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.

BACKGROUND: Erythrophagocytosis constitutes a major pathogenic mechanism of metabolic dysfunction associated fatty liver disease (MAFLD). Our previous research established a quantitative thin-layer chromatography (TLC) technique for sphingomyelin, revealing reduced levels in the red blood cells (erythrocytes) of patients with metabolic dysfunction associated fatty liver disease (MAFLD). This reduction was accompanied by erythrocyte sphingosine accumulation, a driver of pro-inflammatory erythrophagocytosis, though sphingosine 1-phosphate release remained stable. To better understand erythrocyte sphingosine metabolism, we adapted our quantitative TLC method to analyze sphingosine within the erythrocyte-conditioned media (ECM) of MAFLD patients. Methodology Separation was performed on 10X10cm Silica gel 60 F254 plates using a mobile phase of chloroform, methanol, acetic acid, and water (60:50:1:4 v/v/v/v). The dynamic range, linearity, and range of linearity were assessed by analysing sphingosine levels from 0.1 to 10microg/spot. We validated the system precision and sensitivity by performing triplicate analyses of sphingosine standards (1.25, 2.5, and microg). The limits of detection and quantification were derived from the calibration curve slope and standard deviation (3.3 XSD/slope for LOD; 10 XSD/slope for LOQ). Accuracy was assessed via recovery tests at 100%, 200%, and 300% of a 2.5microg load. We confirmed specificity by evaluating the retention factors against other lipid species. This protocol was applied to Folch-extracted lipids from the ECM (5 X 107 cells/ml) of four MAFLD patients and four healthy controls, spiked with 5microg of sphingosine. Findings The calibration model, based on combined Green and Blue color intensities, followed the linear equation y = -11.171x + 353.25(R2 = 0.94). Interday precision values were 0.21%, 1.65%, and 0.44%, while recovery rates (accuracy) ranged from 94.5% to 98.7%. The measured LOD and LOQ were 0.75microg and 1.21microg, respectively. The sensitivity was calculated at 90ng. Statistical analysis showed no significant variance in sphingosine concentrations in erythrocyte-conditioned media between the MAFLD group and the control group. Summary The described thin layer chromatography is accurate, precise, sensitive, with good limits of detection and quantification, and most importantly is low-cost and time-efficient. Using this method, we show that while erythrocytes of MAFLD patients exhibit sphingosine accumulation, the utilisation of exogenous sphingosine from their erythrocytes is not affected. This suggests that the metabolic shift may be driven by increased sphingosine supply from the plasma.

The energy turnover and metabolic flexibility of the human brain extend beyond a primary reliance on carbohydrates and oxygen. Building on work in anesthetized patients with cerebrovascular pathology, we quantified trans-cerebral arteriovenous differences in healthy humans to isolate and characterize the most abundant cerebral metabolite and lipid species. We observed a net release of acylcarnitine from the cerebral circulation, indicating that these species are active in mitochondrial fatty acid {beta}-oxidation occurring in the healthy resting brain. Furthermore, a strong association was apparent between variability in the brains respiratory quotient (RQ) and activity within the purine salvage pathway, particularly with the uptake of guanosine monophosphate. In a larger sample size (n = 210), we further established that biological variability around an RQ of 1.0 - typically interpreted as exclusive carbohydrate oxidation - coincides with coordinated arteriovenous shifts in metabolomic and lipidomic pathways. The variability is not only visible through complex omics pathways, but is also strongly related to the oxygen carbohydrate index of the brain, further supporting that energy substrates other than glucose are exchanged and oxidized across the brain. These findings reveal substantial versatility and redundancy in how the healthy brain maintains its high energetic demands through flexible, interconnected metabolic and lipidomic networks.

Background/ObjectivesMetabolism-level interpretation of metabolomics datasets requires aggregation analyses across metabolites. One highlyused aggregation analysis is pathway enrichment analysis (PEA), which involves detecting pathways enriched with metabolites that are differential between experimental groups. Annotating metabolites with pathway associations is a prerequisite for PEA. While several knowledgebases define pathways and include metabolite-pathway annotations, these definitions are often partially or even grossly incomplete due to limitations in current metabolic knowledge and its curation, which greatly limits the effectiveness of PEA. MethodsIn this work, we used a novel multitask classification, graph convolutional-like neural network to generate high-quality metabolite-pathway annotations for pathways defined across KEGG, MetaCyc, and Reactome. We then included these predicted metabolite-pathway annotations when performing PEA on 990 datasets deposited in Metabolomics Workbench. ResultsWe demonstrate an 8-fold increase in the median number of enriched pathways detected across these datasets compared to using only knowledgebase-derived annotations. ConclusionsThe significant increase in enriched pathways substantially improves the biological and biomedical interpretability of metabolomics datasets.

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.

LC/MS-based food lipidomics provides detailed information on intact lipid species, but the resulting datasets are often difficult to translate into concepts directly useful for food quality, processing, nutritional profiling and authenticity assessment. Here, we present Lipid Food Profile (LFP), a module of the LipidOne platform designed to convert annotated LC/MS lipidomics data into interpretable food-relevant lipid indices. LFP applies an in silico hydrolysis strategy to reconstruct acyl, alkyl and alkenyl chains from intact lipid species while preserving their lipid-class origin. The reconstructed information is then summarized into index categories related to food lipid quality, compositional balance, omega balance, oxidative stability, chain remodelling and ether-linked chain contribution. The interpretative value of LFP was evaluated using three published food lipidomics datasets addressing different analytical questions: X-ray-induced lipid remodelling in Chlorella vulgaris, spatial lipid heterogeneity in Mugil cephalus bottarga, and geographical-origin assessment of camel milk. Across these case studies, LFP recovered the main conclusions of the original lipidomics investigations, including treatment-associated lipid remodelling, inner-outer layer differences in bottarga and regional variation in camel milk. Importantly, LFP reorganized these findings into a smaller number of food-oriented indices, providing additional information on saturation balance, oxidative susceptibility, chain architecture and classification potential. Overall, LFP provides an interpretative layer for LC/MS food lipidomics that complement conventional fatty-acid analysis and molecular-species-based interpretation. By translating complex lipidomic tables into structured lipid index profiles, the module may support more accessible and chemically meaningful analysis of food composition, processing effects, lipid quality and exploratory traceability applications. LFP is freely accessible through the LipidOne web platform (LipidOne.eu). HighlightsO_LILipid Food Profile translates LC/MS food lipidomics into interpretable lipid indices. C_LIO_LIThe workflow preserves chain and lipid-class information without chemical hydrolysis. C_LIO_LIPublished case studies show that LFP recovers and extends previous interpretations. C_LIO_LILFP supports food quality, processing and exploratory origin/authenticity assessment. C_LIO_LIThe module complements conventional fatty-acid analysis and molecular lipidomics. C_LI

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.

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.

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

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.

Background: The Dietary Inflammatory Index (DII) is widely used to assess the inflammatory potential of diet, but it relies on self-reported dietary assessment and does not directly capture individual differences in metabolism as an intermediate connection to inflammation. High-resolution metabolomics provides objective measurements that complement dietary assessment to support precision nutrition to control inflammation. Objective: We developed, tested, and applied a Dietary Metabolite Inflammatory Index (DMII) to assess diet-related chronic inflammation using metabolites measured by liquid chromatography high-resolution mass spectrometry. Methods: DII was calculated using dietaryindex R package with Block Food Frequency Questionnaire (FFQ) data. To develop the DMII, chronic inflammation-related dietary metabolites corresponding to the DII food parameters were found through a literature review. Dietary metabolites were identified and quantified by authentic standards by our established laboratory procedures. DMII uses the same inflammatory effect scores as the DII. Three DMII versions were developed: concentration-based, median-based, and quintile-based DMII. Mean and standard deviation of 29 dietary metabolites were calculated by using 3025 human plasma samples from 3 studies. DMII was tested in the Center for Health Discovery and Well-Being cohort (CHDWB) and the Atlanta African American Maternal and Child cohort (ATLAA) using chronic inflammation biomarkers, including high-sensitivity C-reactive protein (hsCRP), CRP, and IL6. The median-based DMII was further applied to four Alzheimers disease metabolomics datasets as a proof-of-concept application. Results: In the CHDWB study, concentration-based DMII had a weak positive correlation with Block FFQ-derived DII and strongly correlated with median-based and quintile-based DMII. In the same study, all three DMII versions had significant positive correlations with hsCRP and IL6. In the ATLAA study, only concentration-based DMII was positively associated with CRP and IL6. Higher median-based DMII was associated with higher odds of Alzheimers disease. Conclusions: DMII provides a metabolomics-based framework for assessing diet-related chronic inflammation using metabolomics data. This metabolomics approach may complement self-reported dietary assessment to use diet and nutrition to help protect against chronic disease linked to inflammation.

Introduction The coronavirus disease 2019 (COVID-19) pandemic has challenged health care systems worldwide, in certain areas exceeding hospital capacities and human resources. This has underscored the importance of having better tools to predict the outcome of potentially severe respiratory infections such as SARS-CoV-2. Predicting COVID-19 severity may allow physicians to better manage ICU beds and increase the chances of patient survival through appropriate management. During the toughest months of the pandemic, most physicians tried to identify patients that might develop severe forms based primarily on clinical features on admission (e.g., BMI, age). In this context, significant research has focused on identifying comorbidities, clinical manifestations, and routine blood biomarkers to predict disease severity. However, despite the demonstrated value of untargeted metabolomics in assessing severity, limited data exist on its use for identifying novel metabolite biomarkers that could improve both the sensitivity and specificity of outcome prediction. Our goal is to identify metabolite biomarkers that could enhance the predictive accuracy of standard medical biology data and clinical parameters. Methods and analysis This is a retrospective, observational, monocentric cohort study conducted at the Centre Hospitalier Universitaire Grenoble Alpes (CHUGA). The maximum number of eligible patients admitted for PCR-confirmed COVID-19 between March and December 2020 will be included. Severity outcome is defined using the WHO 10-category ordinal scale (mild: categories 4-5; severe: >5). Blood samples were collected within 48 hours of admission and analyzed for 62 routine blood tests and untargeted multiplatform LC-MS/MS metabolomics across four national platforms. Statistical analysis will include logistic regression with variable selection for the primary aim, and multi-block chemometric integration of clinical, biological, and metabolomics data as a secondary aim. Ethics and dissemination A study steering committee has been formed to ensure the accuracy of the collected data by thoroughly reviewing it prior to the data lock. All aspects of the study comply with ethical standards, including approval by the CHUGA institutional review board and adherence to CNIL Reference Methodology MR004 for the protection of participants' rights, privacy, and confidentiality. This study is registered on the French Health Data Hub (number F20210218154851). Results will be disseminated through peer-reviewed publications, presentations at national and international scientific and clinical conferences, and reports shared with key healthcare system stakeholders.

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.

Cassava and cocoyam are major dietary staples in sub-Saharan Africa, commonly processed by natural fermentation before consumption. Although fermentation reduces antinutritional compounds and improves food quality, its molecular effects remain poorly characterized. We used untargeted mass spectrometry-based metabolomics with a computational annotation pipeline to compare fermentation-induced molecular remodeling in the two tubers, which showed distinct responses. In cassava, 718 of 773 significant features (92.9%) were depleted, indicating a predominantly catabolic process. In cocoyam, the response was more balanced, with 385 of 1,013 features (38.0%) enriched, including di- and tripeptides consistent with proteolytic processing. Class analysis, molecular networking, and pathway enrichment revealed tuber-specific signatures: cassava was dominated by purine metabolism, whereas cocoyam showed stronger enrichment of amino acid pathways. Cyanogenic glycoside-related features were depleted, consistent with detoxification. Biotransformation prediction also suggested putative fermentation products absent from current databases, highlighting the under-characterized chemistry of these tubers.

Class imbalance remains a challenge in metabolomics research, where biological and technical variability can affect statistical inference and machine learning (ML) performance. Class-balancing algorithms address this issue by either increasing minority-class observations or reducing the number of majority-class samples. This study evaluated the impact of oversampling and undersampling algorithms on targeted and untargeted metabolomics datasets derived from LC-MS and GC-MS analyses of plasma samples from patients with glioblastoma, meningioma, and controls. Synthetic Minority Oversampling Technique (SMOTE) and Random Undersampling (RUS) were applied to balance the datasets, and their effects on data distribution, inter-feature correlations, and machine learning model performance were compared. RUS preserved the original feature distributions but reduced representativeness by removing the majority-class samples. In contrast, SMOTE introduced synthetic samples that altered covariance structures, increasing the risk of overfitting, particularly in small datasets (n=10). These effects diminished with larger groups (n=30), partially restoring correlations between metabolites. Model performance varied across the class-balancing algorithms. Random Forest classifiers benefited from both balancing methods, with undersampling often yielding higher F1 scores, whereas Support Vector Machine models showed reduced classification performance. These findings highlight the importance of selecting class-balancing strategies based on dataset size, analytical platform, and ML algorithm in metabolomics studies.

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.

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.

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