Metabolomics

Background & Aims: Per- and polyfluoroalkyl substances (PFAS) are persistent endocrine-disrupting chemicals associated with metabolic dysfunction, including metabolic dysfunction-associated steatotic liver disease (MASLD). While PFAS perturb lipid and bile acid (BA) metabolism in a sex-specific manner, the underlying mechanisms remain unclear. We tested whether steroid hormones mediate PFAS-associated metabolic alterations. Methods: In 104 patients with biopsy-characterized MASLD, we performed sex-stratified analyses applied liquid chromatography coupled to mass spectrometry (LC-MS) for chemical analysis, integrating circulating steroids, PFAS exposure, hepatic lipidomics and BA profiles. Results: Steroid hormones were associated with MASLD severity in a sexually-dimorphic manner. Dihydrotestosterone showed consistent inverse associations with steatosis, fibrosis, necroinflammation and insulin resistance, particularly in females. PFAS exposure was associated with altered steroid profiles, predominantly indicating suppressed steroidogenesis in females. These PFAS-associated hormonal changes were linked to downstream alterations in hepatic lipids and BAs. Mediation analysis supported indirect effects of PFAS on metabolic pathways via steroids, including testosterone/epi-testosterone-mediated effects on ether phospholipids and estradiol-mediated effects on lithocholic acid. Females exhibited stronger PFAS-steroid-BA associations, whereas males showed weaker, lipid-centric effects. Conclusions: PFAS exposure is associated with sex-specific disruption of steroid hormone pathways that may link environmental exposure to lipid and BA dysregulation in MASLD. These findings identify steroid hormones as potential key mediators of PFAS-associated metabolic dysfunction and highlight sex as a critical determinant in environmental liver disease.

BackgroundDengue virus (DENV) appears to manipulate several cellular metabolic pathways to permit its replication and immune evasion in the host. Here, we employed high-resolution mass spectrometry (HR-MS) to investigate the serum metabolomic landscape of clinical DENV infection. MethodsSerum specimens from primary dengue (n=11), secondary dengue (n=9) samples, and healthy controls (n=10) were used for untargeted and targeted metabolomic quantification on a Waters Xevo G2-XS QTof Mass Spectrometer. The binding potential of selected ligands against DENV NS1, NS3, and NS5 was evaluated. Crystal structures were retrieved from Protein Data Bank and prepared using the Schrodingers protein preparation wizard. Based on findings from untargeted metabolomics, we validated certain bioactive lipid metabolites using commercial enzyme immunoassays. ResultsSerum metabolomic profiling revealed multiple distinct patterns for primary and secondary dengue versus controls. A consistent peak was observed at 2.06 mins across all samples. Certain bioactive lipid metabolites, such as, lysophospholipids, phosphatidylcholines, phosphatidylserines, and phosphatidylinositols, were detected alongside carnitine fragments, ceramides, diacylglycerols (DAGs), and bile acid conjugates in dengue. Molecular docking showed that DAG consistently exhibited strong binding to all the DENV proteins. Notably, LPC 22:6 showed a selectively strong affinity for NS5. Enzyme validation showed that in the secondary dengue cohort, LPC was significantly elevated than primary and healthy controls (p<0.05). ConclusionsOur investigations of the metabolomic landscaping, unveiled certain characteristic anabolic shift revealing metabolic vulnerabilities in clinical DENV infection, warranting investigations for use as potential biomarkers of inflammation in disease diagnosis and prognosis. Author summaryDengue is a mosquito-borne tropical viral infection that can range in severity from asymptomatic to life-threatening manifestations. Dengue virus (DENV) hijacks cellular machinery to sustain its survival in the host. Using high-resolution mass spectrometry (HR-MS), we studied the serum metabolomic imprints of dengue infection. The binding ability of selected metabolomic ligands against DENV NS1, NS3, and NS5 was studied. We found several distinct retention patterns for the dengue cases, with a consistent peak at 2.06 min across all samples. Further, several bioactive lipid metabolites were detected in the dengue infected cohort. Our molecular docking studies showed that diacylglycerol, a lipid metabolite exhibited strong binding with all the DENV proteins. We concluded that certain unique lipid metabolomic imprints exist in clinical DENV infection. The identified metabolomic signatures reveal significant potential for metabolomics to elucidate host-virus interactions, contributing to the advancement of antiviral and symptomatic treatments, along with prognostic or diagnostic biomarkers of dengue disease.

AbstractO_ST_ABSBackgroundC_ST_ABSShort-chain fatty acids (SCFAs) are widely used as functional readouts of gut microbial activity in vivo. The growing adoption of decentralised study designs and self-collection protocols has amplified the need for reliable room-temperature storage and shipment strategies. However, SCFAs volatility and the persistence of post-collection microbial metabolism raise concerns regarding pre-analytical stability and the interpretability of measured concentrations. MethodsWe assessed the temporal stability of fatty acids (FAs) across intestinal and systemic matrices under room-temperature storage. Untreated stool was compared with two nucleic acid stabilisation devices (eNAT and OMNIgene-GUT), while whole blood, plasma and dried blood spots (DBS) were evaluated as minimally invasive systemic sampling strategies. Profiles were quantified using complementary GC-MS and LC-MS/MS workflows. ResultsUntreated stool showed fermentation-driven increases in major SCFAs, whereas immediate freezing preserved baseline profiles. eNAT maintained faecal FA stability for up to 21 days, while OMNIgene-GUT exhibited baseline and time-dependent alterations. In systemic matrices, plasma and whole blood showed upward drift, whereas DBS declined initially before stabilising after approximately 14 days. ConclusionsFA measurements are highly matrix- and device-dependent. Our findings provide practical guidance for the selection of sampling strategies in microbiome-associated FA studies and emphasise the need for controlled pre-analytical conditions in decentralised microbiome studies.

BackgroundMetabolic reprogramming is a hallmark of breast cancer (BrCa), with alterations in glycolysis, glutamine metabolism, and the urea cycle contributing to tumour progression. Dichloroacetate (DCA), a pyruvate dehydrogenase kinase (PDK) inhibitor, shifts metabolism toward oxidative phosphorylation and has been proposed as a therapeutic agent. While isotope tracing is well-established, natural isotope abundance ({delta}{superscript 1}3C, {delta}{superscript 1}N) is emerging as a biomarker of metabolic alterations in cancer. MethodsWe investigated the relationship between isotope composition and metabolism in BrCa using two BALB/c mouse mammary tumour models (V14 and 4T1) and assessed the effects of DCA treatment using metabolomics, lipidomics and isotopomics. ResultsV14 and 4T1 tumours exhibited isotopic patterns similar to human tumours, with {delta}{superscript 1}3C enrichment and {delta}{superscript 1}N depletion relative to non-cancerous mammary tissue. V14 tumours were more {delta}{superscript 1}N-depleted than 4T1, reflecting differences in nitrogen metabolism. Multivariate analysis integrating isotopic, metabolomic, and lipidomic data revealed isotopic features as key discriminators between tumours and normal tissues. Compared to V14, 4T1 tumours were enriched in TCA intermediates, sphingolipids, and amino acids, whereas V14 tumours showed elevated glutaminolytic and nitrogenous metabolites. DCA treatment differentially affected tumour growth, with V14 tumours more sensitive than 4T1. DCA altered nitrogen metabolism, increasing the arginine-to-ornithine ratio, and modulating {delta}{superscript 1}N values in a tumour-specific manner increasing V14 and decreasing 4T1 {delta}{superscript 1}N values. DCA had little effect on {delta}{superscript 1}3C. {delta}{superscript 1}3C values were primarily determined by the balance between lipid and TCA cycle metabolites, rather than glycolytic flux. {delta}{superscript 1}N variation was linked to nitrogen metabolism, including urea cycle intermediates and sphingolipid composition, with a potential role for choline-related fractionation in {delta}{superscript 1}N depletion. Altered gene expression of Hacd2 and Acot12 in V14 tumours after DCA treatment was reflected in shorter fatty acid tails in phosphatidyl cholines, supporting the lipidomics data. ConclusionsThese findings support the hypothesis that cancer-associated metabolic reprogramming influences natural isotope abundance. Correlations between isotope shifts and metabolic signatures highlight the potential of lipid-derived {delta}{superscript 1}N as a biomarker of tumour metabolic state, with implications for noninvasive metabolic profiling in BrCa. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/710495v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@1589d0eorg.highwire.dtl.DTLVardef@af2ad4org.highwire.dtl.DTLVardef@24e67forg.highwire.dtl.DTLVardef@98da7f_HPS_FORMAT_FIGEXP M_FIG C_FIG

Background & AimsEarly identification of decompensation in patients with cirrhosis is important to enable timely detection, management of complications and for effective treatment. This study investigates the biology of decompensation and aim to identify protein biomarkers for identification of high-risk patients. MethodsThe primary analysis included plasma samples from 46 patients with metabolic dysfunction associated steatotic liver disease (MASLD) related cirrhosis. Plasma samples were depleted for the top 14 most abundant proteins and the proteome was measured by liquid chromatography tandem mass spectrometry. The dataset was divided into a training (14 compensated, 10 decompensated) and a test cohort of compensated patients (11 progressing to decompensation, 11 remaining compensated). Changes in protein levels were determined by ANCOVA and a prognostic model was developed using logistic regression. External validation was performed in an independent cohort of 120 patients with alcohol-related cirrhosis. Time-to-event analyses were conducted in this cohort using Cox regression. Results52 proteins involved in impaired hepatic function, fibrogenesis, immune activation, and metabolic changes were significantly different between compensated and decompensated patients. A prognostic model with four proteins (NBL1, LTBP4, APOC4, GHR), demonstrated predictive ability for future decompensation (AUC=0.93, 73% sensitivity, 100% specificity). In the external validation cohort, the model demonstrated generalizability (AUC=0.78, 72% sensitivity, 82% specificity). Validation cohort time-to-event analyses showed that higher baseline scores were associated with shorter time to liver-related events (HR 1.32; log-rank p = 0.027), underscoring the panels prognostic value. ConclusionOur study indicates that patients with decompensated cirrhosis are characterized by proteomic signatures of fibrogenesis and metabolic dysfunction. Capturing these signatures could help identify patients at risk of complications and potentially those eligible for aetiology directed treatment. Impact and ImplicationsAddressing a critical unmet need for early detection of cirrhosis decompensation, our proteomic study identifies a four-protein panel with predictive ability for decompensation. These findings hold significant implications for hepatologists, clinical researchers, and healthcare systems, offering a novel tool to enhance prognostication and refine treatment strategies, potentially facilitating targeted patient monitoring. However, considering the small discovery sample size and the distinct aetiology of the external validation cohort, further validation is essential before broad clinical integration. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=183 SRC="FIGDIR/small/709475v1_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@6620e2org.highwire.dtl.DTLVardef@f8dfe4org.highwire.dtl.DTLVardef@1331101org.highwire.dtl.DTLVardef@1a195ca_HPS_FORMAT_FIGEXP M_FIG C_FIG

BackgroundPolycystic ovary syndrome (PCOS) is a complex endocrine disorder characterized by metabolic dysregulation. Identifying serum biomarkers can enhance our understanding of its pathophysiology. This study employs an untargeted metabolomic approach to investigate metabolic alterations in PCOS. MethodsSerum samples were collected from 71 women with PCOS and 54 healthy controls. Untargeted Metabolomic profiling was performed using liquid chromatography-mass spectrometry (LC-MS) to identify differentially abundant metabolites. Pathway analysis was conducted to identify key metabolic disruptions, and correlations between identified metabolites and clinical parameters were assessed. ResultsThe metabolomics analysis identified 24 upregulated and 17 downregulated metabolites in PCOS compared with controls. These metabolites mainly include glycerophospholipids, fatty acids, sphingolipids, peptides, ceramides, and steroids. Pathway analysis indicated that these metabolites were enriched in pathways including bile acid biosynthesis, glycerolipid metabolism, tryptophan metabolism, the citric acid cycle, and fatty acid metabolism. Increased levels of branched-chain and aromatic amino acids suggested potential links to insulin resistance. Disruptions in bile acid metabolism pointed to altered gut microbiome interactions. Additionally, metabolites related to oxidative stress and mitochondrial function indicated metabolic dysfunction. Correlation analyses revealed associations between altered metabolites and clinical markers such as insulin resistance and androgen levels. ConclusionThis study reveals distinct serum metabolic alterations in PCOS, emphasizing their association with insulin resistance and inflammation. These findings highlight the potential of metabolomics to identify novel biomarkers for early diagnosis and to develop targeted therapeutic strategies.

Aiming to examine the effect of chronic alcohol exposure on the activity of CYP3A enzymes in human liver, we studied the metabolism of two CYP3A-specific substrates, 7-benzyloxyquinoline (7-BQ) and ivermectin, in 23 preparations of human liver microsomes (HLM) obtained from donors with documented alcohol exposure, grading from non-drinkers to heavy alcoholics. All HLM samples were characterized for the composition of the cytochrome P450 pool and the abundances of other drug-metabolizing and endoplasmic reticulum-stress-related enzymes by global proteomics. Our studies revealed a striking increase in the activities of CYP3A enzymes caused by chronic alcohol exposure. This effect is not associated with changes in CYP3A enzyme levels, which do not correlate with alcohol exposure. Instead, the rates of 7-BQ and ivermectin metabolism correlate with the content of alcohol-inducible CYP2E1. However, this enzyme does not metabolize ivermectin, and its activity with 7-BQ is negligible. These results suggest that the observed acceleration of the elimination of drugs metabolized by CYP3A enzymes by alcohol exposure is due to functional effects of the interaction between CYP3A and CYP2E1. To elucidate the potential mechanism of this effect, we studied the formation of CYP2E1-CYP3A4 complexes in CYP3A4-containing Supersomes with co-incorporated CYP2E1 using tag-transfer chemical crosslinking mass spectrometry (CX-MS). These experiments confirmed physical interactions between the proteins and allowed the identification of CYP3A4 residues at the sites of contact. This information was used to build structural models of the CYP2E1-CYP3A4 complex and to propose possible mechanisms for the observed effects.

The enzyme nitric oxide synthase (NOS) breaks down the semi-essential amino acid L-arginine (L-Arg) in the cell to produce citrulline and nitric oxide (NO). NO is a crucial signalling molecule in cells that controls the metabolism of fats and carbohydrates. The aim of this study was to investigate two important genes in the L-Arg-NOS-NO signalling pathway, AMPK and ACC-1, as markers of the molecular mechanisms that are triggered when liver cells sense elevated L-Arg. Mouse liver epithelial insulin-sensitive BNL CL2 cells were used as a model system and cultured with 0, 400 or 800 {micro}M L-Arg. Cell growth parameters were analysed alongside qRT-PCR based analysis of target transcripts involved in lipid and glucose metabolic pathways. In a further experiment, NOS inhibitor; L-NAME (40 mM) and external NO donor; SNAP (100 {micro}M) were added and the effect on target gene expression analysed. L-Arg addition impacted culture viability and cell growth. AMP-activated protein kinase (AMPK) was regulated in response to L-Arg addition with increasing extracellular concentrations elevating AMPK mRNA and protein expressions. L-NAME decreased target gene expression in an L-Arg addition dependent manner. SNAP (100 {micro}M) addition increased target gene expression after 6 and 24 h. NO, produced as a result of L-Arg addition and the factors L-NAME and SNAP, that regulate NO bioavailability, impacted BNL CL2 cell NO/AMPK/ACC-1 signalling pathways via regulating mRNA expression and subsequently protein expression.

BackgroundMetabolomic changes related to colorectal cancer (CRC) may serve as diagnostic markers to identify patients may develop or have developed CRC. MethodsUntargeted lipidomics were performed on serum from CRC cases and clean-colon controls from the Chicago Colorectal Cancer Consortium (CCCC) and the University of Arizona Cancer Center (UACC). ResultsUntargeted lipidomics in the CCCC CRC series revealed significant alterations in sphingolipids. Targeted lipidomics revealed a signature of five sphingomyelins (SMs) were significantly decreased in CRC patients in CCCC and UACC CRC series. Circulating SMs are degraded primarily by S-SMase and serum S-SMase activity was significantly higher in UACC cases as compared to controls. Serum S-SMase activity was also measured in two series of adenoma patients to determine if S-SMase may serve as a biomarker for development of colorectal neoplasia. While S-SMase activity was significantly higher in adenoma patients compared to controls in the mostly white UACC series, S-SMase activity in samples from the Chicago Black series (CCCC) were indistinguishable from each other and significantly higher than UACC controls. ConclusionsTogether, these studies suggest the potential for S-SMase activity to serve as a biomarker for colorectal neoplasia, with potential implications in some but perhaps not all populations.

BackgroundPolycystic ovary syndrome (PCOS) is a prevalent endocrine disorder with substantial metabolic comorbidities, including obesity, insulin resistance, and dyslipidaemia. Beyond their classical digestive role, bile acids (BAs) function as metabolic signalling molecules that regulate glucose and lipid homeostasis and inflammation through receptors such as the farnesoid X receptor (FXR) and Takeda G-protein receptor 5 (TGR5). However, bile acid dysregulation in PCOS remains inadequately characterised. MethodsTargeted serum bile acid profiling was performed in PCOS (n = 86) and healthy controls (n = 60) using a validated LC-MS/MS method. Individual bile acids were quantified and classified into primary, secondary, and conjugated forms. Multivariate analyses were applied to identify group-level metabolic patterns. Functional bile acid indices reflecting hepatic conjugation and microbial transformation were calculated. Correlation analyses assessed between bile acids and clinical variables. ResultsPCOS women exhibited significantly higher serum levels of cholic acid and conjugated bile acids. Multivariate analyses revealed distinct bile acid signatures differentiating PCOS from controls, with deoxycholic acid, taurocholic acid, and cholic acid contributing most strongly to group separation. Pathway-based indices demonstrated an expanded conjugated bile acid pool, an increased conjugated-to-unconjugated bile acid ratio, and altered secondary-to-primary bile acid balance in PCOS. Several bile acids showed significant associations with androgen levels and gonadotropin ratios. ConclusionPCOS is characterised by coordinated alterations in bile acid metabolism, including hepatic synthesis, conjugation, and gut microbial transformation, highlighting bile acids as integrative metabolic signals linking endocrine and metabolic dysfunction in PCOS.

Background and AimMASLD affects 30-38% of Indian adults, yet the contribution of genetic risk variants to disease susceptibility and fibrosis progression remains poorly characterised. We investigated the association of 12 candidate SNPs with MASLD susceptibility and fibrosis severity in North Indian patients, benchmarking allele frequencies against IndiGenomes and global populations. MethodsSixty-nine MASLD patients (75.4% male; median BMI 29.8 kg/m{superscript 2}) from a tertiary care liver clinic in New Delhi were genotyped for 12 SNPs using Illumina custom BeadChip array and Sanger sequencing. Patients were stratified by liver stiffness measurement (LSM): significant fibrosis ([&ge;]8 kPa, n=38) versus no significant fibrosis (<8 kPa, n=31). Allele frequencies were compared with IndiGenomes ([~]1,020 Indian individuals) and 1000 Genomes populations. ResultsPNPLA3 rs738409 G allele was the strongest within-cohort predictor of significant fibrosis (allelic OR 2.89, 95% CI 1.35-6.19, P=0.006; dominant model OR 3.94, P=0.008), with carriers demonstrating higher LSM (median 15.6 vs. 7.5 kPa, P=0.005). SAMM50 rs3761472 (OR 2.12, P=0.065) and FTO rs9939609 (OR 2.08, P=0.089) showed non-significant trends. In the population-level comparison, APOC3 rs2854116 T allele was the only variant significantly enriched after Bonferroni correction (64.0% vs. 47.9%; OR 1.93, 95% CI 1.35-2.77, P<0.001), followed by PNPLA3 (33.3% vs. 24.1%, OR 1.57, P=0.019) and SAMM50 (31.2% vs. 22.6%, OR 1.55, P=0.028). Notably, APOC3 showed no association with fibrosis (OR 0.96, P=1.000), suggesting a role in susceptibility rather than progression. All SNPs were in Hardy-Weinberg equilibrium. ConclusionsThis study reveals a dissociation between genetic determinants of MASLD susceptibility and fibrosis progression in North Indian patients. APOC3 rs2854116 predisposes to MASLD at the population level, while PNPLA3 rs738409 drives fibrosis severity within established disease, underscoring the need for ancestry-specific genetic risk stratification. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/26347059v1_ufig1.gif" ALT="Figure 1"> View larger version (69K): org.highwire.dtl.DTLVardef@a07808org.highwire.dtl.DTLVardef@12882adorg.highwire.dtl.DTLVardef@9b33a1org.highwire.dtl.DTLVardef@15aa5e8_HPS_FORMAT_FIGEXP M_FIG C_FIG

BackgroundFatal insulin intoxication remains difficult to diagnose because insulin undergoes rapid degradation after death, limiting the reliability of direct biochemical measurements. This creates diagnostic uncertainty when objective molecular confirmation of insulin excess are required. We hypothesised that insulin excess induces systemic metabolic alterations that persist beyond insulin degradation and can be captured using postmortem metabolomics in a forensic setting. MethodsHigh-resolution mass spectrometry (HRMS)-based metabolomics was applied to a national cohort comprising 51 fatal insulin intoxications. Orthogonal partial least squares-discriminant analysis (OPLS-DA) models were trained on cases collected between 2017-2022 to identify insulin-associated metabolite features using a shared-and-unique-structures approach. Performance was evaluated using two temporally distinct test sets (2023-2024): a matched validation cohort and a heterogeneous forensic cohort reflecting biological variability. ResultsHere we show that an insulin-associated metabolomic fingerprint comprising 91 features demonstrated reproducible discrimination across independent cohorts. In the matched cohort (n=59, including 14 insulin cases), insulin intoxication classification achieved 100% sensitivity and 73% specificity within the applicability domain. In the heterogeneous cohort (n=154, including 14 insulin cases), 100% sensitivity was maintained with a 72% specificity despite increased biological variability. Univariate analyses demonstrated significant alterations across multiple metabolite classes, including acylcarnitines, fatty acids/lipids, and purine/nucleoside metabolites, with moderate effect sizes, consistent with systemic effects of insulin-induced hypoglycaemia. ConclusionsFatal insulin intoxication is associated with a reproducible metabolomic fingerprint detectable after death. These findings demonstrate that postmortem metabolomics may serve as a complementary decision-support tool when conventional biomarkers are unreliable.

BackgroundPrediabetes is the transitional stage preceding the onset of type 2 diabetes. Due to its asymptomatic nature and slow progression, conventional screening methods frequently fail to identify individuals in this prediabetic stage. Therefore, this study employs bibliometric methods to reveal the abnormal changes in clinically relevant metabolites in diabetes, providing evidence for early diagnosis, pathogenesis, and precision interventions. MethodsSearch the Web of Science Core Collection database for all papers on metabolomics related to prediabetes and type 2 diabetes clinical research from the databases inception until May 2025. Use VOSviewer and CiteSpace software to perform bibliometric analysis on the included literature. ResultsA total of 1,742 studies were ultimately included in the review of the literature. Researchers from China, the United States, and Germany, as well as institutions, have made significant contributions to metabolomics research in prediabetes and type 2 diabetes. The highest number of publications and citations was from the journals Metabolites and Diabetes. The current research hotspots in this field, as determined by keyword co-occurrence and clustering analysis, include metabolomics, insulin resistance, obesity, risk factors, biomarkers, gut microbiota, amino acids, lipidomics, and mass spectrometry. ConclusionThe outcomes of the study are instrumental in enabling scholars to comprehend the developmental trajectory of metabolites associated with prediabetes and type 2 diabetes and facilitate the rapid identification of emerging research pathways.

Background and aimsCirculating complement is associated with occurrence of alcohol-associated hepatitis (AH) and is a potential biomarker to distinguish AH from alcohol cirrhosis (AC). Complement contributes to kidney injury, a condition often occurring in patients with alcohol-associated liver disease (ALD). However, little is known regarding complement in cross talk between liver and kidney in ALD. Here we tested the hypothesis that urinary complement would provide potential biomarkers for ALD and insights into mechanisms of liver-kidney crosstalk in the pathogenesis of ALD. MethodsPlasma and urine were collected at admission from patients with sAH, healthy controls (HC), and heavy drinkers without liver disease (HD) (from the multicenter Alcohol Hepatitis Network) and with AC (from the Northern Ohio Alcohol Center). Urine was subjected to unbiased proteomics analysis and plasma complement assessed by multiplex/ELISA assays. 30- and 90-day mortality was tracked in patients with sAH. ResultsAll three complement activation pathways were perturbed in plasma and urine of patients with sAH and AC compared to HC and HD. Components of the lectin and classical pathways in urine were associated with 30- and 90-day mortality in patients with sAH. When 4 complement proteins were combined, they distinguished sAH from AC (AUC 0.78), equivalent to that of MELD (AUC 0.65). There was no correlation between complement in plasma and urine, suggesting an independent impact of sAH on complement in kidney and liver. ConclusionThe urinary proteome revealed complement protein signatures associated with sAH and AC, providing valuable insights into the potential for complement biomarkers and the mechanisms of liver-kidney crosstalk in ALD.

Aiming to develop a high-throughput fluorimetric assay for the activity CYP1A2, we introduced 6-Methoxy-2-naphthoic acid (MONA) as a new fluorogenic substrate for this important metabolizer of antidepressants and psychotropic drugs in human liver. We demonstrated that oxidative demethylation of MONA by liver microsomes results in a red shift and a substantial increase in fluorescence. This effect, which is exceptionally well pronounced at alkaline pH, allowed us to develop a sensitive and robust high-throughput assay of MONA metabolism. Probing the activity of 15 individual recombinant human P450 enzymes, we found that only two P450 species exhibited activity in MONA demethylation: CYP1A2 (kcat=11.9{+/-}2.2 min-1, KM=578{+/-}106 {micro}M) and CYP2A6 (kcat=0.48{+/-}0.07 min-1, KM=54{+/-}15 {micro}M). Since the KM values of the two enzymes are well resolved and the turnover rate observed with CYP2A6 is much lower than that of CYP1A2, this new fluorogenic substrate is useful as a specific probe for CYP1A2 activity in HLM. Importantly, MONA is not metabolized by CYP1A1 and CYP2C19, which distinguishes it from all known CYP1A2 fluorogenic substrates. We then used MONA to investigate the effects of chronic alcohol exposure on CYP1A2 activity using a series of 23 proteomically characterized individual HLM preparations from donors with various levels of alcohol consumption. The substrate saturation profiles (SSP) acquired with these preparations were subjected to global kinetic analysis by approximating them with combinations of two Michaelis-Menten equations with globally optimized KM values of 11 and 553 {micro}M. The amplitudes (Vmax values) of both components showed a pronounced increase with increasing alcohol exposure of the liver donors. The Vmax of the minor high-affinity component was best correlated with the abundance of alcohol-inducible CYP2E1 enzyme. The correlation was further improved by combining it with the abundances of CYP2A6 and CPR. This finding suggests that this minor component reflects the activity of CYP2A6 in the complex with alcohol-inducible CYP2E1 protein. In contrast, the Vmax of the predominant CYP1A2-catalyzed low-affinity component revealed a pronounced correlation with the abundances of CYP1A2 and NADPH cytochrome P450 reductase (CPR). These results suggest a considerable increase in the rate of metabolism of drug substrates of CYP1A2 by chronic alcohol exposure that takes place despite an alcohol-induced decrease in CYP1A2 expression.

IntroductionRetinoids are bioactive vitamin A derivatives that regulate cellular differentiation and gene expression, yet their reliable quantification remains challenging due to low abundance, structural isomerism, and sensitivity to ionization conditions while handling. ObjectivesIn this study, we performed a systematic optimization of liquid chromatography-mass spectrometry (LC-MS)-based detection of retinoids across tissues and biofluids. MethodsChromatographic separation, adduct formation, ionization parameters, fragmentation behavior, and extraction procedures were evaluated in an integrated workflow. ResultsChromatographic conditions influenced not only retention time but also the ionic species detected, affecting precursor selection for MS{superscript 2} analysis. Retinoids exhibited compound-dependent responses to electrospray ionization and collision energy, requiring tailored acquisition parameters. Extraction experiments demonstrated differential recovery among retinoid classes and revealed matrix-dependent behavior, indicating that protocols used for tissues cannot be directly transferred to low-abundance biofluids. Using optimized conditions, retinoids were detected in mouse cerebrospinal fluid (CSF) at concentrations approaching the analytical detection limit, where MS{superscript 2} confirmation was necessary for reliable identification. ConclusionTogether, our results provide a framework for reproducible retinoid profiling across biological matrices and enables comparative studies of retinoid biology in low-volume and low-abundance biofluids.

Niemann-Pick Disease Type C1 (NPC1) is a fatal, neurodegenerative disorder, characterized by lysosomal lipid accumulation and dysmyelination. Previous studies have documented some lipid abnormalities in the null mouse (Npc1-/-) focused on the whole brain and liver. However, the specific lipidomic alterations in severely affected brain regions, such as cerebellum and isolated myelin remain understudied. We present a comprehensive LC-MS-based lipidomic analysis of the cerebellum and cortex of Npc1-/- mice during disease progression stages, along with the first comprehensive characterization of the myelin lipidome in NPC1 disease. Our results reveal that the cerebellum accumulates lipid species, including sphingolipids and glycerophospholipids progressively, while the cortex shows an overall decline in lipid levels, indicating region-specific lipid dysregulation. Notably, bis(monoacylglycero)phosphates and their precursors--including lysophosphatidylglycerol and hemibismonoacylglycerophosphate exhibit significant accumulation, with a preference for docosahexaenoic acid (DHA)-containing species. Despite known cholesterol storage defects in NPC1, we observed reduced free cholesterol levels in both regions, which we attribute to myelin loss. Myelin-specific lipidomics demonstrated extensive dysregulation, particularly in cortical myelin, including severe losses in sulfatides, ether-lipids, and acylcarnitine, alongside striking accumulation of hydroxy-ceramides. These findings identify novel lipid alterations in brain subregions and myelin, offering critical insight into the lipid perturbations under the loss of NPC1, and highlight lipid targets that may be crucial for therapeutic intervention and biomarker development.

Pseudomonas putida strain KT2440 is a crucial model organism for synthetic biology and bioengineering applications, yet there currently exists no comprehensive metabolomics database comparable to those available for other model organisms. This gap hinders the use of untargeted metabolomics for exploratory analyses in this system. We developed the P. putida metabolome reference database (PPMDB v1) to address this limitation by consolidating metabolite information from multiple sources and expanding coverage through computational predictions. The database was constructed by curating metabolites from BioCyc, BiGG, and other literature sources, then computationally expanding this collection using BioTransformer environmental transformation predictions to generate additional predicted metabolites. We enhanced the databases utility for molecular annotation in metabolomics studies by incorporating analytical properties including collision cross-sections, tandem mass spectra, and gas-phase infrared spectra. These analytical properties were gathered from existing measurement data or predicted using computational tools. We further augmented the database through inclusion of reaction information and pathway annotations, facilitating biological interpretation of metabolomics data. This publicly available resource fills a critical gap in P. putida research infrastructure, supporting metabolite annotation and biological interpretation in untargeted metabolomics studies and enabling in-depth exploratory analyses of this important synthetic biology platform at the molecular level. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/713193v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@c8828forg.highwire.dtl.DTLVardef@1f3a5c5org.highwire.dtl.DTLVardef@1084535org.highwire.dtl.DTLVardef@1f7ca4a_HPS_FORMAT_FIGEXP M_FIG C_FIG

IntroductionDrug repurposing offers a cost-effective and time-efficient strategy for cancer therapy by leveraging existing drugs with established safety profiles, thus functioning as an alternative therapeutic strategy in demanding diseases such as cancer. Antidiabetic agents, in particular, have demonstrated encouraging anticancer potential. Among them, the non-sulfonylurea insulin secretagogue repaglinide (RPG) has shown emerging anticancer potential, yet its effects on breast and lung cancers remain largely unexplored. Thus, this study investigates the anticancer activity of repaglinide in human breast (MCF-7) and lung (A549) cancer cell lines, focusing on its cytotoxic, pro-apoptotic, anti-proliferative, and anti-migratory effects and the underlying possible molecular mechanisms. Methodology and ResultsMTT cytotoxic assay revealed that RPG reduced cell viability in a dose-/time-dependent manner, with an IC (48h) of 100.8 {+/-} 3.98 {micro}M for MCF-7 and 104 {+/-} 3 {micro}M for A549. Further, the apoptotic effect of RPG on both cell lines was evidenced by double staining assays, comet assay, and western blotting analysis, suggesting that RPG explicitly caused DNA damage and activated intrinsic and extrinsic apoptosis pathways. Additionally, RPG suppressed clonogenicity and enforced G1 arrest in MCF7 and A549 cells by modulating cell cycle regulations as well as cell proliferation pathways. Moreover, RPG markedly suppressed cell motility, as demonstrated by scratch and Transwell migration/invasion assays, which is correlated with reduced MMP-2 and MMP-9 expression, confirmed by gelatin zymography and western blotting. ConclusionConclusively, Repaglinide exerts potent anticancer effects in breast and lung cancer cells by modulating key oncogenic signaling pathways, and thus can be considered a promising candidate for repurposing in cancer therapy.

Identifying metabolites and metabolic reactions specific to a cellular state, such as inflammatory state in immune cells, is of great interest, as it can provide important biomarkers and point to compounds and reactions of specific biological functions. However, many cell state-specific metabolites remain in the unannotated part of metabolome. Here we identified a series of sulfur-containing metabolites that are actively produced in macrophages upon classical activation, but not in resting state or alternative activation state. Isotopic tracing, in vitro assays and genetic perturbations further revealed that they are formed from reactions between free cysteine and several important intermediates in glycolysis and TCA cycle. Upon classical activation, macrophages specifically upregulate the import of cystine via Slc7a11, supporting the production of these adducts. Their production dynamically responds to changes in central metabolism, environmental nutrient levels, and is regulated by nitric oxide. Finally, we confirmed these newly identified compounds also present in human samples, and most of them are significantly elevated in inflammatory granuloma annulare lesions. This work elucidated a previously uncharted part of metabolic network that is associated with inflammation and metabolic stress condition, which has important implications and set foundation for many future discoveries.