierarchical differentiation by annealing. (A) Annealing is the process by which β is transiently decreased and then increased. The decrease in β transitions the cell to a global attractor state corresponding to a multipotent progenitor with an averaged expression profile of terminal cell types. As β increases again, new attractor states are created, which are averages of smaller subsets of the terminal cell types, corresponding to progenitor states with limited potency until, finally, the cell transitions to a terminal cell type. (B) Progenitor states appear in subsets of cell types that have high cosine similarity. In this example, I considered a simple setup where enhancers bind overlapping transcription factors (TFs) (top left, with positive TF binding indicated in black). The dynamics begin from a cell type corresponding to the binding profile of EN1, and EN5 has a slight positive weight w5=0.05. Annealing was simulated as in the top right schematic. The dynamics proceed through progenitors that show multilineage priming (bottom left) and transition from widespread enhancer activity to cell type-specific activity (bottom right). (C) The cosine similarity of the expression profiles of the terminal blood lineages, where expression profiles were calculated using the TFs that show both sufficiently high interlineage variability (see Materials and Methods). Hierarchical clustering according to cosine similarity recreates the ‘classical’ haematopoiesis differentiation tree. (D) UMAP plot of differentiation trajectories, generated by direct simulation of annealing in an EnhancerNet model calibrated by the haematopoietic terminal lineage expression profiles. Trajectories were simulated from an initial homogeneous population with the addition of noise and with adjusted to produce a balanced differentiation profile (see Materials and Methods). Points represent samples of the trajectories at constant time intervals, with the colour corresponding to the identity of the final state. Simulation recreates the observed progenitor states in haematopoiesis, including deviations from tree-like differentiation.
The annealing process proceeds as follows. The accessible and induced cell types, given by the rows of , where is sufficiently large, are all stable when β is very large, while their global average is the only stable state when β is small. This state expresses at low levels the TFs associated with all target lineages, and it corresponds to a multi-potent progenitor cell identity. As β increases, this state loses stability, and new stable states appear transiently. These new states are averages of increasingly restricted subsets of cell types, and they correspond to progenitors of restricted potential. Annealing thus recapitulates the hierarchical Waddingtonian differentiation dynamics and low-level multilineage progenitor expression patterns.
While progenitors generally correspond to averages of terminal cell types, not all averages are equally likely to be observed as stable progenitors. Rather, the stable progenitor states correspond to subsets of cell types with high internal similarity in their expression profiles (measured by cosine distance). As an illustrative example, I considered a simulation of a regulatory network with nine TFs, indexed , and six enhancers, indexed . Each enhancer can be bound by two TFs, and enhancers 1-2, 3-4 and 5-6 overlap by a single TF (Fig. 3B). The initial state corresponded to an expression profile associated with . I also take a slightly larger weight w5 for , corresponding to induction of this state by signalling. Annealing (a decrease in β followed by a gradual increase) transitions the cell through two progenitors: a ‘multipotent progenitor’ (low β), which corresponds to the average expression of all the enhancers; and a ‘restricted potential progenitor’ (intermediate β) that is associated with the average expression of and, finally, differentiation to . Thus, differentiation occurs through Waddingtonian dynamics associated with multilineage priming, allowing transition to an induced cell fate.
The model makes the specific prediction that differentiation hierarchies, including the identity of observed progenitor states, can be estimated by only knowing the identities of the terminal cell states. The model specifically predicts that they will appear only between transcriptional profiles with high internal cosine similarity. To test this, I considered differentiation in haematopoiesis, a system in which differentiation trajectories are complex and have been extensively studied. Using data on haematopoietic lineages in mice (extracted from Haemopedia; Choi et al., 2019), I considered the expression profile of all TFs that showed variability between terminal lineages and used the model to estimate the differentiation hierarchy that generated these lineages.
I first tested the hypothesis that progenitors emerge between correlated expression profiles by performing hierarchical clustering according to pairwise cosine similarity between the expression profiles of terminal cell types (Fig. 3C). Hierarchical clustering provides a heuristic approach to estimate the predicted identity of progenitor states from the terminal expression profiles. It produces a dendrogram (tree) that shows how expression profiles can be progressively grouped into clusters based on their similarity. This resulted in a tree structure that recreates the classic haematopoietic hierarchy (Fig. 3C). Branching points at the dendrogram capture the common lymphoid progenitor (CLP), common myeloid progenitor (CMP), megakaryocyte and erythroid progenitor (MEP), granulocyte and macrophage progenitor (GMP), T and NK cell progenitors (TNK), and B cell-biased lymphoid progenitor (BLP). Thus, a simple unsupervised clustering method with access to only the terminal cell fate information can reproduce a faithful estimate of the known complex differentiation landscape in haematopoiesis.
Motivated by the effectiveness of hierarchical clustering in predicting progenitor states, I developed a computational approach to model differentiation into multiple cell types. This approach is based on annealing in Eqn 2. Annealing trajectories were simulated beginning with a homogeneous initial population representing haematopoietic stem cells. To account for natural variability in differentiation paths, noise was introduced to Eqn 2 (our findings remained robust across various noise magnitudes, Fig. S3). In physiological settings, the production rate of each cell type is regulated by negative feedback, where the terminal cell population size inhibits its own production. This is exemplified by the negative-feedback regulation of red blood count through EPO signalling (Grover et al., 2014). To incorporate this regulation, I implemented a negative-feedback routine to tune , allowing each cell type to inhibit its own production (see Materials and Methods). This resulted in dynamics that generated all cell fates in a balanced manner. The differentiation trajectories were then visualized on a UMAP plot (Fig. 3D), revealing that differentiation dynamics proceed through a series of progenitors corresponding to those observed in haematopoiesis. Thus, our model quantitatively recapitulates differentiation in haematopoiesis without requiring parameter fitting.
Although the heuristic approach of hierarchical clustering produces a tree-like structure that recapitulates the main progenitor states in haematopoiesis, differentiation trajectories in the model need not be tree-like, as can be seen in the simulations of Fig. 3D. As β increases, multiple steady states can appear and disappear, and these new steady states may be averages of overlapping terminal states. The key prediction of the model is that these states will correspond to averages of terminal states with high internal similarity. For example, it is experimentally established that dendritic cells can emerge from both the myeloid lineage through macrophage-dendritic progenitors and from the lymphoid lineage through B cell-biased lymphoid progenitors (Anderson et al., 2021). This observation aligns with our model and simulations (Fig. 3D, see also Fig. S4 for simple examples of tripotent progenitors and deviations from tree structure). The gene expression profile of dendritic cells exhibits high cosine similarity to both B cells and macrophages (and, more generally, myeloid cells), whereas B cells are less similar to myeloid cells. Similarly, neutrophils may arise from distinct monocyte-neutrophil and basophil-eosinophil-neutrophil progenitors, and monocytes from distinct monocyte-neutrophil and monocyte-dendritic cell progenitors (Weinreb et al., 2020; Yampolskaya et al., 2023). These patterns are also recapitulated in our simulations and are related to the similarity structure of the cell types. Higher-level progenitor structures can be captured in the same manner by the model. For example, lymphoid and granulocyte cells have moderate cosine similarity, as do granulocytes and erythro-megakaryocytes. However, lymphoid and erythro-megakaryocytes are poorly correlated. Consequently, each of the first two pairs (lymphoid and granulocyte cells, and granulocytes and erythro-megakaryocytes) can share accessible progenitors, whereas the latter two (lymphoid and erythro-megakaryocytes) are less likely to do so. Indeed, the first two progenitors exist (lympho-myeloid primed progenitors and common myeloid progenitors), whereas the latter, to the best of our knowledge, does not. The model can thus make predictions regarding highly complex transition dynamics by using information on only the terminal cell identities.
Evolution of new cell types
I conclude this study by considering the question of how new cell types can evolve within the regulatory network. Although in principle a new cell type may arise de novo, in practice it is likely to appear by the diversification of an ancestral cell type into sister cell types, a process known as genetic individuation (Arendt et al., 2016; Hobert, 2021). In this process, a cell type associated with a specific combination of TFs evolves into new and distinct cell types associated with new TF combinations.
The EnhancerNet model has unique properties that support the process of genetic individuation of cell types; I propose that these properties may have been instrumental for the evolution of distal cis-regulatory elements with dynamic chromatin in animals. These properties are (1) the ability to support multiple coexisting, highly correlated attractor states; and (2) the ability to have multiple enhancers regulating the same genes.
The importance of the first property is clear when one considers that around the time of the initial diversification of the ancestral cell types, the sister cells have highly correlated expression patterns. At this stage, they need to co-exist as distinct attractor states to be expressed in the body. Thus, in the biological context, the ability to support multiple correlated attractor cell type states is crucial. This is supported by the mechanism underlying the EnhancerNet through the inverse temperature parameter β, which allows cells to discriminate between highly similar expression patterns.
As a concrete example, consider the specification of neuronal identity in C. elegans worms. Reilly et al. investigated the expression of TFs in the mature worm nervous system (Reilly et al., 2020). Their findings showed that each of the 118 neuron classes in C. elegans is defined by a unique combination of homeobox TFs, with 68 TFs exhibiting variable expression across neuronal classes. Many neuronal classes share highly similar TF codes: eight classes differ from another class by only a single TF, whereas 70 classes differ from another by three or fewer TFs. The mechanism underlying Eqn 2, through regulation of the β parameter, successfully supports these patterns, with around a twofold increase in β allowing full specification of all neuronal classes from a single multipotent progenitor (see Materials and Methods).
The second property allows the regulatory network to generate and modify specific cell types without interfering with other cell types that may have common TFs. This relates to the modularity of encoding cell types by enhancer sequences. I illustrate this by a simple example where an ancestral cell type expressing TFs 1, 2, 3 and 4 is diversified into two cell types that express 1, 2 and 3 or 1, 2 and 4 (Fig. 4A,B). The initial attractor state is associated with an enhancer type, denoted EN1, that binds all four TFs. In our example, this enhancer is initially placed in the genome near all four TFs. It is also assumed that multiple copies, known as shadow enhancers (Hong et al., 2008; Cannavò et al., 2016; Kvon et al., 2021), of EN1 are near TF1 and TF2 (Fig. 4C). This phenomenon is widespread and well-established experimentally, and its observed behaviour is in line with model predictions (see Materials and Methods). This setup is illustrated in Fig. 4C, and corresponds to a stable attractor where TFs 1-4 are active.
Fig. 4.
Evolution of new cell types. (A,B) I consider the evolution from a cell type associated with the binding pattern of TF1-4 (EN1 in A, left cell in B) to two sister cell types, each associated with the binding of TF1 and TF2 and either TF3 or TF4 (EN1′ and EN1″ in A, middle two cells in B). This must occur without interference with other cells in the regulatory network that may share TFs with these cell types. (C-E) The evolution occurs in the presence of other cell types in the gene regulatory network. (C) The original setup (left) stabilises only the ancestral cell type: see right three panels with simulations starting from the ancestral state, where TF1-TF4 are active; from sister cell A, where TF1-TF3 are active; and from sister cell B, where TF1, TF2 and TF4 are active. (D) Mutations in specific enhancers can stabilise states near all cell types. (E) Further mutation can destabilise the ancestral state. For all simulations, I used the full dynamics of Eqn 1, with the shade of green corresponding to expression strength of the TF.
Let us consider the possibility that enhancers near TF3 and TF4 evolved so that the enhancer near TF3 does not bind TF4, and the enhancer near TF4 does not bind TF3 (Fig. 4D). Now, there are three stable states: one where TF1-4 are active (with reduced activity of TF3 and TF4) and two where TF1 and TF2, and either TF3 or TF4 are active, which corresponds to the new sister cells. Over time, the shadow enhancers may evolve further, leading to the destabilisation of the original attractor state (Fig. 4E). Throughout this duration, the other cell types remain stable attractors. Thus, the EnhancerNet mechanism allows the evolution of new cell types without interfering with existing cell types.
Model for enhancer activation dynamics in transcriptional feedback networks
I began by deriving a general mathematical model for the feedback regulation of TF expression through their interaction with enhancers (Fig. 1A). Enhancers consist of multiple TF binding motifs (Spitz and Furlong, 2012). The binding of TFs potentiates enhancer activity by recruiting transcriptional co-factors. These co-factors modulate the biochemical properties of enhancer chromatin through processes such as histone acetylation, leading to the recruitment of transcription initiation factors and ultimately resulting in the transcription of enhancer-associated genes (Narita et al., 2021; Panigrahi and O'Malley, 2021).
Fig. 1.
Model for the regulation of cell identity transcription factors by enhancers. (A) Enhancers are cis-regulatory elements that can initiate transcription in distant genes through interaction with specific transcription factors (TFs) and transcriptional machinery. Each enhancer can interact with several TFs, and each TF can be controlled by multiple enhancers. Binding of TFs modulates enhancer chromatin and can increase the transcription initiation rate of the enhancer. (B) Enhancer types (top) bind specific combinations of TFs (bottom) according to binding strengths specified by the matrix, and, in turn, initiate transcription according to the rate matrix . The weight vector determines baseline activity and may be modulated by binding of signalling TFs. (C) Autoregulation is the observation that TFs that are co-expressed in specific cell types co-bind their own enhancers. These enhancers are, in turn, selected and activated in these specific cell types. (D) Different cell types are associated with specific enhancers that may have overlapping TF binding. (E) Autoregulation constrains and implies reciprocity.
To capture these complex molecular mechanisms within an effective mathematical framework, I considered the following setup. An enhancer (indexed i) is characterized by two vectors: a vector, , denoting its binding affinity to different identity TFs (); and a vector, Qi, denoting the rates at which it can induce the transcription of the same TFs (). The binding of TFs to an enhancer modulates the state of the enhancer chromatin, which in turn sets the rate at which the transcriptional initiation machinery is recruited to the enhancer. Specifically, I assumed that the chromatin state sets the energy for the recruitment of the transcription initiation machinery and that, on the timescale of cell identity changes, the recruitment rate is captured by an equilibrium distribution (see Materials and Methods). Taken together, the dynamics of the gene regulatory network are given by:
(1)
where is the vector of TF expression whose entries are xi, is a matrix whose entries are ξi,j, Q is the association matrix whose entries are qi,j, τ is the timescale of the dynamics and β is an effective inverse temperature parameter that depends on the turnover of chromatin modifications (see Materials and Methods, Fig. 1B). The baseline activity vector depends on effects upstream of the cell identity network, such as the binding of effector TFs for signalling pathways (Hnisz et al., 2015). In this study, I will refer to the network model and its associated dynamics as EnhancerNet.
Although Eqn 1 considered different physical enhancers as separate entities, in practice, enhancers with similar binding profiles can appear throughout the genome (Saint-André et al., 2016; Kvon et al., 2021), corresponding to similar rows of . I denote these as enhancer types and note that Eqn 1 can be rewritten so that is a matrix of enhancer types (i.e. with distinct rows) and the matrix corresponds to the overall association between enhancer types and TF expression (see Materials and Methods). Hereafter, I will assume that Eqn 1 captures the dynamics of enhancer types, which can correspond to the activation of multiple physical enhancers.
Autoregulation of TF-enhancer interactions constrains the EnhancerNet model
Eqn 1 can be further constrained by taking into account well-established properties of developmental networks. TFs associated with specific cell identities form densely interconnected networks in which they co-bind adjacent enhancers, which in turn activate the same TFs (Whyte et al., 2013; Hnisz et al., 2013, 2015; Saint-André et al., 2016) (Fig. 1C,D). This implies a strong positive correlation between the rows of , which correspond to binding profiles of enhancers, and the rows of , which correspond to the effect of these enhancers on TF transcription. This was captured by taking (Fig. 1E; relaxing this to assume that are merely correlated does not affect our conclusions). This gives the following model for the dynamics:
(2)
The dynamics captured by Eqn 2 are mathematically similar to models for efficient memory storage in artificial neural networks (Ramsauer et al., 2020 preprint). This relationship can be exploited when analysing various properties of the dynamics. These dynamics are associated with a potential function (or ‘potential landscape’) (see Materials and Methods). The shape of this potential landscape determines how gene expression changes over time, including the stable gene expression patterns associated with different cell identities, which correspond to minima of the potential landscape.
Autoregulation, captured by , implies that the interactions between TFs in the developmental network are reciprocal, i.e. for each pair of TFs k, j, the effect of increasing TF j on the expression rate of TF k is the same as the effect of increasing TF k on the expression rate of TF j (). Moreover, reciprocity and autoregulation are effectively equivalent (see Materials and Methods) (Fig. 1E). This equivalence explains the observation that reciprocal interactions are ubiquitous in developmental networks (Kraut and Levine, 1991; Milo et al., 2002; Alon, 2007; Huang et al., 2007).
Reciprocal interactions between TFs can be positive (mutual activation) or negative (mutual repression), with both possibilities common in developmental networks. This is also captured in Eqn 2, despite the fact that, in the model, enhancers only enhance transcription, and the binding of TFs to enhancers increases their activity. Repression in the model is a by-product of global inhibition by competition over shared transcriptional machinery.
Robust specification of combinatorial cell types through interaction between TFs and enhancers
Cell identity is established through the process of enhancer selection (Heintzman et al., 2009; Hnisz et al., 2013, 2015; Saint-André et al., 2016). In this process, cell type-specific enhancers are marked for activation, leading to the expression of a distinct pattern of transcription factors associated with these enhancers. This is recapitulated in our model when the inverse temperature parameter β is sufficiently large (see Materials and Methods). Under this condition, the cell types encoded by the gene regulatory network, which represent stable attractors for the dynamics, are given by the rows of the enhancer-associated matrix (or, in the general case, by the rows of ; see Materials and Methods). Specifically, the stable fixed points of the network are represented by the vectors ()=() for all enhancer types (Fig. 2A,B). Each stable fixed point corresponds to a cell type with a unique transcription factor expression pattern. At these fixed points, cell type-specific enhancers exhibit maximal activity. These fixed points are robust to a large degree of asymmetry in TF-enhancer interactions (Fig. S1).
Fig. 2.
Specification of cell types and direct reprogramming. (A) The EnhancerNet model was initialized with the TF expression profiles of 45 cell types from Tabula Muris, taking 105 transcription factors (TFs) with high variability between cell types (see Materials and Methods). The resulting matrix (transposed) is plotted, with larger values corresponding to darker colours. (B) At high β each row of is an attractor of the network. PCA transformation of is shown, with transformed individual rows, corresponding to enhancer-binding patterns, plotted in grey. They are all attractor states – trajectories that start in the vicinity of each of these states converge to them. Red lines correspond to trajectories starting from rows of with 25% multiplicative noise. (C) Constitutive overexpression of TFs alters the attractor landscape and can result in a bifurcation where cell types lose stability and transition to another cell type. Arrow indicates transition between gene expression states. (D) The overexpression of Ascl1 transitions fibroblasts to neurons in the model, capturing the known effect of Ascl1 overexpression in fibroblasts. The reprogramming path is plotted in red (lower panel), with black paths capturing other known reprogramming recipes (see Materials and Methods). (E) Expression of TFs (left panel) and enhancer activity (right panel), corresponding to the probability that transcription is initiated from the enhancer.
Signalling activity can induce specific cell fates or restrict inducible cell fates (Perrimon et al., 2012; Grover et al., 2014; Hnisz et al., 2015). This phenomenon is recapitulated in our model through the effect of signalling on , which in turn modulates the basins of attraction for each cell type. Signals that increase the activity of specific enhancers cause the cell types associated with these enhancers to become accessible from a broader range of gene expression states. As discussed later in the context of differentiation, this provides a mechanism to control the relative production of different cell types. Conversely, states with weak induction may become destabilized and thus reprogrammed to an alternative cell type. Importantly, the effect of signalling through changes in does not alter the TF identity of the cell types. Thus, the model provides a mechanism for how signalling can control cell type production without interfering with cell identities.
From a modelling perspective, the observation that cell types correspond to rows of suggests an intriguing possibility: that Eqn 2 can be parameterized using the TF identities of the observed (terminal) cell types, which can be retrieved from gene expression datasets. This leaves the inverse temperature parameter β and the signalling vector as the only free parameters. As I will show, in a variety of settings, there are strong constraints on both parameters. The model can thus provide predictions on the dynamics of the entire transcriptional network by considering only the observed cell types. In the rest of this article, I will scrutinize this possibility and demonstrate that, using this approach, the complex dynamics of the regulatory network can indeed be reconstructed in a variety of settings.
EnhancerNet recapitulates direct reprogramming between cell types and predicts reprogramming recipes
I next tested whether the EnhancerNet model could predict the dynamics of the regulatory network controlling cell identity. Our analysis focused on the two broad classes of cell identity change: direct reprogramming, in which there are direct transitions between cell types; and hierarchical differentiation processes that start from (or transition through) multipotent progenitor states.
Direct reprogramming can be experimentally carried out by overexpressing TFs (Wang et al., 2021). Direct reprogramming has been demonstrated between dozens of cell types by overexpressing a wide range of TFs and TF combinations. Two clear distinguishing features for whether specific TFs can efficiently reprogram into a target cell type are that these factors are: (1) highly expressed in the target cell type and (2) their expression is unique to that cell type (D'Alessio et al., 2015).
The very existence of direct reprogramming between distantly related cell types is beyond the scope of classical models for cell fate bifurcations that are based on competition between a small set of lineage-determining factors. However, it is a straightforward consequence of the dynamics of Eqn 2 (Fig. 2C). The overexpression of the TF xj modulates the basins of attraction of each cell type, which can then encompass other (previously stable) cell type expression patterns. If a cell has initially been placed in one of these distant patterns, i.e. , then, after TF overexpression, it can transition directly to a new pattern: .
Which transcription factors are most efficient in reprogramming to a specific cell type? The optimal transcription factor combination will uniquely increase the basin of attraction of the target cell type, while avoiding increasing the basins of attraction of other cell types. The degree of increase in the basin of attraction of cell type k by the overexpression of TF j is proportional to its binding association ξk,j, which also corresponds to its expression in the target cell type (see Materials and Methods). Thus, TFs that are effective for reprogramming to a given cell type are predicted by the model to have high expression in that cell type and that this high expression is unique to the target cell type. This captures the known properties of reprogramming factors and is consistent with computational methods to identify reprogramming factors (D'Alessio et al., 2015; Rackham et al., 2016).
To test whether the model can recapitulate known reprogramming recipes, I generated a dataset for 45 cell identities using Tabula Muris (Schaum et al., 2018) (see Materials and Methods, Fig. 2A). I then set according to the TFs used in 12 established reprogramming recipes, as well as other TFs with high expression and variability. The transient overexpression of the reprogramming factors recapitulated the known reprogramming behaviour, with the system transitioning from the original attractor state (e.g. fibroblast) to an end attractor state, in line with the experiment (Fig. 2D,E). Thus, the EnhancerNet model can quantitatively recapitulate direct reprogramming, with no fitting parameters and by only considering the observed cell types.
As a specific demonstration of direct reprogramming, consider the reprogramming of a fibroblast into a neuron by overexpression of Ascl1 (Fig. 2E) (Chanda et al., 2014). The cell begins in a fibroblast state, where a specific enhancer type is strongly activated. The transient activation of a specific TF can destabilise this state, resulting in a transition to a neuron state that is associated with the activation of a neuron-specific enhancer type. The dynamics of this transition are one-dimensional and, in the presence of noise, correspond to a stochastic barrier crossing between two potential minima, which is consistent with previous quantitative analyses (Fig. S2) (Pusuluri et al., 2017).
EnhancerNet recapitulates hierarchical differentiation dynamics and predicts progenitor identity
Direct reprogramming is an important experimental phenomenon and may also occur in natural settings after tissue perturbation (Merrell and Stanger, 2016). In homeostatic and developmental settings, however, it is more typical for differentiation trajectories to occur through a series of multipotent progenitor states (Moris et al., 2016). Such differentiation trajectories have been extensively studied in mammalian haematopoiesis, where a population of multipotent progenitors can give rise to many blood lineages (Månsson et al., 2007; Orkin and Zon, 2008). Other examples include the intestinal epithelium (Kim et al., 2016; Singh et al., 2022), stomach epithelium (Qiao et al., 2007) and skin (Toma et al., 2005), as well as throughout development (Briggs et al., 2018).
Differentiation trajectories share several common characteristics. Differentiation proceeds in a directed manner through a series of progenitor states. Each progenitor may acquire one of several terminal fates, with the range of target cell fates becoming more restricted as differentiation proceeds. For example, the cell may initially be in a multipotent progenitor state, and then transition to a bipotent state followed by a unipotent state. These dynamics are most famously conveyed by the Waddington landscape (Waddington, 1957), with the image of a ball rolling down a hill segregated by valleys capturing the progressive restriction of cell fate as it progresses through transitional progenitor states. Progenitors co-express at low levels the lineage-determining TFs associated with their target fates, which is a phenomenon known as multilineage priming (Hu et al., 1997; Olsson et al., 2016; Briggs et al., 2018; Zheng et al., 2018; Martin et al., 2021). Although the Waddington landscape image has gained great popularity for conceptualizing the dynamics of cell fate specification and for developing quantitative models for differentiation dynamics (Zhou et al., 2012; Rand et al., 2021), it is not clear how these complex hierarchical dynamics are implemented by the gene regulatory network.
Here, I show that Waddingtonian cell fate specification dynamics and the existence and identity of the progenitor states is an emergent property of the dynamics captured by Eqn 2. These are due to the second mechanism for cell-type transitions in the model, annealing, where β is transiently decreased by the cell (‘heating up’) and then slowly increased (‘cooling down’) (Fig. 3A). Recall that β is a coarse-grained parameter that depends on the regulation of chromatin by TF binding, and can be controlled by molecular mechanisms. Decreasing β results in a widespread pattern of enhancer activation and gene expression, while increasing β results in the activation of more specific enhancers, in line with experimental knowledge of differentiation hierarchies (Gulati et al., 2020). Specifically, decreasing β destabilises terminal attractor states, while increasing β restabilises them, resulting in a transition towards a new cell type (Fig. 3).
Fig. 3.
Hierarchical differentiation by annealing. (A) Annealing is the process by which β is transiently decreased and then increased. The decrease in β transitions the cell to a global attractor state corresponding to a multipotent progenitor with an averaged expression profile of terminal cell types. As β increases again, new attractor states are created, which are averages of smaller subsets of the terminal cell types, corresponding to progenitor states with limited potency until, finally, the cell transitions to a terminal cell type. (B) Progenitor states appear in subsets of cell types that have high cosine similarity. In this example, I considered a simple setup where enhancers bind overlapping transcription factors (TFs) (top left, with positive TF binding indicated in black). The dynamics begin from a cell type corresponding to the binding profile of EN1, and EN5 has a slight positive weight w5=0.05. Annealing was simulated as in the top right schematic. The dynamics proceed through progenitors that show multilineage priming (bottom left) and transition from widespread enhancer activity to cell type-specific activity (bottom right). (C) The cosine similarity of the expression profiles of the terminal blood lineages, where expression profiles were calculated using the TFs that show both sufficiently high interlineage variability (see Materials and Methods). Hierarchical clustering according to cosine similarity recreates the ‘classical’ haematopoiesis differentiation tree. (D) UMAP plot of differentiation trajectories, generated by direct simulation of annealing in an EnhancerNet model calibrated by the haematopoietic terminal lineage expression profiles. Trajectories were simulated from an initial homogeneous population with the addition of noise and with adjusted to produce a balanced differentiation profile (see Materials and Methods). Points represent samples of the trajectories at constant time intervals, with the colour corresponding to the identity of the final state. Simulation recreates the observed progenitor states in haematopoiesis, including deviations from tree-like differentiation.
The annealing process proceeds as follows. The accessible and induced cell types, given by the rows of , where is sufficiently large, are all stable when β is very large, while their global average is the only stable state when β is small. This state expresses at low levels the TFs associated with all target lineages, and it corresponds to a multi-potent progenitor cell identity. As β increases, this state loses stability, and new stable states appear transiently. These new states are averages of increasingly restricted subsets of cell types, and they correspond to progenitors of restricted potential. Annealing thus recapitulates the hierarchical Waddingtonian differentiation dynamics and low-level multilineage progenitor expression patterns.
While progenitors generally correspond to averages of terminal cell types, not all averages are equally likely to be observed as stable progenitors. Rather, the stable progenitor states correspond to subsets of cell types with high internal similarity in their expression profiles (measured by cosine distance). As an illustrative example, I considered a simulation of a regulatory network with nine TFs, indexed , and six enhancers, indexed . Each enhancer can be bound by two TFs, and enhancers 1-2, 3-4 and 5-6 overlap by a single TF (Fig. 3B). The initial state corresponded to an expression profile associated with . I also take a slightly larger weight w5 for , corresponding to induction of this state by signalling. Annealing (a decrease in β followed by a gradual increase) transitions the cell through two progenitors: a ‘multipotent progenitor’ (low β), which corresponds to the average expression of all the enhancers; and a ‘restricted potential progenitor’ (intermediate β) that is associated with the average expression of and, finally, differentiation to . Thus, differentiation occurs through Waddingtonian dynamics associated with multilineage priming, allowing transition to an induced cell fate.
The model makes the specific prediction that differentiation hierarchies, including the identity of observed progenitor states, can be estimated by only knowing the identities of the terminal cell states. The model specifically predicts that they will appear only between transcriptional profiles with high internal cosine similarity. To test this, I considered differentiation in haematopoiesis, a system in which differentiation trajectories are complex and have been extensively studied. Using data on haematopoietic lineages in mice (extracted from Haemopedia; Choi et al., 2019), I considered the expression profile of all TFs that showed variability between terminal lineages and used the model to estimate the differentiation hierarchy that generated these lineages.
I first tested the hypothesis that progenitors emerge between correlated expression profiles by performing hierarchical clustering according to pairwise cosine similarity between the expression profiles of terminal cell types (Fig. 3C). Hierarchical clustering provides a heuristic approach to estimate the predicted identity of progenitor states from the terminal expression profiles. It produces a dendrogram (tree) that shows how expression profiles can be progressively grouped into clusters based on their similarity. This resulted in a tree structure that recreates the classic haematopoietic hierarchy (Fig. 3C). Branching points at the dendrogram capture the common lymphoid progenitor (CLP), common myeloid progenitor (CMP), megakaryocyte and erythroid progenitor (MEP), granulocyte and macrophage progenitor (GMP), T and NK cell progenitors (TNK), and B cell-biased lymphoid progenitor (BLP). Thus, a simple unsupervised clustering method with access to only the terminal cell fate information can reproduce a faithful estimate of the known complex differentiation landscape in haematopoiesis.
Motivated by the effectiveness of hierarchical clustering in predicting progenitor states, I developed a computational approach to model differentiation into multiple cell types. This approach is based on annealing in Eqn 2. Annealing trajectories were simulated beginning with a homogeneous initial population representing haematopoietic stem cells. To account for natural variability in differentiation paths, noise was introduced to Eqn 2 (our findings remained robust across various noise magnitudes, Fig. S3). In physiological settings, the production rate of each cell type is regulated by negative feedback, where the terminal cell population size inhibits its own production. This is exemplified by the negative-feedback regulation of red blood count through EPO signalling (Grover et al., 2014). To incorporate this regulation, I implemented a negative-feedback routine to tune , allowing each cell type to inhibit its own production (see Materials and Methods). This resulted in dynamics that generated all cell fates in a balanced manner. The differentiation trajectories were then visualized on a UMAP plot (Fig. 3D), revealing that differentiation dynamics proceed through a series of progenitors corresponding to those observed in haematopoiesis. Thus, our model quantitatively recapitulates differentiation in haematopoiesis without requiring parameter fitting.
Although the heuristic approach of hierarchical clustering produces a tree-like structure that recapitulates the main progenitor states in haematopoiesis, differentiation trajectories in the model need not be tree-like, as can be seen in the simulations of Fig. 3D. As β increases, multiple steady states can appear and disappear, and these new steady states may be averages of overlapping terminal states. The key prediction of the model is that these states will correspond to averages of terminal states with high internal similarity. For example, it is experimentally established that dendritic cells can emerge from both the myeloid lineage through macrophage-dendritic progenitors and from the lymphoid lineage through B cell-biased lymphoid progenitors (Anderson et al., 2021). This observation aligns with our model and simulations (Fig. 3D, see also Fig. S4 for simple examples of tripotent progenitors and deviations from tree structure). The gene expression profile of dendritic cells exhibits high cosine similarity to both B cells and macrophages (and, more generally, myeloid cells), whereas B cells are less similar to myeloid cells. Similarly, neutrophils may arise from distinct monocyte-neutrophil and basophil-eosinophil-neutrophil progenitors, and monocytes from distinct monocyte-neutrophil and monocyte-dendritic cell progenitors (Weinreb et al., 2020; Yampolskaya et al., 2023). These patterns are also recapitulated in our simulations and are related to the similarity structure of the cell types. Higher-level progenitor structures can be captured in the same manner by the model. For example, lymphoid and granulocyte cells have moderate cosine similarity, as do granulocytes and erythro-megakaryocytes. However, lymphoid and erythro-megakaryocytes are poorly correlated. Consequently, each of the first two pairs (lymphoid and granulocyte cells, and granulocytes and erythro-megakaryocytes) can share accessible progenitors, whereas the latter two (lymphoid and erythro-megakaryocytes) are less likely to do so. Indeed, the first two progenitors exist (lympho-myeloid primed progenitors and common myeloid progenitors), whereas the latter, to the best of our knowledge, does not. The model can thus make predictions regarding highly complex transition dynamics by using information on only the terminal cell identities.
Evolution of new cell types
I conclude this study by considering the question of how new cell types can evolve within the regulatory network. Although in principle a new cell type may arise de novo, in practice it is likely to appear by the diversification of an ancestral cell type into sister cell types, a process known as genetic individuation (Arendt et al., 2016; Hobert, 2021). In this process, a cell type associated with a specific combination of TFs evolves into new and distinct cell types associated with new TF combinations.
The EnhancerNet model has unique properties that support the process of genetic individuation of cell types; I propose that these properties may have been instrumental for the evolution of distal cis-regulatory elements with dynamic chromatin in animals. These properties are (1) the ability to support multiple coexisting, highly correlated attractor states; and (2) the ability to have multiple enhancers regulating the same genes.
The importance of the first property is clear when one considers that around the time of the initial diversification of the ancestral cell types, the sister cells have highly correlated expression patterns. At this stage, they need to co-exist as distinct attractor states to be expressed in the body. Thus, in the biological context, the ability to support multiple correlated attractor cell type states is crucial. This is supported by the mechanism underlying the EnhancerNet through the inverse temperature parameter β, which allows cells to discriminate between highly similar expression patterns.
As a concrete example, consider the specification of neuronal identity in C. elegans worms. Reilly et al. investigated the expression of TFs in the mature worm nervous system (Reilly et al., 2020). Their findings showed that each of the 118 neuron classes in C. elegans is defined by a unique combination of homeobox TFs, with 68 TFs exhibiting variable expression across neuronal classes. Many neuronal classes share highly similar TF codes: eight classes differ from another class by only a single TF, whereas 70 classes differ from another by three or fewer TFs. The mechanism underlying Eqn 2, through regulation of the β parameter, successfully supports these patterns, with around a twofold increase in β allowing full specification of all neuronal classes from a single multipotent progenitor (see Materials and Methods).
The second property allows the regulatory network to generate and modify specific cell types without interfering with other cell types that may have common TFs. This relates to the modularity of encoding cell types by enhancer sequences. I illustrate this by a simple example where an ancestral cell type expressing TFs 1, 2, 3 and 4 is diversified into two cell types that express 1, 2 and 3 or 1, 2 and 4 (Fig. 4A,B). The initial attractor state is associated with an enhancer type, denoted EN1, that binds all four TFs. In our example, this enhancer is initially placed in the genome near all four TFs. It is also assumed that multiple copies, known as shadow enhancers (Hong et al., 2008; Cannavò et al., 2016; Kvon et al., 2021), of EN1 are near TF1 and TF2 (Fig. 4C). This phenomenon is widespread and well-established experimentally, and its observed behaviour is in line with model predictions (see Materials and Methods). This setup is illustrated in Fig. 4C, and corresponds to a stable attractor where TFs 1-4 are active.
Fig. 4.
Evolution of new cell types. (A,B) I consider the evolution from a cell type associated with the binding pattern of TF1-4 (EN1 in A, left cell in B) to two sister cell types, each associated with the binding of TF1 and TF2 and either TF3 or TF4 (EN1′ and EN1″ in A, middle two cells in B). This must occur without interference with other cells in the regulatory network that may share TFs with these cell types. (C-E) The evolution occurs in the presence of other cell types in the gene regulatory network. (C) The original setup (left) stabilises only the ancestral cell type: see right three panels with simulations starting from the ancestral state, where TF1-TF4 are active; from sister cell A, where TF1-TF3 are active; and from sister cell B, where TF1, TF2 and TF4 are active. (D) Mutations in specific enhancers can stabilise states near all cell types. (E) Further mutation can destabilise the ancestral state. For all simulations, I used the full dynamics of Eqn 1, with the shade of green corresponding to expression strength of the TF.
Let us consider the possibility that enhancers near TF3 and TF4 evolved so that the enhancer near TF3 does not bind TF4, and the enhancer near TF4 does not bind TF3 (Fig. 4D). Now, there are three stable states: one where TF1-4 are active (with reduced activity of TF3 and TF4) and two where TF1 and TF2, and either TF3 or TF4 are active, which corresponds to the new sister cells. Over time, the shadow enhancers may evolve further, leading to the destabilisation of the original attractor state (Fig. 4E). Throughout this duration, the other cell types remain stable attractors. Thus, the EnhancerNet mechanism allows the evolution of new cell types without interfering with existing cell types.
DISCUSSION
Here, I have derived a predictive mechanistic model for the dynamics of cell identity, based on interactions between TFs and enhancers. The model incorporated several features of the architecture of the regulatory network, i.e. that enhancers form dense autoregulatory networks with TFs and that enhancer activity is determined by its chromatin state, which is set by TF binding. These features are sufficient to derive a simple and tractable model that can be used without the need to fit unobserved parameters, and that recapitulates the known processes of cell type specification, reprogramming and differentiation.
The mechanism underlying enhancer selection in EnhancerNet is mathematically related to a recent model for memory storage and retrieval known as Modern Hopfield networks (Ramsauer et al., 2020 preprint; Krotov and Hopfield, 2021). Classical Hopfield networks are based on direct and additive interactions between components (Hopfield, 1982). Classical Hopfield networks have long been considered important conceptual models for memory storage and retrieval in the brain (Hopfield, 1982; Krotov and Hopfield, 2021), and, more recently, they have been employed in pioneering studies as conceptual and predictive models for cell fate specification, and as the basis of computational methods to study cell state dynamics (Lang et al., 2014; Fard et al., 2016; Guo and Zheng, 2017; Conforte et al., 2020; Boukacem et al., 2024). It was not clear how such a network could be implemented mechanistically in cells. Modern Hopfield networks extend Classical Hopfield networks and allow the storage of many patterns through higher-order interactions (Krotov and Hopfield, 2016, 2021). I show that their dynamics arise naturally in cells by the interactions of TFs and enhancers. The mathematical analogy between Modern Hopfield networks and enhancer-TF interactions provides a mechanism through which cells can retrieve many attractor patterns encoded by a biochemical regulatory network.
The model makes specific testable predictions for processes of reprogramming and differentiation, based on the identities of the terminal states. The model can predict reprogramming ‘recipes’ by which TFs can transition a cell from a given cell type to a target cell type. These predictions are consistent with known algorithms for this purpose and recapitulate established reprogramming recipes. The model thus provides a flexible computational framework to model reprogramming dynamics.
For differentiation, the model predicts the identity of progenitors and can predict complex differentiation hierarchies by using only the identities of the terminal cell types. Specifically, the model predicts that, in cases where a single multipotent progenitor gives rise to multiple cell types, more-restricted progenitors will preferentially appear between sets of cell types with similar expression profiles that are distinct from other cell types. The model recapitulates the complex and well-characterized differentiation hierarchy of haematopoiesis with no fitting parameters. In another well-studied system, intestinal stem cells differentiate into secretory progenitors that then give rise to specific progenitors for Paneth and goblet cells and enterochromaffin and non-enterochromaffin enteroendocrine cells (Singh et al., 2022). Singh et al. showed that the progenitors exhibit multilineage priming at both the transcriptional level, with low-level expression of cell type-specific transcripts, and at the chromatin level, with intermediate chromatin accessibility signatures at cell type-specific enhancers. The identity of these progenitors, along with their epigenetic and transcriptional profiles, aligns closely with the model predictions that are based on the similarities among these cell types.
Both the haematopoietic and intestinal systems are thus consistent with an annealing model for differentiation into multiple cell types. From a functional point of view, the annealing strategy corresponds to well-established techniques from physics and optimization to settle a system at a global minimum (Kirkpatrick et al., 1983; Van Laarhoven et al., 1987). From a biological perspective, this allows progenitor and stem cells to identify the cell types induced by signalling pathways (illustrated in Fig. 3B) and direct their differentiation towards them, avoiding convergence to metastable states. The mechanism also supports the balanced production of multiple cell fates, as demonstrated by the simulations for haematopoiesis (Fig. 3D). Annealing thus provides a solution to a key problem of the cell identity network – the need to encode multiple stable configurations (including stem and terminal configurations) while allowing transitions between these configurations, which are sensitive to upstream signalling.
Our analysis predicts that differentiation from a multipotent to a specified cell identity occurs through an increase in β. In our model, β is proportional to the ratio of global production to removal of latent chromatin modifications. A key candidate mechanism for this process is histone acetylation, a hallmark of active enhancers that plays a crucial role in transcription initiation (Creyghton et al., 2010; Kondo et al., 2014; Narita et al., 2021, 2023). The model predicts that a decrease in overall histone acetylation activity, corresponding to a reduction in β, will lead to loss of cell identity, whereas a decrease in the rate of histone deacetylation activity, corresponding to an increase in β, will promote cellular differentiation. These predictions align with well-established effects observed upon the respective inhibition of histone acetyltransferases (Ebrahimi et al., 2019; Gomez et al., 2009; Lipinski et al., 2020; Zhang et al., 2021; Narita et al., 2021; He et al., 2021) and histone deacetylases (Marks et al., 2000; Hsieh et al., 2004; Karantzali et al., 2008; Kondo et al., 2014; Li et al., 2014).
Other mechanisms, in addition to histone acetylation, are known to play a role the regulation of enhancer activity. These include the activity of chromatin remodellers such as the SWI/SNF, Mi-2/NuRD, SET1/MLL and Polycomb complexes, and involve various mechanisms, including the depletion and modification of histone proteins (Gao et al., 2009; Whyte et al., 2012; Iurlaro et al., 2021; Wolf et al., 2023; Chan et al., 2018). In addition to these, the methylation of DNA itself can play a role in regulating enhancer function (Angeloni and Bogdanovic, 2019). Some epigenetic mechanisms may show bistability through local positive-feedback regulation, resulting in digital activation patterns (Dodd et al., 2007; Angel et al., 2011; Haerter et al., 2014; Berry et al., 2017; Sneppen and Ringrose, 2019; Movilla Miangolarra et al., 2024). This property may confer robustness to cell types and protect against spontaneous reprogramming. Specifically, I considered the case where enhancer activity acts as a bifurcation parameter for a bistable switch that inhibits itself (see Materials and Methods). In such a case, a drop in enhancer activity below a critical threshold results in positive feedback that further decreases the basin of attraction for the corresponding cell type, resulting in a barrier for reprogramming.
The model can explain how new cell types can evolve without affecting pre-existing cell types encoded by the network. This applies both to evolutionary dynamics between generations of organisms and to the evolution of cells in diseases such as cancer. The model aligns with the experimentally observed robustness of shadow enhancers, which are thought to play a crucial role in the evolution of cellular diversity. The predictions of the model could be tested by synthetically engineering new cell types, through mutating shadow enhancers as prescribed by the model.
In conclusion, I propose that EnhancerNet provides a simple predictive framework for the dynamics of the gene regulatory network that control cell identity, and a basis for dissecting the complex processes of cell reprogramming, differentiation and evolution.
MATERIALS AND METHODS
Derivation of general EnhancerNet model
I modelled transcriptional activation by considering a process whereby transcription is initiated at enhancer i at rate pi, which results in the transcription of gene j at a coupling rate of qi,j. As in established thermodynamic models for gene regulation, it was assumed that there is a rate-limiting step for transcription that involves the recruitment to a specific site in the genome of transcription initiation machinery (Ackers et al., 1982; Bintu et al., 2005), which may be of high multiplicity (the effect of multiplicity in the model would be to scale overall transcription, which is not important for our conclusions). It was assumed that enhancers compete for the binding of this machinery. I denote using i the energy for transcriptional initiation at enhancer i and assume that the transcriptional machinery is always bound to one of the enhancers. It was assumed that on the timescale of interest, which is related to changes in cell identity (days to weeks), the rate of transcription initiation is captured by equilibrium statistical mechanics. Namely, the rate of transcription initiation at enhancer i is proportional to the Boltzmann distribution:
(3)
where is inverse temperature. The initiation of transcription at enhancer i can then result in the transcription of several associated genes, and each gene can be transcribed after an interaction with one of several enhancers. The dynamics of the expression of gene j, denoted xj, is given by the sum:
(4)
where τ is the timescale of gene expression changes.
Feedback in the model occurs when TFs modulate the activation energies of the enhancers. In classical models for gene regulation in prokaryotes, the binding of activator or repressor molecules directly modulates the rate of transcription initiation, corresponding to the variables i in our model (Bintu et al., 2005). For enhancer-mediated transcription, on the other hand, the dominant mode of enhancer activation appears to be indirect, through the modulation of enhancer chromatin (Eck et al., 2020). Binding of TFs to enhancers leads to loosening of nucleosomes and to the biochemical modification of histone proteins (Calo and Wysocka, 2013; Park et al., 2021; Hansen et al., 2022), resulting in dynamic changes in enhancer chromatin that are closely linked to enhancer activity (Heintzman et al., 2009; Creyghton et al., 2010). A dominant mode of enhancer activation appears to be the TF-mediated recruitment of histone acetyltransferases, which modify enhancer chromatin, resulting in the recruitment of transcription initiation machinery (Narita et al., 2021).
From a biophysical perspective, this can be captured by a model in which i is set by a latent variable mi, which accumulates proportionally to the binding of TFs:
(5)
(6)
where ξi,j is the effective binding rate TF j to enhancer i, and where κ1, κ−1 are the associated ‘on’ and ‘off’ rates for mi, which may be related to the global activity of enzymes such as histone acetyltransferases and histone deacetylases. Experimentally, it was observed that the timescale of these dynamics is of the order of minutes (Weinert et al., 2018; Narita et al., 2021), and thus much faster than the typical timescale of changes in cell identity. The weight parameter determines the energy in the absence of mi or of effects on i that are outside the cell identity feedback network.
The mechanism described in Eqn 6 is generic and may correspond to several underlying biological processes; it is, in essence, similar to modulation of receptor activation energy by methylation in bacterial chemotaxis (Tu, 2013). Taking a quasi-steady-state of mi and denoting by , Eqn 1 is derived, with the entries of the vector given by and the effective inverse temperature given by .
It is also possible to consider a more general model where TF binding to enhancers is modulated at the level of the entire enhancer, e.g. by the binding of ‘pioneer’ factors that increase accessibility for other TFs. In such a case, Eqn 6 may be altered so that:
(7)
where ζi corresponds to enhancer-level gain, which effectively multiplies .
Equivalence of model with physical enhancers to model with enhancer types
I consider the dynamics of Eqn 1 where a subset, , of enhancers has identical association patterns, i.e. ξi,j=ξk,j=ξj for all j and all . Then:
(8)
(9)
(10)
Now:
(11)
(12)
(13)
where . Thus,
(14)
Denoting the denominator by Z, it was noted that:
(15)
(16)
(17)
(18)
(19)
where . Thus, the following may be derived:
(20)
which does not include the physical enhancers of type , as they are replaced with a combined ‘enhancer type’ with the association constant q*,j and weight w*.
Reciprocity and autoregulation in enhancer feedback networks
Here, I will show that reciprocity:
(21)
for all k, j implies that qi,j=ξi,j for all i, j. As the above relation is trivial for j=k, instead j≠k is taken:
(22)
where .
Thus, for an arbitrary x, the equality implies that qi,j=νξi,j:
(23)
Thus, reciprocal interactions between TFs are equivalent to the autoregulation of TFs by binding to their enhancers.
Scalar potential for transcriptional dynamics
Many derivations related to Eqn 2 appear in Ramsauer et al. (2020 preprint), including stability analysis of the fixed points, as well as storage capacity and equivalence with other machine-learning models. Here, for completeness, I will derive the important results that pertain to our paper, and refer the reader to Ramsauer et al. for more in-depth derivations that pertain to other aspects of the model. Considering the symmetric case, I will show that the dynamics are a gradient flow. A scalar potential for Eqn 2 is:
(24)
where . This function is similar to the Lyapunov function of Ramsauer et al. (2020 preprint), and accounts for the bias . To see that the dynamics are a potential flow:
(25)
observe that
(26)
(27)
(28)
(29)
which provides the dynamics of .
Rows of Q are fixed points at high β
Consider the row vectors of given by . The dynamics of Eqn 2 at are given by:
(30)
where θi,j determines the angle between and the magnitude is defined by the Euclidean norm. It was assumed that the rows of are all of magnitude unity, which is equivalent to assuming that they have comparable overall binding affinities. In this case:
(31)
where the last equality holds for sufficiently large β and assuming the rows of are distinct, because, in that case cosθi,k=1 if (and only if) i=k. Thus, for a large enough β, the rows of are fixed points of the dynamics. In practice, it is only important that cell types with high cosine similarity have similar overall binding affinities. Consider, for example, the case where, for pattern k′:
(32)
In such a case, the pattern k will be unstable even at very large β, and thus such a pattern will not be accessible. For this reason, it is expected that similar cell types will have comparable overall binding affinities. In cells, this could be achieved either through evolution of enhancer composition or by population-level negative feedback, such as the coupling of the enhancer-level gain parameter (ζi) to the production of cell type i.
What about the case where the matrix is distinct from ? Consider then dynamics at :
(33)
where now θi,j determines the angle between . Now, in order for the pattern to be a fixed point at high β, it is required that:
(34)
Under the assumption that the rows of are comparable, is a fixed point of the dynamics at sufficiently large β when there is high cosine similarity between compared with the other rows of .
Averages of rows of can be fixed points
Let us denote using a subset of indices of enhancers of magnitude and consider the averaged vector:
(35)
Setting gives the dynamics:
(36)
where in the last equality it was assumed that the rows of are also unity. In the case where all weights wi are similar, this will be zero trivially when β is very small and where is the set of all enhancers (all K rows of ), as in this case:
(37)
This can be generalized in a straightforward manner to the case where some of the weights of w are large and of comparable magnitude. Otherwise, and assuming for simplicity that , it is required that:
(38)
which, at large enough β, occurs when the averaged cosine similarity of a set element with the other elements of the set:
(39)
is both (1) comparable within the set, for , and (2) larger than outside the set, for . An averaged pattern of a subset will therefore be a fixed point if each pattern in the subset is similar to each of the other patterns, and distinct from patterns outside the subset. Condition (1) is specifically easy to satisfy for subsets with only two patterns; in this case, their average (a ‘bipotent progenitor’) will be a fixed point when they have large cosine similarity and are distinct from other patterns.
Global stability of averages depends on β
To probe the global stability of averages of rows of , the scalar potential V can be used. The dynamics proceed from high V to low V so it is expected that states with higher V are less likely to be stable than states with lower V. As the potential function is only defined for the symmetric case, the states are defined as averages of the rows of , which are analogously defined as , where is as before a set of indices:
(40)
Setting , the following may be evaluated:
(41)
Again, taking the magnitude of the rows of to be unity and (equivalent to enhancers with similar binding strengths and weights) gives:
(42)
The energy of averaged patterns thus depends only on their cosine similarity relative to all patterns (first term) and internal cosine similarity (second term). As an illustrative case, let us assume that for and for [considering that, in general, A, B are between (−1, 1)]. Then:
(43)
where K is the overall number of patterns. The case of comparable A≈B, corresponding to random subsets, gives:
(44)
which for large β would correspond to and will thus have high energy when B is low. On the other hand, in the case where A≫B, as a rough approximation:
(45)
When β is very large, the size of the subset |I| does not contribute to V and thus it is minimized when A is maximized, i.e. at single patterns with A=1 (where ). At intermediate β, however, larger subsets may have lower energy. For two subsets where and A1<A2, the larger set will have lower energy when:
(46)
which occurs for a larger range when the difference between the set magnitudes is larger and the difference between their average cosine similarity is smaller. Specifically, the differentiation from a bipotent progenitor to a terminal identity occurs at a critical β:
(47)
Thus, an annealing strategy where β is decreased and then slowly increased results in a transition from large (global) averages, which correspond to multipotent progenitors, to a set of ever more restricted progenitors, similar to the ‘Waddingtonian’ dynamics.
Temperature transitions for cell type evolution
In this section, I will estimate the temperature β required to specify a population of related cell types. These cell types may share activity patterns across many of their TFs and differ in some smaller regions. As an example that will be discussed later, neurons may have similar TF activity patterns across many TFs, yet small differences in TF activity can fine-tune the identity of specific neurons.
I will specifically consider K patterns associated with closely related cell types and K′ patterns associated with other cell types. There are N bits that differ between the related cell types, of which a fraction η are active (drawn at random), N″ bits that are inactive and N′ bits that are active in all other cell types. As patterns have magnitude unity, the activity level of active bits was set to . It was assumed that unrelated patterns have ηN+N′ active TFs drawn at random. The average inner product of the related cell types is given by:
(48)
while the average inner product between random cell types is given by:
(49)
Let denote each of the closely related patterns, and their average. The energy of each pattern is given by:
(50)
It was assumed that the patterns are sufficiently well separated (A≫B) and that β is sufficiently large, such that:
(51)
while the energy of the averaged pattern is:
(52)
In general, as β increases, it would be expected to see first a transition from the global average to the local average of the two patterns, which occurs when V1 drops below V2. This transition occurs at a critical value of β that satisfies (assuming K≫1):
(53)
When β is small then and the l.h.s. is equal to 1−A and thus larger than the r.h.s. When the l.h.s. is negative and thus smaller than the r.h.s. Thus, the critical β occurs around:
(54)
I will now consider the case where a new cell type evolves from . I will assume, without loss of generally, that the evolved pattern differs from the existing pattern by two bits, i.e. ξ1,1=a, ξ′1,1=0, ξ1,2=0, ξ′1,2=a. The energy of the new patterns is given by:
(55)
with C corresponding to the inner product of the new cell types:
(56)
whereas the energy of the averaged pattern is:
(57)
For the second transition, the case of interest is , where the contributions of the (K−1)eβB terms to the energy levels are negligible. In this case, Eqn 47 can be used to give:
(58)
Using Eqns 58 and 54, a ratio χ can be derived that captures the relative increase in β required to evolve and stabilize a new cell type from an existing population of cells:
(59)
The gain χ thus depends only on the TFs that are variable between the cells and, among these TFs, scales with the variance of the active TFs. In the case of neuronal identity specification in C. elegans, the expression code is sparse, with μ=0.1 among variable TFs, with the other parameters being K=118 neuron classes and N=68 TFs exhibiting variable expression across neuronal classes. For this system, this gives:
(60)
implying a further 80% increase in β required from the initial destabilization of a multipotent progenitor to the complete specification of all cell types.
Local positive feedback as a barrier for reprogramming
Consider a modification to the EnhancerNet model where the energy for transcriptional activation is dependent on both the original modification mi and a new modification ui:
(61)
Here, λ is a scalar and ui is autocatalytic and inhibited by enhancer activation. I propose the following dynamics for ui:
(62)
where n≫1 and f(pi) is a decreasing function of enhancer activity pi. Eqn 62 can exhibit either bistability or hysteresis, depending on the value of ρ. It was assumed that initially ui=0 and pi is sufficiently large (as in the multipotent state), maintaining stability at ui=0. As cells differentiate, the activity of specific enhancers decreases while others increase. When the activity of an enhancer drops below a critical threshold, pcrit, Eqn 62 undergoes a bifurcation, causing ui to increase to a much larger value (ui≫1). This increase may persist due to a transition to a new fixed point when the dynamics are bistable. Consequently, the energy required to initiate transcription from the enhancer increases, making reprogramming to the associated cell type (whether induced by signalling, overexpression of specific transcription factors, or noise) less likely. This mechanism effectively creates a barrier to reprogramming.
Direct reprogramming through the overexpression of TFs
Consider a transcription factor xj that is constitutively overexpressed to an extent δj. This constitutive expression alters energy levels i without feedback. This therefore takes into account only the effect of the additional TF production on the energy levels of the enhancers, without altering the coordinates of the stable fixed points. V′ denotes the potential under the perturbed dynamics. Evaluating V′ at pattern k yields, at high β:
(63)
where it was assumed that for all i. Assuming that unperturbed pattern k is stable at a given β, the summand for δj=0 takes its maximum at i=k, where:
(64)
thus:
(65)
Under the assumption that the perturbation magnitude is relatively small (and, generally, for patterns that are stable after the perturbation):
(66)
and thus:
(67)
Note that Eqn 67 readily generalizes to a perturbation in several TFs :
(68)
If the goal is to steer the dynamics toward a pattern k, then the TFs and their overexpression need to be chosen such that is sufficiently larger for i=k than for all other patterns i≠k. That is, the ideal expression for a TF j is such that ξi,j is large for i=k (amplifying δj) and zero for i≠k.
Preprocessing and simulation
The matrix was initialized from transcript counts extracted from murine cells and averaged over cell type. For the Tabula Muris dataset (Schaum et al., 2018), I used all available data across organs and averaged over cell type. For haematopoiesis, I used the Haemopedia dataset (Choi et al., 2019) and averaged all cells that belong to the same terminal lineage. The data were then log-transformed (using a log1+x transformation) and filtered for TFs with mean and std expression larger than log4, as well as for Tabula Muris the TFs that participate in the tested reprogramming pathways (a full list of cell types and TFs used can be found in Tables S1-S4). Although, in principle, a log transformation is not needed for our model, which does not assume log-transformed values of x, it has the statistical advantage of reducing variance in the rows of and thus reducing sensitivity to TF choice. Finally, the rows of were normalized to unity. Simulations were performed using Python, taking τ=1, and the (terminal) β was chosen so that all patterns were stable.
Simulation of reprogramming
To simulate forced TF expression during reprogramming, the following dynamics may be used:
(69)
where is a vector where δi corresponds to the degree of activation of TFi. In general, it was assumed that δi=1 when the TF was in the reprogramming pathway and δi=0 when it was not. However, for a few genes, setting δi as different from unity was necessary to achieve the correct reprogramming (setting δi=1 achieves reprogramming to a closely related cell type). The full list of reprogramming pathways and relevant δ values can be found in Table S5.
Simulation of balanced differentiation
Haematopoiesis was simulated by using annealing from β=0 to β=50 over 50 time units. The initial state corresponded to the averaged value of all terminal states. Simulations were performed by adding additive white noise:
(70)
with σ set at 0.01. To simulate balanced differentiation in haematopoiesis, I used the following feedback procedure that aims to imitate in vivo feedback on differentiation, where there is negative feedback on the production of each cell type by signalling. was initialized as a zero vector. Then, running from k=0 to k=kmax, we performed differentiation by annealing. If the outcome terminal cell corresponded to row i of , I adjusted wi=wi−0.5(1−k/kmax) and then mean-adjusted w to zero. This resulted in a signalling profile, w, that produced all cell fates from the initial progenitor population.
Derivation of general EnhancerNet model
I modelled transcriptional activation by considering a process whereby transcription is initiated at enhancer i at rate pi, which results in the transcription of gene j at a coupling rate of qi,j. As in established thermodynamic models for gene regulation, it was assumed that there is a rate-limiting step for transcription that involves the recruitment to a specific site in the genome of transcription initiation machinery (Ackers et al., 1982; Bintu et al., 2005), which may be of high multiplicity (the effect of multiplicity in the model would be to scale overall transcription, which is not important for our conclusions). It was assumed that enhancers compete for the binding of this machinery. I denote using i the energy for transcriptional initiation at enhancer i and assume that the transcriptional machinery is always bound to one of the enhancers. It was assumed that on the timescale of interest, which is related to changes in cell identity (days to weeks), the rate of transcription initiation is captured by equilibrium statistical mechanics. Namely, the rate of transcription initiation at enhancer i is proportional to the Boltzmann distribution:
(3)
where is inverse temperature. The initiation of transcription at enhancer i can then result in the transcription of several associated genes, and each gene can be transcribed after an interaction with one of several enhancers. The dynamics of the expression of gene j, denoted xj, is given by the sum:
(4)
where τ is the timescale of gene expression changes.
Feedback in the model occurs when TFs modulate the activation energies of the enhancers. In classical models for gene regulation in prokaryotes, the binding of activator or repressor molecules directly modulates the rate of transcription initiation, corresponding to the variables i in our model (Bintu et al., 2005). For enhancer-mediated transcription, on the other hand, the dominant mode of enhancer activation appears to be indirect, through the modulation of enhancer chromatin (Eck et al., 2020). Binding of TFs to enhancers leads to loosening of nucleosomes and to the biochemical modification of histone proteins (Calo and Wysocka, 2013; Park et al., 2021; Hansen et al., 2022), resulting in dynamic changes in enhancer chromatin that are closely linked to enhancer activity (Heintzman et al., 2009; Creyghton et al., 2010). A dominant mode of enhancer activation appears to be the TF-mediated recruitment of histone acetyltransferases, which modify enhancer chromatin, resulting in the recruitment of transcription initiation machinery (Narita et al., 2021).
From a biophysical perspective, this can be captured by a model in which i is set by a latent variable mi, which accumulates proportionally to the binding of TFs:
(5)
(6)
where ξi,j is the effective binding rate TF j to enhancer i, and where κ1, κ−1 are the associated ‘on’ and ‘off’ rates for mi, which may be related to the global activity of enzymes such as histone acetyltransferases and histone deacetylases. Experimentally, it was observed that the timescale of these dynamics is of the order of minutes (Weinert et al., 2018; Narita et al., 2021), and thus much faster than the typical timescale of changes in cell identity. The weight parameter determines the energy in the absence of mi or of effects on i that are outside the cell identity feedback network.
The mechanism described in Eqn 6 is generic and may correspond to several underlying biological processes; it is, in essence, similar to modulation of receptor activation energy by methylation in bacterial chemotaxis (Tu, 2013). Taking a quasi-steady-state of mi and denoting by , Eqn 1 is derived, with the entries of the vector given by and the effective inverse temperature given by .
It is also possible to consider a more general model where TF binding to enhancers is modulated at the level of the entire enhancer, e.g. by the binding of ‘pioneer’ factors that increase accessibility for other TFs. In such a case, Eqn 6 may be altered so that:
(7)
where ζi corresponds to enhancer-level gain, which effectively multiplies .
Equivalence of model with physical enhancers to model with enhancer types
I consider the dynamics of Eqn 1 where a subset, , of enhancers has identical association patterns, i.e. ξi,j=ξk,j=ξj for all j and all . Then:
(8)
(9)
(10)
Now:
(11)
(12)
(13)
where . Thus,
(14)
Denoting the denominator by Z, it was noted that:
(15)
(16)
(17)
(18)
(19)
where . Thus, the following may be derived:
(20)
which does not include the physical enhancers of type , as they are replaced with a combined ‘enhancer type’ with the association constant q*,j and weight w*.
Reciprocity and autoregulation in enhancer feedback networks
Here, I will show that reciprocity:
(21)
for all k, j implies that qi,j=ξi,j for all i, j. As the above relation is trivial for j=k, instead j≠k is taken:
(22)
where .
Thus, for an arbitrary x, the equality implies that qi,j=νξi,j:
(23)
Thus, reciprocal interactions between TFs are equivalent to the autoregulation of TFs by binding to their enhancers.
Scalar potential for transcriptional dynamics
Many derivations related to Eqn 2 appear in Ramsauer et al. (2020 preprint), including stability analysis of the fixed points, as well as storage capacity and equivalence with other machine-learning models. Here, for completeness, I will derive the important results that pertain to our paper, and refer the reader to Ramsauer et al. for more in-depth derivations that pertain to other aspects of the model. Considering the symmetric case, I will show that the dynamics are a gradient flow. A scalar potential for Eqn 2 is:
(24)
where . This function is similar to the Lyapunov function of Ramsauer et al. (2020 preprint), and accounts for the bias . To see that the dynamics are a potential flow:
(25)
observe that
(26)
(27)
(28)
(29)
which provides the dynamics of .
Rows of Q are fixed points at high β
Consider the row vectors of given by . The dynamics of Eqn 2 at are given by:
(30)
where θi,j determines the angle between and the magnitude is defined by the Euclidean norm. It was assumed that the rows of are all of magnitude unity, which is equivalent to assuming that they have comparable overall binding affinities. In this case:
(31)
where the last equality holds for sufficiently large β and assuming the rows of are distinct, because, in that case cosθi,k=1 if (and only if) i=k. Thus, for a large enough β, the rows of are fixed points of the dynamics. In practice, it is only important that cell types with high cosine similarity have similar overall binding affinities. Consider, for example, the case where, for pattern k′:
(32)
In such a case, the pattern k will be unstable even at very large β, and thus such a pattern will not be accessible. For this reason, it is expected that similar cell types will have comparable overall binding affinities. In cells, this could be achieved either through evolution of enhancer composition or by population-level negative feedback, such as the coupling of the enhancer-level gain parameter (ζi) to the production of cell type i.
What about the case where the matrix is distinct from ? Consider then dynamics at :
(33)
where now θi,j determines the angle between . Now, in order for the pattern to be a fixed point at high β, it is required that:
(34)
Under the assumption that the rows of are comparable, is a fixed point of the dynamics at sufficiently large β when there is high cosine similarity between compared with the other rows of .
Averages of rows of can be fixed points
Let us denote using a subset of indices of enhancers of magnitude and consider the averaged vector:
(35)
Setting gives the dynamics:
(36)
where in the last equality it was assumed that the rows of are also unity. In the case where all weights wi are similar, this will be zero trivially when β is very small and where is the set of all enhancers (all K rows of ), as in this case:
(37)
This can be generalized in a straightforward manner to the case where some of the weights of w are large and of comparable magnitude. Otherwise, and assuming for simplicity that , it is required that:
(38)
which, at large enough β, occurs when the averaged cosine similarity of a set element with the other elements of the set:
(39)
is both (1) comparable within the set, for , and (2) larger than outside the set, for . An averaged pattern of a subset will therefore be a fixed point if each pattern in the subset is similar to each of the other patterns, and distinct from patterns outside the subset. Condition (1) is specifically easy to satisfy for subsets with only two patterns; in this case, their average (a ‘bipotent progenitor’) will be a fixed point when they have large cosine similarity and are distinct from other patterns.
Global stability of averages depends on β
To probe the global stability of averages of rows of , the scalar potential V can be used. The dynamics proceed from high V to low V so it is expected that states with higher V are less likely to be stable than states with lower V. As the potential function is only defined for the symmetric case, the states are defined as averages of the rows of , which are analogously defined as , where is as before a set of indices:
(40)
Setting , the following may be evaluated:
(41)
Again, taking the magnitude of the rows of to be unity and (equivalent to enhancers with similar binding strengths and weights) gives:
(42)
The energy of averaged patterns thus depends only on their cosine similarity relative to all patterns (first term) and internal cosine similarity (second term). As an illustrative case, let us assume that for and for [considering that, in general, A, B are between (−1, 1)]. Then:
(43)
where K is the overall number of patterns. The case of comparable A≈B, corresponding to random subsets, gives:
(44)
which for large β would correspond to and will thus have high energy when B is low. On the other hand, in the case where A≫B, as a rough approximation:
(45)
When β is very large, the size of the subset |I| does not contribute to V and thus it is minimized when A is maximized, i.e. at single patterns with A=1 (where ). At intermediate β, however, larger subsets may have lower energy. For two subsets where and A1<A2, the larger set will have lower energy when:
(46)
which occurs for a larger range when the difference between the set magnitudes is larger and the difference between their average cosine similarity is smaller. Specifically, the differentiation from a bipotent progenitor to a terminal identity occurs at a critical β:
(47)
Thus, an annealing strategy where β is decreased and then slowly increased results in a transition from large (global) averages, which correspond to multipotent progenitors, to a set of ever more restricted progenitors, similar to the ‘Waddingtonian’ dynamics.
Temperature transitions for cell type evolution
In this section, I will estimate the temperature β required to specify a population of related cell types. These cell types may share activity patterns across many of their TFs and differ in some smaller regions. As an example that will be discussed later, neurons may have similar TF activity patterns across many TFs, yet small differences in TF activity can fine-tune the identity of specific neurons.
I will specifically consider K patterns associated with closely related cell types and K′ patterns associated with other cell types. There are N bits that differ between the related cell types, of which a fraction η are active (drawn at random), N″ bits that are inactive and N′ bits that are active in all other cell types. As patterns have magnitude unity, the activity level of active bits was set to . It was assumed that unrelated patterns have ηN+N′ active TFs drawn at random. The average inner product of the related cell types is given by:
(48)
while the average inner product between random cell types is given by:
(49)
Let denote each of the closely related patterns, and their average. The energy of each pattern is given by:
(50)
It was assumed that the patterns are sufficiently well separated (A≫B) and that β is sufficiently large, such that:
(51)
while the energy of the averaged pattern is:
(52)
In general, as β increases, it would be expected to see first a transition from the global average to the local average of the two patterns, which occurs when V1 drops below V2. This transition occurs at a critical value of β that satisfies (assuming K≫1):
(53)
When β is small then and the l.h.s. is equal to 1−A and thus larger than the r.h.s. When the l.h.s. is negative and thus smaller than the r.h.s. Thus, the critical β occurs around:
(54)
I will now consider the case where a new cell type evolves from . I will assume, without loss of generally, that the evolved pattern differs from the existing pattern by two bits, i.e. ξ1,1=a, ξ′1,1=0, ξ1,2=0, ξ′1,2=a. The energy of the new patterns is given by:
(55)
with C corresponding to the inner product of the new cell types:
(56)
whereas the energy of the averaged pattern is:
(57)
For the second transition, the case of interest is , where the contributions of the (K−1)eβB terms to the energy levels are negligible. In this case, Eqn 47 can be used to give:
(58)
Using Eqns 58 and 54, a ratio χ can be derived that captures the relative increase in β required to evolve and stabilize a new cell type from an existing population of cells:
(59)
The gain χ thus depends only on the TFs that are variable between the cells and, among these TFs, scales with the variance of the active TFs. In the case of neuronal identity specification in C. elegans, the expression code is sparse, with μ=0.1 among variable TFs, with the other parameters being K=118 neuron classes and N=68 TFs exhibiting variable expression across neuronal classes. For this system, this gives:
(60)
implying a further 80% increase in β required from the initial destabilization of a multipotent progenitor to the complete specification of all cell types.
Local positive feedback as a barrier for reprogramming
Consider a modification to the EnhancerNet model where the energy for transcriptional activation is dependent on both the original modification mi and a new modification ui:
(61)
Here, λ is a scalar and ui is autocatalytic and inhibited by enhancer activation. I propose the following dynamics for ui:
(62)
where n≫1 and f(pi) is a decreasing function of enhancer activity pi. Eqn 62 can exhibit either bistability or hysteresis, depending on the value of ρ. It was assumed that initially ui=0 and pi is sufficiently large (as in the multipotent state), maintaining stability at ui=0. As cells differentiate, the activity of specific enhancers decreases while others increase. When the activity of an enhancer drops below a critical threshold, pcrit, Eqn 62 undergoes a bifurcation, causing ui to increase to a much larger value (ui≫1). This increase may persist due to a transition to a new fixed point when the dynamics are bistable. Consequently, the energy required to initiate transcription from the enhancer increases, making reprogramming to the associated cell type (whether induced by signalling, overexpression of specific transcription factors, or noise) less likely. This mechanism effectively creates a barrier to reprogramming.
Direct reprogramming through the overexpression of TFs
Consider a transcription factor xj that is constitutively overexpressed to an extent δj. This constitutive expression alters energy levels i without feedback. This therefore takes into account only the effect of the additional TF production on the energy levels of the enhancers, without altering the coordinates of the stable fixed points. V′ denotes the potential under the perturbed dynamics. Evaluating V′ at pattern k yields, at high β:
(63)
where it was assumed that for all i. Assuming that unperturbed pattern k is stable at a given β, the summand for δj=0 takes its maximum at i=k, where:
(64)
thus:
(65)
Under the assumption that the perturbation magnitude is relatively small (and, generally, for patterns that are stable after the perturbation):
(66)
and thus:
(67)
Note that Eqn 67 readily generalizes to a perturbation in several TFs :
(68)
If the goal is to steer the dynamics toward a pattern k, then the TFs and their overexpression need to be chosen such that is sufficiently larger for i=k than for all other patterns i≠k. That is, the ideal expression for a TF j is such that ξi,j is large for i=k (amplifying δj) and zero for i≠k.
Preprocessing and simulation
The matrix was initialized from transcript counts extracted from murine cells and averaged over cell type. For the Tabula Muris dataset (Schaum et al., 2018), I used all available data across organs and averaged over cell type. For haematopoiesis, I used the Haemopedia dataset (Choi et al., 2019) and averaged all cells that belong to the same terminal lineage. The data were then log-transformed (using a log1+x transformation) and filtered for TFs with mean and std expression larger than log4, as well as for Tabula Muris the TFs that participate in the tested reprogramming pathways (a full list of cell types and TFs used can be found in Tables S1-S4). Although, in principle, a log transformation is not needed for our model, which does not assume log-transformed values of x, it has the statistical advantage of reducing variance in the rows of and thus reducing sensitivity to TF choice. Finally, the rows of were normalized to unity. Simulations were performed using Python, taking τ=1, and the (terminal) β was chosen so that all patterns were stable.
Simulation of reprogramming
To simulate forced TF expression during reprogramming, the following dynamics may be used:
(69)
where is a vector where δi corresponds to the degree of activation of TFi. In general, it was assumed that δi=1 when the TF was in the reprogramming pathway and δi=0 when it was not. However, for a few genes, setting δi as different from unity was necessary to achieve the correct reprogramming (setting δi=1 achieves reprogramming to a closely related cell type). The full list of reprogramming pathways and relevant δ values can be found in Table S5.
Simulation of balanced differentiation
Haematopoiesis was simulated by using annealing from β=0 to β=50 over 50 time units. The initial state corresponded to the averaged value of all terminal states. Simulations were performed by adding additive white noise:
(70)
with σ set at 0.01. To simulate balanced differentiation in haematopoiesis, I used the following feedback procedure that aims to imitate in vivo feedback on differentiation, where there is negative feedback on the production of each cell type by signalling. was initialized as a zero vector. Then, running from k=0 to k=kmax, we performed differentiation by annealing. If the outcome terminal cell corresponded to row i of , I adjusted wi=wi−0.5(1−k/kmax) and then mean-adjusted w to zero. This resulted in a signalling profile, w, that produced all cell fates from the initial progenitor population.
Supplementary Material
10.1242/develop.202997_sup1
Supplementary information |
Title: Truncated mini LRP1 transports cargo from luminal to basolateral side across the blood brain barrier | Body:
Background
In the healthy brain, the blood brain barrier (BBB) protects the brain from exposure of exogenous and endogenous particles circulating in the blood that could be harmful to the brain [1]. The protection of the brain can be maintained by a functional interplay of different cell types including endothelial cells of the capillary wall, astrocytes and pericytes [2]. To fulfill the protection of the brain, the brain capillary endothelial cells (BCEC) distinguish from the rest of the peripheral endothelial cells by several characteristics [3]. Importantly, BCEC show an absence of fenestrae, an extremely low pinocytotic activity as well as the presence of tight junctions (TJ) and other junctional complexes of high electrical resistance. Together those characteristics provide an effective barrier against molecules and limit paracellular movement by sealing the space between two endothelial cells [4–7]. However, this organized machinery not only provides an effective barrier against molecules, but also regulates the entry as well as the outflow of molecules across the BBB by different transport mechanisms including paracellular diffusion, transport via solute carrier transporters (SLCs), transcellular diffusion, and receptor-mediated transcytosis (RMT) [8, 9]. In particular, RMT represents one of the most important transport mechanisms, as it allows a specific and controlled transport of molecules by different receptor and transporter expression on the luminal and abluminal side of endothelial cells [10, 11]. In this context, a disruption of BBB’s functionality contributes to pathology in a wide range of CNS disorders including multiple sclerosis, stroke, epilepsy, and Alzheimer’s disease (AD). AD is a chronic, neurodegenerative disease leading to dementia with impairment of cognitive and behavioral functions and failure to maintain activities of daily living [12]. AD neuropathology is characterized by intracellular neurofibrillary tangles consisting of the Tau protein and by extracellular amyloid ß (Aß) plaques, which consist of Aß peptides derived from the amyloid precursor protein (APP). The beta-secretase, e.g. BACE1, and the γ-secretase complex sequentially process APP, resulting in the generation of Aß peptides, which occur in a variety of isoforms ranging in length from 36 to 46 amino acids, with Aβ42 being the most toxic variant [13, 14]. The pathophysiology of AD is primarily caused by aberrant accumulation of Aβ42 peptides in the brain, likely due to a decreased clearance of cerebral Aß42 in AD patients compared to healthy individuals. Thus, several strategies including decreasing Aβ production, preventing its aggregation, or improving Aβ clearance from the brain are being considered to slow disease progression. However, curing of the most CNS disorders is mostly limited, as most developed drugs are not able to traverse the BBB and enter the brain. A key challenge in drug development is therefore to design not only therapeutics to treat the disease, but rather treatment strategies allowing a penetration of CNS-active drugs across the BBB. Targeting endogenous receptors, e.g. LRP1 or TfR-1 enabling RMT could be a promising opportunity for enhancing drug delivery across the BBB. RMT is based on endogenous receptors expressed on the luminal side of the BBB. These receptors use vesicular trafficking of ligand-receptor complexes to deliver macromolecule nutrients, such as iron-bound transferrin, insulin, and leptin, into the brain side. The RMT transport pathway necessitates binding to the receptor’s extracellular domain, followed by endocytosis and transcytosis to the capillary endothelium’s abluminal side into the interstitial space. In this context, the majority of research on protein and antibody delivery has focused on the transferrin receptor (TfR) or LRP1, due to their high expression level in BECs [15–18]. However, the use of endogenous receptors, like LRP1 or TfR, that are ubiquitously expressed in different cell types and tissues might induce severe side effects, as the drug delivery is not strictly limited to the CNS. On the one hand, targeting therapeutics could compete with natural ligands, which may perturb the normal biological functions of the receptor. In this context, clinical trials of trontinemab, a new version of the anti-amyloid monoclonal antibody gantenerumab, engineered to more easily cross the BBB by binding to TfR-1, reported some side effects in form anemia. Thereby, iron deficiency is the most frequent cause of anemia [19, 20]. Since trontinemab competes with the binding of natural ligands of TfR-1, e.g. iron-bound transferrin, the normal biological function of TfR, the transport of free iron from the serum into cells, is disrupted accordingly. Additionally, targeting systemically expressed endogenous receptors may induce adverse side effects due to the accumulation of therapeutics in other organs or tissues, as the delivery is not strictly limited to the CNS. Furthermore, in neurological and/or neurodegenerative disorders linked to vascular dysfunction, RMT deficit is anticipated to arise frequently, if not always, in the affected brain regions [21]. This could include deficiencies in TfR and/or LRP1 expression itself and/or anomalies in the endocytic and exocytotic molecular machinery leading to variances in brain shuttle effectiveness in the population afflicted by brain disorders [22].
For this reason, the generation of artificial receptors based on endogenous ones, but lacking their physiological ability to bind natural ligands, could serve as alternative targeting system, due to their similar sorting and transport behavior, but still unique expression to certain tissues. In this context, the use of adeno-associated virus (AAV) based gene therapy offers not only a specific infection of only desired tissues and cells, e.g. endothelial cells of the BBB, but also show a high transduction rate of target organs, low immunogenicity in vivo, and sustainable expression for month or years [23]. Besides targeting receptors at the BBB, the use of liposomes or nanoparticles (NPs) encapsulating different types of drugs seems to be a very sophisticated approach and has been successfully used for drug delivery across the BBB in vitro and in vivo [24–28]. Thereby, the usage of nanoparticles offers many benefits compared to a systemic drug delivery approach including high drug-loading capacity of the nanoparticles as well as the protection of encapsulated substances against enzymatic or chemical degradation while circulating in the blood, which might extend their half-life. Moreover, a modification of nanoparticles’ surfaces also allows them to be actively directed toward a specific tissue including the brain parenchyma following the route of RMT [27, 29]. To date, different targeting strategies can be employed, however the most common form of active targeting is the coupling of antibodies or antibody fragments to the surface of liposomes due to their high specificity. The so-called immunoliposomes are considered as the most promising classes for medical applications nowadays [30, 31]. In this context, the nanoparticulate nonsteroidal anti-inflammatory drug (NSAID) flurbiprofen, which is commonly used for treatment of pain, fever or other inflammatory conditions, was shown to reduce Aß42 levels in an in vitro BBB model [32–35]. Besides their intended use in the treatment of pain, NSAIDs, such as indomethacin, flurbiprofen or ibuprofen were considered for the treatment of AD due to their γ-secretase modulating (GSM) activity. GSMs are small molecules that reduce the levels of the amyloidogenic Aß42 peptides and promote the generation of shorter, less aggregation-prone Aß peptides like Aß38, and were shown to function as allosteric activators of γ-secretase activity [36, 37]. However, NSAIDs such as ibuprofen or flurbiprofen have pharmacological disadvantages, including low GSM activity and brain permeability [38]. More recently, GSMs with favorable pharmacological characteristics and nanomolar potency have been described. For example, after treatment of CHO cells with stable co-expression of human APP and presenilin-1 (PSEN1), the acidic GSM BB25 displayed typical GSM characteristics and decreased Aß42 levels with an IC50 value of 87 nM and a concomitant increase in Aß38 levels [39]. In general, embedding already approved drugs or compounds like GSMs into liposomal formulations that target receptors at the BBB seems to be a promising way enhancing drug delivery into the CNS. For this reason, we designed a proof-of concept study investigating the potential of an artificial truncated LRP1 variant for transport purposes across the BBB. In this proof-of-concept study, the use of the artificial mini LRP1 receptor mLRP1_DIV* as functional antibody and immunoliposome carrier into the CNS was validated. Besides internalization and transport of antibodies and functionalized immunoliposomes across an in vitro model of the BBB, the exact trafficking route of mLRP1_DIV* and corresponding cargo and their abluminal release into the brain parenchyma was demonstrated. Since the γ-secretase modulator BB25 was already shown to modulate Aß generation in vitro, the chemical compound was encapsulated into immunoliposomes. Finally, to confirm the artificial mRLP1_DIV* construct as cargo carrier across the BBB, the modulation of γ-secretase activity by transported liposomal BB25 across endothelial mLRP1_DIV* cells was confirmed in co-culture with CHO cells overexpressing APP and PSEN1. Furthermore, mLRP1_DIV*’s functional role as transport shuttle was explored in the in vivo situation with regard to its relevance for neurodegenerative diseases. To provide an in vivo evidence that LPR1-based receptors can be used as therapeutic strategy, an adeno-associated virus (AAV) that specifically infects only endothelial cells of the BBB was used. Due to the development of this highly specific AAV, only the BBB associated endothelium can be efficiently infected after intravenous injection [40]. mLRP1_DIV*’s expression in the endothelium after intravenous injection was validated by isolation of brain capillaries and endothelial cells from mice, followed by immunofluorescence and lysis of corresponding tissue and western blot analysis.
Methods
Antibodies
A list of antibodies used can be found in the Supplementary Data file 1.
mLRP1_DIV* design and structure
The mLRP1_DIV* transgene is a truncated version of the human LRP1 receptor. The mLRP1_DIV* mini-receptor contains a specific signal peptide (residues 1–19; amino acid sequence of the entry no. Q07954 in the UniProt database), the first five amino acids of the mature protein (5AA linker sequence; residues 20–24), a truncated ligand binding domain IV (residues: 3739–3778), C-terminus of 515 kDa subunit (α-chain; residues 3779–3943) and a full 85 kDa ß-subunit of human LRP1 receptor (β-chain; residues 3944–4544). Here, the generated mini LRP1 receptor construct mLRP1_DIV* consists of a truncated LRP1 DIV, a complete β-chain subunit as well as Myc- and HA tag at the N- and C-terminus, respectively (Fig. 1). Further details about mLRP1_DIV* can be found in the supplementary Data files 2 and 3.
Fig. 1LRP1 construct variants. Full length LRP1 (600 kDa) consisting of the extracellular α-chain and the intracellular ß-chain was truncated to mLRP1 Domain IV (205 kDa). This construct is composed of a complete ß-chain as well as only cluster domain IV of the LRP1’s α-chain. mLRP1 Domain IV was processed to mLRP1_DIV* (120 kDa) by further truncating DIV and by the addition of a Myc- and HA tag at the N- and C-terminus, respectively
Cell culture
For transport experiments, the immortalized mouse brain capillary endothelial cell line bEnd.3 or the human brain microvascular endothelial cells hcMEC/D3 was used. For bEnd.3 cells, DMEM, high glucose (Lonza) containing 10% (v/v) fetal bovine serum, 100 U/ml penicillin and 100 µg/ml streptomycin (all from Gibco) was used. hcMEC/D3 cells were cultured in Endothelial Cell Basal Medium-2 (EBMTM-2) Bulletkit (Lonza). Epithelial CHO 13-5-1 mLRP1_DIV*cell line was used for uptake assays with 9E10. CHO PS70 cells overexpressing human wild type APP751 and human PSEN1 were used for co-culture experiments [41]. α-MEM (Lonza) supplemented with 10% (/v) fetal bovine serum, 100 U/ml penicillin and 100 µg/ml streptomycin was used for CHO cell cultivation. All cells were cultured at 37 °C and 5% C2.
Transfection of endothelial cells
bEnd.3 and hcMEC/D3 cells were transfected with mLRP1_DIV* or pLBCX using Lipofectamine 3000 according to manufacturer’s instructions (L3000001, ThermoFisher). Thereby, pLBCX represents an empty vector backbone control without transgene insertion. Further details on pLBCX or mLRP1_DIV* can be found in the supplementary data files 2–5.
Protein extraction, SDS-PAGE and immunoblotting
Cells were mechanically detached and lysed with cell lysis buffer [50 mmol/l Tris, 150 mmol/l NaCl, 0.02% (w/v) NaN3, 1% (v/v) Nonidet P-40 supplemented with EDTA-free protease inhibitor cocktail (cOmplete™, Roche Applied Science)]. Protein concentrations of the lysates were measured using a BCA assay according to the manufacturer’s protocol (23227, ThermoFisher). SDS-Page and immunoblotting were performed with 20 µg of protein. Densitometric analyses of immunoblotting signals were performed using the ImageJ software (Version 1.52 q) followed by protein level normalization by using β-Actin or α-tubulin signal intensities.
Production of liposomes
Preparation of liposomes
For unloaded liposomes, stock solutions of EPC, Cholesterol, DSPE-PEG2000, 18:1 Liss Rhod PE and DSPE-PEG2000-Maleimide in CHCl3:MeOH (9:1 v/v) were mixed in molar ratios of 59:35:4,94:0,1:0,06. For BB25 immunoliposomes, 2 mg of BB25 were added to the lipid mixture. Organic solvents were removed at 50 °C under continuous nitrogen flow for 30 min and lipid films were dried under reduced pressure at room temperature for 1 h. After the addition of silica beads, lipid films were rehydrated with DPBS and centrifuged in a ZentriMix 380 R dual centrifuge (Hettich AG, Baech, Switzerland). The final lipid concentration was 100 mM.
Antibody thiolation & coupling
Thiolation of anti-Myc 9E10 antibody was performed for 30 min at RT using 10x molar excess of SATA. Detachment of the protection group was achieved with 0,5 M hydroxylamine-HCl over 2 h and the free protection group was removed using Zeba spin desalting columns (Thermo Fisher Scienctific, Zug, Switzerland). Deprotected antibodies were subsequently added to the previously prepared liposomes and coupling occurred over the course of 2 h at RT. Any remaining free maleimide groups were blocked with the addition of 10x molar excess of L-cysteine. For co-culture experiments, BB25–9E10 – IL or unloaded 9E10 – IL were purified directly before use using a Sepharose CL-4B filled Econo-Pac® Chromatography column. The liposomes in this publication are named 9E10 functionalized immunoliposomes (9E10 - IL), unmodified immunoliposomes (unm. - IL), BB25 loaded 9E10 functionalized liposomes (BB25–9E10 - IL) or unloaded 9E10 functionalized liposomes.
(unloaded 9E10 - IL).
In vitro transcytosis studies
Prior to examination of transcellular transport of anti-Myc antibodies or liposomes, transfected bEnd.3 or hcMEC/D3 cells were seeded into transparent membrane inserts (0.4/1 µm) coated with the coating solution. The next day, inserts were placed into the automated cell monitoring system cellZscope (NanoAnalytics) to monitor the transendothelial electrical resistance (TEER) and capacitance (CCI) of the cells. As CCI reached a value of ∼ 1 µF/cm2 or below, cells were stimulated with hydrocortisone (550nM) to enhance tight junction formation. When tight junction formation, barrier function and confluence of the cell monolayer was ensured (TEER > 30 Ω*cm2; CCI = ∼ 1 µF/cm2), the transport of anti-Myc antibodies was performed, approximately 72 h post transfection. Cells were incubated with the antibodies at 37 °C for 1 h. After incubation, cells were placed on ice to stop the transcellular transport. The medium of the abluminal compartment of all wells was collected and proteins within the abluminal and luminal medium were TCA precipitated. To detach the antibodies bound to the cell surface, cells were washed with acidic DPBS (pH 2) 2x for 5 min. The presence of anti-Myc antibodies in the abluminal compartment was analyzed using SDS-PAGE and immunoblotting.
Transport of 9E10 – IL or unm. – IL (3mM) was performed approximately 72 h post transfection for 2 h. As paracellular leakage marker 50 µg/ml of fluorescein isothiocyanate (FITC)-Dextran (3–4 kDa) was used. As a readout, the medium of the abluminal compartment of all wells was collected and analyzed using fluorescence spectroscopy of rhodamine or FITC by the Varioskan LUX multimode microplate reader (SkanIt Software 6.0.2.3) (Ex.: 540 nm / 495 nm, Em.: 591 nm/ 520 nm). For immunofluorescence, hcMEC/D3 cells were transfected with mLRP1_DIV* or control and seeded 24 h later into transparent membrane inserts coated with the coating solution. As CCI reached a value of ∼ 1 µF/cm2 or below, cells were stimulated as described above. Transport of 9E10, mIgG (30 µg/ml), 9E10 – IL or unm. - IL (3mM) was performed approximately 72 h post transfection for 1–2 h.
In vitro transcytosis studies with Rab27a inhibitor
Prior to examination of transcellular transport of anti-Myc antibodies (Alexa Fluor™ 555 (9E10)) or 9E10 liposomes, transfected bEnd.3 or hcMEC/D3 cells were seeded into transparent membrane inserts (1 μm) coated with the coating solution. The next day, inserts were placed into the automated cell monitoring system cellZscope (NanoAnalytics) to monitor the transendothelial electrical resistance (TEER) and capacitance (CCI) of the cells. As CCI reached a value of ∼ 1 µF/cm2 or below, cells were stimulated with hydrocortisone (550nM) to enhance tight junction formation. When tight junction formation, barrier function and confluence of the cell monolayer was ensured (TEER > 30 Ω*cm2; CCI = ∼ 1 µF/cm2), mLRP1_DIV* transfected cells were incubated with the Rab27a inhibitor (10µM) (Nexinhib20; R&D Systems; REF 6089) for 2 h. The transport of cargo was performed, approximately 72 h post transfection with anti-Myc Alexa Fluor™ 555 antibodies (2µg/ml) or with 9E10 – IL (3mM) for 2 h. As paracellular leakage marker 50 µg/ml of fluorescein isothiocyanate (FITC)-Dextran (3–4 kDa) was used. As a readout, the medium of the abluminal compartment of all wells was collected and analyzed using fluorescence spectroscopy of rhodamine, FITC or Alexa Fluor 555 by the Varioskan LUX multimode microplate reader (SkanIt Software 6.0.2.3) (Ex.: 540 nm / 495 nm / 555 nm, Em.: 591 nm/ 520 nm / 568 nm).
Immunofluorescence of endothelial cells
After transport, cells were washed twice with acidic DPBS and fixed in 4% PFA for 15 min. Afterwards, cells were washed with DPBS and membranes of cell culture inserts were cut out using a scalpel. Membranes were placed into a fresh 24 well plate, where cells were permeabilized in 0.1% Triton X-100 for 10 min and blocked under gentle agitation for 1 h at RT. Cells were incubated with primary antibodies on a shaker at 4 °C overnight. The next day, cells were rinsed in PBS 0.05% Triton X-100 and incubated with secondary antibodies for 1 h at RT. Next, cells were washed in PBS and dH2O and stained for nuclei using DAPI for 5 min. Inserts were transferred on superfrost microscope glass slides (#J1810AMNZ, ThermoFisher) and covered with coverslips using DAKO mounting solution (#83023, Dako). For abluminal immunostainings, confocal microscopy was performed using STELLARIS 8 FALCON (Leica Microsystems, Wetzlar, Germany) confocal system equipped with White Light Laser (WLL). Images were acquired with HC PL APO CS2 100x/1.40 OIL objective by using 1024 × 1024 pixel format with pixel sizes of 41 nm. Images were processed with LIGHTNING™ adaptive deconvolution (Leica) using default settings (embedding medium refractive index set to 1.47). All confocal images were prepared using Fiji distribution of ImageJ [42]. Microscope slides were stored at 4 °C.
bEnd.3/ PS70 co-culture model
To confirm mLRP1_DIV* as transport carrier and its ability to transport the liposomal γ-secretase modulator BB25, a co-culture model was established. bEnd.3 cells were transfected with mLRP1_DIV* and seeded on coated cell culture inserts, followed by TEER and CCI monitoring. At the time of TJ stimulation, CHO cells overexpressing wild type human APP751 and human presenilin 1 (PS70 cells) were seeded on 24-well plates. bEnd.3 mLRP1_DIV* cells were divided into four groups similar in TEER and CCI values. The luminal culture media was supplemented with 50 µg/mL (FITC)-Dextran (3–4 kDa) for 24 h. To assess paracellular leakage across the endothelial monolayer and confirm a tight barrier, fluorescence intensities of FITC-dextran in the abluminal compartments were measured 24 h later as described before. After those 24 h, culture inserts with formed and stimulated bEnd.3 cell monolayer were transferred to the wells of the 24-well plates, resulting in a defined luminal compartment and abluminal compartment containing the PS70 cells. Luminal culture media was supplemented with 10µM free BB25, liposomal BB25 (BB25–9E10 - IL), unloaded 9E10 functionalized liposomes (unloaded 9E10 - IL) or DMSO. The administered concentration of the liposomes was adjusted to the free BB25. Transport was performed for 2 h. Afterwards, the amount of transported BB25–9E10 – IL or unloaded 9E10 - IL was investigated. Therefore, 100 µl medium of abluminal compartment was collected 2 h post transport and analyzed using fluorescence spectroscopy of rhodamine by the Varioskan LUX multimode microplate reader (SkanIt Software 6.0.2.3) (Ex.: 540 nm, Em.: 591 nm). After transport, luminal media was replaced by regular culture media. After 48 h, cell culture supernatants of abluminal cultured PS70 cells were collected. The γ-secretase activity was measured by determining the levels of Aβ38 and Aβ42 using a cell-based sandwich enzyme-linked immunosorbent assay (ELISA).
Aß specific ELISA
Aβ38 and Aβ42 peptide levels in cell culture supernatants were quantified using a cell-based ELISA assay as described [39].
Isolation of cerebral microvessels
Murine brain capillaries were isolated based on the dextran gradient centrifugation method followed by a cell-strainer filtration described elsewhere with some modifications [43, 44]. To begin with, cerebral cortices were isolated and devoid of leptomeninges by rolling on blotting paper (Whitman). Next, cortices were fragmentated in ice-cold homogenization buffer (DPBS; 2.5 mM CaCl2; 1.2 mM MgSO4; 15 mM HEPES; 25 mM NaHCO3; 10 mM glucose; 1 mM sodium pyruvate) using a Dounce tissue grinder and centrifuged at 1,000 g for 10 min at 4 °C. Resulting pellet was then thoroughly resuspended in 18% Dextrn/PBS solution (70 kDa, Sigma). The samples were centrifuged at 4000 g for 20 min at 4 °C. Red capillary pellet at the bottom of the tube was collected and filtered through.
40-µm cell nylon-mesh strainer (#352340, Corning). After through washing with ice-cold PBS, vessels remaining on the top of the mesh were collected in 1% BSA/PBS solution and pelleted by centrifugation at 4000 g for 12 min at 4 °C. Samples were lysed in microvessels lysis buffer (50 mM HEPES, pH 7.5; 1% (vol/vol) Triton X-100; 0.5% (wt/vol) sodium deoxycholate; 0.1% (wt/vol) sodium dodecyl sulphate (SDS); 500 mM NaCl; 10 mM MgCl2; 50 mM β-glycerophosphate; 1x protease inhibitor cocktail (cOmplete™); 1x phosphatase inhibitor cocktail (PhosStop™) and used for immunoblot analysis. Alternatively, for immunohistochemistry analysis, capillaries collected at the mesh were fixed with 4% Roti® Histofix for 10 min and collected in 1% BSA/PBS by centrifugation at 4000 g for 20 min at 4 °C. Next, vessels were permeabilized and probed with appropriate primary and Alexa-conjugated secondary antibodies, as described previously [44].
Immunofluorescence (IF) assay - free-floating sections
PFA-perfused mouse brains were embedded in Tissue Freezing Medium (Leica) followed by sectioning using CM3050S cryostat (Leica). 40 µm- thick slices were transferred into 48-well plates filled with cryoprotective solution (25% glycerol, 25% polyethylene glycol, 50% PBS) and stored at 4°C until further use. For IF assay, sections were first transferred to 24-well plate and washed 3 × 5 min with TBS. For mLRP1_DIV* and neurovascular unit components staining, sections were treated with 90% formic acid for 2–5 min and immediately transferred to wells filled with 0.3% Triton X-100/TBS (TBS-Tx) solution for permeabilization. Next, unspecific binding sites were blocked using a 5% (w/v) BSA in TBS-Tx for 1 h at RT. Sections were then incubated with primary antibody overnight at 4°C. On the next day, slices were washed 3 × 5 min in TBS-Tx and subjected to 1 h incubation with fluorophore-conjugated secondary antibody at RT, protected from light. Afterwards, slices were washed 1 × 5min in TBS and nuclei were counterstained with 4’,6-diamidino-2-phenylindole (DAPI) (0.2 µg/mL). Finally, sections were mounted on Superfrost Plus microscope slides (#J1810AMNZ, ThermoFisher) using fluorescent mounting medium (#83023, Dako) and dried for at least 3 h at RT before image analysis.
AAV production
For AAV preparation, pAAV-CMV-teto2-LUC expression cassette containing luciferase reporter gene under control of the cytomegalovirus (CMV) promoter and SV40 poly-A signal embedded between two modified AAV2 Internal Terminal Repeats (ITR) was used as a vector backbone [40]. Briefly, luciferase reporter gene was removed by restriction digestion with PmeI and XbaI restriction enzymes. Subsequently, restriction sites PmeI and XbaI were introduced upstream and downstream of mLRP1_DIV*, respectively, during the PCR amplification to enable ligation. Lastly, a short phosphorylated ssDNA oligomer encoding for the hemagglutinin (HA) or Myc epitope was inserted downstream or upstream of the mLRP1_DIV* construct between last base pair (bp) of LRP1’s cytoplasmatic tail and the stop codon or after the leader sequence N-terminal using HiFi DNA Assembly Master Mix (NEB). DNA was transformed into chemically competent NEB® Stable Competent E. coli (#C3040I, NEB) according to the manufacturer’s protocol. Further details on AAV production can be found in the supplementary Data files 1, 6–9.
AAV expression in mice
To express the mLRP1_DIV* construct into the brain endothelium, 2-month-old 5xFAD wt females were injected intravenously with AAV(BR1)mLRP1_DIV* virus (1 × 1011 genomic particles per mouse). Virus titer was determined by quantitative real-time PCR after purification as described previously using CMV-specific primers: CMV forward 5’ GGG ACT TTC CTA CTT GGC A 3’; CMV reverse 5’ GGC GGA GTT ACG ACA T 3’ [40]. Animals were of similar weight, randomly allocated to treated and control groups. Briefly, a mouse was placed in a restrainer and the tail was warmed up with infrared lamp for several minutes to facilitate vasodilatation of the tail veins. Recombinant AAV aliquots (max vol = 100 µL) were administered into left or right tail vein. Animals were kept under daily supervision for the next two weeks and were sacrificed after 12–16 weeks.
Statistical analysis
For statistical analysis GraphPad Prism (8.4.3) was used. Means and standard error of the mean (SEM) were calculated for all groups and presented graphically. For the evaluation of the statistical significance unpaired t-test or one-way ANOVA followed by Tukey’s multiple comparison test were used, whereby significance limit was p < 0.05. Significant differences are marked. For all experiments, all technical replicates from n = 3 independent experiments were used for statistical analysis and are presented graphically. Images from LSM710 or 8 Falcon stellaris microscopes were arranged and adjusted using the ImageJ software (Version 2.1.0/1.53c) and Microsoft PowerPoint 365. Final images were arranged with CorelDraw2024. Schematic illustrations were created with bioRender.com.
Antibodies
A list of antibodies used can be found in the Supplementary Data file 1.
mLRP1_DIV* design and structure
The mLRP1_DIV* transgene is a truncated version of the human LRP1 receptor. The mLRP1_DIV* mini-receptor contains a specific signal peptide (residues 1–19; amino acid sequence of the entry no. Q07954 in the UniProt database), the first five amino acids of the mature protein (5AA linker sequence; residues 20–24), a truncated ligand binding domain IV (residues: 3739–3778), C-terminus of 515 kDa subunit (α-chain; residues 3779–3943) and a full 85 kDa ß-subunit of human LRP1 receptor (β-chain; residues 3944–4544). Here, the generated mini LRP1 receptor construct mLRP1_DIV* consists of a truncated LRP1 DIV, a complete β-chain subunit as well as Myc- and HA tag at the N- and C-terminus, respectively (Fig. 1). Further details about mLRP1_DIV* can be found in the supplementary Data files 2 and 3.
Fig. 1LRP1 construct variants. Full length LRP1 (600 kDa) consisting of the extracellular α-chain and the intracellular ß-chain was truncated to mLRP1 Domain IV (205 kDa). This construct is composed of a complete ß-chain as well as only cluster domain IV of the LRP1’s α-chain. mLRP1 Domain IV was processed to mLRP1_DIV* (120 kDa) by further truncating DIV and by the addition of a Myc- and HA tag at the N- and C-terminus, respectively
Cell culture
For transport experiments, the immortalized mouse brain capillary endothelial cell line bEnd.3 or the human brain microvascular endothelial cells hcMEC/D3 was used. For bEnd.3 cells, DMEM, high glucose (Lonza) containing 10% (v/v) fetal bovine serum, 100 U/ml penicillin and 100 µg/ml streptomycin (all from Gibco) was used. hcMEC/D3 cells were cultured in Endothelial Cell Basal Medium-2 (EBMTM-2) Bulletkit (Lonza). Epithelial CHO 13-5-1 mLRP1_DIV*cell line was used for uptake assays with 9E10. CHO PS70 cells overexpressing human wild type APP751 and human PSEN1 were used for co-culture experiments [41]. α-MEM (Lonza) supplemented with 10% (/v) fetal bovine serum, 100 U/ml penicillin and 100 µg/ml streptomycin was used for CHO cell cultivation. All cells were cultured at 37 °C and 5% C2.
Transfection of endothelial cells
bEnd.3 and hcMEC/D3 cells were transfected with mLRP1_DIV* or pLBCX using Lipofectamine 3000 according to manufacturer’s instructions (L3000001, ThermoFisher). Thereby, pLBCX represents an empty vector backbone control without transgene insertion. Further details on pLBCX or mLRP1_DIV* can be found in the supplementary data files 2–5.
Protein extraction, SDS-PAGE and immunoblotting
Cells were mechanically detached and lysed with cell lysis buffer [50 mmol/l Tris, 150 mmol/l NaCl, 0.02% (w/v) NaN3, 1% (v/v) Nonidet P-40 supplemented with EDTA-free protease inhibitor cocktail (cOmplete™, Roche Applied Science)]. Protein concentrations of the lysates were measured using a BCA assay according to the manufacturer’s protocol (23227, ThermoFisher). SDS-Page and immunoblotting were performed with 20 µg of protein. Densitometric analyses of immunoblotting signals were performed using the ImageJ software (Version 1.52 q) followed by protein level normalization by using β-Actin or α-tubulin signal intensities.
Production of liposomes
Preparation of liposomes
For unloaded liposomes, stock solutions of EPC, Cholesterol, DSPE-PEG2000, 18:1 Liss Rhod PE and DSPE-PEG2000-Maleimide in CHCl3:MeOH (9:1 v/v) were mixed in molar ratios of 59:35:4,94:0,1:0,06. For BB25 immunoliposomes, 2 mg of BB25 were added to the lipid mixture. Organic solvents were removed at 50 °C under continuous nitrogen flow for 30 min and lipid films were dried under reduced pressure at room temperature for 1 h. After the addition of silica beads, lipid films were rehydrated with DPBS and centrifuged in a ZentriMix 380 R dual centrifuge (Hettich AG, Baech, Switzerland). The final lipid concentration was 100 mM.
Antibody thiolation & coupling
Thiolation of anti-Myc 9E10 antibody was performed for 30 min at RT using 10x molar excess of SATA. Detachment of the protection group was achieved with 0,5 M hydroxylamine-HCl over 2 h and the free protection group was removed using Zeba spin desalting columns (Thermo Fisher Scienctific, Zug, Switzerland). Deprotected antibodies were subsequently added to the previously prepared liposomes and coupling occurred over the course of 2 h at RT. Any remaining free maleimide groups were blocked with the addition of 10x molar excess of L-cysteine. For co-culture experiments, BB25–9E10 – IL or unloaded 9E10 – IL were purified directly before use using a Sepharose CL-4B filled Econo-Pac® Chromatography column. The liposomes in this publication are named 9E10 functionalized immunoliposomes (9E10 - IL), unmodified immunoliposomes (unm. - IL), BB25 loaded 9E10 functionalized liposomes (BB25–9E10 - IL) or unloaded 9E10 functionalized liposomes.
(unloaded 9E10 - IL).
Preparation of liposomes
For unloaded liposomes, stock solutions of EPC, Cholesterol, DSPE-PEG2000, 18:1 Liss Rhod PE and DSPE-PEG2000-Maleimide in CHCl3:MeOH (9:1 v/v) were mixed in molar ratios of 59:35:4,94:0,1:0,06. For BB25 immunoliposomes, 2 mg of BB25 were added to the lipid mixture. Organic solvents were removed at 50 °C under continuous nitrogen flow for 30 min and lipid films were dried under reduced pressure at room temperature for 1 h. After the addition of silica beads, lipid films were rehydrated with DPBS and centrifuged in a ZentriMix 380 R dual centrifuge (Hettich AG, Baech, Switzerland). The final lipid concentration was 100 mM.
Antibody thiolation & coupling
Thiolation of anti-Myc 9E10 antibody was performed for 30 min at RT using 10x molar excess of SATA. Detachment of the protection group was achieved with 0,5 M hydroxylamine-HCl over 2 h and the free protection group was removed using Zeba spin desalting columns (Thermo Fisher Scienctific, Zug, Switzerland). Deprotected antibodies were subsequently added to the previously prepared liposomes and coupling occurred over the course of 2 h at RT. Any remaining free maleimide groups were blocked with the addition of 10x molar excess of L-cysteine. For co-culture experiments, BB25–9E10 – IL or unloaded 9E10 – IL were purified directly before use using a Sepharose CL-4B filled Econo-Pac® Chromatography column. The liposomes in this publication are named 9E10 functionalized immunoliposomes (9E10 - IL), unmodified immunoliposomes (unm. - IL), BB25 loaded 9E10 functionalized liposomes (BB25–9E10 - IL) or unloaded 9E10 functionalized liposomes.
(unloaded 9E10 - IL).
In vitro transcytosis studies
Prior to examination of transcellular transport of anti-Myc antibodies or liposomes, transfected bEnd.3 or hcMEC/D3 cells were seeded into transparent membrane inserts (0.4/1 µm) coated with the coating solution. The next day, inserts were placed into the automated cell monitoring system cellZscope (NanoAnalytics) to monitor the transendothelial electrical resistance (TEER) and capacitance (CCI) of the cells. As CCI reached a value of ∼ 1 µF/cm2 or below, cells were stimulated with hydrocortisone (550nM) to enhance tight junction formation. When tight junction formation, barrier function and confluence of the cell monolayer was ensured (TEER > 30 Ω*cm2; CCI = ∼ 1 µF/cm2), the transport of anti-Myc antibodies was performed, approximately 72 h post transfection. Cells were incubated with the antibodies at 37 °C for 1 h. After incubation, cells were placed on ice to stop the transcellular transport. The medium of the abluminal compartment of all wells was collected and proteins within the abluminal and luminal medium were TCA precipitated. To detach the antibodies bound to the cell surface, cells were washed with acidic DPBS (pH 2) 2x for 5 min. The presence of anti-Myc antibodies in the abluminal compartment was analyzed using SDS-PAGE and immunoblotting.
Transport of 9E10 – IL or unm. – IL (3mM) was performed approximately 72 h post transfection for 2 h. As paracellular leakage marker 50 µg/ml of fluorescein isothiocyanate (FITC)-Dextran (3–4 kDa) was used. As a readout, the medium of the abluminal compartment of all wells was collected and analyzed using fluorescence spectroscopy of rhodamine or FITC by the Varioskan LUX multimode microplate reader (SkanIt Software 6.0.2.3) (Ex.: 540 nm / 495 nm, Em.: 591 nm/ 520 nm). For immunofluorescence, hcMEC/D3 cells were transfected with mLRP1_DIV* or control and seeded 24 h later into transparent membrane inserts coated with the coating solution. As CCI reached a value of ∼ 1 µF/cm2 or below, cells were stimulated as described above. Transport of 9E10, mIgG (30 µg/ml), 9E10 – IL or unm. - IL (3mM) was performed approximately 72 h post transfection for 1–2 h.
In vitro transcytosis studies with Rab27a inhibitor
Prior to examination of transcellular transport of anti-Myc antibodies (Alexa Fluor™ 555 (9E10)) or 9E10 liposomes, transfected bEnd.3 or hcMEC/D3 cells were seeded into transparent membrane inserts (1 μm) coated with the coating solution. The next day, inserts were placed into the automated cell monitoring system cellZscope (NanoAnalytics) to monitor the transendothelial electrical resistance (TEER) and capacitance (CCI) of the cells. As CCI reached a value of ∼ 1 µF/cm2 or below, cells were stimulated with hydrocortisone (550nM) to enhance tight junction formation. When tight junction formation, barrier function and confluence of the cell monolayer was ensured (TEER > 30 Ω*cm2; CCI = ∼ 1 µF/cm2), mLRP1_DIV* transfected cells were incubated with the Rab27a inhibitor (10µM) (Nexinhib20; R&D Systems; REF 6089) for 2 h. The transport of cargo was performed, approximately 72 h post transfection with anti-Myc Alexa Fluor™ 555 antibodies (2µg/ml) or with 9E10 – IL (3mM) for 2 h. As paracellular leakage marker 50 µg/ml of fluorescein isothiocyanate (FITC)-Dextran (3–4 kDa) was used. As a readout, the medium of the abluminal compartment of all wells was collected and analyzed using fluorescence spectroscopy of rhodamine, FITC or Alexa Fluor 555 by the Varioskan LUX multimode microplate reader (SkanIt Software 6.0.2.3) (Ex.: 540 nm / 495 nm / 555 nm, Em.: 591 nm/ 520 nm / 568 nm).
Immunofluorescence of endothelial cells
After transport, cells were washed twice with acidic DPBS and fixed in 4% PFA for 15 min. Afterwards, cells were washed with DPBS and membranes of cell culture inserts were cut out using a scalpel. Membranes were placed into a fresh 24 well plate, where cells were permeabilized in 0.1% Triton X-100 for 10 min and blocked under gentle agitation for 1 h at RT. Cells were incubated with primary antibodies on a shaker at 4 °C overnight. The next day, cells were rinsed in PBS 0.05% Triton X-100 and incubated with secondary antibodies for 1 h at RT. Next, cells were washed in PBS and dH2O and stained for nuclei using DAPI for 5 min. Inserts were transferred on superfrost microscope glass slides (#J1810AMNZ, ThermoFisher) and covered with coverslips using DAKO mounting solution (#83023, Dako). For abluminal immunostainings, confocal microscopy was performed using STELLARIS 8 FALCON (Leica Microsystems, Wetzlar, Germany) confocal system equipped with White Light Laser (WLL). Images were acquired with HC PL APO CS2 100x/1.40 OIL objective by using 1024 × 1024 pixel format with pixel sizes of 41 nm. Images were processed with LIGHTNING™ adaptive deconvolution (Leica) using default settings (embedding medium refractive index set to 1.47). All confocal images were prepared using Fiji distribution of ImageJ [42]. Microscope slides were stored at 4 °C.
bEnd.3/ PS70 co-culture model
To confirm mLRP1_DIV* as transport carrier and its ability to transport the liposomal γ-secretase modulator BB25, a co-culture model was established. bEnd.3 cells were transfected with mLRP1_DIV* and seeded on coated cell culture inserts, followed by TEER and CCI monitoring. At the time of TJ stimulation, CHO cells overexpressing wild type human APP751 and human presenilin 1 (PS70 cells) were seeded on 24-well plates. bEnd.3 mLRP1_DIV* cells were divided into four groups similar in TEER and CCI values. The luminal culture media was supplemented with 50 µg/mL (FITC)-Dextran (3–4 kDa) for 24 h. To assess paracellular leakage across the endothelial monolayer and confirm a tight barrier, fluorescence intensities of FITC-dextran in the abluminal compartments were measured 24 h later as described before. After those 24 h, culture inserts with formed and stimulated bEnd.3 cell monolayer were transferred to the wells of the 24-well plates, resulting in a defined luminal compartment and abluminal compartment containing the PS70 cells. Luminal culture media was supplemented with 10µM free BB25, liposomal BB25 (BB25–9E10 - IL), unloaded 9E10 functionalized liposomes (unloaded 9E10 - IL) or DMSO. The administered concentration of the liposomes was adjusted to the free BB25. Transport was performed for 2 h. Afterwards, the amount of transported BB25–9E10 – IL or unloaded 9E10 - IL was investigated. Therefore, 100 µl medium of abluminal compartment was collected 2 h post transport and analyzed using fluorescence spectroscopy of rhodamine by the Varioskan LUX multimode microplate reader (SkanIt Software 6.0.2.3) (Ex.: 540 nm, Em.: 591 nm). After transport, luminal media was replaced by regular culture media. After 48 h, cell culture supernatants of abluminal cultured PS70 cells were collected. The γ-secretase activity was measured by determining the levels of Aβ38 and Aβ42 using a cell-based sandwich enzyme-linked immunosorbent assay (ELISA).
Aß specific ELISA
Aβ38 and Aβ42 peptide levels in cell culture supernatants were quantified using a cell-based ELISA assay as described [39].
Isolation of cerebral microvessels
Murine brain capillaries were isolated based on the dextran gradient centrifugation method followed by a cell-strainer filtration described elsewhere with some modifications [43, 44]. To begin with, cerebral cortices were isolated and devoid of leptomeninges by rolling on blotting paper (Whitman). Next, cortices were fragmentated in ice-cold homogenization buffer (DPBS; 2.5 mM CaCl2; 1.2 mM MgSO4; 15 mM HEPES; 25 mM NaHCO3; 10 mM glucose; 1 mM sodium pyruvate) using a Dounce tissue grinder and centrifuged at 1,000 g for 10 min at 4 °C. Resulting pellet was then thoroughly resuspended in 18% Dextrn/PBS solution (70 kDa, Sigma). The samples were centrifuged at 4000 g for 20 min at 4 °C. Red capillary pellet at the bottom of the tube was collected and filtered through.
40-µm cell nylon-mesh strainer (#352340, Corning). After through washing with ice-cold PBS, vessels remaining on the top of the mesh were collected in 1% BSA/PBS solution and pelleted by centrifugation at 4000 g for 12 min at 4 °C. Samples were lysed in microvessels lysis buffer (50 mM HEPES, pH 7.5; 1% (vol/vol) Triton X-100; 0.5% (wt/vol) sodium deoxycholate; 0.1% (wt/vol) sodium dodecyl sulphate (SDS); 500 mM NaCl; 10 mM MgCl2; 50 mM β-glycerophosphate; 1x protease inhibitor cocktail (cOmplete™); 1x phosphatase inhibitor cocktail (PhosStop™) and used for immunoblot analysis. Alternatively, for immunohistochemistry analysis, capillaries collected at the mesh were fixed with 4% Roti® Histofix for 10 min and collected in 1% BSA/PBS by centrifugation at 4000 g for 20 min at 4 °C. Next, vessels were permeabilized and probed with appropriate primary and Alexa-conjugated secondary antibodies, as described previously [44].
Immunofluorescence (IF) assay - free-floating sections
PFA-perfused mouse brains were embedded in Tissue Freezing Medium (Leica) followed by sectioning using CM3050S cryostat (Leica). 40 µm- thick slices were transferred into 48-well plates filled with cryoprotective solution (25% glycerol, 25% polyethylene glycol, 50% PBS) and stored at 4°C until further use. For IF assay, sections were first transferred to 24-well plate and washed 3 × 5 min with TBS. For mLRP1_DIV* and neurovascular unit components staining, sections were treated with 90% formic acid for 2–5 min and immediately transferred to wells filled with 0.3% Triton X-100/TBS (TBS-Tx) solution for permeabilization. Next, unspecific binding sites were blocked using a 5% (w/v) BSA in TBS-Tx for 1 h at RT. Sections were then incubated with primary antibody overnight at 4°C. On the next day, slices were washed 3 × 5 min in TBS-Tx and subjected to 1 h incubation with fluorophore-conjugated secondary antibody at RT, protected from light. Afterwards, slices were washed 1 × 5min in TBS and nuclei were counterstained with 4’,6-diamidino-2-phenylindole (DAPI) (0.2 µg/mL). Finally, sections were mounted on Superfrost Plus microscope slides (#J1810AMNZ, ThermoFisher) using fluorescent mounting medium (#83023, Dako) and dried for at least 3 h at RT before image analysis.
AAV production
For AAV preparation, pAAV-CMV-teto2-LUC expression cassette containing luciferase reporter gene under control of the cytomegalovirus (CMV) promoter and SV40 poly-A signal embedded between two modified AAV2 Internal Terminal Repeats (ITR) was used as a vector backbone [40]. Briefly, luciferase reporter gene was removed by restriction digestion with PmeI and XbaI restriction enzymes. Subsequently, restriction sites PmeI and XbaI were introduced upstream and downstream of mLRP1_DIV*, respectively, during the PCR amplification to enable ligation. Lastly, a short phosphorylated ssDNA oligomer encoding for the hemagglutinin (HA) or Myc epitope was inserted downstream or upstream of the mLRP1_DIV* construct between last base pair (bp) of LRP1’s cytoplasmatic tail and the stop codon or after the leader sequence N-terminal using HiFi DNA Assembly Master Mix (NEB). DNA was transformed into chemically competent NEB® Stable Competent E. coli (#C3040I, NEB) according to the manufacturer’s protocol. Further details on AAV production can be found in the supplementary Data files 1, 6–9.
AAV expression in mice
To express the mLRP1_DIV* construct into the brain endothelium, 2-month-old 5xFAD wt females were injected intravenously with AAV(BR1)mLRP1_DIV* virus (1 × 1011 genomic particles per mouse). Virus titer was determined by quantitative real-time PCR after purification as described previously using CMV-specific primers: CMV forward 5’ GGG ACT TTC CTA CTT GGC A 3’; CMV reverse 5’ GGC GGA GTT ACG ACA T 3’ [40]. Animals were of similar weight, randomly allocated to treated and control groups. Briefly, a mouse was placed in a restrainer and the tail was warmed up with infrared lamp for several minutes to facilitate vasodilatation of the tail veins. Recombinant AAV aliquots (max vol = 100 µL) were administered into left or right tail vein. Animals were kept under daily supervision for the next two weeks and were sacrificed after 12–16 weeks.
Statistical analysis
For statistical analysis GraphPad Prism (8.4.3) was used. Means and standard error of the mean (SEM) were calculated for all groups and presented graphically. For the evaluation of the statistical significance unpaired t-test or one-way ANOVA followed by Tukey’s multiple comparison test were used, whereby significance limit was p < 0.05. Significant differences are marked. For all experiments, all technical replicates from n = 3 independent experiments were used for statistical analysis and are presented graphically. Images from LSM710 or 8 Falcon stellaris microscopes were arranged and adjusted using the ImageJ software (Version 2.1.0/1.53c) and Microsoft PowerPoint 365. Final images were arranged with CorelDraw2024. Schematic illustrations were created with bioRender.com.
Results
Here, the truncated mini low-density lipoprotein receptor-related protein 1 mLRP1_DIV* is presented as blood to brain transport carrier, exemplified by antibodies and immunoliposomes using a systematic approach to screen the receptor and its ligands’ route across endothelial cells in vitro. The use of mLRP1_DIV* as liposomal carrier into the CNS was validated based on transport assays across an in vitro model of the BBB using hcMEC/D3 and bEnd.3 cells. Trafficking routes of mLRP1_DIV* and corresponding cargo across endothelial cells were analyzed using immunofluorescence. The transport of liposomes loaded with the GSM BB25 across bEnd.3 mLRP1_DIV* cells and their ability to modulate γ-secretase activity was investigated in co-culture with CHO cells overexpressing APP and PSEN1.
(PS70 cells).
mLRP1_DIV* mediated transcellular transport of anti-myc antibodies across an in vitro model of the BBB
In our first studies mLRP1_DIV* (Fig. 2A) was demonstrated to specifically internalize anti-Myc antibodies in CHO 13-5-1 cells (Supplementary Data 1, Figure S3). Therefore, the mLRP1_DIV* construct was analyzed in the human derived endothelial cell line hcMEC/D3, as these cells consists of a luminal and abluminal polarization. For examination of mLRP1_DIV*’s functionality, an in vitro model of the BBB, which enables to monitor tight junction and barrier formation of the endothelial cells, was used.
Cells were transfected with mLRP1_DIV* or pLBCX and 30 µg/ml of 9E10 or unspecific mouse IgG (mIgG) were luminally applied to the cell monolayer in the in vitro BBB model for 1 h (Fig. 2B). Respective protein amounts were analyzed in the abluminal medium. Regarding transport assay in hcMEC/D3 cells, two bands at about 50 kDa and 25 kDa, corresponding to IgG, became visible in abluminal and luminal medium of cells being transfected with pLBCX or mLRP1_DIV* (Fig. 2C). According to transport of 9E10 in hcMEC/D3 cells, cells transfected with mLRP1_DIV* showed an average transport of 9E10’s heavy chain of 15% after 60 min whereas hcMEC/D3 pLBCX cells showed an average transport of 9E10’s heavy chain of 5.8%. Transport of unspecific mIgG in cells transfected with mLRP1_DIV* averaged out at 4.2% (Fig. 2D). Thereby, a significant higher transcytosis.
(2.5-fold/ 3.3-fold) of 9E10’s heavy chain in hcMEC/D3 mLRP1_DIV* cells could be observed compared to control or unspecific mIgG (p < 0.0001 / p < 0.0001). The transport of the light chain of 9E10 averaged out at 8% after 60 min using hcMEC/D3 mLRP1_DIV* cells and 2.4% using hcMEC/D3 pLBCX cells. mLRP1_DIV* transfected cells showed a transport of unspecific mIgG of around 1.3% (Fig. 2E). Cells expressing the mLRP1_DIV* receptor showed a significant higher transport (3-fold/ 6-fold) of 9E10’s light chain compared to control or mIgG (p < 0.0001 / p < 0.0001). Notably, no significant differences could be observed between transport of 9E10 in control cells and transport of mIgG in hcMEC/D3 mLRP1_DIV* cells (p = 0.55 / p = 0.29). Moreover, no difference in the BBB’s integrity could be observed between the experimental groups, as the transendothelial electrical resistance of hcMEC/D3 pLBCX was about 30.9 Ω*cm2 and 28.4 Ω*cm2 using hcMEC/D3 mLRP1_DIV* cells (p = 0.83 / p = 0.52 / p = 0.85) (Fig. 2F). Together, data clearly demonstrate mLRP1_DIV* mediated transport of 9E10 across an in vitro model of the BBB.
Fig. 2mLRP1_DIV* mediated transcellular transport of anti-Myc antibodies across hcMEC/D3 cells. (A) Schematic illustration of mLRP1_DIV* (B) Schematic illustration of the experimental setup. Representative immunoblotting for (C) luminal and abluminal medium of hcMEC/D3 being transiently transfected with mLRP1_DIV* or pLBCX. At confluence, cells were incubated with 30 µg/ml anti-Myc antibodies (9E10) or unspecific mIgG for 1 h. (D, E) Heavy and light chain protein levels were quantified by densitometric analysis after immunoblotting and normalized to luminal medium saved before transport. The intensity of 9E10 in abluminal medium of hcMEC/D3 pLBCX cells were defined as 100%. (F) TEER at the time of transport was measured by impedance spectroscopy. Data represent the mean ± SEM of ten individual replicates from n = 3 independent experiments. One-way ANOVA followed by Tukey’s multiple comparison test was used for statistical analysis
Luminal to abluminal transport of anti-myc antibodies across hcMEC/D3 cells
As demonstrated above, 9E10 antibodies are transported via the truncated mini LRP1 receptor across human endothelial cells. In the following, the vesicular trafficking route of anti-Myc antibodies (9E10) was further investigated. Cells were co-stained for mLRP1_DIV*, the endocytosis marker Clathrin and Caveolin-1, the early endosome using EEA-1, the lysosome via Lamp-1, recycling endosomes using TfR-1 and for the exocytosis marker Rab27a [45–48]. After entering the cell via a Clathrin – and caveolin-mediated endocytosis, the receptor-antibody complex seems to be routed directly across the cells into recycling endosomes followed by exocytosis via Rab27a positive vesicles (Supplementary Data 1, Figure S4). However, co-localization with Rab27a-positive structures only indicates exocytosis of antibodies in general but cannot allow further specification regarding the side of the release. As 9E10 antibodies should be fused to liposomes as tool for drug delivery into the CNS, a release of the antibodies on the basolateral side must be ensured. Therefore, abluminal exocytosis of 9E10 was further validated by immunofluorescence. Like experiments before, mLRP1_DIV* was transfected into hcMEC/D3 cells, which were then grown in transmembrane inserts until a monolayer was formed. By exposing the luminal side of the cells to 9E10 for different time periods, the abluminal location and release of 9E10 and mLRP1_DIV* was investigated by a co-localization with p-catenin. Besides tight junctions, a different class of junction proteins known as adherens, including cadherins, catenins and junctional adhesion molecules (JAMs) are involved in the formation, stabilization, and organization of the intercellular junctions at the endothelium, mostly at the basolateral membrane [49, 50]. Regarding immunostainings, only a co-localization between mLRP1_DIV* and 9E10, not between mLRP1_DIV* and p-catenin or 9E10 and p-catenin, could be shown within the cell following a 30-minute incubation of hcMEC/D3 mLRP1_DIV* cells with 9E10 antibodies (Fig. 3E1- G1). However, mLRP1_DIV* and 9E10 antibodies were visible at the cell surface along with p-catenin after a 60-minute incubation (Fig. 3E2 - H2). Co-localization of both, mLRP1_DIV* and 9E10 with p-catenin confirms mLRP1_DIV* dependent release of 9E10 on the abluminal side of hcMEC/D3 cells.
Fig. 3Basolateral sorting of 9E10/mLRP1_DIV* complex in co-stainings with p-catenin during transport across hcMEC/D3 cells. Cells were transfected with mLRP1_DIV* and transport of 9E10 was performed 72 h post transfection. Representative confocal images of (A) mLRP1_DIV*, (B) abluminal marker p-catenin, (C) 9E10, (D) nuclei and co-localization (E - H) 30 min (1) and 60 min (2) after incubation with AlexaFluor488-9E10. Cells were washed with acidic PBS, fixed with 4% PFA, permeabilized and stained for mLRP1_DIV* and p-catenin. Images were taken with the Stellaris 8 Falcon confocal laser scanning microscope using a laser at a wavelength of (mLRP1_DIV*) 647 nm, (9E10) 488 nm, (p-catenin) 568 nm and (nuclei) 350 nm. mLRP1_DIV* is depicted in red, 9E10 in yellow, p-catenin in cyan and nuclei in blue. Co-localizations were investigated by (E) merge of A and B, (F) merge of B and C, (G) merge of A and C. Scale bar = 10 μm, z = depth in the cell
mLRP1_DIV* mediated transcellular transport of 9E10 functionalized immunoliposomes across an in vitro model of the BBB
Based on the results obtained in hcMEC/D3 cells concerning internalization of liposomes, mLRP1_DIV*’s functionality of transcytosing liposomes was analyzed (Supplementary Data 1, Figs. S6 – S7). Therefore, the in vitro model of the BBB, which enables to monitor tight junction, barrier formation and confluence of the endothelial cells, was used. Cells were transfected with mLRP1_DIV* or pLBCX and immunoliposomes were luminally applied for 2 h, 72 h after transfection. The amount of transported liposomes was investigated by fluorescence intensity of rhodamine in the medium of the abluminal compartment. The experimental setup is depicted in Fig. 4A. Thereby, cells expressing mLRP1_DIV* showed a significant higher transcytosis (2.28-fold, 1.65-fold, 2.1-fold) of 9E10 functionalized liposomes compared to cells lacking mLRP1_DIV* (p < 0.0001) and compared to an application of unmodified liposomes to mLRP1_DIV* transfected cells (p = 0.0003) or to control cells (p < 0.0001) (Fig. 4B). The transcytosis of 9E10 functionalized liposomes in mLRP1_DIV* cells was further confirmed by an incubation at 37 °C with or without 9E10 antibodies or at 4 °C to compete or inhibit the transcytosis process. Thereby, a significant increased transcytosis of ∼ 50% of 9E10 functionalized liposomes at 37 °C compared to an incubation with 9E10 antibodies (p < 0.0001) or at 4 °C (p < 0.0001) was observed (Fig. 4C). In both transcytosis experiments the integrity of the in vitro BBB was confirmed using FITC-Dextran (3–4 kDa) as permeability marker. For all experimental groups, diffusion of FITC-Dextran was less than 0.5% indicating an intact barrier (Fig. 4D and E). Moreover, no difference in the BBB’s integrity could be observed between the experimental groups, as the transendothelial electrical resistance of hcMEC/D3 pLBCX was about 30.4 Ω*cm2 and 29.7 Ω*cm2 using hcMEC/D3 mLRP1_DIV* cells (Fig. 4F).
Fig. 4mLRP1_DIV* mediated transcellular transport of 9E10 - IL across hcMEC/D3 cells. (A) Schematic illustration of the in vitro BBB model. (B) Cells were transiently transfected with mLRP1_DIV* or pLBCX. At confluence, cells were incubated with 3mM 9E10 functionalized or unmodified liposomes for 2 h. (C) Transcytosis of 3mM 9E10 functionalized liposomes in hcMEC/D3 mLRP1_DIV* cells after 2 h at 37 °C with or without 9E10 antibodies or at 4 °C. (D and E) Paracellular leakage of 50 µg/ml FITC-Dextran (3–4 kDa) in corresponding experimental groups. (B - E) Amount of transcytosed immunoliposomes and FITC-Dextran were analyzed by fluorescence measurement of rhodamine or FITC in the abluminal medium. To quantify the transport of liposomes, a calibration curve for both liposomes was generated. Transported amount of liposomes was calculated according to the calibration curve. Transcytosed amount of FITC-Dextran was calculated percentual to the input saved before the transport. (B) hcMEC/D3 pLBCX cells or (C) hcMEC/D3 mLRP1_DIV* incubated with 9E10 functionalized immunoliposomes at 37 °C were defined as 100%. (F) TEER at the time of transport was measured by impedance spectroscopy. Data represent the mean ± SEM of twelve individual replicates of n = 3 independent experiments. One-way ANOVA followed by Tukey’s multiple comparison test
mLRP1_DIV* mediated luminal to abluminal transport of 9E10 functionalized liposomes across hcMEC/D3 cells
As mentioned previously, the truncated mini LRP1 receptor allows 9E10 functionalized liposomes to pass through human endothelial cells (Supplementary Data 1; Figure S8-S10). After entering the cell by Clathrin- and Caveolin-mediated endocytosis, the receptor-liposome complex appears to be transported straight across the cells into recycling endosomes followed by exocytosis via Rab27a positive vesicles. Here, the basolateral sorting of 9E10 functionalized immunoliposomes was further explored by exposing the luminal side of the cells to 3 mM of 9E10 functionalized liposomes for different time periods. Co-localization of 9E10 functionalized liposomes and mLRP1_DIV* with the abluminal marker p-catenin allowed for an investigation of their abluminal location and release. After incubation of hcMEC/D3 mLRP1_DIV* cells with 9E10 functionalized liposomes for 90 min, only a co-localization between mLRP1_DIV* and liposomes could be detected within the cell, but not between mLRP1_DIV* and p-catenin or 9E10 - IL and p-catenin (Fig. 5E1 - G1). However, after an incubation of 120 min, it was possible to detect mLRP1_DIV* and 9E10 functionalized liposomes at the cell surface, together with p-catenin (Fig. 5E2 - H2). Immunofluorescent stainings clearly demonstrate mLRP1_DIV* dependent transport of 9E10 – IL across hCMEC/D3 cells, followed by their abluminal release.
Fig. 5Basolateral sorting of 9E10 - IL/mLRP1_DIV* complex in co-stainings with p-catenin during transport across hcMEC/D3 cells. Cells were transfected with mLRP1_DIV* and transport of 9E10 - IL was performed 72 h post transfection. Representative confocal images of (A) mLRP1_DIV*, (B) abluminal marker p-catenin, (C) 9E10 - IL, (D) nuclei and co-localization (E - H) for (1) 90 min or (2) 120 min after incubation with the liposomes. Cells were washed with acidic PBS, fixed with 4% PFA, permeabilized and stained for mLRP1_DIV* and p-catenin. Images were taken with the Stellaris 8 Falcon confocal laser scanning microscope using a laser at a wavelength of (mLRP1_DIV*) 647 nm, (rhodamine) 540 nm, (p-catenin) 488 nm and (nuclei) 350 nm. mLRP1_DIV* is depicted in red, 9E10 - IL in yellow, p-catenin in cyan and nuclei in blue. Co-localizations were investigated by (E) merge of A and B, (F) merge of B and C, (G) merge of A and C, (H) merge of all. Scale bar = 10 μm, z = depth in the cell
mLRP1_DIV*/cargo complex is released Rab27a dependent on the abluminal side of endothelial cells
To further explore the mechanism by which the mLRP1_DIV*/cargo complex is exocytosed on the basolateral side of endothelial cells in vitro, a transcytosis of 9E10-immunoliposomes across bEnd.3 mLRP1_DIV* cells or of 9E10 across hcMEC/D3 mLRP1_DIV* was performed with or without the Rab27a inhibitor Nexinhib20 (Fig. 6). Formed and stimulated bEnd.3/hcMEC/D3 mLRP1_DIV* cell monolayer was treated with or without 10µM Rab27a inhibitor for 2 h before and during the transcytosis of 9E10 or 9E10 immunoliposomes. Transport of both, 9E10 or 9E10 immunoliposomes was determined using fluorescence spectroscopy. Thereby, hcMEC/D3 mLRP1_DIV* cells show a 61.3% reduction in transcytosis of 9E10, when cells were incubated with the Rab27a inhibitor compared to DMSO-treated hcMEC/D3 mLRP1_DIV* cells (p < 0.0001) (Fig. 6A). Similar, bEnd.3 mLRP1_DIV* cells also show a 60.2% reduction in transcytosis of 9E10 immunoliposomes when cells were incubated with the Rab27a inhibitor compared to DMSO-treated bEnd.3 mLRP1_DIV* cells (p < 0.0001) (Fig. 6B). Importantly, no difference in the BBB’s integrity could be observed between the experimental groups, as the transendothelial electrical resistance of hcMEC/D3 was about 44.8 Ω*cm2 for both groups and of bEnd.3 cells about 44.7 Ω*cm2 for both treated and non-treated group. For all experimental groups, diffusion of the paracellular leakage marker FITC-Dextran was less than 0.7% indicating an intact barrier (Data not shown). Together data in bEnd.3 or hcMEC/D3 cells clearly demonstrate that the 9E10/mLRP1_DIV* or 9E10 immunoliposomes/ mLRP1_DIV* complex is exocytosed Rab27a dependently.
Fig. 6Transcytosis of 9E10 or 9E10 immunoliposomes across endothelial cells upon Rab27a inhibition. (A) hcMEC/D3 or (B) bEnd.3 cells were transiently transfected with mLRP1_DIV* and cultured in cell culture inserts in an in vitro BBB model. At confluence, cells were incubated with 10µM Rab27a inhibitor or DMSO control and transport of (A) 2 µg/ml of anti-Myc Alexa Fluor™ 555 or (B) 3mM 9E10 immunoliposomes was performed after 2 h for 2 h. Amount of transcytosed Alexa Fluor™ 555 9E10 or 9E10 immunoliposomes was analyzed using fluorescence spectroscopy. To quantify the transport of liposomes, a calibration curve for the liposomes was generated. Transported amount of liposomes was calculated according to the calibration curve. Transcytosed amount of anti-Myc Alexa Fluor™ 555 was calculated percentual to the input saved before the transport. Cells incubated with DMSO control were defined as 100% (Ctrl.). Data represent the mean ± SEM of twelve individual replicates of n = 3 independent experiments. Unpaired t- test was used for statistical analysis
mLRP1_DIV* mediated transport of the liposomal γ-secretase modulator BB25
Preparation and characterization of liposomes
Previous studies reported the small molecule BB25 as a promising γ-secretase modulator (GSM) with nanomolar potency. Following treatment of CHO cells with stable co-expression of human APP751 and PSEN1 (PS70 cells), BB25 displayed the typical characteristics of a GSM with a dose-dependent decrease in Aß42 levels and a concomitant increase in Aß38 levels [39]. To investigate the novel approach to deliver potential drugs across the BBB using the artificial mRLP1_DIV* construct, an in vitro BBB co-culture model was established. Thereby, the transport and functionality of the GSM BB25 embedded in 9E10 functionalized liposomes was analyzed. The liposomes were prepared by a thin film hydration method. The liposomes had a size between 118 and 145 nm. The amount of incorporated BB25 was approx. 4.2 mM (Table 1). The increase in size of BB25 immunoliposomes when compared to unloaded immunoliposomes likely shows the incorporation of BB25 into the liposomal membrane. However, all liposomal formulations tested in this study meet the requirements for BBB targeting nanocarriers regarding size and PDI.
Table 1Physiochemical characteristics of liposomes usedFormulationMean particle diameter (nm)Polydispersity index (PDI)BB25 loading (mM)
BB25–9E10 - IL
144.6 nm0.1654.2
unloaded 9E10 - IL
118.1 nm0.155-
Liposomal BB25 modulated Aß peptide generation in PS70 cells after transport across bEnd.3 in a co-culture model
After validation that immunoliposomes or free BB25 have no impact on endothelial barrier properties (Supplementary Data 1, Figure S15), the functionality of the mLRP1_DIV* mediated drug delivery mechanism was validated by a co-culture model, composed of luminally cultured bEnd.3 mLRP1_DIV* combined with abluminally cultured PS70 cells overexpressing APP751 and PSEN1 (Fig. 7A). When a tight barrier of bEnd.3 cells, monitored by impedance spectroscopy, was achieved, 10 µM BB25, BB25 loaded 9E10 functionalized liposomes, unloaded 9E10 functionalized liposomes, or DMSO were added to the bEnd.3 cells into the luminal compartment for 2 h. Afterwards, luminal medium was removed and replaced by normal culture medium. As a readout for a mLRP1_DIV* mediated transport of liposomal BB25 and subsequent release into the brain parenchyma, abluminal medium of PS70 cells was collected 48 h post transport and an Aβ specific ELISA was used to quantify the levels of Aβ38 and Aβ42 peptides. Furthermore, the transported amount of BB25–9E10 – IL and unloaded 9E10 - IL across the bEnd.3 mLRP1_DIV* monolayer was measured by fluorescence spectroscopy. Luminally administrated free BB25 caused no changes in Aβ38 and Aβ42 peptide levels compared to DMSO vehicle, indicating an insufficient diffusion across the bEnd.3 monolayer (p = 0.99 / p = 0.98) (Fig. 7B and C). In contrast, bEnd.3 mLRP1_DIV* cells showed a transport of liposomal BB25 of approximately
11.5 ± 0.6%, resulting in an 9.3-fold increase of Aß38 as well as a decrease of Aß42 of 56.3% compared to the DMSO control (p < 0.0001, p = 0.0051) (Fig. 7B and C; Table 2). Compared to free BB25, an 8.8-fold increase of Aß38 as well as a decrease of Aß42 of 53.7% could be detected (p < 0.0001,
p = 0 0.0141) (Fig. 7B and C). Similar results were seen after transport of liposomal BB25 compared to unloaded immunoliposomes, as a 11.1-fold increase of Aß38 as well as a decrease of Aß42 of 51.9% could be observed (p < 0.0001, p = 0.024). Although 10.9 ± 2.01% of unloaded 9E10 immunoliposomes were transcytosed, no modulating effect on γ-secretase activity with respect to Aß38 or Aβ42 peptide levels could be detected compared to an application of DMSO vehicle or free BB25 (p = 0.91, p = 0.94 / p = 0.79, p = 0.99) (Fig. 7B and C; Table 2). Notably, Aß38 and Aβ42 peptide levels averaged out at the same concentration after luminal application of DMSO, free BB25, or unloaded 9E10 liposomes across bEnd.3 mLRP1_DIV* cells (Fig. 7B and C). Importantly, all experimental groups showed similar TEER of approximately 28.58 Ω*cm2 as well as the same amount of paracellular diffusion of FITC-Dextran (3-4 kDa) of approximately 0.54%, indicating a comparable tight in vitro BBB between all experimental groups (Fig. 7D and E). These findings clearly demonstrate that BB25 loaded 9E10 functionalized immunoliposomes were transported mLRP1_DIV* dependently across an in vitro model of the BBB, followed by the release of BB25 on the abluminal side, where it modulated γ-secretase activity resulting in decreased Aβ42 and increased Aß38 peptide levels.
Table 2Concentration of rhodamine in the abluminal compartment measured by fluorescence spectroscopyFormulationµM luminally administeredrhodamine abluminally 2 h post transport(µM)% of administered concentrationBB25–9E10 – IL184.8 µM lipid≙10 µM (BB25)21.3 µM lipid≙1.15 µM (BB25)11.5 ± 0.6%unloaded9E10 - IL184.8 µM lipid20.1 µM lipid10.9 ± 2.01%
Fig. 7Aß levels changed after mLRP1_DIV* mediated transport of liposomal BB25 across bEnd.3 cells. (A) Schematic illustration of the co-culture model. PS70 cells overexpressing human APP751 and Presenilin 1 were abluminally co-cultured with transiently transfected bEnd.3 mLRP1_DIV* cells in the luminal compartment. (B, E) Post-confluent bEnd.3 mLRP1_DIV* cells were treated with 10µM BB25, BB25–9E10 - IL, unloaded 9E10 - IL or DMSO for 2 h. The administered concentration of the liposomes was adjusted to the free BB25. Levels of (B) Aβ38 and (C) Aβ42 in abluminal cell culture supernatants were measured 48 h post transport by an Aβ species specific ELISA. (D) TEER at the time of transport was measured by impedance spectroscopy. (E) Paracellular leakage of FITC-Dextran (3–4 kDa) 24 h before transport for 24 h. Data represent the mean ± SEM of twelve individual replicates from n = 3 independent experiments. One-way ANOVA followed by Tukey’s multiple comparison test was used for statistical analysis
AAV-(BR1)-mLRP1_DIV* is expressed in mouse brain capillaries
Based on the overall promising results in vitro, mLRP1_DIV*’s functional role as transport shuttle should be further explored in the in vivo situation with regard to its relevance for neurodegenerative diseases. To provide an in vivo evidence that LPR1-based receptors can be used as therapeutic strategy, an adeno-associated virus (AAV) that specifically infects only endothelial cells of the BBB was used.
(Fig. 8). Due to the development of this highly specific AAV, only the BBB associated endothelium was efficiently infected after intravenous injection Mice were injected with AAV-(BR1)-mLRP1_DIV* or control at age of approximately 10 weeks and sacrificed 3 months later. mLRP1_DIV*’s expression in the endothelium after intravenous injection was validated by isolation of brain capillaries and endothelial cells from 5xFAD (wt) mice, followed by immunofluorescence and lysis of corresponding tissue and western blot analysis. Expression of HA-tag attached to the C-terminus of mLRP1_DIV* was detected in capillaries of AAV-injected mice (AAV(BR)-mLRP1_DIV*) but not of control mice. As expected, no signal can be found in capillary -depleted brain fractions (Fig. 8B). LRP1 expression is increased in capillary fractions of treated mice due to mLRP1_DIV* delivery, while expression in the brain parenchyma is not altered (Fig. 8C). Relative abundance was compared to actin (loading control). mLRP1_DIV*’s expression in the brain microvasculature was further analyzed using immunofluorescent stainings for co-localization of HA tagged mLRP1_DIV* with the neurovascular unit components collagen IV, astrocytic end feet and endothelial cells. Representative cortical images captured with 100-fold magnification show a widespread detection of mLRP1_DIV* (green) throughout the brain microvasculature (red) (Fig. 8D). Moreover, co-localisation of mLRP1_DIV* with the endothelial marker PECAM1, the vascular basement membrane (collagen IV) and astrocytic end feet (aquaporin 4) confirms mLRP1_DIV* enrichment at the blood brain barrier in murine brain slices (Fig. 8E - G). Together, ex vivo data clearly show mLRP1_DIV*’s expression at the murine BBB after intravenous injection of corresponding AAVs.
Fig. 8Ex vivo validation of mLRP1_DIV* expression in mouse brain tissue. (A) Intravenous injection of AAV(BR1)mLRP1_DIV* in 10 weeks old mice. (B, C) Representative immunoblotting for protein expression of mLRP1_DIV* in isolated brain microvasculature (capillaries) and vessel depleted brains of AAV treated 5xFAD wt mice and their littermate controls. ß-actin was used as loading control. Overexpression of mLRP1_DIV* was validated using (B) anti-HA tag antibody or (C) anti LRP1-ß-chain antibody 1704. (D-G) Representative images of murine cortical brain slices. Slices were stained for mLRP1_DIV* (green) and vascular basement membrane (D and E), astrocytic end feet (F) and endothelial cells (G) (red). Scale bars: 100 μm (A) 50 μm – (B - D)
mLRP1_DIV* mediated transcellular transport of anti-myc antibodies across an in vitro model of the BBB
In our first studies mLRP1_DIV* (Fig. 2A) was demonstrated to specifically internalize anti-Myc antibodies in CHO 13-5-1 cells (Supplementary Data 1, Figure S3). Therefore, the mLRP1_DIV* construct was analyzed in the human derived endothelial cell line hcMEC/D3, as these cells consists of a luminal and abluminal polarization. For examination of mLRP1_DIV*’s functionality, an in vitro model of the BBB, which enables to monitor tight junction and barrier formation of the endothelial cells, was used.
Cells were transfected with mLRP1_DIV* or pLBCX and 30 µg/ml of 9E10 or unspecific mouse IgG (mIgG) were luminally applied to the cell monolayer in the in vitro BBB model for 1 h (Fig. 2B). Respective protein amounts were analyzed in the abluminal medium. Regarding transport assay in hcMEC/D3 cells, two bands at about 50 kDa and 25 kDa, corresponding to IgG, became visible in abluminal and luminal medium of cells being transfected with pLBCX or mLRP1_DIV* (Fig. 2C). According to transport of 9E10 in hcMEC/D3 cells, cells transfected with mLRP1_DIV* showed an average transport of 9E10’s heavy chain of 15% after 60 min whereas hcMEC/D3 pLBCX cells showed an average transport of 9E10’s heavy chain of 5.8%. Transport of unspecific mIgG in cells transfected with mLRP1_DIV* averaged out at 4.2% (Fig. 2D). Thereby, a significant higher transcytosis.
(2.5-fold/ 3.3-fold) of 9E10’s heavy chain in hcMEC/D3 mLRP1_DIV* cells could be observed compared to control or unspecific mIgG (p < 0.0001 / p < 0.0001). The transport of the light chain of 9E10 averaged out at 8% after 60 min using hcMEC/D3 mLRP1_DIV* cells and 2.4% using hcMEC/D3 pLBCX cells. mLRP1_DIV* transfected cells showed a transport of unspecific mIgG of around 1.3% (Fig. 2E). Cells expressing the mLRP1_DIV* receptor showed a significant higher transport (3-fold/ 6-fold) of 9E10’s light chain compared to control or mIgG (p < 0.0001 / p < 0.0001). Notably, no significant differences could be observed between transport of 9E10 in control cells and transport of mIgG in hcMEC/D3 mLRP1_DIV* cells (p = 0.55 / p = 0.29). Moreover, no difference in the BBB’s integrity could be observed between the experimental groups, as the transendothelial electrical resistance of hcMEC/D3 pLBCX was about 30.9 Ω*cm2 and 28.4 Ω*cm2 using hcMEC/D3 mLRP1_DIV* cells (p = 0.83 / p = 0.52 / p = 0.85) (Fig. 2F). Together, data clearly demonstrate mLRP1_DIV* mediated transport of 9E10 across an in vitro model of the BBB.
Fig. 2mLRP1_DIV* mediated transcellular transport of anti-Myc antibodies across hcMEC/D3 cells. (A) Schematic illustration of mLRP1_DIV* (B) Schematic illustration of the experimental setup. Representative immunoblotting for (C) luminal and abluminal medium of hcMEC/D3 being transiently transfected with mLRP1_DIV* or pLBCX. At confluence, cells were incubated with 30 µg/ml anti-Myc antibodies (9E10) or unspecific mIgG for 1 h. (D, E) Heavy and light chain protein levels were quantified by densitometric analysis after immunoblotting and normalized to luminal medium saved before transport. The intensity of 9E10 in abluminal medium of hcMEC/D3 pLBCX cells were defined as 100%. (F) TEER at the time of transport was measured by impedance spectroscopy. Data represent the mean ± SEM of ten individual replicates from n = 3 independent experiments. One-way ANOVA followed by Tukey’s multiple comparison test was used for statistical analysis
Luminal to abluminal transport of anti-myc antibodies across hcMEC/D3 cells
As demonstrated above, 9E10 antibodies are transported via the truncated mini LRP1 receptor across human endothelial cells. In the following, the vesicular trafficking route of anti-Myc antibodies (9E10) was further investigated. Cells were co-stained for mLRP1_DIV*, the endocytosis marker Clathrin and Caveolin-1, the early endosome using EEA-1, the lysosome via Lamp-1, recycling endosomes using TfR-1 and for the exocytosis marker Rab27a [45–48]. After entering the cell via a Clathrin – and caveolin-mediated endocytosis, the receptor-antibody complex seems to be routed directly across the cells into recycling endosomes followed by exocytosis via Rab27a positive vesicles (Supplementary Data 1, Figure S4). However, co-localization with Rab27a-positive structures only indicates exocytosis of antibodies in general but cannot allow further specification regarding the side of the release. As 9E10 antibodies should be fused to liposomes as tool for drug delivery into the CNS, a release of the antibodies on the basolateral side must be ensured. Therefore, abluminal exocytosis of 9E10 was further validated by immunofluorescence. Like experiments before, mLRP1_DIV* was transfected into hcMEC/D3 cells, which were then grown in transmembrane inserts until a monolayer was formed. By exposing the luminal side of the cells to 9E10 for different time periods, the abluminal location and release of 9E10 and mLRP1_DIV* was investigated by a co-localization with p-catenin. Besides tight junctions, a different class of junction proteins known as adherens, including cadherins, catenins and junctional adhesion molecules (JAMs) are involved in the formation, stabilization, and organization of the intercellular junctions at the endothelium, mostly at the basolateral membrane [49, 50]. Regarding immunostainings, only a co-localization between mLRP1_DIV* and 9E10, not between mLRP1_DIV* and p-catenin or 9E10 and p-catenin, could be shown within the cell following a 30-minute incubation of hcMEC/D3 mLRP1_DIV* cells with 9E10 antibodies (Fig. 3E1- G1). However, mLRP1_DIV* and 9E10 antibodies were visible at the cell surface along with p-catenin after a 60-minute incubation (Fig. 3E2 - H2). Co-localization of both, mLRP1_DIV* and 9E10 with p-catenin confirms mLRP1_DIV* dependent release of 9E10 on the abluminal side of hcMEC/D3 cells.
Fig. 3Basolateral sorting of 9E10/mLRP1_DIV* complex in co-stainings with p-catenin during transport across hcMEC/D3 cells. Cells were transfected with mLRP1_DIV* and transport of 9E10 was performed 72 h post transfection. Representative confocal images of (A) mLRP1_DIV*, (B) abluminal marker p-catenin, (C) 9E10, (D) nuclei and co-localization (E - H) 30 min (1) and 60 min (2) after incubation with AlexaFluor488-9E10. Cells were washed with acidic PBS, fixed with 4% PFA, permeabilized and stained for mLRP1_DIV* and p-catenin. Images were taken with the Stellaris 8 Falcon confocal laser scanning microscope using a laser at a wavelength of (mLRP1_DIV*) 647 nm, (9E10) 488 nm, (p-catenin) 568 nm and (nuclei) 350 nm. mLRP1_DIV* is depicted in red, 9E10 in yellow, p-catenin in cyan and nuclei in blue. Co-localizations were investigated by (E) merge of A and B, (F) merge of B and C, (G) merge of A and C. Scale bar = 10 μm, z = depth in the cell
mLRP1_DIV* mediated transcellular transport of 9E10 functionalized immunoliposomes across an in vitro model of the BBB
Based on the results obtained in hcMEC/D3 cells concerning internalization of liposomes, mLRP1_DIV*’s functionality of transcytosing liposomes was analyzed (Supplementary Data 1, Figs. S6 – S7). Therefore, the in vitro model of the BBB, which enables to monitor tight junction, barrier formation and confluence of the endothelial cells, was used. Cells were transfected with mLRP1_DIV* or pLBCX and immunoliposomes were luminally applied for 2 h, 72 h after transfection. The amount of transported liposomes was investigated by fluorescence intensity of rhodamine in the medium of the abluminal compartment. The experimental setup is depicted in Fig. 4A. Thereby, cells expressing mLRP1_DIV* showed a significant higher transcytosis (2.28-fold, 1.65-fold, 2.1-fold) of 9E10 functionalized liposomes compared to cells lacking mLRP1_DIV* (p < 0.0001) and compared to an application of unmodified liposomes to mLRP1_DIV* transfected cells (p = 0.0003) or to control cells (p < 0.0001) (Fig. 4B). The transcytosis of 9E10 functionalized liposomes in mLRP1_DIV* cells was further confirmed by an incubation at 37 °C with or without 9E10 antibodies or at 4 °C to compete or inhibit the transcytosis process. Thereby, a significant increased transcytosis of ∼ 50% of 9E10 functionalized liposomes at 37 °C compared to an incubation with 9E10 antibodies (p < 0.0001) or at 4 °C (p < 0.0001) was observed (Fig. 4C). In both transcytosis experiments the integrity of the in vitro BBB was confirmed using FITC-Dextran (3–4 kDa) as permeability marker. For all experimental groups, diffusion of FITC-Dextran was less than 0.5% indicating an intact barrier (Fig. 4D and E). Moreover, no difference in the BBB’s integrity could be observed between the experimental groups, as the transendothelial electrical resistance of hcMEC/D3 pLBCX was about 30.4 Ω*cm2 and 29.7 Ω*cm2 using hcMEC/D3 mLRP1_DIV* cells (Fig. 4F).
Fig. 4mLRP1_DIV* mediated transcellular transport of 9E10 - IL across hcMEC/D3 cells. (A) Schematic illustration of the in vitro BBB model. (B) Cells were transiently transfected with mLRP1_DIV* or pLBCX. At confluence, cells were incubated with 3mM 9E10 functionalized or unmodified liposomes for 2 h. (C) Transcytosis of 3mM 9E10 functionalized liposomes in hcMEC/D3 mLRP1_DIV* cells after 2 h at 37 °C with or without 9E10 antibodies or at 4 °C. (D and E) Paracellular leakage of 50 µg/ml FITC-Dextran (3–4 kDa) in corresponding experimental groups. (B - E) Amount of transcytosed immunoliposomes and FITC-Dextran were analyzed by fluorescence measurement of rhodamine or FITC in the abluminal medium. To quantify the transport of liposomes, a calibration curve for both liposomes was generated. Transported amount of liposomes was calculated according to the calibration curve. Transcytosed amount of FITC-Dextran was calculated percentual to the input saved before the transport. (B) hcMEC/D3 pLBCX cells or (C) hcMEC/D3 mLRP1_DIV* incubated with 9E10 functionalized immunoliposomes at 37 °C were defined as 100%. (F) TEER at the time of transport was measured by impedance spectroscopy. Data represent the mean ± SEM of twelve individual replicates of n = 3 independent experiments. One-way ANOVA followed by Tukey’s multiple comparison test
mLRP1_DIV* mediated luminal to abluminal transport of 9E10 functionalized liposomes across hcMEC/D3 cells
As mentioned previously, the truncated mini LRP1 receptor allows 9E10 functionalized liposomes to pass through human endothelial cells (Supplementary Data 1; Figure S8-S10). After entering the cell by Clathrin- and Caveolin-mediated endocytosis, the receptor-liposome complex appears to be transported straight across the cells into recycling endosomes followed by exocytosis via Rab27a positive vesicles. Here, the basolateral sorting of 9E10 functionalized immunoliposomes was further explored by exposing the luminal side of the cells to 3 mM of 9E10 functionalized liposomes for different time periods. Co-localization of 9E10 functionalized liposomes and mLRP1_DIV* with the abluminal marker p-catenin allowed for an investigation of their abluminal location and release. After incubation of hcMEC/D3 mLRP1_DIV* cells with 9E10 functionalized liposomes for 90 min, only a co-localization between mLRP1_DIV* and liposomes could be detected within the cell, but not between mLRP1_DIV* and p-catenin or 9E10 - IL and p-catenin (Fig. 5E1 - G1). However, after an incubation of 120 min, it was possible to detect mLRP1_DIV* and 9E10 functionalized liposomes at the cell surface, together with p-catenin (Fig. 5E2 - H2). Immunofluorescent stainings clearly demonstrate mLRP1_DIV* dependent transport of 9E10 – IL across hCMEC/D3 cells, followed by their abluminal release.
Fig. 5Basolateral sorting of 9E10 - IL/mLRP1_DIV* complex in co-stainings with p-catenin during transport across hcMEC/D3 cells. Cells were transfected with mLRP1_DIV* and transport of 9E10 - IL was performed 72 h post transfection. Representative confocal images of (A) mLRP1_DIV*, (B) abluminal marker p-catenin, (C) 9E10 - IL, (D) nuclei and co-localization (E - H) for (1) 90 min or (2) 120 min after incubation with the liposomes. Cells were washed with acidic PBS, fixed with 4% PFA, permeabilized and stained for mLRP1_DIV* and p-catenin. Images were taken with the Stellaris 8 Falcon confocal laser scanning microscope using a laser at a wavelength of (mLRP1_DIV*) 647 nm, (rhodamine) 540 nm, (p-catenin) 488 nm and (nuclei) 350 nm. mLRP1_DIV* is depicted in red, 9E10 - IL in yellow, p-catenin in cyan and nuclei in blue. Co-localizations were investigated by (E) merge of A and B, (F) merge of B and C, (G) merge of A and C, (H) merge of all. Scale bar = 10 μm, z = depth in the cell
mLRP1_DIV*/cargo complex is released Rab27a dependent on the abluminal side of endothelial cells
To further explore the mechanism by which the mLRP1_DIV*/cargo complex is exocytosed on the basolateral side of endothelial cells in vitro, a transcytosis of 9E10-immunoliposomes across bEnd.3 mLRP1_DIV* cells or of 9E10 across hcMEC/D3 mLRP1_DIV* was performed with or without the Rab27a inhibitor Nexinhib20 (Fig. 6). Formed and stimulated bEnd.3/hcMEC/D3 mLRP1_DIV* cell monolayer was treated with or without 10µM Rab27a inhibitor for 2 h before and during the transcytosis of 9E10 or 9E10 immunoliposomes. Transport of both, 9E10 or 9E10 immunoliposomes was determined using fluorescence spectroscopy. Thereby, hcMEC/D3 mLRP1_DIV* cells show a 61.3% reduction in transcytosis of 9E10, when cells were incubated with the Rab27a inhibitor compared to DMSO-treated hcMEC/D3 mLRP1_DIV* cells (p < 0.0001) (Fig. 6A). Similar, bEnd.3 mLRP1_DIV* cells also show a 60.2% reduction in transcytosis of 9E10 immunoliposomes when cells were incubated with the Rab27a inhibitor compared to DMSO-treated bEnd.3 mLRP1_DIV* cells (p < 0.0001) (Fig. 6B). Importantly, no difference in the BBB’s integrity could be observed between the experimental groups, as the transendothelial electrical resistance of hcMEC/D3 was about 44.8 Ω*cm2 for both groups and of bEnd.3 cells about 44.7 Ω*cm2 for both treated and non-treated group. For all experimental groups, diffusion of the paracellular leakage marker FITC-Dextran was less than 0.7% indicating an intact barrier (Data not shown). Together data in bEnd.3 or hcMEC/D3 cells clearly demonstrate that the 9E10/mLRP1_DIV* or 9E10 immunoliposomes/ mLRP1_DIV* complex is exocytosed Rab27a dependently.
Fig. 6Transcytosis of 9E10 or 9E10 immunoliposomes across endothelial cells upon Rab27a inhibition. (A) hcMEC/D3 or (B) bEnd.3 cells were transiently transfected with mLRP1_DIV* and cultured in cell culture inserts in an in vitro BBB model. At confluence, cells were incubated with 10µM Rab27a inhibitor or DMSO control and transport of (A) 2 µg/ml of anti-Myc Alexa Fluor™ 555 or (B) 3mM 9E10 immunoliposomes was performed after 2 h for 2 h. Amount of transcytosed Alexa Fluor™ 555 9E10 or 9E10 immunoliposomes was analyzed using fluorescence spectroscopy. To quantify the transport of liposomes, a calibration curve for the liposomes was generated. Transported amount of liposomes was calculated according to the calibration curve. Transcytosed amount of anti-Myc Alexa Fluor™ 555 was calculated percentual to the input saved before the transport. Cells incubated with DMSO control were defined as 100% (Ctrl.). Data represent the mean ± SEM of twelve individual replicates of n = 3 independent experiments. Unpaired t- test was used for statistical analysis
mLRP1_DIV* mediated transport of the liposomal γ-secretase modulator BB25
Preparation and characterization of liposomes
Previous studies reported the small molecule BB25 as a promising γ-secretase modulator (GSM) with nanomolar potency. Following treatment of CHO cells with stable co-expression of human APP751 and PSEN1 (PS70 cells), BB25 displayed the typical characteristics of a GSM with a dose-dependent decrease in Aß42 levels and a concomitant increase in Aß38 levels [39]. To investigate the novel approach to deliver potential drugs across the BBB using the artificial mRLP1_DIV* construct, an in vitro BBB co-culture model was established. Thereby, the transport and functionality of the GSM BB25 embedded in 9E10 functionalized liposomes was analyzed. The liposomes were prepared by a thin film hydration method. The liposomes had a size between 118 and 145 nm. The amount of incorporated BB25 was approx. 4.2 mM (Table 1). The increase in size of BB25 immunoliposomes when compared to unloaded immunoliposomes likely shows the incorporation of BB25 into the liposomal membrane. However, all liposomal formulations tested in this study meet the requirements for BBB targeting nanocarriers regarding size and PDI.
Table 1Physiochemical characteristics of liposomes usedFormulationMean particle diameter (nm)Polydispersity index (PDI)BB25 loading (mM)
BB25–9E10 - IL
144.6 nm0.1654.2
unloaded 9E10 - IL
118.1 nm0.155-
Liposomal BB25 modulated Aß peptide generation in PS70 cells after transport across bEnd.3 in a co-culture model
After validation that immunoliposomes or free BB25 have no impact on endothelial barrier properties (Supplementary Data 1, Figure S15), the functionality of the mLRP1_DIV* mediated drug delivery mechanism was validated by a co-culture model, composed of luminally cultured bEnd.3 mLRP1_DIV* combined with abluminally cultured PS70 cells overexpressing APP751 and PSEN1 (Fig. 7A). When a tight barrier of bEnd.3 cells, monitored by impedance spectroscopy, was achieved, 10 µM BB25, BB25 loaded 9E10 functionalized liposomes, unloaded 9E10 functionalized liposomes, or DMSO were added to the bEnd.3 cells into the luminal compartment for 2 h. Afterwards, luminal medium was removed and replaced by normal culture medium. As a readout for a mLRP1_DIV* mediated transport of liposomal BB25 and subsequent release into the brain parenchyma, abluminal medium of PS70 cells was collected 48 h post transport and an Aβ specific ELISA was used to quantify the levels of Aβ38 and Aβ42 peptides. Furthermore, the transported amount of BB25–9E10 – IL and unloaded 9E10 - IL across the bEnd.3 mLRP1_DIV* monolayer was measured by fluorescence spectroscopy. Luminally administrated free BB25 caused no changes in Aβ38 and Aβ42 peptide levels compared to DMSO vehicle, indicating an insufficient diffusion across the bEnd.3 monolayer (p = 0.99 / p = 0.98) (Fig. 7B and C). In contrast, bEnd.3 mLRP1_DIV* cells showed a transport of liposomal BB25 of approximately
11.5 ± 0.6%, resulting in an 9.3-fold increase of Aß38 as well as a decrease of Aß42 of 56.3% compared to the DMSO control (p < 0.0001, p = 0.0051) (Fig. 7B and C; Table 2). Compared to free BB25, an 8.8-fold increase of Aß38 as well as a decrease of Aß42 of 53.7% could be detected (p < 0.0001,
p = 0 0.0141) (Fig. 7B and C). Similar results were seen after transport of liposomal BB25 compared to unloaded immunoliposomes, as a 11.1-fold increase of Aß38 as well as a decrease of Aß42 of 51.9% could be observed (p < 0.0001, p = 0.024). Although 10.9 ± 2.01% of unloaded 9E10 immunoliposomes were transcytosed, no modulating effect on γ-secretase activity with respect to Aß38 or Aβ42 peptide levels could be detected compared to an application of DMSO vehicle or free BB25 (p = 0.91, p = 0.94 / p = 0.79, p = 0.99) (Fig. 7B and C; Table 2). Notably, Aß38 and Aβ42 peptide levels averaged out at the same concentration after luminal application of DMSO, free BB25, or unloaded 9E10 liposomes across bEnd.3 mLRP1_DIV* cells (Fig. 7B and C). Importantly, all experimental groups showed similar TEER of approximately 28.58 Ω*cm2 as well as the same amount of paracellular diffusion of FITC-Dextran (3-4 kDa) of approximately 0.54%, indicating a comparable tight in vitro BBB between all experimental groups (Fig. 7D and E). These findings clearly demonstrate that BB25 loaded 9E10 functionalized immunoliposomes were transported mLRP1_DIV* dependently across an in vitro model of the BBB, followed by the release of BB25 on the abluminal side, where it modulated γ-secretase activity resulting in decreased Aβ42 and increased Aß38 peptide levels.
Table 2Concentration of rhodamine in the abluminal compartment measured by fluorescence spectroscopyFormulationµM luminally administeredrhodamine abluminally 2 h post transport(µM)% of administered concentrationBB25–9E10 – IL184.8 µM lipid≙10 µM (BB25)21.3 µM lipid≙1.15 µM (BB25)11.5 ± 0.6%unloaded9E10 - IL184.8 µM lipid20.1 µM lipid10.9 ± 2.01%
Fig. 7Aß levels changed after mLRP1_DIV* mediated transport of liposomal BB25 across bEnd.3 cells. (A) Schematic illustration of the co-culture model. PS70 cells overexpressing human APP751 and Presenilin 1 were abluminally co-cultured with transiently transfected bEnd.3 mLRP1_DIV* cells in the luminal compartment. (B, E) Post-confluent bEnd.3 mLRP1_DIV* cells were treated with 10µM BB25, BB25–9E10 - IL, unloaded 9E10 - IL or DMSO for 2 h. The administered concentration of the liposomes was adjusted to the free BB25. Levels of (B) Aβ38 and (C) Aβ42 in abluminal cell culture supernatants were measured 48 h post transport by an Aβ species specific ELISA. (D) TEER at the time of transport was measured by impedance spectroscopy. (E) Paracellular leakage of FITC-Dextran (3–4 kDa) 24 h before transport for 24 h. Data represent the mean ± SEM of twelve individual replicates from n = 3 independent experiments. One-way ANOVA followed by Tukey’s multiple comparison test was used for statistical analysis
Preparation and characterization of liposomes
Previous studies reported the small molecule BB25 as a promising γ-secretase modulator (GSM) with nanomolar potency. Following treatment of CHO cells with stable co-expression of human APP751 and PSEN1 (PS70 cells), BB25 displayed the typical characteristics of a GSM with a dose-dependent decrease in Aß42 levels and a concomitant increase in Aß38 levels [39]. To investigate the novel approach to deliver potential drugs across the BBB using the artificial mRLP1_DIV* construct, an in vitro BBB co-culture model was established. Thereby, the transport and functionality of the GSM BB25 embedded in 9E10 functionalized liposomes was analyzed. The liposomes were prepared by a thin film hydration method. The liposomes had a size between 118 and 145 nm. The amount of incorporated BB25 was approx. 4.2 mM (Table 1). The increase in size of BB25 immunoliposomes when compared to unloaded immunoliposomes likely shows the incorporation of BB25 into the liposomal membrane. However, all liposomal formulations tested in this study meet the requirements for BBB targeting nanocarriers regarding size and PDI.
Table 1Physiochemical characteristics of liposomes usedFormulationMean particle diameter (nm)Polydispersity index (PDI)BB25 loading (mM)
BB25–9E10 - IL
144.6 nm0.1654.2
unloaded 9E10 - IL
118.1 nm0.155-
Liposomal BB25 modulated Aß peptide generation in PS70 cells after transport across bEnd.3 in a co-culture model
After validation that immunoliposomes or free BB25 have no impact on endothelial barrier properties (Supplementary Data 1, Figure S15), the functionality of the mLRP1_DIV* mediated drug delivery mechanism was validated by a co-culture model, composed of luminally cultured bEnd.3 mLRP1_DIV* combined with abluminally cultured PS70 cells overexpressing APP751 and PSEN1 (Fig. 7A). When a tight barrier of bEnd.3 cells, monitored by impedance spectroscopy, was achieved, 10 µM BB25, BB25 loaded 9E10 functionalized liposomes, unloaded 9E10 functionalized liposomes, or DMSO were added to the bEnd.3 cells into the luminal compartment for 2 h. Afterwards, luminal medium was removed and replaced by normal culture medium. As a readout for a mLRP1_DIV* mediated transport of liposomal BB25 and subsequent release into the brain parenchyma, abluminal medium of PS70 cells was collected 48 h post transport and an Aβ specific ELISA was used to quantify the levels of Aβ38 and Aβ42 peptides. Furthermore, the transported amount of BB25–9E10 – IL and unloaded 9E10 - IL across the bEnd.3 mLRP1_DIV* monolayer was measured by fluorescence spectroscopy. Luminally administrated free BB25 caused no changes in Aβ38 and Aβ42 peptide levels compared to DMSO vehicle, indicating an insufficient diffusion across the bEnd.3 monolayer (p = 0.99 / p = 0.98) (Fig. 7B and C). In contrast, bEnd.3 mLRP1_DIV* cells showed a transport of liposomal BB25 of approximately
11.5 ± 0.6%, resulting in an 9.3-fold increase of Aß38 as well as a decrease of Aß42 of 56.3% compared to the DMSO control (p < 0.0001, p = 0.0051) (Fig. 7B and C; Table 2). Compared to free BB25, an 8.8-fold increase of Aß38 as well as a decrease of Aß42 of 53.7% could be detected (p < 0.0001,
p = 0 0.0141) (Fig. 7B and C). Similar results were seen after transport of liposomal BB25 compared to unloaded immunoliposomes, as a 11.1-fold increase of Aß38 as well as a decrease of Aß42 of 51.9% could be observed (p < 0.0001, p = 0.024). Although 10.9 ± 2.01% of unloaded 9E10 immunoliposomes were transcytosed, no modulating effect on γ-secretase activity with respect to Aß38 or Aβ42 peptide levels could be detected compared to an application of DMSO vehicle or free BB25 (p = 0.91, p = 0.94 / p = 0.79, p = 0.99) (Fig. 7B and C; Table 2). Notably, Aß38 and Aβ42 peptide levels averaged out at the same concentration after luminal application of DMSO, free BB25, or unloaded 9E10 liposomes across bEnd.3 mLRP1_DIV* cells (Fig. 7B and C). Importantly, all experimental groups showed similar TEER of approximately 28.58 Ω*cm2 as well as the same amount of paracellular diffusion of FITC-Dextran (3-4 kDa) of approximately 0.54%, indicating a comparable tight in vitro BBB between all experimental groups (Fig. 7D and E). These findings clearly demonstrate that BB25 loaded 9E10 functionalized immunoliposomes were transported mLRP1_DIV* dependently across an in vitro model of the BBB, followed by the release of BB25 on the abluminal side, where it modulated γ-secretase activity resulting in decreased Aβ42 and increased Aß38 peptide levels.
Table 2Concentration of rhodamine in the abluminal compartment measured by fluorescence spectroscopyFormulationµM luminally administeredrhodamine abluminally 2 h post transport(µM)% of administered concentrationBB25–9E10 – IL184.8 µM lipid≙10 µM (BB25)21.3 µM lipid≙1.15 µM (BB25)11.5 ± 0.6%unloaded9E10 - IL184.8 µM lipid20.1 µM lipid10.9 ± 2.01%
Fig. 7Aß levels changed after mLRP1_DIV* mediated transport of liposomal BB25 across bEnd.3 cells. (A) Schematic illustration of the co-culture model. PS70 cells overexpressing human APP751 and Presenilin 1 were abluminally co-cultured with transiently transfected bEnd.3 mLRP1_DIV* cells in the luminal compartment. (B, E) Post-confluent bEnd.3 mLRP1_DIV* cells were treated with 10µM BB25, BB25–9E10 - IL, unloaded 9E10 - IL or DMSO for 2 h. The administered concentration of the liposomes was adjusted to the free BB25. Levels of (B) Aβ38 and (C) Aβ42 in abluminal cell culture supernatants were measured 48 h post transport by an Aβ species specific ELISA. (D) TEER at the time of transport was measured by impedance spectroscopy. (E) Paracellular leakage of FITC-Dextran (3–4 kDa) 24 h before transport for 24 h. Data represent the mean ± SEM of twelve individual replicates from n = 3 independent experiments. One-way ANOVA followed by Tukey’s multiple comparison test was used for statistical analysis
AAV-(BR1)-mLRP1_DIV* is expressed in mouse brain capillaries
Based on the overall promising results in vitro, mLRP1_DIV*’s functional role as transport shuttle should be further explored in the in vivo situation with regard to its relevance for neurodegenerative diseases. To provide an in vivo evidence that LPR1-based receptors can be used as therapeutic strategy, an adeno-associated virus (AAV) that specifically infects only endothelial cells of the BBB was used.
(Fig. 8). Due to the development of this highly specific AAV, only the BBB associated endothelium was efficiently infected after intravenous injection Mice were injected with AAV-(BR1)-mLRP1_DIV* or control at age of approximately 10 weeks and sacrificed 3 months later. mLRP1_DIV*’s expression in the endothelium after intravenous injection was validated by isolation of brain capillaries and endothelial cells from 5xFAD (wt) mice, followed by immunofluorescence and lysis of corresponding tissue and western blot analysis. Expression of HA-tag attached to the C-terminus of mLRP1_DIV* was detected in capillaries of AAV-injected mice (AAV(BR)-mLRP1_DIV*) but not of control mice. As expected, no signal can be found in capillary -depleted brain fractions (Fig. 8B). LRP1 expression is increased in capillary fractions of treated mice due to mLRP1_DIV* delivery, while expression in the brain parenchyma is not altered (Fig. 8C). Relative abundance was compared to actin (loading control). mLRP1_DIV*’s expression in the brain microvasculature was further analyzed using immunofluorescent stainings for co-localization of HA tagged mLRP1_DIV* with the neurovascular unit components collagen IV, astrocytic end feet and endothelial cells. Representative cortical images captured with 100-fold magnification show a widespread detection of mLRP1_DIV* (green) throughout the brain microvasculature (red) (Fig. 8D). Moreover, co-localisation of mLRP1_DIV* with the endothelial marker PECAM1, the vascular basement membrane (collagen IV) and astrocytic end feet (aquaporin 4) confirms mLRP1_DIV* enrichment at the blood brain barrier in murine brain slices (Fig. 8E - G). Together, ex vivo data clearly show mLRP1_DIV*’s expression at the murine BBB after intravenous injection of corresponding AAVs.
Fig. 8Ex vivo validation of mLRP1_DIV* expression in mouse brain tissue. (A) Intravenous injection of AAV(BR1)mLRP1_DIV* in 10 weeks old mice. (B, C) Representative immunoblotting for protein expression of mLRP1_DIV* in isolated brain microvasculature (capillaries) and vessel depleted brains of AAV treated 5xFAD wt mice and their littermate controls. ß-actin was used as loading control. Overexpression of mLRP1_DIV* was validated using (B) anti-HA tag antibody or (C) anti LRP1-ß-chain antibody 1704. (D-G) Representative images of murine cortical brain slices. Slices were stained for mLRP1_DIV* (green) and vascular basement membrane (D and E), astrocytic end feet (F) and endothelial cells (G) (red). Scale bars: 100 μm (A) 50 μm – (B - D)
Discussion
Cerebrovascular diseases such as Alzheimer‘s disease (AD), Parkinson, multiple sclerosis and brain cancer are steadily increasing due to medical progress and the associated increase in life expectancy [51]. To date, curing of the most CNS disorders is mostly limited, as the majority of developed drugs are not able to traverse the BBB and enter the brain. A key challenge in drug development is therefore to design not only therapeutics to treat the disease, but rather treatment strategies allowing CNS-active drugs to cross the BBB. In this proof-of-concept study, the potential of the artificial mini LRP1 receptor mLRP1_DIV*, which harbored an N-terminl myc-tag for cargo attachment and lacking the four functional binding domains, for drug delivery across the BBB was investigated. Importantly, using this artificial mLRP1 construct, higher cargo delivery and reduced risk for systemic side effects is ensured compared to targeting endogenous LRP1 due to the exclusive expression at a certain location and its unique binding site. While expressed in vitro, mLRP1_DIV*’s functionality was analyzed based on internalization assays of antibodies and immunoliposomes that target the receptor as well as an apical to basolateral transport of both across an in vitro model of the BBB. Firstly, mLRP1_DIV* was demonstrated to internalize anti-Myc antibodies with high efficiency compared to control (Supplementary Data 1, Figure S3). To examine a potential transport of anti-Myc antibodies mediated by mLRP1_DIV*, hcMEC/D3 cells were cultured into 24-well transparent inserts, forming a defined luminal (blood) and abluminal (brain) compartment. Immunoblotting of proteins present in the abluminal compartment confirmed a successful transcytosis of 9E10 across the monolayer. Transient expression of mLRP1_DIV* resulted in a 2.5-fold higher transport of 9E10 compared to non-transfected cells or to a transport of unspecific antibodies (mIgG), highlighting the use of mLRP1_DIV* as a transport shuttle (Fig. 2). The functional role of the truncated LRP1 receptor was further investigated using immunoliposomes. Liposomes have been utilized extensively as a drug delivery system to increase medication efficacy and reduce drug-related toxicity or undesirable effects and are highly recommended as a carrier for biologically active ingredients. Importantly, the liposomes’ constituents, i.e. phospholipids and cholesterol render them as biodegradable, immunogenicity-free and chemically inactive, with minimal intrinsic toxicity. However, its lipophilic characteristics and large size prevent a simple diffusion across the BBB with the consequent need for further surface functionalization [52–54]. In this context, as active targeting tool to route any liposomal formulation to its corresponding tissue, antibodies represent the most promising way for active targeting. For this reason, anti-Myc antibodies were fused to the surface of immunoliposomes for actively targeting mLRP1_DIV* at the endothelial surface. Regarding internalization assays and immunofluorescence, the data clearly show a specific mLRP1_DIV* mediated internalization of 9E10 functionalized liposomes and the co-localization to mLRP1_DIV* confirmed their attachment to the receptor even after endocytosis (Supplementary Data 1, Figure S6; S7 A -G). Cells lacking the receptor show only a minor internalization and unmodified liposomes did not show an enhanced internalization by mLRP1_DIV* either (Supplementary Data 1, Figure S6). Endocytosis of unmodified liposomes occurred independently from mLRP1_DIV* (Supplementary Data 1, Figure S7 H - J). The investigation of mLRP1_DIV* in the neurovascular context also demonstrated a specific mLRP1_DIV* mediated transport of only 9E10 functionalized and not unmodified liposomes (Fig. 4). Transient expression of mLRP1_DIV* resulted in a 2.3-fold higher transcytosis of 9E10 functionalized liposomes compared to cells lacking the receptor. Competing or inhibiting transcytosis of 9E10 liposomes across hcMEC/D3 mLRP1_DIV* cells by an application of 9E10 antibodies or an incubation temperature of 4 °C resulted in a 50% reduction in transcytosis compared to control (Fig. 4C). Other studies in this field also reported a facilitated drug delivery into the CNS by conjugating single or multiple ligands to liposomal formulations [55, 56]. The active conjugation of antibodies or endogenous molecules can lead not only to a prolonged half-life of liposomes but also to an increased tissue penetration. In this context, dual conjugation of Angiopep-2, exhibiting high LRP1 binding efficiency, and TAT, providing glioma targeting function, to liposomal formulation (DOX-TAT-Ang-LIP) enhanced brain penetration in vitro. Due to its dual conjugation, DOX-TAT-Ang-LIP was not only transcytosed via LRP1 across the BBB, but also entered glioma cells TAT dependently for their subsequent necrosis due to the release of doxorubicin [57]. As the exact trafficking mechanism as well as the fate of mLRP1_DIV* and its ligands after transport were still elusive, the vesicular trafficking route of 9E10, 9E10 - IL and mLRP1_DIV* during transcytosis was investigated based on immunostainings of endothelial cells using the endocytosis marker Clathrin and Caveolin-1, EEA-1 for the early endosome, Lamp-1 for the lysosome, TfR-1 for recycling endosomes, Rab27a as exocytosis marker and p-catenin as basolateral membrane marker (Figs. 3, 5 and 6 Supplementary Data 1, Figure S4-S5; S8-S13). Obtained results show that unspecific mIgG and unmodified liposomes, which do not bind to mLRP1_DIV*, most likely follow a non-specific pinocytosis from the culture medium. This is accompanied by a lack of co-localization with Clathrin and/or Caveolin-1, membrane molecules that are involved in receptor-mediated endocytosis. In addition, mIgG and unmodified liposomes appear to be partially degraded in lysosomes and partially released back into the extracellular space via exocytosis, however independently from mLRP1_DIV* (Supplementary Data 1, Figure S5, S11-S13). Co-localization of 9E10, 9E10 functionalized liposomes and mLRP1_DIV* with Clathrin and Caveolin-1 shows a specific receptor-mediated endocytosis, and their constant co-localization confirms the specific binding of 9E10 and 9E10 liposomes to mLRP1_DIV during the whole transport. In addition, this binding appears to partially circumvent degradation of 9E10 and 9E10 liposomes in lysosomes (Supplementary Data 1, Figure S4; S8-10; S18). Those findings were further supported, as inhibiting lysosomal activity led to no changes in the transcytosis rate of 9E10 across bEnd.3 cells (Supplementary Data 1, Figure S18). Since mLRP1_DIV* and its ligands are also found in recycling endosomes and exocytosing vesicles, but not in early endosomes, a direct transport of 9E10 and 9E10 liposomes, still bound to mLRP1_DIV* from apical to basolateral membrane across the endothelial cell, followed by their mLRP1_DIV* dependent release via Rab27a in the abluminal compartment is suggested. By inhibiting Rab27a during transcytosis of 9E10 or 9E10 immunoliposomes across hcMEC/D3 or bEnd.3 cells, a 60% reduction of transport of both, 9E10 and 9E10 liposomes has been observed. Together, data clearly show a direct transport of cargo from luminal to abluminal side mediated by mLRP1_DIV* and the Rab27a dependent release on the abluminal side (Figs. 3, 5 and 6; Supplementary Data 1, Figure S8-S10).
Similar studies actively explored antibodies that target receptors at the BBB for receptor mediated transcytosis into the brain parenchyma in more detail. Christensen et al., reported monoclonal antibodies (mAbs) targeting basigin located at brain endothelial cells (BECs) to be sorted into recycling vesicles after internalization and thus avoiding lysosomal degradation [45]. Accompanying, using a unique quantitative mass spectrometry technique, studies from Haqqani et al. monitored the endocytic sorting and transcytosis of several internalizing and BBB-crossing antibody forms in BECs. Thereby, FC5, which interacts with a glycosylated epitope on the luminal side of BEC, was internalized into BEC and dispersed 70%:30% between early and late endosomes, resulting in an efficient release on the BEC monolayer’s abluminal side [58]. However, in present transport studies, only a specific receptor-mediated endocytosis of cargo and their presence in the brain parenchyma in vitro has been reported so far. The vesicular trafficking route of transported cargo and its release at the basolateral surface could not be demonstrated. Notably, our proof-of-concept study clearly highlights mLRP1_DIV*’s role as luminal to basolateral transport carrier of antibodies and liposomes into the CNS and suggest mLRP1_DIV* as promising trojan horse for drug delivery across the BBB. In the past decades, some NSAIDs have been considered for the treatment of AD, due to their ability to modulate γ-secretase activity and Aβ generation, importantly, without interfering with other APP processing pathways or Notch signaling [59]. However, NSAIDs are hampered by low brain penetration and have failed in clinical trials [60–62]. Therefore, mechanisms facilitating drug delivery into the brain, including liposome-based systems have been intensely investigated in the past years [55]. Here, to further explore our concept for improving delivery of therapeutics into the CNS, the capability of mLRP1_DIV* to ferry 9E10 functionalized immunoliposomes loaded with the GSM BB25 across the BBB was investigated. Previous studies had reported that BB25 modulated γ-secretase activity with much higher potency compared to NSAID GSMs such as ibuprofen or flurbiprofen [39]. Consequently, the biological activity of liposomal BB25 was compared to free BB25 after an application to PS70 cells in an initial experiment (Supplementary Data 1, Figure S14). Liposomal BB25 modulated γ-secretase activity in the same way as free BB25 resulting in a 90% reduction of Aß42 levels and a concomitant 1.4-fold increase of Aß38 levels. An application of unloaded liposomes displayed no γ-secretase modulating effect as expected due to the missing embedded GSM (Supplementary Data 1, Figure S14). To show that BB25 loaded 9E10 functionalized liposomes are still intact and more importantly still functional even after a transport across an endothelial monolayer, a co-culture model using bEnd.3 mLRP1_DIV* cells combined with abluminally cultured PS70 cells was established. In this experimental setup, free BB25 did not alter Aß38 or Aß42 levels compared to control, indicating an insufficient diffusion even at high concentrations across the bEnd.3 mLRP1_DIV* monolayer (Fig. 7B and C). In our proof-of-concept study, liposomal BB25 displayed the typical characteristics of a GSM after mLRP1_DIV* dependent transport across bEnd.3 cells, with a decrease of Aß42 and an increase of Aß38 levels. Such an effect could not be demonstrated after application of unloaded 9E10 functionalized liposomes, which were transported with the same efficiency as BB25 loaded 9E10 liposomes (Fig. 7B and C; Table 2). Importantly, the modulation of γ-secretase activity could only be demonstrated after the application of BB25–9E10 - IL to bEnd.3 cells expressing mLRP1_DIV*, whereas the application of BB25–9E10 - IL to empty vector-transfected bEnd.3 cells did not show such an effect (Supplementary Data 1, Figure S16). Consequently, the in vitro co-culture studies clearly demonstrated an mLRP1_DIV* dependent transport of BB25 loaded 9E10 functionalized liposomes, as well as their abluminal release into the brain parenchyma, where they exhibited their indented effect. Importantly, the modulation of γ-secretase activity can be clearly attributed to the mLRP1_DIV* mediated transport of liposomal BB25 and not to differences in the integrity of the BBB between the experimental groups, as TEER and paracellular diffusion of FITC-dextran averaged out at the same level in all groups (Fig. 7D and E). Moreover, liposomal BB25, unloaded liposomes as well as free BB25 did not show any adverse effect on the BBB in vitro, as no changes in endothelial barrier properties could be detected after their application compared to the control condition (Supplementary Data 1, Figure S15). As mentioned above, conjugation of ligands to liposomal formulations facilitate their binding to receptors at the BBB and subsequent transcytosis [55]. In this context, Osthole, a coumarin derivate believed to neutralize Aß induced neurotoxicity through neuroprotective effects, was encapsulated into transferrin functionalized liposomes (Tf-Ost-Lip) [63]. Normally, the drug’s solubility, bioavailability and low BBB permeability restrict its effectiveness. However, corresponding in vitro studies confirm an increase of Osthole into hcMEC/D3 cells followed by an enhanced drug concentration at the BBB, when loaded into Tf-liposomes. Additionally, Tf-Ost-Lip enhanced the accumulation of Ost in the brain and lengthened the cycle time in mice, according to in vivo investigations on pharmacokinetics and the distribution of Ost in brain tissue. Moreover, Tf-Ost-Lip was shown to improve Ost’s ability to reduce pathology associated with Alzheimer’s disease. Based on the overall promising results in vitro, mLRP1_DIV*’s functional role as transport shuttle was further explored in the in vivo situation regarding its relevance for neurodegenerative diseases. To provide an in vivo evidence that LPR1-based receptors can be used as therapeutic strategy, an adeno-associated virus (AAV) that specifically infects only endothelial cells of the BBB was used (Fig. 8). Due to the development of this highly specific AAV, only the BBB associated endothelium was efficiently infected after intravenous injection with AAV-(BR1)-mLRP1_DIV*. In general, mLRP1_DIV* mediated transport of BB25 loaded liposomes as drug delivery mechanism offers decisive advantages compared to current drug delivery strategies as well as to existing AD therapies including Aduhelm, Lecanemab or Donanemab. All are human or humanized IgG monoclonal antibodies against Aß aimed to neutralize Aß levels in the brain and have been shown to slow cognitive and functional decline in early AD [64, 65]. However, clinical trials demonstrated that current amyloid immunotherapies poorly enter the brain parenchyma and linger in the choroid plexus and ventricles five days after infusion. One hypothesis suggests the entry of amyloid immunotherapies at the blood-CSF-barrier (BCSFB) with the consequent accumulation more likely in the CSF rather than the entire brain parenchyma, which explains the moderate effects in clinical trails and their questionable use in the future. Additionally, during several recent Aβ immunotherapy trials, the most frequent and severe adverse event resulting from pathological alterations in the cerebral vasculature was shown to be amyloid-related imaging abnormalities (ARIA), either in form of cerebral edema or microhemorrhages. Currently unknown are the exact physiological and molecular processes via which amyloid immunotherapy amplifies the changes in vascular permeability and microhemorrhages caused by cerebral amyloid angiopathy (CAA) [66]. Most data suggests that due to the entry at the BCSFB anti-Aß antibodies directly interfere with vascular Aß leading to pathological alteration in the vasculature. For this reason, new strategies have already been developed to ensure a drug delivery across the BBB rather than across the BCSFB resulting in a widespread distribution of therapeutic agents across the entire brain parenchyma. In this context, Roche scientist explored alternate delivery methods allowing lower drug dosing with still higher potency. By fusing the anti-amyloid monoclonal antibody gantenerumab to Fc fragments of the human transferrin receptor (TfR-1), extensively expressed at the luminal side of the BBB, researchers achieve an effective CNS delivery and distribution as well as little ARIAs compared to conventional antibody therapies [19]. Although this shuttle system offers an efficient transport of corresponding ligands into the CNS and prevents severe side effects related to pathological vascular alteration, its use may induce other dysfunctional physiological effects. On the one hand, TfR-1 targeting therapeutics compete with endogenous ligands, which may perturb the normal biological functions of the receptor and on the other hand TfR-1 is systemically expressed, thus TfR-1 targeting drugs may induce adverse side effects in other organs and tissues. For this reason, the generation of mLRP1_DIV*, an artificial receptor based on native LRP1 represents an alternative targeting system, which could prevent potential systemic side effects due to its unique binding site and its exclusive expression in the brain endothelium using AAV based gene therapy. Signal transduction is therefore only triggered by corresponding intended exogenous ligands and no natural endogenous ligands compete with the binding to mLRP1_DIV*. Moreover, mLRP1_DIV* targeting CNS-active drugs are only delivered into the CNS and sidesteps other organs and tissues due to mLRP1_DIV*’s specific expression at the BBB. Nevertheless, further in vivo studies regarding the immunogenicity of mLRP1_DIV*, safety of overexpressing an artificial receptor at the BBB and the effect on the expression of other BBB receptors should be conducted. Although, we already showed that an expression of mLRP1_DIV* in endothelial cells had no effect on the protein level of native LRP1 in vitro, the expression of several LDL receptor family proteins should be investigated in the in vivo situation (Supplementary Date 1; Figure S19). Since mLRP1_DIV* is an artificial version of LRP1, with a strongly truncated DIV of LRP1, we assume a high level of safety upon expression. No ligands other than anti-Myc antibodies that normally bind to LRP1, e.g. Aß or tPa have been shown to bind to the receptor (Data not shown). Despite our promising in vitro data of mLRP1_DIV*-mediated delivery of drug-loaded immunoliposomes across an in vitro BBB and its brain endothelial expression in vivo using AAV-based gene therapy, the immunogenicity of this AAV-based gene therapy in vivo should be investigated in the future. Studies have already shown that immunogenicity significantly complicates the safety and efficacy of gene therapies based on AAV vectors and represents an increasing challenge in gene therapy. AAV gene therapies have been associated with mild to severe adverse side effects during clinical development, which has raised strong doubts about the use of such gene therapies. Thereby, humoral and cellular immune response against the viral capsid as well as the transgene protein product remains a serious challenge. The complexity of the immunogenicity of AAV gene therapies arises from the multitude of risk factors associated with their components and the pre-existing immunity of the subjects [67–73]. However, it is not only the immunogenicity of AAV gene therapy that remains a challenge for our new drug delivery mechanism. It is also the use of lipids based nanoparticles (LNPs), especially with regard to pharmacodynamics and pharmacokinetics of water insoluble, poor bioavailable and highly toxic drugs, that should be addressed [74]. The therapeutic potential of nanoparticles as cutting-edge drug delivery technologies that enhance traditional pharmacology has gained widespread recognition throughout the last ten years. LNPs have garnered significant attention in preclinical and clinical research due to their exceptional pharmacological performance and potential therapeutic benefits, among other nanomaterials [75, 76]. Especially, liposomes are superior to typical drug delivery systems, as they allow site-targeting, controlled or prolonged release, protection against drug degradation and clearance, better therapeutic effects, and less harmful side effects due to their biocompatibility, biodegradability, and low immunogenicity. Over the past few decades, a number of liposomal drug products have been approved and successfully used in clinics due to these benefits [29, 76]. Additionally, liposomes can be administered via a variety of routes, such as parenteral, transdermal, pulmonary, ocular, and oral, for both diagnostic and therapeutic purposes [77–80]. However, the chemical and physical stability of liposomes presents significant hurdles. Consequently, the development of liposomes with high stability is crucial since it greatly influences their therapeutic applicability, pharmacodynamics and pharmacokinetics [76, 81]. Despite the many challenges posed by our drug delivery mechanism, including the immunogenicity of AAV-based gene therapies and the hurdles of liposome stability and manufacturing and the resulting dynamics and kinetics of administered drugs, our proof-of-concept study represents a critical first step in the development of new minimally invasive and safe drug delivery strategies. Future in vivo experiments in mice will clarify whether our in vitro drug delivery mechanism can also be transferred to the in vivo situation, in particular with focus on the immunogenicity and safety of AAV gene therapy as well as the efficiency and safety of drug loaded immunoliposomes. Regarding clinical studies on patients, the greatest challenge is already apparent, namely the development of an AAV that specifically infects the human endothelium of the BBB for treatment of CNS disorders.
Conclusion
Our proof-of-concept study verified for the first time the artificial LRP1 mini receptor (mLRP1_DIV*) as auspicious carrier of any cargo into the CNS across an in vitro model of the BBB. It not only provides an endocytosis of cargo into an endothelial cell, but also allows a straight transport of cargo from luminal to abluminal side across an endothelial monolayer and it’s release into brain parenchyma in vitro, where it exhibits its intended therapeutic effect. Further in vivo experiments are needed to clarify the functionality of mLRP1_DIV* mediated delivery of drug loaded liposomes in the physiological context and in clinical applications.
Electronic supplementary material
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Supplementary Material 9 |
Title: Adnexal Masses in Pregnancy: A Single-Centre Prospective Observational Cohort Study | Body:
1. Introduction
Adnexal masses in pregnancy are uncommon, with the incidence ranging from 1 in 76 to 1 in 2328 [1]. Their incidence appears to be rising which is likely to be multifactorial [2]. As ultrasound technology and its availability improves, detection is likely to increase [3]. Additionally, due a steady rise in the age at which women are having their first child, matched with the fact that both benign and malignant adnexal masses are more common with advancing age, the prevalence of adnexal masses in pregnancy is likely to increase [4,5].
The majority are asymptomatic and detected during routine antenatal care [6,7]. Malignancy in this cohort is rare, and the risk of adnexal torsion is 1–6% lower than in non-pregnant women [8,9,10]. As operating during pregnancy is associated with a risk of adverse foetal and maternal outcomes, conservative management is favoured when safe [11].
The assessment of the adnexa during routine antenatal ultrasound is a form of opportunistic screening, for which there is no evidence basis. Ultrasound may be less reliable in pregnancy for many reasons. As the uterus expands, the adnexa may not be visible transvaginal, meaning the transabdominal approach must be employed which is believed to be less accurate [12]. Additionally, alterations in utero-ovarian blood flow may alter Doppler findings [13]. Endometriomas may undergo alterations driven by hormonal changes known as decidualization which mimic malignancy on ultrasound, and rarer masses specific to pregnancy such as luteomas may be detected [14,15,16].
While guidelines exist on the management of adnexal mass in pre- and post-menopausal women, currently none exist for pregnancy, and ultrasound tools such as International Ovarian Tumor Analysis (IOTA) have not been validated in pregnancy [4,17]. With a poor evidence base and a potentially growing incidence, the need to offer evidence-based guidance is paramount [18].
The primary aim of this study was to determine the nature of adnexal masses diagnosed in pregnancy and to monitor their spontaneous resolution, complication, and intervention rate. The secondary aims were to determine if ultrasound by a level II practitioner is a reliable means of assessing this pathology, to assess whether IOTA simple rules are accurate in pregnancy, and when available, to compare histology to ultrasound findings.
2. Materials and Methods
This was a single-centre prospective observational cohort study conducted from January 2019 to August 2021, with approval from the South London Research & Ethics Committee (18/LO/1033). Potential participants were identified either while attending the Early Pregnancy Unit (EPU) for an emergency ultrasound scan in early pregnancy, or by staff performing routine antenatal dating scans. Any pregnant woman (confirmed on ultrasound or by positive urinary or serum human chorionic gonadotropin) over the age of 16 years with at least one adnexal mass (excluding corpora lutea of <30 mm) was eligible [7]. Anyone with a personal history of ovarian malignancy or a Borderline Ovarian Tumor (BOT) was excluded. Patient demographics and medical history were recorded. All participants received study-specific ultrasound assessments of their adnexal mass at the time of detection, at the time of the routine dating scan (11–14 weeks), at the anomaly scan (18–22 weeks), and approximately 6 weeks postpartum. The timing of the ultrasound scans was selected to correspond with critical points in pregnancy management: detection or routine dating scan (around 12 weeks) allowed for early identification of adnexal masses; the anomaly scan (18–22 weeks) enabled further assessment as the pregnancy progresses and the uterus expands, which may impact visibility and mass characteristics; and the postpartum scan at 6 weeks was included to assess the natural resolution of masses after pregnancy when most adnexal masses tend to resolve spontaneously. Scans were performed by level II ultrasound practitioners with appropriate expertise and certification in gynaecology ultrasound [19,20]. All images were subsequently reviewed by level III ultrasound practitioners from the research group (JG or AS), if disagreements or uncertainty occurred, the patient was asked to return for a further scan by a level III practitioner. All ultrasound scans were performed using a Voluson ® E8 or E10 (GE Healthcare Ultrasound, Milwaukee, WI, USA) and findings were documented on Astraia© (Ismaning, Germany). Early pregnancy, 12-week, and postpartum scans were performed transvaginal as the default with conversion to transabdominal as required, using the machine’s ‘Gynecology’ pre-setting. The default for all 20-week scans was a transabdominal approach due to displacement of the adnexa by the expanded uterus and the machine’s ‘obstetric first trimester’ pre-setting was used. The adnexal mass volume (mL) was calculated using the prolate ellipsoid formula (L × H × W × 0.52). A subjective impression based on pattern recognition was assigned and IOTA simple rules features were recorded using a pre-populated proforma on Astraia©, and the resultant impression of ‘benign’, ‘malignant’, or ‘unclassifiable’ was documented [21]. The hospital’s electronic records were accessed for operative notes as well as MRI and histology results. According the consent form, any incomplete data from patients who withdrew or were lost to follow-up were included in the analysis.
Statistical analysis was performed using MedCalc® (MedCalc version 20.010, Ostend, Belgium, 2018) [22]. A power calculation was based on the likelihood of the diagnosis of the least common complication of adnexal masses in pre-menopausal women: malignancy and was determined to be a sample size of 26,616. Statistical significance was determined as a p-value of <0.05. A 95% confidence interval was calculated for incidence rates, paired t-tests were used to assess changes in the adnexal mass volume, and a two-way Chi-squared test was employed to assess the correlation between variables [23,24].
3. Results
During this 31-month period, 13,956 pregnant patients were scanned in the EPU as an emergency, and 14,727 routine 12-week antenatal scans were performed giving a total of 28,683. Adnexal masses were detected in 277 patients, yielding an incidence of 1% (277/28,683). Two patients declined to participate in the study and one was not eligible due to a recent diagnosis of a serous borderline ovarian tumour BOT. As such, 274 patients were included in the analysis. The mean age at diagnosis was 32.03 years (range: 19–45 years). Ethnicity was documented as the following: White in 165/274 (60.2%); Black in 80/274 (29.2%); Asian in 22/274 (8.0%); Arabic in 6/274 (2.2%) and Mixed Race in 1/274 (0.4%). The mean gravida and parity were 1.94 (median: 1.00) and 0.53 (median: 0.00), respectively. The pregnancy was conceived using Assisted Reproductive Techniques (ART) in 15/274 (5.5%) participants. Pregnancy loss (6 miscarriages and 1 termination) occurred in 7/274 (2.6%; incidence rate: [95% CI: 0.01–0.05]) participants, all of which were prior to 12 weekgs gestation—this data was included in the analysis. Symptoms of lower abdominal/pelvic pain or discomfort were reported in 24/274 (8.8%); incidence rate 0.09 (95% CI 0.06–0.13).
Of the 274 women who participated in this study, a unilateral mass was detected in 266/274 (97%) and a bilateral mass in 8/274 (3%). The total adnexal mass count was 282. A total of 21/274 (7.7%; incidence rate 0.77 [95% CI 0.05–0.12]) participants were lost to follow-up, all of whom had a single mass.
An adnexal mass was detected in 114/274 (41.6%) participants during an emergency presentation to the EPU prior to 12 weeks gestation. In 24/114 (21.1%) of these participants, abdominal pain was the reason for presentation, while in 90/114 (78.9%) it was due to vaginal bleeding or hyperemesis. By the time of the 12-week scan, in 17/114 (14.9%) participants, the mass had spontaneously resolved.
At the routine 12-week scan, a mass was detected in 250/274 (91.2%) of participants. These were new masses, detected in asymptomatic participants in 153/250 (61%), while in 97/250 (39%) participants, this was a persistent mass detected during an emergency scan earlier in their pregnancy. By the time of the 20-week scan, in 73/250 (29.2%) participants, the mass had resolved, and 7 participants were lost to follow-up, reducing the denominator to 243.
At the 20-week scan, a mass was detected in 170/243 (70%) of the participants, all of which were believed to be persistent masses detected during the 12-week scan. By the 6-week postpartum scan, in 104/170 (61%) participants, the mass had resolved and 14 were lost to follow-up, further decreasing the denominator to 156.
At the 6-week postpartum scan, a mass was detected in 66/156 (42%) participants. None of these were believed to be new masses, but rather persistent masses detected at the 20-week scan. Overall, in 74% of the participants, the mass spontaneously resolved by the postpartum scan (Figure 1).
The mean volume of the adnexal masses decreased significantly throughout the study. Prior to the 12 weeks, the mean volume was 47.24 cm3 (95% CI: 44.09–50.39 cm3). At the 12-week scan, the mean volume was 32.09 cm3 (95% CI: 28.29–35.88 cm3); a reduction of −15.15 cm3 (95% CI: −18.47 to −11.83; p < 0.0001). At the 20-week scan the mean volume was 22.82 (95% CI: 19.96–26.69 cm3); a reduction of −15.56 cm3 (95% CI: −17.79 to −13.33; p < 0.0001). At the 6-week postpartum scan, the mean volume was 9.94 cm3 (95% CI: 7.68–12.20 cm3); a further reduction of −12.36 cm3 (95% CI: −14.89 to −9.84; p = 0.0001). (Figure 2).
Subjective impression was as follows: simple 208/274 (75.9%); dermoid 25/274 (9.1%); endometrioma 18/274 (6.6%); haemorrhagic 9/274 (3.3%); para-ovarian 7/274 (2.6%); torted simple 2/274 (0.7%); decidualized endometrioma 1/274 (0.4%); fibroma 1/274 (0.4%); theca luteal 1/274 (0.4%); and mucinous BOT 2/274 (0.7%). In the eight patients who had bilateral masses, the subjective impression was the same for both sides. In the 14 cases where histology was available, the subjective impression was correct in 11/14 (79%) and incorrect in 3/14 (21%). During follow-up, there was a change in subjective impression in 14/274 (5.1%) which was not statistically significant (Chi-squared test p = 0.31). The changes were as follows: simple changed to endometrioma (4); simple changed to haemorrhagic (3); haemorrhagic changed to simple (3); endometrioma to haemorrhagic (1); simple to dermoid (1) ovarian changed to para ovarian (1); and para ovarian changed to ovarian (1).
There was a statistically significant correlation between the subjective impression and resolution rate (Chi-Squared test p < 0.0001), with simple cysts being the most common to resolve (Table 1).
A level III gynaecology ultrasound practitioner was asked to review and confirm all the aforementioned cases in which there was a change in subjective impression, the two suspected BOT and one other case. As such, their input was deemed necessary in 18/274 (6.6%) of patients. In all cases, the level III practitioner agreed with the level II practitioner, except for one where the subjective impression was changed from endometrioma to fibroma.
MRI was used in 1/274 (0.4%) participants. Due to this low number, an agreement rate between MRI and ultrasound, and MRI and histology could not be calculated. In this one case, both ultrasound and MRI gave an impression of mucinous borderline ovarian tumour BOT. The subsequent histological diagnosis was a dermoid cyst.
Surgery to remove the adnexal mass was performed in 14/274 (5.1%) participants. There was no statistically significant difference in the timing of surgery (Chi-squared test, p = 0.32): antenatally in 2/14 (14.3%), at the time of caesarean section in 6/14 (42.9%), and postpartum in 6/14 (42.9%). Of the two surgeries performed in the antenatal period, one was a confirmed torsion of a simple cyst and the second was a case with a subjective impression of mucinous BOT but a histological diagnosis of a dermoid cyst. There was no malignancy or BOT diagnosed in the surgical group. A summary of the histological diagnosis is presented in Table 2.
After excluding the seven participants who experienced first-trimester pregnancy losses and the twenty-one participants lost to follow-up, complications due to the adnexal mass occurred in 5/246 (2%) participants. In one case, a simple cyst ruptured and bled leading to significant hemoperitoneum, subsequent intra-abdominal infection, and premature delivery of a live infant at 26 weeks gestation. The participants underwent antenatal surgery as described above, and another two presented with suspected torsion of simple cysts, underwent transabdominal drainage with resolution of symptoms, and had an uncomplicated pregnancy thereafter.
Using IOTA simple rules, 272/274 (99.3%) were classifiable, and 2/274 (0.7% were unclassifiable (p < 0.0001). Only 1/274 (0.4%) had malignant features (p = 0.05). The latter ovarian cyst was a multilocular cyst with increased vascularity (IOTA colour score of 4). The patient had the ovarian cyst removed after delivery and it was a mature cystic teratoma. As there was no malignancy in this cohort, the diagnostic performance of IOTA simple rules could not be calculated.
4. Discussion
This study shows that adnexal masses in pregnancy are uncommon, the majority are incidental findings that will spontaneously resolve; complications are uncommon and malignancy is rare. While the accuracy of IOTA in pregnancy is yet to be established, this study suggests it is likely to be applicable.
There are a number of strengths to this study. At the time of writing, this was the largest prospective study assessing adnexal masses in pregnancy and the second to assess IOTA simple rules in this cohort [25]. The inclusion of women of all ages and ethnic backgrounds as well as those who had conceived as a result of ART should increase the generalizability. The fact that all scans were performed by level II ultrasound practitioners from medical, nursing, and sonography backgrounds, as part of emergency and routine antenatal care, should further increase the generalizability of this paper as it reflects the geographical variations in practitioners performing these scans [26]. Each patient was not followed up by a single operator, which is again reflective of standard practice and may reduce bias. As evidence suggests that pattern recognition has a higher accuracy when performed by more experienced ultrasound practitioners, this study benefited from the involvement of level III gynaecological ultrasound practitioners for masses deemed malignant, unclassifiable, or where there was any form of uncertainty [27].
Limitations of this study include it being single-centred and conducted in a unit with experienced level II and level III gynaecological ultrasound practitioners which may not be the case in other settings. Our sample was heterogenous in that it included both symptomatic and asymptomatic women, however this does mimic clinical practice. Due to the absence of malignancy in this cohort, the performance of IOTA simple rules could not be assessed. Finally, because of the relatively small number of women requiring surgical intervention, the gold standard of histological diagnosis was only available for a few participants, meaning proxy markers of mass resolution/reduction in size had to be used. To overcome these limitations, a prospective multi-centre study is required. Ideally, this would examine unselected, asymptomatic women at fixed points such as 8-, 12- and 20-weeks’ gestation and 6 weeks and 3 months postpartum to give further time for spontaneous resolution.
The incidence rate in this study of 1% was comparable to the results of a large retrospective review [28]. Based on the Royal College of Obstetricians and Gynaecologists (RCOG) nomenclature for discussing risk, patients should be advised that adnexal masses in pregnancy are ‘uncommon’ [29]. The mean age at diagnosis in this study (32.02 years) mirrored that of two recent systematic reviews and also the mean age in the UK for women to have their first child [5,14,16].
The expectation in this cohort was that a large proportion of the masses detected would be physiological cysts that would self-resolve throughout the pregnancy [10]. This was shown to be the case, as the subjective impression was a ‘simple cyst’ in 76% of participants, and of these, 80% resolved. Additionally, when assessing all subjective impressions, in 74% of participants the mass had resolved by the time of the postpartum scan and there was a statistically significant decrease in the mean volume of masses over the course of the study. This information should assist in guiding clinicians when formulating follow-up plans and offering reassurance when counselling patients. The volume of the adnexal masses showed a statistically significant reduction throughout the study. However, it is important to note that the majority of these masses were simple cysts (76%), which are known to resolve spontaneously. Simple cysts accounted for most of the overall volume reduction, while other types of cysts, such as dermoid cysts, are less likely to resolve and tend to persist during pregnancy. Therefore, averaging the volume changes across different cyst types may obscure the distinct behaviours of each. For clarity, we have separated the data for simple and dermoid cysts in the analysis. The resolution rate for simple cysts was 80%, while only 28% of dermoid cysts resolved spontaneously. This distinction is important as it highlights the different natural courses of these cyst types during pregnancy and postpartum. The persistence of dermoid cysts should not be conflated with the spontaneous resolution of simple cysts
The distribution of subjective impressions mimicked that of large previous studies, with simple cysts being the most common, followed by dermoid cysts, endometriomas, and para-ovarian cysts [30]. The fact that a subjective impression was given for all masses in this study is likely a reflection of the scans being performed by experienced level II ultrasound practitioners, with support from level III practitioners.
Subjective impression of adnexal masses is based on pattern recognition and has a quoted accuracy of 92% in non-pregnant women in deciphering benign from malignant masses [31]. There is no data to date comparing this with pregnancy. In this study, histology was available in 14/274 participants. While this number is small, in 11/14 (79%) of cases, the subjective impression was correct. Additionally, the high-resolution rate of simple cysts and perseverance of endometriomas and dermoid cysts in this study suggest subjective impression is reliable during pregnancy.
While the change in subjective impression throughout the study was not statistically significant, it is of interest, as to date there appears to be no data regarding how commonly this occurs either when the same practitioner assesses a mass serially, or when different practitioners assess the same mass. Besides the aforementioned changes in the morphology of endometriomas, no other studies have assessed changes in other histological subtypes during pregnancy. In this study, the most common change was from either a simple to a haemorrhagic cyst or vice versa. This can be explained by spontaneous haemorrhage and resolution within a simple cyst and is commonly seen in clinical practice. A level III ultrasound practitioner was asked to review and confirm all these cases, as well as the two suspected BOT. As such, their input was deemed necessary in 18/274 (6.6%) of patients. In all cases, there was agreement except for one, where the diagnosis of a fibroma was made.
In this study, 99.3% of masses were classifiable based on IOTA simple rules which were higher when compared to studies in the non-pregnant cohort with quoted rates of 76% [21]. The reason for this may be multifactorial. Firstly, in this cohort, simple cysts, endometriomas, and dermoid cysts were most commonly diagnosed and have been shown to be largely classifiable, while malignancy and rarer benign tumours such as luteomas, which were not detected in this study, are more likely to be unclassifiable [21]. Secondly, all practitioners performing the ultrasounds had considerable experience both in gynaecological ultrasound and also in the use of IOTA simple rules, which has been shown to improve rates [27,31]. Thirdly, as most large studies assessing IOTA simple rules were conducted soon after its release in 2008, it would seem likely that with time, clinicians would have become more familiar with it, thus increasing the rates of classification. Supporting this is the fact that smaller, more recent studies of the IOTA simple rules found 89% to 93% of masses in non-pregnant women were classifiable [32,33]. While it was not a primary aim of this study, it was not possible to determine the sensitivity, positive likelihood ratio, negative likelihood ratio, positive predictive value, or accuracy of IOTA simple rules due to the absence of malignancy in our cohort.
As expected, the role of surgical intervention in this cohort was limited, with only two patients undergoing surgery in the antenatal period [7,10]. While one benefited from the detorsion of an ovary and subsequent uncomplicated pregnancy, the second underwent unnecessary intervention for a presumed BOT which was identified as a dermoid on histological examination. The pregnancy continued without complication. In the latter case, MRI findings were consistent with the ultrasound impression of a BOT. Since MRI was only utilized in one case, no conclusions can be drawn about its accuracy. To note, two recent systematic reviews failed to demonstrate the superiority of MRI over ultrasound but instead suggested a propensity for overdiagnosis of malignancy [14,16].
5. Conclusions
We suggest that in the absence of concerns regarding malignancy, torsion, or haemorrhagic rupture, there is no need for further monitoring of adnexal masses during the pregnancy, but rather a transvaginal ultrasound can be offered at least 6 weeks postpartum to give time for resolution and avoid unnecessary intervention. Further work is required to determine the benefit of screening the adnexa during routine antenatal ultrasound and the accuracy of ultrasound tools such as IOTA. |
Title: The role of high mobility group box-1 on the development of diabetes complications: A plausible pharmacological target | Body:
Introduction
Diabetes mellitus (DM) is a metabolic disease characterised by long-lasting hyperglycaemia resulting from defects from insulin secretion, insulin action, or both. Hyperglycaemia places further stress on the β-cells and establishes a negative feedback loop through which metabolic decompensation worsens β-cell failure and insulin resistance. The response of plasma insulin to glucose does not provide information about the health of the β-cell. The β-cell only responds to an increase in plasma glucose concentration with an increase in plasma insulin, and this feedback loop is influenced by the severity of insulin resistance. Thus, β-cell function is best characterized by the insulin secretion/insulin resistance.
1
On average it takes 7 years for a person to be diagnosed with type 2 diabetes (T2D), as symptoms can be mild and may develop gradually. As a result, 30% people with T2D will already have developed complications by the time they are diagnosed. The complications include cardiovascular diseases, retinopathy, nephropathy, and neuropathy.
2
Central to the onset and progression of diabetes complications is uncontrolled hyperglycaemia. From an aetiology perspective, these complications are associated with increases in oxidative stress, low grade inflammation and growth factors expression. Recent developments in diabetes mellitus complications pathogenesis have associated high mobility complex box (HMGB-1) with the onset and progression of diabetes complications, mainly through activation and mediating the expression of inflammatory signalling molecules (refer Figure 1).
3
In this review, we intend to provide and consolidate recent developments on HMGB-1 and diabetes complications pathogenesis. Furthermore, we intend to explore HMGB-1 antagonism as a plausible pharmacological target for the management of diabetes complications. We envisage this review shall be more instrumental in persuading more studies towards exploring HMGB-1 suppression and antagonism in the delaying the onset and progression of diabetes complications.Figure 1.The potential involvement of HMGB-1 in type 2 diabetes is linked to early inflammation occurring in adipose tissue and pancreatic islets, leading to the necrosis of adipose-derived stromal cells and islet cells. When these cells undergo necrosis, they release HMGB-1, which then activates Toll-like receptors (TLRs) and the receptor for advanced glycation end products (RAGE) on macrophages and dendritic cells. This activation of TLRs and RAGE prompts the translocation of NF-κB into the nucleus, where it promotes the expression of inflammatory genes, including HMGB-1 itself. Furthermore, the activated macrophages and dendritic cells actively secrete HMGB-1, thereby exacerbating the necrosis of adipose tissue and pancreatic islets.
HMGB-1 box
Initially recognized for its involvement in gene expression regulation, HMGB-1, a nuclear protein, has recently been implicated in alarming pathogenic activities.
3
This includes its role in activating proinflammatory responses upon passive release from necrotic cells or active secretion by activated immune cells into the extracellular environment.
4
Once released into the extracellular environment, HMGB-1, partakes in various processes, including immune response, cell migration, cell differentiation, proliferation, and tissue regeneration. Notably, HMGB-1 has been associated with numerous inflammatory diseases, including cancer, trauma, arthritis,
5
ischemia-reperfusion injury,
6
sepsis,
7
cardiovascular shock, diabetes, and autoimmune diseases. Furthermore, HMGB-1 has demonstrated its pivotal role as an acute inflammation coordinator across various stress models (refer Figure 1).3,8
HMGB-1, widely distributed across mammalian tissues and prevalent in all vertebrate nuclei, was initially identified as a nuclear protein. Within the nucleus, HMGB-1 binds to DNA and oversees several crucial DNA processes, including gene transcription, DNA replication, and DNA repair to mention a few. For a considerable time, it was exclusively acknowledged as a nuclear protein until 1999 when Wang et al (1999), first reported its involvement as a late inflammatory mediator, linking it to the pathogenesis of sepsis. Subsequently, recognition grew regarding HMGB-1’s significant role in inflammation processes.
9
In the late 1990s, HMGB-1, initially known for its role in regulating gene expression as a non-histone nuclear protein, was rediscovered as an endogenous danger signal molecule.
4
HMGB-1 is released from cells through two mechanisms: passively or actively.
10
When cells are damaged or undergo necrosis, they passively release their nuclear HMGB-1, triggering an immediate inflammatory response mediated by pro-inflammatory cytokines such as tumor necrosis factor α (TNF-α).
11
Cells lacking HMGB-1 show a delayed onset of the inflammatory response, as they are unable to activate monocytes during necrosis, as demonstrated by Scaffidi and colleagues in 2002.
11
Additionally, HMGB-1 can be actively secreted from various cell types, including immune cells, endothelial cells, platelets, neurons, astrocytes, and cancer cells, either in response to stress or as a reinforcement to other damage-associated molecular pattern (DAMP) signals.
10
HMGB-1’s release into the extracellular environment was found to trigger inflammatory responses. This finding coincided with the recognition of similarities in the inflammatory responses contributing to the development of T2D.
8
Studies have shown HMGB-1’s significant role in insulin resistance and diabetes.
Under physiological conditions, HMGB-1 is present in the plasma at relatively low levels. Studies have reported normal plasma concentrations of HMGB-1 in healthy individuals ranging from 0 to 10 ng/mL.
12
In individuals with T2D, HMGB-1 levels are significantly elevated compared to healthy controls.
13
This elevation is associated with chronic inflammation, oxidative stress, and insulin resistance characteristic of T2D.
14
Research indicates that plasma HMGB-1 levels in patients with T2D can be higher than 10 ng/mL, often reaching up to 20 ng/mL or more.
15
Elevated HMGB-1 levels in these patients are correlated with poor glycaemic control and complications such as cardiovascular disease.
16
Similarly, obesity, which is often linked to T2D, also shows increased levels of HMGB-1 in the plasma.
13
Obesity is characterized by a state of chronic low-grade inflammation, and HMGB-1 is thought to contribute to this inflammatory milieu.
17
Plasma HMGB-1 levels in obese individuals are often elevated, with studies showing increases comparable to those seen in T2D patients.18,19 The levels in obese individuals can vary but typically range between 5 and 15 depending on the severity of obesity and associated metabolic disturbances.
18
It is worth noting that the concentration of HMGB-1 in the serum can serve as an indicator of glucose toxicity, atherosclerosis, and diminished β-cell function in individuals with diabetes.
20
There is a positive correlation between serum HMGB-1 levels and glucose metabolism indicators such as HbA1c and fasting plasma glucose (FPG).
20
HMGB-1 enhances the expression of ATG7 and the LC3B II/I ratio, decreases p62 expression, and promotes autophagy as studied by Zhang et al.
21
Furthermore, serum HMGB-1 levels show a positive correlation with fasting insulin (FINS) and homeostasis model assessment-insulin resistance (HOMA-IR), and a negative correlation with HOMA-β, indicating that HMGB-1 might impair β-cell function and heighten insulin resistance.
20
HMGB-1 contributes to insulin resistance by upregulating receptor for advanced glycation end-products (RAGE) expression, activating the TLR4/JNK/NF-κB pathway, and inhibiting the IRS-1 signaling pathway.22,23
The association between diabetes and HMGB-1
Insulin resistance denotes a diminished ability of insulin to facilitate glucose uptake by the target tissues. Essentially, it signifies a condition wherein cells inadequately respond to circulating insulin.
24
Peripheral tissues such as the liver, skeletal muscle, and adipose tissue can all serve as sites for insulin resistance.
25
Research has shown that infiltration of macrophages into adipose tissue correlates with elevated serum insulin concentration, suggesting that macrophage-mediated inflammatory responses may significantly contribute to insulin resistance.
26
Recent research indicates a close correlation between diabetes and HMGB-1.
An increase NF-κB activity has been observed in obese animal models, and blocking NF-κB has been shown to protect mice on a high-fat diet from insulin resistance.
27
Activation of the NF-κB signalling pathway can be initiated by pattern recognition receptors like toll-like receptors (TLRs) and the RAGE, both of which interact with HMGB-1.
8
This suggests that HMGB-1 might play a crucial role in insulin resistance through NF-κB signalling (refer Figure 2).
28
Several human studies in obese or T2D individuals have reported elevated circulating levels of HMGB-1, which positively correlate with insulin resistance as measured by the HOMA-IR.
28
Figure 2.HMGB-1, a pro-inflammatory molecule, plays a significant role in the development of insulin resistance, a key factor in diabetes complications. It contributes to this condition by activating inflammatory pathways, notably NF-κB and JNK. Activation of these pathways leads to the disruption of insulin receptor substrate (IRS) function, which is crucial for the proper signalling of insulin. When IRS function is impaired, the insulin signalling pathway is compromised, resulting in decreased expression of glucose transporter 4 (GLUT4) on the cell surface. GLUT4 is essential for the uptake of glucose into cells, and its reduced expression leads to decreased glucose uptake and elevated blood glucose levels. Additionally, HMGB-1 activation increases the production of inflammatory cytokines, further exacerbating insulin resistance and inflammation. This combination of impaired insulin signalling and increased inflammation contributes to the overall metabolic dysfunction seen in diabetes and its complications, such as cardiovascular disease, neuropathy, and nephropathy.
Additionally, Dasu et al (2010),
29
found higher circulating levels of HMGB-1 in T2D patients compared to controls, a phenomenon also supported by Škrha et al (2012).
30
Hagiwara et al (2008), demonstrated that hyperglycaemia induced by glucose infusion in a rat model was linked to elevated serum HMGB-1 levels.
30
Further research by Dasu and colleagues (2010), revealed elevated levels of HMGB-1 in individuals with T2D, positively correlating with TLR2 and TLR4, body mass index (BMI), and HOMA-IR.
29
Additionally, the expression of MyD88 and NF-κB p65 was increased. The activation of TLR-MyD88-NF-κB signalling resulted in elevated cytokine levels.
28
Subsequently, Chen et al. found significantly upregulated expression of HMGB-1, NF-κB, TNF-α, and vascular endothelial growth factor (VEGF) in T2D retinas and in cells treated with high glucose concentration. HMGB-1 blockade significantly attenuated NF-κB activity and VEGF secretion in these cells.
31
Further studies by Chen et al (2013), indicated that HMGB-1 was significantly upregulated by high blood glucose concentration via NF-κB signalling, associated with increased expression of proinflammatory cytokines.
32
HMGB-1 inhibition reduced the upregulation of proinflammatory cytokines in response to high blood glucose concentration. Therefore, HMGB-1 may play a role in the development of insulin resistance by activating NF-κB signalling and contributing to the elevated expression of proinflammatory mediators (refer Figure 2).28,32 HMGB-1 is believed to have a significant impact on insulin resistance by influencing NF-κB signalling.
28
Following these reports, Chen and colleagues (2013), discovered that HMGB-1 expression, along with NF-κB and TNF-α/VEGF concentrations, were significantly elevated in the retinas of T2D patients and in ARPE-19 cells treated with high glucose concentration.
33
Furthermore, blocking HMGB-1 resulted in reduced NF-κB activity and VEGF secretion in high glucose-stimulated ARPE-19 cells.
28
Moreover, another study by Chen and colleagues (2015), demonstrated that high glucose upregulated HMGB-1 expression via NF-κB signalling both in vivo and in vitro, leading to increased levels of proinflammatory cytokines.
34
Inhibition of HMGB-1 reduced the high glucose-induced upregulation of proinflammatory cytokines. Therefore, these findings suggest that HMGB-1 may contribute to the development of insulin resistance by activating NF-κB signalling and promoting the expression of proinflammatory mediators (refer Figure 2).28,35
Cardiovascular complications: Implications of HMGB-1
Cardiovascular complications associated with diabetes mellitus include atherosclerosis, hypertension, cerebrovascular disease, and coronary heart disease where arteries and veins are affected (refer Figure 3).15,36Figure 3.The HMGB-1 system influences various complications of diabetes, including coronary artery disease (CAD), cerebrovascular disease (CVD), diabetic retinopathy (DR), chronic kidney disease (CKD), diabetic neuropathy (DN), and peripheral artery disease (PAD). HMGB-1 affects these conditions by interacting with Toll-Like Receptors (TLRs), the Receptor for Advanced Glycation End Products (RAGE), and other pathways, leading to the production of inflammatory cytokines such as interleukin 1β (IL1β), interleukin 6 (IL6), and Tumor Necrosis Factor-α (TNFα). Additionally, HMGB-1 activates nuclear factor kappa-light-chain-enhancer of activated B cells (NFkB) and promotes the generation of reactive oxygen species (ROS), further exacerbating inflammation. This inflammatory response contributes to the pathogenesis of various diabetic complications by promoting endothelial dysfunction, increasing the expression of adhesion molecules such as Intercellular Adhesion Molecule 1 (ICAM-1) and Vascular Cell Adhesion Molecule 1 (VCAM-1), inducing the secretion of Monocyte Chemoattractant Protein 1 (MCP-1), and enhancing the activity of Interferon γ (IFNγ) and Lipopolysaccharide (LPS).
Vascular effects
The central pathological mechanism in cardiovascular disease is the process of atherosclerosis which leads to narrowing of arteries hence increased blood pressure.
2
The risk of atherosclerosis is increased by the elevated circulating triglycerides and low-density lipoprotein (LDL) and decreased high density lipoprotein (HDL).
37
Hypertension in diabetes is attributed to both haemodynamic and metabolic disturbances.38,39 Glucose homeostatic disturbances have been reported to cause endothelium dysfunction through various mechanisms. The endothelial dysfunction is associated with decreased nitric oxide (NO) and prostacyclin concentration and increased endothelin-1 concentration.
40
Endothelin-1 is a well-known vasoconstrictor which also increases the expression of adhesion molecules such as intercellular adhesion molecules (ICAM) and vascular adhesion molecules (VCAM) which are implicated in cell to cell interaction, resulting in arterial stiffening (refer Figure 3).15,
41
Chronic hypertension has been shown to be associated with diastolic dysfunction, cardiac muscle hypertrophy and cardiomyopathy due to increased cardiac workload.
42
There are various markers which have been shown to predict the risk of cardiomyopathy. Pro-inflammatory cytokines such as TNF-α and the interleukin 6 (IL-6) family including cardiotropin-1 and C reactive protein (CRP) are highly expressed in cardiac failure.
43
Similar to other macrovascular complications of diabetes, the development of diabetic cerebrovascular disease (CVD) is associated with various mechanisms including vascular endothelial dysfunction, increased arterial stiffness, and systemic inflammation. These elements contribute both to the atherosclerosis of cerebral vessels and to worse outcomes following an acute event.44,45
HMGB-1 has been implicated in the pathogenesis and progression of cardiovascular complications. HMGB-1 can be released passively from damaged pancreatic β cells or actively secreted by dendritic cells (DCs) and macrophages that infiltrate the islets. HMGB-1 is reported to be involved in the autoimmune response that leads to the destruction of pancreatic β cells in type 1 diabetes (T1D).
14
It acts as a pro-inflammatory cytokine and a damage-associated molecular pattern (DAMP), contributing to the chronic inflammatory environment observed in T1D.
46
In T1D, extracellular HMGB-1 exacerbates autoimmune progression by disrupting regulatory T cells.
47
Individuals with T2D exhibit a low-grade systemic inflammatory condition, where HMGB-1, known as a late-stage inflammation mediator, plays a significant role in the development of T2D.
48
A study by Zhang et al (2014), illustrated that metformin protected against hyperglycaemia-induced cardiomyocyte injury by suppressing the expression of RAGE and HMGB-1.
49
Currently, there is limited evidence on the role of HMGB-1 in diabetic CVD (refer Figure 3). Nonetheless, similar to other diabetic complications, HMGB-1 might play a crucial role in diabetic stroke.50,51 In streptozotocin (STZ) induced diabetic rats, middle cerebral artery occlusion led to elevated serum levels of HMGB-1 and matrix metallopeptidase-9 (MMP-9), and increased expression of MMP-9, RAGE, and TLR-4 in brain tissue, especially in microglia was observed.
51
A study by Ye and colleagues (2011) discussed treatment with niaspan, a slow-release form of niacin, reduced levels of HMGB-1, MMP-9, RAGE, and TLR-4.
51
Niacin has a protective effect after cerebrovascular injury, reducing inflammation and enhancing endothelial function. It also increases levels of angiopoietin-1, which is involved in neuronal differentiation, vascular remodelling, and endothelial cell survival.51,52 Thus, niaspan could be a potential treatment to improve neurological outcomes after stroke.
Additionally, a study by Hu and colleagues (2016) found that intravenous injection of bone marrow stromal cells into T2D rats with experimentally induced middle artery occlusion reduced HMGB-1 and RAGE expression in the ischemic brain, improved functional recovery, and decreased blood–brain barrier leakage by increasing desmin and zonula occludens-1 (ZO-1) levels.
50
These findings contrast with a previous study on T1D rats with middle artery occlusion, where Chen and colleagues (2011) observed that bone marrow stromal cell treatment decreased survival rates, increased blood–brain barrier leakage, brain haemorrhage, and vascular density, and promoted atherosclerosis by increasing intima thickness and collagen production in the internal carotid artery.
53
Due to these conflicting results, further research is needed to clarify the role of bone marrow stromal cells in diabetic stroke as a potential therapeutic strategy for diabetic CVD.
Myocardial effects
Current research suggests a close association between diabetic myocardial ischemia/reperfusion injury, oxidative stress, reactive oxygen species (ROS) elevation, and mitochondrial dysfunction.
54
Oxidative-reductive reactions can trigger HMGB-1 translocation, leading to sustained activation of proinflammatory pathways and exacerbating myocardial injury via binding with RAGE.
55
Some experts consider diabetic heart disease as primarily a mitochondrial disorder. HMGB-1 plays a crucial role in regulating mitochondrial autophagy.
49
HMGB-1 also modulates heat shock protein beta 1 (HSPB1) to maintain mitochondrial morphology and control mitochondrial autophagy, thus contributing to myocardial remodelling following diabetic ischemia/reperfusion.
55
However, excessive autophagy during ischemia/reperfusion in diabetic cardiomyopathy can worsen myocardial injury.
56
Studies indicate that reducing HMGB-1 expression in diabetic rats can decrease infarct volume, enhance haemodynamics, and alleviate inflammation. Inhibition of HMGB-1 can mitigate myocardial ischemia/reperfusion injury by suppressing autophagy.
57
Wu and colleagues (2017), demonstrated that HMGB-1 promotes myocardial ischemia/reperfusion injury in diabetic mice through mediation of mitochondrial autophagy.
54
In both in vitro and in vivo settings, hyperglycaemia induces HMGB-1 ribonucleic acid (RNA) expression and increases HMGB-1 protein levels in myocardial cells and fibroblasts.
58
Volz and colleagues (2010), reported diabetic mice with post-myocardial infarction remodelling exhibit elevated HMGB-1 levels, leading to enhanced inflammation and fibrosis. Knockdown of HMGB-1 and RAGE genes in these mice reduces inflammation and infarct size.
55
Additionally, mice lacking RAGE show decreased HMGB-1 expression and reduced NF-κB activity in the heart, indicating that HMGB-1 action in myocardial cells is primarily mediated by RAGE and NF-Kb.
55
In diabetic mice, HMGB-1 contributes to cardiomyocyte apoptosis through activation of the extracellular signal-regulated kinase 1 (ERK1) ERK/Ets-1 pathway, which regulates cell growth, proliferation, and apoptosis. Inhibition of HMGB-1 with HMGB-1siRNA reduces ERK and Ets-1 phosphorylation induced by hyperglycaemia.
59
Furthermore, HMGB-1 is implicated in myocardial fibrosis.
29
Wang and colleagues (2014), demonstrated that HMGB-1 increases TGF-β1 levels in cardiac fibroblasts, enhancing MMP activity, collagen I and collagen III expression. The inhibition of HMGB-1 reduces signalling through p38MAPK, ERK1/2, and JNK, crucial pathways in cardiac hypertrophy, fibrosis, and cytokine-mediated inflammation.
60
Song and colleagues (2016), found cardiac expression of HMGB-1 in a hyperglycaemic environment is mediated by the PI3Kgamma/Akt pathway, and treatment with an antioxidant prevents PI3Kgamma/Akt signalling and HMGB-1 production, suggesting a potential therapeutic target for diabetic cardiomyopathy.
61
Moreover, Zhang and colleagues (2010), demonstrated in diabetic mice that treatment with resveratrol induces a cardioprotective effect by reducing HMGB-1 expression and downregulating RAGE, TLR-4, and NF-κB signalling.
62
Resveratrol also reduces oxidative stress, ameliorates myocardial fibrosis and inflammation, and decreases TNF-α and iNOS levels, highlighting its potential as a treatment strategy for cardiac dysfunction.
62
Neutralizing antibodies against HMGB-1 effectively suppress HMGB-1 release from the heart, lower the LC3-II/I ratio, attenuate mitochondrial autophagy, and alleviate myocardial injury in diabetic mice.
63
Therefore, anti-HMGB-1 therapy emerges as a promising approach to mitigate HMGB-1-induced inflammatory damage, suppress autophagy, and improve patient outcomes.
54
Diabetic nephropathy: implications of HMGB-1
Renal complications are amongst the leading causes of mortality and morbidity for both type 1 diabetes (T1D) and T2D and most patients experience the condition before they are diagnosed.
64
Renal complications are precipitated directly or indirectly through four main molecular pathways which involve advance glycated end products (AGEs), the polyol pathway, protein kinase C (PKC) and hexosamine pathways.
65
These pathways result in increased oxidative stress which ultimately influences and activates the renin angiotensin aldosterone system.
66
The clinical hallmarks of renal complications include increased albumin excretion and a decrease in the glomerular filtration rate (GFR), both of which are associated with high blood pressure and electrolyte handling disturbances.
67
Albuminuria doesn’t only predict renal disease but also serves as an independent cardiovascular risk factor. 66,68 These functional changes in the kidney occur as a consequence of morphological changes which include the thickening of the glomerular basement membrane, mesangial cell expansion and loss of podocyte, extracellular matrix deposition, glomerulosclerosis as well as tubular-interstitial fibrosis.69–71 These morphological changes are as a result of increased inflammation (TNF-α, IL-2) and expression of growth factors such as transforming growth factor (TGF) and vascular endothelial growth factor.
72
Various studies have indicated elevated levels of HMGB-1 in diabetic nephropathy (DN), suggesting its involvement in the development of this condition. For instance, Kim and colleagues (2011), observed heightened expression of HMGB-1 in renal glomerular and tubular cells of diabetic rats, particularly in comparison to normal controls.
73
Additionally, diabetic rats exhibited increased levels of RAGE and NF-κB, which are implicated in kidney injury by regulating various mediators such as TNF-α, IL-6, IL-1, ICAM-1, and GM-CSF (refer Figure 3).15,73
Other research has also shown elevated levels of TLRs, HMGB-1, and cytokines like TNF-α, IL-6, IL-1β, TGF-β1, ICAM-1, and MCP-1 in DN.74–77 Specifically, Lin and colleagues (2012), demonstrated increased expression of TLR-4 and HMGB-1 in renal tubules of individuals with diabetic nephropathy, with TLR-4 levels correlating with interstitial macrophage infiltration and glycosylated haemoglobin.
78
Furthermore, hyperglycaemia induced TLR-4 expression via PKC, leading to elevated levels of IL-6 and CCL-2 through the IkB/NF-κB pathway. Knockdown of TLR gene in mice resulted in reduced IL-6 and CCL-2 levels, thus exerting a protective effect on the kidney.
78
In contrast, Mudaliar and colleagues (2013), showed that high glucose concentrations exposure increased levels of both TLR-2 and HMGB-1, with HMGB-1 stimulating the NF-κB pathway. Silencing of TLR-2 interrupted NF-κB nuclear expression and HMGB-1-induced NF-κB-DNA binding.
74
HMGB-1 is also believed to be implicated in diabetic nephropathy development through its modulation of autophagy.
79
The Inhibition of HMGB-1 reduces apoptosis and injury in podocytes, delaying glomerular function deterioration caused by diabetes through the activation of Akt/mTOR signaling pathway and inhibition of autophagy.
80
Interestingly, Zhang and colleagues (2017), demonstrated that glycyrrhizic acid (GL), an HMGB-1 inhibitor, reduced expression of HMGB-1, RAGE, TLR-4, and activation of ERK, p38 MAPK, and NF-κB pathways in kidney tissue of diabetic rats.
77
GL also decreased serum and kidney levels of TNF-α, IL-6, IL-1β, MCP-1, ICAM-1, and TGF-β1.
77
Moreover, a recent study by Jigheh et al. indicated that empaglifozin reduced renal levels of HMGB-1, RAGE, and TLR-4, thereby alleviating renal inflammation, suggesting a potential therapeutic approach for the disease.
81
Diabetic retinopathy: implications of HMGB-1
Diabetic retinopathy (DR) is the leading cause of visual disability and blindness in people with diabetes.
82
Diabetic retinopathy is characterised by retinal vessel micro aneurysms, haemorrhages, and oedema.
2
One of the primary changes in diabetic retinopathy involves loss of pericytes in retinal capillaries, which may lead to vascular failure and chronic hypoxia.
83
Hypoxia is one of the major inducers of angiogenesis.
36
Hypoxic conditions lead to the upregulation of hypoxia- inducible factor (HIF) and VEGF, which then promote the rapid formation of neovessels, resulting in exacerbated angiogenesis.
84
The sudden establishment of angiogenic vessels leads to leakiness and malfunctioning of vascular system.
36
Vitreous hemorrhage is also observed in DR, due to leaking of newly formed blood vessels, and jell-like substance fills the centre of the eye, thus impairing vision.
85
Retinal detachment is also observed in DR, due to abnormal new blood vessels which promote scar tissue growth, the later pulls the retina away, ultimately causing spot floating vision.
86
Moreover, leaking and new growth of blood vessels interrupt the fluid flow in the eyes and this causes the pressure to accumulate, in severe cases this damages the optic nerve which leads to permanent blindness.
HMGB-1 plays a crucial role in causing inflammation in the retina. It acts as a receptor for danger-associated protein patterns and can detect elevated glycemic levels as a stress signal evident in T1D and T2D.
87
Patients with advanced diabetic retinopathy often show elevated levels of HMGB-1. HMGB-1 exerts its pro-inflammatory effects in retinal cells through binding to TLR-4
88
and RAGE, as well as activation of ERK1/2 and NF-kB signaling pathways (refer Figure 3).15,89 Intravitreal administration of HMGB-1 enhances these pathways and downregulates TLR-2 and occludin expression, increasing retinal vaso-permeability.
89
Additionally, HMGB-1 seems to inhibit insulin signaling in retinal cells via its link to RAGE and TLR-4.
88
However, conflicting findings exist regarding HMGB-1’s direct role in retinal and choroidal neovascularization.90,91 While some studies suggest direct mediation of endothelial cells by HMGB-1, others propose that HMGB-1 induces pericyte death, responsible for vasopermeability and endothelial proliferation, through TLR-4-dependent production of reactive oxygen species and cytokines by glial cells.
90
Notably, subretinal injection of HMGB-1 in rats did not induce neovascularization or modify expression of VEGF-A in glial cells.
91
Furthermore, HMGB-1 promotes oxidative stress in the retina, inducing ROS-derived apoptosis in retinal cells. The administration of glycyrrhizic acid inhibits this effect. In diabetic rats, treatment with Polygonum cuspidatum extract i.e. a potent antioxidant known for its potential health benefits. Polygonum cuspidatum reduces HMGB-1, RAGE, and NF-κB expression, improving vascular retinal permeability and inhibiting tight junction leakage.
92
Intravitreal injection of HMGB-1 siRNA in rats reduces retinal damage and cellular death, improving retinal function. In human retinal endothelial cells treated with high glucose, HMGB-1 siRNA reduces oxidative stress and cellular apoptosis. Additionally, exosomes derived from mesenchymal stem cells overexpressing miRNA-126 suppress HMGB-1 expression and NF-κB and NLRP3 inflammasome activity in human retinal endothelial cells.
93
Lastly, protein kinase A (PKA) appears to inhibit cytoplasmic HMGB-1.
94
Recent studies indicate an association between autophagy and the mechanisms underlying pathological neovascularization and neurodegeneration mediated by HMGB-1, thus influencing the development and progression of DR.
95
The role of autophagy in DR is multifaceted. While autophagy may promote cell survival in the early stages of DR, excessive autophagy can lead to necrosis and exacerbate the condition. Intermittent hyperglycemic oxidative stress can modulate autophagy in retinal pigment epithelial cells, promoting cell survival by upregulating HMGB-1.
96
HMGB-1 can translocate to the lysosome via the autophagy-lysosome pathway, leading to the release of lysosomal enzyme B into the cytoplasm, thereby inducing inflammation and apoptosis.
97
Feng et al. demonstrated that HMGB-1 participates in lysosomal membrane penetration (LMP) and autophagy inhibition in retinal pigment epithelial (RPE) cells. Decreased HMGB-1 expression restored autophagic degradation, reduced inflammatory cytokine and VEGF expression, and protected RPE cells during the early stages of DR.
98
Diabetic neuropathy: Implications of HMGB-1
In clinical practice, diabetic neuropathy (DNE) is defined as signs and symptoms of peripheral nerve dysfunction in a diabetic patient where other causes of peripheral nerve dysfunction have been excluded.
64
DNE has caused more hospitalization of diabetes mellitus patients than other complications, with half of the patients having some degree of the disease such as polyneuropathy and mononeuropathy.
99
The prevalence of DNE has been found to be 66% and 59% for T1D and T2D, respectively.
100
This complication is heterogeneous by symptoms and signs, risk factors, underlying mechanism and pathologic alterations. Mono and polyneuropathies, plexopathies and radiculopathies are observed in DNE.
101
Autonomic neuropathy has been considered irreversible and life threatening since there is high risk of mortality. However, some studies have shown that cardiac denervation regresses with high glycaemic control.
102
Pachydaki et al (2006)
103
and Yu et al (2015), noted increased expression of HMGB-1 in the retinas of individuals with diabetes and rat models with retinopathy.
104
As a proinflammatory mediator, HMGB-1 is involved in diabetic neuropathy by interacting with RAGE and TLR4 and regulating autophagy. Elevated HMGB-1 levels stimulate glutamate release and mediate neurotoxicity.
7
Neuronal cells release HMGB-1 during seizures, accompanied by increased TLR4 expression.
105
Guo and colleagues (2019), observed increased HMGB-1 and TLR4 protein expression levels and exacerbated neuronal apoptosis in KKAy mice subjected to intermittent hypoxia to induce diabetic neuropathy. Furthermore, HMGB-1 siRNA significantly reduced HMGB-1 and TLR4 protein expression, regulating autophagy, and reducing neuronal apoptosis.
106
Several studies have explored the connection between HMGB-1 and DNE, building upon previous research highlighting HMGB-1’s involvement in the nervous system. For instance, HMGB-1 has been implicated in central ischemic damage, where it is released into the extracellular space following ischemic insult, promoting neuroinflammation (refer Figure 3).15,107 Inhibition of HMGB-1 expression has been shown to reduce infarct size and microglia activation.
107
Additionally, in rat models, HMGB-1 promotes pain hypersensitivity after peripheral nerve injury, likely through RAGE activation, while treatment with anti-HMGB-1 antibodies alleviates hyperalgesia. Nerve injury also upregulates HMGB-1 mRNA expression in dorsal root ganglia (DRG) and spinal nerves.
108
Furthermore, Zhao and colleagues (2016), demonstrated that calmodulin-dependent protein kinase IV (CaMKIV), a protein kinase involved in neuropathic pain, and HMGB-1 are upregulated in the dorsal root ganglia of rats treated with STZ, a substance used to induce diabetes and neuropathic pain.
109
Inhibition of phosphorylated CaMKIV (pCaMKIV) decreases HMGB-1 levels and reduces thermal hyperalgesia and mechanical allodynia in diabetic rats, confirming the role of HMGB-1 in neuropathic pain.
110
The expression of HMGB-1 in neuropathic pain also appears to be correlated with the Sigma-1 receptor (Sigma-1R), a receptor involved in nociception. STZ treatment induces expression of Sigma-1R and HMGB-1 in DRG, resulting in increased tactile allodynia and thermal hyperalgesia in rat models.
111
Conversely, knockdown of Sigma-1R in rats shows modest tactile allodynia and thermal hyperalgesia in the absence of increased cytoplasmic levels of HMGB-1, suggesting a role of Sigma-1R in promoting HMGB-1 expression.
111
Furthermore, the relationship between HMGB-1 and neuropathic changes induced by hyperglycaemia has been investigated in retinal neuropathy.112,113 Diabetes mellitus is associated with the activation of HMGB-1, activation of the ERK1/2 pathway, activation of cleaved caspase-3 (an apoptosis executer enzyme), and glutamate signalling pathways in rat retinas. Diabetic retinas also exhibit decreased levels of Glyoxalase-1 (GLO1), an enzyme important for detoxifying AGEs.
Targeting HMGB-1: A promising approach
The understanding of HMGB-1 signalling and involvement in the onset and progression of diabetic complications could open further avenues for the prevention of diabetic complications. Despite tight glycaemic control afforded by antihyperglycaemics, diabetes complications inevitably develop, which underscore the necessity for extra-glycaemic protective agents. Aminoguanidine an AGEs formation inhibitor, failed in clinical trials primarily due to safety concerns and lack of efficacy. It’s therefore prudent to search for target that could yield similar protective effects. As we have highlighted above, HMGB-1 antagonism could protect against inflammation, oxidative stress, and AGEs. From a pharmacology perspective, HMGB-1 present yet another approach to prevent and delay the onset of diabetic complications which of often manifests despite glycaemic control. Metformin, a primary antidiabetic medication, is known to possess anti-inflammatory properties. Tsoyi et al (2011), showed that metformin significantly reduced HMGB-1 expression in LPS-treated RAW264.7 cells.
113
Glycyrrhizin (GL) is a natural compound found in the liquorice plant, Glycyrrhiza glabra. It has been shown to directly bind directly to and inhibit HMGB-1, as reported by Mollica et al. (2007).
114
GL and its derivatives exhibit significant anti-diabetic effects in diabetes mellitus and its associated complications.
115
These effects include reducing blood glucose and insulin concentrations, improving insulin resistance and glucose tolerance, regulating lipid metabolism, and enhancing insulin secretion.
115
However, GL’s bioavailability is limited, prompting the development of formulations such as nanoparticles or conjugation with various metals. GL-loaded nanoparticles have demonstrated efficacy in lowering blood glucose levels and improving lipid profiles.
115
Notably, the dosages required when using nanoparticles are only one-quarter of those needed for pure GL, making nanoparticles a more potent option for GL delivery.
116
Other HMGB-1 inhibitors include those that prevent the translocation of HMGB-1 from the cytoplasm and render its unavailability for extracellular proteins. Amongst these include ethyl pyruvate which has further been shown to inhibit RAGEs. Indeed, a recent study Jung et al., demonstrated that ethyl pyruvate prevents renal damage induced by methylglyoxal-derived advanced glycation end products.
116
Wang et al recently demonstrated that ethyl pyruvate can reduce the inflammatory response after diabetic intracerebral haemorrhage and may inhibit the activation of inflammasomes by the HMGB-1/TLR4 pathway.
117
Gabexate mesylate, a synthetic protease inhibitor is believed to inhibit plasminogen activator inhibitor-1 (PAI-1), and protease-activated receptor-2 (PAR-2), consequently, indirectly inhibiting HMGB-1 and mitigating tissue damage and/or diabetic complications.
118
Other promising HMGB-1 inhibitors that could potential be considered for diabetes complications management include triptolide and diflunisal which have been shown to attenuate inflammation associated with HMGB-1.
119
With a clear HMGB-1 understanding, we envisage it to be more appealing for drug discovery workers, to design and explore more potential HMGB-1 inhibitors.
120
Conclusion
HMGB-1, a chromosomal protein expressed widely and preserved throughout evolution, plays various roles in inflammation regulation. Mounting evidence suggests that HMGB-1 plays a crucial role in both the initiation and progression of diabetes. Elevated levels of HMGB-1 have been detected in the serum, islets, and other tissues such as adipose, liver, and muscle in individuals with diabetes and animal models. Moreover, diabetes is associated with increased expression levels of receptors like RAGE and TLRs, which play pivotal roles in triggering proinflammatory cytokines. The functional interplay between HMGB-1, RAGE, and TLRs exacerbates inflammation in T2D, including inflammation induced by obesity, insulin resistance, and islet inflammation. Thus, inhibiting HMGB-1 and its receptors emerges as a promising therapeutic strategy for managing inflammation in T2D. |
Title: A Wireless Artificial Mechanoreceptor in 180-nm CMOS | Body: |
Title: Impact of Bacterial Etiology on Procalcitonin, C-reactive Protein and Hematological Parameters: Evaluating Mean Platelet Volume for Differentiating Gram-Negative and Gram-Positive Bacteria in Odontogenic Versus Non-odontogenic Head and Neck Abscesses | Body:
Introduction
An abscess, characterized by inflammation, is a localized accumulation of pus. Abscesses in the head and neck region are common yet potentially serious infections that can arise from a variety of causes, broadly categorized into odontogenic (originating from dental structures) and non-odontogenic (arising from other sources) [1,2]. Non-odontogenic abscesses usually arise from an infection that enters the soft tissues through various injuries to the oral mucosa or the skin in the head and neck region [3]. To identify an abscess as odontogenic, it is crucial to determine the presence of a causative tooth from which the inflammatory process begins, often referred to as the gateway of infection [1]. When the dental pulp chamber is exposed or opened, the root canals can become colonized by various strains of aerobic or anaerobic microorganisms [1]. These abscesses are not only a clinical challenge due to their potential for rapid progression and severe complications but also because of the diverse bacterial pathogens that can cause them. Understanding the bacterial etiology, particularly the distinction between Gram-positive and Gram-negative organisms, is crucial for effective diagnosis and treatment.
Odontogenic abscesses are typically caused by Gram-positive bacteria (GPB), such as Streptococcus and Staphylococcus species, which invade through dental caries, failed endodontic treatments, or other dental pathologies [1,4]. These infections can lead to localized pus formation, which, if not managed promptly, may spread to surrounding tissues or even disseminate systemically [1,4]. On the other hand, non-odontogenic abscesses, often associated with Gram-negative bacteria (GNB) such as Escherichia coli or Klebsiella species, usually result from trauma to the mucosal surfaces or skin in the head and neck region or from systemic infections that localize in these areas [5]. The inflammatory response to these bacterial infections can vary significantly depending on the type of pathogen involved. Biomarkers such as procalcitonin (PCT) and C-reactive protein (CRP) are critical in assessing the body's response to these infections. PCT is particularly valuable in identifying bacterial infections, often rising significantly in response to Gram-negative sepsis, whereas CRP levels generally increase in response to both GPB and GNB infections, reflecting the overall inflammatory burden [6].
In addition to these biomarkers, hematological parameters such as white blood cell count (WBC), mean platelet volume (MPV), and platelet count (PLT) provide further insight into the systemic effects of these infections. Platelets are crucial in various pathophysiological processes, including hemostasis, thrombosis, inflammation, and the body’s defense against microbial infections [7]. Upon activation, platelets engage with white blood cells (WBCs) such as lymphocytes, monocytes, and macrophages, contributing to anti-inflammatory responses [8]. Parameters related to platelets, like PLT and MPV, which are easily measured through routine blood tests, have gained recognition for their significance in managing inflammatory diseases. MPV serves as an indicator of platelet activation, and together with PLT and the MPV-to-PLT ratio (MPI), these markers are valuable in assessing the level of inflammatory activity and the effectiveness of treatment in infectious diseases [6]. The nature of the bacterial pathogen, whether GPB or GNB, can influence these parameters differently, affecting the severity and course of the infection [6].
This study aims to investigate the impact of bacterial etiology on PCT, CRP, and various hematological parameters, and to assess the diagnostic performance of MPV in differentiating between GNB and GPB in adults with odontogenic and non-odontogenic abscesses of the head and neck. By analyzing these factors, the research seeks to enhance our understanding of how different bacterial pathogens influence the clinical presentation and progression of abscesses in the head and neck region. The findings could lead to more tailored therapeutic strategies, improving outcomes for patients with these complex infections.
Materials and methods
This is a retrospective reporting of a prospective study. The design is an observational comparison-group study. The same was conducted after approval by the Institutional Review Board of Medical University "Prof. Dr. Paraskev Stoyanov", Varna, Bulgaria (approval number: 101/2021). It included, studied and analyzed all 80 patients with odontogenic (n=50) and non-odontogenic (n=30) head and neck abscesses, who for a period of one year (from the beginning of July 2021 to the end of June 2022) were hospitalized in the Clinic of Maxillofacial Surgery at the University Hospital St. Marina, Varna, Bulgaria. In all of them, the diagnosis of abscess was confirmed initially during the physical examination by an oral or maxillofacial surgeon and then during the operative treatment in the volume of incision, lavage and drainage, during which a different amount of purulent exudate was evacuated.
Inclusion criteria were that all patients were 18 years of age or older and had been hospitalized and operated on for an odontogenic or non-odontogenic head or neck abscess.
The exclusion criteria included patients under the age of 18 and those with conditions or diseases that could artificially elevate levels of PCT, CRP, WBC count and PLT count. These conditions encompassed other infections (viral, bacterial, or fungal), parasitic infestations, recent major trauma or surgical procedures, fever, burns, oncological disorders, paraneoplastic syndromes, medications that promote cytokine production, cardiogenic shock, tissue hypoperfusion, bronchial asthma, and pulmonary pneumonia. Additionally, for parameters of complete blood count, the exclusion criteria also included immunosuppression, emotional stress, pregnancy, confirmed diagnoses of blood disorders (e.g., leukemia, thalassemia, immune thrombocytopenia, and multiple myeloma), and recent treatment, including anticoagulants, antiplatelet agents, hemostatic agents, granulocyte boosters, or clinical transfusion therapy (e.g., platelets, red blood cells, or plasma).
In the present study, we determined the mean values of key hematological parameters, including WBC, neutrophil (Neu) count, lymphocyte (Ly) count, MPV, PLT count and MPI. Blood samples were collected preoperatively from all participants following the diagnosis of head and neck abscesses. Whole blood was collected in vacutainers containing dipotassium ethylenediaminetetraacetic acid (K2EDTA) as an anticoagulant. Total WBC, Neu, Ly, and PLT counts were derived from routine complete blood count analyses performed using an automated 5-Diff hematology analyzer (ADVIA 2120, Siemens Healthineers, Erlangen, Germany). This advanced flow cytometry-based system employs light scatter, differential leukocytes lysis, and myeloperoxidase and oxazine 750 staining to yield a comprehensive blood cell profile. The results were reported in the following units: Nx10^9/L for WBC and PLT, Nx10^9/L or % for Neu and Ly count. MPI was calculated by dividing MPV by PLT and expressed as numerical value.
For CRP and PCT analyses, serum was separated by centrifugation at 2500 G for 15 minutes from blood collected in vacutainers equipped with gel separators. CRP levels were quantified via immunoturbidimetric analysis utilizing latex-enhanced particles on the Cobas® 6000 platform (Roche Diagnostics Corporation, Indianapolis, IN, USA), while PCT levels were measured using a latex-enhanced immunoturbidimetric assay on the ADVIA 1800 biochemical analyzer (Siemens Healthineers) in conjunction with a reagent kit provided by Diazyme Laboratories, Inc. (Poway, CA, USA). The results were reported in the following units: mg/L for CRP and ng/mL for PCT.
Statistical analyses were executed using the SPSS software package, version 19 (modified May 21, 2021; IBM Corp., Armonk, NY, USA) on a Windows 10.0 platform (Microsoft Corporation, Redmond, WA, USA). Numerical data were expressed as mean values ± standard deviation (SD). Descriptive statistics were initially employed to determine the central tendency and dispersion of the data. Pearson's correlation coefficient (r) was used to perform correlation analysis, evaluating the linear relationship between variables. Linear regression analysis, both univariate and multivariate, was utilized to examine relationships between independent and dependent variables. Factor analysis, coupled with independent-samples T-tests was conducted to identify significant differences between sample means. For the analysis of nominal data, non-parametric statistical methods such as the Chi-square test of independence were applied. Receiver operating characteristic (ROC) analysis and the calculation of the area under the curve (AUC) were employed to assess the sensitivity (Se) and specificity (Sp) of laboratory parameters, which facilitated the derivation of optimal cut-off values. A significance level (α) of 0.05 was maintained across all analyses, with the null hypothesis being rejected for p-values less than α (p<0.05).
Results
The study included 80 patients divided into two groups: 50 patients with odontogenic abscesses (56% men) and 30 with non-odontogenic abscesses of the head and neck (66.7% men). Gender distribution was comparable between the groups (χ²=0.889, p=0.346). The mean age was 41.6±18.18 years in the odontogenic abscess group and 44.53±15.49 years in the non-odontogenic abscess group, with no significant difference (F=1.345, p=0.250).
In 25 (31.3%) of all patients, no microorganisms were isolated as the causative agents of the infection (sterile cultures). In 28 (35%) patients, representatives of the resident oral microflora were isolated, indicating a polyinfection caused by normal inhabitants of the human oral cavity. GNB were isolated in seven (8.8%) patients and GPB were isolated in 17 (21.3%) patients.
The predominant Gram-negative bacteria were Escherichia coli and Klebsiella pneumoniae, while the predominant Gram-positive bacteria were Staphylococcus aureus, Staphylococcus anginosus, and Staphylococcus epidermidis. The distribution of isolated bacteria was comparable between patients from the odontogenic abscess group and the non-odontogenic abscess group (χ²=2.931, p=0.569).
Data from the 80 patients are presented in Table 1. CRP and Neu were significantly higher in patients with odontogenic abscesses compared to non-odontogenic ones: 104.94±111.75 mg/l vs 36.85±56.29 mg/l, p=0.003 for CRP and 73.67±11.49% vs 68.11±12.45%, p=0.046 for Neu. PLT, PCT and Ly were lower in patients with odontogenic abscesses vs those with non-odontogenic abscesses: PLT (268.38±80.46x10^9/L vs 301.77±133.02x10^9/L, p=0.035); PCT (0.816±1.02 ng/ml vs 1.26±1.53 ng/ml, p=0.006); Ly (16.59±9.27% vs 22.37±10.57%; 1.64±0.70x10^9/L vs 2.13±0.91x10^9/L) (Table 1).
Table 1
Baseline characteristics: Mean values of laboratory parameters in clinical groups with head and neck abscesses. Independent-samples T-test was conducted to identify significant differences between sample means.
n: number; SD: standard deviation; WBC: white blood cells; Neu: neutrophil count; Ly: lymphocyte count; MPV: mean platelet volume; PLT: platelet count; MPI: mean platelet volume-to-platelet count ratio; CRP: C-reactive protein; PCT: procalcitonin
Studied marker
Patients with odontogenic abscesses (n=50)
Patients with non-odontogenic abscesses (n=30)
Reference ranges
p-value
Mean value
SD
Mean value
SD
WBC (10^9/L)
11.17
4.429
10.33
3.19
3.79-10.33
non-significant
Neu (10^9/L)
8.45
4.34
7.24
3.23
1.78-7.00
non-significant
Neu (%)
73.67
11.49
68.11
12.45
39-77
0.046
Ly (%)
16.59
9.27
22.37
10.57
20-44
0.008
Ly (10^9/L)
1.64
0.70
2.13
0.91
1.07-3.12
0.013
MPV (fl)
8.63
1.21
8.57
1.36
6.0-10.0
non-significant
PLT (10^9/L)
268.38
80.46
301.77
133.02
140-440
0.035
MPI (numerical value)
0.038
0.028
0.034
0.015
/
non-significant
CRP (mg/l)
104.94
111.75
36.85
56.29
0-5
0.003
PCT (ng/ml)
0.816
1.02
1.26
1.53
0-0.05
0.006
When comparing the average levels of analyzed parameters against the causative agents of the infection, individuals with GPB exhibited higher values of Ly, MPV, MPI and PCT compared to those with GNB (p<0.05). Conversely, statistically significant differences favoring GNB were noted for WBC, Neu and PLT (p<0.05), as shown in Table 2. Although higher average values of CRP were reported in patients with GNB, the observed differences were not statistically significant (Table 2).
Table 2
Mean values of analyzed parameters of patients with isolated Gram-negative and Gram-positive bacteria. Independent-samples T-test was conducted to identify significant differences between sample means.
n: number; SD: standard deviation; GNB: Gram-negative bacteria; GPB: Gram-positive bacteria; WBC: white blood cells; Neu: neutrophil; Ly: lymphocytes; MPV: mean platelet volume; PLT: platelet count; MPI: mean platelet volume-to-platelet count ratio; CRP: C-reactive protein; PCT: procalcitonin
Studied marker
Patients with GNB (n=7)
Patients with GPB (n=17)
Reference ranges
p-value
Mean value
SD
Mean value
SD
WBC (10^9/L)
12.29
1.68
9.81
3.21
3.79-10.33
0.035
Neu (10^9/L)
9.85
2.09
7.15
2.80
1.78-7.00
0.033
Neu (%)
79.50
6.23
71.71
11.82
39-77
0.048
Ly (10^9/L)
1.57
0.49
1.77
0.86
20-44
non-significant
Ly (%)
13.19
4.89
19.26
10.23
1.07-3.12
0.05
MPV (fl)
7.93
0.65
8.79
1.21
6.0-10.0
0.035
PLT (10^9/L)
447.29
144.14
255.47
58.31
140-440
<0.001
MPI (numerical value)
0.019
0.007
0.036
0.011
/
<0.001
CRP (mg/l)
124.12
106.20
84.26
129.36
0-5
non-significant
PCT (ng/ml)
0.93
1.51
1.15
1.26
0-0.05
<0.001
Additionally, in individuals with isolated GNB, significant correlations were observed between WBC and Neu (r=0.974, p<0.005); WBC and Ly (r=-0.760, p=0.047); Neu and Ly (r=-0.867, p=0.011), and CRP and PCT (r=0.862, p=0.013). In patients with isolated GPB, notable correlations included WBC and Neu (r=0.942, p<0.005); Neu and Ly (r=-0.648, p=0.005); WBC and CRP (r=0.413, p=0.099); Neu and CRP (r=0.519, p=0.033), and Ly and CRP (r=-0.566, p=0.018). Across all studied patients, significant associations were observed between WBC and CRP (r=0.549, p<0.05), WBC and Neu (r=0.583, p<0.05), WBC and Ly (r=-0.539, p<0.05), Neu and Ly (r=-0.270, p=0.016), Neu and CRP (r=0.594, p<0.05), and Ly and CRP (r=-0.343, p=0.02).
Furthermore, a significant positive correlation was found between MPV and Ly (r=0.294, p=0.008), and a negative association between MPV and Neu (r=-0.294, p=0.008) in all studied patients.
To establish the linear relationship between MPV as а dependent variable and Neu and Ly as independent variables, a multiple linear regression analysis was conducted. A statistically significant combination of the variables Ly % and Neu % was identified for predicting the value of MPV in adults with head and neck abscesses, F=3.795, p=0.027. The regression constant was 9.381, p=0.04. The equation found for the relationship between the variables was MPV(fl)=9.381+Ly(%)x0.154-Neu(%)x0.152.
The multiple linear regression equation, resulting from the analyzed data, predicts the dependent variable (MPV) based on the two independent variables, Ly and Neu. The relationship between the combined variables was moderate, with R=0.300. The adjusted R² was 0.09, indicating that 9% of the variation in MPV values is explained by the regression model. To verify homogeneity of variance, standardized residual plots were examined. Statistical tests and a histogram confirmed a normal Gaussian distribution of MPV in studied patients (Figure 1). The histogram illustrates the distribution of the variable in the form of bars. The P-P Plot shows the regression line closely follows the diagonal (Figure 2). Figure 3 and Figure 4 present the regression curves of analysis. The regression graphs illustrate how changes in the relative count of Neu and Ly influence MPV, specifically showing an inverse relationship between Neu and MPV, and a direct relationship between Ly and MPV.
Figure 1
Histogram for normal distribution as a prerequisite for regression analysis
MPV: mean platelet volume
Figure 2
P-P Plot for normal distribution as a prerequisite for regression analysis
MPV: mean platelet volume
Figure 3
Regression curve: MPV (fl) as dependent variable; Neu (%) as predictor. On the x-axis, Neu values (%) are presented; on the y-axis, MPV values (fL) are presented.
MPV: mean platelet volume, Neu: neutrophil
Figure 4
Regression curve: MPV (fl) as dependent variable; Ly (%) as predictor. On the x-axis, Ly values (%) are presented; on the y-axis, MPV values (fL) are presented.
MPV: mean platelet volume, Ly: lymphocyte
To determine the cut-off values of MPV, MPI and PCT that distinguish groups with GPB from those with GNB, ROC curve analysis was applied. MPI exhibited an AUC-ROC of 0.776, p=0.018. MPV and PCT did not show good prognostic value, with an AUC-ROC for MPV=0.541, p=0.725, and an AUC-ROC for PCT=0.568, p=0.564.
A cut-off value of 0.029 was derived for MPI, yielding a sensitivity of 70.6% and specificity of 80%. The calculated likelihood ratios were LR(+)=3.5 and LR(-)=0.42 (Figure 5).
Figure 5
AUC-ROC curve to estimate a prognostic value of MPI, MPV and PCT in distinguishing patients with GPB from those with GNB
AUC-ROC: area under the curve of the receiver operating characteristic; MPI: mean platelet volume-to-platelet count ratio; MPV: mean platelet volume; PCT: procalcitonin; GPB: Gram-positive bacteria; GNB: Gram-negative bacteria
Discussion
While the etiology and clinical progression of maxillofacial infections are well-documented, identifying factors unique to odontogenic and non-odontogenic infections requires a thorough investigation. These infections have distinct origins, influenced by various factors that modify their course, severity, and predisposition [9]. To address these complexities, this study offers a comprehensive analysis of head and neck infections, comparing odontogenic and non-odontogenic maxillofacial infections.
The results of our study on gender and age distribution of patients are similar to previous studies [9,10]. Considering the average age in the group of odontogenic and non-odontogenic abscesses, we confirm the highest frequency of these infections between the ages of 25-45, with a slight predominance in men, though statistically insignificant [9-11]. Therefore, we accept the thesis that similar risk factors influence the development of both odontogenic and non-odontogenic abscesses, with a tendency for higher incidence in young men aged between 25-45 years [9]. Some of the most commonly discussed predisposing risk factors are smoking and alcohol consumption. They cause damage to the mucous membrane of the mouth and throat, which is the first immune barrier against the spread of microorganisms. Disruption of the physiological immune balance can lead to the development of both odontogenic and non-odontogenic maxillofacial infections [9]. Another predisposing factor for maxillofacial infections is poor oral hygiene [9]. Traditionally attributed as an important risk factor for odontogenic infections, it can also significantly impair local immune defenses and promote the spread of potentially benign pharyngeal infections [1,9]. In the 25-45 age group, the consequences of neglecting oral hygiene, caries, and periodontitis may be most clearly marked, which, combined with limited access to a dentist, may result in a higher risk of maxillofacial infections. Finally, in this age group, pathologies related to the eruption of the lower third molars are most common, which are postulated as a frequent cause of severe odontogenic infections [9,12].
In 31.3% (n=25) of the 80 patients studied (50 with odontogenic abscesses and 30 with non-odontogenic abscesses), no microorganisms were isolated as the causative agents of the infection. Several factors could account for this, including improper sample collection, where the sample might contain only pus, which is often low in microorganisms, instead of also including tissue material adjacent to the abscess, which is typically rich in bacterial content. Other potential reasons include improper storage conditions, such as inappropriate environment, temperature, humidity, or exposure to sunlight, as well as an excessively long interval between sample collection and testing.
The second most common finding is the resident bacterial microflora, typically consisting of two or more bacterial species, predominantly Gram-positive (35%, n=28). This pattern is characteristic of neck abscesses from various origins, such as odontogenic, rhinogenic, dermatogenic, and post-traumatic [1,2]. The resident flora often protects the body against disease-causing organisms. However, under certain conditions, microorganisms that are part of a person’s resident flora may cause disease. Such conditions include the use of antibiotics, injury or surgery, and a weakened immune system (as occurs in people with HIV infection or cancer, those taking corticosteroids, and those undergoing cancer chemotherapy) [13].
In line with previous studies, we found that the primary microorganisms responsible for head and neck abscesses in patients over 18 years of age are GPB [2,9,13]. One of the most common species causing human diseases is Staphylococcus epidermidis, a finding that our study also supports [14]. These bacteria typically colonize the skin and mucous membranes without causing infections under normal conditions [14]. However, when the skin and mucosal epithelium are injured or in cases of immune system disorders, they can lead to purulent infections. They are less virulent than Staphylococcus aureus, a conclusion also confirmed by our study [2,14]. GNB are significantly less common in neck infections, with no specific reason identified for this occurrence [14]. Our findings indicate that GPB are three times more prevalent than GNB, a result that aligns with numerous studies in the literature [9,10,13,14].
In our analysis, several significant differences in the studied laboratory parameters were identified, though the distribution of isolated bacteria was similar between the groups. Additionally, when comparing the average levels of analyzed parameters relative to the causative agents of the infection, significant differences were observed across all variables, except for CRP and the absolute value of Ly.
In contrast to previous studies that found comparable results for CRP, we observed significantly higher values for this indicator, as well as a higher relative value of Neu in the group with odontogenic abscesses compared to those with non-odontogenic abscesses [6,9]. For WBC, we observed a similar trend, but it did not reach statistical significance. A higher level of CRP is linked to a more severe progression of maxillofacial infections [15]. The average CRP value we observed in the group with odontogenic abscesses was 104.94±111.75 mg/l, which is nearly three times higher than the value observed in the group with non-odontogenic abscesses, 36.85±56.29 mg/l, p=0.003. According to Pham Dang et al., patients with a CRP level exceeding 200 mg/l face a 27% risk of requiring multiple surgeries due to odontogenic infections [12]. Conversely, a CRP level below 50 mg/l, coupled with immunodepression, may predispose individuals to a more severe course of odontogenic infections [12]. Neu count and CRP levels are typically elevated, while Ly count is decreased in patients who develop abnormal inflammatory responses. According to Rosca et al. (2023), the association between CRP and Neu to Ly ratio was found to increase the risk of severe odontogenic infections by 7.28 times. The ROC analysis of CRP-Neu-to-Ly ratio yielded an AUC of 0.889, with high Se (79.6%) and high Sp (85.1%) for predicting severe odontogenic infections based on biomarkers measured at hospital admission (p<0.001) [15]. Kaminski et al. (2024) noted that these findings are attributed to the higher morbidity observed in patients with odontogenic infections compared to those with non-odontogenic infections [9].
Furthermore, we observed more severe leukocytosis, due to neutrophilia in patients with GNB, compared to those with GPB. In general, acute inflammation is characterized by an increase in Neu count, whereas chronic inflammation is typically associated with a rise in Ly levels. Inflammatory responses are characterized by the detection of damaged tissues by inflammatory cells, the selective accumulation of certain leukocyte subsets, and the subsequent elimination of harmful agents. In cases of systemic bacterial inflammation, a decrease in Ly count and an increase in Neu count are typically observed [16]. Lymphopenia is thought to result from the margination and redistribution of Ly within the lymphatic system, while neutrophilia is driven by the accumulation of Neu at the infection site, delayed apoptosis, and stem cell activation [16]. According to several authors, GNB are generally more harmful than GPB for several reasons [2,4,6,9]. They have a robust outer membrane that shields them from many antibiotics, making them more difficult to treat. Furthermore, when their cell walls are compromised, they release endotoxins that can intensify symptoms and provoke severe inflammatory reactions. This heightened resistance to antibiotics results in greater morbidity and mortality, thereby rendering GNB significantly more harmful than GPB [9]. In acccordance with Kaminski et al. (2024), we found no significant differences in CRP levels between GNB and GPB groups [9].
The positive correlation between CRP and WBC, as well as with the Neu count, has been extensively studied by various authors, whereas a negative correlation with the Ly count has also been observed [15-17]. Consequently, the results of our study confirm these associations, regardless of the abscess origin (odontogenic or non-odontogenic) or the etiological agent (GNB or GPB).
Our findings regarding the intergroup differences in average PCT levels were unexpected. We observed higher levels of this marker in individuals with non-odontogenic abscesses compared to those with odontogenic abscesses, as well as higher levels in GPB compared to GNB. This contrasts with several studies that have reported significantly higher PCT levels in patients with GNB compared to those with GPB [6,9]. The endotoxins on the cell walls of GNB may directly stimulate PCT production. Thomas-Ruddel et al. and Yu et al. noted that PCT serum concentrations can be influenced by the infection site, potentially limiting its diagnostic value for GNB [18,19]. A meta-analysis concluded that PCT is generally more effective than CRP for diagnosing GNB infections [20]. The discrepancy in PCT levels observed in our study may be due to differences in inflammatory responses and systemic inflammatory responses induced by these bacterial classes through distinct signaling pathways or possibly the limited size of the studied cohort. Recently, the role of the delta neutrophil index (DNI) in the diagnosis, follow-up and prediction of the outcome of the disease in inflammatory infections in the head and neck region has been discussed [21].
Platelets contribute to the pathogenesis of infectious diseases in addition to their primary role in hemostasis. Infections can lead to changes in platelet size, with an increase in MPV often seen during severe infections [6]. This increase in MPV is thought to result from the rapid release of platelets from the spleen, making MPV a useful inflammatory marker in the early stages of infection. Elevated MPV levels have been observed in conditions such as acute pyelonephritis, peritonsillar abscesses, acidic fluid infections, severe community-acquired pneumonia requiring hospitalization, and infective endocarditis [22,23]. The results of our study demonstrated that platelet parameters such as PLT, MPV, and MPI were significantly different between the GPB and GNB groups. Infections caused by GPB, such as Staphylococcus, Streptococcus and Peptostreptococcus spp, often lead to a localized inflammatory response [24]. This typically results in a smaller increase in MPV relative to the overall PLT count, potentially leading to a lower MPI. In contrast, infections caused by GNB, such as Escherichia coli and Pseudomonas, are associated with more widespread inflammation and septic responses. This systemic inflammation can cause a more significant increase in MPV due to the production of larger, more reactive platelets, which might result in a higher MPI [22]. The inverse relationship between the PLT count and MPV has previously been described [6]. We observed a similar trend in our study, but it did not reach statistical significance.
In contrast to Gao et al. (2021), we found that MPV and MPI in the GPB group were significantly higher than those in the GNB group, while the PLT count was significantly lower in the GPB group [6]. Although GNB trigger a more intense inflammatory response in the body, leading to higher concentrations of inflammatory factors than GPB, some studies have indicated that GPB are associated with greater platelet activation, consistent with our findings [25]. This difference can result in changes in platelet volume, aggregation, and volume distribution. However, the precise differences in hematological levels of these non-specific biomarkers still require further investigation at the molecular level to better understand the distinct pathogenic mechanisms involved. Furthermore, patient-specific factors and variations in the inflammatory response can affect MPV and PLT levels, making it essential to interpret these measurements in the context of the overall clinical picture [6].
Additionally, we observed a significant association between the MPV parameter and the relative counts of Ly and Neu across all studied patients, regardless of the abscess origin or etiological factor. Аccording to several authors, increased MPV is correlated with neutrophilia and lymphocytopenia, indicating a stronger inflammatory response in the body [16,22,23]. This correlation reflects the role of PLT as active participants in the immune response during infections, where larger, more reactive PLT are produced alongside an increase in Neu and a decrease in Ly. In summary, the association between increased MPV, neutrophilia, and lymphocytopenia underscores the dynamic nature of the immune response, highlighting how the body adapts to and manages inflammation and infection [16]. We were surprised to find an inverse correlation between MPV and both Neu and Ly in our investigation. Possible explanations for this phenomenon are as follows: during inflammatory or immune responses, both PLT and Ly can become activated. An increased MPV typically indicates heightened PLT activation and production, while elevated Ly counts reflect their role in the inflammatory process [8]. The body produces larger PLT in response to infection, and Ly, particularly T-cells, are mobilized to combat the pathogen. Conversely, conditions such as autoimmune disorders or chronic infections can result in both elevated MPV and increased Ly counts. The persistent inflammatory environment can drive the production of larger, more reactive PLT and enhance Ly proliferation [8].
Our study found that the diagnostic value of MPI was superior to either MPV or PCT in distinguishing GNB from GPB. MPI showed an AUC-ROC of 0.776 (p=0.018), highlighting its effectiveness as a diagnostic marker in this context. Additionally, we derived a cut-off value of 0.029 for MPI, which demonstrated a Se of 70.6% and Sp of 80%.
Limitations
As limitations of this article, it can be noted that it does not study the pediatric population, it covers only a one-year time interval during which the patients were studied, and it considers patients who were hospitalized in only one clinic of one hospital.
Conclusions
In conclusion, our research provides valuable insights into the impact of bacterial etiology on inflammatory and hematological markers in head and neck abscesses. Inflammatory markers such as CRP and PCT showed significant variations based on bacterial etiology and abscess origin. Specifically, CRP and PCT levels were higher in patients with odontogenic abscesses compared to non-odontogenic ones, while MPV and PLT levels differed according to bacterial type. Notably, our study found that the diagnostic value of MPI was superior to either MPV or PCT in distinguishing GNB from GPB. MPV as a standalone marker does not have sufficient diagnostic accuracy. We derived a cut-off value of 0.029 for MPI, which demonstrated a Se of 70.6% and a Sp of 80%. These findings underscore the importance of precise bacterial identification and relevant laboratory tests for optimal treatment of these complex infections. Enhanced understanding of these relationships can lead to more accurate diagnoses and more effective therapeutic strategies, ultimately improving clinical outcomes for patients with head and neck abscesses. |
Title: From diagnosis to survivorship addressing the sexuality of women during cancer | Body:
Sexual health and cancer: the dismissed concern
For women diagnosed with cancer, sexual dysfunction is associated with higher symptom burden, anxiety, and depression. However, most women are left in the dark without any guidance from their oncology teams regarding potential side effects and treatment options.1-3 As a result, sexual dysfunction often goes untreated, impacting quality of life, mental and physical health, and relationships.1,3
Sexual dysfunction in women after cancer treatment has been well documented. Persistent sexual concerns, as a result of oncologic treatment and psychological distress, have been reported in up to 90% of women with gynecological cancer, up to 75% of women diagnosed with breast cancer, and 77% of women with lung cancer.1,4-7 Certain cancer treatments can result in specific sexual health toxicities. For example, 29%-49% of female stem cell transplant recipients may experience gynecological graft versus host disease symptoms, such as vaginal dryness, dyspareunia, vulvovaginal scarring, and vaginal stenosis.8
Multiple professional medical organizations have recognized the importance of sexuality and wellness after cancer diagnosis and have issued guidelines or consensus statements regarding the assessment and treatment of sexual dysfunction in patients with cancer.9-11 These guidelines recommend regular assessments and treatments of sexuality throughout cancer diagnosis, treatment, and follow-up.
Despite the existence of these guidelines, the reality is that only a few women with cancer are asked about sexuality concerns that result from cancer treatments.3 A survey of patients in a radiation oncology clinic reported that 87% had the impact on sexual function, however only 27.9% were ever asked about sexuality by a medical professional.12 Gender disparities exist within this topic, in that 53% of men vs 22% of women with cancer were directly assessed for sexual concerns, fueling the already existing gender disparities in cancer care.12 Research from Living Beyond Breast Cancer reveals that patients feel oncology teams do not address sexual health and 64% of young women with breast cancer reported sexuality concerns that their provider was unable to address.13
Oncology professionals can meet this need by addressing sexuality concerns throughout the spectrum of cancer care, offering mitigating strategies and referrals to specialists when needed. Cancer centers can work to address this unmet need by developing sexual health programs where patients can receive a bio-psycho-social assessment and treatment options.
This review will address the barriers medical professionals face in addressing sexuality in women, the incidence of sexual dysfunction in women with cancer, treatment options, and the potential role of social media in mitigating these issues.
Lack of inclusion in routine oncology care
Despite the importance of sexuality to overall well-being, sexuality in cancer care is often a forgotten and avoided subject.2,14 Research reports that sexuality concerns are avoided topics by providers.2 Providers cite barriers such as not feeling qualified or prepared to discuss sexuality with oncology patients, lack of time, patient resources for referrals, lack of knowledge, and not knowing how to bring up the subject as reasons for why they do not discuss sexuality (Figure 1).2,15,16 In addition, intersectional identities impact the discussion of sexuality during routine oncology care. Patients who are women, LGBTQ+, and patients who are religious minorities or religiously observant are less likely to receive discussions about sexuality as bias, lack of knowledge and training, or concern about offending patients in these groups can be greater.14,16-19
Figure 1.
Top reasons why oncology providers do not feel comfortable discussing sexuality.
Gender Bias
Studies indicate that disparities exist regarding the discussion of sexuality. In France, a nationwide survey found a statistically significant difference (P < .001) in the discussion of sexuality for patients with cancer, reporting that 11.1% of women and 36.7% of men with cancer had sexual health discussions with a provider.17 The likelihood of discussion was also impacted by cancer type.17 Patients with prostate and cervical cancers had a higher chance of receiving discussions, though the likelihood was higher in patients with prostate cancer (56.3%) compared to patients with cervical cancer (39.6%).17 These findings were reaffirmed by data from the US reporting that sexuality was assessed at consult for 13% of patients with cervical cancer and 89% of patients with prostate cancer.20 More so, other studies reveal that discussions varied by cancer type: 80% of patients with breast cancer and 82% of patients with blood or marrow cancer reported having no discussion of sexuality at all.18 Notably, conversations about the sexuality impact of treatments were more likely to be brought up by female oncologists (P = .02).18
LGBTQ+ patients
Sexual orientation and gender identity data are not routinely collected in oncology care, leading to a general lack of information on patients who identify as sexual and gender minorities (SGM).14 Providers may feel uncomfortable or untrained in addressing sexuality in patients who identify as SGM, perpetuating the marginalization that SGM groups continue to experience.14 More so, lack of provider knowledge and bias is often exacerbated in transgender/gender diverse (TGD) individuals because of insufficient research centering patients who are TGD and greater implicit biases from providers.14 In a qualitative study on the reasons why providers did not discuss sexuality with SGM patients, not knowing how to bring up the topic, insufficient time in appointments, bias against LGBTQ+ individuals, not having the tools to help, and fear of offending the patient were cited as reasons by providers.19 For TGD individuals, lack of provider knowledge and formalized clinical guidelines significantly impacts gender-affirming care practices during cancer treatment, adding to the isolation that many of these patients experience.21 Gender-affirming care is a part of holistic sexual and reproductive health practices, indicating the importance of provider education and subsequent discussion of these topics in oncology care.22
Religious minorities or religiously observant
Religion and spirituality can have a positive impact on cancer care and are important to many patients.23 Researchers have emphasized the importance of culture when discussing sexuality.24 In a study of 433 medical oncologists, 23.7% reported a patient’s culture or religion as a reason for avoiding a sexual discussion.16 As religions have different views on sexuality and sexual health, discussions and treatment should be individualized to the patient’s needs.25 Cultural humility is a core tenant of equitable care, and as such, learning about a patient’s religious practice is critical to understanding the psychosocial and sexual health needs of patients.26
Gender Bias
Studies indicate that disparities exist regarding the discussion of sexuality. In France, a nationwide survey found a statistically significant difference (P < .001) in the discussion of sexuality for patients with cancer, reporting that 11.1% of women and 36.7% of men with cancer had sexual health discussions with a provider.17 The likelihood of discussion was also impacted by cancer type.17 Patients with prostate and cervical cancers had a higher chance of receiving discussions, though the likelihood was higher in patients with prostate cancer (56.3%) compared to patients with cervical cancer (39.6%).17 These findings were reaffirmed by data from the US reporting that sexuality was assessed at consult for 13% of patients with cervical cancer and 89% of patients with prostate cancer.20 More so, other studies reveal that discussions varied by cancer type: 80% of patients with breast cancer and 82% of patients with blood or marrow cancer reported having no discussion of sexuality at all.18 Notably, conversations about the sexuality impact of treatments were more likely to be brought up by female oncologists (P = .02).18
LGBTQ+ patients
Sexual orientation and gender identity data are not routinely collected in oncology care, leading to a general lack of information on patients who identify as sexual and gender minorities (SGM).14 Providers may feel uncomfortable or untrained in addressing sexuality in patients who identify as SGM, perpetuating the marginalization that SGM groups continue to experience.14 More so, lack of provider knowledge and bias is often exacerbated in transgender/gender diverse (TGD) individuals because of insufficient research centering patients who are TGD and greater implicit biases from providers.14 In a qualitative study on the reasons why providers did not discuss sexuality with SGM patients, not knowing how to bring up the topic, insufficient time in appointments, bias against LGBTQ+ individuals, not having the tools to help, and fear of offending the patient were cited as reasons by providers.19 For TGD individuals, lack of provider knowledge and formalized clinical guidelines significantly impacts gender-affirming care practices during cancer treatment, adding to the isolation that many of these patients experience.21 Gender-affirming care is a part of holistic sexual and reproductive health practices, indicating the importance of provider education and subsequent discussion of these topics in oncology care.22
Religious minorities or religiously observant
Religion and spirituality can have a positive impact on cancer care and are important to many patients.23 Researchers have emphasized the importance of culture when discussing sexuality.24 In a study of 433 medical oncologists, 23.7% reported a patient’s culture or religion as a reason for avoiding a sexual discussion.16 As religions have different views on sexuality and sexual health, discussions and treatment should be individualized to the patient’s needs.25 Cultural humility is a core tenant of equitable care, and as such, learning about a patient’s religious practice is critical to understanding the psychosocial and sexual health needs of patients.26
Status of research about sexual health and cancer
Status of research
Despite overall low reports of discussions for women,17 data on the incidence, types of sexual dysfunction, and rates of where it is addressed highlight the importance of improving sexuality care for patients with cancer. In a meta-analysis encompassing 5483 women with cancer, the prevalence of sexual dysfunction, encompassing concerns with arousal, orgasm, interest, and pain, was 66% based on the female sexual dysfunction index (FSFI).27 Notably, there are inconsistencies across reports; in a systematic review, there was a significant variation in the reported incidence of sexual dysfunction, ranging from 30% to 80%.28 Further, it is reported that the risk of developing sexual dysfunction was 2.7 to 3.5 times higher in women with cancer than those who did not have cancer.28
Women with cancer in reproductive organs often report the highest level of sexual dysfunction27,28; studies estimate that 90% of patients with gynecologic cancer experience changes in sexual experiences, preferences, and are at increased risk of dyspareunia and vaginal dryness.29,30 In cancer types where sexual health is less likely to be discussed, such as lung, breast, colorectal, and head and neck cancer, the prevalence and areas of sexuality concerns have also been reported.6,7,31 In the SHAWL study, 77% of women with lung cancer reported sexual dysfunction.6 Among patients affected by breast cancer it is reported that that 75% of women report sexual concerns7 and 89.5% report changes to sexuality from treatment.32 Sexual function has been reported as the most significantly affected quality-of-life measure during breast cancer treatment, with the most common sexual concerns including pain with penetration, vulvovaginal dryness, and decreased desire and arousal.33,34 Research centering on patients with colorectal and anal cancer revealed 98% of patients had FSFI scores associated with significant sexual dysfunction.31 Sexuality is also impacted in head and neck cancer as treatment can involve surgery, chemotherapy, and radiation, which can be disfiguring, impact saliva production, and induce hormonal changes; in fact, sexuality and body image are altered in 79.2% of female head and neck cancer survivors.35
Status of research
Despite overall low reports of discussions for women,17 data on the incidence, types of sexual dysfunction, and rates of where it is addressed highlight the importance of improving sexuality care for patients with cancer. In a meta-analysis encompassing 5483 women with cancer, the prevalence of sexual dysfunction, encompassing concerns with arousal, orgasm, interest, and pain, was 66% based on the female sexual dysfunction index (FSFI).27 Notably, there are inconsistencies across reports; in a systematic review, there was a significant variation in the reported incidence of sexual dysfunction, ranging from 30% to 80%.28 Further, it is reported that the risk of developing sexual dysfunction was 2.7 to 3.5 times higher in women with cancer than those who did not have cancer.28
Women with cancer in reproductive organs often report the highest level of sexual dysfunction27,28; studies estimate that 90% of patients with gynecologic cancer experience changes in sexual experiences, preferences, and are at increased risk of dyspareunia and vaginal dryness.29,30 In cancer types where sexual health is less likely to be discussed, such as lung, breast, colorectal, and head and neck cancer, the prevalence and areas of sexuality concerns have also been reported.6,7,31 In the SHAWL study, 77% of women with lung cancer reported sexual dysfunction.6 Among patients affected by breast cancer it is reported that that 75% of women report sexual concerns7 and 89.5% report changes to sexuality from treatment.32 Sexual function has been reported as the most significantly affected quality-of-life measure during breast cancer treatment, with the most common sexual concerns including pain with penetration, vulvovaginal dryness, and decreased desire and arousal.33,34 Research centering on patients with colorectal and anal cancer revealed 98% of patients had FSFI scores associated with significant sexual dysfunction.31 Sexuality is also impacted in head and neck cancer as treatment can involve surgery, chemotherapy, and radiation, which can be disfiguring, impact saliva production, and induce hormonal changes; in fact, sexuality and body image are altered in 79.2% of female head and neck cancer survivors.35
Treatment of sexual health
The American Society of Clinical Oncology (ASCO) guidelines recommend that medical professionals should assess the following domains of sexual function and recommended interventions: the genitourinary syndrome of menopause, sexual response—libido, arousal, and orgasm, dyspareunia, and psychosocial concerns including depression, anxiety, body image concerns, and relationship issues (Table 1).9
Table 1.
Treatment recommendations.
Domain
Professional society recommendations
Assessment
ASCO: Discussion initiated by healthcare team regarding sexual health and dysfunction resulting from cancer or its treatment at time of diagnosis and continue to readdress during treatment course and survivorshipNCCN: Ask about sexual health at regular intervals
Libido
ASCO: Psychosocial or psychosexual counseling, couple’s interventions, regular stimulation (including masturbation), flibanserin for pre-menopausal womenNCCN: Psychosocial counseling, Discussion of medications including androgens, bupropion, buspirone, flibanserin, and bremelanotide
Genitourinary syndrome of menopause
ASCO: Vaginal moisturizers, lubricants, and vaginal estrogenNCCN: Non-hormonal treatments (vaginal moisturizers, gels, hyaluronic acid, oils), lubricants for sexual activity, local estrogen, and DHEANAMS/ISSWSH: Non-hormonal therapies are generally first-line therapyACOG: Silicone, polycarbophil, and water-based lubricants such as hyaluronic acid, polyacrylic acid, and vitamin E and D suppositories should be considered first-line treatment for urogenital symptoms in individuals with a history of estrogen-dependent breast cancer
Dyspareunia
ASCO: Cognitive-behavioral therapy, pelvic floor therapy, exercise, vaginal dilatorsNCCN: Topical vaginal therapies, vaginal dilators, ospemifene, DHEA, pelvic physical therapy, topical analgesics
Orgasm (less intense, difficulty achieving, pain)
NCCN: Discuss options including vibrator or clitoral stimulatory device with referral to appropriate specialist and consider pelvic floor physical therapy
Vaginal hormones in hormone sensitive breast cancer
ASCO: For those who do not respond or whose symptoms are more severe at presentation, low-dose vaginal estrogen can be used. For women with hormone-positive breast cancer who are symptomatic and not responding to conservative measures, low-dose vaginal estrogen can be considered after a thorough discussion of risks and benefits.NCCN: Limited data in breast cancer survivors suggest minimal systemic absorption with rings and suppositories. Therefore, if estrogen-based treatment is warranted, rings and suppositories are preferred over creams for survivors of hormonally sensitive tumors.NAMS/ISSWSH: Women with severe symptoms where nonhormone treatments have failed may still be candidates for local hormone therapies after review with the woman’s oncologist vs consider switching to tamoxifenACOG: If non-hormonal treatments have failed to adequately address symptoms, after discussion of risks and benefits, low-dose vaginal estrogen may be used in individuals with a history of breast cancer, including those taking tamoxifen. For individuals taking aromatase inhibitors (Ais), low-dose vaginal estrogen can be used after shared decision-making between the patient, gynecologist, and oncologist.
ASCO: American Society of Clinical OncologyNCCN: National Comprehensive Center NetworkNAMS/ISSWSH: North American Menopause Society/International Society for the Study of Women’s Sexual HealthACOG: American College of Obstetrics and Gynecology
Assessment
Sexual function and distress related to sexual concerns should be assessed in female patients with cancer regularly. There are multiple methods available to assess sexuality, including asking during routine visits, utilizing a paper checklist, and incorporating it into standard side effect assessments.36 One helpful tool is to use a ubiquity statement to normalize the discussion of sexuality concerns, such as “many women on aromatase inhibitors experience vaginal dryness, low libido, or changes in sexual health followed with “have you experienced these concerns?.”13 It is important that this is a regular discussion over the course of follow-up.
Libido
Low libido is a common concern after cancer treatment. If this is distressing to the patient, then interventions can be recommended. Psychosocial counseling, sex therapy, and couples counseling are first-line treatments for low libido.9 Sensate focus, mindfulness, exercise, and hypnosis have also shown benefit.37-40 A recent systematic review of randomized trials identified that multi-modal interventions, including education, acceptance, mindfulness, and communication/relationship skills, effectively improved sexual function.39 Outside of these modalities, flibanserin (a dual serotonin 1A receptor agonist/2A receptor antagonist) may improve libido, the number satisfying sexual events, and decrease distress in women with breast cancer on endocrine therapy.41 Bupropion has been suggested as another agent to help with low libido; however, a randomized trial of the anti-depressant bupropion compared to placebo in patients with breast cancer showed no difference in desire score on the FSFI.42 The lack of difference may have been due to untreated physical concerns, such as vaginal dryness, decreased lubrication, and pain with penetration.42 This underscores the importance of the bio-psycho-social assessment and treatment of sexual dysfunction in women with cancer in a holistic manner.
Genitourinary syndrome of menopause
The genitourinary syndrome of menopause (GSM) includes vaginal and vulvar dryness, decreased lubrication, vaginal narrowing or shortening, and urinary symptoms.11,43-45 GSM is common in patients who are affected by cancer and is caused by cancer treatments and menopause.44 Non-hormonal moisturizers, lubricants, and low-dose local vaginal hormones are used to treat GSM. Use of a vaginal moisturizer containing hyaluronic acid three to five times per week can improve vulvovaginal tissue and sexual function.46,47 Patients should be educated about the appropriate use of vaginal lubricants, including recommendations of products with the appropriate pH of around 4.5 for vaginal products and osmolality of <1200 mOsm/kg as recommended by the World Health Organization, as well as the differences between water and silicone-based lubricants.48 The use of local vaginal hormones in hormone receptor-positive breast cancer is included in all the noted guidelines after a discussion of risks and benefits with the patient. Both a cohort study and a nested case-control study did not show an increased risk of cancer recurrence in patients with breast cancer on endocrine therapy who used vaginal estrogen when comparing quantity of use to non-use respectively.49,50 The Danish observational cohort study reported that while, overall, women with breast cancer who used local estrogen did not have an increased risk of cancer recurrence, the subgroup of patients on aromatase inhibitors did have an increased risk of cancer recurrence (1.39 [95% CI = 1.04-1.85), without an increase in mortality.51 Limitations of this study include that the doses of vaginal estrogen were not reported and may have been higher than the currently used low-dose vaginal estrogen, the study period pre-dated HER2 testing, which significantly affects the risk of recurrence, and that many patients were treated without any endocrine therapy.51 Another more recent retrospective study evaluated a group of 42 113 women diagnosed with genitourinary syndrome of menopause (GSM) following a breast cancer diagnosis, of which 3.9% utilized prescriptions for vaginal estrogen.52 The risk of recurrence was comparable between vaginal estrogen and the control group. Among the 10 584 patients with documented estrogen receptor-positive breast cancer, 3.9% used vaginal estrogen, and the risk of recurrence was similar. In a very small subgroup of patients identified to have concurrent prescriptions for vaginal estrogen and an aromatase inhibitor, there was an increased risk of breast cancer recurrence; however, results were not controlled for stage, grade, or nodal status.52 Lastly, a recent study of 49 237 women with breast cancer with 5% of patients using vaginal estrogen therapy found no increased risk of breast cancer-specific mortality (HR, 0.77; 95% CI, 0.63-0.94).53
Dyspareunia
Pain with penetration or dyspareunia has been reported in patients with breast cancer, gynecologic cancers, lung cancer, and colorectal cancer6,30,31,54,55 Dyspareunia can be caused by vaginal stenosis or narrowing, insertional dyspareunia, or pelvic floor dysfunction.56,57 Vaginal dilators help treat vaginal stenosis and should be recommended after pelvic radiation to reduce the risk of vaginal stenosis.56 Pelvic floor dysfunction frequently contributes to dyspareunia and can be treated with pelvic floor physical therapy and, in some cases surgery.57 For patients with insertional dyspareunia, the application of 4% topical aqueous lidocaine can reduce symptoms of pain at the time of penetration, however, lidocaine may cause reduced sensitivity for a penetrating partner.58-60
Sexual positions that limit discomfort
After a cancer diagnosis, certain sex positions that were once pleasurable may now cause symptoms such as fatigue, pain and/or discomfort, and shortness of breath.61 People experiencing decreased energy from diagnosis and treatment may benefit from side-lying positions and/or having their partner assume the active role.61 This may be particularly beneficial for patients with breast cancer or individuals with ostomy bags to avoid rubbing against sensitive areas.61 More so, patients with breathing concerns may want to avoid positions where they are lying flat on their back and may benefit from side-by-side and superior positions, which allow for better breathing and control.61,62 It is important that partners communicate with each other and try out different ways and positions to create an enjoyable experience. As symptoms may change over time, there is not one position that will be right for everyone every time. Pillows of different shapes and forms are a helpful tool to provide additional support to painful or uncomfortable areas.63 Certain areas that were once erogenous zones, such as the breasts, may no longer be after cancer, and identifying other pleasurable areas in the body should be encouraged by providers as patients learn new areas of stimulation and discover their new body during or after cancer treatments.63
Complementary medicine
Sexual concerns often occur due to changes in both physical and mental health.5,64 A cancer diagnosis can result in feelings and symptoms such as anxiety, depression, fear, guilt, anger, and loneliness, which can subsequently impact body image, sexual desire, and sexual functioning.64 Medications often used to treat anxiety and depression can result in sexual side effects such as decreased libido or inhibition of orgasm. This is further exacerbated in patients with cancer who identify as LGBTQI+ as they experience worse mental health outcomes, higher levels of depression and anxiety, discrimination, and cancer-related distress.65
The ASCO Clinical Practice guideline recommends that psychosocial and/or psychosexual counseling be offered to all patients with cancer, aiming to improve overall sexual functioning, body image perception, intimacy, and relationship issues.9 Studies utilizing cognitive-behavioral therapy, couples-based sexual education, self-healing training, and group therapy with guided imagery have shown benefit in female survivors of cancer.66 Pelvic floor physical therapy may be helpful for patients experiencing symptoms of potential pelvic floor dysfunction such as persistent pain and urinary and/or fecal leakage.66 Integrative medicine options such as acupuncture, slow-breathing techniques and hypnosis have also shown some benefit and are overall low-cost interventions.67-69 Throughout such ventures, though, lack of insurance coverage and availability of trained and knowledgeable professionals remains a barrier to implementation.66
Assessment
Sexual function and distress related to sexual concerns should be assessed in female patients with cancer regularly. There are multiple methods available to assess sexuality, including asking during routine visits, utilizing a paper checklist, and incorporating it into standard side effect assessments.36 One helpful tool is to use a ubiquity statement to normalize the discussion of sexuality concerns, such as “many women on aromatase inhibitors experience vaginal dryness, low libido, or changes in sexual health followed with “have you experienced these concerns?.”13 It is important that this is a regular discussion over the course of follow-up.
Libido
Low libido is a common concern after cancer treatment. If this is distressing to the patient, then interventions can be recommended. Psychosocial counseling, sex therapy, and couples counseling are first-line treatments for low libido.9 Sensate focus, mindfulness, exercise, and hypnosis have also shown benefit.37-40 A recent systematic review of randomized trials identified that multi-modal interventions, including education, acceptance, mindfulness, and communication/relationship skills, effectively improved sexual function.39 Outside of these modalities, flibanserin (a dual serotonin 1A receptor agonist/2A receptor antagonist) may improve libido, the number satisfying sexual events, and decrease distress in women with breast cancer on endocrine therapy.41 Bupropion has been suggested as another agent to help with low libido; however, a randomized trial of the anti-depressant bupropion compared to placebo in patients with breast cancer showed no difference in desire score on the FSFI.42 The lack of difference may have been due to untreated physical concerns, such as vaginal dryness, decreased lubrication, and pain with penetration.42 This underscores the importance of the bio-psycho-social assessment and treatment of sexual dysfunction in women with cancer in a holistic manner.
Genitourinary syndrome of menopause
The genitourinary syndrome of menopause (GSM) includes vaginal and vulvar dryness, decreased lubrication, vaginal narrowing or shortening, and urinary symptoms.11,43-45 GSM is common in patients who are affected by cancer and is caused by cancer treatments and menopause.44 Non-hormonal moisturizers, lubricants, and low-dose local vaginal hormones are used to treat GSM. Use of a vaginal moisturizer containing hyaluronic acid three to five times per week can improve vulvovaginal tissue and sexual function.46,47 Patients should be educated about the appropriate use of vaginal lubricants, including recommendations of products with the appropriate pH of around 4.5 for vaginal products and osmolality of <1200 mOsm/kg as recommended by the World Health Organization, as well as the differences between water and silicone-based lubricants.48 The use of local vaginal hormones in hormone receptor-positive breast cancer is included in all the noted guidelines after a discussion of risks and benefits with the patient. Both a cohort study and a nested case-control study did not show an increased risk of cancer recurrence in patients with breast cancer on endocrine therapy who used vaginal estrogen when comparing quantity of use to non-use respectively.49,50 The Danish observational cohort study reported that while, overall, women with breast cancer who used local estrogen did not have an increased risk of cancer recurrence, the subgroup of patients on aromatase inhibitors did have an increased risk of cancer recurrence (1.39 [95% CI = 1.04-1.85), without an increase in mortality.51 Limitations of this study include that the doses of vaginal estrogen were not reported and may have been higher than the currently used low-dose vaginal estrogen, the study period pre-dated HER2 testing, which significantly affects the risk of recurrence, and that many patients were treated without any endocrine therapy.51 Another more recent retrospective study evaluated a group of 42 113 women diagnosed with genitourinary syndrome of menopause (GSM) following a breast cancer diagnosis, of which 3.9% utilized prescriptions for vaginal estrogen.52 The risk of recurrence was comparable between vaginal estrogen and the control group. Among the 10 584 patients with documented estrogen receptor-positive breast cancer, 3.9% used vaginal estrogen, and the risk of recurrence was similar. In a very small subgroup of patients identified to have concurrent prescriptions for vaginal estrogen and an aromatase inhibitor, there was an increased risk of breast cancer recurrence; however, results were not controlled for stage, grade, or nodal status.52 Lastly, a recent study of 49 237 women with breast cancer with 5% of patients using vaginal estrogen therapy found no increased risk of breast cancer-specific mortality (HR, 0.77; 95% CI, 0.63-0.94).53
Dyspareunia
Pain with penetration or dyspareunia has been reported in patients with breast cancer, gynecologic cancers, lung cancer, and colorectal cancer6,30,31,54,55 Dyspareunia can be caused by vaginal stenosis or narrowing, insertional dyspareunia, or pelvic floor dysfunction.56,57 Vaginal dilators help treat vaginal stenosis and should be recommended after pelvic radiation to reduce the risk of vaginal stenosis.56 Pelvic floor dysfunction frequently contributes to dyspareunia and can be treated with pelvic floor physical therapy and, in some cases surgery.57 For patients with insertional dyspareunia, the application of 4% topical aqueous lidocaine can reduce symptoms of pain at the time of penetration, however, lidocaine may cause reduced sensitivity for a penetrating partner.58-60
Sexual positions that limit discomfort
After a cancer diagnosis, certain sex positions that were once pleasurable may now cause symptoms such as fatigue, pain and/or discomfort, and shortness of breath.61 People experiencing decreased energy from diagnosis and treatment may benefit from side-lying positions and/or having their partner assume the active role.61 This may be particularly beneficial for patients with breast cancer or individuals with ostomy bags to avoid rubbing against sensitive areas.61 More so, patients with breathing concerns may want to avoid positions where they are lying flat on their back and may benefit from side-by-side and superior positions, which allow for better breathing and control.61,62 It is important that partners communicate with each other and try out different ways and positions to create an enjoyable experience. As symptoms may change over time, there is not one position that will be right for everyone every time. Pillows of different shapes and forms are a helpful tool to provide additional support to painful or uncomfortable areas.63 Certain areas that were once erogenous zones, such as the breasts, may no longer be after cancer, and identifying other pleasurable areas in the body should be encouraged by providers as patients learn new areas of stimulation and discover their new body during or after cancer treatments.63
Complementary medicine
Sexual concerns often occur due to changes in both physical and mental health.5,64 A cancer diagnosis can result in feelings and symptoms such as anxiety, depression, fear, guilt, anger, and loneliness, which can subsequently impact body image, sexual desire, and sexual functioning.64 Medications often used to treat anxiety and depression can result in sexual side effects such as decreased libido or inhibition of orgasm. This is further exacerbated in patients with cancer who identify as LGBTQI+ as they experience worse mental health outcomes, higher levels of depression and anxiety, discrimination, and cancer-related distress.65
The ASCO Clinical Practice guideline recommends that psychosocial and/or psychosexual counseling be offered to all patients with cancer, aiming to improve overall sexual functioning, body image perception, intimacy, and relationship issues.9 Studies utilizing cognitive-behavioral therapy, couples-based sexual education, self-healing training, and group therapy with guided imagery have shown benefit in female survivors of cancer.66 Pelvic floor physical therapy may be helpful for patients experiencing symptoms of potential pelvic floor dysfunction such as persistent pain and urinary and/or fecal leakage.66 Integrative medicine options such as acupuncture, slow-breathing techniques and hypnosis have also shown some benefit and are overall low-cost interventions.67-69 Throughout such ventures, though, lack of insurance coverage and availability of trained and knowledgeable professionals remains a barrier to implementation.66
Sexuality and social media
Disseminating existing treatment guidelines for sexual health in a way that is accessible and understandable to patients is vital.9 Notably, people are increasingly turning to social media for health information and advice.70,71 More so, it is theorized that traditional approaches to cancer care will not be able to manage the increase in survivorship care demands,72 especially given the rising number of cancer diagnoses each year and the increasing incidence of cancer in younger adults.73 This may be especially relevant to sexuality, as oncologists may not feel comfortable or well-trained to discuss the topic, and patients may be reluctant to bring up sexual concerns during their visits. Social media can play a role in studying sexual health and in sexuality interventions, particularly in adolescent and young adult (AYA) patients with cancer, who are more likely to seek such information online.74 Additionally, the ability to remain anonymous online may help patients with cancer feel more comfortable discussing sexuality.75,76 Social media is also used as a communication method to discuss sexuality education in LGBTQI+ communities, who are generally excluded from sexual education, indicating the potential use of social media to close the gap between the oncology community and sexuality for SGM and TGD individuals.77 While this remains a largely unexplored frontier, studies have utilized social media to describe the impact of cancer on sexuality.
Adams et al conducted a mixed methods study focusing on the psychosocial needs of survivors of gynecologic cancer.78 The authors analyzed discussion board posts made by survivors of gynecologic cancer on the American Cancer Society website and demonstrated that nearly 19% of the posts were related to the psychosocial experience of survivorship, including conversations on sexuality and intimacy.78 A survey conducted by Taylor et al, including, patients with multiple types of cancer, found that nearly 9 out of 10 respondents experienced changes after cancer treatment that resulted in a negative impact on their sexual health.12 The survey was administered electronically in a clinic setting and on social media (Facebook and Twitter) with 87% of participants expressing sexual dysfunction after cancer treatment.12 Breast cancer was one of the most common cancer diagnoses, and survivors reported sexual toxicities, including dyspareunia, difficulty achieving orgasm, distortion of body image resulting in intimacy issues, and infertility. Yet, only 44% of respondents had been told that cancer treatment could impact their sexuality and less than a third had been formally asked about their sexuality by a provider.12 The Women’s Insights in Sexual Health After Breast Cancer (WISH-BREAST) study surveyed women who had or have breast cancer on sexuality concerns and was disseminated using a social media platform (Instagram). There were 1775 respondents, highlighting that social media can serve as an effective research tool for people impacted by cancer.32 Research on sexuality and cancer can be enhanced through increased social media utilization, thereby leading to improved trial recruitment, and decreasing barriers to discussion in clinical settings.79,80 Despite this, social media currently remains a widely untapped resource. Digital health interventions using internet-based platforms, including educational information, interactive methods, and cognitive behavior therapy-based interventions, have led to improvements in sexual function, desire, and psychological well-being.72 However, these studies have been somewhat limited due to high drop-out rates and poor adherence rates, a common factor in digital health interventions.72 Proposed factors to improve engagement in the digital health space include institutional accreditation/health care practitioner endorsement, expert-led intervention development without commercial bias, security and confidentiality, platform usability, and personalization or tailoring of interventions.72 These factors must be leveraged in the social media space as well.
As many patients do not receive information on sexual health from their oncology providers,16,81 using social media for education and effective communication interventions may bridge this gap but requires healthcare professionals to be present and engaged on social media platforms that are predominantly patient facing. The WISH-BREAST study reported that 80% of respondents sought information about sexuality on social media, primarily from medical professional accounts.32 A growing number of medical professional accounts, patient-facing organizations, and healthcare organizations are providing this information on social media (Table 2). Drizin et al conducted semi-structured interviews to evaluate HCP perceived barriers and facilitators to social media communication about sexual health and strategies to help HCPs navigate social media use for this purpose.82 This was a small study of HCPs who had provided medical or supportive care to AYA cancer patients and survivors. Notable themes identified suggested that social media has the potential to facilitate patient-centered communication through a variety of ways: normalization of sexual health, encouragement to patients to engage in their sexuality care, and helping HCPs learn about the patient perspectives and needs.82 The importance of effective communication and learning about patient perspectives is exceedingly pertinent for SGM and TGD communities as oncology providers are often undereducated when it comes to these patient populations.14,21 However, challenges remain in doing this effectively such as concerns for professional social media use, lack of social media training, time constraints, and the need for brevity in social media posts.82
Table 2.
Patient resources. Please note that this list is not all-inclusive, just a few suggestions.
Type
Resource
Social media
Twitter
◦ @SGMCancerCARE◦ @cancersexnet◦ @drteplinsky◦ @oriordanliz◦ @LailaAgrawalMD◦ @NarjustFlorez
Instagram
◦ @oriordanliz◦ @drteplinsky◦ @drlailaagrawal◦ @kellycasperson◦ @drmennobgyn◦ @lgbtcancernetwork◦ @menopause_and_cancer
Podcasts
Interlude Women’s Cancer Stories with Dr. Eleonora Teplinsky
◦ Episode 125 “Sexual Health”◦ Episode 139 “Let’s Talk About Lung Cancer, Sexual Health, and Cancer Disparities”
You Are Not Broken, Kelly CaspersonMenopause and Cancer
Websites
Prosayla.com
—
An expert and patient reviewed source, supported by ISSWSH
Scientific Network on Female Sexual Health and Cancer
Cancer Support Community
American Cancer Society—Sex and the Adult Female with CancerBreastcancer.org
Home (meetrosy.com)
National Cancer Institute
Videos
How cancer treatment affects sexuality in women | Dana-Farber Cancer Institute
—
YouTube
Books
Come as You Are by Dr. Emily NagoskiSex and Cancer Intimacy, Romance, and Love After Diagnosis and Treatment by Dr. Saketh Guntupalli and Maryann KarinchWoman Cancer Sex by Dr. Anne KatzThe Better Sex Through Mindfulness Workbook by Dr. Lori BrottoYou are Not Broken by Dr. Kelly Casperson
The prevalence of misinformation on social media can make it challenging for patients to identify accurate information.83,84 In fact, in a quality review of social media articles on cancer treatment, 32.5% of articles contained misinformation.84 This is a significant concern and ensuring information comes from accredited sources and is backed by providers, evidence, government organizations, and cancer centers can mitigate this.85,86 The use of social media by these sources can improve the amount of misinformation available as disseminating accredited information through social media improves the chances that individuals seeking information online will find it from a credited source.84,86 Moreover, encouraging patients to feel comfortable discussing online sexual health resources with their HCPs is one way of “vetting” the information.
How to begin the conversation about sexuality
Providers and patients interested in starting a discussion about sexuality may need guidance on where to start the discussion.15 Guidelines for non-judgmental questions providers may use are available in Figure 2. Opening phrases patients may use to catalyze a conversation about sexuality are available in Figure 3. Clinicians can share this figure for patient education.
Figure 2.
Top seven ways for oncologist to discuss sexuality non-judgmentally36.
Figure 3.
Seven phases for patients to discuss sexuality with oncologists87.
Conclusions
Despite the existence of clinical guidelines for the treatment of sexuality concerns, this topic remains under-discussed and understudied, leading many people with cancer to be left in the dark.2,12 Current interventions and data on the incidence of sexual dysfunction and how it presents itself can be used to tailor programs so that they best meet the needs of varying cancer types and diverse populations. Social media is a largely untapped resource that can be used for research and to disseminate accessible information about sexuality to patients. Treatment guidelines, patient resources, and discussion frameworks as presented in this paper can provide oncologists with a framework to incorporate inclusive sexuality care in their practice. |
Title: LINC01857 promotes cell proliferation and migration while dampening cell apoptosis in pancreatic cancer by upregulating CDC42EP3 via miR-450b-5p | Body:
1
Introduction
As one of the most lethal malignancies, pancreatic cancer (PC) occurs in the pancreas where is a gland consisted of endocrine and exocrine cells [1]. The malignancy is mainly diagnosed in men and elderly adults from 60 to 85 years old but gradually become prevalent in young patients [2]. The etiology of PC is complex and diverse, and risk factors for PC development include smoking, family heredity, type 2 diabetes, and obesity [3]. The 5-year survival rate of PC patients is below 10 % [4]. Tumor metastasis to distant organs can be quickly developed in PC, posing challenges for treatment [5]. Chemotherapy, surgical resection, targeted therapy, radiotherapy, and combination regimens were common treatment options [6]. As to the targeted therapy, the identification of abnormally expressed genes and exploration of gene functions and related mechanisms are quite necessary.
Long noncoding RNAs (lncRNAs) are composed of overall 200 nucleotides at length and lack the protein coding capability [7]. Abnormally expressed lncRNAs in human cancer are implicated with carcinogenesis by regulating proliferation, drug resistance, apoptosis and metastasis [8]. As to mechanisms, previous studies discovered that lncRNAs can act as mediators of RNA interference, scaffolds for RNA complexes, decoys for transcription factors or miRNAs, or chromatin‐modifying proteins targeting specific genomic loci, and regulators of cis‐ or trans‐transcriptional processes [9]. Among the various mechanism, the roles of lncRNAs in regulating protein-coding genes at the transcriptional or post-transcriptional level have gained increasing attention [10,11]. At the post-transcriptional level, lncRNA is known to serve as a competing endogenous RNA (ceRNA) to interact with microRNAs (miRNAs/miRs) and thus hamper the suppressive impact of miRs on the expression of specific tumorigenesis-related target genes [12,13]. Recently, many lncRNAs have been reported to promote or repress malignant behavior of PC cells by mediating ceRNA networks. For example, lncRNA forkhead box D1 antisense RNA 1 (FOXD1-AS1) promotes tumorigenesis and self-renewal of cancer stem cells in PC [14]. LncRNA nuclear paraspeckle assembly transcript 1 (NEAT1) has recently been revealed to suppress PC cell metastasis and invasion by sponging miR-146b-5p and thus upregulating tumor necrosis factor receptor-associated factor 6 (TRAF6) [15]. LncRNA leucine zipper tumor suppressor 1 antisense RNA 1 (LZTS1-AS1) promotes PC cell proliferation and migration while repressing autophagy through increasing the expression of twist family bHLH transcription factor 1 (TWIST1) via interfering the inhibitory impact of miR-532 on TWIST1 [16].
LINC01857 has been confirmed to participate in many types of cancer, such as gastric cancer [17], breast cancer [18], glioma [19], lymphoma [20], and endometrial carcinoma [21]. Importantly, a report indicates that LINC01857 is highly expressed in PC tissues [22]. Another study validated the high expression of LINC01857 in pancreatic ductal adenocarcinoma and revealed the promoting effect of LINC01857 on epithelial-mesenchymal transition via binding with miR-19a-3p to alter the expression of secreted protein acidic and rich in cysteine-related modular calcium binding protein 2 (SMOC2) [23]. Compared with this article, our finding proposed a novel ceRNA network mediated by LINC01857 and focused on cell apoptosis in addition to cell proliferation and migration.
The miRNAs are single stranded and noncoding RNAs with 21–23 nucleotides at length, [24]. MiR-450b-5p was identified and explored in the present study. It was preliminary analyzed to be decreased in patients with ampullary adenocarcinoma, a malignancy similar to PC [25]. However, its role in PC were not reported yet. Cell division cycle 42 effector protein 3 (CDC42EP3) was chosen to be the target gene of miR450b-5p in this work. Though CDC42EP3 is a gene frequently reported in different types of cancer, there are no articles focusing on the role of CDC42EP3 in PC. The current study explored the function of two novel downstream factors of LINC01857 in PC.
In summary, the study aimed to explore the effects of LINC01857 on PC cell proliferation, migration, and apoptosis as well as a novel ceRNA network mediated by LINC01857. The study provides a promising therapeutic strategy for PC.
2
Materials and methods
2.1
Cell culture
Four PC cell lines (BXPC-3, CFPAC, MIA PaCa-2, PANC-1) and one human pancreatic ductal epithelial cell line (HPDE) were procured from ATCC (Manassas, USA). These cell lines were verified to be mycoplasma free. All the cell lines were authenticated through examination of morphology and growth characteristics and were confirmed to be free of mycoplasma. These cells were cultured in RPMI-1640 medium (Gibco, Thermo Fisher Scientific, Waltham, USA) with 10 % fetal bovine serum and 1 % penicillin/streptomycin (Gibco) at 37 °C with 5 % CO2. The passage numbers for cell lines are 2–3.
2.2
Reverse transcription and quantitative polymerase chain reaction (RT-qPCR)
TRIzol reagent (Invitrogen, USA) was utilized for RNA extraction. PrimeScript™ II Reverse Transcriptase (Takara, Kusatsu, Japan) was utilized for cDNA synthesis through RNA reverse transcription. ABI StepOne Plus PCR system (Applied Biosystems, Foster City, USA) together with SYBR Green PCR Kit (Takara) were employed for qPCR analysis. Each PCR was performed in triplicate. Gene expression was identified via the 2−ΔΔCt method and normalized to GAPDH or U6. Sequences of primers used in qPCR were listed in Table 1.Table 1Sequences of primers used for reverse transcription-quantitative PCR.Table 1GeneSequence (5’→3′)LINC01857Forward: CTCCACTGCGCTTTGTCCATReverse: GAGGCTTTGAGGATGGGGACmiR-2052Forward: TGTTTTGATAACAGTAATGTReverse: GAACATGTCTGCGTATCTCmiR-450b-5pForward: ACACTCCAGCTGGGTTTTGCAATATGTTCCReverse: TGGTGTCGTGGAGTCGmiR-580-5pForward: TTGAGAATGATGAATCATTAGReverse: GAACATGTCTGCGTATCTCmiR-4645-5pForward: ACAATATTTCTTGCCTGGTReverse: CAGTGCGTGTCGTGGAGTCDC42EP3Forward: CCTGGAAACCAGGAGAAAGCACReverse: GGAGATGGCATTTTTGAGCACCGFOXN3Forward: TGCAAATGCACCTACTGGGTGGReverse: CACCACAACGACCCTTTCCCAAAUTS2Forward: CCTCCTCATCACAGCAACTTCCReverse: GAAGGCATTGCCACCAACTGCTGAPDHForward: GTCTCCTCTGACTTCAACAGCGReverse: ACCACCCTGTTGCTGTAGCCAAU6Forward: CTCGCTTCGGCAGCACATReverse: TTTGCGTGTCATCCTTGCG
2.3
Cell transfection
The siRNAs specifically targeting LINC01857 (si-LINC01857) or CDC42EP3 (si-CDC42EP3) were obtained from Gene Pharma (Shanghai, China) to knock down LINC01857 or CDC42EP3 expression, and the corresponding nonspecific siRNAs were utilized as negative control (si-NC). miR-450b-5p mimics and NC mimics were procured form RiboBio (Guangzhou, China) to amplify miR-450b-5p expression. LINC01857 full-length sequences were inserted into pcDNA vector (Geenseed Biotech, Guangzhou, China) for upregulation of LINC01857 in PC cells, with the empty vector served as control. For cell transfection, a total of 50 nM si-LINC01857, 50 nM si-CDC42EP3, 50 nM si-NC, 30 nM miR-450b-5p mimics, 30 nM NC mimics, 30 nM pcDNA-LINC01857 or 30 nM pcDNA-NC were subjected to cell transfection utilizing Lipofectamine 3000 (Invitrogen) for 48 h. Transfection efficiency was verified by detecting LINC01857, miR-450b-5p, or CDC42EP3 expression before and after plasmid transfection using RT-qPCR.
2.4
Cell counting Kit-8 (CCK-8) assay
CCK-8 reagent (Beyotime, Shanghai, China) was employed for detection of cell viability. Briefly, PC cells were inoculated to 96-well plates (2 × 103 cells/well) in 100 μl of complete medium for one, two or three days of incubation. At the end of indicated days, 10 μl of CCK-8 reagent was added to each well for another 2 h of cell culture at 37 °C. The value of optical density at the wavelength of 450 nm was measured using a spectrophotometer (Molecular Devices, Gaithersburg, USA). Cell viability in the pcDNA3.1-LINC01587 group (LINC01587) was normalized to the control vector group (Control), while that in the si-LINC01857#1/2 group was compared to the si-NC group. The viability of cells in the LINC01857+si-CDC42EP3#1 group was compared to that in the LINC01857 group. Each reaction was repeated in triplicate, and the mean ± standard deviation was calculated for each value.
2.5
Colony formation
PC cells (500 cells/well) were put into culture plates (6-well) for 2 weeks of incubation. For every two or three days, the culture medium was changed with new medium. After that, colonies were first fixed with 4 % paraformaldehyde and then subjected to staining with 0.5 % crystal violet (Sigma Aldrich, St. Louis, USA). The colonies were quantified using the “ColonyArea” plugin of ImageJ software (National Institutes of Health, Bethesda, USA) after images were captured. The number of colonies containing ≥50 cells is counted and presented in a line graph.
2.6
Wound healing assay
PC cells were put to 24-well plates (1 × 105 cells/well) for 24 h of incubation. Upon reaching 95%–100 % confluency, scratches were made on cell monolayers with a 10 μL pipette tip to make wounds. At 0 h and 48 h, images of these scratches were captured under a microscope (Nikon, Tokyo, Japan). For each wound, images were captured from five different areas. ImageJ software was used to calculate the wound closure rate according to the formula of (wound areas at 0 h– the actual wound areas at 48 h)/(wound areas at 0 h). Data are shown as the mean ± standard deviation.
2.7
Flow cytometry
In accordance with user guides, Annexin V-FITC/PI kit (Solarbio, Beijing, China) was used to detect cell apoptosis. PC cells were rinsed and then subjected to resuspension in binding buffer. Next, PC cells were dyed by 5 μl Annexin V-FITC and 5 μl PI in dark. After that, the flow cytometer (Becton Dickinson, USA) was applied to detect the percentage of apoptotic cells. Cells in the (AnnexinV-FITC)+/PI+ (Q2) area and (AnnexinV-FITC)+/PI- (Q3) area were regarded as apoptotic cells. Cell apoptosis in the pcDNA3.1-LINC01587 group (LINC01587) was normalized to the control vector group (Control), while that in the si-LINC01857#1/2 group was compared to the si-NC group. The apoptotic rate of cells in the LINC01857+si-CDC42EP3#1 group was compared to that in the LINC01857 group.
2.8
Western blot
Cell lysis was performed with radio immunoprecipitation lysis buffer (Beyotime, 50 mL, 100 μL). Proteins were extracted from PC cells and quantified by bicinchoninic acid methods. Then the protein was isolated on 12 % SDS-PAGE and then shifted to PVDF membranes. Next, the membranes were blocked using 5 % fat-free milk. Primary antibodies (Thermo Fisher Scientific, USA) of anti-CDC42EP3 (PA5-97076) and anti-GAPDH (39–8600) at the dilution of 1/500 were added to membranes for incubation overnight at 4 °C. After that, horseradish peroxidase (HRP)-conjugated secondary antibodies were supplemented for another 2 h incubation. Antibodies were validated using the Labome website (https://www.labome.com/index.html). After membrane was washed by the TBST, protein signals were detected by ECL substance (Thermo Fisher Scientific) and analyzed by the ImageJ program. GAPDH served as the loading control.
2.9
FISH
RNA FISH probe specific to LINC01857 was synthesized by RiboBio (Guangzhou, China). Streptavidin-biotin system was used to detect lncRNA. Air-dried cells were incubated with 40 nmol/L probes in a hybridization buffer (RiboBio), and the nuclei were stained by DAPI. Analysis and imaging were performed using a fluorescence microscope (Olympus). The fluorescence signal intensity was processed using ImageJ software.
2.10
RNA pulldown assay
Pierce Magnetic RNA-Protein Pull-Down Kit (Thermo Fisher Scientific) was utilized for this assay. Transfection with biotinylated LINC01857 or biotinylated NC into PC cells for 48 h was first conducted, and then cells were incubated with lysis buffer. Next, 30 μl of Streptavidin magnetic beads (11641778001, Roche, Basel, Switzerland) was supplemented to the cell lysate after digestion with DNase I. RNA-protein mixture was treated with proteinase K, and the isolated RNA was quantified by RT-qPCR. The enrichment of miRNAs in the Bio-LINC01857 group were calculated compared with those in the bio-NC group.
2.11
Luciferase reporter gene assay
The putative binding site between LINC01857 and miR-450b-5p was predicted with the bioinformatics tool DIANA, and that between miR-450b-5p and CDC42EP3 3′-UTR was predicted via ENCORI database. LINC01857 or CDC42EP3 3′UTR segments having the binding area with miR-450b-5p were cloned to pmirGLO luciferase reporter vector (Promega, USA) for establishing the pmirGLO LINC01857-WT/Mut or CDC42EP3 3′UTR-WT/Mut. After that, the established plasmids were subjected to cotransfection with miR-450b-4p mimics/NC mimics into PC cells for 48 h using Lipofectamine 3000 (Invitrogen). Dual-Luciferase Reporter Assay System (#E1910, Promega) was applied to examine luciferase activities of fireflies (Photinus pyralis) and Renilla reniformis. The ratio of firefly luciferase activity to Renilla activity was defined as relative luciferase activity. The relative luciferase activity of LINC01857-Wt/Mut in PC cells transfected with miR-450b-5p mimics was compared to its activity in PC cells with NC mimics.
2.12
Statistical analyses
GraphPad PRISM 8 (GraphPad, La Jolla, USA) was used for statistical analysis. Data are presented as mean ± SD, n = 3 for all experiments. Comparison of differences among groups was evaluated using Student's t-test (for two groups) or one-way ANOVA (for three groups) followed by Tukey's post hoc analysis. The value of p < 0.05 was deemed as statistically significant. Specific p values were listed in Supplementary Table 1.
2.1
Cell culture
Four PC cell lines (BXPC-3, CFPAC, MIA PaCa-2, PANC-1) and one human pancreatic ductal epithelial cell line (HPDE) were procured from ATCC (Manassas, USA). These cell lines were verified to be mycoplasma free. All the cell lines were authenticated through examination of morphology and growth characteristics and were confirmed to be free of mycoplasma. These cells were cultured in RPMI-1640 medium (Gibco, Thermo Fisher Scientific, Waltham, USA) with 10 % fetal bovine serum and 1 % penicillin/streptomycin (Gibco) at 37 °C with 5 % CO2. The passage numbers for cell lines are 2–3.
2.2
Reverse transcription and quantitative polymerase chain reaction (RT-qPCR)
TRIzol reagent (Invitrogen, USA) was utilized for RNA extraction. PrimeScript™ II Reverse Transcriptase (Takara, Kusatsu, Japan) was utilized for cDNA synthesis through RNA reverse transcription. ABI StepOne Plus PCR system (Applied Biosystems, Foster City, USA) together with SYBR Green PCR Kit (Takara) were employed for qPCR analysis. Each PCR was performed in triplicate. Gene expression was identified via the 2−ΔΔCt method and normalized to GAPDH or U6. Sequences of primers used in qPCR were listed in Table 1.Table 1Sequences of primers used for reverse transcription-quantitative PCR.Table 1GeneSequence (5’→3′)LINC01857Forward: CTCCACTGCGCTTTGTCCATReverse: GAGGCTTTGAGGATGGGGACmiR-2052Forward: TGTTTTGATAACAGTAATGTReverse: GAACATGTCTGCGTATCTCmiR-450b-5pForward: ACACTCCAGCTGGGTTTTGCAATATGTTCCReverse: TGGTGTCGTGGAGTCGmiR-580-5pForward: TTGAGAATGATGAATCATTAGReverse: GAACATGTCTGCGTATCTCmiR-4645-5pForward: ACAATATTTCTTGCCTGGTReverse: CAGTGCGTGTCGTGGAGTCDC42EP3Forward: CCTGGAAACCAGGAGAAAGCACReverse: GGAGATGGCATTTTTGAGCACCGFOXN3Forward: TGCAAATGCACCTACTGGGTGGReverse: CACCACAACGACCCTTTCCCAAAUTS2Forward: CCTCCTCATCACAGCAACTTCCReverse: GAAGGCATTGCCACCAACTGCTGAPDHForward: GTCTCCTCTGACTTCAACAGCGReverse: ACCACCCTGTTGCTGTAGCCAAU6Forward: CTCGCTTCGGCAGCACATReverse: TTTGCGTGTCATCCTTGCG
2.3
Cell transfection
The siRNAs specifically targeting LINC01857 (si-LINC01857) or CDC42EP3 (si-CDC42EP3) were obtained from Gene Pharma (Shanghai, China) to knock down LINC01857 or CDC42EP3 expression, and the corresponding nonspecific siRNAs were utilized as negative control (si-NC). miR-450b-5p mimics and NC mimics were procured form RiboBio (Guangzhou, China) to amplify miR-450b-5p expression. LINC01857 full-length sequences were inserted into pcDNA vector (Geenseed Biotech, Guangzhou, China) for upregulation of LINC01857 in PC cells, with the empty vector served as control. For cell transfection, a total of 50 nM si-LINC01857, 50 nM si-CDC42EP3, 50 nM si-NC, 30 nM miR-450b-5p mimics, 30 nM NC mimics, 30 nM pcDNA-LINC01857 or 30 nM pcDNA-NC were subjected to cell transfection utilizing Lipofectamine 3000 (Invitrogen) for 48 h. Transfection efficiency was verified by detecting LINC01857, miR-450b-5p, or CDC42EP3 expression before and after plasmid transfection using RT-qPCR.
2.4
Cell counting Kit-8 (CCK-8) assay
CCK-8 reagent (Beyotime, Shanghai, China) was employed for detection of cell viability. Briefly, PC cells were inoculated to 96-well plates (2 × 103 cells/well) in 100 μl of complete medium for one, two or three days of incubation. At the end of indicated days, 10 μl of CCK-8 reagent was added to each well for another 2 h of cell culture at 37 °C. The value of optical density at the wavelength of 450 nm was measured using a spectrophotometer (Molecular Devices, Gaithersburg, USA). Cell viability in the pcDNA3.1-LINC01587 group (LINC01587) was normalized to the control vector group (Control), while that in the si-LINC01857#1/2 group was compared to the si-NC group. The viability of cells in the LINC01857+si-CDC42EP3#1 group was compared to that in the LINC01857 group. Each reaction was repeated in triplicate, and the mean ± standard deviation was calculated for each value.
2.5
Colony formation
PC cells (500 cells/well) were put into culture plates (6-well) for 2 weeks of incubation. For every two or three days, the culture medium was changed with new medium. After that, colonies were first fixed with 4 % paraformaldehyde and then subjected to staining with 0.5 % crystal violet (Sigma Aldrich, St. Louis, USA). The colonies were quantified using the “ColonyArea” plugin of ImageJ software (National Institutes of Health, Bethesda, USA) after images were captured. The number of colonies containing ≥50 cells is counted and presented in a line graph.
2.6
Wound healing assay
PC cells were put to 24-well plates (1 × 105 cells/well) for 24 h of incubation. Upon reaching 95%–100 % confluency, scratches were made on cell monolayers with a 10 μL pipette tip to make wounds. At 0 h and 48 h, images of these scratches were captured under a microscope (Nikon, Tokyo, Japan). For each wound, images were captured from five different areas. ImageJ software was used to calculate the wound closure rate according to the formula of (wound areas at 0 h– the actual wound areas at 48 h)/(wound areas at 0 h). Data are shown as the mean ± standard deviation.
2.7
Flow cytometry
In accordance with user guides, Annexin V-FITC/PI kit (Solarbio, Beijing, China) was used to detect cell apoptosis. PC cells were rinsed and then subjected to resuspension in binding buffer. Next, PC cells were dyed by 5 μl Annexin V-FITC and 5 μl PI in dark. After that, the flow cytometer (Becton Dickinson, USA) was applied to detect the percentage of apoptotic cells. Cells in the (AnnexinV-FITC)+/PI+ (Q2) area and (AnnexinV-FITC)+/PI- (Q3) area were regarded as apoptotic cells. Cell apoptosis in the pcDNA3.1-LINC01587 group (LINC01587) was normalized to the control vector group (Control), while that in the si-LINC01857#1/2 group was compared to the si-NC group. The apoptotic rate of cells in the LINC01857+si-CDC42EP3#1 group was compared to that in the LINC01857 group.
2.8
Western blot
Cell lysis was performed with radio immunoprecipitation lysis buffer (Beyotime, 50 mL, 100 μL). Proteins were extracted from PC cells and quantified by bicinchoninic acid methods. Then the protein was isolated on 12 % SDS-PAGE and then shifted to PVDF membranes. Next, the membranes were blocked using 5 % fat-free milk. Primary antibodies (Thermo Fisher Scientific, USA) of anti-CDC42EP3 (PA5-97076) and anti-GAPDH (39–8600) at the dilution of 1/500 were added to membranes for incubation overnight at 4 °C. After that, horseradish peroxidase (HRP)-conjugated secondary antibodies were supplemented for another 2 h incubation. Antibodies were validated using the Labome website (https://www.labome.com/index.html). After membrane was washed by the TBST, protein signals were detected by ECL substance (Thermo Fisher Scientific) and analyzed by the ImageJ program. GAPDH served as the loading control.
2.9
FISH
RNA FISH probe specific to LINC01857 was synthesized by RiboBio (Guangzhou, China). Streptavidin-biotin system was used to detect lncRNA. Air-dried cells were incubated with 40 nmol/L probes in a hybridization buffer (RiboBio), and the nuclei were stained by DAPI. Analysis and imaging were performed using a fluorescence microscope (Olympus). The fluorescence signal intensity was processed using ImageJ software.
2.10
RNA pulldown assay
Pierce Magnetic RNA-Protein Pull-Down Kit (Thermo Fisher Scientific) was utilized for this assay. Transfection with biotinylated LINC01857 or biotinylated NC into PC cells for 48 h was first conducted, and then cells were incubated with lysis buffer. Next, 30 μl of Streptavidin magnetic beads (11641778001, Roche, Basel, Switzerland) was supplemented to the cell lysate after digestion with DNase I. RNA-protein mixture was treated with proteinase K, and the isolated RNA was quantified by RT-qPCR. The enrichment of miRNAs in the Bio-LINC01857 group were calculated compared with those in the bio-NC group.
2.11
Luciferase reporter gene assay
The putative binding site between LINC01857 and miR-450b-5p was predicted with the bioinformatics tool DIANA, and that between miR-450b-5p and CDC42EP3 3′-UTR was predicted via ENCORI database. LINC01857 or CDC42EP3 3′UTR segments having the binding area with miR-450b-5p were cloned to pmirGLO luciferase reporter vector (Promega, USA) for establishing the pmirGLO LINC01857-WT/Mut or CDC42EP3 3′UTR-WT/Mut. After that, the established plasmids were subjected to cotransfection with miR-450b-4p mimics/NC mimics into PC cells for 48 h using Lipofectamine 3000 (Invitrogen). Dual-Luciferase Reporter Assay System (#E1910, Promega) was applied to examine luciferase activities of fireflies (Photinus pyralis) and Renilla reniformis. The ratio of firefly luciferase activity to Renilla activity was defined as relative luciferase activity. The relative luciferase activity of LINC01857-Wt/Mut in PC cells transfected with miR-450b-5p mimics was compared to its activity in PC cells with NC mimics.
2.12
Statistical analyses
GraphPad PRISM 8 (GraphPad, La Jolla, USA) was used for statistical analysis. Data are presented as mean ± SD, n = 3 for all experiments. Comparison of differences among groups was evaluated using Student's t-test (for two groups) or one-way ANOVA (for three groups) followed by Tukey's post hoc analysis. The value of p < 0.05 was deemed as statistically significant. Specific p values were listed in Supplementary Table 1.
3
Results
3.1
LINC01857 facilitates PC cell proliferation and migration while hampering cell apoptosis
As shown by expression analysis using a bioinformatics tool GEPIA, LINC01857 is markedly upregulated in pancreatic adenocarcinoma (PAAD) tissues (Fig. 1A, p < 0.05). RT-qPCR was then conducted to verify LINC01857 level in PC cells compared to that in HPDE cells. It was illustrated that LINC01857 was highly expressed in four PC cell lines, including BXPC-3 (p = 0.006), CFPAC (p = 0.0003), MIA PaCa-2 (p < 0.0001), and PANC-1 cells (p < 0.0001), especially in PANC-1 and MIA PaCa-2 cells compared with its expression in HPDE cells (8.9 folds and 7.1 folds) (Fig. 1B). Therefore, the two cell lines were identified for subsequent assays. Then, functional experiments were carried out to explore the biological role of LINC01857 in PC cells. PCR revealed that LINC01857 expression was effectively amplified through transfection of pcDNA-LINC01857 (p < 0.0001) and its expression was successfully reduced post transfection of si-LINC01857#1 and #2 in PANC-1 and MIA PaCa-2 cells (p < 0.05) (Fig. 1C). CCK-8 assays indicated that the OD value was promoted by LINC01857 overexpression (p < 0.01) and repressed by LINC01857 depletion (p < 0.05), suggesting that cell viability could be facilitated by LINC01857 overexpression and inhibited by the silencing of LINC01857 (Fig. 1D and E). The quantity of colonies formed was elevated via the transfection of pcDNA3.1-LINC01857 and was declined in the presence of LINC01857 depletion (Fig. 1F–G, p < 0.0001). The finding indicated the promoting role of LINC01857 in cell proliferation. Moreover, wound healing assays displayed that the number of migrated cells was elevated in the pcDNA-LINC01857 group (p < 0.0001) and was decreased in the si-LINC01857 cell group (p < 0.0005), indicating that LINC01857 facilitates cell migratory capability (Fig. 1H and I). As showed by flow cytometry, cell apoptosis rate was suppressed by LINC01857 upregulation and was enhanced in the context of LINC01857 knockdown (Fig. 1J, p ≤ 0.0001). In short, LINC01857 is abundantly expressed in PC cells and promotes PC malignant cell behavior.Fig. 1LINC01857 promotes malignant behavior of PC cells.(A) GEPIA database was utilized to predict LINC01857 expression in PAAD and normal tissues. (B) LINC01857 expression in PC cells and HPDE cells was tested via RT-qPCR. (C) The transfection efficiency of pcDNA3.1-LINC01857 or si-LINC01857 in PANC-1 and MIA PaCa-2 cells was measured via RT-qPCR. (D–G) Cell viability and proliferation were assessed via CCK-8 and colony formation assays after transfection with pcDNA-LINC01857 or si-LINC01857. (H–I) Wound healing assays were utilized to estimate cell migration after interference of LINC01857 expression. (J) Flow cytometry was performed to evaluate PC cell apoptosis after LINC01857 overexpression or depletion. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.Fig. 1
3.2
LINC01857 binds with miR-450b-5p
As showed by FISH, LINC01857 majorly localized in cytoplasm of PANC-1 and MIA PaCa-2 cells, implying that LINC01857 may exert its function post-transcriptionally (Fig. 2A). Hence, LINC01857 has the potential to function as a ceRNA to interact with miRNAs in PC. DIANA database was utilized to predict the possible miRNAs for LINC01857 [26]. According to the score ranking, the top four miRNAs were selected (Fig. 2B). RNA pulldown assay illustrated that only miR-450b-5p was prominently enriched in response to biotinylated LINC01857 (Bio-LINC01857) probe compared with its enrichment in the Bio-NC group (PANC-1: 12 folds; MIA PaCa-2: 13 folds, p < 0.0001), suggesting that LINC01857 could interact with miR-450b-5p (Fig. 2C). Furthermore, RT-qPCR showed the low levels of miR-450b-5p in PC cells (Fig. 2D, p < 0.0001). RT-qPCR results also showed the successful transfection of miR-450b-5p mimics, because miR-450b-5p level was markedly increased in PANC-1 and MIA PaCa-2 cells overexpressing miR-450b-5p in contrast to its expression in NC mimics group (5.6 folds and 4.8 folds) (Fig. 2E, p < 0.0001). The binding area of LINC01857 and miR-450b-5p was obtained in the DIANA database (Fig. 2F). Then, it was found that miR-450b-5p upregulation declined the luciferase activity of LINC01857-WT in PANC-1 cells (68 % decrease, p < 0.0001) and MIA PaCa-2 cells (70 % decrease, p < 0.0001), whereas LINC01857-Mut activity was not significantly changed between miR-450b-5p mimics group and NC mimics group (PANC-1: 1 vs 0.96, p = 0.8135; MIA PaCa-2: 1 vs 1.04, p = 0.8027) (Fig. 2G). These data confirmed that LINC01857 could combine with miR-450b-5p. Additionally, miR-450b-5p expression was elevated via LINC01857 depletion (7.9 and 5.8 folds) (Fig. 2H, p < 0.0001). Overall, LINC01857 binds to miR-450b-5p and inversely modulates its expression in PC cells.Fig. 2LINC01857 binds with miR-450b-5p.(A) FISH assay was utilized to determine the subcellular location of LINC01857 in PANC-1 and MIA PaCa-2 cells. (B) DIANA database was utilized to predict the candidate miRNAs for LINC01857. (C) RNA pulldown assay was employed for validating the combination of LINC01857 and candidate miRNAs (miR-2052/miR-450b-5p/miR-580-5p/miR-4645-5p). (D) MiR-450b-5p expression in PC cells was tested via RT-qPCR. (E) The transfection efficiency of miR-450b-5p mimics was measured via RT-qPCR. (F) DIANA database was applied for predicting the binding site of LINC01857 and miR-450b-5p. (G) The binding of LINC01857 and miR-450b-5p was validated through luciferase reporter assay. (H) RT-qPCR was employed for analyzing miR-4645-5p expression when LINC01857 was overexpressed or silenced. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.Fig. 2
3.3
CDC42EP3 is a target of miR-450b-5p
Downstream mRNAs that can be regulated by miR-450b-5p were further explored. In miRDB (http://mirdb.org/) databases with the searching criterion of Target Score = 100, three potential mRNAs were identified (Fig. 3A). RT-qPCR results illustrated that CDC42EP3 expression was decreased by miR-450b-5p overexpression (64 % decrease, p < 0.0001), while the expression of the other two mRNAs was almost unchanged (Fig. 3B–p > 0.8). Western blot further illustrated that CDC42EP3 level was repressed following miR-450b-5p upregulation (Fig. 3C). GEPIA database displays that CDC42EP3 is highly expressed in PAAD samples (Fig. 3D). In our experiments, CDC42EP3 was discovered to be abundantly expressed in four PC cells (Fig. 3E, p = 0.0013, p = 0.0006, p < 0.0001, p < 0.0001). The binding area of CDC42EP3 and miR-450b-5p was predicted from ENCORI (https://rnasysu.com/encori/) database (Fig. 3F). CDC42EP3-Wt luciferase activity was reduced by miR-450b-5p overexpression (p < 0.0001), while the CDC42EP3-Mut activity was not significantly altered by miR-450b-5p upregulation in PANC-1 and MIA PaCa-2 cells (Fig. 3G–H, p = 0.8873, p = 0.4031). RNA pulldown assays further validated the binding of miR-450b-5p to CDC42EP3. As shown by Fig. 3I, CDC42EP3 abundantly existed in the Bio-miR-450b-5p-Wt groups compared with its level in the Bio-NC group (56 and 49 folds, p < 0.0001). Additionally, there was no significant changes between Bio-NC and Bio-miR-450b-5p-Mut groups (Fig. 3I, p = 0.9771, p = 0.9942). The mRNA and protein levels of CDC42EP3 were elevated in response to LINC01857 upregulation and were reduced in the context of LINC01857 knockdown (Fig. 3J–K, p < 0.0001). In summary, CDC42EP3 is targeted and negatively modulated by miR-450b-5p in PC cells. Additionally, CDC42EP3 expression is positively correlated with LINC01857 expression.Fig. 3CDC42EP3 is targeted by miR-450b-5p.(A) The candidate mRNAs targeted by miR-450b-5p were predicted by miRDB database. (B) The expression of CDC42EP3, FOXN3 and AUTS2 was tested by RT-qPCR in PC cells overexpressing miR-450b-5p. (C) Western blot was utilized to test CDC42EP3 protein level in response to miR-450b-5p overexpression. Full images of blots can be found in supplementary material. (D) GEPIA database was utilized to predict CDC42EP3 expression in PAAD and normal tissues. (E) CDC42EP3 expression in PC cells and HPDE cells was tested via RT-qPCR. (F) The ENCORI database was applied for predicting the binding site of miR-450b-5p and CDC42EP3. (G–H) The binding of miR-450b-5p and CDC42EP3 was further validated through the luciferase reporter assay. (I) RNA pulldown assays were carried out to further explore the interaction of miR-450b-5p and CDC42EP3. (J–K) Western blot was carried out for measurement of CDC42EP3 RNA and protein levels in response to LINC01857 overexpression or depletion. Full images of blots are available in supplementary material. ∗∗p < 0.01, ∗∗∗p < 0.001.Fig. 3
3.4
LINC01857 influences cell malignant behavior in PC by upregulating CDC42EP3
At last, rescue assays were conducted to verify whether LINC01857 promotes malignant PC cell behavior by upregulating CDC42EP3. First, CDC42EP3 was silenced in PC cells via the transfection of si-CDC42EP3 plasmids. It was manifested that the levels of CDC42EP3 were inhibited by the si-CDC42EP3 transfection (p < 0.0001, Fig. 4A and B). Cell viability promoted by LINC01857 amplification could be reversed via CDC42EP3 downregulation (Fig. 4C and D, ∗p < 0.05, ∗∗p < 0.01). Further, colony formation assays indicated that CDC42EP3 silencing counteracted the promoting impact of LINC01857 overexpression on cell proliferation (Fig. 4E, p < 0.0001). Wound healing assays manifested that the promotive role of LINC01857 in cell migration was offset by knockdown of CDC42EP3 (Fig. 4F–G, p ≤ 0.0008). According to flow cytometry, cell apoptosis was inhibited by LINC01857 upregulation, while co-transfection of si-CDC42EP3 counteracted the inhibitory effect (Fig. 4H, p < 0.0001). In short, LINC01857 facilitates PC cell process via upregulating CDC42EP3 expression. More experiments were performed to investigate whether overexpressed CDC42EP3 can rescue the inhibitory effect of LINC01857 knockdown on malignant behavior of PC cells. As shown by Fig. 5A and B, CDC42EP3 mRNA and protein expression levels were successfully amplified post transfection of pcDNA3.1-CDC42EP3 vectors (p < 0.0001). Cells in the si-LINC01857 group had relatively low viability and proliferative capability compared with the si-NC group, and the trend was countervailed by CDC42EP3 upregulation (Fig. 5C–D, p < 0.0001). In addition, the reduction of PC cell migratory ability mediated by LINC01857 depletion was improved by overexpressed CDC42EP3 (Fig. 5E–p ≤ 0.0001). On the contrary, the silencing of LINC01857 contributed to a high apoptotic rate of PC cells (p < 0.0001), and the alteration was suppressed by CDC42EP3 overexpression in PANC-1 and MIA PaCa-2 cells (p = 0.0002, p = 0.0001) (Fig. 5F). The above findings demonstrated that LINC01857 promotes PC cell process by upregulating CDC42EP3.Fig. 4LINC01857 promotes malignant cell behavior in PC by upregulating CDC42EP3.(A–B) RT-qPCR and Western blot were conducted for estimating the transfection efficiency of si-CDC42EP3. Full images of blots can be found in supplementary material. (C–E) Cell viability and proliferation were assessed using CCK-8 and colony formation assays in the control group, the pcDNA3.1-LINC01857 group, and the pcDNA3.1-LINC01857+si-CDC42EP3#1 group. (F–G) Wound healing assay was performed for estimating cell migration in the above three groups. (H) Flow cytometry was utilized for measuring cell apoptosis in the above three groups. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.Fig. 4Fig. 5LINC01857 depletion inhibits PC cell process via downregulation of CDC42EP3.(A–B) RT-qPCR and western blotting were performed to measure the overexpression efficiency of CDC42EP3 in PANC-1 and MIA PaCa-2 cells. Full images of uncropped blots are available in Supplementary material. (C–D) CCK-8 and colony formation assays were performed to examine PC cell viability and proliferation in the si-NC, si-LINC01857#1, and si-LINC01857#1 + CDC42EP3 groups. (E–F) Wound healing assays and flow cytometry analyses were conducted to assess PC cell migration and apoptosis in the si-NC, si-LINC01857#1, and si-LINC01857#1 + CDC42EP3 groups. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.Fig. 5
3.1
LINC01857 facilitates PC cell proliferation and migration while hampering cell apoptosis
As shown by expression analysis using a bioinformatics tool GEPIA, LINC01857 is markedly upregulated in pancreatic adenocarcinoma (PAAD) tissues (Fig. 1A, p < 0.05). RT-qPCR was then conducted to verify LINC01857 level in PC cells compared to that in HPDE cells. It was illustrated that LINC01857 was highly expressed in four PC cell lines, including BXPC-3 (p = 0.006), CFPAC (p = 0.0003), MIA PaCa-2 (p < 0.0001), and PANC-1 cells (p < 0.0001), especially in PANC-1 and MIA PaCa-2 cells compared with its expression in HPDE cells (8.9 folds and 7.1 folds) (Fig. 1B). Therefore, the two cell lines were identified for subsequent assays. Then, functional experiments were carried out to explore the biological role of LINC01857 in PC cells. PCR revealed that LINC01857 expression was effectively amplified through transfection of pcDNA-LINC01857 (p < 0.0001) and its expression was successfully reduced post transfection of si-LINC01857#1 and #2 in PANC-1 and MIA PaCa-2 cells (p < 0.05) (Fig. 1C). CCK-8 assays indicated that the OD value was promoted by LINC01857 overexpression (p < 0.01) and repressed by LINC01857 depletion (p < 0.05), suggesting that cell viability could be facilitated by LINC01857 overexpression and inhibited by the silencing of LINC01857 (Fig. 1D and E). The quantity of colonies formed was elevated via the transfection of pcDNA3.1-LINC01857 and was declined in the presence of LINC01857 depletion (Fig. 1F–G, p < 0.0001). The finding indicated the promoting role of LINC01857 in cell proliferation. Moreover, wound healing assays displayed that the number of migrated cells was elevated in the pcDNA-LINC01857 group (p < 0.0001) and was decreased in the si-LINC01857 cell group (p < 0.0005), indicating that LINC01857 facilitates cell migratory capability (Fig. 1H and I). As showed by flow cytometry, cell apoptosis rate was suppressed by LINC01857 upregulation and was enhanced in the context of LINC01857 knockdown (Fig. 1J, p ≤ 0.0001). In short, LINC01857 is abundantly expressed in PC cells and promotes PC malignant cell behavior.Fig. 1LINC01857 promotes malignant behavior of PC cells.(A) GEPIA database was utilized to predict LINC01857 expression in PAAD and normal tissues. (B) LINC01857 expression in PC cells and HPDE cells was tested via RT-qPCR. (C) The transfection efficiency of pcDNA3.1-LINC01857 or si-LINC01857 in PANC-1 and MIA PaCa-2 cells was measured via RT-qPCR. (D–G) Cell viability and proliferation were assessed via CCK-8 and colony formation assays after transfection with pcDNA-LINC01857 or si-LINC01857. (H–I) Wound healing assays were utilized to estimate cell migration after interference of LINC01857 expression. (J) Flow cytometry was performed to evaluate PC cell apoptosis after LINC01857 overexpression or depletion. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.Fig. 1
3.2
LINC01857 binds with miR-450b-5p
As showed by FISH, LINC01857 majorly localized in cytoplasm of PANC-1 and MIA PaCa-2 cells, implying that LINC01857 may exert its function post-transcriptionally (Fig. 2A). Hence, LINC01857 has the potential to function as a ceRNA to interact with miRNAs in PC. DIANA database was utilized to predict the possible miRNAs for LINC01857 [26]. According to the score ranking, the top four miRNAs were selected (Fig. 2B). RNA pulldown assay illustrated that only miR-450b-5p was prominently enriched in response to biotinylated LINC01857 (Bio-LINC01857) probe compared with its enrichment in the Bio-NC group (PANC-1: 12 folds; MIA PaCa-2: 13 folds, p < 0.0001), suggesting that LINC01857 could interact with miR-450b-5p (Fig. 2C). Furthermore, RT-qPCR showed the low levels of miR-450b-5p in PC cells (Fig. 2D, p < 0.0001). RT-qPCR results also showed the successful transfection of miR-450b-5p mimics, because miR-450b-5p level was markedly increased in PANC-1 and MIA PaCa-2 cells overexpressing miR-450b-5p in contrast to its expression in NC mimics group (5.6 folds and 4.8 folds) (Fig. 2E, p < 0.0001). The binding area of LINC01857 and miR-450b-5p was obtained in the DIANA database (Fig. 2F). Then, it was found that miR-450b-5p upregulation declined the luciferase activity of LINC01857-WT in PANC-1 cells (68 % decrease, p < 0.0001) and MIA PaCa-2 cells (70 % decrease, p < 0.0001), whereas LINC01857-Mut activity was not significantly changed between miR-450b-5p mimics group and NC mimics group (PANC-1: 1 vs 0.96, p = 0.8135; MIA PaCa-2: 1 vs 1.04, p = 0.8027) (Fig. 2G). These data confirmed that LINC01857 could combine with miR-450b-5p. Additionally, miR-450b-5p expression was elevated via LINC01857 depletion (7.9 and 5.8 folds) (Fig. 2H, p < 0.0001). Overall, LINC01857 binds to miR-450b-5p and inversely modulates its expression in PC cells.Fig. 2LINC01857 binds with miR-450b-5p.(A) FISH assay was utilized to determine the subcellular location of LINC01857 in PANC-1 and MIA PaCa-2 cells. (B) DIANA database was utilized to predict the candidate miRNAs for LINC01857. (C) RNA pulldown assay was employed for validating the combination of LINC01857 and candidate miRNAs (miR-2052/miR-450b-5p/miR-580-5p/miR-4645-5p). (D) MiR-450b-5p expression in PC cells was tested via RT-qPCR. (E) The transfection efficiency of miR-450b-5p mimics was measured via RT-qPCR. (F) DIANA database was applied for predicting the binding site of LINC01857 and miR-450b-5p. (G) The binding of LINC01857 and miR-450b-5p was validated through luciferase reporter assay. (H) RT-qPCR was employed for analyzing miR-4645-5p expression when LINC01857 was overexpressed or silenced. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.Fig. 2
3.3
CDC42EP3 is a target of miR-450b-5p
Downstream mRNAs that can be regulated by miR-450b-5p were further explored. In miRDB (http://mirdb.org/) databases with the searching criterion of Target Score = 100, three potential mRNAs were identified (Fig. 3A). RT-qPCR results illustrated that CDC42EP3 expression was decreased by miR-450b-5p overexpression (64 % decrease, p < 0.0001), while the expression of the other two mRNAs was almost unchanged (Fig. 3B–p > 0.8). Western blot further illustrated that CDC42EP3 level was repressed following miR-450b-5p upregulation (Fig. 3C). GEPIA database displays that CDC42EP3 is highly expressed in PAAD samples (Fig. 3D). In our experiments, CDC42EP3 was discovered to be abundantly expressed in four PC cells (Fig. 3E, p = 0.0013, p = 0.0006, p < 0.0001, p < 0.0001). The binding area of CDC42EP3 and miR-450b-5p was predicted from ENCORI (https://rnasysu.com/encori/) database (Fig. 3F). CDC42EP3-Wt luciferase activity was reduced by miR-450b-5p overexpression (p < 0.0001), while the CDC42EP3-Mut activity was not significantly altered by miR-450b-5p upregulation in PANC-1 and MIA PaCa-2 cells (Fig. 3G–H, p = 0.8873, p = 0.4031). RNA pulldown assays further validated the binding of miR-450b-5p to CDC42EP3. As shown by Fig. 3I, CDC42EP3 abundantly existed in the Bio-miR-450b-5p-Wt groups compared with its level in the Bio-NC group (56 and 49 folds, p < 0.0001). Additionally, there was no significant changes between Bio-NC and Bio-miR-450b-5p-Mut groups (Fig. 3I, p = 0.9771, p = 0.9942). The mRNA and protein levels of CDC42EP3 were elevated in response to LINC01857 upregulation and were reduced in the context of LINC01857 knockdown (Fig. 3J–K, p < 0.0001). In summary, CDC42EP3 is targeted and negatively modulated by miR-450b-5p in PC cells. Additionally, CDC42EP3 expression is positively correlated with LINC01857 expression.Fig. 3CDC42EP3 is targeted by miR-450b-5p.(A) The candidate mRNAs targeted by miR-450b-5p were predicted by miRDB database. (B) The expression of CDC42EP3, FOXN3 and AUTS2 was tested by RT-qPCR in PC cells overexpressing miR-450b-5p. (C) Western blot was utilized to test CDC42EP3 protein level in response to miR-450b-5p overexpression. Full images of blots can be found in supplementary material. (D) GEPIA database was utilized to predict CDC42EP3 expression in PAAD and normal tissues. (E) CDC42EP3 expression in PC cells and HPDE cells was tested via RT-qPCR. (F) The ENCORI database was applied for predicting the binding site of miR-450b-5p and CDC42EP3. (G–H) The binding of miR-450b-5p and CDC42EP3 was further validated through the luciferase reporter assay. (I) RNA pulldown assays were carried out to further explore the interaction of miR-450b-5p and CDC42EP3. (J–K) Western blot was carried out for measurement of CDC42EP3 RNA and protein levels in response to LINC01857 overexpression or depletion. Full images of blots are available in supplementary material. ∗∗p < 0.01, ∗∗∗p < 0.001.Fig. 3
3.4
LINC01857 influences cell malignant behavior in PC by upregulating CDC42EP3
At last, rescue assays were conducted to verify whether LINC01857 promotes malignant PC cell behavior by upregulating CDC42EP3. First, CDC42EP3 was silenced in PC cells via the transfection of si-CDC42EP3 plasmids. It was manifested that the levels of CDC42EP3 were inhibited by the si-CDC42EP3 transfection (p < 0.0001, Fig. 4A and B). Cell viability promoted by LINC01857 amplification could be reversed via CDC42EP3 downregulation (Fig. 4C and D, ∗p < 0.05, ∗∗p < 0.01). Further, colony formation assays indicated that CDC42EP3 silencing counteracted the promoting impact of LINC01857 overexpression on cell proliferation (Fig. 4E, p < 0.0001). Wound healing assays manifested that the promotive role of LINC01857 in cell migration was offset by knockdown of CDC42EP3 (Fig. 4F–G, p ≤ 0.0008). According to flow cytometry, cell apoptosis was inhibited by LINC01857 upregulation, while co-transfection of si-CDC42EP3 counteracted the inhibitory effect (Fig. 4H, p < 0.0001). In short, LINC01857 facilitates PC cell process via upregulating CDC42EP3 expression. More experiments were performed to investigate whether overexpressed CDC42EP3 can rescue the inhibitory effect of LINC01857 knockdown on malignant behavior of PC cells. As shown by Fig. 5A and B, CDC42EP3 mRNA and protein expression levels were successfully amplified post transfection of pcDNA3.1-CDC42EP3 vectors (p < 0.0001). Cells in the si-LINC01857 group had relatively low viability and proliferative capability compared with the si-NC group, and the trend was countervailed by CDC42EP3 upregulation (Fig. 5C–D, p < 0.0001). In addition, the reduction of PC cell migratory ability mediated by LINC01857 depletion was improved by overexpressed CDC42EP3 (Fig. 5E–p ≤ 0.0001). On the contrary, the silencing of LINC01857 contributed to a high apoptotic rate of PC cells (p < 0.0001), and the alteration was suppressed by CDC42EP3 overexpression in PANC-1 and MIA PaCa-2 cells (p = 0.0002, p = 0.0001) (Fig. 5F). The above findings demonstrated that LINC01857 promotes PC cell process by upregulating CDC42EP3.Fig. 4LINC01857 promotes malignant cell behavior in PC by upregulating CDC42EP3.(A–B) RT-qPCR and Western blot were conducted for estimating the transfection efficiency of si-CDC42EP3. Full images of blots can be found in supplementary material. (C–E) Cell viability and proliferation were assessed using CCK-8 and colony formation assays in the control group, the pcDNA3.1-LINC01857 group, and the pcDNA3.1-LINC01857+si-CDC42EP3#1 group. (F–G) Wound healing assay was performed for estimating cell migration in the above three groups. (H) Flow cytometry was utilized for measuring cell apoptosis in the above three groups. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.Fig. 4Fig. 5LINC01857 depletion inhibits PC cell process via downregulation of CDC42EP3.(A–B) RT-qPCR and western blotting were performed to measure the overexpression efficiency of CDC42EP3 in PANC-1 and MIA PaCa-2 cells. Full images of uncropped blots are available in Supplementary material. (C–D) CCK-8 and colony formation assays were performed to examine PC cell viability and proliferation in the si-NC, si-LINC01857#1, and si-LINC01857#1 + CDC42EP3 groups. (E–F) Wound healing assays and flow cytometry analyses were conducted to assess PC cell migration and apoptosis in the si-NC, si-LINC01857#1, and si-LINC01857#1 + CDC42EP3 groups. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.Fig. 5
4
Discussion
PC is regarded as a fatal malignancy because the disease can quickly invade surrounding tissue and organs with no early symptoms [2]. Therefore, understanding the molecular mechanism of PC progression is vital to develop new treatments. Increasing evidence has confirmed that lncRNAs exert crucial functions in cancer tumorigenesis [27]. In PC, many lncRNAs are identified to be upregulated or downregulated in human cancers and participates in regulating PC progression, such as BCAB-AS1 [28], LINC01268 [29], and GATA6-AS1 [30]. In this study, LINC01857 was verified to be upregulated in PC cells compared with its expression in pancreatic ductal epithelial cells. The finding is consistent with the conclusion of a previous article authored by Giulietti et al., which identified the upregulation of LINC01857 in PC tissues [22]. This work further investigated the biological role of LINC01857 in PC. The silencing of LINC01857 repressed cell proliferative and migratory capabilities while promoting cell apoptosis in PC. LINC01857 overexpression had the opposite effect, indicating the oncogenic role of LINC01857 in PC. Results of functional experiments regarding the effect of LINC01857 on cell proliferation and migration were in line with those reported in a recent study [23]. Moreover, the carcinogenic role of LINC01857 has also been mentioned in other types of cancer such as diffuse large B-cell lymphoma and gastric cancer [17,20].
Recently, the ceRNA regulatory network of lncRNA/miRNA/mRNA has been confirmed in various cancers [27]. The regulatory axis involves the interaction of lncRNAs with miRNAs, leading to indirect regulation of mRNA levels at a post-transcriptional stage [31]. The ceRNA network represents a promising regulatory mechanism for RNA interactions, and it has been experimentally validated to govern the progression and development of cancer [13,32]. The prerequisite for ceRNA mechanism is the cytoplastic localization of lncRNA [33]. Consistent with available literature, LINC01857, in this study, was discovered to predominantly exist in cytoplasm of PC cells. The ceRNA function of LINC01857 verified in this study is in accordance with previous articles centered on its role in glioma [19] and lymphoma [20].
Moreover, miR-450b-5p was verified to be the downstream miRNA inversely regulated by LINC01857. Like its role in the present work, miR-450b-5p was frequently reported to participate in ceRNA networks mediated by lncRNAs. For example, it was reported to interact with LINC00441 and target RAB10 in cervical cancer [34]. miR-450b-5p acts as a downstream factor of LINC00641 in colorectal cancer and constitutes the LINC00641/miR-450b-5p/GOLPH3 axis [35]. In addition, the low expression of miR-450b-5p in PC cells in the current work is in line with the results of miRNA profiling in PC authored by Calatayud et al., which also confirmed the prognostic role of miR-450b-5p for patients with PC [25].
CDC42EP3 is a member of Cdc42 effector protein family and exerts a crucial role in assorted cellular process, including cell polarization and neural progression [36]. In the present study, CDC42EP3 was targeted and negatively modulated by miR-450b-5p in PC cells. High CDC42EP3 expression levels in PC cells are in accordance with bioinformatics analysis using GEPIA (data source: The Cancer Genome Atlas) [37]. Moreover, rescue experiments manifested that CDC42EP3 depletion offset the promotive impact of LINC01857 upregulation on malignant cellular behavior, while overexpressed CDC42EP3 reversed the suppressive impact of LINC01857 knockdown on PC cell process. The current findings verified the oncogenic role of CDC42EP3 in regulating PC cells, and LINC01857 facilitates PC cell proliferation and metastasis while repressing apoptosis by upregulating CDC42EP3. The expression and function of CDC42EP3 were first reported in PC. Its carcinogenic role in PC is not aberrant from previous reports on CDC42EP3 in other types of cancer. For example, CDC42EP3 facilitates colorectal cancer progression by modulating cell proliferation and migration [38]. CDC42EP3 is a critical modulator participating in the development of gastric cancer [39].
Overall, this study demonstrates that overexpression of LINC01857 facilitates cell proliferation and migration via interacting with miR-450b-5p to elevate CDC42EP3 expression. These discoveries may provide new therapeutic targets for precision medicine in PC. The limitations of this study lie in the absence of in vivo experiments for further verification of the regulatory axis mediated by LINC01857 in PC and the lack of exploration regarding the upstream transcription factor of LINC01857 and the signaling pathways mediated by CDC42EP3. Animal experiments and in-depth exploration of molecules involved in the axis can be the direction for future work.
Ethics approval
Not applicable.
Funding
This work is supported by the 10.13039/501100001809National Natural Science Foundation of China (No.81902501).
Data availability statement
The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.
CRediT authorship contribution statement
Jian-Xin Zhang: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Yan-Bin Shen: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Dan-Dan Ma: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation. Zhong-Hu Li: Supervision, Software, Resources, Project administration, Methodology. Zhi-Yong Zhang: Validation, Supervision, Software, Resources. Wei-Dong Jin: Supervision, Software.
Declaration of competing interest
None. |
Title: Case report: Pancreatic metastasis from small-cell lung cancer appears as primary G2 pancreatic neuroendocrine tumor on combined contrast PET imaging with three probes | Body:
1
Introduction
Lung carcinoma rarely metastasizes to the pancreas, which accounts for approximately 0.6% of metastatic lung cancer. Small-cell lung cancer (SCLC) is the most common histological type of lung carcinoma metastasizing to pancreas, because it has a strong propensity to metastasize (1). Due to the special anatomical location of the pancreas and the high rate of complications of needle biopsy, therefore, the commonly used diagnostic criteria for pancreatic lesions is mainly based on clinical history and radiologic imaging. With the development of nuclear medicine diagnostics and treatment, the application of multi-tracer imaging modalities provided a strong basis for the precise diagnosis and effective treatment of malignant tumors; especially in cases where pathology acquisition conditions are limited, it can provide reference information for physicians. However, when it comes to the pancreatic metastases, which belongs to endocrine organ, it will be challenging to use the metabolic imaging of PET/computed tomography (CT) for differential diagnosis, and there will be a possibility of misdiagnosis; hence, the gold standard for diagnosis is confirmed by histology or cytology.
In this case, we will report on a patient with metachronous multiple primary cancers of SCLC treated 5 years ago and prostate cancer 1 years ago. The patient underwent 18F-fluoro-2-deoxy-2-D-glucose (18F-FDG) PET/CT because of bone pain, and it revealed space-occupying lesion in pancreas and left acetabulum, which was considered to be both metastatic cancers. In order to identify the primary lesion, he successively underwent examination of 18F-PSMA-1007 PET/CT and 18F-NOTA-JR11 PET/CT. By comparing tumors uptake of three different tracers, the result indicated a high possibility of primary grade 2 pancreatic neuroendocrine neoplasm; then, we performed endoscopic ultrasound-guided fine-needle aspiration biopsy and arrived at a final diagnosis of pancreatic metastasis and bone metastasis of SCLC. This report illustrates the complex diagnostic process and describes a very rare case of pancreatic metastasis of small-cell lung carcinoma, which may provide a valuable reference for the diagnosis of this type of patient. Moreover, it emphasizes the irreplaceability of pathology and the non-specific on PET/CT of pancreatic metastasis.
2
Case report
We present the case of a 69-year-old man with a history of 20 pack-years of smoking, who underwent CT scan due to 4 months of a dry cough under no obvious inducement on 14 December 2018. The CT revealed a dense mass in the upper lobe of the left lung which measured approximately 2.8 cm × 1.5 cm in size; moreover, there were soft-tissue density lesions in the anterior mediastinum and left hilar area, which sized about 4.3 cm × 3.1cm and 3.4 cm × 3.0 cm, respectively (
Figures 1A, C, E
). The result of bronchoscopic transbronchial lung biopsy supported SCLC based on the immunohistochemistry analysis that revealed a positive staining for pan-cytokeratin (CKpan), thyroid transcription factor 1 (TTF-1), protein phosphatase 1 (Ki-67) (approximately 90%), synaptophysin (Syn), and cluster of differentiation 56 (CD56) and a negative staining for Tumor prot ein 63 (P63), Cytokeratins (CKs) 5 and 6 (CK5/6), and cytokeratin (CK7). Therefore, he was diagnosed with limited-stage SCLC and treated with concurrent cisplatin and etoposide chemotherapy combined with thoracic radiotherapy and standard prophylactic brain radiation. The last time for treatment was 17 July 2019, and the CT scan reported that the lesions size was about 1.1 cm × 0.5 cm in the upper lobe of the left lung and 2.0 cm × 1.3 cm and 1.9 cm × 1.8 cm in the anterior mediastinum and left hilar area, respectively (
Figures 1B, D, F
). Subsequently, the patient had regular follow-up examinations.
Figure 1
CT showed the comparison of lung lesions before (A, C, E) and after (B, D, F) the treatment of concurrent chemoradiotherapy. (A, B) show the tumor location of the primary lesion in the left upper lobe before and after treatment. (C, D) show the location of the tumor before and after treatment of the left hilar lymph node metastases. (E, F) show the tumor location before and after treatment of anterior mediastinal lymph node metastases.
The patient was admitted on 3 March 2022 because his prostate-specific antigen (PSA) continued to rise over 6 months. Further magnetic resonance imaging (MRI) showed uneven signal in the central gland with Prostate Imaging Reporting and Data System (PI-RADS) assessment category 5, and the signal in the peripheral zone was reduced with PI-RADS 2. Targeted prostate biopsy was performed, which identified prostate adenocarcinoma component within the lesion. Therefore, he underwent robot assisted laparoscopic radical prostatectomy and abdominal mass resection. Postoperative pathology confirmed adenocarcinoma of the prostate and seminal vesicles. The Gleason score was 4 + 4; moreover, it also contained a small amount of Gleason 5 component (<5%). The tumor accounted for approximately 50% of the total prostate volume, involving bilateral lobes of the prostate and periprostatic adipose tissue; in addition, the infiltration of blood vessels, nerves, and lateral resection margin of prostate urethra could be seen. The immunohistochemistry analysis revealed a positive staining for Ki-67 (approximately 10%), PSA, P504S, and Syn and a negative staining for P63, PCgA, and 34βE12. Therefore, he was diagnosed with stage pT3aN0M0, and the administration of Flutamide started in May 2022, in which the dose was 750 mg/day, divided into three doses orally.
On 6 October 2023, he underwent 18F-FDG PET/CT for bone pain, which demonstrated that the dense streak shadow with low glucose metabolism in the upper lobe of the left lung was consistent with change after treatment and tumor activity was inhibited. Moreover, the presentation of PET was also consistent with postoperative manifestation of prostate cancer, and no abnormal hypermetabolic lesions were found in the surgical area. Hypermetabolic activity and osteolytic bone destruction sized about 3.8 cm × 3.2 cm were distinguished in left acetabulum lesion, which was considered bone metastasis (SUV max = 7.3). In addition, there were lesions with hypermetabolic activity in the pancreas, which was also supposed to be metastases (SUV max = 4.2) (
Figures 2A, D
), and the largest one sized about 5.1 cm × 2.8 cm on MRI (
Figures 3A, C
). The conclusion of 18F-PSMA-1007 PET/CT was in accordance with 18F-FDG PET/CT (
Figures 2B, E
). Then, 18F-NOTA-JR11 PET/CT was performed to compare with 18F-PSMA-1007 PET/CT and 18F-FDG PET/CT, which showed multiple hypermetabolic lesions in the head and tail of the pancreas, exhibiting an abnormally high 18F-NOTA-JR11 uptake (SUV max = 17.6) (
Figures 2C, F
) but a moderate 18F-FDG and 18F-PSMA-1007 uptake. Therefore, the high possibility of G2 grade primary neuroendocrine tumors (NETs) of the pancreas was considered. Moreover, the lesion in the left acetabulum presented the similar pattern of uptake (SUV max = 31.9), which was considered pancreatic NET with bone metastasis. The serum levels of tumor markers were as follows: Carbohydrate antigen 199 (CA199) of 33.68 U/mL and Neu-ron specific enolase (NSE) of 23.74 ng/mL. To identify the primary lesion, we performed endoscopic ultrasound-guided fine-needle aspiration biopsy and percutaneous puncture under ultrasound guidance for bone biopsy. The result of bone puncture biopsy supported small-cell carcinoma, which was likely to originate from the lung based on the immunohistochemistry and morphology analysis, and immunohistochemistry results were a positive staining for Ki-67 (approximately 80%), CKpan, TTF-1, CgA, Syn, Insulinoma-associated protein 1 (INSM1), Retinoblastoma gene product (RBGP), and P53 (approximately 80%) and a strongly positive staining for SSTR2; simultaneously, it indicated a negative staining for NapsinA, P504S, PSA, Vimentin, CK7, P40, and NKX3.1 (
Figure 4C
). For the tissue of pancreas, abnormal cells were found in the coagulation tissue, which supported the diagnosis of small-cell neuroendocrine carcinoma (NEC), and immunohistochemistry analysis showed a positive staining for Ki-67 (approximately 40%), P53, CK, Syn, CD56, CK19, INSM1, and alpha-thalassemia mental retardation X-linked (ATRX) and a negative staining for CK7, CK20, villin, CEA, CgA, and RbGP (
Figures 4A, B
). In the meanwhile, pancreas tissue sample was obtained, and a comprehensive genomic profiling was performed by high-throughput sequencing using a 520 cancer-related gene panel, which demonstrated RB1 p.Y454 and TP53 p.G266R variant (germline mutation). The treatment consisted of cisplatin + etoposide + Atezolizumab + denosumab starting on 15 November, and, after 2 courses of treatment, the overall efficacy was evaluated as partial response, and the largest pancreas lesion size was about 3.4 cm × 1.5 cm (
Figures 3B, D
).
Figure 2
PET/CT showed comparison of the new lesions using 18F-FDG (A, D), 18F-PSMA (B, E), and 18F-JR11 (C, F) tracers.
Figure 3
MRI showed the comparison of pancreatic lesions before (A, C) and after (B, D) the treatment of chemotherapy. (A, B) are MRI images of horizontal pancreas before and after treatment, and (C, D) are MRI images of coronal pancreas before and after treatment. The red line arrows point to the location of pancreatic lesions before and after treatment.
Figure 4
The pathological images of the pancreatic (A, B) (×100) and bone metastases (C) (×10), respectively.
3
Discussion
In this paper, it was difficult to determine the nature of the lesions because the patient had a history of malignant tumors including SCLC and prostate cancer, which both have a strong predilection to metastasize to bone. SCLC is characterized by its extremely aggression and ability to metastasize to a variety of anatomic sites such as the bones, liver, and brain (2), with a 27%–41% incidence of bone metastases (3). Meanwhile, bone is also a preferred site for prostate cancer cells; approximately 5%–10% of newly diagnosed prostate cancer patients showed evidence of bone metastasis (4, 5); therefore, bone space-occupying lesions may be secondary metastases of one of the two malignant tumors mentioned above. On the other hand, whether lung cancer or prostate cancer, it is considerably rare to spread to the pancreas. Preliminary PET/CT results revealed that no increased 18F-FDG uptake was seen in the lungs or prostate, whereas pancreatic and bone lesions were likely to be metastases. Therefore, it is important to identify the primary tumor in metastatic disease. Moreover, it is necessary to clarify whether the pancreas and bone lesions belonged to the same pathological type. Although pathology diagnosis is known as the gold standard for disease diagnosis, the pancreas is a retroperitoneal organ and it has a deep anatomical position with complicated surrounding structures; biopsies could be painful and carry risks of complications, toward which patient had negative attitudes. With the rapid development of computer technology and medical imaging technology, PET/CT imaging has been widely recognized in clinical practice for diagnosis and clinical staging for the advantages of functional and anatomical structural imaging as well as non-invasiveness, which has become an important part of precision medicine. To respect patient wishes and to avoid as much as possible the use of an invasive operation, thereby, we wanted to make a further diagnosis through imaging technology. Considering the patient was diagnosed with prostate cancer last year, therefore, it is necessary to firstly identify whether new lesions originated from the prostate. Nowadays, prostate-specific membrane antigen (PSMA) PET/CT is an emerging imaging method, which is becoming a promising method for staging in prostate cancer (6). PSMA, a type II transmembrane glycoprotein, is highly overexpressed in prostate cancer epithelial cells, which was 100–1,000 times higher than that in normal cells (6, 7). Moreover, PSMA expression increased incrementally with increasing cancer stages and tumor grades in prostate cancer, especially in high-grade, metastatic, and castration-resistant disease (8). Therefore, PSMA is an excellent target for specific imaging and targeted therapy for prostate cancer; with 18F-PSMA-1007 PET/CT, disease recurrence could be identified at a low level of serum PSA (9). The patients underwent PET with 18F-labeled PSMA ligands, aiming to determine whether there was evidence of progressive prostate cancer, whereas imaging test identified no increased metabolic activity in prostate cancer post-operative area; meanwhile, the mean SUV of the bone and pancreas lesion reported was slightly high which were 4.9 and 7.5, respectively. Considering the patient’s PSA was within the reference value, we estimated that new lesions were less likely to be metastasized from prostate cancer.
Considering the history of SCLC, therefore, another non-invasive method to verify the possibility of pulmonary source was urgently needed. To meet the needs of clinical treatment and diagnosis, promising imaging technology on the basis of specific biological targets to cancer are being explored. Somatostatin receptor (SSTR) is overexpressed on the majority of neuroendocrine neoplasms cell surface, and somatostatin receptor subtype 2 (SSTR2) has become an essential target for diagnosis and radionuclide therapy of neuroendocrine neoplasms (10). However, the expression of SSTR in tumors is not limited to NETs and can also be found in a variety of other solid tumors, including SCLC cancer (11). JR11 was recently developed as an SSTR2-specific antagonist for PET tracer, which can bind to significantly more receptor sites than the SSTR agonists, showing a more favorable pharmacokinetics, better contrast, and better lesion detection rate (12, 13). Currently, 18F-NOTA-JR11 PET/CT imaging has a huge potential in clinical practice for identifying the primary lesion, clinical staging, and restaging of neuroendocrine neoplasms (11, 14, 15). What is noteworthy is that neuroendocrine neoplasm gathers a heterogeneous group of tumors with histological, distinct clinical, and genetic characteristics, which is classified into well-differentiated NETs and poorly differentiated NECs. SSTR2 positive is significantly lower in poorly differentiated than in well-differentiated neuroendocrine neoplasm, whereas 18F-FDG uptake has been proven to be significantly higher in poorly differentiated than in well-differentiated neuroendocrine neoplasm (16). In NECs, 18F-FDG uptake is usually increased and about half of cases have increased SSTR-PET uptake; therefore, it will be significantly higher in sensitivity of differential diagnosis when using dual-tracer PET/CT (18F-FDG and 18F-NOTA-JR11) than FDG/PET imaging alone (17, 18). The patient received a 18F-NOTA-JR11 PET/CT, and the image showed a high uptake of 18F-NOTA-JR11 in pancreatic occupying lesion and bone lesion, which suggested a possible G2 grade primary pancreatic NET with bone metastasis. Given the patient’s prior history of SCLC and multiple primary cancers, we tended to think that new lesions were more likely to be metastasized from SCLC when it comes to the new neuroendocrine neoplasm of the pancreas. Accurate differential diagnosis of neuroendocrine neoplasm is important for treatment selection and assessment of prognosis, and it was difficult to identify the differentiation level based on imaging data alone. Comprehensive communication with patient was conducted, and he finally consented to undergo biopsy. Therefore, we performed biopsy of pancreatic lesions by endoscopic ultrasound-guided fine-needle aspiration and percutaneous biopsy of bone lesions. The diagnosis of bone lesion was clear based on pathology and immunohistochemistry, which was confirmed a bone metastasis from SCLC. In addition, SSTR2 expression levels (score 3+) in tumor tissue explained the reason why uptake of 18F-NOTA-JR11 increased in the lesions, increasing difficulty diagnosis. The differentiation between neuroendocrine tumor G3 and neuroendocrine carcinoma is challenging due to the limited sample tissue available from pancreatic lesions, constraining the reliability of current morphological and immunohistochemical criteria. Therefore, the immunohistochemical staining of TP53, RB1, and ATRX was conducted to assist in differential diagnosis. The expected immunohistochemical phenotype is usually TP53(−), RbGP(+), and ATRX(+/−) in well-differentiated NETs but TP53(+), RbGP(−), and ATRX(+) in poorly differentiated NECs (19). The report was TP53(+), RbGP(−), and ATRX(+) in our case; therefore, he was definitively diagnosed with SCLC developing pancreatic metastasis. For lung cancer patients with pancreatic and bone metastases, a comprehensive treatment based on systemic therapy is the main principle for them. Two recent independent large-scale phase III clinical studies of CASPIAN and IMpower133 have provided robust evidence that immunotherapy plus chemotherapy can extend the overall survival of patients with extensive stage SCLC (20, 21). The patient experienced relapse beyond 6 months after the first-line therapy, and no immunotherapy has been used in the first line; therefore, he was treated with the treatment regimen including Etoposide plus Cisplatin plus Atezolizumab; simultaneously, Denosumab was used for bone metastases. The patient tolerated the treatment well, and, after two courses of treatment, the lesion was significantly smaller than before, which confirmed the accuracy of our diagnosis. This case emphasizes the unsubstitutability of pathology. Although the three imaging probes—18F-FDG, 18F-PSMA-1007, and 18F-NOTA-JR11—were combined, diverse imaging equipment, imaging mode, and, more importantly, receptor expression in tumor cells may lead to many differences in diagnosis results.
In addition to the rarity of metastatic sites, what is also rare in this case is that patient developed a second primary cancer 4 years after successful treatment for limited-stage SCLC. Given the highly aggressive nature of small cell lung carcinoma (SCLC), a subtype of lung cancer known for its poor prognosis, the five-year relative survival rate stands at approximately 10% to 13% (22). As a consequence, little attention has been paid to second primary cancers after SCLC. However, with the development of comprehensive anti-tumor treatment, the prolonged survival outcomes increased risk of developing metachronous second primary malignancies. Previous research predicted that the incidence rate of second primary malignant tumors after limited-stage SCLC treatment is approximately 2.8% (23). Given the special nature of the case, the genetic testing was completed, which identified somatic mutations in TP53 and RB1. TP53 and RB1 are widely believed to be tumor-suppressor genes in multiple tumors, which plays an important role in regulating cell division (24, 25). In the meanwhile, inactivation of tumor suppressor genes of TP53 and RB1 is common in almost all cases of SCLC (26). These mutational loads may promote the development of second primary cancer. Future studies on comprehensive molecular analysis may shed more light on underlying mechanisms of these tumor-suppressor genes in the development and progression of SCLC and second primary cancer.
In this article, we report the diagnostic process of a rare case of multiple primary cancers with SCLC metastasizing to pancreas and bone. The patient developed metastases five years after successful treatment for limited-stage SCLC and 1 year after prostate cancer. After combining the three imaging probes—18F-FDG, 18F-PSMA-1007, and 18F-NOTA-JR11, the imaging characteristics of pancreatic lesion on PET/CT were consistent with G2 grade primary pancreatic NET, However, it was confirmed by pathology that malignant cells were SCLC. Indeed, the past medical history of multiple primary cancers poses a major challenge to our differential diagnosis; in addition, the presentation of the combined three imaging probes was inconsistent with our pathological diagnosis because the diverse molecular biology may cause the expression of some special cancer phenotype, which will interfere diagnosis; therefore, it should alert clinicians to the need for pathological diagnosis. We use a variety of tracers to improve diagnostic sensitivity and resolution; however, different radiopharmaceuticals present a higher radiation exposure to patients, which is a reminder that we can reduce the use of radiopharmaceuticals with the help of whole-body PET/CT to reduce radiation exposure (27, 28). Moreover, our case enrich data of the second primary cancer after SCLC; although genetic testing studies have been performed, additional research are needed to identify the critical role of TP53/RB1 in the second primary cancer. Clearly, future studies will unravel the underlying molecular mechanisms in more detail, bringing us closer to precision medicine. |
Title: Metabolomic Profiling and Network Toxicology: Mechanistic Insights into Effect of Gossypol Acetate Isomers in Uterine Fibroids and Liver Injury | Body:
1. Introduction
Uterine leiomyoma (UL) is a hormone-dependent benign tumor that occurs in the female myometrium and is most common in women aged 30–50 years, with women who are around 50 years old and near menopause representing the peak age of the onset of this disease [1]. Most patients in the early stage of uterine fibroid formation are asymptomatic, as these are often only found incidentally during pelvic or ultrasound examinations. Due to the production of tumor masses in the uterine tissue, the uterine cavity and adjacent organs (the ovaries, fallopian tubes) are compressed, resulting in uterine bleeding, infertility, ectopic pregnancies, spontaneous abortion, anemia and other clinical manifestations [2]. When fibroids grow, they mostly protrude from the serosal surface of the uterus, and the blood vessels connected to the tumor mass can easily be twisted and torn, causing severe acute pain in the abdomen [3]. It is generally believed that the abnormal secretion of sex hormones such as estrogen and progesterone is the main reason for the production and growth of uterine fibroids, which can promote mitosis, the proliferation of smooth muscle cells and the growth of leiomyomas [4,5]. Uterine fibroids are benign tumors with a malignancy rate of 0.4% to 1.25% and are usually associated with a risk of fibroid progression when they grow rapidly in the short term [6]. Therefore, reducing fibroid cell proliferation, shrinking the volume of uterine fibroids and removing tumors are the primary therapeutic goals for uterine fibroids.
Uterine fibroids are mainly treated via surgery or the administration of drugs. The former can directly remove the tumor; however, it causes significant trauma and the recurrence rate after surgery is high [2]. In terms of drug treatments, the most commonly used drug is the anti-progestin preparation mifepristone. Another drug compound listed as suitable for treating uterine fibroids is gossypol acetate, which is a non-hormonal drug administered in the form of a tablet, and can effectively inhibit the secretion of steroidal hormone receptors in the uterine smooth muscle, endometrium and other parts of the uterus. It leads to a reduction in or elimination of fibroids and plays a role in treating and relieving symptoms of uterine fibroids. But the elevated hepatic SGPT level and GSH content were induced by mixed gossypol. Different doses of mixed gossypol acetate can cause significant pathological changes in rat liver tissue, such as mitochondrial vacuolization, endoplasmic reticulum dilation, perinuclear space widening and glial fiber proliferation in Disse space. Meanwhile, mixed gossypol acetate produced a large amount of O2 and H2O2, which affected the binding of liver to microsomal proteins [7,8]. The effect of mixed gossypol on the liver is revealed at the pathological level.
Because gossypol is a racemate and a natural product of polyphenol bisaldehyde that is primarily isolated from cottonseed [9,10], it has a variety of biological activities, including anti-infective [9], antimalarial [11], antiviral [12], antifertility [7], antitumor [13] and antioxidant activities [14]. The biological activity and toxic effects of (+)-gossypol and (−)-gossypol have been reported to differ [15,16]. The antitumor effect of (−)-GA is more potent than that of its racemate (+)-GA [17,18,19,20]. However, (+)-gossypol has a stronger destructive effect on DNA bonds in normal human leukocytes than (−)-gossypol [16].
The differences in efficacy and adverse effects of gossypol optical isomers have been elucidated in previous studies [21]. To further clarify the mechanism of the effect of acetate gossypol optical isomers on uterine fibroids and liver injury, 1H-NMR technology was used to perform metabolomics analysis of rat serum to investigate the mechanism of gossypol optical isomers on uterine fibroids. It was found that gossypol could significantly improve the abnormality of tricarboxylic acid cycle, immune function, glycolysis and gluconeogenesis metabolism caused by uterine fibroids. Network toxicology was used to explore the mechanism of liver injury caused by acetate gossypol optical isomers. The potential targets of liver injury (HSP90AA1, HSP90AB1, SRC, MAPK1, AKT1, EGFR, BCL2, CASP3) and the molecular mechanisms of liver injury in rats (cancer pathway, PPAR signaling pathway, gluconeogenesis/glycolysis and Th17 cell differentiation) were elucidated. It has provided a research basis and reference for the further exploration of the drug use of optical isomers.
2. Results
2.1. Untargeted Serum Metabolomics
2.1.1. Analysis and Attribution of Rat Serum’s 1H-NMR Metabolite Profiles
As shown in Figure 1A, we have generated attribution maps of rat serum from the normal control group, the uterine fibroid model group, the high-dose (+)-gossypol acetate group, the high-dose (–)-gossypol acetate group and the positive control group. The 1H-NMR spectra of these five groups’ rat serum were segmented, and the resulting integral values were analyzed via PLS-DA to obtain spatial distribution maps (3D plots) (Figure 1B). In the PLS-DA analysis, R2X = 0.452, R2Y = 0.331, Q2 = 0.263 and each group occupies an independent space, indicating that each group’s serum has different metabolic components.
2.1.2. Analysis of Rat Serum OPLS-DA Results
The OPLS-DA mode was used to compare the serum of rats in the model group of uterine fibroids with the normal control group, while the rats in the drug groups and rats in the model group were compared and analyzed to determine the differential metabolites between the two groups and analyze their differences. As can be seen from Figure 2, the distribution of each group in the two comparisons is completely separated, indicating that the serum of the two groups of rats has obvious differences in its metabolic components. In this analysis, the distribution of the metabolites in the normal control group and the model group is completely separate: R2X = 0.374, R2Y = 0.917, Q2 = 0.63 (Figure 2A). R2X = 0.435, R2Y = 0.978, Q2 = 0.831 are the values seen for the positive control group versus the model group (Figure 2B), while the high-dose (+)-gossypol acetate group and model group generate R2X = 0.271, R2Y = 0.928, Q2 = 0.536 (Figure 2C). Furthermore, these values were R2X = 0.524, R2Y = 0.873, and Q2 = 0.63 for the comparison of the high-dose (−)-gossypol acetate group and the model group (Figure 2D).
2.1.3. Statistics and Analysis of Serum Metabolic Differentiators in All Groups of Rats
The differential metabolic components in the serum of all groups of rats were statistically obtained from nuclear magnetic resonance hydrogen spectroscopy and then summarized and tabulated. The results showed that there were significant differences between the serum of rats in the positive control group, the high-dose (+)-gossypol acetate group, the high-dose (–)-gossypol acetate group, the normal control group and the uterine fibroid model group. When the correlation coefficient r > 0 (or r < 0), it indicates that there is a difference in the content of a metabolite between two groups and that the content in one group shows a decreasing (or increasing) trend.
Elevated levels of lysine, glutamic acid, alpha-aminobutyric acid, lactic acid, unsaturated fatty acids, proline and glycine were seen in the serum of blank control rats compared to that of the uterine fibroid model group, while their levels of arginine, β-glucose, citrulline, α-glucose and glycerol decreased (p < 0.05). The levels of lactic acid, glutamic acid, urea and formic acid in the serum of the high-dose (+)-gossypol acetate group were increased, while its lipid, proline, β-glucose and α-glucose contents were decreased (p < 0.05). The levels of cholesterol, glutamate, lipids, acetoacetate, lactate and unsaturated fatty acids were significantly higher in the differential metabolites of the serum from the high-dose (–)-gossypol acetate group, while its glutamine, proline, α-glucose, β-glucose, citrulline and glycerol levels were significantly decreased (p < 0.05). The levels of cholesterol, lactic acid, glutamic acid, pyruvate and unsaturated fatty acids in the serum of the positive control group increased, while the levels of arginine, proline, α-glucose, β-glucose, citrulline and glycerol decreased (p < 0.05). The data on the chemical shifts in the differential metabolites and their attributions and correlation coefficients are detailed in Table 1 and Table 2.
pharmaceuticals-17-01363-t001_Table 1
Table 1
Comparison of correlation coefficients of major serum metabolites in each group (n = 8).
Serial Number
Metabolites
Comparison of Normal Control and Model Groups
Comparison of (+)-Gossypol Acetate and Model Groups
Comparison of (−)-Gossypol Acetate and Model Groups
Comparison of Positive Control and Model Groups
1
Cholesterol
−0.60
0.41
−0.78
−0.69
2
Isoleucine
−0.54
—
—
−0.38
3
Leucine
—
0.55
—
—
4
Lipid
−0.61
0.69
−0.80
−0.52
5
Lactic acid
−0.69
−0.84
−0.74
−0.88
6
Alanine
−0.49
−0.23
0.40
—
7
Acetic acid
—
—
—
−0.47
8
Lysine
−0.78
—
—
—
9
Glutamic acid
−0.74
−0.65
−0.76
−0.63
10
Methionine
—
—
—
−0.37
11
Acetoacetate
−0.59
0.43
−0.52
−0.38
12
Glutamine
—
0.49
0.67
—
13
Pyruvic acid
−0.42
−0.45
—
−0.67
14
Citrate
—
−0.52
—
—
15
γ-aminobutyric Acid
−0.77
—
0.56
—
16
Choline
−0.48
−0.60
—
−0.57
17
Arginine
0.73
—
0.62
0.81
18
Proline
−0.77
0.78
0.75
0.92
19
β-glucose
0.90
0.98
0.99
0.96
20
α-glucose
0.84
0.71
0.90
0.95
21
Glycine
−0.67
0.60
—
—
22
Citrulline
0.84
0.61
0.90
0.86
23
Glycerin
0.74
0.47
0.89
0.89
24
Creatine
−0.38
—
—
—
25
Unsaturated Fatty acids
−0.70
−0.33
−0.87
−0.71
26
Urea
—
−0.77
—
—
27
Formic acid
—
−0.35
—
—
Note: The metabolites with positive correlation coefficients in this table are metabolites with a reduced content compared to the model group. Metabolites with a negative correlation coefficient are those with an increased content compared to the model group, while “—” indicates no change.
2.2. Network Toxicology Analysis
2.2.1. Prediction of Targets
A total of 569 target components of gossypol acetate were predicted by Pharm Mapper and the Swiss Target Prediction platform. After removing repeated targets, a total of 301 target components were obtained. A total of 190 hepatotoxic targets related to gossypol optical isomers were screened by drawing on the cross-section of a Venn diagram (Figure 3A).
2.2.2. Construction and Analysis of Protein Interaction Networks
The 190 targets acquired were imported into the STRING database in order to download the results of their protein interactions; the confidence level was set to 0.700, the discrete nodes were hidden and the results were imported into Cytoscape software 3.9.1 to plot their PPI networks (Figure 3B). The degree of inter-target interactions varies, with nodes denoting proteins and edges denoting inter-protein associations. Node sizes and color depths are positively correlated with their degree value. The larger the degree value, the higher the score and the more critical the role of the target. The network graph has 175 nodes and 730 edges; its average node degree is 7.64 and its average local clustering coefficient is 0.433.
2.2.3. GO Gene Function and KEGG Pathway Analyses
The results of the GO analysis included three branches: biological processes, molecular functions and cellular components (Figure 3C). There were 474 results for biological processes (BPs), which mainly involved the “response to hormones”, “protein phosphorylation”, “positive regulation of phosphorylation” and other processes. A total of 28 results were enriched cell components (CCs), and these mainly involved the “vesicle cavity”, “receptor complex”, “side of membrane”, etc. A total of 35 results were obtained from the molecular function (MF) analysis, and these mainly included “phosphotransferase activity, receptor alcohol group”, “kinase binding”, “oxygen multireductase activity” and other functions.
A total of 91 pathways were enriched in the KEGG pathway analysis. The key pathways included pathways related to cancer, the PPAR signaling pathway, glycolysis/gluconeogenesis and Th17 cell differentiation (Figure 3D).
2.1. Untargeted Serum Metabolomics
2.1.1. Analysis and Attribution of Rat Serum’s 1H-NMR Metabolite Profiles
As shown in Figure 1A, we have generated attribution maps of rat serum from the normal control group, the uterine fibroid model group, the high-dose (+)-gossypol acetate group, the high-dose (–)-gossypol acetate group and the positive control group. The 1H-NMR spectra of these five groups’ rat serum were segmented, and the resulting integral values were analyzed via PLS-DA to obtain spatial distribution maps (3D plots) (Figure 1B). In the PLS-DA analysis, R2X = 0.452, R2Y = 0.331, Q2 = 0.263 and each group occupies an independent space, indicating that each group’s serum has different metabolic components.
2.1.2. Analysis of Rat Serum OPLS-DA Results
The OPLS-DA mode was used to compare the serum of rats in the model group of uterine fibroids with the normal control group, while the rats in the drug groups and rats in the model group were compared and analyzed to determine the differential metabolites between the two groups and analyze their differences. As can be seen from Figure 2, the distribution of each group in the two comparisons is completely separated, indicating that the serum of the two groups of rats has obvious differences in its metabolic components. In this analysis, the distribution of the metabolites in the normal control group and the model group is completely separate: R2X = 0.374, R2Y = 0.917, Q2 = 0.63 (Figure 2A). R2X = 0.435, R2Y = 0.978, Q2 = 0.831 are the values seen for the positive control group versus the model group (Figure 2B), while the high-dose (+)-gossypol acetate group and model group generate R2X = 0.271, R2Y = 0.928, Q2 = 0.536 (Figure 2C). Furthermore, these values were R2X = 0.524, R2Y = 0.873, and Q2 = 0.63 for the comparison of the high-dose (−)-gossypol acetate group and the model group (Figure 2D).
2.1.3. Statistics and Analysis of Serum Metabolic Differentiators in All Groups of Rats
The differential metabolic components in the serum of all groups of rats were statistically obtained from nuclear magnetic resonance hydrogen spectroscopy and then summarized and tabulated. The results showed that there were significant differences between the serum of rats in the positive control group, the high-dose (+)-gossypol acetate group, the high-dose (–)-gossypol acetate group, the normal control group and the uterine fibroid model group. When the correlation coefficient r > 0 (or r < 0), it indicates that there is a difference in the content of a metabolite between two groups and that the content in one group shows a decreasing (or increasing) trend.
Elevated levels of lysine, glutamic acid, alpha-aminobutyric acid, lactic acid, unsaturated fatty acids, proline and glycine were seen in the serum of blank control rats compared to that of the uterine fibroid model group, while their levels of arginine, β-glucose, citrulline, α-glucose and glycerol decreased (p < 0.05). The levels of lactic acid, glutamic acid, urea and formic acid in the serum of the high-dose (+)-gossypol acetate group were increased, while its lipid, proline, β-glucose and α-glucose contents were decreased (p < 0.05). The levels of cholesterol, glutamate, lipids, acetoacetate, lactate and unsaturated fatty acids were significantly higher in the differential metabolites of the serum from the high-dose (–)-gossypol acetate group, while its glutamine, proline, α-glucose, β-glucose, citrulline and glycerol levels were significantly decreased (p < 0.05). The levels of cholesterol, lactic acid, glutamic acid, pyruvate and unsaturated fatty acids in the serum of the positive control group increased, while the levels of arginine, proline, α-glucose, β-glucose, citrulline and glycerol decreased (p < 0.05). The data on the chemical shifts in the differential metabolites and their attributions and correlation coefficients are detailed in Table 1 and Table 2.
pharmaceuticals-17-01363-t001_Table 1
Table 1
Comparison of correlation coefficients of major serum metabolites in each group (n = 8).
Serial Number
Metabolites
Comparison of Normal Control and Model Groups
Comparison of (+)-Gossypol Acetate and Model Groups
Comparison of (−)-Gossypol Acetate and Model Groups
Comparison of Positive Control and Model Groups
1
Cholesterol
−0.60
0.41
−0.78
−0.69
2
Isoleucine
−0.54
—
—
−0.38
3
Leucine
—
0.55
—
—
4
Lipid
−0.61
0.69
−0.80
−0.52
5
Lactic acid
−0.69
−0.84
−0.74
−0.88
6
Alanine
−0.49
−0.23
0.40
—
7
Acetic acid
—
—
—
−0.47
8
Lysine
−0.78
—
—
—
9
Glutamic acid
−0.74
−0.65
−0.76
−0.63
10
Methionine
—
—
—
−0.37
11
Acetoacetate
−0.59
0.43
−0.52
−0.38
12
Glutamine
—
0.49
0.67
—
13
Pyruvic acid
−0.42
−0.45
—
−0.67
14
Citrate
—
−0.52
—
—
15
γ-aminobutyric Acid
−0.77
—
0.56
—
16
Choline
−0.48
−0.60
—
−0.57
17
Arginine
0.73
—
0.62
0.81
18
Proline
−0.77
0.78
0.75
0.92
19
β-glucose
0.90
0.98
0.99
0.96
20
α-glucose
0.84
0.71
0.90
0.95
21
Glycine
−0.67
0.60
—
—
22
Citrulline
0.84
0.61
0.90
0.86
23
Glycerin
0.74
0.47
0.89
0.89
24
Creatine
−0.38
—
—
—
25
Unsaturated Fatty acids
−0.70
−0.33
−0.87
−0.71
26
Urea
—
−0.77
—
—
27
Formic acid
—
−0.35
—
—
Note: The metabolites with positive correlation coefficients in this table are metabolites with a reduced content compared to the model group. Metabolites with a negative correlation coefficient are those with an increased content compared to the model group, while “—” indicates no change.
2.1.1. Analysis and Attribution of Rat Serum’s 1H-NMR Metabolite Profiles
As shown in Figure 1A, we have generated attribution maps of rat serum from the normal control group, the uterine fibroid model group, the high-dose (+)-gossypol acetate group, the high-dose (–)-gossypol acetate group and the positive control group. The 1H-NMR spectra of these five groups’ rat serum were segmented, and the resulting integral values were analyzed via PLS-DA to obtain spatial distribution maps (3D plots) (Figure 1B). In the PLS-DA analysis, R2X = 0.452, R2Y = 0.331, Q2 = 0.263 and each group occupies an independent space, indicating that each group’s serum has different metabolic components.
2.1.2. Analysis of Rat Serum OPLS-DA Results
The OPLS-DA mode was used to compare the serum of rats in the model group of uterine fibroids with the normal control group, while the rats in the drug groups and rats in the model group were compared and analyzed to determine the differential metabolites between the two groups and analyze their differences. As can be seen from Figure 2, the distribution of each group in the two comparisons is completely separated, indicating that the serum of the two groups of rats has obvious differences in its metabolic components. In this analysis, the distribution of the metabolites in the normal control group and the model group is completely separate: R2X = 0.374, R2Y = 0.917, Q2 = 0.63 (Figure 2A). R2X = 0.435, R2Y = 0.978, Q2 = 0.831 are the values seen for the positive control group versus the model group (Figure 2B), while the high-dose (+)-gossypol acetate group and model group generate R2X = 0.271, R2Y = 0.928, Q2 = 0.536 (Figure 2C). Furthermore, these values were R2X = 0.524, R2Y = 0.873, and Q2 = 0.63 for the comparison of the high-dose (−)-gossypol acetate group and the model group (Figure 2D).
2.1.3. Statistics and Analysis of Serum Metabolic Differentiators in All Groups of Rats
The differential metabolic components in the serum of all groups of rats were statistically obtained from nuclear magnetic resonance hydrogen spectroscopy and then summarized and tabulated. The results showed that there were significant differences between the serum of rats in the positive control group, the high-dose (+)-gossypol acetate group, the high-dose (–)-gossypol acetate group, the normal control group and the uterine fibroid model group. When the correlation coefficient r > 0 (or r < 0), it indicates that there is a difference in the content of a metabolite between two groups and that the content in one group shows a decreasing (or increasing) trend.
Elevated levels of lysine, glutamic acid, alpha-aminobutyric acid, lactic acid, unsaturated fatty acids, proline and glycine were seen in the serum of blank control rats compared to that of the uterine fibroid model group, while their levels of arginine, β-glucose, citrulline, α-glucose and glycerol decreased (p < 0.05). The levels of lactic acid, glutamic acid, urea and formic acid in the serum of the high-dose (+)-gossypol acetate group were increased, while its lipid, proline, β-glucose and α-glucose contents were decreased (p < 0.05). The levels of cholesterol, glutamate, lipids, acetoacetate, lactate and unsaturated fatty acids were significantly higher in the differential metabolites of the serum from the high-dose (–)-gossypol acetate group, while its glutamine, proline, α-glucose, β-glucose, citrulline and glycerol levels were significantly decreased (p < 0.05). The levels of cholesterol, lactic acid, glutamic acid, pyruvate and unsaturated fatty acids in the serum of the positive control group increased, while the levels of arginine, proline, α-glucose, β-glucose, citrulline and glycerol decreased (p < 0.05). The data on the chemical shifts in the differential metabolites and their attributions and correlation coefficients are detailed in Table 1 and Table 2.
pharmaceuticals-17-01363-t001_Table 1
Table 1
Comparison of correlation coefficients of major serum metabolites in each group (n = 8).
Serial Number
Metabolites
Comparison of Normal Control and Model Groups
Comparison of (+)-Gossypol Acetate and Model Groups
Comparison of (−)-Gossypol Acetate and Model Groups
Comparison of Positive Control and Model Groups
1
Cholesterol
−0.60
0.41
−0.78
−0.69
2
Isoleucine
−0.54
—
—
−0.38
3
Leucine
—
0.55
—
—
4
Lipid
−0.61
0.69
−0.80
−0.52
5
Lactic acid
−0.69
−0.84
−0.74
−0.88
6
Alanine
−0.49
−0.23
0.40
—
7
Acetic acid
—
—
—
−0.47
8
Lysine
−0.78
—
—
—
9
Glutamic acid
−0.74
−0.65
−0.76
−0.63
10
Methionine
—
—
—
−0.37
11
Acetoacetate
−0.59
0.43
−0.52
−0.38
12
Glutamine
—
0.49
0.67
—
13
Pyruvic acid
−0.42
−0.45
—
−0.67
14
Citrate
—
−0.52
—
—
15
γ-aminobutyric Acid
−0.77
—
0.56
—
16
Choline
−0.48
−0.60
—
−0.57
17
Arginine
0.73
—
0.62
0.81
18
Proline
−0.77
0.78
0.75
0.92
19
β-glucose
0.90
0.98
0.99
0.96
20
α-glucose
0.84
0.71
0.90
0.95
21
Glycine
−0.67
0.60
—
—
22
Citrulline
0.84
0.61
0.90
0.86
23
Glycerin
0.74
0.47
0.89
0.89
24
Creatine
−0.38
—
—
—
25
Unsaturated Fatty acids
−0.70
−0.33
−0.87
−0.71
26
Urea
—
−0.77
—
—
27
Formic acid
—
−0.35
—
—
Note: The metabolites with positive correlation coefficients in this table are metabolites with a reduced content compared to the model group. Metabolites with a negative correlation coefficient are those with an increased content compared to the model group, while “—” indicates no change.
2.2. Network Toxicology Analysis
2.2.1. Prediction of Targets
A total of 569 target components of gossypol acetate were predicted by Pharm Mapper and the Swiss Target Prediction platform. After removing repeated targets, a total of 301 target components were obtained. A total of 190 hepatotoxic targets related to gossypol optical isomers were screened by drawing on the cross-section of a Venn diagram (Figure 3A).
2.2.2. Construction and Analysis of Protein Interaction Networks
The 190 targets acquired were imported into the STRING database in order to download the results of their protein interactions; the confidence level was set to 0.700, the discrete nodes were hidden and the results were imported into Cytoscape software 3.9.1 to plot their PPI networks (Figure 3B). The degree of inter-target interactions varies, with nodes denoting proteins and edges denoting inter-protein associations. Node sizes and color depths are positively correlated with their degree value. The larger the degree value, the higher the score and the more critical the role of the target. The network graph has 175 nodes and 730 edges; its average node degree is 7.64 and its average local clustering coefficient is 0.433.
2.2.3. GO Gene Function and KEGG Pathway Analyses
The results of the GO analysis included three branches: biological processes, molecular functions and cellular components (Figure 3C). There were 474 results for biological processes (BPs), which mainly involved the “response to hormones”, “protein phosphorylation”, “positive regulation of phosphorylation” and other processes. A total of 28 results were enriched cell components (CCs), and these mainly involved the “vesicle cavity”, “receptor complex”, “side of membrane”, etc. A total of 35 results were obtained from the molecular function (MF) analysis, and these mainly included “phosphotransferase activity, receptor alcohol group”, “kinase binding”, “oxygen multireductase activity” and other functions.
A total of 91 pathways were enriched in the KEGG pathway analysis. The key pathways included pathways related to cancer, the PPAR signaling pathway, glycolysis/gluconeogenesis and Th17 cell differentiation (Figure 3D).
2.2.1. Prediction of Targets
A total of 569 target components of gossypol acetate were predicted by Pharm Mapper and the Swiss Target Prediction platform. After removing repeated targets, a total of 301 target components were obtained. A total of 190 hepatotoxic targets related to gossypol optical isomers were screened by drawing on the cross-section of a Venn diagram (Figure 3A).
2.2.2. Construction and Analysis of Protein Interaction Networks
The 190 targets acquired were imported into the STRING database in order to download the results of their protein interactions; the confidence level was set to 0.700, the discrete nodes were hidden and the results were imported into Cytoscape software 3.9.1 to plot their PPI networks (Figure 3B). The degree of inter-target interactions varies, with nodes denoting proteins and edges denoting inter-protein associations. Node sizes and color depths are positively correlated with their degree value. The larger the degree value, the higher the score and the more critical the role of the target. The network graph has 175 nodes and 730 edges; its average node degree is 7.64 and its average local clustering coefficient is 0.433.
2.2.3. GO Gene Function and KEGG Pathway Analyses
The results of the GO analysis included three branches: biological processes, molecular functions and cellular components (Figure 3C). There were 474 results for biological processes (BPs), which mainly involved the “response to hormones”, “protein phosphorylation”, “positive regulation of phosphorylation” and other processes. A total of 28 results were enriched cell components (CCs), and these mainly involved the “vesicle cavity”, “receptor complex”, “side of membrane”, etc. A total of 35 results were obtained from the molecular function (MF) analysis, and these mainly included “phosphotransferase activity, receptor alcohol group”, “kinase binding”, “oxygen multireductase activity” and other functions.
A total of 91 pathways were enriched in the KEGG pathway analysis. The key pathways included pathways related to cancer, the PPAR signaling pathway, glycolysis/gluconeogenesis and Th17 cell differentiation (Figure 3D).
3. Discussion
3.1. Analysis of the Mechanism of Action of Gossypol Optical Isomers on Serum Metabolomics in Rats with Uterine Fibroids
The preliminary study of 1H-NMR metabolomics found that there were differences in the metabolites in the serum of rats from the uterine fibroid model group and the normal control group. The serum levels of isoleucine, alanine, lysine, glutamic acid, glycine, proline, acetoacetate, choline, pyruvate, lactic acid, γ-aminobutyric acid, lipids (including LDL and VLDL), cholesterol, creatine, unsaturated fatty acids and other metabolites in the model group were significantly decreased, while the levels of glucose, glycerol, arginine and citrulline were increased. The above metabolites constitute the metabolomic features of the uterine fibroid model rat.
The changes in the levels of various amino acids in the uterine fibroid model group indicated that uterine fibroids disrupt amino acid metabolism. The essential amino acids isoleucine, leucine, alanine and lysine are branched-chain amino acids that participate in the synthesis and decomposition of proteins in the body [22]. Amino acids can also be used as energy sources to meet the energy needs of the body and provide energy regulation, thus maintaining the nitrogen balance of the body [23]. Alanine is very important for cell growth and physiological metabolism. It is one of the most important amino acids that make up proteins [24]. Arginine, glutamate and proline can be combined with alanine to form glutamine, which can be deaminated to α-ketoglutarate and NH4+ by glutamine dehydrogenase, or deaminated to α-ketoglutarate by alanine transaminase. α-ketoglutarate then enters the tricarboxylic acid cycle to meet the energy needs of the body. Glutamine, glutamate and other metabolites play an important role in maintaining the normal immune function of the body; glutamine and glutamate, as precursors of the synthesis of the natural antioxidant glutathione (GSH), have an important antioxidant effect on the body’s cells [25]. Glutamine and creatine are also basic metabolites that maintain the normal structure of cells. The changes in the levels of these serum metabolites in the uterine fibroid model group reflect a disorder of amino acid metabolism in the body, which causes abnormal energy metabolism and weakened immune function, suggesting that uterine fibroids may cause damage to the body’s immune function in vivo.
The significant increase in serum glucose and the decrease in lactic acid seen in this group may be due to abnormal changes in the glycolysis metabolism process. When the body’s glucose content increases, its cells undergo aerobic oxidation to carry out glycolysis, releasing a large amount of lactic acid in the process [26]. However, when the amount of glucose in the body is increased, the aerobic oxidation occurring in the cells is abnormal and glycolysis is not carried out in time, resulting in a significant reduction in the content of lactic acid, the product of the glycolysis process, indicating that the body’s glycolytic metabolism is abnormal. Pyruvate and lactic acid are important intermediates in gluconeogenesis, and pyruvate can be converted to lactic acid under the catalysis of related enzymes. However, the serum levels of pyruvate and lactate were reduced in the model group, suggesting that these rats’ gluconeogenesis was abnormal. At the same time, pyruvate and lactate are also the key intermediates of energy metabolism (the tricarboxylic acid cycle); when their serum levels are reduced, as in the model group, this slows down the body’s fatty acid β-oxidation, which in turn causes a decrease in the body’s levels of lipids, cholesterol and unsaturated fatty acids and an increase in its level of glycerol, suggesting a disturbance in the body’s energy metabolism.
A comparison of the differential metabolites in the serum of the groups administered gossypol acetate and that of the model group revealed a trend toward higher levels of lactate, cholesterol, leucine, alanine and glutamate metabolites and lower levels of glutamine, arginine, proline and glucose metabolites in the administered groups. It was shown that (−)-gossypol acetate, (+)-gossypol acetate and the positive control drug had similar effects; all of them could regulate the glycolytic metabolism, amino acid metabolism and energy metabolism disorders caused by uterine leiomyomas in the body, improve the immune function of the body and play a positive role in the treatment and prevention of uterine leiomyomas.
Metabolomics is an emerging technology, but there are still some limitations to the analysis of its data. Metabolomics generates a very large amount of data which can only be analyzed and interpreted through sophisticated statistical methods and pattern recognition techniques. Although there are software and algorithms that can help with this data analysis, they can usually only handle specific types of data and the reliability of their analyses needs to be further verified. Sample preparation is also a critical step in metabolomics research, which directly affects the results of subsequent analyses. However, deviations in the sample preparation process may lead to inaccurate results.
Although modern analytical techniques can detect a large number of metabolites, their identification is still a challenge, especially for those metabolites with a low content or complex structure.
3.2. Network Toxicology Prediction of Gossypol Optical Isomers
Our research group has carried out preliminary pharmacodynamic experiments which have proven that both (−)-acetate gossypol and (+)-acetate gossypol have effects on the liver and kidney and that the effect of (+)-acetate gossypol on liver function is more obvious [21].
The potential liver injury targets of gossypol isomers were predicted by network toxicology, and their interaction showed that HSP90AA1, HSP90AB1, SRC, MAPK1, AKT1, EGFR, BCL2 and CASP3 were highly related to liver injuries. Through a further KEGG enrichment analysis, it was found that the molecular mechanism by which gossypol optical isomers induced liver injury in rats may be related to the pathways activated in cancer, the PPAR signaling pathway, glycolysis/glycolysis gluconeogenesis and Th17 cell differentiation.
SRC can phosphorylate STAT3, AKT and EGFR to regulate various biological activities [27,28,29]. SRC is engaged in the activation of HSCs and hepatic fibrosis, while activation of primary hepatic stellate cells (HSCs) and hepatic fibrosis is associated with an increase in SRC family kinases [30]. AKT promotes the proliferation, migration and transcription of cells while disrupting apoptosis [31]. HSCs’ proliferation and migration are important for wound healing and liver fibrosis during liver injury [32]. Studies have shown that acetaldehyde and lipopolysaccharide lead to a remarkable increase in HSC proliferation and migration. Silencing Akt1 and Akt2 reduces acetaldehyde- and lipopolysaccharide-mediated proliferation [31]. AKT1 is a protein upstream of the PI3K/Akt signaling channel. AKT1 phosphorylation activates this pathway, thereby affecting tumor cell multiplication and apoptosis [33]. Bcl2 is widely recognized as an important anti-apoptotic molecule in both tumor and normal cells [34,35]. CASP3 is an effector caspase that causes the fragmentation of nuclear DNA in cells during apoptosis [36]. CASP3 is also involved in the MAPK signaling pathway and inflammatory responses [37]. The hyperactivation of CASP3 is strongly associated with myocardial infarction, alcoholic hepatitis, hepatitis B and other diseases [38]. Changes in the PPAR signaling pathway may induce hepatic lipid metabolism disorders [39]. Increased PPAR protein expression is a common feature of steatotic livers. PPAR contributes to the maintenance of the steatosis phenotype of hepatocytes [40]. PPARγ can activate the expression of genes related to TG accumulation in hepatocytes and promote the production of a fatty liver [41]. An important indicator of the progression of liver fibrosis is an abnormal expression of Th17 cells and associated cytokines [42]. Large numbers of Th17 cells have been reported in patients with hepatitis B and cirrhosis, and experiments have shown that an excess of Th17 cells can advance liver fibrosis [43]. Other studies in mice have shown that liver fibrosis is associated with an abnormal increase in Th17 cells and a high expression of Th17-related cytokines [44,45]. The liver is a complex and critical organ for glucose, fatty acid and amino acid metabolism, which has a broad impact on systemic metabolism [46]. In the early stages of liver injury or inflammation, the hepatocyte microenvironment becomes hypoxic, resulting in a failure of oxidative energy production and a switch to the glycolytic and gluconeogenic pathways of producing ATP [47].
Network toxicology is an emerging discipline that combines network science and toxicology, using network analysis techniques to predict and assess the toxicity of chemical substances. It has unique advantages, the first of which is rapid screening: network toxicology is able to quickly identify and assess potentially toxic substances through database searching and network analyses, thus shortening research time. The second is its high-throughput analyses: by using high-throughput sequencing technology, network toxicology can analyze the expression of thousands of genes at once to gain a more comprehensive understanding of the effects of a toxicant on a cell or organism. However, there are data-dependent drawbacks; network toxicology relies on a large number of data resources, and this may lead to inaccurate predictions if the data are incomplete or contain errors. Network toxicology is based on virtual computing and database searching, and further in vivo and in vitro experiments are required if the mechanism of liver injury of specific spin isomers from medroxyprogesterone acetate is to be explored. Future research from our group will be centered around particular pathways and use cell and animal experiments to further explore the mechanism of the hepatotoxicity of GA.
3.3. The Significance of This Study
Spin splitting is important in clinical medicine and drug development for several reasons: (1) It improves drug safety. Many drug molecules are chiral, i.e., they exist in both left- and right-handed forms, and the metabolism, efficacy and toxicity of these two forms of drugs in the human body may be different. Through spin splitting, the left-rotation and right-rotation forms of these drugs can be separated and studied separately, allowing us to better understand the mechanism of the drug’s action and improve drug safety. (2) It can optimize the efficacy of drugs. Spin splitting can help us understand the absorption, distribution, metabolism and excretion processes of different chiral forms of drugs in the body. For example, the left-handed form of some drugs may have better efficacy, while their right-handed form may have no efficacy or poor efficacy. Through spin splitting, the most effective form of a drug can be identified and the efficacy of that drug can be improved. (3) It can lead to a reduction in drug side effects. Different chiral forms of a drug may have different metabolic and excretory processes in the body, which may lead to different side effects. Through spin splitting, the left-handed and right-handed forms of a drug can be studied separately, thus allowing us to reduce the side effects of a drug.
Our group found that both (−)-gossypol acetate and (+)-gossypol acetate caused hypokalemic reactions by determining the concentration of potassium ions in rat serum. By determining the ALT, ALP, AST, CRE and BUN in the serum, it was found that the levels of ALT, ALP, AST and BUN were significantly elevated in all dosing groups compared to the normal group, while the levels of CRE were also elevated to varying degrees. Moreover, only ALP was significantly lower in the (−)-gossypol acetate group than in the (+)-gossypol acetate group. This suggests that both (−)-gossypol acetate and (+)-gossypol acetate have an effect on the liver and kidney and that (+)-gossypol acetate has a more pronounced effect on liver function. The mechanism of action of the hepatic injury caused by gossypol acetate needs to be followed up on through detailed experiments.
3.1. Analysis of the Mechanism of Action of Gossypol Optical Isomers on Serum Metabolomics in Rats with Uterine Fibroids
The preliminary study of 1H-NMR metabolomics found that there were differences in the metabolites in the serum of rats from the uterine fibroid model group and the normal control group. The serum levels of isoleucine, alanine, lysine, glutamic acid, glycine, proline, acetoacetate, choline, pyruvate, lactic acid, γ-aminobutyric acid, lipids (including LDL and VLDL), cholesterol, creatine, unsaturated fatty acids and other metabolites in the model group were significantly decreased, while the levels of glucose, glycerol, arginine and citrulline were increased. The above metabolites constitute the metabolomic features of the uterine fibroid model rat.
The changes in the levels of various amino acids in the uterine fibroid model group indicated that uterine fibroids disrupt amino acid metabolism. The essential amino acids isoleucine, leucine, alanine and lysine are branched-chain amino acids that participate in the synthesis and decomposition of proteins in the body [22]. Amino acids can also be used as energy sources to meet the energy needs of the body and provide energy regulation, thus maintaining the nitrogen balance of the body [23]. Alanine is very important for cell growth and physiological metabolism. It is one of the most important amino acids that make up proteins [24]. Arginine, glutamate and proline can be combined with alanine to form glutamine, which can be deaminated to α-ketoglutarate and NH4+ by glutamine dehydrogenase, or deaminated to α-ketoglutarate by alanine transaminase. α-ketoglutarate then enters the tricarboxylic acid cycle to meet the energy needs of the body. Glutamine, glutamate and other metabolites play an important role in maintaining the normal immune function of the body; glutamine and glutamate, as precursors of the synthesis of the natural antioxidant glutathione (GSH), have an important antioxidant effect on the body’s cells [25]. Glutamine and creatine are also basic metabolites that maintain the normal structure of cells. The changes in the levels of these serum metabolites in the uterine fibroid model group reflect a disorder of amino acid metabolism in the body, which causes abnormal energy metabolism and weakened immune function, suggesting that uterine fibroids may cause damage to the body’s immune function in vivo.
The significant increase in serum glucose and the decrease in lactic acid seen in this group may be due to abnormal changes in the glycolysis metabolism process. When the body’s glucose content increases, its cells undergo aerobic oxidation to carry out glycolysis, releasing a large amount of lactic acid in the process [26]. However, when the amount of glucose in the body is increased, the aerobic oxidation occurring in the cells is abnormal and glycolysis is not carried out in time, resulting in a significant reduction in the content of lactic acid, the product of the glycolysis process, indicating that the body’s glycolytic metabolism is abnormal. Pyruvate and lactic acid are important intermediates in gluconeogenesis, and pyruvate can be converted to lactic acid under the catalysis of related enzymes. However, the serum levels of pyruvate and lactate were reduced in the model group, suggesting that these rats’ gluconeogenesis was abnormal. At the same time, pyruvate and lactate are also the key intermediates of energy metabolism (the tricarboxylic acid cycle); when their serum levels are reduced, as in the model group, this slows down the body’s fatty acid β-oxidation, which in turn causes a decrease in the body’s levels of lipids, cholesterol and unsaturated fatty acids and an increase in its level of glycerol, suggesting a disturbance in the body’s energy metabolism.
A comparison of the differential metabolites in the serum of the groups administered gossypol acetate and that of the model group revealed a trend toward higher levels of lactate, cholesterol, leucine, alanine and glutamate metabolites and lower levels of glutamine, arginine, proline and glucose metabolites in the administered groups. It was shown that (−)-gossypol acetate, (+)-gossypol acetate and the positive control drug had similar effects; all of them could regulate the glycolytic metabolism, amino acid metabolism and energy metabolism disorders caused by uterine leiomyomas in the body, improve the immune function of the body and play a positive role in the treatment and prevention of uterine leiomyomas.
Metabolomics is an emerging technology, but there are still some limitations to the analysis of its data. Metabolomics generates a very large amount of data which can only be analyzed and interpreted through sophisticated statistical methods and pattern recognition techniques. Although there are software and algorithms that can help with this data analysis, they can usually only handle specific types of data and the reliability of their analyses needs to be further verified. Sample preparation is also a critical step in metabolomics research, which directly affects the results of subsequent analyses. However, deviations in the sample preparation process may lead to inaccurate results.
Although modern analytical techniques can detect a large number of metabolites, their identification is still a challenge, especially for those metabolites with a low content or complex structure.
3.2. Network Toxicology Prediction of Gossypol Optical Isomers
Our research group has carried out preliminary pharmacodynamic experiments which have proven that both (−)-acetate gossypol and (+)-acetate gossypol have effects on the liver and kidney and that the effect of (+)-acetate gossypol on liver function is more obvious [21].
The potential liver injury targets of gossypol isomers were predicted by network toxicology, and their interaction showed that HSP90AA1, HSP90AB1, SRC, MAPK1, AKT1, EGFR, BCL2 and CASP3 were highly related to liver injuries. Through a further KEGG enrichment analysis, it was found that the molecular mechanism by which gossypol optical isomers induced liver injury in rats may be related to the pathways activated in cancer, the PPAR signaling pathway, glycolysis/glycolysis gluconeogenesis and Th17 cell differentiation.
SRC can phosphorylate STAT3, AKT and EGFR to regulate various biological activities [27,28,29]. SRC is engaged in the activation of HSCs and hepatic fibrosis, while activation of primary hepatic stellate cells (HSCs) and hepatic fibrosis is associated with an increase in SRC family kinases [30]. AKT promotes the proliferation, migration and transcription of cells while disrupting apoptosis [31]. HSCs’ proliferation and migration are important for wound healing and liver fibrosis during liver injury [32]. Studies have shown that acetaldehyde and lipopolysaccharide lead to a remarkable increase in HSC proliferation and migration. Silencing Akt1 and Akt2 reduces acetaldehyde- and lipopolysaccharide-mediated proliferation [31]. AKT1 is a protein upstream of the PI3K/Akt signaling channel. AKT1 phosphorylation activates this pathway, thereby affecting tumor cell multiplication and apoptosis [33]. Bcl2 is widely recognized as an important anti-apoptotic molecule in both tumor and normal cells [34,35]. CASP3 is an effector caspase that causes the fragmentation of nuclear DNA in cells during apoptosis [36]. CASP3 is also involved in the MAPK signaling pathway and inflammatory responses [37]. The hyperactivation of CASP3 is strongly associated with myocardial infarction, alcoholic hepatitis, hepatitis B and other diseases [38]. Changes in the PPAR signaling pathway may induce hepatic lipid metabolism disorders [39]. Increased PPAR protein expression is a common feature of steatotic livers. PPAR contributes to the maintenance of the steatosis phenotype of hepatocytes [40]. PPARγ can activate the expression of genes related to TG accumulation in hepatocytes and promote the production of a fatty liver [41]. An important indicator of the progression of liver fibrosis is an abnormal expression of Th17 cells and associated cytokines [42]. Large numbers of Th17 cells have been reported in patients with hepatitis B and cirrhosis, and experiments have shown that an excess of Th17 cells can advance liver fibrosis [43]. Other studies in mice have shown that liver fibrosis is associated with an abnormal increase in Th17 cells and a high expression of Th17-related cytokines [44,45]. The liver is a complex and critical organ for glucose, fatty acid and amino acid metabolism, which has a broad impact on systemic metabolism [46]. In the early stages of liver injury or inflammation, the hepatocyte microenvironment becomes hypoxic, resulting in a failure of oxidative energy production and a switch to the glycolytic and gluconeogenic pathways of producing ATP [47].
Network toxicology is an emerging discipline that combines network science and toxicology, using network analysis techniques to predict and assess the toxicity of chemical substances. It has unique advantages, the first of which is rapid screening: network toxicology is able to quickly identify and assess potentially toxic substances through database searching and network analyses, thus shortening research time. The second is its high-throughput analyses: by using high-throughput sequencing technology, network toxicology can analyze the expression of thousands of genes at once to gain a more comprehensive understanding of the effects of a toxicant on a cell or organism. However, there are data-dependent drawbacks; network toxicology relies on a large number of data resources, and this may lead to inaccurate predictions if the data are incomplete or contain errors. Network toxicology is based on virtual computing and database searching, and further in vivo and in vitro experiments are required if the mechanism of liver injury of specific spin isomers from medroxyprogesterone acetate is to be explored. Future research from our group will be centered around particular pathways and use cell and animal experiments to further explore the mechanism of the hepatotoxicity of GA.
3.3. The Significance of This Study
Spin splitting is important in clinical medicine and drug development for several reasons: (1) It improves drug safety. Many drug molecules are chiral, i.e., they exist in both left- and right-handed forms, and the metabolism, efficacy and toxicity of these two forms of drugs in the human body may be different. Through spin splitting, the left-rotation and right-rotation forms of these drugs can be separated and studied separately, allowing us to better understand the mechanism of the drug’s action and improve drug safety. (2) It can optimize the efficacy of drugs. Spin splitting can help us understand the absorption, distribution, metabolism and excretion processes of different chiral forms of drugs in the body. For example, the left-handed form of some drugs may have better efficacy, while their right-handed form may have no efficacy or poor efficacy. Through spin splitting, the most effective form of a drug can be identified and the efficacy of that drug can be improved. (3) It can lead to a reduction in drug side effects. Different chiral forms of a drug may have different metabolic and excretory processes in the body, which may lead to different side effects. Through spin splitting, the left-handed and right-handed forms of a drug can be studied separately, thus allowing us to reduce the side effects of a drug.
Our group found that both (−)-gossypol acetate and (+)-gossypol acetate caused hypokalemic reactions by determining the concentration of potassium ions in rat serum. By determining the ALT, ALP, AST, CRE and BUN in the serum, it was found that the levels of ALT, ALP, AST and BUN were significantly elevated in all dosing groups compared to the normal group, while the levels of CRE were also elevated to varying degrees. Moreover, only ALP was significantly lower in the (−)-gossypol acetate group than in the (+)-gossypol acetate group. This suggests that both (−)-gossypol acetate and (+)-gossypol acetate have an effect on the liver and kidney and that (+)-gossypol acetate has a more pronounced effect on liver function. The mechanism of action of the hepatic injury caused by gossypol acetate needs to be followed up on through detailed experiments.
4. Materials and Methods
4.1. Drugs and Reagents
The following compounds were used in this study: D2O (American CIL Corporation, Room 1106, North 3rd Floor, No. 58, Rangchun Road, Vertical New Town, Chongming District, Shanghai, China), dipotassium hydrogen phosphate (Tianjin Guangfu Fine Chemical Co., Ltd., Nankai University Farm, Nankai District, Tianjin, China), sodium dihydrogen phosphate (Tianjin Guangfu Fine Chemical Research Institute), and sodium chloride (Tianjin GuangFU Technology Development Co., Ltd., No.29 Huacheng Middle Road, Caozili Township, Wuqing District, Tianjin, China).
4.2. Laboratory Animals
One hundred and seventeen healthy, clean-grade, 8-week-old, sexually mature SD rats that were female, not pregnant and had a body mass of (180 ± 20) g were selected for this study and provided by the Animal Experimentation Centre of Xinjiang Medical University, License No.: SCXK (Xin) 2018-0003. Due to the effect of the rat sexual cycle on their estrogen and progesterone levels, rats born on the same week were selected for modeling. The animals were housed in the Animal Experimentation Centre of Xinjiang Medical University in an animal laboratory that was an SPF environment with a room temperature of 20 ± 2 °C and a relative humidity of 20–40%. All the rats were given water and food and acclimatized for 1 week. All the experimental procedures were approved by the Animal Ethics Committee of Xinjiang Medical University.
4.3. Animal Grouping, Modeling, and Drug Administration
These SD rats were randomly divided into the following nine groups: the normal control group; model control group; positive control group (administered compound medroxyprogesterone acetate tablets); high-, medium- and low-dose (−)-gossypol acetate groups; and high-, medium- and low-dose (+)-gossypol acetate groups. The normal control group was injected intraperitoneally with saline, at 1 mL/100 g, once daily for 6 weeks, while the remaining eight groups were injected intraperitoneally with 0.5 mg/kg of estradiol benzoate once daily and received an intramuscular progesterone injection of 4 mg/kg once every weekday for 5 weeks; this was changed to a simultaneous injection of both hormones at the same dosage in the 6th week. At the end of the modeling period, one rat was randomly selected from each group to observe the formation of uterine fibroids in each group, and their uterus-related indexes were examined to determine whether each model was successfully established.
After the end of modeling, the rats in each group were subjected to a drug intervention: the normal and model control groups were gavaged daily with equal volumes of saline, while the positive control group was administered a dose of 20 mg/kg (equivalent to 20 times the daily dose–weight ratio of adults). The suspension was made with drinking water as the solvent and administered once a day; the volume of the gavage was l mL/100 g. The drugs administered to the (–)-gossypol acetate and (+)-gossypol acetate groups were compound preparations, the main drug of which was (–)-gossypol acetate or (+)-gossypol acetate, while their excipients were vitamin B1, B6 and potassium chloride (with the drug-positive compound gossypol acetate tablets as a reference: gossypol acetate, 20 mg; vitamin B1, 10 mg; vitamin B6, 10 mg; potassium chloride, 250 mg). The dose of the main drug was the standard adhered to; the concentrations of the low-, medium- and high-dose groups were 25 mg/kg, 50 mg/kg and 100 mg/kg. We added the corresponding dose of the excipients, which are documented in Table 1, made a suspension with drinking water as the solvent, and administered this once a day at a gavage volume of l mL/100 g for 4 weeks (Table 3).
4.4. Sample Collection
After the completion of the gavage, the rats were anesthetized (0.35 mL/100 g) with 10% chloral hydrate solution administered via an intraperitoneal injection. After anesthesia, abdominal aorta blood was taken from each group, and after the blood was placed at room temperature for more than 6 h, its serum was centrifuged at 3500 r/min for 10 min and then isolated and stored in a −80 °C refrigerator for later use.
4.5. Untargeted Serum Metabolomics Studies
4.5.1. Sample Preparation
The configuration of the phosphate-buffered solution used was as follows: 10 mL of D2O and 40 mL of ultrapure water were used as the solutions, and K2HPO4 at 0.4169 g, NaH2PO4 at 0.0713 g and NaCl at 0.4519 g were placed in these solutions and mixed by shaking at pH = 7.0. The rat serum sample was taken out of the −80 °C freezer and thawed at 4 °C, and then 200 μL of it was accurately pipetted, along with 400 μL of phosphate-buffered solution, into a centrifuge tube. The sample was allowed to stand for 10 min at room temperature and then centrifuged at 10,000 r/min and 4 °C for 10 min, before 550 μL of the upper part of the clarified liquid was pipetted into a 5 mm NMR tube, and the processed sample was stored in a 4 °C freezer for further measurements.
4.5.2. 1H-NMR Test of Serum
Serum samples were measured using a 600 M NMR spectrometer, using the Cpmg pulse train mode. The test temperature was 30 °C, the cumulative number of scans was 128, the sampling data point was 32 k, the spectral width was 104 Hz and the water peak was suppressed using the presaturation method. The 1H-NMR spectra of the serum samples from each group were recorded using the same NMR spectrometer, which was conducive to the identification of their metabolic components.
4.5.3. 1H-NMR Pattern Processing
The 1H-NMR NMR spectra of each group of rat serum were processed and analyzed using NMR processing software (MestReNova15). The lactic acid chemical shift value (δ1.331 ppm) was used as the standard calibration for manual correction, the baseline was manually corrected, the δ4.68~δ5.10 ppm water peak region was removed, the spectra of the δ0.10~δ9.00 ppm region were segmented into equal widths, and all the maps were segmented with an integration interval of 0.003 ppm. The obtained integral data were normalized for multivariate statistical analyses.
4.5.4. Statistical Processing
SIMCA software14.1 was used to perform the partial least squares discriminant analysis (PLS-DA), orthogonal partial least squares discriminant analysis (OPLS-DA) and displacement test, and R2X and Q2 were used as the quality evaluation indicators of the established models. R2X describes the optimization degree of the model, R2Y describes the percentage of variation in the reaction variable Y, and the cumulative prediction degree of the model is described by the cross-check parameter Q2, which indicates the authenticity of the prediction results. In this experiment, the metabolite correlation coefficient was used to determine whether the metabolites were different between the groups, and α = 0.05 was used as the test standard. Significant differences in Pearson’s correlation coefficient |r| > 0.632 (n = 8) were used to detect whether the change in the metabolite content had a significance threshold. Metabolites represented by correlation coefficients |r| > 0.632 are statistically significant. Larger values of |r| indicate greater variability.
4.6. Network Toxicology Studies
4.6.1. Obtaining Information about the Gossypol Acetate Compound
We download the sdf. two-dimensional conformational format map of gossypol acetate and the canonical SMILE sequence from the official website of Pubchem for our network toxicological analysis.
4.6.2. Drug Target Acquisition
We uploaded the sdf. format to the PharmMapper platform, set the “reserved target match number” to 300, obtained the drug target of gossypol acetate and then imported the protein target UniProt ID number of gossypol acetate in the UniProt KB search interface of the UniProt database. We then selected “Homo sapiens” and obtained the gene targets of gossypol acetate after its retrieval and transformation. The SMILE sequence file of gossypol acetate was imported into the SwissTargetPrediction platform, and the species was also set to “Homo sapiens” to obtain the potential gene targets of gossypol acetate. We finally integrated the target components predicted by the PharmMapper and Swiss Target Prediction technology platforms and removed duplicates to obtain the final target components of gossypol acetate.
4.6.3. Hepatotoxicity Target Acquisition
By entering the keywords “liver toxicity, liver damage, liver disease, liver harm” into the GeneCards database, the Comparative Toxicogenomics Database (CTD) and Online Mendelian Inheritance in Man (OMIM), the reported gene targets related to liver damage were searched, duplicate genes and false-positive genes were removed and disease targets related to liver toxicity were obtained.
4.6.4. Common Target Acquisition
The gossypol acetate target components obtained in Section 4.6.2 and the hepatotoxicity-related targets obtained in Section 4.6.3 were introduced into the Vennn2.1.0 platform https://bioinfogp.cnb.csic.es/tools/venny/ (accessed on 2 August 2023) for the screening of common targets.
4.6.5. Protein Interaction Network Construction and Analysis
The common targets obtained in Section 4.6.4 were imported into the STRING database, the species was limited to humans, their protein–protein interactions were obtained, the results were imported into Cytoscape software3.9.1 to map the PPI network and the PPI network was then analyzed. The size and color of the nodes in the PPI network diagram are related to the “degree” of the node; that is, the larger the “degree” value of the node is, the larger the node is and the redder its color is. The width of an edge indicates the strength of the interaction between the two nodes connected by that edge; that is, the stronger the interaction, the wider the edge.
4.6.6. GO Bioprocess and KEGG Pathway Enrichment Analysis
We imported the common targets in Section 4.6.4 into the Metascape database, set the species to “Homo sapiens”, ran GO biological processes and KEGG pathway enrichment analyses and obtained GO analysis results that included biological processes (BPs), molecular functions (MFs) and cellular components (CCs). The results of the KEGG enrichment analysis were saved as TSV format files. The results were imported into the bioinformatics http://www.bioinformatics.com.cn/ (accessed on 2 August 2023) platform for visualization and mapping, and a signal pathway bubble map was drawn.
4.1. Drugs and Reagents
The following compounds were used in this study: D2O (American CIL Corporation, Room 1106, North 3rd Floor, No. 58, Rangchun Road, Vertical New Town, Chongming District, Shanghai, China), dipotassium hydrogen phosphate (Tianjin Guangfu Fine Chemical Co., Ltd., Nankai University Farm, Nankai District, Tianjin, China), sodium dihydrogen phosphate (Tianjin Guangfu Fine Chemical Research Institute), and sodium chloride (Tianjin GuangFU Technology Development Co., Ltd., No.29 Huacheng Middle Road, Caozili Township, Wuqing District, Tianjin, China).
4.2. Laboratory Animals
One hundred and seventeen healthy, clean-grade, 8-week-old, sexually mature SD rats that were female, not pregnant and had a body mass of (180 ± 20) g were selected for this study and provided by the Animal Experimentation Centre of Xinjiang Medical University, License No.: SCXK (Xin) 2018-0003. Due to the effect of the rat sexual cycle on their estrogen and progesterone levels, rats born on the same week were selected for modeling. The animals were housed in the Animal Experimentation Centre of Xinjiang Medical University in an animal laboratory that was an SPF environment with a room temperature of 20 ± 2 °C and a relative humidity of 20–40%. All the rats were given water and food and acclimatized for 1 week. All the experimental procedures were approved by the Animal Ethics Committee of Xinjiang Medical University.
4.3. Animal Grouping, Modeling, and Drug Administration
These SD rats were randomly divided into the following nine groups: the normal control group; model control group; positive control group (administered compound medroxyprogesterone acetate tablets); high-, medium- and low-dose (−)-gossypol acetate groups; and high-, medium- and low-dose (+)-gossypol acetate groups. The normal control group was injected intraperitoneally with saline, at 1 mL/100 g, once daily for 6 weeks, while the remaining eight groups were injected intraperitoneally with 0.5 mg/kg of estradiol benzoate once daily and received an intramuscular progesterone injection of 4 mg/kg once every weekday for 5 weeks; this was changed to a simultaneous injection of both hormones at the same dosage in the 6th week. At the end of the modeling period, one rat was randomly selected from each group to observe the formation of uterine fibroids in each group, and their uterus-related indexes were examined to determine whether each model was successfully established.
After the end of modeling, the rats in each group were subjected to a drug intervention: the normal and model control groups were gavaged daily with equal volumes of saline, while the positive control group was administered a dose of 20 mg/kg (equivalent to 20 times the daily dose–weight ratio of adults). The suspension was made with drinking water as the solvent and administered once a day; the volume of the gavage was l mL/100 g. The drugs administered to the (–)-gossypol acetate and (+)-gossypol acetate groups were compound preparations, the main drug of which was (–)-gossypol acetate or (+)-gossypol acetate, while their excipients were vitamin B1, B6 and potassium chloride (with the drug-positive compound gossypol acetate tablets as a reference: gossypol acetate, 20 mg; vitamin B1, 10 mg; vitamin B6, 10 mg; potassium chloride, 250 mg). The dose of the main drug was the standard adhered to; the concentrations of the low-, medium- and high-dose groups were 25 mg/kg, 50 mg/kg and 100 mg/kg. We added the corresponding dose of the excipients, which are documented in Table 1, made a suspension with drinking water as the solvent, and administered this once a day at a gavage volume of l mL/100 g for 4 weeks (Table 3).
4.4. Sample Collection
After the completion of the gavage, the rats were anesthetized (0.35 mL/100 g) with 10% chloral hydrate solution administered via an intraperitoneal injection. After anesthesia, abdominal aorta blood was taken from each group, and after the blood was placed at room temperature for more than 6 h, its serum was centrifuged at 3500 r/min for 10 min and then isolated and stored in a −80 °C refrigerator for later use.
4.5. Untargeted Serum Metabolomics Studies
4.5.1. Sample Preparation
The configuration of the phosphate-buffered solution used was as follows: 10 mL of D2O and 40 mL of ultrapure water were used as the solutions, and K2HPO4 at 0.4169 g, NaH2PO4 at 0.0713 g and NaCl at 0.4519 g were placed in these solutions and mixed by shaking at pH = 7.0. The rat serum sample was taken out of the −80 °C freezer and thawed at 4 °C, and then 200 μL of it was accurately pipetted, along with 400 μL of phosphate-buffered solution, into a centrifuge tube. The sample was allowed to stand for 10 min at room temperature and then centrifuged at 10,000 r/min and 4 °C for 10 min, before 550 μL of the upper part of the clarified liquid was pipetted into a 5 mm NMR tube, and the processed sample was stored in a 4 °C freezer for further measurements.
4.5.2. 1H-NMR Test of Serum
Serum samples were measured using a 600 M NMR spectrometer, using the Cpmg pulse train mode. The test temperature was 30 °C, the cumulative number of scans was 128, the sampling data point was 32 k, the spectral width was 104 Hz and the water peak was suppressed using the presaturation method. The 1H-NMR spectra of the serum samples from each group were recorded using the same NMR spectrometer, which was conducive to the identification of their metabolic components.
4.5.3. 1H-NMR Pattern Processing
The 1H-NMR NMR spectra of each group of rat serum were processed and analyzed using NMR processing software (MestReNova15). The lactic acid chemical shift value (δ1.331 ppm) was used as the standard calibration for manual correction, the baseline was manually corrected, the δ4.68~δ5.10 ppm water peak region was removed, the spectra of the δ0.10~δ9.00 ppm region were segmented into equal widths, and all the maps were segmented with an integration interval of 0.003 ppm. The obtained integral data were normalized for multivariate statistical analyses.
4.5.4. Statistical Processing
SIMCA software14.1 was used to perform the partial least squares discriminant analysis (PLS-DA), orthogonal partial least squares discriminant analysis (OPLS-DA) and displacement test, and R2X and Q2 were used as the quality evaluation indicators of the established models. R2X describes the optimization degree of the model, R2Y describes the percentage of variation in the reaction variable Y, and the cumulative prediction degree of the model is described by the cross-check parameter Q2, which indicates the authenticity of the prediction results. In this experiment, the metabolite correlation coefficient was used to determine whether the metabolites were different between the groups, and α = 0.05 was used as the test standard. Significant differences in Pearson’s correlation coefficient |r| > 0.632 (n = 8) were used to detect whether the change in the metabolite content had a significance threshold. Metabolites represented by correlation coefficients |r| > 0.632 are statistically significant. Larger values of |r| indicate greater variability.
4.5.1. Sample Preparation
The configuration of the phosphate-buffered solution used was as follows: 10 mL of D2O and 40 mL of ultrapure water were used as the solutions, and K2HPO4 at 0.4169 g, NaH2PO4 at 0.0713 g and NaCl at 0.4519 g were placed in these solutions and mixed by shaking at pH = 7.0. The rat serum sample was taken out of the −80 °C freezer and thawed at 4 °C, and then 200 μL of it was accurately pipetted, along with 400 μL of phosphate-buffered solution, into a centrifuge tube. The sample was allowed to stand for 10 min at room temperature and then centrifuged at 10,000 r/min and 4 °C for 10 min, before 550 μL of the upper part of the clarified liquid was pipetted into a 5 mm NMR tube, and the processed sample was stored in a 4 °C freezer for further measurements.
4.5.2. 1H-NMR Test of Serum
Serum samples were measured using a 600 M NMR spectrometer, using the Cpmg pulse train mode. The test temperature was 30 °C, the cumulative number of scans was 128, the sampling data point was 32 k, the spectral width was 104 Hz and the water peak was suppressed using the presaturation method. The 1H-NMR spectra of the serum samples from each group were recorded using the same NMR spectrometer, which was conducive to the identification of their metabolic components.
4.5.3. 1H-NMR Pattern Processing
The 1H-NMR NMR spectra of each group of rat serum were processed and analyzed using NMR processing software (MestReNova15). The lactic acid chemical shift value (δ1.331 ppm) was used as the standard calibration for manual correction, the baseline was manually corrected, the δ4.68~δ5.10 ppm water peak region was removed, the spectra of the δ0.10~δ9.00 ppm region were segmented into equal widths, and all the maps were segmented with an integration interval of 0.003 ppm. The obtained integral data were normalized for multivariate statistical analyses.
4.5.4. Statistical Processing
SIMCA software14.1 was used to perform the partial least squares discriminant analysis (PLS-DA), orthogonal partial least squares discriminant analysis (OPLS-DA) and displacement test, and R2X and Q2 were used as the quality evaluation indicators of the established models. R2X describes the optimization degree of the model, R2Y describes the percentage of variation in the reaction variable Y, and the cumulative prediction degree of the model is described by the cross-check parameter Q2, which indicates the authenticity of the prediction results. In this experiment, the metabolite correlation coefficient was used to determine whether the metabolites were different between the groups, and α = 0.05 was used as the test standard. Significant differences in Pearson’s correlation coefficient |r| > 0.632 (n = 8) were used to detect whether the change in the metabolite content had a significance threshold. Metabolites represented by correlation coefficients |r| > 0.632 are statistically significant. Larger values of |r| indicate greater variability.
4.6. Network Toxicology Studies
4.6.1. Obtaining Information about the Gossypol Acetate Compound
We download the sdf. two-dimensional conformational format map of gossypol acetate and the canonical SMILE sequence from the official website of Pubchem for our network toxicological analysis.
4.6.2. Drug Target Acquisition
We uploaded the sdf. format to the PharmMapper platform, set the “reserved target match number” to 300, obtained the drug target of gossypol acetate and then imported the protein target UniProt ID number of gossypol acetate in the UniProt KB search interface of the UniProt database. We then selected “Homo sapiens” and obtained the gene targets of gossypol acetate after its retrieval and transformation. The SMILE sequence file of gossypol acetate was imported into the SwissTargetPrediction platform, and the species was also set to “Homo sapiens” to obtain the potential gene targets of gossypol acetate. We finally integrated the target components predicted by the PharmMapper and Swiss Target Prediction technology platforms and removed duplicates to obtain the final target components of gossypol acetate.
4.6.3. Hepatotoxicity Target Acquisition
By entering the keywords “liver toxicity, liver damage, liver disease, liver harm” into the GeneCards database, the Comparative Toxicogenomics Database (CTD) and Online Mendelian Inheritance in Man (OMIM), the reported gene targets related to liver damage were searched, duplicate genes and false-positive genes were removed and disease targets related to liver toxicity were obtained.
4.6.4. Common Target Acquisition
The gossypol acetate target components obtained in Section 4.6.2 and the hepatotoxicity-related targets obtained in Section 4.6.3 were introduced into the Vennn2.1.0 platform https://bioinfogp.cnb.csic.es/tools/venny/ (accessed on 2 August 2023) for the screening of common targets.
4.6.5. Protein Interaction Network Construction and Analysis
The common targets obtained in Section 4.6.4 were imported into the STRING database, the species was limited to humans, their protein–protein interactions were obtained, the results were imported into Cytoscape software3.9.1 to map the PPI network and the PPI network was then analyzed. The size and color of the nodes in the PPI network diagram are related to the “degree” of the node; that is, the larger the “degree” value of the node is, the larger the node is and the redder its color is. The width of an edge indicates the strength of the interaction between the two nodes connected by that edge; that is, the stronger the interaction, the wider the edge.
4.6.6. GO Bioprocess and KEGG Pathway Enrichment Analysis
We imported the common targets in Section 4.6.4 into the Metascape database, set the species to “Homo sapiens”, ran GO biological processes and KEGG pathway enrichment analyses and obtained GO analysis results that included biological processes (BPs), molecular functions (MFs) and cellular components (CCs). The results of the KEGG enrichment analysis were saved as TSV format files. The results were imported into the bioinformatics http://www.bioinformatics.com.cn/ (accessed on 2 August 2023) platform for visualization and mapping, and a signal pathway bubble map was drawn.
4.6.1. Obtaining Information about the Gossypol Acetate Compound
We download the sdf. two-dimensional conformational format map of gossypol acetate and the canonical SMILE sequence from the official website of Pubchem for our network toxicological analysis.
4.6.2. Drug Target Acquisition
We uploaded the sdf. format to the PharmMapper platform, set the “reserved target match number” to 300, obtained the drug target of gossypol acetate and then imported the protein target UniProt ID number of gossypol acetate in the UniProt KB search interface of the UniProt database. We then selected “Homo sapiens” and obtained the gene targets of gossypol acetate after its retrieval and transformation. The SMILE sequence file of gossypol acetate was imported into the SwissTargetPrediction platform, and the species was also set to “Homo sapiens” to obtain the potential gene targets of gossypol acetate. We finally integrated the target components predicted by the PharmMapper and Swiss Target Prediction technology platforms and removed duplicates to obtain the final target components of gossypol acetate.
4.6.3. Hepatotoxicity Target Acquisition
By entering the keywords “liver toxicity, liver damage, liver disease, liver harm” into the GeneCards database, the Comparative Toxicogenomics Database (CTD) and Online Mendelian Inheritance in Man (OMIM), the reported gene targets related to liver damage were searched, duplicate genes and false-positive genes were removed and disease targets related to liver toxicity were obtained.
4.6.4. Common Target Acquisition
The gossypol acetate target components obtained in Section 4.6.2 and the hepatotoxicity-related targets obtained in Section 4.6.3 were introduced into the Vennn2.1.0 platform https://bioinfogp.cnb.csic.es/tools/venny/ (accessed on 2 August 2023) for the screening of common targets.
4.6.5. Protein Interaction Network Construction and Analysis
The common targets obtained in Section 4.6.4 were imported into the STRING database, the species was limited to humans, their protein–protein interactions were obtained, the results were imported into Cytoscape software3.9.1 to map the PPI network and the PPI network was then analyzed. The size and color of the nodes in the PPI network diagram are related to the “degree” of the node; that is, the larger the “degree” value of the node is, the larger the node is and the redder its color is. The width of an edge indicates the strength of the interaction between the two nodes connected by that edge; that is, the stronger the interaction, the wider the edge.
4.6.6. GO Bioprocess and KEGG Pathway Enrichment Analysis
We imported the common targets in Section 4.6.4 into the Metascape database, set the species to “Homo sapiens”, ran GO biological processes and KEGG pathway enrichment analyses and obtained GO analysis results that included biological processes (BPs), molecular functions (MFs) and cellular components (CCs). The results of the KEGG enrichment analysis were saved as TSV format files. The results were imported into the bioinformatics http://www.bioinformatics.com.cn/ (accessed on 2 August 2023) platform for visualization and mapping, and a signal pathway bubble map was drawn.
5. Conclusions
In our metabolomics experiment, the levels of isoleucine, alanine, lysine, glutamic acid, glycine, proline, acetoacetate, choline, pyruvate, lactic acid, γ-aminobutyric acid, lipids (including LDL and VLDL), cholesterol, creatine, unsaturated fatty acids and other metabolites in the serum from the model group were significantly decreased. The levels of glucose, glycerol, arginine and citrulline were increased. These metabolites together constitute the metabolomic characteristics of the uterine fibroid model rat. Uterine fibroids caused a disorder of amino acid metabolism in the body and led to abnormal energy metabolism, weakened immune function and abnormal glycolysis and gluconeogenesis metabolism. The effects of (−)-gossypol acetate and (+)-gossypol acetate are similar to those of the positive control drug, which regulates the glycolysis and gluconeogenesis metabolism, amino acid metabolism and energy metabolism disorders caused by uterine fibroids, improves the body’s immune ability and plays a positive role in the treatment and prevention of uterine fibroids.
Our research group’s pharmacodynamic experiments have proven that the therapeutic effects of gossypol optical isomers [(−)-acetate gossypol and (+)-acetate gossypol] in the treatment of uterine fibroids are different. We also discussed some of the possible toxic reactions caused by the two drugs. The mechanism of liver injury induced by gossypol optical isomers in the treatment of uterine fibroids was elucidated via network toxicology. This has important implications for the development of single optical isomer drugs for the treatment of uterine fibroids. |
Title: TMEM132E ablation suppresses tumor progression and restores tamoxifen sensitivity by inducing ERα expression in triple-negative breast cancer | Body:
Ethics declaration
All animal experiments were approved by the Institutional Animal Care and Use Committee of Shandong University (approval No. ECSBMSSDU2022-2-19). All animal housing and experiments were conducted in strict accordance with the institutional guidelines for the care and use of laboratory animals.
Conflict of interests
The authors declare that they have no comepeting interests.
Funding
This work was supported by grants from the National Key R&D Program of China (No. 2022YFC2703701 to Qiji Liu), the National Natural Science Foundation of China (No. 82271901, 32070586), the Shandong Provincial Natural Science Foundation, China (No. ZR2020MH086), Taishan Scholar Program of Shandong Province (tsqn202211318), and NHC Key Laboratory of Birth Defects Prevention, China (No. ZD202101). |
Title: Teledermatology: an evidence map of systematic reviews | Body:
Introduction
Teledermatology has been introduced in the hope of increasing access to care and improving health outcomes for patients while reducing healthcare costs to both patients and providers [1]. With the proliferation of the internet and advancements in technology, telemedicine has been implemented in a wide number of clinical specialties and institutions. The main modes of teledermatology consultations are the transmission of digital photographs for review (referred to as asynchronous or store and forward (SF)) or real-time (referred to as synchronous, live-interactive (LI), or face-to-face virtual communication): sometimes with methods used in combination [1]. Telemedicine has been used for a wide range of dermatological conditions (e.g., acne, melanoma, psoriasis) and for different populations in the community (e.g., children, older people, military veterans) [2–5].
A key advantage of using teledermatology is to remove physical or geographical barriers to dermatologic care for patients who would otherwise have difficulty accessing such care. Another advantage of teledermatology, as perceived by teledermatologists, is the ability to help patients who would find it costly to have a face-to-face consultation [6]. Other reported benefits include shorter waiting times for patients to receive a diagnosis and management [7], whilst achieving diagnostic and treatment concordance with face-to-face consultations [8]. Economic evaluations have demonstrated that a teledermatology consultation can be more cost-effective than a face-to-face consultation [9, 10].
There is an increasing amount of literature evaluating heterogeneous interventions for teledermatology, with services delivered to various participants in diverse settings in different ways. This growth in research is evident when searching “teledermatology” on the PubMed database with 70 records between 1995 (i.e., the start of PubMed was in 1996) and 2000, 240 records from 2001 to 2010, and 700 records between 2011 and 2020, and 697 records in the three and half years between 2021 and June 2024. At the same time, there is a lack of robust evidence for some teledermatology applications as not all conditions, settings, approaches, and patient groups have been researched in equal measure. With the growing number of systematic reviews of teledermatology, it is beneficial to map the available evidence, identifying gaps in the literature and research needs. Our evidence map aimed to describe the landscape of teledermatology research by mapping the existing evidence in systematic reviews.
Methods
The review was registered at https://www.researchregistry.com/(Unique Identifying Number: reviewregistry878). The Campbell Evidence and Gap Map conduct standards [11] were used for methodological guidance.
Search strategy
The search included articles published between 01st January 2004 and 31st January 2023, from five databases (CINAHL, Embase, PubMed, Scopus, and Web of Science), two systematic review repositories (Cochrane Library and JBI Database of Systematic Reviews and Implementation Reports), and the grey literature database OpenGray. The search strategies used are shown in Appendix 1. Searches were supplemented by screening the reference lists of review articles.
Inclusion and exclusion criteria
Any systematic review of teledermatology involving humans, with or without meta-analysis, and published in English was considered eligible for inclusion. Reviews were excluded if they were non-systematic reviews (e.g., narrative reviews) or if they were abstracts, conference and meeting proceedings, editorials, commentaries, or letters. Included SRs were classified using a typology of systematic reviews [12]. Those that were specifically designed to explore the breadth or depth of literature, map and summarize evidence, or identify knowledge gaps were classified as scoping reviews [13].
Screening and selection of systematic reviews
Two reviewers independently screened the titles and abstracts of citations to remove duplicates and citations that did not meet the inclusion criteria and assessed the eligibility of the full-text articles. Disagreements between reviewers were resolved by discussion with a senior author.
Data extraction
Two reviewers independently extracted the following data into a spreadsheet: (1) reference of systematic review; (2) systematic review publication includes statement about prior registration or publication of a protocol (i.e., yes or no); (3) focus of systematic review (i.e., interventions, diagnostic test accuracy, qualitative studies, observational studies, outcomes, outcome measures); (4) conflict of interest declared as stated in the publication (i.e., conflict, no conflict, or no comment); (5) funding statement (i.e., yes or no); (6) information source (e.g., electronic bibliographies, trials registries); (7) aim and research question of the systematic review; (8) dermatological conditions included in the systematic review; (9) number of included primary studies; (10) study designs of included primary studies; (11) details of research participants in included primary studies (gender, number of adults (i.e. ≥ 18 years) and children participants, range, mean, median, standard deviation); (12) quality assessment of studies by systematic review (instrument used for quality assessment and the findings from the quality assessment); and (13) main findings of the systematic review. As before, disagreements were discussed with a senior author. The data are presented as frequencies where appropriate.
Overlap of primary studies
The overlap of primary studies included in two or more systematic reviews was analyzed using the corrected covered area (CCA) [14]. The CCA measures the amount of overlap by dividing the frequency of repeated occurrences of the primary study in other systematic reviews by the product of the total number of primary studies and the total number of systematic reviews, reduced by the number of primary studies. The corrected covered area is an indicator of the amount of overlap (i.e., < = 5% indicating a slight overlap, 6% to 10% indicating a moderate overlap, 11% to 15% indicating a high overlap, and more than 15% indicating a very high overlap). To further analyze the CCA, reviews were grouped into pairs. We used the GROOVE (Graphical Representation of Overlap for OVErviews) tool for this calculation [15].
Search strategy
The search included articles published between 01st January 2004 and 31st January 2023, from five databases (CINAHL, Embase, PubMed, Scopus, and Web of Science), two systematic review repositories (Cochrane Library and JBI Database of Systematic Reviews and Implementation Reports), and the grey literature database OpenGray. The search strategies used are shown in Appendix 1. Searches were supplemented by screening the reference lists of review articles.
Inclusion and exclusion criteria
Any systematic review of teledermatology involving humans, with or without meta-analysis, and published in English was considered eligible for inclusion. Reviews were excluded if they were non-systematic reviews (e.g., narrative reviews) or if they were abstracts, conference and meeting proceedings, editorials, commentaries, or letters. Included SRs were classified using a typology of systematic reviews [12]. Those that were specifically designed to explore the breadth or depth of literature, map and summarize evidence, or identify knowledge gaps were classified as scoping reviews [13].
Screening and selection of systematic reviews
Two reviewers independently screened the titles and abstracts of citations to remove duplicates and citations that did not meet the inclusion criteria and assessed the eligibility of the full-text articles. Disagreements between reviewers were resolved by discussion with a senior author.
Data extraction
Two reviewers independently extracted the following data into a spreadsheet: (1) reference of systematic review; (2) systematic review publication includes statement about prior registration or publication of a protocol (i.e., yes or no); (3) focus of systematic review (i.e., interventions, diagnostic test accuracy, qualitative studies, observational studies, outcomes, outcome measures); (4) conflict of interest declared as stated in the publication (i.e., conflict, no conflict, or no comment); (5) funding statement (i.e., yes or no); (6) information source (e.g., electronic bibliographies, trials registries); (7) aim and research question of the systematic review; (8) dermatological conditions included in the systematic review; (9) number of included primary studies; (10) study designs of included primary studies; (11) details of research participants in included primary studies (gender, number of adults (i.e. ≥ 18 years) and children participants, range, mean, median, standard deviation); (12) quality assessment of studies by systematic review (instrument used for quality assessment and the findings from the quality assessment); and (13) main findings of the systematic review. As before, disagreements were discussed with a senior author. The data are presented as frequencies where appropriate.
Overlap of primary studies
The overlap of primary studies included in two or more systematic reviews was analyzed using the corrected covered area (CCA) [14]. The CCA measures the amount of overlap by dividing the frequency of repeated occurrences of the primary study in other systematic reviews by the product of the total number of primary studies and the total number of systematic reviews, reduced by the number of primary studies. The corrected covered area is an indicator of the amount of overlap (i.e., < = 5% indicating a slight overlap, 6% to 10% indicating a moderate overlap, 11% to 15% indicating a high overlap, and more than 15% indicating a very high overlap). To further analyze the CCA, reviews were grouped into pairs. We used the GROOVE (Graphical Representation of Overlap for OVErviews) tool for this calculation [15].
Results
We included fourteen systematic reviews published between 2004 and 2023 were finally included in this evidence map [9, 16–28]. Figure 1 shows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram [29].Fig. 1PRISMA 2009 flow diagram
Characteristics of included systematic reviews
Table 1 shows the types of SRs included. Two were costs/economic evaluation reviews, two diagnostic test accuracy reviews, one experiential review, one a combination of experiential and psychometric, one combined diagnostic test accuracy with a costs/economic evaluation, and seven scoping reviews.
Table 1Characteristics of included systematic reviewsReference of systematic reviewYear of PublicationCountryType of systematic ReviewAim(s) of the systematic reviewDermatological conditions included in the systematic reviewTotal Number of included primary studiesFuertes-Guiro, F., & Girabent-Farrés, M. (2017). Opportunity cost of the dermatologist’s consulting time in the economic evaluation of teledermatology. Journal of Telemedicine and Telecare, 23(7), 657-664.2017Spain• Costs/Economic EvaluationTo evaluate the opportunity cost through an economic evaluation of teledermatology consultation and conventional dermatology consultation.Not mentioned in systematic review8van der Heijden, J. P., Spuls, P. I., Voorbraak, F. P., de Keizer, N. F., Witkamp, L., & Bos, J. D. (2010). Tertiary teledermatology: a systematic review. Telemedicine and e-health, 16(1), 56-62.2010The Netherlands• Scoping reviewTo provide an overview of tertiary teledermatology studies focusing on what tertiary teledermatology is used for and to compare tertiary teledermatology with secondary teledermatology.Not mentioned in systematic review11Snoswell, C., Finnane, A., Janda, M., Soyer, H. P., & Whitty, J. A. (2016). Cost-effectiveness of store-and-forward teledermatology: a systematic review. JAMA Dermatology, 152(6), 702-708.2016Australia• Costs/Economic EvaluationTo evaluate and compare the cost effectiveness of store-and-forward teledermatology with conventional face-to-face care.Psoriasis, suspected cancer, ambulatory skin conditions, nonmelanoma skin cancer or fast-growth vascular tumours, and some were not specified.11Demiris, G., Speedie, S. M., & Hicks, L. L. (2004). Assessment of patients' acceptance of and satisfaction with teledermatology. Journal of medical systems, 28(6), 575-579.2004USA• Experiential• PsychometricTo review and analyse published literature and measurements of patients’ satisfaction with teledermatology to propose and develop a framework for a reliable and valid satisfaction instrumentNot mentioned in systematic review14Chuchu, N., Dinnes, J., Takwoingi, Y., Matin, R. N., Bayliss, S. E., Davenport, C., ... & Walter, F. M. (2018). Teledermatology for diagnosing skin cancer in adults. Cochrane Database of Systematic Reviews, 12.2018United Kingdom• Diagnostic Test AccuracyTo assess whether teledermatology is accurate enough to identify which people with skin lesions require referrals to a dermatologist to evaluate whether the lesion is malignant.Skin cancer22Wallace, D. L., Hussain, A., Khan, N., & Wilson, Y. T. (2012). A systematic review of the evidence for telemedicine in burn care: with a UK perspective. Burns, 38(4), 465-480.2012United Kingdom• Scoping reviewTo assess the evidence for the use of telemedicine in acute burn care and outpatient-based management.Skin burns24Clark, A. K., Bosanac, S., Ho, B., & Sivamani, R. K. (2018). Systematic review of mobile phone-based teledermatology. Archives of dermatological research, 310(9), 675-689.2018USA• Diagnostic Test AccuracyTo provide an overview of the mobile phone-based teledermatology, to compare the accuracy and concordance of diagnosis and clinical management of skin conditions between mobile teledermatology and face-to-face dermatology, and to assess how data was managed in teledermatology studies.Not mentioned in systematic review26Mounessa, J. S., Chapman, S., Braunberger, T., Qin, R., Lipoff, J. B., Dellavalle, R. P., & Dunnick, C. A. (2018). A systematic review of satisfaction with teledermatology. Journal of Telemedicine and Telecare, 24(4), 263-270.2018USA• ExperientialTo review assessments of patient and provider satisfaction with store-and-forward and live-interactive teledermatology.Not mentioned in systematic review40Warshaw, E. M., Hillman, Y. J., Greer, N. L., Hagel, E. M., MacDonald, R., Rutks, I. R., & Wilt, T. J. (2011). Teledermatology for diagnosis and management of skin conditions: a systematic review. Journal of the American Academy of Dermatology, 64(4), 759-772.2011USA• Costs/Economic Evaluation• Diagnostic Test AccuracyTo compare the diagnostic accuracy and clinical management of skin conditions between teledermatology and clinic dermatology, to compare the clinical outcomes between teledermatology and clinic dermatology, and to compare the cost between teledermatology and clinic dermatology.Rashes (e.g. papulosquamous, eczematous) and circumscribed lesions (isolated skin growths), pigmented, nonpigmented, and circumscribed lesions.78Eminović, N., De Keizer, N. F., Bindels, P. J. E., & Hasman, A. (2007). Maturity of teledermatology evaluation research: a systematic literature review. British Journal of Dermatology, 156(3), 412-419.2007The Netherlands• Scoping reviewTo describe the maturity status of teledermatology evaluation research and to explore the outcome measures used in the various evaluation phases.Not mentioned in systematic review99Trettel, A., Eissing, L., & Augustin, M. (2018). Telemedicine in dermatology: findings and experiences worldwide–a systematic literature review. Journal of the European Academy of Dermatology and Venereology, 32(2), 215-224.2018Germany• Scoping reviewTo identify the use and current state of teledermatology across the world with regard to geographical distribution of published studies, treated indications, research questions, and its reliability in diagnosis and therapy compared to classic face-to-face consultations.Skin cancer, wounds, psoriasis, atopic dermatitis, acne, leprosy, rash, tinea, and some were not specified.204Elsner, P. (2020). Teledermatology in the times of COVID‐19–a systematic review. JDDG: Journal der Deutschen Dermatologischen Gesellschaft, 18(8), 841-845.2020Germany• Scoping reviewTo summarise teledermatological procedures used by dermatologists and the experiences of using teledermatological procedures in dermatological practices and clinics during the COVID-19 pandemic.Acne, chronic inflammatory dermatoses, dermatological consultations for suspected COIVD-19, and dermatologic complications in oncologic patients7Loh, C. H., Chong Tam, S. Y., & Oh, C. C. (2021). Teledermatology in the COVID-19 pandemic: A systematic review. JAAD Int, 5, 54-64.2021Singapore• Scoping reviewTo analyse and report the worldwide utilisation of teledermatology for patient care during the COVID-19 pandemic.Not mentioned in systematic review27Miller, J., & Jones, E. (2022). Shaping the future of teledermatology: a literature review of patient and provider satisfaction with synchronous teledermatology during the COVID-19 pandemic. Clin Exp Dermatol.;47(11):1903-9.2022USA• Scoping reviewTo identify the patient and provider satisfaction levels of synchronous teledermatology used during the COVID-19 pandemicNot mentioned in systematic review15
Characteristics of the included systematic reviews are shown in Table 2. The reviews were undertaken in Europe, the USA, Australia, and Singapore.
Table 2Inclusion and exclusion criteria used in systematic reviewsReference of systematic reviewDate range of publication searchedElectronic database(s) usedInclusion criteriaExclusion criteriaDemiris, G., Speedie, S. M., & Hicks, L. L. (2004). Assessment of patients' acceptance of and satisfaction with teledermatology. Journal of medical systems, 28(6), 575-579.1966 to 20031) Embase2) Medline3) Science Citation Index4) Telemedicine Information Exchange1) Studies published in English2) Studies that used quantitative and/or qualitative methods to investigate patient satisfaction with teledermatology applications in a prospective or retrospective manner1) Reviews and concept papersEminović, N., De Keizer, N. F., Bindels, P. J. E., & Hasman, A. (2007). Maturity of teledermatology evaluation research: a systematic literature review. British Journal of Dermatology, 156(3), 412-419.1966 to 20061) Medline1) Studies published in English2) Original full papers reporting on the evaluation of a specific teledermatology service3) Papers on a telemedicine application for several specialties (i.e. only if the results on dermatology were separately reported)1) Literature reviews, comments, abstracts, letters, and editorials2) Papers not about dermatology but about another specialty (e.g. radiology, pathology)3) Papers where the evaluation of a specific teledermatology service was not the primary aim4) Papers from conference proceedings were excluded if a full journal paper on the same study was obtained in the selection procedurevan der Heijden, J. P., Spuls, P. I., Voorbraak, F. P., de Keizer, N. F., Witkamp, L., & Bos, J. D. (2010). Tertiary teledermatology: a systematic review. Telemedicine and e-health, 16(1), 56-62.No limit reported1) Cochrane Library2) Medline3) Scopus1) All articles on tertiary teledermatology, including original research, comments, letters, and editorials.2) No language restrictions3) During the title scan, references were included if one of the words ‘‘teledermatology,’’ ‘‘dermatol*,’’ or ‘‘skin*’’ was found in the title. References with the word ‘‘telemedicine’’ in the title were only included if no specialty (other than dermatology) was mentioned in the title.4) References without an abstract in the database were subject to a second title scan, where references were only included if the title included the word ‘‘teledermatology’’5) During the full text screening, references were included if the main subject of the article was the use of teledermatology between dermatologists, or a dermatology resident and a specialized dermatologist.1) Conference proceedings and errata2) During the full text screening, references were excluded if the referrer was a primary care physician or specialist other than a dermatologist, the article was excludedWarshaw, E. M., Hillman, Y. J., Greer, N. L., Hagel, E. M., MacDonald, R., Rutks, I. R., & Wilt, T. J. (2011). Teledermatology for diagnosis and management of skin conditions: a systematic review. Journal of the American Academy of Dermatology, 64(4), 759-772.1990 to 20091) Medline2) PubMed1) Clinical trials, systematic reviews, cost studies, and implementation papers involving human participants2) Controlled trial studies3) Store and forward or live interactive teledermatology studies4) Clinical trials of teledermatology with a clinic dermatology control group (in-person examination) if they provided information related to diagnostic and management accuracy or concordance as defined by the authors5) Teledermatology studies without control groups that compare clinical outcomes (i.e. clinical course, satisfaction, quality of life, visits avoided) with clinic dermatology6) Teledermatology studies without control groups that compare the cost with clinic dermatology1) Teledermatology involving mobile telephones.2) Nonteledermatology settings (e.g., imaging analyses, telemedicine studies other than teledermatology, videomicroscopy studies, basic science, imaging techniques)3) Dermatopathology studies4) Reviews, teledermatology program descriptions, and historical summaries of teledermatology (unless relevant to questions 3 or 4)5) Studies of computer-aided diagnoses only (e.g., computerized pattern recognition for pigmented lesions)6) Survey studies addressing outcomes other than those defined research questions7) Teledermatology as an educational tool for primary care physicians or residents8) Technology assessment only9) Remote monitoring of known diagnoses (e.g., leg ulcers, postoperative wounds)10) Teledermatology involving patient-generated photographs, history, or both (without a referring provider)11) Non-English language12) Case series with no control group (questions 1 and 2 only)13) Commentaries, editorials, or meeting abstracts14) Studies involving only one or two diagnoses (of, leprosy, acne, warts); studies of one category of skin conditions (e.g., pigmented lesions that could have multiple diagnoses) were included15) Duplicate publications; if both preliminary and final reports were published, final datawere usedWallace, D. L., Hussain, A., Khan, N., & Wilson, Y. T. (2012). A systematic review of the evidence for telemedicine in burn care: with a UK perspective. Burns, 38(4), 465-480.1966 to 20101) Arts and Humanities Citation Index2) CINAHL3) Cochrane Controlled Trials Register4) EMBASE5) Medline6) Science Citation Index7) Social Sciences Citation Index8) Telemedicine Information Exchange databases1) Studies published in peer-reviewed journals about burn injury care with an application that involved the transfer of visual images2) No language restrictions1) Studies not published in peer-reviewed journals or given as presentations2) Studies about technical comments relating to information technology for burn injury assessment without involving direct clinical careSnoswell, C., Finnane, A., Janda, M., Soyer, H. P., & Whitty, J. A. (2016). Cost-effectiveness of store-and-forward teledermatology: a systematic review. JAMA Dermatology, 152(6), 702-708.No limit reported1) CINAHL2) Cochrane3) EconLit4) EMBASE5) Google Scholar6) Medline7) PubMed1) Studies related to any population requiring dermatological care2) Studies that include store-and-forward teledermatology intervention, regardless of the device or individual used to capture the images3) Studies that compared the intervention with conventional face-to-face consultation4) Studies which had outcomes expressed in terms of any kind of economic analysis5) Only full-text journal articles available in English were includedNone stated
The number of electronic bibliographic databases used in the SRs ranged from 1 to 14 (Table 3). Five used hand-searching [9, 16, 19–21]. Only three SRs included a statement about prior registration or publication of a protocol [9, 22, 27]. All but one review [16] included a statement regarding any conflict of interest, and three reviews reported conflicts of interest [9, 23, 26]. Five reviews did not report a funding statement [16–19, 27], five reviews stated there was no financial support for the review [20, 23–25, 27, 28], and three reported receiving funding from medical councils or government agencies [9, 21, 22] (Fig. 2).
Table 3Information sources used in the 14 systematic reviews to identify primary studiesFig. 2Protocol registration, Protocol publication, conflict of interest declaration, and funding statement reported in the Systematic Reviews
Dermatological conditions
Eight of the SRs did not specify the dermatological conditions of interest [16–18, 21, 24, 25, 27, 28]. The remaining focused on burns [20, 22], rashes [19, 23], skin lesions [19], psoriasis [9, 23], skin cancer and other associated indications [22, 23], dermatologic complications amongst oncological patients [27], suspected malignant lesions [9], nonmelanoma skin cancer or fast-growth vascular tumor suitable for surgery under local anesthesia [9], acne [23], wounds [23], atopic dermatitis [23], tinea [23], leprosy [23], circumscribed lesions [19], pigmented and non-pigmented skin lesions [19], chronic inflammatory dermatoses [27], dermatological consultations for suspected COVID-19 [27], and any or unspecified conditions [9, 19, 23]. As a single diagnosis acne was addressed most often, it was featured specifically in four reviews (Fig. 3).Fig. 3Dermatological conditions forming the focus of the systematic review
Overlap of primary studies
The overall CCA was 4.9% which suggested a slight overlap of primary studies in the 14 included systematic reviews, and the overlap between pairs of studies ranged between 0.0% and 43.9%. Of the 91 pairs of systematic reviews, three pairs were categorized as having very high overlap (i.e., 15 and 17 = 43.9%; 17 and 21 = 25.9%; 15 and 21 = 22.2%) and four pairs were categorized as having high overlap (i.e., 24 and 25 = 13.3%; 17 and 20 = 12.4%; 14 and 15 = 10.7%; 19 and 21 = 10/1%). Nine pairs were categorized as having moderate overlap, and 75 pairs were categorized as having none to slight overlap (Table 4).
Table 4Analysis of overlap of primary studies in each of the 78 pairs of systematic reviews
Quality assessment of the studies included in the systematic reviews
Seven of the 14 SRs reported conducting a quality assessment [9, 17, 19, 20, 23. 25, 26] (Fig. 4). Tools used were the Quality Assessment of Diagnostic Accuracy Studies (QUADAS and QUADAS-2) [19, 21, 22], the Consolidated Health Economic Evaluation Reporting Standards checklist (CHEERS) [9], the rating scheme provided by the Oxford Centre for Evidence-Based Medicine [28], and the abridged version [25]. In one instance the review [25] did not describe their findings in detail but commented on a low risk of bias in their included primary studies. Another review [27] did not report the quality assessment in the published article but did append their results on Mendeley Data (https://doi.org/10.17632/xd6ftfpgmc.1), while another did not report any quality assessment results [28].Fig. 4Overview of number of primary studies included and quality assessments conducted on primary studies
Among the four reviews that reported quality assessment in detail, three described that at least half of the primary studies were at risk of bias [9, 19, 22]. The two reviews using the 14 QUADAS quality assessment made very different observations. In one, the proportion of primary studies that reported at least 10 QUADAS items was only 29% of 78 primary studies [19], in the other one it was 85% of 26 primary studies [21]. The systematic review that used the QUADAS-2 reported their findings in detail [22], highlighting that at least half of the 22 included primary studies were at high or unclear risk of bias for participant selection, reference standard, and flow and timing domains, while the majority were at low risk for the index test. In summary, they concluded that the quality of the studies included was of concern. Another systematic review of 11 primary studies [9] reported a wide range of quality scores using the CHEERS checklist (7 to 21 out of a total score of 24). The authors reported that the lower scores were due to a failure of the primary studies to report or discuss economic principles or justify the analytic approach used [9]. For the store and forward studies, the most relevant principles that were not included were study duration, appropriate financial conversions, and financial referencing.
Main findings of two systematic reviews addressing cost/economic evaluation
Published in 2016 [9] and 2017 [24] these two reviews had a 5.6% overlap. Snoswell et al. [9] concluded that, while the evidence was sparse, SF teledermatology can be cost-effective when used as a triage mechanism to reduce the number of conventional face-to-face appointments. They identified three studies supporting the increasing cost-effectiveness of SF teledermatology when patients need to travel long distances to access dermatology services.
Fuertes-Guiro and Girrabent-Farres’s review [24] found that a teledermatology consultation requires more time (7.54 min extra) than a conventional consultation to make a diagnosis and management plan. In 2017 this represented an additional cost of 29.25 Euros for remote consultation; in addition, SF teledermatology was less costly than LI teledermatology. The authors observe that while there are some cost-utility and cost-effectiveness studies in the literature that indicate that telemedicine can reduce costs, these have not always been attentive to the cost of the dermatologist’s time, i.e., opportunity cost.
Main findings of systematic reviews addressing the accuracy of telemedicine
One review [21] found that the diagnostic accuracy of mobile phone-based teledermatology was inferior to traditional face-to-face dermatology when comparing the clinical diagnosis with histopathology (weighted mean absolute difference 7.2%). Diagnostic concordance, defined as the agreement between teledermatology diagnosis and face-to-face teledermatology diagnosis, was generally good, and higher than the levels previously reported for SF. Only one study addressed management accuracy (matching management with histopathology) but found very high agreement when comparing the management decision based on teledermatology dermatoscopy and clinical images with histological-based management. Overall management concordance rates were very good, with a weighted average concordance of 80%. Whilst the review concluded that mobile teledermatology has yet to achieve a level of accuracy to replace conventional dermatology diagnosis, they described how over time mobile phone technology had developed for data capture, transmission, display, and storage improving the accessibility and convenience of mobile teledermatology.
The other systematic review (n = 22) addressed accuracy and focussed on teledermatology for detecting skin cancer in adults [22]. Data from four studies suggests that fewer than 7% of malignant skin lesions were missed by teledermatology. However, the applicability of these findings to the development of clinical services may be limited as participants were largely recruited from secondary or tertiary care clinics rather than the primary care setting where teledermatology is often used to triage and patients require referral to secondary care.
Main findings of experiential systematic reviews of patient satisfaction
Demiris, Speedie, and Hicks looked at the quality of evidence about patient satisfaction with teledermatology [16]. They identified 13 primary studies that used self-administered questionnaires to measure patient satisfaction and one study that used phone interviews. The psychometric evaluation of the existing instruments was weak: content, construct, or reliability testing were not reported in any of the primary studies. Patients accepted teledermatology as a mode of care delivery but had concerns relating to privacy, embarrassment of being photographed, limited opportunities to express their problems and concerns, completeness of information transmitted, anxiety about the unfamiliar technology, and frustration with technical problems. The authors noted that the definition of satisfaction differed across the primary studies. They suggested that SF and LI need distinct evaluation tools.
Mounessa et al. reviewed 40 studies focussing on patient and provider satisfaction with SF and LI teledermatology [25]. Dissatisfaction with SF teledermatology was reported in 1 of 24 studies assessing patients and 3 out of 17 studies assessing teledermatology providers; it was noted that eight of these studies assessed both patient and teledermatology provider satisfaction with SF teledermatology. For SF services 96% of patients and 82% of providers were satisfied, and for LI teledermatology 89% of patients and 100% of providers were satisfied. It was noted that two LI teledermatology studies surveyed non-physician providers, and five studies included both patient and teledermatology providers.
Main findings of combination-type review
One systematic review [19] was a combination of diagnostic test accuracy and cost/economic evaluation. Using 78 primary studies, Warshaw et al. [19] compared the diagnostic accuracy, clinical management, clinical outcomes, and the cost between teledermatology and clinic dermatology [19]. The authors reported that clinic dermatology had higher diagnostic accuracy than SF teledermatology (i.e., six studies, 19% better) and LI teledermatology (i.e., 11 studies, 11% better) that teledermatology accuracy rates improved by up to 15% with teledermatoscopy, and that the diagnostic concordance with clinic dermatology of SF teledermatology was good but better for LI teledermatology. Regarding management accuracy, the overall rates were similar but teledermatology and teledermatoscopy were inferior to clinic dermatology for malignant lesions. Regarding management concordance, rates were moderate to very good for both SF and LI teledermatology. The authors reported that there was insufficient evidence to evaluate the effect of teledermatology on clinical outcomes and that patient satisfaction and preferences for teledermatology were comparable with clinic dermatology. The time to treatment was significantly shorter and in-person visits to the dermatology clinic were avoided when patients had a teledermatology consultation. The SR reported that teledermatology was cost-effective compared to clinic dermatology on key considerations such as distance traveled by the patient, the volume of teledermatology, and the costs of clinic dermatology. However, the authors were unable to pool the data for analysis because these cost studies analyzed different outcome parameters.
Main findings of scoping reviews
The first scoping review (N = 99 studies, 101 publications) aimed to describe the maturity status of teledermatology evaluation research and to explore the outcome measures [17]. It reported that while the number of feasibility studies increased, there was a lack of randomized controlled trials (RCTs), simulation cost studies, and post-implementation studies. Regarding outcome measures, the authors reported diagnostic accuracy as the most common (53 studies). Regarding study design, there were 43 intervention studies with the same patients as controls, 30 studies using an uncontrolled study design, 12 RCTs, seven intervention studies with different primary studies and patients as controls, and seven observational studies, SF teledermatology was most frequently used in the primary studies (62%), followed by LI teledermatology (30%), and combination of SF and LI teledermatology (2%). No data was available for the remaining studies.
The second scoping review (N = 11) aimed to provide an overview of the use of tertiary teledermatology [18], identifying four categories of tertiary teledermatology use: expertise (i.e., seeking advice from a dermatologist specialized in a specific area), continuing medical education (i.e., learning from other dermatologists), supervision of residents in training programs, and second opinion from dermatologists. The review identified three modalities of use (i.e. teledermatology consultation application in seven studies, website in two studies, and email list in one study). Regarding the type of teledermatology used, seven primary studies used SF teledermatology, and three used a combination of SF and LI teledermatology, but it was unclear what type of teledermatology was used in one study. Next, the authors reported that the outcome measure commonly reported was the effect of teledermatology on learning, followed by development cost, image quality, efficiency improvement, diagnostic validity, diagnostic reliability, diagnostic accuracy, patient satisfaction, and physician satisfaction.
The third scoping review included 24 primary studies and aimed to assess the evidence for the use of telemedicine in acute burn care and outpatient-based management [20]. Of the 24 included studies, seven studies evaluated clinical decision-making for acute burn care, eight studies assessed technical feasibility and clinical validation, and nine studies evaluated outpatient care. Wallace et al. [20] also reported that 14 primary studies assessed SF teledermatology, seven assessed LI teledermatology, and three assessed a combination of SF and LI teledermatology. This review found that teledermatology for burn care was rated as comparable to face-to-face assessment and as a tool that could improve clinical decision-making. The authors added that patients were satisfied and benefited from cost-savings in time and travel, but healthcare providers benefited from limited cost-savings only when a large volume of teledermatology was used. Regarding methodology, the authors commented that they did not find any RCTs, and of the 24 primary studies in their review, only 8 studies had controls. The primary studies in this review did not report a priori power calculation and were mainly subjective reports about teledermatology use rather than formal comparisons.
The fourth scoping review included in our evidence map review aimed to identify the use and current state of teledermatology across the world with regard to the geographical distribution of published studies, treated indications, research questions, and its reliability in diagnosis and therapy compared to classic face-to-face consultations [23]. Based on 204 primary studies included in this review, Trettel et al. [23] reported that the most common category of research questions posed by them was validity, concordance, or feasibility (n = 154), followed by effectiveness (i.e. comparison of teledermatology with face-to-face consultations; n = 33), costs, cost-effectiveness or cost–benefits of teledermatology (n = 24), quality of life (n = 4), and safety issues (n = 1). Regarding the comparison of teledermatology with face-to-face consultations, 138 studies reported that teledermatology was feasible, reliable, or effective under certain conditions, 34 studies found teledermatology to be superior to face-to-face consultations, 25 studies reported outcomes to be equivalent, and 15 studies reported outcomes to be inferior to face-to-face consultations. This scoping review included primary studies from a diverse range of clinical areas using teledermatology. Out of 204 primary studies, 127 studies reported either “various skin diseases” or did not specify them, 52 studies focused on skin cancer and associated diagnoses, 11 studies focused on wounds, 7 studies were on psoriasis, 4 studies were on atopic dermatitis, and single studies addressed acne, leprosy, rash, or tinea. Lastly, regarding the application of teledermatology, 105 primary studies were unspecified general evaluations, 59 studies were about patient management (e.g., referral from primary care physician to dermatologist) and triage, 23 studies were about the diagnosis or consultation of patients in remote locations, 17 studies were about the monitoring and consultation of patients in the nursing home or home care setting, and one study was about emergency diagnosis.
The fifth scoping review aimed to summarize teledermatology studies performed during the COVID-19 pandemic in 2020 [26]. Elsner [26] reported that two of the seven included studies were surveys among dermatologists showing that more than 80% offered teledermatology. The five remaining studies were retrospective cohort studies of low quality. Three of them investigated teledermatology in acne and inflammatory skin diseases, one the care of oncological patients with dermatological complications, and one teleconsultation in suspected COVID-19 cases. In all studies, teledermatology largely reduced the number of personal consultations. The review concludes that teledermatology could at least partly compensate for the limitations of in-person dermatological care during the COVID-19 pandemic.
The sixth scoping review included 27 primary studies and aimed to analyze the global utilization of teledermatology for patient care during the COVID-19 pandemic [27]. Out of 27 primary studies, 10 studies were about SF teledermatology, 6 studies were about LI teledermatology, 8 studies were about the combination of SF and LI teledermatology, and 3 studies did not specify the type of teledermatology used. Loh et al. [27] reported that teledermatology was useful in assessing and managing common ambulatory dermatoses. However, the authors highlighted concerns raised in the primary studies about low-quality images used in SF and LI teledermatology that reduced the accuracy of clinical assessments. During the COVID-19 pandemic, the authors reported that teledermatology decreased unnecessary face-to-face consultations, which reduced the risk of infections and the use of personal protective supplies. The authors also reported that teledermatology was used for the diagnosis of cutaneous manifestations of COVID-19 infection and the follow-up of onco-dermatology patients.
The final scoping review included 15 primary studies and aimed to identify the satisfaction levels of patients and providers of synchronous teledermatology during the COVID-19 pandemic, including the likelihood of patients and providers using teledermatology in the future [28]. Most studies reported that patients were willing to continue using synchronous teledermatology. Regarding satisfaction levels, Miller and Jones [28] reported that patients were satisfied with the patient–provider relationship and increased access to care. It was also noted that patients were generally satisfied with the technical quality and sound quality of their teledermatology consultation sessions. However, patients were reportedly not satisfied with the physical examination or quality compared with face-to-face care. As for the teledermatology providers, the authors reported that they were generally dissatisfied with the video or image quality and the quality of the teledermatology visit compared with face-to-face care. Despite these areas of dissatisfaction, it was noted that both the patients and providers were satisfied with visits meeting patient needs. The authors also observed that most questions asked when assessing satisfaction levels focused on quality of care and technical aspects of teledermatology, rather than access to care, overall satisfaction, and the patient-provider relationship.
Characteristics of included systematic reviews
Table 1 shows the types of SRs included. Two were costs/economic evaluation reviews, two diagnostic test accuracy reviews, one experiential review, one a combination of experiential and psychometric, one combined diagnostic test accuracy with a costs/economic evaluation, and seven scoping reviews.
Table 1Characteristics of included systematic reviewsReference of systematic reviewYear of PublicationCountryType of systematic ReviewAim(s) of the systematic reviewDermatological conditions included in the systematic reviewTotal Number of included primary studiesFuertes-Guiro, F., & Girabent-Farrés, M. (2017). Opportunity cost of the dermatologist’s consulting time in the economic evaluation of teledermatology. Journal of Telemedicine and Telecare, 23(7), 657-664.2017Spain• Costs/Economic EvaluationTo evaluate the opportunity cost through an economic evaluation of teledermatology consultation and conventional dermatology consultation.Not mentioned in systematic review8van der Heijden, J. P., Spuls, P. I., Voorbraak, F. P., de Keizer, N. F., Witkamp, L., & Bos, J. D. (2010). Tertiary teledermatology: a systematic review. Telemedicine and e-health, 16(1), 56-62.2010The Netherlands• Scoping reviewTo provide an overview of tertiary teledermatology studies focusing on what tertiary teledermatology is used for and to compare tertiary teledermatology with secondary teledermatology.Not mentioned in systematic review11Snoswell, C., Finnane, A., Janda, M., Soyer, H. P., & Whitty, J. A. (2016). Cost-effectiveness of store-and-forward teledermatology: a systematic review. JAMA Dermatology, 152(6), 702-708.2016Australia• Costs/Economic EvaluationTo evaluate and compare the cost effectiveness of store-and-forward teledermatology with conventional face-to-face care.Psoriasis, suspected cancer, ambulatory skin conditions, nonmelanoma skin cancer or fast-growth vascular tumours, and some were not specified.11Demiris, G., Speedie, S. M., & Hicks, L. L. (2004). Assessment of patients' acceptance of and satisfaction with teledermatology. Journal of medical systems, 28(6), 575-579.2004USA• Experiential• PsychometricTo review and analyse published literature and measurements of patients’ satisfaction with teledermatology to propose and develop a framework for a reliable and valid satisfaction instrumentNot mentioned in systematic review14Chuchu, N., Dinnes, J., Takwoingi, Y., Matin, R. N., Bayliss, S. E., Davenport, C., ... & Walter, F. M. (2018). Teledermatology for diagnosing skin cancer in adults. Cochrane Database of Systematic Reviews, 12.2018United Kingdom• Diagnostic Test AccuracyTo assess whether teledermatology is accurate enough to identify which people with skin lesions require referrals to a dermatologist to evaluate whether the lesion is malignant.Skin cancer22Wallace, D. L., Hussain, A., Khan, N., & Wilson, Y. T. (2012). A systematic review of the evidence for telemedicine in burn care: with a UK perspective. Burns, 38(4), 465-480.2012United Kingdom• Scoping reviewTo assess the evidence for the use of telemedicine in acute burn care and outpatient-based management.Skin burns24Clark, A. K., Bosanac, S., Ho, B., & Sivamani, R. K. (2018). Systematic review of mobile phone-based teledermatology. Archives of dermatological research, 310(9), 675-689.2018USA• Diagnostic Test AccuracyTo provide an overview of the mobile phone-based teledermatology, to compare the accuracy and concordance of diagnosis and clinical management of skin conditions between mobile teledermatology and face-to-face dermatology, and to assess how data was managed in teledermatology studies.Not mentioned in systematic review26Mounessa, J. S., Chapman, S., Braunberger, T., Qin, R., Lipoff, J. B., Dellavalle, R. P., & Dunnick, C. A. (2018). A systematic review of satisfaction with teledermatology. Journal of Telemedicine and Telecare, 24(4), 263-270.2018USA• ExperientialTo review assessments of patient and provider satisfaction with store-and-forward and live-interactive teledermatology.Not mentioned in systematic review40Warshaw, E. M., Hillman, Y. J., Greer, N. L., Hagel, E. M., MacDonald, R., Rutks, I. R., & Wilt, T. J. (2011). Teledermatology for diagnosis and management of skin conditions: a systematic review. Journal of the American Academy of Dermatology, 64(4), 759-772.2011USA• Costs/Economic Evaluation• Diagnostic Test AccuracyTo compare the diagnostic accuracy and clinical management of skin conditions between teledermatology and clinic dermatology, to compare the clinical outcomes between teledermatology and clinic dermatology, and to compare the cost between teledermatology and clinic dermatology.Rashes (e.g. papulosquamous, eczematous) and circumscribed lesions (isolated skin growths), pigmented, nonpigmented, and circumscribed lesions.78Eminović, N., De Keizer, N. F., Bindels, P. J. E., & Hasman, A. (2007). Maturity of teledermatology evaluation research: a systematic literature review. British Journal of Dermatology, 156(3), 412-419.2007The Netherlands• Scoping reviewTo describe the maturity status of teledermatology evaluation research and to explore the outcome measures used in the various evaluation phases.Not mentioned in systematic review99Trettel, A., Eissing, L., & Augustin, M. (2018). Telemedicine in dermatology: findings and experiences worldwide–a systematic literature review. Journal of the European Academy of Dermatology and Venereology, 32(2), 215-224.2018Germany• Scoping reviewTo identify the use and current state of teledermatology across the world with regard to geographical distribution of published studies, treated indications, research questions, and its reliability in diagnosis and therapy compared to classic face-to-face consultations.Skin cancer, wounds, psoriasis, atopic dermatitis, acne, leprosy, rash, tinea, and some were not specified.204Elsner, P. (2020). Teledermatology in the times of COVID‐19–a systematic review. JDDG: Journal der Deutschen Dermatologischen Gesellschaft, 18(8), 841-845.2020Germany• Scoping reviewTo summarise teledermatological procedures used by dermatologists and the experiences of using teledermatological procedures in dermatological practices and clinics during the COVID-19 pandemic.Acne, chronic inflammatory dermatoses, dermatological consultations for suspected COIVD-19, and dermatologic complications in oncologic patients7Loh, C. H., Chong Tam, S. Y., & Oh, C. C. (2021). Teledermatology in the COVID-19 pandemic: A systematic review. JAAD Int, 5, 54-64.2021Singapore• Scoping reviewTo analyse and report the worldwide utilisation of teledermatology for patient care during the COVID-19 pandemic.Not mentioned in systematic review27Miller, J., & Jones, E. (2022). Shaping the future of teledermatology: a literature review of patient and provider satisfaction with synchronous teledermatology during the COVID-19 pandemic. Clin Exp Dermatol.;47(11):1903-9.2022USA• Scoping reviewTo identify the patient and provider satisfaction levels of synchronous teledermatology used during the COVID-19 pandemicNot mentioned in systematic review15
Characteristics of the included systematic reviews are shown in Table 2. The reviews were undertaken in Europe, the USA, Australia, and Singapore.
Table 2Inclusion and exclusion criteria used in systematic reviewsReference of systematic reviewDate range of publication searchedElectronic database(s) usedInclusion criteriaExclusion criteriaDemiris, G., Speedie, S. M., & Hicks, L. L. (2004). Assessment of patients' acceptance of and satisfaction with teledermatology. Journal of medical systems, 28(6), 575-579.1966 to 20031) Embase2) Medline3) Science Citation Index4) Telemedicine Information Exchange1) Studies published in English2) Studies that used quantitative and/or qualitative methods to investigate patient satisfaction with teledermatology applications in a prospective or retrospective manner1) Reviews and concept papersEminović, N., De Keizer, N. F., Bindels, P. J. E., & Hasman, A. (2007). Maturity of teledermatology evaluation research: a systematic literature review. British Journal of Dermatology, 156(3), 412-419.1966 to 20061) Medline1) Studies published in English2) Original full papers reporting on the evaluation of a specific teledermatology service3) Papers on a telemedicine application for several specialties (i.e. only if the results on dermatology were separately reported)1) Literature reviews, comments, abstracts, letters, and editorials2) Papers not about dermatology but about another specialty (e.g. radiology, pathology)3) Papers where the evaluation of a specific teledermatology service was not the primary aim4) Papers from conference proceedings were excluded if a full journal paper on the same study was obtained in the selection procedurevan der Heijden, J. P., Spuls, P. I., Voorbraak, F. P., de Keizer, N. F., Witkamp, L., & Bos, J. D. (2010). Tertiary teledermatology: a systematic review. Telemedicine and e-health, 16(1), 56-62.No limit reported1) Cochrane Library2) Medline3) Scopus1) All articles on tertiary teledermatology, including original research, comments, letters, and editorials.2) No language restrictions3) During the title scan, references were included if one of the words ‘‘teledermatology,’’ ‘‘dermatol*,’’ or ‘‘skin*’’ was found in the title. References with the word ‘‘telemedicine’’ in the title were only included if no specialty (other than dermatology) was mentioned in the title.4) References without an abstract in the database were subject to a second title scan, where references were only included if the title included the word ‘‘teledermatology’’5) During the full text screening, references were included if the main subject of the article was the use of teledermatology between dermatologists, or a dermatology resident and a specialized dermatologist.1) Conference proceedings and errata2) During the full text screening, references were excluded if the referrer was a primary care physician or specialist other than a dermatologist, the article was excludedWarshaw, E. M., Hillman, Y. J., Greer, N. L., Hagel, E. M., MacDonald, R., Rutks, I. R., & Wilt, T. J. (2011). Teledermatology for diagnosis and management of skin conditions: a systematic review. Journal of the American Academy of Dermatology, 64(4), 759-772.1990 to 20091) Medline2) PubMed1) Clinical trials, systematic reviews, cost studies, and implementation papers involving human participants2) Controlled trial studies3) Store and forward or live interactive teledermatology studies4) Clinical trials of teledermatology with a clinic dermatology control group (in-person examination) if they provided information related to diagnostic and management accuracy or concordance as defined by the authors5) Teledermatology studies without control groups that compare clinical outcomes (i.e. clinical course, satisfaction, quality of life, visits avoided) with clinic dermatology6) Teledermatology studies without control groups that compare the cost with clinic dermatology1) Teledermatology involving mobile telephones.2) Nonteledermatology settings (e.g., imaging analyses, telemedicine studies other than teledermatology, videomicroscopy studies, basic science, imaging techniques)3) Dermatopathology studies4) Reviews, teledermatology program descriptions, and historical summaries of teledermatology (unless relevant to questions 3 or 4)5) Studies of computer-aided diagnoses only (e.g., computerized pattern recognition for pigmented lesions)6) Survey studies addressing outcomes other than those defined research questions7) Teledermatology as an educational tool for primary care physicians or residents8) Technology assessment only9) Remote monitoring of known diagnoses (e.g., leg ulcers, postoperative wounds)10) Teledermatology involving patient-generated photographs, history, or both (without a referring provider)11) Non-English language12) Case series with no control group (questions 1 and 2 only)13) Commentaries, editorials, or meeting abstracts14) Studies involving only one or two diagnoses (of, leprosy, acne, warts); studies of one category of skin conditions (e.g., pigmented lesions that could have multiple diagnoses) were included15) Duplicate publications; if both preliminary and final reports were published, final datawere usedWallace, D. L., Hussain, A., Khan, N., & Wilson, Y. T. (2012). A systematic review of the evidence for telemedicine in burn care: with a UK perspective. Burns, 38(4), 465-480.1966 to 20101) Arts and Humanities Citation Index2) CINAHL3) Cochrane Controlled Trials Register4) EMBASE5) Medline6) Science Citation Index7) Social Sciences Citation Index8) Telemedicine Information Exchange databases1) Studies published in peer-reviewed journals about burn injury care with an application that involved the transfer of visual images2) No language restrictions1) Studies not published in peer-reviewed journals or given as presentations2) Studies about technical comments relating to information technology for burn injury assessment without involving direct clinical careSnoswell, C., Finnane, A., Janda, M., Soyer, H. P., & Whitty, J. A. (2016). Cost-effectiveness of store-and-forward teledermatology: a systematic review. JAMA Dermatology, 152(6), 702-708.No limit reported1) CINAHL2) Cochrane3) EconLit4) EMBASE5) Google Scholar6) Medline7) PubMed1) Studies related to any population requiring dermatological care2) Studies that include store-and-forward teledermatology intervention, regardless of the device or individual used to capture the images3) Studies that compared the intervention with conventional face-to-face consultation4) Studies which had outcomes expressed in terms of any kind of economic analysis5) Only full-text journal articles available in English were includedNone stated
The number of electronic bibliographic databases used in the SRs ranged from 1 to 14 (Table 3). Five used hand-searching [9, 16, 19–21]. Only three SRs included a statement about prior registration or publication of a protocol [9, 22, 27]. All but one review [16] included a statement regarding any conflict of interest, and three reviews reported conflicts of interest [9, 23, 26]. Five reviews did not report a funding statement [16–19, 27], five reviews stated there was no financial support for the review [20, 23–25, 27, 28], and three reported receiving funding from medical councils or government agencies [9, 21, 22] (Fig. 2).
Table 3Information sources used in the 14 systematic reviews to identify primary studiesFig. 2Protocol registration, Protocol publication, conflict of interest declaration, and funding statement reported in the Systematic Reviews
Dermatological conditions
Eight of the SRs did not specify the dermatological conditions of interest [16–18, 21, 24, 25, 27, 28]. The remaining focused on burns [20, 22], rashes [19, 23], skin lesions [19], psoriasis [9, 23], skin cancer and other associated indications [22, 23], dermatologic complications amongst oncological patients [27], suspected malignant lesions [9], nonmelanoma skin cancer or fast-growth vascular tumor suitable for surgery under local anesthesia [9], acne [23], wounds [23], atopic dermatitis [23], tinea [23], leprosy [23], circumscribed lesions [19], pigmented and non-pigmented skin lesions [19], chronic inflammatory dermatoses [27], dermatological consultations for suspected COVID-19 [27], and any or unspecified conditions [9, 19, 23]. As a single diagnosis acne was addressed most often, it was featured specifically in four reviews (Fig. 3).Fig. 3Dermatological conditions forming the focus of the systematic review
Overlap of primary studies
The overall CCA was 4.9% which suggested a slight overlap of primary studies in the 14 included systematic reviews, and the overlap between pairs of studies ranged between 0.0% and 43.9%. Of the 91 pairs of systematic reviews, three pairs were categorized as having very high overlap (i.e., 15 and 17 = 43.9%; 17 and 21 = 25.9%; 15 and 21 = 22.2%) and four pairs were categorized as having high overlap (i.e., 24 and 25 = 13.3%; 17 and 20 = 12.4%; 14 and 15 = 10.7%; 19 and 21 = 10/1%). Nine pairs were categorized as having moderate overlap, and 75 pairs were categorized as having none to slight overlap (Table 4).
Table 4Analysis of overlap of primary studies in each of the 78 pairs of systematic reviews
Quality assessment of the studies included in the systematic reviews
Seven of the 14 SRs reported conducting a quality assessment [9, 17, 19, 20, 23. 25, 26] (Fig. 4). Tools used were the Quality Assessment of Diagnostic Accuracy Studies (QUADAS and QUADAS-2) [19, 21, 22], the Consolidated Health Economic Evaluation Reporting Standards checklist (CHEERS) [9], the rating scheme provided by the Oxford Centre for Evidence-Based Medicine [28], and the abridged version [25]. In one instance the review [25] did not describe their findings in detail but commented on a low risk of bias in their included primary studies. Another review [27] did not report the quality assessment in the published article but did append their results on Mendeley Data (https://doi.org/10.17632/xd6ftfpgmc.1), while another did not report any quality assessment results [28].Fig. 4Overview of number of primary studies included and quality assessments conducted on primary studies
Among the four reviews that reported quality assessment in detail, three described that at least half of the primary studies were at risk of bias [9, 19, 22]. The two reviews using the 14 QUADAS quality assessment made very different observations. In one, the proportion of primary studies that reported at least 10 QUADAS items was only 29% of 78 primary studies [19], in the other one it was 85% of 26 primary studies [21]. The systematic review that used the QUADAS-2 reported their findings in detail [22], highlighting that at least half of the 22 included primary studies were at high or unclear risk of bias for participant selection, reference standard, and flow and timing domains, while the majority were at low risk for the index test. In summary, they concluded that the quality of the studies included was of concern. Another systematic review of 11 primary studies [9] reported a wide range of quality scores using the CHEERS checklist (7 to 21 out of a total score of 24). The authors reported that the lower scores were due to a failure of the primary studies to report or discuss economic principles or justify the analytic approach used [9]. For the store and forward studies, the most relevant principles that were not included were study duration, appropriate financial conversions, and financial referencing.
Main findings of two systematic reviews addressing cost/economic evaluation
Published in 2016 [9] and 2017 [24] these two reviews had a 5.6% overlap. Snoswell et al. [9] concluded that, while the evidence was sparse, SF teledermatology can be cost-effective when used as a triage mechanism to reduce the number of conventional face-to-face appointments. They identified three studies supporting the increasing cost-effectiveness of SF teledermatology when patients need to travel long distances to access dermatology services.
Fuertes-Guiro and Girrabent-Farres’s review [24] found that a teledermatology consultation requires more time (7.54 min extra) than a conventional consultation to make a diagnosis and management plan. In 2017 this represented an additional cost of 29.25 Euros for remote consultation; in addition, SF teledermatology was less costly than LI teledermatology. The authors observe that while there are some cost-utility and cost-effectiveness studies in the literature that indicate that telemedicine can reduce costs, these have not always been attentive to the cost of the dermatologist’s time, i.e., opportunity cost.
Main findings of systematic reviews addressing the accuracy of telemedicine
One review [21] found that the diagnostic accuracy of mobile phone-based teledermatology was inferior to traditional face-to-face dermatology when comparing the clinical diagnosis with histopathology (weighted mean absolute difference 7.2%). Diagnostic concordance, defined as the agreement between teledermatology diagnosis and face-to-face teledermatology diagnosis, was generally good, and higher than the levels previously reported for SF. Only one study addressed management accuracy (matching management with histopathology) but found very high agreement when comparing the management decision based on teledermatology dermatoscopy and clinical images with histological-based management. Overall management concordance rates were very good, with a weighted average concordance of 80%. Whilst the review concluded that mobile teledermatology has yet to achieve a level of accuracy to replace conventional dermatology diagnosis, they described how over time mobile phone technology had developed for data capture, transmission, display, and storage improving the accessibility and convenience of mobile teledermatology.
The other systematic review (n = 22) addressed accuracy and focussed on teledermatology for detecting skin cancer in adults [22]. Data from four studies suggests that fewer than 7% of malignant skin lesions were missed by teledermatology. However, the applicability of these findings to the development of clinical services may be limited as participants were largely recruited from secondary or tertiary care clinics rather than the primary care setting where teledermatology is often used to triage and patients require referral to secondary care.
Main findings of experiential systematic reviews of patient satisfaction
Demiris, Speedie, and Hicks looked at the quality of evidence about patient satisfaction with teledermatology [16]. They identified 13 primary studies that used self-administered questionnaires to measure patient satisfaction and one study that used phone interviews. The psychometric evaluation of the existing instruments was weak: content, construct, or reliability testing were not reported in any of the primary studies. Patients accepted teledermatology as a mode of care delivery but had concerns relating to privacy, embarrassment of being photographed, limited opportunities to express their problems and concerns, completeness of information transmitted, anxiety about the unfamiliar technology, and frustration with technical problems. The authors noted that the definition of satisfaction differed across the primary studies. They suggested that SF and LI need distinct evaluation tools.
Mounessa et al. reviewed 40 studies focussing on patient and provider satisfaction with SF and LI teledermatology [25]. Dissatisfaction with SF teledermatology was reported in 1 of 24 studies assessing patients and 3 out of 17 studies assessing teledermatology providers; it was noted that eight of these studies assessed both patient and teledermatology provider satisfaction with SF teledermatology. For SF services 96% of patients and 82% of providers were satisfied, and for LI teledermatology 89% of patients and 100% of providers were satisfied. It was noted that two LI teledermatology studies surveyed non-physician providers, and five studies included both patient and teledermatology providers.
Main findings of combination-type review
One systematic review [19] was a combination of diagnostic test accuracy and cost/economic evaluation. Using 78 primary studies, Warshaw et al. [19] compared the diagnostic accuracy, clinical management, clinical outcomes, and the cost between teledermatology and clinic dermatology [19]. The authors reported that clinic dermatology had higher diagnostic accuracy than SF teledermatology (i.e., six studies, 19% better) and LI teledermatology (i.e., 11 studies, 11% better) that teledermatology accuracy rates improved by up to 15% with teledermatoscopy, and that the diagnostic concordance with clinic dermatology of SF teledermatology was good but better for LI teledermatology. Regarding management accuracy, the overall rates were similar but teledermatology and teledermatoscopy were inferior to clinic dermatology for malignant lesions. Regarding management concordance, rates were moderate to very good for both SF and LI teledermatology. The authors reported that there was insufficient evidence to evaluate the effect of teledermatology on clinical outcomes and that patient satisfaction and preferences for teledermatology were comparable with clinic dermatology. The time to treatment was significantly shorter and in-person visits to the dermatology clinic were avoided when patients had a teledermatology consultation. The SR reported that teledermatology was cost-effective compared to clinic dermatology on key considerations such as distance traveled by the patient, the volume of teledermatology, and the costs of clinic dermatology. However, the authors were unable to pool the data for analysis because these cost studies analyzed different outcome parameters.
Main findings of scoping reviews
The first scoping review (N = 99 studies, 101 publications) aimed to describe the maturity status of teledermatology evaluation research and to explore the outcome measures [17]. It reported that while the number of feasibility studies increased, there was a lack of randomized controlled trials (RCTs), simulation cost studies, and post-implementation studies. Regarding outcome measures, the authors reported diagnostic accuracy as the most common (53 studies). Regarding study design, there were 43 intervention studies with the same patients as controls, 30 studies using an uncontrolled study design, 12 RCTs, seven intervention studies with different primary studies and patients as controls, and seven observational studies, SF teledermatology was most frequently used in the primary studies (62%), followed by LI teledermatology (30%), and combination of SF and LI teledermatology (2%). No data was available for the remaining studies.
The second scoping review (N = 11) aimed to provide an overview of the use of tertiary teledermatology [18], identifying four categories of tertiary teledermatology use: expertise (i.e., seeking advice from a dermatologist specialized in a specific area), continuing medical education (i.e., learning from other dermatologists), supervision of residents in training programs, and second opinion from dermatologists. The review identified three modalities of use (i.e. teledermatology consultation application in seven studies, website in two studies, and email list in one study). Regarding the type of teledermatology used, seven primary studies used SF teledermatology, and three used a combination of SF and LI teledermatology, but it was unclear what type of teledermatology was used in one study. Next, the authors reported that the outcome measure commonly reported was the effect of teledermatology on learning, followed by development cost, image quality, efficiency improvement, diagnostic validity, diagnostic reliability, diagnostic accuracy, patient satisfaction, and physician satisfaction.
The third scoping review included 24 primary studies and aimed to assess the evidence for the use of telemedicine in acute burn care and outpatient-based management [20]. Of the 24 included studies, seven studies evaluated clinical decision-making for acute burn care, eight studies assessed technical feasibility and clinical validation, and nine studies evaluated outpatient care. Wallace et al. [20] also reported that 14 primary studies assessed SF teledermatology, seven assessed LI teledermatology, and three assessed a combination of SF and LI teledermatology. This review found that teledermatology for burn care was rated as comparable to face-to-face assessment and as a tool that could improve clinical decision-making. The authors added that patients were satisfied and benefited from cost-savings in time and travel, but healthcare providers benefited from limited cost-savings only when a large volume of teledermatology was used. Regarding methodology, the authors commented that they did not find any RCTs, and of the 24 primary studies in their review, only 8 studies had controls. The primary studies in this review did not report a priori power calculation and were mainly subjective reports about teledermatology use rather than formal comparisons.
The fourth scoping review included in our evidence map review aimed to identify the use and current state of teledermatology across the world with regard to the geographical distribution of published studies, treated indications, research questions, and its reliability in diagnosis and therapy compared to classic face-to-face consultations [23]. Based on 204 primary studies included in this review, Trettel et al. [23] reported that the most common category of research questions posed by them was validity, concordance, or feasibility (n = 154), followed by effectiveness (i.e. comparison of teledermatology with face-to-face consultations; n = 33), costs, cost-effectiveness or cost–benefits of teledermatology (n = 24), quality of life (n = 4), and safety issues (n = 1). Regarding the comparison of teledermatology with face-to-face consultations, 138 studies reported that teledermatology was feasible, reliable, or effective under certain conditions, 34 studies found teledermatology to be superior to face-to-face consultations, 25 studies reported outcomes to be equivalent, and 15 studies reported outcomes to be inferior to face-to-face consultations. This scoping review included primary studies from a diverse range of clinical areas using teledermatology. Out of 204 primary studies, 127 studies reported either “various skin diseases” or did not specify them, 52 studies focused on skin cancer and associated diagnoses, 11 studies focused on wounds, 7 studies were on psoriasis, 4 studies were on atopic dermatitis, and single studies addressed acne, leprosy, rash, or tinea. Lastly, regarding the application of teledermatology, 105 primary studies were unspecified general evaluations, 59 studies were about patient management (e.g., referral from primary care physician to dermatologist) and triage, 23 studies were about the diagnosis or consultation of patients in remote locations, 17 studies were about the monitoring and consultation of patients in the nursing home or home care setting, and one study was about emergency diagnosis.
The fifth scoping review aimed to summarize teledermatology studies performed during the COVID-19 pandemic in 2020 [26]. Elsner [26] reported that two of the seven included studies were surveys among dermatologists showing that more than 80% offered teledermatology. The five remaining studies were retrospective cohort studies of low quality. Three of them investigated teledermatology in acne and inflammatory skin diseases, one the care of oncological patients with dermatological complications, and one teleconsultation in suspected COVID-19 cases. In all studies, teledermatology largely reduced the number of personal consultations. The review concludes that teledermatology could at least partly compensate for the limitations of in-person dermatological care during the COVID-19 pandemic.
The sixth scoping review included 27 primary studies and aimed to analyze the global utilization of teledermatology for patient care during the COVID-19 pandemic [27]. Out of 27 primary studies, 10 studies were about SF teledermatology, 6 studies were about LI teledermatology, 8 studies were about the combination of SF and LI teledermatology, and 3 studies did not specify the type of teledermatology used. Loh et al. [27] reported that teledermatology was useful in assessing and managing common ambulatory dermatoses. However, the authors highlighted concerns raised in the primary studies about low-quality images used in SF and LI teledermatology that reduced the accuracy of clinical assessments. During the COVID-19 pandemic, the authors reported that teledermatology decreased unnecessary face-to-face consultations, which reduced the risk of infections and the use of personal protective supplies. The authors also reported that teledermatology was used for the diagnosis of cutaneous manifestations of COVID-19 infection and the follow-up of onco-dermatology patients.
The final scoping review included 15 primary studies and aimed to identify the satisfaction levels of patients and providers of synchronous teledermatology during the COVID-19 pandemic, including the likelihood of patients and providers using teledermatology in the future [28]. Most studies reported that patients were willing to continue using synchronous teledermatology. Regarding satisfaction levels, Miller and Jones [28] reported that patients were satisfied with the patient–provider relationship and increased access to care. It was also noted that patients were generally satisfied with the technical quality and sound quality of their teledermatology consultation sessions. However, patients were reportedly not satisfied with the physical examination or quality compared with face-to-face care. As for the teledermatology providers, the authors reported that they were generally dissatisfied with the video or image quality and the quality of the teledermatology visit compared with face-to-face care. Despite these areas of dissatisfaction, it was noted that both the patients and providers were satisfied with visits meeting patient needs. The authors also observed that most questions asked when assessing satisfaction levels focused on quality of care and technical aspects of teledermatology, rather than access to care, overall satisfaction, and the patient-provider relationship.
Discussion
Main findings of the evidence map of teledermatology
Our evidence map review identified 14 systematic reviews published between 2004 and 2022, that were from Western countries with the exception of one from Singapore. LI teledermatology is more costly than SF teledermatology. SF teledermatology is cost-effective as a triage mechanism to reduce face-to-face consultations but dermatologists reportedly spend more time during teledermatology consultations than in-person consultations [9, 24]. Mobile teledermatology has good diagnostic concordance with face-to-face dermatology when used in a tertiary setting; there remains a lack of data to support its use for triage in the primary care setting [22]. Although the accessibility and convenience of mobile teledermatology have improved, there is a lack of evidence to support it replacing face-to-face dermatology [21, 22]. Most patients and service providers were satisfied with SF and LI teledermatology [25] but have concerns about privacy, communication (accuracy and completeness) with the doctor, and technical requirements to use the service [16]. The accuracy of teledermatology increases with teledermatoscopy, but face-to-face dermatology had higher diagnostic and management accuracy than SF and LI teledermatology [19]. LI teledermatology was also reported to have higher diagnostic concordance than SF teledermatology, while management concordance was rated as moderate to very good for LI and SF teledermatology. Teledermatology was also reported to be cost-effective compared to face-to-face dermatology when considering the distance traveled by the patient, volume of teledermatology consultations, and costs of operating clinic dermatology.
Clinical areas where teledermatology was commonly researched were skin cancer, wounds, psoriasis, atopic dermatitis, acne, leprosy, rash, and tinea [17, 18, 20, 23, 26, 27]. The application of teledermatology included general evaluations, patient management and triage, diagnosis, consultation, or monitoring in remote locations, nursing homes, or home care settings [17, 18, 20, 23, 26, 27]. During the COVID-19 pandemic, most healthcare professionals reported using teledermatology as an alternative to face-to-face consultations, to minimize the risk of infections and reduce the need to use personal protective supplies [17, 18, 20, 23, 26–28].
Gaps in the literature highlighted
While feasibility studies are common, there is a lack of RCTs, simulation cost studies, and post-implementation studies, all methodologies that researchers could consider when designing future studies. There was a high level of heterogeneity in the study methodology, the skin conditions included, and the outcome parameters used. Systematic reviews were unable to pool the data for analysis and draw generalizable conclusions. Although there was a large number of studies that assessed patient and provider satisfaction with teledermatology, the definition of satisfaction differed between studies. There is a paucity of studies that address in detail the reasons for dissatisfaction, and yet this is a fundamental requirement when developing interventions to improve satisfaction. Most studies compared the diagnostic accuracy, diagnostic concordance, management accuracy, and management concordance of teledermatology to care by a specialist dermatologist, there is a lack of studies that compare teledermatology with dermatologic care provided by non-dermatologists (e.g., primary care). There were no systematic reviews of articles addressing the safety of teledermatology; safety includes the clinical aspect of teledermatology, but also the security of data exchanged during teledermatology, especially since this is a concern that has been highlighted by patients in different studies. Next, the systematic reviews included in our evidence map all originated from developed countries. This uneven global distribution of manuscript origin is not dissimilar to the findings of a bibliometric analysis of teledermatology publications between 1980 and 2013, which found the top three countries were the USA, the UK, and Australia [30]. Teledermatology may be particularly beneficial in countries where the distances between health care facilities are large, where transport is difficult, and where specialist care is scarce. Next, there seems to be a gap in the literature regarding the effects on work processes and workflows due to the implementation of teledermatology in a clinic. While teledermatology is meant to help healthcare professionals, there might be greater indirect costs and opportunity costs that may deem teledermatology to be less cost-effective.
Strengths and limitations of our overview
The broad search strategy and stringent screening processes used give confidence that the map of teledermatology evidence created reflects the current state of the teledermatology literature. We searched registries to look for any unpublished studies. However, a limitation is the exclusion of reviews published in languages other than English. Another limitation is that the inter-rater agreement could have been recorded.
Recommendations and implications
The heterogeneity of outcomes addressed, and the outcome measurement instruments used limit the pooling of data. Moving forward it would be beneficial to develop a core outcome set for teledermatology research [31]. Secondly, as technology advances, research about the accuracy, concordance, cost, and safety of teledermatology needs to be updated, to confirm that the technological advances bring clinical benefit and are cost-effective. There is a lack of studies that compare teledermatology with dermatologic care provided by non-dermatologists (e.g., primary care). Fourth, future teledermatology studies should include non-Western low and middle-income countries, to assess the utility and feasibility of teledermatology in areas that may require it most (e.g., remote areas where patients have to travel long distances for dermatological care). Lastly, future studies could include patient involvement as part of the study design as this may lead to better-designed research that is more relevant with clearer outcomes.
Main findings of the evidence map of teledermatology
Our evidence map review identified 14 systematic reviews published between 2004 and 2022, that were from Western countries with the exception of one from Singapore. LI teledermatology is more costly than SF teledermatology. SF teledermatology is cost-effective as a triage mechanism to reduce face-to-face consultations but dermatologists reportedly spend more time during teledermatology consultations than in-person consultations [9, 24]. Mobile teledermatology has good diagnostic concordance with face-to-face dermatology when used in a tertiary setting; there remains a lack of data to support its use for triage in the primary care setting [22]. Although the accessibility and convenience of mobile teledermatology have improved, there is a lack of evidence to support it replacing face-to-face dermatology [21, 22]. Most patients and service providers were satisfied with SF and LI teledermatology [25] but have concerns about privacy, communication (accuracy and completeness) with the doctor, and technical requirements to use the service [16]. The accuracy of teledermatology increases with teledermatoscopy, but face-to-face dermatology had higher diagnostic and management accuracy than SF and LI teledermatology [19]. LI teledermatology was also reported to have higher diagnostic concordance than SF teledermatology, while management concordance was rated as moderate to very good for LI and SF teledermatology. Teledermatology was also reported to be cost-effective compared to face-to-face dermatology when considering the distance traveled by the patient, volume of teledermatology consultations, and costs of operating clinic dermatology.
Clinical areas where teledermatology was commonly researched were skin cancer, wounds, psoriasis, atopic dermatitis, acne, leprosy, rash, and tinea [17, 18, 20, 23, 26, 27]. The application of teledermatology included general evaluations, patient management and triage, diagnosis, consultation, or monitoring in remote locations, nursing homes, or home care settings [17, 18, 20, 23, 26, 27]. During the COVID-19 pandemic, most healthcare professionals reported using teledermatology as an alternative to face-to-face consultations, to minimize the risk of infections and reduce the need to use personal protective supplies [17, 18, 20, 23, 26–28].
Gaps in the literature highlighted
While feasibility studies are common, there is a lack of RCTs, simulation cost studies, and post-implementation studies, all methodologies that researchers could consider when designing future studies. There was a high level of heterogeneity in the study methodology, the skin conditions included, and the outcome parameters used. Systematic reviews were unable to pool the data for analysis and draw generalizable conclusions. Although there was a large number of studies that assessed patient and provider satisfaction with teledermatology, the definition of satisfaction differed between studies. There is a paucity of studies that address in detail the reasons for dissatisfaction, and yet this is a fundamental requirement when developing interventions to improve satisfaction. Most studies compared the diagnostic accuracy, diagnostic concordance, management accuracy, and management concordance of teledermatology to care by a specialist dermatologist, there is a lack of studies that compare teledermatology with dermatologic care provided by non-dermatologists (e.g., primary care). There were no systematic reviews of articles addressing the safety of teledermatology; safety includes the clinical aspect of teledermatology, but also the security of data exchanged during teledermatology, especially since this is a concern that has been highlighted by patients in different studies. Next, the systematic reviews included in our evidence map all originated from developed countries. This uneven global distribution of manuscript origin is not dissimilar to the findings of a bibliometric analysis of teledermatology publications between 1980 and 2013, which found the top three countries were the USA, the UK, and Australia [30]. Teledermatology may be particularly beneficial in countries where the distances between health care facilities are large, where transport is difficult, and where specialist care is scarce. Next, there seems to be a gap in the literature regarding the effects on work processes and workflows due to the implementation of teledermatology in a clinic. While teledermatology is meant to help healthcare professionals, there might be greater indirect costs and opportunity costs that may deem teledermatology to be less cost-effective.
Strengths and limitations of our overview
The broad search strategy and stringent screening processes used give confidence that the map of teledermatology evidence created reflects the current state of the teledermatology literature. We searched registries to look for any unpublished studies. However, a limitation is the exclusion of reviews published in languages other than English. Another limitation is that the inter-rater agreement could have been recorded.
Recommendations and implications
The heterogeneity of outcomes addressed, and the outcome measurement instruments used limit the pooling of data. Moving forward it would be beneficial to develop a core outcome set for teledermatology research [31]. Secondly, as technology advances, research about the accuracy, concordance, cost, and safety of teledermatology needs to be updated, to confirm that the technological advances bring clinical benefit and are cost-effective. There is a lack of studies that compare teledermatology with dermatologic care provided by non-dermatologists (e.g., primary care). Fourth, future teledermatology studies should include non-Western low and middle-income countries, to assess the utility and feasibility of teledermatology in areas that may require it most (e.g., remote areas where patients have to travel long distances for dermatological care). Lastly, future studies could include patient involvement as part of the study design as this may lead to better-designed research that is more relevant with clearer outcomes.
Conclusions
Teledermatology, leveraging technology for remote dermatological consultations, aims to enhance access, reduce costs, and improve health outcomes. This evidence map reviews 14 systematic reviews (2004–2022) to understand teledermatology’s landscape. Advantages include overcoming barriers to care and cost-effectiveness, particularly in triaging face-to-face appointments. However, the evidence is heterogeneous, lacking robust research across diverse conditions, settings, and patient groups. Asynchronous (store and forward) and real-time consultations prevail. Teledermatology’s benefits encompass shorter waiting times, cost-effectiveness, and comparable diagnostic concordance with face-to-face consultations. The review identifies gaps, emphasizing the need for more randomized controlled trials, standardized outcome measures, and exploration of non-Western contexts. While patient and provider satisfaction is generally positive, concerns persist about privacy, communication, and technical aspects. Notably, teledermatology’s role during the COVID-19 pandemic is acknowledged, reducing in-person visits and preserving resources. The review suggests future research should address dissatisfaction reasons, safety concerns, and global disparities in teledermatology literature, urging inclusivity and patient involvement for comprehensive insights.
Supplementary Information
Supplementary Material 1: Appendix S1. Search strategy. |
Title: Description of FDG and Prostate-Specific Membrane Antigen PET/CT Findings in Korean Patients With Advanced Metastatic Castration-Resistant Prostate Cancer | Body:
INTRODUCTION
Prostate-specific membrane antigen (PSMA) is overexpressed in prostate cancer cells and has been a much-studied target in prostate cancer [1]. Radiopharmaceuticals that bind to PSMA for diagnostic and therapeutic purposes have been developed, and clinical trials have shown remarkable results [234], leading to their rapid implementation in daily practice [5]. In contrast, [18F]fluorodeoxyglucose (FDG) PET/CT with its wide-range of application in oncology had limited use in diagnosis of prostate cancer as malignant lesions can exhibit low FDG uptake because of their inherent biological properties [67]. Nonetheless, FDG uptake in prostate cancer is correlated with cancer aggressiveness and the overall prognosis [8]. Bauckneht et al. [9] demonstrated that metabolic tumor volume measured using FDG PET/CT serves as an independent predictor of overall survival in patients with prostate cancer. At the cellular level, one study showed that an increase in the proliferation rate and migratory potential of primary prostate cancer cells is associated with enhanced FDG uptake and decreased PSMA retention [10].
Prostate cancer demonstrates a heterogeneous array of biological and clinical characteristics [11]. Although the incidence of prostate cancer is relatively low in Asia [12], it has been increasing recently in China, Japan, and Korea [13]. A significant proportion of 604 Korean men who underwent radical prostatectomy showed poor differentiation regardless of serum prostate-specific antigen (PSA) level or clinical stage and a greater rate of PSA failure in a multicenter study [14]. In addition, the incidence of high-grade or advanced-stage prostate cancer is reportedly higher in Korean men than in Caucasian men [1516]. Black men with prostate cancer have shown worse outcomes for various reasons [17]. Although different clinical manifestations among racial groups have been studied, studies comparing the imaging findings of prostate cancer among Asians are scarce, especially in the field of molecular imaging.
Limited coverage issues of FDG PET/CT prevent its routine utilization for decision-making in patients with prostate cancer in Korea despite its prognostic potential; no study has compared FDG and PSMA uptake patterns in Asian men with advanced prostate cancer. PSMA PET/CT is a prerequisite prior to PSMA-targeting radioligand therapy (RLT); however, no current international consensus exists on the use of FDG PET/CT. The VISION trial did not require FDG PET/CT to be eligible for the study [3], and the supplementary data reported a relatively poor response in the Asian group. Owing to the few Asian participants (n = 15) in the VISION trial and insufficient information regarding ethnicity in other trials, it is unknown whether there is a true disparity in the response to RLT in Asian men. FDG and PSMA are two clinically available imaging biomarkers of prostate cancer that can guide treatment decisions; however, data regarding their dynamics in Korean men at advanced stages is unavailable. In this study, we aimed to describe the FDG and PSMA PET/CT findings in Korean men with advanced metastatic castration-resistant prostate cancer (mCRPC).
MATERIALS AND METHODS
Patients
Paired FDG and PSMA PET/CT images, collected consecutively between October 2022 and July 2023, were retrospectively reviewed. PET/CT studies assessed the eligibility for PSMA-targeting RLT with [177Lu]Ludotadipep [18] through clinical trials or expanded access programs in men with mCRPC who had evidence of progression after exhausting all therapeutic options, including anti-androgens and chemotherapy. Clinical data (age, time from initial diagnosis to PSMA PET/CT imaging, Gleason score, PSA level at the time of imaging, complete blood count, and blood chemistry profile) were collected from medical records. This study was approved by the Institutional Review Board of Seoul St. Mary’s Hospital (IRB Nos. KC20MDSF0483, KC22MDSS0440, and KC23MOSC0134).
FDG and PSMA PET/CT
After 6 hours of fasting, 284 ± 36 MBq of FDG was injected. No intravenous contrast agents were administered. Images were acquired using PET/CT scanner, Discovery 710 (GE Healthcare, Chicago, IL, USA) or Biograph TruePoint (Siemens Medical Solutions, Knoxville, TN, USA). CT began at the vertex and progressed to the upper thigh using the following standard protocol: 120 kV, 50 mA, 5 mm slice thickness (Biograph TruePoint); 120 kVp, variable mAs adjusted by topographic image, 2.5 mm slice thickness (Discovery 710). PET followed immediately over the same body region. The acquisition time was 2–3 minutes per bed position. CT data were used for attenuation correction, and PET images were reconstructed using standard ordered-subset expectation maximization. The same method was used to obtain PSMA PET/CT 90 minutes after the intravenous injection of 185 ± 19 MBq of [18F]Florastamin (FutureChem, Seoul, Korea), a PSMA targeting diagnostic radiopharmaceutical [19].
Image Analysis
Lesions with FDG or PSMA uptake intensity higher than that of the liver on visual assessment were considered positive and noted for each patient and tumor site. Tumor sites were categorized as prostate bed, lymph nodes, bone, and visceral organ. The site was considered positive with at least one lesion with uptake higher than that of the liver at that site. The readers independently looked for lesions with FDG uptake intensity unequivocally higher than that of the PSMA uptake. Visual assessments were performed using the liver as the reference organ. Two readers reviewed the images together to reach a consensus in case of disagreement for uncertain lesions. The total tumor volumes from the FDG and PSMA PET/CT images were manually measured with a fixed threshold of SUV 4.0 using Mirada XD3 software (Mirada Medical, Oxford, UK). A case was considered to be “FDG-dominant” when the total tumor volume computed from FDG PET/CT was greater than the volume computed from PSMA PET/CT by either 1) an absolute difference ≥50 mL, or 2) a factor ≥2. Guidelines on how to compute and compare tumor volumes using FDG and PSMA PET/CT remain nonexistent. The authors borrowed an arbitrary 50 mL absolute value from the threshold used to define a large prostate volume [20], and the relative volume factor was adapted from the PSA doubling time used in clinical practice to express tumor aggressiveness [21]. Additionally, the time from the initial diagnosis to PSMA PET/CT imaging (disease duration), Gleason score at staging, and laboratory parameters (PSA level, complete blood count, and blood chemistry profile) obtained within a week of PSMA PET/CT were retrieved. We compared the clinical findings between FDG-dominant patients and other patients using the Mann–Whitney U test. The statistically significant threshold was set at P < 0.05.
Patients
Paired FDG and PSMA PET/CT images, collected consecutively between October 2022 and July 2023, were retrospectively reviewed. PET/CT studies assessed the eligibility for PSMA-targeting RLT with [177Lu]Ludotadipep [18] through clinical trials or expanded access programs in men with mCRPC who had evidence of progression after exhausting all therapeutic options, including anti-androgens and chemotherapy. Clinical data (age, time from initial diagnosis to PSMA PET/CT imaging, Gleason score, PSA level at the time of imaging, complete blood count, and blood chemistry profile) were collected from medical records. This study was approved by the Institutional Review Board of Seoul St. Mary’s Hospital (IRB Nos. KC20MDSF0483, KC22MDSS0440, and KC23MOSC0134).
FDG and PSMA PET/CT
After 6 hours of fasting, 284 ± 36 MBq of FDG was injected. No intravenous contrast agents were administered. Images were acquired using PET/CT scanner, Discovery 710 (GE Healthcare, Chicago, IL, USA) or Biograph TruePoint (Siemens Medical Solutions, Knoxville, TN, USA). CT began at the vertex and progressed to the upper thigh using the following standard protocol: 120 kV, 50 mA, 5 mm slice thickness (Biograph TruePoint); 120 kVp, variable mAs adjusted by topographic image, 2.5 mm slice thickness (Discovery 710). PET followed immediately over the same body region. The acquisition time was 2–3 minutes per bed position. CT data were used for attenuation correction, and PET images were reconstructed using standard ordered-subset expectation maximization. The same method was used to obtain PSMA PET/CT 90 minutes after the intravenous injection of 185 ± 19 MBq of [18F]Florastamin (FutureChem, Seoul, Korea), a PSMA targeting diagnostic radiopharmaceutical [19].
Image Analysis
Lesions with FDG or PSMA uptake intensity higher than that of the liver on visual assessment were considered positive and noted for each patient and tumor site. Tumor sites were categorized as prostate bed, lymph nodes, bone, and visceral organ. The site was considered positive with at least one lesion with uptake higher than that of the liver at that site. The readers independently looked for lesions with FDG uptake intensity unequivocally higher than that of the PSMA uptake. Visual assessments were performed using the liver as the reference organ. Two readers reviewed the images together to reach a consensus in case of disagreement for uncertain lesions. The total tumor volumes from the FDG and PSMA PET/CT images were manually measured with a fixed threshold of SUV 4.0 using Mirada XD3 software (Mirada Medical, Oxford, UK). A case was considered to be “FDG-dominant” when the total tumor volume computed from FDG PET/CT was greater than the volume computed from PSMA PET/CT by either 1) an absolute difference ≥50 mL, or 2) a factor ≥2. Guidelines on how to compute and compare tumor volumes using FDG and PSMA PET/CT remain nonexistent. The authors borrowed an arbitrary 50 mL absolute value from the threshold used to define a large prostate volume [20], and the relative volume factor was adapted from the PSA doubling time used in clinical practice to express tumor aggressiveness [21]. Additionally, the time from the initial diagnosis to PSMA PET/CT imaging (disease duration), Gleason score at staging, and laboratory parameters (PSA level, complete blood count, and blood chemistry profile) obtained within a week of PSMA PET/CT were retrieved. We compared the clinical findings between FDG-dominant patients and other patients using the Mann–Whitney U test. The statistically significant threshold was set at P < 0.05.
RESULTS
A total of 42 pairs of FDG- and PSMA-PET/CT studies were reviewed. Patient characteristics are shown in Table 1.
Of the 42 patients, 41 and 40 showed positive lesions on PSMA PET/CT and FDG PET/CT, respectively. On patient-based analysis, two patients revealed PSMA+/FDG- findings (no tumor with positive FDG uptake), one patient showed PSMA-/FDG+ (no tumor with PSMA uptake), and 39 patients showed PSMA+/FDG+ (one or more lesions positive on both images). On site-based analysis, the discordance (PSMA+/FDG- or PSMA-/FDG+) rates were 9.5% (4/42) for prostate beds, 11.9% (5/42) for LNs, 9.5% (4/42) for bones, and 11.9% (5/42) for visceral organs (Fig. 1).
Unequivocally higher FDG uptake than PSMA uptake in at least one tumor site was observed in 54.8% (23/42) of patients. Patients with a greater total tumor volume on FDG PET/CT than that on PSMA PET/CT, that is, FDG-dominant patients, accounted for 28.6% (12/42). The FDG-dominant group showed significantly shorter disease duration (median 25 months vs. 62 months, P = 0.049), higher AST (median 28.5 U/L vs. 22.5 U/L, P = 0.027), and higher LDH levels than their counter group (median 341.5 U/L vs. 224.5 U/L, P = 0.010) (Fig. 2). Representative cases are shown in Figure 3.
DISCUSSION
In the current study of Korean patients with advanced mCRPC that progressed after standard anti-androgen and chemotherapy regimens, most patients exhibited positive findings on both PSMA and FDG PET/CT images. Upon analysis by tumor site, discordant PSMA and FDG positivity was observed in approximately 10% of patients. Comparing individual lesions, over half (23/42) of the patients included in this study cohort had at least one lesion that exhibited higher FDG uptake than PSMA uptake. Furthermore, considering the overall tumor volume, 12 of the 42 patients (28.6%) demonstrated predominance on FDG PET/CT.
While a few studies have compared the diagnostic performance of FDG and PSMA PET/CT for the staging or biochemical recurrence of prostate cancer [2223], the patterns of uptake in the two PET/CT modalities in mCRPC remain unclear. For decades, FDG PET/CT has been widely utilized to characterize various solid tumors; however, its role has been relatively limited in prostate cancer in Asian men. We aimed to fill this gap in the literature by examining the FDG uptake pattern, a known surrogate for tumor aggressiveness, and PSMA expression in Koreans. As demonstrated in our study, while most patients exhibited positive findings on both PSMA and FDG PET/CT, the patterns of uptake varied per individual and tumor site, which has implications for treatment planning.
PSMA-targeting RLT has been approved for adult patients with PSMA-positive mCRPC who progress from androgen receptor pathway inhibition to taxane-based chemotherapy [24]. In the VISION trial, FDG PET/CT was not required to select patients for treatment [3]. However, in the TheraP trial, PSMA and FDG PET/CT were used to assess treatment eligibility [2]. Although some patients who did not meet the eligibility criteria of the TheraP trial also showed response, patient selection using this criteria resulted in better treatment outcomes [25]. Despite its importance in the patient selection process, imaging criteria for PSMA-targeting RLT have not been studied in Korean men.
Reports have indicated the poor prognosis associated with GLUT expression in prostate cancer specimens obtained through surgery [26]. A recent study showed that patients with a metabolic tumor volume greater than 200 mL on FDG PET/CT had lower odds of PSA response after PSMA-targeting RLT [27]. In another study of patients with mCRPC undergoing RLT, those with at least one FDG+/PSMA- lesion at baseline had a significantly lower overall survival than patients without any discordant lesions [28]. In theory, a tumor lesion that is PSMA- will not respond to PSMA RLT, and FDG+ lesions have the potential to drive disease progression. These studies suggested that evaluating FDG uptake may be as important as examining PSMA overexpression before RLT.
In our study, patients with higher tumor volumes on FDG PET/CT had elevated levels of AST and LDH, and a shorter time from the diagnosis of prostate cancer to disease progression. In a previous study, elevated baseline LDH levels were associated with an increased risk of disease progression after RLT [29]. If the FDG-dominant pattern demonstrates biologically aggressive tumors, targeting PSMA alone may be insufficient to effectuate clinical benefits in approximately 29% of patients who are RLT candidates, as seen in this study cohort. This rate is somewhat higher than the 18% reported previously from Germany [30]; however, a direct comparison is not possible because the definition of mismatch was different. Further studies are required to determine the true rate of discordance and whether the FDG-dominant group exhibits differences at the molecular and genetic levels.
The first limitation of this study is its single-center design with a small sample size. Second, as a surrogate for PSMA expression, we utilized Florastamin, a novel PSMA-targeting tracer with no in vivo data on its compatibility with the more widely used [68Ga]Ga-PSMA-11 or [18F]DCFPyL. However, direct comparison studies among various PSMA-targeting tracers are scarce, and it is generally agreed that a readily available PMSA-targeting tracer can be used to determine the feasibility of PSMA-targeted therapy [31]. Third, while we examined the associations between FDG and PSMA uptake patterns and certain clinical features, we did not directly correlate the imaging findings with actual patient treatment responses or survival outcomes at this stage. However, given the scarcity of reports on the imaging patterns in Korean patients with advanced mCRPC, our findings may serve as a basis for future prospective diagnostic and therapeutic studies.
In conclusion, most patients with advanced Mcrpc in our study were positive on both PSMA and FDG PET/CT, and approximately half of the patients had tumor lesion(s) with higher FDG uptake than PSMA uptake. Approximately 29% of the patients exhibited a predominantly higher FDG volume overall, which may be associated with more aggressive clinical features. Therefore, it may be beneficial to conduct both PSMA- and FDG-PET/CT for treatment planning in Korean patients with advanced Mcrpc. |
Title: Nutritional Profile and Chlorophyll Intake of Collard Green as a Convenience Food | Body:
1. Introduction
Brassicaceae, commonly called Cruciferous, is one of the largest families of Angiosperms, with more than 360 genera and nearly 4000 species spread across several continents [1,2]. In recent years, Cruciferous vegetables have gained increasing attention for being an exceptional source of nutrients, such as proteins, vitamins, and minerals. The characteristic that distinguishes Cruciferous vegetables is the high content of glucosinolates, which convert into bioactive compounds with anticancer properties, such as isothiocyanates and indoles. Isothiocyanates actively contribute to protection against cancer by promoting the activation of phase 2 enzymes, involved in the detoxification of carcinogens, arresting cell cycle progression and inducing apoptosis of damaged cells. Studies in animal models have confirmed that isothiocyanates exert antiproliferative and pro-apoptotic effects through the accumulation of reactive oxygen species, crucial for the control of tumor growth [3]. Additionally, cruciferous vegetables stand out for their potent antioxidant effects, reducing oxidative stress and inflammation. Their diverse range of phytonutrients, including phenols and flavonoids, not only supports immune function but also provides antimicrobial and cardioprotective benefits. Moreover, their ability to influence detoxification pathways and promote cellular health adds to their exceptional profile [3,4,5].
Collard green (Brassica oleraceae var. viridis) is part of the Acephala group, which contains different morphotypes [6]. The name “Acephala” refers to a group of leafy cabbages “without head” [7]. The vegetables of the Acephala group of Brassica oleracea are biennial cultivars and originated in the Mediterranean region but have gained popularity worldwide due to their strong tolerance toward unfavorable environmental conditions, as well as their nutritional aspects and versatility in recipes [8]. Collard greens are known variously as couve (Brazil), couve-galega (Portugal), kovi or kobi (Spanish-speaking countries), haak (Kashmir), and sukuma wiki (East Africa). Popular cultivars of green collard include “Georgia Southern”, “Morris Heading”, and “Couve-Manteiga” [9,10].
The green color of plants is attributed to chlorophylls, the most abundant pigments on Earth, synthesized by plants, algae, and some bacteria. More than a hundred different structures of chlorophyll have been identified. The structures of chlorophyll a (Chla) and b (Chlb) are the most abundant in green foods [11].
Interest in chlorophylls is increasing sharply, as recent studies have highlighted their ability to be absorbed and metabolized by the body and their protective role against carcinogens. The main mechanisms by which chlorophylls exert an antitumor action include antioxidant activity, which reduces oxidative stress and DNA damage, and the ability to complex mutagens in the gastrointestinal tract, limiting their systemic absorption and decreasing the risk of carcinogenesis. In addition, chlorophylls modulate the detoxification enzymatic pathways of xenobiotic compounds and can induce apoptosis in cancer cells, thereby contributing to their elimination and control of cancerous proliferation [12,13]. These aspects are increasingly attracting the attention of scientific research and the food industry [11,14,15,16].
Chlorophylls can undergo significant changes during the digestive process due to changes in pH and enzymatic reactions. This can lead to the pheophytinization and oxidation of ingested chlorophylls, transforming them into derivatives such as pheophytins and pheophorbides [14,17]. Despite these transformations, it is crucial to include sources of chlorophyll in the diet, as chlorophyll derivatives, once absorbed, can also benefit health. The ability of chlorophylls and their derivatives to be micellarized and absorbed changes, but even minimal absorption is physiologically significant, enhancing their protective potential in the body. Once micellized, these compounds can enter cells, where they exert protective effects through antioxidant activity, modulation of detoxification processes, and regulation of oxidative stress and inflammatory pathways [13,14].
Chlorophyll-derived color additives, identified as E140 and E141 [11], are authorized by European legislation and commonly used to provide stable and vibrant colors [18,19]. However, the food market is progressively evolving toward solutions that respect the concept of “clean label”, favoring “natural ingredients” that not only have coloring properties but also maintain the main compounds of the original plant-based food matrix. In response to these needs, research is focusing on plant sources rich in natural chlorophylls to develop concentrates in the form of juices, pastes, powders, and the like, which can be used as food coloring, replacing traditional natural or synthetic coloring additives [20,21]. Additionally, if these concentrates are obtained from plant by-products of the agrifood industry, it helps to reduce food waste and promote the circular economy of the food chain [22].
Among the drying methods, freeze drying is widely preferred for its ability to preserve food quality best. Studies have shown that freeze-dried leaves, such as kale and chives, maintain a significantly higher concentration of chlorophyll than other drying methods, with retention rates of up to 100% [23,24,25]. The use of these concentrated functional ingredients of chlorophylls and other bioactive components represents a promising way to increase the consumption of chlorophyll among the population, especially among the youngest, by proposing it in different and more palatable forms. Saidi et al. (2023) [26] have developed an artisanal ice cream with improved nutritional value thanks to the inclusion of green mustard leaves, both in powder and puree form. Waseem et al. (2024) [27] have obtained an unleavened bread enriched with spinach powder, significantly improving its nutritional and functional characteristics. Fanesi et al. (2023) [28] developed functional biscuits containing flour derived from broccoli by-products rich in vitamins, glucosinolates, carotenoids and chlorophylls.
A recent study on chronic intake of green chlorophylls in Europe, based on EFSA data, reported an average intake of 207.12 mg/day in European adults, with wide variations between countries and age groups. For example, in adults, the intake ranges from 44.40 mg/day in Denmark to 434.99 mg/day in the Netherlands. Similar ranges were observed for adolescents and the elderly. In the infant group, values range from 2.37 mg/day in Italy to 124.77 mg/day in France, with increased intake during adolescence. In Italy, the average consumption of chlorophyll increases with age, from 2.37 mg/day in children to 156.04 mg/day in adolescents, 152.28 mg/day in adults, and reaching 162.32 mg/day in the elderly [16]. In Italy, childhood obesity is among the highest in Europe [29], with 19% of children overweight and 9.8% obese. In addition, 25.9% consume fruit and vegetables less than once a day [30], far from the five portions recommended by the Italian guidelines and the World Health Organization (WHO) [31,32]. Even among adults, only 7% of Italians between 18 and 69 years of age include the recommended five portions of fruit and vegetables in their daily diet. In total, 52% of the population consumes only 1–2 servings a day, 38% takes 3–4 servings, and 3% consumes none [33]. Among the main obstacles that separate the modern consumer from an adequate consumption of vegetables are the cost, the lack of time for preparation, availability when eating out, preference, and social support [34,35,36]. However, the barriers related to preparation and cooking seem to have a greater impact than those related to purchase [34]. Plant-based convenience foods, with their high nutritional quality, offer a promising solution to improve diets and overcome the barriers related to vegetable preparation [36,37,38,39].
Several authors have documented the impact of domestic and industrial processing on the nutritional quality of Brassicaceae. Sous vide cooking has emerged as a method that offers numerous advantages over traditional techniques. It preserves vitamins and minerals, reduces oxidation, prevents moisture loss, and maintains volatile aroma [40,41,42,43,44]. Additionally, vacuum sealing inhibits bacterial growth, thereby prolonging the shelf-life of food products and reducing food waste. This is a crucial step in improving food safety in global food distribution and storage [38,44,45,46,47].
Starting from these considerations, this work aims to provide an in-depth analysis of the nutritional profile of the “Couve-Manteiga” cultivar grown in Italy.
Given its recent introduction into Italian agriculture, there is a notable absence of studies investigating the mineral, amino acid, and fatty acid profiles of this cultivar at a national level. This study addresses this gap by analyzing these aspects in depth, with a focus on the beneficial roles that these components play for consumers.
In addition, the potential of collard green leaves (CGLs) in the food industry is being examined through their application in convenience food, offering convenient consumption options that minimize the time and effort required for cleaning, preparing, and consuming plant-based foods. This attention is also motivated by the results of a recent study of 2024 that highlights the low levels of chlorophyll intake among certain age groups in the European population; in particular, Italian children have the lowest reported consumption [16]. Given the proven health benefits of chlorophyll, this research also investigates variations in chlorophyll content in different CGL preparations, including fresh-cut, fifth-range (cooked and vacuum-packed), and freeze-dried formats. The aim is to identify simple methods to increase chlorophyll intake in daily diets, thus making chlorophyll consumption more accessible and practical, while also promoting greater consumer confidence in these alternatives.
2. Materials and Methods
2.1. Plant Material
All the CGLs, cultivar “Couve-Manteiga”, were supplied by Azienda Agraria Evangelisti (Cesena, Italy), with the harvesting site located at a latitude of 44°8′0″ N and a longitude of 12°14′0″ E. The same company directly provided a sample of 5 kg of fresh-cut CGLs and provided a sample of 2 kg of the parts discarded during sorting, such as the outermost and hardest leaves, those damaged or with shape defects. A total of 2 kg of samples of the fifth range, pasteurized and vacuum-packed, was supplied by the Ghisetti company (Badia Polesine, Rovigo, Italy), specialized in the production of ready-to-eat vegetable products. All samples were transported to the laboratory in thermal boxes.
2.2. Preparation of Freeze-Dried CGLs
To ensure adequate representativeness, 100 leaves were randomly selected from the fresh-cut sample received and freeze-dried at a pressure of 28 mbar, at –50 °C, for four days, using a freeze-dryer (Edwards Italy, Milan, Italy). After the freeze-drying phase, the samples were ground with a mortar and pestle to obtain a homogeneous sample with a particle size of less than 500 μm. Then, the powder was vacuum-packed and stored at –20 °C until further analysis. The same process was applied to the by-products, freeze drying the entire amount of product received.
2.3. Determination of Water Activity
Activity water (aw) was measured using the LabMaster-aw instrument (Novasina AG, Lachen, Switzerland) with an accuracy of ±0.01 at 25 °C. After calibration, the samples were placed in a sampling chamber until the equilibrium was reached. Each measurement was performed in triplicate.
2.4. Proximate Composition of CGLs
Moisture was measured using the oven-drying method at 105 °C for 24 h. Ash content was measured by weighing the samples before and after burning them in a muffle furnace at 550 °C for 6 h, as described in Methods 942.05 and 934.01 by the Association of Official Analytical Chemists (AOAC). Crude protein content was evaluated using the Kjeldahl method, and the results were multiplied by the nitrogen conversion factor of 6.25 (AOAC, Method 981.10). The crude fiber was evaluated by boiling the leaves in 0.26 M sulfuric acid for 30 min. The insoluble residue obtained was filtered and washed, and the filtrate was boiled in 0.31 M sodium hydroxide and filtered and rewashed. The final filtrate was dried at 130 °C for 120 min. Weight loss was measured at 350 °C. Crude lipid content was determined according to the Soxhlet technique, using a Soxtec™ 2046 extraction system (FOSS, Hillerød, Denmark). Available carbohydrate was obtained by difference [48,49], and, finally, the energy value was calculated using Equation (1):(1)Energy kcal100g product =4×g protein+g available carbohydrates +2×g dietary fiber+9×g fat
2.5. Amino Acids Profile Determination
Amino acids were analyzed following a method adapted from European Pharmacopoeia and previously described by Ebrahimi et al. (2022) [50]. For the separation and quantification of amino acids (AAs) in CGLs, an Agilent 1260 Infinity High-Performance Liquid Chromatography (Agilent, Santa Clara, CA, United States) with a reversed-phase C18 column (CORTECS C18, 2.7 µm, 2.1 × 150 mm), maintained at 45 °C, and a diode array detector (Agilent 1260 Series, DAD VL+) was used. AAs were analyzed after acid hydrolysis and pre-column derivatization with 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate, separated by RP-HPLC, and analyzed by UV detection, following the method described by Bosch et al. (2006) [51]. Briefly, for amino acid determination, the sample protein was hydrolyzed with 6 M acid hydrochloride at 105 °C for 24 h. To determine cysteine (Cys), a method involving a reaction with 3,3-dithiodipropionic acid to form a mixed disulfide was used, followed by acid hydrolysis. After hydrolysis, the samples were neutralized with 8 M sodium hydroxide, adjusted to volume, and filtered through 0.45 µm filters. Next, the derivatization step was conducted by adding AccQ-Tag Ultra borate buffer and the filtered sample, followed by adding the derivatization agent dissolved in acetonitrile and heating for 10 min at 55 °C. The sample was then diluted and injected into HPLC. Tryptophan (Try) was determined following a method adapted from Directive 2000/45/EC. The sample was hydrolyzed in Teflon vials with barium hydroxide and water at 105 °C for 24 h and then neutralized and diluted with a 1 M sodium borate buffer. After filtration, the sample was injected into the column (Xselect HSS T3, 5 µm; 4.6 × 250 mm), and separation was performed by an isocratic elution system consisting of sodium acetate/acetonitrile.
2.6. Fatty Acids Profile Determination
Fatty acids (FAs) content in CGLs was analyzed by two-dimensional gas chromatography (GC × GC) using an Agilent 7890A gas chromatograph with an Agilent 7683 autosampler, an Agilent Flame Ionization Detector (FID), and an Agilent CFT modulator. To prepare the sample for FAs analysis, 40 mg of CMLs was added to 1 mL of sodium methoxide in MeOH (0.5 M), followed by incubation at 50 °C for 15 min and cooling to room temperature. Next, 1.5 mL of MeOH containing 5% HCl was added, and the mixture was incubated at 80 °C for 15 min, followed by cooling to room temperature. After that, 2 mL of hexane and 2 mL of 6% potassium carbonate were added to the cooled mixture. After vortex agitation for 30 s and centrifugation at 4000× g at 4 °C for 5 min, the supernatant containing fatty acid methyl esters (FAMEs) was injected into the GC. The GC temperature program was set with an initial oven temperature of 40 °C (holding time of 2 min), followed by heating to 170 °C at a rate of 50 °C/min (holding time of 25 min) and then increasing the temperature to 250 °C at a rate of 2 °C/min (holding time 14 min). The injection port and detector temperatures were 270 °C and 300 °C, respectively. The volume injected was 1 µL in split mode (split ratio 160:1), using hydrogen as the carrier gas. The columns used were a Supelco SP-2560 (Merck, Darmstadt, Germany) as the primary column and an Agilent J&W HP-5 ms as the secondary column. The resulting two-dimensional chromatograms were processed with GC × GC Image R 2.2 software from Zoex Corp., Houston, TX, USA. Individual FAs were expressed as a percentage of TFAs. Chemicals used as standards were high-purity grade and were purchased from Sigma-Aldrich (Merck, Milano, Italy).
2.7. Laboratory Sous Vide Cooking of CGLs
In order to monitoring the variations in color and chlorophyll content due to cooking, the fresh-cut leaves were weighed (20 g) and vacuum-sealed in ORVED vacuum-embossed kitchen bags (30 cm × 25 cm) with layers of OPA/PP and oxygen permeability of 30 cm3/m2/24 h/bar (23 °C, 50% RH), using an automatic packaging machine (Dito Sam, mod 600528, Pordenone, Italy). The vacuum-packed CGLs were placed in a water bath maintained at the selected temperature of 100 °C. The samples were heated for 5, 10, 15, 20, and 25 min, respectively. The untreated sample was the control. As soon as the treatment was completed, the samples were immersed in a basin with water and ice to stop further post-cooking biochemical changes. The vacuum packets were used for the determination of colorimetric parameters and chlorophylls of CGLs.
2.8. Colorimetric Properties of CGLs
Colorimetric parameters were evaluated using the Chroma Meter CR-300 colorimeter (Konica Minolta, Milan, Italy), set to illuminant D65 and an observation angle of 10°. Prior to testing, the device was calibrated with a standard white tile (L* = 84.1, a* = 0.32, b* = 0.33; Konica Minolta, Milan, Italy). The negative value of a* was considered the parameter of green (−a*). For each sample, 10 measurements were taken. For the leaves, both fresh and cooked, measurements were made with a measuring area of 8 mm in the leaf blade, while values on the freeze-dried powder were taken over a homogeneous area of 50 mm.
2.9. Determination of Chlorophyll
The extraction was performed according to the methods reported by Ebrahimi et al. (2024), with some modifications [52]. As an extraction solvent, 75% EtOH was used, and 2 g of ground sample was added to a falcon containing 20 mL solvent. The solid/liquid ratio was 1:10 (w/v). The spectrum of the extract was recorded from 400 to 700 nm, with reading ranges of 5 nm, using a spectrophotometer (Varian Carry 50 Bio UV/Vis, Agilent Technologies, Santa Clara, CA, USA). The absorbance values at 664 nm and 648.6 nm were recorded separately. The concentration of Chla and Chlb was determined spectrophotometrically, using the method described by Lichtenthaler and Buschmann (2001) [53]. Since Chla and Chlb have no absorbance at 750 nm, each absorbance value was corrected by subtracting that of the mixture at 750 nm from the measured absorbance. The contents of Chla and Chlb were calculated using Equations (2) and (3), respectively. Total chlorophyll is given by the sum of chlorophyll a and b.
(2)Chlaμgg=(13.36·(A664.1−A750)−5.19·(A648.6−A750))·Vex(mL)wex (g)
(3)Chlbμgg=(27.43·(A648.6−A750)−8.12·(A664.1−A750))·VexmLwex g
where Chla represents chlorophyll a; Chlb is chlorophyll b; A is the absorbance at 664.1, 648.6, and 750 nm; Vex is the volume of solvent in the extraction; and Wex is the weight of the sample in the extraction.
2.10. Data Analysis
All analyses were performed in triplicate (n = 3), except for colorimetric measurements, which were taken tenfold (n = 10), for statistical analysis, and the results were expressed as a mean ± standard deviation. The data collected were analyzed using Excel® for Microsoft 365 (Microsoft, Redmond, WA, USA) and Origin Pro 2024 (OriginLab, Northampton, MA, USA). An analysis of variance (ANOVA) was performed on the data, and Tukey’s test was used for comparisons, with significance and confidence levels set at 0.05 and 95%, respectively. The charts were generated using Origin Pro 2024 (OriginLab, Northampton, MA, USA).
2.1. Plant Material
All the CGLs, cultivar “Couve-Manteiga”, were supplied by Azienda Agraria Evangelisti (Cesena, Italy), with the harvesting site located at a latitude of 44°8′0″ N and a longitude of 12°14′0″ E. The same company directly provided a sample of 5 kg of fresh-cut CGLs and provided a sample of 2 kg of the parts discarded during sorting, such as the outermost and hardest leaves, those damaged or with shape defects. A total of 2 kg of samples of the fifth range, pasteurized and vacuum-packed, was supplied by the Ghisetti company (Badia Polesine, Rovigo, Italy), specialized in the production of ready-to-eat vegetable products. All samples were transported to the laboratory in thermal boxes.
2.2. Preparation of Freeze-Dried CGLs
To ensure adequate representativeness, 100 leaves were randomly selected from the fresh-cut sample received and freeze-dried at a pressure of 28 mbar, at –50 °C, for four days, using a freeze-dryer (Edwards Italy, Milan, Italy). After the freeze-drying phase, the samples were ground with a mortar and pestle to obtain a homogeneous sample with a particle size of less than 500 μm. Then, the powder was vacuum-packed and stored at –20 °C until further analysis. The same process was applied to the by-products, freeze drying the entire amount of product received.
2.3. Determination of Water Activity
Activity water (aw) was measured using the LabMaster-aw instrument (Novasina AG, Lachen, Switzerland) with an accuracy of ±0.01 at 25 °C. After calibration, the samples were placed in a sampling chamber until the equilibrium was reached. Each measurement was performed in triplicate.
2.4. Proximate Composition of CGLs
Moisture was measured using the oven-drying method at 105 °C for 24 h. Ash content was measured by weighing the samples before and after burning them in a muffle furnace at 550 °C for 6 h, as described in Methods 942.05 and 934.01 by the Association of Official Analytical Chemists (AOAC). Crude protein content was evaluated using the Kjeldahl method, and the results were multiplied by the nitrogen conversion factor of 6.25 (AOAC, Method 981.10). The crude fiber was evaluated by boiling the leaves in 0.26 M sulfuric acid for 30 min. The insoluble residue obtained was filtered and washed, and the filtrate was boiled in 0.31 M sodium hydroxide and filtered and rewashed. The final filtrate was dried at 130 °C for 120 min. Weight loss was measured at 350 °C. Crude lipid content was determined according to the Soxhlet technique, using a Soxtec™ 2046 extraction system (FOSS, Hillerød, Denmark). Available carbohydrate was obtained by difference [48,49], and, finally, the energy value was calculated using Equation (1):(1)Energy kcal100g product =4×g protein+g available carbohydrates +2×g dietary fiber+9×g fat
2.5. Amino Acids Profile Determination
Amino acids were analyzed following a method adapted from European Pharmacopoeia and previously described by Ebrahimi et al. (2022) [50]. For the separation and quantification of amino acids (AAs) in CGLs, an Agilent 1260 Infinity High-Performance Liquid Chromatography (Agilent, Santa Clara, CA, United States) with a reversed-phase C18 column (CORTECS C18, 2.7 µm, 2.1 × 150 mm), maintained at 45 °C, and a diode array detector (Agilent 1260 Series, DAD VL+) was used. AAs were analyzed after acid hydrolysis and pre-column derivatization with 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate, separated by RP-HPLC, and analyzed by UV detection, following the method described by Bosch et al. (2006) [51]. Briefly, for amino acid determination, the sample protein was hydrolyzed with 6 M acid hydrochloride at 105 °C for 24 h. To determine cysteine (Cys), a method involving a reaction with 3,3-dithiodipropionic acid to form a mixed disulfide was used, followed by acid hydrolysis. After hydrolysis, the samples were neutralized with 8 M sodium hydroxide, adjusted to volume, and filtered through 0.45 µm filters. Next, the derivatization step was conducted by adding AccQ-Tag Ultra borate buffer and the filtered sample, followed by adding the derivatization agent dissolved in acetonitrile and heating for 10 min at 55 °C. The sample was then diluted and injected into HPLC. Tryptophan (Try) was determined following a method adapted from Directive 2000/45/EC. The sample was hydrolyzed in Teflon vials with barium hydroxide and water at 105 °C for 24 h and then neutralized and diluted with a 1 M sodium borate buffer. After filtration, the sample was injected into the column (Xselect HSS T3, 5 µm; 4.6 × 250 mm), and separation was performed by an isocratic elution system consisting of sodium acetate/acetonitrile.
2.6. Fatty Acids Profile Determination
Fatty acids (FAs) content in CGLs was analyzed by two-dimensional gas chromatography (GC × GC) using an Agilent 7890A gas chromatograph with an Agilent 7683 autosampler, an Agilent Flame Ionization Detector (FID), and an Agilent CFT modulator. To prepare the sample for FAs analysis, 40 mg of CMLs was added to 1 mL of sodium methoxide in MeOH (0.5 M), followed by incubation at 50 °C for 15 min and cooling to room temperature. Next, 1.5 mL of MeOH containing 5% HCl was added, and the mixture was incubated at 80 °C for 15 min, followed by cooling to room temperature. After that, 2 mL of hexane and 2 mL of 6% potassium carbonate were added to the cooled mixture. After vortex agitation for 30 s and centrifugation at 4000× g at 4 °C for 5 min, the supernatant containing fatty acid methyl esters (FAMEs) was injected into the GC. The GC temperature program was set with an initial oven temperature of 40 °C (holding time of 2 min), followed by heating to 170 °C at a rate of 50 °C/min (holding time of 25 min) and then increasing the temperature to 250 °C at a rate of 2 °C/min (holding time 14 min). The injection port and detector temperatures were 270 °C and 300 °C, respectively. The volume injected was 1 µL in split mode (split ratio 160:1), using hydrogen as the carrier gas. The columns used were a Supelco SP-2560 (Merck, Darmstadt, Germany) as the primary column and an Agilent J&W HP-5 ms as the secondary column. The resulting two-dimensional chromatograms were processed with GC × GC Image R 2.2 software from Zoex Corp., Houston, TX, USA. Individual FAs were expressed as a percentage of TFAs. Chemicals used as standards were high-purity grade and were purchased from Sigma-Aldrich (Merck, Milano, Italy).
2.7. Laboratory Sous Vide Cooking of CGLs
In order to monitoring the variations in color and chlorophyll content due to cooking, the fresh-cut leaves were weighed (20 g) and vacuum-sealed in ORVED vacuum-embossed kitchen bags (30 cm × 25 cm) with layers of OPA/PP and oxygen permeability of 30 cm3/m2/24 h/bar (23 °C, 50% RH), using an automatic packaging machine (Dito Sam, mod 600528, Pordenone, Italy). The vacuum-packed CGLs were placed in a water bath maintained at the selected temperature of 100 °C. The samples were heated for 5, 10, 15, 20, and 25 min, respectively. The untreated sample was the control. As soon as the treatment was completed, the samples were immersed in a basin with water and ice to stop further post-cooking biochemical changes. The vacuum packets were used for the determination of colorimetric parameters and chlorophylls of CGLs.
2.8. Colorimetric Properties of CGLs
Colorimetric parameters were evaluated using the Chroma Meter CR-300 colorimeter (Konica Minolta, Milan, Italy), set to illuminant D65 and an observation angle of 10°. Prior to testing, the device was calibrated with a standard white tile (L* = 84.1, a* = 0.32, b* = 0.33; Konica Minolta, Milan, Italy). The negative value of a* was considered the parameter of green (−a*). For each sample, 10 measurements were taken. For the leaves, both fresh and cooked, measurements were made with a measuring area of 8 mm in the leaf blade, while values on the freeze-dried powder were taken over a homogeneous area of 50 mm.
2.9. Determination of Chlorophyll
The extraction was performed according to the methods reported by Ebrahimi et al. (2024), with some modifications [52]. As an extraction solvent, 75% EtOH was used, and 2 g of ground sample was added to a falcon containing 20 mL solvent. The solid/liquid ratio was 1:10 (w/v). The spectrum of the extract was recorded from 400 to 700 nm, with reading ranges of 5 nm, using a spectrophotometer (Varian Carry 50 Bio UV/Vis, Agilent Technologies, Santa Clara, CA, USA). The absorbance values at 664 nm and 648.6 nm were recorded separately. The concentration of Chla and Chlb was determined spectrophotometrically, using the method described by Lichtenthaler and Buschmann (2001) [53]. Since Chla and Chlb have no absorbance at 750 nm, each absorbance value was corrected by subtracting that of the mixture at 750 nm from the measured absorbance. The contents of Chla and Chlb were calculated using Equations (2) and (3), respectively. Total chlorophyll is given by the sum of chlorophyll a and b.
(2)Chlaμgg=(13.36·(A664.1−A750)−5.19·(A648.6−A750))·Vex(mL)wex (g)
(3)Chlbμgg=(27.43·(A648.6−A750)−8.12·(A664.1−A750))·VexmLwex g
where Chla represents chlorophyll a; Chlb is chlorophyll b; A is the absorbance at 664.1, 648.6, and 750 nm; Vex is the volume of solvent in the extraction; and Wex is the weight of the sample in the extraction.
2.10. Data Analysis
All analyses were performed in triplicate (n = 3), except for colorimetric measurements, which were taken tenfold (n = 10), for statistical analysis, and the results were expressed as a mean ± standard deviation. The data collected were analyzed using Excel® for Microsoft 365 (Microsoft, Redmond, WA, USA) and Origin Pro 2024 (OriginLab, Northampton, MA, USA). An analysis of variance (ANOVA) was performed on the data, and Tukey’s test was used for comparisons, with significance and confidence levels set at 0.05 and 95%, respectively. The charts were generated using Origin Pro 2024 (OriginLab, Northampton, MA, USA).
3. Results and Discussion
3.1. Proximate Composition
The proximate composition of the product is shown in Table 1. All data are expressed as 100 g of fresh weight (FW), specifically referring to the edible part of the product. The CGLs have a high water content (88.94 ± 0.55 g/100 g FW) and an aw of 0.9813 ± 0.0046. The fat content is low (0.94 ± 0.5 g/100 g FW), and the FAs profile is characterized by the prevalence of polyunsaturated fatty acids (PUFAs). Dietary fibers are the most abundant component (3.39 ± 0.08 g/100 g FW), followed by proteins (3.01 ± 0.04 g/100 g FW), which contribute significantly to the caloric value of the vegetable (30.66 ± 0.21 kcal/100 g FW), which nevertheless remains a low-calorie product [54]. Available carbohydrates are very low (0.85 ± 0.05 g/100 g FW). Proteins account for most of the weight and contribute 39.15% to the product’s energy, making this vegetable a “source of protein” product. For every 100 kcal, 11.06 g of total fiber is provided, making the product also a “source of fiber” [54]. CGLs contain slightly more protein (3.01 ± 0.04 g/100 g FW) than other green leafy vegetables commonly consumed in Italy, such as cabbage (2.1 g/100 g FW), lettuce (1.8 g/100 g FW), arugula (2.6 g/100 g FW), and endive (0.9 g/100 g FW) [55].
Italian guidelines recommend consuming at least five portions of fruit and vegetables a day [32]. According to these guidelines, a serving of vegetables corresponds to about 200 g, which would translate into 6.77 g of dietary fiber provided by one serving of CGLs, equaling just over one-fourth of the official recommendations that suggest a dietary fiber intake of 25 g per day for adults [55]. In addition, CGLs also contain slightly more fiber (3.39 ± 0.08 g/100 g FW) than other varieties of the same species commonly consumed in Italy, such as cauliflower (2.4 g/100 g FW), cabbage (2.6 g/100 g FW), or broccoli (3 g/100 g FW) [55].
3.2. Soluble Sugars
As regards the composition of soluble sugars (Table 2), the main sugar detected was glucose (0.44 ± 0.01 g/100 g FW), followed by fructose (0.18 ± 0.03 g/100 g FW) and rhamnose (0.08 ± 0.01 g/100 g FW). Glucose is the most abundant soluble sugar, as demonstrated also by a recent study on the phytochemical composition of selected genotypes of organic kale [6]; however, this finding is in contrast with what was reported by another study, where fructose was found to be the most abundant sugar [56]. It is also important to note that both the stage of development of the plant and the environmental conditions affect both the profile and the amounts of sugars. In this respect, it has been reported that sucrose decreased during the development of the plant, whereas fructose increased in cabbages grown at 2 °C, improving their sweetness [57].
3.3. Mineral Composition
The mineral composition of CGLs is shown in Table 3. Calcium (333.09 ± 1.020 mg/100 g FW) was the main mineral found in the analyzed samples, followed by potassium (215.53 ± 1.06 mg/100 g FW) and sulfur (108 ± 0.19 mg/100 g FW). The latter, typical of the genus Brassica, is part of many bioactive components important for health, the first of all glucosinolates in Cruciferous vegetables derived from the breakdown of some isothiocyanates [58].
The calcium content in 100 g of this product is significantly higher than that found in other green leafy vegetables commonly consumed in Italy, such as endive (93 mg/100 g FW), lettuce (45 mg/100 g FW), and spinach (78 mg/100 g FW). It also surpasses the calcium levels typically found in other Brassica species, which a range from 30 to 60 mg/100 g FW [55].
A 200 g serving of CGLs would provide 666.18 mg/100 g FW of calcium and 431.05 mg/100 g FW of potassium, covering 83% of the daily requirement of calcium (800 mg/day) and just over 20% the daily requirement of potassium (2000 mg/day) [59]. One portion is therefore an important source of calcium and potassium [54].
This combination of minerals supports several vital functions, contributing to the overall well-being of humans [60]. Plant-based nutrition is becoming increasingly popular among athletes; it is critical that these diets meet the rigorous nutritional requirements needed to ensure optimal performance and effective recovery. Among the essential micronutrients that vegan athletes need to pay special attention to are iron, vitamin B12, calcium, vitamin D, zinc, and omega-3 fatty acids [61]. Calcium is crucial for bone health, preventing conditions such as osteoporosis, and for vital functions such as muscle contraction, nerve transmission, and blood clotting. In addition, it plays an important role in glucose regulation and thyroid function, both of which are crucial for energy metabolism and athletic performance. To ensure adequate calcium intake, athletes must include calcium-rich vegetables in their diet, such as kale, spinach, cabbage, broccoli, artichokes, and green turnips [61,62]. Considering the high calcium content in CGLs, concentrated solutions of the CGLs as freeze-dried products to be used to fortify snacks or energy drinks for athletes could be a way to ensure a constant intake of calcium even in these diets with dietary restrictions. Furthermore, it emerges that, in CGLs, the ratio of sodium to potassium is less than 0.6 mg/mg, suggesting that this vegetable is suitable for hypertensive consumers [63]. Magnesium (29.45 ± 0.07 mg/100 g FW) is a very important macronutrient for plants, as it is a structural element of the chlorophyll molecule; it activates more than 300 enzymes and contributes to the stabilization of some subcellular structures, such as ribosomes; and it also plays an important role in plant photosynthesis, carbohydrate transport, and nucleic acid and protein synthesis, as well as in the generation of reactive species oxygen [64]. Since magnesium deficiency frequently occurs in old age, a balanced magnesium intake may contribute to healthy aging [65]. A 200 g serving of CGLs has 58.90 mg/100 g FW of magnesium, 15.7% of the daily requirement [54,59].
3.4. Amino Acid Profile
Table 4 summarizes the content of the 18 AAs commonly found in proteins. For a better explanation, the identified amino acids were divided into two groups: essential amino acids (EAAs) and non-essential amino acids (NEAAs). The nine EAAs are leucine (Leu), isoleucine (Ile), valine (Val), phenylalanine (Phe), threonine (Thr), tryptophan (Trp), Methionine (Met), lysine (Lys), and histidine (His). Conversely, alanine (Ala), serine (Ser), proline (Pro), arginine (Arg), aspartic acid (Asp), tyrosine (Tye), glutamic acid (Glu), and cysteine (Cys) are NEAAs [66,67,68].
Histidine, although synthesized by the body, in some conditions, such as chronic obstructive pulmonary disease (COPD) and chronic kidney disease (CKD) [69,70], is not produced in sufficient quantities to meet physiological needs, making it an indispensable amino acid. Its deficiency can lead to significant reductions in hemoglobin levels [68,70]. Furthermore, supplementation with doses of 4.0–4.5 g histidine and increased dietary histidine intake are associated with decreased BMI, adiposity, markers of glucose homeostasis (e.g., HOMA–IR, fasting blood glucose, and 2 h postprandial blood glucose), proinflammatory cytokines, and oxidative stress [70]. The sum of total amino acids (TAAs) in CGLs is 2746.91 ± 18.96 mg/100 g FW, that of the NEAAs is 1598.96 ± 0.34 mg/100 g FW, and that of the EAAs is 1147.95 ± 19.30 mg/100 g FW. These data indicate a good ratio of EAAs/NEAAs in CGLs. According to the ideal amino acid composition proposed by the Food and Agriculture Organization of the United Nations (FAO)/WHO/United Nations University (UNU), the EAA/TAAs ratio should be ≈40%, and that of the EAA/NEAA should be ≥ 60% [69]. In the present study, whole leaves were examined, but previous studies have shown that leaf blades contain more amino acids than the parts of stems involved in nutrient transport [71,72].
The most abundant AAs are acidic AAs Glu and Asp, respectively, at 470.25 ± 5.20 mg/100 g FW and 324.62 ± 0.53 mg/100 g FW, as also reported in previous studies [56,72,73]. In contrast, the AAs present at the lowest quantities in CGLs are the sulfur AAs Cys (31.36 ± 0.23 mg/100 g FW) and Met (29.59 ± 5.47 mg/100 g FW), as reported in previous studies too [56,72,74,75]. Table 5 reports the EAAs content of CGLs in comparison with the recommended daily allowances (RDA, in a reference 70 kg man) of EAAs, according to the FAO/WHO/UNU (2007), for adults (>18 years) [68]. The AAs profile of CGLs in respect to RDA was also compared with the profiles of other plants, as previously reported [76,77]. Among vegetables, soy is known to have an AAs profile close to that of animal proteins, except that it has a lower content of sulfur AAs [78,79]. As a matter of fact, among vegetables, soy is the closest to the RDA, providing per 100 g of edible product an excess of all the EAAs, both as individual EAAs and as their sum. Soy is the legume with the highest amount of protein [55,80]. In contrast, for the same amount of edible product, beans (legumes) and wheat (cereals) proved to be much more deficient than soybeans, both in terms of individual amino acids and their sum. Cereals are limited in lysine content, whereas all legumes are deficient in sulfur amino acids [78,80]. Other vegetables have a much lower protein content than even cereals and legumes do, and, consequently, 100 g of their edible part is even more away from the RDA of a 70 kg man.
However, in CGLs, the amino acids content reported as mg/g of protein and the amino acids profile indicated by the WHO/FAO/UNU (2007) [68] interestingly indicate that all the essential amino acids are provided in sufficient or even abundant amounts compared to the EAAs content of the “ideal” protein, with the exception of [Met + Cyt], which, however, is only marginally reduced (Table 6). The relative contents of His, Lys, [Phe + Tyr], Trp, and Thr are particularly abundant, and the sum of EEAs (mg/g protein) is 1.5 times higher than that shown in the WHO/FAO/UNU indices (2007) and significantly higher than that in other vegetables, such as wheat, potatoes, spinach, and cauliflower, which are limited in several essential amino acids. On the basis of these evaluations, concentrating the dry matter of CGLs via freeze drying and, thus, increasing the protein content could result in functional ingredients with an extremely favorable amino acids profile.
These considerations have been made on the fresh product, but although it can be eaten raw, thinly sliced, or in centrifuges [81], generally, consumers tend to consume this type of vegetable cooked. Cooking can cause changes in the nutritional value of protein, decreasing the AAs content. A previous study conducted by Lisiewska et al. (2007) [72] showed that the cultivar Winterbor F1 kale boiled for 15 min had about 78% of the TAAs content found in fresh leaves, where the most important decreases were in the AAs Val, Ile, Cys, Met, Ala, and Phe. The authors report that the loss of TAAs was broadly similar to the loss of dry weight (DW) mainly influenced by the leaching of constituents during cooking. In this sense, vacuum packs in sous vide cooking reduce the release of components such as AAs or organic acids [82].
3.5. Fatty Acid Composition and Content
As shown in Table 7, in CGLs, the predominant FAs are unsaturated fatty acids (USFAs), categorized into 58.87% PUFAs and 22.80% monounsaturated fatty acids (MUFAs). Meanwhile, saturated fatty acids (SFAs) account for only 18.34% of the total fatty acids (TFAs), with palmitic acid (C16:0) being present in the highest amount. The balance is therefore totally shifted toward USFAs, with PUFAs prevailing where α-linolenic acid (C18:3n3) of the omega-3 series (ω-3) and linoleic acid (C18:2n6) of the omega-6 series (ω-6) are the main representatives, making up, respectively, 42.28% and 12.07% of the total fatty acids. α-Linolenic acid (C18:3n3) is the fatty acid present in the highest percentage of the TFAs. These results are consistent with those identified in a previous study of Brassica oleracea var. acephala [56]. It is widely recognized how crucial a healthy diet is to human health. Unbalanced diets are associated with an increase in lifestyle-related diseases, which are particularly common among populations in industrialized countries [83,84,85,86,87]. The importance of proper dietary FAs intake is well documented and relevant to overall health. Diets in industrialized countries tend to include excessive amounts of SFAs at the expense of USFAs. This imbalance contributes to the increase in chronic non-communicable diseases related to lifestyle [83,88,89,90]. The WHO recommends that adults reduce their intake of SFAs to 10% of total energy. In addition, the WHO dietary fat guidelines suggest a further reduction in SFAs intake to below 10% and replacing them with PUFAs and MUFAs from plant sources or fiber-rich carbohydrates [91]. Essential fatty acids (EFAs) refer to those PUFAs that must be provided by foods because they cannot be synthesized in the body but are necessary for health. There are two families of EFAs, ω-3 and ω-6, based on the position of the first double bond from the ω end of the fatty acid [92]. In both families, there are many forms of PUFAs: α-linolenic acid (ALA), eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA) of the ω-3 family; and linoleic acid (LA), dihomo-γ-linolenic acid (DGLA), and arachidonic acid (AA) of the ω-6 family are the PUFAs that are important for human health. Both ω-3 and ω-6 are competitively metabolized by the same set of enzymes. Excessive consumption of ω-6 with low ω-3 intake is strongly associated with the pathogenesis of many chronic diseases related to the modern diet [93]. Currently, the ratio of the PUFAs ω-6 to ω-3 is between 20:1 and 50:1, much higher than the recommended ratio of 4:1 or 5:1 [32]. Due to this imbalance, higher amounts of lipid mediators derived from LA and AA are produced, which are responsible for the formation of thrombi and atheromas, allergic and inflammatory disorders, cell proliferation, and overactivity of the endocannabinoid system [85,89,92]. Although the FAs levels in CGLs are not high since they contain an average of 1% fat on fresh product, it is interesting to note that the FAs profile is advantageous with a ω-3/ω-6 ratio of 3.2.
3.6. Chlorophyll Content in CGL Convenience Food
In light of the growing evidence of the health benefits of chlorophyll pigments and the limited consumption of these important pigments in many European countries, especially among children [16], the study aimed to explore the chlorophyll content in CGLs in practical and ready-to-use solutions for the modern consumer, who has little time to devote to food preparation and the search for healthy, fast, and convenient solutions [45,46,94]. In this regard, fresh-cut [95], fifth-range, and freeze-dried CGLs were considered (Figure 1).
In the present study, the CGLs analyzed were vacuum-cooked in an industrial autoclave, with an equivalent heat treatment ≥10 min at 90 °C at the core of the product, capable of bringing at least 6 decimal reductions in the spores of non-proteolytic Clostridium botulinum microorganisms, taken as a reference for Refrigerated Processed Foods of Extended Durability (REPFEDs) [43,96,97]. Fresh plant-based foods are not always available all year round, and due to their high water content and aw value, they are very perishable. In this respect, the aw of fresh CGLs was 0.98 ± 0.01. For this reason, drying can allow for long-term consumption and facilitates handling, transport, and storage, and freeze drying is especially known for producing high-quality food powders [23]. The lyophilized CGL powders showed an aw of 0.31 ± 0.01. Since at an aw value of 0.61, there is no bacterial proliferation, products below this activity water range have a long shelf-life [98,99]. Moreover, freeze-dried powders based on vegetables and fruits can be used to obtain instant drinks with high nutritional value [100], additions to soups, confectionery, breakfast cereals, baked goods, snacks, condiment sauces, etc. [23,100]. In addition, freeze drying makes it possible to obtain value-added products even starting from plant by-products [101,102,103,104,105]. In fact, cutting residues and the hardest and most damaged outer leaves were used to obtain the freeze-dried CGLs. Table 8 shows the Chla, Chlb, and total Chl (Chla+b) content, expressed in μg/g FW; and the greenness parameter (−a* value) of the products concerned. For the fifth-range and the freeze-dried products, the % of chlorophyll retention compared to the fresh-cut product on a DW basis is also reported.
Photosynthetic pigments, such as chlorophylls and carotenoids, are essential for photosynthesis and contribute significantly to plant color. Chlorophylls, mainly Chla and Chlb, absorb blue and red light, giving the leaves their characteristic green color. In addition to capturing light for photosynthesis, these pigments protect plants from oxidative damage due to their antioxidant properties [15,17,106]. The sous vide-pasteurized product in the industrial autoclave had a total chlorophyll retention of about 83.5% compared to the fresh product. The freeze-dried product had more than ten times the total chlorophyll content compared to the fresh product, and when comparing the values based on the dry weight, freeze drying resulted in an overall chlorophyll retention of 97.66%. Similar retention rates have been identified for freeze-dried chives [24]. As far as the green color is concerned, clearly, the cooking has led to a significant decrease (p < 0.05) in the greenness parameter (–a*) due to the degradation of chlorophylls and the formation of pheophytin, a phenomenon that increases with the increase in the cooking time [107]. In contrast, in freeze-dried CGLs, there is an increase in the greenness parameter (–a*) due to the drastic reduction in the volume of plant matter, which concentrates chlorophyll and other pigments, making the green color more saturated than in the fresh leaf. One teaspoon (~5 g) of freeze-dried CGLs provides 17.30 mg of chlorophyll, which is 7.30 times the current average daily consumption of Italian children and about half that of European children. One tablespoon (~15 g) provides about 51.91 mg of chlorophyll, more than 30% of the current daily consumption of adolescents, adults, and the elderly in Italy; and 34% of that of European adolescents, 25% of that of European adults, and 28% of that of the elderly in European countries [16]. The use of these chlorophyll concentrates and other bioactive compounds (polyphenols, glucosinolates, fibers, vitamins, minerals, etc.) for the enrichment of food and beverages represents an innovative and sustainable approach. This would not only allow the full use of plant by-products from the agrifood industry to produce functional ingredients but would also facilitate an increase in chlorophyll intake in the population. Such supplementation could have beneficial effects both in nutritional and public health terms, contributing to the prevention of diseases related to oxidative stress and to the improvement of the nutritional profile of foods intended for consumption. A 200 g serving of sous vide-cooked CGLs provides 71.47 mg of chlorophyll, equivalent to 30 times the current daily consumption of Italian children and 45% of that of adolescents, adults, and the elderly. At the European level, it represents two times the current daily consumption of children, 47% of that of adolescents, 34.5% for adults and 38.4% for the elderly [16]. These data raise intriguing questions about the potential impact of sous vide cooking on chlorophyll content.
The elderly represent the fastest growing segment of the population and are at the greatest risk of chronic diseases. According to the WHO, between 2015 and 2050, the percentage of the world’s population over 60 will almost double, from 12% to 22% [108]. Although the consumption of fruits and vegetables is protective, many people do not reach the recommended levels and prefer starchy vegetables to more nutritious ones, such as dark green and orange vegetables. Older age is often associated with decreased appetite and oral health problems, which can further limit the intake of fruits and vegetables. Additionally, older adults with disabilities or functional limitations face specific difficulties in buying, preparing, and consuming fresh food. Deterioration of physical health, as well as loneliness, in the elderly is generally accompanied by a decline in diet quality, further aggravating nutritional risks. Finally, many older adults who live alone and who consume small portions tend to avoid large packages to prevent waste, especially in regard to vegetables [109,110,111,112]. The introduction of single-serving solutions of green leafy vegetables, already cooked and, therefore, with a softer consistency would represent an advantageous nutritional option for the elderly. These products, easily stored in the refrigerator and characterized by a long shelf-life, i.e., of several weeks or months [113,114,115], would reduce the need for frequent purchases and simplify vegetable intake for individuals with mobility limitations or difficulties in food preparation. In addition, these products, rich in essential nutrients, such as chlorophyll, vitamins, and minerals, could help improve the intake of micronutrients, facilitating the increase in vegetable consumption among the elderly population. A 200 g serving of fresh-cuts CGLs would provide 36 times the amount of chlorophyll currently consumed daily by children in Italy and almost double the current average daily consumption of children in Europe. In addition, a single serving would account for between 53% and 56% of the current daily consumption of chlorophyll for adolescents, adults, and the elderly in Italy, and between 41% and 56% for the same age groups in Europe [16]. However, these last considerations refer to the consumption of fresh leaves, for example, in salads or smoothies. Cooking leads to a reduction in chlorophyll content and, consequently, the green pigmentation to varying degrees, depending on several factors. These include the cooking method applied, the cooking duration, the cooking temperature, the pH of the cooking medium, the presence of salts or other additives, etc. [106,116,117,118].
3.7. Impact of Sous Vide-Cooking Processing on Color and Chlorophyll Content
Many vegetables are consumed after different cooking methods that induce chemical and physical changes to the product, affecting the bioavailability and the content of chemopreventive compounds in vegetables [106,119,120]. Chlorophyll is the pigment responsible for the green color of plants and is a very important factor that influences the quality of vegetables. Generally, plants have Chla and Chlb in a ratio of 3:1, which can vary due to environmental factors, growing conditions, and high sun exposure [15,121,122]. Weak light transmission (shading) typically results in a decrease in the proportion of red light absorbed by Chla and an increase in the proportion of blue light absorbed by Chlb [123]. Chlb is more stable than Chla during thermal processing, and Chla is more susceptible to pheophytinization during heating and is even more susceptible under acidic conditions [116,117,124,125,126]. When the temperature exceeds 60 °C, the membranes surrounding the chloroplast begin to become damaged, exposing the chlorophyll to the plant’s natural acids [127]. In the sous vide treatment, many soluble nutrients are retained that would be leached by cooking in water; however, the latter would also allow the dispersion in water of organic acids that are retained in the sous vide treatment and influence the degradation of chlorophyll through a slight lowering of the pH [53,116]. Table 9 reports the changes in Chla, Chlb, and total chlorophyll (Chla+b) content, expressed in μg/g FW; ratio of Chla to Chlb (Chla/Chlb); greenness parameter (−a* value); and pH for samples of CGLs (20 g) cooked sous vide in boiling water (100 °C) for 5, 10, 15, 20, and 25 min, respectively.
As the data reported in Table 8 show, changes in chlorophyll content are reflected in changes in green color parameters. There is an initial increase in green color and chlorophyll content in the first 5 min of treatment and then a gradual decrease that has a more important impact on Chla than Chlb, progressively lowering the Chla/Chlb ratio (Figure 2).
As widely reported by previous studies, in the first minute of heat treatment, there is the removal of air around the fine hairs on the surface of the plant and the expulsion of air between the cells. This would lead to an alteration of the reflective properties of the surface, resulting in a brighter green [128,129,130,131]. Furthermore, this increase is also due to the initial effect of cooking that softens plant tissues, facilitating the extraction of bioactive compounds, such as chlorophylls, from the cellular matrix [132,133,134]. After the first 5 min, the bright green color begins to degrade and turns more and more toward olive green. The higher the temperature, the greater the increase in color, but at the same time, the sooner the color begins to decay [128]. Chemical reactions are triggered that lead to the formation of chlorophyll derivatives, mainly chlorophyll isomers (chlorophylls a’ and b’) and pheophytins a and b, phenomena also encouraged by the slight and gradual lowering of pH, also found in previous studies [40]. Chlorophylls a’ and b’ have the same absorption spectra, and therefore their formation does not cause color changes. However, the formation of pheophytins, caused by the exchange of Mg2+ with H+ at the center of the porphyrin ring of chlorophyll, is accompanied by a change in color from bright green to olive brown [128,135] with a relative drop in absorbance in the red region (650–680 nm) [53,136] of the absorption spectrum, as shown in Figure 3.
3.1. Proximate Composition
The proximate composition of the product is shown in Table 1. All data are expressed as 100 g of fresh weight (FW), specifically referring to the edible part of the product. The CGLs have a high water content (88.94 ± 0.55 g/100 g FW) and an aw of 0.9813 ± 0.0046. The fat content is low (0.94 ± 0.5 g/100 g FW), and the FAs profile is characterized by the prevalence of polyunsaturated fatty acids (PUFAs). Dietary fibers are the most abundant component (3.39 ± 0.08 g/100 g FW), followed by proteins (3.01 ± 0.04 g/100 g FW), which contribute significantly to the caloric value of the vegetable (30.66 ± 0.21 kcal/100 g FW), which nevertheless remains a low-calorie product [54]. Available carbohydrates are very low (0.85 ± 0.05 g/100 g FW). Proteins account for most of the weight and contribute 39.15% to the product’s energy, making this vegetable a “source of protein” product. For every 100 kcal, 11.06 g of total fiber is provided, making the product also a “source of fiber” [54]. CGLs contain slightly more protein (3.01 ± 0.04 g/100 g FW) than other green leafy vegetables commonly consumed in Italy, such as cabbage (2.1 g/100 g FW), lettuce (1.8 g/100 g FW), arugula (2.6 g/100 g FW), and endive (0.9 g/100 g FW) [55].
Italian guidelines recommend consuming at least five portions of fruit and vegetables a day [32]. According to these guidelines, a serving of vegetables corresponds to about 200 g, which would translate into 6.77 g of dietary fiber provided by one serving of CGLs, equaling just over one-fourth of the official recommendations that suggest a dietary fiber intake of 25 g per day for adults [55]. In addition, CGLs also contain slightly more fiber (3.39 ± 0.08 g/100 g FW) than other varieties of the same species commonly consumed in Italy, such as cauliflower (2.4 g/100 g FW), cabbage (2.6 g/100 g FW), or broccoli (3 g/100 g FW) [55].
3.2. Soluble Sugars
As regards the composition of soluble sugars (Table 2), the main sugar detected was glucose (0.44 ± 0.01 g/100 g FW), followed by fructose (0.18 ± 0.03 g/100 g FW) and rhamnose (0.08 ± 0.01 g/100 g FW). Glucose is the most abundant soluble sugar, as demonstrated also by a recent study on the phytochemical composition of selected genotypes of organic kale [6]; however, this finding is in contrast with what was reported by another study, where fructose was found to be the most abundant sugar [56]. It is also important to note that both the stage of development of the plant and the environmental conditions affect both the profile and the amounts of sugars. In this respect, it has been reported that sucrose decreased during the development of the plant, whereas fructose increased in cabbages grown at 2 °C, improving their sweetness [57].
3.3. Mineral Composition
The mineral composition of CGLs is shown in Table 3. Calcium (333.09 ± 1.020 mg/100 g FW) was the main mineral found in the analyzed samples, followed by potassium (215.53 ± 1.06 mg/100 g FW) and sulfur (108 ± 0.19 mg/100 g FW). The latter, typical of the genus Brassica, is part of many bioactive components important for health, the first of all glucosinolates in Cruciferous vegetables derived from the breakdown of some isothiocyanates [58].
The calcium content in 100 g of this product is significantly higher than that found in other green leafy vegetables commonly consumed in Italy, such as endive (93 mg/100 g FW), lettuce (45 mg/100 g FW), and spinach (78 mg/100 g FW). It also surpasses the calcium levels typically found in other Brassica species, which a range from 30 to 60 mg/100 g FW [55].
A 200 g serving of CGLs would provide 666.18 mg/100 g FW of calcium and 431.05 mg/100 g FW of potassium, covering 83% of the daily requirement of calcium (800 mg/day) and just over 20% the daily requirement of potassium (2000 mg/day) [59]. One portion is therefore an important source of calcium and potassium [54].
This combination of minerals supports several vital functions, contributing to the overall well-being of humans [60]. Plant-based nutrition is becoming increasingly popular among athletes; it is critical that these diets meet the rigorous nutritional requirements needed to ensure optimal performance and effective recovery. Among the essential micronutrients that vegan athletes need to pay special attention to are iron, vitamin B12, calcium, vitamin D, zinc, and omega-3 fatty acids [61]. Calcium is crucial for bone health, preventing conditions such as osteoporosis, and for vital functions such as muscle contraction, nerve transmission, and blood clotting. In addition, it plays an important role in glucose regulation and thyroid function, both of which are crucial for energy metabolism and athletic performance. To ensure adequate calcium intake, athletes must include calcium-rich vegetables in their diet, such as kale, spinach, cabbage, broccoli, artichokes, and green turnips [61,62]. Considering the high calcium content in CGLs, concentrated solutions of the CGLs as freeze-dried products to be used to fortify snacks or energy drinks for athletes could be a way to ensure a constant intake of calcium even in these diets with dietary restrictions. Furthermore, it emerges that, in CGLs, the ratio of sodium to potassium is less than 0.6 mg/mg, suggesting that this vegetable is suitable for hypertensive consumers [63]. Magnesium (29.45 ± 0.07 mg/100 g FW) is a very important macronutrient for plants, as it is a structural element of the chlorophyll molecule; it activates more than 300 enzymes and contributes to the stabilization of some subcellular structures, such as ribosomes; and it also plays an important role in plant photosynthesis, carbohydrate transport, and nucleic acid and protein synthesis, as well as in the generation of reactive species oxygen [64]. Since magnesium deficiency frequently occurs in old age, a balanced magnesium intake may contribute to healthy aging [65]. A 200 g serving of CGLs has 58.90 mg/100 g FW of magnesium, 15.7% of the daily requirement [54,59].
3.4. Amino Acid Profile
Table 4 summarizes the content of the 18 AAs commonly found in proteins. For a better explanation, the identified amino acids were divided into two groups: essential amino acids (EAAs) and non-essential amino acids (NEAAs). The nine EAAs are leucine (Leu), isoleucine (Ile), valine (Val), phenylalanine (Phe), threonine (Thr), tryptophan (Trp), Methionine (Met), lysine (Lys), and histidine (His). Conversely, alanine (Ala), serine (Ser), proline (Pro), arginine (Arg), aspartic acid (Asp), tyrosine (Tye), glutamic acid (Glu), and cysteine (Cys) are NEAAs [66,67,68].
Histidine, although synthesized by the body, in some conditions, such as chronic obstructive pulmonary disease (COPD) and chronic kidney disease (CKD) [69,70], is not produced in sufficient quantities to meet physiological needs, making it an indispensable amino acid. Its deficiency can lead to significant reductions in hemoglobin levels [68,70]. Furthermore, supplementation with doses of 4.0–4.5 g histidine and increased dietary histidine intake are associated with decreased BMI, adiposity, markers of glucose homeostasis (e.g., HOMA–IR, fasting blood glucose, and 2 h postprandial blood glucose), proinflammatory cytokines, and oxidative stress [70]. The sum of total amino acids (TAAs) in CGLs is 2746.91 ± 18.96 mg/100 g FW, that of the NEAAs is 1598.96 ± 0.34 mg/100 g FW, and that of the EAAs is 1147.95 ± 19.30 mg/100 g FW. These data indicate a good ratio of EAAs/NEAAs in CGLs. According to the ideal amino acid composition proposed by the Food and Agriculture Organization of the United Nations (FAO)/WHO/United Nations University (UNU), the EAA/TAAs ratio should be ≈40%, and that of the EAA/NEAA should be ≥ 60% [69]. In the present study, whole leaves were examined, but previous studies have shown that leaf blades contain more amino acids than the parts of stems involved in nutrient transport [71,72].
The most abundant AAs are acidic AAs Glu and Asp, respectively, at 470.25 ± 5.20 mg/100 g FW and 324.62 ± 0.53 mg/100 g FW, as also reported in previous studies [56,72,73]. In contrast, the AAs present at the lowest quantities in CGLs are the sulfur AAs Cys (31.36 ± 0.23 mg/100 g FW) and Met (29.59 ± 5.47 mg/100 g FW), as reported in previous studies too [56,72,74,75]. Table 5 reports the EAAs content of CGLs in comparison with the recommended daily allowances (RDA, in a reference 70 kg man) of EAAs, according to the FAO/WHO/UNU (2007), for adults (>18 years) [68]. The AAs profile of CGLs in respect to RDA was also compared with the profiles of other plants, as previously reported [76,77]. Among vegetables, soy is known to have an AAs profile close to that of animal proteins, except that it has a lower content of sulfur AAs [78,79]. As a matter of fact, among vegetables, soy is the closest to the RDA, providing per 100 g of edible product an excess of all the EAAs, both as individual EAAs and as their sum. Soy is the legume with the highest amount of protein [55,80]. In contrast, for the same amount of edible product, beans (legumes) and wheat (cereals) proved to be much more deficient than soybeans, both in terms of individual amino acids and their sum. Cereals are limited in lysine content, whereas all legumes are deficient in sulfur amino acids [78,80]. Other vegetables have a much lower protein content than even cereals and legumes do, and, consequently, 100 g of their edible part is even more away from the RDA of a 70 kg man.
However, in CGLs, the amino acids content reported as mg/g of protein and the amino acids profile indicated by the WHO/FAO/UNU (2007) [68] interestingly indicate that all the essential amino acids are provided in sufficient or even abundant amounts compared to the EAAs content of the “ideal” protein, with the exception of [Met + Cyt], which, however, is only marginally reduced (Table 6). The relative contents of His, Lys, [Phe + Tyr], Trp, and Thr are particularly abundant, and the sum of EEAs (mg/g protein) is 1.5 times higher than that shown in the WHO/FAO/UNU indices (2007) and significantly higher than that in other vegetables, such as wheat, potatoes, spinach, and cauliflower, which are limited in several essential amino acids. On the basis of these evaluations, concentrating the dry matter of CGLs via freeze drying and, thus, increasing the protein content could result in functional ingredients with an extremely favorable amino acids profile.
These considerations have been made on the fresh product, but although it can be eaten raw, thinly sliced, or in centrifuges [81], generally, consumers tend to consume this type of vegetable cooked. Cooking can cause changes in the nutritional value of protein, decreasing the AAs content. A previous study conducted by Lisiewska et al. (2007) [72] showed that the cultivar Winterbor F1 kale boiled for 15 min had about 78% of the TAAs content found in fresh leaves, where the most important decreases were in the AAs Val, Ile, Cys, Met, Ala, and Phe. The authors report that the loss of TAAs was broadly similar to the loss of dry weight (DW) mainly influenced by the leaching of constituents during cooking. In this sense, vacuum packs in sous vide cooking reduce the release of components such as AAs or organic acids [82].
3.5. Fatty Acid Composition and Content
As shown in Table 7, in CGLs, the predominant FAs are unsaturated fatty acids (USFAs), categorized into 58.87% PUFAs and 22.80% monounsaturated fatty acids (MUFAs). Meanwhile, saturated fatty acids (SFAs) account for only 18.34% of the total fatty acids (TFAs), with palmitic acid (C16:0) being present in the highest amount. The balance is therefore totally shifted toward USFAs, with PUFAs prevailing where α-linolenic acid (C18:3n3) of the omega-3 series (ω-3) and linoleic acid (C18:2n6) of the omega-6 series (ω-6) are the main representatives, making up, respectively, 42.28% and 12.07% of the total fatty acids. α-Linolenic acid (C18:3n3) is the fatty acid present in the highest percentage of the TFAs. These results are consistent with those identified in a previous study of Brassica oleracea var. acephala [56]. It is widely recognized how crucial a healthy diet is to human health. Unbalanced diets are associated with an increase in lifestyle-related diseases, which are particularly common among populations in industrialized countries [83,84,85,86,87]. The importance of proper dietary FAs intake is well documented and relevant to overall health. Diets in industrialized countries tend to include excessive amounts of SFAs at the expense of USFAs. This imbalance contributes to the increase in chronic non-communicable diseases related to lifestyle [83,88,89,90]. The WHO recommends that adults reduce their intake of SFAs to 10% of total energy. In addition, the WHO dietary fat guidelines suggest a further reduction in SFAs intake to below 10% and replacing them with PUFAs and MUFAs from plant sources or fiber-rich carbohydrates [91]. Essential fatty acids (EFAs) refer to those PUFAs that must be provided by foods because they cannot be synthesized in the body but are necessary for health. There are two families of EFAs, ω-3 and ω-6, based on the position of the first double bond from the ω end of the fatty acid [92]. In both families, there are many forms of PUFAs: α-linolenic acid (ALA), eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA) of the ω-3 family; and linoleic acid (LA), dihomo-γ-linolenic acid (DGLA), and arachidonic acid (AA) of the ω-6 family are the PUFAs that are important for human health. Both ω-3 and ω-6 are competitively metabolized by the same set of enzymes. Excessive consumption of ω-6 with low ω-3 intake is strongly associated with the pathogenesis of many chronic diseases related to the modern diet [93]. Currently, the ratio of the PUFAs ω-6 to ω-3 is between 20:1 and 50:1, much higher than the recommended ratio of 4:1 or 5:1 [32]. Due to this imbalance, higher amounts of lipid mediators derived from LA and AA are produced, which are responsible for the formation of thrombi and atheromas, allergic and inflammatory disorders, cell proliferation, and overactivity of the endocannabinoid system [85,89,92]. Although the FAs levels in CGLs are not high since they contain an average of 1% fat on fresh product, it is interesting to note that the FAs profile is advantageous with a ω-3/ω-6 ratio of 3.2.
3.6. Chlorophyll Content in CGL Convenience Food
In light of the growing evidence of the health benefits of chlorophyll pigments and the limited consumption of these important pigments in many European countries, especially among children [16], the study aimed to explore the chlorophyll content in CGLs in practical and ready-to-use solutions for the modern consumer, who has little time to devote to food preparation and the search for healthy, fast, and convenient solutions [45,46,94]. In this regard, fresh-cut [95], fifth-range, and freeze-dried CGLs were considered (Figure 1).
In the present study, the CGLs analyzed were vacuum-cooked in an industrial autoclave, with an equivalent heat treatment ≥10 min at 90 °C at the core of the product, capable of bringing at least 6 decimal reductions in the spores of non-proteolytic Clostridium botulinum microorganisms, taken as a reference for Refrigerated Processed Foods of Extended Durability (REPFEDs) [43,96,97]. Fresh plant-based foods are not always available all year round, and due to their high water content and aw value, they are very perishable. In this respect, the aw of fresh CGLs was 0.98 ± 0.01. For this reason, drying can allow for long-term consumption and facilitates handling, transport, and storage, and freeze drying is especially known for producing high-quality food powders [23]. The lyophilized CGL powders showed an aw of 0.31 ± 0.01. Since at an aw value of 0.61, there is no bacterial proliferation, products below this activity water range have a long shelf-life [98,99]. Moreover, freeze-dried powders based on vegetables and fruits can be used to obtain instant drinks with high nutritional value [100], additions to soups, confectionery, breakfast cereals, baked goods, snacks, condiment sauces, etc. [23,100]. In addition, freeze drying makes it possible to obtain value-added products even starting from plant by-products [101,102,103,104,105]. In fact, cutting residues and the hardest and most damaged outer leaves were used to obtain the freeze-dried CGLs. Table 8 shows the Chla, Chlb, and total Chl (Chla+b) content, expressed in μg/g FW; and the greenness parameter (−a* value) of the products concerned. For the fifth-range and the freeze-dried products, the % of chlorophyll retention compared to the fresh-cut product on a DW basis is also reported.
Photosynthetic pigments, such as chlorophylls and carotenoids, are essential for photosynthesis and contribute significantly to plant color. Chlorophylls, mainly Chla and Chlb, absorb blue and red light, giving the leaves their characteristic green color. In addition to capturing light for photosynthesis, these pigments protect plants from oxidative damage due to their antioxidant properties [15,17,106]. The sous vide-pasteurized product in the industrial autoclave had a total chlorophyll retention of about 83.5% compared to the fresh product. The freeze-dried product had more than ten times the total chlorophyll content compared to the fresh product, and when comparing the values based on the dry weight, freeze drying resulted in an overall chlorophyll retention of 97.66%. Similar retention rates have been identified for freeze-dried chives [24]. As far as the green color is concerned, clearly, the cooking has led to a significant decrease (p < 0.05) in the greenness parameter (–a*) due to the degradation of chlorophylls and the formation of pheophytin, a phenomenon that increases with the increase in the cooking time [107]. In contrast, in freeze-dried CGLs, there is an increase in the greenness parameter (–a*) due to the drastic reduction in the volume of plant matter, which concentrates chlorophyll and other pigments, making the green color more saturated than in the fresh leaf. One teaspoon (~5 g) of freeze-dried CGLs provides 17.30 mg of chlorophyll, which is 7.30 times the current average daily consumption of Italian children and about half that of European children. One tablespoon (~15 g) provides about 51.91 mg of chlorophyll, more than 30% of the current daily consumption of adolescents, adults, and the elderly in Italy; and 34% of that of European adolescents, 25% of that of European adults, and 28% of that of the elderly in European countries [16]. The use of these chlorophyll concentrates and other bioactive compounds (polyphenols, glucosinolates, fibers, vitamins, minerals, etc.) for the enrichment of food and beverages represents an innovative and sustainable approach. This would not only allow the full use of plant by-products from the agrifood industry to produce functional ingredients but would also facilitate an increase in chlorophyll intake in the population. Such supplementation could have beneficial effects both in nutritional and public health terms, contributing to the prevention of diseases related to oxidative stress and to the improvement of the nutritional profile of foods intended for consumption. A 200 g serving of sous vide-cooked CGLs provides 71.47 mg of chlorophyll, equivalent to 30 times the current daily consumption of Italian children and 45% of that of adolescents, adults, and the elderly. At the European level, it represents two times the current daily consumption of children, 47% of that of adolescents, 34.5% for adults and 38.4% for the elderly [16]. These data raise intriguing questions about the potential impact of sous vide cooking on chlorophyll content.
The elderly represent the fastest growing segment of the population and are at the greatest risk of chronic diseases. According to the WHO, between 2015 and 2050, the percentage of the world’s population over 60 will almost double, from 12% to 22% [108]. Although the consumption of fruits and vegetables is protective, many people do not reach the recommended levels and prefer starchy vegetables to more nutritious ones, such as dark green and orange vegetables. Older age is often associated with decreased appetite and oral health problems, which can further limit the intake of fruits and vegetables. Additionally, older adults with disabilities or functional limitations face specific difficulties in buying, preparing, and consuming fresh food. Deterioration of physical health, as well as loneliness, in the elderly is generally accompanied by a decline in diet quality, further aggravating nutritional risks. Finally, many older adults who live alone and who consume small portions tend to avoid large packages to prevent waste, especially in regard to vegetables [109,110,111,112]. The introduction of single-serving solutions of green leafy vegetables, already cooked and, therefore, with a softer consistency would represent an advantageous nutritional option for the elderly. These products, easily stored in the refrigerator and characterized by a long shelf-life, i.e., of several weeks or months [113,114,115], would reduce the need for frequent purchases and simplify vegetable intake for individuals with mobility limitations or difficulties in food preparation. In addition, these products, rich in essential nutrients, such as chlorophyll, vitamins, and minerals, could help improve the intake of micronutrients, facilitating the increase in vegetable consumption among the elderly population. A 200 g serving of fresh-cuts CGLs would provide 36 times the amount of chlorophyll currently consumed daily by children in Italy and almost double the current average daily consumption of children in Europe. In addition, a single serving would account for between 53% and 56% of the current daily consumption of chlorophyll for adolescents, adults, and the elderly in Italy, and between 41% and 56% for the same age groups in Europe [16]. However, these last considerations refer to the consumption of fresh leaves, for example, in salads or smoothies. Cooking leads to a reduction in chlorophyll content and, consequently, the green pigmentation to varying degrees, depending on several factors. These include the cooking method applied, the cooking duration, the cooking temperature, the pH of the cooking medium, the presence of salts or other additives, etc. [106,116,117,118].
3.7. Impact of Sous Vide-Cooking Processing on Color and Chlorophyll Content
Many vegetables are consumed after different cooking methods that induce chemical and physical changes to the product, affecting the bioavailability and the content of chemopreventive compounds in vegetables [106,119,120]. Chlorophyll is the pigment responsible for the green color of plants and is a very important factor that influences the quality of vegetables. Generally, plants have Chla and Chlb in a ratio of 3:1, which can vary due to environmental factors, growing conditions, and high sun exposure [15,121,122]. Weak light transmission (shading) typically results in a decrease in the proportion of red light absorbed by Chla and an increase in the proportion of blue light absorbed by Chlb [123]. Chlb is more stable than Chla during thermal processing, and Chla is more susceptible to pheophytinization during heating and is even more susceptible under acidic conditions [116,117,124,125,126]. When the temperature exceeds 60 °C, the membranes surrounding the chloroplast begin to become damaged, exposing the chlorophyll to the plant’s natural acids [127]. In the sous vide treatment, many soluble nutrients are retained that would be leached by cooking in water; however, the latter would also allow the dispersion in water of organic acids that are retained in the sous vide treatment and influence the degradation of chlorophyll through a slight lowering of the pH [53,116]. Table 9 reports the changes in Chla, Chlb, and total chlorophyll (Chla+b) content, expressed in μg/g FW; ratio of Chla to Chlb (Chla/Chlb); greenness parameter (−a* value); and pH for samples of CGLs (20 g) cooked sous vide in boiling water (100 °C) for 5, 10, 15, 20, and 25 min, respectively.
As the data reported in Table 8 show, changes in chlorophyll content are reflected in changes in green color parameters. There is an initial increase in green color and chlorophyll content in the first 5 min of treatment and then a gradual decrease that has a more important impact on Chla than Chlb, progressively lowering the Chla/Chlb ratio (Figure 2).
As widely reported by previous studies, in the first minute of heat treatment, there is the removal of air around the fine hairs on the surface of the plant and the expulsion of air between the cells. This would lead to an alteration of the reflective properties of the surface, resulting in a brighter green [128,129,130,131]. Furthermore, this increase is also due to the initial effect of cooking that softens plant tissues, facilitating the extraction of bioactive compounds, such as chlorophylls, from the cellular matrix [132,133,134]. After the first 5 min, the bright green color begins to degrade and turns more and more toward olive green. The higher the temperature, the greater the increase in color, but at the same time, the sooner the color begins to decay [128]. Chemical reactions are triggered that lead to the formation of chlorophyll derivatives, mainly chlorophyll isomers (chlorophylls a’ and b’) and pheophytins a and b, phenomena also encouraged by the slight and gradual lowering of pH, also found in previous studies [40]. Chlorophylls a’ and b’ have the same absorption spectra, and therefore their formation does not cause color changes. However, the formation of pheophytins, caused by the exchange of Mg2+ with H+ at the center of the porphyrin ring of chlorophyll, is accompanied by a change in color from bright green to olive brown [128,135] with a relative drop in absorbance in the red region (650–680 nm) [53,136] of the absorption spectrum, as shown in Figure 3.
4. Conclusions
This study highlights the nutritional value of the leaves of the collard green (Brassica oleracea var. viridis) cultivar “Couve-Manteiga” and their potential as convenience foods to increase the intake of essential nutrients, especially chlorophyll, in the daily diet. CGLs’ profile of being low in calories and high in fiber, protein, and minerals such as calcium and potassium, support its suitability as a functional food. Chlorophyll, an essential bioactive compound found in satisfactory amounts in CGLs, is essential for reducing oxidative stress, preventing DNA damage, and offering potential antitumor benefits. The high chlorophyll retention rates achieved through sous vide cooking and freeze drying demonstrate the effectiveness of these methods in preserving the nutritional value of chlorophyll, making them suitable for the development of practical and nutrient-rich food products. A single serving of sous vide CGLs provides ≈ 45% of the current daily consumption of chlorophyll in adolescents, adults, and the elderly in Italy, while freeze-dried products can provide even higher concentrations. A teaspoon provides 7.30 times the average current daily consumption of Italian children, while a tablespoon provides just over 30% of the daily consumption of Italian adolescents, adults, and the elderly. These findings make collard green-based convenience foods ideal for filling dietary gaps in chlorophyll consumption, particularly in children and older populations. This study suggests that incorporating chlorophyll-rich convenience foods into daily meals could offer significant health benefits, promoting better oxidative defense mechanisms and overall nutritional status in different population groups.
A potential limitation of this study is its focus on the specific cultivar “Couve-Manteiga” cultivated in Italy. While the results offer valuable insights into this cultivar’s nutritional profile and chlorophyll retention, their generalizability to other collard green varieties or different geographic regions may need to be improved. Variability in climate, agricultural practices, and genetic differences among cultivars could influence the nutritional profile and bioactive compound stability. Future research is needed to explore these variations and determine whether similar conclusions can be extended to other varieties and regions. Additionally, future studies should assess chlorophyll’s stability during the shelf-life of these convenience foods. Monitoring how storage conditions, packaging, and environmental factors affect chlorophyll degradation will provide valuable insights for optimizing these products for maximum health benefits. Exploring the long-term health impact of consuming collard green-based convenience foods will further clarify their potential benefits and implications.
Finally, freeze-dried CGL powder offers promising applications in various food products, including doughs, ice creams, sauces, emulsions, and beverages. This broader utilization could enhance the nutritional profile of diverse food products, expanding the role of collard greens in improving dietary health. |
Title: Correlation of Deglutitive Striated Esophagus Motor Function and Pharyngeal Phase Swallowing Biomechanical Events | Body: |
Title: Tumor Hypoxia on | Body:
Introduction
Long-term survival of patients with head and neck squamous cell carcinoma (HNSCC) remains approximately 50% to 60% across all subsites and exceeds 75% for human papillomavirus (HPV)-positive disease.1,2 While chemoradiotherapy (CRT) improves locoregional control,3 distant metastasis (DM) occurs in 10% to 15% of patients, with poor prognosis.1,2 Novel biomarkers are needed to identify patients at high risk of DM who may benefit from escalated therapeutic strategies, including novel chemotherapy and immunotherapy regimens.4 This is particularly important in patients with early-stage disease, for whom risk of locoregional failure is low.
Tumor hypoxia is associated with resistance to CRT and propensity for metastasis in preclinical models and clinical data across many cancer types.5,6,7 While clinical assessment of tumor hypoxia has historically been challenging, 18F-fluoromisonidazole (FMISO) positron emission tomography (PET) has emerged as a method for reliable, noninvasive measurement.8,9,10,11,12,13
In HNSCC, tumor hypoxia on FMISO is associated with locoregional failure after CRT.14,15,16,17,18 While limited evidence suggests that hypoxia on FMISO PET is also associated with DM, consistent with preclinical models, this association is inadequately explored.19,20 To address this knowledge gap, we pooled data from prospective nonrandomized clinical trials to perform what is, to our knowledge, the largest analysis of tumor hypoxia on FMISO PET as a biomarker of DM risk after CRT for HNSCC.
Methods
Study Design and Participants
This cohort study was approved by the Memorial Sloan Kettering Cancer Center institutional review board and followed Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. Patients with nonmetastatic HNSCC preparing to undergo CRT as part of definitive management at a single academic institution were enrolled in 2 prospective phase II studies that incorporated FMISO PET (Lee et al16 [2016], Riaz et al,17 and Lee et al18 [2024]), reported previously. Written informed consent was obtained from all patients who enrolled. Patients treated on these studies between August 2004 and January 2021 were considered for inclusion in this unplanned secondary analysis. Lee et al (2016)16 and Riaz et al,17 which enrolled patients from 2004 to 2016, included patients with HPV-positive and HPV-negative HNSCC with various primary sites. Lee et al (2024)18 cohort A, which enrolled patients from 2017 to 2021, included HPV-positive HNSCC of the oropharynx or unknown primary site, T stage 0 to 2, and N stage 1 to 2c per American Joint committee on Cancer (AJCC), seventh edition staging. In this protocol, oropharyngeal primary cancers were resected before CRT, although a negative margin was not required. FMISO PET imaging before and 1 to 2 weeks after starting CRT were required for inclusion. Completion of the planned course of CRT was required for inclusion. A flow diagram illustrating the number of patients included from each of the prospective studies is provided (eFigure 1 in Supplement 1). Tumor hypoxia status was evaluated by nuclear medicine physicians. Protocols for obtaining and interpreting FMISO PET are described previously.12,16,17,18 Patients with oropharyngeal primary cancers who were negative for intratreatment hypoxia were eligible for 30 Gy de-escalated CRT.
The primary outcome was time to DM from CRT completion. DM was identified as biopsy-proven HNSCC outside the primary site and regional lymph nodes. The date of DM was based on the earliest imaging study in which the biopsied metastatic lesion was identifiable.
Statistical Analysis
DM, with death as a competing risk, was assessed with cumulative incidence estimation, Gray test, and the Fine-Gray subdistribution hazard model. Patients were censored at the date of the last computed tomography study encompassing the chest, abdomen, and pelvis. Overall survival (OS) was assessed as a secondary outcome. OS was assessed with the Kaplan-Meier method and Cox proportional hazards model. Patients were censored at the last date on which they were known to be alive.
Univariable model hazard ratios (HRs) are reported for selected baseline characteristics. Multivariable models were not evaluated given few DM and OS events. Associations of hypoxia with other baseline characteristics were evaluated with univariable logistic regression and Fisher exact test. Statistical significance was defined as P < .05. Analyses were conducted using R statistical software version 4.3.0 (R Project for Statistical Computing). Analysis was conducted from May 2023 to May 2024.
Study Design and Participants
This cohort study was approved by the Memorial Sloan Kettering Cancer Center institutional review board and followed Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. Patients with nonmetastatic HNSCC preparing to undergo CRT as part of definitive management at a single academic institution were enrolled in 2 prospective phase II studies that incorporated FMISO PET (Lee et al16 [2016], Riaz et al,17 and Lee et al18 [2024]), reported previously. Written informed consent was obtained from all patients who enrolled. Patients treated on these studies between August 2004 and January 2021 were considered for inclusion in this unplanned secondary analysis. Lee et al (2016)16 and Riaz et al,17 which enrolled patients from 2004 to 2016, included patients with HPV-positive and HPV-negative HNSCC with various primary sites. Lee et al (2024)18 cohort A, which enrolled patients from 2017 to 2021, included HPV-positive HNSCC of the oropharynx or unknown primary site, T stage 0 to 2, and N stage 1 to 2c per American Joint committee on Cancer (AJCC), seventh edition staging. In this protocol, oropharyngeal primary cancers were resected before CRT, although a negative margin was not required. FMISO PET imaging before and 1 to 2 weeks after starting CRT were required for inclusion. Completion of the planned course of CRT was required for inclusion. A flow diagram illustrating the number of patients included from each of the prospective studies is provided (eFigure 1 in Supplement 1). Tumor hypoxia status was evaluated by nuclear medicine physicians. Protocols for obtaining and interpreting FMISO PET are described previously.12,16,17,18 Patients with oropharyngeal primary cancers who were negative for intratreatment hypoxia were eligible for 30 Gy de-escalated CRT.
The primary outcome was time to DM from CRT completion. DM was identified as biopsy-proven HNSCC outside the primary site and regional lymph nodes. The date of DM was based on the earliest imaging study in which the biopsied metastatic lesion was identifiable.
Statistical Analysis
DM, with death as a competing risk, was assessed with cumulative incidence estimation, Gray test, and the Fine-Gray subdistribution hazard model. Patients were censored at the date of the last computed tomography study encompassing the chest, abdomen, and pelvis. Overall survival (OS) was assessed as a secondary outcome. OS was assessed with the Kaplan-Meier method and Cox proportional hazards model. Patients were censored at the last date on which they were known to be alive.
Univariable model hazard ratios (HRs) are reported for selected baseline characteristics. Multivariable models were not evaluated given few DM and OS events. Associations of hypoxia with other baseline characteristics were evaluated with univariable logistic regression and Fisher exact test. Statistical significance was defined as P < .05. Analyses were conducted using R statistical software version 4.3.0 (R Project for Statistical Computing). Analysis was conducted from May 2023 to May 2024.
Results
Baseline characteristics of the 281 patients (median [range] age at CRT, 58.7 [25.5-85.6] years; 251 male [89.3%]) are summarized in Table 1. Most patients had primary oropharyngeal tumors (242 patients [86.1%]) and HPV-positive disease by p16 immunohistochemistry or RNA in situ hybridization (266 patients [94.7%]). Most patients had early-stage disease, including 217 patients (77.2%) with T stage 1 or 2 and 231 patients (82.2%) with N stage 2b or less. Approximately one-half of patients had primary tumor resection before CRT (148 patients [52.7%]). Approximately one-half of patients received 30 Gy de-escalated CRT (144 patients [51.2%]), and the remainder received standard 70 Gy CRT. Most patients received cisplatin-based chemotherapy (239 patients [85.1%]).
Table 1.
Patient, Disease, and Treatment Characteristics
Characteristic
Patients, No. (%) (N =281)
Clinical trial
Lee et al, 201616 and Riaz et al, 202117
129 (45.9)
Lee et al 202418
152 (54.1)
Age at chemoradiotherapy, median (range), y
58.7 (25.5-85.6)
Sex
Male
251 (89.3)
Female
30 (10.7)
Karnofsky performance status score <80
5 (1.8)
Smoking history
At least 1 pack-year
124 (44.1)
Ongoing at time of chemoradiation
13 (4.6)
Primary site
Oropharynx
242 (86.1)
Base of tongue
100 (35.6)
Tonsil
142 (50.5)
Oral cavity
1 (0.4)
Hypopharynx
3 (1.1)
Larynx
3 (1.1)
Unknown
32 (11.4)
HPV statusa
Positive
266 (94.7)
Negative
11 (3.9)
Unknown
4 (1.4)
T stage
T0
32 (11.4)
T1
108 (38.4)
T2
109 (38.8)
T3
21 (7.5)
T4
11 (3.9)
N stage
N0
4 (1.4)
N1
36 (12.8)
N2a
28 (10.0)
N2b
163 (58)
N2c
48 (17.1)
N3
2 (0.7)
Primary tumor resection before chemoradiotherapy
148 (52.7)
Radiotherapy regimen
Standard (70 Gy in 35 fractions)
137 (48.8)
De-escalated (30 Gy in 15 fractions)
144 (51.2)
Concurrent systemic therapy regimen
Cisplatin-based
239 (85.1)
Platinum-based
273 (97.2)
Cetuximab only
4 (1.4)
Hypoxia on FMISO PET
Negative pretreatment
73 (26.0)
Positive pretreatment, negative intratreatment
138 (49.1)
Positive pretreatment, positive intratreatment
70 (24.9)
Abbreviation: FMISO PET, 18F-fluoromisonidazole positron emission tomography; HPV, human papillomavirus.
a
Determined by p16 immunohistochemistry or HPV in situ hybridization.
FMISO PET showed that 73 patients (26.0%) had hypoxia-negative disease before CRT, 138 patients (49.1%) had hypoxia-positive disease before CRT and subsequently hypoxia-negative disease during CRT, and 70 patients (24.9%) persistently had hypoxia-positive disease before and during CRT. Representative FMISO PET imaging of patients in each of the 3 categories is provided in eFigure 2 in Supplement 1. N stage 2c or greater was associated with persistent intratreatment hypoxia (odds ratio, 2.40; 95% CI, 1.19-4.81; P = .01).
Characteristics of 12 patients who experienced DM are summarized in the eTable in Supplement 1. Of the 12 patients, 4 (33.3%) experienced locoregional recurrence prior to DM. Metastatic sites included the lungs (11 patients [91.7%]), bone (3 patients [25.0%]), and liver (2 patients [16.7%]). Median (IQR) time to latest radiographic follow-up among censored patients was 24 (12-34) months. Cumulative incidence estimates of DM at 2 years were 10.2% (95% CI, 3.6%-20.7%) among patients with persistent intratreatment hypoxia and 2.4% (95% CI, 0.8%-5.7%) among those without (HR, 3.51; 95% CI, 1.05-11.79; P = .04) (Figure, A). Among cases with hypoxia-negative disease before CRT, none experienced DM (Gray test P = .03) (Figure, B). HPV-positive disease was associated with lower risk of DM (HR, 0.13; 95% CI, 0.02-0.92; P = .04) (Table 2).
Figure.
Distant Metastasis and Overall Survival After Chemoradiotherapy by Pretreatment and Intratreatment Hypoxia Status
Table 2.
Univariable Associations Between Baseline Characteristics and Outcomes
Characteristic
Distant metastasisa
Overall survivalb
HR (95% CI)
P value
HR (95% CI)
P value
Age at chemoradiation
1.03 (0.94-1.13)
.53
1.13 (1.08-1.19)
<.001
Smoking history 1 pack-year or greater
1.25 (0.43-3.68)
.68
1.67 (0.71-3.92)
.24
Oropharyngeal primary
0.74 (0.16-3.42)
.70
0.43 (0.16-1.16)
.10
Base of tongue primary
2.31 (0.72-7.40)
.16
1.06 (0.45-2.5)
.89
Tonsil primary
0.34 (0.09-1.28)
.11
0.59 (0.25-1.40)
.23
T stage ≥3
1.10 (0.16-7.52)
.93
5.52 (2.35-12.97)
<.001
N stage ≥2c
0.70 (0.12-3.98)
.68
1.71 (0.69-4.24)
.25
Human papillomavirus–positive
0.13 (0.02-0.92)
.04
0.16 (0.05-0.49)
.001
Primary tumor resection
0.28 (0.07-1.21)
.09
0.20 (0.06-0.68)
.01
Radiotherapy de-escalated to 30 Gy
0.25 (0.06-1.08)
.06
0.46 (0.16-1.33)
.15
Cisplatin-based chemotherapy
1.51 (0.23-9.81)
.67
0.18 (0.08-0.43)
<.001
Hypoxia-positive pretreatmentc
NA
NA
1.45 (0.49-4.29)
.50
Hypoxia-positive intratreatment
3.51 (1.05-11.79)
.04
2.66 (1.14-6.19)
.02
Abbreviations: HR, hazard ratio; NA, not applicable.
a
Competing risk regression with death as a competing risk.
b
Cox regression.
c
HR estimation for distant metastasis was not possible due to no events among patients with hypoxia-negative disease pretreatment.
Of 22 deaths, 6 were related to metastatic HNSCC progression. Median (IQR) time to latest clinical follow-up among censored patients was 58 (46-91) months. OS estimates at 5 years were 89.1% (95% CI, 81.8%-97.1%) among patients with persistent intratreatment hypoxia and 95.5% (95% CI, 92.5%-98.7%) among those without (HR, 2.66; 95% CI, 1.14-6.19, P = .02) (Figure, C). Advanced age (HR, 1.13; 95% CI, 1.08-1.19; P < .001) and T stage 3 or greater (HR, 5.52; 95% CI, 2.35-12.97; P < .001) were also associated with worse OS. HPV-positive disease (HR, 0.16; 95% CI, 0.05-0.49; P = .001) and cisplatin-based CRT (HR, 0.18; 95% CI, 0.08-0.43; P < .001) were associated with improved OS (Table 2).
A subset of patients with HPV-positive oropharyngeal primary tumors who received platinum-based chemotherapy (228 patients) was analyzed separately. The HR for DM risk was 2.79 (95% CI, 0.73-10.58). (eFigure 3 in Supplement 1).
Discussion
There is ample preclinical evidence demonstrating that hypoxia promotes essentially every step of the metastatic cascade through activation of the hypoxia-inducible transcription factors.5,6 In the context of localized disease, a hypoxic tumor microenvironment promotes immune evasion and epithelial-mesenchymal transition, which are precursors for metastasis.
In the clinical management of HNSCC, FMISO PET has been incorporated as a noninvasive method to assess tumor hypoxia and is associated with locoregional recurrence.14,15,16,17,18 However, there are limited data supporting its association with DM19,20 despite the underlying biological rationale. Because DM is a relatively rare event in HNSCC, a major challenge has been accumulating a sufficient sample size to address this question. Most prospective clinical series exploring the role of FMISO PET in HNSCC have been small, typically ranging from 15 to 53 patients.14,15,16,17,19,20
In contrast, in this cohort study, we have compiled a prospective series of 281 patients to evaluate whether tumor hypoxia on FMISO PET is associated with DM risk after CRT for HNSCC. Persistent intratreatment hypoxia was associated with increased DM risk. Conversely, no patients with hypoxia-negative disease before CRT experienced DM. Poorer OS was associated with persistent intratreatment hypoxia but not with pretreatment hypoxia. Compared with prior studies focusing on pretreatment hypoxia,19,20 this analysis suggests that intratreatment hypoxia is of major clinical importance.
While there is an established but small-magnitude association of stage with DM risk,1,2 hypoxia had a larger magnitude of association with DM than T or N stage in this study. Because most patients had T stage 2 or less and N stage 2b or less, it is unclear if the results can be extrapolated to more advanced disease. In future studies, which would ideally include a greater proportion of locally advanced disease and a greater number of DM events, stage and tumor hypoxia should be evaluated as independent risk factors for DM. Nevertheless, it is notable that hypoxia was associated with increased DM risk even in a predominantly early-stage cohort.
Higher rates of DM among patients with hypoxia-positive disease are expected to translate to worse OS. Given the small number of DM events in a predominantly early-stage cohort, a minority of deaths were related to DM progression. Nevertheless, we observed a signal for worse OS among patients with intratreatment hypoxia.
Prior work has shown that the lack of hypoxia on FMISO PET can be used to select patients for safe, effective de-escalation of CRT. In a series of prospective clinical trials,16,17,18 patients with HPV-positive HNSCC who were negative for tumor hypoxia 1 to 2 weeks after initiating CRT were eligible for de-escalated CRT over 3 weeks (30 Gy) compared with standard CRT over 7 weeks (70 Gy). With this selective de-escalation strategy, no differences in progression-free survival or OS were observed.18 Conversely, the current study shows that persistent tumor hypoxia on FMISO PET is a biomarker of DM risk and may guide patient selection for escalated therapeutic strategies, including novel systemic therapy regimens. Pembrolizumab has shown efficacy in treating metastatic HNSCC, independent of cytotoxic chemotherapy,4 and may reduce DM risk in the upfront setting, for a well-selected high-risk patient population. Ultimately, both the presence and absence of tumor hypoxia may be used as biomarkers to guide individualized therapy.
Limitations
Our study is not without limitations. Despite the large size of this dataset, there were still few DM events. This limited the ability to assess hypoxia as an independent risk factor in multivariable and subgroup analyses. Additionally, treatment strategies were heterogenous. Patients with and without primary tumor resection were included, as were patients who received standard and de-escalated CRT.
Limitations
Our study is not without limitations. Despite the large size of this dataset, there were still few DM events. This limited the ability to assess hypoxia as an independent risk factor in multivariable and subgroup analyses. Additionally, treatment strategies were heterogenous. Patients with and without primary tumor resection were included, as were patients who received standard and de-escalated CRT.
Conclusions
In this cohort study using pooled analysis of prospective nonrandomized clinical trials, we evaluated tumor hypoxia on FMISO PET as a biomarker of DM risk after CRT for HNSCC. Persistent hypoxia during CRT was associated with increased risk of DM. Conversely, all patients with hypoxia-negative disease before CRT remained free of DM. These findings suggest that tumor hypoxia on FMISO PET may serve as a biomarker of DM risk. |
Title: Food safety issues associated with sesame seed value chains: Current status and future perspectives | Body:
1
Introduction
Sesame (Sesamum indicum) is an ancient oilseed crop mainly cultivated for its edible seeds from which oil is produced [1]. Sesame seeds comprise up to 60 % oil, the highest content of all major oilseed crops [2,3]. Sesame is a functional food as it is a source of nutritional and nutraceutical components. Sesame oil is a rich source of polyunsaturated fatty acids (PUFA), such as oleic and linolenic acids [4,5]. Sesame is also an excellent source of proteins, carbohydrates, vitamins, and minerals, including phosphorus, manganese, copper, and iron [6]. Recent studies have reported that sesame is an important natural food source of phytosterols (3–8 mg/g), melatonin (0.04–298.62 ng/g) and tocopherols (530–1000 mg/kg) [[7], [8], [9]]. In addition, lignans such as sesamin, sesamol, sesamolin, and sesaminol are another major group of bioactive compounds found in sesame [9]. These components are associated with various biological and pharmacological activities, including antioxidant, anti-inflammatory, cardioprotective, anticancer and anti-neurodegenerative effects [10].
Consequently, sesame has diverse uses across the food, cosmetic and pharmaceutic industries, and increased demand is driving the growth of the sesame market. The mainstreaming of indigenous foods and ingredients such as hummus, tahini, and halva, particularly in Western diets, have contributed to the increasing demand for sesame seeds [11]. Sesame oil has pleasant sensorial characteristics, and the presence of antioxidants confers increased resistance to rancidity compared to other oils [4,6]. In addition, different applications of sesame include soap and cosmetic production and as a delivery vehicle for fat-soluble drugs [12,13].
The top producers of sesame are in Africa and Asia, where sesame significantly contributes to the local economy through job creation and foreign exchange revenue [1,14]. However, microbial and chemical hazards in this commodity constitute a significant barrier to the global trade of sesame seeds [15,16]. Salmonella spp. and mycotoxin contamination are frequently reported in sesame and sesame-based products. Salmonella spp. and mycotoxins were the most significant hazards in the “nuts, nut products and seeds” category in foods exported into the European Union (EU) in 2018 and 2019 [17]. Recent reports have also highlighted the scale of Salmonella contamination in sesame imported into the EU [16,18]. In addition, outbreaks of salmonellosis associated with sesame and sesame-related products have been reported worldwide [[19], [20], [21]]. Sesame seeds contaminated with mycotoxins have been observed at different stages of the value chain, suggesting this is a widespread problem [22,23]. Pesticides are also an emerging concern. In September 2020, sesame seeds contaminated with ethylene oxide were reported to the EU's Rapid Alert System for Food and Feed (RASFF). This major incident led to several recalls and withdrawals of sesame-containing foods across Europe [24]. Sesame seeds often serve as ingredients in a wide variety of products. Therefore, the presence of hazards in sesame can have severe and widespread health and economic consequences.
Sesame can be exposed to various contaminants at all stages of the value chain. Poor agricultural practices during cultivation, harvesting and storage can allow for microbial and chemical contamination of sesame seeds [25]. Furthermore, the warm and humid conditions characteristic of tropical and subtropical regions where sesame is grown may also create an optimal environment for the growth of foodborne pathogens or the production of microbial toxins, further exacerbating the problem [22].
Several reviews discussing sesame have been published. However, these mainly focus on single aspects such as nutritional or nutraceutical components [4,9,26] or economic value [1,27] to producing countries. Olaimat et al. [28] reviewed the microbial safety of oil-based food products but focused on foodborne pathogens. However, no comprehensive overview of microbial and chemical contamination is focused explicitly on sesame and sesame-based products. Therefore, this review is necessary to summarise current knowledge on the safety of sesame-based foods. It also highlights data gaps for future research and suggests interventions to strengthen the sesame value chain.
2
The global sesame seed market and value chain
Sesame is a highly valued crop worldwide because of its various uses for its seeds and oil in the food, nutraceutical, and pharmaceutical industries. Increased consumer awareness of sesame's health benefits, changing consumption patterns, and a growing population have increased the demand for sesame [27].
The sesame market is projected to grow at a compound annual growth rate of 2.6 % to USD 8.7 billion by 2029 [29]. Global sesame seed production exceeded 7 million tonnes in 2022, an increase of almost 200 % over the last three decades (Fig. 1). Africa and Asia produce over 95 % of the world's supply of sesame. In 2022, the highest producing countries were Sudan, India, Myanmar, the United Republic of Tanzania, and China, accounting for over 60 % of global production (Table 1).Fig. 1Global production of sesame seed between 1992 and 2022. Source: Food and Agriculture Organisation Statistical Databases [30].Fig. 1Table 1Major producing countries of sesame seeds.Table 1S/NCountryProduction Quantity (tonnes)Exports (tonnes)Export to production ratio (%)Export value (USD million)1Sudan1,231,701356,643295092India788,740234,458304223Myanmar760,926101,565131444United Republic of Tanzania700,000120,987171445China872,79590,208102026Nigeria450,000297,022663317Burkina Faso208,79658,85828688Chad201,91369,749351029Central African Republic190,9171150.060.0110Ethiopia180,000107,71960183ND - No data available.Source: Food and Agriculture Organisation Statistical Databases. Data for 2022 [30].
In producing countries, sesame is gaining recognition as a high-value export crop. Over 2.0 million tonnes of unprocessed sesame seeds, valued at USD 3 billion, were traded globally in 2022 [30]. In Nigeria, almost 70 % of domestic sesame production was exported in 2022 (Table 1), and sesame seeds are the third most valuable export product after cocoa and herbs [31]. In addition, Ethiopia, Chad, and India exported 60 %, 35 %, and 30 % of their cultivated sesame in 2022 (Table 1). Asia and Europe are the primary destinations for sesame seeds (Fig. 2). China, Turkey, and Japan are the largest importers of sesame seeds, accounting for about 56 % (almost 1.2 million tonnes) of global imports, valued at nearly USD 1.9 billion [30]. The European Union (EU) is also a growing market for imported sesame seeds primarily used in the food industry to supplement local production [11]. Consequently, sesame is gaining attention as a priority crop, and increasing production has become the focus of many national and international efforts [1,13,32,33].Fig. 2Major importers of sesame seeds. Data represent import values (USD million) in 2022. Source: Food and Agriculture Organisation Statistical Databases [30].Fig. 2
The supply chain connecting sesame producers with consumers is global and complex (Fig. 3). In major producing regions, sesame is grown predominantly by smallholder farmers, with a minor contribution from a few large-scale farmers. Producers sell individually or through cooperative unions to wholesalers, the principal actors in the sesame value chain. Exporters purchase the bulk of the seeds, while smaller amounts are sold to processors and local retailers [31,32,34]. Several constraints to the value chain in many low and middle-income sesame-producing countries include access to high-yielding and well-adapted cultivars, seed supply systems, and credit. In addition, there is limited use of modern agricultural production technologies, post-harvest crop management infrastructure and systems [14,35,36]. Sesame value chains are poorly organised in the world's major producing regions. They are, therefore, more vulnerable to foodborne hazards that may pose health risks to consumers.Fig. 3The sesame supply chain identifying major agents.Fig. 3
3
Salmonella and other microbial hazards in sesame seeds and associated products
Sesame seeds and sesame seed products such as tahini (sesame paste) and halva are classified as low water activity (aw) foods (aw < 0.70) that typically have an extended shelf life of several months [28]. Low aw does not support the growth of pathogenic and spoilage bacteria [37]. Therefore, these foods are usually considered microbiologically safe. However, factors influencing pathogen survival in low aw foods are poorly understood and vary among foods [38]. The oil content of sesame-based foods may protect some pathogens from preservative measures such as heat treatment and gamma irradiation during processing [39,40].
There have been several reports of imported sesame-based foods contaminated with pathogenic bacteria, notably Salmonella, with severe consequences, including border rejections, product recalls, and foodborne outbreaks [16,20]. Many of these products are purchased as ready-to-eat (RTE) products without a further inactivation step. Therefore, their safety is of paramount importance.
Salmonella has emerged as a significant hazard in sesame seeds and sesame-based products (Table 2) and is becoming increasingly recognised as a source of outbreaks [41]. A notable example was the 2016–2017 outbreak of salmonellosis, with 47 confirmed cases across five European countries. The causative agent was identified as a novel Salmonella enterica subspecies enterica serotype (11: z41: e,n,z15). A traceback investigation implicated sesame paste produced in Greece and sesame seeds imported from Nigeria as the vehicles of transmission [20]. More recently, the European Food Safety Authority (EFSA) reported an outbreak associated with sesame-containing products (halva and tahini) imported from Syria. In total, 135 confirmed cases from five European countries (Denmark, Germany, Netherlands, Norway, Sweden), Canada and the United States of America were infected with six Salmonella enterica serotypes between January 2019 and October 2021 [42]. Other outbreaks of salmonellosis linked to sesame-based foods have been reported in New Zealand [43], Australia [21,43], the United States of America [19,44], and Canada [45].Table 2Microbial hazards in sesame seeds and sesame-based products.Table 2MicroorganismProduct(s)Sample collection pointCountry (Country of origin)aPrevalence (n/N)bAnalytical methodReferenceSalmonella spp.Sesame seedsRetailItaly (Nigeria)3/36Conventional[46]S. Montevideo,S. Stanleyville,S. TileneSalmonella spp.Sesame seedsRetailMexico (U)12/100Conventional[47]Salmonella spp.Sesame seedsPoint of exportBurkina Faso95/359Conventional[48]Salmonella spp.Sesame seedsPoint of importUnited States of America (U)20/177Conventional[49]Salmonella spp.Sesame seedsPoint of importUnited States of America (U)23/233Conventional[50]Salmonella Offa, Salmonella TenneseeSesame seedsRetailGermany (U)2/16Conventional[51]Salmonella Typhimurium DT104TahiniRetailGermany (Turkey)1/12ConventionalSalmonella Typhimurium DT104, Salmonella PoonaHalvahRetailGermany (Turkey)8/71ConventionalThermotolerant coliformsSesame seeds, Sesame-based snacksRetailBurkina Faso32/75Conventional[52]MicroorganismProduct(s)Sample collection pointCountry (Country of origin)cPrevalence (n/N)bAnalytical methodReferenceSalmonella spp.Sesame seedsRetailUnited Kingdom (U)13/771Conventional[53]Escherichia coli8/771Salmonella enterica subspecies enterica serotype (11:z41:e,n,z15)Sesame spreadRetail, HouseholdGermany, Luxembourg (Sudan)NDConventional, Whole Genome sequencing[20]Sesame seedsProcessorGermany (Nigeria)NDSushi containing sesameProcessorUnited Kingdom (U)NDSalmonellaTahiniRetailLebanon7/42Conventional[54]Escherichia coli18/42Bacillus cereusSesame seedsRetailJapan (U)1/6MALDI TOF-MS[55]Bacillus spp.Sesame seedsRetailUnited States of America (India, China, Mexico, Unknown)6/1016S rRNA amplicon sequencing[56]ND: Not Documented.aBrackets indicate the country where samples were collected. Where no brackets are used, the country where samples were collected was the same as the country of origin of the seeds. (U): Undeclared country of origin.bn/N: n, number of contaminated samples; N, Total number of samples.cCountry where samples were collected. Where no brackets are used, the country where samples were collected was the same as the country of origin of the seeds. (U): Undeclared country of origin.
It is important to note that all these outbreaks have involved imported sesame products or raw materials, highlighting the role of the supply chain in the transmission of this microbial hazard. Salmonella is recognised as a significant hazard in sesame seeds imported from Africa into the EU. Fifty-six percent (56 %) of the notifications in the RASFF database arising from pathogenic organisms in foods imported into the EU between 2009 and 2019 were due to Salmonella-contaminated sesame seeds [16]. Similarly, Salmonella contamination was frequently observed in sesame seeds exported into Europe from the Asia-Pacific region between 2000 and 2020 [18]. Van Doren et al. [49] observed that almost 10 % of 229 shipments of sesame seeds imported into the United States of America within a six-month period were contaminated with Salmonella. Conversely, Zhang et al. [57] did not detect Salmonella in 527 samples of imported sesame seeds collected from retail establishments in the United States of America between 2013 and 2014. In addition, Compaore et al. [48] noted that 27 % of 359 sesame samples intended for export from Burkina Faso over a 10-year period were contaminated with Salmonella.
Consequently, RTE sesame seeds and associated products are regarded as high-risk foods and have been subjected to increased official controls in several countries at various times [58,59]. These findings have significant implications for producers, particularly in low- and middle-income countries, where sesame is an essential source of foreign revenue and jobs contributing to socioeconomic development [34].
The prevalence of pathogenic and indicator bacteria in retailed sesame seeds and products made from sesame has also been investigated. Willis et al. [53] studied the prevalence of Salmonella and Escherichia coli in 771 sesame seed samples collected from retail outlets in the United Kingdom. They reported 1.7 % and 1 % prevalence rates for Salmonella and E. coli, respectively. Juarez-Arana et al. [47] also observed that 12 % of sesame seeds sold in Mexican markets were contaminated with Salmonella. Alaouie et al. [54] also reported the presence of Salmonella and E. coli in 47 % and 43 % respectively, of tahini samples collected in Lebanese markets.
Contamination with enteric pathogens such as Salmonella is an indication of unhygienic practices during food production and storage. Sesame seeds are susceptible to microbial hazards from contaminated soil, irrigation water, livestock, equipment surfaces and human handling [25,60]. Salmonella can persist in soil for extended periods and be transferred to water and cultivated crops [61]. Post-harvest handling is a significant challenge in many sesame-producing countries. An important post-harvest treatment of sesame seeds is drying to reduce the moisture content of seeds and prevent spoilage during storage. In several producing countries, this process usually occurs on the farm, under the sun, or in the open, exposing sesame seeds to hazards in the farm environment [14,36]. Potential sources of enteric pathogens include contaminated aerosols or dust, manure and animal droppings, and the harvest stage, which are increasingly considered critical for Salmonella contamination [48,51]. Many sesame-based products such as halva and tahini undergo further processing, e.g., cooking or the addition of sugar, which should inhibit the growth of pathogens like Salmonella. Therefore, cross-contamination from food handlers is also a possible source of contamination where good manufacturing practices are not utilised.
Other pathogenic or indicator bacteria have been linked to products from sesame seeds. Tahini contaminated with Listeria monocytogenes has been recalled from retail outlets in New Zealand [62], and other Listeria species have been isolated from hummus [63]. In addition, survival challenge studies have shown that L. monocytogenes can survive in sesame seed products under various environmental conditions and should be considered a safety concern [64,65]. Bacillus spp. including B. cereus, has also been linked to retailed sesame seeds [56,55]. Compaore et al. [52] evaluated the sanitary quality of sesame seeds and sesame based RTE foods in Burkina Faso. Although they did not detect any pathogenic Escherichia coli or Salmonella in the 75 samples collected, more than 30 % of the samples did not meet the microbiological criteria for dehydrated products.
Food safety remains a significant global public health challenge. The World Health Organisation (WHO) estimates that 1 in 10 people fall ill, and over 400,000 people, mainly under the age of 5, die each year after eating contaminated food [66]. The role of food as a vehicle for the transmission of biological hazards is well documented, and in an increasingly complex and global food chain, safeguarding the health of consumers, both domestic and international, remains a crucial goal. Salmonella outbreaks linked to sesame are a significant public health concern. Results from large-scale surveillance studies suggest that the prevalence of pathogenic organisms in sesame is low [49,50]. However, there are only a few of these studies and surveillance data from producing countries is sparse. Many sesame-based foods are sold as RTE with a long shelf life, which may put consumers' health at risk [46].
Furthermore, more information must be provided on the microbiological quality and safety of raw and processed sesame marketed for domestic consumption in producing countries. Most reports on microbial hazards and foodborne outbreaks linked to sesame are from importing countries [48]. In addition, very few studies investigate the whole supply chain to assess and evaluate critical control points to reduce contamination (Table 2). These are significant research gaps that require further investigation.
4
Chemical hazards in sesame seeds
4.1
Mycotoxins
Mycotoxins are toxic secondary metabolites of fungal species mainly belonging to the genera Aspergillus, Fusarium and Penicillium. These natural contaminants of food and feed are a growing public health concern, especially in low and middle-income countries [[67], [68], [69], [70]]. The most widely recognised classes of mycotoxins of concern are aflatoxins (AF), ochratoxin A (OTA), fumonisins, deoxynivalenol (DON) and other trichothecenes, and zearalenone (ZEA) [[71], [72], [73]].
Aspergillus flavus and A. parasiticus are the primary producers of aflatoxins [74]. Aflatoxin exposure can lead to acute aflatoxicosis, and long-term exposure is a risk factor for hepatocellular carcinoma [75]. Aflatoxin B1 (AFB1) is considered the most toxic and has been classified as a Group 1 carcinogen by the International Agency for Research on Cancer [76]. Contamination with multiple mycotoxins occurs frequently and can lead to severe health problems for consumers as the cytotoxic effects can impair the function of several organs, such as the liver and kidney, as well as the immune and nervous systems [77,78]. Chronic exposure to mycotoxins has also been associated with childhood stunting [79,80].
The frequent isolation of fungal species which have the potential to produce mycotoxins, particularly during the storage of sesame seeds, is a cause for concern. Aspergillus flavus and Fusarium spp. were reported as the dominant fungi in retailed sesame seeds in Nigeria [81]. Ajmal et al. [82] reported an increase in the prevalence of Aspergillus flavus and the concentration of aflatoxins during the storage of sesame seeds.
Sesame seeds are susceptible to fungal contamination at different stages of production and processing. The farm environment can be a source of fungal spores. Post-harvest storage of sesame seeds is common as sesame cultivation is seasonal, and storage provides supply between harvests or before seeds can be exported [35,83]. The storage period can range from a few weeks to several months [84]. Harvested produce is usually stored in non-hermetic packaging and non-climate-controlled facilities, which can support microbial growth. Temperature and water activity are the major extrinsic factors influencing fungal growth and mycotoxin production in food [85,86]. Storage at high humidity may increase water activity. Many sesame-producing countries are in tropical regions, and the warmer temperatures may provide suitable conditions for any fungal spores in the seeds to germinate during storage, thus producing mycotoxins [82,87,88].
Exposure to mycotoxins in food and feed is a major issue for human and animal health, nutrition, and the food trade [89]. International, regional, and national agencies have set maximum tolerable limits (MTLs) for mycotoxins in food to mitigate dietary exposure to mycotoxins and safeguard public health. For example, the European Commission has maximum levels for AFB1, total aflatoxins and ochratoxin A at 2, 4 and 5 μg/kg, respectively [90], while the United States Food and Drug Administration (U.S. FDA) recommends a maximum limit of 20 μg/kg for aflatoxins in foods intended for human consumption [91].
Several studies have reported a low prevalence of mycotoxins in sesame (Table 3). Ezekiel et al. [84] demonstrated that no detectable aflatoxins or fumonisins were present in sesame seeds collected from farmers (stored for less than 30 days after harvest) in Nigeria. These seeds also complied with international standards for regulated mycotoxins. These data corroborate results by Pongpraket et al. [23], where only 2 out of 200 (1 %) samples of retailed sesame seeds in Thailand were above the European Commission (EC) regulatory limits for aflatoxins. Tabata et al. [92] observed aflatoxins in 5 of 47 (10.6 %) sesame samples in Japan, noting concentrations of AFB1 between 0.6 and 2.4 μg/kg. Similarly, Hosseininia et al. [93] observed that 50 % of 269 samples from five shipments of sesame seeds imported into Iran contained less than 1 μg/kg of total aflatoxins. Esan et al. [94] reported a prevalence of 12 % and 7 % for total aflatoxins and Fumonisin B1, respectively, in sesame samples collected from retail markets in Nigeria. Ochratoxin A (OTA) was not detected in any of the samples in the study. It should be noted that in most of these studies, only specific mycotoxins were investigated. The full spectrum of mycotoxins and fungal metabolites in food products must be determined to accurately assess dietary mycotoxin exposure from consuming such foods. Furthermore, consuming foods contaminated with multiple mycotoxins, even at low concentrations over a prolonged period, may pose a health risk due to the possible synergistic effects of metabolite combinations [72,95].Table3Occurrence and contamination level of mycotoxins in sesame seeds and sesame-based products.TableSesame productMycotoxin typeaSamples (N)Positive samples (n)Number of samples > MTLbMean (μg/kg)Range (μg/kg)Analytical methodcCountryReferenceHarvested seedsAFB1100922421.61.2–60TLCPakistan[82]Stored seedsAFB1100998030.615–60SeedsAF4623713.670.79–60.5Scanning densitometerNigeria[81]SeedsAFB11823391.62HPLCIran[96]Seeds (Black)AF9552030.2–16TLCMyanmar[87]Seeds (White)AF110350.3–7SeedsAF301052.950.9–61.8ELISAUganda[97]OTA302611.450.1–3.1DON3021194.40.8–955.3SeedsAFB19696966.363.95–11.75HPLCNigeria[98]SeedsAF597516.90.29–88.5LC-MS/MSNigeria[94]FB159413.05.60–24.0SeedsDON1715288–76LC-MS/MSNigeria[84]SeedsAFB12433.60.4–7.2LC-MS/MSNigeria[22]FB124517.37.3–26.7DON241478.328–171Sesame productMycotoxin typeaSamples (n)Positive samplesNumber of samples > MTLbMean (μg/kg)Range (μg/kg)Analytical methodcCountryReferenceSeedsAF4022151.95HPLCIran[99]TahiniAF4018141.10Tahini halvaAF4013110.72SeedsAF26913681.430.4–48.18HPLCIran[93]Seeds, Tahini, Tahini halva, Sesame barsAFB1302380.1–8.6HPLCGreece[100]PasteAFB110037124.310.39–20.45Fluorimetry, LCChina[101]AF96.750.54–56.89SeedsOTA1919138.141.90–15.66HPLCNigeria[102]SeedsAFB120010101.440.84–2.17LC-MS/MSThailand[23]BEA358.891.39–37.8SeedsAFB1870.900.54–1.82ELISAMalaysia[103]SeedsAFB128252533.7HPLCEgypt[104]TahiniAFB111739256.550.2–238.1HPLCEgypt[105]SeedsAFB14750.6–2.4HPTLCJapan[92](−): Data not available.aAF: Total Aflatoxin; AFB1: Aflatoxin B1; BEA: Beauvericin; DON: Deoxynivalenol; FB1: Fumonisin B1; OTA: Ochratoxin A.bMTL: Maximum Tolerable Limit based on European Commission (EC, 2006) regulations for Total AF (4 μg/kg), AFB1 (2 μg/kg), OTA (5 μg/kg).cHPLC: High-Performance Liquid chromatography; HPTLC: High-Performance Thin Layer Chromatography; LC: Liquid chromatography; MS: Mass spectrophotometry; TLC: Thin Layer Chromatography.
An analysis of aflatoxin contamination in sesame seeds in this report has shown that contaminated samples at the retail or household level regularly exceed regulatory limits (Table 3). Elaigwu et al. [98] observed concentrations of AFB1 above 2 μg/kg in all sesame seed samples (n = 96) collected in Nigeria. Heshmati et al. [99] reported that 25 % of sesame seeds from the Iranian market were contaminated with AFB1 above the EC ML. In the same study, 18 % and 15 % of tahini and tahini-halva samples, respectively, were above the EC ML for AFB1. Overall, 38 %, 35 % and 11 % of the sesame seeds, tahini, and tahini-halva samples contained total aflatoxins above the EC limit. A study in China investigating the occurrence of aflatoxins in sesame paste collected from both small-scale and industrial manufacturers noted that 37 % of the samples were contaminated with AFB1. The maximum AFB1 concentration recorded was 20.45 μg/kg, and 12 % of samples had concentrations above 2 μg/kg [101]. Echodu et al. [97] observed that 13 % of sesame seed samples collected from households in Northern Uganda exceeded the EC ML for aflatoxins. In tahini samples from Egypt, 21 % exceeded the Egyptian ML of 2 μg/kg [105].
Ochratoxin has been demonstrated to be genotoxic and carcinogenic in animals with the kidney as the primary target organ, and it is classified as a Group 2B possible carcinogen [106,107]. There are few reports of OTA contamination of sesame seeds. Makun et al. [102] investigated the prevalence of OTA in sesame samples from Nigeria. They reported that all sesame seed samples in their study (n = 19) were contaminated with OTA, and EC limits were exceeded in 13 % of the samples. This contrasts with Echodu et al. [97], where only 3 % of collected samples had OTA concentrations exceeding EC limits.
Only a few major producing countries have set regulatory limits for mycotoxins, specifically for sesame seeds and products, and where these exist, focus on international trade [108]. In addition to potential risks to consumer health, mycotoxin contamination of sesame seeds could have severe economic consequences due to border rejections and recalls.
4.2
Pesticides
Controlling the growth of microorganisms and pests in sesame is critical for improving food quality and safety. Some previously used biological control methods for reducing microbial hazards in harvested sesame include irradiation, fumigation with carbon dioxide (CO2) or propylene oxide, and the addition of salts [25,109]. Furthermore, plant protection products, such as pesticides, are used at different stages of cultivation to reduce post-harvest losses due to pest infestation and pathogens. However, there is growing concern about the potential adverse effects of pesticide residues on consumers and the environment [110,111].
Recently, global attention was drawn to the issue of pesticide contamination due to consumer exposure to ethylene oxide after its detection in sesame seeds imported into Europe from India in 2020 [112]. The use of ethylene oxide as a plant protection product is not approved in the EU as it has been classified as a Group 1 carcinogen [113]. However, ethylene oxide was detected at over 1000 times the maximum residue level (MRL) of 0.05 mg/kg [114,115]. This incident led to an unprecedented recall and withdrawal of sesame-based foods across the Member States and non-EU Member States [24]. As a result, new legislation has been implemented to increase import controls on sesame originating from India [15].
Between January 2020 and March 2024, there were 419 notifications regarding pesticide residues in sesame seeds in the EU RASFF system. Most of the notifications concerned sesame seeds originating from India (349, 83.3 %). The main contaminant was ethylene oxide (312 out of 349) and its derivatives, 2-chloroethanol, chlorate and iprobenfos. There have been reduced notifications from India since 2020 (262 notifications in 2020, 78 notifications in 2021, 8 notifications in 2022 and 1 notification in 2023). This is probably because of the increased frequency of checks and import control by importing countries. As of April 2024, there are only 5 notifications regarding pesticide residues in sesame seeds entering the EU for 2024. Four of the notifications were from Nigeria, with Chlorpyrifos (more than two times the MRL) and Chlorate (more than 8 times the MRL) reported in sesame seeds from Nigeria [116].
Some pesticide residues, including lindane, chlorpyriphos, and metalaxyl, have been observed in sesame seeds and oil [117,118]. Pesticide residues are not only found in the sesame seeds but could also be carried over into the processed products. For example, ethylene oxide was detected in caramelised nuts made with sesame seeds from Nigeria [119], in baking mixes made with sesame seeds from India [120], in spice mixes made with sesame seeds from India [121] and in bread baking mixes made with sesame seeds from India [122]. A residue of ethylene oxide, 2-chloroethane, was also detected in baking mixes made with sesame seeds from Nigeria [123].
The presence and persistence of pesticides in sesame seeds and their products raise the urgent need for research and development of alternative pest control strategies. This will eliminate the need to use these unsafe chemicals in foods. Furthermore, there have been repeated notifications of ethylene oxide in sesame seeds imported into the EU. This suggests a need for continuous monitoring and surveillance of these chemicals in sesame seeds and their products. This is particularly important in producing countries for which there is limited data.
4.3
Allergens
Sesame allergy is a growing concern as it triggers hypersensitivities that lead to symptoms including vomiting, diarrhoea, contact dermatitis and systematic anaphylaxis [124,125]. Sesame allergens have been classified into three major groups: lipid, protein, and unknown allergens [126]. Protein allergens are classified into eight groups, Ses i 1 to Ses i 8 and are associated with IgE-mediated immediate hypersensitivity reactions. Lipid allergens initiate both immediate (seeds) and delayed (oil) hypersensitivity reactions [127].
Reports on the prevalence of sesame allergies globally vary widely from about 0.1 % to 0.8 %, as this depends on how much sesame is consumed within the local diet [125,128,129]. Sesame has been recognised as a source of food allergens in the Middle East, where it is used extensively in the diet. Sesame ranked third as the most common food allergy after eggs and milk in Israeli children [126]. A study in Saudi Arabia noted that sesame was the third most common cause of anaphylaxis, accounting for 15 % of cases prescribed antihistamines over a 2-year period [130]. In Turkey, an estimated 20 % of children with food allergies are allergic to sesame [131]. However, sesame allergies are reported in several other parts of the world. For example, although sesame-induced anaphylaxis rates were reported to be higher in the Middle East than in North America [132], sesame allergy is a substantial burden in the United States. An estimated 0.49 % of the population report a current sesame allergy, and 17 % of children with an IgE-mediated food allergy are estimated to have a sesame allergy [133,134]. Consequently, it is thought that the burden of sesame allergies may be higher than reported [135].
Several countries have established regulatory food labelling on products containing sesame to protect consumers and reduce the risk of unintentional exposure to sesame allergens. Since 2023, it has been required by law in the United States to label sesame as an allergen on food and dietary supplement packaging. This requirement also exists in the European Union, Canada, Australia, New Zealand, and other parts of the world [136]. In addition, a joint FAO-WHO Expert Committee recommended that sesame be considered a priority allergen [137].
There is scarce information on the prevalence of sesame allergies and their regulation in many sesame-producing countries worldwide, particularly those in Africa. This could be because sesame seeds are produced for export rather than local consumption. However, it has also been noted that there are significant data gaps on food allergens in many low-resource countries that bear a significant burden of other food-related challenges, e.g., malnutrition [138].
As observed with microbial hazards, there is limited information on the prevalence and human health risks of chemical hazards in sesame seeds and sesame-based products. Inadequate food safety and quality regulatory and monitoring systems and a lack of public awareness are important limitations in many producing countries [87,97]. To address this critical food safety issue, a better understanding of the routes of contamination of sesame seeds and routine surveillance in producing countries is required. This will serve as a baseline for developing evidence-based strategies for risk assessment and identifying intervention strategies to reduce exposure to these hazards.
4.1
Mycotoxins
Mycotoxins are toxic secondary metabolites of fungal species mainly belonging to the genera Aspergillus, Fusarium and Penicillium. These natural contaminants of food and feed are a growing public health concern, especially in low and middle-income countries [[67], [68], [69], [70]]. The most widely recognised classes of mycotoxins of concern are aflatoxins (AF), ochratoxin A (OTA), fumonisins, deoxynivalenol (DON) and other trichothecenes, and zearalenone (ZEA) [[71], [72], [73]].
Aspergillus flavus and A. parasiticus are the primary producers of aflatoxins [74]. Aflatoxin exposure can lead to acute aflatoxicosis, and long-term exposure is a risk factor for hepatocellular carcinoma [75]. Aflatoxin B1 (AFB1) is considered the most toxic and has been classified as a Group 1 carcinogen by the International Agency for Research on Cancer [76]. Contamination with multiple mycotoxins occurs frequently and can lead to severe health problems for consumers as the cytotoxic effects can impair the function of several organs, such as the liver and kidney, as well as the immune and nervous systems [77,78]. Chronic exposure to mycotoxins has also been associated with childhood stunting [79,80].
The frequent isolation of fungal species which have the potential to produce mycotoxins, particularly during the storage of sesame seeds, is a cause for concern. Aspergillus flavus and Fusarium spp. were reported as the dominant fungi in retailed sesame seeds in Nigeria [81]. Ajmal et al. [82] reported an increase in the prevalence of Aspergillus flavus and the concentration of aflatoxins during the storage of sesame seeds.
Sesame seeds are susceptible to fungal contamination at different stages of production and processing. The farm environment can be a source of fungal spores. Post-harvest storage of sesame seeds is common as sesame cultivation is seasonal, and storage provides supply between harvests or before seeds can be exported [35,83]. The storage period can range from a few weeks to several months [84]. Harvested produce is usually stored in non-hermetic packaging and non-climate-controlled facilities, which can support microbial growth. Temperature and water activity are the major extrinsic factors influencing fungal growth and mycotoxin production in food [85,86]. Storage at high humidity may increase water activity. Many sesame-producing countries are in tropical regions, and the warmer temperatures may provide suitable conditions for any fungal spores in the seeds to germinate during storage, thus producing mycotoxins [82,87,88].
Exposure to mycotoxins in food and feed is a major issue for human and animal health, nutrition, and the food trade [89]. International, regional, and national agencies have set maximum tolerable limits (MTLs) for mycotoxins in food to mitigate dietary exposure to mycotoxins and safeguard public health. For example, the European Commission has maximum levels for AFB1, total aflatoxins and ochratoxin A at 2, 4 and 5 μg/kg, respectively [90], while the United States Food and Drug Administration (U.S. FDA) recommends a maximum limit of 20 μg/kg for aflatoxins in foods intended for human consumption [91].
Several studies have reported a low prevalence of mycotoxins in sesame (Table 3). Ezekiel et al. [84] demonstrated that no detectable aflatoxins or fumonisins were present in sesame seeds collected from farmers (stored for less than 30 days after harvest) in Nigeria. These seeds also complied with international standards for regulated mycotoxins. These data corroborate results by Pongpraket et al. [23], where only 2 out of 200 (1 %) samples of retailed sesame seeds in Thailand were above the European Commission (EC) regulatory limits for aflatoxins. Tabata et al. [92] observed aflatoxins in 5 of 47 (10.6 %) sesame samples in Japan, noting concentrations of AFB1 between 0.6 and 2.4 μg/kg. Similarly, Hosseininia et al. [93] observed that 50 % of 269 samples from five shipments of sesame seeds imported into Iran contained less than 1 μg/kg of total aflatoxins. Esan et al. [94] reported a prevalence of 12 % and 7 % for total aflatoxins and Fumonisin B1, respectively, in sesame samples collected from retail markets in Nigeria. Ochratoxin A (OTA) was not detected in any of the samples in the study. It should be noted that in most of these studies, only specific mycotoxins were investigated. The full spectrum of mycotoxins and fungal metabolites in food products must be determined to accurately assess dietary mycotoxin exposure from consuming such foods. Furthermore, consuming foods contaminated with multiple mycotoxins, even at low concentrations over a prolonged period, may pose a health risk due to the possible synergistic effects of metabolite combinations [72,95].Table3Occurrence and contamination level of mycotoxins in sesame seeds and sesame-based products.TableSesame productMycotoxin typeaSamples (N)Positive samples (n)Number of samples > MTLbMean (μg/kg)Range (μg/kg)Analytical methodcCountryReferenceHarvested seedsAFB1100922421.61.2–60TLCPakistan[82]Stored seedsAFB1100998030.615–60SeedsAF4623713.670.79–60.5Scanning densitometerNigeria[81]SeedsAFB11823391.62HPLCIran[96]Seeds (Black)AF9552030.2–16TLCMyanmar[87]Seeds (White)AF110350.3–7SeedsAF301052.950.9–61.8ELISAUganda[97]OTA302611.450.1–3.1DON3021194.40.8–955.3SeedsAFB19696966.363.95–11.75HPLCNigeria[98]SeedsAF597516.90.29–88.5LC-MS/MSNigeria[94]FB159413.05.60–24.0SeedsDON1715288–76LC-MS/MSNigeria[84]SeedsAFB12433.60.4–7.2LC-MS/MSNigeria[22]FB124517.37.3–26.7DON241478.328–171Sesame productMycotoxin typeaSamples (n)Positive samplesNumber of samples > MTLbMean (μg/kg)Range (μg/kg)Analytical methodcCountryReferenceSeedsAF4022151.95HPLCIran[99]TahiniAF4018141.10Tahini halvaAF4013110.72SeedsAF26913681.430.4–48.18HPLCIran[93]Seeds, Tahini, Tahini halva, Sesame barsAFB1302380.1–8.6HPLCGreece[100]PasteAFB110037124.310.39–20.45Fluorimetry, LCChina[101]AF96.750.54–56.89SeedsOTA1919138.141.90–15.66HPLCNigeria[102]SeedsAFB120010101.440.84–2.17LC-MS/MSThailand[23]BEA358.891.39–37.8SeedsAFB1870.900.54–1.82ELISAMalaysia[103]SeedsAFB128252533.7HPLCEgypt[104]TahiniAFB111739256.550.2–238.1HPLCEgypt[105]SeedsAFB14750.6–2.4HPTLCJapan[92](−): Data not available.aAF: Total Aflatoxin; AFB1: Aflatoxin B1; BEA: Beauvericin; DON: Deoxynivalenol; FB1: Fumonisin B1; OTA: Ochratoxin A.bMTL: Maximum Tolerable Limit based on European Commission (EC, 2006) regulations for Total AF (4 μg/kg), AFB1 (2 μg/kg), OTA (5 μg/kg).cHPLC: High-Performance Liquid chromatography; HPTLC: High-Performance Thin Layer Chromatography; LC: Liquid chromatography; MS: Mass spectrophotometry; TLC: Thin Layer Chromatography.
An analysis of aflatoxin contamination in sesame seeds in this report has shown that contaminated samples at the retail or household level regularly exceed regulatory limits (Table 3). Elaigwu et al. [98] observed concentrations of AFB1 above 2 μg/kg in all sesame seed samples (n = 96) collected in Nigeria. Heshmati et al. [99] reported that 25 % of sesame seeds from the Iranian market were contaminated with AFB1 above the EC ML. In the same study, 18 % and 15 % of tahini and tahini-halva samples, respectively, were above the EC ML for AFB1. Overall, 38 %, 35 % and 11 % of the sesame seeds, tahini, and tahini-halva samples contained total aflatoxins above the EC limit. A study in China investigating the occurrence of aflatoxins in sesame paste collected from both small-scale and industrial manufacturers noted that 37 % of the samples were contaminated with AFB1. The maximum AFB1 concentration recorded was 20.45 μg/kg, and 12 % of samples had concentrations above 2 μg/kg [101]. Echodu et al. [97] observed that 13 % of sesame seed samples collected from households in Northern Uganda exceeded the EC ML for aflatoxins. In tahini samples from Egypt, 21 % exceeded the Egyptian ML of 2 μg/kg [105].
Ochratoxin has been demonstrated to be genotoxic and carcinogenic in animals with the kidney as the primary target organ, and it is classified as a Group 2B possible carcinogen [106,107]. There are few reports of OTA contamination of sesame seeds. Makun et al. [102] investigated the prevalence of OTA in sesame samples from Nigeria. They reported that all sesame seed samples in their study (n = 19) were contaminated with OTA, and EC limits were exceeded in 13 % of the samples. This contrasts with Echodu et al. [97], where only 3 % of collected samples had OTA concentrations exceeding EC limits.
Only a few major producing countries have set regulatory limits for mycotoxins, specifically for sesame seeds and products, and where these exist, focus on international trade [108]. In addition to potential risks to consumer health, mycotoxin contamination of sesame seeds could have severe economic consequences due to border rejections and recalls.
4.2
Pesticides
Controlling the growth of microorganisms and pests in sesame is critical for improving food quality and safety. Some previously used biological control methods for reducing microbial hazards in harvested sesame include irradiation, fumigation with carbon dioxide (CO2) or propylene oxide, and the addition of salts [25,109]. Furthermore, plant protection products, such as pesticides, are used at different stages of cultivation to reduce post-harvest losses due to pest infestation and pathogens. However, there is growing concern about the potential adverse effects of pesticide residues on consumers and the environment [110,111].
Recently, global attention was drawn to the issue of pesticide contamination due to consumer exposure to ethylene oxide after its detection in sesame seeds imported into Europe from India in 2020 [112]. The use of ethylene oxide as a plant protection product is not approved in the EU as it has been classified as a Group 1 carcinogen [113]. However, ethylene oxide was detected at over 1000 times the maximum residue level (MRL) of 0.05 mg/kg [114,115]. This incident led to an unprecedented recall and withdrawal of sesame-based foods across the Member States and non-EU Member States [24]. As a result, new legislation has been implemented to increase import controls on sesame originating from India [15].
Between January 2020 and March 2024, there were 419 notifications regarding pesticide residues in sesame seeds in the EU RASFF system. Most of the notifications concerned sesame seeds originating from India (349, 83.3 %). The main contaminant was ethylene oxide (312 out of 349) and its derivatives, 2-chloroethanol, chlorate and iprobenfos. There have been reduced notifications from India since 2020 (262 notifications in 2020, 78 notifications in 2021, 8 notifications in 2022 and 1 notification in 2023). This is probably because of the increased frequency of checks and import control by importing countries. As of April 2024, there are only 5 notifications regarding pesticide residues in sesame seeds entering the EU for 2024. Four of the notifications were from Nigeria, with Chlorpyrifos (more than two times the MRL) and Chlorate (more than 8 times the MRL) reported in sesame seeds from Nigeria [116].
Some pesticide residues, including lindane, chlorpyriphos, and metalaxyl, have been observed in sesame seeds and oil [117,118]. Pesticide residues are not only found in the sesame seeds but could also be carried over into the processed products. For example, ethylene oxide was detected in caramelised nuts made with sesame seeds from Nigeria [119], in baking mixes made with sesame seeds from India [120], in spice mixes made with sesame seeds from India [121] and in bread baking mixes made with sesame seeds from India [122]. A residue of ethylene oxide, 2-chloroethane, was also detected in baking mixes made with sesame seeds from Nigeria [123].
The presence and persistence of pesticides in sesame seeds and their products raise the urgent need for research and development of alternative pest control strategies. This will eliminate the need to use these unsafe chemicals in foods. Furthermore, there have been repeated notifications of ethylene oxide in sesame seeds imported into the EU. This suggests a need for continuous monitoring and surveillance of these chemicals in sesame seeds and their products. This is particularly important in producing countries for which there is limited data.
4.3
Allergens
Sesame allergy is a growing concern as it triggers hypersensitivities that lead to symptoms including vomiting, diarrhoea, contact dermatitis and systematic anaphylaxis [124,125]. Sesame allergens have been classified into three major groups: lipid, protein, and unknown allergens [126]. Protein allergens are classified into eight groups, Ses i 1 to Ses i 8 and are associated with IgE-mediated immediate hypersensitivity reactions. Lipid allergens initiate both immediate (seeds) and delayed (oil) hypersensitivity reactions [127].
Reports on the prevalence of sesame allergies globally vary widely from about 0.1 % to 0.8 %, as this depends on how much sesame is consumed within the local diet [125,128,129]. Sesame has been recognised as a source of food allergens in the Middle East, where it is used extensively in the diet. Sesame ranked third as the most common food allergy after eggs and milk in Israeli children [126]. A study in Saudi Arabia noted that sesame was the third most common cause of anaphylaxis, accounting for 15 % of cases prescribed antihistamines over a 2-year period [130]. In Turkey, an estimated 20 % of children with food allergies are allergic to sesame [131]. However, sesame allergies are reported in several other parts of the world. For example, although sesame-induced anaphylaxis rates were reported to be higher in the Middle East than in North America [132], sesame allergy is a substantial burden in the United States. An estimated 0.49 % of the population report a current sesame allergy, and 17 % of children with an IgE-mediated food allergy are estimated to have a sesame allergy [133,134]. Consequently, it is thought that the burden of sesame allergies may be higher than reported [135].
Several countries have established regulatory food labelling on products containing sesame to protect consumers and reduce the risk of unintentional exposure to sesame allergens. Since 2023, it has been required by law in the United States to label sesame as an allergen on food and dietary supplement packaging. This requirement also exists in the European Union, Canada, Australia, New Zealand, and other parts of the world [136]. In addition, a joint FAO-WHO Expert Committee recommended that sesame be considered a priority allergen [137].
There is scarce information on the prevalence of sesame allergies and their regulation in many sesame-producing countries worldwide, particularly those in Africa. This could be because sesame seeds are produced for export rather than local consumption. However, it has also been noted that there are significant data gaps on food allergens in many low-resource countries that bear a significant burden of other food-related challenges, e.g., malnutrition [138].
As observed with microbial hazards, there is limited information on the prevalence and human health risks of chemical hazards in sesame seeds and sesame-based products. Inadequate food safety and quality regulatory and monitoring systems and a lack of public awareness are important limitations in many producing countries [87,97]. To address this critical food safety issue, a better understanding of the routes of contamination of sesame seeds and routine surveillance in producing countries is required. This will serve as a baseline for developing evidence-based strategies for risk assessment and identifying intervention strategies to reduce exposure to these hazards.
5
Discussion and recommendations
Sesame seeds have high economic value and immense potential in enabling producing countries to achieve Sustainable Development Goals focused on poverty alleviation and food security. Sesame is mainly grown as an export crop in producing countries, providing employment and income for producers and processors. While the global sesame market is anticipated to grow [139], compliance with food safety regulations remains a significant barrier to the international trade of sesame seeds. Some major hazards affecting the sesame seed trade identified in this review include Salmonella mycotoxins and pesticide residues.
Currently, there is a limited understanding of which stages of sesame production and processing are most vulnerable to contamination. Many studies investigating the occurrence of hazards in sesame focus on the storage and retail stages of the value chain. During production, contaminants can be introduced through pollution from the farm environment, the use of contaminated soil amendments, irrigation water and pesticide use [140]. Further contamination could occur due to poor harvesting, drying, storage and transportation practices and unhygienic conditions during the processing and retail stages [98,141].
There is a dearth of data from sesame-producing countries describing the link between local agricultural practices, particularly at the pre-harvest stage, and the occurrence of microbial and chemical hazards in sesame. Although some good agricultural practices have been recommended to improve the quality of sesame seeds [142], systematic investigations are needed to identify the critical points where contamination occurs in the value chain. This information is important to better target control strategies to minimise the contamination of sesame. This will contribute to food security for many smallholder farmers in producing countries and overall food safety for consumers.
Research could also focus on infrastructural interventions such as alternative drying procedures and hermetic technologies for seed storage [142,143]. In humid climates, in addition to drying, seeds need to be packed in moisture-proof packaging to prevent rehydration [144]. Hermetic technologies such as the Purdue Improved Crop Storage [145] and Super Grain Pro [146] are moisture-proof and prevent oxygen from getting into the seeds. Microorganisms and pests require oxygen for respiration; therefore, oxygen concentrations are reduced to concentrations which cannot support their growth [144]. This is particularly important as conditions that support fungal growth will lead to mycotoxin contamination. In addition, better pest control reduces the need for the use and abuse of pesticides. Consequently, hermetic packaging has been promoted in many low-resource, tropical countries to reduce post-harvest losses of several crops [147,148]. There are relatively few studies exploring the use of hermetic packaging for sesame seed storage that focus on microbial hazards [143]. Sesame seeds stored in hermetic bags had lower levels of fungal infestation and mycotoxins compared to standard packaging in polypropylene and jute bags over a six-month storage period [149]. The effect of environmental factors, storage periods and affordable packaging technologies on sesame safety and quality is an important research priority in producing countries.
Regular surveillance is required to detect contamination sources and measure the effectiveness of mitigation strategies for mycotoxin contamination. For pesticides in sesame seeds, there is a need to conduct a risk assessment of their presence in sesame seeds and how these are carried over to sesame-based products. Furthermore, it is essential to develop and employ novel rapid detection methods for determining contaminants across the value chain to mitigate post-harvest and economic losses where possible. Alternative pest management strategies, which are sustainable and environmentally-friendly, should be developed and deployed to avoid using unapproved pesticides in the sesame seed value chain.
The safety of sesame seeds for domestic consumption must also be prioritised as a research need in producing countries. Knowledge transfer between researchers, producers, and processors of sesame seeds on food safety is essential. This will give producers and processors the knowledge and tools to produce sesame seeds that meet the food safety requirements for local consumption and the international market. Researchers should regularly network with stakeholders in the sesame seeds value chain to identify emerging food safety challenges and make these research priorities for action (Fig. 4).Fig. 4Recommendations for reducing microbial and chemical hazards in the sesame value chain.Fig. 4
Funding
AO gratefully acknowledges the award of a PhD scholarship by the Tertiary Education Trust Fund (TETFund), Nigeria.
Data availability
All data to support the conclusions in this review have been provided in the manuscript.
CRediT authorship contribution statement
Amarachukwu Anyogu: Writing – review & editing, Writing – original draft, Visualization, Supervision, Project administration, Conceptualization. Yinka M. Somorin: Writing – review & editing, Writing – original draft, Visualization, Supervision, Conceptualization. Abigail Oluseye Oladipo: Writing – original draft. Saki Raheem: Writing – review & editing, Writing – original draft.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
Title: Strain-release driven reactivity of a chiral SuFEx reagent provides stereocontrolled access to sulfinamides, sulfonimidamides, and sulfoximines | Body:
Introduction
Sulfur in its range of oxidation states is ubiquitous in nature and the modern industrial world. From the organosulfur compounds found in essential biomolecules such as amino acids (cysteine and methionine) and vitamins (biotin), to life saving antibiotics (penicillin) and antidiabetic medicines (glibenclamide), sulfur’s rich chemical and structural diversity has profound impacts on all living organisms1–3. Sulfonylureas, such as glibenclamide, represent an important class of S(VI) functionality that is found in pharmaceuticals and agrochemicals (Fig. 1A)4–6. Recent medicinal chemistry efforts towards the investigation of bioisosteric replacement groups and the exploration of more sp3-rich chemical space have led to an increased interest in multi-dimensional groups such as sulfonimidoyls (1,
2), the aza-derivatives of sulfonyls7–13. These S(VI) groups consist of an additional spatial vector and stereogenic sulfur center, have served as bioisoteres for carboxylic acids and sulfonamides7,14, and are known to have favorable physiochemical properties such as permeability and total polar surface area10–13, making them advantageous for pharmaceutical development.Fig. 1Introduction and background of sulfinyl and sulfonimidoyl functionality.A Pharmaceutical and catalysis examples. B Synthetic strategies of sulfonimidoyl ureas. C Utilizing t-BuSF as a trifunctional reagent for the asymmetric synthesis of sulfinyl and sulfonimidoyl ureas (this work). TBS = tert-butyldimethylsilyl, PG = protecting group, [M] = metal, 1° = primary, * = chiral center, t-Bu = tert-butyl, i-Pr = isopropyl, e.e. =enantiomeric excess.
One of the first bioactive sulfonimidoyl ureas was reported by Eli Lilly where the observed oncolytic activity was determined to be dependent on the S-chirality of 115. More recently, sulfonimidoyl ureas have taken center stage in the development of NLRP3 inhibitors as immunomodulators16, garnering attention from companies such as Genentech (2)17 and Novartis (DFV-890)18–20—with DFV-890 currently under clinical evaluation in multiple USFDA trials21. In addition to promising therapeutics, the S(IV) derivatives have been developed as highly enantioselective organocatalysts for asymmetric aza-Henry reactions (3)22, 1,4-conjugate additions23, and β-amino olefin reductions (4)24,25.
Conventional synthetic approaches for sulfonyl and sulfonimidoyl ureas rely on the addition of the S(VI) functionality to isocyanates at the final stage of a synthesis, prohibiting subsequent derivatization at sulfur (Fig. 1B)15–20,26,27. The requisite sulfonimidamides are obtained as racemates through the S(VI) interconversion of N-protected sulfonamides (5) via deoxychlorination and amine addition28, or treatment with organolithium and Grignard reagents to achiral S(IV) electrophiles (e.g., 6, SO2)29–32 followed by oxidative amination and N-protection. Although additional methods for the synthesis of sulfonimidamides exist33–38, they are not commonly employed for sulfonimidoyl ureas. The relatively high step count to key precursors, lack of stereochemical control, and the synthetic restrictions related to isocyanates, make these routes less tractable for future discoveries and manufacturing of this important compound class. Therefore, there is an unmet need to develop practical and modular methods that utilize diverse and readily available building blocks with stereocontrol at sulfur, which was surmised to be possible using a trifunctional chiral S(IV) synthon that can be fully elaborated with carbon and nitrogen nucleophiles (Fig. 1B, right).
Inspired by our chiral bifunctional SuFEx reagent t-BuSF39, we envisioned switching the role of the N,N-diisopropyl urea protecting group through an activation strategy that would introduce a third functional feature of this reagent platform, increasing the overall utility and addressing the current synthetic limitations of sulfonimidoyl ureas (Fig. 1C). Here we disclose an additional reactivity mode of t-BuSF in which the bulky N,N-diisopropylurea serves as an enabling group and undergoes facile sulfinyl urea (7) and sulfonimidoyl urea (8) amine exchange, providing two independent routes for rapid diversification around the S(IV) (9) and S(VI) (10) core with stereochemical control. Moreover, tert-butyl sulfoximines derived from t-BuSF serve as stereogenically stable S(IV) surrogates that circumvent the traditional stereochemical lability associated with sulfinamides40,41 by direct conversion to sulfonimidoyl ureas in a single step. The methods herein have expanded the accessible S(IV) and S(VI) chemical space from t-BuSF, as demonstrated in over seventy examples, have provided significant improvements in the targeted synthesis of clinical candidates and pharmaceutical derivatives, and were applied to a combinatorial chemistry approach for rapid generation of 75 diverse S(IV) and S(VI) derivatives within one workday for a single chemist.
Results and discussion
Reaction discovery
The successful development of t-BuSF as a chiral SuFEx reagent was contingent on an imido protecting group that provided selective S-reactivity as well as reagent and product stability39. By employing a sterically encumbered and electron rich N,N-diisopropyl carbamoyl group, bifunctional asymmetric manipulation at sulfur became possible via sulfinyl urea intermediates, such as 7 (Fig. 2A). The ease in which the protecting group is removed, especially for 2° sulfonimidamides 8 (DMSO/H2O, 60 °C), prompted further investigations into its reactivity and structural features as an additional point of diversification.Fig. 2The N,N-diisopropyl carbamoyl enabled sulfinyl and sulfonimidoyl urea amine exchange.A Switching the roles of the t-BuSF protecting group. B Investigating amine exchange efficiency of sulfinyl ureas with N,N-substituents of varying size. aConversion to 9a was determined by LC–MS. bDihedral angle was calculated in Maestro after energy minimizations were performed using MacroModel. C One-pot S-activation and amine exchange of tert-butyl sulfoximines and evaluation of S(IV) sulfinyl urea enantiostability. cEnantiomeric excess (e.e.) was determined by chiral HPLC. dAmine exchange was performed for 3 h at 23 °C. PG = protecting group, EG = enabling group, eq. = equivalents, ND = not detected, TMP = 2,2,6,6,-tetramethylpiperidine, Temp. = temperature.
Conformational analysis of N,N-diisopropyl sulfinyl and sulfonimidoyl ureas revealed out-of-plane distortions about the amide bonds as a result of torsional strain induced by two N-isopropyl substituents (dihedral angle of 159°). In the presence of an α-NH proton, the inherent strain and increased N-pyramidalization42–45 converts the carbamoyl protecting group (PG) to a strain-activated enabling group (EG) —providing entry to S(IV) (9) and S(VI) (10) derivatives. The torsional strain-release promoted by 1° and 2° amines was found to be highly efficient with N,N-diisopropyl sulfinyl ureas, exhibiting nearly full conversions in three hours at room temperature, while heating (60–80 °C) was required for sulfonimidoyl ureas. The observed thermodynamic barrier for S(VI)-urea amine exchange is presumably due to the tautomeric shift required for N,N-diisopropyl carbamoyl activation along with the decreased amide torsional strain relative to the S(IV) variant.
To investigate this reactivity, a series of phenyl sulfinyl ureas (11) were used to evaluate the effects of torsional strain and steric bulk around the tertiary amide bond (Fig. 2B). Smaller substituents, such as N,N-dimethyl and -diethyl, adopt a near-planar conformation (dihedral angles of 177–175°) and exhibit low reactivity (5–7% conversion to 9a within 3 h) in the presence of morpholine (1 eq.) at room temperature. Conversely, the larger N-isopropyl-N-methyl group’s increased torsional strain (dihedral angle of 172°) revealed slightly higher conversion (10%). The strain-release reactivity trend continued with symmetrical N,N-diisopropyl substituent having the largest change in dihedral angle (159°) and subsequently increasing conversion to 94%. Attempts to exaggerate the torsional strain further with N-tert-butyl-N-isopropyl (dihedral angle of 139°) and 2,2,6,6-tetramethylpiperidine (TMP) (dihedral angle of 105°) proved impractical due to product instability.
Based on our current understanding of the S(IV)-urea amine exchange in conjunction with similar studies of sterically congested electron deficient amides43 and ureas44,45, an addition-elimination pathway at the crowded urea carbonyl is unlikely. Alternatively, elimination of N,N-diisopropyl amine in the presence of a protic amine to form a sulfinyl isocyanate intermediate followed by addition of the less bulky amine is more plausible; although our attempts to identify such an intermediate by analytical or synthetic means have proven unfruitful. Aside from the reaction mechanism, the capriciousness of S(IV) stereocenters under neutral conditions38,39 implored us to examine the effects, if any, this transformation has on the distal chiral center. Enantiopure tert-butyl phenyl sulfoximine 12 was thermally activated using t-BuOK over two hours then neutralized to give sulfinyl urea intermediate 7 that was subsequently treated with morpholine (9a) or aminomethylcyclohexane (9b) at varying temperatures and reaction times (Fig. 2C). To our delight, the stereogenic sulfur center was unaffected at room temperature (21–23 °C) or 60 °C. Although not required, prolonged heating at 80 °C resulted in stereochemical erosion for secondary and tertiary sulfinyl urea derivatives, and eventual decomposition (see Supplementary Information section 1. VII for additional details). The S(IV) stereochemical fidelity analysis suggested enantiopurity would be conserved for reactions conducted at room temperature. The balanced steric influence of N, N-diisopropyl group on S(IV)-urea amine exchange, SuFEx chemistry, and overall chemical stability offers a unique versatile synthetic handle for sulfinyl and sulfonimidoyl synthesis.
Sulfinyl urea scope
With optimal reaction conditions in hand, the scope of the S(IV) reaction was evaluated (Fig. 3). Aryl, heteroaryl and alkyl tert-butyl sulfoximines were prepared from t-BuSF and used as models for scope analysis. Following S-activation of their respective sulfoximines, the resulting sulfinyl urea intermediates were treated at room temperature for three to four hours with amines of varying complexity, from building blocks to advanced pharmaceutical intermediates and drugs. Additionally, the enantiopurity for each class of sulfinyl derivative was determined after isolation.Fig. 3Reaction scope and application of N,N-diisopropyl sulfinyl urea amine exchange.All reactions were performed on 0.1–0.25 mmol scales unless otherwise stated. Isolated yields are reported. Enantiomeric excess (% e.e.) was determined by chiral HPLC. aTFA (1 eq.) was used as an additive. bCommercial cyclohexyl isocyanate was used. cIsolated as a mixture of diastereomers at the epimeric carbon. Boc = tert-butyloxycarbonyl, Lit. = literature.
Primary aliphatic amines were first evaluated using less sterically hindered benzylic amines and aminomethyl cyclic amines which exchanged smoothly to provide sulfinyl ureas 9b–9e in excellent yields (86–98%), with no observable change in enantiomeric excess (>99% e.e.). Furthermore, amino oxetane 9f and N-Boc protected azetidine 9 g were prepared uneventfully, representing common medicinal chemistry fragments. Interestingly, tert-butyl amine exchanged with N,N-diisopropyl amine in nearly quantitative yield (9h), revealing no steric limitation for primary amine substrates. In addition to primary aliphatic amines, 1-amino piperidine was also a compatible nucleophile giving rise to 9i in 85% yield and providing entry to an underexplored class of sulfinyl ureas.
The decreased basicity and nucleophilicity of aromatic amines contributed to lesser reactivity under optimized conditions, reaching an equilibrium with the starting N,N-diisopropyl sulfinyl ureas and diminished yields (ca. 50–60%). Gratifyingly, Brønsted acid additives were found to enhance the exchange with less basic amines and improving target yields up to 88%. Trifluoroacetic acid (TFA) was selected as the ideal additive due to its role in S-activation and observed reactivity enhancement, making it suitable for one-pot and telescoped protocols. When optimal conditions were employed for aromatic amine substrates (1 eq. of TFA, 2-MeTHF or THF, rt), S(IV) derivatives 9j–9m bearing electron withdrawing/donating groups and ortho-substituents were obtained in 79–86% yields with no erosion of enantiopurity. Additionally, heteroaryl amines, 3-aminopyridine 9n and 2-aminothiazole 9o, were obtained in high yields. Secondary aliphatic amines delivered the target sulfinyl ureas within three hours at room temperature and in high yields without consequence for enantiopurity, as demonstrated by morpholine and N-hexynyl piperazine derivatives 9a,
9p, and 9q. In addition, unsymmetrical secondary amines provide sulfinyl ureas 9r and 9s with functional hydroxyl and alkynyl handles suitable for further downstream manipulation.
Late-stage compatibility and (real-world) functional group tolerance were exemplified using eight pharmaceutically relevant amines of varying complexity. The primary amines of the antiviral oseltamivir and calcium channel blocker amlodipine performed exceptionally well giving sulfinyl urea derivatives 9t and 9u exclusively in the presence of esters, secondary amides and 1,4-dihydropyridine. Additionally, the β-amine of sitagliptin was successfully functionalized to 9v in nearly quantitative yield. An aza-indole sulfinyl urea derivative of a JAK2 inhibitor46 (9w) was made readily accessible by displacement of N,N-diisopropyl amine, introducing S(IV) functionality to commonly encountered kinase pharmacophores. Antibiotic analogs of moxifloxacin (9x) and sarafloxacin (9y) were obtained uneventfully by engaging their 1H-pyrrolo[3,4-b]pyridine and piperazine motifs respectfully. Notable site selectivity was observed for evobrutinib, favoring the piperidine over a diaminopyrimidine leading to the sulfinyl derivative 9z in good yield (76%). Lastly, S(IV)-urea amine exchange was employed as a bimolecular linking strategy between a sulfinyl urea analog of celecoxib and an E3 ligase ligand (9aa) that could be leveraged for the development of proteolysis targeting chimeras (PROTACs).
Synthetic applications of S(IV)-urea amine exchange
Sulfinyl urea organocatalysts impart asymmetric control through chiral hydrogen–bonding environments and have been largely restricted to tert-butyl S-substituents21–24. These limitations are due in part to the steric bulk offered by the tert-butyl group, availability of the sulfinamide starting material, and most notably, a lack of synthetic methods to efficiently introduce structural and electronic diversity around the chiral S-center. Although t-BuSF could serve as the chiral template for catalyst design, enantiopure (R)-tert-butyl sulfinamide 13 was employed to prepare sulfinyl urea organocatalysts in a single step. Carbamoylation of sulfinamide 13 affords diversifiable intermediate 14 that was directly treated with amines to give sulfinyl ureas 15–17, improving the yield of 16 by 32% and providing a protecting group-free synthesis of 17—subsequently reducing the overall step count22. The practicality and scalability of this method was demonstrated in the gram-scale preparation of 17 and by replacing isocyanates with widely available amine building blocks.
Sulfonimidoyl urea scope
After establishing the sulfinyl urea amine exchange, our focus shifted to the analogous S(VI) transformation of sulfonimidamides. Two secondary sulfonimidamides, N-aryl 8a and N-alkyl 8b, were chosen to examine the reaction scope due to their differences in structure and tautomerization potential (Fig. 4). In general, sulfonimidoyl ureas are less reactive than their S(IV) counterparts and require elevated temperatures to undergo the desired transformation. A similar set of structurally diverse primary and secondary amines were evaluated under thermal conditions (60–80 °C) in THF and MeCN. It was quickly determined that N-aryl derivative 8a undergoes the exchange more readily at 60 °C while N-alkyl derivative 8b required a slightly more elevated temperature (80 °C). Despite the known stereogenic stability of sulfonimidamides47,48, enantiopurity was assessed for each class of sulfonimidoyl ureas.Fig. 4Reaction scope of N,N-diisopropyl sulfonimidoyl urea amine exchange.All reactions were performed on 0.1–0.25 mmol scales. Isolated yields are reported. Enantiomeric excess (e.e.) determined by chiral HPLC. aTFA (1 eq.) was used as an additive.
Both N-substituted N,N-diisopropyl sulfonimidoyl derivatives (8a and 8b) readily reacted with primary aliphatic amines (10a–10h) in good to excellent yields (72–96%) without impacting enantiopurity (>99% e.e.). Benzylic amines and heterocycle-containing primary amines afforded 10a–10c and 10d–10f respectively. Sterically congested valinol provided sulfonimidoyl urea 10g preferentially via S(VI)-urea amine exchange in 84% yield with no observed reactivity at the less hindered alcohol. An arene bioisostere was introduced as a bicyclopentyl (BCP) unit via amine exchange to give S(VI) derivative 10h in high yield. Aromatic amines exhibited similar reactivity trends to the analogous sulfinyl ureas, therefore the same tactic was employed using TFA as an additive. Even though diminished reactivity was observed, good to high yields (56–85%) were achievable for anilinic substrates 10i–10l with no effect on the S-stereocenter. On the other hand, secondary amines readily exchange with N,N-diisopropyl amine producing 10m and 10n uneventfully while maintaining enantiopurity (>99% e.e.). N-Substituted piperazines 10o–10q, 3-hydroxypyrrolidine 10r, and 3-N-Boc-piperidine 10s all delivered the desired sulfonimidoyl ureas in excellent yields. Venturing further away from flat land chemical space, sp3-rich spirocyclic amines were successfully introduced granting access to 10t–10w, which represent untapped chemical scaffolds.
The sulfonimidoyl urea scope was further expanded to encompass the complexity often encountered in drug discovery programs and to verify late-stage introduction of sulfonimidoyl functionality. Amlodipine, alogliptin, and oseltamivir were efficiently exchanged with N,N-diisopropylamine to afford 10x,
10y, and 10z as their respective sulfonimidoyl urea derivatives in the presence of esters, nitriles, amides, and α,β-unsaturated esters. Additionally, the primary aromatic amine of afatinib’s pharmacophore gave rise to 10aa despite the large o-substituent and weak nucleophilicity. Sarafloxacin and moxifloxacin exhibited exclusive reactivity at the secondary amine over condensation with carboxylic acids or 1,4-additions, providing both 10ab and 10ac in 91% yield and 10ad in 85% yield. These representative pharmaceutical examples highlight the selectivity and utility of the sulfonimidoyl urea amine exchange, revealing opportunities for late-stage diversification and derivatization of clinically optimized scaffolds.
While secondary sulfonimidoyl ureas can be functionalized under mild conditions, tertiary substrates were unreactive at elevated temperatures (120 °C, dioxane) or with the addition of Brønsted and Lewis acids (see Supplementary Information section 1. XIII for additional details). To capture this class of sulfonimidoyl derivatives, S(IV)-urea amine exchange and S-activation of sulfinyl ureas were leveraged (Fig. 5). A practical one-pot procedure was developed as a streamlined approach that mitigates the handling of reactive and less stable intermediates while showcasing the degree of modularity and diversity accessible. Tertiary sulfonimidamide 18 was prepared in enantiopure form from the corresponding tert-butyl sulfoximine in a single step (56% yield) via the sulfonimidoyl chloride intermediate. Alternatively, sulfinyl urea intermediates (9) can be fluorinated to provide isolable bifunctionalized S(VI) electrophiles, as demonstrated by sulfonimidoyl fluoride 19, that can undergo additional functionalization. A minor decrease in enantiopurity ( >99% to 99% e.e.) was observed during the fluorination event for 19, however, the stereospecific addition of an amine and turbo-Grignard reagent afforded 20 and 21 in 87% and 76% yield, respectively.Fig. 5Direct asymmetric functionalization of tert-butyl sulfoximines to sulfonimidoyl ureas.Enantiomeric excess (e.e.) was determined by chiral HPLC. aIsolated yield from tert-butyl sulfoximine via sulfonimidoyl chloride. bIsolated yield from S-activation/flourination of a tert-butyl sulfoximine. cIsolated yield from sulfonimidoyl fluoride 19 using a turbo-amide or turbo-Grignard. dIsolated yield from t-BuSF SuFEx. eYields for each step were not reported.
Synthetic applications
The practical utility and versatility of sulfinyl and sulfonimidoyl urea amine exchanges were further demonstrated in target- and diversity-oriented syntheses of clinically relevant sulfonimidoyl ureas. In two steps, t-BuSF was trifunctionally elaborated in an asymmetric fashion (Fig. 5). An aza-analog of the sulfonylurea antidiabetic drug chloropropamide was prepared in 54% yield from t-BuSF via the trifunctionalization strategy leveraging sulfoximine 22 and n-propyl amine (23). The corresponding S(IV) intermediate 24 was subjected to oxidative amination with t-BuOCl and ammonia providing enantiopure 25 in 63% yield from sulfoximine 22 in a single step. To our knowledge, S-chlorination of secondary sulfinyl ureas has not been reported. The same tactic was applied in a targeted asymmetric synthesis of sulfonimidoyl stereoisomer 1 reported by Eli Lilly15. Starting from t-BuSF, enantiopure sulfoximine 26 was prepared in 74% yield after recrystallization, which was subsequently transformed to 1 in one step (75% yield, > 99% e.e.)—providing an asymmetric route with a decreased step count and improved overall yield. Furthermore, an NLRP3 inhibitor developed by Genentech16 was prepared asymmetrically from sulfoximine 29 that was obtained from t-BuSF in 98% e.e. without recrystallization. Upon S-activation of sulfoximine 29 with TFA, sulfinyl urea 31 was directly exchanged with aniline 30 followed by oxidative amination. This one-step protocol gave rise to sulfonimidoyl urea 32 in 65% yield (45% from t-BuSF) and excellent enantiopurity (98% e.e.), significantly improving the reported 10 step racemic synthesis17 while affording a derivatization platform for future analog development.
One of the most clinically significant sulfonimidoyl compounds to date is Novartis’ NLRP3 inhibitor DFV-890, which is currently under evaluation in six USFDA clinical trials across multiple indications including cancer, heart disease, osteoarthritis, auto-inflammatory syndromes, and COVID-1921. The reported routes to DFV-890 rely on preparative chiral HPLC, isocyanate 33, and sulfonyl to sulfonimidoyl (34) interconversion (Fig. 6A). The requisite sulfonamide 35 is prepared from three different thiazole building blocks (36–38) and sulfur dioxide in varying yields and step counts. Depending on the initial heterocycle chosen, DFV-890 can be prepared in 6–9 steps using at least one protecting group with an overall yield of 0.5–4% and 97.5% e.e. after chiral chromatography.Fig. 6Application of t-BuSF trifunctionalization for the preparation of clinical inhibitor DFV-890 and derivatives.A Analysis of reported synthetic routes for DFV-890. B A medicinal chemistry route for the asymmetric synthesis of DFV-890 derivatives from t-BuSF. C A scalable targeted asymmetric synthesis of DFV-890 as a mock process route. TMEDA = tetramethylethylenediamine, 2-MeTHF = 2-methyltetrahydrofuran.
To address the synthetic drawbacks associated with DFV-890, the trifunctionalization of t-BuSF was applied. Starting from enantiopure (S)-t-BuSF, SuFEx using lithiated thiazole 39 (1st point of diversification) provided tert-butyl sulfoximine 37 with >99% e.e. in 66% yield after recrystallization on gram-scale (Fig. 6B). It is worth noting that the sulfonimidoyl transfer reaction deviated from previously reported conditions39 and was conducted in 2-MeTHF with TMEDA as an additive to aid in dianion formation. Sulfoximine 40 was then subjected to TFA mediated S-activation followed by treatment with an aromatic amine (2nd point of diversification), then oxidative amination with propargyl amine or ammonia (3rd point of diversification) to deliver clickable chemical probe derivatives of DFV-890 41 and 42 in good yields as single stereoisomers, illustrating an advantageous medicinal chemistry route. The same strategy was used in a target-oriented, mock process approach for the gram-scale synthesis of DFV-890 (Fig. 6C). Two different thiazoles (37,
38) were used to prepare gram quantities of enantiopure sulfoximine 40 with no chromatography. S-Activation afforded sulfinyl urea intermediate 43 which was directly converted to DFV-890 via S(IV)-urea amine exchange with aniline 30 followed by in situ oxidative amination providing over one gram of DFV-890 ( >99% e.e.) in 77% yield without the need for chromatographic purification. This modular two-step asymmetric synthesis of DFV-890 reduced the overall synthetic step count (8to 2 steps), improved the yield 10-fold, provided the target compound in enantiopure form, and removed the need for isocyanates, protecting groups, and chromatography.
Combinatorial trifunctionalization of t-BuSF
The modularity, efficiency, and chemical space accessible from t-BuSF were further demonstrated by applying the methods herein to a combinatorial chemistry workflow as a diversity-oriented synthesis platform for S(IV) and S(VI) library generation (Fig. 7A). tert-Butyl sulfoximines (from t-BuSF) serve as the central building block (A) to which the remaining diversity is introduced during sulfinyl urea amine exchange (B) and subsequent S-activation/additions with amines (C) or turbo-Grignards (D). For this compound set, the S-substituent was fixed to phenyl and representatives for building blocks B (maroon), C and D (teal) are shown in Fig. 7A. Individual reactions were set to specific time points (eight total hours, representing a typical workday for a single chemist) and were not independently optimized. Analysis was performed by LC–MS to monitor conversions for yield estimations of target compounds, which were cross-checked by isolation.Fig. 7Applying the trifunctionalization of t-BuSF to a combinatorial workflow.A Combinatorial approach to sulfonimidoyl ureas from t-BuSF. Representative nucleophile building blocks were used and reaction profile analysis was performed by LC–MS. B Structural representatives for the sulfonimidoyl classes obtained from each quadrant. LC–MS analysis legend: Dark green =75–100% yield, 15–60 mg of product; light green = 50–75% yield, 10–45 mg of product; yellow = 25–50% yield, 5–30 mg of product; red = 0–25% yield, 0–15 mg of product.
Starting from tert-butyl sulfoximine A, S-activation was carried out on a gram-scale using t-BuOK in 2-MeTHF at 80 °C for 2 h then partitioned into four quadrants (Q1–Q4) throughout a 48-well reaction block. The S(IV)-urea amine exchange was initiated by the addition of 22 amine building blocks (B). After three hours, the newly formed sulfinyl ureas could be split, purified, or further transformed via S-activation; in this proof-of-concept experiment, 44 unique S(VI) products were targeted. Quadrant 1 was subjected to oxidative amination with t-BuOCl and NH3 to obtain 10 primary sulfonimidamides, while quadrant 2 was designed to give 12 products captured in the sulfonimidoyl urea scope (Fig. 4) as controls for method validation. Quadrant 3 contained a diverse set of 12 secondary and tertiary sulfonimidamides, and quadrant 4 was devoted to the synthesis of sulfoximine derivatives via sulfonimidoyl fluorides and turbo-Grignards D1 and D2.
Estimated yields (with the overall mass of target compounds) after four transformations are color-coded within the 48 well plate according to the legend in Fig. 7A. Two examples from each quadrant were isolated and shown to be in agreement with the estimated yields (Fig. 7B). To our delight, 42 examples out of 44 were estimated to have >50% yield (from building block A). The two examples that fell short of the 50% cut off were Q4 sulfoximines treated with D1 having estimated yields of 40%. Within the course of eight hours, 34 sulfonimidamides and 10 sulfoximines were prepared, including 25 sulfinyl ureas, 34 sulfonimidoyl chlorides, and 5 sulfonimidoyl fluoride intermediates. The chemical and structural diversity created within this proof-of-concept workflow signifies the impact t-BuSF will have in the chemical sciences. With the translation to automated liquid handling coupled with the wide variety of available amine building blocks, it is anticipated that high-throughput variations of this approach will be feasible.
In all, a new functional feature of t-BuSF has been developed resulting in a trifunctional chiral SuFEx reagent platform. The key N,N-diisopropyl urea protecting group of t-BuSF was transformed to an enabling group that induces torsional strain-release with primary and secondary amines for the asymmetric synthesis of structurally diverse sulfinyl and sulfonimidoyl ureas. This reactivity mode allows for selective and efficient carbamoyl derivatization with amines at either the S(IV) or S(VI) stage, providing multiple synthetic route options that negates the need for isocyanates and laborious functional group interconversions. The reaction compatibility was explored using myriad amines ranging in structural complexity offering over seventy sulfinamide, sulfonimidamide, and sulfoximine examples with enantiopurities up to >99% e.e. Scalable one-pot protocols were established for rapid construction of the target sulfur functionality in two steps (five transformations) from t-BuSF that were highlighted in five synthetic applications and successfully applied to a combinatorial chemistry workflow. Most notably, the significant synthetic improvements these methods provide for important clinical candidates and derivatives illustrate the impact that the t-BuSF SuFEx platform will have on the discovery sciences. Additional reactivity modes and activation strategies of t-BuSF and intermediates are currently under investigation and will be reported in due course.
Reaction discovery
The successful development of t-BuSF as a chiral SuFEx reagent was contingent on an imido protecting group that provided selective S-reactivity as well as reagent and product stability39. By employing a sterically encumbered and electron rich N,N-diisopropyl carbamoyl group, bifunctional asymmetric manipulation at sulfur became possible via sulfinyl urea intermediates, such as 7 (Fig. 2A). The ease in which the protecting group is removed, especially for 2° sulfonimidamides 8 (DMSO/H2O, 60 °C), prompted further investigations into its reactivity and structural features as an additional point of diversification.Fig. 2The N,N-diisopropyl carbamoyl enabled sulfinyl and sulfonimidoyl urea amine exchange.A Switching the roles of the t-BuSF protecting group. B Investigating amine exchange efficiency of sulfinyl ureas with N,N-substituents of varying size. aConversion to 9a was determined by LC–MS. bDihedral angle was calculated in Maestro after energy minimizations were performed using MacroModel. C One-pot S-activation and amine exchange of tert-butyl sulfoximines and evaluation of S(IV) sulfinyl urea enantiostability. cEnantiomeric excess (e.e.) was determined by chiral HPLC. dAmine exchange was performed for 3 h at 23 °C. PG = protecting group, EG = enabling group, eq. = equivalents, ND = not detected, TMP = 2,2,6,6,-tetramethylpiperidine, Temp. = temperature.
Conformational analysis of N,N-diisopropyl sulfinyl and sulfonimidoyl ureas revealed out-of-plane distortions about the amide bonds as a result of torsional strain induced by two N-isopropyl substituents (dihedral angle of 159°). In the presence of an α-NH proton, the inherent strain and increased N-pyramidalization42–45 converts the carbamoyl protecting group (PG) to a strain-activated enabling group (EG) —providing entry to S(IV) (9) and S(VI) (10) derivatives. The torsional strain-release promoted by 1° and 2° amines was found to be highly efficient with N,N-diisopropyl sulfinyl ureas, exhibiting nearly full conversions in three hours at room temperature, while heating (60–80 °C) was required for sulfonimidoyl ureas. The observed thermodynamic barrier for S(VI)-urea amine exchange is presumably due to the tautomeric shift required for N,N-diisopropyl carbamoyl activation along with the decreased amide torsional strain relative to the S(IV) variant.
To investigate this reactivity, a series of phenyl sulfinyl ureas (11) were used to evaluate the effects of torsional strain and steric bulk around the tertiary amide bond (Fig. 2B). Smaller substituents, such as N,N-dimethyl and -diethyl, adopt a near-planar conformation (dihedral angles of 177–175°) and exhibit low reactivity (5–7% conversion to 9a within 3 h) in the presence of morpholine (1 eq.) at room temperature. Conversely, the larger N-isopropyl-N-methyl group’s increased torsional strain (dihedral angle of 172°) revealed slightly higher conversion (10%). The strain-release reactivity trend continued with symmetrical N,N-diisopropyl substituent having the largest change in dihedral angle (159°) and subsequently increasing conversion to 94%. Attempts to exaggerate the torsional strain further with N-tert-butyl-N-isopropyl (dihedral angle of 139°) and 2,2,6,6-tetramethylpiperidine (TMP) (dihedral angle of 105°) proved impractical due to product instability.
Based on our current understanding of the S(IV)-urea amine exchange in conjunction with similar studies of sterically congested electron deficient amides43 and ureas44,45, an addition-elimination pathway at the crowded urea carbonyl is unlikely. Alternatively, elimination of N,N-diisopropyl amine in the presence of a protic amine to form a sulfinyl isocyanate intermediate followed by addition of the less bulky amine is more plausible; although our attempts to identify such an intermediate by analytical or synthetic means have proven unfruitful. Aside from the reaction mechanism, the capriciousness of S(IV) stereocenters under neutral conditions38,39 implored us to examine the effects, if any, this transformation has on the distal chiral center. Enantiopure tert-butyl phenyl sulfoximine 12 was thermally activated using t-BuOK over two hours then neutralized to give sulfinyl urea intermediate 7 that was subsequently treated with morpholine (9a) or aminomethylcyclohexane (9b) at varying temperatures and reaction times (Fig. 2C). To our delight, the stereogenic sulfur center was unaffected at room temperature (21–23 °C) or 60 °C. Although not required, prolonged heating at 80 °C resulted in stereochemical erosion for secondary and tertiary sulfinyl urea derivatives, and eventual decomposition (see Supplementary Information section 1. VII for additional details). The S(IV) stereochemical fidelity analysis suggested enantiopurity would be conserved for reactions conducted at room temperature. The balanced steric influence of N, N-diisopropyl group on S(IV)-urea amine exchange, SuFEx chemistry, and overall chemical stability offers a unique versatile synthetic handle for sulfinyl and sulfonimidoyl synthesis.
Sulfinyl urea scope
With optimal reaction conditions in hand, the scope of the S(IV) reaction was evaluated (Fig. 3). Aryl, heteroaryl and alkyl tert-butyl sulfoximines were prepared from t-BuSF and used as models for scope analysis. Following S-activation of their respective sulfoximines, the resulting sulfinyl urea intermediates were treated at room temperature for three to four hours with amines of varying complexity, from building blocks to advanced pharmaceutical intermediates and drugs. Additionally, the enantiopurity for each class of sulfinyl derivative was determined after isolation.Fig. 3Reaction scope and application of N,N-diisopropyl sulfinyl urea amine exchange.All reactions were performed on 0.1–0.25 mmol scales unless otherwise stated. Isolated yields are reported. Enantiomeric excess (% e.e.) was determined by chiral HPLC. aTFA (1 eq.) was used as an additive. bCommercial cyclohexyl isocyanate was used. cIsolated as a mixture of diastereomers at the epimeric carbon. Boc = tert-butyloxycarbonyl, Lit. = literature.
Primary aliphatic amines were first evaluated using less sterically hindered benzylic amines and aminomethyl cyclic amines which exchanged smoothly to provide sulfinyl ureas 9b–9e in excellent yields (86–98%), with no observable change in enantiomeric excess (>99% e.e.). Furthermore, amino oxetane 9f and N-Boc protected azetidine 9 g were prepared uneventfully, representing common medicinal chemistry fragments. Interestingly, tert-butyl amine exchanged with N,N-diisopropyl amine in nearly quantitative yield (9h), revealing no steric limitation for primary amine substrates. In addition to primary aliphatic amines, 1-amino piperidine was also a compatible nucleophile giving rise to 9i in 85% yield and providing entry to an underexplored class of sulfinyl ureas.
The decreased basicity and nucleophilicity of aromatic amines contributed to lesser reactivity under optimized conditions, reaching an equilibrium with the starting N,N-diisopropyl sulfinyl ureas and diminished yields (ca. 50–60%). Gratifyingly, Brønsted acid additives were found to enhance the exchange with less basic amines and improving target yields up to 88%. Trifluoroacetic acid (TFA) was selected as the ideal additive due to its role in S-activation and observed reactivity enhancement, making it suitable for one-pot and telescoped protocols. When optimal conditions were employed for aromatic amine substrates (1 eq. of TFA, 2-MeTHF or THF, rt), S(IV) derivatives 9j–9m bearing electron withdrawing/donating groups and ortho-substituents were obtained in 79–86% yields with no erosion of enantiopurity. Additionally, heteroaryl amines, 3-aminopyridine 9n and 2-aminothiazole 9o, were obtained in high yields. Secondary aliphatic amines delivered the target sulfinyl ureas within three hours at room temperature and in high yields without consequence for enantiopurity, as demonstrated by morpholine and N-hexynyl piperazine derivatives 9a,
9p, and 9q. In addition, unsymmetrical secondary amines provide sulfinyl ureas 9r and 9s with functional hydroxyl and alkynyl handles suitable for further downstream manipulation.
Late-stage compatibility and (real-world) functional group tolerance were exemplified using eight pharmaceutically relevant amines of varying complexity. The primary amines of the antiviral oseltamivir and calcium channel blocker amlodipine performed exceptionally well giving sulfinyl urea derivatives 9t and 9u exclusively in the presence of esters, secondary amides and 1,4-dihydropyridine. Additionally, the β-amine of sitagliptin was successfully functionalized to 9v in nearly quantitative yield. An aza-indole sulfinyl urea derivative of a JAK2 inhibitor46 (9w) was made readily accessible by displacement of N,N-diisopropyl amine, introducing S(IV) functionality to commonly encountered kinase pharmacophores. Antibiotic analogs of moxifloxacin (9x) and sarafloxacin (9y) were obtained uneventfully by engaging their 1H-pyrrolo[3,4-b]pyridine and piperazine motifs respectfully. Notable site selectivity was observed for evobrutinib, favoring the piperidine over a diaminopyrimidine leading to the sulfinyl derivative 9z in good yield (76%). Lastly, S(IV)-urea amine exchange was employed as a bimolecular linking strategy between a sulfinyl urea analog of celecoxib and an E3 ligase ligand (9aa) that could be leveraged for the development of proteolysis targeting chimeras (PROTACs).
Synthetic applications of S(IV)-urea amine exchange
Sulfinyl urea organocatalysts impart asymmetric control through chiral hydrogen–bonding environments and have been largely restricted to tert-butyl S-substituents21–24. These limitations are due in part to the steric bulk offered by the tert-butyl group, availability of the sulfinamide starting material, and most notably, a lack of synthetic methods to efficiently introduce structural and electronic diversity around the chiral S-center. Although t-BuSF could serve as the chiral template for catalyst design, enantiopure (R)-tert-butyl sulfinamide 13 was employed to prepare sulfinyl urea organocatalysts in a single step. Carbamoylation of sulfinamide 13 affords diversifiable intermediate 14 that was directly treated with amines to give sulfinyl ureas 15–17, improving the yield of 16 by 32% and providing a protecting group-free synthesis of 17—subsequently reducing the overall step count22. The practicality and scalability of this method was demonstrated in the gram-scale preparation of 17 and by replacing isocyanates with widely available amine building blocks.
Sulfonimidoyl urea scope
After establishing the sulfinyl urea amine exchange, our focus shifted to the analogous S(VI) transformation of sulfonimidamides. Two secondary sulfonimidamides, N-aryl 8a and N-alkyl 8b, were chosen to examine the reaction scope due to their differences in structure and tautomerization potential (Fig. 4). In general, sulfonimidoyl ureas are less reactive than their S(IV) counterparts and require elevated temperatures to undergo the desired transformation. A similar set of structurally diverse primary and secondary amines were evaluated under thermal conditions (60–80 °C) in THF and MeCN. It was quickly determined that N-aryl derivative 8a undergoes the exchange more readily at 60 °C while N-alkyl derivative 8b required a slightly more elevated temperature (80 °C). Despite the known stereogenic stability of sulfonimidamides47,48, enantiopurity was assessed for each class of sulfonimidoyl ureas.Fig. 4Reaction scope of N,N-diisopropyl sulfonimidoyl urea amine exchange.All reactions were performed on 0.1–0.25 mmol scales. Isolated yields are reported. Enantiomeric excess (e.e.) determined by chiral HPLC. aTFA (1 eq.) was used as an additive.
Both N-substituted N,N-diisopropyl sulfonimidoyl derivatives (8a and 8b) readily reacted with primary aliphatic amines (10a–10h) in good to excellent yields (72–96%) without impacting enantiopurity (>99% e.e.). Benzylic amines and heterocycle-containing primary amines afforded 10a–10c and 10d–10f respectively. Sterically congested valinol provided sulfonimidoyl urea 10g preferentially via S(VI)-urea amine exchange in 84% yield with no observed reactivity at the less hindered alcohol. An arene bioisostere was introduced as a bicyclopentyl (BCP) unit via amine exchange to give S(VI) derivative 10h in high yield. Aromatic amines exhibited similar reactivity trends to the analogous sulfinyl ureas, therefore the same tactic was employed using TFA as an additive. Even though diminished reactivity was observed, good to high yields (56–85%) were achievable for anilinic substrates 10i–10l with no effect on the S-stereocenter. On the other hand, secondary amines readily exchange with N,N-diisopropyl amine producing 10m and 10n uneventfully while maintaining enantiopurity (>99% e.e.). N-Substituted piperazines 10o–10q, 3-hydroxypyrrolidine 10r, and 3-N-Boc-piperidine 10s all delivered the desired sulfonimidoyl ureas in excellent yields. Venturing further away from flat land chemical space, sp3-rich spirocyclic amines were successfully introduced granting access to 10t–10w, which represent untapped chemical scaffolds.
The sulfonimidoyl urea scope was further expanded to encompass the complexity often encountered in drug discovery programs and to verify late-stage introduction of sulfonimidoyl functionality. Amlodipine, alogliptin, and oseltamivir were efficiently exchanged with N,N-diisopropylamine to afford 10x,
10y, and 10z as their respective sulfonimidoyl urea derivatives in the presence of esters, nitriles, amides, and α,β-unsaturated esters. Additionally, the primary aromatic amine of afatinib’s pharmacophore gave rise to 10aa despite the large o-substituent and weak nucleophilicity. Sarafloxacin and moxifloxacin exhibited exclusive reactivity at the secondary amine over condensation with carboxylic acids or 1,4-additions, providing both 10ab and 10ac in 91% yield and 10ad in 85% yield. These representative pharmaceutical examples highlight the selectivity and utility of the sulfonimidoyl urea amine exchange, revealing opportunities for late-stage diversification and derivatization of clinically optimized scaffolds.
While secondary sulfonimidoyl ureas can be functionalized under mild conditions, tertiary substrates were unreactive at elevated temperatures (120 °C, dioxane) or with the addition of Brønsted and Lewis acids (see Supplementary Information section 1. XIII for additional details). To capture this class of sulfonimidoyl derivatives, S(IV)-urea amine exchange and S-activation of sulfinyl ureas were leveraged (Fig. 5). A practical one-pot procedure was developed as a streamlined approach that mitigates the handling of reactive and less stable intermediates while showcasing the degree of modularity and diversity accessible. Tertiary sulfonimidamide 18 was prepared in enantiopure form from the corresponding tert-butyl sulfoximine in a single step (56% yield) via the sulfonimidoyl chloride intermediate. Alternatively, sulfinyl urea intermediates (9) can be fluorinated to provide isolable bifunctionalized S(VI) electrophiles, as demonstrated by sulfonimidoyl fluoride 19, that can undergo additional functionalization. A minor decrease in enantiopurity ( >99% to 99% e.e.) was observed during the fluorination event for 19, however, the stereospecific addition of an amine and turbo-Grignard reagent afforded 20 and 21 in 87% and 76% yield, respectively.Fig. 5Direct asymmetric functionalization of tert-butyl sulfoximines to sulfonimidoyl ureas.Enantiomeric excess (e.e.) was determined by chiral HPLC. aIsolated yield from tert-butyl sulfoximine via sulfonimidoyl chloride. bIsolated yield from S-activation/flourination of a tert-butyl sulfoximine. cIsolated yield from sulfonimidoyl fluoride 19 using a turbo-amide or turbo-Grignard. dIsolated yield from t-BuSF SuFEx. eYields for each step were not reported.
Synthetic applications
The practical utility and versatility of sulfinyl and sulfonimidoyl urea amine exchanges were further demonstrated in target- and diversity-oriented syntheses of clinically relevant sulfonimidoyl ureas. In two steps, t-BuSF was trifunctionally elaborated in an asymmetric fashion (Fig. 5). An aza-analog of the sulfonylurea antidiabetic drug chloropropamide was prepared in 54% yield from t-BuSF via the trifunctionalization strategy leveraging sulfoximine 22 and n-propyl amine (23). The corresponding S(IV) intermediate 24 was subjected to oxidative amination with t-BuOCl and ammonia providing enantiopure 25 in 63% yield from sulfoximine 22 in a single step. To our knowledge, S-chlorination of secondary sulfinyl ureas has not been reported. The same tactic was applied in a targeted asymmetric synthesis of sulfonimidoyl stereoisomer 1 reported by Eli Lilly15. Starting from t-BuSF, enantiopure sulfoximine 26 was prepared in 74% yield after recrystallization, which was subsequently transformed to 1 in one step (75% yield, > 99% e.e.)—providing an asymmetric route with a decreased step count and improved overall yield. Furthermore, an NLRP3 inhibitor developed by Genentech16 was prepared asymmetrically from sulfoximine 29 that was obtained from t-BuSF in 98% e.e. without recrystallization. Upon S-activation of sulfoximine 29 with TFA, sulfinyl urea 31 was directly exchanged with aniline 30 followed by oxidative amination. This one-step protocol gave rise to sulfonimidoyl urea 32 in 65% yield (45% from t-BuSF) and excellent enantiopurity (98% e.e.), significantly improving the reported 10 step racemic synthesis17 while affording a derivatization platform for future analog development.
One of the most clinically significant sulfonimidoyl compounds to date is Novartis’ NLRP3 inhibitor DFV-890, which is currently under evaluation in six USFDA clinical trials across multiple indications including cancer, heart disease, osteoarthritis, auto-inflammatory syndromes, and COVID-1921. The reported routes to DFV-890 rely on preparative chiral HPLC, isocyanate 33, and sulfonyl to sulfonimidoyl (34) interconversion (Fig. 6A). The requisite sulfonamide 35 is prepared from three different thiazole building blocks (36–38) and sulfur dioxide in varying yields and step counts. Depending on the initial heterocycle chosen, DFV-890 can be prepared in 6–9 steps using at least one protecting group with an overall yield of 0.5–4% and 97.5% e.e. after chiral chromatography.Fig. 6Application of t-BuSF trifunctionalization for the preparation of clinical inhibitor DFV-890 and derivatives.A Analysis of reported synthetic routes for DFV-890. B A medicinal chemistry route for the asymmetric synthesis of DFV-890 derivatives from t-BuSF. C A scalable targeted asymmetric synthesis of DFV-890 as a mock process route. TMEDA = tetramethylethylenediamine, 2-MeTHF = 2-methyltetrahydrofuran.
To address the synthetic drawbacks associated with DFV-890, the trifunctionalization of t-BuSF was applied. Starting from enantiopure (S)-t-BuSF, SuFEx using lithiated thiazole 39 (1st point of diversification) provided tert-butyl sulfoximine 37 with >99% e.e. in 66% yield after recrystallization on gram-scale (Fig. 6B). It is worth noting that the sulfonimidoyl transfer reaction deviated from previously reported conditions39 and was conducted in 2-MeTHF with TMEDA as an additive to aid in dianion formation. Sulfoximine 40 was then subjected to TFA mediated S-activation followed by treatment with an aromatic amine (2nd point of diversification), then oxidative amination with propargyl amine or ammonia (3rd point of diversification) to deliver clickable chemical probe derivatives of DFV-890 41 and 42 in good yields as single stereoisomers, illustrating an advantageous medicinal chemistry route. The same strategy was used in a target-oriented, mock process approach for the gram-scale synthesis of DFV-890 (Fig. 6C). Two different thiazoles (37,
38) were used to prepare gram quantities of enantiopure sulfoximine 40 with no chromatography. S-Activation afforded sulfinyl urea intermediate 43 which was directly converted to DFV-890 via S(IV)-urea amine exchange with aniline 30 followed by in situ oxidative amination providing over one gram of DFV-890 ( >99% e.e.) in 77% yield without the need for chromatographic purification. This modular two-step asymmetric synthesis of DFV-890 reduced the overall synthetic step count (8to 2 steps), improved the yield 10-fold, provided the target compound in enantiopure form, and removed the need for isocyanates, protecting groups, and chromatography.
Combinatorial trifunctionalization of t-BuSF
The modularity, efficiency, and chemical space accessible from t-BuSF were further demonstrated by applying the methods herein to a combinatorial chemistry workflow as a diversity-oriented synthesis platform for S(IV) and S(VI) library generation (Fig. 7A). tert-Butyl sulfoximines (from t-BuSF) serve as the central building block (A) to which the remaining diversity is introduced during sulfinyl urea amine exchange (B) and subsequent S-activation/additions with amines (C) or turbo-Grignards (D). For this compound set, the S-substituent was fixed to phenyl and representatives for building blocks B (maroon), C and D (teal) are shown in Fig. 7A. Individual reactions were set to specific time points (eight total hours, representing a typical workday for a single chemist) and were not independently optimized. Analysis was performed by LC–MS to monitor conversions for yield estimations of target compounds, which were cross-checked by isolation.Fig. 7Applying the trifunctionalization of t-BuSF to a combinatorial workflow.A Combinatorial approach to sulfonimidoyl ureas from t-BuSF. Representative nucleophile building blocks were used and reaction profile analysis was performed by LC–MS. B Structural representatives for the sulfonimidoyl classes obtained from each quadrant. LC–MS analysis legend: Dark green =75–100% yield, 15–60 mg of product; light green = 50–75% yield, 10–45 mg of product; yellow = 25–50% yield, 5–30 mg of product; red = 0–25% yield, 0–15 mg of product.
Starting from tert-butyl sulfoximine A, S-activation was carried out on a gram-scale using t-BuOK in 2-MeTHF at 80 °C for 2 h then partitioned into four quadrants (Q1–Q4) throughout a 48-well reaction block. The S(IV)-urea amine exchange was initiated by the addition of 22 amine building blocks (B). After three hours, the newly formed sulfinyl ureas could be split, purified, or further transformed via S-activation; in this proof-of-concept experiment, 44 unique S(VI) products were targeted. Quadrant 1 was subjected to oxidative amination with t-BuOCl and NH3 to obtain 10 primary sulfonimidamides, while quadrant 2 was designed to give 12 products captured in the sulfonimidoyl urea scope (Fig. 4) as controls for method validation. Quadrant 3 contained a diverse set of 12 secondary and tertiary sulfonimidamides, and quadrant 4 was devoted to the synthesis of sulfoximine derivatives via sulfonimidoyl fluorides and turbo-Grignards D1 and D2.
Estimated yields (with the overall mass of target compounds) after four transformations are color-coded within the 48 well plate according to the legend in Fig. 7A. Two examples from each quadrant were isolated and shown to be in agreement with the estimated yields (Fig. 7B). To our delight, 42 examples out of 44 were estimated to have >50% yield (from building block A). The two examples that fell short of the 50% cut off were Q4 sulfoximines treated with D1 having estimated yields of 40%. Within the course of eight hours, 34 sulfonimidamides and 10 sulfoximines were prepared, including 25 sulfinyl ureas, 34 sulfonimidoyl chlorides, and 5 sulfonimidoyl fluoride intermediates. The chemical and structural diversity created within this proof-of-concept workflow signifies the impact t-BuSF will have in the chemical sciences. With the translation to automated liquid handling coupled with the wide variety of available amine building blocks, it is anticipated that high-throughput variations of this approach will be feasible.
In all, a new functional feature of t-BuSF has been developed resulting in a trifunctional chiral SuFEx reagent platform. The key N,N-diisopropyl urea protecting group of t-BuSF was transformed to an enabling group that induces torsional strain-release with primary and secondary amines for the asymmetric synthesis of structurally diverse sulfinyl and sulfonimidoyl ureas. This reactivity mode allows for selective and efficient carbamoyl derivatization with amines at either the S(IV) or S(VI) stage, providing multiple synthetic route options that negates the need for isocyanates and laborious functional group interconversions. The reaction compatibility was explored using myriad amines ranging in structural complexity offering over seventy sulfinamide, sulfonimidamide, and sulfoximine examples with enantiopurities up to >99% e.e. Scalable one-pot protocols were established for rapid construction of the target sulfur functionality in two steps (five transformations) from t-BuSF that were highlighted in five synthetic applications and successfully applied to a combinatorial chemistry workflow. Most notably, the significant synthetic improvements these methods provide for important clinical candidates and derivatives illustrate the impact that the t-BuSF SuFEx platform will have on the discovery sciences. Additional reactivity modes and activation strategies of t-BuSF and intermediates are currently under investigation and will be reported in due course.
Methods
General procedure 1 (GP-1): synthesis of tert-butyl sulfoximines from t-BuSF
To a 50 mL flame dried round-bottom flask equipped with a magnetic stir bar under argon (balloon) was added aryl bromide (1–1.5 eq.) followed by anhydrous Et2O (to make a 0.1 M solution). The mixture was then cooled to –78 °C and n-BuLi (1 eq., 2.5 M in hexanes) or t-BuLi (1 eq., 1.7 M in pentane) was added dropwise and stirred for 1–4 h (for lithium-halogen exchange). t-BuSF (1 eq.) in Et2O was added dropwise at –78 °C and the reaction mixture was stirred at –78 °C for 1–3 h. Upon completion (checked by TLC and LC–MS) the reaction mixture was quenched with MeOH and saturated aqueous NH4Cl solution. The mixture was extracted with EtOAc (3 times) using a separatory funnel. The combined organic layer was washed with water (2 times) and brine (1 time), dried over anhydrous Na2SO4, filtered, and concentrated under reduced pressure. The crude compound was subjected to chromatographic purification to obtain the desired tert-butyl sulfoximines.
General procedure 2 (GP-2): one pot synthesis of sulfinyl ureas from tert-butyl sulfoximines via base-mediated S-activation/amine exchange
tert-Butyl sulfoximines (1 eq.) were taken in a flame-dried scintillation vial equipped with a stir bar and septum. Dry THF (or 2-MeTHF) was added (to make a 0.3 M solution) followed by addition of t-BuOK (3 eq.) at room temperature and the vial was sealed with a cap and Teflon tape. The mixture was heated at 80 °C for 2 h. The reaction mixture was cooled to –78 °C and a solution of TFA (3 eq.) in 2 mL THF (or 2-MeTHF) was added dropwise to make the final reaction concentration 0.1 M. The desired amine (1 eq.) was added to the reaction mixture at room temperature and stirred for 3 h. After completion (monitored by TLC and LC–MS) the solvent was evaporated below 25 °C using a rotary evaporator. The crude compounds were then triturated with Et2O/hexanes two times and decanted or purified via flash chromatography (silica gel) to obtain pure sulfinamides 9.
General procedure (GP-3): One pot synthesis of sulfinyl ureas from tert-butyl sulfoximines via acid-mediated S-activation followed by amine exchange
To a 2-dram vial containing tert-butyl sulfoximine (1 eq.) equipped with a stir bar, a 40% TFA (5 eq.) solution in DCM was added at room temperature. The reaction mixture was stirred at room temperature for 15 min. After completion, monitored by TLC and LC–MS, 2-MeTHF was added to the reaction mixture (to achieve a 0.1 M concentration) and cooled to –78 °C. t-BuOK (3 eq.) was then added portion-wise, and the reaction mixture warmed to room temperature. Amine (1 eq.) was then added at room temperature and the reaction mixture stirred for 3 h. After completion, monitored by TLC and LC–MS, the solvent was evaporated while maintaining a temperature below 25 °C. The crude compounds were then triturated with Et2O/hexanes two times and decanted or purified via flash chromatography (silica gel) to obtain pure sulfinamides 9.
General procedure (GP-4) for synthesis of tert-butyl sulfinyl ureas
Commercially available tert-butyl sulfinamide 13 (1 eq.) was taken up in dry THF (to make a 0.1 M solution) and cooled to 0 °C. NaH (2.5 eq.) was added portion-wise over 5 min. The mixture was stirred at 0 °C for 10 min and carbamoyl chloride, ClCON(i-Pr)2 (1 eq.), was added and stirred at 0 °C for 1 h. After completion (monitored by TLC), AcOH (1.5 eq.) was added to reaction mixture at –30 °C (neutralization) then warmed to room temperature. The desired amine (1 eq.) was then added and stirred for 3 h. After completion (monitored by TLC and LC–MS), the solvent was evaporated and the crude compound purified by silica gel column chromatography to afford the tert-butyl sulfinyl urea.
General procedure 5 (GP-5): Sulfonimidamide amine exchange
Sulfonimidamide 8a or 8b (1 eq.) was taken up in dry MeCN or THF (1.0 mL) in a screw-capped vial followed by the addition of an amine (1 eq.). The reaction vial was securely capped and sealed with Teflon tape then heated to 60 °C (N-aryl substrates) or 80 °C (N-alkyl substrates) for 6–24 h. After completion (monitored by TLC and LC–MS), the solvent was evaporated and the crude compound purified by flash chromatography (silica gel) to afford the desired sulfonimidamide compound 10.
General procedure 1 (GP-1): synthesis of tert-butyl sulfoximines from t-BuSF
To a 50 mL flame dried round-bottom flask equipped with a magnetic stir bar under argon (balloon) was added aryl bromide (1–1.5 eq.) followed by anhydrous Et2O (to make a 0.1 M solution). The mixture was then cooled to –78 °C and n-BuLi (1 eq., 2.5 M in hexanes) or t-BuLi (1 eq., 1.7 M in pentane) was added dropwise and stirred for 1–4 h (for lithium-halogen exchange). t-BuSF (1 eq.) in Et2O was added dropwise at –78 °C and the reaction mixture was stirred at –78 °C for 1–3 h. Upon completion (checked by TLC and LC–MS) the reaction mixture was quenched with MeOH and saturated aqueous NH4Cl solution. The mixture was extracted with EtOAc (3 times) using a separatory funnel. The combined organic layer was washed with water (2 times) and brine (1 time), dried over anhydrous Na2SO4, filtered, and concentrated under reduced pressure. The crude compound was subjected to chromatographic purification to obtain the desired tert-butyl sulfoximines.
General procedure 2 (GP-2): one pot synthesis of sulfinyl ureas from tert-butyl sulfoximines via base-mediated S-activation/amine exchange
tert-Butyl sulfoximines (1 eq.) were taken in a flame-dried scintillation vial equipped with a stir bar and septum. Dry THF (or 2-MeTHF) was added (to make a 0.3 M solution) followed by addition of t-BuOK (3 eq.) at room temperature and the vial was sealed with a cap and Teflon tape. The mixture was heated at 80 °C for 2 h. The reaction mixture was cooled to –78 °C and a solution of TFA (3 eq.) in 2 mL THF (or 2-MeTHF) was added dropwise to make the final reaction concentration 0.1 M. The desired amine (1 eq.) was added to the reaction mixture at room temperature and stirred for 3 h. After completion (monitored by TLC and LC–MS) the solvent was evaporated below 25 °C using a rotary evaporator. The crude compounds were then triturated with Et2O/hexanes two times and decanted or purified via flash chromatography (silica gel) to obtain pure sulfinamides 9.
General procedure (GP-3): One pot synthesis of sulfinyl ureas from tert-butyl sulfoximines via acid-mediated S-activation followed by amine exchange
To a 2-dram vial containing tert-butyl sulfoximine (1 eq.) equipped with a stir bar, a 40% TFA (5 eq.) solution in DCM was added at room temperature. The reaction mixture was stirred at room temperature for 15 min. After completion, monitored by TLC and LC–MS, 2-MeTHF was added to the reaction mixture (to achieve a 0.1 M concentration) and cooled to –78 °C. t-BuOK (3 eq.) was then added portion-wise, and the reaction mixture warmed to room temperature. Amine (1 eq.) was then added at room temperature and the reaction mixture stirred for 3 h. After completion, monitored by TLC and LC–MS, the solvent was evaporated while maintaining a temperature below 25 °C. The crude compounds were then triturated with Et2O/hexanes two times and decanted or purified via flash chromatography (silica gel) to obtain pure sulfinamides 9.
General procedure (GP-4) for synthesis of tert-butyl sulfinyl ureas
Commercially available tert-butyl sulfinamide 13 (1 eq.) was taken up in dry THF (to make a 0.1 M solution) and cooled to 0 °C. NaH (2.5 eq.) was added portion-wise over 5 min. The mixture was stirred at 0 °C for 10 min and carbamoyl chloride, ClCON(i-Pr)2 (1 eq.), was added and stirred at 0 °C for 1 h. After completion (monitored by TLC), AcOH (1.5 eq.) was added to reaction mixture at –30 °C (neutralization) then warmed to room temperature. The desired amine (1 eq.) was then added and stirred for 3 h. After completion (monitored by TLC and LC–MS), the solvent was evaporated and the crude compound purified by silica gel column chromatography to afford the tert-butyl sulfinyl urea.
General procedure 5 (GP-5): Sulfonimidamide amine exchange
Sulfonimidamide 8a or 8b (1 eq.) was taken up in dry MeCN or THF (1.0 mL) in a screw-capped vial followed by the addition of an amine (1 eq.). The reaction vial was securely capped and sealed with Teflon tape then heated to 60 °C (N-aryl substrates) or 80 °C (N-alkyl substrates) for 6–24 h. After completion (monitored by TLC and LC–MS), the solvent was evaporated and the crude compound purified by flash chromatography (silica gel) to afford the desired sulfonimidamide compound 10.
Supplementary information
Supplementary Information
Peer Review File
Supplementary information
Supplementary Information
Peer Review File |
Title: Myrrh Essential Oil Improves DSS-Induced Colitis by Modulating the MAPK Signaling Pathway: In vitro and in vivo Studies | Body:
Introduction
Colitis is a nonspecific chronic inflammatory disease of the intestine with a high rate of recurrence and cancer of the digestive system.1 Patients with colitis mainly present with symptoms such as diarrhea, mucus in stool, pus, and blood in stool.2 Some studies have reported that approximately 30% of colitis require colectomy.3 Most of the clinical drugs currently used to alleviate the symptoms of colitis are salicylic acid agents, immunosuppressive agents, and biological agents. However, these drugs generally have many adverse effects, leading to easy disease recurrence, are costly, and have poor patient compliance.4 Currently, the research value of “aromatherapy”, which uses essential oil extracts from aromatic plants for therapeutic purposes, is increasing in medicine because of the anti-inflammatory, antibacterial, and antioxidant effects of plant-based oil.5 Therefore, in this study, we considered aromatherapy a potential therapeutic approach for treating colitis.
Myrrh (Commiphora myrrha), obtained from trees or shrubs of the genus Commiphora (Burseraceae), is an aromatic oleogum resin.6 The chemical composition of myrrh is complex and diverse, mainly including resin, gum, essential oil, salts and acids,7 among which essential oil is the characteristic component of myrrh, and is the basic substance that plays the pharmacological function and fragrance in myrrh.8 Its components are complex and have significant anti-inflammatory and analgesic effects.9,10 Myrrh essential oil (MEO) contains active components including Germacrene,11 Elemene, Lindestrene, Furanocalypt-1,3-diene12 and Curzerene. These components have anti-inflammatory,13 analgesic14 and immunostimulatory15 effects; therefore, they are commonly used to treat inflammatory and infectious diseases.16 Holleran reported that myrrh can reduce tumor necrosis factor (TNF)-α and interleukin (IL)-1β in the colon,17 but its mechanism of action is not clear. Thus, in this study, we explored the mechanisms of action of MEO components in the treatment of colitis.
RNA Sequencing (RNA-seq) is commonly used to study possible changes in human disease-associated gene expression changes.18 However, there are no studies related to the transcriptome sequencing of MEO for treating mice with DSS-induced colitis. Thus, we used a transcriptomic approach to explore the mechanism of action of MEO in the treatment of colitis. Traditional network pharmacology neglects the effect of component content,19 therefore, we used weighting coefficients combined with network pharmacology to reduce the effect of content differences in drug components by weighting the drug components and their corresponding oral bioavailability (OB) as two key indicators to explore the mechanisms of action of drugs. Weighted gene co-expression network analysis (WGCNA) constructs gene co-expression networks between genes and phenotypes and clusters them according to gene expression for each sample,20 obtaining important modules that are highly correlated with clinical traits and screening hub genes to explore the molecular mechanism of colitis.21
We based on in vitro cell experiments, MEO was used to treat LPS-induced RAW264.7 macrophages to observe its therapeutic effects. In addition, using animal experiments, analysis of RNA-seq, WGCNA, and weighting coefficients combined with network pharmacology and pharmacodynamic verification, we investigated the mechanisms of action of orally administered MEO in DSS-induced colitis mice.
Materials and Methods
Analysis and Identification of the Components of MEO
Gas Chromatography–Mass Spectrometry (GC–MS) Analysis of MEO
MEO (extracted from dried resin of Commiphora molmol in Somalia, Africa, LOT. 20210220) was purchased from Poli Aromatic Pharmaceutical Technology Co. Ltd. (Shanghai, China). The components in MEO were identified via GC-MS analysis. The GC-MS analysis utilized an HP5-MS column; the carrier gas was high-purity He, shunt ratio was set to 15:1, and injection volume was 1 μL. The temperature of the inlet port was 260 °C, and a programmed temperature increase was used, with an initial temperature of 90 °C, an increase to 175 °C (rate 2 °C/min), and a hold for 5 min, followed by an increase to 210 °C (rate 1 °C/min), and a hold for 10 min. The mass range of the MS was 35–400 amu, the ionization source was EI, and ion source temperature was 230 °C.
MEO Components Identification
GC-MS results were analyzed using Data Analysis software and compared with the NIST database. MEO components were screened according to their matching degree, retention index (RI), and corresponding literature. Formula for the RI19 is shown below (1–1):
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\begin{document}$$RI = 100n + 100\left[{{t_r}\left(M \right) - {t_r}\left(n \right)} \right]/\left[{{t_r}\left({n + 1} \right) - {t_r}\left(n \right)} \right]$$\end{document}
where tr is the retention time, M is the compound to be analyzed, and n and n + 1 are the number of carbon atoms in two adjacent n-alkanes before and after the analyte, respectively, such that tr(n) < tr(M) < tr(n + 1).
Cell Culture
Complete culture medium was prepared by adding 10% Fetal Bovine Serum (FBS) and 1% penicillin-streptomycin to Dulbecco’s modified Eagle’s medium (DMEM) and mixing well. RAW264.7 macrophages (Procell Life Science&Technology Co., Ltd., Wuhan, China) were cultured in complete culture medium and incubated in a cell culture incubator (37 °C, 5% CO2).22
3-(4,5-Dimethylthiazol-2-Yl)-2,5-Diphenyl Tetrazolium Bromide (MTT) Assay to Detect the Effect of MEO on the Viability of RAW264.7 Macrophages
The RAW264.7 cell density was adjusted to 2×105/mL, seeded at 100 µL per well in 96-well plates, and incubated in a cell culture incubator (37 °C, 5% CO2). After 12 h, the culture medium was discarded, and different concentrations of MEO (0.0625, 0.125, 0.25, 0.5, and 1 µg/g) were added to each well, and five replicate wells were set per concentration. The blank and control groups were established with five replicate wells for each group. Following the instructions of the MTT assay kit, the optical density (OD) value was measured at 490 nm with microplate reader (FC, Thermo Fisher Scientific Co., Ltd, Shanghai, China), and the cell viability was calculated to determine the optimal concentration. The calculation formula is as follows (1–2):
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\begin{document}$$Cell\ viability = \left({m - a} \right)/\left({n - a} \right)$$\end{document}
where a is the mean OD of the blank wells, m is the mean OD of the measurement wells, and n is the mean OD of the control wells.
The Levels of Nitric Oxide (NO), TNF-α and IL-1β in RAW264.7 Macrophages
Establishment of Cell Inflammation Model
The cell concentration was set at 2×106/mL, 100 µL per well was seeded in 96-well plates, and put it in at 37 °C, 5% CO2 incubator and incubate overnight. Different concentrations of MEO (0.0625, 0.125, and 0.25 µg/g) were added to each well, and five replicate wells were set for each concentration. After 1 hour, 1µg/mL of Lipopolysaccharides (LPS) was added and incubated overnight to measure the levels of inflammatory factors.23
Assay of NO
The NO assay kit was used according to the manufacturer’s instructions. The cell supernatants (100 µL) were collected and operated according to the manufacturer’s instructions, incubated for 15 min, and the OD value was measured at 550 nm using a microplate reader.
The calculation formula is as follows (1–3):
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\begin{document}$$NO\ content\left({\mu mol/L} \right) = \left({b - a} \right)/\left({c - a} \right) \times d\times N$$\end{document}
where, a is the OD value of the blank wells, b is the OD value of the measurement wells, c is the OD value of the standard wells, d is the concentration of the standard (20 µmol/L), and N is the dilution time (four times).
Determinations of TNF-α and IL-1β
The cell inflammation model was established according to 2.2.2.1 Establishment of cell inflammation model. The cell culture medium was collected in sterile tubes and centrifuged at 2500 rpm for 20 min, and the supernatants were collected. Following the instructions of the TNF-α and IL-1β Enzyme-linked immunosorbent assay (ELISA) kits, the OD value was measured at 450 nm using a microplate reader.
Establishment of the Colitis Mice Model
Specific-pathogen-free (SPF) BALB/c male mice (Chengdu Dashuo Laboratory Animal Co., Ltd., Chengdu, China), weighing 18–22 g and animal license SCXK(Chuan)2020–030. Constant temperature, pressure, and acclimatization for one week. Animal experiments were approved by the Animal Ethics Committee of Shaanxi University of Traditional Chinese Medicine, Xianyang, China (Number: SUCMDC20220530003). The mice were divided into the following four groups (10 mice per group) according to the random number table method: control, model, positive, and MEO groups. The control group was administered drinking water only; the other groups were administered MP Biomedicals 3% dextran sulfate sodium (DSS) (lot number:0216011050) solution instead of water to induce colitis. Drug administration was started on the second day of modeling. Rapeseed oil, purchased from Sichuan Guanghan Oil & Grease Co., Ltd. (Sichuan, China) was gavaged at 0.01 mL/g/body weight in the control group. Mesalazine, purchased from Shanghai Ethypharm Pharmaceutical Co., Ltd. (Shanghai, China) was gavaged at 0.01 mL/g/body weight in the positive group. Rapeseed oil as a carrier for MEO was gavaged at 0.01 mL/g/body weight in the MEO (7.5 mg/kg) groups, and the body weight of the mice was measured daily for 7 days. The disease activity index (DAI) scores were calculated (Table 1).24 At the end of the final administration, the mice were subjected to fasting without water for 24 h. Mice were anesthetized with isoflurane and blood was collected from their eyes. The blood samples were centrifuged (4 °C, 4000 rpm) for 10 min to obtain serum, which was then divided and stored at −20 °C. The mice were euthanized, their colons were dissected, and their natural lengths were recorded.25 One part of the colon was fixed using paraformaldehyde, and the other parts were stored at −80 °C after quick-freezing in liquid nitrogen.26,27Table 1Disease Activity Index (DAI) Score TableScoreWeight Loss Rate (%)Stool ConsistencyBlood in Stool00NormalNegative11–526–10Loose stoolPositive311–15415DiarrheaRectal bleeding
ELISA in Colitis Mice
TNF-α (MM-0132M1, Jiangsu Meimian Industrial Co., Ltd., Yancheng, China) and IL-1β (MM-0040M1, Jiangsu Meimian Industrial Co., Ltd., Yancheng, China) levels in the mouse serum were determined by ELISA. The assays were performed according to the manufacturer’s instructions.
Transcriptome Sequencing of the Colon Tissues of Mice
Total RNA was extracted from the colon tissues, which were stored at −80 °C, of three mice each in the control, model, and MEO groups, and poly(A)-mRNAs were purified. The purified mRNAs were fragmented and reverse-transcribed into double-stranded cDNA, followed by preparation of a double-stranded cDNA library.28 Data analyses were performed using the Omicsmart (https://www.omicsmart.com) application platform with DESeq2 to identify differentially expressed genes (DEG). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed.
WGCNA Analysis
Scale-free networks were constructed based on scale-free topology criteria for differential genes. The dynamic segmentation method was used to identify modules, a correlation analysis of all modules was carried out in pairs, and a heat map was drawn. Correlation analysis was also performed between the expression of each gene and the module eigenvalue. The TNF-α and IL-β data of the correlation groups were established as trait files and correlation analysis was performed using module characteristic values. The modules most correlated with traits and phenotypes were used as target modules for the KEGG enrichment analysis.
Component–Target Network Pharmacological Analyses
Acquisition of MEO Components and Colitis Targets
PubChem (https://pubchem.ncbi.nlm.nih.gov), Index-Calcnet-TargetNet (http://targetnet.scbdd.com/calcnet/calc_text), and Swiss Target Prediction (http://swisstargetprediction.ch) databases were used to identify the relevant active component targets. GeneCards (https://www.genecards.org), Online Mendelian Inheritance in Man (OMIM, https://omim.org), DisGeNET (https://www.disgenet.org), and Comparative Toxicogenomics Database (CTD, http://ctdbase.org) databases were used to search for the term “colitis” to obtain relevant colitis targets, and duplicate targets were removed.
Constructing Component–Target–Disease Interaction and Protein-Protein Interaction (PPI) Network
In the Venny 2.1.0 (https://bioinfogp.cnb.csic.es/tools/venny) platform, the intersection targets of “MEO-related component targets”, “colitis targets”, and “differential gene targets” was obtained and visualized in Cytoscape 3.7.1. The network for “active components-critical target” was constructed using the “differential gene targets” and imported into Cytoscape 3.7.1. The aforementioned intersection targets were imported into the STRING (https://cn.string-db.org) database,29 the source was selected as “Homo sapiens” and adjusted the confidence to hide the free proteins in the network. The PPI results were visualized using Cytoscape 3.7.1. The PPI network was constructed using the key targets, with topological parameter degree> averaged degree values of each node.
Establishing the Weighting Coefficients
MEO extracted from different herbs or purchased from different manufacturers can differ greatly in the content of the active components and their corresponding OBs. OB is crucial for measuring the pharmacokinetic processes and drug-forming properties of drugs. Therefore, we multiplied the relative content of MEO components by their OBs to obtain the weighting coefficient. The weighting coefficient (T) and weighting coefficients were calculated as follows:
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\begin{document}$$T = w\times OB$$\end{document}
where w denotes the relative content of each component;
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\begin{document}$$A = \mathop \sum \limits_{i = 1}^n {T_i}$$\end{document}
where A denotes the weight coefficient of each target and Ti denotes the weighting coefficient of each component included in A.
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\begin{document}$$B = \mathop \sum \limits_{i = 1}^n {A_i}$$\end{document}
where B denotes the weighting coefficient of each pathway and Ai denotes the weight coefficient of each target included in B.
GO and KEGG Pathway Enrichment Analyses
The intersected targets under 1.5.2 were analysed by GO and KEGG using the “clusterProfiler30”, “org.Hs.eg.db31”, and “ggplot232” packages in R to obtain the pathway ranking results. The enriched pathway entries were ranked based on their weighting coefficients.
Molecular Docking
Molecular docking was performed for the key targets CACN, RAS, ERK, and JNK, which were obtained by pathway enrichment. The 3D protein structures were downloaded from the PCSB-PDB (https://www.rcsb.org) database.33 Specific ligands corresponding to key targets were obtained from the DrugBank database (https://go.drugbank.com/), and the 2D structures of the components and specific ligands were downloaded from the PubChem database. We imported the 3D structure of the key target protein as receptors and the 2D structure of the MEO components and the specific ligands as ligands into Discovery Studio (DS) v4.0. The LibDock module34 was used for docking and energy calculations.
Pharmacodynamic Tests
Animal Grouping and Establishment of the Colitis Mice Model
The strains, suppliers and feeding environment of mice was the same as under 2.3 Establishment of colitis mice model. The difference, however, was that BALB/c mice were divided into the following six groups (10 mice per group) based on the random number table method: control, model, positive group, and low-dose, medium-dose, high-dose MEO group. The control group was administered only drinking water, whereas the other groups were administered a 3% DSS solution instead of drinking water to induce colitis. Drug administration was started on the second day of modeling. The control group was gavaged with rapeseed oil at 0.01 mL/g. On the other hand, the low- (3.75 mg/kg), medium- (7.5 mg/kg), and high- (15 mg/kg) dose MEO groups were gavaged with MEO in rapeseed oil at 0.01 mL/g for 7 days and calculated DAI score.
Biological Sample Collection and Processing
At the end of the final administration, the biological sample collection and processing was performed according to the procedure mentioned in 2.3 Establishment of colitis mice model.
TNF-A and IL-1β ELISA Assays
ELISA was performed to determine the levels of TNF-α (MM-0132M1) and IL-1β (MM-0040M1) according to the procedure described in 2.3.1 ELISA in colitis mice.
Hematoxylin-Eosin (HE) Staining
Colonic tissues of each group of mice were collected, dehydrated using an ethanol gradient, and embedded in paraffin. The tissues were stained with hematoxylin, restained with eosin, dehydrated to transparency, and sealed.
Immunohistochemistry
Colon tissues of each mouse group were fixed in 4% paraformaldehyde, washed, dehydrated, immersed in wax, embedded, and sectioned. After dewaxing with xylene, samples were hydrated using an ethanol gradient. Nonimmune normal sheep serum was added to remove nonspecific antigens and the samples were washed with PBS. Then, Primary antibodies were added and the samples were incubated overnight at 4 °C. The following day, samples were incubated at room temperature for 40 min and washed again with PBS. Thereafter, horseradish peroxidase (HRP)-labeled secondary antibodies (rabbit antibodies) were added, and the samples were incubated for 1 h at 37 °C, then washed again with PBS, stained with DAB, restained with hematoxylin, dehydrated using an ethanol gradient, and blocked with xylene after transparent treatment.35
Based on relevant research showing that TNF-α is a pro-inflammatory cytokine produced by various cell types and an important mediator in the inflammatory response and host defence,36 involved in the regulation of the immune system, cell survival signaling pathways, proliferation and regulation of metabolic processes.37 When the level of TNF-α in the body exceeds the normal level, it will disrupt the immune homeostasis of the body, induce and promote the production and release of other inflammatory mediators, amplify the level of inflammatory cascade response, and cause colitis. Cui et al38 detected the expression of TNF-α in DSS-induced colitis mice by immunohistochemistry and found to be highly expressed in the DSS group, whereas TNF-α levels were significantly reduced after administration of resveratrol treatment. When colitis occurs, inflammation cytokines such as TNF-α, IL-1 and IL-6 can cause intestinal fibroblasts,39 neutrophils and macrophages accumulate in the intestines, where fibroblasts will cause intestinal stenosis in patients with colitis. In addition to this, there are several pieces of evidence that TNF-α is associated with both human IBD and murine colitis.40–43 Therefore, we considered using immunohistochemistry to detect the level of inflammatory factor TNF-α in colonic tissues after DSS induction.
Western Blot
The colonic tissues of three mice from each group were weighed. A mixture of RIPA lysate and protease inhibitor was added and the samples were homogenized using a high-speed homogenizer and centrifuged at 4 °C for 5 min at 12000 rpm. Serum protein concentration was measured using a BCA kit.44 Electrophoresis was performed on a sodium dodecyl sulfate-polyacrylamide gel, and at the end of the gel, the proteins were transferred to a PVDF membrane and blocked with 5% skim milk for 1 h. Added the Primary antibodies phosphorylated amino-terminal protein kinase (p-JNK) (Triple Eagle, 1:1000), c-Jun amino-terminal kinase (JNK) (Triple Eagle, 1:3000), phosphorylated extracellular signal-regulated kinase (p-ERK) (Saville, 1:400), and extracellular signal-regulated kinase (ERK) (Abcam, 1:1000), and the internal reference glyceraldehyde-3-phosphate dehydrogenase (GAPDH) (Huaan Bio, 1:5000) then incubated overnight at 4 °C. The membranes were then washed three times with TBST at room temperature, followed by the addition of HRP-labeled secondary antibodies (Jackson, 1:5000), incubated for 30 min at room temperature, washed with TBST, and developed using an ELC chromogen. Proteins were visualized using an imager45 and the relative levels of the target proteins were calculated.
Statistical Analysis
GraphPad Prism 9.0.0 software was used to statistically analyse all data. One-way analysis of variance was used to compare differences among the groups. P< 0.05 was considered statistically different.
Analysis and Identification of the Components of MEO
Gas Chromatography–Mass Spectrometry (GC–MS) Analysis of MEO
MEO (extracted from dried resin of Commiphora molmol in Somalia, Africa, LOT. 20210220) was purchased from Poli Aromatic Pharmaceutical Technology Co. Ltd. (Shanghai, China). The components in MEO were identified via GC-MS analysis. The GC-MS analysis utilized an HP5-MS column; the carrier gas was high-purity He, shunt ratio was set to 15:1, and injection volume was 1 μL. The temperature of the inlet port was 260 °C, and a programmed temperature increase was used, with an initial temperature of 90 °C, an increase to 175 °C (rate 2 °C/min), and a hold for 5 min, followed by an increase to 210 °C (rate 1 °C/min), and a hold for 10 min. The mass range of the MS was 35–400 amu, the ionization source was EI, and ion source temperature was 230 °C.
MEO Components Identification
GC-MS results were analyzed using Data Analysis software and compared with the NIST database. MEO components were screened according to their matching degree, retention index (RI), and corresponding literature. Formula for the RI19 is shown below (1–1):
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\begin{document}$$RI = 100n + 100\left[{{t_r}\left(M \right) - {t_r}\left(n \right)} \right]/\left[{{t_r}\left({n + 1} \right) - {t_r}\left(n \right)} \right]$$\end{document}
where tr is the retention time, M is the compound to be analyzed, and n and n + 1 are the number of carbon atoms in two adjacent n-alkanes before and after the analyte, respectively, such that tr(n) < tr(M) < tr(n + 1).
Gas Chromatography–Mass Spectrometry (GC–MS) Analysis of MEO
MEO (extracted from dried resin of Commiphora molmol in Somalia, Africa, LOT. 20210220) was purchased from Poli Aromatic Pharmaceutical Technology Co. Ltd. (Shanghai, China). The components in MEO were identified via GC-MS analysis. The GC-MS analysis utilized an HP5-MS column; the carrier gas was high-purity He, shunt ratio was set to 15:1, and injection volume was 1 μL. The temperature of the inlet port was 260 °C, and a programmed temperature increase was used, with an initial temperature of 90 °C, an increase to 175 °C (rate 2 °C/min), and a hold for 5 min, followed by an increase to 210 °C (rate 1 °C/min), and a hold for 10 min. The mass range of the MS was 35–400 amu, the ionization source was EI, and ion source temperature was 230 °C.
MEO Components Identification
GC-MS results were analyzed using Data Analysis software and compared with the NIST database. MEO components were screened according to their matching degree, retention index (RI), and corresponding literature. Formula for the RI19 is shown below (1–1):
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\begin{document}$$RI = 100n + 100\left[{{t_r}\left(M \right) - {t_r}\left(n \right)} \right]/\left[{{t_r}\left({n + 1} \right) - {t_r}\left(n \right)} \right]$$\end{document}
where tr is the retention time, M is the compound to be analyzed, and n and n + 1 are the number of carbon atoms in two adjacent n-alkanes before and after the analyte, respectively, such that tr(n) < tr(M) < tr(n + 1).
Cell Culture
Complete culture medium was prepared by adding 10% Fetal Bovine Serum (FBS) and 1% penicillin-streptomycin to Dulbecco’s modified Eagle’s medium (DMEM) and mixing well. RAW264.7 macrophages (Procell Life Science&Technology Co., Ltd., Wuhan, China) were cultured in complete culture medium and incubated in a cell culture incubator (37 °C, 5% CO2).22
3-(4,5-Dimethylthiazol-2-Yl)-2,5-Diphenyl Tetrazolium Bromide (MTT) Assay to Detect the Effect of MEO on the Viability of RAW264.7 Macrophages
The RAW264.7 cell density was adjusted to 2×105/mL, seeded at 100 µL per well in 96-well plates, and incubated in a cell culture incubator (37 °C, 5% CO2). After 12 h, the culture medium was discarded, and different concentrations of MEO (0.0625, 0.125, 0.25, 0.5, and 1 µg/g) were added to each well, and five replicate wells were set per concentration. The blank and control groups were established with five replicate wells for each group. Following the instructions of the MTT assay kit, the optical density (OD) value was measured at 490 nm with microplate reader (FC, Thermo Fisher Scientific Co., Ltd, Shanghai, China), and the cell viability was calculated to determine the optimal concentration. The calculation formula is as follows (1–2):
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\begin{document}$$Cell\ viability = \left({m - a} \right)/\left({n - a} \right)$$\end{document}
where a is the mean OD of the blank wells, m is the mean OD of the measurement wells, and n is the mean OD of the control wells.
The Levels of Nitric Oxide (NO), TNF-α and IL-1β in RAW264.7 Macrophages
Establishment of Cell Inflammation Model
The cell concentration was set at 2×106/mL, 100 µL per well was seeded in 96-well plates, and put it in at 37 °C, 5% CO2 incubator and incubate overnight. Different concentrations of MEO (0.0625, 0.125, and 0.25 µg/g) were added to each well, and five replicate wells were set for each concentration. After 1 hour, 1µg/mL of Lipopolysaccharides (LPS) was added and incubated overnight to measure the levels of inflammatory factors.23
Assay of NO
The NO assay kit was used according to the manufacturer’s instructions. The cell supernatants (100 µL) were collected and operated according to the manufacturer’s instructions, incubated for 15 min, and the OD value was measured at 550 nm using a microplate reader.
The calculation formula is as follows (1–3):
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\begin{document}$$NO\ content\left({\mu mol/L} \right) = \left({b - a} \right)/\left({c - a} \right) \times d\times N$$\end{document}
where, a is the OD value of the blank wells, b is the OD value of the measurement wells, c is the OD value of the standard wells, d is the concentration of the standard (20 µmol/L), and N is the dilution time (four times).
Determinations of TNF-α and IL-1β
The cell inflammation model was established according to 2.2.2.1 Establishment of cell inflammation model. The cell culture medium was collected in sterile tubes and centrifuged at 2500 rpm for 20 min, and the supernatants were collected. Following the instructions of the TNF-α and IL-1β Enzyme-linked immunosorbent assay (ELISA) kits, the OD value was measured at 450 nm using a microplate reader.
3-(4,5-Dimethylthiazol-2-Yl)-2,5-Diphenyl Tetrazolium Bromide (MTT) Assay to Detect the Effect of MEO on the Viability of RAW264.7 Macrophages
The RAW264.7 cell density was adjusted to 2×105/mL, seeded at 100 µL per well in 96-well plates, and incubated in a cell culture incubator (37 °C, 5% CO2). After 12 h, the culture medium was discarded, and different concentrations of MEO (0.0625, 0.125, 0.25, 0.5, and 1 µg/g) were added to each well, and five replicate wells were set per concentration. The blank and control groups were established with five replicate wells for each group. Following the instructions of the MTT assay kit, the optical density (OD) value was measured at 490 nm with microplate reader (FC, Thermo Fisher Scientific Co., Ltd, Shanghai, China), and the cell viability was calculated to determine the optimal concentration. The calculation formula is as follows (1–2):
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\begin{document}$$Cell\ viability = \left({m - a} \right)/\left({n - a} \right)$$\end{document}
where a is the mean OD of the blank wells, m is the mean OD of the measurement wells, and n is the mean OD of the control wells.
The Levels of Nitric Oxide (NO), TNF-α and IL-1β in RAW264.7 Macrophages
Establishment of Cell Inflammation Model
The cell concentration was set at 2×106/mL, 100 µL per well was seeded in 96-well plates, and put it in at 37 °C, 5% CO2 incubator and incubate overnight. Different concentrations of MEO (0.0625, 0.125, and 0.25 µg/g) were added to each well, and five replicate wells were set for each concentration. After 1 hour, 1µg/mL of Lipopolysaccharides (LPS) was added and incubated overnight to measure the levels of inflammatory factors.23
Assay of NO
The NO assay kit was used according to the manufacturer’s instructions. The cell supernatants (100 µL) were collected and operated according to the manufacturer’s instructions, incubated for 15 min, and the OD value was measured at 550 nm using a microplate reader.
The calculation formula is as follows (1–3):
(1-3)\documentclass[12pt]{minimal}
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\usepackage{amsfonts}
\usepackage{amssymb}
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\DeclareSymbolFont{linotext}{T1}{linotext}{m}{n}
\DeclareSymbolFontAlphabet{\mathLINOTEXT}{linotext}
\begin{document}$$NO\ content\left({\mu mol/L} \right) = \left({b - a} \right)/\left({c - a} \right) \times d\times N$$\end{document}
where, a is the OD value of the blank wells, b is the OD value of the measurement wells, c is the OD value of the standard wells, d is the concentration of the standard (20 µmol/L), and N is the dilution time (four times).
Determinations of TNF-α and IL-1β
The cell inflammation model was established according to 2.2.2.1 Establishment of cell inflammation model. The cell culture medium was collected in sterile tubes and centrifuged at 2500 rpm for 20 min, and the supernatants were collected. Following the instructions of the TNF-α and IL-1β Enzyme-linked immunosorbent assay (ELISA) kits, the OD value was measured at 450 nm using a microplate reader.
Establishment of Cell Inflammation Model
The cell concentration was set at 2×106/mL, 100 µL per well was seeded in 96-well plates, and put it in at 37 °C, 5% CO2 incubator and incubate overnight. Different concentrations of MEO (0.0625, 0.125, and 0.25 µg/g) were added to each well, and five replicate wells were set for each concentration. After 1 hour, 1µg/mL of Lipopolysaccharides (LPS) was added and incubated overnight to measure the levels of inflammatory factors.23
Assay of NO
The NO assay kit was used according to the manufacturer’s instructions. The cell supernatants (100 µL) were collected and operated according to the manufacturer’s instructions, incubated for 15 min, and the OD value was measured at 550 nm using a microplate reader.
The calculation formula is as follows (1–3):
(1-3)\documentclass[12pt]{minimal}
\usepackage{wasysym}
\usepackage[substack]{amsmath}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage[mathscr]{eucal}
\usepackage{mathrsfs}
\DeclareFontFamily{T1}{linotext}{}
\DeclareFontShape{T1}{linotext}{m}{n} {linotext }{}
\DeclareSymbolFont{linotext}{T1}{linotext}{m}{n}
\DeclareSymbolFontAlphabet{\mathLINOTEXT}{linotext}
\begin{document}$$NO\ content\left({\mu mol/L} \right) = \left({b - a} \right)/\left({c - a} \right) \times d\times N$$\end{document}
where, a is the OD value of the blank wells, b is the OD value of the measurement wells, c is the OD value of the standard wells, d is the concentration of the standard (20 µmol/L), and N is the dilution time (four times).
Determinations of TNF-α and IL-1β
The cell inflammation model was established according to 2.2.2.1 Establishment of cell inflammation model. The cell culture medium was collected in sterile tubes and centrifuged at 2500 rpm for 20 min, and the supernatants were collected. Following the instructions of the TNF-α and IL-1β Enzyme-linked immunosorbent assay (ELISA) kits, the OD value was measured at 450 nm using a microplate reader.
Establishment of the Colitis Mice Model
Specific-pathogen-free (SPF) BALB/c male mice (Chengdu Dashuo Laboratory Animal Co., Ltd., Chengdu, China), weighing 18–22 g and animal license SCXK(Chuan)2020–030. Constant temperature, pressure, and acclimatization for one week. Animal experiments were approved by the Animal Ethics Committee of Shaanxi University of Traditional Chinese Medicine, Xianyang, China (Number: SUCMDC20220530003). The mice were divided into the following four groups (10 mice per group) according to the random number table method: control, model, positive, and MEO groups. The control group was administered drinking water only; the other groups were administered MP Biomedicals 3% dextran sulfate sodium (DSS) (lot number:0216011050) solution instead of water to induce colitis. Drug administration was started on the second day of modeling. Rapeseed oil, purchased from Sichuan Guanghan Oil & Grease Co., Ltd. (Sichuan, China) was gavaged at 0.01 mL/g/body weight in the control group. Mesalazine, purchased from Shanghai Ethypharm Pharmaceutical Co., Ltd. (Shanghai, China) was gavaged at 0.01 mL/g/body weight in the positive group. Rapeseed oil as a carrier for MEO was gavaged at 0.01 mL/g/body weight in the MEO (7.5 mg/kg) groups, and the body weight of the mice was measured daily for 7 days. The disease activity index (DAI) scores were calculated (Table 1).24 At the end of the final administration, the mice were subjected to fasting without water for 24 h. Mice were anesthetized with isoflurane and blood was collected from their eyes. The blood samples were centrifuged (4 °C, 4000 rpm) for 10 min to obtain serum, which was then divided and stored at −20 °C. The mice were euthanized, their colons were dissected, and their natural lengths were recorded.25 One part of the colon was fixed using paraformaldehyde, and the other parts were stored at −80 °C after quick-freezing in liquid nitrogen.26,27Table 1Disease Activity Index (DAI) Score TableScoreWeight Loss Rate (%)Stool ConsistencyBlood in Stool00NormalNegative11–526–10Loose stoolPositive311–15415DiarrheaRectal bleeding
ELISA in Colitis Mice
TNF-α (MM-0132M1, Jiangsu Meimian Industrial Co., Ltd., Yancheng, China) and IL-1β (MM-0040M1, Jiangsu Meimian Industrial Co., Ltd., Yancheng, China) levels in the mouse serum were determined by ELISA. The assays were performed according to the manufacturer’s instructions.
Transcriptome Sequencing of the Colon Tissues of Mice
Total RNA was extracted from the colon tissues, which were stored at −80 °C, of three mice each in the control, model, and MEO groups, and poly(A)-mRNAs were purified. The purified mRNAs were fragmented and reverse-transcribed into double-stranded cDNA, followed by preparation of a double-stranded cDNA library.28 Data analyses were performed using the Omicsmart (https://www.omicsmart.com) application platform with DESeq2 to identify differentially expressed genes (DEG). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed.
WGCNA Analysis
Scale-free networks were constructed based on scale-free topology criteria for differential genes. The dynamic segmentation method was used to identify modules, a correlation analysis of all modules was carried out in pairs, and a heat map was drawn. Correlation analysis was also performed between the expression of each gene and the module eigenvalue. The TNF-α and IL-β data of the correlation groups were established as trait files and correlation analysis was performed using module characteristic values. The modules most correlated with traits and phenotypes were used as target modules for the KEGG enrichment analysis.
ELISA in Colitis Mice
TNF-α (MM-0132M1, Jiangsu Meimian Industrial Co., Ltd., Yancheng, China) and IL-1β (MM-0040M1, Jiangsu Meimian Industrial Co., Ltd., Yancheng, China) levels in the mouse serum were determined by ELISA. The assays were performed according to the manufacturer’s instructions.
Transcriptome Sequencing of the Colon Tissues of Mice
Total RNA was extracted from the colon tissues, which were stored at −80 °C, of three mice each in the control, model, and MEO groups, and poly(A)-mRNAs were purified. The purified mRNAs were fragmented and reverse-transcribed into double-stranded cDNA, followed by preparation of a double-stranded cDNA library.28 Data analyses were performed using the Omicsmart (https://www.omicsmart.com) application platform with DESeq2 to identify differentially expressed genes (DEG). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed.
WGCNA Analysis
Scale-free networks were constructed based on scale-free topology criteria for differential genes. The dynamic segmentation method was used to identify modules, a correlation analysis of all modules was carried out in pairs, and a heat map was drawn. Correlation analysis was also performed between the expression of each gene and the module eigenvalue. The TNF-α and IL-β data of the correlation groups were established as trait files and correlation analysis was performed using module characteristic values. The modules most correlated with traits and phenotypes were used as target modules for the KEGG enrichment analysis.
Component–Target Network Pharmacological Analyses
Acquisition of MEO Components and Colitis Targets
PubChem (https://pubchem.ncbi.nlm.nih.gov), Index-Calcnet-TargetNet (http://targetnet.scbdd.com/calcnet/calc_text), and Swiss Target Prediction (http://swisstargetprediction.ch) databases were used to identify the relevant active component targets. GeneCards (https://www.genecards.org), Online Mendelian Inheritance in Man (OMIM, https://omim.org), DisGeNET (https://www.disgenet.org), and Comparative Toxicogenomics Database (CTD, http://ctdbase.org) databases were used to search for the term “colitis” to obtain relevant colitis targets, and duplicate targets were removed.
Constructing Component–Target–Disease Interaction and Protein-Protein Interaction (PPI) Network
In the Venny 2.1.0 (https://bioinfogp.cnb.csic.es/tools/venny) platform, the intersection targets of “MEO-related component targets”, “colitis targets”, and “differential gene targets” was obtained and visualized in Cytoscape 3.7.1. The network for “active components-critical target” was constructed using the “differential gene targets” and imported into Cytoscape 3.7.1. The aforementioned intersection targets were imported into the STRING (https://cn.string-db.org) database,29 the source was selected as “Homo sapiens” and adjusted the confidence to hide the free proteins in the network. The PPI results were visualized using Cytoscape 3.7.1. The PPI network was constructed using the key targets, with topological parameter degree> averaged degree values of each node.
Establishing the Weighting Coefficients
MEO extracted from different herbs or purchased from different manufacturers can differ greatly in the content of the active components and their corresponding OBs. OB is crucial for measuring the pharmacokinetic processes and drug-forming properties of drugs. Therefore, we multiplied the relative content of MEO components by their OBs to obtain the weighting coefficient. The weighting coefficient (T) and weighting coefficients were calculated as follows:
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\begin{document}$$T = w\times OB$$\end{document}
where w denotes the relative content of each component;
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where A denotes the weight coefficient of each target and Ti denotes the weighting coefficient of each component included in A.
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\begin{document}$$B = \mathop \sum \limits_{i = 1}^n {A_i}$$\end{document}
where B denotes the weighting coefficient of each pathway and Ai denotes the weight coefficient of each target included in B.
GO and KEGG Pathway Enrichment Analyses
The intersected targets under 1.5.2 were analysed by GO and KEGG using the “clusterProfiler30”, “org.Hs.eg.db31”, and “ggplot232” packages in R to obtain the pathway ranking results. The enriched pathway entries were ranked based on their weighting coefficients.
Molecular Docking
Molecular docking was performed for the key targets CACN, RAS, ERK, and JNK, which were obtained by pathway enrichment. The 3D protein structures were downloaded from the PCSB-PDB (https://www.rcsb.org) database.33 Specific ligands corresponding to key targets were obtained from the DrugBank database (https://go.drugbank.com/), and the 2D structures of the components and specific ligands were downloaded from the PubChem database. We imported the 3D structure of the key target protein as receptors and the 2D structure of the MEO components and the specific ligands as ligands into Discovery Studio (DS) v4.0. The LibDock module34 was used for docking and energy calculations.
Acquisition of MEO Components and Colitis Targets
PubChem (https://pubchem.ncbi.nlm.nih.gov), Index-Calcnet-TargetNet (http://targetnet.scbdd.com/calcnet/calc_text), and Swiss Target Prediction (http://swisstargetprediction.ch) databases were used to identify the relevant active component targets. GeneCards (https://www.genecards.org), Online Mendelian Inheritance in Man (OMIM, https://omim.org), DisGeNET (https://www.disgenet.org), and Comparative Toxicogenomics Database (CTD, http://ctdbase.org) databases were used to search for the term “colitis” to obtain relevant colitis targets, and duplicate targets were removed.
Constructing Component–Target–Disease Interaction and Protein-Protein Interaction (PPI) Network
In the Venny 2.1.0 (https://bioinfogp.cnb.csic.es/tools/venny) platform, the intersection targets of “MEO-related component targets”, “colitis targets”, and “differential gene targets” was obtained and visualized in Cytoscape 3.7.1. The network for “active components-critical target” was constructed using the “differential gene targets” and imported into Cytoscape 3.7.1. The aforementioned intersection targets were imported into the STRING (https://cn.string-db.org) database,29 the source was selected as “Homo sapiens” and adjusted the confidence to hide the free proteins in the network. The PPI results were visualized using Cytoscape 3.7.1. The PPI network was constructed using the key targets, with topological parameter degree> averaged degree values of each node.
Establishing the Weighting Coefficients
MEO extracted from different herbs or purchased from different manufacturers can differ greatly in the content of the active components and their corresponding OBs. OB is crucial for measuring the pharmacokinetic processes and drug-forming properties of drugs. Therefore, we multiplied the relative content of MEO components by their OBs to obtain the weighting coefficient. The weighting coefficient (T) and weighting coefficients were calculated as follows:
(1-4)\documentclass[12pt]{minimal}
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\begin{document}$$T = w\times OB$$\end{document}
where w denotes the relative content of each component;
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\begin{document}$$A = \mathop \sum \limits_{i = 1}^n {T_i}$$\end{document}
where A denotes the weight coefficient of each target and Ti denotes the weighting coefficient of each component included in A.
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\begin{document}$$B = \mathop \sum \limits_{i = 1}^n {A_i}$$\end{document}
where B denotes the weighting coefficient of each pathway and Ai denotes the weight coefficient of each target included in B.
GO and KEGG Pathway Enrichment Analyses
The intersected targets under 1.5.2 were analysed by GO and KEGG using the “clusterProfiler30”, “org.Hs.eg.db31”, and “ggplot232” packages in R to obtain the pathway ranking results. The enriched pathway entries were ranked based on their weighting coefficients.
Molecular Docking
Molecular docking was performed for the key targets CACN, RAS, ERK, and JNK, which were obtained by pathway enrichment. The 3D protein structures were downloaded from the PCSB-PDB (https://www.rcsb.org) database.33 Specific ligands corresponding to key targets were obtained from the DrugBank database (https://go.drugbank.com/), and the 2D structures of the components and specific ligands were downloaded from the PubChem database. We imported the 3D structure of the key target protein as receptors and the 2D structure of the MEO components and the specific ligands as ligands into Discovery Studio (DS) v4.0. The LibDock module34 was used for docking and energy calculations.
Pharmacodynamic Tests
Animal Grouping and Establishment of the Colitis Mice Model
The strains, suppliers and feeding environment of mice was the same as under 2.3 Establishment of colitis mice model. The difference, however, was that BALB/c mice were divided into the following six groups (10 mice per group) based on the random number table method: control, model, positive group, and low-dose, medium-dose, high-dose MEO group. The control group was administered only drinking water, whereas the other groups were administered a 3% DSS solution instead of drinking water to induce colitis. Drug administration was started on the second day of modeling. The control group was gavaged with rapeseed oil at 0.01 mL/g. On the other hand, the low- (3.75 mg/kg), medium- (7.5 mg/kg), and high- (15 mg/kg) dose MEO groups were gavaged with MEO in rapeseed oil at 0.01 mL/g for 7 days and calculated DAI score.
Biological Sample Collection and Processing
At the end of the final administration, the biological sample collection and processing was performed according to the procedure mentioned in 2.3 Establishment of colitis mice model.
TNF-A and IL-1β ELISA Assays
ELISA was performed to determine the levels of TNF-α (MM-0132M1) and IL-1β (MM-0040M1) according to the procedure described in 2.3.1 ELISA in colitis mice.
Hematoxylin-Eosin (HE) Staining
Colonic tissues of each group of mice were collected, dehydrated using an ethanol gradient, and embedded in paraffin. The tissues were stained with hematoxylin, restained with eosin, dehydrated to transparency, and sealed.
Immunohistochemistry
Colon tissues of each mouse group were fixed in 4% paraformaldehyde, washed, dehydrated, immersed in wax, embedded, and sectioned. After dewaxing with xylene, samples were hydrated using an ethanol gradient. Nonimmune normal sheep serum was added to remove nonspecific antigens and the samples were washed with PBS. Then, Primary antibodies were added and the samples were incubated overnight at 4 °C. The following day, samples were incubated at room temperature for 40 min and washed again with PBS. Thereafter, horseradish peroxidase (HRP)-labeled secondary antibodies (rabbit antibodies) were added, and the samples were incubated for 1 h at 37 °C, then washed again with PBS, stained with DAB, restained with hematoxylin, dehydrated using an ethanol gradient, and blocked with xylene after transparent treatment.35
Based on relevant research showing that TNF-α is a pro-inflammatory cytokine produced by various cell types and an important mediator in the inflammatory response and host defence,36 involved in the regulation of the immune system, cell survival signaling pathways, proliferation and regulation of metabolic processes.37 When the level of TNF-α in the body exceeds the normal level, it will disrupt the immune homeostasis of the body, induce and promote the production and release of other inflammatory mediators, amplify the level of inflammatory cascade response, and cause colitis. Cui et al38 detected the expression of TNF-α in DSS-induced colitis mice by immunohistochemistry and found to be highly expressed in the DSS group, whereas TNF-α levels were significantly reduced after administration of resveratrol treatment. When colitis occurs, inflammation cytokines such as TNF-α, IL-1 and IL-6 can cause intestinal fibroblasts,39 neutrophils and macrophages accumulate in the intestines, where fibroblasts will cause intestinal stenosis in patients with colitis. In addition to this, there are several pieces of evidence that TNF-α is associated with both human IBD and murine colitis.40–43 Therefore, we considered using immunohistochemistry to detect the level of inflammatory factor TNF-α in colonic tissues after DSS induction.
Western Blot
The colonic tissues of three mice from each group were weighed. A mixture of RIPA lysate and protease inhibitor was added and the samples were homogenized using a high-speed homogenizer and centrifuged at 4 °C for 5 min at 12000 rpm. Serum protein concentration was measured using a BCA kit.44 Electrophoresis was performed on a sodium dodecyl sulfate-polyacrylamide gel, and at the end of the gel, the proteins were transferred to a PVDF membrane and blocked with 5% skim milk for 1 h. Added the Primary antibodies phosphorylated amino-terminal protein kinase (p-JNK) (Triple Eagle, 1:1000), c-Jun amino-terminal kinase (JNK) (Triple Eagle, 1:3000), phosphorylated extracellular signal-regulated kinase (p-ERK) (Saville, 1:400), and extracellular signal-regulated kinase (ERK) (Abcam, 1:1000), and the internal reference glyceraldehyde-3-phosphate dehydrogenase (GAPDH) (Huaan Bio, 1:5000) then incubated overnight at 4 °C. The membranes were then washed three times with TBST at room temperature, followed by the addition of HRP-labeled secondary antibodies (Jackson, 1:5000), incubated for 30 min at room temperature, washed with TBST, and developed using an ELC chromogen. Proteins were visualized using an imager45 and the relative levels of the target proteins were calculated.
Animal Grouping and Establishment of the Colitis Mice Model
The strains, suppliers and feeding environment of mice was the same as under 2.3 Establishment of colitis mice model. The difference, however, was that BALB/c mice were divided into the following six groups (10 mice per group) based on the random number table method: control, model, positive group, and low-dose, medium-dose, high-dose MEO group. The control group was administered only drinking water, whereas the other groups were administered a 3% DSS solution instead of drinking water to induce colitis. Drug administration was started on the second day of modeling. The control group was gavaged with rapeseed oil at 0.01 mL/g. On the other hand, the low- (3.75 mg/kg), medium- (7.5 mg/kg), and high- (15 mg/kg) dose MEO groups were gavaged with MEO in rapeseed oil at 0.01 mL/g for 7 days and calculated DAI score.
Biological Sample Collection and Processing
At the end of the final administration, the biological sample collection and processing was performed according to the procedure mentioned in 2.3 Establishment of colitis mice model.
TNF-A and IL-1β ELISA Assays
ELISA was performed to determine the levels of TNF-α (MM-0132M1) and IL-1β (MM-0040M1) according to the procedure described in 2.3.1 ELISA in colitis mice.
Hematoxylin-Eosin (HE) Staining
Colonic tissues of each group of mice were collected, dehydrated using an ethanol gradient, and embedded in paraffin. The tissues were stained with hematoxylin, restained with eosin, dehydrated to transparency, and sealed.
Immunohistochemistry
Colon tissues of each mouse group were fixed in 4% paraformaldehyde, washed, dehydrated, immersed in wax, embedded, and sectioned. After dewaxing with xylene, samples were hydrated using an ethanol gradient. Nonimmune normal sheep serum was added to remove nonspecific antigens and the samples were washed with PBS. Then, Primary antibodies were added and the samples were incubated overnight at 4 °C. The following day, samples were incubated at room temperature for 40 min and washed again with PBS. Thereafter, horseradish peroxidase (HRP)-labeled secondary antibodies (rabbit antibodies) were added, and the samples were incubated for 1 h at 37 °C, then washed again with PBS, stained with DAB, restained with hematoxylin, dehydrated using an ethanol gradient, and blocked with xylene after transparent treatment.35
Based on relevant research showing that TNF-α is a pro-inflammatory cytokine produced by various cell types and an important mediator in the inflammatory response and host defence,36 involved in the regulation of the immune system, cell survival signaling pathways, proliferation and regulation of metabolic processes.37 When the level of TNF-α in the body exceeds the normal level, it will disrupt the immune homeostasis of the body, induce and promote the production and release of other inflammatory mediators, amplify the level of inflammatory cascade response, and cause colitis. Cui et al38 detected the expression of TNF-α in DSS-induced colitis mice by immunohistochemistry and found to be highly expressed in the DSS group, whereas TNF-α levels were significantly reduced after administration of resveratrol treatment. When colitis occurs, inflammation cytokines such as TNF-α, IL-1 and IL-6 can cause intestinal fibroblasts,39 neutrophils and macrophages accumulate in the intestines, where fibroblasts will cause intestinal stenosis in patients with colitis. In addition to this, there are several pieces of evidence that TNF-α is associated with both human IBD and murine colitis.40–43 Therefore, we considered using immunohistochemistry to detect the level of inflammatory factor TNF-α in colonic tissues after DSS induction.
Western Blot
The colonic tissues of three mice from each group were weighed. A mixture of RIPA lysate and protease inhibitor was added and the samples were homogenized using a high-speed homogenizer and centrifuged at 4 °C for 5 min at 12000 rpm. Serum protein concentration was measured using a BCA kit.44 Electrophoresis was performed on a sodium dodecyl sulfate-polyacrylamide gel, and at the end of the gel, the proteins were transferred to a PVDF membrane and blocked with 5% skim milk for 1 h. Added the Primary antibodies phosphorylated amino-terminal protein kinase (p-JNK) (Triple Eagle, 1:1000), c-Jun amino-terminal kinase (JNK) (Triple Eagle, 1:3000), phosphorylated extracellular signal-regulated kinase (p-ERK) (Saville, 1:400), and extracellular signal-regulated kinase (ERK) (Abcam, 1:1000), and the internal reference glyceraldehyde-3-phosphate dehydrogenase (GAPDH) (Huaan Bio, 1:5000) then incubated overnight at 4 °C. The membranes were then washed three times with TBST at room temperature, followed by the addition of HRP-labeled secondary antibodies (Jackson, 1:5000), incubated for 30 min at room temperature, washed with TBST, and developed using an ELC chromogen. Proteins were visualized using an imager45 and the relative levels of the target proteins were calculated.
Statistical Analysis
GraphPad Prism 9.0.0 software was used to statistically analyse all data. One-way analysis of variance was used to compare differences among the groups. P< 0.05 was considered statistically different.
Results
Results of Analysis and Identification of MEO Components
The ion mass spectra of MEO are shown in Figure 1A, and the data were matched with the NIST database, and 23 active components were screened based on their matching degree. The specific components are listed in Table 2. The structures of the components are shown in Figure 1B.Table 2Qualitative Results of the Components in MEO by GC-MSNO.Library/IDCASRIPct Total1delta-EIemene20307–84-01337.920.9962Isolongifolene1135–66-61389.596.5983beta-Elemene515–13-91395.237.7154Longifolene475–20-71406.410.2195gamma-Maaliene20071–49-21419.550.6906Germacrene B15423–57-11433.490.7787gamma-Selinene515–17-31474.521.8618gamma-Muurolene30021–74-01480.761.9449(-)-beta-Selinene17066–67-01485.891.51910Curzerene17910–09-71502.4917.02811Eremophilene10219–75-71506.701.09712(+)-delta-Cadinene483–76-11523.240.39013(-)-alpha-Gurjunene489–40-71555.855.20214Menthofuran494–90-61578.940.84315Furanoeudesma 1.3-diene87605–93-41577.9221.98416Lindestrene2221–88-71578.789.22317Atractylone6989–21-51581.878.89418Germacrone6902–91-61588.420.705194,4’-Dimethoxybiphenyl2132–80-11670.737.488207-Acetoxy-p-mentha-1-ene-3-one56248–42-11766.740.360214-Methoxybenzhydrol720–44-51773.142.382221-Acetyl-4,6,8-trimethylazulene834–97-91866.130.51823(4aS,10aS)-4a-Methyl-3,4,4a,9,10,10a-hexahydrophenanthren-1(2H)-one62318–99-41952.310.752
Figure 1GC-MS chromatogram of MEO and the structure of its components. (A) Total ion flow map of MEO. (B) Structures of 23 components in MEO.
The Anti-Inflammatory Effect of MEO on RAW264.7 Macrophages
Effect of MEO on Cell Viability
As shown in Figure 2A, when the concentration of MEO was 0.0625, 0.125, and 0.25 µg/g, the cell survival rate was above 95%, indicating no adverse effect on cell viability. Therefore, the selected concentrations were used for NO, TNF-α, and IL-1β experiments.
Figure 2The effect of MEO on cell viability in RAW264.7 macrophages. (A) Cell viability was measured by MTT cell viability assay. (B) Effect of MEO on NO production in LPS-induced RAW264.7 macrophages. (C) Effect of MEO on TNF-α production in RAW264.7. (D) Effect of MEO on IL-1β production in RAW264.7.Notes: ##P< 0.01 vs control group. **P< 0.01 vs model group.
Effect of MEO on the Levels of NO, TNF-α, and IL-1β in LPS-Induced RAW264.7 Macrophages
As shown in Figure 2B and D, compared with the control group, the NO, TNF-α, and IL-1β levels in the cells of the model group were significantly higher (P< 0.01). Compared to the model group, the levels of NO, TNF-α, and IL-1β at the three concentrations of MEO were significantly decreased (P< 0.01).
DAI, Colon Morphology and Serum Levels of TNF-α and IL-1β in Colitis Mice
The body weight of mice in the control group gradually increased. The model and MEO groups of mice had diluted feces on the third day with severe weight loss after modeling and blood in stool on the fourth day. However, the MEO group showed a gradual increase in body weight starting on day 5 and improved stool properties. Figure 3A and B illustrates the changes in DAI scores and body weights of the mice in each group. Images of the colon of each mouse group are shown in Figure 3C, and the differences in colon length are shown in Figure 3D. We observed that the colons of mice in the MEO group were longer and showed no mucosal congestion or swelling. Therefore, MEO effectively relieved colitis in mice.
Figure 3The DAI, colon morphology and serum levels of TNF-α and IL-1β in colitis mice. (A) DAI scores. (B) Changes in the body weight in each group. (C) Morphological analysis of the colon in each group. (D) Differences in colon length in each mice group. (E) The levels of TNF-α in colon tissues of colitis mice. (F) The levels of IL-1β in colon tissues of colitis mice.Notes: ##P< 0.01 vs control group. *P< 0.05, **P< 0.01 vs model group.
The serum TNF-α and IL-1β levels were significantly higher in the model group (P< 0.0001). In contrast, the levels of TNF-α and IL-1β in the MEO group decreased to different degrees (P< 0.001). The results are shown in Figure 3E and F).
Results of RNA-Seq in Colitis Mice
Sequencing results were analyzed using the Omicsmart platform. The parameters were set as follows: false discovery rate (FDR) ≤ 0.05, |log2FC| = 1. Detected DEGs 3021 A total of 1688 upregulated and 1333 downregulated genes. The sample clustering (PCA) plot is shown in Figure 4A, differential gene volcano plot is shown in Figure 4B, and differential gene heat map is shown in Figure 4C. KEGG and GO enrichment analyses of the identified DEGs were performed. As shown in Figure 4D, 388 KEGG pathways were enriched, including the MAPK, PI3K-Akt, and P53 signaling pathway. The GO enrichment results are shown in Figure 4E.
Figure 4Results of RNA-seq analysis in colitis mice with MEO. (A) PCA graph. (B) Volcano graph. (C) Heat map of DEGs. (D) KEGG enrichment results. (E) GO enrichment results.
WGCNA Analysis Results
A scale-free network with a soft threshold of eight is shown in Figure 5A. The left figure shows the scale-free network fitting index R2 corresponding to different soft thresholds; the red horizontal line represents the correlation coefficient of 0.9, and the right figure represents the average network connectivity corresponding to each β value. A total of 19 gene modules were identified using the dynamic cutting method (Figure 5B). The number of genes contained in each gene module is shown in Figure 5C, among which the blue module contained the largest number of genes, 5261 in total; the orange module contained the least number of genes, 62 in total; and the grey module indicates that the 25 genes did not aggregate into any other modules. The correlation between the two modules is shown in Figure 5D, and that between gene expression and modules is shown in Figure 5E. The correlation between traits and modules is shown in Figure 5F, in which the modules with highly positive correlations with the traits is blue modules, and the gene of the blue module was imported into the R software. KEGG enrichment analysis was performed, and 336 pathways were found to be enriched; the specific pathway results are shown in Figure 5G. Among them, the MAPK signaling pathway is located in the sixth position, which indicates that this pathway is important for the treatment of colitis with MEO.
Figure 5Results of WGCNA. (A) Selection of the soft threshold value. (B) Gene clustering tree and module division. (C) Gene number distribution in each module. (D) Heat map for correlation analysis between two modules. (E) Heat map of gene expression and inter-module correlation analysis. (F) Results of correlation analysis of trait modules. (G) KEGG results enriched by the blue modules.
Component–Target Network Analysis of MEO
A total of 568 targets were de-duplicated for MEO, 25941 targets were de-duplicated for colitis, and 2500 DEGs were obtained using the Omicsmart platform. The intersection targets of the three genes were identified using the Venny2.1.0 platform; and 71 key targets of MEO for colitis treatment were obtained (Figure 6A). By importing the data into Cytoscape 3.7.1, we obtained a component–target network diagram of MEO for colitis (Figure 6B), where blue represents the intersection targets and orange represents the active components. The STRING platform was used to construct a PPI network diagram using the intersecting targets, the results are presented in Figure 6C.
Figure 6The results of MEO component–target network analysis and KEGG pathway enrichment analysis. (A) Intersection map of MEO targets, ulcerative colitis targets, and DEGs. (B) Component-target map of MEO. (C) PPI network map of the key targets. (D) KEGG enrichment analysis before introducing weighting coefficients. (E) KEGG enrichment analysis after introducing weighting coefficients.
Reordered KEGG Enrichment Analysis After Introducing Weighting Coefficients
71 intersecting targets were imported into the R software for KEGG enrichment analyses. In total, 17 enrichments were identified by KEGG pathway analysis (Figure 6D). After introducing weighting coefficients, the order of KEGG pathways was changed, and the results are shown in Figure 6E. The results revealed that the MAPK signaling pathway had a higher rank (from 7 to 6). Bai et al46 suggest MAPK is a key pathway in colitis treatment; therefore, this pathway may be important for treating colitis using MEO.
Molecular Docking Results
Molecular docking of the key targets CACNA2D1, RAS, ERK, and JNK in the MAPK pathway. Gabapentin, Sotorasib, Acetylsalicylic acid, and Halicin were selected as specific ligands for the corresponding targets and were evaluated based on their binding energy scoring scores. The results are presented in Figure 7 and Table 3. The results showed that gamma-Muurolene, Curzerene, beta-Elemene, and Furanoeudesma 1.3-diene components had higher scores than the specific ligands. Therefore, we can presume that these four active components of MEO play pivotal roles in colitis treatment.Table 3The Docking Scores of Key Targets with Active Components and Specific LigandsKey TargetActive ComponentsDocking ScoreSpecific ligandsDocking ScoreCACNA2D1gamma-Muurolene71.9261Gabapentin61.2152CACNA2D1Eremophilene71.739CACNA2D1Germacrone71.6017CACNA2D14-Methoxybenzhydrol71.5981CACNA2D1(+)-delta-Cadinene71.5767RASCurzerene83.5249Sotorasib82.9067RASLindestrene81.4724RASFuranoeudesma 1.3-diene80.6225RASGermacrone77.6854RAS(+)-delta-Cadinene76.104ERKbeta-EIemene72.8193Acetylsalicylic acid58.4676ERKCurzerene72.1743ERKLindestrene72.0177ERKgamma-Selinene71.8568ERKdelta-EIemene71.8414JNKFuranoeudesma 1.3-diene77.9532Halicin72.6008JNK4,4’-Dimethoxybiphenyl76.2019JNKLindestrene73.3771JNKbeta-Elemene72.9658JNKGermacrone71.7237
Figure 7Molecular docking and thermogram results of the key targets with active components and specific ligands. (A) the docking results of 7mix protein of CACNA2D1 target with the specific ligand Gabapentin. (B) the docking results of 7mix protein of CACNA2D1 target with the gamma-Muurolene component. (C) the docking results of 3kkq protein of RAS target with the specific ligand Sotorasib. (D) the docking results of 3kkq protein of RAS target with the Curzerene component. (E) the docking results of 2ok1 protein of ERK target with the specific ligand Acetylsalicylic acid. (F) the docking results of 2ok1 protein of ERK target with the beta-Elemene component. (G) the docking results of 3v6r protein of JNK target with the specific ligand Halicin. (H) the docking results of 3v6r protein of JNK target with the Furanoeudesma 1.3-diene component. (I) the heat map of molecular docking.
DAI and Colon Morphology in Colitis Mice
The body weight of mice in the control group gradually increased. The model and MEO groups of mice had loose or even diluted feces and severe weight loss on the third day after modeling, and blood in the stool on the fourth day. However, the symptoms of mice in the MEO group started to improve on the fifth day, with a gradual increase in body weight and improved stool properties. Figure 8A and B) illustrates the changes in DAI scores and body weights of the mice in each group. These results indicated that MEO can effectively relieve colitis in mice. The colons of mice in the model group were short in length, and the mucous membrane was congested and swollen, whereas the colons of mice in the MEO group were longer. Images of the colons of each mouse group are shown in Figure 8C, and the differences in colon length are shown in Figure 8D.
Figure 8The DAI score and colon morphology in colitis mice. (A) DAI scores. (B) Changes in the body weight of in each group. (C) Morphological analysis of the colon in different group. (D) Differences in colon length in each mice group. ##P< 0.01 vs control group.Note: **P< 0.01 vs model group.
Effects of Different MEO Doses on Serum TNF-α and IL-1β Levels
The results are shown in Figure 9A and B. The serum TNF-α and IL-1β (P< 0.0001) levels were significantly higher in the model group. Furthermore, TNF-α and IL-1β levels were greatly decreased to different degrees in the MEO group (P< 0.01).
Figure 9The Results of MEO inhibition of serum inflammatory factor levels and effects on colonic histopathology in colitis mice. (A) Serum TNF-α levels of each mice group. (B) Serum IL-1β levels of each mice group. (C) Effect of MEO on DSS-induced histopathological changes in the colon. The yellow arrows show goblet cells in the normal colon, and the black arrow indicates inflammatory infiltration.Note: **P< 0.01 vs model group, ##P< 0.01 vs control group.
HE Staining Results
The HE staining results of the colon tissue of mice are shown in Figure 9C. The intestinal tissues of mice in the control group had a basic normal structure. The model group showed obvious abnormalities with a large number of inflammatory cells infiltrating the tissue. The high-dose group was normal with a small amount of inflammatory cell infiltration and no necrosis. These results suggested that the high-dose MEO group had better inhibitory effects against colitis.
Immunohistochemical Analysis
The results are shown in Figure 10A large number of positive cells was observed in the colon tissues of the model group. The area of positive staining in colon tissues was reduced to varying degrees in the MEO group. Our results suggest that MEO effectively reduced the expression of p-JNK, p-ERK, and TNF-α in colon tissues.
Figure 10The Results Immunohistochemistry. (A) Effect of MEO on the levels of p-JNK, p-ERK, and TNF-α protein in DSS-induced colon tissues. (B) Protein levels of p-JNK in colon tissues of each mice group. (C) Protein levels of P-ERK in colon tissues of each mice group. (D) Protein levels of TNF-α in colon tissues of each mice group.Note: ##P< 0.01 vs control group, *P< 0.05, **P< 0.01 vs model group.
Western Blot Results
The results showed that, the levels of p-JNK, JNK, p-ERK, and ERK were significantly higher in model group (P< 0.01). However, the levels decreased in the MEO group (P< 0.05), and the effect was similar to that in the positive group. Taken together, the results suggest that MEO can effectively inhibit the activation of the MAPK signaling pathway in the colon after DSS induction; the results are shown in Figure 11.
Figure 11The results of Western blot. (A) Changes in the levels of P-JNK, JNK, P-ERK, and ERK in DSS-induced colon tissues after treatment with MEO. (B) Protein levels of p-JNK/JNK in colon tissues of each mice group. (C) Protein levels of p-ERK/ERK in colon tissues of each mice group.Notes: ##P< 0.01 vs control group. *P< 0.05 vs model group.
Results of Analysis and Identification of MEO Components
The ion mass spectra of MEO are shown in Figure 1A, and the data were matched with the NIST database, and 23 active components were screened based on their matching degree. The specific components are listed in Table 2. The structures of the components are shown in Figure 1B.Table 2Qualitative Results of the Components in MEO by GC-MSNO.Library/IDCASRIPct Total1delta-EIemene20307–84-01337.920.9962Isolongifolene1135–66-61389.596.5983beta-Elemene515–13-91395.237.7154Longifolene475–20-71406.410.2195gamma-Maaliene20071–49-21419.550.6906Germacrene B15423–57-11433.490.7787gamma-Selinene515–17-31474.521.8618gamma-Muurolene30021–74-01480.761.9449(-)-beta-Selinene17066–67-01485.891.51910Curzerene17910–09-71502.4917.02811Eremophilene10219–75-71506.701.09712(+)-delta-Cadinene483–76-11523.240.39013(-)-alpha-Gurjunene489–40-71555.855.20214Menthofuran494–90-61578.940.84315Furanoeudesma 1.3-diene87605–93-41577.9221.98416Lindestrene2221–88-71578.789.22317Atractylone6989–21-51581.878.89418Germacrone6902–91-61588.420.705194,4’-Dimethoxybiphenyl2132–80-11670.737.488207-Acetoxy-p-mentha-1-ene-3-one56248–42-11766.740.360214-Methoxybenzhydrol720–44-51773.142.382221-Acetyl-4,6,8-trimethylazulene834–97-91866.130.51823(4aS,10aS)-4a-Methyl-3,4,4a,9,10,10a-hexahydrophenanthren-1(2H)-one62318–99-41952.310.752
Figure 1GC-MS chromatogram of MEO and the structure of its components. (A) Total ion flow map of MEO. (B) Structures of 23 components in MEO.
The Anti-Inflammatory Effect of MEO on RAW264.7 Macrophages
Effect of MEO on Cell Viability
As shown in Figure 2A, when the concentration of MEO was 0.0625, 0.125, and 0.25 µg/g, the cell survival rate was above 95%, indicating no adverse effect on cell viability. Therefore, the selected concentrations were used for NO, TNF-α, and IL-1β experiments.
Figure 2The effect of MEO on cell viability in RAW264.7 macrophages. (A) Cell viability was measured by MTT cell viability assay. (B) Effect of MEO on NO production in LPS-induced RAW264.7 macrophages. (C) Effect of MEO on TNF-α production in RAW264.7. (D) Effect of MEO on IL-1β production in RAW264.7.Notes: ##P< 0.01 vs control group. **P< 0.01 vs model group.
Effect of MEO on the Levels of NO, TNF-α, and IL-1β in LPS-Induced RAW264.7 Macrophages
As shown in Figure 2B and D, compared with the control group, the NO, TNF-α, and IL-1β levels in the cells of the model group were significantly higher (P< 0.01). Compared to the model group, the levels of NO, TNF-α, and IL-1β at the three concentrations of MEO were significantly decreased (P< 0.01).
Effect of MEO on Cell Viability
As shown in Figure 2A, when the concentration of MEO was 0.0625, 0.125, and 0.25 µg/g, the cell survival rate was above 95%, indicating no adverse effect on cell viability. Therefore, the selected concentrations were used for NO, TNF-α, and IL-1β experiments.
Figure 2The effect of MEO on cell viability in RAW264.7 macrophages. (A) Cell viability was measured by MTT cell viability assay. (B) Effect of MEO on NO production in LPS-induced RAW264.7 macrophages. (C) Effect of MEO on TNF-α production in RAW264.7. (D) Effect of MEO on IL-1β production in RAW264.7.Notes: ##P< 0.01 vs control group. **P< 0.01 vs model group.
Effect of MEO on the Levels of NO, TNF-α, and IL-1β in LPS-Induced RAW264.7 Macrophages
As shown in Figure 2B and D, compared with the control group, the NO, TNF-α, and IL-1β levels in the cells of the model group were significantly higher (P< 0.01). Compared to the model group, the levels of NO, TNF-α, and IL-1β at the three concentrations of MEO were significantly decreased (P< 0.01).
DAI, Colon Morphology and Serum Levels of TNF-α and IL-1β in Colitis Mice
The body weight of mice in the control group gradually increased. The model and MEO groups of mice had diluted feces on the third day with severe weight loss after modeling and blood in stool on the fourth day. However, the MEO group showed a gradual increase in body weight starting on day 5 and improved stool properties. Figure 3A and B illustrates the changes in DAI scores and body weights of the mice in each group. Images of the colon of each mouse group are shown in Figure 3C, and the differences in colon length are shown in Figure 3D. We observed that the colons of mice in the MEO group were longer and showed no mucosal congestion or swelling. Therefore, MEO effectively relieved colitis in mice.
Figure 3The DAI, colon morphology and serum levels of TNF-α and IL-1β in colitis mice. (A) DAI scores. (B) Changes in the body weight in each group. (C) Morphological analysis of the colon in each group. (D) Differences in colon length in each mice group. (E) The levels of TNF-α in colon tissues of colitis mice. (F) The levels of IL-1β in colon tissues of colitis mice.Notes: ##P< 0.01 vs control group. *P< 0.05, **P< 0.01 vs model group.
The serum TNF-α and IL-1β levels were significantly higher in the model group (P< 0.0001). In contrast, the levels of TNF-α and IL-1β in the MEO group decreased to different degrees (P< 0.001). The results are shown in Figure 3E and F).
Results of RNA-Seq in Colitis Mice
Sequencing results were analyzed using the Omicsmart platform. The parameters were set as follows: false discovery rate (FDR) ≤ 0.05, |log2FC| = 1. Detected DEGs 3021 A total of 1688 upregulated and 1333 downregulated genes. The sample clustering (PCA) plot is shown in Figure 4A, differential gene volcano plot is shown in Figure 4B, and differential gene heat map is shown in Figure 4C. KEGG and GO enrichment analyses of the identified DEGs were performed. As shown in Figure 4D, 388 KEGG pathways were enriched, including the MAPK, PI3K-Akt, and P53 signaling pathway. The GO enrichment results are shown in Figure 4E.
Figure 4Results of RNA-seq analysis in colitis mice with MEO. (A) PCA graph. (B) Volcano graph. (C) Heat map of DEGs. (D) KEGG enrichment results. (E) GO enrichment results.
WGCNA Analysis Results
A scale-free network with a soft threshold of eight is shown in Figure 5A. The left figure shows the scale-free network fitting index R2 corresponding to different soft thresholds; the red horizontal line represents the correlation coefficient of 0.9, and the right figure represents the average network connectivity corresponding to each β value. A total of 19 gene modules were identified using the dynamic cutting method (Figure 5B). The number of genes contained in each gene module is shown in Figure 5C, among which the blue module contained the largest number of genes, 5261 in total; the orange module contained the least number of genes, 62 in total; and the grey module indicates that the 25 genes did not aggregate into any other modules. The correlation between the two modules is shown in Figure 5D, and that between gene expression and modules is shown in Figure 5E. The correlation between traits and modules is shown in Figure 5F, in which the modules with highly positive correlations with the traits is blue modules, and the gene of the blue module was imported into the R software. KEGG enrichment analysis was performed, and 336 pathways were found to be enriched; the specific pathway results are shown in Figure 5G. Among them, the MAPK signaling pathway is located in the sixth position, which indicates that this pathway is important for the treatment of colitis with MEO.
Figure 5Results of WGCNA. (A) Selection of the soft threshold value. (B) Gene clustering tree and module division. (C) Gene number distribution in each module. (D) Heat map for correlation analysis between two modules. (E) Heat map of gene expression and inter-module correlation analysis. (F) Results of correlation analysis of trait modules. (G) KEGG results enriched by the blue modules.
Component–Target Network Analysis of MEO
A total of 568 targets were de-duplicated for MEO, 25941 targets were de-duplicated for colitis, and 2500 DEGs were obtained using the Omicsmart platform. The intersection targets of the three genes were identified using the Venny2.1.0 platform; and 71 key targets of MEO for colitis treatment were obtained (Figure 6A). By importing the data into Cytoscape 3.7.1, we obtained a component–target network diagram of MEO for colitis (Figure 6B), where blue represents the intersection targets and orange represents the active components. The STRING platform was used to construct a PPI network diagram using the intersecting targets, the results are presented in Figure 6C.
Figure 6The results of MEO component–target network analysis and KEGG pathway enrichment analysis. (A) Intersection map of MEO targets, ulcerative colitis targets, and DEGs. (B) Component-target map of MEO. (C) PPI network map of the key targets. (D) KEGG enrichment analysis before introducing weighting coefficients. (E) KEGG enrichment analysis after introducing weighting coefficients.
Reordered KEGG Enrichment Analysis After Introducing Weighting Coefficients
71 intersecting targets were imported into the R software for KEGG enrichment analyses. In total, 17 enrichments were identified by KEGG pathway analysis (Figure 6D). After introducing weighting coefficients, the order of KEGG pathways was changed, and the results are shown in Figure 6E. The results revealed that the MAPK signaling pathway had a higher rank (from 7 to 6). Bai et al46 suggest MAPK is a key pathway in colitis treatment; therefore, this pathway may be important for treating colitis using MEO.
Molecular Docking Results
Molecular docking of the key targets CACNA2D1, RAS, ERK, and JNK in the MAPK pathway. Gabapentin, Sotorasib, Acetylsalicylic acid, and Halicin were selected as specific ligands for the corresponding targets and were evaluated based on their binding energy scoring scores. The results are presented in Figure 7 and Table 3. The results showed that gamma-Muurolene, Curzerene, beta-Elemene, and Furanoeudesma 1.3-diene components had higher scores than the specific ligands. Therefore, we can presume that these four active components of MEO play pivotal roles in colitis treatment.Table 3The Docking Scores of Key Targets with Active Components and Specific LigandsKey TargetActive ComponentsDocking ScoreSpecific ligandsDocking ScoreCACNA2D1gamma-Muurolene71.9261Gabapentin61.2152CACNA2D1Eremophilene71.739CACNA2D1Germacrone71.6017CACNA2D14-Methoxybenzhydrol71.5981CACNA2D1(+)-delta-Cadinene71.5767RASCurzerene83.5249Sotorasib82.9067RASLindestrene81.4724RASFuranoeudesma 1.3-diene80.6225RASGermacrone77.6854RAS(+)-delta-Cadinene76.104ERKbeta-EIemene72.8193Acetylsalicylic acid58.4676ERKCurzerene72.1743ERKLindestrene72.0177ERKgamma-Selinene71.8568ERKdelta-EIemene71.8414JNKFuranoeudesma 1.3-diene77.9532Halicin72.6008JNK4,4’-Dimethoxybiphenyl76.2019JNKLindestrene73.3771JNKbeta-Elemene72.9658JNKGermacrone71.7237
Figure 7Molecular docking and thermogram results of the key targets with active components and specific ligands. (A) the docking results of 7mix protein of CACNA2D1 target with the specific ligand Gabapentin. (B) the docking results of 7mix protein of CACNA2D1 target with the gamma-Muurolene component. (C) the docking results of 3kkq protein of RAS target with the specific ligand Sotorasib. (D) the docking results of 3kkq protein of RAS target with the Curzerene component. (E) the docking results of 2ok1 protein of ERK target with the specific ligand Acetylsalicylic acid. (F) the docking results of 2ok1 protein of ERK target with the beta-Elemene component. (G) the docking results of 3v6r protein of JNK target with the specific ligand Halicin. (H) the docking results of 3v6r protein of JNK target with the Furanoeudesma 1.3-diene component. (I) the heat map of molecular docking.
DAI and Colon Morphology in Colitis Mice
The body weight of mice in the control group gradually increased. The model and MEO groups of mice had loose or even diluted feces and severe weight loss on the third day after modeling, and blood in the stool on the fourth day. However, the symptoms of mice in the MEO group started to improve on the fifth day, with a gradual increase in body weight and improved stool properties. Figure 8A and B) illustrates the changes in DAI scores and body weights of the mice in each group. These results indicated that MEO can effectively relieve colitis in mice. The colons of mice in the model group were short in length, and the mucous membrane was congested and swollen, whereas the colons of mice in the MEO group were longer. Images of the colons of each mouse group are shown in Figure 8C, and the differences in colon length are shown in Figure 8D.
Figure 8The DAI score and colon morphology in colitis mice. (A) DAI scores. (B) Changes in the body weight of in each group. (C) Morphological analysis of the colon in different group. (D) Differences in colon length in each mice group. ##P< 0.01 vs control group.Note: **P< 0.01 vs model group.
Effects of Different MEO Doses on Serum TNF-α and IL-1β Levels
The results are shown in Figure 9A and B. The serum TNF-α and IL-1β (P< 0.0001) levels were significantly higher in the model group. Furthermore, TNF-α and IL-1β levels were greatly decreased to different degrees in the MEO group (P< 0.01).
Figure 9The Results of MEO inhibition of serum inflammatory factor levels and effects on colonic histopathology in colitis mice. (A) Serum TNF-α levels of each mice group. (B) Serum IL-1β levels of each mice group. (C) Effect of MEO on DSS-induced histopathological changes in the colon. The yellow arrows show goblet cells in the normal colon, and the black arrow indicates inflammatory infiltration.Note: **P< 0.01 vs model group, ##P< 0.01 vs control group.
HE Staining Results
The HE staining results of the colon tissue of mice are shown in Figure 9C. The intestinal tissues of mice in the control group had a basic normal structure. The model group showed obvious abnormalities with a large number of inflammatory cells infiltrating the tissue. The high-dose group was normal with a small amount of inflammatory cell infiltration and no necrosis. These results suggested that the high-dose MEO group had better inhibitory effects against colitis.
Immunohistochemical Analysis
The results are shown in Figure 10A large number of positive cells was observed in the colon tissues of the model group. The area of positive staining in colon tissues was reduced to varying degrees in the MEO group. Our results suggest that MEO effectively reduced the expression of p-JNK, p-ERK, and TNF-α in colon tissues.
Figure 10The Results Immunohistochemistry. (A) Effect of MEO on the levels of p-JNK, p-ERK, and TNF-α protein in DSS-induced colon tissues. (B) Protein levels of p-JNK in colon tissues of each mice group. (C) Protein levels of P-ERK in colon tissues of each mice group. (D) Protein levels of TNF-α in colon tissues of each mice group.Note: ##P< 0.01 vs control group, *P< 0.05, **P< 0.01 vs model group.
Western Blot Results
The results showed that, the levels of p-JNK, JNK, p-ERK, and ERK were significantly higher in model group (P< 0.01). However, the levels decreased in the MEO group (P< 0.05), and the effect was similar to that in the positive group. Taken together, the results suggest that MEO can effectively inhibit the activation of the MAPK signaling pathway in the colon after DSS induction; the results are shown in Figure 11.
Figure 11The results of Western blot. (A) Changes in the levels of P-JNK, JNK, P-ERK, and ERK in DSS-induced colon tissues after treatment with MEO. (B) Protein levels of p-JNK/JNK in colon tissues of each mice group. (C) Protein levels of p-ERK/ERK in colon tissues of each mice group.Notes: ##P< 0.01 vs control group. *P< 0.05 vs model group.
Discussion
In this study, in vitro experiments were conducted to determine the anti-inflammatory effects of MEO on LPS-induced RAW264.7 macrophages. It was found that MEO can reduce the levels of NO, TNF-α, and IL-1β Subsequently, in vivo experiments established a DSS-induced colitis mouse model, and DAI scores showed that high doses of MEO were more effective. The transcript levels of TNF-α and IL-1β were significantly reduced in colitis mice after high-dose MEO administration, which inhibited the colitis inflammatory response to some extent. Western blot was performed and the results showed that the protein expression of p-JNK and p-ERK was most reduced in the high dose group of MEO. We hypothesize that MEO may reduce inflammation by inhibiting the activation of the MAPK signaling pathway, thereby ameliorating colitis in mice.
Many studies have indicated that the mechanisms involved in anti-colitis effects include the MAPK-mediated pathway, JAK/STAT3 pathway, P13k/Akt pathway, and NF-κB pathway. Gao et al47 suggest that the serum p-ERK and p-JNK levels were significantly increased in a DSS mouse model, and the MAPK signaling pathway was successfully activated. These findings indicate that the MAPK pathway may play a crucial role in colitis pathogenesis. In addition, growing evidences showed that the pathogenesis of colitis is related to MAPK signaling cascade.48,49 When the transmembrane receptor CACN on the cell membrane receives an external signal stimulus, it releases Ca2+ and transmits them to RasGRP, which exchanges GDP for GTP, thereby activating Ras. After Ras activation, it recruits and activates downstream oncogene Raf. The activated Raf gradually stimulates MEK1/2 and activates ERK1/2. Among them, ERK participates in and mediates inflammatory responses, regulates inflammatory factor production, promotes epithelial cell proliferation and differentiation, and inhibits apoptosis of small intestinal epithelial cells.50 Simultaneously, RAS indirectly activates JNK protein through the activation of MEKK and MKK.51 It has been shown that JNK is a key mediator in most pathological signaling pathways of IBD. In the colon of IBD patients, JNK activity is significantly elevated, significantly increasing susceptibility to bacterial components and cytokines.52 It binds to the transcription factor AP-1 (oncogene transcription factor), leading to AP-1 activation and release of a large number of inflammatory factors such as TNF-α and IL-6,53,54 thus exacerbating the progression of colitis disease.
Molecular docking of the key targets CACNA2D1, RAS, ERK, and JNK in the MAPK signaling pathway with their specific ligands and active components in MEO revealed that gamma-Muurolene, Curzerene, beta-Elemene, and Furanoeudesma 1.3-diene may be key components associated with the MAPK pathway-based therapeutic effect of MEO in colitis.
By performing WGCNA analysis of differentially expressed genes in mice, 19 gene modules were obtained by dynamic cleavage, and TNF-α and IL-β trait data were correlated with each module to derive the module with the most significant positive correlation. Genes in the modules were enriched in KEGG pathway analysis. The combined analysis of transcriptome sequencing results, WGCNA analysis, and KEGG pathway analysis after the introduction of weighting coefficients showed that the MAPK signaling pathway ranked high, ranked 5th in transcriptome KEGG enrichment results and ranked 6th in WGCNA analysis, especially after the introduction of weighting coefficients ranked from 7th to 6th, so we considered it to be an important pathway of MEO against colitis.
In this study, we found that the therapeutic mechanism of colitis may be MEO acts on the CACN transmembrane transporter protein CACNA2D1 through its gamma-Muurolene component, which reduces Ca2+ influx and attenuates RAS activation. Simultaneously, Curzerene directly acts on RAS, further inhibiting its activity. In a cascade reaction, the inhibition of phosphorylated RAS (p-RAS) reduces the activation of downstream Raf and MEK. MEK is a dual-specificity kinase, and its inhibited activity simultaneously weakens the activation of ERK1 and ERK2.55 The β-elemene component in MEO binds to ERK, reducing the levels of p-ERK, which in turn decreases the phosphorylation of transcription factors such as c-Fos and c-Jun in the cell nucleus. It is well known that c-Fos and c-Jun together form heterodimers to form the transcription factor complex AP-1, which promotes the proliferation and differentiation of epithelial cells and inhibits apoptosis in small intestinal epithelial cells.56 Meanwhile, the inhibition of RAS activation leads to decreased MEKK1 activity, due to the high selectivity of MEKK1 to MKK4 in vivo, the content of phosphorylated MKK4 decreases sharply, which ultimately leads to the decrease in the downstream p-JNK level.57 In addition, Furanoeudesma 1.3-diene in MEO acts directly on JNK and reduces p-JNK activity. These two aspects simultaneously decrease the number of downstream AP-1 binding proteins and inhibit the MAPK signaling pathway, resulting in a decrease in the production of TNF-α and IL-1β,58 which reduces the incidence of colon injury, improves the colon condition, and alleviates colitis.
In summary, we used weighting coefficients combined with network pharmacology methods to predict the mechanism of action of MEO on colitis, which was validated by pharmacodynamic experiments. The results indicated that the mechanism of MEO for colitis may involve the interaction of γ-Muurolene, Curzerene, β-Elemene and Furanoeudesma 1.3-Diene components with CACNA2D1, RAS, ERK and JNK targets, thereby inhibiting the expression of p-ERK and p-JNK in the MAPK pathway. The mechanism of which is shown in Figure 12. Nonetheless, our study exhibits many shortcomings. Although our research has made some progress, there are some limitations. Multiple pathways were enriched by network pharmacology methods, but only verified the MAPK signaling pathway, and did preliminary mechanistic studies and pharmacodynamic evaluation of this pathway, without considering the relationship between other signaling pathways and colitis. In addition, monomeric anti-inflammatory pharmacodynamic studies should be performed for the four active components γ-Muurolene, Curzerene, β-Elemene and Furanoeudesma 1.3-Diene derived from molecular docking to verify the specific anti-inflammatory effects of each MEO component.
Figure 12The mechanism of MEO in the treatment of colitis through the MAPK pathway.
Conclusions
In this study, we found that MEO exhibited the protective effect against DSS-induced colitis in mice and inhibited the expression of inflammatory factors by LPS in RAW264.7 cells. Pharmacodynamic experiments revealed that MEO inhibited the release of inflammatory factors by regulating p-ERK and p-JNK in the MAPK signaling pathway through γ-Muurolene, Curzerene, β-Elemene, and Furanoeudesma 1.3- diene, thereby alleviating colitis symptoms. This indicates that MEO therapy for colitis operates through a multi component, multi-target, and multi-pathway, which provides a reference for future studies on the use of MEO to treat inflammation, and provides a new idea for the subsequent development of new anti-colitis drugs. |
Title: Self-Medication Paths | Body:
METHODS
Study Design and Population
This cross-sectional descriptive study was performed in a convenience sample of adults living with chronic pain. More specifically, to be eligible, participants had to: (1) live with pain for more than 3 months (ICD-11 definition50); (2) currently use medical and/or nonmedical cannabis or have used cannabis in the last year (all reasons for use considered); (3) be at least 18 years old; (4) be able to answer questions over the phone in French. No exclusion criteria were applied. The study received ethics approval from the Université du Québec en Abitibi-Témiscamingue’s research ethics committee (#2022-11–Audet, C.). In terms of patient engagement, a person with lived experience of chronic pain (CB), was a full-fledged team member and participated in the project conceptualization, questionnaire development, and interpretation of results.
Recruitment and Data Collection Methods
As a potential participant pool, the list of individuals included in a previous study, that is, the Chronic Pain Treatment (COPE) Cohort,51 was utilized. This cohort comprises 1935 French-speaking adults living with pain for more than 3 months, recruited through the web in all regions of the province of Quebec (Canada) between June and October 2019. COPE Cohort participants were previously found to be comparable to random (representative) samples of Canadians living with chronic pain in terms of age, employment status, level of education, pain duration, mean pain intensity, and most common pain locations.51 Most (n=1114) expressed their interest in being approached by e-mail for future studies conducted by our research team.
For the present study, e-mail invitations were sent to potential participants (convenience sample) until we obtained a sample size of 73 individuals. This sample served as a well-balanced compromise, allowing for study completion within an acceptable timeframe and precise estimation of descriptive statistics, all while ensuring the feasibility of conducting telephone interviews. As compared with a web-based questionnaire, this data collection approach was chosen for several reasons, including the novelty and anticipated complexity of the phenomenon of self-medication with medical and/or nonmedical cannabis, and the possibility to minimize missing data. In fact, telephone interviews allowed the research team the opportunity to clarify misunderstandings and provide examples. At this level, we consider this choice to be a strength of the study. Furthermore, participants of the COPE Cohort had expressed a desire for opportunities to have more personal contact with the research team (as opposed to web-based self-administered questionnaires). As compared with a web-based questionnaire, administering the interviews was thus deemed to be an adequate methodological decision and the research team was able to recommend adequate resources to many participants in case of psychological distress.
From January 10 to April 25, 2023, e-mail invitations were sent to potential participants by the principal investigator of the COPE Cohort (above-mentioned pool of 1114 participants who expressed their interest in being approached for future studies; until the reach of our sample size). These e-mails included a description of the study, along with an information and consent letter in an attachment. If interested, individuals were invited to respond to the e-mail, providing their name, phone number, and the best time for a telephone interview. Participants were generally contacted within a week of their response. Informed consent was confirmed by responding to the invitation e-mail and initiating the phone interview. The interviews, lasting ∼60 minutes, were conducted by a Master’s degree student and registered social worker, and were computer-assisted (LimeSurvey). A standardized interview guide (questionnaire—Appendix 1, Supplemental Digital Content 1, http://links.lww.com/CJP/B146) was developed by our multidisciplinary research team (a patient, a social worker, a pain clinic anesthesiologist, a nursing research scientist, an opioid misuse and addiction scientist, and an epidemiologist) and pretested with 6 individuals with lived experience of chronic pain, including men and women with different levels of education. These pretests were used to estimate completion times and to determine any modifications required, which led to specify minor elements, such as adding examples to statements.
Study Variable
Cannabis Self-Medication for Pain Relief
There is currently no consensus on the definition of self-medication.36,38,39,52 However, we retained the concept of individual action,52 without the guidance of a health care professional. In this study, cannabis use for pain relief and the conditions under which cannabis was used were explored using questions developed by the research team to cover 3 topics: (1) medical cannabis authorization (yes/no); (2) use of legal nonmedical cannabis (yes/no); (3) if they received guidance from a registered HCP for their cannabis use (yes/no). Before answering these questions, participants were carefully explained the difference between medical (medical authorization under the Cannabis Act) and legal nonmedical cannabis (accessed through the recreational route; Government-operated in-person and online stores in the province of Quebec). They were presented with examples of registered HCPs, such as physicians, nurses, and pharmacists, who can play a role in supporting cannabis use. Details were provided to operationalize “guidance,” that is, accompaniment, expert advice, recommendations, insights, and assistance. Since it is known that patients who receive authorization through a medical cannabis program do not necessarily have cannabis-related medical follow-up,44 participants could report using cannabis without the guidance of a health care professional (self-medication), whether it was authorized (medical cannabis) or not (legal nonmedical cannabis) by a HCP.
Self-Perceived Cannabis Effectiveness and Safety
Self-perceived cannabis effectiveness and safety were measured using 5-points Likert scales, as used in the Medical Cannabis Access Survey, to facilitate data comparison:53 (1) “In your experience, what is (or has been) the effectiveness of cannabis in managing your pain? (not at all effective/slightly effective/moderately effective/very effective/extremely effective/don’t know); (2) In your opinion, is the use of cannabis for pain management a risky practice for your health?” (no risk/minimal risk/moderate risk/high risk/don’t know). Using closed-ended questions, participants were also asked if they perceived that cannabis could lead to addiction, if consuming cannabis was riskier, less risky, or equivalent to consuming prescription opioids (eg, morphine, fentanyl, hydromorphone were provided), and if consuming cannabis was riskier, less risky, or equal to consuming illicit drugs in general (examples that resonate with patients such as speed, ecstasy, GHB, cocaine, magic mushrooms were provided).
Cannabis Use Characteristics
The questionnaire administered over the phone contained items related to cannabis use, including reasons for use, products used, methods of use (semiclosed-ended questions, allowing participants to select all options that applied), concentration of their products (THC-dominant, CBD-dominant, balanced products, multiple products with different combinations of THC and CBD), and frequency of use (times per day). These items were based on the 2022 Canadian Cannabis Survey,54 the 2022 Quebec Cannabis Survey,55 and the Medical Cannabis Access Survey53 to facilitate data comparison. A question about cannabis use before the age of 2456 (yes/no) was also added.
Pain and Psychological Variables
The pain profile section of the questionnaire covered pain location (1 item), duration (1 item), and intensity (11-point numeric rating scale about average pain intensity in the last 7 days).57 Neuropathic component to the participant’s pain was evaluated using the 4-item DN4 (Douleur Neuropathique en 4 Questions)—Interview part (a score >3/7 indicates a likely presence of a neuropathic component).58 The DN4 is one of the most used and validated screeners of neuropathic pain.59 Pain interference was measured using the Brief Pain Inventory (BPI) 7-item interference scale.60 Items include general activity, mood, walking ability, normal work, relations with others, sleep, enjoyment of life, personal care, recreational activities, and social activities in the past 7 days. The BPI is one of the most commonly used validated pain measures.61 Pain catastrophizing was assessed using the 4-item Brief Pain Catastrophizing Scale (BriefPCS),62 an abbreviated version of the PCS validated for a quick screen of exaggerated negative orientation toward pain. Psychological distress was measured using the 4-item Patient Health Questionnaire (PHQ),63 an ultrabrief screener for anxiety and depression validated in a great diversity of clinical and nonclinical populations.64
Sociodemographic Variables
Information was collected on participants’ age, sex at birth, sex identity and country of birth (as social determinants of health65), region of residence (remote vs. nonremote), employment status, family annual income, and education.
Statistical Analysis
Descriptive statistics, such as counts, proportions, means, SD, medians, minimums, and maximums were used to compile participant characteristics and cannabis product utilization. To facilitate the comparison of our results with those of the 2022 Quebec Cannabis Survey,55 common variables measured in both data collections were depicted using dual bar charts (comparisons of the proportions without statistical testing). To address the first objective about cannabis self-medication, the 3 above-mentioned questions (medical authorization yes/no, use of legal nonmedical cannabis, and guidance from a registered HCP) were combined to create a tree diagram (conceptual map). Proportions of individuals self-medicating, with medical authorization, and/or using legal nonmedical cannabis were computed for the entire sample, and then stratified by sex and age groups (women vs. men; individuals 65 years or older vs. below 65 years old; χ2 tests). 95% CI were computed to assess the precision of the estimation of our primary statistics of interest. In an exploratory analysis (the sample size was not planned for an analytical study design/multivariable analyses), characteristics of individuals self-medicating versus not self-medicating were explored using bivariate comparisons (t tests and χ2 tests). To address the second objective, self-perceived cannabis effectiveness and safety were analyzed using descriptive statistics. All data were analyzed using SPSS Statistics 19 (IBM Corp., Armonk, NY).
Study Design and Population
This cross-sectional descriptive study was performed in a convenience sample of adults living with chronic pain. More specifically, to be eligible, participants had to: (1) live with pain for more than 3 months (ICD-11 definition50); (2) currently use medical and/or nonmedical cannabis or have used cannabis in the last year (all reasons for use considered); (3) be at least 18 years old; (4) be able to answer questions over the phone in French. No exclusion criteria were applied. The study received ethics approval from the Université du Québec en Abitibi-Témiscamingue’s research ethics committee (#2022-11–Audet, C.). In terms of patient engagement, a person with lived experience of chronic pain (CB), was a full-fledged team member and participated in the project conceptualization, questionnaire development, and interpretation of results.
Recruitment and Data Collection Methods
As a potential participant pool, the list of individuals included in a previous study, that is, the Chronic Pain Treatment (COPE) Cohort,51 was utilized. This cohort comprises 1935 French-speaking adults living with pain for more than 3 months, recruited through the web in all regions of the province of Quebec (Canada) between June and October 2019. COPE Cohort participants were previously found to be comparable to random (representative) samples of Canadians living with chronic pain in terms of age, employment status, level of education, pain duration, mean pain intensity, and most common pain locations.51 Most (n=1114) expressed their interest in being approached by e-mail for future studies conducted by our research team.
For the present study, e-mail invitations were sent to potential participants (convenience sample) until we obtained a sample size of 73 individuals. This sample served as a well-balanced compromise, allowing for study completion within an acceptable timeframe and precise estimation of descriptive statistics, all while ensuring the feasibility of conducting telephone interviews. As compared with a web-based questionnaire, this data collection approach was chosen for several reasons, including the novelty and anticipated complexity of the phenomenon of self-medication with medical and/or nonmedical cannabis, and the possibility to minimize missing data. In fact, telephone interviews allowed the research team the opportunity to clarify misunderstandings and provide examples. At this level, we consider this choice to be a strength of the study. Furthermore, participants of the COPE Cohort had expressed a desire for opportunities to have more personal contact with the research team (as opposed to web-based self-administered questionnaires). As compared with a web-based questionnaire, administering the interviews was thus deemed to be an adequate methodological decision and the research team was able to recommend adequate resources to many participants in case of psychological distress.
From January 10 to April 25, 2023, e-mail invitations were sent to potential participants by the principal investigator of the COPE Cohort (above-mentioned pool of 1114 participants who expressed their interest in being approached for future studies; until the reach of our sample size). These e-mails included a description of the study, along with an information and consent letter in an attachment. If interested, individuals were invited to respond to the e-mail, providing their name, phone number, and the best time for a telephone interview. Participants were generally contacted within a week of their response. Informed consent was confirmed by responding to the invitation e-mail and initiating the phone interview. The interviews, lasting ∼60 minutes, were conducted by a Master’s degree student and registered social worker, and were computer-assisted (LimeSurvey). A standardized interview guide (questionnaire—Appendix 1, Supplemental Digital Content 1, http://links.lww.com/CJP/B146) was developed by our multidisciplinary research team (a patient, a social worker, a pain clinic anesthesiologist, a nursing research scientist, an opioid misuse and addiction scientist, and an epidemiologist) and pretested with 6 individuals with lived experience of chronic pain, including men and women with different levels of education. These pretests were used to estimate completion times and to determine any modifications required, which led to specify minor elements, such as adding examples to statements.
Study Variable
Cannabis Self-Medication for Pain Relief
There is currently no consensus on the definition of self-medication.36,38,39,52 However, we retained the concept of individual action,52 without the guidance of a health care professional. In this study, cannabis use for pain relief and the conditions under which cannabis was used were explored using questions developed by the research team to cover 3 topics: (1) medical cannabis authorization (yes/no); (2) use of legal nonmedical cannabis (yes/no); (3) if they received guidance from a registered HCP for their cannabis use (yes/no). Before answering these questions, participants were carefully explained the difference between medical (medical authorization under the Cannabis Act) and legal nonmedical cannabis (accessed through the recreational route; Government-operated in-person and online stores in the province of Quebec). They were presented with examples of registered HCPs, such as physicians, nurses, and pharmacists, who can play a role in supporting cannabis use. Details were provided to operationalize “guidance,” that is, accompaniment, expert advice, recommendations, insights, and assistance. Since it is known that patients who receive authorization through a medical cannabis program do not necessarily have cannabis-related medical follow-up,44 participants could report using cannabis without the guidance of a health care professional (self-medication), whether it was authorized (medical cannabis) or not (legal nonmedical cannabis) by a HCP.
Self-Perceived Cannabis Effectiveness and Safety
Self-perceived cannabis effectiveness and safety were measured using 5-points Likert scales, as used in the Medical Cannabis Access Survey, to facilitate data comparison:53 (1) “In your experience, what is (or has been) the effectiveness of cannabis in managing your pain? (not at all effective/slightly effective/moderately effective/very effective/extremely effective/don’t know); (2) In your opinion, is the use of cannabis for pain management a risky practice for your health?” (no risk/minimal risk/moderate risk/high risk/don’t know). Using closed-ended questions, participants were also asked if they perceived that cannabis could lead to addiction, if consuming cannabis was riskier, less risky, or equivalent to consuming prescription opioids (eg, morphine, fentanyl, hydromorphone were provided), and if consuming cannabis was riskier, less risky, or equal to consuming illicit drugs in general (examples that resonate with patients such as speed, ecstasy, GHB, cocaine, magic mushrooms were provided).
Cannabis Use Characteristics
The questionnaire administered over the phone contained items related to cannabis use, including reasons for use, products used, methods of use (semiclosed-ended questions, allowing participants to select all options that applied), concentration of their products (THC-dominant, CBD-dominant, balanced products, multiple products with different combinations of THC and CBD), and frequency of use (times per day). These items were based on the 2022 Canadian Cannabis Survey,54 the 2022 Quebec Cannabis Survey,55 and the Medical Cannabis Access Survey53 to facilitate data comparison. A question about cannabis use before the age of 2456 (yes/no) was also added.
Pain and Psychological Variables
The pain profile section of the questionnaire covered pain location (1 item), duration (1 item), and intensity (11-point numeric rating scale about average pain intensity in the last 7 days).57 Neuropathic component to the participant’s pain was evaluated using the 4-item DN4 (Douleur Neuropathique en 4 Questions)—Interview part (a score >3/7 indicates a likely presence of a neuropathic component).58 The DN4 is one of the most used and validated screeners of neuropathic pain.59 Pain interference was measured using the Brief Pain Inventory (BPI) 7-item interference scale.60 Items include general activity, mood, walking ability, normal work, relations with others, sleep, enjoyment of life, personal care, recreational activities, and social activities in the past 7 days. The BPI is one of the most commonly used validated pain measures.61 Pain catastrophizing was assessed using the 4-item Brief Pain Catastrophizing Scale (BriefPCS),62 an abbreviated version of the PCS validated for a quick screen of exaggerated negative orientation toward pain. Psychological distress was measured using the 4-item Patient Health Questionnaire (PHQ),63 an ultrabrief screener for anxiety and depression validated in a great diversity of clinical and nonclinical populations.64
Sociodemographic Variables
Information was collected on participants’ age, sex at birth, sex identity and country of birth (as social determinants of health65), region of residence (remote vs. nonremote), employment status, family annual income, and education.
Cannabis Self-Medication for Pain Relief
There is currently no consensus on the definition of self-medication.36,38,39,52 However, we retained the concept of individual action,52 without the guidance of a health care professional. In this study, cannabis use for pain relief and the conditions under which cannabis was used were explored using questions developed by the research team to cover 3 topics: (1) medical cannabis authorization (yes/no); (2) use of legal nonmedical cannabis (yes/no); (3) if they received guidance from a registered HCP for their cannabis use (yes/no). Before answering these questions, participants were carefully explained the difference between medical (medical authorization under the Cannabis Act) and legal nonmedical cannabis (accessed through the recreational route; Government-operated in-person and online stores in the province of Quebec). They were presented with examples of registered HCPs, such as physicians, nurses, and pharmacists, who can play a role in supporting cannabis use. Details were provided to operationalize “guidance,” that is, accompaniment, expert advice, recommendations, insights, and assistance. Since it is known that patients who receive authorization through a medical cannabis program do not necessarily have cannabis-related medical follow-up,44 participants could report using cannabis without the guidance of a health care professional (self-medication), whether it was authorized (medical cannabis) or not (legal nonmedical cannabis) by a HCP.
Self-Perceived Cannabis Effectiveness and Safety
Self-perceived cannabis effectiveness and safety were measured using 5-points Likert scales, as used in the Medical Cannabis Access Survey, to facilitate data comparison:53 (1) “In your experience, what is (or has been) the effectiveness of cannabis in managing your pain? (not at all effective/slightly effective/moderately effective/very effective/extremely effective/don’t know); (2) In your opinion, is the use of cannabis for pain management a risky practice for your health?” (no risk/minimal risk/moderate risk/high risk/don’t know). Using closed-ended questions, participants were also asked if they perceived that cannabis could lead to addiction, if consuming cannabis was riskier, less risky, or equivalent to consuming prescription opioids (eg, morphine, fentanyl, hydromorphone were provided), and if consuming cannabis was riskier, less risky, or equal to consuming illicit drugs in general (examples that resonate with patients such as speed, ecstasy, GHB, cocaine, magic mushrooms were provided).
Cannabis Use Characteristics
The questionnaire administered over the phone contained items related to cannabis use, including reasons for use, products used, methods of use (semiclosed-ended questions, allowing participants to select all options that applied), concentration of their products (THC-dominant, CBD-dominant, balanced products, multiple products with different combinations of THC and CBD), and frequency of use (times per day). These items were based on the 2022 Canadian Cannabis Survey,54 the 2022 Quebec Cannabis Survey,55 and the Medical Cannabis Access Survey53 to facilitate data comparison. A question about cannabis use before the age of 2456 (yes/no) was also added.
Pain and Psychological Variables
The pain profile section of the questionnaire covered pain location (1 item), duration (1 item), and intensity (11-point numeric rating scale about average pain intensity in the last 7 days).57 Neuropathic component to the participant’s pain was evaluated using the 4-item DN4 (Douleur Neuropathique en 4 Questions)—Interview part (a score >3/7 indicates a likely presence of a neuropathic component).58 The DN4 is one of the most used and validated screeners of neuropathic pain.59 Pain interference was measured using the Brief Pain Inventory (BPI) 7-item interference scale.60 Items include general activity, mood, walking ability, normal work, relations with others, sleep, enjoyment of life, personal care, recreational activities, and social activities in the past 7 days. The BPI is one of the most commonly used validated pain measures.61 Pain catastrophizing was assessed using the 4-item Brief Pain Catastrophizing Scale (BriefPCS),62 an abbreviated version of the PCS validated for a quick screen of exaggerated negative orientation toward pain. Psychological distress was measured using the 4-item Patient Health Questionnaire (PHQ),63 an ultrabrief screener for anxiety and depression validated in a great diversity of clinical and nonclinical populations.64
Sociodemographic Variables
Information was collected on participants’ age, sex at birth, sex identity and country of birth (as social determinants of health65), region of residence (remote vs. nonremote), employment status, family annual income, and education.
Statistical Analysis
Descriptive statistics, such as counts, proportions, means, SD, medians, minimums, and maximums were used to compile participant characteristics and cannabis product utilization. To facilitate the comparison of our results with those of the 2022 Quebec Cannabis Survey,55 common variables measured in both data collections were depicted using dual bar charts (comparisons of the proportions without statistical testing). To address the first objective about cannabis self-medication, the 3 above-mentioned questions (medical authorization yes/no, use of legal nonmedical cannabis, and guidance from a registered HCP) were combined to create a tree diagram (conceptual map). Proportions of individuals self-medicating, with medical authorization, and/or using legal nonmedical cannabis were computed for the entire sample, and then stratified by sex and age groups (women vs. men; individuals 65 years or older vs. below 65 years old; χ2 tests). 95% CI were computed to assess the precision of the estimation of our primary statistics of interest. In an exploratory analysis (the sample size was not planned for an analytical study design/multivariable analyses), characteristics of individuals self-medicating versus not self-medicating were explored using bivariate comparisons (t tests and χ2 tests). To address the second objective, self-perceived cannabis effectiveness and safety were analyzed using descriptive statistics. All data were analyzed using SPSS Statistics 19 (IBM Corp., Armonk, NY).
RESULTS
Sample Characteristics
Table 1 presents the characteristics of the 73 participants included in the study. Most participants were females (sex at birth; 76.7%) and identified as women (sex identity; 75.3%). The mean age of the participants was 54.36±12.05 years old (range: 30 to 79). The most reported specific pain condition was fibromyalgia (n=23, 31.5%). When looking at pain locations, most participants experienced back pain (76.7%) and 67 participants had multisite pain (91.8%). Twenty-seven participants presented moderate to severe psychological distress (38.0%), and most participants presented moderate to high levels of pain catastrophizing (n=51, 71.8%). A total of 62 participants (84.9%) were current cannabis users and 10 participants (13.70%) stopped using it in the last 12 months (1 missing value). The majority (82.2%) of participants used cannabis at least once a day in the previous 12 months.
TABLE 1
Sample Demographics and Cannabis Use Characteristics
Sociodemographic profile
Participants (n=73), n (%)
Age (y), mean±SD
54.4±12.1
Range
(30-79)
Median
55
Sex at birth
Female
56 (76.7)
Male
17 (23.3)
Sex identity*
Woman
55 (75.3)
Man
17 (23.3)
Questioning
1 (1.4)
Country of birth
Canada
71 (97.3)
Other
2 (2.7)
Region of residence†
Remote
15 (20.8)
Nonremote
57 (79.2)
Employed (full-time or part-time)
27 (37.0)
Family annual income
Under $25,000
13 (17.8)
$25,000-$49,999
20 (27.4)
$50,000-$74,999
15 (20.6)
$75,000-$99,999
9 (12.3)
$100,000 and over
15 (20.6)
Prefers not to answer
1 (1.4)
Postsecondary education
58 (79.5)
Pain profile
Most common self-reported pain locations‡
Back
56 (76.7)
Legs
18 (24.7)
Shoulders
17 (23.3)
Neck
15 (20.5)
Knees
14 (19.2)
Generalized pain (yes vs. no)
27 (37.0)
Multisite pain (≥2 sites)
67 (91.8)
Most common self-reported pain conditions‡
Fibromyalgia
23 (31.5)
Osteoarthritis
18 (24.7)
Herniated disc
13 (17.8)
Pain duration (≥10 years)
59 (83.1)
Average pain intensity in the past 7 days (0 to 10 NRS), mean±SD
Range
5.2±1.7
Median
(1-10) 5
Recoded
Mild (1-4)
25 (35.2)
Moderate (5-7)
42 (59.2)
Severe (8-10)
4 (5.6)
Presence of neuropathic pain (DN4)
43 (58.9)
Pain interference (BPI score, 0-10), mean±SD
5.2±2.1
Catastrophizing (0-16)
Low: 0-5
20 (28.2)
Moderate: 6-8
16 (22.5)
High: 9-16
35 (49.3)
Psychological distress (PHQ-4)
None: 0-2
17 (23.9)
Mild: 3-5
27 (38.0)
Moderate: 6-8
16 (22.5)
Severe: 9-12
11 (15.5)
Cannabis use profile
Reasons for use‡
Pain
60 (96.8)
Sleep
49 (79.0)
Stress and anxiety
26 (41.9)
For pleasure
21 (33.9)
Mood
18 (29.0)
Appetite
9 (14.5)
Cannabis products used in the last 12 months‡
Liquid extracts or concentrates (eg, oil)
49 (67.1)
Dried flowers or leaves, buds
36 (49.3)
Pills and capsules
24 (32.9)
Edibles
19 (26.0)
Hashish (resin or pollen)
12 (16.4)
Beverages
5 (6.9)
Extracts or solid concentrates
2 (2.7)
Concentrations
THC-dominant products
13 (17.8)
CBD-dominant products
20 (27.4)
Balanced products
4 (5.5)
Several products with different combinations
33 (45.2)
Do not know
3 (4.1)
Frequency of use (number of times/day), mean±SD
2.0±1.6
Cannabis use before the age of 24 (yes vs. no)
41 (56.2)
0% missing values for all presented variables except for the region of residence (1 missing value).
BPI indicates Brief Pain Inventory; CBD, Cannabidiol; NRS, Numeric Rating Scale; PHQ-4, Patient Health Questionnaire; THC, Δ-9-thetrahydrocannabinol.
*
Sex identity choices: woman, man, nonbinary, genderqueer, transgender, 2-spirit, questioning, none of the above, other, and I prefer not to answer.
†
Six remote resource regions as defined by Revenu Quebec (ie, the provincial revenue agency) are: Bas-Saint-Laurent, Saguenay–Lac-Saint-Jean, Abitibi-Témiscamingue, Côte-Nord, Nord-du-Québec, Gaspésie–Îles-de-la-Madeleine. Nonremote regions are near a major urban center.
‡
Nonmutually exclusive categories.
Figure 1 compares the methods of cannabis use between the present study population living with chronic pain and the general population (2022 Quebec Cannabis Survey55) that includes individuals with and without chronic pain (comparisons of the proportions without statistical testing). While smoked cannabis predominated in the general population (81.6% vs. 42.5% in our sample), people of the community were less likely to opt for oil-based cannabis products (29.5% vs. 67.1% in our sample).
FIGURE 1
Methods of cannabis use. Oral drops and sprays include all oil-based products. Dabbing: inhalation with a hot knife or nail.
Cannabis Self-Medication for Pain Relief
Figure 2 presents the tree diagram showing the distribution of participants according to their user profiles (authorization for medical cannabis, legal nonmedical cannabis use, and guidance by a health care professional). Up to 61.6% (n=45) of our sample reported using cannabis for pain relief without the guidance of a HCP, that is, were self-medicating with cannabis (95% CI: 49.52-72.79). Among those, 40.0% (n=18) held a medical authorization. In the whole sample, this represented a total of 24.7% who reported holding an authorization for medical cannabis and being self-medicating.
FIGURE 2
Tree diagram (conceptual map) of the possible uses of cannabis. Medical cannabis: cannabis used by individuals holding medical authorization under the Cannabis Act in Canada. Nonmedical cannabis: cannabis purchased from a legal recreational cannabis store. Professional guidance: expert advice, recommendations, insights, assistance from a health care professional.
None of the sex identity-stratified or age-stratified results yielded statistically significant differences. Sex identity-stratified results revealed no statistically significant differences between the proportion of women and men self-medicating (58.2% vs. 70.6%; P=0.284). As much as 60.00% of women in our sample held authorization for medical cannabis, whereas 41.2% of men did, but this difference was not statistically significant (χ2
P=0.198). A similar proportion of women and men participants used legal nonmedical cannabis (65.5% vs. 64.7%; χ2
P=0.917). Those proportions are not mutually exclusive as participants can use both. Participants aged 65 and over (vs. below 65) had similar profiles of self-medication with cannabis (63.2% vs. 61.1%; χ2
P=0.875). When comparing older and younger participants, no statistically significant differences were found, that is, proportions using legal nonmedical cannabis were 73.7% vs. 63.0% (Fisher exact test P=0.290); proportions using medical cannabis were 47.4% vs. 57.4% (χ2
P=0.450).
Table 2 shows the characteristics of participants depending on whether they were self-medicating with cannabis or using it under the guidance of a registered HCP. As compared with individuals not self-medicating, self-medicating participants were younger (t test P=0.027), more often started the use of cannabis before the age of 24 (χ2
P=0.022), and more often used dried cannabis (χ2
P=0.001). There was no significant difference found between participants who self-medicated and those who did not in terms of the frequency of moderate to severe psychological distress (χ2
P=0.954) or high levels of pain catastrophizing (χ2
P=0.179). Participants with mild pain were mostly self-medicating (68.0% vs. 32.0%), as were those with moderate (57.1% vs. 42.9%) and severe (100.0% vs. 0.0%) pain.
TABLE 2
Sociodemographic Profiles According to Self-Medication Status
Cannabis self-medication (n=45), n (%)
Using cannabis under professional guidance (n=28), n (%)
P
Age (y), mean±SD
52.22±12.08
57.79±11.40
0.027
Sex at birth
Female
33 (73.3)
23 (82.1)
0.284
Male
12 (26.7)
5 (17.9)
Sex identity*
Woman
32 (71.1)
23 (82.1)
0.478
Man
12 (26.7)
5 (17.9)
Questioning
1 (2.2)
0
Region of residence†
Remote
11 (24.4)
4 (14.8)
0.253
Nonremote
34 (75.6)
23 (85.2)
Employed (full-time or part-time)
20 (44.5)
7 (25.0)
0.094
Family annual income
Under $25,000
9 (20.0)
4 (14.3)
0.889
$25,000-$49,999
12 (26.7)
8 (28.6)
$50,000-$74,999
10 (22.2)
5 (17.9)
$75,000-$99,999
5 (11.1)
4 (14.3)
$100,000 and over
8 (17.8)
7 (25.0)
Prefers not to answer
1 (2.2)
0
Postsecondary education
36 (80.0)
22 (78.6)
0.883
Cannabis use before the age of 24 (yes vs. no)
30 (66.7)
11 (39.3)
0.022
Cannabis products used in the last 12 months‡
28 (62.2)
21 (75.0)
0.258
Liquid extracts or concentrates (eg, oil)
29 (64.4)
7 (25.0)
0.001
Dried flowers or leaves, buds Pills and capsules
13 (28.9)
11 (39.3)
0.358
Frequency of use (number of times/day)
1.92±1.67
2.13±1.57
0.343
*
Sex identity choices: woman, man, nonbinary, genderqueer, transgender, 2-spirited, questioning, none of the above, other, and I prefer not to answer.
†
One missing value. Remote resource regions as defined by Revenu Quebec (ie, the provincial revenue agency) are: Bas-Saint-Laurent, Saguenay–Lac-Saint-Jean, Abitibi- Témiscamingue, Côte-Nord, Nord-du-Québec, Gaspésie–Îles-de-la-Madeleine. Nonremote regions are near a major urban center.
‡
Nonmutually exclusive categories.
Self-Perceived Cannabis Effectiveness and Safety
Figure 3 presents the self-perceived effectiveness of cannabis in reducing pain. These results show that 90% of the participants perceived cannabis to be at least slightly effective, and 49% perceived cannabis to be “very” or “extremely” effective. The 10 participants of 73 (13.7%) who stopped using cannabis it in the last 12 months reported various reasons for stopping including no effectiveness (n=7), adverse side effects (n=6), and too expensive (n=2).
FIGURE 3
Self-perceived cannabis effectiveness in reducing pain. Cannabis refers to all cannabis-based products, including laboratory-synthesized cannabinoids (eg, nabilone).
Most participants (72.6%) estimated that cannabis use posed no or minimal health risk, whereas 27.4% believed it presented a moderate to high health risk; 86.3% of participants believed that cannabis use could lead to an addiction problem, 5.5% did not know, and 8.2% believed that it could not lead to addiction. When compared with opioids, 83.6% of participants felt that cannabis was safer, and when compared with other illicit drugs, 100% they also felt that cannabis was safer. When comparing self-medicating and nonself-medicating participants, 37.8% versus 10.7% perceived cannabis to pose moderate to high health risk (Fisher exact test P=0.10) and 93.3% versus 85.7% perceived cannabis to be effective in some way in reducing pain (Fisher exact test P=0.249).
Sample Characteristics
Table 1 presents the characteristics of the 73 participants included in the study. Most participants were females (sex at birth; 76.7%) and identified as women (sex identity; 75.3%). The mean age of the participants was 54.36±12.05 years old (range: 30 to 79). The most reported specific pain condition was fibromyalgia (n=23, 31.5%). When looking at pain locations, most participants experienced back pain (76.7%) and 67 participants had multisite pain (91.8%). Twenty-seven participants presented moderate to severe psychological distress (38.0%), and most participants presented moderate to high levels of pain catastrophizing (n=51, 71.8%). A total of 62 participants (84.9%) were current cannabis users and 10 participants (13.70%) stopped using it in the last 12 months (1 missing value). The majority (82.2%) of participants used cannabis at least once a day in the previous 12 months.
TABLE 1
Sample Demographics and Cannabis Use Characteristics
Sociodemographic profile
Participants (n=73), n (%)
Age (y), mean±SD
54.4±12.1
Range
(30-79)
Median
55
Sex at birth
Female
56 (76.7)
Male
17 (23.3)
Sex identity*
Woman
55 (75.3)
Man
17 (23.3)
Questioning
1 (1.4)
Country of birth
Canada
71 (97.3)
Other
2 (2.7)
Region of residence†
Remote
15 (20.8)
Nonremote
57 (79.2)
Employed (full-time or part-time)
27 (37.0)
Family annual income
Under $25,000
13 (17.8)
$25,000-$49,999
20 (27.4)
$50,000-$74,999
15 (20.6)
$75,000-$99,999
9 (12.3)
$100,000 and over
15 (20.6)
Prefers not to answer
1 (1.4)
Postsecondary education
58 (79.5)
Pain profile
Most common self-reported pain locations‡
Back
56 (76.7)
Legs
18 (24.7)
Shoulders
17 (23.3)
Neck
15 (20.5)
Knees
14 (19.2)
Generalized pain (yes vs. no)
27 (37.0)
Multisite pain (≥2 sites)
67 (91.8)
Most common self-reported pain conditions‡
Fibromyalgia
23 (31.5)
Osteoarthritis
18 (24.7)
Herniated disc
13 (17.8)
Pain duration (≥10 years)
59 (83.1)
Average pain intensity in the past 7 days (0 to 10 NRS), mean±SD
Range
5.2±1.7
Median
(1-10) 5
Recoded
Mild (1-4)
25 (35.2)
Moderate (5-7)
42 (59.2)
Severe (8-10)
4 (5.6)
Presence of neuropathic pain (DN4)
43 (58.9)
Pain interference (BPI score, 0-10), mean±SD
5.2±2.1
Catastrophizing (0-16)
Low: 0-5
20 (28.2)
Moderate: 6-8
16 (22.5)
High: 9-16
35 (49.3)
Psychological distress (PHQ-4)
None: 0-2
17 (23.9)
Mild: 3-5
27 (38.0)
Moderate: 6-8
16 (22.5)
Severe: 9-12
11 (15.5)
Cannabis use profile
Reasons for use‡
Pain
60 (96.8)
Sleep
49 (79.0)
Stress and anxiety
26 (41.9)
For pleasure
21 (33.9)
Mood
18 (29.0)
Appetite
9 (14.5)
Cannabis products used in the last 12 months‡
Liquid extracts or concentrates (eg, oil)
49 (67.1)
Dried flowers or leaves, buds
36 (49.3)
Pills and capsules
24 (32.9)
Edibles
19 (26.0)
Hashish (resin or pollen)
12 (16.4)
Beverages
5 (6.9)
Extracts or solid concentrates
2 (2.7)
Concentrations
THC-dominant products
13 (17.8)
CBD-dominant products
20 (27.4)
Balanced products
4 (5.5)
Several products with different combinations
33 (45.2)
Do not know
3 (4.1)
Frequency of use (number of times/day), mean±SD
2.0±1.6
Cannabis use before the age of 24 (yes vs. no)
41 (56.2)
0% missing values for all presented variables except for the region of residence (1 missing value).
BPI indicates Brief Pain Inventory; CBD, Cannabidiol; NRS, Numeric Rating Scale; PHQ-4, Patient Health Questionnaire; THC, Δ-9-thetrahydrocannabinol.
*
Sex identity choices: woman, man, nonbinary, genderqueer, transgender, 2-spirit, questioning, none of the above, other, and I prefer not to answer.
†
Six remote resource regions as defined by Revenu Quebec (ie, the provincial revenue agency) are: Bas-Saint-Laurent, Saguenay–Lac-Saint-Jean, Abitibi-Témiscamingue, Côte-Nord, Nord-du-Québec, Gaspésie–Îles-de-la-Madeleine. Nonremote regions are near a major urban center.
‡
Nonmutually exclusive categories.
Figure 1 compares the methods of cannabis use between the present study population living with chronic pain and the general population (2022 Quebec Cannabis Survey55) that includes individuals with and without chronic pain (comparisons of the proportions without statistical testing). While smoked cannabis predominated in the general population (81.6% vs. 42.5% in our sample), people of the community were less likely to opt for oil-based cannabis products (29.5% vs. 67.1% in our sample).
FIGURE 1
Methods of cannabis use. Oral drops and sprays include all oil-based products. Dabbing: inhalation with a hot knife or nail.
Cannabis Self-Medication for Pain Relief
Figure 2 presents the tree diagram showing the distribution of participants according to their user profiles (authorization for medical cannabis, legal nonmedical cannabis use, and guidance by a health care professional). Up to 61.6% (n=45) of our sample reported using cannabis for pain relief without the guidance of a HCP, that is, were self-medicating with cannabis (95% CI: 49.52-72.79). Among those, 40.0% (n=18) held a medical authorization. In the whole sample, this represented a total of 24.7% who reported holding an authorization for medical cannabis and being self-medicating.
FIGURE 2
Tree diagram (conceptual map) of the possible uses of cannabis. Medical cannabis: cannabis used by individuals holding medical authorization under the Cannabis Act in Canada. Nonmedical cannabis: cannabis purchased from a legal recreational cannabis store. Professional guidance: expert advice, recommendations, insights, assistance from a health care professional.
None of the sex identity-stratified or age-stratified results yielded statistically significant differences. Sex identity-stratified results revealed no statistically significant differences between the proportion of women and men self-medicating (58.2% vs. 70.6%; P=0.284). As much as 60.00% of women in our sample held authorization for medical cannabis, whereas 41.2% of men did, but this difference was not statistically significant (χ2
P=0.198). A similar proportion of women and men participants used legal nonmedical cannabis (65.5% vs. 64.7%; χ2
P=0.917). Those proportions are not mutually exclusive as participants can use both. Participants aged 65 and over (vs. below 65) had similar profiles of self-medication with cannabis (63.2% vs. 61.1%; χ2
P=0.875). When comparing older and younger participants, no statistically significant differences were found, that is, proportions using legal nonmedical cannabis were 73.7% vs. 63.0% (Fisher exact test P=0.290); proportions using medical cannabis were 47.4% vs. 57.4% (χ2
P=0.450).
Table 2 shows the characteristics of participants depending on whether they were self-medicating with cannabis or using it under the guidance of a registered HCP. As compared with individuals not self-medicating, self-medicating participants were younger (t test P=0.027), more often started the use of cannabis before the age of 24 (χ2
P=0.022), and more often used dried cannabis (χ2
P=0.001). There was no significant difference found between participants who self-medicated and those who did not in terms of the frequency of moderate to severe psychological distress (χ2
P=0.954) or high levels of pain catastrophizing (χ2
P=0.179). Participants with mild pain were mostly self-medicating (68.0% vs. 32.0%), as were those with moderate (57.1% vs. 42.9%) and severe (100.0% vs. 0.0%) pain.
TABLE 2
Sociodemographic Profiles According to Self-Medication Status
Cannabis self-medication (n=45), n (%)
Using cannabis under professional guidance (n=28), n (%)
P
Age (y), mean±SD
52.22±12.08
57.79±11.40
0.027
Sex at birth
Female
33 (73.3)
23 (82.1)
0.284
Male
12 (26.7)
5 (17.9)
Sex identity*
Woman
32 (71.1)
23 (82.1)
0.478
Man
12 (26.7)
5 (17.9)
Questioning
1 (2.2)
0
Region of residence†
Remote
11 (24.4)
4 (14.8)
0.253
Nonremote
34 (75.6)
23 (85.2)
Employed (full-time or part-time)
20 (44.5)
7 (25.0)
0.094
Family annual income
Under $25,000
9 (20.0)
4 (14.3)
0.889
$25,000-$49,999
12 (26.7)
8 (28.6)
$50,000-$74,999
10 (22.2)
5 (17.9)
$75,000-$99,999
5 (11.1)
4 (14.3)
$100,000 and over
8 (17.8)
7 (25.0)
Prefers not to answer
1 (2.2)
0
Postsecondary education
36 (80.0)
22 (78.6)
0.883
Cannabis use before the age of 24 (yes vs. no)
30 (66.7)
11 (39.3)
0.022
Cannabis products used in the last 12 months‡
28 (62.2)
21 (75.0)
0.258
Liquid extracts or concentrates (eg, oil)
29 (64.4)
7 (25.0)
0.001
Dried flowers or leaves, buds Pills and capsules
13 (28.9)
11 (39.3)
0.358
Frequency of use (number of times/day)
1.92±1.67
2.13±1.57
0.343
*
Sex identity choices: woman, man, nonbinary, genderqueer, transgender, 2-spirited, questioning, none of the above, other, and I prefer not to answer.
†
One missing value. Remote resource regions as defined by Revenu Quebec (ie, the provincial revenue agency) are: Bas-Saint-Laurent, Saguenay–Lac-Saint-Jean, Abitibi- Témiscamingue, Côte-Nord, Nord-du-Québec, Gaspésie–Îles-de-la-Madeleine. Nonremote regions are near a major urban center.
‡
Nonmutually exclusive categories.
Self-Perceived Cannabis Effectiveness and Safety
Figure 3 presents the self-perceived effectiveness of cannabis in reducing pain. These results show that 90% of the participants perceived cannabis to be at least slightly effective, and 49% perceived cannabis to be “very” or “extremely” effective. The 10 participants of 73 (13.7%) who stopped using cannabis it in the last 12 months reported various reasons for stopping including no effectiveness (n=7), adverse side effects (n=6), and too expensive (n=2).
FIGURE 3
Self-perceived cannabis effectiveness in reducing pain. Cannabis refers to all cannabis-based products, including laboratory-synthesized cannabinoids (eg, nabilone).
Most participants (72.6%) estimated that cannabis use posed no or minimal health risk, whereas 27.4% believed it presented a moderate to high health risk; 86.3% of participants believed that cannabis use could lead to an addiction problem, 5.5% did not know, and 8.2% believed that it could not lead to addiction. When compared with opioids, 83.6% of participants felt that cannabis was safer, and when compared with other illicit drugs, 100% they also felt that cannabis was safer. When comparing self-medicating and nonself-medicating participants, 37.8% versus 10.7% perceived cannabis to pose moderate to high health risk (Fisher exact test P=0.10) and 93.3% versus 85.7% perceived cannabis to be effective in some way in reducing pain (Fisher exact test P=0.249).
DISCUSSION
The present descriptive study described cannabis self-medication for pain relief in people living with chronic pain. Their perceptions toward the effectiveness and safety of cannabis were also explored. To our knowledge, this is the first postlegalization study to investigate cannabis self-medication in people living with chronic pain in Canada that covers both medical and nonmedical users. Positive perceptions regarding the effectiveness and safety of cannabis, as well as the widespread practice of self-medication (61.6%), should be a matter of concern. Surprising pathways to self-medication were revealed.
Our sample has demonstrated similar characteristics (age, postsecondary education, pain intensity, presence of neuropathic pain, reasons for cannabis use, and concentrations) when compared with other chronic pain populations studies,28,66–69 except regarding the proportion of female participants, pain duration, and the methods of cannabis use. The proportion of female participants in the current study (77%) was observed to be higher than that reported in chronic pain random samples (55% to 65%).51 When compared with studies on cannabis users, this difference is even more important with proportions of females ranging from 45% to 55%.28 First, this could be explained by the COPE Cohort51 and the present spin-off study web-based recruitment methods. Given that females are more likely to work in an online environment70 and use social media,71 this could have influenced the proportion of females in the present sample (which is greater than random samples of individuals living with chronic pain). It is probably not sex differences in cannabis use that explain this trend in our sample, as the prevalence of cannabis use among women and men living with chronic pain was found to be similar.25 This over-representation of women thus calls for the stratification of prevalence estimates to determine if results are genuinely extrapolatable. In our case, no statistically significant difference was found between women and men regarding the prevalence of self-medication, minimizing the possibility of sampling bias. About duration of pain, the proportion of participants living with pain ≥10 years was higher (83.1%) in this study than what is found in other random and nonrandom samples of people living with chronic pain (47% to 53%51,66,67,72). We could hypothesize that cannabis is not the first treatment attempted by patients, which is consistent with other findings.33 In terms of methods of use, sprays and drops were preferred by people living with chronic pain in our sample, a trend markedly less prevalent within the general population using cannabis.54,55 In other studies involving medical and nonmedical cannabis users with chronic pain, sprays and drops methods were less prevalent (smoked cannabis was preferred).28 These differences between the present sample and the general population or previous studies in individuals with chronic pain could possibly be explained by the proportion of participants (55%) using medical cannabis. Two recent studies revealed that oil-based cannabis products were the most commonly used among individuals authorized for medical cannabis.73,74 All things considered, these differences underscore the importance of specific studies on the reality of people living with chronic pain to guide the establishment of policies, preventive efforts, and interventions promoting optimal use. This subgroup is present within the general population (1 of 5 individuals lives with chronic pain), but our study suggests that it does not present the same profile as found in large federal and provincial surveys (eg, less pulmonary risk associated with smoked cannabis).
Cannabis Self-Medication for Pain Relief
Our study revealed that 61.6% of individuals living with chronic pain and using cannabis are self-medicating. Legalization of nonmedical (recreational) cannabis in Canada in 2018 has facilitated access to cannabis, opening the door to self-medication. Before the legalization (when considering medical cannabis only), 15% of people living with chronic pain in Canada self-medicated with cannabis.26 To the best of our knowledge, no study has yet estimated this prevalence among individuals living with chronic pain since the legalization. A study on self-medication with cannabis (for any health problems) conducted in Quebec reported that 53% of the participants (n=489) self-medicated with cannabis for reducing pain.45
An important proportion of individuals living with chronic pain and using cannabis reported using both medical and legal nonmedical cannabis (21%). Similar results showing an overlap between medical and nonmedical cannabis have been reported in a previous Canadian study, where ∼80% of the medical cannabis users were also using nonmedical cannabis.75 This poses a challenge for the therapeutic application of cannabis and its medical oversight,76 as people who use both medical and nonmedical cannabis are more likely to exhibit cannabis use problems.75 As compared with medical cannabis, using nonmedical cannabis is associated with various disadvantages and risks such as uncertainty about strains, doses, and purity, risk of using more cannabis as well as the lack of insurance coverage.43,44,75 Although it was expected that using medical cannabis would have the advantage of receiving professional guidance, interestingly, participants in the present study who held medical cannabis authorization were not necessarily accompanied or supervised by a HCP. This highlights one of the most important limitations of the Cannabis Act in Canada,41 in that it does not ensure that patients consuming cannabis for medical purposes will receive support from a HCP, as the law does not mandate this medical guidance.42 Furthermore, HCPs who would be interested in getting involved in follow-up care currently have limited guidelines to rely upon.77 Globally, in a real-life context, medical cannabis and legal nonmedical cannabis are interconnected since a large proportion of patients utilize both.75 The introduction of the Cannabis Act has enabled the procurement of products from diverse sources for the treatment of medical conditions according to the patients’ preferences (eg, patients perceive legal nonmedical products as being more cost-effective).78 In addition to this reality, there is the presence of self-medication, which complicates the consumption profiles of users and exposes them to various health risks (eg, dosing challenges, no evaluation of potential interaction with medications, no support regarding occupational hazards).43,44 This raises concerns regarding the risks of self-medication and highlights the need for medical supervision related to cannabis use. We should note that those are potential risks as we did not find any evidence comparing the actual risks between patients self-medicating and those who are not. Moreover, in the present study, all 4 participants who reported severe pain were self-medicating. Globally, our descriptive study represents a first attempt to describe self-medication practices, but further studies should be conducted at this level.
Different unexpected pathways to self-medication were revealed in the present study. This opens the door to a variety of new research questions that could be explored, for example, through qualitative studies: What motivates individuals to self-medicate? What are the unmet needs that could lead to self-medications? Why do some holders of medical cannabis authorization lack the follow-up support? How do HCPs operationalize support for nonmedical cannabis use? How do illegal sources of nonmedical cannabis come into play? While frameworks exist for self-medication with over-the-counter medications,38 new frameworks should be established to guide research specifically on cannabis use among people living with chronic pain.
Self-Perceived Cannabis Effectiveness and Safety
The majority of participants felt that cannabis was effective in some ways in alleviating pain. It is worth mentioning that this assessment reflects the overall impression of pain improvement felt by participants, rather than a quantifiable measure of pain intensity before and after cannabis use. These results are consistent with previous findings showing that 78% of patients using cannabis to alleviate their pain perceived a moderate improvement in their condition.26 From a qualitative perspective, the literature suggests that patients often report a sense of relief when using cannabis, even if a decrease in pain intensity is not observed on the visual analog scale or other measurement tools79 underlining the importance of diversification of outcome measures in cannabis efficacy and effectiveness studies.
As for the perceived safety of cannabis, participants in this study felt somewhat confident, with less than a third of them reporting a moderate to high health risk linked to its utilization. This proportion is notably small compared with the general populations, where three-quarter of individuals perceive cannabis as posing a moderate to high health risk.54,55 Cannabis may mitigate experiences of pain and benefit mental health for those with chronic pain,80 without causing related cannabis problems.81 Han et al82 also suggested that the risk perception among patients with disabling illnesses may decrease considering changing priorities in the setting of chronic or advanced disease.82 However, the large majority of participants in the present study (86%) believed that cannabis use presented a risk of addiction. This proportion is higher than what was found in a recent study from Goodman and Hammond,83 where 51% to 62% of the participants perceived that cannabis use presented a risk of addiction. In that study, which included participants from Canada and the United States, the Canadian participants had a higher perception of risk compared with their American counterparts. This disparity among Canadian participants and the participants in our study could be attributed to the numerous cannabis consumption awareness campaigns broadcasted in Canada,84,85 particularly in Quebec.86 In the present study, it was surprising to see that so many people perceive this risk but still engage in self-medication with cannabis. In fact, when comparing self-medicating and nonself-medicating participants, those who self-medicated more often perceived cannabis to pose a moderate to high health risk. Florimbio et al87 suggest that the risk perception vary by cannabis consumption patterns and method. Therefore, the self-medicating participants in our study were more likely to use dried flower and smoking methods, which may have influenced their risk perception. Another hypothesis is that individuals self-medicating with cannabis recognize their limited knowledge of dosage, interactions with medications, and overall safety. Without health care guidance, reliance on anecdotal information may heighten concerns about potential adverse effects and health risks. In addition, the lack of professional oversight may lead to uncertainties about the quality and purity of cannabis products, further increasing perceived health risks among self-medicating users. Such hypotheses should be further explored in future studies.
Strengths and Limitations
This study has several strengths, including the utilization of telephone interviews, a standardized questionnaire, and questions derived from existing surveys and recognized validated measurement scales. The study incorporated a community sample of participants from 16 of the 17 administrative regions of the province of Quebec, ensuring diversity, particularly among individuals and regions that are often underrepresented in studies where recruitment is conducted in university-affiliated clinics. However, some limitations are worth mentioning. Much like all studies involving cannabis users, individuals who are well informed about or have a positive attitude or tolerance toward cannabis might have been more inclined to participate in the study, thereby creating the potential for selection bias. While telephone interviews led to a number of strengths, they were not anonymous (possibility of social desirability bias). In fact, cannabis still carries an illicit connotation, even though it has been legal in Canada for 5 years.55 The present descriptive study is, however, a first step toward understanding self-medication among individuals living with chronic pain who use cannabis. Future studies with larger sample sizes and different designs (eg, case-control studies, observational studies) will be needed to deepen the understanding of the biopsychosocial determinants of self-medication using multivariable analyses. One should also keep in mind that generalizing results is difficult outside the legislative context in Canada regarding cannabis use. Finally, this study is also limited as it focuses on medical and legal nonmedical cannabis use. Nonmedical cannabis obtained from illegal sources should be the focus of further investigations.
Cannabis Self-Medication for Pain Relief
Our study revealed that 61.6% of individuals living with chronic pain and using cannabis are self-medicating. Legalization of nonmedical (recreational) cannabis in Canada in 2018 has facilitated access to cannabis, opening the door to self-medication. Before the legalization (when considering medical cannabis only), 15% of people living with chronic pain in Canada self-medicated with cannabis.26 To the best of our knowledge, no study has yet estimated this prevalence among individuals living with chronic pain since the legalization. A study on self-medication with cannabis (for any health problems) conducted in Quebec reported that 53% of the participants (n=489) self-medicated with cannabis for reducing pain.45
An important proportion of individuals living with chronic pain and using cannabis reported using both medical and legal nonmedical cannabis (21%). Similar results showing an overlap between medical and nonmedical cannabis have been reported in a previous Canadian study, where ∼80% of the medical cannabis users were also using nonmedical cannabis.75 This poses a challenge for the therapeutic application of cannabis and its medical oversight,76 as people who use both medical and nonmedical cannabis are more likely to exhibit cannabis use problems.75 As compared with medical cannabis, using nonmedical cannabis is associated with various disadvantages and risks such as uncertainty about strains, doses, and purity, risk of using more cannabis as well as the lack of insurance coverage.43,44,75 Although it was expected that using medical cannabis would have the advantage of receiving professional guidance, interestingly, participants in the present study who held medical cannabis authorization were not necessarily accompanied or supervised by a HCP. This highlights one of the most important limitations of the Cannabis Act in Canada,41 in that it does not ensure that patients consuming cannabis for medical purposes will receive support from a HCP, as the law does not mandate this medical guidance.42 Furthermore, HCPs who would be interested in getting involved in follow-up care currently have limited guidelines to rely upon.77 Globally, in a real-life context, medical cannabis and legal nonmedical cannabis are interconnected since a large proportion of patients utilize both.75 The introduction of the Cannabis Act has enabled the procurement of products from diverse sources for the treatment of medical conditions according to the patients’ preferences (eg, patients perceive legal nonmedical products as being more cost-effective).78 In addition to this reality, there is the presence of self-medication, which complicates the consumption profiles of users and exposes them to various health risks (eg, dosing challenges, no evaluation of potential interaction with medications, no support regarding occupational hazards).43,44 This raises concerns regarding the risks of self-medication and highlights the need for medical supervision related to cannabis use. We should note that those are potential risks as we did not find any evidence comparing the actual risks between patients self-medicating and those who are not. Moreover, in the present study, all 4 participants who reported severe pain were self-medicating. Globally, our descriptive study represents a first attempt to describe self-medication practices, but further studies should be conducted at this level.
Different unexpected pathways to self-medication were revealed in the present study. This opens the door to a variety of new research questions that could be explored, for example, through qualitative studies: What motivates individuals to self-medicate? What are the unmet needs that could lead to self-medications? Why do some holders of medical cannabis authorization lack the follow-up support? How do HCPs operationalize support for nonmedical cannabis use? How do illegal sources of nonmedical cannabis come into play? While frameworks exist for self-medication with over-the-counter medications,38 new frameworks should be established to guide research specifically on cannabis use among people living with chronic pain.
Self-Perceived Cannabis Effectiveness and Safety
The majority of participants felt that cannabis was effective in some ways in alleviating pain. It is worth mentioning that this assessment reflects the overall impression of pain improvement felt by participants, rather than a quantifiable measure of pain intensity before and after cannabis use. These results are consistent with previous findings showing that 78% of patients using cannabis to alleviate their pain perceived a moderate improvement in their condition.26 From a qualitative perspective, the literature suggests that patients often report a sense of relief when using cannabis, even if a decrease in pain intensity is not observed on the visual analog scale or other measurement tools79 underlining the importance of diversification of outcome measures in cannabis efficacy and effectiveness studies.
As for the perceived safety of cannabis, participants in this study felt somewhat confident, with less than a third of them reporting a moderate to high health risk linked to its utilization. This proportion is notably small compared with the general populations, where three-quarter of individuals perceive cannabis as posing a moderate to high health risk.54,55 Cannabis may mitigate experiences of pain and benefit mental health for those with chronic pain,80 without causing related cannabis problems.81 Han et al82 also suggested that the risk perception among patients with disabling illnesses may decrease considering changing priorities in the setting of chronic or advanced disease.82 However, the large majority of participants in the present study (86%) believed that cannabis use presented a risk of addiction. This proportion is higher than what was found in a recent study from Goodman and Hammond,83 where 51% to 62% of the participants perceived that cannabis use presented a risk of addiction. In that study, which included participants from Canada and the United States, the Canadian participants had a higher perception of risk compared with their American counterparts. This disparity among Canadian participants and the participants in our study could be attributed to the numerous cannabis consumption awareness campaigns broadcasted in Canada,84,85 particularly in Quebec.86 In the present study, it was surprising to see that so many people perceive this risk but still engage in self-medication with cannabis. In fact, when comparing self-medicating and nonself-medicating participants, those who self-medicated more often perceived cannabis to pose a moderate to high health risk. Florimbio et al87 suggest that the risk perception vary by cannabis consumption patterns and method. Therefore, the self-medicating participants in our study were more likely to use dried flower and smoking methods, which may have influenced their risk perception. Another hypothesis is that individuals self-medicating with cannabis recognize their limited knowledge of dosage, interactions with medications, and overall safety. Without health care guidance, reliance on anecdotal information may heighten concerns about potential adverse effects and health risks. In addition, the lack of professional oversight may lead to uncertainties about the quality and purity of cannabis products, further increasing perceived health risks among self-medicating users. Such hypotheses should be further explored in future studies.
Strengths and Limitations
This study has several strengths, including the utilization of telephone interviews, a standardized questionnaire, and questions derived from existing surveys and recognized validated measurement scales. The study incorporated a community sample of participants from 16 of the 17 administrative regions of the province of Quebec, ensuring diversity, particularly among individuals and regions that are often underrepresented in studies where recruitment is conducted in university-affiliated clinics. However, some limitations are worth mentioning. Much like all studies involving cannabis users, individuals who are well informed about or have a positive attitude or tolerance toward cannabis might have been more inclined to participate in the study, thereby creating the potential for selection bias. While telephone interviews led to a number of strengths, they were not anonymous (possibility of social desirability bias). In fact, cannabis still carries an illicit connotation, even though it has been legal in Canada for 5 years.55 The present descriptive study is, however, a first step toward understanding self-medication among individuals living with chronic pain who use cannabis. Future studies with larger sample sizes and different designs (eg, case-control studies, observational studies) will be needed to deepen the understanding of the biopsychosocial determinants of self-medication using multivariable analyses. One should also keep in mind that generalizing results is difficult outside the legislative context in Canada regarding cannabis use. Finally, this study is also limited as it focuses on medical and legal nonmedical cannabis use. Nonmedical cannabis obtained from illegal sources should be the focus of further investigations.
CONCLUSION
Our study showed that among adults living with chronic pain and reporting cannabis use, 6 of 10 (61.6%) endorsed self-medicating with cannabis, and many of them did so despite having authorization for medical cannabis. Unexpected pathways to self-medication have been uncovered and deserve further exploration through more extensive research. Moreover, 1 participant of 5 used both medical and legal nonmedical cannabis. Since cannabis research is often organized around medical versus legal nonmedical cannabis, it is appropriate to say that these 2 silos need to be broken, considering that patients can use cannabis obtained through recreational channels for therapeutic purposes. This descriptive study showed that in the real world, medical and legal nonmedical cannabis use are 2 connected vessels. This highlights the urgent need for HCPs to become more involved in the supervision of cannabis use to mitigate patient risks. Interested parties, including governments, researchers, HCPs, and funding agencies, need to take this into account. In a context where several countries are considering loosening their laws regarding the use of cannabis, our results are timely and will contribute to the scientific knowledge. |
Title: Mosapride stimulates human 5-HT | Body:
Introduction
Mosapride (Fig. 1A) was developed by a Japanese company (reviewed in: Katoh et al. 2003) and is approved in patients and mainly sold in Japan and some other Asian countries. Mosapride is intended to treat various gastrointestinal diseases (Curran and Robinson 2008). Mosapride has at least one active metabolite (Katoh et al. 2003), called des-fluorobenzyl-mosapride (Fig. 1A), which we studied for comparison. Mosapride acts functionally as a partial agonist compared to 5-HT at 5-HT4 receptors in ligand-binding studies (Tsubouchi et al. 2018). Like many other benzamides (e.g. zacopride), mosapride is also an antagonist at 5-HT3 receptors (Park and Sung 2019). One had developed mosapride because previous work showed that agonists at 5-HT4 receptors were promising agents for treating gastric diseases (Katoh et al. 2003). However, the authors argued that other drugs that increase gastric motility show agonistic or antagonistic effects at other receptors, in addition to their stimulatory effect on 5-HT4 receptors (Katoh et al. 2003). For example, they gave metoclopramide or cisapride (Katoh et al. 2003). For instance, metoclopramide is not only an agonist at 5-HT4-receptors but also acts as an antagonist at D2-dopamine receptors.Fig. 1A Structural formulae of serotonin, des-fluorobenzyl-mosapride and mosapride. Note the benzamide structure in mosapride, the different side chain of mosapride compared to serotonin and the metabolism of mosapride to its active metabolite des-fluorobenzyl-mosapride. B (Scheme): Mechanism(s) of action of serotonin and mosapride in cardiomyocytes. A heptahelical 5-HT4-serotonin receptor is depicted in sarcolemma. The agonist serotonin (5-HT) activates the 5-HT4-serotonin receptor. Thereby, the stimulatory G-protein (Gs) augments the ability of adenylyl cyclases (AC) to generate cAMP. This cAMP can activate cAMP-dependent protein kinases (PKA). Thereafter, PKA phosphorylates and activates target proteins like the L-type calcium channel (LTCC) in the sarcolemma and the ryanodine receptor (RyR) in the sarcoplasmic reticulum (SR). Phosphorylation of phospholamban increases the activity of SR-Ca ATPAse (SERCA). Phosphodiesterase (PDE) III converts cAMP to inactive 5′-AMP in the human heart and is inhibited by cilostamide. Mosapride and des-fluorobenzyl-mosapride may activate human cardiac 5-HT4-serotonin receptors
Thus, metoclopramide can lead to Parkinson-like side effects and elevated prolactin levels (Katoh et al. 2003, Athavale et al. 2020). In parallel, soon after cisapride entered the market, cardiac arrhythmias were reported that were explained by the inhibitory action of cisapride on human ventricular potassium channels (Tack et al. 2012). This manifested in prolonging QT interval on surface ECG (e.g. dog: Matsunaga et al. 2011). At least in isolated perfused rabbit hearts, mosapride concentration dependently increased the duration of the QT interval and increased the incidence of early after-depolarisation (Kii and Ito 2002). Prolongation of QT intervals sometimes leads to torsade de pointes and deadly ventricular fibrillation. Mosapride has the advantage of being about three orders of magnitude less potent than cisapride to inhibit ventricular potassium channels and, thus, less likely to prolong the QT interval and induce arrhythmias by this electrophysiological mechanism (rabbit: Carlsson et al. 1997; rat: Kii et al. 2001, dog: Matsunaga et al. 2011). However, another potassium channel that is expressed in the heart, Kv4.3, is also inhibited by mosapride (Sung and Hahn 2013). This may result in a propensity for arrhythmias in vulnerable patients. Indeed, a clinical registry study noted an increased incidence of arrhythmias in patients taking mosapride compared to non-users (Song et al. 2020).
Moreover, the cardiac effects of mosapride might be beneficial. For instance, doxorubicin is an important anti-cancer drug. Its use is limited by the cardiotoxic effects of doxorubicin. For example, doxorubicin leads to arrhythmia and heart failure in humans (Nishiuchi et al. 2022). Interestingly, in mice treated with doxorubicin as a model of human heart failure, an improvement in cardiac contractility was observed when mosapride was given together with doxorubicin (Nishiuchi et al. 2022). This raises the option of starting a clinical trial with mosapride in cancer patients who need chemotherapy that includes doxorubicin. In this regard, it is crucial to study mosapride in the human heart before such trials can be initiated in earnest.
The data in rabbits, rats and dogs are consistent with an inhibitory action of mosapride on potassium channels but tell nothing about the involvement of 5-HT4 receptors (Fig. 1B) because 5-HT4 receptors are not functionally present in rabbit cardiomyocytes (review: Neumann et al. 2017, 2023a). However, stimulation of 5-HT4 receptors in the heart can lead to cardiac arrhythmias (Neumann et al. 2017, 2023a, Keller et al. 2018). Mosapride has never been studied for its functional effects on human 5-HT4 receptors in the heart. This fact motivated us to initiate the present study.
All inotropic and chronotropic effects of serotonin are mediated via 5-HT4 receptors on human cardiomyocytes (reviews: Kaumann and Levy 2006, Neumann et al. 2017, Neumann et al. 2023a, Fig. 1B). These 5-HT4 receptors are lacking in a functional manner in mouse hearts: serotonin does not increase the force of contraction in isolated mouse cardiac preparations from wild-type mice (WT, Gergs et al. 2010, 2013). To facilitate the study of human 5-HT4 receptors, we previously established a transgenic mouse with overexpression of this receptor (5-HT4-TG) only in the heart, which responds with positive inotropic and positive chronotropic effects to serotonin and other 5-HT4 receptor agonists (Gergs et al. 2010; review: Neumann et al. 2017, 2023a, 2023b). Hence, we decided to test whether mosapride would exert positive inotropic and chronotropic effects in this 5-HT4-TG and not in littermate WT. If that were the case, one would also expect mosapride to stimulate the 5-HT4 receptors in the human heart and thereby increase the force of contraction (Fig. 1B). We used this reasoning with some success in the past. For instance, we found that metoclopramide, cisapride, bufotenin and prucalopride stimulated 5-HT4 receptors in the atrium of 5-HT4-TG as well as the human atrial preparations (HAP) in vitro (e.g. Keller et al. 2018, Neumann et al. 2021a, 2023b).
Hence, we tested the following hypotheses:Mosapride increases the force of contraction and spontaneous beating rate in atrial preparations from 5-HT4-TG (and not WT).Mosapride and des-fluorobenzyl-mosapride increase the force of contraction in HAP via 5-HT4 receptors.
A progress report has been published in abstract form (Neumann et al. 2023c).
Materials and methods
Contractile studies in mice
Mice in this study included transgenic mice (CD1 background), where the full-length human 5-HT4 receptor is overexpressed in the heart driven by α-myosin heavy-chain promoter (5-HT4-TG). The generation and initial characterisation of these mice at biochemical and functional levels was reported some years ago (Gergs et al. 2010). The initial founder was then crossed in mice of the strain CD-1. For comparison, we used littermate wild-type animals (WT). We used mice of random sex about 130 days of age.
In brief, the right or left atrial preparations in the mice were isolated and mounted in organ baths, as previously described (Gergs et al. 2013; Neumann et al. 1998). The bathing solution of the organ baths contained 119.8 mM NaCI, 5.4 mM KCI, 1.8 mM CaCl2, 1.05 mM MgCl2, 0.42 mM NaH2PO4, 22.6 mM NaHCO3, 0.05 mM Na2EDTA, 0.28 mM ascorbic acid and 5.05 mM glucose. The solution was continuously gassed with 95% O2 and 5% CO2 and maintained at 37 °C and pH 7.4 (Neumann et al. 1998). The force of contraction was quantified in electrically paced isolated left atrial preparations. The duration of electrical stimulation with a rectangular impulse of direct current was 5 ms. The voltage was 10% higher than necessary to initiate contraction and the stimulation rate was one beat per second (1 Hz). Muscles were stretched such that the maximum basal force was generated and then allowed to stabilise for 30 min before drug application. Spontaneously beating right atrial preparations in mice were used to study any chronotropic effects. The drug application was as follows: After equilibration was reached, mosapride was added cumulatively to the left or right atrial preparations to establish concentration–response curves.
Next, where indicated, serotonin was cumulatively applied to the preparations to compare the efficacy of mosapride and serotonin. We studied WT (n = 5) and 5-HT4-TG (n = 6) from both genders. The average age was 144 days.
Contractile studies on human preparations
Contractile studies on human preparations were done using the same setup and buffer used in the mouse studies. In brief, the force of contraction was quantified in electrically paced isolated left atrial preparations. The duration of electrical stimulation with a rectangular impulse of direct current was 5 ms. The voltage was 10% higher than necessary to initiate contraction. Muscles were stretched such that the maximum basal force was generated and then allowed to stabilise for 30 min before drug application started. Basal developed force can be seen in the relevant diagrams in this paper labelled with millinewton (mN) in the ordinates under the condition labelled control conditions (Ctr). The samples were obtained from ten male and four female patients aged 45–83. The patients suffered from coronary diseases (two- and three-vessel diseases), atrial fibrillation and hypertension. Drug therapy included metoprolol, furosemide, apixaban, statins and acetylsalicylic acid. The methods used for atrial contraction studies in human samples have been previously published and were not altered in this study (Gergs et al. 2009, 2017). Informed written consent was obtained from all patients.
The drug application was as follows: After equilibration was reached, mosapride was cumulatively added to HAP to establish concentration–response curves. In separate experiments, the first 1 μM cilostamide was given. We waited until a positive inotropic effect on cilostamide had developed and reached a plateau. Then, we constructed a concentration–response curve for mosapride. In some preparations, an antagonist was added (either tropisetron or GR 125487). Then, where indicated, serotonin was cumulatively applied to the preparations. In other experiments, 10 µM mosapride alone was added, and then serotonin was cumulatively applied. These results were compared with separate preparations in which only a concentration response to serotonin was constructed.
Data analysis
Data shown are the means ± standard error of the mean. Statistical significance was estimated using analysis of variance (ANOVA) followed by Bonferroni’s t-test. A p-value < 0.05 was considered significant.
Drugs and materials
( −)-Isoprenaline-( +)-bitartrate, des-4-fluorobenzyl-mosapride (4-amino-5-chloro-2-ethoxy-N-(2-morpholinylmethyl)-benzamide, mosapride (4-amino-5-chloro-2-ethoxy-N-[[4-[(4-fluorophenyl)methyl]-2-morpholinyl]methyl]-benzamide) citrate, serotonin hydrochloride, GR 125487 [1-[2-(methanesulfonamido)ethyl]piperidin-4-yl]methyl 5-fluoro-2-methoxy-1H-indole-3-carboxylate) sulfamate, tropisetron (ICS 205–930, [(1R,5S)-8-methyl-8-azabicyclo[3.2.1]octan-3-yl] 1H-indole-3-carboxylate) hydrochloride and cilostamide (N-cyclohexyl-N-methyl-4-(1,2-dihydro-2-oxo-6-quinolyloxy) butyramide) were purchased from Cayman (via Biomol, Hamburg, Germany), Selleckchem (Cologne, Germany), Tocris (Wiesbaden-Nordenstadt, Germany) or Sigma-Aldrich (now: Merck, Derieich, Germany), respectively. All other chemicals were of the highest purity grade commercially available. Deionised water was used throughout the experiments. Stock solutions were prepared fresh daily.
Contractile studies in mice
Mice in this study included transgenic mice (CD1 background), where the full-length human 5-HT4 receptor is overexpressed in the heart driven by α-myosin heavy-chain promoter (5-HT4-TG). The generation and initial characterisation of these mice at biochemical and functional levels was reported some years ago (Gergs et al. 2010). The initial founder was then crossed in mice of the strain CD-1. For comparison, we used littermate wild-type animals (WT). We used mice of random sex about 130 days of age.
In brief, the right or left atrial preparations in the mice were isolated and mounted in organ baths, as previously described (Gergs et al. 2013; Neumann et al. 1998). The bathing solution of the organ baths contained 119.8 mM NaCI, 5.4 mM KCI, 1.8 mM CaCl2, 1.05 mM MgCl2, 0.42 mM NaH2PO4, 22.6 mM NaHCO3, 0.05 mM Na2EDTA, 0.28 mM ascorbic acid and 5.05 mM glucose. The solution was continuously gassed with 95% O2 and 5% CO2 and maintained at 37 °C and pH 7.4 (Neumann et al. 1998). The force of contraction was quantified in electrically paced isolated left atrial preparations. The duration of electrical stimulation with a rectangular impulse of direct current was 5 ms. The voltage was 10% higher than necessary to initiate contraction and the stimulation rate was one beat per second (1 Hz). Muscles were stretched such that the maximum basal force was generated and then allowed to stabilise for 30 min before drug application. Spontaneously beating right atrial preparations in mice were used to study any chronotropic effects. The drug application was as follows: After equilibration was reached, mosapride was added cumulatively to the left or right atrial preparations to establish concentration–response curves.
Next, where indicated, serotonin was cumulatively applied to the preparations to compare the efficacy of mosapride and serotonin. We studied WT (n = 5) and 5-HT4-TG (n = 6) from both genders. The average age was 144 days.
Contractile studies on human preparations
Contractile studies on human preparations were done using the same setup and buffer used in the mouse studies. In brief, the force of contraction was quantified in electrically paced isolated left atrial preparations. The duration of electrical stimulation with a rectangular impulse of direct current was 5 ms. The voltage was 10% higher than necessary to initiate contraction. Muscles were stretched such that the maximum basal force was generated and then allowed to stabilise for 30 min before drug application started. Basal developed force can be seen in the relevant diagrams in this paper labelled with millinewton (mN) in the ordinates under the condition labelled control conditions (Ctr). The samples were obtained from ten male and four female patients aged 45–83. The patients suffered from coronary diseases (two- and three-vessel diseases), atrial fibrillation and hypertension. Drug therapy included metoprolol, furosemide, apixaban, statins and acetylsalicylic acid. The methods used for atrial contraction studies in human samples have been previously published and were not altered in this study (Gergs et al. 2009, 2017). Informed written consent was obtained from all patients.
The drug application was as follows: After equilibration was reached, mosapride was cumulatively added to HAP to establish concentration–response curves. In separate experiments, the first 1 μM cilostamide was given. We waited until a positive inotropic effect on cilostamide had developed and reached a plateau. Then, we constructed a concentration–response curve for mosapride. In some preparations, an antagonist was added (either tropisetron or GR 125487). Then, where indicated, serotonin was cumulatively applied to the preparations. In other experiments, 10 µM mosapride alone was added, and then serotonin was cumulatively applied. These results were compared with separate preparations in which only a concentration response to serotonin was constructed.
Data analysis
Data shown are the means ± standard error of the mean. Statistical significance was estimated using analysis of variance (ANOVA) followed by Bonferroni’s t-test. A p-value < 0.05 was considered significant.
Drugs and materials
( −)-Isoprenaline-( +)-bitartrate, des-4-fluorobenzyl-mosapride (4-amino-5-chloro-2-ethoxy-N-(2-morpholinylmethyl)-benzamide, mosapride (4-amino-5-chloro-2-ethoxy-N-[[4-[(4-fluorophenyl)methyl]-2-morpholinyl]methyl]-benzamide) citrate, serotonin hydrochloride, GR 125487 [1-[2-(methanesulfonamido)ethyl]piperidin-4-yl]methyl 5-fluoro-2-methoxy-1H-indole-3-carboxylate) sulfamate, tropisetron (ICS 205–930, [(1R,5S)-8-methyl-8-azabicyclo[3.2.1]octan-3-yl] 1H-indole-3-carboxylate) hydrochloride and cilostamide (N-cyclohexyl-N-methyl-4-(1,2-dihydro-2-oxo-6-quinolyloxy) butyramide) were purchased from Cayman (via Biomol, Hamburg, Germany), Selleckchem (Cologne, Germany), Tocris (Wiesbaden-Nordenstadt, Germany) or Sigma-Aldrich (now: Merck, Derieich, Germany), respectively. All other chemicals were of the highest purity grade commercially available. Deionised water was used throughout the experiments. Stock solutions were prepared fresh daily.
Results
As seen in this original recording, mosapride exerted a concentration- and time-dependent positive inotropic effect in the left atrial preparations from 5-HT4-TG (Fig. 2B). In contrast, mosapride failed to raise the force of contraction in the left atrial preparations from WT (Fig. 2A). The latter finding is consistent with our previous research. 5-HT cannot raise force in the atrium from WT (Gergs et al. 2010). The expression of the 5-HT4 receptor or the coupling of the 5-HT4 receptor is too small to affect contractility in the mouse heart (discussed in Gergs et al. 2010). If mosapride behaves like 5-HT, mosapride should affect the beating rate in right atrial preparations of 5-HT4-TG. Furthermore, we noticed a small time- and concentration-dependent positive chronotropic effect of mosapride in right atrial preparations from 5-HT4-TG (Fig. 2D), which is plotted in Fig. 3D in an original recording.Fig. 2Original recordings in electrically driven left atrial preparations from 5-HT4-TG (B) or from WT (A). Mosapride induced a time- and concentration-dependent positive inotropic effect in 5-HT4-TG (B) but not WT (A). Original recordings of the effect of mosapride in spontaneously beating right atrial preparations from 5-HT4-TG (D) or from WT (C). Mosapride induced a time- and concentration-dependent positive chronotropic effect in 5-HT4-TG (D) but not WT (C). Ordinates in panels A and B in millinewton (mN). Ordinates in panels C and D in beats per minute (bpm). Horizontal bars indicate time in minutes (min). Arrows indicate at what time points mosapride was cumulatively applied to the organ bath. Concentrations of mosapride are given in negative logarithmic units in the horizontal arrow in the topFig. 3Summarised concentration–response curves for the effect of mosapride on force of contraction in % of pre-drug value (A) in mN (B). Furthermore, the rate of tension development and the rate of relaxation are given in panel C. The effect of mosapride on time to peak tension (T1, D) and time of relaxation (T2, D). The effect of mosapride on beating rate is given in panel E. Ordinates in panels A and B are in milliseconds (ms). Rate of contraction and rate of relaxation are in panel C in mN/s. Contraction times are in ms in panel D. Abscissae in panels A–E indicate concentrations of mosapride in negative logarithm of molar concentrations. Significant differences versus 5-HT4-TG are indicated by x. Numbers in brackets mean number of experiments
In contrast, we detected no positive inotropic effect in the right atrial preparations from WT (Figs. 2C and 3C). We also assessed muscle tension parameters in the left atrium. Several such experiments are summarised regarding the force of contraction measured as the percent of pre-drug value or mN in the left atrium, as seen in Fig. 3A and B. This effect gained statistical significance at 1 µM mosapride. Furthermore, the rate of tension development and the rate of relaxation were not changed in absolute values by mosapride (Fig. 3C). Moreover, we were interested in the effect of mosapride on the times of contraction. It turned out that mosapride did not reduce the time to peak to tension (T1 in Fig. 3D) or the time of relaxation (T2 in Fig. 3D). Finally, mosapride tended to increase the beating rate (Fig. 3E).
In the original recordings, we depicted that in separate experiments, first applying mosapride and then subsequently applying 5-HT increased the force of contraction further in left atrial preparations from 5-HT4-TG (Figs. 4B, 5A and 5B). In contrast, the subsequent application of 5-HT did not increase the force of contraction in WT (Fig. 4A, Gergs et al. 2010, 2013).Fig. 4Original recording in mouse left atrial preparation from 5-HT4-TG. Mosapride induced a time- and concentration-dependent positive inotropic effect in 5-HT4-TG that was followed by a further positive inotropic effect in the additional presence of serotonin (B). Original recordings of the effect of mosapride and serotonin in left atrial preparation from WT (A). Mosapride induced a time- and concentration-dependent positive chronotropic effect in right atrial preparations from 5-HT4-TG that was augmented by additionally applied serotonin in right atrial preparations from 5-HT4-T (D) but not from WT (C). Horizontal bars indicate time in minutes (min). Arrows indicate at what time points mosapride or serotonin were cumulatively applied to the organ bath. Concentrations of mosapride and serotonin are given in negative logarithmic units in the horizontal arrow in the topFig. 5A Summarised data for the concentration dependent effect of cumulatively applied mosapride followed by serotonin in left atrial preparations from 5-HT4-TG for force of contraction in % of pre-drug value (A) in mN (B), rate of tension development (C: dF/dtmax), rate of tension relaxation (C: dF/dtmin) both in mN/s and on time to peak tension (T1, D) and on time of relaxation (D: T2). Ordinates give force of contraction in panel A in % of pre-drug value or in panel B in mN, the rate of contraction and rate of relaxation in panel C in mN/s, the contraction times in panel D in milliseconds (ms) and in panel E in beats per minute (bpm). Abscissae in panels A–E indicate concentrations of mosapride and of serotonin in negative logarithm of molar concentrations. Significant differences versus 5-HT4-TG are indicated in x. Numbers in brackets mean number of experiments
Likewise, following mosapride, additionally applied 5-HT increased the beating rate further in 5-HT4-TG, suggesting that mosapride is not a full agonist in relation to the beating rate (Figs. 4D and 5E). In contrast, neither mosapride nor serotonin augmented the beating rate in the right atrial preparations from WT (Fig. 4C, Gergs et al. 2010, 2013).
We also assessed muscle tension parameters in the left atrium under these conditions (i.e. Fig. 4B). Several such experiments are summarised regarding the force of contraction measured as the percent of pre-drug value or mN in the left atrium (Fig. 5A, B). This effect gained statistical significance at 0.3 µM serotonin. Furthermore, the rate of tension development and the rate of relaxation were not augmented in absolute values by mosapride, but by subsequent serotonin (Fig. 5C). Moreover, we were interested in the effect of mosapride on the times of contraction. The result was that mosapride did not reduce the time to peak tension (Fig. 5D) or the time of relaxation (T2, Fig. 3D). Finally, serotonin after mosapride further increased the beating rate (Fig. 5E).
Next, we wanted to test the effects of mosapride in the human heart similarly to those in mice, as shown in Fig. 4. To that end, we constructed concentration–response relationships for cumulatively applied serotonin alone or in the presence of increasing concentrations of mosapride. As seen in the original recording, while serotonin concentration-dependently increased (Fig. 6A), mosapride (1 µM: Fig. 6C; or 10 µM: Fig. 6B) alone did not increase but reduced the force of contraction. However, the concentration-dependent mosapride shifted the concentration-dependent effect of serotonin to the right (Fig. 6C, B). Several such experiments are summarised in Fig. 7. Mosapride shifted the effect of serotonin on the force of contraction in a concentration-dependent manner, as shown in mN (Fig. 7A) or % of the pre-drug value (Fig. 7B). Similarly, while serotonin alone raised the rate of tension development or rate of relaxation, these effects were concentration dependently reduced by mosapride (Fig. 7C). A similar pattern was seen at the time of relaxation (T2, Fig. 7D).Fig. 6A Original recording of the concentration- and time-dependent positive inotropic effect of serotonin alone in millinewton (mN) in electrically stimulated human right atrial muscle strips. B Original recording of the concentration- and time-dependent positive inotropic effect of serotonin after application of 10 µM mosapride in millinewton (mN) in electrically stimulated human right atrial muscle strips. C Original recording of the concentration- and time-dependent positive inotropic effect of serotonin after application of 1 µM mosapride in millinewton (mN) in electrically stimulated human right atrial muscle strips. Horizontal bar indicates time axis in minutes (min). Concentrations of mosapride are given in negative logarithmic units in the horizontal arrow in the topFig. 7A force of contraction in % of pre-drug value. B Force of contraction in mN, C rate of contraction (dF/dtmax), D rate of relaxation (dF/dtmin). D Time to peak tension (T1), D time to relaxation (T2). Ordinates in panels A and B force of contraction in % of pre-drug value or millinewton (mN). Ordinates give rate of contraction and rate of relaxation in panel C in mN/s. Ordinates in panel D indicate milliseconds (ms). Significant difference versus serotonin alone is indicated by x. Numbers in brackets mean number of experiments. Abscissae in panels A–D indicate concentrations of serotonin in the presence of increasing concentrations of mosapride, both given in negative logarithms of molar concentrations. Significant difference versus control (Ctr; pre-drug value) or mosapride are indicated in * or #. Number of experiments were 3 to 9
Next, we reversed the application of serotonin and mosapride. First, we established a concentration–response curve for serotonin, saturating cardiac 5-HT4 receptors (Fig. 8A). After that, increasing concentrations of mosapride were added (Fig. 8A). Here, we noted a negative inotropic effect of mosapride, displacing serotonin from receptors due to high concentrations of mosapride. This was seen in an original experiment (Fig. 8A). Data from several experiments are depicted in Fig. 8. Similar inotropic effects of mosapride were noted in absolute values for the force of contraction expressed in mN or % of the pre-drug value (Fig. 8B, C), for the rate of tension development and for the rate of relaxation (Fig. 8D). Moreover, we were interested in the effect of mosapride on the times of contraction. It turned out that mosapride reversed the effect of serotonin on the time of relaxation (T2, Fig. 8E).Fig. 8Effect of mosapride (1 µM) after serotonin (100 nM) on force of contraction in HAP. A An original recording of the concentration- and time-dependent positive inotropic effect of mosapride in millinewton (mN, vertical axis) in electrically stimulated HAP. First serotonin was applied then mosapride was given as a single concentration. Horizontal bar indicates time axis in minutes (min). B Force of contraction in % of pre-drug value. C Force of contraction in mN, D positive maximum rate of contraction (dF/dtmax) and negative minimum rate of relaxation (E: dF/dtmin). F Time to peak tension (T1) and time of relaxation (G: T2). Abscissae indicate molar concentrations of mosapride or serotonin in negative logarithms. Significant difference versus control (Ctr; pre-drug value) or serotonin are indicated in * or #. Numbers in brackets mean number of experiments
While mosapride alone failed to raise the force of contraction but reduced the force of contraction (Fig. 6), when force had been raised by cilostamide, a phosphodiesterase III inhibitor (which we have used before to raise force in HAP: e.g. Gergs et al. 2024), then additionally applied mosapride exerted a positive inotropic effect that could be reduced by receptor antagonists. The original recording is depicted in Fig. 9A. Data from several experiments are depicted in Fig. 9. Cilostamide increased force, and this effect was amplified by mosapride and antagonised by tropisetron, here used as an inhibitor of human 5-HT4 serotonin receptors, in HAP used before by others (Kaumann et al. 1990) (Fig. 9C). Similar inotropic effects of mosapride were noted in absolute values for the rate of tension development and the rate of relaxation (Fig. 9D).Fig. 9Effect of mosapride after cilostamide on force of contraction in HAP. A An original recording of the concentration- and time-dependent positive inotropic effect of mosapride in millinewton (mN, vertical axis) in electrically stimulated HAP. First cilostamide was applied then mosapride was given and finally tropisetron. Horizontal bar indicates time axis in minutes (min). B Force of contraction in % of pre-drug value. C Force of contraction in mN, D rate of contraction (dF/dtmax), E rate of relaxation (dF/dtmin), F time to peak tension (T1), G time to relaxation (T2). Ordinates in panels A, C and B force of contraction in millinewton (mN) or % of pre-drug values. Rate of contraction and rate of relaxation in panels C and E in mN/s. Ordinates in panel E time to peak tension (T1) and F gives time of relaxation (T2) in milliseconds (ms). H Rate of contraction (dF/dtmax), I rate of relaxation (dF/dtmin). For normalisation, we have defined the maximum effect of mosapride as 100%. Hence, the ordinates in panels H and I give force related to this maximum effect of mosapride. Significant difference versus control (Ctr; pre-drug value) or mosapride is indicated with * or #. Numbers in brackets mean number of experiments. In the bar diagram, pre-drug values (Ctr), the effect of cilostamide alone (Cilo), the effect of additional mosapride (Mosa) or additional tropisetron (Tropi) are indicated. The p-values of panel B of Mosa vs. Ctr to p ≤ 0.0001 or Mosa vs. Cilo to p = 0.0319 or Mosa vs. Tropi to p = 0.3805. The p-values of panel C of Ctr vs. Cilo and Ctr vs. Mosa to p ≤ 0.0001 or Ctr vs. Tropi to p = 0.0016 and Mosa vs. Cilo to p = 0.057 or Mosa vs. Tropi to p = 0.8059. The p-values in panel D amount for Ctr vs. Cilo to p = 0.0006 or Ctr vs. Mosa p ≤ 0.0001 or Ctr vs. Tropi p = 0.0177 and Mosa vs. Cilo p = 0.0303 or Mosa vs. Tropi p = 0.9769. The p-values in panel E amount for Ctr vs. Cilo to p = 0.0001 or Ctr vs. Mosa p ≤ 0.0001 or Ctr vs. Tropi p = 0.0024 and Mosa vs. Cilo p = 0.0096 or Mosa vs. Tropi p = 0.9924. The p-values in panel F amount for Ctr vs. Cilo to p = 0.836 or Ctr vs. Mosa and Ctr vs. Tropi p ≥ 0.999 and Mosa vs. Cilo p ≤ 0.0001 or Mosa vs. Tropi p = 0.8937. The p-values in panel G amount for Ctr vs. Cilo to p = 0.027 or Ctr vs. Mosa p = 0.0023 or Ctr vs. Tropi p = 0.0099 and Mosa vs. Cilo p = 0.0005 or Mosa vs. Tropi p = 0.2754. The p-values in panel H amount for Mosa vs. Ctr to p ≤ 0.0001 or Mosa vs. Cilo to p = 0.0024 or Mosa vs. Tropi to p ≥ 0.999. The p-values in panel I amount for Mosa vs. Ctr to p ≤ 0.0001 or Mosa vs. Cilo to p = 0.0014 or Mosa vs. Tropi to p ≥ 0.999
Moreover, we were interested in the effect of mosapride on the times of contraction. Cilostamide and additional mosapride did not alter the time to peak tension (Fig. 9F). Cilostamide itself reduced the time of relaxation (T2, Fig. 9G); this effect was accentuated by mosapride, but was not reversed by additional tropisetron (Fig. 9G). Finally, we calculated the percent increase in the rate of tension development and the rate of relaxation. In other words, pre-drug values before cilostamide (Fig. 8A) were arbitrarily set to 100%. If this normalisation procedure is used, it becomes more apparent that cilostamide alone increased the rate of tension development and this was accentuated by mosapride (Fig. 9H). Likewise, cilostamide alone increased the rate of tension relaxation and this was accentuated by mosapride (Fig. 9I).
Finally, the question arose of how the primary metabolite of mosapride would affect the force of contraction in HAP. We observed the same pattern as with the mother compound, mosapride. Specifically, as seen in the original recording, cilostamide slowly increased the force of contraction (Fig. 10A). After that, mosapride concentration dependently increased the force of contraction; this increase could be antagonised by GR125487, an antagonist at human cardiac 5-HT4 serotonin receptors (e.g. Gergs et al. 2010, 2013) (Fig. 10A). Several similar experiments were then summarised. We report similar positive inotropic effects of des-mosapride for the force of contraction expressed in mN or % of the maximum value (Fig. 10B, C) for the rate of tension development and the rate of relaxation (Fig. 10D) in absolute values by mosapride (Fig. 10E). Moreover, we were interested in the effect of mosapride on the times of contraction. It turned out that mosapride reversed the effect of serotonin not on time to peak tension (T1, Fig. 10F) but on time of relaxation (T2, Fig. 10G).Fig. 10Original recording of the concentration- and time-dependent positive inotropic effect of des-4-fluoro-benzyl-mosapride (Des-Mosa) in millinewton (mN) in electrically stimulated HAP. First cilostamide (Cilo) was applied then des-4-fluoro-benzyl-mosapride was cumulatively applied thereafter GR125487 (GR). Horizontal bar indicates time axis in minutes (min). Ordinates in panels A, B and C force of contraction in milliNewton (mN) or % of maximum effect of value to Des-Mosa. Rate of contraction and rate of relaxation in panels D and E in mN/s. Ordinates in panel F give time to peak tension (T1) and time of relaxation (T2) in panel G in milliseconds (ms). Abscissae indicate molar concentrations of des-4-fluoro-benzyl-mosapride in negative decadic logarithms. Significant difference versus Des-Mosa or Ctr (pre-dug value) is indicated by # or *. Numbers in brackets mean number of experiments
Discussion
Main new findings
The primary new finding is that mosapride can function as a partial functional agonist at 5-HT4 receptors in transgenic mouse hearts and HAP.
Mechanism of mosapride
We suggest that mosapride increased force and beating rate as an agonist at cardiac human 5-HT4 receptors because mosapride only increased the force of contraction in the left atrium from 5-HT4-TG and not in WT. Because the maximum inotropic effect of mosapride in atrial preparations of 5-HT4-TG could be further stimulated by additional serotonin, we tentatively conclude that mosapride acts here as a partial agonist. Likewise in HAP, mosapride could act as an agonist and could increase force of contraction via 5-HT4 receptors. However, this positive inotropic effect of mosapride was only seen in the presence of cilostamide, a phosphodiesterase III inhibitory drug. In contrast, in the absence of cilostamide (Fig. 6), mosapride reduced force of contraction. This could mean that mosapride can also reduce force of contraction via 5-HT4 receptors. Conceivably, mosapride might in this case act as an inverse agonist at 5-HT4 receptors.
Role of phosphorylation of regulatory proteins
The general assumption is that 5-HT4 receptor stimulation increases the phosphorylation of protein substrates for cAMP-dependent protein kinase (Fig. 1B). We and others described that serotonin via 5-HT4 receptors can increase the phosphorylation state of phospholamban (Gergs et al. 2009, Neumann et al. 2019, 2023b). These phosphorylations can partly explain why mosapride increased the force in atrial preparations from 5-HT4-TG.
Mosapride functioned as an agonist at 5-HT4 receptors in the isolated HAP. We conclude this because the positive inotropic effect of mosapride (in the presence of cilostamide) is antagonised by 5-HT4 antagonists like tropisetron and GR 125487. Moreover, mosapride, as in 5-HT4-TG, acts as a partial agonist. Mosapride shifted the concentration–response curve (on the force of contraction) of serotonin to the right in HAP, which was expected from a 5-HT4 antagonist. Moreover, mosapride attenuated the positive inotropic effect of serotonin. Such partial agonisms in the human heart are not without precedence for 5-HT4 receptor agonists. For inotropy, cisapride and metoclopramide are partial agonists in 5-HT4-TG but also in HAP (Chai et al. 2012, Keller et al. 2018, Neumann et al. 2021b). Of note, cisapride and metoclopramide are structurally similar to mosapride, further supporting our conclusion.
Species differences
A significant merit of this study is that we used a small animal model (5-HT4-TG) to test for the inotropic effects of mosapride. In previous papers, rats, rabbits or dogs were used to study the cardiac effects of mosapride. While these studies are well suited to investigate the effects of mosapride via potassium channels (notably hERG), they are not useful to study the effects of mosapride or its metabolites on 5-HT4 receptors; dog, rabbit and rat hearts (and WT mouse hearts) do not contain functional 5-HT4 receptors that couple to force of contraction (reviewed in Neumann et al. 2017, 2023a). One could study mosapride in porcine hearts, but pigs are more expensive than mice; the sequence of the 5-HT4 receptor is similar, but not identical, in pigs and humans. In 5-HT4-TG, we encountered the same sequence as in human hearts because we chose to overexpress the human 5-HT4 receptor.
Moreover, our comparative study on 5-HT4-TG and HAP showed another intriguing species difference. Whereas mosapride alone was an agonist in the atrial preparations of 5-HT4-TG, mosapride alone was ineffective in raising the force of contraction in HAP. At least two reasons could explain these differences between mice and humans, which are not mutually exclusive. First, the overexpression of 5-HT4 receptor is so high in 5-HT4-TG that even an inverse agonist can stimulate the receptor. Alternatively, the signal transduction proteins (e.g. Gs, AC, PKA, Fig. 1A) are so different between mice and humans that stimulation of mosapride leads to different steps in signal transduction. It would be interesting to address this issue in subsequent work. There is also evidence in the gastroenterological tract that mosapride can act as a partial agonist (Yoshida and Ito 1994).
Notably, mosapride acted more potently to raise force in transgenic mice than in the human atrium. This is consistent with our previous work on cisapride, prucalopride or metoclopramide (Keller et al. 2018, Neumann et al. 2021b). We assume this is due to the much higher level of expression of 5-HT4 receptors in mouse hearts than in human hearts (Neumann et al. 2021a). We argue that the 5-HT4-TG offer the possibility of amplifying any effect of agonists at 5-HT4 receptors. On the other hand, if a putative 5-HT4-agonist does not act in 5-HT4-TG, it is unlikely to work as an agonist in human tissue. In isolated rabbit hearts, 10 μM of mosapride prolonged the QT interval (Kii and Ito 2002). This was explained by the inhibition of potassium currents.
In this study (Fig. 3D), we did not detect a reduction in time of relaxation, which is typical of 5-HT4-mediated effects by serotonin alone (Gergs et al. 2009, 2010). We speculate that any shortening via 5-HT4 receptor stimulation is offset by the inhibition of potassium channels that prolong the duration of the contraction. In HAP, we did not observe a further reduction in the time of relaxation (Fig. 9G) compared to cilostamide. We assume that cilostamide had already maximally reduced the time of relaxation, and thus no additional shortening by mosapride was detectable. Alternatively, one might speculate that in the human atrium, some degree of inhibition of the potassium channels might have led to the mixed effect of mosapride.
On the other hand, as seen in Fig. 10G, Des-Mosa reduced the time of relaxation, even in the additional presence of cilostamide. A potential explanation for the different effects of mosapride and De-Mosa on the time of relaxation might reside in the following: De-Mosa might not be able to inhibit potassium channels in human hearts and might solely act on 5-HT4 receptors, therefore reducing the time parameters. However, one would need to know how Des-Mosa acts on the action potential in HAP to confirm this assumption.
Effects on the beating rate
We assume that, like 5-HT, mosapride stimulated 5-HT4 receptors in the right atrium of 5-HT4-TG. This conclusion is based on the observation that the effect is absent in the right atrium from the WT. Mosapride acted like various other agonists (cisapride, prucalopride, metoclopramide) as a partial agonist compared to the chronotropic effect of 5-HT (Keller et al. 2018, Neumann et al. 2021b). The data on the beating rate might have clinical relevance because it is rarely possible to obtain sinus node cells from patients and to test mosapride in such spontaneously beating cells. However, our data predict that mosapride can lead to tachycardia in human hearts. In contrast, we cannot rule out from our contraction data in the HAP that mosapride might act as an antagonist in the human sinus node. This should result in bradycardia, because serotonin is present in the human atrium in thrombocytes and continuously forms in the human atrium.
Clinical relevance
To our knowledge, this is the first study on the effects of mosapride on the force of contraction in isolated HAP. Hence, this is the first report of any effect of mosapride in the human heart and its mediation via the 5-HT4 receptor, which adds to the clinical knowledge about mosapride.
We predict that tachycardia after treatment with mosapride in patients could be blocked by tropisetron, an approved drug. However, this prediction must be confirmed in a clinical study. On the other hand, if mosapride mainly acts as an antagonist, mosapride should reduce the beating rate. In a study of healthy volunteers, 10 mg mosapride per os changed the spectral form of the ECG. The QT was shorter after the mosapride, but not significantly different. The heart rate was lower with mosapride, but this was also not significant (Endo et al. 2002). Peak therapeutic plasma levels of mosapride when taking 40 mg per mouth in healthy volunteers amounted to 282 ng/ml (0.67 µM). Hence, the concentrations tested here in vitro for mosapride might be achieved in humans. Moreover, in intoxications, much higher plasma levels of mosapride are expected. Mosapride is degraded mainly by CYP3A4 (Katoh et al. 2003). This enzyme is inhibited by antifungal azoles and some antibiotics, such as erythromycin.
A Japanese study found that the metabolism of mosapride in healthy volunteers was impaired by erythromycin (Katoh et al. 2003). This drug–drug interaction increased plasma concentrations of mosapride from about 42 to about 67 ng/ml and prolonged the half-life of mosapride (Katoh et al. 2003). In contrast to cisapride, which blocks ventricular potassium ion channels in humans, mosapride is 400–1000-fold less potent than cisapride in inhibiting these channels (Katoh et al. 2003).
Mosapride and its metabolites are primarily eliminated by the kidneys (Katoh et al. 2003). As kidney function declines with ageing, the elimination half-life of mosapride is probably augmented in the elderly. This kinetic behaviour is problematic, as the cumulation of mosapride and an increase in plasma mosapride concentration are expected. Seniors are also more likely to develop atrial fibrillation in the first place. Moreover, many drugs would inhibit the metabolism of mosapride, further increasing plasma concentrations and potential cardiac side effects of mosapride. One could question the relevance of our findings regarding cilostamide and mosapride. Usually, patients take mosapride in the absence of a phosphodiesterase inhibitor. However, in heart failure patients, the phosphodiesterase inhibitors pimobendan, milrinone or levosimendan are sometimes given. Moreover, many patients drink coffee, which contains the phosphodiesterase inhibitor caffeine.
Finally, our work adds to our knowledge by showing that the primary metabolite of mosapride is active at 5-HT4 receptors. From this, one might predict that the cardiac action of mosapride may last longer than predicted from the half-life of mosapride because, after that, the metabolite might still be active in humans.
Limitations of the study
One can argue that we have not tested the effects on the sinus node of man directly. Such a study would require access to a human pacemaker. Such studies were beyond the scope of this initial study. Furthermore, due to a lack of access to that tissue, we did not have the opportunity to study contractility in human ventricle tissue.
Figure 6 shows that mosapride alone could decrease the force of contraction in the HAP. The reasons for this negative inotropic effect have not been studied here. An attractive hypothesis is that mosapride might act as an inverse agonist at the 5-HT4 serotonin receptors in HAP. We have shown that in human HAP, serotonin can be produced (Gergs et al. 2017). This serotonin might continuously stimulate HAP, which may be antagonised by mosapride.
In summary, we can now address the hypotheses raised in the ‘Introduction.’ First, mosapride raised the force of contraction and beating rate in atrial preparations from 5-HT4-TG but not from WT. Second, mosapride and its primary metabolite elevated the force of contraction in the HAP via 5-HT4 receptors.
Main new findings
The primary new finding is that mosapride can function as a partial functional agonist at 5-HT4 receptors in transgenic mouse hearts and HAP.
Mechanism of mosapride
We suggest that mosapride increased force and beating rate as an agonist at cardiac human 5-HT4 receptors because mosapride only increased the force of contraction in the left atrium from 5-HT4-TG and not in WT. Because the maximum inotropic effect of mosapride in atrial preparations of 5-HT4-TG could be further stimulated by additional serotonin, we tentatively conclude that mosapride acts here as a partial agonist. Likewise in HAP, mosapride could act as an agonist and could increase force of contraction via 5-HT4 receptors. However, this positive inotropic effect of mosapride was only seen in the presence of cilostamide, a phosphodiesterase III inhibitory drug. In contrast, in the absence of cilostamide (Fig. 6), mosapride reduced force of contraction. This could mean that mosapride can also reduce force of contraction via 5-HT4 receptors. Conceivably, mosapride might in this case act as an inverse agonist at 5-HT4 receptors.
Role of phosphorylation of regulatory proteins
The general assumption is that 5-HT4 receptor stimulation increases the phosphorylation of protein substrates for cAMP-dependent protein kinase (Fig. 1B). We and others described that serotonin via 5-HT4 receptors can increase the phosphorylation state of phospholamban (Gergs et al. 2009, Neumann et al. 2019, 2023b). These phosphorylations can partly explain why mosapride increased the force in atrial preparations from 5-HT4-TG.
Mosapride functioned as an agonist at 5-HT4 receptors in the isolated HAP. We conclude this because the positive inotropic effect of mosapride (in the presence of cilostamide) is antagonised by 5-HT4 antagonists like tropisetron and GR 125487. Moreover, mosapride, as in 5-HT4-TG, acts as a partial agonist. Mosapride shifted the concentration–response curve (on the force of contraction) of serotonin to the right in HAP, which was expected from a 5-HT4 antagonist. Moreover, mosapride attenuated the positive inotropic effect of serotonin. Such partial agonisms in the human heart are not without precedence for 5-HT4 receptor agonists. For inotropy, cisapride and metoclopramide are partial agonists in 5-HT4-TG but also in HAP (Chai et al. 2012, Keller et al. 2018, Neumann et al. 2021b). Of note, cisapride and metoclopramide are structurally similar to mosapride, further supporting our conclusion.
Species differences
A significant merit of this study is that we used a small animal model (5-HT4-TG) to test for the inotropic effects of mosapride. In previous papers, rats, rabbits or dogs were used to study the cardiac effects of mosapride. While these studies are well suited to investigate the effects of mosapride via potassium channels (notably hERG), they are not useful to study the effects of mosapride or its metabolites on 5-HT4 receptors; dog, rabbit and rat hearts (and WT mouse hearts) do not contain functional 5-HT4 receptors that couple to force of contraction (reviewed in Neumann et al. 2017, 2023a). One could study mosapride in porcine hearts, but pigs are more expensive than mice; the sequence of the 5-HT4 receptor is similar, but not identical, in pigs and humans. In 5-HT4-TG, we encountered the same sequence as in human hearts because we chose to overexpress the human 5-HT4 receptor.
Moreover, our comparative study on 5-HT4-TG and HAP showed another intriguing species difference. Whereas mosapride alone was an agonist in the atrial preparations of 5-HT4-TG, mosapride alone was ineffective in raising the force of contraction in HAP. At least two reasons could explain these differences between mice and humans, which are not mutually exclusive. First, the overexpression of 5-HT4 receptor is so high in 5-HT4-TG that even an inverse agonist can stimulate the receptor. Alternatively, the signal transduction proteins (e.g. Gs, AC, PKA, Fig. 1A) are so different between mice and humans that stimulation of mosapride leads to different steps in signal transduction. It would be interesting to address this issue in subsequent work. There is also evidence in the gastroenterological tract that mosapride can act as a partial agonist (Yoshida and Ito 1994).
Notably, mosapride acted more potently to raise force in transgenic mice than in the human atrium. This is consistent with our previous work on cisapride, prucalopride or metoclopramide (Keller et al. 2018, Neumann et al. 2021b). We assume this is due to the much higher level of expression of 5-HT4 receptors in mouse hearts than in human hearts (Neumann et al. 2021a). We argue that the 5-HT4-TG offer the possibility of amplifying any effect of agonists at 5-HT4 receptors. On the other hand, if a putative 5-HT4-agonist does not act in 5-HT4-TG, it is unlikely to work as an agonist in human tissue. In isolated rabbit hearts, 10 μM of mosapride prolonged the QT interval (Kii and Ito 2002). This was explained by the inhibition of potassium currents.
In this study (Fig. 3D), we did not detect a reduction in time of relaxation, which is typical of 5-HT4-mediated effects by serotonin alone (Gergs et al. 2009, 2010). We speculate that any shortening via 5-HT4 receptor stimulation is offset by the inhibition of potassium channels that prolong the duration of the contraction. In HAP, we did not observe a further reduction in the time of relaxation (Fig. 9G) compared to cilostamide. We assume that cilostamide had already maximally reduced the time of relaxation, and thus no additional shortening by mosapride was detectable. Alternatively, one might speculate that in the human atrium, some degree of inhibition of the potassium channels might have led to the mixed effect of mosapride.
On the other hand, as seen in Fig. 10G, Des-Mosa reduced the time of relaxation, even in the additional presence of cilostamide. A potential explanation for the different effects of mosapride and De-Mosa on the time of relaxation might reside in the following: De-Mosa might not be able to inhibit potassium channels in human hearts and might solely act on 5-HT4 receptors, therefore reducing the time parameters. However, one would need to know how Des-Mosa acts on the action potential in HAP to confirm this assumption.
Effects on the beating rate
We assume that, like 5-HT, mosapride stimulated 5-HT4 receptors in the right atrium of 5-HT4-TG. This conclusion is based on the observation that the effect is absent in the right atrium from the WT. Mosapride acted like various other agonists (cisapride, prucalopride, metoclopramide) as a partial agonist compared to the chronotropic effect of 5-HT (Keller et al. 2018, Neumann et al. 2021b). The data on the beating rate might have clinical relevance because it is rarely possible to obtain sinus node cells from patients and to test mosapride in such spontaneously beating cells. However, our data predict that mosapride can lead to tachycardia in human hearts. In contrast, we cannot rule out from our contraction data in the HAP that mosapride might act as an antagonist in the human sinus node. This should result in bradycardia, because serotonin is present in the human atrium in thrombocytes and continuously forms in the human atrium.
Clinical relevance
To our knowledge, this is the first study on the effects of mosapride on the force of contraction in isolated HAP. Hence, this is the first report of any effect of mosapride in the human heart and its mediation via the 5-HT4 receptor, which adds to the clinical knowledge about mosapride.
We predict that tachycardia after treatment with mosapride in patients could be blocked by tropisetron, an approved drug. However, this prediction must be confirmed in a clinical study. On the other hand, if mosapride mainly acts as an antagonist, mosapride should reduce the beating rate. In a study of healthy volunteers, 10 mg mosapride per os changed the spectral form of the ECG. The QT was shorter after the mosapride, but not significantly different. The heart rate was lower with mosapride, but this was also not significant (Endo et al. 2002). Peak therapeutic plasma levels of mosapride when taking 40 mg per mouth in healthy volunteers amounted to 282 ng/ml (0.67 µM). Hence, the concentrations tested here in vitro for mosapride might be achieved in humans. Moreover, in intoxications, much higher plasma levels of mosapride are expected. Mosapride is degraded mainly by CYP3A4 (Katoh et al. 2003). This enzyme is inhibited by antifungal azoles and some antibiotics, such as erythromycin.
A Japanese study found that the metabolism of mosapride in healthy volunteers was impaired by erythromycin (Katoh et al. 2003). This drug–drug interaction increased plasma concentrations of mosapride from about 42 to about 67 ng/ml and prolonged the half-life of mosapride (Katoh et al. 2003). In contrast to cisapride, which blocks ventricular potassium ion channels in humans, mosapride is 400–1000-fold less potent than cisapride in inhibiting these channels (Katoh et al. 2003).
Mosapride and its metabolites are primarily eliminated by the kidneys (Katoh et al. 2003). As kidney function declines with ageing, the elimination half-life of mosapride is probably augmented in the elderly. This kinetic behaviour is problematic, as the cumulation of mosapride and an increase in plasma mosapride concentration are expected. Seniors are also more likely to develop atrial fibrillation in the first place. Moreover, many drugs would inhibit the metabolism of mosapride, further increasing plasma concentrations and potential cardiac side effects of mosapride. One could question the relevance of our findings regarding cilostamide and mosapride. Usually, patients take mosapride in the absence of a phosphodiesterase inhibitor. However, in heart failure patients, the phosphodiesterase inhibitors pimobendan, milrinone or levosimendan are sometimes given. Moreover, many patients drink coffee, which contains the phosphodiesterase inhibitor caffeine.
Finally, our work adds to our knowledge by showing that the primary metabolite of mosapride is active at 5-HT4 receptors. From this, one might predict that the cardiac action of mosapride may last longer than predicted from the half-life of mosapride because, after that, the metabolite might still be active in humans.
Limitations of the study
One can argue that we have not tested the effects on the sinus node of man directly. Such a study would require access to a human pacemaker. Such studies were beyond the scope of this initial study. Furthermore, due to a lack of access to that tissue, we did not have the opportunity to study contractility in human ventricle tissue.
Figure 6 shows that mosapride alone could decrease the force of contraction in the HAP. The reasons for this negative inotropic effect have not been studied here. An attractive hypothesis is that mosapride might act as an inverse agonist at the 5-HT4 serotonin receptors in HAP. We have shown that in human HAP, serotonin can be produced (Gergs et al. 2017). This serotonin might continuously stimulate HAP, which may be antagonised by mosapride.
In summary, we can now address the hypotheses raised in the ‘Introduction.’ First, mosapride raised the force of contraction and beating rate in atrial preparations from 5-HT4-TG but not from WT. Second, mosapride and its primary metabolite elevated the force of contraction in the HAP via 5-HT4 receptors. |
Title: Enzalutamide Monotherapy in the EMBARK Trial Should Be Practice-changing and Existing Data Suggest How to Mitigate Toxicity | Body: |
Title: Chronic Viral Reactivation and Associated Host Immune Response and Clinical Outcomes in Acute COVID-19 and Post-Acute Sequelae of COVID-19 | Body:
Introduction
Viruses employ a variety of strategies to enhance their persistence and dissemination, including establishing chronic infection1–3. This strategy is exemplified by several human-infecting viruses, particularly those belonging to the Herpesviridae and Anelloviridae families, with these viruses establishing lifelong infections in a significant portion of the human population4–6. Although primary infection typically remains asymptomatic in immunocompetent individuals, some chronic viral infections contribute to the development of autoimmune disorders and cancers, among other adverse health outcomes2,7–13. These viruses typically remain dormant, but can reactivate during periods of stress, sleep deprivation, surgery, hormonal imbalances, or in the setting of critical illness14–17. The full range of immunological consequences stemming from these chronic viral reactivations remains largely unknown.
Since its emergence in 2019, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has resulted in over 774 million cases of coronavirus disease 2019 (COVID-19) and 7 million deaths18,19. Due to the physiological stress introduced by SARS-CoV-2 infection, underlying chronic viral infections may reactivate and potentially contribute to the immunological consequences of COVID-19. For example, reactivation of Herpesviridae, including EBV (Epstein-Barr Virus/Human Herpesvirus 4 (HHV4)), CMV (Cytomegalovirus/Human Herpesvirus 5 (HHV5)), Human Herpesvirus 6 (HHV6), and Human Herpesvirus 8 (HHV8), is associated with worse acute clinical outcomes in patients with COVID-1920–24. Reactivation of CMV and EBV, in particular, has been linked to more severe outcomes, including increased mortality20,25. Additionally, patients with “Long COVID”, also known as Post-Acute Sequelae of COVID-19 (PASC), develop elevated EBV antibody titers, raising the possibility that reactivation of these viruses may contribute to PASC22,26.
Many of the foundational COVID-19 viral reactivation studies have been limited by small sample sizes, have focused on only a subset of Herpesviridae, or have relied solely on evaluating antibody responses to assess viral reactivation20,22–24,26–30, as opposed to measuring transcripts of actively replicating virus. As such, important gaps remain in our understanding of the dynamics and biology of chronic viral reactivation during acute COVID-19, and their role in PASC.
To address the knowledge gap in viral reactivation in COVID-19, we leveraged samples and data from a longitudinal prospective observational study of 1,154 patients hospitalized for COVID-19 enrolled in the Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC), with patients evaluated during acute hospitalization and for 12 months post hospital discharge. We carried out longitudinal, multi-omic analyses of nasal swabs, peripheral blood mononuclear cells (PBMCs), and endotracheal aspirates and found significant reactivation of chronic viruses, particularly from the Herpesviridae and Anelloviridae families, associated with acute COVID-19 severity. By integrating host and viral transcriptomics, cytokine profiling, cellular immunophenotyping, metabolomics, and proteomics, we observed distinct viral reactivation dynamics, and striking associations between viral reactivation, clinical outcomes, immunologic features, and patient demographics, both during acute COVID-19 and PASC. Our results provide novel insights into the endogenous virological landscape of COVID-19 patients, highlighting the complex interplay between SARS-CoV-2 infection, latent viral reactivation, host immune responses, and clinical outcomes.
Results
IMPACC Cohort
The IMPACC consortium enrolled 1,154 patients hospitalized for COVID-19 across 20 US hospitals between May 2020 and March 2021 (Figure 1A). All participants were COVID-19 vaccine-naive at the time of enrollment. To assess COVID-19 severity, participants were assigned to one of five trajectory groups (TG) using latent class mixed modeling of respiratory status over the first 28 days31. Groups were classified as mild (TG1), moderate (TG2), severe (TG3), critical (TG4), or fatal within 28 days (TG5). From each participant, bulk RNA sequencing was performed on PBMCs, nasal swabs, and for mechanically ventilated patients, endotracheal aspirates (EA), at up to ten visits during one-year post-hospital admission (Figure 1B). In addition, we assessed whole blood immune cell populations by mass cytometry by time of flight (CyTOF), serum anti-EBV and anti-CMV antibody titers, serum cytokine levels by proximity extension assay (PEA), and the plasma proteome and metabolome by mass spectrometry at participant visits.
RNA-sequencing Identifies Transcripts from the Human Virome
From RNA-seq data, we identified viral RNA transcripts in nasal, EA and PBMC samples and found a diverse number of human infecting viruses beyond SARS-CoV-2 including EBV, CMV, HHV6, HSV1, HSV2, and several Anelloviridae and Enteroviridae species (Figure 1C). Unsurprisingly, SARS-CoV-2 was the most prevalent virus identified, and was primarily found in NS and EA samples, with detection in PBMCs only in 10 participants near time of admission (Figure 1C). We confirmed that SARS-CoV-2 abundance measured by RNA-seq reads per million (rpM) highly correlated with RT-qPCR cycle threshold (Supplemental Figure S1A–B).
Among Herpesviridae, HSV1/2, EBV, and CMV transcripts were commonly detected across compartments during acute COVID-19 (defined as the first 40 days after hospital admission), with a notable lack of detection during the convalescent period (>3 months post admission) (Figure 1C). In addition, we also detected a diverse number of Anelloviridae and Enteroviridae species (Figure S1C–D), with their collective viral load at the family taxonomic level used for analyses.
Interestingly, we found that each viral species displayed unique temporal dynamics of reactivation relative to hospital admission (Figure 1D). For example, EBV reactivated early in the disease course, with ~20% of participants having detectable transcripts at the time of admission, followed by a gradual decline in detection over time. Unlike EBV, the frequency of Anelloviridae transcripts remained constant up to day 20 post-admission, followed by a slow decline. In contrast, HSV1/2 and CMV reactivated later in disease, with HSV1 detected in up to 40% of EA samples and CMV in ~8% of PBMC samples 19–24 days post admission (Figure 1D & S1D–E).
The detection of each virus varied across compartments, with EBV transcripts more common in PBMCs and HSV1/2 transcripts notably more common in NS and EA samples. Evaluation of viral rpM across nasal, EA and PBMC compartments demonstrated that transcript abundance for individual viruses was often correlated across the three compartments (Figure 1E, Supplemental Figure S1F).
Activation of the Human Virome is Associated with COVID-19 Clinical Outcomes
Next, we evaluated how detection of viral transcripts in the first 40 days post hospital admission is associated with COVID-19 severity, using the previously published IMPACC trajectory groups (TG)31 (Figure 2A, and S2A, and Supplementary Data 1). Cumulative linked modeling of the TGs demonstrated significant associations between COVID-19 severity and the detection of Herpesviridae and Anelloviridae transcripts (Supplementary Data 1). More specifically, we found associations between severity and the detection of transcripts from Anelloviridae (PBMC adj.p = 2.37E-05), CMV (nasal adj.p = 3.16E-04, PBMC adj.p = 9.91E-04), EBV (nasal adj.p = 7.33E-06, PBMC adj.p =8.06E-10), HSV1 (nasal adj.p =1.05E-05), and HSV2 (nasal adj.p = 6.61E-04 transcripts. When further limiting to severely ill TG4 patients (still hospitalized after 28 days), we observed that patients with CMV transcripts in any compartment were more likely to die within one year (nasal adj.p = 4.79E-03, EA adj.p = 6.66E-03, PBMC adj.p = 6.66E-03). This was also the case for patients with detectably expressed EBV transcripts in the upper respiratory tract (adj.p = 0.0067), HSV1 (adj.p = 0.0067), or HSV2 (adj.p = 0.0067) (Figure 2A). Of note, there was no difference in prevalence of chronic viruses between TG4 and TG5.
We then evaluated whether the detection of chronically infecting viral transcripts varied with age, while adjusting for COVID-19 severity (TGs), and found a significant positive association with Anelloviridae transcripts in the PBMCs with increasing age (Figure 2B, adj.p = 0.044, Supplementary Data 1). When assessing associations between race or ethnicity, we found that Hispanic ethnicity was significantly associated with detection of both CMV (adj.p = 0.03) and EBV transcripts (adj.p = 0.03, Figure S2B, Supplementary Data 1). However, we found no significant associations between viral transcripts and biological sex, or treatment with either remdesivir or steroids (Figure S2C–E).
To further extend our analysis, we also evaluated the association of viral transcripts with comorbidities, medication usage, and complications (Figure 2C, Supplementary Data 1). Anelloviridae transcripts in PBMCs were significantly associated with a history of solid organ transplantation and immunosuppression, as well as shock and ST-elevation myocardial infarction (STEMI). CMV in the nasal compartment was linked to pneumothorax, whereas CMV in the PBMCs was associated with bacteremia, pulmonary vascular disease, renal complications, shock, and stroke. Interestingly, CMV in the nasal compartment was also inversely associated with azithromycin use. EBV transcripts in the nasal compartment were primarily associated with ICU-level care and shock, while detection of EBV transcripts in PBMCs correlated with liver failure, concurrent infections, shock, and the overall number of complications. Also, detection of EBV transcripts in the nasal compartment and the PBMCs was associated with a secondary infection besides SARS-CoV-2. Detection of HSV1 in the nasal compartment had a significant association with acute venous thromboembolism and shock and was inversely associated with liver disease. Similarly, HSV2 reads in the nasal compartment were associated with shock and inversely associated with convalescent plasma.
Viral Reactivation Results in Elevated Antibody Titers and Changes in Immune Cell Frequencies
We next leveraged our multi-omic data to further validate chronic viral reactivation in COVID-19 and characterize the associated responses of the host’s immune system, incorporating serum EBV and CMV antibody levels, immune cell frequencies, serum cytokines, plasma metabolomics, and host transcriptomics.
First, we observed that patients with detectable EBV transcripts in PBMCs had persistently elevated EBV IgG and IgA antibody titers (Figure 3A, Supplementary Data 2). Similarly, patients with CMV transcripts had significantly higher CMV seropositivity rates at baseline (Figure 3B, Supplementary Data 2). Furthermore, using unbiased mass spectrometry proteomics32,33, we assessed circulating HSV1 proteins in participant plasma. These proteins were more common in participants from whom HSV1 transcripts were identified in nasal swabs (Figure 3C, p=0.02, Supplementary Data 2), and significantly more common in TG4 patients with severe COVID-19 (Figure S3B, p=0.0003, Supplementary Data 2).
Using mass cytometry by time of flight (CyTOF), we next evaluated relationships between viral transcript detection and blood immune cell frequencies. We observed significant associations between EBV transcription in PBMCs and increased proportions of B-cell plasmablasts, the primary host cells of the virus (Figure 3D, Supplementary Data 2). Furthermore, we observed that detection of CMV transcripts was associated with a significant reduction in the frequency of CD4 and CD8 central memory T cells, CD27low effector memory CD4 T cells, and CD56low CD16hi CD57low NK cells. Interestingly, detectable expression of both EBV and CMV was associated with a higher frequency of activated CD4+ and CD8+ T cells.
To assess whether changes in circulating cell frequencies may explain the association between Herpesviridae and Anelloviridae transcripts and COVID-19 severity we repeated our analysis of viral transcripts across the TGs while controlling for immune cell frequencies, which vary with disease severity34. This analysis demonstrated that even when controlling for changes in these underlying cell frequencies, viral transcripts were still significantly associated with increasing COVID-19 severity (Figure S3A, Supplementary Data 2).
Reactivation of the Human Virome Correlates with Changes in Inflammatory Cytokines and Chemokines
Next, we asked whether detection of Herpesviridae and Anelloviridae transcripts was associated with changes in inflammatory protein levels. Using generalized additive mixed modeling (gamm), we compared the longitudinal dynamics of cytokines in patients with or without evidence of viral reactivation, while controlling for COVID-19 severity (TGs), sex, and age (Figure 4A, Supplementary Data 3).
Interestingly, we found reactivation of different chronic viruses associated with several unique cytokine, chemokine, and inflammatory soluble protein signatures (Figure 4A and S4). However, there was also a set of shared cytokines including CXCL10 (Figure 4B), CXCL11 (Figure 4C), and IL18 (Figure 4D) that were correlated with several chronic viruses.
EBV in the PBMC was associated with elevation of several key cytokines including IL6 (Figure 4E), CCL7, CCL2, IL10 (Figure 4F), and CXCL10 all of which have been associated with COVID-19 severity35–37 (Figure 4A and S4). Interestingly, from the cytokines associated with EBV in PBMC, only CXCL10 was also associated with EBV in the nasal transcriptomics. In addition, EBV in the nasal compartment was also correlated with increases in IL18, CXCL11, CXCL10, CD274, IL18R1, IL15RA, IL22RA1, CCL8, IFNG, HGF, and a decrease in KITLG. Thus, these data suggest that the host immune response towards chronic viruses may differ between different compartments, with EBV in the nasal compartment possibly reflecting a more severe state of EBV viral reactivation.
Similar to EBV, the other detected herpetic viruses (HSV1 and HSV2 in the nasal and CMV in the PBMC transcriptomics) associated with pro-inflammatory serum cytokines, with many in common between the viruses. HSV1, HSV2, and CMV were all associated with increases in IL18, CXCL11, CXCL10, CD274, and TNF, with HSV1 and CMV also correlated with elevations in IL18R1, IL15RA, CDCP1, CD40, CD8A, FGF23. Furthermore, HSV2 was associated with increases in CCL8, TGFA, and OSM, HSV1 with elevations of IL22RA1, CCL25, CCL20, CD5, SLAMF1, MMP10, and TNFRSF9, and CMV with increases in CCL8, IFNG (Figure 4G), HGF, CCL7, CXCL9, CX3CL1, LIFR, ADA1, CXCL8, and decreases in MMP1 and IL4. Finally, Anelloviridae were only associated with an increase in CXCL11 and IL18, and a decrease in TNFSF11. We also observed compartment-specific differences in the relationship between cytokine expression and viral replication. For instance, of the cytokines associated with EBV transcripts in PBMCs, only CXCL10 was also associated with EBV in the upper airway.
Reactivation of the Human Virome Correlates with Changes in the Metabolome
Using the same longitudinal gamm analysis, we evaluated the association of chronic viral reactivation with plasma metabolites as measured by liquid chromatography-mass spectrometry (Supplementary Data 4). As has been observed with other viral infections38, reactivation was associated with changes in metabolites belonging to amino acid and lipid metabolism38 (Figure 5A and 5B). Of the viruses with detectably expressed transcripts, CMV was associated the greatest number of changes in the metabolome, and in particular with higher levels of urea and TMAP, metabolites previously linked to kidney injury (Figure 5A, 5C and S5B)39,40. Additionally, detection of CMV and Anelloviridae transcripts were both associated with increases in several long chain fatty acids such as erucate, arachidate, and docosadienoate (Figure 5D and S5C–D) and regulators of nitric oxide synthesis, dimethylarginine (SDMA + ADMA)41–44 (Figure S5E). We also identified a shared metabolomic signature common across multiple viruses (Figure S5A). For example, both S-methylcysteine sulfoxide and 6-bromotryptophan were reduced with reactivation of Anelloviridae, HSV1, HSV2, CMV, and EBV (Figure 5E and S5F).
Reactivation of Chronic Viruses is Associated with Changes in the Host Transcriptome
To identify a signature for each chronic virus independent of COVID-19 severity and participant demographics, we evaluated nasal and PBMC transcriptomic data for differentially expressed host genes while controlling for COVID-19 severity, SARS-CoV-2 nasal viral load, sex, age, and days from hospital admission (Supplementary Data 5).
In the PBMC transcriptomics, detection of viral transcripts in both the PBMC or nasal compartments were associated with changes in host PBMC gene expression (Figure 6A and S6, Supplementary Data 5). Hypergeometric enrichment analysis demonstrated that Anelloviridae in the PBMCs, CMV in the PBMCs, and HSV1/2 in the nasal compartment were all associated with downregulation of a diverse number of pathways pertaining to RNA processing and protein translation. Similarly, EBV, CMV, HSV1/2 were associated with an upregulation of pathways pertaining to cellular replication. In addition to these shared pathways, Anelloviridae in the PBMCs, CMV in the PBMCs, and HSV1/2, and CMV in the nasal compartment were also strongly associated with signatures of neutrophil degranulation. Finally, EBV in the PBMCs was uniquely associated with platelet activation and signaling.
When evaluating the nasal transcriptomics, viruses that reactivated in the nasal compartment (EBV, CMV, and HSV1/2) had the strongest associations with changes in upper airway gene expression (Figure 6B and S6, Supplementary Data 5), with inflammatory signaling pathways significantly upregulated in patients with reactivation of these viruses. For example, CMV, EBV, and HSV1/2 transcripts were all associated with upregulation of interleukin signaling, lymphocyte immunoregulatory interactions, and neutrophil degranulation. Of note, interleukin signaling included Interleukin-10 signaling, which was elevated in the serum of participants with CMV and EBV reactivation. Additionally, CMV and EBV replication in the upper airway was also associated with increases in pathways pertaining to a type 2 immune response and phagocytosis. Detection of HSV1 or EBV transcripts in the nasal compartment was associated with upregulation of methylation, and EBV was specifically associated with increases in pathways pertaining to T-cell activation. Finally, Anelloviridae in the PBMCs was also associated with changes in the nasal transcriptome, with the strongest associations pertaining to upregulation of genes involved in keratinization and downregulation of noncanonical NF-Kb signaling.
Detection of Anelloviridae Transcripts Associates with Physical Disability and Fatigue in PASC patients
Given recent reports linking chronic viral reactivation to PASC22,26,27, we evaluated how viral reactivation correlated with our previously identified patient reported outcome (PRO) groups using convalescent survey data45. The three PASC groups consisted of physical (characterized by physical disability and fatigue), cognitive (characterized by cognitive impairment), and global deficits (characterized by both physical and cognitive deficits). These groups were compared to a fourth PRO group reporting minimal deficits (minimal). To probe the relationship between chronic viral reactivation and PASC PRO groups, we examined whether viral reactivation was more prevalent in specific PRO groups during either acute or convalescent stages of COVID-19.
Upon evaluating the relationship between viral reactivation during the acute stage of COVID-19, no significant relationship was found with the PRO groups (Figure 7A, Supplementary Data 6). However, due to the association of chronic viruses with mortality and the participant drop-out in the convalescent stage, the sample size was limited (Figure S7A, Supplementary Data 6). Despite the lack of statistical significance during the acute stage, the relationship between specific viruses and PASC trended in the direction of previous reports, with CMV reactivation associated with lower rates of PASC 22.
We then investigated the correlation between viral detection during the convalescent period (> 60 days post hospitalization) and PRO groups. Interestingly, among the examined viruses, Anelloviridae and Enteroviridae were the most frequently detected viruses in RNA-seq of convalescent samples (Figure 1C and 7B, Supplementary Data 6). Notably, we found that Anelloviridae transcripts were significantly more prevalent in participants from the Physical PRO group (X2 = 9.95, adj.p = 0.038), which was characterized by high scores on the Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function survey. This finding suggests that detection of Anelloviridae transcripts may serve as a potential biomarker for persistent physical disability in PASC patients. We further observed the same Anelloviridae gene expression signature during both the acute and convalescent periods (i.e. an upregulation of genes involved in neutrophil degranulation and downregulation of genes involved in RNA processing, Figure 6A and 7C).
IMPACC Cohort
The IMPACC consortium enrolled 1,154 patients hospitalized for COVID-19 across 20 US hospitals between May 2020 and March 2021 (Figure 1A). All participants were COVID-19 vaccine-naive at the time of enrollment. To assess COVID-19 severity, participants were assigned to one of five trajectory groups (TG) using latent class mixed modeling of respiratory status over the first 28 days31. Groups were classified as mild (TG1), moderate (TG2), severe (TG3), critical (TG4), or fatal within 28 days (TG5). From each participant, bulk RNA sequencing was performed on PBMCs, nasal swabs, and for mechanically ventilated patients, endotracheal aspirates (EA), at up to ten visits during one-year post-hospital admission (Figure 1B). In addition, we assessed whole blood immune cell populations by mass cytometry by time of flight (CyTOF), serum anti-EBV and anti-CMV antibody titers, serum cytokine levels by proximity extension assay (PEA), and the plasma proteome and metabolome by mass spectrometry at participant visits.
RNA-sequencing Identifies Transcripts from the Human Virome
From RNA-seq data, we identified viral RNA transcripts in nasal, EA and PBMC samples and found a diverse number of human infecting viruses beyond SARS-CoV-2 including EBV, CMV, HHV6, HSV1, HSV2, and several Anelloviridae and Enteroviridae species (Figure 1C). Unsurprisingly, SARS-CoV-2 was the most prevalent virus identified, and was primarily found in NS and EA samples, with detection in PBMCs only in 10 participants near time of admission (Figure 1C). We confirmed that SARS-CoV-2 abundance measured by RNA-seq reads per million (rpM) highly correlated with RT-qPCR cycle threshold (Supplemental Figure S1A–B).
Among Herpesviridae, HSV1/2, EBV, and CMV transcripts were commonly detected across compartments during acute COVID-19 (defined as the first 40 days after hospital admission), with a notable lack of detection during the convalescent period (>3 months post admission) (Figure 1C). In addition, we also detected a diverse number of Anelloviridae and Enteroviridae species (Figure S1C–D), with their collective viral load at the family taxonomic level used for analyses.
Interestingly, we found that each viral species displayed unique temporal dynamics of reactivation relative to hospital admission (Figure 1D). For example, EBV reactivated early in the disease course, with ~20% of participants having detectable transcripts at the time of admission, followed by a gradual decline in detection over time. Unlike EBV, the frequency of Anelloviridae transcripts remained constant up to day 20 post-admission, followed by a slow decline. In contrast, HSV1/2 and CMV reactivated later in disease, with HSV1 detected in up to 40% of EA samples and CMV in ~8% of PBMC samples 19–24 days post admission (Figure 1D & S1D–E).
The detection of each virus varied across compartments, with EBV transcripts more common in PBMCs and HSV1/2 transcripts notably more common in NS and EA samples. Evaluation of viral rpM across nasal, EA and PBMC compartments demonstrated that transcript abundance for individual viruses was often correlated across the three compartments (Figure 1E, Supplemental Figure S1F).
Activation of the Human Virome is Associated with COVID-19 Clinical Outcomes
Next, we evaluated how detection of viral transcripts in the first 40 days post hospital admission is associated with COVID-19 severity, using the previously published IMPACC trajectory groups (TG)31 (Figure 2A, and S2A, and Supplementary Data 1). Cumulative linked modeling of the TGs demonstrated significant associations between COVID-19 severity and the detection of Herpesviridae and Anelloviridae transcripts (Supplementary Data 1). More specifically, we found associations between severity and the detection of transcripts from Anelloviridae (PBMC adj.p = 2.37E-05), CMV (nasal adj.p = 3.16E-04, PBMC adj.p = 9.91E-04), EBV (nasal adj.p = 7.33E-06, PBMC adj.p =8.06E-10), HSV1 (nasal adj.p =1.05E-05), and HSV2 (nasal adj.p = 6.61E-04 transcripts. When further limiting to severely ill TG4 patients (still hospitalized after 28 days), we observed that patients with CMV transcripts in any compartment were more likely to die within one year (nasal adj.p = 4.79E-03, EA adj.p = 6.66E-03, PBMC adj.p = 6.66E-03). This was also the case for patients with detectably expressed EBV transcripts in the upper respiratory tract (adj.p = 0.0067), HSV1 (adj.p = 0.0067), or HSV2 (adj.p = 0.0067) (Figure 2A). Of note, there was no difference in prevalence of chronic viruses between TG4 and TG5.
We then evaluated whether the detection of chronically infecting viral transcripts varied with age, while adjusting for COVID-19 severity (TGs), and found a significant positive association with Anelloviridae transcripts in the PBMCs with increasing age (Figure 2B, adj.p = 0.044, Supplementary Data 1). When assessing associations between race or ethnicity, we found that Hispanic ethnicity was significantly associated with detection of both CMV (adj.p = 0.03) and EBV transcripts (adj.p = 0.03, Figure S2B, Supplementary Data 1). However, we found no significant associations between viral transcripts and biological sex, or treatment with either remdesivir or steroids (Figure S2C–E).
To further extend our analysis, we also evaluated the association of viral transcripts with comorbidities, medication usage, and complications (Figure 2C, Supplementary Data 1). Anelloviridae transcripts in PBMCs were significantly associated with a history of solid organ transplantation and immunosuppression, as well as shock and ST-elevation myocardial infarction (STEMI). CMV in the nasal compartment was linked to pneumothorax, whereas CMV in the PBMCs was associated with bacteremia, pulmonary vascular disease, renal complications, shock, and stroke. Interestingly, CMV in the nasal compartment was also inversely associated with azithromycin use. EBV transcripts in the nasal compartment were primarily associated with ICU-level care and shock, while detection of EBV transcripts in PBMCs correlated with liver failure, concurrent infections, shock, and the overall number of complications. Also, detection of EBV transcripts in the nasal compartment and the PBMCs was associated with a secondary infection besides SARS-CoV-2. Detection of HSV1 in the nasal compartment had a significant association with acute venous thromboembolism and shock and was inversely associated with liver disease. Similarly, HSV2 reads in the nasal compartment were associated with shock and inversely associated with convalescent plasma.
Viral Reactivation Results in Elevated Antibody Titers and Changes in Immune Cell Frequencies
We next leveraged our multi-omic data to further validate chronic viral reactivation in COVID-19 and characterize the associated responses of the host’s immune system, incorporating serum EBV and CMV antibody levels, immune cell frequencies, serum cytokines, plasma metabolomics, and host transcriptomics.
First, we observed that patients with detectable EBV transcripts in PBMCs had persistently elevated EBV IgG and IgA antibody titers (Figure 3A, Supplementary Data 2). Similarly, patients with CMV transcripts had significantly higher CMV seropositivity rates at baseline (Figure 3B, Supplementary Data 2). Furthermore, using unbiased mass spectrometry proteomics32,33, we assessed circulating HSV1 proteins in participant plasma. These proteins were more common in participants from whom HSV1 transcripts were identified in nasal swabs (Figure 3C, p=0.02, Supplementary Data 2), and significantly more common in TG4 patients with severe COVID-19 (Figure S3B, p=0.0003, Supplementary Data 2).
Using mass cytometry by time of flight (CyTOF), we next evaluated relationships between viral transcript detection and blood immune cell frequencies. We observed significant associations between EBV transcription in PBMCs and increased proportions of B-cell plasmablasts, the primary host cells of the virus (Figure 3D, Supplementary Data 2). Furthermore, we observed that detection of CMV transcripts was associated with a significant reduction in the frequency of CD4 and CD8 central memory T cells, CD27low effector memory CD4 T cells, and CD56low CD16hi CD57low NK cells. Interestingly, detectable expression of both EBV and CMV was associated with a higher frequency of activated CD4+ and CD8+ T cells.
To assess whether changes in circulating cell frequencies may explain the association between Herpesviridae and Anelloviridae transcripts and COVID-19 severity we repeated our analysis of viral transcripts across the TGs while controlling for immune cell frequencies, which vary with disease severity34. This analysis demonstrated that even when controlling for changes in these underlying cell frequencies, viral transcripts were still significantly associated with increasing COVID-19 severity (Figure S3A, Supplementary Data 2).
Reactivation of the Human Virome Correlates with Changes in Inflammatory Cytokines and Chemokines
Next, we asked whether detection of Herpesviridae and Anelloviridae transcripts was associated with changes in inflammatory protein levels. Using generalized additive mixed modeling (gamm), we compared the longitudinal dynamics of cytokines in patients with or without evidence of viral reactivation, while controlling for COVID-19 severity (TGs), sex, and age (Figure 4A, Supplementary Data 3).
Interestingly, we found reactivation of different chronic viruses associated with several unique cytokine, chemokine, and inflammatory soluble protein signatures (Figure 4A and S4). However, there was also a set of shared cytokines including CXCL10 (Figure 4B), CXCL11 (Figure 4C), and IL18 (Figure 4D) that were correlated with several chronic viruses.
EBV in the PBMC was associated with elevation of several key cytokines including IL6 (Figure 4E), CCL7, CCL2, IL10 (Figure 4F), and CXCL10 all of which have been associated with COVID-19 severity35–37 (Figure 4A and S4). Interestingly, from the cytokines associated with EBV in PBMC, only CXCL10 was also associated with EBV in the nasal transcriptomics. In addition, EBV in the nasal compartment was also correlated with increases in IL18, CXCL11, CXCL10, CD274, IL18R1, IL15RA, IL22RA1, CCL8, IFNG, HGF, and a decrease in KITLG. Thus, these data suggest that the host immune response towards chronic viruses may differ between different compartments, with EBV in the nasal compartment possibly reflecting a more severe state of EBV viral reactivation.
Similar to EBV, the other detected herpetic viruses (HSV1 and HSV2 in the nasal and CMV in the PBMC transcriptomics) associated with pro-inflammatory serum cytokines, with many in common between the viruses. HSV1, HSV2, and CMV were all associated with increases in IL18, CXCL11, CXCL10, CD274, and TNF, with HSV1 and CMV also correlated with elevations in IL18R1, IL15RA, CDCP1, CD40, CD8A, FGF23. Furthermore, HSV2 was associated with increases in CCL8, TGFA, and OSM, HSV1 with elevations of IL22RA1, CCL25, CCL20, CD5, SLAMF1, MMP10, and TNFRSF9, and CMV with increases in CCL8, IFNG (Figure 4G), HGF, CCL7, CXCL9, CX3CL1, LIFR, ADA1, CXCL8, and decreases in MMP1 and IL4. Finally, Anelloviridae were only associated with an increase in CXCL11 and IL18, and a decrease in TNFSF11. We also observed compartment-specific differences in the relationship between cytokine expression and viral replication. For instance, of the cytokines associated with EBV transcripts in PBMCs, only CXCL10 was also associated with EBV in the upper airway.
Reactivation of the Human Virome Correlates with Changes in the Metabolome
Using the same longitudinal gamm analysis, we evaluated the association of chronic viral reactivation with plasma metabolites as measured by liquid chromatography-mass spectrometry (Supplementary Data 4). As has been observed with other viral infections38, reactivation was associated with changes in metabolites belonging to amino acid and lipid metabolism38 (Figure 5A and 5B). Of the viruses with detectably expressed transcripts, CMV was associated the greatest number of changes in the metabolome, and in particular with higher levels of urea and TMAP, metabolites previously linked to kidney injury (Figure 5A, 5C and S5B)39,40. Additionally, detection of CMV and Anelloviridae transcripts were both associated with increases in several long chain fatty acids such as erucate, arachidate, and docosadienoate (Figure 5D and S5C–D) and regulators of nitric oxide synthesis, dimethylarginine (SDMA + ADMA)41–44 (Figure S5E). We also identified a shared metabolomic signature common across multiple viruses (Figure S5A). For example, both S-methylcysteine sulfoxide and 6-bromotryptophan were reduced with reactivation of Anelloviridae, HSV1, HSV2, CMV, and EBV (Figure 5E and S5F).
Reactivation of Chronic Viruses is Associated with Changes in the Host Transcriptome
To identify a signature for each chronic virus independent of COVID-19 severity and participant demographics, we evaluated nasal and PBMC transcriptomic data for differentially expressed host genes while controlling for COVID-19 severity, SARS-CoV-2 nasal viral load, sex, age, and days from hospital admission (Supplementary Data 5).
In the PBMC transcriptomics, detection of viral transcripts in both the PBMC or nasal compartments were associated with changes in host PBMC gene expression (Figure 6A and S6, Supplementary Data 5). Hypergeometric enrichment analysis demonstrated that Anelloviridae in the PBMCs, CMV in the PBMCs, and HSV1/2 in the nasal compartment were all associated with downregulation of a diverse number of pathways pertaining to RNA processing and protein translation. Similarly, EBV, CMV, HSV1/2 were associated with an upregulation of pathways pertaining to cellular replication. In addition to these shared pathways, Anelloviridae in the PBMCs, CMV in the PBMCs, and HSV1/2, and CMV in the nasal compartment were also strongly associated with signatures of neutrophil degranulation. Finally, EBV in the PBMCs was uniquely associated with platelet activation and signaling.
When evaluating the nasal transcriptomics, viruses that reactivated in the nasal compartment (EBV, CMV, and HSV1/2) had the strongest associations with changes in upper airway gene expression (Figure 6B and S6, Supplementary Data 5), with inflammatory signaling pathways significantly upregulated in patients with reactivation of these viruses. For example, CMV, EBV, and HSV1/2 transcripts were all associated with upregulation of interleukin signaling, lymphocyte immunoregulatory interactions, and neutrophil degranulation. Of note, interleukin signaling included Interleukin-10 signaling, which was elevated in the serum of participants with CMV and EBV reactivation. Additionally, CMV and EBV replication in the upper airway was also associated with increases in pathways pertaining to a type 2 immune response and phagocytosis. Detection of HSV1 or EBV transcripts in the nasal compartment was associated with upregulation of methylation, and EBV was specifically associated with increases in pathways pertaining to T-cell activation. Finally, Anelloviridae in the PBMCs was also associated with changes in the nasal transcriptome, with the strongest associations pertaining to upregulation of genes involved in keratinization and downregulation of noncanonical NF-Kb signaling.
Detection of Anelloviridae Transcripts Associates with Physical Disability and Fatigue in PASC patients
Given recent reports linking chronic viral reactivation to PASC22,26,27, we evaluated how viral reactivation correlated with our previously identified patient reported outcome (PRO) groups using convalescent survey data45. The three PASC groups consisted of physical (characterized by physical disability and fatigue), cognitive (characterized by cognitive impairment), and global deficits (characterized by both physical and cognitive deficits). These groups were compared to a fourth PRO group reporting minimal deficits (minimal). To probe the relationship between chronic viral reactivation and PASC PRO groups, we examined whether viral reactivation was more prevalent in specific PRO groups during either acute or convalescent stages of COVID-19.
Upon evaluating the relationship between viral reactivation during the acute stage of COVID-19, no significant relationship was found with the PRO groups (Figure 7A, Supplementary Data 6). However, due to the association of chronic viruses with mortality and the participant drop-out in the convalescent stage, the sample size was limited (Figure S7A, Supplementary Data 6). Despite the lack of statistical significance during the acute stage, the relationship between specific viruses and PASC trended in the direction of previous reports, with CMV reactivation associated with lower rates of PASC 22.
We then investigated the correlation between viral detection during the convalescent period (> 60 days post hospitalization) and PRO groups. Interestingly, among the examined viruses, Anelloviridae and Enteroviridae were the most frequently detected viruses in RNA-seq of convalescent samples (Figure 1C and 7B, Supplementary Data 6). Notably, we found that Anelloviridae transcripts were significantly more prevalent in participants from the Physical PRO group (X2 = 9.95, adj.p = 0.038), which was characterized by high scores on the Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function survey. This finding suggests that detection of Anelloviridae transcripts may serve as a potential biomarker for persistent physical disability in PASC patients. We further observed the same Anelloviridae gene expression signature during both the acute and convalescent periods (i.e. an upregulation of genes involved in neutrophil degranulation and downregulation of genes involved in RNA processing, Figure 6A and 7C).
RNA-sequencing Identifies Transcripts from the Human Virome
From RNA-seq data, we identified viral RNA transcripts in nasal, EA and PBMC samples and found a diverse number of human infecting viruses beyond SARS-CoV-2 including EBV, CMV, HHV6, HSV1, HSV2, and several Anelloviridae and Enteroviridae species (Figure 1C). Unsurprisingly, SARS-CoV-2 was the most prevalent virus identified, and was primarily found in NS and EA samples, with detection in PBMCs only in 10 participants near time of admission (Figure 1C). We confirmed that SARS-CoV-2 abundance measured by RNA-seq reads per million (rpM) highly correlated with RT-qPCR cycle threshold (Supplemental Figure S1A–B).
Among Herpesviridae, HSV1/2, EBV, and CMV transcripts were commonly detected across compartments during acute COVID-19 (defined as the first 40 days after hospital admission), with a notable lack of detection during the convalescent period (>3 months post admission) (Figure 1C). In addition, we also detected a diverse number of Anelloviridae and Enteroviridae species (Figure S1C–D), with their collective viral load at the family taxonomic level used for analyses.
Interestingly, we found that each viral species displayed unique temporal dynamics of reactivation relative to hospital admission (Figure 1D). For example, EBV reactivated early in the disease course, with ~20% of participants having detectable transcripts at the time of admission, followed by a gradual decline in detection over time. Unlike EBV, the frequency of Anelloviridae transcripts remained constant up to day 20 post-admission, followed by a slow decline. In contrast, HSV1/2 and CMV reactivated later in disease, with HSV1 detected in up to 40% of EA samples and CMV in ~8% of PBMC samples 19–24 days post admission (Figure 1D & S1D–E).
The detection of each virus varied across compartments, with EBV transcripts more common in PBMCs and HSV1/2 transcripts notably more common in NS and EA samples. Evaluation of viral rpM across nasal, EA and PBMC compartments demonstrated that transcript abundance for individual viruses was often correlated across the three compartments (Figure 1E, Supplemental Figure S1F).
Activation of the Human Virome is Associated with COVID-19 Clinical Outcomes
Next, we evaluated how detection of viral transcripts in the first 40 days post hospital admission is associated with COVID-19 severity, using the previously published IMPACC trajectory groups (TG)31 (Figure 2A, and S2A, and Supplementary Data 1). Cumulative linked modeling of the TGs demonstrated significant associations between COVID-19 severity and the detection of Herpesviridae and Anelloviridae transcripts (Supplementary Data 1). More specifically, we found associations between severity and the detection of transcripts from Anelloviridae (PBMC adj.p = 2.37E-05), CMV (nasal adj.p = 3.16E-04, PBMC adj.p = 9.91E-04), EBV (nasal adj.p = 7.33E-06, PBMC adj.p =8.06E-10), HSV1 (nasal adj.p =1.05E-05), and HSV2 (nasal adj.p = 6.61E-04 transcripts. When further limiting to severely ill TG4 patients (still hospitalized after 28 days), we observed that patients with CMV transcripts in any compartment were more likely to die within one year (nasal adj.p = 4.79E-03, EA adj.p = 6.66E-03, PBMC adj.p = 6.66E-03). This was also the case for patients with detectably expressed EBV transcripts in the upper respiratory tract (adj.p = 0.0067), HSV1 (adj.p = 0.0067), or HSV2 (adj.p = 0.0067) (Figure 2A). Of note, there was no difference in prevalence of chronic viruses between TG4 and TG5.
We then evaluated whether the detection of chronically infecting viral transcripts varied with age, while adjusting for COVID-19 severity (TGs), and found a significant positive association with Anelloviridae transcripts in the PBMCs with increasing age (Figure 2B, adj.p = 0.044, Supplementary Data 1). When assessing associations between race or ethnicity, we found that Hispanic ethnicity was significantly associated with detection of both CMV (adj.p = 0.03) and EBV transcripts (adj.p = 0.03, Figure S2B, Supplementary Data 1). However, we found no significant associations between viral transcripts and biological sex, or treatment with either remdesivir or steroids (Figure S2C–E).
To further extend our analysis, we also evaluated the association of viral transcripts with comorbidities, medication usage, and complications (Figure 2C, Supplementary Data 1). Anelloviridae transcripts in PBMCs were significantly associated with a history of solid organ transplantation and immunosuppression, as well as shock and ST-elevation myocardial infarction (STEMI). CMV in the nasal compartment was linked to pneumothorax, whereas CMV in the PBMCs was associated with bacteremia, pulmonary vascular disease, renal complications, shock, and stroke. Interestingly, CMV in the nasal compartment was also inversely associated with azithromycin use. EBV transcripts in the nasal compartment were primarily associated with ICU-level care and shock, while detection of EBV transcripts in PBMCs correlated with liver failure, concurrent infections, shock, and the overall number of complications. Also, detection of EBV transcripts in the nasal compartment and the PBMCs was associated with a secondary infection besides SARS-CoV-2. Detection of HSV1 in the nasal compartment had a significant association with acute venous thromboembolism and shock and was inversely associated with liver disease. Similarly, HSV2 reads in the nasal compartment were associated with shock and inversely associated with convalescent plasma.
Viral Reactivation Results in Elevated Antibody Titers and Changes in Immune Cell Frequencies
We next leveraged our multi-omic data to further validate chronic viral reactivation in COVID-19 and characterize the associated responses of the host’s immune system, incorporating serum EBV and CMV antibody levels, immune cell frequencies, serum cytokines, plasma metabolomics, and host transcriptomics.
First, we observed that patients with detectable EBV transcripts in PBMCs had persistently elevated EBV IgG and IgA antibody titers (Figure 3A, Supplementary Data 2). Similarly, patients with CMV transcripts had significantly higher CMV seropositivity rates at baseline (Figure 3B, Supplementary Data 2). Furthermore, using unbiased mass spectrometry proteomics32,33, we assessed circulating HSV1 proteins in participant plasma. These proteins were more common in participants from whom HSV1 transcripts were identified in nasal swabs (Figure 3C, p=0.02, Supplementary Data 2), and significantly more common in TG4 patients with severe COVID-19 (Figure S3B, p=0.0003, Supplementary Data 2).
Using mass cytometry by time of flight (CyTOF), we next evaluated relationships between viral transcript detection and blood immune cell frequencies. We observed significant associations between EBV transcription in PBMCs and increased proportions of B-cell plasmablasts, the primary host cells of the virus (Figure 3D, Supplementary Data 2). Furthermore, we observed that detection of CMV transcripts was associated with a significant reduction in the frequency of CD4 and CD8 central memory T cells, CD27low effector memory CD4 T cells, and CD56low CD16hi CD57low NK cells. Interestingly, detectable expression of both EBV and CMV was associated with a higher frequency of activated CD4+ and CD8+ T cells.
To assess whether changes in circulating cell frequencies may explain the association between Herpesviridae and Anelloviridae transcripts and COVID-19 severity we repeated our analysis of viral transcripts across the TGs while controlling for immune cell frequencies, which vary with disease severity34. This analysis demonstrated that even when controlling for changes in these underlying cell frequencies, viral transcripts were still significantly associated with increasing COVID-19 severity (Figure S3A, Supplementary Data 2).
Reactivation of the Human Virome Correlates with Changes in Inflammatory Cytokines and Chemokines
Next, we asked whether detection of Herpesviridae and Anelloviridae transcripts was associated with changes in inflammatory protein levels. Using generalized additive mixed modeling (gamm), we compared the longitudinal dynamics of cytokines in patients with or without evidence of viral reactivation, while controlling for COVID-19 severity (TGs), sex, and age (Figure 4A, Supplementary Data 3).
Interestingly, we found reactivation of different chronic viruses associated with several unique cytokine, chemokine, and inflammatory soluble protein signatures (Figure 4A and S4). However, there was also a set of shared cytokines including CXCL10 (Figure 4B), CXCL11 (Figure 4C), and IL18 (Figure 4D) that were correlated with several chronic viruses.
EBV in the PBMC was associated with elevation of several key cytokines including IL6 (Figure 4E), CCL7, CCL2, IL10 (Figure 4F), and CXCL10 all of which have been associated with COVID-19 severity35–37 (Figure 4A and S4). Interestingly, from the cytokines associated with EBV in PBMC, only CXCL10 was also associated with EBV in the nasal transcriptomics. In addition, EBV in the nasal compartment was also correlated with increases in IL18, CXCL11, CXCL10, CD274, IL18R1, IL15RA, IL22RA1, CCL8, IFNG, HGF, and a decrease in KITLG. Thus, these data suggest that the host immune response towards chronic viruses may differ between different compartments, with EBV in the nasal compartment possibly reflecting a more severe state of EBV viral reactivation.
Similar to EBV, the other detected herpetic viruses (HSV1 and HSV2 in the nasal and CMV in the PBMC transcriptomics) associated with pro-inflammatory serum cytokines, with many in common between the viruses. HSV1, HSV2, and CMV were all associated with increases in IL18, CXCL11, CXCL10, CD274, and TNF, with HSV1 and CMV also correlated with elevations in IL18R1, IL15RA, CDCP1, CD40, CD8A, FGF23. Furthermore, HSV2 was associated with increases in CCL8, TGFA, and OSM, HSV1 with elevations of IL22RA1, CCL25, CCL20, CD5, SLAMF1, MMP10, and TNFRSF9, and CMV with increases in CCL8, IFNG (Figure 4G), HGF, CCL7, CXCL9, CX3CL1, LIFR, ADA1, CXCL8, and decreases in MMP1 and IL4. Finally, Anelloviridae were only associated with an increase in CXCL11 and IL18, and a decrease in TNFSF11. We also observed compartment-specific differences in the relationship between cytokine expression and viral replication. For instance, of the cytokines associated with EBV transcripts in PBMCs, only CXCL10 was also associated with EBV in the upper airway.
Reactivation of the Human Virome Correlates with Changes in the Metabolome
Using the same longitudinal gamm analysis, we evaluated the association of chronic viral reactivation with plasma metabolites as measured by liquid chromatography-mass spectrometry (Supplementary Data 4). As has been observed with other viral infections38, reactivation was associated with changes in metabolites belonging to amino acid and lipid metabolism38 (Figure 5A and 5B). Of the viruses with detectably expressed transcripts, CMV was associated the greatest number of changes in the metabolome, and in particular with higher levels of urea and TMAP, metabolites previously linked to kidney injury (Figure 5A, 5C and S5B)39,40. Additionally, detection of CMV and Anelloviridae transcripts were both associated with increases in several long chain fatty acids such as erucate, arachidate, and docosadienoate (Figure 5D and S5C–D) and regulators of nitric oxide synthesis, dimethylarginine (SDMA + ADMA)41–44 (Figure S5E). We also identified a shared metabolomic signature common across multiple viruses (Figure S5A). For example, both S-methylcysteine sulfoxide and 6-bromotryptophan were reduced with reactivation of Anelloviridae, HSV1, HSV2, CMV, and EBV (Figure 5E and S5F).
Reactivation of Chronic Viruses is Associated with Changes in the Host Transcriptome
To identify a signature for each chronic virus independent of COVID-19 severity and participant demographics, we evaluated nasal and PBMC transcriptomic data for differentially expressed host genes while controlling for COVID-19 severity, SARS-CoV-2 nasal viral load, sex, age, and days from hospital admission (Supplementary Data 5).
In the PBMC transcriptomics, detection of viral transcripts in both the PBMC or nasal compartments were associated with changes in host PBMC gene expression (Figure 6A and S6, Supplementary Data 5). Hypergeometric enrichment analysis demonstrated that Anelloviridae in the PBMCs, CMV in the PBMCs, and HSV1/2 in the nasal compartment were all associated with downregulation of a diverse number of pathways pertaining to RNA processing and protein translation. Similarly, EBV, CMV, HSV1/2 were associated with an upregulation of pathways pertaining to cellular replication. In addition to these shared pathways, Anelloviridae in the PBMCs, CMV in the PBMCs, and HSV1/2, and CMV in the nasal compartment were also strongly associated with signatures of neutrophil degranulation. Finally, EBV in the PBMCs was uniquely associated with platelet activation and signaling.
When evaluating the nasal transcriptomics, viruses that reactivated in the nasal compartment (EBV, CMV, and HSV1/2) had the strongest associations with changes in upper airway gene expression (Figure 6B and S6, Supplementary Data 5), with inflammatory signaling pathways significantly upregulated in patients with reactivation of these viruses. For example, CMV, EBV, and HSV1/2 transcripts were all associated with upregulation of interleukin signaling, lymphocyte immunoregulatory interactions, and neutrophil degranulation. Of note, interleukin signaling included Interleukin-10 signaling, which was elevated in the serum of participants with CMV and EBV reactivation. Additionally, CMV and EBV replication in the upper airway was also associated with increases in pathways pertaining to a type 2 immune response and phagocytosis. Detection of HSV1 or EBV transcripts in the nasal compartment was associated with upregulation of methylation, and EBV was specifically associated with increases in pathways pertaining to T-cell activation. Finally, Anelloviridae in the PBMCs was also associated with changes in the nasal transcriptome, with the strongest associations pertaining to upregulation of genes involved in keratinization and downregulation of noncanonical NF-Kb signaling.
Detection of Anelloviridae Transcripts Associates with Physical Disability and Fatigue in PASC patients
Given recent reports linking chronic viral reactivation to PASC22,26,27, we evaluated how viral reactivation correlated with our previously identified patient reported outcome (PRO) groups using convalescent survey data45. The three PASC groups consisted of physical (characterized by physical disability and fatigue), cognitive (characterized by cognitive impairment), and global deficits (characterized by both physical and cognitive deficits). These groups were compared to a fourth PRO group reporting minimal deficits (minimal). To probe the relationship between chronic viral reactivation and PASC PRO groups, we examined whether viral reactivation was more prevalent in specific PRO groups during either acute or convalescent stages of COVID-19.
Upon evaluating the relationship between viral reactivation during the acute stage of COVID-19, no significant relationship was found with the PRO groups (Figure 7A, Supplementary Data 6). However, due to the association of chronic viruses with mortality and the participant drop-out in the convalescent stage, the sample size was limited (Figure S7A, Supplementary Data 6). Despite the lack of statistical significance during the acute stage, the relationship between specific viruses and PASC trended in the direction of previous reports, with CMV reactivation associated with lower rates of PASC 22.
We then investigated the correlation between viral detection during the convalescent period (> 60 days post hospitalization) and PRO groups. Interestingly, among the examined viruses, Anelloviridae and Enteroviridae were the most frequently detected viruses in RNA-seq of convalescent samples (Figure 1C and 7B, Supplementary Data 6). Notably, we found that Anelloviridae transcripts were significantly more prevalent in participants from the Physical PRO group (X2 = 9.95, adj.p = 0.038), which was characterized by high scores on the Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function survey. This finding suggests that detection of Anelloviridae transcripts may serve as a potential biomarker for persistent physical disability in PASC patients. We further observed the same Anelloviridae gene expression signature during both the acute and convalescent periods (i.e. an upregulation of genes involved in neutrophil degranulation and downregulation of genes involved in RNA processing, Figure 6A and 7C).
Discussion
Chronically infecting viruses are prevalent in the general population, with individuals estimated to carry an average of 8 to 12 chronic viral infections at any given time2. These viruses are generally innocuous and typically do not result in acute infectious symptoms, however, emerging evidence suggests that their reactivation is associated with and may contribute to multiple diseases, including autoimmune syndromes, malignancies, and chronic fatigue syndrome 2,7–13,46. Additionally, Herpesviridae, such as EBV and CMV, have been found to reactivate in severe infections and sepsis16,47,48 and have more recently been implicated in the pathophysiology of Long COVID/PASC22,26,27. Thus, there is an urgent need to better understand how chronically infecting viruses reactivate in COVID-19 and to develop preventative and therapeutic strategies to combat their reactivation.
Here, we conducted a prospective, multi-omic analysis of blood and respiratory samples from >1000 hospitalized COVID-19 patients, which revealed widespread reactivation of chronic viral infections, specifically from the Herpesviridae and Anelloviridae families. By integrating data from multiple platforms, including cellular and cytokine immunophenotyping, metabolomics, and transcriptomics, our findings expand on the complex interplay between SARS-CoV-2 infection and chronic viral reactivation, and provide a deeper understanding of the host immune response. Importantly, our findings demonstrate association of viral co-infections on the clinical course of COVID-19 and PASC.
Despite prior studies evaluating viral reactivation in acute COVID-1920,21,25, the exact timing of reactivation for different viruses has not been clearly established. Here, leveraging a large and longitudinally sampled cohort, we define timing and duration of viral reactivations. Specifically, we found that EBV transcripts in blood peaked early in acute disease following hospital admission, and then decreased over time. Similarly, detection of Anelloviridae transcripts from blood was most common early during hospitalization and began to decline several weeks later. In contrast, CMV, HSV1, and HSV2 displayed later reactivation and were found primarily in respiratory samples, peaking at approximately 22 days post hospitalization. Interestingly, patients who were EBV transcript positive, in either the nasal or PBMC compartments at hospital admission, had higher relative EBV IgG antibody titers, suggesting that EBV reactivation may happen prior to hospitalization for some patients. This may also be the case for HSV1, HSV2, and CMV as reactivation of these viruses may be present in tissues before they become detectable in PBMC or mucosal secretions. These results provide novel insights into the dynamics of the diverse virological landscape of COVID-19 and reveal that the time course of reactivation varies for different viruses.
Reactivation of Herpesviridae and Anelloviridae was associated with key clinical outcomes, including severe disease and death. CMV reactivation in all tested compartments (upper and lower respiratory tract and blood) was associated with increased mortality; while EBV reactivation in the nasal compartment and HSV1/2 reactivation in the respiratory compartment served as prognostic markers of mortality. Renal complications were more common with CMV reactivation, while venous thromboembolism was most associated with HSV1. Immunocompromised patients were most likely to experience Anelloviridae reactivation, as previously reported49–51. Shock was associated with multiple viral reactivations across anatomical compartments including Anelloviridae in PBMCs, CMV and EBV in the upper respiratory tract and PBMCs, and HSV1/2 in the upper respiratory tract, while EBV emerged as the virus with the most significant association to the overall number of complications.
Interestingly, azithromycin, a macrolide antibiotic, known to have antiviral activity52 and that exhibits anti-inflammatory effects that could influence microbial infections53,54, was associated with a decrease in CMV prevalence in the nasal compartment. These findings collectively raise the possibility of a multifaceted role of viral reactivation in the exacerbation of acute COVID-19, underscoring the need for integrated viral surveillance across body compartments and integration of viral reactivation data into the management of acute COVID-19 patients.
Regarding demographic features associated with virome reactivation, our findings are in line with prior epidemiological studies demonstrating higher rates of reactivation for EBV and CMV in individuals of Hispanic/Latino ethnicity55. Detection of Anelloviridae transcripts was significantly associated with increasing age, aligning with previous findings of higher Anelloviridae viral loads in older adults56. This finding suggests that aside from the risk of more severe disease, age on its own may not be a critical factor in herpetic viral reactivations.
Interestingly, there was a unique signature of cytokines, chemokines, and other inflammatory proteins associated with reactivation for the various viruses. For example, CMV reactivation correlated with a myriad of proteins, including IL10, CXCL10, CXCL11, sCD40, sPDL1 (CD274), and sCD8A. Increases in sCD40, sPDL1, and sCD8A, in conjunction with the increased circulating activated CD4+ and CD8+ T-cells during CMV reactivation, suggests that CMV may elicit activation of T-cells, as previously reported57.
Similarly to CMV, EBV was also associated with a significant increase in plasma IL10, as has been observed in previous studies58. Increasing serum concentrations of IL10 may stem from both viral upregulation of human IL10, coupled with possible displacement of human IL10 from IL10R by virally produced IL10 competitive agonists encoded by both CMV and EBV58–60. Other than IL10, EBV was also associated with increases in four additional cytokines and chemokines: IL6, CXCL10, CCL7, and CCL2. IL6 and CXCL10 have been strongly correlated with COVID-19 severity 34,36,61,62, and given that EBV reactivation occurs early during acute COVID-19, these findings raise the possibility that EBV may play a role in the production of these cytokines in critically ill COVID-19 patients. Collectively, these findings suggest that viruses may either exploit existing excessive inflammation to reactivate, or magnify production of specific inflammatory cytokines, with both mechanisms potentially contributing to acute COVID-19 severity.
In addition to perturbations of cytokines, we also observed significant changes in the host transcriptome, particularly PBMCs, possibly reflecting responses to viral reactivation. Broadly, reactivation of Herpesviridae in the blood was associated with increased expression of genes involved in cellular replication, potentially mirroring the expanding lymphocyte response to the reactivation. In the nasal compartment, reactivation of Herpesviridae correlated with upregulation of localized inflammatory signaling.
We also observed associations between virome reactivation and levels of metabolites. In particular, levels of methylcysteine sulfoxide and 6-bromotryptophan, which are involved in protection against oxidative stress63,64, were negatively associated with detection of Herpesviridae transcripts, suggesting that viral reactivation may contribute to oxidative stress and cellular damage. Furthermore, 6-bromotryptophan has previously been associated with COVID-19 complications34,65 and impaired kidney function63,66, the persistently low levels of this metabolite in the context of viral reactivation could imply additional risk to kidney health, compounded by the setting of hospitalization. This finding aligns with the pronounced and sustained elevation of urea and TMAP in CMV-infected patients which is indicative of renal impairment and suggests the need for monitoring of CMV reactivation in COVID-19 patients presenting with acute kidney damage. Additionally, we observed elevations in long chain fatty acids and dimethylarginine with CMV reactivation, which have been previously implicated in higher inflammatory states during acute COVID-1941,43 and suggest a potential disturbance in endothelial function and systemic inflammation42,44,67. Collectively, these data suggest that viral reactivation is associated with specific metabolic perturbations, possibly reflecting strategies by these pathogens to manipulate host cell metabolism for their advantage38.
PASC, also known as Long COVID, is a disorder that is characterized by heterogeneous symptoms that persist for months to years’ post-acute COVID-19 infection, which can profoundly impact patients’ health and often results in disability and loss of income45,68–70. While true prevalence is unknown due to evolving definition of PASC and variability of study design across studies, it is estimated to be ~10% or higher in patients post COVID-1971–73. Given the high prevalence and lack of treatments for PASC, there is an urgent need for a better understanding of the underlying PASC pathophysiology to guide the development of novel therapeutics. One of the leading emerging factors associated with PASC is chronic viral reactivation, particularly reactivation of EBV, for which PASC patients with neurocognitive symptoms have elevated antibody titers22.
In this study, we demonstrate that EBV reactivation is extensive in severe acute COVID-19 and establish the timing of EBV reactivation relative to SARS-CoV-2 infection and hospitalization. Although we did not find a direct association of EBV transcripts in the acute phase of COVID-19 with convalescent deficits, we observed a trend towards higher rates of PASC in participants with EBV reactivation and a trend toward lower rates in those with CMV reactivation, consistent with prior published reports which highlight the potential role of EBV reactivation in the development of PASC22,26. Of note, the PROs in this study were designed early in the pandemic prior to emergence of PASC, and thus may not fully capture PASC phenotypes, which may have limited our ability to detect association between EBV viral reactivation and PASC in our dataset. Furthermore, prior reports connecting EBV with PASC have focused strictly on EBV antibody titers, which was collected only at hospital admission for half our cohort (n=479). Thus, future work delving into the dynamics of antibody titers compared to viral transcription and reactivation may continue to elucidate EBV’s role in PASC.
Nonetheless, we report a novel significant association between the PASC Physical PRO group and detection of Anelloviridae transcripts in the convalescent period. Anelloviridae are a large family of negative-sense DNA stranded viruses, with some members of this family (e.g. Torque teno virus) found in ~80–90% of the population74,75. Interestingly, Anelloviridae have been previously linked with chronic conditions, such as chronic fatigue syndrome and multiple sclerosis76–78.These conditions often present with physical symptoms similar to those reported by PASC patients, including fatigue, cognitive dysfunction, and post- exertional malaise. Thus, our findings suggest that detection of Anelloviridae transcripts may serve as a biomarker of persistent physical symptoms among PASC patients. Our data highlights the need for future research to dissect the role of viral reactivation, particularly of Anelloviridae, in the development and persistence of PASC, as well as to explore potential therapeutic interventions targeting this virus family in PASC patients.
Strengths of our study include a large multicenter prospective cohort, diverse bioassays enabling comprehensive immunophenotyping, detailed clinical phenotyping, and the use of RNA sequencing to measure actively replicating viruses. However, there were also several limitations to our study. First, the usage of transcripts to identify viral loads is seldom performed when compared to common clinical RT-qPCR tests. However, the extensive sequencing depth of our samples (targeted read depth 50,000,000 reads) provided sufficient depth to identify viral reads. Additionally, we had only six collection timepoints during the acute stage of disease, limiting the ability to absolutely rule out that participants did not have viral reactivation that might have occurred between collection timepoints. However, using the samples from >1000 participants and the fact that the exact date of samples varied from participant to participant due to the nature of observation and voluntary human studies, we calculated the overall global trends of reactivation over time. Another limitation was substantial participant drop out during the convalescent stage of the study, particularly in patients who had acute viral reactivation, particularly with EBV, limiting the power of analyses testing the association of acute viral reactivation with PASC. Lastly, IMPACC patients were unvaccinated and primarily exposed to ancestral strains of SARS-CoV-2, and thus further studies are needed to ascertain effects of more recent SARS-CoV-2 strains on viral reactivation in acute COVID-19 and PASC in population with hybrid immunity.
Conclusion
In this study, we integrated clinical, immunologic, virologic, and multi-omic data from cellular and cytokine immunophenotyping, metabolomics, proteomics, and transcriptomics in a longitudinal cohort of >1000 COVID-19 patients to investigate for reactivation of chronic viral infections and their association with clinical outcomes in one of the largest studies to date. We found that multiple chronic viruses reactivate during acute COVID-19 infection, particularly from the Herpesviridae and Anelloviridae families. Furthermore, we delineate the temporal dynamics of reactivation for various viruses and report their associations with the host immune response, molecular pathways, as well as acute and chronic clinical sequelae of COVID-19. Notably, our results raise the possibility that viral reactivation may contribute to the development of PASC. This finding underscores the pressing need to address chronic viral reactivation in the evaluation and management of acute COVID-19 and PASC.
Methods
Study Design and Participant Recruitment
IMPACC is a prospective longitudinal study that enrolled >1,000 hospitalized COVID-19 patients, as previously described34,45,62,79–81. Participants 18 years and older were recruited from 20 hospitals across 15 academic institutes within the United States (Figure 1A). All participants were confirmed to be SARS-CoV-2 positive by reverse transcription PCR (RT-PCR) testing and no participants were vaccinated for SARS-CoV-2 at time of enrollment. Nasal swabs, blood, and endotracheal aspirate (for ventilated patients) were collected within 72 hours of hospital admission (visit 1) and on days 4, 7, 14, 21, and 28 post hospital admission in addition to convalescent samples at 3, 6, 9, and 12 months. Participants were characterized into one of five trajectory groups (TGs) based on latent mixed class modeling of a 7-point ordinal scale that characterized degree of respiratory illness and reflected acute COVID-19 severity31.
The Department of Health and Human Services Office for Human Research Protections (OHRP) and NIAID concurred that the IMPACC study qualified for public health surveillance exemption. The study protocol was sent for review to each site’s institutional review board (IRB), with twelve sites conducting as a public health surveillance study, and three sites integrating the IMPACC study into IRB-approved protocols (The University of Texas at Austin, IRB 2020-04-0117; University of California San Francisco, IRB 20–304-97; Case Western Reserve University, IRB STUDY20200573) with participants providing informed consent. Participants enrolled at sites operating as a public health surveillance study were provided information sheets describing the study including the samples to be collected and plans for analysis and data de-identification. Participants who requested not to participate after review of the study plan and information were not enrolled. Participants were not compensated while hospitalized but were subsequently compensated for outpatient visits and surveys. This study was registered at clinicaltrials.gov (NCT0438777) and followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.
Sample Processing and Assays
Samples were processed as previously described34,45,62,79–81, with the sample protocol extensively documented in the IMPACC study design and protocol paper79. Briefly, 10 mL of blood and nasal swabs were collected at each visit, with blood processed within 6 hours of collection. Blood was collected in both a 2.5 mL Greiner Vacuette CAT Serum Separating Tube (SST) (Cat: 454243P) for serum and a 7.5 mL Sarstedt Venous blood collection monovette EDTA (Cat: NC9453456) for whole blood, PBMCs, and plasma. The SST was kept vertical at room temperature (RT) for at least 30 minutes before centrifuging at RT for 10 minutes at 1000g. Serum was then aliquoted at 100 μL for downstream assays.
From the EDTA tube, it was briefly inverted to mix before aliquoting 270 uL of whole blood twice for both Cytometry Time of Flight (CyTOF) and genome wide association sequencing (GWAS) which was stored at −80C until shipment to their respective processing core. The remaining blood was centrifuged at RT for 10 minutes at 1000g before aliquoting and storing 500 uL of plasma at −80C for proteomic and metabolomics. PBMCs were then isolated from the remaining sample using the SepMate and Lymphoprep system (StemCell) following manufacturer protocol and as previously described79. PBMCs were then stored at 2.5 × 105 cells in 200 μL of RLT Buffer (Qiagen) and beta-mercaptoethanol at −80C.
Interior nasal turbinate swabs (herein referred to as nasal swabs) were collected and stored in 1 mL of Zymo-DNA/RNA shield reagent (Zymo Research), before RNA was extracted twice in parallel from 250 μL of sample and purified with the KingFisher Flex sample purification system (ThermoFisher) and the quick DNA-RNA MagBead kit (Zymo Research). The duplicated RNA was pooled and aliquoted at 20 μL for the downstream assays (SARS-CoV-2 RT-qPCR and RNA-sequencing).
When participants were ventilated, an endotracheal aspirate (EA) was also collected in a 40 cc Argyle specimen trap and was processed within 2 hours of collection. First, 500 uL of 1:1 diluted EA with Maxpar PBS (Ca+2 −Mg+2 free) was mixed with 500 uL of DNA/RNA shield in a Zymo tube with lysis beads and subsequently stored at −80C for bulk RNA-sequencing.
Collected samples were then shipped and processed for nasal, PBMC, and EA RNA-sequencing, plasma proteomics, serum cytokine proximity extension assay (PEA), serum EBV and CMV antibody titers, whole blood CyTOF, and plasma metabolomics at their respective processing cores as previously described34,45,62,79–81. Each assay is described briefly below with additional technical details in the prior publications34,45,62,79–81.
PBMC, EA, and Nasal RNA-seq were sequenced on a NovaSeq 6000 (Illumina) at 100 bp paired-end read length, and data was aligned using STAR (v2.4.2a or v2.4.3) against the GRCh38 reference genome. Gene counts were generated using HTSeq-count (v0.4.1).
Nasal SARS-CoV-2 viral load was also measured by nasal swab RT-qPCR conducted using two separate sets of primers and probes for the N1 and N2 genes.
Whole Blood CyTOF used a panel of 43 antibodies to quantify the frequency of 65 cell subsets using a Fluidigm Helios mass cytometer, with a semi-automatic gating strategy used for cell type assignment.
Serum anti-viral antibodies for EBV and CMV were measured via a Luminex platform and were normalized to Assay Chex control beads by Radix BioSolutions with batch regressed as previously described80. This assay was only performed for half of the cohort (n=479).
Plasma proteomics was evaluated with a EVOSEP one liquid chromatography connected to a TIMSTOF Pro (Bruker) as previously described32,33.
Plasma metabolomics were measured using liquid chromatography-mass spectrometry and was conducted by Metabolon using in-house standards34,82,83.
Serum cytokines and chemokines were measured using O-Link’s multiplex PEA for 92 proteins known to be involved in human inflammation (Olink Bioscience, Uppsala, Sweden).
Of note and unique to this IMPACC manuscript, taxonomic alignments for human infecting viruses from the PBMC, Nasal, and EA RNA-seq data was obtained from CZID84, which removes host reads before aligning remaining reads against the National Center for Biotechnology Information (NCBI) nucleotide and non-redundant databases. A sample was considered “positive” for a virus if it had at least one read that mapped to both the nucleotide and non-redundant database. No water control samples from any of the RNA-sequencing had any reads for the human-infecting viruses evaluated in this manuscript, supporting this low threshold for positivity.
Statistics
All analyses were executed in R v4.0.3. All p-values calculated in this manuscript were adjusted using the Benjamini-Hochberg procedure where appropriate and are indicated by “adj.p” or “adjusted p-value” (circumstances where no adjustments were necessary will instead report “p” or “p-value”).
Clinical Features and Demographics
For testing the association of detection of chronic viral transcripts with TGs and age, we used cumulative linked mixed modeling from the ordinal (v 2019.12–10) R package. Due to both COVID-19 severity and age quantiles being ordinal, cumulative linked mixed modeling allowed for this ordinal relationship to be accounted for in addition to including enrollment site as a random effect. In the age quantile ordinal model, trajectory groups were also used as a main effect to control for COVID-19 severity.
Trajectory_group~virus_status,random=enrollment_siteAdmit_age_quintile~virus_status+trajectory_group,random=enrollment_site
For association testing of specific clinical complications, comorbidities, medication, and other clinical outcomes, linear mixed effect modeling from the lme4 R package (v1.1–28) was used with viral status, sex, admit_age_quintile, and trajectory group as main effects and enrollment site as a random effect.
Clinical_feature~virus_status+sex+admit_age_quintile+trajectory_group+(1|enrollment_site)
Serum Cytokines and Plasma Metabolomics
To evaluate both the serum PEA cytokine assay and the plasma metabolomics, generalized additive mixed modeling (gamm) from the gamm4 (v 0.2–6) R package was used to evaluate for differences in individual analytes between patients who had detected transcripts for a chronic virus compared to patients who never had human-infecting viral transcripts detected (other than for SARS-CoV-2). Analytes were modeled against days from admission using cubic regression splines with interactions of both status for a given chronic virus (binary: positive or negative based on detected transcripts at any collected timepoint) and TG in addition to fixed effects of status for the chronic virus being evaluated, TG, sex, age at time of admission sorted into quintiles, and SARS-CoV-2 nasal viral RPM at the time of that sample. As chronic viral status is both a main effect and interaction term in the model, we used a lower adjusted p-value cutoff of 0.01 to account for the fact that a feature was significant if either term was significant.
Analyte~s(days,bs=‘cr’)+s(days,bs=‘cr’,by=‘virus_status’)+s(days,bs=‘cr’,by=‘trajectory_group’)+virus_status+sex+trajectory_group+admit_age_quintile+sarscovs2_nasal_log_rpm,random=~(1|enrollment_site/participant_id)
Nasal and PBMC RNA-Sequencing
To analyze the signature of host gene expression associated with chronic viruses, the limma (v 3.46.0) R package was used for both the nasal and PBMC RNA-sequencing to evaluate differential expressed genes associated with participants who had detectable chronic viral transcripts.
~admit_age_quintile+sex+visit_number+trajectory_group+sarscov2_nasal_log_rpm+virus_status
Pathway enrichment on both the upregulated and downregulated differentially expressed genes was conducted using hypergeometric enrichment testing from the R package clusterProfiler (v3.18.1) with the Reactome pathway database.
Study Design and Participant Recruitment
IMPACC is a prospective longitudinal study that enrolled >1,000 hospitalized COVID-19 patients, as previously described34,45,62,79–81. Participants 18 years and older were recruited from 20 hospitals across 15 academic institutes within the United States (Figure 1A). All participants were confirmed to be SARS-CoV-2 positive by reverse transcription PCR (RT-PCR) testing and no participants were vaccinated for SARS-CoV-2 at time of enrollment. Nasal swabs, blood, and endotracheal aspirate (for ventilated patients) were collected within 72 hours of hospital admission (visit 1) and on days 4, 7, 14, 21, and 28 post hospital admission in addition to convalescent samples at 3, 6, 9, and 12 months. Participants were characterized into one of five trajectory groups (TGs) based on latent mixed class modeling of a 7-point ordinal scale that characterized degree of respiratory illness and reflected acute COVID-19 severity31.
The Department of Health and Human Services Office for Human Research Protections (OHRP) and NIAID concurred that the IMPACC study qualified for public health surveillance exemption. The study protocol was sent for review to each site’s institutional review board (IRB), with twelve sites conducting as a public health surveillance study, and three sites integrating the IMPACC study into IRB-approved protocols (The University of Texas at Austin, IRB 2020-04-0117; University of California San Francisco, IRB 20–304-97; Case Western Reserve University, IRB STUDY20200573) with participants providing informed consent. Participants enrolled at sites operating as a public health surveillance study were provided information sheets describing the study including the samples to be collected and plans for analysis and data de-identification. Participants who requested not to participate after review of the study plan and information were not enrolled. Participants were not compensated while hospitalized but were subsequently compensated for outpatient visits and surveys. This study was registered at clinicaltrials.gov (NCT0438777) and followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.
Sample Processing and Assays
Samples were processed as previously described34,45,62,79–81, with the sample protocol extensively documented in the IMPACC study design and protocol paper79. Briefly, 10 mL of blood and nasal swabs were collected at each visit, with blood processed within 6 hours of collection. Blood was collected in both a 2.5 mL Greiner Vacuette CAT Serum Separating Tube (SST) (Cat: 454243P) for serum and a 7.5 mL Sarstedt Venous blood collection monovette EDTA (Cat: NC9453456) for whole blood, PBMCs, and plasma. The SST was kept vertical at room temperature (RT) for at least 30 minutes before centrifuging at RT for 10 minutes at 1000g. Serum was then aliquoted at 100 μL for downstream assays.
From the EDTA tube, it was briefly inverted to mix before aliquoting 270 uL of whole blood twice for both Cytometry Time of Flight (CyTOF) and genome wide association sequencing (GWAS) which was stored at −80C until shipment to their respective processing core. The remaining blood was centrifuged at RT for 10 minutes at 1000g before aliquoting and storing 500 uL of plasma at −80C for proteomic and metabolomics. PBMCs were then isolated from the remaining sample using the SepMate and Lymphoprep system (StemCell) following manufacturer protocol and as previously described79. PBMCs were then stored at 2.5 × 105 cells in 200 μL of RLT Buffer (Qiagen) and beta-mercaptoethanol at −80C.
Interior nasal turbinate swabs (herein referred to as nasal swabs) were collected and stored in 1 mL of Zymo-DNA/RNA shield reagent (Zymo Research), before RNA was extracted twice in parallel from 250 μL of sample and purified with the KingFisher Flex sample purification system (ThermoFisher) and the quick DNA-RNA MagBead kit (Zymo Research). The duplicated RNA was pooled and aliquoted at 20 μL for the downstream assays (SARS-CoV-2 RT-qPCR and RNA-sequencing).
When participants were ventilated, an endotracheal aspirate (EA) was also collected in a 40 cc Argyle specimen trap and was processed within 2 hours of collection. First, 500 uL of 1:1 diluted EA with Maxpar PBS (Ca+2 −Mg+2 free) was mixed with 500 uL of DNA/RNA shield in a Zymo tube with lysis beads and subsequently stored at −80C for bulk RNA-sequencing.
Collected samples were then shipped and processed for nasal, PBMC, and EA RNA-sequencing, plasma proteomics, serum cytokine proximity extension assay (PEA), serum EBV and CMV antibody titers, whole blood CyTOF, and plasma metabolomics at their respective processing cores as previously described34,45,62,79–81. Each assay is described briefly below with additional technical details in the prior publications34,45,62,79–81.
PBMC, EA, and Nasal RNA-seq were sequenced on a NovaSeq 6000 (Illumina) at 100 bp paired-end read length, and data was aligned using STAR (v2.4.2a or v2.4.3) against the GRCh38 reference genome. Gene counts were generated using HTSeq-count (v0.4.1).
Nasal SARS-CoV-2 viral load was also measured by nasal swab RT-qPCR conducted using two separate sets of primers and probes for the N1 and N2 genes.
Whole Blood CyTOF used a panel of 43 antibodies to quantify the frequency of 65 cell subsets using a Fluidigm Helios mass cytometer, with a semi-automatic gating strategy used for cell type assignment.
Serum anti-viral antibodies for EBV and CMV were measured via a Luminex platform and were normalized to Assay Chex control beads by Radix BioSolutions with batch regressed as previously described80. This assay was only performed for half of the cohort (n=479).
Plasma proteomics was evaluated with a EVOSEP one liquid chromatography connected to a TIMSTOF Pro (Bruker) as previously described32,33.
Plasma metabolomics were measured using liquid chromatography-mass spectrometry and was conducted by Metabolon using in-house standards34,82,83.
Serum cytokines and chemokines were measured using O-Link’s multiplex PEA for 92 proteins known to be involved in human inflammation (Olink Bioscience, Uppsala, Sweden).
Of note and unique to this IMPACC manuscript, taxonomic alignments for human infecting viruses from the PBMC, Nasal, and EA RNA-seq data was obtained from CZID84, which removes host reads before aligning remaining reads against the National Center for Biotechnology Information (NCBI) nucleotide and non-redundant databases. A sample was considered “positive” for a virus if it had at least one read that mapped to both the nucleotide and non-redundant database. No water control samples from any of the RNA-sequencing had any reads for the human-infecting viruses evaluated in this manuscript, supporting this low threshold for positivity.
Statistics
All analyses were executed in R v4.0.3. All p-values calculated in this manuscript were adjusted using the Benjamini-Hochberg procedure where appropriate and are indicated by “adj.p” or “adjusted p-value” (circumstances where no adjustments were necessary will instead report “p” or “p-value”).
Clinical Features and Demographics
For testing the association of detection of chronic viral transcripts with TGs and age, we used cumulative linked mixed modeling from the ordinal (v 2019.12–10) R package. Due to both COVID-19 severity and age quantiles being ordinal, cumulative linked mixed modeling allowed for this ordinal relationship to be accounted for in addition to including enrollment site as a random effect. In the age quantile ordinal model, trajectory groups were also used as a main effect to control for COVID-19 severity.
Trajectory_group~virus_status,random=enrollment_siteAdmit_age_quintile~virus_status+trajectory_group,random=enrollment_site
For association testing of specific clinical complications, comorbidities, medication, and other clinical outcomes, linear mixed effect modeling from the lme4 R package (v1.1–28) was used with viral status, sex, admit_age_quintile, and trajectory group as main effects and enrollment site as a random effect.
Clinical_feature~virus_status+sex+admit_age_quintile+trajectory_group+(1|enrollment_site)
Serum Cytokines and Plasma Metabolomics
To evaluate both the serum PEA cytokine assay and the plasma metabolomics, generalized additive mixed modeling (gamm) from the gamm4 (v 0.2–6) R package was used to evaluate for differences in individual analytes between patients who had detected transcripts for a chronic virus compared to patients who never had human-infecting viral transcripts detected (other than for SARS-CoV-2). Analytes were modeled against days from admission using cubic regression splines with interactions of both status for a given chronic virus (binary: positive or negative based on detected transcripts at any collected timepoint) and TG in addition to fixed effects of status for the chronic virus being evaluated, TG, sex, age at time of admission sorted into quintiles, and SARS-CoV-2 nasal viral RPM at the time of that sample. As chronic viral status is both a main effect and interaction term in the model, we used a lower adjusted p-value cutoff of 0.01 to account for the fact that a feature was significant if either term was significant.
Analyte~s(days,bs=‘cr’)+s(days,bs=‘cr’,by=‘virus_status’)+s(days,bs=‘cr’,by=‘trajectory_group’)+virus_status+sex+trajectory_group+admit_age_quintile+sarscovs2_nasal_log_rpm,random=~(1|enrollment_site/participant_id)
Nasal and PBMC RNA-Sequencing
To analyze the signature of host gene expression associated with chronic viruses, the limma (v 3.46.0) R package was used for both the nasal and PBMC RNA-sequencing to evaluate differential expressed genes associated with participants who had detectable chronic viral transcripts.
~admit_age_quintile+sex+visit_number+trajectory_group+sarscov2_nasal_log_rpm+virus_status
Pathway enrichment on both the upregulated and downregulated differentially expressed genes was conducted using hypergeometric enrichment testing from the R package clusterProfiler (v3.18.1) with the Reactome pathway database.
Clinical Features and Demographics
For testing the association of detection of chronic viral transcripts with TGs and age, we used cumulative linked mixed modeling from the ordinal (v 2019.12–10) R package. Due to both COVID-19 severity and age quantiles being ordinal, cumulative linked mixed modeling allowed for this ordinal relationship to be accounted for in addition to including enrollment site as a random effect. In the age quantile ordinal model, trajectory groups were also used as a main effect to control for COVID-19 severity.
Trajectory_group~virus_status,random=enrollment_siteAdmit_age_quintile~virus_status+trajectory_group,random=enrollment_site
For association testing of specific clinical complications, comorbidities, medication, and other clinical outcomes, linear mixed effect modeling from the lme4 R package (v1.1–28) was used with viral status, sex, admit_age_quintile, and trajectory group as main effects and enrollment site as a random effect.
Clinical_feature~virus_status+sex+admit_age_quintile+trajectory_group+(1|enrollment_site)
Serum Cytokines and Plasma Metabolomics
To evaluate both the serum PEA cytokine assay and the plasma metabolomics, generalized additive mixed modeling (gamm) from the gamm4 (v 0.2–6) R package was used to evaluate for differences in individual analytes between patients who had detected transcripts for a chronic virus compared to patients who never had human-infecting viral transcripts detected (other than for SARS-CoV-2). Analytes were modeled against days from admission using cubic regression splines with interactions of both status for a given chronic virus (binary: positive or negative based on detected transcripts at any collected timepoint) and TG in addition to fixed effects of status for the chronic virus being evaluated, TG, sex, age at time of admission sorted into quintiles, and SARS-CoV-2 nasal viral RPM at the time of that sample. As chronic viral status is both a main effect and interaction term in the model, we used a lower adjusted p-value cutoff of 0.01 to account for the fact that a feature was significant if either term was significant.
Analyte~s(days,bs=‘cr’)+s(days,bs=‘cr’,by=‘virus_status’)+s(days,bs=‘cr’,by=‘trajectory_group’)+virus_status+sex+trajectory_group+admit_age_quintile+sarscovs2_nasal_log_rpm,random=~(1|enrollment_site/participant_id)
Nasal and PBMC RNA-Sequencing
To analyze the signature of host gene expression associated with chronic viruses, the limma (v 3.46.0) R package was used for both the nasal and PBMC RNA-sequencing to evaluate differential expressed genes associated with participants who had detectable chronic viral transcripts.
~admit_age_quintile+sex+visit_number+trajectory_group+sarscov2_nasal_log_rpm+virus_status
Pathway enrichment on both the upregulated and downregulated differentially expressed genes was conducted using hypergeometric enrichment testing from the R package clusterProfiler (v3.18.1) with the Reactome pathway database.
Supplementary Material
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Supplement 8 |
Title: LINC01116-dependent upregulation of RNA polymerase I transcription drives oncogenic phenotypes in lung adenocarcinoma | Body:
Introduction
The transcription of ribosomal DNA (rDNA) into ribosomal RNA (rRNA) by RNA Polymerase I (Pol I) is a rate-limiting step in ribosome biogenesis, directly affecting cellular translational capacity, thus influencing growth, proliferation, differentiation, and apoptosis [1]. Hyperactive Pol I transcription, often accompanied by upregulation of its core transcriptional machinery, is a molecular anomaly frequently observed in diverse cancer types [2]. Moreover, a multitude of epigenetic alterations within the rDNA loci have been implicated in oncogenic processes [3]. Recently, a specific N6-methyladenosine modification in 18S rRNA has shown to promote tumorigenesis and chemoresistance [4]. Notably, heightened Pol I transcription has been associated with adverse prognosis [5, 6], therapeutic resistance [7], and epithelial-mesenchymal transition (EMT) [8]. These findings underscore the indispensable role of hyperactive Pol I transcription in driving oncogenic processes.
The initiation of Pol I transcription is orchestrated by class-specific machinery, including Selectivity Factor 1 (SL1), a complex of TATA-binding protein (TBP) and four TBP-associated factors (TAFs—TAF1A, TAF1B, TAF1C, and TAF1D), Upstream Binding Factor (UBF), RRN3, and Pol I enzyme [9]. These core components assemble at the rDNA promoter to form the pre-initiation complex (PIC), a crucial step for initiating transcription. Remarkably, these protein–protein and protein-rDNA interactions are direct targets of signaling circuitry governing cell growth and proliferation [10]. Oncoprotein c-Myc stimulates PIC-rDNA promoter interactions primarily through Ras/MAPK, PI3K, and mTOR pathways [11]. Meanwhile, tumor suppressors such as pRb and p53 restrain PIC activity [11–13]. Oncogenic aberrations disrupting this balance amplify PIC activity on the rDNA promoter, resulting in unrestrained rRNA synthesis critical for malignant proliferation [14]. Recently, we reported a novel microRNA-circularRNA-mediated post-transcriptional mechanism contributing to the upregulation of Pol I transcription in lung adenocarcinoma (LUAD) [15]. However, the intricate molecular mechanisms, particularly those involving regulatory RNAs, contributing to the hyperactivation of Pol I transcription in cancer, remain largely elusive.
Long noncoding RNAs (lncRNAs), a class of regulatory RNAs, are integral to modulating gene expression programs. LncRNAs exert their functions through diverse interactive mechanisms, including molecular scaffolding, decoying, competitive binding, and epigenetic modulation [16]. These multifaceted interactions of lncRNAs with cellular proteins and nucleic acids profoundly influence crucial cellular processes, including growth, proliferation, apoptosis, and cell fate determination in health and disease. Accumulating evidence revealed that lncRNAs exhibit dual roles as drivers of both tumor suppression and oncogenesis across various cancer types, underscoring their intricate role in the regulatory landscape of cancer [17].
Recent studies have identified LINC01116 as significantly upregulated in various cancers, playing an oncogenic role in several cancer hallmarks and contributes to therapeutic resistance [18–20]. However, the underlying mechanisms through which LINC01116 exerts its oncogenic effects remain largely unclear. This study unveils a novel oncogenic mechanism of LINC01116 through Pol I transcription. We demonstrated that LINC01116 enhances Pol I transcription by directly binding to the rDNA promoter and promoting PIC assembly in LUAD cell lines. Notably, LINC01116-dependent activation of Pol I transcription is vital for its oncogenic function. Inhibition of Pol I transcription mitigated LINC01116-induced tumor promoting processes. Furthermore, we identified c-Myc as a driver of LINC01116 upregulation in LUAD. These findings underscore the c-Myc-LINC01116-Pol I axis as a novel oncogenic pathway and propose LINC01116 as a potential therapeutic target for modulating Pol I transcription-mediated oncogenic phenotypes in LUAD.
Materials and methods
All the primers, probes, and antibodies used in the study are listed in Additional file 1: Table S1.
In-silico analysis
Interactions between lncRNAs and Pol I transcriptional machinery were predicted using RNAct [21], SFPEL-LPI [22], and catRAPIDomics [23]. The Cancer Genome Atlas (TCGA) LUAD RNA-sequencing data was analyzed using RNAInter (www.rnainter.org) to predict LINC01116 interactors, which were then subjected to Gene Set Enrichment Analysis (GSEA) using GSEA v4.2.3 application available on molecular signatures database MSigDB (www.gsea-msigdb.org/gsea/index.jsp). RNA-sequencing data for LINC01116 and c-Myc in LUAD were retrieved from TCGA and Clinical Proteomic Tumor Analysis Consortium (CPTAC) using cBioPortal (https://www.cbioportal.org/) and analysed for correlation. Putative c-Myc binding sites on the LINC01116 promoter were predicted using the LASAGNA application (https://biogrid-lasagna.engr.uconn.edu).
Molecular cloning
The full-length transcript of LINC01116 was PCR amplified using gene-specific cloning primers and cloned into MluI and BamHI restriction sites of pCMV6 expression vector (Origene, Rockville, Maryland, USA). Sense and anti-sense shRNA oligos targeting nucleotides 201-221 of LINC01116 were synthesized (Eurofins, Bangalore, India), and oligo ends were phosphorylated using T4 polynucleotide kinase (Invitrogen), annealed and cloned into AgeI and EcoRI restriction sites of pLKO.1 puro expression vector (#8453 Addgene, Watertown, MA, USA) as per Addgene protocol. LINC01116 promoter region was PCR-amplified from human genomic DNA (Promega) and cloned into MluI and KpnI restriction sites of pGL3-basic vector (Promega). Insert sequences were confirmed by Sanger sequencing.
Cell culture and transfections
A549, H23, and HEK293T cells were purchased from the National Centre for Cell Science, Pune, India. Cells were authenticated at source by short tandem repeat analysis and monitored for Mycoplasma contamination. Cells were maintained in RPMI 1640 medium (A549 and H23) or DMEM (HEK293T) (Gibco, Thermo Fisher Scientific, Waltham, MA, USA), supplemented with 10% fetal bovine serum (FBS) (Gibco) and 100 U/mL penicillin–streptomycin (Gibco, Thermo Fisher Scientific) at 37 °C and 5% CO2 in a humidified incubator (Forma Steri-Cycle i60, Thermo Fisher Scientific). Cells were transfected with pCMV6 empty vector (eV) or pCMV6-LINC01116 expression vector using Lipofectamine 2000 (Invitrogen). To generate LINC01116-overexpressing stable cell lines, A549 and H23 cells were transfected with 1 µg of pCMV6-eV or pCMV6-LINC01116 using Lipofectamine 2000. After 48 h, cells were cultured in selection media containing 1000 µg/ml G418 (Roche, Sigma-Aldrich, St. Louis, MO, USA) until individual colonies formed. For stable LINC01116-shRNA transfection, A549 and H23 cells were transfected with 1 µg of pLKO.1-eV or LINC01116-shRNA, followed by clone selection using media containing 1 µg/mL Puromycin (Roche). For reconstitution of LINC01116 expression, LINC01116 stable knockdown cells were transiently transfected with pCMV6-LINC01116 expression vector using Lipofectamine 2000.
RNA isolation and quantitative real-time PCR
Total RNA from cells was isolated using TRIzol (Ambion, Thermo Fisher Scientific) according to the manufacturer’s protocol, and reverse transcribed using High-Capacity cDNA reverse transcription kit (Applied Biosystems, Thermo Fisher Scientific). The quantitative real-time PCR (qPCR) was performed using SYBR green chemistry (Applied Biosystems) on Quant Studio 5 qPCR system (Applied Biosystems, Thermo Fisher Scientific). For gene expression analysis, Gamma-Actin, or small nuclear RNA U6 were used as internal controls.
LUAD RNA samples
RNA isolated from LUAD or adjacent normal samples were purchased from the National Cancer Tissue Bank at the Indian Institute of Technology Madras, Chennai, India.
RNA Immunoprecipitation
RNA immunoprecipitation (RIP) was performed as previously [24]. Briefly, 1 × 106 A549 cells or A549 or H23 cells stably overexpressing LINC01116 or LINC01116-shRNA were cultured in a 10 cm dish for 24 h and cross-linked using 1% formaldehyde (Sigma-Aldrich), and the cross-linking was quenched using glycine (final concentration of 0.25 M). Next, cells were washed twice with 1X DPBS and, resuspended in lysis buffer, and sonicated at 32% amplitude and 15 cycles with 30 s on/off using Sinaptec ultrasonicator (Lezennes, France) and subjected to DNase (Invitrogen) treatment (250 U/mL) for 30 min at 37 °C. For immunoprecipitation, Protein A beads (Invitrogen) were bound to either IgG isotype control or TAF1A or TAF1D antibodies, followed by incubation with DNase-treated cell lysate supernatant. Next, the antibody-bound RNA and 1% Input samples were subjected to 18 µL Proteinase K (Invitrogen) treatment for 30 min at 55 °C. Total RNA was isolated using the TRIzol method, and LINC01116 association with TAF1A and TAF1D was validated using qPCR.
Fluorescence in-situ hybridization
Fluorescence in-situ hybridization (FISH) was performed as previously [25]. Briefly, 1 × 104 A549 cells stably overexpressing LINC01116 or LINC01116-shRNA were cultured overnight on coverslips in a 12 well plate. Cells were fixed with 3.7% formaldehyde for 10 min at room temperature (RT) and permeabilized with 70% ethanol for 1 h at RT. The fixed cells were probed with LINC01116-specific 6-FAM-tagged probe (Merck) at 8 nM concentration and incubated with NPM1/TAF1A/TAF1D primary antibody in hybridization buffer (20% formamide (Sigma-Aldrich), 0.02% RNAse-free BSA (Himedia Laboratories, India), 50 μg salmon sperm DNA (Sigma-Aldrich), 2X SSC in a humidified chamber at 37 °C and then incubated overnight in a dark chamber. Then, the cells were washed thrice with wash buffer and incubated with hybridization solution containing fluorescence-tagged secondary antibody in dark for 1 h at 37 °C. The cells were then counterstained with Hoechst 33342 (Invitrogen) and imaged using a fluorescence microscope (DMi8, Leica Microsystems, Wetzlar, Germany).
Nucleolar SL1 pulldown
Nucleolar SL1 pull-down was performed as previously [26], with minor modifications. Briefly, 1 × 106 A549 cells were cultured overnight in a 10 cm dish. Cells were cross-linked with 1% formaldehyde and quenched with 0.125 M Glycine for 10 min at RT. The cross-linked cells were lysed and sonicated (SinapTec Lab 120, France) to generate 100–600 bp DNA fragments. Sheared chromatin was immunoprecipitated with TAF1B antibody. Immunoprecipitated (IP) samples were aliquoted for Protein, Chromatin, and RNA isolation. The IP samples were eluted with 4X LDS sample buffer (Invitrogen) for protein isolation and proceeded for immunoblot. Sonicated chromatin was treated with RNase and Proteinase K, and subsequently, chromatin DNA was eluted using the QIAGEN PCR purification kit (QIAGEN). RNA was isolated using the Trizol method.
Chromatin immunoprecipitation
Chromatin immunoprecipitation (ChIP) assays were performed as described previously [27]. Briefly, 1 × 106 A549 cells stably expressing pCMV6-eV or pCMV6-LINC01116 or pLKO.1-eV or LINC01116-shRNA were cultured until 70% confluency in a 10 cm dish. Cells were cross-linked with 1% formaldehyde, and subsequently reaction was quenched using 0.125 M Glycine for 10 min at RT. The cross-linked cells were lysed and sonicated (32% amplitude for 15 cycles with 30 s on/off) (SinapTec Lab 120, France) to generate 200–500 bp DNA fragments. For immunoprecipitation, IgG or, TAF1A or TAF1D or TAF1B or POLR1B antibodies were bound to Protein A magnetic beads for 3 h at RT, followed by incubation with sonicated chromatin. Bead-bound DNA–protein complexes were extracted using an extraction buffer (1% SDS, 0.1 M NaHCO3 and Protease inhibitor). The extracts were RNAse and Proteinase K treated sequentially, and ChIP DNA was eluted using QIAGEN PCR purification kit (QIAGEN). The relative enrichment of TAF1A, TAF1D, TAF1B, and POLR1B on the rDNA promoter was analyzed using qPCR.
Chromatin isolation by RNA purification
Chromatin isolation by RNA purification (ChIRP) was performed as described previously [28]. Briefly, 1 × 106 cells with stable overexpression of LINC01116 or A549 cells with stable knockdown of LINC01116 were cultured in 10 cm dish up to 70% confluency and cross-linked with 1% formaldehyde, and the cross-linking was quenched using 0.125 M glycine. Next, cells were lysed and sonicated at 32% amplitude for 15 cycles with 30 s on/off. The sonicated cell lysate was hybridized with biotinylated LINC01116 probe (Merck) in hybridization buffer for 4 h at RT. Next, the biotin probe-bound complexes were captured by streptavidin-conjugated magnetic beads (Biobharathi Life Sciences, India) and separated into two fractions. Each fraction was subjected to DNA elution or RNA elution with respective elution buffers, and the elutes were then treated with 5 µL Proteinase K (20 mg/mL) (Invitrogen) for 45 min at 50 °C. RNA was reverse transcribed and analyzed for LINC01116, and DNA was analyzed for rDNA using primers specific to the rDNA core promoter on qPCR. Further, the eluted DNA was PCR amplified and subjected to Sanger sequencing.
Ethynyl uridine incorporation assay
A549 or H23 cells stably overexpressing LINC01116 or LINC01116-shRNA were seeded in a 6-well plate at 1.5 × 105 cells/ well. After 48 h, the cells were incubated with 100 µM of ethynyl uridine (EU) (Sigma-Aldrich) for 1 h at 37 °C the dark. Next, the EU-labeled cells were cross-linked using 3.7% formaldehyde and permeabilized using 0.01% Triton-X 100. Next, the permeabilized cells were stained with 15 µM Azide fluor (Sigma-Aldrich). Stained cells were washed thrice with 1X phosphate-buffered saline, counterstained with Hoechst 33342 (Invitrogen), and imaged using a fluorescence microscope (Leica).
Cell proliferation assay
A549 or H23 cells stably overexpressing LINC01116 or LINC01116-shRNA were seeded at a density of 3000 cells/well in a 96-well plate. After overnight incubation, cells were then treated with 1 µM BMH-21 (Sigma-Aldrich), and control cells received DMSO. After 24 h, cells were incubated with the Alamar Blue Cell Viability Reagent (Invitrogen), and the absorbance was measured at 570 nm using a Clariostar plate reader (BMG Labtech, Ortenberg, Germany).
Cell cycle and apoptosis assays
A549 or H23 cells stably overexpressing LINC01116 or with stable knockdown of LINC01116 were cultured in a 6-well plate (1.5 × 105/well) and treated with BMH-21 (1 µM) for 24 h. Subsequently, cells were trypsinized and utilized for cell cycle and apoptosis assays. For cell cycle analysis, cells were fixed in 66.6% ice-cold ethanol at 4 ℃ for 2 h and then stained using the propidium iodide (PI) Flow Cytometry Kit (Abcam). For apoptosis analysis, cells were stained with the Annexin-V-PI Apoptosis Detection Kit I from BD Biosciences. Cells were analyzed on a BD C6 Plus flow cytometer (BD Biosciences, New Jersey, USA) and quantified using FlowJo software (BD Biosciences).
Scratch assay
A549 cells stably overexpressing LINC01116 or LINC01116-shRNA were seeded in a 6-well plate (1.5 × 105/well). Cells were treated with BMH-21 (1 µM). After 24 h, a scratch was made on the monolayer of cells and imaged at an interval of 24 h for a total of 72 h using an EVOS XL core light microscope (Invitrogen, Waltham, Massachusetts, USA).
Invasion assay
Matrigel (Corning, USA) was mixed at a 1:5 ratio with ice-cold serum-free media, 500 µL of this diluted Matrigel was applied to the upper section of a Transwell chamber and incubated at 37 °C for 2 h. Subsequently, BMH-21 treated A549 cells (1.5 × 105/well) stably overexpressing pCMV6-eV or pCMV6-LINC01116 or shRNA against LINC01116 introduced into the Matrigel-coated invasion chamber. Lower chamber of the well was filled with culture media containing 10% FBS, and incubated for 24 h. Later, the chambers were fixed using 100% ice-cold methanol for 15 min and stained with a 0.01% solution of crystal violet (Sigma-Aldrich) for 20 min at RT. Cells located on the upper side of the membrane were gently wiped off with a sterile cotton swab, and the remaining cells were visualized using a light microscope (Evos).
Clonogenicity assay
A549 or H23 cells stably overexpressing LINC01116 or with stable knockdown of LINC01116 were seeded in a six-well plate (103 cells/plate). The cells were treated with BMH-21 and incubated in the humidified CO2 incubator at 37 °C until the appearance of isolated colonies. The colonies were then fixed with 100% ice-cold methanol for 5 min and stained using 0.01% crystal violet (Sigma-Aldrich) solution for 20 min at RT. Colony images were manually captured and counted using ImageJ software.
Generation of A549 and H23 spheroids
Tumor cell spheroids were generated as previously [29]. Briefly, A549 or H23 cells stably overexpressing LINC01116 or LINC01116-shRNA were seeded in a 1 × 104/ well in agarose-coated 96-well plates cultured till the formation of visible spheroids. Spheroids were treated with 1 µM BMH-21 for 24 h. Next, the spheroids were analyzed for EU incorporation assay using the previously discussed protocol or processed for Ki-67 – immunofluorescence assay. Briefly, spheroids were fixed with 3.7% formaldehyde, blocked with 5% BSA for 1 h, and incubated with Ki67 primary antibody overnight, followed by fluorescence-tagged secondary antibody, and further counterstained with Hoechst 33342. The spheroids were imaged using a DMi8 fluorescence microscope (Leica).
Generation of cisplatin and doxorubicin resistant A549 cells
Cisplatin (Cis) and Doxorubicin (Dox) resistant A549 cells were generated by dose-escalation method. Drug treatment was initiated by culturing A549 cells with 1 µM Cis (Sigma-Aldrich) or 0.2 µM Dox (Sigma-Aldrich) for 48 h. Every month, apoptosis measurement was performed to assess the resistance development, and subsequently, the drug treatment was gradually increased to a final concentration of 10 µM Cis and 1 µM Dox. After six months of continuous exposure to Cis and Dox, drug resistance was validated by relative resistance to cell death compared with parental A549 cells.
Luciferase reporter assays
The dual luciferase assay was performed as previously [27]. Briefly, 1 × 105 HEK293T cells were seeded onto a 12-well plate and incubated overnight. Next, cells were co-transfected with 0.5 µg of pGL3 basic plasmid containing LINC01116-promoter construct, 0.5 µg of c-Myc (#16,011 Addgene), or 0.5 µg of eV and 50 ng of pRL-CMV (Promega) as an internal control using Lipofectamine 2000 (Invitrogen). 48 h post-transfection, cells were processed using Dual-Luciferase® Reporter Assay kit (Promega), and the luciferase activities were measured using GloMax® Navigator (Promega). The results were calculated by normalizing firefly luciferase to that of Renilla luciferase.
Statistics
All experiments were conducted with a minimum of three independent biological replicates. Results are depicted as the mean ± standard error of the mean (SEM). Statistical analysis was performed using GraphPad Prism (v. 8.2). Pearson analysis was employed for correlation assessment. A two-tailed Student’s t-test was employed to compare the means between the two groups, multiple group comparisons were performed using one-way analysis of variance (ANOVA), followed by Tukey’s post-hoc test. Results were considered statistically significant for p-values ≤ 0.05.
In-silico analysis
Interactions between lncRNAs and Pol I transcriptional machinery were predicted using RNAct [21], SFPEL-LPI [22], and catRAPIDomics [23]. The Cancer Genome Atlas (TCGA) LUAD RNA-sequencing data was analyzed using RNAInter (www.rnainter.org) to predict LINC01116 interactors, which were then subjected to Gene Set Enrichment Analysis (GSEA) using GSEA v4.2.3 application available on molecular signatures database MSigDB (www.gsea-msigdb.org/gsea/index.jsp). RNA-sequencing data for LINC01116 and c-Myc in LUAD were retrieved from TCGA and Clinical Proteomic Tumor Analysis Consortium (CPTAC) using cBioPortal (https://www.cbioportal.org/) and analysed for correlation. Putative c-Myc binding sites on the LINC01116 promoter were predicted using the LASAGNA application (https://biogrid-lasagna.engr.uconn.edu).
Molecular cloning
The full-length transcript of LINC01116 was PCR amplified using gene-specific cloning primers and cloned into MluI and BamHI restriction sites of pCMV6 expression vector (Origene, Rockville, Maryland, USA). Sense and anti-sense shRNA oligos targeting nucleotides 201-221 of LINC01116 were synthesized (Eurofins, Bangalore, India), and oligo ends were phosphorylated using T4 polynucleotide kinase (Invitrogen), annealed and cloned into AgeI and EcoRI restriction sites of pLKO.1 puro expression vector (#8453 Addgene, Watertown, MA, USA) as per Addgene protocol. LINC01116 promoter region was PCR-amplified from human genomic DNA (Promega) and cloned into MluI and KpnI restriction sites of pGL3-basic vector (Promega). Insert sequences were confirmed by Sanger sequencing.
Cell culture and transfections
A549, H23, and HEK293T cells were purchased from the National Centre for Cell Science, Pune, India. Cells were authenticated at source by short tandem repeat analysis and monitored for Mycoplasma contamination. Cells were maintained in RPMI 1640 medium (A549 and H23) or DMEM (HEK293T) (Gibco, Thermo Fisher Scientific, Waltham, MA, USA), supplemented with 10% fetal bovine serum (FBS) (Gibco) and 100 U/mL penicillin–streptomycin (Gibco, Thermo Fisher Scientific) at 37 °C and 5% CO2 in a humidified incubator (Forma Steri-Cycle i60, Thermo Fisher Scientific). Cells were transfected with pCMV6 empty vector (eV) or pCMV6-LINC01116 expression vector using Lipofectamine 2000 (Invitrogen). To generate LINC01116-overexpressing stable cell lines, A549 and H23 cells were transfected with 1 µg of pCMV6-eV or pCMV6-LINC01116 using Lipofectamine 2000. After 48 h, cells were cultured in selection media containing 1000 µg/ml G418 (Roche, Sigma-Aldrich, St. Louis, MO, USA) until individual colonies formed. For stable LINC01116-shRNA transfection, A549 and H23 cells were transfected with 1 µg of pLKO.1-eV or LINC01116-shRNA, followed by clone selection using media containing 1 µg/mL Puromycin (Roche). For reconstitution of LINC01116 expression, LINC01116 stable knockdown cells were transiently transfected with pCMV6-LINC01116 expression vector using Lipofectamine 2000.
RNA isolation and quantitative real-time PCR
Total RNA from cells was isolated using TRIzol (Ambion, Thermo Fisher Scientific) according to the manufacturer’s protocol, and reverse transcribed using High-Capacity cDNA reverse transcription kit (Applied Biosystems, Thermo Fisher Scientific). The quantitative real-time PCR (qPCR) was performed using SYBR green chemistry (Applied Biosystems) on Quant Studio 5 qPCR system (Applied Biosystems, Thermo Fisher Scientific). For gene expression analysis, Gamma-Actin, or small nuclear RNA U6 were used as internal controls.
LUAD RNA samples
RNA isolated from LUAD or adjacent normal samples were purchased from the National Cancer Tissue Bank at the Indian Institute of Technology Madras, Chennai, India.
RNA Immunoprecipitation
RNA immunoprecipitation (RIP) was performed as previously [24]. Briefly, 1 × 106 A549 cells or A549 or H23 cells stably overexpressing LINC01116 or LINC01116-shRNA were cultured in a 10 cm dish for 24 h and cross-linked using 1% formaldehyde (Sigma-Aldrich), and the cross-linking was quenched using glycine (final concentration of 0.25 M). Next, cells were washed twice with 1X DPBS and, resuspended in lysis buffer, and sonicated at 32% amplitude and 15 cycles with 30 s on/off using Sinaptec ultrasonicator (Lezennes, France) and subjected to DNase (Invitrogen) treatment (250 U/mL) for 30 min at 37 °C. For immunoprecipitation, Protein A beads (Invitrogen) were bound to either IgG isotype control or TAF1A or TAF1D antibodies, followed by incubation with DNase-treated cell lysate supernatant. Next, the antibody-bound RNA and 1% Input samples were subjected to 18 µL Proteinase K (Invitrogen) treatment for 30 min at 55 °C. Total RNA was isolated using the TRIzol method, and LINC01116 association with TAF1A and TAF1D was validated using qPCR.
Fluorescence in-situ hybridization
Fluorescence in-situ hybridization (FISH) was performed as previously [25]. Briefly, 1 × 104 A549 cells stably overexpressing LINC01116 or LINC01116-shRNA were cultured overnight on coverslips in a 12 well plate. Cells were fixed with 3.7% formaldehyde for 10 min at room temperature (RT) and permeabilized with 70% ethanol for 1 h at RT. The fixed cells were probed with LINC01116-specific 6-FAM-tagged probe (Merck) at 8 nM concentration and incubated with NPM1/TAF1A/TAF1D primary antibody in hybridization buffer (20% formamide (Sigma-Aldrich), 0.02% RNAse-free BSA (Himedia Laboratories, India), 50 μg salmon sperm DNA (Sigma-Aldrich), 2X SSC in a humidified chamber at 37 °C and then incubated overnight in a dark chamber. Then, the cells were washed thrice with wash buffer and incubated with hybridization solution containing fluorescence-tagged secondary antibody in dark for 1 h at 37 °C. The cells were then counterstained with Hoechst 33342 (Invitrogen) and imaged using a fluorescence microscope (DMi8, Leica Microsystems, Wetzlar, Germany).
Nucleolar SL1 pulldown
Nucleolar SL1 pull-down was performed as previously [26], with minor modifications. Briefly, 1 × 106 A549 cells were cultured overnight in a 10 cm dish. Cells were cross-linked with 1% formaldehyde and quenched with 0.125 M Glycine for 10 min at RT. The cross-linked cells were lysed and sonicated (SinapTec Lab 120, France) to generate 100–600 bp DNA fragments. Sheared chromatin was immunoprecipitated with TAF1B antibody. Immunoprecipitated (IP) samples were aliquoted for Protein, Chromatin, and RNA isolation. The IP samples were eluted with 4X LDS sample buffer (Invitrogen) for protein isolation and proceeded for immunoblot. Sonicated chromatin was treated with RNase and Proteinase K, and subsequently, chromatin DNA was eluted using the QIAGEN PCR purification kit (QIAGEN). RNA was isolated using the Trizol method.
Chromatin immunoprecipitation
Chromatin immunoprecipitation (ChIP) assays were performed as described previously [27]. Briefly, 1 × 106 A549 cells stably expressing pCMV6-eV or pCMV6-LINC01116 or pLKO.1-eV or LINC01116-shRNA were cultured until 70% confluency in a 10 cm dish. Cells were cross-linked with 1% formaldehyde, and subsequently reaction was quenched using 0.125 M Glycine for 10 min at RT. The cross-linked cells were lysed and sonicated (32% amplitude for 15 cycles with 30 s on/off) (SinapTec Lab 120, France) to generate 200–500 bp DNA fragments. For immunoprecipitation, IgG or, TAF1A or TAF1D or TAF1B or POLR1B antibodies were bound to Protein A magnetic beads for 3 h at RT, followed by incubation with sonicated chromatin. Bead-bound DNA–protein complexes were extracted using an extraction buffer (1% SDS, 0.1 M NaHCO3 and Protease inhibitor). The extracts were RNAse and Proteinase K treated sequentially, and ChIP DNA was eluted using QIAGEN PCR purification kit (QIAGEN). The relative enrichment of TAF1A, TAF1D, TAF1B, and POLR1B on the rDNA promoter was analyzed using qPCR.
Chromatin isolation by RNA purification
Chromatin isolation by RNA purification (ChIRP) was performed as described previously [28]. Briefly, 1 × 106 cells with stable overexpression of LINC01116 or A549 cells with stable knockdown of LINC01116 were cultured in 10 cm dish up to 70% confluency and cross-linked with 1% formaldehyde, and the cross-linking was quenched using 0.125 M glycine. Next, cells were lysed and sonicated at 32% amplitude for 15 cycles with 30 s on/off. The sonicated cell lysate was hybridized with biotinylated LINC01116 probe (Merck) in hybridization buffer for 4 h at RT. Next, the biotin probe-bound complexes were captured by streptavidin-conjugated magnetic beads (Biobharathi Life Sciences, India) and separated into two fractions. Each fraction was subjected to DNA elution or RNA elution with respective elution buffers, and the elutes were then treated with 5 µL Proteinase K (20 mg/mL) (Invitrogen) for 45 min at 50 °C. RNA was reverse transcribed and analyzed for LINC01116, and DNA was analyzed for rDNA using primers specific to the rDNA core promoter on qPCR. Further, the eluted DNA was PCR amplified and subjected to Sanger sequencing.
Ethynyl uridine incorporation assay
A549 or H23 cells stably overexpressing LINC01116 or LINC01116-shRNA were seeded in a 6-well plate at 1.5 × 105 cells/ well. After 48 h, the cells were incubated with 100 µM of ethynyl uridine (EU) (Sigma-Aldrich) for 1 h at 37 °C the dark. Next, the EU-labeled cells were cross-linked using 3.7% formaldehyde and permeabilized using 0.01% Triton-X 100. Next, the permeabilized cells were stained with 15 µM Azide fluor (Sigma-Aldrich). Stained cells were washed thrice with 1X phosphate-buffered saline, counterstained with Hoechst 33342 (Invitrogen), and imaged using a fluorescence microscope (Leica).
Cell proliferation assay
A549 or H23 cells stably overexpressing LINC01116 or LINC01116-shRNA were seeded at a density of 3000 cells/well in a 96-well plate. After overnight incubation, cells were then treated with 1 µM BMH-21 (Sigma-Aldrich), and control cells received DMSO. After 24 h, cells were incubated with the Alamar Blue Cell Viability Reagent (Invitrogen), and the absorbance was measured at 570 nm using a Clariostar plate reader (BMG Labtech, Ortenberg, Germany).
Cell cycle and apoptosis assays
A549 or H23 cells stably overexpressing LINC01116 or with stable knockdown of LINC01116 were cultured in a 6-well plate (1.5 × 105/well) and treated with BMH-21 (1 µM) for 24 h. Subsequently, cells were trypsinized and utilized for cell cycle and apoptosis assays. For cell cycle analysis, cells were fixed in 66.6% ice-cold ethanol at 4 ℃ for 2 h and then stained using the propidium iodide (PI) Flow Cytometry Kit (Abcam). For apoptosis analysis, cells were stained with the Annexin-V-PI Apoptosis Detection Kit I from BD Biosciences. Cells were analyzed on a BD C6 Plus flow cytometer (BD Biosciences, New Jersey, USA) and quantified using FlowJo software (BD Biosciences).
Scratch assay
A549 cells stably overexpressing LINC01116 or LINC01116-shRNA were seeded in a 6-well plate (1.5 × 105/well). Cells were treated with BMH-21 (1 µM). After 24 h, a scratch was made on the monolayer of cells and imaged at an interval of 24 h for a total of 72 h using an EVOS XL core light microscope (Invitrogen, Waltham, Massachusetts, USA).
Invasion assay
Matrigel (Corning, USA) was mixed at a 1:5 ratio with ice-cold serum-free media, 500 µL of this diluted Matrigel was applied to the upper section of a Transwell chamber and incubated at 37 °C for 2 h. Subsequently, BMH-21 treated A549 cells (1.5 × 105/well) stably overexpressing pCMV6-eV or pCMV6-LINC01116 or shRNA against LINC01116 introduced into the Matrigel-coated invasion chamber. Lower chamber of the well was filled with culture media containing 10% FBS, and incubated for 24 h. Later, the chambers were fixed using 100% ice-cold methanol for 15 min and stained with a 0.01% solution of crystal violet (Sigma-Aldrich) for 20 min at RT. Cells located on the upper side of the membrane were gently wiped off with a sterile cotton swab, and the remaining cells were visualized using a light microscope (Evos).
Clonogenicity assay
A549 or H23 cells stably overexpressing LINC01116 or with stable knockdown of LINC01116 were seeded in a six-well plate (103 cells/plate). The cells were treated with BMH-21 and incubated in the humidified CO2 incubator at 37 °C until the appearance of isolated colonies. The colonies were then fixed with 100% ice-cold methanol for 5 min and stained using 0.01% crystal violet (Sigma-Aldrich) solution for 20 min at RT. Colony images were manually captured and counted using ImageJ software.
Generation of A549 and H23 spheroids
Tumor cell spheroids were generated as previously [29]. Briefly, A549 or H23 cells stably overexpressing LINC01116 or LINC01116-shRNA were seeded in a 1 × 104/ well in agarose-coated 96-well plates cultured till the formation of visible spheroids. Spheroids were treated with 1 µM BMH-21 for 24 h. Next, the spheroids were analyzed for EU incorporation assay using the previously discussed protocol or processed for Ki-67 – immunofluorescence assay. Briefly, spheroids were fixed with 3.7% formaldehyde, blocked with 5% BSA for 1 h, and incubated with Ki67 primary antibody overnight, followed by fluorescence-tagged secondary antibody, and further counterstained with Hoechst 33342. The spheroids were imaged using a DMi8 fluorescence microscope (Leica).
Generation of cisplatin and doxorubicin resistant A549 cells
Cisplatin (Cis) and Doxorubicin (Dox) resistant A549 cells were generated by dose-escalation method. Drug treatment was initiated by culturing A549 cells with 1 µM Cis (Sigma-Aldrich) or 0.2 µM Dox (Sigma-Aldrich) for 48 h. Every month, apoptosis measurement was performed to assess the resistance development, and subsequently, the drug treatment was gradually increased to a final concentration of 10 µM Cis and 1 µM Dox. After six months of continuous exposure to Cis and Dox, drug resistance was validated by relative resistance to cell death compared with parental A549 cells.
Luciferase reporter assays
The dual luciferase assay was performed as previously [27]. Briefly, 1 × 105 HEK293T cells were seeded onto a 12-well plate and incubated overnight. Next, cells were co-transfected with 0.5 µg of pGL3 basic plasmid containing LINC01116-promoter construct, 0.5 µg of c-Myc (#16,011 Addgene), or 0.5 µg of eV and 50 ng of pRL-CMV (Promega) as an internal control using Lipofectamine 2000 (Invitrogen). 48 h post-transfection, cells were processed using Dual-Luciferase® Reporter Assay kit (Promega), and the luciferase activities were measured using GloMax® Navigator (Promega). The results were calculated by normalizing firefly luciferase to that of Renilla luciferase.
Statistics
All experiments were conducted with a minimum of three independent biological replicates. Results are depicted as the mean ± standard error of the mean (SEM). Statistical analysis was performed using GraphPad Prism (v. 8.2). Pearson analysis was employed for correlation assessment. A two-tailed Student’s t-test was employed to compare the means between the two groups, multiple group comparisons were performed using one-way analysis of variance (ANOVA), followed by Tukey’s post-hoc test. Results were considered statistically significant for p-values ≤ 0.05.
Results
LINC01116 directly interacts with transcriptionally active SL1 subunits TAF1A and TAF1D in the nucleolus
LncRNAs can regulate gene transcription by modulating the activity and recruitment of transcription factors [30, 31]. Since SL1 functions as an essential transcription factor and regulatory hub for Pol I transcription, we focused on identifying lncRNAs interacting with SL1 components. Utilizing multiple lncRNA-protein interaction tools, we identified LINC01116 as a high-confidence interactor with SL1 subunits TAF1A and TAF1D. LINC01116 demonstrated optimal binding affinity evidenced by catRAPIDomics star rating system, which integrates normalized interaction propensity, RNA/DNA binding domains, and known RNA binding motifs. Furthermore, LINC01116 was significantly upregulated in LUAD tissues, with elevated expression correlating with poor overall survival. This unique profile distinguished LINC01116 from other predicted lncRNAs, prioritizing it for functional characterization (Fig. 1A and Additional file 2, Figure S1). Strikingly, GSEA revealed a significant enrichment of LINC01116 target genes in pathways related to ribosome biogenesis (Fig. 1B, Additional file 3, Table S2) and rRNA metabolic processes (Fig. 1C, Additional file 4, Table S3). Further, using catRAPID fragments and graphic modules, we identified a specific binding region between TAF1A/TAF1D and LINC01116, spanning nucleotides 201–360 (Additional File 5, Figure S2). In addition, a significant positive correlation was observed between the expression of LINC01116 and 47S rRNA in LUAD tumors compared to adjacent normal tissues (Fig. 1D), suggesting a potential role of LINC01116 in Pol I transcription. LINC01116 and Pol I transcription are highly upregulated and associated with oncogenic roles in LUAD [15, 32]. Thus, we sought to investigate the regulatory link between LINC01116 and Pol I transcription in LUAD cell lines. To investigate the endogenous interaction between LINC01116 and TAF1A and TAF1D, we performed RIP assays in A549 and H23 cells stably overexpressing LINC01116 (Fig. 1E) or LINC01116-shRNA (Fig. 1F). Remarkably, LINC01116 overexpression resulted in a substantial increase in TAF1A and TAF1D enrichment compared to controls. Conversely, knockdown of LINC01116 markedly diminished this enrichment, confirming the interaction between LINC01116 and, TAF1A and TAF1D (Fig. 1G and H). Pol I transcription is confined to the nucleolus [9]. We examined nucleolar localization of LINC01116 by immunofluorescence microscopy. Cells overexpressing LINC01116 exhibited a notable co-localization of LINC01116 along with TAF1A, TAF1D and the nucleolar marker NPM1 (Fig. 1I), which was diminished in LINC01116 knockdown cells (Fig. 1J). Transcriptionally competent TAF1A and TAF1D are integral components of the SL1 complex bound to rDNA promoter [33]. To test whether the interaction of LINC01116 is specific to the TAF1A/TAF1D subunits within the SL1 complex or free forms, we immunoprecipitated the SL1 complex bound to the rDNA-promoter and assessed the presence of SL1 components, LINC01116 and rDNA (scheme Fig. 1K). Immunoblotting analysis confirmed the presence of TAF1A-D, indicating successful isolation of the SL1 complex (Fig. 1L). Subsequent qPCR analysis of the Immunoprecipitated chromatin and RNA revealed significant enrichment of the rDNA core promoter (Fig. 1M) and LINC01116 (Fig. 1N). These findings strongly indicate the interaction between LINC01116 and TAF1A/TAF1D with transcriptionally competent SL1 complex bound to the rDNA promoter, suggesting a potential regulatory role for LINC01116 in Pol I transcription.Fig. 1LINC01116 interacts with the SL1 components. A Computational prediction indicating LINC01116 interaction with SL1 components TAF1A and TAF1D. B and C Gene Set Enrichment Analysis showing enrichment of LINC01116 expression in ribosome biogenesis and rRNA metabolic processes in LUAD. D qPCR analysis reveals a significant positive correlation (Pearson) between LINC01116 and 47S rRNA expression, n = 6). E and F qPCR data demonstrating the stable overexpression of LINC01116 or stable knockdown of LINC01116 in A549 and H23 cells. G and H qPCR demonstrating relative enrichment of TAF1A or TAF1D in LINC01116 overexpression and knockdown cells. (I and J). Fluorescent in-situ hybridization images showing the co-localization of LINC01116 with NPM1, TAF1A, and TAF1D in LINC01116 overexpression cells or LINC01116 knockdown cells. K Schematic representation of immunoprecipitation of rDNA-bound SL1 complex. L Immunoblot analysis of SL1 complex immunoprecipitated from rDNA-promoter-bound chromatin. TAF1A-D subunits are detected, confirming successful SL1 complex isolation. (M and N). qPCR data confirming the relative enrichment of rDNA core promoter and LINC01116 after rDNA-bound SL1 immunoprecipitation. *P ≤ 0.05, **P ≤ 0.005, ***P ≤ 0.0005, ****P ≤ 0.00005. Error bars indicate mean ± SEM. NPM1, Nucleophosmin 1
LINC01116 upregulates Pol I transcription by promoting pre-initiation complex formation at the rDNA promoter
To investigate whether LINC01116 affects PIC formation on the rDNA promoter, we performed ChIP assays with antibodies against PIC components. ChIP-qPCR analysis revealed a significant increase in SL1 components and POLR1B at the rDNA promoter upon LINC01116 overexpression (Fig. 2A). Conversely, LINC01116 knockdown significantly reduced this enrichment, indicating its critical role in recruiting PIC components to the rDNA promoter via TAF1A and TAF1D (Fig. 2B). LncRNAs can recruit transcription factors to regulatory sequences through direct DNA interactions [34]. Strikingly, alignment of the LINC01116 sequence with rDNA regulatory loci revealed a remarkable sequence complementarity between LINC01116 and the rDNA core promoter (+ 20 to -45) and the upstream control element (UCE) (-107 to -177) (Fig. 2C). To validate this, we conducted ChIRP assays. qPCR analysis of ChIRP-derived RNA and DNA fractions revealed a significant enrichment of both LINC01116 (Fig. 2D) and the rDNA core promoter (Fig. 2E) in LINC01116-overexpressing cells compared to controls. In contrast, stable knockdown of LINC01116 reversed the enrichment of both LINC01116 (Fig. 2F) and the rDNA core promoter (Fig. 2G). To further validate the sequence complementarity between LINC01116 and the rDNA promoter/UCE, we performed ChIRP-qPCR assays scanning the entire rDNA repeat. Notably, LINC01116 overexpression led to significant enrichment of core promoter and UCE, whereas LINC01116 knockdown resulted in reduced signals. In contrast, other regions of the rDNA repeat remained unaffected, highlighting the specificity of this interaction (Additional file 6, Figure S3). Sanger sequencing of the ChIRP DNA fraction confirmed the direct binding of LINC01116 to the rDNA core promoter (Fig. 2H). Next, we investigated the impact of LINC01116 on Pol I transcription by analyzing both steady-state levels and de novo synthesis of 47S pre-rRNA in cells with stable LINC01116 overexpression, knockdown, and reconstituted expression in knockdown cells (Fig. 2I). qPCR analysis revealed that LINC01116 overexpression elevated steady-state 47S rRNA levels, whereas knockdown resulted in decreased levels, notably, reconstitution of LINC01116 in knockdown cells significantly rescued 47S rRNA (Fig. 2J). Furthermore, EU incorporation assays demonstrated that LINC01116 overexpression enhanced de novo rRNA transcription, while knockdown reduced synthesis. Remarkably, reconstitution of LINC01116 in knockdown cells also restored de novo rRNA transcription (Fig. 2K). Serum starvation dampens Pol I transcription, resulting in reduced rRNA synthesis; this effect can be reversed upon serum reconstitution [35]. To study the involvement of LINC01116 in stimulus-mediated activation of Pol I transcription, cells with LINC01116 overexpression or knockdown were subjected to serum starvation, and rRNA de novo synthesis was measured. As anticipated, serum deprivation significantly decreased rRNA synthesis, as evidenced by reduced EU incorporation. However, upon serum re-stimulation, LINC01116-overexpressing cells exhibited a notable increase in rRNA levels, exceeding basal levels (Fig. 2L). Conversely, LINC01116 knockdown cells displayed impaired Pol I transcriptional reactivation upon serum reconstitution (Fig. 2M). Collectively, these findings strongly establish a positive regulatory role of LINC01116 on Pol I transcription.Fig. 2LINC01116 upregulates Pol I transcription. (A and B). ChIP-qPCR data showing the relative promoter occupancy of PIC components after LINC01116 overexpression or, after LINC01116 knockdown. C Sequence alignment showing complementarity between LINC01116 and rDNA promoter. D and E Chromatin isolation by RNA purification-qPCR data confirming the relative binding of LINC01116 to the rDNA promoter in LINC01116 overexpressing cells, (F and G). in LINC01116 knockdown cells. H Sanger sequencing histogram confirming ChIRP DNA as rDNA promoter. I. qPCR analysis showing relative expression levels of LINC01116 in cells with stable overexpression (OE), knockdown (shRNA), and reconstitution (shRNA + OE) of LINC01116. J qPCR analysis showing changes in 47S rRNA expression levels in cells with stable LINC01116 overexpression, knockdown, and reconstitution. K EU incorporation assay illustrating changes in Pol I transcriptional activity in cells with LINC01116 overexpression, knockdown, and reconstitution. Bar graphs illustrating the normalized fluorescence intensity. L EU incorporation assay demonstrating enhanced Pol I transcription in LINC01116 overexpression cells following serum reconstitution. M EU incorporation assay showing reduced Pol I reactivation in LINC01116 knockdown cells after serum reconstitution. Error bars indicate mean ± SEM. *P ≤ 0.05, **P ≤ 0.005,***P ≤ 0.0005, ****P ≤ 0.00005, #P ≤ E-7, ##P ≤ E-8. rDNA, ribosomal DNA, UCE, Upstream control element, EU, Ethynyl Uridine
LINC01116-driven Pol I transcription is essential for enhanced cell proliferation, clonogenicity and reduced apoptosis
Given that hyperactive Pol I transcription drives malignant proliferation [36], and LINC01116 upregulation is associated with increased proliferation in various cancers [37–39], we investigated the plausible link between LINC01116 and Pol I transcription controlling cell proliferation. We administered the specific Pol I inhibitor BMH-21 [40] to cells with either overexpression or knockdown of LINC01116 and assessed cell proliferation. Overexpression of LINC01116 resulted in a significant increase in cell proliferation, which was substantially reduced by BMH-21 treatment relative to controls. Conversely, in LINC01116 knockdown cells, BMH-21 treatment caused a further decrease in cell proliferation compared to the effect of knockdown alone (Fig. 3A). To further investigate the combined effects of BMH-21 treatment and LINC01116 knockdown on cell viability, we performed a dose–response analysis in A549 and H23 cells stably expressing shRNA-LINC01116. As shown in Fig. 3B, LINC01116 knockdown significantly potentiated the anti-proliferative effects of BMH-21 treatment, resulting in a synergistic decrease in cell viability in both cell lines. Tumor spheroids closely mimic in vivo tumors, and provide a more realistic environment for studying cell proliferation [41]. We generated tumor spheroids from A549 and H23 cells stably overexpressing LINC01116 or LINC01116-shRNA (Fig. 3C). LINC01116-overexpressing spheroids exhibited increased rRNA synthesis, while LINC01116-shRNA spheroids showed reduced rRNA levels, as measured by EU incorporation. Notably, BMH-21 treatment reduced rRNA levels in LINC01116-overexpressing spheroids and caused a synergistic decrease in rRNA transcription in LINC01116 knockdown spheroids (Fig. 3D). In addition, LINC01116 overexpressing spheroids also displayed higher Ki67 expression, a proliferation marker, compared to controls, while knockdown spheroids had reduced Ki67 expression. BMH-21 significantly decreased Ki67 levels in LINC01116-overexpressing spheroids, with an even greater reduction in knockdown spheroids (Fig. 3E). These results indicate that LINC01116 promotes cell proliferation through Pol I transcription. Further, BMH-21 treatment had a marginal effect on the clonogenic capacity of A549 and H23 cells overexpressing LINC01116. In contrast, LINC01116 knockdown cells exhibited a significant reduction in clonogenicity upon BMH-21 treatment. Notably, reconstitution of LINC01116 in knockdown cells rescued the clonogenic capacity (Fig. 3F). The rate of Pol I transcription is closely linked to cell cycle progression [42]. To investigate whether LINC01116-mediated Pol I transcription influences cell cycle dynamics, we analyzed the cell cycle distribution in LINC01116-overexpressing, knockdown or reconstituted cells. LINC01116 overexpression resulted in a significant decrease in G1-phase and an increase in the S-phase population, whereas knockdown of LINC01116 increased G1 and reduced the S-phase population. Notably, treatment with the Pol I inhibitor BMH-21 caused G1 arrest and diminished the S-phase enrichment observed in LINC01116-overexpressing cells. Additionally, BMH-21 treatment further decreased the S-phase population in LINC01116 knockdown cells, suggesting a synergistic effect. Moreover, reconstitution of LINC01116 expression in knockdown cells substantially rescued the cell cycle phenotype, mitigating the G1-phase accumulation and S-phase depletion induced by LINC01116 depletion, in both control and BMH-21-treated conditions (Fig. 3G, and Additional file 7, Figure S4A and S4B). Cyclin-dependent kinases (CDKs) CDK2, CDK4, and CDK6 are key regulators of the G1 to S-phase transition [43, 44]. Remarkably, LINC01116 overexpression led to increased CDK2, CDK4, and CDK6 expression, which was reduced to basal levels upon BMH-21 treatment. Furthermore, BMH-21 treatment synergistically decreased the expression of these CDKs in LINC01116 knockdown cells. Notably, reconstitution of LINC01116 expression rescued CDK expression levels in knockdown cells with or without BMH-21 treatment. These results indicate that LINC01116 promotes cell cycle progression through upregulation of G1 to S-phase regulators in a Pol I transcription-dependent manner (Fig. 3H). LINC01116 has been shown to suppress apoptosis [38], and rRNA levels are inversely correlated with apoptosis [15]. To investigate the apoptotic link between LINC01116 and Pol I transcription, we treated A549 and H23 cells overexpressing or knocked down for LINC01116 with BMH-21 and performed apoptosis assays. Notably, LINC01116 overexpression significantly reduced apoptosis, while BMH-21 treatment only marginally increased apoptosis in these overexpressing cells. In contrast, BMH-21 treatment significantly augmented apoptosis in LINC01116 knockdown cells. In addition, reconstitution of LINC01116 expression in knockdown cells restored apoptotic resistance, rescuing cells from increased apoptosis in both untreated and BMH-21-treated conditions (Fig. 3I and Additional file 7: Fig. S4C and S4D).Fig. 3LINC01116-driven Pol I transcription modulates cell proliferation, clonogenicity, and apoptosis. A Alamar blue cell proliferation assay showing decreased proliferation of A549 and H23 cells stably overexpressing LINC01116 or LINC01116-shRNA treated with BMH-21. B Dose–response curves illustrating enhanced sensitivity to BMH-21 in LINC01116 knockdown cells, demonstrating a synergistic effect on cell viability. C qPCR showing the expression of LINC01116 in tumor spheroids generated from A549 and H23 cells stably overexpressing LINC01116 or LINC01116-shRNA. D EU incorporation assay showing Pol I transcription levels in tumor spheroids stably overexpressing LINC01116 or LINC01116-shRNA with or without inhibiting Pol I transcription by BMH-21. E Immunofluorescence images showing increased Ki67 expression in tumor spheroids stably overexpressing LINC01116 or LINC01116-shRNA in response to Pol I transcription inhibition. F Representative images of clonogenic assays showing the effect of BMH-21 treatment on A549 and H23 cells stably overexpressing LINC01116 (OE), Knockdown of LINC01116 (shRNA) or Reconstituted LINC01116 expression in knockdown cells (shRNA + OE), bar graphs illustrating relative colony counts for each condition normalized to untreated controls. G Bar graphs showing G1, S, and G2/M phase distributions in LINC01116-overexpressing (OE), LINC01116-shRNA, and reconstituted LINC01116 expression (shRNA + OE) cells with or without BMH-21 treatment. H qPCRs showing altered expression of cell cycle regulators CDK2, CDK4, and CDK6 in BMH-21 treated cells stably overexpressing LINC01116,LINC01116-shRNA or reconstitution in knockdown cells. I Bar graphs showing the apoptosis percentage in BMH-21 treated A549 or H23 cells stably overexpressing, knockdown or reconstitution of LINC01116. J. qPCRs showing the expression of pro-apoptotic and anti-apoptotic gene expression in BMH-21 treated cells with stable LINC01116 overexpression, knockdown or reconstitution in LINC01116 knockdown cells. Data represented as mean ± SEM. *P ≤ 0.05, **P ≤ 0.005,***P ≤ 0.0005, ****P ≤ 0.00005, ###P ≤ E-9, $P ≤ E-11,$$P ≤ E-13
Subsequently, we analyzed the expression of apoptotic markers in A549 and H23 cells with LINC01116 overexpression and knockdown and treated with BMH-21. qPCR analysis revealed that LINC01116 overexpression significantly decreased the expression of the apoptotic marker BAX, with BMH-21 treatment marginally increasing BAX expression in these cells. Conversely, BMH-21 treatment significantly upregulated BAX expression in LINC01116 knockdown cells. Additionally, BMH-21 treatment marginally decreased the anti-apoptotic marker Bcl2 in LINC01116 overexpression cells, while LINC01116 knockdown significantly reduced Bcl2 expression. Notably, reconstitution of LINC01116 in knockdown cells rescued BAX and Bcl2 expression changes (Fig. 3J). Overall, our results highlight the crucial role of heightened Pol I transcription in mediating the oncogenic effects of LINC01116 in LUAD cells.
Pol I transcription is essential for LINC01116-mediated EMT
Recent studies have linked Pol I transcription [8] and LINC01116 to EMT processes [45]. We investigated the effect of LINC01116-Pol I transcription interplay on cell migration and invasion. Remarkably, LINC01116 overexpression significantly increased cell migration, which was abrogated by inhibition of Pol I transcription by BMH-21 treatment. However, BMH-21 treatment had marginally reduced migration in LINC01116 knockdown cells. Notably, reconstituting LINC01116 expression in knockdown cells rescued the migratory phenotype (Fig. 4A). Furthermore, BMH-21 treatment significantly dampened the increased invasiveness of LINC01116-overexpressing cells, with an even more pronounced decrease in invasiveness observed in LINC01116 knockdown cells. Remarkably, reconstituted LINC01116 expression partially rescued the invasive phenotype in knockdown cells (Fig. 4B). These findings indicate that the LINC01116-Pol I transcription axis plays a crucial role in promoting cell migration and invasion potential of LUAD cells.Fig. 4Effect of LINC01116-mediated Pol I transcription on EMT. A Wound healing assay showing decreased cell migration in BMH-21 treated A549 cells with stable overexpression or knockdown of LINC01116, reconstitution of LINC01116 expression rescues migration defects, bar graphs indicate relative cell migration. B The trans-well invasion assay demonstrating reduced invasion of A549 cells with stable LINC01116 overexpression or knockdown, after BMH-21 treatment. LINC01116 reconstitution restores invasive capabilities, bar graphs illustrate relative cell invasion. C qPCR data of BMH-21 treated A549 cells with stable LINC01116 overexpression, knockdown or reconstitution showing changes in the EMT marker genes. Data represented as mean ± SEM. *P ≤ 0.05, **P ≤ 0.005,***P ≤ 0.0005, ****P ≤ 0.00005
Upregulation of Pol I transcription promotes EMT by modulating the expression of key genes involved [15]. To investigate whether LINC01116-dependent upregulation of Pol I transcription alters the expression of genes essential for EMT, we treated LINC01116-overexpressing and knockdown cells with BMH-21 and evaluated the expression of EMT markers. Overexpression of LINC01116 reduced the expression of the epithelial marker CDH1, and BMH-21 treatment further repressed CDH1 expression compared to controls. Conversely, CDH1 expression was significantly upregulated in LINC01116 knockdown cells upon Pol I inhibition compared to controls. Moreover, LINC01116 overexpression significantly upregulated mesenchymal markers SLUG, TWIST, VIM, and ZEB1, which were markedly reduced upon BMH-21 treatment. In LINC01116 knockdown cells, BMH-21 treatment further significantly decreased the expression of mesenchymal markers. Notably, Reconstituting LINC01116 in knockdown cells rescued CDH1 downregulation, and restored SLUG, TWIST, VIM, and ZEB1 expression levels in knockdown cells (Fig. 4C). In summary, our data strongly indicate that Pol I transcription plays a crucial role in regulating LINC01116-mediated EMT in LUAD.
LINC01116 confers chemoresistance through upregulating Pol I transcription
Increased Pol I transcriptional activity [7] and LINC01116 expression [20] have been correlated to chemoresistance. To evaluate the mechanistic role of LINC01116-Pol I transcription in chemoresistance, we first treated A549 cells, either overexpressing LINC01116 or with LINC01116 knockdown, with Cis and Dox. LINC01116 knockdown significantly increased apoptosis in response to Cis and Dox, while LINC01116 overexpression reduced this effect, indicating the role of LINC01116 in modulating chemosensitivity (Fig. 5A, B, and Additional file 8: Figure S5A). Next, to investigate the impact of LINC01116-Pol I transcription on drug sensitivity, we generated A549 cells resistant to Cis or Dox. Flow cytometry demonstrated a significant decrease in sensitivity to Cis (Fig. 5C, Additional file 8: Figure S5B) and Dox (Fig. 5D, Additional file 8: Figure S5C) in the resistant cells compared to the parental A549 cells, indicating the gain of a resistant phenotype. Subsequently, we measured LINC01116 expression in these resistant cells. Interestingly, LINC01116 is upregulated in A549-CisR and A549-DoxR cells compared to control A549 cells (Fig. 5E). Further, qPCR analysis revealed a significant increase in the expression of 47S rRNA levels in A549-CisR and A549-DoxR cells, compared to control cells (Fig. 5F). Next, we overexpressed or knocked down the expression of LINC01116 in A549-CisR and A549-DoxR cells growing in Cis or Dox respectively, with or without BMH-21, and evaluated apoptosis. Intriguingly, in A549-CisR cells, LINC01116 overexpression reduced cisplatin sensitivity and decreased apoptosis, whereas knockdown enhanced cisplatin sensitivity and increased apoptosis. Pol I inhibition with BMH-21 further augmented apoptosis in the knockdown group, while LINC01116-mediated Pol I upregulation reduced the apoptotic response to BMH-21 (Fig. 5G, and Additional file 8: Fig. S5D). Similarly, in A549-DoxR cells. Overexpression of LINC01116 reduced doxorubicin sensitivity, while knockdown reversed this effect. Treating the transfected cells with BMH-21 resulted in a marginal increase in apoptosis. However, BMH-21 significantly augmented apoptosis in the LINC01116 knockdown group (Fig. 5H, and Additional file 8: Fig. S5E). These findings emphasize the pivotal role of LINC01116-mediated Pol I transcription in Cis and Dox response.Fig. 5LINC01116 contributes to chemoresistance through Pol I transcription. A and B Bar graph showing apoptosis percentage of Cis and Dox treated A549 cells with stable LINC01116 overexpression or knockdown. C Bar graph showing apoptotic cell percentage in Cis-treated A549 and A549-CisR cells. D Bar graph showing apoptotic cell percentage in Dox-treated A549 and A549-DoxR cells. E qPCR data showing LINC01116 upregulation in A549-CisR and A549-DoxR cells compared to A549 cells. F qPCR data showing upregulation of 47S rRNA in A549-CisR and A549-DoxR cells compared to A549 cells. G Bar graph showing apoptotic cell percentage in the A549-CisR cells with overexpression or knockdown of LINC01116 and treated with BMH-21. H Bar graph showing apoptotic cell percentage in the A549-DoxR cells overexpressing or knockdown of LINC01116 and treated with BMH-21. Data represented as mean ± SEM. *P ≤ 0.05, **P ≤ 0.005,***P ≤ 0.0005, ****P ≤ 0.00005. Cis, Cisplatin; Dox, Doxorubicin
c-Myc transcriptionally activates LINC01116 expression
LINC01116 is upregulated in various cancers [45], but the underlying regulatory mechanisms are unclear. We identified putative c-Myc binding sites on the LINC01116 promoter at − 1 to − 48 bp upstream of the transcription start site (Fig. 6A). To investigate this, using transcriptomic data of LUAD on TCGA, we first performed a correlation analysis and found a significant positive correlation between c-Myc and LINC01116 expression (Fig. 6B). Notably, a significant positive correlation has been observed between c-Myc and LINC01116 expression LUAD tumors compared to normal (Fig. 6C). Furthermore, overexpression of c-Myc markedly upregulated LINC01116 expression (Fig. 6D), whereas shRNA-mediated c-Myc knockdown resulted in a marked decrease in LINC01116 levels (Fig. 6E) in A549 cells, indicating a transcriptional regulation of LINC01116 by c-Myc. In addition, we performed ChIP assays to validate the binding of c-Myc to the LINC01116 promoter. ChIP-qPCR with an anti-Myc antibody confirmed significant c-Myc enrichment on the LINC01116 promoter (Fig. 6F). To further validate this interaction, we transfected HEK293T cells with a luciferase reporter construct containing the LINC01116 promoter sequence along with either a c-Myc expression vector or an empty vector control. Remarkably, luciferase activity significantly increased upon c-Myc overexpression compared to the control, indicating specific c-Myc binding to the LINC01116 promoter and subsequent transcriptional activation (Fig. 6G). These findings confirm that c-Myc directly interacts with the LINC01116 promoter, driving its transcriptional activation in A549 cells.Fig. 6c-Myc regulates LINC01116 expression. A Computational prediction demonstrating putative c-Myc binding sites on the LINC01116 promoter. B Correlation analysis using TCGA data showing a positive correlation between c-Myc and LINC01116 expression in LUAD. C QPCR validation in LUAD tumor tissues confirms a strong positive correlation between c-Myc and LINC01116 expression. D qPCR data illustrating LINC01116 upregulation with c-Myc overexpression. E qPCR data showing c-Myc knockdown downregulates LINC01116 expression. F ChIP-qPCR data showing significant enrichment of c-Myc on the LINC01116 promoter. G Luciferase assay data demonstrating the functional binding of c-Myc on the LINC01116 promoter. Data represented as mean ± SEM. **P ≤ 0.005,***P ≤ 0.0005
LINC01116 directly interacts with transcriptionally active SL1 subunits TAF1A and TAF1D in the nucleolus
LncRNAs can regulate gene transcription by modulating the activity and recruitment of transcription factors [30, 31]. Since SL1 functions as an essential transcription factor and regulatory hub for Pol I transcription, we focused on identifying lncRNAs interacting with SL1 components. Utilizing multiple lncRNA-protein interaction tools, we identified LINC01116 as a high-confidence interactor with SL1 subunits TAF1A and TAF1D. LINC01116 demonstrated optimal binding affinity evidenced by catRAPIDomics star rating system, which integrates normalized interaction propensity, RNA/DNA binding domains, and known RNA binding motifs. Furthermore, LINC01116 was significantly upregulated in LUAD tissues, with elevated expression correlating with poor overall survival. This unique profile distinguished LINC01116 from other predicted lncRNAs, prioritizing it for functional characterization (Fig. 1A and Additional file 2, Figure S1). Strikingly, GSEA revealed a significant enrichment of LINC01116 target genes in pathways related to ribosome biogenesis (Fig. 1B, Additional file 3, Table S2) and rRNA metabolic processes (Fig. 1C, Additional file 4, Table S3). Further, using catRAPID fragments and graphic modules, we identified a specific binding region between TAF1A/TAF1D and LINC01116, spanning nucleotides 201–360 (Additional File 5, Figure S2). In addition, a significant positive correlation was observed between the expression of LINC01116 and 47S rRNA in LUAD tumors compared to adjacent normal tissues (Fig. 1D), suggesting a potential role of LINC01116 in Pol I transcription. LINC01116 and Pol I transcription are highly upregulated and associated with oncogenic roles in LUAD [15, 32]. Thus, we sought to investigate the regulatory link between LINC01116 and Pol I transcription in LUAD cell lines. To investigate the endogenous interaction between LINC01116 and TAF1A and TAF1D, we performed RIP assays in A549 and H23 cells stably overexpressing LINC01116 (Fig. 1E) or LINC01116-shRNA (Fig. 1F). Remarkably, LINC01116 overexpression resulted in a substantial increase in TAF1A and TAF1D enrichment compared to controls. Conversely, knockdown of LINC01116 markedly diminished this enrichment, confirming the interaction between LINC01116 and, TAF1A and TAF1D (Fig. 1G and H). Pol I transcription is confined to the nucleolus [9]. We examined nucleolar localization of LINC01116 by immunofluorescence microscopy. Cells overexpressing LINC01116 exhibited a notable co-localization of LINC01116 along with TAF1A, TAF1D and the nucleolar marker NPM1 (Fig. 1I), which was diminished in LINC01116 knockdown cells (Fig. 1J). Transcriptionally competent TAF1A and TAF1D are integral components of the SL1 complex bound to rDNA promoter [33]. To test whether the interaction of LINC01116 is specific to the TAF1A/TAF1D subunits within the SL1 complex or free forms, we immunoprecipitated the SL1 complex bound to the rDNA-promoter and assessed the presence of SL1 components, LINC01116 and rDNA (scheme Fig. 1K). Immunoblotting analysis confirmed the presence of TAF1A-D, indicating successful isolation of the SL1 complex (Fig. 1L). Subsequent qPCR analysis of the Immunoprecipitated chromatin and RNA revealed significant enrichment of the rDNA core promoter (Fig. 1M) and LINC01116 (Fig. 1N). These findings strongly indicate the interaction between LINC01116 and TAF1A/TAF1D with transcriptionally competent SL1 complex bound to the rDNA promoter, suggesting a potential regulatory role for LINC01116 in Pol I transcription.Fig. 1LINC01116 interacts with the SL1 components. A Computational prediction indicating LINC01116 interaction with SL1 components TAF1A and TAF1D. B and C Gene Set Enrichment Analysis showing enrichment of LINC01116 expression in ribosome biogenesis and rRNA metabolic processes in LUAD. D qPCR analysis reveals a significant positive correlation (Pearson) between LINC01116 and 47S rRNA expression, n = 6). E and F qPCR data demonstrating the stable overexpression of LINC01116 or stable knockdown of LINC01116 in A549 and H23 cells. G and H qPCR demonstrating relative enrichment of TAF1A or TAF1D in LINC01116 overexpression and knockdown cells. (I and J). Fluorescent in-situ hybridization images showing the co-localization of LINC01116 with NPM1, TAF1A, and TAF1D in LINC01116 overexpression cells or LINC01116 knockdown cells. K Schematic representation of immunoprecipitation of rDNA-bound SL1 complex. L Immunoblot analysis of SL1 complex immunoprecipitated from rDNA-promoter-bound chromatin. TAF1A-D subunits are detected, confirming successful SL1 complex isolation. (M and N). qPCR data confirming the relative enrichment of rDNA core promoter and LINC01116 after rDNA-bound SL1 immunoprecipitation. *P ≤ 0.05, **P ≤ 0.005, ***P ≤ 0.0005, ****P ≤ 0.00005. Error bars indicate mean ± SEM. NPM1, Nucleophosmin 1
LINC01116 upregulates Pol I transcription by promoting pre-initiation complex formation at the rDNA promoter
To investigate whether LINC01116 affects PIC formation on the rDNA promoter, we performed ChIP assays with antibodies against PIC components. ChIP-qPCR analysis revealed a significant increase in SL1 components and POLR1B at the rDNA promoter upon LINC01116 overexpression (Fig. 2A). Conversely, LINC01116 knockdown significantly reduced this enrichment, indicating its critical role in recruiting PIC components to the rDNA promoter via TAF1A and TAF1D (Fig. 2B). LncRNAs can recruit transcription factors to regulatory sequences through direct DNA interactions [34]. Strikingly, alignment of the LINC01116 sequence with rDNA regulatory loci revealed a remarkable sequence complementarity between LINC01116 and the rDNA core promoter (+ 20 to -45) and the upstream control element (UCE) (-107 to -177) (Fig. 2C). To validate this, we conducted ChIRP assays. qPCR analysis of ChIRP-derived RNA and DNA fractions revealed a significant enrichment of both LINC01116 (Fig. 2D) and the rDNA core promoter (Fig. 2E) in LINC01116-overexpressing cells compared to controls. In contrast, stable knockdown of LINC01116 reversed the enrichment of both LINC01116 (Fig. 2F) and the rDNA core promoter (Fig. 2G). To further validate the sequence complementarity between LINC01116 and the rDNA promoter/UCE, we performed ChIRP-qPCR assays scanning the entire rDNA repeat. Notably, LINC01116 overexpression led to significant enrichment of core promoter and UCE, whereas LINC01116 knockdown resulted in reduced signals. In contrast, other regions of the rDNA repeat remained unaffected, highlighting the specificity of this interaction (Additional file 6, Figure S3). Sanger sequencing of the ChIRP DNA fraction confirmed the direct binding of LINC01116 to the rDNA core promoter (Fig. 2H). Next, we investigated the impact of LINC01116 on Pol I transcription by analyzing both steady-state levels and de novo synthesis of 47S pre-rRNA in cells with stable LINC01116 overexpression, knockdown, and reconstituted expression in knockdown cells (Fig. 2I). qPCR analysis revealed that LINC01116 overexpression elevated steady-state 47S rRNA levels, whereas knockdown resulted in decreased levels, notably, reconstitution of LINC01116 in knockdown cells significantly rescued 47S rRNA (Fig. 2J). Furthermore, EU incorporation assays demonstrated that LINC01116 overexpression enhanced de novo rRNA transcription, while knockdown reduced synthesis. Remarkably, reconstitution of LINC01116 in knockdown cells also restored de novo rRNA transcription (Fig. 2K). Serum starvation dampens Pol I transcription, resulting in reduced rRNA synthesis; this effect can be reversed upon serum reconstitution [35]. To study the involvement of LINC01116 in stimulus-mediated activation of Pol I transcription, cells with LINC01116 overexpression or knockdown were subjected to serum starvation, and rRNA de novo synthesis was measured. As anticipated, serum deprivation significantly decreased rRNA synthesis, as evidenced by reduced EU incorporation. However, upon serum re-stimulation, LINC01116-overexpressing cells exhibited a notable increase in rRNA levels, exceeding basal levels (Fig. 2L). Conversely, LINC01116 knockdown cells displayed impaired Pol I transcriptional reactivation upon serum reconstitution (Fig. 2M). Collectively, these findings strongly establish a positive regulatory role of LINC01116 on Pol I transcription.Fig. 2LINC01116 upregulates Pol I transcription. (A and B). ChIP-qPCR data showing the relative promoter occupancy of PIC components after LINC01116 overexpression or, after LINC01116 knockdown. C Sequence alignment showing complementarity between LINC01116 and rDNA promoter. D and E Chromatin isolation by RNA purification-qPCR data confirming the relative binding of LINC01116 to the rDNA promoter in LINC01116 overexpressing cells, (F and G). in LINC01116 knockdown cells. H Sanger sequencing histogram confirming ChIRP DNA as rDNA promoter. I. qPCR analysis showing relative expression levels of LINC01116 in cells with stable overexpression (OE), knockdown (shRNA), and reconstitution (shRNA + OE) of LINC01116. J qPCR analysis showing changes in 47S rRNA expression levels in cells with stable LINC01116 overexpression, knockdown, and reconstitution. K EU incorporation assay illustrating changes in Pol I transcriptional activity in cells with LINC01116 overexpression, knockdown, and reconstitution. Bar graphs illustrating the normalized fluorescence intensity. L EU incorporation assay demonstrating enhanced Pol I transcription in LINC01116 overexpression cells following serum reconstitution. M EU incorporation assay showing reduced Pol I reactivation in LINC01116 knockdown cells after serum reconstitution. Error bars indicate mean ± SEM. *P ≤ 0.05, **P ≤ 0.005,***P ≤ 0.0005, ****P ≤ 0.00005, #P ≤ E-7, ##P ≤ E-8. rDNA, ribosomal DNA, UCE, Upstream control element, EU, Ethynyl Uridine
LINC01116-driven Pol I transcription is essential for enhanced cell proliferation, clonogenicity and reduced apoptosis
Given that hyperactive Pol I transcription drives malignant proliferation [36], and LINC01116 upregulation is associated with increased proliferation in various cancers [37–39], we investigated the plausible link between LINC01116 and Pol I transcription controlling cell proliferation. We administered the specific Pol I inhibitor BMH-21 [40] to cells with either overexpression or knockdown of LINC01116 and assessed cell proliferation. Overexpression of LINC01116 resulted in a significant increase in cell proliferation, which was substantially reduced by BMH-21 treatment relative to controls. Conversely, in LINC01116 knockdown cells, BMH-21 treatment caused a further decrease in cell proliferation compared to the effect of knockdown alone (Fig. 3A). To further investigate the combined effects of BMH-21 treatment and LINC01116 knockdown on cell viability, we performed a dose–response analysis in A549 and H23 cells stably expressing shRNA-LINC01116. As shown in Fig. 3B, LINC01116 knockdown significantly potentiated the anti-proliferative effects of BMH-21 treatment, resulting in a synergistic decrease in cell viability in both cell lines. Tumor spheroids closely mimic in vivo tumors, and provide a more realistic environment for studying cell proliferation [41]. We generated tumor spheroids from A549 and H23 cells stably overexpressing LINC01116 or LINC01116-shRNA (Fig. 3C). LINC01116-overexpressing spheroids exhibited increased rRNA synthesis, while LINC01116-shRNA spheroids showed reduced rRNA levels, as measured by EU incorporation. Notably, BMH-21 treatment reduced rRNA levels in LINC01116-overexpressing spheroids and caused a synergistic decrease in rRNA transcription in LINC01116 knockdown spheroids (Fig. 3D). In addition, LINC01116 overexpressing spheroids also displayed higher Ki67 expression, a proliferation marker, compared to controls, while knockdown spheroids had reduced Ki67 expression. BMH-21 significantly decreased Ki67 levels in LINC01116-overexpressing spheroids, with an even greater reduction in knockdown spheroids (Fig. 3E). These results indicate that LINC01116 promotes cell proliferation through Pol I transcription. Further, BMH-21 treatment had a marginal effect on the clonogenic capacity of A549 and H23 cells overexpressing LINC01116. In contrast, LINC01116 knockdown cells exhibited a significant reduction in clonogenicity upon BMH-21 treatment. Notably, reconstitution of LINC01116 in knockdown cells rescued the clonogenic capacity (Fig. 3F). The rate of Pol I transcription is closely linked to cell cycle progression [42]. To investigate whether LINC01116-mediated Pol I transcription influences cell cycle dynamics, we analyzed the cell cycle distribution in LINC01116-overexpressing, knockdown or reconstituted cells. LINC01116 overexpression resulted in a significant decrease in G1-phase and an increase in the S-phase population, whereas knockdown of LINC01116 increased G1 and reduced the S-phase population. Notably, treatment with the Pol I inhibitor BMH-21 caused G1 arrest and diminished the S-phase enrichment observed in LINC01116-overexpressing cells. Additionally, BMH-21 treatment further decreased the S-phase population in LINC01116 knockdown cells, suggesting a synergistic effect. Moreover, reconstitution of LINC01116 expression in knockdown cells substantially rescued the cell cycle phenotype, mitigating the G1-phase accumulation and S-phase depletion induced by LINC01116 depletion, in both control and BMH-21-treated conditions (Fig. 3G, and Additional file 7, Figure S4A and S4B). Cyclin-dependent kinases (CDKs) CDK2, CDK4, and CDK6 are key regulators of the G1 to S-phase transition [43, 44]. Remarkably, LINC01116 overexpression led to increased CDK2, CDK4, and CDK6 expression, which was reduced to basal levels upon BMH-21 treatment. Furthermore, BMH-21 treatment synergistically decreased the expression of these CDKs in LINC01116 knockdown cells. Notably, reconstitution of LINC01116 expression rescued CDK expression levels in knockdown cells with or without BMH-21 treatment. These results indicate that LINC01116 promotes cell cycle progression through upregulation of G1 to S-phase regulators in a Pol I transcription-dependent manner (Fig. 3H). LINC01116 has been shown to suppress apoptosis [38], and rRNA levels are inversely correlated with apoptosis [15]. To investigate the apoptotic link between LINC01116 and Pol I transcription, we treated A549 and H23 cells overexpressing or knocked down for LINC01116 with BMH-21 and performed apoptosis assays. Notably, LINC01116 overexpression significantly reduced apoptosis, while BMH-21 treatment only marginally increased apoptosis in these overexpressing cells. In contrast, BMH-21 treatment significantly augmented apoptosis in LINC01116 knockdown cells. In addition, reconstitution of LINC01116 expression in knockdown cells restored apoptotic resistance, rescuing cells from increased apoptosis in both untreated and BMH-21-treated conditions (Fig. 3I and Additional file 7: Fig. S4C and S4D).Fig. 3LINC01116-driven Pol I transcription modulates cell proliferation, clonogenicity, and apoptosis. A Alamar blue cell proliferation assay showing decreased proliferation of A549 and H23 cells stably overexpressing LINC01116 or LINC01116-shRNA treated with BMH-21. B Dose–response curves illustrating enhanced sensitivity to BMH-21 in LINC01116 knockdown cells, demonstrating a synergistic effect on cell viability. C qPCR showing the expression of LINC01116 in tumor spheroids generated from A549 and H23 cells stably overexpressing LINC01116 or LINC01116-shRNA. D EU incorporation assay showing Pol I transcription levels in tumor spheroids stably overexpressing LINC01116 or LINC01116-shRNA with or without inhibiting Pol I transcription by BMH-21. E Immunofluorescence images showing increased Ki67 expression in tumor spheroids stably overexpressing LINC01116 or LINC01116-shRNA in response to Pol I transcription inhibition. F Representative images of clonogenic assays showing the effect of BMH-21 treatment on A549 and H23 cells stably overexpressing LINC01116 (OE), Knockdown of LINC01116 (shRNA) or Reconstituted LINC01116 expression in knockdown cells (shRNA + OE), bar graphs illustrating relative colony counts for each condition normalized to untreated controls. G Bar graphs showing G1, S, and G2/M phase distributions in LINC01116-overexpressing (OE), LINC01116-shRNA, and reconstituted LINC01116 expression (shRNA + OE) cells with or without BMH-21 treatment. H qPCRs showing altered expression of cell cycle regulators CDK2, CDK4, and CDK6 in BMH-21 treated cells stably overexpressing LINC01116,LINC01116-shRNA or reconstitution in knockdown cells. I Bar graphs showing the apoptosis percentage in BMH-21 treated A549 or H23 cells stably overexpressing, knockdown or reconstitution of LINC01116. J. qPCRs showing the expression of pro-apoptotic and anti-apoptotic gene expression in BMH-21 treated cells with stable LINC01116 overexpression, knockdown or reconstitution in LINC01116 knockdown cells. Data represented as mean ± SEM. *P ≤ 0.05, **P ≤ 0.005,***P ≤ 0.0005, ****P ≤ 0.00005, ###P ≤ E-9, $P ≤ E-11,$$P ≤ E-13
Subsequently, we analyzed the expression of apoptotic markers in A549 and H23 cells with LINC01116 overexpression and knockdown and treated with BMH-21. qPCR analysis revealed that LINC01116 overexpression significantly decreased the expression of the apoptotic marker BAX, with BMH-21 treatment marginally increasing BAX expression in these cells. Conversely, BMH-21 treatment significantly upregulated BAX expression in LINC01116 knockdown cells. Additionally, BMH-21 treatment marginally decreased the anti-apoptotic marker Bcl2 in LINC01116 overexpression cells, while LINC01116 knockdown significantly reduced Bcl2 expression. Notably, reconstitution of LINC01116 in knockdown cells rescued BAX and Bcl2 expression changes (Fig. 3J). Overall, our results highlight the crucial role of heightened Pol I transcription in mediating the oncogenic effects of LINC01116 in LUAD cells.
Pol I transcription is essential for LINC01116-mediated EMT
Recent studies have linked Pol I transcription [8] and LINC01116 to EMT processes [45]. We investigated the effect of LINC01116-Pol I transcription interplay on cell migration and invasion. Remarkably, LINC01116 overexpression significantly increased cell migration, which was abrogated by inhibition of Pol I transcription by BMH-21 treatment. However, BMH-21 treatment had marginally reduced migration in LINC01116 knockdown cells. Notably, reconstituting LINC01116 expression in knockdown cells rescued the migratory phenotype (Fig. 4A). Furthermore, BMH-21 treatment significantly dampened the increased invasiveness of LINC01116-overexpressing cells, with an even more pronounced decrease in invasiveness observed in LINC01116 knockdown cells. Remarkably, reconstituted LINC01116 expression partially rescued the invasive phenotype in knockdown cells (Fig. 4B). These findings indicate that the LINC01116-Pol I transcription axis plays a crucial role in promoting cell migration and invasion potential of LUAD cells.Fig. 4Effect of LINC01116-mediated Pol I transcription on EMT. A Wound healing assay showing decreased cell migration in BMH-21 treated A549 cells with stable overexpression or knockdown of LINC01116, reconstitution of LINC01116 expression rescues migration defects, bar graphs indicate relative cell migration. B The trans-well invasion assay demonstrating reduced invasion of A549 cells with stable LINC01116 overexpression or knockdown, after BMH-21 treatment. LINC01116 reconstitution restores invasive capabilities, bar graphs illustrate relative cell invasion. C qPCR data of BMH-21 treated A549 cells with stable LINC01116 overexpression, knockdown or reconstitution showing changes in the EMT marker genes. Data represented as mean ± SEM. *P ≤ 0.05, **P ≤ 0.005,***P ≤ 0.0005, ****P ≤ 0.00005
Upregulation of Pol I transcription promotes EMT by modulating the expression of key genes involved [15]. To investigate whether LINC01116-dependent upregulation of Pol I transcription alters the expression of genes essential for EMT, we treated LINC01116-overexpressing and knockdown cells with BMH-21 and evaluated the expression of EMT markers. Overexpression of LINC01116 reduced the expression of the epithelial marker CDH1, and BMH-21 treatment further repressed CDH1 expression compared to controls. Conversely, CDH1 expression was significantly upregulated in LINC01116 knockdown cells upon Pol I inhibition compared to controls. Moreover, LINC01116 overexpression significantly upregulated mesenchymal markers SLUG, TWIST, VIM, and ZEB1, which were markedly reduced upon BMH-21 treatment. In LINC01116 knockdown cells, BMH-21 treatment further significantly decreased the expression of mesenchymal markers. Notably, Reconstituting LINC01116 in knockdown cells rescued CDH1 downregulation, and restored SLUG, TWIST, VIM, and ZEB1 expression levels in knockdown cells (Fig. 4C). In summary, our data strongly indicate that Pol I transcription plays a crucial role in regulating LINC01116-mediated EMT in LUAD.
LINC01116 confers chemoresistance through upregulating Pol I transcription
Increased Pol I transcriptional activity [7] and LINC01116 expression [20] have been correlated to chemoresistance. To evaluate the mechanistic role of LINC01116-Pol I transcription in chemoresistance, we first treated A549 cells, either overexpressing LINC01116 or with LINC01116 knockdown, with Cis and Dox. LINC01116 knockdown significantly increased apoptosis in response to Cis and Dox, while LINC01116 overexpression reduced this effect, indicating the role of LINC01116 in modulating chemosensitivity (Fig. 5A, B, and Additional file 8: Figure S5A). Next, to investigate the impact of LINC01116-Pol I transcription on drug sensitivity, we generated A549 cells resistant to Cis or Dox. Flow cytometry demonstrated a significant decrease in sensitivity to Cis (Fig. 5C, Additional file 8: Figure S5B) and Dox (Fig. 5D, Additional file 8: Figure S5C) in the resistant cells compared to the parental A549 cells, indicating the gain of a resistant phenotype. Subsequently, we measured LINC01116 expression in these resistant cells. Interestingly, LINC01116 is upregulated in A549-CisR and A549-DoxR cells compared to control A549 cells (Fig. 5E). Further, qPCR analysis revealed a significant increase in the expression of 47S rRNA levels in A549-CisR and A549-DoxR cells, compared to control cells (Fig. 5F). Next, we overexpressed or knocked down the expression of LINC01116 in A549-CisR and A549-DoxR cells growing in Cis or Dox respectively, with or without BMH-21, and evaluated apoptosis. Intriguingly, in A549-CisR cells, LINC01116 overexpression reduced cisplatin sensitivity and decreased apoptosis, whereas knockdown enhanced cisplatin sensitivity and increased apoptosis. Pol I inhibition with BMH-21 further augmented apoptosis in the knockdown group, while LINC01116-mediated Pol I upregulation reduced the apoptotic response to BMH-21 (Fig. 5G, and Additional file 8: Fig. S5D). Similarly, in A549-DoxR cells. Overexpression of LINC01116 reduced doxorubicin sensitivity, while knockdown reversed this effect. Treating the transfected cells with BMH-21 resulted in a marginal increase in apoptosis. However, BMH-21 significantly augmented apoptosis in the LINC01116 knockdown group (Fig. 5H, and Additional file 8: Fig. S5E). These findings emphasize the pivotal role of LINC01116-mediated Pol I transcription in Cis and Dox response.Fig. 5LINC01116 contributes to chemoresistance through Pol I transcription. A and B Bar graph showing apoptosis percentage of Cis and Dox treated A549 cells with stable LINC01116 overexpression or knockdown. C Bar graph showing apoptotic cell percentage in Cis-treated A549 and A549-CisR cells. D Bar graph showing apoptotic cell percentage in Dox-treated A549 and A549-DoxR cells. E qPCR data showing LINC01116 upregulation in A549-CisR and A549-DoxR cells compared to A549 cells. F qPCR data showing upregulation of 47S rRNA in A549-CisR and A549-DoxR cells compared to A549 cells. G Bar graph showing apoptotic cell percentage in the A549-CisR cells with overexpression or knockdown of LINC01116 and treated with BMH-21. H Bar graph showing apoptotic cell percentage in the A549-DoxR cells overexpressing or knockdown of LINC01116 and treated with BMH-21. Data represented as mean ± SEM. *P ≤ 0.05, **P ≤ 0.005,***P ≤ 0.0005, ****P ≤ 0.00005. Cis, Cisplatin; Dox, Doxorubicin
c-Myc transcriptionally activates LINC01116 expression
LINC01116 is upregulated in various cancers [45], but the underlying regulatory mechanisms are unclear. We identified putative c-Myc binding sites on the LINC01116 promoter at − 1 to − 48 bp upstream of the transcription start site (Fig. 6A). To investigate this, using transcriptomic data of LUAD on TCGA, we first performed a correlation analysis and found a significant positive correlation between c-Myc and LINC01116 expression (Fig. 6B). Notably, a significant positive correlation has been observed between c-Myc and LINC01116 expression LUAD tumors compared to normal (Fig. 6C). Furthermore, overexpression of c-Myc markedly upregulated LINC01116 expression (Fig. 6D), whereas shRNA-mediated c-Myc knockdown resulted in a marked decrease in LINC01116 levels (Fig. 6E) in A549 cells, indicating a transcriptional regulation of LINC01116 by c-Myc. In addition, we performed ChIP assays to validate the binding of c-Myc to the LINC01116 promoter. ChIP-qPCR with an anti-Myc antibody confirmed significant c-Myc enrichment on the LINC01116 promoter (Fig. 6F). To further validate this interaction, we transfected HEK293T cells with a luciferase reporter construct containing the LINC01116 promoter sequence along with either a c-Myc expression vector or an empty vector control. Remarkably, luciferase activity significantly increased upon c-Myc overexpression compared to the control, indicating specific c-Myc binding to the LINC01116 promoter and subsequent transcriptional activation (Fig. 6G). These findings confirm that c-Myc directly interacts with the LINC01116 promoter, driving its transcriptional activation in A549 cells.Fig. 6c-Myc regulates LINC01116 expression. A Computational prediction demonstrating putative c-Myc binding sites on the LINC01116 promoter. B Correlation analysis using TCGA data showing a positive correlation between c-Myc and LINC01116 expression in LUAD. C QPCR validation in LUAD tumor tissues confirms a strong positive correlation between c-Myc and LINC01116 expression. D qPCR data illustrating LINC01116 upregulation with c-Myc overexpression. E qPCR data showing c-Myc knockdown downregulates LINC01116 expression. F ChIP-qPCR data showing significant enrichment of c-Myc on the LINC01116 promoter. G Luciferase assay data demonstrating the functional binding of c-Myc on the LINC01116 promoter. Data represented as mean ± SEM. **P ≤ 0.005,***P ≤ 0.0005
Discussion
Hyperactive Pol I transcription is recognized as a canonical molecular aberration linked to various cancer hallmarks [46, 47]. Despite this, the precise molecular mechanisms driving this dysregulation remain elusive. Our study is the first to identify a novel regulatory pathway involving LINC01116, which drives the upregulation of Pol I transcription in LUAD. Importantly, this LINC01116-mediated upregulation of Pol I transcription plays a pivotal role in promoting various oncogenic processes, highlighting the significance of LINC01116-Pol I in the molecular etiology of LUAD.
Hyperactive Pol I transcription necessitates increased recruitment of pre-initiation complex (PIC) components to the rDNA promoter [35]. Our study shows that oncogenic LINC01116 operates as a scaffold, facilitating the assembly of essential transcription factors (TFs) at the rDNA promoter, critical for initiating rRNA synthesis. Previous studies have demonstrated that SLERT, a snoRNA-ended lncRNA, enhances pre-rRNA transcription by binding to the DEAD-box RNA helicase DDX21 and altering rDNA topology. Intriguingly, these findings connect SLERT-dependent regulation of Pol I transcription to ribosome biogenesis, underscoring the intricate regulatory networks linking Pol I transcription to ribosome biogenesis [48, 49]. Notably, lncRNA-TF scaffolding has also been observed to modulate Pol II-driven transcription by preventing the recruitment of Pol II or specific transcription factors. For example, the tumor-suppressor lncRNA GAS5, dysregulated in several cancers, promotes growth arrest, apoptosis, and inhibits cell migration by blocking the glucocorticoid receptor from binding to glucocorticoid response elements, thereby regulating target gene transcription [50, 51]. Intriguingly, lncRNA B2 RNA interacts with the Pol II σ-holoenzyme, preventing the assembly of a functional pre-initiation complex, and thereby suppressing Pol II transcription initiation [52]. Moreover, lncRNA–TF interactions have demonstrated both tumor suppressor and oncogenic roles in cancer. For instance, HAND2-AS1 binds E2F4 at the C16orf74 promoter to downregulate its expression and repress cervical cancer progression [53], whereas HNF1A-AS1 binds PBX3 to upregulate OTX1 expression, promoting angiogenesis in colon cancer [54]. These findings underscore the critical role of lncRNA-TF interactions in regulating transcription and their profound impact on cancer progression, highlighting their potential as therapeutic targets.
Recent studies have linked LINC01116 to promoting cell proliferation and cycle progression in cancer cells [55]. However, these studies predominantly explored the indirect effects mediated by microRNA-LINC01116 interactions. In this study, we uncover the direct impact of LINC01116 on cancer cell proliferation through the activation of Pol I transcription. Given that accelerated Pol I transcription is pivotal for malignant proliferation, LINC01116-dependent activation of Pol I transcription can be a potential target to reduce tumor burden. The rate of Pol I transcription is tightly coordinated with cell cycle progression to meet the varying demands for protein synthesis during different phases of the cell cycle [56]. Pol I transcription is moderately active during G1 phase and peaks as cells enter the S-phase, where the demand for protein synthesis is high [57]. We found that LINC01116-dependent activation of Pol I transcription is essential for driving cells into S-phase, indicating that LINC01116 plays a crucial role in facilitating cell cycle progression by ensuring sufficient rRNA synthesis to support heightened protein production needs during DNA replication. Elevated Pol I transcription promotes the synthesis and function of key cell cycle regulators, including cyclins, cyclin-dependent kinases (CDKs), and proteins involved in the Rb and p53 pathways [58, 59]. This process is tightly integrated with the functions of CDK2, CDK4, and CDK6, particularly during the S phase of the cell cycle. Our study indicates that LINC01116-mediated Pol I transcription upregulates CDK2, CDK4, and CDK6, highlighting a crucial mechanism that drives cell cycle progression. Interestingly, other oncogenic lncRNAs have similar roles. For example, SNHG6 promotes G1-S transition and proliferation in NSCLC cells. Additionally, MAFG-AS1 upregulates CDK2 expression via miR-339-5p sponging, thereby accelerating the G1-S transition [60]. These findings underscore the importance of lncRNA-mediated regulation of cell cycle progression in cancer.
EMT initiation coincides with rDNA transcription activation, and inhibiting rRNA synthesis disrupts EMT and reduces metastasis [8]. Our recent findings demonstrate that miRNA-mediated inhibition of Pol I transcription significantly reduces A549 cell migration and downregulates ZEB1, a key EMT modulator [15]. Activation of Pol I transcription during EMT enhances ribosome biogenesis and protein synthesis, supporting the production of essential EMT proteins, including ZEB transcription factors. ZEB1 and ZEB2 repress epithelial markers and promote mesenchymal markers, stabilizing the mesenchymal state. This state requires continuous protein synthesis, maintained by Pol I activity. Our investigation revealed that LINC01116-dependent modulation of invasion and migration, and associated EMT markers expression requires activation of Pol I transcription. This interplay between Pol I and EMT mediated through LINC01116, highlights a critical juncture where Pol I activity governs the expression of crucial EMT regulators.
The association between Pol I transcription and chemoresistance is primarily due to hyperactive Pol I transcription driving proliferation, enhancing EMT processes, decreasing apoptosis, and activating survival pathways [7]. Our previous work showed that inhibiting Pol I transcription increased chemosensitivity in A549 cells [15]. In this study, we demonstrate that LINC01116-dependent upregulation of Pol I transcription reduces sensitivity to Cis and Dox, especially in drug-resistant cells. While LINC01116 role in chemoresistance has been linked predominantly to miRNA-dependent mechanisms [55], our findings suggest that increased Pol I transcription, facilitated by LINC01116, is a significant factor in chemoresistance.
Oncogenic c-Myc is a bona fide activator of Pol I transcription, directly binding to rDNA promoters, recruiting essential transcription factors, and enhancing rRNA synthesis [61]. This activation is considered a major oncogenic event in tumorigenesis. Our data suggests that c-Myc transcriptionally activates LINC01116 expression. Thus, c-Myc functions as an upstream regulatory node connecting both LINC01116 and Pol I transcription, creating a synergistic mechanism that drives crucial tumorigenic processes.
In summary, our study uncovers the critical link between oncogenic LINC01116 and Pol I transcription, offering valuable insights into the molecular basis of cancer pathogenesis. Also, highlights the intricate regulatory networks governing Pol I transcription in cancer.
Conclusion
In summary, our study reveals the synergistic role of LINC01116 and Pol I transcription in promoting oncogenic phenotypes in LUAD, including enhanced cell proliferation, clonogenicity, cell cycle progression, and chemoresistance, while suppressing apoptosis. The c-Myc-LINC01116-Pol I axis emerges as a crucial pathway in cancer development and progression, offering promising targets for therapeutic intervention. Our findings underscore the importance of lncRNA-mediated regulation of Pol I transcription and open new avenues for targeted cancer therapies aimed at disrupting this pathway to inhibit tumor growth and metastasis.
Supplementary Information
Additional file 1: Table S1. List of primers, probes, and antibodies used in this studyAdditional file 2: Figure S1. Computational predictions of lncRNAs interacting with Pol I proteome. A. Top four lncRNAs—LINC01116, LINC00471, LINC02449, and LINC00313 interacting with TAF1A and TAF1D, ranked by catRAPID star rating score. B. Box plots illustrating LINC01116, LINC00471, LINC02449, and LINC00313 expression levels in normal lung tissues and LUAD tumors from TCGA. C. Kaplan-Meier survival analysis of LINC01116, LINC00471, LINC02449, and LINC00313 expression in TCGA LUAD samples. Survival plots demonstrating significantly poorer survival in patients with high LINC01116 expression compared to low expression. FPKM, Fragments Per Kilobase of transcript per Million mapped readsAdditional file 3: Table S2. GSEA results summary for ribosome biogenesisAdditional file 4: Table S3. GSEA results summary for rRNA metabolic processesAdditional file 5: Figure S2. Interaction prediction between LINC01116 and TAF1A/TAF1D. A. catRAPID fragments histogram depicting the interaction profile between LINC01116 and TAF1A. B. Matrix showing the interaction predictions between LINC01116 nucleotides and TAF1A amino acids. C. catRAPID fragments histogram illustrating the interaction profile between LINC01116 and TAF1D. D. Interaction matrix showing the interaction predictions between LINC01116 nucleotides and TAF1D amino acidsAdditional file 6: Figure S3: LINC01116 selectively binds to the rDNA UCE and core promoter regions. A. Schematic representation of the rDNA repeat. B. ChIRP-qPCR analysis demonstrating significant enrichment of LINC01116 at the UCE and core promoter regions, with no significant changes in other regionsAdditional file 7: Figure S4. Cell cycle and apoptosis analysis.. Flow cytometry histograms of cell cycle analysis of BMH-21 treated A549 and H23 cells stably overexpressing LINC01116,LINC01116-shRNA,or reconstitution of LINC01116 in knockdown cells.. Flow cytometry histograms of apoptosis assay of BMH-21 treated A549 and H23 cells stably overexpressing LINC01116,LINC01116-shRNA or reconstitution of LINC01116 in knockdown cellsAdditional file 8: Figure S5. Apoptosis analysis in drug resistant cells. Flow cytometry histograms of apoptosis assay. A. Flow cytometry histogram of apoptosis assay of Cis and Dox treated A549 cells with stable LINC01116 overexpression or knockdown. B. Flow cytometry histograms of apoptosis assay of Cis-treated A549 or A549-CisR cells. C. Flow cytometry histograms of apoptosis assay of Dox-treated A549 or A549-DoxR cells. D. Flow cytometry histograms of apoptosis assay of BMH-21 treated A549-CisR cells overexpressing or knockdown of LINC01116. E. Flow cytometry histograms of apoptosis assay of BMH-21 treated A549-DoxR cells overexpressing or knockdown of LINC01116. |
Title: Exploring socioeconomic status, lifestyle factors, and cardiometabolic disease outcomes in the United States: insights from a population-based cross-sectional study | Body:
Background
The relationship between socioeconomic status (SES) and cardiometabolic diseases has been an essential area of public health and medicine research [1, 2]. Cardiometabolic diseases, which include cardiovascular disease, type 2 diabetes, and lung disease, pose a significant public health challenge worldwide [3]. Their prevalence has increased recently, with important implications for individuals, communities, and healthcare systems [3]. SES, a complex determinant, has emerged as a crucial factor associated with the development and progression of these diseases [4].
Despite the growing body of literature exploring the connection between SES and cardiometabolic diseases [1, 2, 5–7], there are still gaps in our understanding. Prior studies have focused on establishing the association itself, often overlooking the complex pathways through which SES affects cardiometabolic health. Moreover, limited attention has been given to the potential role of lifestyle factors as mediators in this intricate relationship. Although some research has explored the individual impacts of lifestyle choices on cardiometabolic diseases [8, 9], there is a lack of comprehensive investigations examining lifestyle’s potential to mediate the effects of SES on these health outcomes. Thus, delving deeper into the interplay between SES, lifestyle, and cardiometabolic diseases is important.
It is crucial to understand the impact of socioeconomic status on cardiometabolic diseases, especially in the current context where health disparities persist and evolve [4, 10]. The COVID-19 pandemic has highlighted the vulnerabilities individuals with lower SES face, making this investigation urgent [11, 12].
This study investigates the association between socioeconomic status and cardiometabolic diseases in the United States and how lifestyle factors may mediate this relationship. Data from the Health Information National Trends Survey (HINTS) 5, a nationally representative survey conducted by the National Cancer Institute, will be analyzed. The study hypothesizes that specific lifestyle factors, such as physical activity and smoking, may significantly mediate the relationship between socioeconomic status and cardiometabolic diseases. The findings from this research could provide valuable insights for potential targeted public health interventions and policy strategies aimed at reducing health disparities.
Methods
Participants
This study utilized data from the Health Information National Trends Survey (HINTS) [13], a nationally representative survey conducted by the National Cancer Institute (NCI) since 2003. HINTS provides valuable insights into the American public’s knowledge, attitudes, and use of cancer- and health-related information to enhance health communication strategies across diverse populations. The HINTS 5 survey data was used for this study, which targeted non-institutionalized civilians aged 18 years or older residing in the United States. No direct contact was made with the study participants. Therefore, informed consent for the present analysis was not necessary as secondary data analysis does not involve interaction with participants. Ethical approval for HINTS was obtained through expedited review by the Westat Institutional Review Board and subsequently deemed exempt by the U.S. National Institutes of Health Office of Human Subjects Research Protections. HINTS adheres to established international and local ethical standards and protocols. Approval to use the HINTS dataset was granted by the National Cancer Institute. The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. They determined that no formal ethical consent was required to conduct research using this data source.
The study was conducted by sending a self-administered questionnaire to a sample of addresses in the United States. The addresses were randomly selected from a database maintained by Marketing Systems Group (MSG), which contains all non-vacant residential addresses in the United States, including P.O. boxes and seasonal addresses. A modified Dillman approach used for the mailing protocol included an initial questionnaire dispatch, a reminder postcard, and up to two more questionnaire mailings for households that did not respond. Respondents were given toll-free telephone numbers for inquiries or concerns. The sampling strategy consisted of a two-stage plan, where a stratified sample of residential addresses was selected, considering both rural and urban areas and areas with high and low concentrations of minority adult populations. The sampling frame was divided into four explicit sampling strata, enabling oversampling of high-minority and rural strata to enhance estimates for these subpopulations. Within each stratum, an equal probability sample of addresses was chosen, totalling 29,600 addresses for HINTS 5. Data for HINTS 5 was compiled between April 6 and May 11, 2021, which is still relevant for our research objectives.
Measures
To determine the respondents’ socioeconomic status, their education level, household income, and occupation were taken into account. Respondents were asked about their highest level of education using a 5-point Likert scale. The choices ranged from “Less than high school” to “post-baccalaureate degree.” Similarly, household income was assessed using a 5-point Likert scale ranging from “Less than $20,000” to “$75,000 or More.” Occupation classifications included employed, homemaker, student, retired, disabled, multiple occupation status, unemployed for one year or more, unemployed for less than one year, and other occupations. In our study, socioeconomic status (SES) is defined and measured as a composite variable that includes educational level, household income, and occupation status. These specific demographic characteristics were selected based on their established importance in the literature as key indicators of socioeconomic position.
Additionally, lifestyle factors such as physical activity, alcohol consumption, and smoking were considered. The number of minutes per week of at least moderate-intensity exercise was used to determine physical activity, and respondents were categorized as either active (> 150 min) or inactive (≤ 150 min) based on guidelines from established organizations like the World Health Organization (WHO) [14]. It’s worth noting that this cutoff is commonly used in epidemiological studies [14, 15]. Alcohol consumption was categorized as either never or currently, and smoking was categorized as current, former, or non-smokers. Respondents’ self-reported frequency and quantity were recorded for alcohol consumption and smoking to provide a more comprehensive picture of these lifestyle factors.
Cardiometabolic diseases were assessed based on diabetes, heart conditions, and lung disease. To determine diabetes, respondents were asked if a doctor or other health professional had ever told them that they had diabetes or high blood sugar. The response options were “yes” and “no”. The presence of a heart condition was assessed by asking respondents if a doctor or other health professional had ever diagnosed them with a heart condition such as heart attack, angina, or congestive heart failure. Finally, respondents were asked whether they had ever been diagnosed with chronic lung disease, asthma, emphysema, or chronic bronchitis to measure lung disease.
This study also considered other relevant variables, such as race and perceived discrimination. Respondents were asked to select their race from the options: Non-Hispanic White, Non-Hispanic Black or African American, Hispanic, Non-Hispanic Asian, and Non-Hispanic Other. To measure perceived discrimination, respondents were asked if they had ever received unfair treatment or discrimination in medical care because of their race or ethnicity. They had the option to choose “yes” or “no”. This measure helps assess the potential impact of perceived discrimination on health outcomes, including cardiometabolic diseases.
The sociodemographic variables considered in this study include age, gender, and marital status. Age was classified into five ranges, gender as male or female, and marital status as married, divorced/separated, widowed, or single/never married.
Statistical analysis
This study analysed the data using STATA SE version 14.2 (Stata Corp, College Station, TX) and Intellectus Statistics [16]. The data [17] was first analyzed descriptively to summarise the relevant variables. A regression analysis was conducted to investigate the association between socioeconomic status and cardiometabolic diseases. This regression aimed to determine the relationship between the independent variable (socioeconomic status) and the dependent variable (Cardiometabolic diseases). Furthermore, structural equation modelling (SEM) was utilized to explore the role of lifestyle as a mediator in the association between socioeconomic status and cardiometabolic diseases. SEM is a statistical technique that examines complex relationships between multiple variables.
The regression analysis between socioeconomic status (independent variable) and cardiometabolic diseases (dependent variable) can be written as Y = β0 + β1*X + ε. Here, Y represents Cardiometabolic diseases (dependent variable), X represents socioeconomic status (independent variable), β0 is the intercept (representing the expected value of Y when X is equal to 0), β1 is the regression coefficient (representing the change in Y associated with a one-unit change in X), and ε means the error term (accounting for the variability in Y that the model does not explain).
The SEM equation can be written as follows: Cardiometabolic diseases = λ0 + λ1Socioeconomic status + λ2Lifestyle + δ. Here, cardiometabolic diseases represent the dependent variable, Socioeconomic status represents the independent variable, Lifestyle represents the mediating variable, λ0 represents the direct effect of the intercept on cardiometabolic diseases, λ1 represents the direct effect of socioeconomic status on cardiometabolic diseases, λ2 represents the direct effect of lifestyle on cardiometabolic diseases, and δ represents the error term (accounting for the variability in cardiometabolic diseases that the model does not explain).
The analysis assessed the reliability and validity of the sample size. Multicollinearity was conducted to examine the squared multiple correlations (R²) and the determinant of the correlation matrix. No variables had R² > 0.90, and the determinant was 0.56, indicating no multicollinearity. A Chi-Square Goodness of Fit Test was conducted to determine if the Structural Equation Model (SEM) accurately fits the data. It is a standard practice to include the Chi-square test in SEM. However, this test is highly sensitive to sample size, which almost always rejects the null hypothesis and indicates a poor model fit when the sample size is large [18]. Additionally, fit indices such as the Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), and Standardized Root Mean Square Residual (SRMR) were employed to evaluate the model fit. The significance level for the statistical tests was 0.05. Results with p ≤ .05 were considered statistically significant.
Participants
This study utilized data from the Health Information National Trends Survey (HINTS) [13], a nationally representative survey conducted by the National Cancer Institute (NCI) since 2003. HINTS provides valuable insights into the American public’s knowledge, attitudes, and use of cancer- and health-related information to enhance health communication strategies across diverse populations. The HINTS 5 survey data was used for this study, which targeted non-institutionalized civilians aged 18 years or older residing in the United States. No direct contact was made with the study participants. Therefore, informed consent for the present analysis was not necessary as secondary data analysis does not involve interaction with participants. Ethical approval for HINTS was obtained through expedited review by the Westat Institutional Review Board and subsequently deemed exempt by the U.S. National Institutes of Health Office of Human Subjects Research Protections. HINTS adheres to established international and local ethical standards and protocols. Approval to use the HINTS dataset was granted by the National Cancer Institute. The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. They determined that no formal ethical consent was required to conduct research using this data source.
The study was conducted by sending a self-administered questionnaire to a sample of addresses in the United States. The addresses were randomly selected from a database maintained by Marketing Systems Group (MSG), which contains all non-vacant residential addresses in the United States, including P.O. boxes and seasonal addresses. A modified Dillman approach used for the mailing protocol included an initial questionnaire dispatch, a reminder postcard, and up to two more questionnaire mailings for households that did not respond. Respondents were given toll-free telephone numbers for inquiries or concerns. The sampling strategy consisted of a two-stage plan, where a stratified sample of residential addresses was selected, considering both rural and urban areas and areas with high and low concentrations of minority adult populations. The sampling frame was divided into four explicit sampling strata, enabling oversampling of high-minority and rural strata to enhance estimates for these subpopulations. Within each stratum, an equal probability sample of addresses was chosen, totalling 29,600 addresses for HINTS 5. Data for HINTS 5 was compiled between April 6 and May 11, 2021, which is still relevant for our research objectives.
Measures
To determine the respondents’ socioeconomic status, their education level, household income, and occupation were taken into account. Respondents were asked about their highest level of education using a 5-point Likert scale. The choices ranged from “Less than high school” to “post-baccalaureate degree.” Similarly, household income was assessed using a 5-point Likert scale ranging from “Less than $20,000” to “$75,000 or More.” Occupation classifications included employed, homemaker, student, retired, disabled, multiple occupation status, unemployed for one year or more, unemployed for less than one year, and other occupations. In our study, socioeconomic status (SES) is defined and measured as a composite variable that includes educational level, household income, and occupation status. These specific demographic characteristics were selected based on their established importance in the literature as key indicators of socioeconomic position.
Additionally, lifestyle factors such as physical activity, alcohol consumption, and smoking were considered. The number of minutes per week of at least moderate-intensity exercise was used to determine physical activity, and respondents were categorized as either active (> 150 min) or inactive (≤ 150 min) based on guidelines from established organizations like the World Health Organization (WHO) [14]. It’s worth noting that this cutoff is commonly used in epidemiological studies [14, 15]. Alcohol consumption was categorized as either never or currently, and smoking was categorized as current, former, or non-smokers. Respondents’ self-reported frequency and quantity were recorded for alcohol consumption and smoking to provide a more comprehensive picture of these lifestyle factors.
Cardiometabolic diseases were assessed based on diabetes, heart conditions, and lung disease. To determine diabetes, respondents were asked if a doctor or other health professional had ever told them that they had diabetes or high blood sugar. The response options were “yes” and “no”. The presence of a heart condition was assessed by asking respondents if a doctor or other health professional had ever diagnosed them with a heart condition such as heart attack, angina, or congestive heart failure. Finally, respondents were asked whether they had ever been diagnosed with chronic lung disease, asthma, emphysema, or chronic bronchitis to measure lung disease.
This study also considered other relevant variables, such as race and perceived discrimination. Respondents were asked to select their race from the options: Non-Hispanic White, Non-Hispanic Black or African American, Hispanic, Non-Hispanic Asian, and Non-Hispanic Other. To measure perceived discrimination, respondents were asked if they had ever received unfair treatment or discrimination in medical care because of their race or ethnicity. They had the option to choose “yes” or “no”. This measure helps assess the potential impact of perceived discrimination on health outcomes, including cardiometabolic diseases.
The sociodemographic variables considered in this study include age, gender, and marital status. Age was classified into five ranges, gender as male or female, and marital status as married, divorced/separated, widowed, or single/never married.
Statistical analysis
This study analysed the data using STATA SE version 14.2 (Stata Corp, College Station, TX) and Intellectus Statistics [16]. The data [17] was first analyzed descriptively to summarise the relevant variables. A regression analysis was conducted to investigate the association between socioeconomic status and cardiometabolic diseases. This regression aimed to determine the relationship between the independent variable (socioeconomic status) and the dependent variable (Cardiometabolic diseases). Furthermore, structural equation modelling (SEM) was utilized to explore the role of lifestyle as a mediator in the association between socioeconomic status and cardiometabolic diseases. SEM is a statistical technique that examines complex relationships between multiple variables.
The regression analysis between socioeconomic status (independent variable) and cardiometabolic diseases (dependent variable) can be written as Y = β0 + β1*X + ε. Here, Y represents Cardiometabolic diseases (dependent variable), X represents socioeconomic status (independent variable), β0 is the intercept (representing the expected value of Y when X is equal to 0), β1 is the regression coefficient (representing the change in Y associated with a one-unit change in X), and ε means the error term (accounting for the variability in Y that the model does not explain).
The SEM equation can be written as follows: Cardiometabolic diseases = λ0 + λ1Socioeconomic status + λ2Lifestyle + δ. Here, cardiometabolic diseases represent the dependent variable, Socioeconomic status represents the independent variable, Lifestyle represents the mediating variable, λ0 represents the direct effect of the intercept on cardiometabolic diseases, λ1 represents the direct effect of socioeconomic status on cardiometabolic diseases, λ2 represents the direct effect of lifestyle on cardiometabolic diseases, and δ represents the error term (accounting for the variability in cardiometabolic diseases that the model does not explain).
The analysis assessed the reliability and validity of the sample size. Multicollinearity was conducted to examine the squared multiple correlations (R²) and the determinant of the correlation matrix. No variables had R² > 0.90, and the determinant was 0.56, indicating no multicollinearity. A Chi-Square Goodness of Fit Test was conducted to determine if the Structural Equation Model (SEM) accurately fits the data. It is a standard practice to include the Chi-square test in SEM. However, this test is highly sensitive to sample size, which almost always rejects the null hypothesis and indicates a poor model fit when the sample size is large [18]. Additionally, fit indices such as the Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), and Standardized Root Mean Square Residual (SRMR) were employed to evaluate the model fit. The significance level for the statistical tests was 0.05. Results with p ≤ .05 were considered statistically significant.
Results
The results presented in Table 1 gives a detailed description of the demographic and health-related characteristics of the surveyed individuals. The results suggest a more significant proportion of people in the 50–64 age group compared to the 18–34 age category, while fewer people in the 65–74 and 75+ age groups. Furthermore, most respondents are females, making up 57% of the population. Educational attainment appears to be evenly distributed, which suggests that the participants come from diverse educational backgrounds.
Regarding household income, 41.5% of participants earn “$75,000 or more,” which implies that the surveyed population may be skewed towards higher economic brackets. Occupational status is also noteworthy, as retirees comprise a substantial 26.4% of the group, indicating that the participants may be older or financially stable. Additionally, there is a near parity in alcohol consumption and smoking habits, which could merit further investigation into the group’s lifestyle choices or social norms. Chronic illnesses are prevalent, with 19.9% and 14.8% of participants experiencing diabetes and lung disease, respectively. Additionally, 10.3% reported heart-related ailments.
Table 1Demographic and health-related factors within the studied populationVariablesProportionStd. Err.[95% Conf.Interval]
Age group
18–3415.3%0.0070.1400.16735–4921.3%0.0080.1980.22950–6430.5%0.0090.2880.32265–7422.2%0.0080.2070.23875+10.7%0.0060.0960.119
Gender
Male43.0%0.0090.4120.448Female57.0%0.0090.5520.588
Marital Status
Married55.8%0.0090.5390.576Divorced/Separated17.5%0.0070.1620.190Widowed8.9%0.0050.0790.100Single17.8%0.0070.1650.193
Race
Non-Hispanic White62.6%0.0090.6080.644Non-Hispanic Black or African American12.5%0.0060.1130.138Hispanic16.5%0.0070.1520.179Non-Hispanic Asian4.7%0.0040.0400.055Non-Hispanic Other3.7%0.0040.0310.045
Education level
Less than High school5.7%0.0040.0490.067High school graduate15.9%0.0070.1450.172Some College29.5%0.0090.2780.312Bachelor’s degree28.2%0.0080.2650.298Post - Baccalaureate degree20.8%0.0080.1930.223
Household income
Less than $20,00014.6%0.0070.1330.159$20,000 to < $35,00012.7%0.0060.1150.139$35,000 to < $50,00013.1%0.0060.1190.144$50,000 to < $75,00018.2%0.0070.1680.197$75,000 or more41.5%0.0090.3960.433
Occupation
Employed only49.4%0.0090.4750.512Homemaker only3.5%0.0030.0290.042Student only1.0%0.0020.0070.014Retired only26.4%0.0080.2480.280Disabled only4.3%0.0040.0360.051Multiple Occupation statuses selected10.8%0.0060.0970.120Unemployed for 1 year or more only1.9%0.0030.0140.025Unemployed for less than 1 year only2.0%0.0030.0160.026Other Occupation only0.8%0.0020.0050.012
Alcohol intake
Never52.0%0.0090.5020.539Current48.0%0.0090.4610.498
Smoking
Current11.2%0.0060.1010.124Former25.1%0.0080.2350.267Never63.7%0.0090.6190.655
Physical activity
Inactive62.1%0.0090.6020.638Active37.9%0.0090.3620.398
Diabetes
Yes19.9%0.0080.1840.214No80.1%0.0080.7860.816
Lung Disease
Yes14.8%0.0070.1350.161No85.2%0.0070.8390.865
Heart Condition
Yes10.3%0.0060.0920.115No89.7%0.0060.8850.908Std. Err = Standard Eroor; 95% Conf. Interval = 95% confidence interval
This study used a Structural Equation Modeling (SEM) approach to evaluate the effectiveness of latent variables, including socioeconomic status, lifestyle, and cardiometabolic diseases. Firstly, the model’s reliability was established based on the sample size. Subsequently, the Chi-square goodness-of-fit test and fit indices were applied to evaluate the results, detailed in Table 2. The correlations between latent variables are presented in Table 3, and the node diagram is illustrated in Fig. 1.
Regressions were analyzed using an alpha level of 0.05. Socioeconomic status was found to be a significant predictor for CMD (cardiometabolic diseases), with B = 0.06, z = 8.13, p < .001, indicating that a unit rise in socioeconomic status is associated with a 0.06 unit increase in expected CMD. However, socioeconomic status was not found to have a significant connection with lifestyle, B = 0.07, z = 0.90, p = .371, indicating the absence of a direct relationship. Interestingly, lifestyle was a significant predictor for CMD, B = 0.02, z = 2.16, p = .030, suggesting that improving lifestyle factors could marginally increase CMD by 0.02 units.
Although there is no direct link between socioeconomic status (SES) and lifestyle, conducting a mediation analysis is important. This is because lifestyle factors are believed to play a significant role in the connection between SES and cardiometabolic diseases (CMD). Previous research indicates that lifestyle behaviors often mediate the relationship between SES and health outcomes [19, 20]. By investigating these pathways, we can determine whether SES influences CMD through its impact on lifestyle, providing a more thorough understanding of these complex connections. Including lifestyle as a mediator ensures that our model accurately reflects the proposed relationships and allows us to test our theoretical framework rigorously.
The mediation analysis examined whether lifestyle acted as an intermediary between socioeconomic status and cardiometabolic diseases. The direct effect between socioeconomic status and CMD negated the possibility of full mediation by lifestyle, but left room for potential partial mediation. This hypothesis was explored further using the indirect and total effects. The indirect impact of lifestyle on the relationship between socioeconomic status and CMD was insignificant, B = 0.001, z = 0.86, p = .390, indicating that socioeconomic status does not influence CMD through lifestyle changes. In contrast, the total effect was significant, B = 0.06, z = 8.34, p < .001, showing that socioeconomic status independently affects the prevalence of CMD. The non-significance of the indirect effect implies that lifestyle does not support partial mediation, necessitating further exploration into other potential mediating factors. “B” represents the unstandardized regression coefficient, and “z” refers to the z-value, a test statistic for the regression coefficient.
Table 2Unstandardized loadings (standard errors), standardized loadings, and significance levels for each parameter in the structural equation model (N = 2820)Parameter EstimateUnstandardizedStandardized
p
Loadings
SES → Household income1.00(0.00)0.82< 0.001 SES → Education0.49(0.03)0.52< 0.001 SES → Occupation-0.72(0.05)-0.41< 0.001 Lifestyle → Alcohol intake1.00(0.00)0.15< 0.001 Lifestyle → Smoking-0.04(0.02)-0.060.041 Lifestyle → Physical activity0.738(0.04)0.710.350 CMD → Diabetes1.00(0.00)0.44< 0.001 CMD → lung disease0.54(0.08)0.26< 0.001 CMD → Heart condition0.80(0.10)0.46< 0.001
Regressions
SES → CMD0.06(0.007)0.42< 0.001 SES → Lifestyle0.07(0.08)0.080.371 Lifestyle → CMD0.02(0.008)0.100.030 Indirect Effect of SES on CMD by Lifestyle0.001(0.001)0.0080.390 Total Effect of SES on CMD0.06(0.007)0.43< 0.001
Errors
Error in SES1.47(0.11)1.00< 0.001 Error in Lifestyle1.12(1.23)0.990.361 Error in CMD0.02(0.004)0.81< 0.001 Error in Occupation3.73(0.11)0.83< 0.001 Error in Household income0.73(0.10)0.33< 0.001 Error in Education0.97(0.03)0.73< 0.001 Error in Physical activity1.634(1.85)0.500.348 Error in Alcohol intake0.456(1.78)0.98< 0.001 Error in Smoking0.47(0.01)1.00< 0.001 Error in Heart condition0.07(0.003)0.79< 0.001 Error in Diabetes0.13(0.005)0.81< 0.001 Error in Lung Disease0.12(0.003)0.93< 0.001χ2(24) = 352.43, p = Significance level; SES = Socioeconomic status; CMD = Cardiometabolic diseases
Table 3Correlation table for the latent variablesVariableSESLifestyleCMDSES1.00----Lifestyle0.081.00--CMD0.430.141.00SES = Socioeconomic status; CMD = Cardiometabolic diseases
Fig. 1Structural Equation Model (SEM) depicting the relationships among Socioeconomic Status (SES), Lifestyle, and Cardiometabolic Diseases (CMD)
The numbers shown in Fig. 1 represent the path coefficients (standardized regression weights) in the structural equation model (SEM). These coefficients indicate the strength and direction of relationships between socioeconomic status (SES), lifestyle factors, and cardiometabolic diseases (CMD). Higher coefficients indicate stronger relationships, with positive or negative values reflecting the direction of these relationships. The numbers next to the endogenous variables represent the squared multiple correlations (R²), which show the proportion of variance explained by the predictor variables in the model.
The following fit indices were used to assess the model fit: root mean square error of approximation (RMSEA), comparative fit index (CFI), Tucker-Lewis index (TLI), and standardized root mean square residual (SRMR). The TLI was only 0.69, indicating a poor model fit, while the CFI was 0.79, suggesting a poor fit. However, the RMSEA index was 0.07, with a 90% CI of [0.06, 0.08], indicating a good model fit, and the SRMR was 0.06, implying that the model fits the data adequately. The fit indices are illustrated in Table 4. The Chi-square goodness of fit test results were significant, χ2(24) = 352.43, p < .001, indicating that the model did not fit the data accurately.
Table 4Fit indices for the structural equation modelNFITLICFIRMSEASRMR0.780.690.790.070.06NFI = Normed Fit Index; TLI = Tucker-Lewis index; CFI = Comparative Fit Index; RMSEA = Root Mean Square Error of Approximation; SRMR = Standardized Root Mean Square Residual
Discussion
This study had 134 participants for every 1 item, with a sample size of 2,820 and 21 variables included. According to the N: q ratio rule of thumb, this sample size is sufficient to produce reliable results. Our study shows that physical activity levels present a significant health concern, as a troubling 62.1% of participants admit to being sedentary, outnumbering the 37.9% active. This suggests a potential need for targeted health interventions to promote physical activity, although research is required to confirm these findings.
Further, this study explored the relationship between socioeconomic status (SES), lifestyle, and cardiometabolic diseases (CMD). Results from the structural equation model (SEM) showed significant associations and gave insights into the mediation effects of lifestyle on the relationship between SES and CMD.
Regarding the mediation results, it was discovered that lifestyle didn’t fully mediate the relationship between SES and CMD. Although there was a significant direct effect of SES on CMD, there was no significant indirect effect through lifestyle. This suggests that lifestyle factors may not be the primary mechanism through which SES is associated with CMD risk, as measured in this study. These findings align with previous studies that have reported mixed results regarding the mediation effects of lifestyle between SES and health outcomes [21–23]. However, due to the self-reported nature of the data, these results should be interpreted with caution. The complexity of the relationship between these variables, with lifestyle factors influenced by various social, economic, and cultural factors, makes it challenging to establish a clear mediating role [10, 24]. Additionally, unmeasured variables may contribute to the relationship between SES and CMD, thus attenuating the mediating effect of lifestyle [1, 25].
Hicks et al. (2021) studied the relationship between lifestyle factors, and cardiovascular disease risk [26]. They found that lifestyle factors may partially mediate the association between SES and cardiovascular disease risk, supporting the idea that lifestyle behaviours explain some socioeconomic disparities in health outcomes. In contrast, Liu et al. (2023) conducted a similar study and reported no significant mediating effect of lifestyle on the relationship between SES and health outcomes [27]. These contrasting findings highlight the complexity of the relationship and suggest that other factors beyond lifestyle may also play a role in the socioeconomic disparities in CMD [28–30].
Furthermore, the present study revealed significant associations between SES, lifestyle, and CMD individually. SES was significantly associated with CMD, suggesting that higher SES is associated with an increased risk of CMD, contrary to the common belief that higher SES is generally linked to better health outcomes [19, 31, 32]. However, it aligns with previous studies that reported similar associations between higher SES and increased CMD risk [33]. The prevalence of risk factors such as a sedentary lifestyle and psychosocial stressors among individuals with higher SES may contribute to this association. In our research, we define higher socioeconomic status (SES) as individuals who score higher on the composite measure of SES, which takes into account their educational level, household income, and occupation status. Specifically, people with higher SES typically have higher levels of education, greater household income, and are more likely to have prestigious or stable occupations.
Lifestyle was significantly associated with CMD, suggesting that individuals with unhealthier lifestyles have a higher risk of CMD. This result is consistent with a vast body of literature linking unhealthy behaviours, such as physical inactivity, smoking, and excessive alcohol consumption, with increased risk of CMD [34–37]. Numerous studies have consistently demonstrated the detrimental effects of unhealthy lifestyle factors on cardiovascular health and CMD outcomes [34–37].
The results of this study provide important insights into the associations between lifestyle, SES, and CMD. The findings suggest that while lifestyle factors play a role in CMD risk, they may not fully mediate the relationship between SES and CMD. This suggests that other factors, such as psychosocial stressors, access to healthcare, environmental factors, and genetic predispositions, may contribute to the socioeconomic disparities in CMD [21, 38–40]. Future research should explore these additional factors to understand better the complex associations between SES, lifestyle, and CMD, using more robust methods to mitigate the limitations of self-reported data.
The findings of this study have significant policy implications for addressing cardiometabolic diseases and reducing socioeconomic disparities in health outcomes. The findings of this study emphasize the relevance of public health by highlighting the complex relationship between socioeconomic status (SES), lifestyle factors, and cardiometabolic diseases (CMD). Using a robust structural equation model (SEM) to analyze data from a large, nationally representative sample, our research offers new insights into the associations between SES, CMD, and lifestyle behaviors. These insights are essential for designing specific public health interventions to address the disparities in CMD prevalence associated with SES. Significantly, this study adds to the existing body of research by showing that while lifestyle factors are significantly associated with CMD risk, they do not fully mediate the SES-CMD relationship. This suggests the need for comprehensive strategies that consider multiple health determinants. The novelty of this paper lies in its thorough examination of the role of lifestyle in the SES-CMD link, its focus on the complexity of these relationships, and its implications for creating comprehensive public health policies that address both behavioral and structural health determinants.
Although the mediation analysis did not support lifestyle as the primary mechanism that explains the relationship between SES and CMD, it does not undermine the importance of lifestyle interventions in preventing and managing CMD [41–44]. Public health policies and interventions may consider promoting healthy lifestyles and addressing the risk factors associated with CMD while acknowledging the preliminary nature of these findings due to the reliance on self-reported data. However, it is crucial to recognize that multifaceted factors beyond lifestyle may influence socioeconomic disparities in CMD [24, 45]. Policymakers should consider implementing broader structural interventions to tackle the underlying socioeconomic determinants of health. This could involve improving access to healthcare services, addressing social inequalities, reducing poverty, and providing educational and employment opportunities to individuals from disadvantaged socioeconomic backgrounds [46, 47]. Additionally, efforts should be made to raise awareness about the complex relationship between lifestyle, SES, and CMD among healthcare professionals, policymakers, and the general population. To develop effective and fair strategies for preventing, early detecting, and managing cardiovascular and metabolic diseases, it may be necessary to take a holistic approach that considers the various factors contributing to socioeconomic disparities. However, it is essential to make these suggestions cautiously and support them with further research.
The study has several strengths that contribute to its validity. Firstly, structural equation modelling allowed for a comprehensive analysis of the complex relationships among socioeconomic status, lifestyle, and cardiometabolic diseases. This approach provides a robust statistical framework to evaluate the hypothesized associations, although caution is warranted in interpreting the findings due to reliance on self-reported data. Additionally, the introduction of multiple measures and using validated scales enhance the reliability and validity of the study’s findings.
However, it is essential to acknowledge some limitations. Firstly, the study relied on self-report measures, which might introduce response biases and recall errors. Future research could incorporate objective measures, such as clinical assessments and biomarkers, to enhance the accuracy of the data. Secondly, the study’s cross-sectional design limits the ability to establish causal relationships among the variables. Longitudinal or interventional studies would provide more robust evidence regarding the directionality of the associations. Lastly, the study was conducted in a specific population, which may limit the generalizability of the findings to other demographics or cultural contexts.
Conclusion
This study explored the link between socioeconomic status, lifestyle, and cardiometabolic diseases (CMD). The results revealed that socioeconomic status significantly predicted CMD, while lifestyle significantly predicted cardiometabolic diseases. However, the mediation analysis failed to produce significant results, indicating that lifestyle did not fully mediate the link between SES and CMD. The study emphasizes addressing socioeconomic disparities and promoting healthy lifestyles to prevent and manage cardiometabolic diseases. Policymakers should prioritise interventions aimed at reducing inequalities and encouraging healthier lifestyles. Although the study has some limitations, its findings contribute to the growing body of knowledge in this field and highlight the need for further research to comprehend the complex relationship between these factors fully. |
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