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2020-08-13T15:28:18.510Z | 2020-08-13T00:00:00.000 | 221110110 | {
"extfieldsofstudy": [
"Chemistry",
"Medicine"
],
"oa_license": "CCBY",
"oa_status": "GOLD",
"oa_url": "https://www.nature.com/articles/s41467-020-17862-6.pdf",
"pdf_hash": "09060fb073fe545d861ddf3264af691c2fb603ac",
"pdf_src": "PubMedCentral",
"provenance": "20241012_202828_00062_s4wg2_d7ea2846-036c-420f-8433-dd5c6d8a39fe.zst:3",
"s2fieldsofstudy": [
"Biology"
],
"sha1": "09060fb073fe545d861ddf3264af691c2fb603ac",
"year": 2020
} | pes2o/s2orc | ABHD11 maintains 2-oxoglutarate metabolism by preserving functional lipoylation of the 2-oxoglutarate dehydrogenase complex
2-oxoglutarate (2-OG or α-ketoglutarate) relates mitochondrial metabolism to cell function by modulating the activity of 2-OG dependent dioxygenases involved in the hypoxia response and DNA/histone modifications. However, metabolic pathways that regulate these oxygen and 2-OG sensitive enzymes remain poorly understood. Here, using CRISPR Cas9 genome-wide mutagenesis to screen for genetic determinants of 2-OG levels, we uncover a redox sensitive mitochondrial lipoylation pathway, dependent on the mitochondrial hydrolase ABHD11, that signals changes in mitochondrial 2-OG metabolism to 2-OG dependent dioxygenase function. ABHD11 loss or inhibition drives a rapid increase in 2-OG levels by impairing lipoylation of the 2-OG dehydrogenase complex (OGDHc)—the rate limiting step for mitochondrial 2-OG metabolism. Rather than facilitating lipoate conjugation, ABHD11 associates with the OGDHc and maintains catalytic activity of lipoyl domain by preventing the formation of lipoyl adducts, highlighting ABHD11 as a regulator of functional lipoylation and 2-OG metabolism.
T he ability to sense and respond to nutrient abundance is a fundamental requirement for cell survival, and to achieve this, cells have evolved several strategies that link metabolic function to transcriptional adaptation. One such strategy is the coupling of 2-oxoglutarate (2-OG) metabolism to gene transcription, whereby 2-OG, a key component of TCA cycle, can facilitate cell function by modulating the activity of 2-OG dependent dioxygenases involved in the hypoxia inducible factor (HIF) response, DNA methylation, and histone modifications 1 .
The relevance of 2-OG in modulating the activity of these dioxygenases is exemplified by changes in the relative abundance of cellular 2-OG. An increased 2-OG/succinate ratio promotes embryonic stem cell pluripotency 2 , and antagonises the growth of solid organ tumours 3 through increased hydroxymethylation of DNA (5hmC) and histone demethylation. Conversely, elevated cellular 2-OG can drive its own reduction to L-2-hydroxyglutarate (L-2-HG), which counterintuitively inhibits 2-OG dependent dioxygenases, leading to decreased DNA hydroxymethylation and histone demethylation, activation of the HIF response, altered T cell fate, and haematopoietic cell differentiation [4][5][6][7][8][9] . Consequently, understanding how 2-OG metabolism is regulated has broad biological implications.
Central to maintaining cellular 2-OG homeostasis is the 2oxoglutarate dehydrogenase complex (OGDHc, also known as the α-ketoglutarate dehydrogenase complex), the rate-limiting enzyme within the TCA cycle that oxidatively decarboxylates 2oxoglutarate to succinyl-CoA. This evolutionarily conserved enzyme also requires lipoic acid, a redox sensitive cofactor that is synthesised within the mitochondria and conjugated to a single lysine within the OGDHc E2 subunit, dihydrolipoamide Ssuccinyltransferase (DLST) [10][11][12] . The cyclical reduction and oxidation of the two thiols of conjugated lipoic acid (lipoamide to dihydrolipoamide) serves as a redox intermediate, coupling the formation of succinyl CoA to generation of NADH. The importance of DLST and its lipoylation is highlighted by the recent identification of genetic mutations leading to human disease. Patients with germline mutations in lipoic acid synthesis genes develop a severe variant of the neurological condition, Leigh syndrome 13 , and loss of heterozygosity mutations in the OGDHc lead to angiogenic tumours (pheochromocytomas and paragangliomas), similar to other hereditary cancer syndromes activating the HIF pathway 14 . However, how OGDHc function and 2-OG abundance is regulated is unclear.
Here, we use the sensitivity of the HIF pathway to 2-OG abundance to gain insights into how 2-OG metabolism is controlled. Using genome-wide CRISPR/Cas9 mutagenesis screens, we identify an uncharacterised protein, αβ-hydrolase domaincontaining 11 (ABHD11), as a mitochondrial enzyme that impairs OGDHc activity when depleted or inhibited. ABHD11 loss leads to the accumulation of 2-OG and formation of L-2-HG, which inhibits 2-OG dependent dioxygenases involved in the HIF response and DNA hydroxymethylation, similarly to genetic disruption of the OGDHc. ABHD11 also associates with the OGDHc and is required for catalytic activity and TCA cycle function. However, ABHD11 does not alter the constituent levels of the OGDHc. Instead, ABHD11 maintains functional lipoylation of the OGDHc, preserving the catalytic activity of DLST. Together, these studies identify a key role for ABHD11 in 2-OG metabolism, and demonstrate that lipoylation provides a previously unappreciated mechanism for mediating an adaptive transcriptional response to changes in OGDHc function.
ABHD11 mediates activity of 2-OG dependent dioxygenases.
To find genes involved in 2-OG metabolism we utilised the sensitivity of the HIF response to 2-OG availability, and carried out CRISPR/Cas9 mutagenesis screens in human cells using a fluorescent HIF reporter we developed 4,15 . This reporter encodes the consensus HIF responsive element (HRE) in triplicate that drives the expression of GFP fused to the oxygen and 2-OG sensitive region of HIF-1α ( Supplementary Fig. 1a) 4,15 . Therefore, reporter stability is dependent on 2-OG dependent dioxygenase activity of the prolyl hydroxylases (PHDs or EGLNs) 16,17 , which was confirmed with treatment with the PHD inhibitor dimethyloxalylglycine (DMOG), cell permeable 2-OG (dimethyl 2-OG) or incubation in 1% oxygen ( Supplementary Fig. 1b-d) 4,7,15 .
Two genome-wide CRISPR sgRNA libraries were used to identify genes that when mutated activated the HIF reporter: the Brunello human genome-wide library (containing 76,441 sgRNA) 18 , and the Toronto genome-wide knockout library (containing 176,500 sgRNA) 19 . HeLa cells stably expressing the HRE-GFP ODD reporter and Cas9 were transduced with each genome-wide library and iteratively sorted for GFP HIGH cells by fluorescence-activated cell sorting (FACS) at day 10 and day 18 (Fig. 1a). SgRNAs enriched by FACS were identified by Illumina HiSeq and compared to a population of mutagenized cells that had not undergone phenotypic selection (Fig. 1a, b) (Supplementary Dataset 1). All screens were conducted in aerobic conditions (21% oxygen), thereby preventing oxygen availability limiting PHD function.
Both screens identified genes involved in the canonical pathway for HIF stability (VHL, EGLN1 (PHD2)) and 2-OG metabolism (OGDHc components, lipoic acid synthesis pathway), validating the approach (Fig. 1b, c). Other biological processes that were significantly enriched for sgRNA included intracellular iron metabolism, the mTOR pathway, and transcriptional regulation (Fig. 1b, c). The reliance of the HIF pathway on these processes is well substantiated and in line with our prior studies using gene-trap mutagenesis in haploid cells 4,15 . In addition to these known pathways, we identified an uncharacterised α/β hydrolase, ABHD11, that was highly enriched for sgRNA in both screens (Fig. 1b, c).
We next asked if ABHD11 loss resulted in HIF-1α stabilisation through impaired 2-OG dependent dioxygenase activity. PHD function can be readily assessed by measuring HIF-1α prolyl hydroxylation using a HIF prolyl hydroxy-specific antibody. Mixed knockout populations of ABHD11 stabilised HIF-1α in a non-hydroxylated form, similar to the HIF-1α stabilisation with DMOG ( Supplementary Fig. 1g). In contrast, inhibition of the VHL E3 ligase with VH298 20 , which stabilises HIF-1α by preventing ubiquitination and proteasome-mediated degradation showed high levels of hydroxylated HIF-1α ( Supplementary Fig. 1g). To verify that the decreased prolyl hydroxylation was due to impaired PHD activity, we directly measured prolyl hydroxylation of a recombinant HIF-1α protein in control or ABHD11 deficient lysates 4 . Rapid prolyl hydroxylation was observed with a HeLa control lysate but this was markedly reduced in the ABHD11 depleted cells, similarly to loss of OGDHc function 4 ( Supplementary Fig. 1h, i). This PHD inhibition activated a transcriptional HIF response, promoting activation of HIF-1α target genes, VEGF and carbonic anhydrase 9, similarly to loss of VHL or OGDH (Fig. 1h, i).
We also explored whether ABHD11 loss altered the activity of other 2-OG dependent dioxygenases involved in transcription. ABHD11 KO cells showed a marked decrease in total DNA 5hydroxymethylcytosine (5hmC) levels, similar to those observed when OGDHc function is impaired 4 (Fig. 1j, Supplementary Fig. 1j), indicating that Ten-eleven translocation (TET) activity was impaired. However, the steady state levels of selected histone marks were not altered by ABHD11 depletion ( Supplementary Fig. 1k). As levels of methylation depend on transferase activity, demethylation and nucleosome turnover, lysine demethylases (KDM) may still be affected by ABHD11 loss. Despite these differences between TET and KDM activity, these studies suggested that ABHD11 loss had broader implications for 2-OG dependent dioxygenase function, aside from PHDs. that ABHD11 may be involved in 2-OG metabolism. Therefore, we first examined the consequences of ABHD11 loss on 2-OG levels and other TCA cycle intermediates. HeLa cells were depleted of ABHD11 and small molecule metabolites traced by incubating cells with uniformly 13 C labelled ([U-13 C 5 ]) glutamine, followed by liquid chromatography mass spectrometry (LC-MS) (Fig. 2a). Cells deficient in OGDH were used as a control to measure perturbations of 2-OG metabolism. ABHD11 depletion resulted in 2-OG accumulation, similarly to OGDH loss (Fig. 2b). This increase in 2-OG was not due to activation of the HIF response, as we previously demonstrated that PHD2 deficiency does not perturb 2-OG levels 4 . 13 C tracing confirmed that ABHD11 depletion impaired OGDHc function, as TCA cycle metabolites downstream of the OGDHc were decreased (succinate, fumarate and malate) (Fig. 2c-e), and cells adapted by showing a shift from oxidative metabolism to reductive carboxylation 4,21 , with a relative decrease in m + 4 and m + 2 citrate, and an increase in m + 5 and m + 3 citrate isotopologues (Fig. 2f).
To substantiate that ABHD11 levels altered OGDHc function, we measured OGDHc enzymatic activity in isolated mitochondria, using a colorimetric assay which detects oxidation of exogenous 2-OG with a redox sensitive probe (Fig. 2g). OGDHc activity was decreased in ABHD11 deficient mitochondria, similarly to levels observed with depletion of the OGDH subunit (Fig. 2g). Loss of OGDHc function was not due to HIF stabilisation, as VHL depletion had no effect on OGDHc activity (Fig. 2g). Bioenergetic profiling also showed that ABHD11 depletion impaired oxygen consumption rates (Fig. 2h, i), consistent with a major defect in the TCA cycle and oxidative phosphorylation.
Three enzymes are implicated in the formation of L-2-HG from 2-OG: lactate dehydrogenase A (LDHA), malate dehydrogenase 1 and malate dehydrogenase 2 6,8,9 . Reductive carboxylation and an acidic environment potentiate the reduction of 2-OG to L-2-HG and inhibition of LDHA alone is sufficient to prevent L-2-HG formation 4,7,8 . Therefore, to confirm that L-2-HG was responsible for decreased 2-OG dependent dioxygenase activity, we treated cells with sodium oxamate, which inhibits LDHA as well as decreasing 2-OG formation from glutamine 4,7 , or the selective LDHA inhibitor GSK-2837808A, and measured HIF-1α levels by immunoblot (Fig. 2l, m). Both treatments restored HIF-1α turnover in ABHD11 deficient HeLa cells. Together, these experiments confirmed that impaired OGDHc function and L-2-HG accumulation was responsible for the decreased PHD activity and activation of the HIF response.
ABHD11 is a mitochondrial hydrolase. ABHD11 is a member of the alpha-beta hydrolase family, which contains 19 known genes, and encodes an α/β hydrolase fold ( Supplementary Fig. 2a), typical of many proteases and lipases 22 . Unlike most alpha-beta hydrolase family members, ABHD11 is predicted to localise to the mitochondria through a classical mitochondrial targeting sequence (Fig. 3a, Supplementary Fig. 2a). Therefore, we used immunofluorescence microscopy to determine whether ABHD11 resided within mitochondria. Endogenous ABHD11 could not be readily detected by immunofluorescence, but exogenously expressed ABHD11 fused to GFP (ABHD11-GFP), which still retained function (see Fig. 4f), colocalised with MitoTracker DeepRed (Fig. 3b, c).
We biochemically confirmed ABHD11's endogenous localisation using isolated mitochondria and a Proteinase K protection assay. Cytoskeletal and outer membrane proteins were rapidly lost with the addition of Proteinase K (30 min at 37°C), but ABHD11 levels were unaffected, suggesting localisation inside of the outer membrane (Fig. 3d). Furthermore, ABHD11 was still retained in mitoplasts, irrespective of proteinase K treatment, consistent with its localisation to the mitochondrial matrix (Fig. 3d).
As the stable isotope tracing demonstrated that ABHD11 loss altered OGDHc activity, we determined if ABHD11 associated with components of the complex. Both OGDH and DLST immunoprecipitated with HA conjugated ABHD11 (Fig. 3e), and ABHD11-GFP colocalised with the OGDH (Fig. 3f). We also subjected immunoprecipitated ABHD11-HA to mass spectrometry, which confirmed the association with OGDH and DLST (Supplementary Table 1). Furthermore, our findings were consistent with a prior unbiased mass spectrometry analysis of interactions between mitochondrial proteins, which identified that ABHD11 associated with OGDH with high confidence 23 .
We next examined if ABHD11 enzymatic activity was required for its effect on HIF-1α stability. Structural modelling of ABHD11 predicted a typical α/β hydrolase fold with two catalytic motifs (Fig. 3a, g). Hydrolase activity is predicted to arise from the serine nucleophile motif (GXSXG), but ABHD11 also encodes a putative Fig. 1 Identification of ABHD11 as a mediator of 2-OG dependent dioxygenase activity. a HeLa HRE-GFP ODD cells were transduced and mutagenised with genome-wide sgRNA libraries (Brunello and Toronto KO). GFP HIGH cells were selected by iterative FACS and sgRNA identified by Illumina HiSeq. b, c Comparative bubble plot (b) and table (c) of sgRNA enriched in the GFP HIGH cells between the two genome-wide sgRNA libraries compared to a mutagenised population of HRE-GFP ODD cells that had not been phenotypically selected. Genes enriched for sgRNA clustered into six main groups: (1) the canonical HIF pathway, (2) OGDHc, Lipoylation (Lp) and 2-HG related pathways, (3) intracellular iron metabolism (iron, lysosomal), (4) mTOR, (5) Transcription and (6) Uncharacterised. Unadjusted p value calculated using MaGECK robust rank aggregation (RRA); FDR = Benjamini-Hochberg false discovery rate (multiple hypothesis adjustment of RRA p value). EGLN1 = PHD2, EGLN3 = PHD3. d-f HeLa HRE-GFP ODD (d), MCF-7 (e) and Hep G2 (f) cells stably expressing Cas9 were transduced with up to three different sgRNA targeting ABHD11. Reporter GFP or endogenous HIF-1α levels were measured by flow cytometry (d) or immunoblot (e, f) respectively after 10-13 days. Endogenous ABHD11 levels were measured by immunoblot and β-actin served as a loading control. g Reconstitution of mixed KO population of ABHD11 with exogenous ABHD11. HeLa cells expressing Cas9 were transduced with sgRNA targeting ABHD11 as described. Targeted cells were also transduced with exogenous ABHD11 with the PAM site mutated. Cells depleted of PHD2 served as a control for ABHD11 reconstitution. h-i Quantitative PCR (qPCR) of the HIF-1α target genes (VEGF and CAIX) in HeLa cells following ABHD11 depletion by sgRNA (n = 3, SEM, *p < 0.028, **p < 0.0065, two-tailed one-sample t test of ratio). sgRNA targeting OGDH and VHL were used as control for HIF-1α activation. j Genomic DNA was extracted from Hela control or mixed KO populations of ABHD11, LIAS or VHL, and 5hmC levels measured by immunoblot relative to total DNA content ( Supplementary Fig. 1j). 5hmC levels were quantified using ImageJ. n = 3, Mean ± SEM **p = 0.010; ns:p = 0.39, VHL compared to control; two-tailed t test (not adjusted for multiple comparison). Ct = control.
To confirm that these mutations were altering ABHD11 enzymatic activity, we purified wildtype and S141A ABHD11 and measured hydrolysis of p-nitrophenyl ester, a substrate validated for generic α/β hydrolase activity 22,24 . Wildtype ABHD11 protein and the S141A mutant were isolated by Fig. 4). ABHD11 predominantly migrated as a single species but a slower migrating form was apparent in the cell extract and purified protein, consistent with an immature form prior to mitochondrial insertion ( Supplementary Fig. 4a). Mass spectrometry analysis confirmed ABHD11's identity and demonstrated that the mitochondrial targeting sequence was lost in the predominantly expressed form (Supplementary Fig. 4b) (the slower migrating species was of too low abundance). Size exclusion chromatography identified two peaks but full length ABHD11 was only detected in the second peak, at an elution volume consistent with a monomeric species ( Supplementary Fig. 4c, d). Hydrolysis of the p-nitrophenyl ester confirmed ABHD11 enzymatic activity, but this was lost with the S141A mutant and following heat treatment (Fig. 3i). Thus, ABHD11 is a mitochondrial hydrolase that associates with the OGDHc, and loss of its enzymatic activity leads to HIF-1α accumulation.
ABHD11 loss impairs lipoylation of the OGDHc. Conversion of 2-OG to succinyl-CoA by the OGDHc requires decarboxylation and the formation of succinyl intermediate (succinyl-dihydrolipoate), dependent on the cyclical reduction and oxidation of the lipoylated DLST subunit (Fig. 4a). Therefore, to understand how ABHD11 is required for OGDHc function, we first examined whether protein levels of core OGDHc components or its lipoylation were altered. ABHD11 depletion did not alter total levels of the OGDHc subunits (OGDH, DLST or DLD) in HeLa cells ( Fig. 4a, b). However, using a specific anti-lipoate antibody that detects conjugated lipoamide, we observed a reproducible loss of the faster migrating lipoylated protein species, attributed to the lipoylated DLST subunit of the OGDHc (Fig. 4b, c). Immunoprecipitation of endogenous DLST confirmed loss of lipoylation following ABHD11 depletion, without altering total DLST levels ( Supplementary Fig. 5a), and this decreased DLST lipoylation was observed in several cell types (Fig. 4d, e). Furthermore, in contrast to complete disruption of lipoic acid synthesis by LIAS depletion, ABHD11 loss preferentially decreased DLST lipoylation, without altering the other abundantly lipoylated protein within the mitochondria, the DLAT (dihydrolipoamide acetyltransferase) subunit of the pyruvate dehydrogenase complex (PDHc) ( Fig. 4b-e, Supplementary Fig. 5b). Indeed, PDHc function, as measured by [U-13 C 6 ] glucose stable isotope tracing, was not impaired in the ABHD11 deficient HeLa cells (Supplementary Fig. 6a-g), and lactate production was not increased compared to control HeLa cells ( Supplementary Fig. 6h). Complementation studies were used to determine whether the enzymatic activity of ABHD11 was required for lipoylation of the OGDHc. Exogenous wildtype or mutant ABHD11 were expressed in mixed ABHD11 KO populations and lipoylation levels measured by immunoblot. DLST lipoylation was restored with the wildtype ABHD11 but not with the S141A or H296A mutants (Fig. 4f). HIF-1α levels were only reduced to basal levels by reconstituting with wildtype ABHD11 but not the nucleophile mutants ( Fig. 4f), as previously shown.
ABHD11 maintains functional lipoylation of DLST. The finding that ABHD11 loss showed a selective loss of DLST lipoylation was unexpected, as prior genetic studies of lipoate conjugation had not shown a requirement for an additional enzyme 26 . Furthermore, we confirmed that ABHD11 loss differed to depletion of other components of the lipoic acid synthesis pathway by generating CRISPR/Cas9 mixed KO populations of the key enzymes involved ( Supplementary Fig. 7a, b). Lipoyl(octanoyl) transferase 2 (LIPT2), LIAS, and lipoyltransferase 1 (LIPT1) all reduced DLAT and DLST lipoylation in HeLa cells to a similar level, but only ABHD11 showed a selective loss of DLST lipoylation ( Supplementary Fig. 7b). This preferential decrease in DLST lipoylation following ABHD11 loss argued against a general role for ABHD11 in lipoyl synthesis, and while it remained possible that ABHD11 was required for the final catalysis of DLST lipoylation, prior genetic studies suggested that LIPT1 was sufficient for this step [26][27][28] .
Rather than acting as a conjugating enzyme, we hypothesized that ABHD11 may directly or indirectly be involved in maintaining a functional lipoate moiety on the OGDHc complex. We observed that ABHD11 loss in HeLa cells led to decreased cell viability after prolonged passage for three weeks. However, it was unlikely that general growth inhibition was responsible for the lipoylation phenotype as ML226 treatment, which efficiently inhibited ABHD11, did not alter cell growth ( Supplementary Fig. 8a). We also examined whether ABHD11 activity altered the mitochondrial redox environment, which could influence the reduction and oxidation of lipoylated DLST. Stable isotope tracing showed no overall change in cellular glutathione (GSH) Fig. 2 ABHD11 is required for OGDHc function. a Schematic of the TCA cycle (oxidative metabolism) and reductive carboxylation (reductive metabolism), illustrating the fate of 13 C carbons upon incubation with [U-13 C 5 ]-glutamine. b-g Stable isotope tracing of control HeLa cells compared to mixed CRISPR KO populations (sgRNA) of ABHD11 or OGDH incubated with [U-13 C 5 ] glutamine. 2-oxoglutarate (b), succinate (c), fumarate (d), malate (e) and citrate (f), divided by metabolite isotopologues (m + 0 to m + 5) are indicated. Two biologically independent replicates, n = 5 technical replicates per sample, mean ± SD (g) OGDHc activity in isolated mitochondria. Mitochondria were extracted from control or mixed CRISPR KO populations of ABHD11, OGDH or VHL HeLa cells and OGDHc activity measured by a redox sensitive colorimetric probe for 2-OG oxidation. n = 3 biologically independent samples, mean ± SEM, **p = 0.0082, two-tailed t test. h Bioenergetic assays of oxygen consumption rates (OCR) in control, ABHD11 deficient or OGDH deficient HeLa cells (mixed KO populations). ABHD11 and OGDH were depleted as described, and analysed by using a Seahorse XF e 24 Extracellular Flux Analyzer (n = 4 technical replicates per sample, mean ± SD). Three basal measurements were made at 9 min intervals followed by three measurements per treatment (1 μM oligomycin, 1 μM FCCP and 1 μM antimycin/rotenone). OCR was normalised to total cell number. i Comparison technical repeats at first basal measurement from (h); ***p = 6.3 × 10 −5 , two-tailed t test. j Measurement of 2-hydroxyglutarate (2-HG) levels following [U-13 C 5 ] glutamine stable isotope tracing in control HeLa cells compared to mixed CRISPR KO populations (sgRNA) of ABHD11 or OGDH. Metabolite isotopologues (m + 0 to m + 5) are indicated. Two biologically independent samples are shown; n = 5 technical replicates per sample, mean ± SD. k Relative quantification of 2-HG enantiomers upon derivatisation with diacetyl-L-tartaric anhydride and LC-MS analysis. l, m Inhibition of lactate dehydrogenase A (LDHA) in ABHD11 deficient HeLa cells. Mixed CRISPR KO ABHD11, VHL or PHD2 cells were treated with sodium oxamate (Ox) (l) or GSK-2837808A (m) as indicated for 24 h. HIF-1α levels were measured by immunoblot. levels ( Supplementary Fig. 8b). Small changes in mitochondrial ROS were observed with ABHD11 loss, using MitoSOX Red, similarly to OGDH or LIAS depletion ( Supplementary Fig. 8c, d). However, ML226 treatment, showed no change MitoSOX Red levels, and importantly, Antimycin A, which increased mitochondrial ROS to higher levels than ABHD11 inhibition, was not sufficient to activate the HIF reporter ( Supplementary Fig. 8e). Thus, alterations in mitochondrial ROS were unlikely to account for the HIF stabilisation or altered lipoylation following ABHD11 loss or inactivation of the OGDHc.
To explore further how ABHD11 activity altered DLST lipoylation we used mass spectrometry analysis of the lipoate ARTICLE moiety. Immunoprecipitated DLST was treated with a reducing agent and then incubated with N-ethylmaleimide (NEM), forming an NEM-lipoyl conjugate, which had previously been shown to aid detection of the lipoate moiety 29 (Fig. 5a, Supplementary Fig. 9a). Interestingly, NEM treatment prevented detection of immunoprecipitated lipoylated DLST by immunoblot ( Supplementary Fig. 9a), demonstrating that the anti-lipoate antibody only detected the functional lipoate and not the NEMmodified form, suggesting that the apparent loss of DLST lipoylation in ABHD11 deficient cells may be due to modification of the lipoate moiety. We next measured levels of DLST lipoylation (NEM-lipoyl) by label-free quantification on immunoprecipitated DLST from wildtype HeLa cells or those deficient in LIAS or ABHD11 (Fig. 5a, b, Supplementary Fig. 9b).
To account for potential differences in DLST protein abundance around the lipoylated region (DK*TSVQVPSPA), we normalised these peptides to the sum of all DLST peptide label-free quantification values. Approximately 50% of the DK*TSVQVPSPA DLST peptide in wildtype cells was modified with lipoate compared to the unmodified form and as expected, nearly all the lipoate detected was modified with NEM (Fig. 5b). DLST lipoylation was nearly completely lost in the LIAS deficient cells, with the majority of the DLST lipoylated peptide region found to be unmodified (Fig. 5b), confirming that this approach could readily identify a defect in lipoyl synthesis and conjugation. However, ABHD11 deficiency did not result in an accumulation of the unmodified DK*TSVQVPSPA peptide, which could have been expected with a defect in conjugation. Instead, both the unconjugated or NEM-lipoyl DK*TSVQVPSPA peptide were barely detectable, with a 10-fold decrease in abundance compared to the control or LIAS null cells (Fig. 5b). This decrease was not due to less total DLST, as DK*TSVQVPSPA levels were normalised to other DLST peptides upstream or downstream of the lipoylated region. Therefore, a modification of the DK*TSVQVPSPA peptide of undefined mass accounted for the apparent decrease in peptide abundance. Common posttranslational modifications (e.g., ubiquitination, phosphorylation or acetylation), combinations of modifications, or known DLST intermediates (e.g., succinyl-dihydrolipoamide, acyldihydrolipoamide or S-glutathionylation) (Supplementary Dataset 2) did not account for the peptide loss of the lipoylated DLST region, suggesting the formation of lipoyl adducts that were not detectable by mass spectrometry.
The thiols within the lipoamide moiety are sensitive to attack by lipid peroxidation products, which disrupt OGDHc catalysis by preventing the cyclical oxidation and reduction of the lipoyl conjugate 30,31 (Fig. 5c). We measured whether common lipid peroxidation products formed in cells (4-hydroxy-2-nonenal (4-HNE) or 4-oxononenal (4-ONE)) 30-32 modified the lipoate moiety on immunoprecipitated DLST, but these modifications did not account for the unassigned mass of the DLST peptide (Supplementary Dataset 2). However, the complex nature of lipid based adducts of undefined and variable lengths may preclude their detection.
While the exact nature of the lipoyl adduct formed in ABHD11 deficient cells was unclear, we examined whether exogenous treatment with 4-HNE could alter DLST lipoylation, similarly to ABHD11 depletion. 4-HNE treatment of cell lysates preferentially decreased detection of DLST lipoylation by immunoblot (Fig. 5d), consistent with the formation of lipoyl adducts preventing binding to the antibody. DLAT lipoylation was only affected at high concentrations (5 mM) of 4-HNE (Fig. 5d), suggesting that DLAT may be more resistant to lipoyl adduct formation than DLST, and consistent with our findings that ABHD11 loss preferentially effects the OGDHc.
Finally, to explore whether ABHD11 protected against to the formation lipoyl adducts, such as those formed by 4-HNE, we measured if ABHD11 loss or inhibition made the OGDHc more susceptible to lipid peroxidation damage. Control or ABHD11 depleted HeLa cells or lysates were treated with 4-HNE, and lipoylation detected by immunoblot. 4-HNE decreased the detection of DLST preferentially to DLAT within cell lysates (Fig. 5d), consistent with lipoyl adducts preventing detection of the lipoyl moiety by immunoblot. 4-HNE treatment of cells also decreased DLST functional lipoylation preferentially to DLAT, and ABHD11 deficient cells were more susceptible to 4-HNE treatment compared to the control cells (Fig. 5e). Overexpression of ABHD11 inactive mutants competed with endogenous ABHD11 to also show an increase lipoyl-adduct formation following 4-HNE treatment (Supplementary Fig. 9c). ABHD11 overexpression did not increase DLST lipoylation compared to control cells (Supplementary Fig. 9c) but this finding is consistent with exogenous ABHD11 not increasing total lipoylation levels and reflect that OGDHc lipoylation is tightly regulated, with only 50% of DLST modified by lipoylation. While these studies demonstrated that 4-HNE could disrupt functional lipoylation, we were concerned that the concentrations required were higher than prior reports 33,34 , and considered that this may be due to the presence of L-cysteine within media. Therefore, we repeated these assays using lower concentrations of 4-HNE in media without Lcysteine (Fig. 5f). We now observed impaired DLST lipoylation with low concentrations of 4-HNE (40 µM) in control HeLa cells, Fig. 3 ABHD11 is a serine hydrolase that associates with the OGDHc. a Schematic of ABHD11 with the putative mitochondrial targeting sequence (MTS), mitochondrial processing peptidase (MPP) cleavage site and catalytic residues indicated (Serine 141, and Histidine 296). Modelled from UniProtKB-Q8NFV4, and MitoFates prediction tool 55 . b Confocal micrograph of HeLa cells lentivirally transduced with ABHD11-GFP. Mitochondria were visualised with MitoTracker Deep Red FM (MitoDeepRed); Scale = 10 μm, representative example from two biologically independent experiments and 14 images (c) Pearson correlation coefficient (Pearson r) comparing colocalisation of GFP and MitoTracker in HeLa cells expressing ABHD11-GFP (n = 14). HeLa cells expressing GFP under an SFFV promoter without a localisation signal served as a cytosolic control (pSFFV-GFP; n = 14) ***p = 7.0 × 10 −6 , two-tailed Mann-Whitney U test. d Mitochondrial protease protection assay. Mitochondria were extracted using the Qproteome Mitochondria Isolation Kit (Qiagen). Proteinase K was added to the final concentrations indicated, and incubation at 37°C for 30 min. e Immunoprecipitation of ABHD11-HA with endogenous OGDHc components. ABHD11-HA or the inactive mutants (S141A and H296A) were transduced into HeLa cells, lysed and immunoprecipitated using the HA tag. TMEM199, a membrane bound protein tagged with HA (TM-HA) was used as a control. f Colocalisation of ABHD11 with the mitochondrial matrix protein, OGDH. HeLa cells expressing ABHD11-GFP were fixed in paraformaldehyde. ABHD11 and OGDH subcellular localisation was visualised by immunofluorescence confocal microscopy. Scale = 10 μm, representative image of five technical repeats. g In silico modelling of ABHD11 with putative catalytic site and key residues S141 and H296 (Phyre2 structural prediction against a template of murine epoxide hydrolase, PDB: 1cr6, and visualised using PyMOL 2.3). h Reconstitution of mixed KO population of ABHD11 with exogenous ABHD11, or enzymatic inactive mutants. i p-nitrophenyl esterase activity of purified ABHD11-FLAG. Purified wildtype or S141A ABHD11-FLAG were incubated with p-nitrophenyl acetate and hydrolysis measured by rate of increase in absorbance at 405 nm (37°C for 40 min). An empty FLAG vector (EV), that had undergone affinity purification, was used as a control. ABHD11 enzymatic activity was also measured following heat inactivation of the protein (90°C for 5 min). n = 4, Mean ± SEM, ***p = 0.0006, two-tailed t test.
with complete loss of DLST in ABHD11 deficient cells at 20 µM 4-HNE (Fig. 5f). Furthermore, while 4-HNE preferentially altered DLST, DLAT lipoylation was also decreased in the ABHD11 null cells compared to the controls (Fig. 5f). Similar findings were observed with ABHD11 inhibition, consistent with a requirement for ABHD11 to maintain functional lipoylation in the context of lipid peroxidation products. In conclusion, while the nature of adducts formed on lipoylated DLST remain to be fully determined, these studies demonstrate that ABHD11 is required for functional lipoylation of the OGDHc, and may protect against the formation of lipoyl adducts, such as those formed by 4-HNE (Fig. 6).
Discussion
This study identifies ABHD11 as a mitochondrial enzyme required for OGDHc function, and to our knowledge, is the first example of a mitochondrial pathway that maintains TCA cycle integrity by preserving functional OGDHc lipoylation (Fig. 6). Moreover, we demonstrate that ABHD11 inhibition allows 2-OG metabolism to be modulated in multiple cells and in a reversible manner, with potential broad implications for altering cell fatedecisions and manipulating 2-OG abundance in tumours. The selective loss of lipoylated DLST following ABHD11 depletion initially suggested that it may be necessary for OGDHc lipoate conjugation. However, a requirement for ABHD11 in lipoate synthesis had not been previously observed 13,26,35 , and LIPT1 deletion or human loss of function mutations prevent PDHc and OGDHc lipoylation [26][27][28] . It was possible that ABHD11 transfers lipoate moieties between 2-oxoacid dehydrogenases, but our mass spectrometry findings argued against this. If ABHD11 was required for lipoate transfer, the unmodified DLST peptide should accumulate in ABHD11 depleted cells. Instead, we found an absence of both the modified and unmodified lipoylated region of DLST by mass spectrometry (Fig. 5b, c). Similar coverage of DLST peptides upstream and downstream of the lipoylated region confirmed that there was no change in total DLST levels following ABHD11 loss. Therefore, a peptide of undefined mass must account for the apparent loss of this region, indicating a posttranslational modification other than lipoylation or the formation of a lipoyl adduct.
Common post-translational modifications (e.g. ubiquitination, phosphorylation or acetylation), combinations of modifications, or known DLST intermediates (e.g. succinyl-dihydrolipoamide, acyl-dihydrolipoamide or S-glutathionylation) (Supplementary Dataset 2) did not account for the peptide loss of the lipoylated DLST region, suggesting that this DLST peptide was not uniformly modified. Lipid peroxidation products (hydroxyalkenals, such as 4-HNE), arise from free radical propagation through phospholipids 31 , and can easily react with thiol groups, inactivating 2-oxoacid dehydrogenases by forming lipoyl adducts 30 . We did not observe 4-HNE lipoyl adducts on immunoprecipitated DLST, but the hydrophobicity and complex nature of these adducts 32 may preclude their detection by mass spectrometry, accounting for the apparent loss of the DLST DK*TSVQVPSPA peptide that we observed (Fig. 5b). We also required high concentrations of 4-HNE to decrease detectable lipoylation in immunoprecipitated DLST, and 0.1 mM 4-HNE in cells incubated in serum-free media. High levels or prolonged treatment of 4HNE may lead to depletion of cellular antioxidant levels and apoptosis 36,37 , but we did not observe changes in DLAT lipoylation at 0.1 mM consistent with a selective effect on DLST, as previously observed 38 . Furthermore, treatment of HeLa cells with 4-HNE in media without L-cysteine resulted in selective loss of DLST lipoylation at lower, biologically relevant 4-HNE concentrations 33,34 .
The apparent specificity of ABHD11 for DLST may relate to the preponderance of lipoyl adducts formed by the OGDHc compared to other lipoylated proteins, or selectively binding to the OGDHc, rather than all 2-oxoacid dehydrogenases. It is possible that ABHD11 may regulate DLST indirectly, but this is unlikely to be due to altered mitochondrial ROS ( Supplementary Fig. 8). The association with DLST and OGDH (Fig. 3e), and marked accumulation of 2-OG (Fig. 2) are also consistent with a direct role on OGDHc function. These findings are also supported by prior mass spectrometry interactome studies, showing an association of ABHD11 with OGDH 23 . Whether ABHD11 interacts with other 2-oxoacid dehydrogenases will be of interest to explore further. We observed that ABHD11 can interact with DLAT by mass spectrometry (Supplementary Table 1), and a small decrease in DLAT lipoylation following ABHD11 loss was observed. ABHD11 deficient cells were also more susceptible to impaired lipoylation following 4-HNE treatment. However, ABHD11 loss did not increase pyruvate levels in HeLa cells ( Supplementary Fig. 5), which would be expected if PDHc activity was significantly impaired.
ABHD11 is one of a family of alpha-beta hydrolase domaincontaining enzymes, which as a group are poorly characterised. Of these, only ABHD10 and ABHD11 are known to be mitochondrial 39 . ABHD10 is has recently been shown to be an acyl protein thioesterase, with S-depalmitoylase activity against the anti-oxidant protein peroxiredoxin-5 40 . These findings would be consistent with our observations for ABHD11 regulating the thiol-containing lipoate moiety on DLST. However, ABHD11 has less sequence identity with ABHD10 than other ABHD family members ( Supplementary Fig. 2), and is not inhibited by ML226 25 .
This work provides insights into the functional role of ABHD11, for which no physiological substrate or role has been identified previously. ABHD11 is one of~26 genes included in the 7q11.23 hemizygous deletion of Williams-Beuren syndrome, a rare multisystem disorder often characterised by developmental and cardiac abnormalities 41 . While the phenotype of cardiac and soft tissue disease is felt largely due to loss of Tropoelastin 1 (ELN1) 41 , a functional understanding of ABHD11 may offer some insights into aspects of this syndrome. It will also be of interest to explore further the biological consequences of ABHD11 loss compared to other lipoate enzymes. ABHD11 loss disrupts the TCA cycle, impairing oxidative phosphorylation and promoting reductive metabolism, but ABHD11 has a distinct metabolic phenotype compared to loss of other lipoic acid pathway enzymes. Germline mutations in LIAS, LIPT2 and LIPT1 result in impaired PDHc and OGDHc activity 13,27,42,43 , Fig. 4 ABHD11 loss impairs lipoylation of the OGDHc. a OGDHc compromises 3 subunits: OGDH (E1) catalyses the oxidation of 2-OG to form the succinyl moiety, releasing carbon dioxide (CO 2 ) and reducing the lipoate to form a succinyl-dihydrolipoate intermediate. DLST (E2) catalyses transfer of the succinyl moiety to Coenzyme A (CoA), forming succinyl CoA and releasing dihydrolipoylated DLST. Dihydrolipoate is oxidised by DLD (E3) to reform lipoate, with the free electrons reducing NAD + to NADH. Thus 2-OG oxidation is coupled to cyclical lipoate reduction/oxidation and NADH formation. b, c Immunoblot and quantification of OGDHc subunits and lipoylation in ABHD11 or LIAS deficient cells. HeLa cells were transduced with sgRNA targeting ABHD11 or LIAS to generate mixed KO populations, and probed for OGDHc components (b). Lipoate (Lp) antibody detects lipoylated proteins. The predominant lipoylated proteins, DLAT and DLST, are indicated. ImageJ quantification of lipoyl-DLST (left) and DLST (right), n = 5 biologically independent samples (c). Mean ± SEM, ***p = 0.0004, ns:p = 0.19, two-tailed one sample t test. Ct = control (d, e) Immunoblot OGDHc subunits and lipoylation in ABHD11 or LIAS deficient THP-1 or MCF-7 cells. THP-1 (d) or MCF-7 (e) cells were transduced with sgRNA targeting ABHD11 or LIAS to generate mixed KO populations, and probed for OGDHc components or lipoate. β-actin served as a loading control. f Reconstitution of mixed KO population of ABHD11 with exogenous ABHD11-GFP, or enzymatic inactive mutants. g ML226 treatment in vitro. Purified wildtype ABHD11-FLAG was incubated with p-nitrophenyl acetate and hydrolysis measured by rate of increase in absorbance at 405 nm (37°C for 30 min), with addition of ML226 at the indicated concentration. Hydrolysis activity was subtracted from a background control without ABHD11-FLAG, and is normalised to the activity of ABHD11-FLAG with vehicle control. ϕ = vehicle control; n = 3. h, i ML266 treatment in HeLa cells (h) and C2C12 myoblasts (i). Cells were treated for 24 h with ML226 at the indicated concentrations and lipoylation measured by immunoblot. j HeLa cells were treated with 1μM ML226, or 0.075% DMSO as a vehicle (veh) control. ML226 was washed out after 24 h and lipoylation recovery measured by immunoblot. but ABHD11 loss did not significantly alter DLAT lipoylation in several cancer lines. PDHc activity is required for lipogenesis, by providing acetyl-CoA, and LIPT1 mutations result in defective lipid synthesis 44 . As ABHD11 acts predominantly on the OGDHc, it is possible that the preserved activity of PDHc fuels lipogenesis, and cell survival, and it will be important to determine whether ABHD11 activity, and the resulting HIF activation, feeds back on lipid synthesis and the mitochondrial fatty acid pathway.
This study and prior reports demonstrate that human mutations in lipoylation enzymes increase 2-OG levels and promote L-2-HG formation 4,42 . However, how L-2-HG inhibits 2-OG sensitive enzymes when 2-OG is abundant remains to be fully resolved. It is possible that L-2-HG may allosterically inhibit the enzymes, or that additional factors aside from L-2-HG result in decreased dioxygenase activity. The reasons for the propensity to form L-2-HG under certain conditions of 2-OG accumulation also remains to be determined, as in other cellular responses, such as embryonic stem cell pluripotency 2 , 2-OG treatment does not result in inhibition of 2-OG dependent dioxygenases. These discrepancies may relate to the reductive environment that occurs when OGDHc activity is impaired (which is likely to occur with Lipoic acid has been traditionally described as an essential cofactor for 2-oxoacid dehydrogenases, but only 50% of DLST in HeLa cells was observed to be lipoylated in resting cells (Fig. 5b), and OGDHc lipoylation was rapidly stored after washout of ML226, suggesting a reserve capacity to alter lipoate levels and increase OGDHc activity (Fig. 4j). These findings show that lipoylation is a dynamic modification that must be maintained, which is further supported by recent observations that SIRT4 act as a lipoamidase, altering PDHc function 29 , and that increased lipoylation can enhance brown adipose tissue function, decreasing age-associated obesity 45 . Therefore, modulating lipoylation through ABHD11 activity provides an attractive approach to manipulating 2-OG metabolism. Moreover, these studies extend the role of lipoylation beyond an enzymatic cofactor, to a dynamic modification that couples the mitochondria to a transcriptional adaptive response mediated by 2-OG and oxygen sensitive enzymes.
Full details of reagents and antibodies used are shown in Supplementary Table 2.
Constructs. CRISPR sgRNAs were cloned into a lentiviral sgRNA expression vector pKLV-U6gRNA(BbsI)-PGKpuro2ABFP 46 . All sgRNA sequences are detailed in Supplementary Table 3. ABHD11 constructs were generated from the I.M.A.G.E. cDNA clone (IRATp970F0688D, Source Bioscience), cloned into the pHRSIN pSFFV backbone with pGK-blasticidin resistance (a gift from Paul Lehner), using NEBuilder HiFi (NEB). Prior to assembly, silent mutations were introduced inside the sequence targeted by ABHD11 sgRNA 2, using PCR primers detailed in Supplementary Table 3. Mutations of catalytic residues serine 141 to alanine (S141A) and histidine 296 to alanine (H296A) of ABHD11 was created using NEBuilder HiFi, with primers detailed in Supplementary Table 3. Lentiviral expression vectors (pHRSIN) for ABHD11, S141A ABHD11 and H296A ABHD11 with C-terminal eGFP tags or HA tags were created using NEBuilder HiFi (NEB). ABHD11 was also cloned into a transfection vector, pCEFL 3xFLAG mCherry vector, encoding a C-terminal 3X FLAG tag and mCherry under a separate promoter (a gift from David Ron) 47 , using Gibson Assembly (NEB) and NEBuilder HiFi. 250 µl of virus with 5 × 10 4 cells in a 24-well plate made up to 1 ml media. For the screens, a titration of increasing volumes of virus was used, with 10 6 HeLa cells in a 6-well plate. Cell plates were centrifuged for 1 h at 37°C at 750 × g immediately after addition of virus.
Whole-genome CRISPR/Cas9 forward genetic screens. HeLa HRE-GFP ODD cells were transduced with Streptococcus pyogenes Cas9 (pHRSIN-FLAG-NLS-CAS9-NLS-pGK-Hygro) 49 and selected for Cas9 expression using hygromycin. 5 × 10 7 (Brunello) or 10 8 (TKO) HeLa HRE-GFP ODD cells were transduced with the appropriate volume of pooled sgRNA virus (multiplicity of infection (MOI) of~0.3), maintaining at least 150-fold sgRNA coverage. After 30 h, cells were treated with puromycin 1 µg/ml for 5 days. Representation was maintained throughout the screen such that no selection event occurred where the library was cultured at fewer than 200 times the number of sgRNA sequences in the library. The library was pooled immediately before any selection event. FACS was performed by harvesting 10 8 cells, washing the cells in PBS, and then resuspending them in PBS containing 2% foetal calf serum and 10 mM HEPES (Sigma H0887). Cells were sorted using an Influx cell sorter (BD); GFP-high cells were chosen in a gate set at one log 10 unit above the mode of the untreated population.
Genomic DNA was extracted using a Gentra Puregene Core kit (Qiagen). Lentiviral sgRNA inserts were amplified in a two-step PCR (with Illumina adapters added on the second PCR), as previously described 49 . For the TKO screen, the forward inner PCR and sequencing primers were modified (Supplemental Table 3).
Sequencing analysis was performed by first extracting the raw sequencing reads, trimming the first 20 bp (FASTX-toolkit), and aligning against the appropriate sgRNA library using Bowtie 50 . Read counts for each sgRNA were compared between conditions, and Benjamini-Hochberg false discovery rates for each gene calculated, using MAGeCK 51 (Supplementary Dataset 1). The analysis presented compares DNA extracted following the second sort to an unsorted DNA library taken at the same timepoint. Immunoblotting and immunoprecipitation. Cells were lysed in an SDS lysis buffer containing 2% SDS, 50 mM Tris pH 7.4, 150 mM NaCl, 1 mM dithiothreitol, 10% glycerol and 1:200 Benzonase nuclease (Sigma), for 15 min at room temperature, then heated at 90°C for 5 min. Proteins were separated with SDS-PAGE electrophoresis, transferred to a PVDF membrane, and probed using appropriate primary antibodies and a secondary with HRP conjugate. Densitometry measurements were made using ImageJ 52 .
To identify protein interactions with ABHD11, HeLa cells lentivirally transduced with ABHD11 with a C-terminal HA tag were lysed in a buffer containing 100 mM Tris pH 8.0, 140 mM NaCl, 1% IGEPAL CA-630 (Sigma), 1 mM PMSF (Sigma P7626) and cOmplete Protease Inhibitor Cocktail (Roche). After centrifugation at 17,000 × g, the supernatant was pre-cleared using Sepharose CL-4B (GE Heathcare) and then incubated with EZView HA Red Anti-HA Affinity Gel (Sigma E6779) overnight on a rotator. Resins were washed with Tris-buffered saline containing 0.1% IGEPAL CA-630, and a further two washes with Trisbuffered saline. Proteins were eluted using an SDS lysis buffer (4% SDS, 100 mM Tris pH 7.4, 300 mM NaCl, 2 mM dithiothreitol, and 20% glycerol) heated at 90°C for 5 min, and separated using SDS-PAGE.
Confocal microscopy. Mitochondrial labelling was performed using MitoTracker Deep Red FM (Thermo M22426). Cells were cultured overnight on a 1 cm glass coverslip, incubated with 250 nM Mitotracker Deep Red FM for 40 min, and after washing with PBS fixed with 4% paraformaldehyde for 20 min. Cells were mounted to slides using ProLong Gold Antifade Mountant with DAPI (Thermo).
Quantitative PCR. Total RNA was extracted using the RNeasy Plus minikit (Qiagen) and reverse transcribed using Super RT reverse transcriptase (HT Biotechnology Ltd). PCR was performed on the ABI 7900HT Real-Time PCR system (Applied Biosystems; software: Quantstudio 1.3) using SYBR Green Master mix (Applied Biosystems). Reactions were performed with 125 ng of template cDNA. Transcript levels of genes were normalised to GAPDH.
Measurements of 2-OG dependent dioxygenase activity. The in vitro HIF-1α prolyl hydroxylation activity of HeLa cell lysates against a His-tagged protein corresponding to residues 530-652 of human HIF-1α protein 4,15 was performed by incubating 10 μM HIF-1α 530-652 with 50 μl HeLa cell extracts in 20 mM HEPES (pH 7.5, 150 mM NaCl and 1 mM DTT) for 15 min at 37°C. Reactions were terminated by the addition of SDS loading buffer, and proteins visualised by SDS-PAGE. Images were quantified using ImageJ, and are presented as the ratio of densitometry of hydroxylated to total HIF-1α ODD peptide at the 15 min timepoint, normalised to PHD2.
For the 5hmC dot blot assay, genomic DNA was extracted from HeLa cells using a Gentra Puregene kit (Qiagen), and dot blotting for 5hmC levels performed by serial dilutions of denatured genomic DNA on Hybond NX membranes 4 . Following UV crosslinking the membranes were blocked with 1% bovine serum albumin and 5% milk powder prior to probing with a rabbit polyclonal antibody to 5hmC (Active Motif). Total DNA levels were evaluated by methylene blue staining, and relative densitometry measured using ImageJ.
For the KDM panel, cells were lysed in SDS lysis buffer and probed for selected H3 methylation marks.
Mitochondrial protease protection assay. Mitochondria from 10 7 HeLa cells were extracted using a Qproteome Mitochondria Isolation Kit (Qiagen). After the final wash with mitochondria storage buffer, mitochondria were divided into tubes, pelleted by centrifugation at 6000 × g and resuspended in either 10 mM Tris-HCl pH 8.0 with 250 mM sucrose (for whole mitochondria), or 10 mM Tris-HCl pH 8.0 (for mitoplasts), to a final protein concentration of 1 mg/ml. Proteinase K (Sigma P2308) was added to a final concentration of 12 or 24 µg/ml, based on methods previously described 53 . Following incubation at 37°C for 30 min, the reaction was quenched with 1 mM PMSF. Mitochondria or mitoplasts were then pelleted again by centrifugation at 6000 × g, lysed in SDS buffer and analysed by immunoblot.
Purification of ABHD11 from HEK293T cells. HEK293T cells were transfected with the pCEFL-ABHD11 -3XFLAG tag plasmid. In brief, cells were seeded in a 14 cm dish at 70% confluency and transfected using 270 μg polyethylenimine (Sigma) with 22.5 µg DNA in 6 ml Opti-MEM. Cells were harvested after 48 h, and lysed in TBS buffer (100 mM Tris-HCl pH 8.0, 140 mM NaCl) with 1% Triton X-100 and cOmplete Protease Inhibitor Cocktail (Roche). After centrifugation at 17,000 × g, the supernatant was pre-cleared using Sepharose CL-4B (GE Healthcare) and incubated overnight with FLAG M2 antibody conjugated beads (Sigma). Following five washes with TBS, ABHD11-FLAG was eluted using 100 mg/l 3xFLAG peptide (Sigma F4799), filtered using a 0.22 µm PVDF filter and separated using a Superdex 75 10/300 column on an Äkta-Pure liquid chromatography system (software: Unicorn 6.3). 500 µl fractions were collected and protein content visualised by SDS-PAGE and Coomassie staining. Protein identity was confirmed by LC-MS/MS.
Liquid chromatography mass spectrometry. Samples were reduced, alkylated and digested in-gel using either trypsin, GluC or AspN. The resulting peptides were analysed by LC-MS/MS using an Orbitrap Fusion Lumos coupled to an Ultimate 3000 RSLC nano UHPLC equipped with a 100 µm ID × 2 cm Acclaim PepMap Precolumn (Thermo Fisher Scientific) and a 75 µm ID × 50 cm, 2 µm particle Acclaim PepMap RSLC analytical column. Loading solvent was 0.1% formic acid with analytical solvents A: 0.1% formic acid and B: 80% acetonitrile + 0.1% formic acid. Samples were loaded at 5 µl/min loading solvent for 5 min before beginning the analytical gradient. The analytical gradient was 3-40% B over 42 min rising to 95% B by 45 min followed by a 4 min wash at 95% B and equilibration at 3% solvent B for 10 min. Columns were held at 40°C. Data were acquired in a DDA fashion with the following settings: MS1: 375-1500 Th, 120,000 resolution, 4 × 10 5 AGC target, 50 ms maximum injection time. MS2: Quadrupole isolation at an isolation width of m/z 1.6, HCD fragmentation (NCE 30) with fragment ions scanning in the Orbitrap from m/z 110, 5 × 10 4 AGC target, 100 ms maximum injection time. Dynamic exclusion was set to +/−10 ppm for 60 s. MS2 fragmentation was trigged on precursors 5 × 10 4 counts and above.
Raw files were processed using PEAKS Studio (version 8.0, Bioinformatics Solutions Inc.). Searches were performed with either trypsin, GluC or AspN against a Homo sapiens database (UniProt reference proteome downloaded 26/01/18 containing 25,813 sequences) and an additional contaminant database (containing 246 common contaminants). Variable modifications at PEAKS DB stage included oxidation (M) and carbamidomethylatation with 479 built in modifications included at PEAKS PTM stage.
p-Nitrophenyl ester hydrolysis assay. Hydrolase activity of ABHD11-FLAG (or a heat-inactivated control made by incubation at 90°C for 5 min) was assayed by incubation in an assay buffer containing 50 mM Tris-HCl pH 7.4, 150 mM NaCl, 0.01% bovine serum albumin, 1.4% methanol, and 500 µM p-nitrophenyl acetate (Sigma N8130). 1.5 µg enzyme was added to 200 µl assay buffer, and the formation of p-nitrophenol assayed using a Clariostar plate reader (BMG Labtech), recording absorbance at 405 nm while incubating at 37°C for 40 min. The rate of formation of p-nitrophenol was calculated from the slope of the absorbance curve, subtracting the slope of a blank containing only assay buffer and substrate, and calibrated against a standard curve of p-nitrophenol (software: Microsoft Excel for Mac 16).
OGDHc activity assay. OGDHc activity was measured in whole cell lysates using a Biovision ketoglutarate dehydrogenase activity assay (Biovision K678), according to the manufacturer's protocol. HeLa cells were lysed by three freeze-thaw cycles followed by passing 10 times through a 26-gauge needle. Activity was subtracted from a background control containing cell lysate but no substrate.
Mitochondrial bioenergetics assay. Dynamic measurements of oxygen consumption rate and extracellular acidification were recorded using a Seahorse XFe24 (Agilent; software: Wave 2.3). HeLa cells were seeded 24 h beforehand at 1.5 × 10 4 cells/well, and assayed using the manufacturer's Mito Stress Test protocol.
Stable isotope tracing by LC-MS. HeLa cells were seeded in five replicates in 6well plates 27 h prior to metabolite extraction, with a sixth well per condition used for cell count. Twenty-four hours prior to extraction, media was changed to either DMEM without L-glutamine (Sigma D6546) supplemented with 10% FCS and 4 mM [U-13 C 5 ]L-glutamine (Cambridge Isotopes CLM-1822), or DMEM without glucose (Gibco 11966-025) supplemented with 10% FCS, 1 mM sodium pyruvate and 25 mM [U-13 C 6 ]D-glucose (Cambridge Isotopes CLM-1396). Two biological replicates were undertaken, with independent lentiviral transductions of the same cell line on different days. Metabolites were extracted on dry ice, after washing with ice cold PBS, with 1 ml per 10 6 cells of extraction buffer, containing 50% methanol, 30% acetonitrile, 20% water and 100 ng/ml HEPES. To quantify the two enantiomers of hydroxyglutarate, a subset was derivatised using 50 mg/ml diacetyl-Ltartaric anhydride in 20% acetic acid/80% acetonitrile 4 .
For mass spectrometry analysis, in-gel AspN digest and sample analysis were performed as previously described. To identify possible modifications of DLST K110, raw files were processed using PEAKS Studio Label-free quantitation values were obtained by processing raw files with MaxQuant (version 1.6.6.0) with the following parameters: specific AspN digestion; Human database (UniProt reference proteome downloaded 18 Dec 2018 containing 21066 proteins); oxidation, lipoylation, 2x NEM lipoylation, N terminal acetylation as variable modifications; carbamidomethylation as a fixed modification; label-free quantification enabled. Label-free quantification values were normalised to the sum of DLST peptides label-free quantification values. ABHD11 structural prediction. A structural model of ABHD11 was obtained using the NCBI reference sequence for ABHD11 transcript variant 1 (NP_683710.1), modelled with Phyre2 against a template of murine epoxide hydrolase (PDB: 1cr6) 54 and visualised using PyMOL 2.3 (Schrödinger, LLC). The mitochondrial targeting sequence was mapped with the MitoFates prediction tool 55 . Phylogenetic analysis and multiple sequence alignment of ABHD family members was performed with protein sequences obtained from Uniprot (canonical transcript variant) and aligned using Clustal Omega (EBI) 56 .
Statistics and reproducibility. Statistical analysis of the screens was performed using MAGeCK version 0.5.5 51 , testing the sgRNA read counts obtained following the second sort against sgRNA read counts obtained from unsorted cells lysed at the same timepoint. Quantification and data analysis of other experiments are expressed as mean ± SEM and P values were calculated using two-tailed Student's ttest for pairwise comparisons, unless otherwise stated, and were calculated using Graphpad Prism version 8. Metabolomic samples were blinded and randomised prior to their evaluation. Qualitative experiments were repeated independently to confirm accuracy. Specifically, Figs. 1e, f, g; 3e, h; 4f, i, j; 5d, f; Supplementary Figs. 1e, g, k, 5a, b; 9c were repeated twice with similar findings. Figs. 2l, m; 3d; 4d, e, h; 5e; Supplementary Figs. 1f; 4a, d; 7b and 9a were performed independently at least 3 times. Representative data are shown in the figures. Uncropped original scans of all immunoblots are displayed in Supplementary Fig. 11. | v3-fos-license |
2020-11-19T09:13:07.488Z | 2020-11-01T00:00:00.000 | 227066197 | {
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} | pes2o/s2orc | Methionine Supplementation Affects Metabolism and Reduces Tumor Aggressiveness in Liver Cancer Cells
Liver cancer is one of the most common cancer worldwide with a high mortality. Methionine is an essential amino acid required for normal development and cell growth, is mainly metabolized in the liver, and its role as an anti-cancer supplement is still controversial. Here, we evaluate the effects of methionine supplementation in liver cancer cells. An integrative proteomic and metabolomic analysis indicates a rewiring of the central carbon metabolism, with an upregulation of the tricarboxylic acid (TCA) cycle and mitochondrial adenosine triphosphate (ATP) production in the presence of high methionine and AMP-activated protein kinase (AMPK) inhibition. Methionine supplementation also reduces growth rate in liver cancer cells and induces the activation of both the AMPK and mTOR pathways. Interestingly, in high methionine concentration, inhibition of AMPK strongly impairs cell growth, cell migration, and colony formation, indicating the main role of AMPK in the control of liver cancer phenotypes. Therefore, regulation of methionine in the diet combined with AMPK inhibition could reduce liver cancer progression.
Introduction
Liver cancer is the second leading cause of cancer-related death worldwide. Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer and is associated with chronic liver damage, which can be caused by viral infections, such as hepatitis B virus (HBV) and hepatitis C virus (HCV) infection or by alcoholic liver diseases and non-alcoholic steatohepatitis (NASH). Potentially curative treatments for very early/early stage HCC patients include surgical resection, liver transplantation and percutaneous ablation. At an advanced/late stage surgery is no longer applicable, and the currently available therapies are effective only in small groups of patients [1][2][3]. Very few therapeutic options, with unsatisfactory antitumor effects and toxicity, are nowadays available, thus, prognosis remains very poor. In 2007 Sorafenib was the first VEGFR TKI (vascular endothelial growth factor receptor Thus, since methionine/SAM administration in liver seems to have an opposite role than in other cancer types, more studies are necessary to support a therapeutic role of methionine or SAM supplementation in patients.
Here, we investigate the effect of methionine supplementation in liver cancer cells in vitro, to explore the possibility that alterations in methionine dietary consumption could help treatment of liver cancer.
HepG2, Huh7, SW480 and A549 cells were cultured using RPMI1640 supplemented with 10% (v/v) FBS, 2 mM L-glutamine, 100 U/mL penicillin, and 100 µg/mL streptomycin. MCF7 were cultured using EMEM/NEAA supplemented with 10% (v/v) FBS, 2 mM L-glutamine, 100 U/mL penicillin, and 100 µg/mL streptomycin. Cells were maintained at 37 • C in a humidified 5% CO 2 incubator. Methionine was dissolved in water at 45 mg/mL and added at a final concentration of 1.5 mg/mL to RPMI1640 medium; in the control medium, the same amount of water was added. Compound C (Calbiochem, San Diego, CA, USA) was dissolved in DMSO and added to the medium at a final concentration of 2 µM for HepG2, SW480, A549, and MCF7 cells, and at 1.5 µM for Huh7 cells.
Growth Curves
1.5 × 10 5 cells were plated in 6 well plates, the day after the medium was changed (control medium, high methionine, Compound C or High methionine and Compound C). Cells were counted at t = 0, 48 h and 72 h.
Migration Assay
Cell migration was assessed using transwell permeable supports (Costar) with 8.0 µm filter membranes. Cells were treated with high methionine and/or Compound C for 24 h, and then serum starved for 24 h. 5 × 10 4 HepG2 cells and 3.5 × 10 4 Huh7 cells were resuspended in 100 µL of serum free medium (always in the presence or absence of high methionine and/or Compound C), plated onto each filter and 500 µL of complete medium (containing 10% FBS) were placed in the lower chamber. After 24 h, filters were washed, fixed and stained with 0.5% Coomassie brilliant blue (in 10% acetic acid, 45% methanol). Cells on the upper surface of the filters were removed with cotton swabs. Cells that had invaded to the lower surface of the filter were counted under the microscope.
Clonogenic Assay
A total of 2500 cells were plated in a 6 well plates, treated with high methionine and/or Compound C for 10-15 days (the medium was changed every 3-4 days). Then, colonies were fixed with 70% ethanol for 5 min, stained with 0.5% crystal violet in 10% ethanol for 15 min, finally, washed with water and manually counted.
Shotgun Mass Spectrometry and Label Free Quantification
Four technical replicates were performed for each HepG2 sample, grown for 48 h in the presence or absence of high methionine and/or Compound C. Proteins were lysed in RapiGest 0.1% (RG, Waters Corporation, Milford, MA, USA), reduced with 13 mM DTE (30 min at 55 • C) and alkylated with 26 mM iodoacetamide (30 min at 23 • C). Protein digestion was performed using sequence-grade trypsin (Roche) for 16 h at 37 • C using a protein/trypsin ratio of 20:1. The proteolytic digested was desalted using Zip-Tip C18 (Millipore, Burlington, MA, USA) before MS analysis [27]. LC-ESI-MS/MS analysis was performed on a Dionex UltiMate 3000 HPLC System with a PicoFrit ProteoPrep C18 column (200 mm, internal diameter of 75 µm). Gradient: 2% ACN in 0.1% formic acid for 10 min, 2-4% ACN in 0.1% formic acid for 6 min, 4-30% ACN in 0.1% formic acid for 147 min, and 30-50% ACN in 0.1% formic for 3 min, at a flow rate of 0.3 µL/min. The eluate was electrosprayed into an LTQ OrbitrapVelos (Thermo Fisher Scientific, Bremen, Germany) through a Proxeon nanoelectrospray ion source (Thermo Fisher Scientific), as reported in [28]. The LTQ-Orbitrap was operated in positive mode in data-dependent acquisition mode to automatically alternate between a full scan (m/z 350-2000) in the Orbitrap (at resolution 60,000, AGC target 1,000,000) and subsequent CID MS/MS in the linear ion trap of the 20 most intense peaks from full scan (normalized collision energy of 35%). Data acquisition was controlled by Xcalibur 2.0 and Tune 2.4 software (Thermo Fisher Scientific).
A database search was conducted against the Homo Sapiens Uniprot sequence database (release 6 March 2019) with MaxQuant (version 1.6.0.1) software, using the following parameters: the initial maximum allowed mass deviation of 15 ppm for monoisotopic precursor ions and 0.5 Da for MS/MS peaks, trypsin enzyme specificity, a maximum of 2 missed cleavages, carbamidomethyl cysteine as fixed modification, N-terminal acetylation, methionine oxidation, asparagine/glutamine deamidation and serine/threonine/tyrosine phosphorylation as variable modifications. Quantification was performed using the built in XIC-based label-free quantification (LFQ) algorithm using fast LFQ [29]. False protein identifications (1%) were estimated by searching MS/MS spectra against the corresponding reversed-sequence (decoy) database. Statistical analysis was performed using the Perseus software (version 1.5.5.3, https://maxquant.net/perseus/). Only proteins present and quantified in at least 75% of the repeats were positively identified and used for statistical analysis. An ANOVA test (cut-off at 0.05 p-value) was carried out to identify proteins differentially expressed among the four conditions. Focusing on specific comparisons, proteins were considered differentially expressed if they were present only in one condition or showed significant t-test difference (Student's t-test p value ≤ 0.05) [30]. Bioinformatic analyses were carried out by Ingenuity ® Pathway Analysis software (IPA ® -QIAGEN) to cluster enriched annotation groups of Biological Processes, Pathways, and Networks within the set of identified proteins. Functional grouping was based on Fischer's exact test p value ≤ 0.05 (i.e., −log10 ≥ 1.3) and at least 3 counts. Comparison between the proteomic and metabolomic data was performed by IPA and by MetaboAnalyst software R3.0 [31]. Enrichment analysis aimed to evaluate whether the observed genes and metabolites in a particular pathway are significantly enriched (Fisher's exact test 0.05), while the topology analysis aimed to evaluate whether a given gene or metabolite plays an important role in a biological response, based on its position within a pathway (pathway impact).
The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository, with the dataset identifier 7MlKNZqC.
Chemicals for Metabolomics Analysis
All chemicals and solvents used for extraction buffer and for liquid chromatography were LC-MS Chromasolv purity grade. Acetonitrile, methanol, 2-Propanol, and water was purchased from Honeywell, while chloroform and formic acid were purchased from Sigma-Aldrich.
Metabolites Extraction for GC-MS Analysis
Cells were quickly rinsed with 0.9% NaCl and quenched with 800 µL of 1:1 ice-cold methanol:water and collected by scraping. Cells were sonicated 5 s for 5 pulses at 70% power twice and then 400 µL of chloroform were added. Samples were vortexed at 4 • C for 20 min, and then centrifuged at 12,000 g for 10 min at 4 • C. The aqueous phase was collected in a glass insert for solvent evaporation in a centrifugal vacuum concentrator (Concentrator plus/ Vacufuge ® plus, Eppendorf) at 30 • C for about 2.5 h.
Metabolites Extraction for LC-MS Analysis
Cells were quickly rinsed with 0.9% NaCl and quenched with 500 µL ice-cold 70:30 acetonitrile-water. Plates were placed at −80 • C for 10 min, then the cells were collected by scraping. Cells were sonicated as above and then centrifuged at 12,000 g for 10 min at 4 • C. The supernatant was collected in a glass insert and dried as above. Samples were then resuspended with 150 µL of H 2 O prior to analyses.
GC-MS Metabolic Profiling
Derivatization was performed using automated sample prep WorkBench instrument (Agilent Technologies, Santa Clara, CA, USA). Dried polar metabolites were dissolved in 60 µL of 2% methoxyamine hydrochloride in pyridine (ThermoFisher) and held at 40 • C for 6 h. After the reaction, 90 µL of MSTFA (N-Methyl-N-(trimethylsilyl) trifluoroacetamid) was added, and samples were incubated at 60 • C for 1 h. Derivatized samples were analyzed by GC-MS using a DB-35MS column (30 m × 0.25 mm i.d. × 0.25 µm) installed in an Agilent 7890B gas chromatograph (GC) interfaced with an Agilent 7200 Accurate-Mass Quadrupole Time-of-Flight (QTOF) mass spectrometer (MS), operating under electron impact (EI) ionization at 70 eV. Samples (1 µL) were injected in a splitless mode at 250 • C, using helium as the carrier gas at a flow rate of 1 mL/min. The GC oven temperature was held at 100 • C for 2 min and increased to 325 • C at 10 • C/min. GC/MS data processing was performed using Agilent Muss Hunter software. Relative metabolites abundance was carried out after normalization to internal standard d27 Myristic acid and cell number and statistical analyses were performed using MetaboAnalyst 4.0 [32].
Metabolites Quantification in the Media Samples
Absolute quantification of glucose, lactate, glutamine, and glutamate in spent media was determined enzymatically using YSI2950 bioanalyzer (YSI Incorporated, Yellow Springs, OH, USA).
Bioenergetics by Seahorse Technology
Mitochondrial oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) were determined by using Seahorse XFe96 Analyzer (Agilent Technologies). HepG2 and Huh7 cells were seeded in Agilent Seahorse cell culture microplates at density of 2 × 10 4 cells/well for HepG2 or 1 × 10 4 cell/well for Huh7 72 h prior to the assay. A total of 24 h after seeding cells were treated with high methionine (1.5 g/L) and/or Compound C (2 µM for HepG2 and at 1.5 µM for Huh7 cells) for 48 h, and then analyzed by using the Seahorse XF ATP rate assay kit (Agilent Technologies), according to manufacturer instructions. Three measurements of OCR and ECAR were taken for the baseline and after the sequential injection of mitochondrial inhibitors (1.5 µM oligomycin and 1.5 µM rotenone/antimycin A). OCR and ECAR from each well were normalized by protein content by using the Bradford assay.
High Methionine and Compound C Induce Proteomic Changes
We recently published findings that methionine activates AMPK and increases mitochondrial metabolism and respiration in the model organism Saccharomyces cerevisiae, especially in cells lacking Snf1/AMPK activity [33].
To investigate whether this is a conserved mechanism triggered by methionine, we explored the effect of high methionine concentrations in liver cancer cells, in the presence or absence of Compound C, which mimics AMPK inhibition. We performed a proteomic analysis on HepG2 cells grown for 48 h (CTR), in the presence of Compound C (CTRCC), in the presence of high methionine (MET), and in double treated cells (METCC) by a quantitative shotgun label free strategy. The comparison of the four data sets by an ANOVA test (p-value 0.05) showed proteins exclusively expressed in each condition, as well as 717 proteins common to all data sets, among which, 46 were statistically different. IPA analysis carried out on these proteins to find possible interactions highlighted that 25 out of 46 proteins differentially expressed belonged to a network classified as: cancer, protein synthesis, RNA damage and repair ( Figure 1A). The upstream regulator analysis, based on prior knowledge of expected effects between transcriptional regulators and their target genes in IPA, suggested that two transcription regulators were probably involved, hepatocyte nuclear factor 1 (HNF1A, p-value 0.0005) and hepatocyte nuclear factor 4 (HNF4A, p-value 0.001) ( Figure 1B), which target 6/46 and 12/46 of the proteins differentially expressed, respectively. This result suggests that the presence of methionine and Compound C significantly affects transcription, in keeping with Wang and coworkers who reported an impact on methionine metabolism by HNF4A [34].
Specific analyses were then carried out by comparing MET vs. CTR, METCC vs. MET, CTRCC vs. CTR. A summary of the results obtained is reported in Figure 3, while Table S2-S4 report the list of the proteins differentially expressed (either increased or decreased), or exclusively expressed in one condition in the three comparisons. The corresponding volcano plots are shown in Figure 1C. These proteins were further analyzed to find possible enrichment in GO Biological Processes ( Figure 2) and pathways (Figure 3), in the comparison between control and treatments with high methionine or/and Compound C. Differentially expressed proteins mainly belonged to the classes of metabolic processes, mitochondrion, and mitochondrial dysfunction, translation, RNA and protein processing and response to oxidative stress. As shown in Figure 2A,B and in Table S1, the expression of proteins related to oxidation-reduction events were altered, either up or downregulated, indicating a general impact on these processes in the presence of methionine and/or Compound C compared to the control. Many proteins involved in mitochondrial respiratory chain were upregulated by the double treatment with high methionine and Compound C. In particular, high methionine induced an increase of proteins involved in the mitochondrial electron transport and in complex I assembly, while Compound C treatment mainly increased the level of tricarboxylic acid (TCA) cycle and lipid metabolic processes (Figure 2A). Methionine and Compound C presence upregulated proteins involved in the cellular response to oxidative stress, manly at the endoplasmic reticulum level (Figure 2A, Figure 3), while Compound C alone induced the expression of proteins involved in membrane organization, transmembrane transport, and cell-cell adhesion (Figure 2A).
Interestingly, methionine and Compound C altered sirtuin signaling pathway ( Figure 3). This pathway is a master regulator of several cellular processes and known to both extend lifespan and regulate spontaneous tumor development. It is well known that S-adenosyl-methionine, sirtuin, and mTOR pathway are strictly related [35]. In keeping with these data and with results reported above, the proteomic analysis suggested that mTOR signaling was altered mainly in response to increased methionine in the medium (Figure 3).
Among the processes more altered in HepG2 cells treated with methionine and Compound C, RNA processing and mitochondrial translation were downregulated mainly by methionine, while Compound C had more impact on general translation ( Figure 2B). Overall, the double treatment with high methionine and Compound C induced a decrease in the level of ribosomal proteins RPS9, RPL23A, RPL34, mitochondrial ribosomal proteins MRPL37, MRPS18C, and translational initiation factors, such as EIF1 and EIF3M, as well as proteins of the SRP (signal-recognition-particle) co-translational process (SRP14, SSR3, SRP9), suggesting a general reduction of translation in this condition (Table S4).
High Methionine and Compound C Induce a Metabolic Rewiring
Since methionine supplementation induced deep changes of proteins associated with cellular metabolism, we further investigated this feature by performing metabolomics analysis in cells grown with high methionine and/or Compound C. Extracellular metabolites were analyzed by YSI biochemical analyzer. In HepG2 cells, only the double treatment (high methionine and Compound C) showed an increase of glucose and glutamine consumption, with a proportional increase in lactate and glutamate secretion ( Figure 4A). However, no changes in extracellular metabolites were observed in Huh7 cells in any condition. This difference could be due to the lower growth rate of Huh7 cells, or to their higher basal metabolism compared to HepG2 cells; indeed, their basal glucose uptake and their lactate secretion were three times higher than those of HepG2 cells ( Figure 4A,B). We then measured relative abundance of intracellular metabolites in HepG2 cells treated for 48 h with high methionine and/or Compound C, by an untargeted metabolomic GC/MS and LC/MS analysis. This analysis revealed a strong metabolic change due to high methionine, but also to Compound C addition (Supplementary Figure S1, Figure 4C). The most affected pathways were those related to amino acids metabolism, like Cys-Met (as expected upon methionine supplementation), Ala-Asp-Glu, Arg-Pro, and Gly-Ser-Thr ( Figure 4C), but also those related to aminoacyl-tRNA biosynthesis, coherently with the downregulation of translation associated proteins ( Figure 2B). In addition, alterations in the pathways of glutathione metabolism (which is directly associated to methionine metabolism), of the sulfur-containing molecules taurine and hypotaurine, in the pathways of purine and nucleotide metabolism and of fatty acid metabolism were identified ( Figure 4C). As expected, methionine supplementation in the medium (either in the presence or absence of Compound C) increased the intracellular level of methionine, but also of its derived metabolites, such as S-adenosyl-methionine and S-adenosyl-homocysteine, which are part of the methionine cycle ( Figure 4D). In addition, high methionine treatment induced an increase of cysteine, which can derive from homocysteine and serine, through conversion to cystathionine, and of glutathione, which is synthesized from Cys, Glu, and Gly. In high methionine condition there was also an increase of two direct methionine derivatives, the regulatory metabolite methylthioadenosine (MTA) and the toxic compound methionine sulfoxide ( Figure 4D). Remarkably, the MTA level negatively correlates with growth potential in the liver [36,37]. In fact, it has been reported that MTA decreases after partial hepatectomy, when the replicative response of hepatocytes is higher [38], and MTA administration in vitro reduces liver cell growth [39]. Furthermore, MTA inhibits the synthesis of polyamines [36], whose level is correlated with proliferation and which were indeed decreased in high methionine condition (see spermidine and putrescine, Figure 4E). On the contrary, the MTA derivatives adenine and adenosine increased in high methionine condition ( Figure 4E). High methionine also induced an increase of metabolites related to the urea cycle, such as arginine, ornithine, uric acid ( Figure 4E), which was probably increased to convert excess nitrogen (given by the high methionine supplied in the medium) into urea.
High Methionine and Compound C Increase Mitochondrial Functionality
The metabolomics analysis revealed also that many metabolites of the TCA cycle were modulated by high methionine, with higher levels of cis-aconitate, citrate, isocitrate, α-ketoglutarate, succinate and malate and lower levels of fumarate and oxaloacetate compared to the control ( Figure 4F,G).
Since the metabolomics and proteomics data suggested an increase of the TCA cycle, we measured the rate of ATP production from glycolysis and from mitochondrial respiration in HepG2 and Huh7 cells, through Agilent Seahorse technology ( Figure 4H). Although the amount of ATP deriving from glycolysis was only slightly altered by treatments, cells grown in high methionine condition (with or without Compound C) produced a higher fraction of ATP through mitochondrial respiration ( Figure 4H). The increase of mitochondrial-derived ATP was clearly evident in HepG2 cells and it was also confirmed, albeit with a lower increase, in Huh7 cells.
According to systems biology, the phenotype of a cell results from the interaction of several component, in which the emergent behavior is wider than the sum of their parts [40]. As a result of this, we decided to integrate the results from metabolomic and proteomic analysis, using the IPA and MetaboAnalyst softwares.
This integration better highlighted that TCA cycle was one of the most affected process in all three comparisons (MET versus CTR, CTRCC versus CTR, METCC versus CTR) ( Figure 5A-C). However, as reported in Figure 5D,E, focusing on the first 10 more significant pathways in terms of enrichment (p-values) and topology (pathway impact) in the three comparisons, the integration analysis suggested that citrate and TCA cycle were more affected by the presence of Compound C than methionine, which, in turn, had a major impact on amino acid metabolism, especially of those amino acids directly linked to the nitrogen cycle, such as Arg, Gln, and Glu. In keeping with the effect on TCA cycle, pyruvate metabolism and the synthesis and degradation of ketone bodies prevailed in the presence of Compound C if compared to the supplementation of methionine ( Figure 5D,E). Strikingly, the pentose phosphate pathway, Arg/Pro metabolism, glyoxylate, and dicarboxylate metabolism were altered only upon double treatment ( Figure 5D). In addition, glutathione metabolism and metabolism of xenobiotics, which were affected by single treatments, were no more altered upon double treatment ( Figure 5D). These results indicate that the combination of Compound C and high methionine does not result in a simple synergistic effect of the single treatments, but rather leads to the emergence of new features in liver cancer cell metabolism.
High Methionine Activates AMPK and mTOR Pathways
Since we previously reported that methionine can activate Snf1/AMPK in budding yeast, we then investigated the effect of methionine supplementation in liver cancer cells. AMPK showed a dose-dependent activation after 24 h in HepG2 cells ( Figure 6A), detectable as increased phosphorylation on T172, as well as increased phosphorylation of Acc1-S79, the main target of AMPK, often used as reporter of its activity. It is known that AMPK activation is mainly due to an energy reduction [41], although it can also be activated without any ATP decrease [42]. Since data presented above ( Figure 4H) indicate that ATP level did not decreased, but rather increased after methionine treatment, we can speculate that AMPK activation was not a result of energy deficiency. (A) HepG2 were treated with methionine for 24 h and AMPK activation state was assayed by Western analysis, using the pT172-AMPK antibody (against pT172 in the activation loop) and using the anti-pS79-Acc1 antibody (against the target site of AMPK on Acc1).
An anti-AMPK total antibody and an anti-vinculin antibody were used as controls. (B) HepG2 and Huh7cells were gown in control medium and 1.5 g/L methionine was added to the cultures at time 0. Samples were collected at the indicated time points to evaluate AMPK activation, using an anti-pT172-AMPK antibody and an anti-pS79-Acc1 antibody. An anti-AMPK total antibody and an anti-tubulin antibody were used as controls. (C) HepG2 and Huh7 cells were gown for 48 h in the absence or presence of Compound C. Then 1.5 g/L methionine was added to the cultures, and samples were collected at the indicated time points to evaluate mTOR activation, using anti-pS6K antibody and Akt activation using anti-pS473-Akt antibody. Anti-Akt total antibody and anti-tubulin antibody were used as controls.
To better investigate AMPK activation, we performed time-course experiments in both HepG2 and Huh7 cell lines. A total of 1.5 g/L of methionine was added to cells growing in regular medium and phosphorylation of AMPK-T172 and of Acc1-S79 were detected by Western blot analysis ( Figure 6B). AMPK phosphorylation, as well as the phosphorylation of its target Acc1, increased in both cell lines, with a peak at 0.5 h and 1 h after methionine addition, for HepG2 and Huh7 respectively ( Figure 6B).
Since amino acids can activate mTOR, the master regulator of cell growth [43] and, since mTOR involvement was suggested by our proteomics analysis (Figure 3), we investigated the activation of the mTOR pathway. mTOR was activated in response to high methionine in the medium, as observed by increased pS6K phosphorylation ( Figure 6C), in keeping with previously reported data [44], both in HepG2 and in Huh7 cells. Phosphorylation increased after 30-min treatment, remaining high until 16 h ( Figure 6C). This increase was more evident in cells pre-treated with Compound C, in which the release of mTOR inhibition by AMPK resulted in a higher pS6K phosphorylation, both at time 0 and in response to high methionine ( Figure 6C). High methionine also induced activation of the Akt pathway, involved in cell proliferation and survival [45], in both cell lines, although in HepG2 cells, it was less persistent over time when AMPK was inhibited ( Figure 6C).
High Methionine Reduces Cancer Associated Phenotypes
We previously showed that, in yeast cells, methionine induced a slow-down of growth rate, especially in cells lacking Snf1/AMPK activity [33]. The increase of methionine concentration in the medium (up to 1.5 g/L, versus 15 mg/L in regular medium) induced a slight slow-down of growth rate both in HepG2 and in Huh7 cell lines ( Figure 7A,B). Inhibition of AMPK with Compound C slightly reduced growth rate in both cell lines, but drastically impaired growth when combined with high methionine in the medium ( Figure 7A,B). These results suggest that the effect of methionine on cellular proliferation is a conserved feature that deserves further investigation.
In addition to a higher proliferation, one of the most relevant features of cancer cells is their ability to migrate and to form colonies from single cells, to give metastasis [46]. To analyze the effect of methionine on cell migration, we used Boyden chambers and migration of serum starved cells was assessed in the presence or absence of high methionine concentration and/or Compound C. Cell migration through the membrane of the transwell was significantly impaired in high methionine in both cell lines, even more when AMPK was inhibited ( Figure 7C,D).
We then tested the ability of single cells to form colonies in the presence of high methionine and/or inhibition of AMPK. As shown in Figure 7E,F, colony formation was reduced by high methionine supplementation, especially in the presence of Compound C ( Figure 7E,F).
Altogether, our results suggest that high methionine supplementation inhibits cancer associated phenotypes, especially in combination with AMPK inhibition.
The Effect of High Methionine and Compound C is Specific for Liver Cancer Cells
Although Compound C has been extensively used to inhibit AMPK activity, it may also inhibit other kinases. To discern whether the effect of Compound C on growth inhibition in high methionine condition was specifically due to AMPK inhibition, we silenced the expression of AMPKα/α' subunits in HepG2 and Huh7 cells and tested their growth in the presence of high methionine in the medium ( Figure 8A,B). We found that cells with siAMPKα/α' showed a reduced growth at 72 h in the presence of high methionine, thus, confirming that high methionine had a negative effect on growth when AMPK activity was low (either due to chemical inhibition or to reduction of the catalytic subunit).
These data confirm that the effect of Compound C on growth rate was mediated by AMPK inhibition, and not by non-specific effects of the compound. Finally, to analyze whether the effect of high methionine and AMPK inhibition was specific for liver cancer cells or could be observed also on other cancer cell types, we performed the growth assay and the clonogenic assay on three cell lines deriving from different tumors: SW480 colorectal cancer cells, A549 lung cancer cells and MCF7 breast cancer cells. High methionine and/or Compound C had no effect on the growth of SW480 and A549 cells, while high methionine slightly reduced the growth of breast MCF7 cells also in combination with AMPK inhibition ( Figure 8C). However, the ability of forming colonies from single cells was not impaired in none of the cell lines tested ( Figure 8D), although in MCF7 cells high methionine induced the formation of smaller colonies (not shown).
Thus, we can assume that the effect of this treatment on cancer associated phenotypes is specific for liver cancer cells.
Discussion
The role of methionine has been long investigated in many different fields [13]. Indeed, it is well known that methionine restriction extends lifespan in different model systems, from budding yeast to Drosophila melanogaster, Caenorhabditis elegans, and mammalian cells [47][48][49][50]. Methionine restriction also affects the cardiovascular system [51] and bone development [52].
On the contrary, the relationship between methionine and human cancers progression is still very ambiguous, most probably because it is cancer specific. Different studies showed that methionine restriction delays cancer progression. This was reported for instance in colon and prostate cancer animal models [53,54], as well as in breast cancer in vitro and in vivo [55,56]. On the contrary, other reports indicate that methionine supplementation induces cell-cycle arrest and transcriptional alterations in breast and prostate cancer cells [57,58].
It was reported that S-adenosyl-methionine (SAM) supplementation inhibits liver cancer cell invasion in vitro [19], by inducing changes in the methylation state of DNA, that lead to downregulation of genes involved in growth and metastasis, already known to be upregulated in liver cancer cells. In addition, in a rat model of hepatocarcinogenesis [59], as well as in a mouse model for inflammation-mediated HCC [20], SAM administration exerted a chemopreventive effect on HCC development. However, although a short-term treatment with SAM showed positive effects in the mouse model, a long-term administration did not affect tumor growth and hepatocyte proliferation [20]. Here, we explored the combination of methionine administration (the precursor of SAM) and AMPK inhibition in vitro. AMPK is a dual role kinase, being either anti-or pro-tumorigenic depending on the context, on the stage of tumor development and on the cancer type [6]. We showed that, in liver cancer cells, high methionine concentration in the medium reduces cell growth inhibits colony formation and cell migration in two different liver cancer cell lines ( Figure 7) and, remarkably, these phenotypes were increased when high methionine was combined with AMPK inhibition (Figures 7 and 8). This is perfectly in line with what we observed in budding yeast, where growth rate reduction due to methionine in the medium was largely dependent on the presence of an active Snf1/AMPK pathway [33]. Moreover, methionine induces an activation of Snf1/AMPK in S. cerevisiae, as we observe in liver cancer cells (Figure 6), highlighting that AMPK involvement in the response to methionine is a conserved feature in eukaryotic systems. An intriguing aspect of methionine response is the activation of the mTOR pathway ( Figure 6), which is coherent with the reported effect of SAM (the first metabolite of methionine) on mTOR activation through SAM-sensor upstream of mTOR1 (SAMTOR) [44]. However, the intracellular level of most amino acids was downregulated in cells grown in high methionine (Figure 4), and this probably leads to the observed downregulation of proteins involved in translation and tRNA synthesis (Figures 1, 2, 4 and 5) and to the reduction of growth rate (Figure 7). This condition-active mTOR with reduced translational capacity-reminds the condition of cycloheximide treatment, in which mTOR phosphorylates pS6K [60] although growth is impaired, thus, producing the effect of a counter circuit in the cell.
High methionine has also a strong effect on metabolome and proteome remodeling, as also reported in yeast cells [33], especially when combined with Compound C. In fact, reduction of intracellular amino acid levels and alterations in metabolites of the TCA cycle were found in both yeast and liver cancer cells. Interestingly, the observed increase of proteins and metabolites of the TCA cycle could be a direct consequence of methionine metabolism to homocysteine, which can be then converted to α-keto-butyrate and enter the mitochondria. However, in yeast cells, the effect of methionine on mitochondrial functionality was much more evident, probably due to the fact that yeast cells have a fermentative metabolism in the presence of glucose. On the contrary, in human cells, which have a mixed respirative and fermentative metabolism, the effect on mitochondria better emerges by integrating metabolomics and proteomics data. These results, together with the reduction of polyamine levels (which are associated to growth rate), could explain at least in part the observed reduction of cancer phenotypes.
Why does methionine metabolism have this anti-tumor role on liver cancer cell lines, contrary to other cancer cells? It should be noted that methionine metabolism in the liver is very peculiar, since the liver is the organ where most of the methionine is converted to S-adenosyl-methionine and where only the gene MAT1A is expressed. Therefore, most of the observed effects could be due to this liver-specific metabolism, although we cannot exclude the possibility that methionine could carry out also other functions more related to protein synthesis. In fact, Mato and Lu suggest that liver cancer cells, in contrast to normal non-proliferating, differentiated hepatocytes, tend to utilize methionine mainly for protein synthesis [17].
An interesting translational application of our results could be to directly increase methionine uptake through the diet, both in animal models and in human patients. Methionine should easily reach the liver, since it is the physiological organ where it is metabolized. It should have no side effect on normal hepatocytes, since SAM was shown to have negligible effects on primary untransformed liver cells [19]. Therefore, further investigations should explore the possibility that alterations in methionine dietary consumption, in combination with pharmacological treatments, could have clinically relevant outcomes in liver cancer patients. Figure S1: Hierarchical clustering heatmap from One-way ANOVA analysis of the entire set of metabolites differentially expressed. | v3-fos-license |
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} | pes2o/s2orc | Isolation, identification and growth conditions of calcite producing bacteria from urea-rich soil
Bacterial chemical reactions, such as urea hydrolysis can induce calcium carbonate precipitation. The induced production of calcium carbonate formed by microorganisms has been widely used in environmental and engineering applications. The present study aimed to isolate, identify and optimize growth conditions of urease positive bacteria from urea rich soil in Gaza Strip. Bacterial isolates, which tolerated ≥10% urea concentration, were selected for the investigation. Eight isolates recovered and identified to be spore forming, urease positive, alkaliphile, halotolerant, and presumptively belonged to Bacillus species. All isolates showed best growth at temperature 37°C, and pH 9-9.5. After exposure to UV irradiation, most isolates showed improved tolerance to urea concentration, however, other strains showed a decline in their adaption to urea concentrations. The mutant form of isolate in soil sample #3 showed the highest tolerance to urea concentrations at all exposure intervals, when compared with wild type. Moreover, all isolates precipitated calcium carbonate. The locally recovered isolates are promising contributors in the process of calcite Biomineralizaion and may be utilized in the remediation of concrete cracks, increase of compressive strength of concrete, decrease water permeability, and solve the problems of soil erosions.
INTRODUCTION
Biological precipitation of minerals (Bio-mineralization) is a widespread phenomenon in the microorganism's world, and is mediated by bacteria, fungi, protists, and even by plants. Calcium carbonate (Calcite) is one of those minerals that naturally precipitate as a by-product of microbial metabolic activities (Seifan and Berenjian, 2019).
Microbial metabolic activities facilitate calcium carbonate (calcite) precipitation, in a well-studied process called microbial induced calcium carbonate precipitation (MICP) (Zambare et al., 2019). MICP usually occurs due to the chemical alteration of the environment induced by the microbial activity (Sarikaya, 1999;Stocks-Fischer et al., 1999;Warren et al., 2001;De Muynck et al., 2010a). Bacteria can be invested as a major player in the MICP phenomenon through various mechanisms. The most significant mechanism is the bacterial ureolytic activity (Stocks-Fischer et al., 1999;Warren et al., 2001;Krajewska, 2018). Urea hydrolysis can be facilitated by *Corresponding author. E-mail: [email protected]. Tel: 00970594157573. Author(s) agree that this article remain permanently open access under the terms of the Creative Commons Attribution License 4.0 International License bacteria that can produce urease (urea amidohydrolase) enzyme and thus are able to induce CaCO 3 precipitation (Stocks-Fischer et al., 1999;Hammes and Verstraete, 2002;Phillips et al., 2013;Bhaduri et al., 2016). Calcium carbonate precipitation is a chemical process controlled mainly by four key factors: (1) calcium ions concentration, (2) dissolved inorganic carbon (DIC) levels, (3) the pH, and (4) the availability of nucleation sites (Hammes and Verstraete, 2002;Seifan and Berenjian, 2018).
Over recent years, MICP has received considerable attention and has been proposed as a potent solution to address many environmental and engineering issues (Seifan and Berenjian, 2019). It has been intensely investigated in bulk systems, sand columns (Dhami et al., 2013a;Seifan et al., 2016;Tziviloglou et al., 2016), and bio-cementation processes (Seifan et al., 2016). It has been found that MICP may drive many potential applications in civil engineering such as enhancing stability of slopes and dams, reducing the liquefaction potential of soil, road construction, prevention of soil erosion, increase durability and compressive strength of concrete, as well as the repair of the cracks in concrete (De Muynck et al., 2010a;Stabnikov et al., 2011, Shashank et al., 2016. Many bacterial species have been studied to exploit their abilities in the biomineralizing of calcite (MICP). One of the most robust ureolytic bacteria is Sporosarcina pasteurii (formerly known as Bacillus pasteurii). S. pasteurii is facultative anaerobes, spore forming, bacilli bacteria. It utilizes urea as an energy source and eliminates ammonia which increases the pH in the environment and generates carbonate, causing Ca 2+ and CO 3 2to be precipitated as CaCO 3 (Clive, 1990, Stocks-Fischer et al., 1999Chahal et al., 2011). Other studies showed the role of bacteria that are mostly related to Bacillus spp. in the process of MICP (Stocks-Fischer et al., 1999;Elmanama and Alhour, 2013;Ali et al., 2020). The aim of this study is to isolate, identify, and optimize growth conditions of locally isolated urease-producing bacteria that are able to produce calcite crystals.
Sample sources and characteristics
This study utilized soil samples varying in urea content from Gaza Strip. About 100 g of each sample were collected during June 2020 from the following sources: (1) Sea coastal sand from Rafah city beach, (2) outlet sewage water (treated sewage), (3) inlet sewage water (untreated sewage), (4) soil sample with cat`s urine from Gaza city, (5) coastal sand with dog`s urine from Rafah city beach, (6) sand with dog`s urine from Rafah city, (7) agricultural soil with dog`s urine from Gaza city, (8) agricultural soil with dog`s urine from Rafah city, (9) urea rich soil from greenhouse in Gaza city, and (10) ammonia rich soil from a greenhouse in Gaza city.
Sample processing and bacterial isolation
Five grams of soil samples were mixed with 20 ml sterile saline (stock suspension) and dilutions 10 -1 and 10 -2 were made. A volume of 0.1 ml of the stock suspension as well as the two dilutions were cultured onto 2 and 3% urea containing Nutrient Agar (NA) plates (HiMedia, India). The media were prepared according to HiMedia manufacturer recommendations. Extra pure urea suspensions (Honeywell Riedel-de Haen, Germany) were filtered, then added to media after autoclaving and cooling to 50°C. Cultures were incubated at 37°C, and plates were examined after 24 and again after 48 h.
Bacterial tolerance to high urea concentration
Bacterial isolates were obtained as a pure culture and then cultured on 5, 8, 10, 12, and 15% urea enriched NA media, incubated at 37°C for 48-72 h, and after the incubation period bacteria were harvested to be cultivated in nutrient broth and agar plates. Bacterial isolates tolerated ≥ 10% urea concentration were selected for further testing.
pH profile
The isolates were inoculated into 3 ml of Nutrient Broth tubes with pH scale of 1 to 14. A bacterial suspension was made and the turbidity was adjusted to 0.5% McFarland standard, incubated for 24 h at 37°C, and growth has been measured as turbidity at O.D 600 nm using CT-2200 spectrophotometer (Chrom Tech, Taiwan). Results were recorded against a blank of bacterial suspension. 1N HCl (HiMedia, India) and 1N NaOH (Frutarom, Palestine) were used to adjust the pH. An additional nutrient broth tubes at pH 7 were inoculated with the bacterial isolates, incubated at 37°C, and the change in pH was monitored during growth, using a pH meter (Jenway pH Meter 3510 /mV, USA ) results were recorded after 30 min, 1 , 2, 4, 8, 24, and 32 h of inoculation.
Effect of temperature on growth
Bacterial isolates were inoculated into nutrient broth tubes (HiMedia, India), adjusted to 0.5% McFarland standard, incubated for 24 h at 0, 4, 25, 37, 45 and 60°C, and the turbidity was measured using spectrophotometer at O.D 600 nm.
Ultraviolet (UV) induced mutagenesis for bacterial isolates
The selected isolates were grown overnight in NB + 2% urea in a shaking incubator (Boeco, Germany) at 37°C. The isolates were washed three times with sterile phosphate-buffered saline, resuspended in urea free and sterile NB. The turbidity of cell suspensions was adjusted to a 0.5% McFarland reagent and exposed to UV light using a Philips 20 W germicidal lamp for 2-20 min with 2 min intervals. From each exposure interval, a loopful of the exposed bacteria was cultured onto urea-based agar (HiMedia, India). After incubation of 24 h at 37°C, a single, well-defined colony was chosen, cultivated on NA plates, and then inoculated onto NA with varying urea concentrations; 5, 8, 10, 12 and 15% respectively. After incubation, bacterial growth was observed and compared to wild type growth on the different urea concentrations.
Mini-scale of calcium carbonate precipitation
Bacterial isolates were subjected to calcium carbonate production test as described previously (Ghosh et al., 2019). Alive bacterial isolates were inoculated into nutrient broth (NB) containing both urea and calcium chloride (NBUC), NB with only urea (NBU), and NB with only calcium chloride (NBC). The same procedures were repeated with autoclave killed bacterial suspension (pellet and supernatant filtrate). To all tubes, a concentration of 0.012 g/L phenol red was used as a pH indicator. NBUC and NBU were prepared to contain 2% of urea. NBUC and NBC were prepared to contain 2.8 g/L of calcium chloride. Urea and calcium chloride solutions were filter sterilized and separately added to phenol red containing NB before bacterial inoculation. A urease enzyme reagent obtained from Blood urea nitrogen kit (Biosystems, Spain) was used as a positive control, while Escherichia coli ATCC 25922 (urease negative) was used as a negative control. Non-inoculated tubes were used as a validity control. All tubes were incubated at 37°C for 24 h. The trial was performed in triplicate.
Bacterial isolates characteristics
After the incubation of soil samples cultures, one to four colonies of isolated bacteria were selected from those plates containing few and well isolated colonies. Colonies were creamy white or pale yellow to bright orange colored and slightly convex with an entire margin.
Selection of the suitable urease producing isolates
By culturing all isolates on 5, 8, 10, 12, and 15% urea agar media, those that tolerated ≥ 10% urea concentration has been chosen to proceed with.
Bacterial Identification and biochemical characterization
All selected isolates were spore-forming, Gram-positive bacilli, catalase and urease positive. Table 2 shows the phenotypic characteristics of the eight isolates and indicates the biochemical tests that have been used in the identification process. ABIS online Software has been used in bacterial identification (Costin and Lonut, 2017). Table 3 shows the presumptive identification of the selected isolates according to the ABIS online Software.
Growth conditions
The optimal pH at which all selected isolates showed the highest turbidity and rapid growth ranged from 7 to 10, with a preference to the pH 9 (Figure 1), thus all tested isolates are moderate alkaliphiles. For most isolates, the pH of media has increased during growth to reach the maximum of 9 to 9.5. All isolates showed significant growth at temperature 37°C (Table 4). Most isolates showed halophilic characteristic as they grew at NaCl concentration up to 5%.
Urea tolerance for bacterial isolates after UV exposure
In general, most isolates showed improved tolerance to urea concentration after exposure to UV light when difference of tolerance to urea between wild type and mutant form.
Mini-scale calcium carbonate precipitation experiment
NBUC tubes for all live isolates, and the pure urease enzyme showed change in pH from neutral to alkaline (yellow to pink), and a precipitate of calcium carbonate were noticed at the bottom of the tubes. NBU tubes for all live isolates and urease enzyme showed only change in pH. NBC tubes for all live isolates and urease enzyme showed neither a change in pH nor calcium carbonate precipitation. Changes in color and pH indicate ureolytic activity (Table 6). A comparison of NBUC of live bacteria versus killed isolates (both killed cells and supernatant) and E. coli, all of them were unable to change pH, so there was no urea hydrolytic activity due to the absence of the enzyme. Consequently, there was no calcium carbonate precipitation. Unchanged non-inoculated tubes suggests that results obtained are reproducible and representative (Table 6). Precipitate containing and non-containing tubes were examined under light microscope to confirm the presence of calcium carbonate crystal (Figure 2).
DISCUSSION
The present study was conducted to isolate, characterize, and optimize locally adapted urease-releasing bacteria that inhabits urea rich soils. Microbial activity that involves the cleavage of urea into ammonia and carbon dioxide by the urease enzyme, leading to the precipitation of carbonate ions as calcium carbonates. This potentially useful application explains the need to enhance urease production by various methods among candidate microorganisms (Vempada et al., 2011). The biochemical profile of the selected isolates showed that all isolates belong to the genus Bacillus (Table 3). This is similar to a previous study that isolated and characterized urease positive bacteria from urea rich soils, in which several isolates were mostly related to the Bacillus group (Ali et al., 2020).
Despite the differences in their characteristics, the obtained isolates showed similar behavior in their ureolytic capability. This is in agreement with the findings of Stocks-Fischer et al. (1999); Hammes et al. (2003) and Stabnikov et al. (2011) that reported the same ureolytic Bacillus strains that can be isolated and cultivated using the same followed protocols of isolation and cultivation. Phenotypic and biochemical profiles of the isolates were matched to those Bacillus species reported previously that proved active in MICP process (Stocks-Fischer et al., 1999;Elmanama and Alhour, 2013).
Ureolysis-driven MICP is a phenomenon that has many applications for biochemical and engineering purposes (Omoregie et al., 2020). It has been widely investigated for soil stabilization, healing of concrete cracks, restoration of limestone surfaces, preventing soil erosions, and treatment of industrial wastewater and removing heavy metals (Whiffin et al., 2007;Sarda et al., 2009;Van paassen, 2009;De Muynck et al., 2010a;De Muynck et al., 2010b;Wu et al., 2019).
All obtained isolates showed ureolytic activity, tolerance to high urea concentrations, as well as calcium carbonate production. This suggests that isolates are potential candidates for the applications of MICP. Isolates 10.1 that was identified as B. mycoides, has been previously isolated and showed an efficient role of increased sand consolidation and compressive strength of cement (Elmanama and Alhour, 2013). Isolate 8.3 has been identified as B. licheniformis, has been reported in a previous study that it was able to precipitate calcium carbonate by ureolysis (Helmi et al., 2016).
Bacteria are previously known to breakdown urea in order to: (1) elevate the ambient pH (Burne and Marquis, 2000), (2) consume it as a nitrogen source (Burne and Chen, 2001), and (3) use it as a source of energy (Mobley and Hausinger, 1989). The amount and rate of urea that can be cleaved were influenced by the urea and calcium source (Wang et al., 2017). In this system, urea is the source of the carbonate. The more urea is supplied, the more CaCO 3 can be produced, if a sufficient amount of calcium ions is available (Wu et al., 2019). In this study, isolates that were selected tolerated and grew in the presence of 10 -15% urea concentration. This because urease activity, as well as, calcium carbonate production rate depend on urea concentration. A previous study utilized S. pasteurii and emphasized the role of urea containing cultural medium in the proliferation of bacteria. Moreover, it reported that bacteria cultivated with urea displayed a healthier cell surface and more negative surface charge for calcium ion binding than the bacteria have been cultivated without urea (Ma et al., 2020).
Increasingly, it has been reported that the bacterial concentration and ureolytic activity are important contributors in the efficiency of MICP process. The urea hydrolysis is an extremely slow process, whereas the presence of urease enzyme can substantially increase the hydrolysis of urea (De Belie et al., 2018). Therefore, the selection of the bacterial isolates with higher ureolytic activity is desirable for the higher production of calcium carbonate.
However, it has been shown that when the content of urea is excessive, bacterial growth and ureolytic activity are inhibited. For instance, when the urea concentration was greater than 0.75 mol/L, the amount of urea breakdown was decreased and thus appears as an inhibitory component. The reason could be due to too high urea molecule transportation over the cell
A B
membrane into the cell, at elevated urea concentrations, inhibiting other cellular processes. Therefore, a certain amount of bacteria can only metabolize a certain amount of urea hydrolysis (Wu et al., 2019). In our study, the local isolates were halo-tolerant, and corroborate with the findings of previous studies (Stabnikov et al., 2013). The observation of the pH tolerance profile of bacterial isolates showed a common moderate alkaliphile property. The best growth was at pH range 7-10 with a preference to pH 9. This is in agreement with a previous study that showed the good alkali tolerance of B. cereus which was successfully used to heal concrete cracks (Stabnikov et al., 2013;Wu et al., 2019).
Generally, the optimal pH range for bacterial growth is 7 to 8. Under higher alkaline conditions (pH 9 -12) bacteria can still grow but at a much-declined rate. Although the pH is relatively high in fresh concrete, the pH at cracks may drop to 8-11 due to carbonation, exposure, and humidity (De Muynck et al., 2010a). Above pH 11, the bacteria have a limited capacity to precipitate CaCO 3 , thus limited ability to heal cracks. This implies that bacterial spores will keep dormant after being embedded in the concrete matrix (pH > 12), and only start to become active after cracks appear and crack surface pH drops (Wang et al., 2017;Wu et al., 2019). Therefore, alkaline pH is the primary factor by which bacteria promote calcite precipitation (Castanier et al., 2000;Fujita et al., 2000). Another study showed that the calcium carbonate yield (mg calcium carbonate/CFU) in the presence of Bacillus species increases when bacteria grown at a relatively high pH in compared with those bacteria that grown at uncontrolled pH solution (Seifan et al., 2017). Another study investigated the factors affecting the S. pasteurii induced biomineralization process, reported that the rise in medium pH to 9.5 accelerate bacterial growth (Ma et al., 2020). This may be promising that Bacillus isolates in this study can be used to heal concrete cracks. Especially, in the pH range of 7-11, bacteria will have a remarkable ureolytic activity, which ensures the decomposition of urea and the precipitation of CaCO 3 . This meets also with (Phang et al., 2018) findings, It has been reported that some bacterial ureases exhibited high activity in alkaline conditions at pH of 9.
In the present study, the effect of temperature on isolates growth showed a temperatures range from 25 to 40°C. Bacterial mediated urea hydrolysis is an enzymatic reaction controlled by many factors including temperature. It has been reported in the literature that temperature affects bacterial activity, urease activity, and therefore reaction rate. Hence, the rate of formation of biogenic CaCO 3 and crack healing efficiency will be affected as well.
Urease activity is stable between 15 and 25°C, and an increase in temperature (until 60°C) results in increased urease activity (Whiffin, 2004;Peng and Liu, 2019).
Isolates that were exposed to UV irradiation were compared with their corresponding wild type isolates for the ability to tolerate higher urea concentrations. Most isolates showed improved tolerance to urea concentration. However, other strains showed a decline in their adaption to urea concentrations. This suggests that the mutagenesis process is random and did not correlate to the time of exposure to UV light. This is similar to the findings of a previous study, which used UV irradiation on S. pasteurii in order to improve urease activity (Wu et al., 2019).
The established calcium carbonate precipitation process showed that all NBUC tubes containing the viable isolates showed accompanied ureolytic and calcite precipitation activity. On the other hand, NBUC tubes containing the autoclave-killed isolates (pellet or supernatant) showed neither ureolytic nor calcite precipitation activity. This suggests that bacteria activity and urease positivity is a principal contributing to pH change due to urea cleavage, as well as calcium carbonate precipitation. In all NBC tubes inoculated with the viable isolates there was no calcium carbonate precipitation observed. This suggests that calcium carbonate production is enhanced by the change of pH. In NBC tubes (without urea), there was no difference in color change or calcium carbonate precipitation between live bacteria, killed bacteria, or E. coli. All NBU tubes inoculated with the viable isolates showed a change in pH as a proof for the ureolytic activity they possess. Negative control (E. coli) showed no change in pH or calcite production. These findings matched a previous study that reported the ability of urease producing bacteria S. pasteurii to produce calcium carbonate crystals under the same conditions (Ghosh et al., 2019). This is in agreement with the previous studies that reported that Bacillus sp. is with high respect in compared with other genus and that this might be due to their physiological ability to adapt to stressed conditions (Helmi et al., 2016).
In conclusion, this study successfully and easily isolated several Bacillus species from locally collected soil samples. These strains are alkaliphile, grow well at pH 7-10, and tolerate high urea concentrations. They showed calcite biomineralizing properties and may be employed in bacterial remediation of concrete cracks, increasing the compressive strength of concrete, decreasing water permeability, and solve the problems of soil erosions. Further studies on a larger scale are recommended to confirm the findings. | v3-fos-license |
2018-08-13T13:04:43.119Z | 2018-08-13T00:00:00.000 | 51965916 | {
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} | pes2o/s2orc | AromaDb: A Database of Medicinal and Aromatic Plant’s Aroma Molecules With Phytochemistry and Therapeutic Potentials
In traditional, herbal medicine, and aromatherapy, use of essential oils and their aroma compounds have been known since long, for the management of various human diseases. The essential oil is a mixture of highly complex, naturally occurring volatile aroma compounds synthesized by medicinal and aromatic plants as secondary metabolites. Essential oils widely used in pharmaceutical, cosmetic, sanitary, food industry and agriculture for their antibacterial, antiviral, antifungal, antiparasitic, insecticidal, anticancer, neuroprotective, psychophysiological, and anti-aging activities. Moreover, volatile aroma compounds comprise a chemically diverse class of low molecular weight organic compounds with significant vapor pressure. However, aroma compounds produced by plants, mainly attract pollinators, seed dispersers and provide defense against pests or pathogens. However, in humans, about 300 active olfactory receptor genes are involved to detect thousands of different aroma compounds and modulates expression of different metabolic genes regulating human psychophysiological activity, brain function, pharmacological signaling, and therapeutic potential. Keeping in mind this importance, present database, namely, AromaDb (http://bioinfo.cimap.res.in/aromadb/) covers information of plant varieties/chemotypes, essential oils, chemical constituents, GC-MS profile, yield variations due to agro-morphological parameters, trade data, aroma compounds, fragrance type, and bioactivity details. The database includes 1,321 aroma chemical structures, bioactivities of essential oil/aroma compounds, 357 fragrance type, 166 commercially used plants, and their high yielding 148 varieties/chemotypes. Also includes calculated cheminformatics properties related to identification, physico-chemical properties, pharmacokinetics, toxicological, and ecological information. Also comprises interacted human genes affecting various diseases related cell signaling pathways correlating the use of aromatherapy. This database could be a useful resource to the plant’s growers/producers, an aroma/fragrance industrialist, health professionals, and researchers exploring the potential of essential oils and aroma compounds in the development of novel formulations against human diseases.
In traditional, herbal medicine, and aromatherapy, use of essential oils and their aroma compounds have been known since long, for the management of various human diseases. The essential oil is a mixture of highly complex, naturally occurring volatile aroma compounds synthesized by medicinal and aromatic plants as secondary metabolites. Essential oils widely used in pharmaceutical, cosmetic, sanitary, food industry and agriculture for their antibacterial, antiviral, antifungal, antiparasitic, insecticidal, anticancer, neuroprotective, psychophysiological, and anti-aging activities. Moreover, volatile aroma compounds comprise a chemically diverse class of low molecular weight organic compounds with significant vapor pressure. However, aroma compounds produced by plants, mainly attract pollinators, seed dispersers and provide defense against pests or pathogens. However, in humans, about 300 active olfactory receptor genes are involved to detect thousands of different aroma compounds and modulates expression of different metabolic genes regulating human psychophysiological activity, brain function, pharmacological signaling, and therapeutic potential. Keeping in mind this importance, present database, namely, AromaDb (http://bioinfo.cimap.res.in/aromadb/) covers information of plant varieties/chemotypes, essential oils, chemical constituents, GC-MS profile, yield variations due to agromorphological parameters, trade data, aroma compounds, fragrance type, and bioactivity details. The database includes 1,321 aroma chemical structures, bioactivities of essential oil/aroma compounds, 357 fragrance type, 166 commercially used plants, and their high yielding 148 varieties/chemotypes. Also includes calculated cheminformatics properties related to identification, physico-chemical properties, pharmacokinetics, toxicological, and ecological information. Also comprises interacted human genes affecting various diseases related cell signaling pathways correlating the INTRODUCTION Both flowers and leaves of plants emit aroma compound that moved through the air and is detected by the olfactory system of animals. The complexity of aroma is still challenging because smell of any flower or plant is not due to a single chemical compound. Although plants and flowers consist of many chemical compounds, all of them do not contribute in the aroma. As the rose scent majorly influenced by major constituent compound (-) -cis-rose oxide and minor constituent compounds, namely, beta-damascenone (family -rose ketones) and betaionone of the plant's essential oil, while other compounds like geraniol, nerol, (-) -citronellol, farnesol, and linalool contributions as minor.
Aroma is a mixture of volatile compounds with a molecular weight <300 and high vapor pressure, but the complete group of volatile compounds comprises thousands of inorganic and organic compounds stemming from major pathways of secondary metabolism (Frauendorfer and Schieberle, 2006). There are three significant pathways which involved in the biosynthesis process of the main aroma components in plants, such as the shikimic acid pathway by which eugenol (cloves) biosynthesized, degradation of lipids for the formation of short-chain alcohols and aldehydes and terpenoid pathway by which geraniol (Rose) and menthol (Peppermint) are synthesized. These aroma molecules act as semiochemical (mixture or chemical that carries a message for the purpose of communication), pheromones, defense mechanism, allow animals to recognize and detect individuals. The pheromone aroma molecules are essential for mating choices, sexual behavior, fertilization, and nursing and to warn kin in situations of danger (alarm pheromones) and to defend against predators. Aroma molecules are also involved in communication and interaction like plant-plant interaction (Das et al., 2013) and plant-animal interactions (Herrera and Pellmyr, 2002). These interactions and communications were performed through pollination (Schaefer et al., 2004), while another property of aroma molecule is a plant's defense response against herbivores (Glaum and Kessler, 2017). Bacteria also emit a wealth of aroma molecules with an influence on plants, fungi, animals, and bacteria (Vet and Dicke, 1992;Heil and Silva Bueno, 2007). Aroma compounds can also be found in food, spices, perfumes, wine, fragrance oils, and essential oils. These molecules form biochemically during ripening of fruits and other crops. Aroma fragrance molecules are of great commercial interest, resulting in many applications of volatile aroma molecules in research, health, food, cosmetic, and health industries.
A comprehensive information of plants based aroma molecules (2D and 3D structures), aroma or fragrance type, essential oils, respective commercial plants varieties available for cultivation in India and abroad, therapeutic potential in terms of biological activities, physicochemical, stereo, and pharmacokinetics (ADMET) properties of aroma molecules, interacted human genes and corresponding essential oil's export/import trade data trend information are not publicly available for both scientific research and industrial use. It was, therefore, our goal to develop a database AromaDb which cover basic and advance information of aroma molecules and provide a platform for comparative analysis and quantitative structure aroma relationship studies (QSAR).
The AromaDb database would be helpful to answers the queries of researchers, industries, and growers related to aroma compounds and essential oils. However, a variety of database resources of, essential oils, scent, fragrance and flavor (synthetic) components are already reported but still limited at some extent focused on certain subgroups, for example, SuperScent (Dunkel et al., 2009), OdorDB, Pherobase, EssOilDB (Kumari et al., 2014); ScentBase 1 , AroChemBase 2 , and Flavornet 3 ( Table 1). Consequently, these databases are good but useful for special purposes, and therefor there is a need for a comprehensive listing of commercially important volatile aroma molecules found in plant's essential oils from different local and globally grown aromatic plants with information of chemotype-specific varieties, type of fragrance (aroma), physicochemical properties of aroma molecule, chemical identification, chemical structures (2D & 3D) for free downloads, Pharmacokinetic properties (ADMET), effect of aroma on human genes, their vapor pressure and logP, " and export and import trade data trends in terms of global demand and business turnover in India and abroad, so that to guide the future research directions for researchers, growers, farmers, and industries.
Data Selection and Resources
Data were retrieved from the national and international literature and various web database resources or papers published in national and international journals. The database includes records from more than 100 scientific journals related to aroma (fragrance) and flavor. Following web resources were used to select the required data, such as selection of floral (Baldwin et al., 2006) ( Table 2). The abstracts were screened searched against chemical names and synonyms of chemical compounds on given literature search engines. The information regarding adverse effect or allergic responses (skin irritation toxicity) by some aroma molecules is covered in the AromaDb database under fields "physical and ADMET properties" which was calculated through TOPKAT module of Discovery Studio v3.5 software (Accelrys, San Diego, CA, United States). Compounds human genes interaction data was retrieved from the EPA (United States Environmental Protection Agency) 4 and ACToR 5 information portal by searching all compounds using CAS ID.
Database Development Backend Information
AromaDb database is developed on Apache HTTP server, which is platform independent and available as open-source software. The database is developed on MySQL for storing the information in the backend. The database website front end is developed in PHP, HTML, CSS, and JavaScript. AromaDb comprises basic and advances molecular information retrieved from different resources as shown in Figures 1A,B.
Structure Similarity Search Tool Application and Structure Editor
Structure editor and structure similarity search tools in AromaDb for structural similarity within the database and from the extended database, i.e., ChemAxon SMILES based search option was used, for displaying 3D structures, JSmol was used which is available as open-source JavaScript viewer for chemical structures 4 https://www.epa.gov/ 5 https://actor.epa.gov/actor/home.xhtml in 3D 6 . JSmol is the extension of the Java-based molecular visualization applet Jmol 7 as an HTML5 JavaScript-only web app. Similarly, JSME (JavaScript Molecular Editor) was applied for the built-in molecule editor, which allows the user to screen with self-edited molecules. JSME is a free molecule editor written in JavaScript 8 .
Database Content
The major parameters in the database were plants, variety, essential oils, chemical molecules, chemical group, trade data, structural data, and biological activity of essential oil, compounds and their interacting genes, etc. For better performance, all the data is kept distributed in several interrelated tables logically. For wide complex searching search engine was used to check each link of the database website and accordingly showed the matched results ( Figure 1B). All the information in AromaDb database can be categorized into three categories: primary, secondary, and tertiary information. The database information was manually curated and selected from various sources such as published international literature, CSIR-CIMAP, Lucknow 9 essential oils monograph, web link annual reports, journal (JMAPS; Journal of Medicinal and Aromatic Plant Sciences; Baldwin et al., 2006), newsletters, and books. The primary information has been retrieved from the literature, these information's consists of the following major fields namely: (i) plant details (ii) essential oil name, and (iii) plant variety. The secondary information, which was derived from the plant essential oil (primary information) includes the essential oil description details, chemical constituents, major minor compound details, content percentage, and export-import essential oil trade data. The tertiary information is further derived from secondary information which comprises detailed information about the essential oil compounds as followed: IUPAC name, Chemical class biochemical classes (e.g., terpene hydrocarbons and oxygenated compounds: phenols, alcohols, aldehydes, ketones, esters, lactones, coumarins, ethers, and oxides), Fragrance type, Physical and chemical properties, Absorption and Metabolism information, Toxicological information, Ecological information, Hazards information, and compound bioactivity data for therapeutic use or drug formulation development. This information provides the user a comprehensive aroma molecules database, together with substantial options, such as chemical compounds search based on structural similarity using compound Canonical SMILES within databases. Figure 2 summarizes snapshots of database home page (Figure 2A), plant and varieties details ( Figure 2B), and essential oil details ( Figure 2C).
Results of Structure Editor and Structure Similarity Search Tools
The AromaDb database provides the diverse and commercially important aroma molecules, fragrance types, essential oils, aromatic plants, trade data, and other industrially important information as required while formulating any herbal product formulation for human uses. We have incorporated various tools for making the database easy and more convenient for users. The database contents could be accessible with different search tools, options, e.g., simple and advance search tools. The simple search tool is powered by Google search engine, which search data from within the AromaDb database and also from public databases, where another mining tool search the query within the database, the Advance search tool option further divided into two types: (i) search based on structural similarity (within AromaDb database) and (ii) search based on structural physicochemical and pharmacokinetics (ADMET) properties. The database search fields enable the user to look for compounds using physicochemical properties, plants, varieties, chemotype, essential oil, aroma molecules, chemical classification, biological activity, etc. Database allows the user to choose certain functional groups, species or range of molecular weights, which search whole entries and retrieve the user required results. To mine the database user require a 2D molecular structure or structure canonical SMILES code (Simplified Molecular Input Line Entry System) or a MOLfile of interested aroma compounds. Structure drawing/editing option is provided in the AromaDb database with the help of JSME (JavaScript Molecular Editor; JSME Homepage 10 ). With the help of JSME user can draw either full structure or part of it. The most similar database entries are listed in the order of structural similarity. For each compound, details of plant, plant name, variety, essential oil constituents, chemical class, ADMET properties, 2D/3D chemical structures, trade data, and the SMILES based similarity search percentage are presented. Furthermore, a similarity search to find the most similar compounds analogs are provided. Additionally, similar information can be viewed separately by accessing database header fields or the menu.
Information About Putative Therapeutic Free Human Targets or Genes in AromaDb
The other useful feature of the AromaDb database is the information of aroma molecules and related interacting human therapeutic genes or proteins involved to modulate the biological system biological pathways and for unraveling the underlying mechanism of action and therapeutic potential of these aroma molecules along with supporting references. Also enlisted the therapeutic important aroma molecules along with the option to see the related aromatic plants, essential oils, export and import trade data, and other information.
Display of Essential Oil Trade Data Information
The graphical analysis of trade data suggests the consumption and demand data trend in India and the world or own country (India) as a guideline or forecasting tool to the aroma or essential oil industries and growers (farmers or producers) to streamline their resources, economy, and time according to expected demand of respective essential oils in the world and therefore indirectly helps to improve the socioeconomical condition of farmers and producers. Here, the database entries have been clustered according to the quality of their aroma properties. A manually verified upload option through email allows the scientific community to contribute to the database. Here, the user can import a MOL-file together with corresponding information of the compound. The AromaDb database will be updated on a quarterly basis.
Comparative Display of Physical Properties and Safety Prediction Data
The database features the properties related to aroma molecule, hazard identification, exposure controls and personal protection, physical and chemical properties, toxicological and ecological information. Compound identification properties include chemical name, IUPAC name and chemical class. Hazards identification includes properties related to physical hazards, health hazards such as skin corrosion and ocular irritancy property. Toxicological properties such as irritation, absorption level, aqueous solubility level, LogP (octanol/water partition coefficient), polar surface area (PSA), and blood-brain barrier level indicate the precautionary properties related to exposure controls and personal protection (eye/face protection, skin protection, respiratory protection, and thermal hazards). Physical and chemical properties indicated by molecular weight, molecular formula, fragrance type, LogP (n-octanol/water), H-bond donor and H-bond acceptor, rotatable bond, topological polar surface area (TPSA), IUPAC name, InChIKey, aqueous solubility level, vapor pressure, PubChem (NCBI, United States) (Wang et al., 2009) database ID and SMILES information. Toxicological information includes properties related to information on likely routes of exposure such as inhalation, skin contact, eye contact, ingestion, symptoms related to the physical, chemical and toxicological characteristics, information on toxicological effects, e.g., acute toxicity (LD 50 -dermal/oral, rabbit/rat, mg/kg), skin corrosion/irritation, serious eye damage/eye irritation, respiratory or skin sensitization, germ cell mutagenicity (genotoxic), carcinogenicity, reproductive toxicity, specific target organ toxicity (single exposure/repeated exposure), and aspiration hazard. However, the ecological information includes properties related to eco-toxicity (environmentally hazardous), persistence, and degradability. Since these aroma molecules used in aroma based natural therapy (aromatherapy) in traditional herbal medicine system of India (Ayurveda and Unani) and other East Asian countries, therefore, database covered properties related to pharmacokinetics such as absorption, distribution, metabolism, excretion, and toxicity (ADMET). These properties indicate drug-likeness properties, ADME compliance, and toxicity risk assessment data. Moreover, the database also includes information related to the molecular interaction of aroma molecules/essential oils with human proteins (genes) directly or indirectly affecting the metabolic processes and therefore causing their useful biological activity or responses. This information is supported with cross-references or proper evidence based on reported publications and therefore offer a possibility for biological interpretation of these aroma molecules. Moreover, an investigation of medical effects is also possible by browsing the "biological activity" field for each essential oils/aroma molecules. The comparative trends or pattern plots shows the emitted aroma compounds of the chosen species compared with all other plants species, which emit these compounds. The comparative data analysis through graph plots shows the unique pattern of aroma molecules (fingerprints), essential oil, yield, major constituent percentage (chemotype), trade analysis trend, and others based on changes in different agromorphological parameters, e.g., soil type, stress conditions, temperature, weather type, months wise oil yield, etc., which are important feature for distinguishing between more or less useful features/parameters during agriculture practices and/or cultivation by farmers, or industrial growers. As an example, a snapshot represents these calculated properties of aroma showing molecule, chemical identification, description and 3D structure for download snapshot represented in the given figure ( Figure 3C).
DISCUSSION
It was difficult to explore such information about plants, its variety, essential oil constituents, and essential oil trade data, on a single platform in addition to the aroma molecules information about its physicochemical properties, absorption and metabolism information, toxicological information, ecological information, Hazards identification, and compound biological activity with its fragrance and interacting human genes information.
Browsing and Searching Database Contents
Database contents can be seen in two ways: (i) browsing different data fields available on the header menu on the home page such as, plants, essential oils, aroma molecules, chemical classes of aroma molecules, biological activity, and trade data, and (ii) searching database contents through advanced search option and wild search through Google's custom search option. Users can search any text within the database based on text similarity concept through Google's custom search option (wild search or complex search) available in the header of the database. A similar analysis with the help of representing snapshots of the database showing database home page search parameters (Figure 2A), plant and varieties details (Figure 2B), and essential oil details ( Figure 2C).
Properties Search by Entering Values
Besides, users can also limit searches using ranges of physiochemical data, e.g., maximum and minimum values of TPSA (Topological Polar Surface Area) (Figures 3A-C). Database snapshots of these properties and parameters showing chemical constituents of essential oil, GC-MS data and months wise variations in oil yield (Figure 3A), essential oil trade data and comparative graphical plots (Figure 3B), and aroma molecule description and 3D structure for downloads ( Figure 3C).
Similar Structures Search and Downloading of Aroma Molecules
Users can search and download interested aroma molecules (2D and 3D chemical structures) through structure search option. In this option, users can draw or edit any structure of their interest and convert it to SMILES and MOL file format and subsequently search whole AromaDb database aroma molecules and resulted most similar matched structures available in the database and can easily see details of these matched small molecules or free downloads the structures. For example, if the user draws the phenyl ring in the JME editor and enter the option either "get SMILES" or get MOL file' resulted in SMILES or MOL file data would be shown on the other side of JME editor and subsequently enter the key similarity search within database, results of matched structures showed in the new web page, with no exact match aroma molecule found, and enlisted the similar matched compounds, e.g., (E)-cinnamyl acetate, 1,1-diisobutoxy-2-phenylethane, 1,1-dimethoxy-2benzylideneheptane, etc. All these matched structures have phenyl ring in common. Moreover, the user can see further details of each matched compound by entering key on "details" button. Details of aroma compound include calculated properties related to aroma molecule identification, hazards, physical and chemical properties, pharmacokinetic properties, toxicological information, and ecological information. The database also represents the 2D structure, 3D structure visualization in 3D structure viewer window with spin-on/off option so that to see the structural conformations quickly with free download option. The compounds search outputs are oriented to explore the matched compounds by following the "details" hyperlink to the table of physical, ADMET and safety properties described earlier. Figure 4 showing snapshots of advanced search options based on different properties, fragrance type and toxicological parameters of aroma molecule (Figure 4A), drawing tool for structure-based search option as well as SMILES or MOL file basis (Figure 4B), and results of structure-based search based on SMILES basis structural similarity (exact match and similar hits; Figure 4C).
Search by Selecting Data Fields
Similarly, naive users can directly see and select the interesting data through search menu buttons provided at the end of the home page of aroma database and the further user may browse more information without wasting time on thinking about aroma (fragrance), aroma molecules name, essential oils, plant varieties, and plants. This type of search display option will help the fresh researchers, scholars, and students. For example, a new user can directly browse the home page header menu field "Plants, " which will display the list of all aromatic plants with their common and scientific names and a number of their available plant varieties or highly yielding chemotypes. Users can see more details of these plants by clicking on the button "View Detail." For example, if the user wishes to search for Menthol mint plant (Mentha arvensis), which included at present 11 mint plant varieties or chemotypes based on different aroma molecules constituent's ratio variations.
Users can see more details such as brief introduction, family, localization, uses, essential oils types, and the name of 11 mint varieties used by Indian growers or farmers. Users have the option to print or save this data. In this page, users have two options to see further details; (i) essential oil types and (ii) variety details. For example, if the user searches for M. arvensis MAS-1 essential oil details, database will display brief introduction, major constituent Menthol with 84% with multiple minor components such as Menthone (5.8%), etc., comparative graph plot showing percentage ratio of different chemical constituents of M. arvensis MAS-1 essential oil (Figure 2C), and the Indian scientists or researchers contribution, if any, showed in the references. Users have the option to print (or save) this data and graph plot. Also, the user can directly move to see details of major or minor compounds and yield percentage by simply enter on the name of compounds. Moreover, if the user enters on M. arvensis variety Gomti button, the database will display a tabular text data showing brief details of plant, variety name, major constituents of given variety such as Menthol 74% in ratio, menthone 12.6%, isomenthone 3.7%, and methyl acetate 2.9%. Beside this database show details of essential oil, GC-MS graph (if available) of essential oil, major compounds peak in GC data, compound property, year of plant variety release and complete reference (Figures 3A-C).
Agronomic Parameters Based Gaphical Data Analysis
At the same time, the database also covers data related to different agronomic parameters based and provide an option to the user to see either in tabular or graphical forms. For example, in the case of M. arvensis Gomti variety, the database covers five observation readings revealing variations in Z-Linalool oxide compound percentage ranges from 2.2 to 2.7% from January to May 2015. This data suggest that the highest yield of Z-Linalool oxide was 2.7 February. Likewise, the user can see other parameter based graph plot analysis and get benefited from future cultivation, for example, if the user sees the Himalaya variety of M. arvensis, there are five readings based on the age of the plant (in days) and menthol compound yield percentage. This data showed that the study was performed for 30 days old plants to 150 days old plant (menthol mint) and menthol yield ranges from 70.66 to 82.18%. Graphical analysis revealed that highest yield obtained at 120 days old plant (Figure 2). A similar analysis with the help of representing snapshots of the database showing database home page search parameters (Figure 2A), plant and varieties details (Figure 2B), and essential oil details ( Figure 2C).
CONCLUSION
The AromaDb database is a useful tool to retrieve information about aroma molecules, aroma or fragrance types, essential oils, plants varieties, bioactivity of essential oils or aroma molecules, toxicological and ecological data, and trade data. The database provides, 3D structures of aroma compounds for free downloads and option to see the essential oil yields or constituents percentage variation trends at different agromorphological conditions during plant growth. The included data on aroma molecules along with a focus on associated plants and their essential oils chemotype (varieties) will enable systematic experimental approaches on the relation between structural similarities, essential oils, and aroma (fragrance) type and aroma chemical classes. Besides, last 18 years global export and import trade data of plants essential oils will educate the growers or farmers to prioritize the cultivation of aromatic plants based on expected global demand. Furthermore, structure comparisons of self-edited molecules with the database aroma molecules as well as the external database may allow a first rough estimation of the potential aroma of new chemicals. The AromaDb database is a free resource with embedded screening functions for aroma molecule based on molecular weight, plants, varieties, essential oils, fragrance or aroma type, toxicological and ecological information (allergic or toxic responses).
AUTHOR CONTRIBUTIONS
YK carried out the database entry, data searching and retrieval, download, designed 2D and 3D structures, participated in the data analysis, and manuscript writing. OP developed the preliminary database offline using PHP and MySQL for initial data entry. HT add the trade data of essential oil based plants commodities and economic profile in Indian currency. ST provided 25 aroma molecules to the database and other properties. MG also provided small molecules and corrected chemical classes of aroma molecules and corrected the chemistry part. L-UR provided data related to Indian plants varieties, essential oils details, constituents, and contributed in the design of the database. RL guided to add published in-house data related to aroma molecules, essential oils, aroma plants, and bioactivity. MS coordinated in database web hosting and IT support. FK conceived the study, contributed to its design, ER relationship, and drafted the manuscript. MD Addition of updated bioactivity (in vitro) data for essential oil/aroma molecules with their cros references. All authors read and approved the final manuscript.
ACKNOWLEDGMENTS
We are thankful to the Director, CSIR-CIMAP, Lucknow, India for rendering essential research facilities and support. YK, OP, | v3-fos-license |
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} | pes2o/s2orc | An endoplasmic reticulum stress-specific caspase cascade in apoptosis. Cytochrome c-independent activation of caspase-9 by caspase-12.
Activation of caspase-12 from procaspase-12 is specifically induced by insult to the endoplasmic reticulum (ER) (Nakagawa, T., Zhu, H., Morishima, N., Li, E., Xu, J., Yankner, B. A., and Yuan, J. (2000) Nature 403, 98-103), yet the functional consequences of caspase-12 activation have been unclear. We have shown that recombinant caspase-12 specifically cleaves and activates procaspase-9 in cytosolic extracts. The activated caspase-9 catalyzes cleavage of procaspase-3, which is inhibitable by a caspase-9-specific inhibitor. Although cytochrome c released from mitochondria has been believed to be required for caspase-9 activation during apoptosis (Zou, H., Henzel, W. J., Liu, X., Lutschg, A., and Wang, X. (1997) Cell 90, 405-413, Li, P., Nijhawan, D., Budihardjo, I., Srinivasula, S. M., Ahmad, M., Alnemri, E. S., and Wang, X. (1997) Cell 91, 479-489), caspase-9 as well as caspase-12 and -3 are activated in cytochrome c-free cytosols in murine myoblast cells under ER stress. These results suggest that caspase-12 can activate caspase-9 without involvement of cytochrome c. To examine the role of caspase-12 in the activation of downstream caspases, we used a caspase-12-binding protein, which we identified in a yeast two-hybrid screen, for regulation of caspase-12 activation. The binding protein protects procaspase-12 from processing in vitro. Stable expression of the binding protein renders procaspase-12 insensitive to ER stress, thereby suppressing apoptosis and the activation of caspase-9 and -3. These data suggest that procaspase-9 is a substrate of caspase-12 and that ER stress triggers a specific cascade involving caspase-12, -9, and -3 in a cytochrome c-independent manner.
The caspase protease family plays a central role in the implementation of apoptosis in vertebrates (4,5). Caspases are constitutively expressed in healthy cells, where they are synthesized as precursor proteins (procaspases). Caspases are activated upon processing of procaspases into ϳ20-kDa (p20) and 10-kDa (p10) mature fragments, in addition to the N-terminal prodomain. The caspase family is broadly divided into two groups: initiator caspases (caspase-8, -9, and -12) and effector caspases (caspase-3, -6, and -7). Initiator caspases undergo autoprocessing for activation in response to apoptotic stimuli. Active initiator caspases in turn process precursors of the effector caspases responsible for dismantling cellular structures.
Recent studies have suggested the existence of a novel apoptotic pathway in which caspase-12 functions as the initiator caspase in response to a toxic insult to the ER, 1 such as by treatment with tunicamycin (an inhibitor of glycosylation), thapsigargin (an inhibitor of the ER-specific calcium ATPase), or calcium ionophores (1). Caspase-12 is specifically activated in cells subjected to ER stress. Furthermore, caspase-12-deficient cells are resistant to inducers of ER stress, suggesting that caspase-12 is significant in ER stress-induced apoptosis (1). ER stress has received growing attention because it is considered a cause of pathologically relevant apoptosis, and it is particularly implicated in neurodegenerative disorders (6). However, the mechanism of caspase-12-mediated apoptosis has been unknown, mainly due to the lack of identification of caspase-12 substrates. In this study, we have examined the susceptibility of procaspases to active caspase-12 and have shown that procaspase-9 can specifically be cleaved by caspase-12 in vitro.
Recent studies show that multiple death signals converge on the mitochondrion (7). Damaged mitochondria release cytochrome c, which facilitates conformational changes in Apaf-1, the specific activator of procaspase-9 (2, 3). The cytochrome c⅐Apaf-1 complex called an apoptosome (8,9) is thought to recruit procaspase-9 through interaction between Apaf-1 and procaspase-9 and facilitate autoactivation of caspase-9. Active caspase-9 then activates caspase-3, the major effector caspase that is responsible for destruction of various substrates (4,5). Cytochrome c release from mitochondria has also been observed in ER stress-induced apoptosis of several cell lines, including mouse embryonic fibroblast cells (10,11). The in vitro cleavage of procaspase-9 by caspase-12 described above can be achieved in the absence of cytochrome c, suggesting the presence of the ER stress-specific caspase cascade, which comprises caspase-12, -9, and -3 in this order. For examination of the role of caspase-12 in activation of the caspase cascade in vivo, however, it would be desirable to use conditions in which cytochrome c is not released from mitochondria; otherwise, caspase-9 could be activated by the cytochrome c⅐Apaf-1 mechanism, independent of caspase-12. We thus used a murine myoblast cell line, C2C12, to study caspase-12, because our preliminary data showed that ER stress induces the activation of caspase-12 and apoptosis in the cell line without the release of cytochrome c from mitochondria. This result suggests that cytochrome c release is not essential for ER stress-induced apoptosis. We took advantage of the fact that cytochrome c is not released to examine the mechanism of caspase cascade activation in the absence of mitochondrial damage, focusing on events that occur downstream of caspase-12 activation.
Examination of Mitochondrial Transmembrane Potential-Apoptosis was induced in C2C12 cells, and then the cells were stained with the MitoSensor reagent (CLONTECH) according to the manufacturer's protocol.
Preparation of S-100 from C2C12 Cells-The C2C12 cell 100,000 ϫ g supernatant was prepared according to the method described in Liu and Wang (12). Briefly, cells were disrupted in buffer containing 250 mM sucrose by a Dounce homogenizer. The supernatant was centrifuged in a microcentrifuge for 10 min, and subsequently at 100,000 ϫ g for 30 min in a tabletop ultracentrifuge (Beckman Coulter, Inc.).
Yeast Two-hybrid Screening-The split LexA protein system was used for two-hybrid screening according to the method of Brent as described by Gyuris et al. (16). The caspase-12 p10 fragment (Thr 319 -Asn 419 ) was used as the bait for the screening of a HeLa cell cDNA library. From ϳ2 ϫ 10 7 transformants, we obtained 15 positive clones, all of which contained sequences derived from the MAGE-3 mRNA. The 5Ј ends of the cDNAs were located between codons 81 and 94. We cloned the full-length coding region of MAGE-3 (314 amino acids) for further analysis by polymerase chain reaction amplification of a human testis cDNA library (CLONTECH).
GST Fusion Protein Pull-down Assay-GST-MAGE-3 protein (1 g) and histidine-tagged caspase-12 (0.1 g) were incubated with 10 l of glutathione Sepharose-4B beads (Amersham Biosciences) for 1 h at room temperature in 150 l of 20 mM phosphate buffer, pH 7.0, containing 200 mM NaCl and 0.02% Triton X-100. Anti-hexahistidine monoclonal antibody (CLONTECH) was used for the detection of caspase-12 p20. Proteins that bound glutathione resin were analyzed by Western blotting.
In Vitro Cleavage of Radiolabeled Procaspases-In vitro synthesis of 35 S-labeled proteins and their detection by autoradiography were achieved as described previously (15). For mutant analysis, mutations at specific aspartic acid residues in procaspases were introduced by the QuikChange Site-Directed Mutagenesis Kit (Stratagene). Introduction of mutation was confirmed by DNA sequencing. 35 S-labeled procaspase (0.2 l of the labeled protein solution) was incubated for a cleavage assay at 37°C with caspase-12 (0.28 g) for 4 h. Resistance of procaspase-12 to cleavage by active caspase-12 was examined by addition of recombinant MAGE-3 to the procaspase-12 cleavage assay solution. Procaspase-12 whose active site Cys residue had been replaced with Ser was synthesized in vitro in the presence of [ 35 S]methionine. 35 S-Labeled mutant procaspase-12 (0.2 l of the labeled protein solution) was incubated with active caspase-12 (0.28 g) for 45 min in the presence or absence of MAGE-3.
In Vitro Activation of Caspase-9 in S-100 by Caspase-12-Cytochrome c-free cytosol from C2C12 cells (10 g of proteins) was treated with recombinant caspase-12 p30 (0.8 g) at 37°C for 4 h. Activation of caspase-9 and -3 was examined by Western blot analysis. Five micrograms of proteins were loaded on each lane. As a positive control for caspase-9 activation, the cytochrome c-free cytosol was incubated with 10 M bovine cytochrome c (Sigma-Aldrich) and 1 mM dATP for 60 min at 37°C. For inhibition of caspase-9 activity, LEHD-fluoromethylketone (BioVision, Palo Alto, CA) was added to the cytosol before the addition of caspase-12.
Stable Cell Lines-MAGE-3 stable cell lines of C2C12 were generated as follows. MAGE-3 cDNA was cloned into pcDNA3.1(Ϫ) vector (Invitrogen). The plasmid DNA was linearized by ScaI digestion before transfection. Transfection was performed with a Superfect transfection reagent (QIAGEN) according to the manufacturer's protocol. MAGE-3 cDNA cloned into the pcDNA3.1(Ϫ) vector (Invitrogen) was used for stable transfection. Stable transfectants were grown in medium containing 600 g/ml G418 (Invitrogen) for 2 weeks before cloning.
RESULTS
Procaspase-9 Is a Substrate of Caspase-12-We examined how caspase-12 processing is linked to the activation of other caspases. For an in vitro cleavage assay, we produced recombinant caspase-12 (p30) whose N-terminal prodomain had been removed and replaced with a hexahistidine tag. The p30 protein undergoes efficient autoprocessing into p20 and p10 peptides when overexpressed in E. coli (p30* in Fig. 1A). A mutant p30 (p30C/S), whose active site Cys is substituted with Ser, is not processed in E. coli (Fig. 1A). The mature caspase-12 (p30*) exhibits proteolytic activity and cleaves procaspase-12 into 35and 12-kDa fragments (Fig. 1B, lane 16). The cleavage site was located at Asp318, because a procaspase-12 mutant in which Asp318 was replaced with Ser was resistant to caspase-12 digestion (data not shown). Asp318 is also the cleavage site for autoprocessing in E. coli (Fig. 1A), as revealed by amino acid sequencing of p10 by the Edman degradation method (data not shown). p30* cleaves caspase-9 ( Fig. 1, B and C) but not other caspase precursors (murine caspase-1 and -2, and human caspase-3, -6, and -8) under the experimental conditions. Note that processing site sequences between p20 and p10 are highly conserved between murine and human caspase-3, -6, -7, and -8. Mutation analysis of caspase-9 ( Fig. 1C) indicates that caspase-12 cleaves at specific Asp residues in the linker region between p20 and p10 in the procaspase-9 polypeptide (LDSD349 and SEPD353 in the murine caspase-9, PEPD315 in the human caspase-9). Asp 353 of the murine caspase-9 and Asp 315 of human caspase-9 have been reported to be the cleavage sites for the activation of procaspase-9 (17,18). The caspase-9 cleavage observed in vitro thus suggests the possibility that caspase-9 can be activated by caspase-12 during ER stress-induced apoptosis. Under the experimental conditions used, murine caspase-9 contains another cleavage site(s) for caspase-12 in vitro, cleavage at which generates 27-and 20-kDa fragments. We did not further analyze these additional cleavage site(s) because we could detect neither 27-nor 20-kDa caspase-9 fragments in apoptotic cells (described below; data not shown). Procaspase-7 seems to be only slightly processed by active caspase-12 (Fig. 1B, lane 10). The in vitro cleavage of procaspase-7 was not studied further because processing of procaspase-7 is undetectable in C2C12 cells subjected to ER stress (Fig. 1D).
Cytochrome c Is Not Essential for ER Stress-induced Caspase Activation-Several reports have demonstrated that ER stress causes mitochondrial damage, which results in cytochrome c release from mitochondria (e.g., Refs. 10,11). Cytochrome c in cytosol and Apaf-1 can induce activation of caspase-9 (2, 3). To examine whether there is an ER stress-specific caspase cascade that is initiated by caspase-12, we used a murine myoblast cell line, C2C12, because this cell line undergoes ER stress-induced apoptosis without cytochrome c release from mitochondria. Cytosolic extracts (S-100) of tunicamycin-or thapsigargintreated C2C12 cells contain cytochrome c at the same level as that detected in S-100 fractions prepared from untreated cells ( Fig. 2A). Nevertheless, more than 50% of the cells undergo apoptosis (see below). Cytochrome c release per se, however, is functional in C2C12 cells, because treatment of C2C12 cells with etoposide or serum deprivation induces apoptosis at a similar level of lethality and with a significant release of cytochrome c. After apoptosis was induced by ER stress inducers, the mitochondrial transmembrane potential was maintained in apoptotic cells (small cells with condensed nuclei), as in the case of untreated cells, which was exhibited by mitochondrial accumulation of fluorochromes and their conversion to emit the orange color (Fig. 2B). Etoposide-treatment of C2C12 cells resulted in decrease in mitochondrial transmembrane potential, which was monitored by the green color of the fluorochromes in the cytosol (Fig. 2B). These results suggest that mitochondria in C2C12 cells do not suffer severe damages from ER stress, thus releasing little cytochrome c into cytosol.
Treatment of C2C12 cells with ER stress inducers, either tunicamycin or thapsigargin, results in the processing of procaspase-12 (48 kDa, Fig. 2C) and apoptosis. A 35-kDa fragment was detected by antibodies specific to the p20 region (1). Caspase-9 and caspase-3 are also activated in C2C12 cells treated with ER stress inducers (Fig. 2C). The activation of caspase-3, one of the most downstream caspases, suggests that the ER stress-specific caspase cascade comprises caspase-12, -9, and -3. It has been suggested that calpain is involved in activation of caspases in cultured glial cells after deprivation of oxygen and glucose (19). In the apoptotic C2C12 cells, however, cleavage of a calpain substrate, Bcl-XL, was not detected (Fig. 2C), suggesting that caspase activation in C2C12 cells treated with ER stress inducers is independent of calpain.
Direct Activation of Caspase-9 by Caspase-12-We then examined whether caspase-9 activation occurs by the cleavage of procaspase-9 by active caspase-12 without the release of cytochrome c in cell extracts. Incubation of the S-100 fraction of untreated C2C12 cells with active caspase-12 results in a pattern of cleavage of procaspase-9 that is similar to that observed in S-100 of apoptotic C2C12 cells, the cleavage products being a doublet of 35-kDa fragments (Fig. 3A, lanes 2 and 4). A FIG. 1. Procaspase-9 is a substrate of caspase-12. A, purification of the caspase-12 p30 protein overexpressed in E. coli. Either wild-type p30* or the inactive mutant (C/S) protein was tagged with hexahistidine at the N terminus and purified by Ni-column affinity chromatography. Proteins were detected by Coomassie Brilliant Blue staining. B, procaspase-9 and procaspase-12 are specifically cleaved by active caspase-12. 35 S-Labeled procaspases were incubated with (ϩ) or without (Ϫ) active caspase-12 at 37°C for 4 h and analyzed by SDS-polyacrylamide gel electrophoresis as described previously (15). Arrowheads indicate cleavage products. C, cleavage sites within procaspase-9 are processing sites for activation. Mutation of specific Asp residues (Asp-349 and Asp-353 in murine procaspase-9 and Asp-315 in human procaspase-9, respectively) significantly reduces cleavage by caspase-12 (ϩ). Arrowheads indicate cleavage fragments. D, caspase-7 is not activated in C2C12 cells under ER stress (TG, thapsigargin; TUN, tunicamycin). The Western blot was probed with an anti-caspase-7 monoclonal antibody.
control experiment showed that addition of cytochrome c and dATP to S-100 of untreated cells also caused processing of procaspase-9 into 35-kDa fragments that appeared as a doublet on the blot (Fig. 3B, lane 3). The lower band was less intense than the upper band in the case of caspase-12-induced processing (Fig. 3B, lane 2) and the apoptotic S-100 fractions (lane 4). The ratio of these 35-kDa fragments was different from that observed in the cytochrome c-treated S-100 (Fig. 3B, lane 3). It remains to be revealed whether the difference in the ratio of these fragments reflects a difference in mechanism of processing.
Suppression of Procaspase-12 Processing by Its Binding Protein-We have recently isolated by yeast two-hybrid screening from a HeLa cell cDNA library a human cancer antigen, MAGE-3, as a protein that specifically binds the caspase-12 p10 fragment (see "Experimental Procedures"). Because MAGE-3 can suppress the activity of procaspase-12, as described below, we used the protein to examine the significance of caspase-12 activation in ER stress-induced apoptosis in C2C12 cells. MAGE-3 is a member of the MAGE gene family and is expressed in various types of tumor but not in normal tissues except for the testis (24). Although the specific interac- tion between caspase-12 and MAGE-3 is intriguing, it remains unclear whether MAGE-3 plays any role in caspase regulation in human cells (see "Discussion"). MAGE-3 does not bind to other caspases, such as caspase-9 (of either murine or human origin), as tested by the two-hybrid assay (results of murine caspase-1, -9, and -11 and human caspase-3, -6, and -7 are shown in Fig. 4A).
The MAGE-3 protein can also bind both the caspase-12 p10 fragment and procaspase-12 in mammalian cells. When MAGE-3 is expressed in COS-1 cells by transient transfection it can be co-precipitated with FLAG-tagged p10 (Fig. 4B, lane 3) or FLAG-tagged procaspase-12 (lane 7) using an anti-FLAG antibody. MAGE-3 was not co-precipitated with FLAG-tagged p10 fragments of murine caspase-2 and human caspase-8, whose binding ability could not be examined by the two-hybrid assay because of significant background activity (data not shown). Fig. 4C shows that p30C/S (unprocessed p30) co-precipitates with GST-tagged MAGE-3 (lanes 3 and 4). Under the same conditions, however, p30* (processed) is not efficiently co-precipitated by GST-MAGE-3 (Fig. 4C, lanes 1 and 2), suggesting that MAGE-3 does not efficiently bind the p10 fragment in active caspase-12. It is possible that the p10 fragment within mature caspase-12 is not fully accessible to MAGE-3 because of steric hindrance by the p20 portion. X-ray crystallographic analyses of caspase-1 and caspase-3 have suggested that they undergo a conformational change upon maturation (25)(26)(27). This conformational change may occur in caspase-12 and result in the p10 fragment being less exposed for binding to MAGE-3.
Consistent with the binding of MAGE-3 to unprocessed caspase-12, MAGE-3 protects procaspase-12 from cleavage by active p30* in a dose-dependent manner (Fig. 4D, lanes 2-7). Substitution of MAGE-3 with bovine serum albumin fails to inhibit cleavage (Fig. 4D, lane 9). It is less likely that MAGE-3 blocks active caspase-12 by acting as a competitive inhibitor. In Fig. 4D, lane 7, small amounts of the 35-and 12-kDa fragments can be detected, indicating the presence of caspase-12 activity. Under such conditions, excessive levels of active caspase-12 are expected to be protected from inhibition by MAGE-3. However, an enhancement of cleavage was not detected in the presence of 4-fold higher levels of caspase-12 (lane 8). In contrast, when twice as much substrate is added to the reaction mixture in the presence of MAGE-3, both p35 and p12 cleavage products are produced at the same levels as in the absence of MAGE-3 (Fig. 4E, lane 2). It is more likely that MAGE-3 protects procaspase-12 from processing by specifically binding the p10 portion of the precursor. This result is consistent with our observation that the affinity of MAGE-3 for p30C/S is much higher than that for active caspase-12 (Fig. 4C).
Suppression of Caspase-12 Activation Resulted in Suppression of Caspase-9 Activation and Apoptosis in Vivo-To examine the involvement of caspase-12 in the activation of the caspase cascade, we established stable transfectants (C2C12/ MA21) of C2C12 cells that overexpress MAGE-3 (Fig. 5A). Colocalization of MAGE-3 with endogenous caspase-12, an ERassociated protein (1), in C2C12/MA21 was observed by double immunostaining (Fig. 5B), although signals of free MAGE-3 proteins (red color) were still evident in the merged image. This observation was supported by a cell fractionation experiment, where MAGE-3 was detected in the microsomal fraction as well as in S-100 (Fig. 5C). Treatment of either parental C2C12 cells FIG. 4. A caspase-12 binding protein suppresses processing of procaspase-9. A, MAGE-3 binds to caspase-12 p10 but not to other caspases (p10) in the yeast two-hybrid system. Positive control (P), the active Gal4 transcription factor; negative control (N), empty vector. B, MAGE-3 binds to caspase-12 p10 in cells. Cell lysates were prepared from COS-1 cells transfected with plasmids bearing MAGE-3 (lanes 1-8) or FLAG-tagged caspase-12 (procaspase-12, lanes 1 and 3; caspase-12 p10, lanes 5 and 7), and proteins were precipitated using the anti-FLAG affinity gel. Lanes 2, 4, 6, and 8, a vector control for FLAG-caspase-12. MAGE-3 proteins were detected by Western blot analysis with an anti-MAGE-3 rabbit polyclonal antibody. C, coprecipitation of the GST-MAGE-3 fusion protein with an unprocessed form of caspase-12 (p30C/S). Lanes 1 and 2, p30*; lanes 3 and 4, p30C/S. Lanes 1 and 3, GST control; lanes 2 and 4, GST-MAGE-3. D, procaspase-12 bound to MAGE-3 is resistant to cleavage by active caspase-12. 35 S-Labeled procaspase-12 was prepared by in vitro transcription and translation (15). Lane 1, intact procaspase-12; lanes 2-7, procaspase-12 incubated with active caspase-12 (0. or a vector control line (C2C12/vec2) with ER stress inducers leads to morphological changes typical of apoptosis. Over 50% of C2C12/vec2 cells exhibit apoptotic morphology after 24 h treatment with tunicamycin or thapsigargin, as indicated by the small round shape of the cells (Fig. 5D). The nuclei of these round cells are fully condensed, as visualized by staining with Hoechst 33342 (data not shown). However, C2C12/MA21 cells undergo apoptosis at the same low background level (Ͻ 5%) observed in untreated cells under the same conditions (Fig. 5D). Activation of caspase-12 is almost completely suppressed in C2C12/MA21 cells treated with ER stress inducers (Fig. 5E). Processing of caspase-9 and caspase-3 also does not take place in MAGE-3 overexpressing cells. Both C2C12/MA21 cells and C2C12/vec2 cells respond to ER stress and elicit the unfolded protein response (reviewed in Ref. 28), as demonstrated by the induction of BiP, an ER-specific heat shock protein (Fig. 5F). These data indicate that MAGE-3 overexpression renders cells resistant to ER stress by suppressing the activation of caspase-12. Therefore, caspase-12 is a critical component of the apoptotic machinery that responds to ER stress, confirming the previous observation (1) that caspase-12 null mice are resistant to the toxic effects of ER stress (e.g., intraperitoneal injection of tunicamycin). Furthermore, concomitant inhibition of the activation of other caspases (caspase-9 and -3) in stably transfected C2C12/MA21 cells strongly suggests that caspase-9 and -3 are located downstream of caspase-12 in the ER stress-specific caspase cascade. These results suggest that procaspse-9 is a substrate of caspase-12 in vivo as well as in vitro. Both C2C12/ MA21 and C2C12/vec2 cells undergo apoptosis when treated with staurosporine, a protein kinase inhibitor (data not shown), indicating that the apoptotic machinery per se is functional. This result supports the idea that the suppressive effect of MAGE-3 is specific for the ER stress-induced apoptotic pathway mediated by caspase-12.
DISCUSSION
Our data suggest the following: 1) caspase-12 activation triggers the caspase cascade in response to ER stress; 2) pro-caspase-9 is a substrate of caspase-12 and caspase-9 activation can be achieved in cells without the release of cytochrome c from mitochondria; and 3) proteolytic signals in the cascade are transmitted from caspase-12 to an effector caspase (caspase-3) via caspase-9 (Fig. 6). An Apaf-1/cytochrome c-independent mechanism of caspase-9 activation has recently been reported for dexamethasone-induced apoptosis of multiple myeloma cells (29). Because recombinant caspase-9 prepared from E. coli exhibits protease activity (30), it is obvious that Apaf-1 (and cytochrome c) is not essential for the activation of caspase-9. However, the lack of cytochrome c release in C2C12 cells does not exclude the possibility that Apaf-1/cytochrome c is involved in other cell lines. Cytochrome c release has been observed in both mouse and rat embryonic fibroblast cells subjected to ER stress (10,11). It is likely that caspase-9 activation can be achieved by caspase-12-dependent cleavage, by an Apaf-1/cytochrome c mechanism, or by both means (Fig. 6). A similarly complex mechanism by which apoptosis is triggered has been described previously for the death receptor mediated pathway (31). Stimulation of death receptors (e.g., Fas) results in the activation of caspase-8, which in turn activates effector caspases in a direct manner. Alternatively, caspase-8 may cleave Bid, a pro-apoptotic member of the Bcl-2 family, and the cleaved Bid may in turn induce cytochrome c release through mitochondrial damage (32,33). Our studies present another example of redundancy in the mechanisms by which apoptosis is executed. It is unclear, then, how cytochrome c release is induced by ER stress in cell lines other than C2C12 cells. ER stress induces cytochrome c release in rat fibroblast cells in a caspase-8-and Bid-independent manner (11). Possible mediators linking the ER to mitochondria, as suggested by recent studies, include the c-Abl tyrosine kinase (10) and calcium (34). The present study reveals that C2C12 cells are useful for the study of the ER stress-specific caspase cascade because a simpler mechanism probably operates in these cells. Comparison of C2C12 cells with other cell lines would contribute to the dissection of the mechanism of ER stress-induced apoptosis.
To conclude that caspase-12 initiates the ER-specific caspase cascade in a direct manner, it should be critical to show that caspase-12 cleaves procaspase-9 at the processing site for activation, and the cleavage product (caspase-9) is active. We have demonstrated the specific cleavage and activation of procaspase-9 by purified caspase-12. Furthermore, we have shown direct correlation between suppression of caspase-12 activation and suppression of caspase-9 activation (and apoptosis) in vivo using the caspase-12 binding protein. These data strongly suggest that caspase-12, activated in response to ER stress, cleaves procaspase-9 to initiate the ER stress specific caspase cascade. During preparation of this article, Ellerby's group reported that Apaf-1 Ϫ/Ϫ knockout cells undergo ER stressinduced apoptosis (35). This result indicates that the cytochrome c⅐Apaf-1 complex is not essential for apoptosis induced by ER stress. They also showed that transient overexpression of a catalytic mutant of caspase-12 results in partial resistance of the knockout cells to ER stress and demonstrated that procaspase-9 can be cleaved by microsomal fractions, although the cleavage site has not been determined. These data are consistent with our findings described above in terms of the dependence of caspase-9 activation on caspase-12.
In this study, we also identify MAGE-3 as a protein that specifically binds procaspase-12. MAGE-3 has been detected in tumor cell lines, including melanoma cell lines (24). Because the precise human ortholog of murine caspase-12 is not yet known (36), it remains unclear whether MAGE-3 plays any role in caspase regulation in human cells. Our preliminary data show that endogenous MAGE-3 is detected in the microsomal fraction as well as in the S-100 fraction in several human tumor cell lines so far examined (e.g., Jurkat, HeLa), although the abundance in the microsomal fraction depends on cell lines (data not shown). Our results also show that overexpression of antisense MAGE-3 rendered Jurkat cells less resistant to ER stress induced by A23187, whereas the sense construct did not affect the resistance of Jurkat cells (data not shown). These results support the theory that specific expression of MAGE-3 in tumor cells may be involved in resistance of tumor cells to ER stress. It is interesting to note that MAGE-3 is more often expressed by metastatic melanomas than primary tumors (24). Although the involvement of MAGE-3 in resistance to ER stress has not been studied in detail, a correlation between malignancy and resistance to thapsigargin is evident in human melanoma cells (37,38). These observations together suggest the possibility that MAGE-3 may regulate the human caspase-12 ortholog. We have shown that murine caspase-12 can cleave procaspse-9 of both murine and human origins, although their cleavage site sequences are not identical (Fig. 1C). It is interesting to note that the processing sites within procaspase-9 of both origins are functionally conserved so that they can be cleaved by caspase-12, implying the presence of a functional homolog of caspase-12 in human cells.
Prolonged ER stress contributes to cell death and is linked to the pathogenesis of several different neurodegenerative disorders (39). It is possible that suppression of caspase-12 activation per se generates little toxicity in mammalian bodies, because caspase-12 null mutant mice do not show abnormalities during either development or adulthood (1). Therefore, a study of the specific interactions between MAGE-3 and procaspase-12 may provide a basis for the development of therapeutic reagents against unwanted activation of caspases caused by ER stress. | v3-fos-license |
2014-10-01T00:00:00.000Z | 2008-09-24T00:00:00.000 | 38636532 | {
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} | pes2o/s2orc | 1,1′-Dimethyl-4,4′-(propane-1,3-diyl)dipyridinium tetrabromidocadmate(II)
In the cation of the title compound, (C15H20N2)[CdBr4], the dihedral angle between the two pyridine rings is 70.85 (5)°. An intermolecular π–π interaction between the pyridine rings [centroid–centroid distance = 3.900 (4) Å] is observed. The CdII atom has a distorted tetrahedral coordination.
In the cation of the title compound, (C 15 H 20 N 2 )[CdBr 4 ], the dihedral angle between the two pyridine rings is 70.85 (5) . An intermolecularinteraction between the pyridine rings [centroid-centroid distance = 3.900 (4) Å ] is observed. The Cd II atom has a distorted tetrahedral coordination.
Compound (I), as shown in Fig. 1, consists of a 1,3-bis(1-methyl-4-pyridinium)propane cation and a tetrabormocadmate anion. As result of the flexible propane chain, the two pyridine rings have seriously torsion with the dihedral angle of 70.85 (5)°. The Cd II atom is coordinated by four Br atoms to a tetrahedral divalent anion.
The mixture was stirred for 20 min at room temperature and then sealed in a Teflon-lined stainless steel autoclave with a 23 ml capacity at 428 K for 72 h. After cooling to room temperature, the filtered solution was slowly evaporates and 7 days later colourless block-shaped crystals were obtained; these were washed with deionized water, filtered, and dried in air (yield 46% based on Cd).
S3. Refinement
H atoms were placed geometrically (C-H = 0.93-0.97 Å) and refined as riding, with U iso (H) = 1.2U eq (C) or 1.5U eq (methyl C) . The highest residual electron density peak is located at 1.223 (3) Å from Cd atom. The molecular structure of (I), with the atom-labeling scheme and 30% probability displacement ellipsoids.
Figure 2
A partial packing view of (I) along the c axis. For the sake of clarity, H atoms have been omitted.
Special details
Geometry. All e.s.d.'s (except the e.s.d. in the dihedral angle between two l.s. planes) are estimated using the full covariance matrix. The cell e.s.d.'s are taken into account individually in the estimation of e.s.d.'s in distances, angles and torsion angles; correlations between e.s.d.'s in cell parameters are only used when they are defined by crystal symmetry. An approximate (isotropic) treatment of cell e.s.d.'s is used for estimating e.s.d.'s involving l.s. planes. Refinement. Refinement of F 2 against ALL reflections. The weighted R-factor wR and goodness of fit S are based on F 2 , conventional R-factors R are based on F, with F set to zero for negative F 2 . The threshold expression of F 2 > σ(F 2 ) is used only for calculating R-factors(gt) etc. and is not relevant to the choice of reflections for refinement. R-factors based on F 2 are statistically about twice as large as those based on F, and R-factors based on ALL data will be even larger. | v3-fos-license |
2018-04-03T01:01:57.413Z | 2017-01-01T00:00:00.000 | 27832178 | {"extfieldsofstudy":["Medicine","Chemistry"],"oa_license":"CCBY","oa_status":"GOLD","oa_url":"https:(...TRUNCATED) | pes2o/s2orc | "Primary mono- and bis-sulfonamides obtained via regiospecific sulfochlorination of N-arylpyrazoles:(...TRUNCATED) | v3-fos-license |
2019-11-14T17:13:53.344Z | 2019-11-04T00:00:00.000 | 209725024 | {"extfieldsofstudy":["Chemistry","Medicine"],"oa_license":"CCBY","oa_status":"GOLD","oa_url":"https:(...TRUNCATED) | pes2o/s2orc | "Iron oxide encapsulated by copper-apatite: an efficient magnetic nanocatalyst for N-arylation of im(...TRUNCATED) | v3-fos-license |
2020-02-12T14:04:21.041Z | 2020-02-01T00:00:00.000 | 211079385 | {"extfieldsofstudy":["Medicine","Chemistry"],"oa_license":"CCBY","oa_status":"GOLD","oa_url":"https:(...TRUNCATED) | pes2o/s2orc | "HPTLC-DESI-HRMS-Based Profiling of Anthraquinones in Complex Mixtures—A Proof-of-Concept Study Us(...TRUNCATED) | v3-fos-license |
2016-03-22T00:56:01.885Z | 2014-07-01T00:00:00.000 | 11790460 | {"extfieldsofstudy":["Chemistry","Medicine"],"oa_license":"CCBY","oa_status":"GOLD","oa_url":"http:/(...TRUNCATED) | pes2o/s2orc | "Base Flip in DNA Studied by Molecular Dynamics Simulations of Differently-Oxidized Forms of Methyl-(...TRUNCATED) | v3-fos-license |
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