Metabolomics

High dietary fructose consumption is a major contributor to the development of obesity and related cardiometabolic diseases, highlighting the need for accurate assessment of fructose metabolism in humans. Stable isotope tracer approaches, such as 13C6-fructose, require highly sensitive and specific analytical methods to quantify both concentrations and isotopic enrichments. In this study, we developed and validated a robust gas chromatography-triple quadrupole mass spectrometry (GC-QQQ)-based method for the simultaneous measurement of unlabeled and 13C6-fructose in human plasma. The method employs oximation and per-acetate derivatization, and demonstrates high specificity and accuracy. Intra- and inter-assay precision were below 10%, with no detectable carry-over, and a lower limit of quantification (LLOQ) of 0.1 nmol/mL for concentration and 0.02 molar percent excess (MPE%) for enrichment and no interference from glucose. We further compared data acquisition using multiple reaction monitoring (MRM) and selected ion monitoring (SIM). MRM showed superior performance at the low concentrations and enrichment levels characteristic of clinical plasma samples, resulting in improved sensitivity and lower LLOQs compared to SIM. Overall, this validated method provides a sensitive and reliable approach for fructose tracer studies in humans. Its application will facilitate robust investigations into fructose metabolism, and its role in metabolic dysregulation and obesity-related disease.

Abstract: Background: Children born very preterm (VPT) are at elevated risk for adverse neurobehavioral outcomes and are vulnerable to environmental exposures. We used untargeted metabolomics analysis to identify environmentally relevant metabolites associated with childhood behavioral problems and to characterize related endogenous metabolic pathways. Methods: We studied 226 children born <30 weeks gestation in a multi-site cohort study. Urine collected at age 4 was profiled using untargeted ultra-high-performance liquid chromatography high-resolution mass spectrometry (UHPLC-HRMS). Associations between 30 exogenous environmental metabolites and behavioral problems score at age 4 were evaluated cross-sectionally using multivariable linear regression, followed by metabolome-wide association and pathway enrichment analyses. Results: Three exogenous metabolites were nominally associated with child behavior problems. Specifically, 2-(Acetylamino)-3-( henylthiol)propanoic acid was positively associated with overall ({beta}=1.42, p=0.006), externalizing ({beta}=1.50, p=0.006), and internalizing ({beta}=1.34, p=0.021) behavior problems. Diethofencarb was positively associated with overall ({beta}=0.34, p=0.018) and externalizing ({beta}=0.39, p=0.010) behavior problems, while protocatechuic acid was inversely associated with internalizing problems ({beta}=-0.68, p=0.027). No associations remained significant after false discovery rate (FDR) correction. Nevertheless, these metabolites were associated with 151, 131, and 530 endogenous metabolic features, respectively, at FDR<0.05, highlighting perturbations in lipid metabolism, mitochondrial energetics, inflammatory signaling, amino acid metabolism, and neuroactive pathways. Conclusions: Environmental metabolites associated with behavior problems were linked to broad endogenous metabolic differences relevant to neurodevelopment. These findings highlight potentially modifiable environmental influences, including exposures related to combustion-derived pollutants, pesticides, and dietary sources, that may contribute to behavioral vulnerability among children born very preterm

BackgroundChronic obstructive pulmonary disease, primarily caused by exposure to cigarette smoke, is a heterogeneous lung condition characterized by complex metabolic alterations. The metabolic changes associated with smoking status have not been thoroughly investigated. Our study aims to explore the metabolite profile of COPD patients categorised by their smoking habits, including smokers, ex-smokers, and non-smokers. MethodsIn this study, the plasma metabolome of smoking stratified COPD patients were assessed using gas chromatography coupled to mass spectrometry. We applied multivariate and univariate statistical analysis to identify the differentially abundant metabolites. ResultsWe identified 23 altered metabolites in the smokers and 36 in the ex-smokers COPD subgroups. Interestingly, in comparison to the control group, no significant alteration was observed in the plasma of non-smoker COPD patients. Additionally, pathway enrichment analysis revealed top dysregulated metabolic pathways, including biosynthesis of unsaturated fatty acids, galactose metabolism, phenylalanine, tyrosine, and tryptophan biosynthesis, and glycosylphosphatidylinositol (GPI)-anchor biosynthesis. The receiver operating characteristic curve screened five metabolites, such as tetradecanoic acid, 2,4-di-tert-butylphenol, chloroxylenol, tetradecanal, and 1-dodecene, with the highest diagnostic performance (AUC > 0.8). ConclusionThis study reveals distinct plasma metabolic signatures across COPD subgroups categorized by cigarette smoking history.

Mass Spectrometry Metabolomics Identification Connection Algorithm (MSMICA) is an algorithm for automated metabolite identification in untargeted liquid chromatography-high-resolution mass spectrometry (LC-HRMS) analyses. Limitations in metabolite identification can occur due to the availability and cost of standards and prevent recognition of metabolic factors impacting human health and disease. MSMICA performs mass-to-charge-ratio matching with chemical structures and clusters of LC-HRMS features for adduct and isotope forms. A local optimization is then used to integrate retention time prediction, metabolite precursor-product and transporter correlations, and biospecimen-specific abundance information for metabolite identification. Applying MSMICA to various internal and external mammalian datasets, validation results showed a 96.2 +- 5.1% correct rate of metabolite identification. When multiple LC-HRMS datasets were used, MSMICA enabled greater metabolite identifications, expanded metabolic pathway coverage, and data harmonization. Thus, MSMICA applies multiple pieces of evidence to substantially improve metabolite identification coverage and accuracy for known metabolites.

Background and AimsThe genus Solanum including the wild species S. dulcamara, is rich in specialized metabolites such as steroidal glycoalkaloids (SGAs). Yet, much of this chemical diversity remains poorly characterized. This study aims to provide a comprehensive assessment of intra-specific chemodiversity in S. dulcamara. Using a dataset generated from 42 globally distributed accessions, we tested whether metabolic profiles differ among plant organs. We postulated that metabolic richness and abundance vary across accessions. Additionally, we hypothesized that differences in geographic origin or altitude affect SGA chemodiversity. MethodsAn untargeted metabolomic approach was applied to leaf, flower and root samples of 42 S. dulcamara accessions. Plants were grown in the greenhouse, and the extracted metabolites were analyzed using UHPLC-HRMS/MS in positive and negative ionization modes. Data processing and metabolite annotation were performed with a tailored bioinformatics workflow. Multivariate analyses were performed to evaluate chemical variation across organs and accessions. Key ResultsOur analyses revealed both organ and accession-specific metabolic diversity. Principal component analysis and clustering analyses revealed metabolic differentiation between leaves, flowers and roots. Leaves showed the highest metabolite richness and abundance, while roots showed the lowest. Alkaloids, especially SGAs, dominated positive mode profiles in roots, whereas shikimates and phenylpropanoids were prominent in negative mode profiles. Based on the leaf and flower SGAs profiles, four chemotypes were identified. Analyses of flavonoid and cinnamic acid derivatives, however, did not reveal chemotypes. Feature-based molecular network analyses confirmed that metabolite clusters are associated with plant organs, but not with altitude or geographic origin of the accessions. ConclusionsThe intraspecific chemodiversity within S. dulcamara is mainly driven by organ and accession-specific metabolic differences. We identified four SGA leaf and flower chemotypes, suggesting possible functional and ecological roles of this aboveground chemodiversity. These insights may contribute to applied research in plant resistance breeding and crop production.

Blood-based metabolomic profiling has been widely investigated for breast cancer (BC) detection; however, clinical implementation remains limited due to variability in sample handling, analytical reproducibility, and overfitting during statistical analysis. We established a plasma GC/MS metabolomics workflow for discriminating BC from healthy controls (HC) using conventional machine-learning algorithms. Plasma samples (n = 360; BC = 180, HC = 180) were collected prospectively under standardized preanalytical conditions before surgery and the initiation of systematic anticancer therapy and analyzed using a quantitative GC/MS platform with automated derivatization. Feature selection and model development were conducted using three machine-learning (ML) algorithms (Lasso logistic regression (LR), random forest classifier (RFC), and support vector machine (SVM)). A total of 45 metabolite candidate biomarkers were identified, and the optimal number of metabolite features for each algorithm was estimated by a recursive feature elimination (RFE)-based strategy. The best-performing models achieved area under the ROC curve values (AUC) of 0.910 (LR), 0.893 (RFC), and 0.843 (SVM). We selected prioritizing candidate biomarkers consistently expressed across the multi-algorithm pipeline. A bagging ensemble model improved stability (AUC = 0.911) and reduced false-positive predictions in the independent HC dataset. In addition, model stability with respect to false-positive predictions was assessed using an independent HC cohort (n = 15) that was collected at a separate institution. These results indicate that a plasma metabolomics workflow combined with conventional multi-algorithm ML, algorithm-specific feature selection, and independent assessment provides stable discrimination between BC and HC in a moderately sized cohort.

Type 2 diabetes (T2D) is a prevalent metabolic disorder affecting millions worldwide, characterized by insulin resistance and impaired glucose homeostasis. While mammalian models are widely used, Drosophila melanogaster provides a powerful alternative due to its conserved insulin signaling pathways, genetic tractability, and suitability for high throughput studies. In addition to glucose dysregulation, lipid metabolism plays a crucial role in T2D pathophysiology, as alterations in lipid composition contribute to insulin resistance and metabolic dysfunction. Lipidomic studies have emerged as an essential approach to identify metabolic signatures and potential biomarkers for disease progression and therapeutic targeting. In this study, T2D like model was established by inducing insulin resistance through knockdown of the insulin receptor in brain insulin-producing cells using the dilp2-Gal4>UAS-InRRNAi system. This genetic manipulation resulted in significant metabolic dysregulation, including elevated glucose, trehalose, and triacylglyceride levels, along with increased oxidative stress indicators. Additionally, mRNA expression analysis of key insulin signaling components, including insulin receptor substrate 1, dilp2, dilp3, dilp5, and phosphorylated Akt, further validated the model. To further investigate metabolic alterations, Lipid profiling was performed using ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) in non targeted LC-MS-based metabolomics approach to identify lipid biomarkers associated with T2D. Multivariate statistical analyses, including PCA and PLS-DA, revealed distinct lipid signatures between wild-type and T2D flies. Notably, specific phosphatidylglycerol species PG 34:0, PG 34:4, PA 38:3, PIP 38:1, PIP2 38:6, and LPS 24:0 demonstrated an area under the curve (AUC) of 1, indicating their strong reliability as lipid biomarkers for T2D diagnosis.

Excessive alcohol intake is frequently associated with hypertriglyceridemia, a condition that increases the risk of severe complications including acute pancreatitis and cardiovascular disease. The very low-density lipoprotein (VLDL) receptor (VLDLR) promotes uptake of apoE-containing VLDL particles by peripheral tissues and plays an important role in maintaining plasma triglyceride (TG) homeostasis. Brown adipose tissue (BAT) is a major metabolic organ that contributes to circulating lipid clearance during thermogenic activation. It was reported that cold-induced thermogenesis upregulates VLDLR expression in BAT and reduces plasma TG via VLDL uptake. However, whether BAT VLDLR-mediated VLDL uptake regulates alcohol-induced hypertriglyceridemia remains unknown. Here, we generated BAT-specific fatty acid synthase (FASN) knockout mice (FASNBKO) and subjected them to binge and acute-on-chronic alcohol feeding paradigms. We found that BAT FASN deficiency enhanced thermogenic function and promoted VLDL uptake, resulting in attenuation of alcohol-induced elevations in plasma TG. Consistent with these findings, pharmacological inhibition of FASN by TVB3664 treatment in differentiated brown adipocytes (bADs) increased thermogenic gene expression and VLDL uptake under both control and alcohol-exposed conditions. In addition, FASNBKO mice were protected from alcohol-induced hepatic steatosis, which was accompanied by increased hepatic AMP-activated-protein kinase (AMPK) activation and enhanced {beta}-oxidation. Furthermore, FASNBKO mice exhibited upregulated FGF21 mRNA expression in the BAT and elevated circulating FGF21 levels. Similarly, TVB3664-treated differentiated bADs showed higher FGF21 expression and increased FGF21 content in culture medium. Taken together, these findings identify the important role of brown adipocyte FASN in regulating thermogenic function and TG homeostasis during alcohol exposure and suggest that enhancing thermogenic lipid utilization in BAT may represent a potential therapeutic strategy for mitigating alcohol-associated increases in plasma TG and hepatic fat accumulation.

Dehydroascorbic acid (DHA), the oxidized form of vitamin C, exhibits specific toxicity against KRAS-mutant cancer cells; however, the early metabolic reprogramming mechanisms leading to cell death remain incompletely understood. In this study, we reveal the non-linear, dose-dependent response of the intracellular metabolome during the ultra-early phase of 1-hour DHA exposure. Under 1 mM DHA exposure, an early significant accumulation of the primary catabolite erythrulose (p = 0.030) and moderate consumption of glutamate (p = 0.0086) were observed, while core TCA cycle homeostasis and cell viability were preserved. In contrast, under 10 mM DHA exposure, an acute and profound alteration of the metabolic profile was observed. Along with the significant accumulation of glycerate, indicative of a glycolytic blockade, and the terminal peroxidation product oxalate, core TCA cycle intermediates such as -ketoglutarate and malate showed rapid depletion (Log2FC < -2.0, p < 10-7). Furthermore, coupled with this acute energy depletion, a statistically significant accumulation of free methionine (Log2FC = 1.14, p = 0.011), which was below the limit of detection in the control, was identified alongside profound depletions of glutamate (p < 0.001) and cysteine (p = 0.0027). These results strongly suggest that the initial response to high-dose DHA is not merely a linear accumulation of oxidative stress, but rather an "abrupt metabolic switch" characterized by rapid TCA cycle dysfunction followed by the early signature of a methionine trap.

BackgroundEmerging evidence suggests that PFAS can cross blood-brain barrier and lead to neurotoxicity. Recent evidence also suggest that PFAS can bioaccumulate in gut microbiota resident in the gut. However, how gut microbes influence PFAS-driven reorganization of metabolic homeostasis in the brain is poorly understood. MethodsTo address this gap, we investigated how gut microbiota influences brain metabolomic and lipidomic responses to PFAS exposure. Specific pathogen-free (SPF) and germ-free (GF) mice were fed an obesogenic diet for 8 weeks to promote metabolic disturbance. After 1 week of acclimation, half received control water and half received water containing a PFAS mixture (PFHxS, GenX, PFOA, PFOS, and FTOH mixture). Plasma and brain samples (cortex, subcortex, cerebellum, olfactory bulb, and brainstem) were collected after 8 weeks. Untargeted analyses were performed for lipidomic, metabolomic and PFAS using high resolution liquid chromatography tandem mass spectrometry (LC-MS/MS). Data was processed using MassCube with open-sources libraries. ResultsPFHxS, GenX, PFOA, PFOS, PFDA, and PFDS were detected in plasma. PFHxS, PFOA, PFOS, and PFDS were detected across all five brain regions, with PFOS as the predominant brain-enriched species. Pathway analysis identified nicotinate and nicotinamide metabolism as the most consistently PFAS-altered pathway in both SPF and GF mice. PFAS exposure induced region-specific metabolic remodeling, with gut microbiota differentially modulating responses in the cortex, cerebellum, and brainstem, whereas the olfactory bulb showed a largely microbiota-independent response. In addition to local effects within individual brain regions, plasma-brain analysis suggested systemic metabolic responses across tissues, with association strength varying by brain region and microbiome status. Gut microbiota also shaped PFAS-induced lipid dysregulation in the brain, and methylnicotinamide and delta-valerobetaine were among the most responsive metabolites. ConclusionThis study is the first to demonstrate that resident microbiota impact PFAS-associated metabolic remodeling across the gut-plasma-brain axis. HighlightsO_LIPFAS-induced metabolic remodeling in the brain is modified by gut microbiota. C_LIO_LIPFAS exposure alters nicotinate and nicotinamide metabolism throughout the brain. C_LIO_LIPFAS-induced brain metabolic responses are region specific and microbiota dependent. C_LIO_LIPlasma-brain analysis suggests potential systemic metabolic disruption by PFAS. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/743341v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@15301deorg.highwire.dtl.DTLVardef@9fac0aorg.highwire.dtl.DTLVardef@d7f0f4org.highwire.dtl.DTLVardef@10c29c2_HPS_FORMAT_FIGEXP M_FIG C_FIG

Prostate cancer (PCa) is the second most frequent cancer in men and represents a major public health issue worldwide. To complement current therapeutic approaches, our work aims to understand the impact of nutritional interventions on PCa evolution. Recent studies showed that supplementation with omega-3 fatty acids reduces tumour growth in PCa mouse models. Several beneficial effects were observed, particularly on the gut microbiome, suggesting indirect effects on tumours, and whether these fatty acids have a direct molecular impact on the prostate remains elusive. Here, we hypothesized that the prostate directly takes up dietary fatty acids to reprogram metabolism, which could contribute to the beneficial effects on cancer treatment. To test this hypothesis, five series of patient-derived organoids (PDOs), with paired lines originating from normal and tumour samples, were treated with omega-3 and omega-6 fatty acids. While heterogeneity was observed in growth capacities of PDO lines, omega-3 supplementation overall decreased PDO growth, with a stronger decrease in PCa lines. This effect was stronger when co-treated with selenoneine, a nutrient enriched in some marine foods with high omega-3 content. Lipidomic experiments were then performed, revealing that PDOs can take up fatty acids, both omega-3 and omega-6. Furthermore, PDOs could metabolize omega-3 fatty acids into downstream metabolites, even if high inter-patient heterogeneity was observed. Altogether, this study, using PDOs as a working model to study the human prostate, provides a better understanding of the molecular impacts of fatty acid supplementation on prostate and PCa biology for the development of future nutritional interventions.

Hexose-6-phosphate dehydrogenase (H6PD) catalyzes the first two steps of an endoplasmic reticulum-specific pentose phosphate pathway, regenerating luminal NADPH levels in the process. Its function remains insufficiently well understood. Since expression of H6PD is notably high in the liver, we aimed to assess its role in hepatic metabolism. Considering the central role of the liver in lipid synthesis, breakdown and storage, we focused our efforts specifically on studying the effect of H6PD on hepatic lipid metabolism. An H6PD knockout mice strain was generated and characterized by liquid chromatography-high-resolution mass spectrometry (LC-HRMS)-based lipidomic and proteomic analyses of liver tissue. Lipidomics analysis revealed an overall increase in hepatic triglycerides and a specific increase in unsaturated long-chain triglycerides in H6PD knockout mice. Intracellular lipid accumulation was confirmed through Nile Red staining of liver sections. Functional enrichment analysis of proteomics data from the H6PD deficient mice identified a corresponding upregulation of multiple fatty acid metabolism-associated pathways. Additionally, an H6PD knockout AML12 cell line was generated through CRISPR/Cas9 and characterized by lipid staining and functional assays to assess metabolic outcomes. Loss of H6PD led to intracellular lipid accumulation, reduced mitochondrial {beta}-oxidation and increased sensitivity to lipotoxicity, even though fatty acids remained the cells' primary mitochondrial fuel. Ultimately, our results indicate that H6PD plays an as-of-yet undescribed role in hepatic lipid metabolism, implying a link between the availability of NADPH within the endoplasmic reticulum and fatty acid homeostasis.

Volatile organic compounds (VOCs) define the distinctive aroma of cannabis and critically influence consumer preference, cultivar authentication, and breeding programs. However, systematic characterization of VOC diversity across commercial drug-type cultivars remains limited. This study presents a comprehensive volatilomics-based phenotypic characterization of 165 commercial drug-type cannabis accessions using gas chromatography with flame ionization detection and mass spectrometry (GC-FID/MS). We identified 61 high-confidence VOCs assigned to three biosynthetic classes: terpenoids (n = 45), fatty acid-derived volatiles (n = 12) and amino acid-derived volatiles (n = 4), resolved into 12 subclasses. Analysis of variance revealed highly significant among-accession differences for all compounds (p < 0.001; 2 = 0.67-0.97), with repeatability estimates averaging 0.81 (range 0.50-0.95). Unsupervised clustering partitioned accessions into three distinct chemotypes (n = 90, 53, and 22), supported by principal component and t-SNE analyses. Machine learning-based feature selection identified a consensus panel of 12 discriminative compounds (camphene, -fenchene, sabinene, -terpinene, ({+/-})-limonene, -humulene, linalool, endo-fenchol, {Delta}3-carene, -thujene, {gamma}-terpinene and -phellandrene) that recovered the chemotype assignment of 32 of 33 held-out accessions. Estimated odor-activity screening ranked prenylthiol, -pinene, ({+/-})-limonene, linalool and myrcene highest among the odor-active compounds. All three chemotypes shared a prenylthiol-dominated core (67-77% of summed OAV) and were distinguished by the extent and nature of terpenoid modulation of that core: minimally modulated (Cluster ZERO), citrus-floral modulated (Cluster ONE) and pine-terpenic modulated (Cluster TWO). These findings indicate that volatile diversity in this panel can be summarized by three reproducible chemotypes, providing a quantitative basis for accession characterization and a foundation for future breeding and quality-assessment studies.

Acetyl-CoA and other acyl-CoA thioesters are critical intermediates in the metabolic reactions that cells rely on to produce energy and carry out biosynthesis. Therefore, the analysis of acyl-CoA provides valuable information about the metabolic activity of cells, especially when combined with stable isotope tracing. Acyl-CoA species are routinely monitored by reversed-phase liquid chromatography coupled to tandem mass spectrometry (RPLC-MS/MS). However, drastic differences in the hydrophobicity of short-chain versus long-chain acyl-CoA species have been challenging to accommodate with a single set of RPLC conditions. Here, we describe a convenient method based on hydrophilic interaction liquid chromatography (HILIC-MS/MS) for the concurrent detection of both short-chain and long-chain acyl-CoA and their corresponding acyl-carnitine species. Using this strategy, we tracked the metabolism of isotope-labeled fatty acids in multiple cell lines, which revealed differences in their propensities for fatty acid oxidation and the extent to which isotope incorporation into acyl-CoA mirrored that of acyl-carnitine. We also applied the HILIC-MS/MS workflow to the analysis of NADH and ATP, making it a useful technique for gauging cellular bioenergetics as reflected by the acetyl-CoA/CoA, NADH/NAD+, and ATP/ADP ratios. Altogether, this HILIC-MS/MS platform enables a streamlined analysis of acyl-CoA species and other key intermediates in cell metabolism.

Background and aims Plant volatile organic compounds (VOCs) change dynamically with plant development and in response to environmental conditions. However, their potential as non-invasive indicators of phenological progression remains poorly explored. In this study, we developed a framework integrating automated VOC sampling, time-resolved VOC profiling, and machine-learning analysis for the non-invasive assessment of plant phenology. Using soybean (Glycine max (L.) Merr.), we investigated whether development-associated temporal variation in VOC emissions could delineate and predict developmental phases. Methods We collected VOCs daily under controlled environmental conditions from 16 to 43 days after sowing, spanning the transition from vegetative to reproductive stages, using an automated sampling system coupled with thermal desorption-gas chromatograph-mass spectrometer (TD-GC-MS). To characterise temporal changes in VOC profiles associated with phenological progression, we analysed the daily VOC data using a multi-step pipeline combining statistical filtering and similarity-based network analysis. We defined VOC-derived developmental phases from similarity patterns in the VOC profiles, then developed and evaluated machine-learning models to predict these phases. Key results Seven VOCs exhibited distinct phase-dependent dynamics, including green leaf volatiles and monoterpenes showing characteristic temporal changes during phenological progression. Network-based clustering of VOC profiles resolved five developmental phases closely aligned with conventional developmental stages. A machine-learning model predicted these phases from the VOC profiles with high predictive accuracy on independent test data, demonstrating that phenological progression could be quantitatively inferred from VOC emission patterns. Conclusions Our findings support VOC profiling as a reliable and non-invasive approach for assessing phenological progression in soybean. By extracting temporally structured VOC signals, this framework captures developmental information that may be difficult to obtain through visual observation alone, particularly after canopy closure. VOC profiling offers a practical tool for monitoring crop developmental dynamics and has broader potential for plant phenotyping and precision crop management.

Background: Succinate dehydrogenase (SDH) is a heterotetrameric enzyme in both the tricarboxylic acid (TCA) cycle and the electron transport chain. SDH-deficiency has been shown to drive the oncogenesis of cancer types including gastrointestinal stromal tumors (GIST), paraganglioma/pheochromocytoma, and clear cell renal cell carcinoma. Previous work on SDH loss has shown increases in succinate, decreases in fumarate, as well as increase in the succinate to -ketoglutarate ratio, and correlated these changes to metabolite signaling and epigenetic dysregulation. Alterations to other TCA intermediates, including succinyl-CoA, the acyl-donor for succinylation, and the resulting cellular effects has not been fully explored. Methods: We used liquid chromatography high resolution mass spectrometry to quantify TCA cycle metabolites across the NCI-60 panel of cell lines, three SDH-competent and SDH-deficient model systems, and human tumor samples. We measured histone post-translational modifications in SHD-deficient and SDH-competent models and examined the correlation and potential pharmacological targeting of described desuccinylase enzymes by protein and cell assays. Results: We found an increase in succinyl-CoA in multiple SDH-deficient models, but no correlation between succinate to fumarate ratio and succinyl-CoA concentrations in the NCI-60. Interestingly, we found that while there was an increase in succinyl-CoA in the SDH-deficient cells, there was no corresponding increase in histone succinylation. We also observed a decrease in lysine malonylation and glutarylation, two other anionic lysine modifications. Cell viability and clonogenic assays revealed very weak or no selective effect of desuccinylase targeting in SDH-deficient models. Similarly, using SDH-deficient versus SDH-competent GIST patient samples, we examined both protein expression and metabolite abundance at the bulk tumor level and found no significant increase in succinyl-CoA or desuccinylase expression, despite the increase in succinate to fumarate ratio. Conclusions: We found that SDH-deficiency led to altered central carbon metabolism including increases to succinate and succinyl-CoA. Despite data that suggested the upregulation of desuccinylases may mediate a disconnect between succinyl-CoA concentration and histone succinylation, we found weak or no reductions in cell viability in response to pharmacological inhibition of desuccinylases. Additionally, no correlation of SDH loss with desuccinylase expression and succinyl-CoA in bulk tumor samples was observed.

Hepatocellular Carcinoma (HCC) arising from Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) is an increasing public health burden with high mortality, highlighting the need for improved early detection strategies. Current surveillance tools, including Alpha-fetoprotein (AFP) and ultrasound, lack sufficient sensitivity for early-stage HCC detection. We analyzed serum samples from 131 patients, including 58 with cirrhosis and 73 with MASLD-related HCC (42 early-stage, 31 late-stage), using an nLC-stepped HCD-PRM-MS/MS workflow for targeted N-glycome profiling of glycopeptides derived from haptoglobin and vitronectin. Combining targeted glycopeptides with AFP significantly improved HCC detection compared with AFP alone. The optimal panel for all HCC versus cirrhosis (AFP + VTNC_169_A2G2F0S1 + VTNC_242_A3G3F2S2) achieved an AUC of 0.859 and 76.7% sensitivity at 90% specificity. For early-stage HCC, AFP + HP_184_A3G3F1S3 + VTNC_169_A2G2F0S1 yielded an AUC of 0.890 with 66.7% sensitivity at 1% specificity. A SHAP-selected Gaussian Naive Bayes model based on seven molecular/glycopeptide features, without demographic variables, further improved performance, achieving ROC-AUC values of 0.9985 in training and 1.0000 in independent testing cohorts, with accuracies of 98.1% and 100.0%, respectively.

Diet can rapidly change the fecal metabolome, but less is known about recovery after the original diet is restored. We used untargeted UPLC-MS metabolomics to analyze 72 fecal samples from nine Pumi dogs during an owner-managed switch from dry food to raw food and back to dry food. Diet phase accounted for a large proportion of variation in both ionization modes. More than 13,000 LC-MS features changed at the first sampling point after the switch to raw food, with a similarly large response after return to dry food. Among features significant in both comparisons, more than 99% changed in opposite directions. At the final sampling point, no positive-mode (ESI+) features and only 13 negative-mode (ESI-) features differed from the second dry-food baseline under the same threshold. BARF-associated patterns persisted in analyses excluding individual dogs and in pedigree-adjusted candidate models, although individual feature effects depended on normalization. Putative metabolites from several biochemical classes differed in their response and recovery. The fecal metabolome therefore changed rapidly and returned largely toward baseline, with differences among dogs.

Brook trout (Salvelinus fontinalis) exhibit strain-level variation in growth performance, environmental tolerance, and survival, yet the biochemical mechanisms underlying these differences remain poorly understood. We developed and applied a ratiometric biochemical framework integrating the pentose-phosphate pathway (PPP) and glutathione metabolism to characterize strain-specific hepatic metabolic organization in brook trout. Five strains reared under standardized conditions differed significantly in hepatic soluble protein density, glutathione pool size, total NADP(H) concentration, and activities of glucose-6-phosphate dehydrogenase (G6PDH), glutathione reductase (GR), and transketolase (TKT). These differences were not uniformly coordinated across pathways, demonstrating that metabolic phenotype cannot be inferred from individual biomarkers alone. Derived ratios describing oxidative-to-non-oxidative PPP capacity (G6PDH/TKT) and glutathione buffering relative to recycling capacity ((GSH+GSSG)/GR) resolved distinct patterns of metabolic allocation among strains. Despite shared ancestry, the Temiscamie (TEM) strain and its domestic x TEM hybrid (TXD) exhibited markedly divergent metabolic phenotypes, demonstrating that closely related strains can differ substantially in hepatic metabolic organization. Together, these findings identify relative allocation among interconnected metabolic pathways as an axis of physiologic diversity and establish a ratiometric approach for comparing metabolic organization across populations and species. Graphical abstractHepatic metabolic phenotypes of brook trout strains were characterized by integrating pentose phosphate pathway enzyme capacities, glutathione metabolism, NADP(H) availability, and soluble protein into a ratiometric framework. Ratios distinguish investment in oxidative versus non-oxidative PPP capacity (G6PDH/TKT), antioxidant buffering versus glutathione recycling capacity (total glutathione/GR), and hepatic protein density (soluble protein/liver mass), revealing distinct metabolic organization among strains. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=88 SRC="FIGDIR/small/743818v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1694676org.highwire.dtl.DTLVardef@90f2d4org.highwire.dtl.DTLVardef@365327org.highwire.dtl.DTLVardef@8d56ca_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIA ratiometric framework was developed to characterize hepatic metabolic organization in brook trout C_LIO_LIGlutathione buffering and recycling capacity distinguish alternative redox phenotypes C_LIO_LIInvestment in oxidative and non-oxidative PPP capacity varies independently among strains C_LIO_LIG6PDH/TKT and total glutathione (GSH+GSSG)/GR reveal distinct metabolic phenotypes C_LIO_LIRatiometric indices provide a framework for interpreting redox metabolism and carbon allocation C_LI

Angiotensinogen (AGT) deletion in hepatocytes reduces Western diet-induced adiposity and hepatic steatosis in mice maintained under conventional room-temperature (RT) housing. Given the high metabolic activity of mice, this temperature imposes adaptive metabolic responses in this species. Whether this metabolic protection persists independent of increased thermogenic demand remains unclear. In this study, we first determined whether thermoneutral housing (TN, 30 {degrees}C) alters Western diet-induced metabolic phenotypes compared with RT housing (20 {degrees}C) in wild-type mice. Although body weight did not differ significantly between housing conditions, Western diet-fed mice housed at TN exhibited brown adipose tissue whitening and more pronounced hepatic steatosis than mice housed at RT, confirming that thermoneutrality exacerbated diet-induced metabolic dysfunction. We then housed hepatocyte Agt deficient (hepAGT-/-) mice and wild-type (hepAGT+/+) littermates at TN and fed them Western diet for 12 weeks. Despite enhanced metabolic dysfunction under TN, hepatocyte AGT deletion resulted in reductions in diet-induced body weight gain, fat mass, liver weight, and hepatic triglyceride accumulation. Bulk RNA sequencing of liver revealed hepatocyte AGT deficiency-dependent alterations in lipid-metabolic pathways. Cross-temperature analysis of RT and TN housing identified 35 shared differentially expressed genes, including 27 concordantly downregulated genes enriched in lipid metabolism and transport. Extended Western diet feeding for 24 weeks confirmed sustained reductions in body weight gain, liver weight, and hepatic lipid accumulation in hepAGT-/- mice. These findings demonstrate that hepatocyte AGT deletion provides sustained protection against Western diet-induced metabolic dysfunction under thermoneutral housing, a condition that more closely recapitulates human basal metabolism. NEW & NOTEWORTHYThis study investigated hepatocyte angiotensinogen (AGT) biology during Western diet feeding in mice under thermoneutral housing, a condition relevant to human metabolism. By minimizing adaptive thermogenesis induced by standard room temperature housing, thermoneutrality more closely recapitulates human basal metabolic conditions. Under this condition, hepatocyte AGT deletion remains protective against adipo and hepatic lipid accumulation, despite exacerbated Western diet-induced metabolic dysfunction in wild-type mice, demonstrating that this protection persists in a human-relevant thermal environment. GRAPHIC ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=132 SRC="FIGDIR/small/742617v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@1ac7094org.highwire.dtl.DTLVardef@131cfforg.highwire.dtl.DTLVardef@d4dba6org.highwire.dtl.DTLVardef@a09acc_HPS_FORMAT_FIGEXP M_FIG C_FIG