Metabolomics

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.

Background & Aims: Metabolic Dysfunction Associated Steatotic Liver Disease (MASLD) is the leading cause of chronic liver disease in children. However, accurate, noninvasive diagnostic tools remain limited. Current screening methods are invasive or lack sensitivity. Breath-based volatile organic compound (VOC) analysis offers a simple approach with potential for point of care screening. This study aimed to identify and validate breath VOC signatures of pediatric MASLD. Approach & Results: We conducted a prospective IRB approved cohort study at the Childrens Hospital of Philadelphia (CHOP). Children aged between 7 and 20 years with MASLD (n=22), as defined by hepatic steatosis either by liver biopsy or imaging and 1 cardiometabolic risk factor, and a control group without MASLD (n=20) were enrolled. Breath samples were collected using a standardized protocol and analyzed by untargeted comprehensive two-dimensional gas chromatography-mass spectrometry (GCGCMS). Machine learning and unsupervised clustering were applied to identify discriminatory VOCs and assess heterogeneity. Untargeted GCGCMS analysis identified a distinct breath VOC signature in children with MASLD compared with non MASLD controls. A Random Forest model achieved a sensitivity of 73% and specificity of 65%, with AUC of 0.84. The VOC 2,4-dimethyl-1-heptene demonstrated strong diagnostic performance in the discovery cohort with a sensitivity of 85%, specificity of 77% and an AUC of 0.81. Unsupervised clustering revealed four MASLD subgroups with distinct volatile phenotypes associated with differences in liver enzymes and metabolic parameters. External validation in a second pediatric cohort confirmed reproducible reductions in o/p-xylene in subjects with MASLD. Conclusions: Pediatric MASLD is associated with a reproducible breath VOC signature identified by untargeted GCGCMS. These findings support breath analysis as a scalable, noninvasive screening and stratification tool for pediatric MASLD and warrant validation in larger, longitudinal studies.

A multi-omic approach utilizing a single biospecimen is important to avoid intra-sample heterogeneity associated with testing multiple omic single-samples, and for more efficient use of small volumes of precious biopsies (<30 mg). This is especially true for the microanatomy of post-mortem human brain samples. Using post-mortem human brain biospecimens from the NIH NeuroBioBank, a penta-omic sequential extraction method is described, Simultaneous Metabolomic, Proteomic, Lipidomic - DNA, RNA Extraction (SiMPL-DREx). Each sequential omic extract was compared to those obtained by the gold standard single omic method. Preserving RIN is critical for brain and tissue banks, as it is a primary measure of tissue quality. For all five omic extracts, the tissue integrity numbers and omic profiles did not significantly differ from those obtained by the respective omic gold standard method. Unlike past multi-omic studies, this study quantified the relative solvent percentages and upstream losses for both the organic and aqueous phases, confirming an omics loss of under 5%.

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.

Metabolomics enables the profiling of small-molecule metabolites and thereby captures the biochemical state of a living organism at a given moment and enables to monitor its cellular responses to stimuli. This technique has become a powerful tool in pharmaceutical research, the food industry, and microbial research. Metabolomics aims to obtain an unbiased metabolic profile; however, this is complicated by compound instability, complex and often extensive sample processing, and nonlinear responses in mass spectrometry. Therefore, correcting for metabolite loss and mass spectrometry-related artifacts is essential, typically achieved through relative quantification against an isotopically labelled internal standard for each metabolite of interest. This article describes how to produce 13C-labelled yeast extract and its use as internal standard for metabolomics. More specifically, it provides step-by-step protocols for the fed-batch fermentation, quenching, metabolite extraction, and LC-MS and GC-MS characterization of the internal standard. It also includes a protocol explaining how to use the internal standard for the quantification of metabolites in yeast samples.

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.

Understanding how plants respond to high temperature is critical under global warming. Metabolite markers can provide insights into stress-responsive mechanisms and help guide strategies to maintain crop quality. However, heat-associated metabolite markers in grape berries remain poorly defined, particularly at the green stage, a critical phase of berry development during which early metabolic perturbations can influence subsequent ripening and ultimately determine berry composition and quality. Here, we applied berry-scale heat treatments of eight durations of two major wine cultivars, Cabernet Sauvignon and Merlot. Untargeted LC-MS profiling revealed both conserved and cultivar-dependent responses to heat. Based on these patterns, three time points were selected for targeted GC-MS analysis, and subsequent statistical analyses identified robust "cultivar-common heat markers": glycine decreased, whereas galactinol increased consistently across time points and cultivars. "Cultivar-dependent heat markers" were identified: xylose, lyxose, citrulline, quinic acid, and glutamine, that consistently distinguished CS and Merlot fruits under heat stress. Notably, xylose, lyxose, citrulline, and quinic acid also differentiated the two cultivars under ambient conditions, underscoring their potential as stable cultivar-discriminating metabolites. Together, these results reveal dynamic metabolic remodeling in grape berries under heat stress, particularly in amino acid, nitrogen, central carbon metabolism, raffinose family oligosaccharides pathway and the glutathione-ascorbate cycle.

Prenatal alcohol exposure (PAE) is a major cause of neurodevelopmental disorders, yet most children are diagnosed late or misdiagnosed. Neuroplacentology suggest that placental factors released into maternal and/or umbilical cord blood contribute to fetal brain development. Consistently, a preclinical inter-organ transcriptomic database revealed that PAE disrupts the expression ratio of angiogenic and inflammatory factors suggesting an angio-inflammatory response. This study aimed i) to assay, by multiplex immunoassay, angiogenic and inflammatory factors in maternal and umbilical cord blood from alcohol-consuming women and ii) to perform a maternofetal analysis according to neonatal sex. Afterwards, dysregulated factors from mothers who gave birth to females or males were submitted to STRING and ShinyGO analyses. Results showed that PAE differently altered the distribution profiles of dysregulated angiogenic and inflammatory factors in maternal and umbilical cord blood. Moreover, sex-specific differences were observed, with 36% of dysregulated proteins specific to males, 48% to females, and 16% common to both. STRING analysis revealed robust functional protein-protein interactions linking together inflammatory and angiogenic clusters while the ShinyGO analysis identified enriched pathways related to vascular shear stress. These findings provide the first maternofetal analysis of combined angiogenic and inflammatory factors from alcohol-consuming mothers.

It was recently reported that Methylobacterium extorquens AM1 produces the citrate-hydroxamate siderophore N-deoxyschizokinen A, identified by LC-HRMS. Multiple properties were inconsistent with the assignment: the feature eluted far later than the other schizokinen derivatives (17 min versus 6-8 min), a reversed-phase shift larger than a single-hydroxyl difference in a molecule can explain, further its accurate mass deviated from the calculated one by 28 ppm, well outside the error on the co-analyzed standards and its diagnostic m/z 105 and 77 fragments suggest a molecule with an aromatic moiety. A replicate comparison of identical samples in plastic versus glass autosampler vials was decisive: the m/z 387 feature was reproducibly present with plastic vials and absent with glass. We therefore conclude that the reported detection of N-deoxyschizokinen A in M. extorquens AM1 is an artifact, and recommend glass-vial and solvent-blank controls, an explicit accurate-mass threshold, and narrow MS/MS isolation when assigning trace siderophore-like features from complex extracts.

Principal component analysis (PCA) is widely used in mass spectrometry-based metabolomics for exploratory data mining. Statistical testing of loading values can extract metabolite features associated with score patterns, but this approach requires principal components (PCs) to remain orthogonal while loadings are defined as correlation coefficients between PC scores and variables. Adjustment for Confounding PCA (AC-PCA) was previously developed to explore biologically meaningful components from data matrices affected by biological and technical confounders. However, AC-PCA does not simultaneously ensure PC orthogonality and a correlation-coefficient definition of loadings, limiting the statistical interpretation of its loadings. Here, we reformulated AC-PCA as Orthogonal Adjustment for Confounding effects in PCA (OAC-PCA). In OAC-PCA, PCs remain orthogonal, and loadings retain this correlation-coefficient interpretation. These properties enable statistical testing of metabolite associations while accounting for confounding effects.

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.

Aroma perception during food consumption results from the combined effects of food composition, oral processing (such as chewing and saliva action), the release and transport of volatile compounds toward the olfactory epithelium, followed by cognitive integration in the brain. Recent advances in real-time analytical techniques, particularly Proton Transfer Reaction-Time-of-Flight Mass Spectrometry (PTR-ToF-MS), enable in vivo monitoring of aroma release with high temporal resolution and have become widely used for analyzing the composition of exhaled air. However, the interpretation of aroma release kinetics remains challenging due to substantial intra- and inter-individual variability caused by differences in physiology, anatomy, oral behavior, and respiratory patterns. In this context, the present study was designed to quantify aroma release associated with different food oral processing (FOP) mechanisms, such as chewing and swallowing, using simple model matrices containing a single aroma compound, and to document inter- and intra-individual variability among subjects. Real-time PTR-MS measurements were combined with self-reported oral events and simultaneous respiratory monitoring to analyze aroma release from aqueous solutions and gummy discs flavored with isoamyl acetate. The results showed that inter-individual variability was higher than intra-individual variability and allowed its quantification in aroma release. Significant differences in aroma release kinetics were observed depending on FOP protocols. The importance of considering swallowing events when analyzing aroma release data was also highlighted.

BACKGROUND: Perfluorooctane sulfonate (PFOS) is a widely distributed persistent organic pollutant in the environment and has been associated with an increased risk of atherosclerosis. However, the underlying pathogenic mechanisms remain largely unclear. This study aimed to investigate the effects of PFOS on atherosclerosis and its associated gut-vascular axis. METHODS: Pseudo-germ-free mouse models and fecal microbiota transplantation (FMT) were used to determine the role of the gut microbiota in PFOS-induced atherosclerosis. Metagenomic sequencing was performed to characterize alterations in gut microbial composition following PFOS exposure, and targeted metabolomics was used to assess bile acid profiles in the ileum and plasma. Transcriptomic analysis of Bacteroides caecimuris (B.caecimuris) was conducted to explore the reasons for the increased abundance of B.caecimuris after PFOS exposure. In addition, intestinal transcriptomics and ChIP-qPCR were performed to validate transcriptional regulation within the FXR-TLR3 signaling axis. RESULTS: Among 127 participants with paired serum and fecal samples, including 82 patients undergoing coronary angiography with Gensini scores (GS score), fecal PFOS levels were significantly associated with lipid profiles and GS score, whereas serum PFOS showed no such association. Mechanistically, PFOS exposure promotes intestinal enrichment of B. caecimuris by upregulating its tolC gene, thereby enhancing efflux capacity. This microbial shift was accompanied by reduced levels of tauro-ursodeoxycholic acid (TUDCA) and aberrant activation of intestinal FXR signaling. Further analyses demonstrated that FXR activation upregulated TLR3 expression and promoted inflammatory responses and atherosclerosis progression via the TLR3-NF-{kappa}B signaling axis. Both intestinal epithelial-specific FXR deficiency (Fxr{Delta}IE) and TUDCA supplementation significantly suppressed pathway activation and alleviated disease phenotypes.Functional experiments identified TLR3 as a key downstream effector of FXR. Overexpression of TLR3 abolished the protective effects observed in Fxr{Delta}IE mice. Moreover, pharmacological inhibition of TLR3 using CU CPT-4a significantly improved established atherosclerotic lesions in vivo. CONCLUSIONS: This study identifies a gut microbiota-driven FXR-TLR3 signaling axis that mediates PFOS-induced atherosclerosis. These findings provide new mechanistic insights into environmentally induced cardiovascular disease and suggest potential targets for risk assessment and therapeutic intervention.

Background and AimsAs the older population (aged 65 years and older) continues to expand and more people drink alcohol, aging has been linked to the development of alcohol-associated liver disease (ALD) and to worse disease outcomes. The aim of this study was to explore the mechanisms by which advanced age and alcohol exacerbate alcohol-induced liver injury. MethodsTwo-to-three-month-old and twenty-to-twenty-two-month-old male C57BL/6N mice were subjected to chronic-on-binge alcohol feeding (the Gao-binge model). Serum alanine aminotransferase, aspartate aminotransferase, and triglyceride content were determined using biochemical assays. The levels of lipogenesis, fatty acid-metabolizing proteins, inflammatory markers, mitochondrial and autophagy-related proteins, and senescence-associated proteins were determined by immunoblotting, immunohistochemistry, and real-time quantitative polymerase chain reaction (RT-qPCR). Liver tissues were also subjected to RNA sequencing and metabolomics analyses. Proteomics analysis was performed on serum samples. Tail-vein adenovirus-TFEB was injected to overexpress hepatic TFEB in 22-month-old C57BL/6N male mice, followed by Gao-binge alcohol feeding. ResultsHepatic triglyceride content was significantly increased in aged, alcohol-fed mice, whereas serum ALT and AST levels remained relatively similar between alcohol-fed young and aged mice. Gao-binge alcohol increased the hepatic levels of diacylglycerol and acyl-carnitine species in both aged and young livers. RNA sequencing, proteomic analysis, and serum cytokine array analysis showed that inflammatory cytokines, including Ccr2, Cxcl1, and CCL6, and pro-inflammatory antibody fragments were increased in aged, alcohol-fed mice. Increased gene and protein expression of the senescent markers p21 and p27, along with increased senescent-associated (SA) {beta}-galactosidase activity in ethanol-fed aged mice compared to young mice. Gene and protein expression of TFEB was downregulated in ethanol-fed young and aged animals, along with decreased levels of lysosomal ATPases and hepatic dipeptide content. Overexpression of TFEB in the livers of aged, Gao-binge-fed mice was associated with reduced Ly6G-positive cells, reduced protein levels of the innate immune mediators cGAS, IRF-7, IRF3, and NLRP3, and reduced caspase-1 activity as well as serum ALT levels. ConclusionsOur findings indicate that advanced age perpetuates the detrimental effects of excessive alcohol consumption on various homeostatic processes and promotes steatosis and inflammation in the liver. Modulations in hepatic TFEB could be effective in mitigating pro-inflammatory signaling that occurs due to the synergistic effect of both heavy alcohol consumption and advanced age.

Coenzyme A is an essential cofactor synthesized from pantothenate, cysteine, and ATP, and is involved in numerous processes of cellular metabolism through its ability to carry activated acyl groups. Coenzyme A participates in catabolism of carbohydrate, fat and amino acids; biosynthesis of fatty acids, cholesterol and heme; and protein modification including acetylation and 4-phosphopantetheinylation. Despite CoAs critical functions, the regulation of CoA levels and the rate of CoA synthesis in different cell types and disease states are not well understood. One reason for this gap is that many acyl-CoA species are analytically challenging to measure due to factors including instability, poor ionization, and the wide range of biochemical properties conferred by different acyl chain lengths. In addition, most current methods do not support analysis of CoA isotopic labeling, which is required to quantify CoA synthesis rate or to measure absolute concentration using isotope-labeled internal standards. Here, we describe a method to quantify the concentration and isotopic labeling of total CoA, defined as the sum of CoASH plus all acyl-CoA species. Acyl-CoA species are hydrolyzed using sodium hydroxide to remove acyl chains, then CoA is derivatized on the thiol with N-ethylmaleimide (NEM). Following protein precipitation and solid phase extraction, samples are analyzed by liquid chromatography-mass spectrometry. This method is linear in a wide range that captures mouse tissue CoA levels, with accuracy within 15% error and precision below 15% relative standard deviation for both pure standards and tissue samples. We applied this method to measure total CoA concentration in five tissues from male and female mice, and total CoA synthesis rate in mouse liver via infusion of 13C-15N-pantothenate. Overall, this method offers a tractable approach to measure total CoA concentration and isotopic labeling to enable study of total CoA synthesis rates and concentrations in health and disease.

Background and AimsSteatotic liver disease (SLD) is characterized by excessive lipid accumulation in hepatocytes, and alcohol consumption may modify the disease course, but the evidence is inclusive. This systematic review and meta-analysis aimed to holistically evaluate the impact of mild, moderate, and high levels of alcohol consumption on hepatic and extrahepatic outcomes in SLD. MethodsWe systematically searched EMBASE, MEDLINE, Web of Science, and the Cochrane Central Register of Controlled Trials for relevant studies. The study outcomes included liver related events, malignancy, mortality and cardiovascular disease among adults with SLD who consumed alcohol. ResultsOf 2228 records identified, twenty-six studies comprising 466611 adults with SLD were included. High alcohol consumption was associated with an increased risk of liver-related events compared with abstinence (2.97, 95% CI 1.61-5.50; p<0.001), and a similar association was observed among alcohol drinkers overall (HR 1.93, 95% CI 1.60-2.33; p<0.001). Moderate alcohol consumption was associated with a higher incidence of malignancy (HR 1.41, 95% CI 1.13-1.78; p=0.677). In contrast, mild alcohol consumption was associated with lower all-cause mortality compared with abstinence (HR 0.88, 95% CI 0.78-0.98; p=0.001). No association was observed between alcohol consumption and cardiovascular disease incidence or hepatocellular carcinoma ConclusionsAlcohol intake may increase the risk of liver-related complications and cancer risk in individuals with SLD. Mild alcohol consumption was associated with lower all-cause mortality, and alcohol intake showed no association with cardiovascular disease incidence. Further studies are needed to clarify the dose-dependent effects of alcohol on hepatic and extrahepatic outcomes in SLD.

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

BackgroundThe ketogenic diet is being explored as an adjuvant intervention in breast cancer because it lowers circulating glucose and elevates ketone bodies such as {beta}-hydroxybutyrate (BHB), but how individual ER+ breast cancer subtypes adapt to these conditions remains poorly characterized. We examined metabolic responses to BHB supplementation under glucose restriction in two ER+ breast cancer cell lines, asking whether metabolic adaptation patterns differ between models. MethodsMCF-7 and T47D cells were cultured under high glucose, glucose-restricted (5% of standard), or glucose-restricted with 10 mM BHB conditions and profiled by comprehensive two-dimensional gas chromatography-mass spectrometry (GCxGC-MS). Pairwise Welchs t-tests with Benjamini-Hochberg false discovery rate (FDR) correction were applied to identify treatment-responsive metabolites. Targeted assays quantified intracellular glycine, SHMT1 protein, and total branched-chain amino acid (BCAA) concentrations across a BHB dose range (2.5-15 mM). Patient tumor transcriptomic data from TCGA (n=1,084) and paired tumor-normal samples from GSE58135 (n=20) were analyzed for genes involved in one-carbon, ketone body, and BCAA metabolism. ResultsMCF-7 and T47D cells exhibited markedly divergent metabolic responses to BHB. In MCF-7 cells, BHB supplementation produced a broad pattern-level metabolic shift: 75% of detected metabolites trended upward when BHB was added to glucose-restricted cultures (C vs. B comparison), with 1,4-butanediol reaching nominal significance (FC=2.35, p=0.016) and a 4.1-fold trend increase in lactic acid (p=0.11), although no individual metabolite survived FDR correction. T47D cells showed essentially no metabolic response to BHB at the global level. Targeted assays detected an elevation in glycine at 5 mM BHB in both cell lines that did not follow a monotonic dose response and was not accompanied by changes in SHMT1 protein expression. Total BCAA levels were elevated by BHB in T47D cells but remained unchanged in MCF-7 cells. In paired patient samples, OXCT1 (log2FC = -1.41), SHMT1 (log2FC = -1.31), and ACAT1 (log2FC = -1.07) were significantly downregulated in ER+ tumors relative to matched normal tissue (adjusted p < 0.001 for all three). ConclusionsER+ breast cancer cell lines show heterogeneous metabolic responses to BHB supplementation under glucose restriction. The broad pattern of metabolite elevation in MCF-7 but not T47D cells suggests that capacity to utilize ketone bodies as metabolic substrate varies between ER+ models. The downregulation of OXCT1, ACAT1, and SHMT1 in ER+ tumors compared to normal tissue identifies these enzymes as candidate biomarkers that may help stratify which patients are likely to benefit from ketogenic interventions. Findings related to individual metabolites should be regarded as exploratory and require validation in larger, adequately powered cohorts.

Background/ObjectivesHuman plasma lipidomics provides valuable information on dietary and metabolic phenotypes, but the interpretation of high-dimensional lipid datasets remains challenging. We developed the Nutritional-Metabolic Lipid Profile (NMLP) module within LipidOne to translate plasma lipidomics data into interpretable nutritional-metabolic indices, functional categories, visual outputs, and biological statements. Subjects/MethodsNMLP calculates lipid indices reflecting cardiometabolic lipid status, fatty acid remodelling, overall lipid quality, oxidative protection, and omega-3/essential fatty acid status. The module was applied to three human plasma lipidomics public datasets: a randomized crossover glycemic-load feeding study, a eucaloric high-fat diet intervention in normal-weight women, and a large public dataset stratified by insulin sensitivity. ResultsAcross datasets, NMLP converted complex lipidomic matrices into coherent nutritional-metabolic profiles. In the glycemic-load study, the module highlighted metabolic lipid shifts not captured by standard clinical lipid panels, mainly involving cardiometabolic lipid status, oxidative protection, and fatty acid remodelling. In the high-fat diet intervention, NMLP tracked temporal lipid remodelling across pre-diet, on-diet, and post-diet states, consistent with metabolic adaptation to increased dietary fat exposure. In the insulin-sensitivity dataset, insulin-resistant subjects showed a storage-oriented lipid phenotype characterized by increased neutral lipid storage indices and altered lipid quality and oxidative-protection features. Category-level clustering further revealed heterogeneous nutritional-metabolic states within insulin-resistant subjects. ConclusionsNMLP provides a deeper and clearer interpretative framework for human plasma lipidomics in nutrition and metabolic health research. By translating lipid species into functional indices and category-level readouts, the module may facilitate the use of lipidomics in clinical nutrition, metabolic phenotyping, and precision nutrition studies. NMLP is freely accessible as part of the online LipidOne platform.

Comprehensive detection of steroids, beyond the limited panels typically analyzed in clinical chemistry laboratories, has become increasingly important given their pivotal roles in diverse biological processes. However, steroid quantification poses several analytical challenges, including differences in ionization efficiency and structural similarities across the entire steroid metabolic network. To address these challenges, we developed a targeted ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) assay to analyze 21 steroids using reverse-phase chromatography combined with rapid polarity switching. Mass spectrometry (MS) analysis was performed in scheduled multiple reaction monitoring (sMRM) mode. Depending on the steroid and matrix, the validated lower limits of quantitation (LLOQ) ranged from 12.0 pM to 1216 pM in plasma and 41.1 pM to 384 pM in fecal sample homogenates. In adipose tissue, it was from 0.01 pmol/g to 9 pmol/g. Measured steroid concentrations obtained from the commercial control samples (MassTrak Steroid Serum QC Set 1 and the MassCheck Steroid Panel 1 Serum Control) showed close agreement with the reference values. As a proof of concept, the method was successfully applied to 469 plasma samples in several projects, 15 adipose tissue samples, and 332 fecal samples, demonstrating its applicability to large-scale studies. In conclusion, the method enables sensitive, derivatization-free quantification of an expanded steroid panel in plasma and complex biological matrices, including adipose tissue and fecal samples, representing a significant advancement in comprehensive steroid profiling.