Metabolomics最新文献

Background: Post-stroke cognitive impairment (PSCI) is a common long-term complication of acute ischemic stroke often accompanied by metabolic disorder, yet early metabolomic predictors remain poorly characterized. This study aimed to identify acute-phase serum metabolomic signatures associated with PSCI and develop a prediction model for early PSCI risk stratification.

Methods: In this prospective study, 130 acute ischemic stroke patients were enrolled. Serum samples collected within 24 h of stroke onset were subjected to untargeted metabolomic profiling. Cognitive status was assessed at 3 months after stroke. KEGG pathway and MECNA analyses were performed to assess pathway-level and metabolite-metabolite correlation patterns. Bootstrap-LASSO stability selection and logistic regression were used to develop a prediction model, which was evaluated by stratified 10-fold cross-validation. Calibration curves and decision curve analysis assessed model performance.

Results: A total of 51 candidate differential metabolites were identified between PSCI and Non-PSCI patients, involving purine/caffeine-related metabolism, bile acid metabolism, lipid and fatty acid metabolism, and amino acid-related metabolism. MECNA identified 15,120 differential metabolite-metabolite correlation pairs, suggesting distinct acute-phase serum correlation patterns in patients who later developed PSCI. Six metabolites were retained in the final model: 6-Hydroxymellein, 21-Deoxycortisol, Inosine, 2-Hydroxy-3-methylbutyric acid, Isoleucyl-Arginine, and Propylparaben. The metabolite-only model achieved an AUC of 0.774 (95% CI, 0.690-0.853), showing higher discrimination than the core clinical-only model and the combined clinical-metabolomic model.

Conclusions: Acute-phase serum metabolomic profiling identified multi-domain metabolic features associated with PSCI. The internally validated six-metabolite model showed moderate performance for early PSCI risk stratification.

Background: Neuroborreliosis (LNB) poses a significant diagnostic challenge in paediatrics due to its non-specific clinical presentation and the limited sensitivity of standard tests. Metabolomics offers the possibility of direct insight into the biochemical changes occurring in the central nervous system during infection.

Objective: The aim of the study was to characterise the metabolic profile of paired serum and cerebrospinal fluid (CSF) in children with LNB to identify candidate immunometabolic signatures associated with the infection.

Methods: Samples were analysed using NMR spectroscopy and LC-MS/MS. Data gaps were filled using a Random Forest algorithm, followed by univariate and multivariate statistical tests (PLS-DA) and pathway enrichment analysis based on the KEGG database.

Results: Profound dysregulation of purine metabolism was demonstrated, manifested by elevated concentrations of hypoxanthine, xanthine and uric acid, which may reflect a combination of non-specific infection-related responses, oxidative stress, and potentially the pathogen's metabolic demand for host purines. Significant changes were observed in the biosynthesis of aromatic amino acids (particularly L-tyrosine) and in glycerophospholipid metabolism, suggesting damage to the blood-brain barrier. In CSF, alterations in choline and glutamate levels were identified as candidate metabolic signatures potentially associated with neuroinflammation and excitotoxicity.

Conclusions: Metabolomic analysis enables precise mapping of the biochemical response in paediatric LNB. The identified abnormalities in purine metabolism, neuroinflammation and cell membrane integrity represent a promising starting point for future biomarker discovery research.

Introduction: Despite technological advancements over the last three decades in small-molecule omics, compound annotation remains a major bottleneck in untargeted metabolomics and non-targeted environmental analysis. This is especially true for exposomics applications, which remain significantly affected by the limited chemical space coverage.

Objectives: This work aims at describing the development of an MS/MS spectral library containing > 170 relevant xenobiotics from different classes of food, environmental, and microbial toxicants.

Methods: LC-MS/MS data was acquired using collision-induced dissociation in data dependent acquisition mode under 13 different single collision energies with four additional collision energy spread experiments. A diverse set of compounds including natural toxins produced by bacteria, fungi (mycotoxins), and plants (phytotoxins), as well as anthropogenic chemicals such as bisphenols, phthalates, PFAS chemicals, drugs, consumer care products ingredients, and pesticides, and additional toxicologically relevant chemical classes were screened. Metabolic products for which commercially available reference standards and/or MS/MS spectra are not available in any public or commercial database have been included (e.g. colibactin-DNA-adduct, cereulide, deoxynivalenol-3-glucuronide). Library generation was performed in mzmine.

Results: Open-format data based on representative spectra are provided.This new resource, available at https://zenodo.org/records/20715576 , is aimed at providing a ready-to-use tool for the annotation of key exogenous compounds which are frequently overlooked in clinical metabolomics but may exert potent biological effects. A detailed discussion from a user perspective is provided regarding the library generation workflow in mzmine, aiming at facilitating the work of fellow researchers in the creation of their own in-house libraries.

Conclusion: We intend to provide the metabolomics community with better tools for exposomics research and to reduce perceived barriers in developing specialized MS/MS libraries for widening chemical space coverage and increasing quality and confidence.

Introduction: Untargeted metabolomics often results in a significant portion of unannotated metabolites, or "metabolic dark matter," which hinders biological interpretation.

Objectives: A two-step analytical approach was developed to systematically prioritize and interpret unannotated metabolites using plasma LC-MS/MS data from pregnant women with obesity as a biologically relevant test dataset.

Methods: The first step involved clustering 1,021 known metabolites into ten structurally coherent groups based on the Tanimoto similarity, thus defining the biologically relevant chemical space of the dataset. These metabolites were further characterized by Absorption, Distribution, Metabolism, and Excretion (ADME) profiling, protein target prediction, molecular docking and Kyoto Encyclopedia of Genes and Genomes pathway mapping analysis, to establish biological plausibility and functional perspective. Candidate structures for 1,836 unannotated features were retrieved from PubChem using molecular formula and molecular weight matching within a ±0.5 Da tolerance.

Results: This search yielded 569,115 candidate structures, of which 368,197 unique structures were retained after curation. Tanimoto coefficient filtering reduced the candidate pool to 19,868 structurally plausible candidates, and retention time-based prioritization further refined this set to 418 high confidence candidate annotations, including 83 database-supported candidates identified through HMDB and LIPID MAPS structure database cross-referencing. RT-based prioritization effectively distinguished positional isomers sharing the same molecular formula by incorporating agreement between predicted and experimentally observed retention times.

Conclusion: This improved discrimination among structurally similar candidates, expanded metabolite annotation confidence, and provided a scalable framework for prioritizing dark matter metabolites in untargeted metabolomics.

Introduction: Resistance training (RT) imposes repeated mechanical stimuli triggering systemic metabolic reprogramming whose molecular underpinnings remain incompletely characterized. Univariate approaches fail to capture pathway-level shifts defining the metabolomic response to RT in protocols differing in load.

Objectives: To evaluate whether high-load (HL) and low-load (LL) RT protocols performed to volitional failure induce serum biochemical patterns specific to training status and loading condition, undetectable by univariate approaches.

Methods: Seventeen healthy young men completed an 8-week RT intervention (HL: 80% 1-RM, n = 9; LL: 30% 1-RM, n = 8). Fasting serum profiles were acquired by untargeted ¹H-NMR spectroscopy. Univariate comparisons used paired t-tests and one-way ANOVA with Benjamini-Hochberg correction. Random Forest models were validated by stratified 5 × 5-fold cross-validation and 1000-iteration permutation testing, with group separation assessed by sensitivity, specificity, and AUC. Discriminant metabolites were independently mapped onto established metabolic pathways to assess biochemical plausibility.

Results: Of 10 metabolites significantly altered, five were consistently modulated across both protocols (3-hydroxyisovalerate, 3-hydroxybutyrate, acetone, isobutyrate, and lactate), reflecting shared adaptations in amino acid turnover and ketone body metabolism. Training-status separation achieved AUC = 1.00 (p < 0.001), with 3-hydroxyisovalerate as the dominant feature. Load-specific divergence was captured only by an exploratory multivariate signature of choline, glucose, and alanine (AUC = 0.94; p = 0.008), none of which was individually significant in univariate testing. Pathway integration demonstrated that discriminant metabolites are consistently related to well-established metabolic pathways: leucine catabolism, ketone body turnover, and glycolytic-oxidative rebalancing.

Conclusions: RT induces biologically coherent, load-modulated serum metabolomic shifts detectable only through multivariate analysis. These findings are hypothesis-generating and require external validation in independent cohorts before applied implementation.

Introduction: Chronic Obstructive Pulmonary disease (COPD) and Idiopathic Pulmonary Fibrosis (IPF) are chronic pulmonary disorders with distinct pathologies but shared risk factors. Metabolomics may provide insights into mechanisms.

Objectives: To identify metabolites associated with COPD and IPF, and to characterize shared and disease-specific signatures.

Methods: Plasma metabolomic profiling was conducted in the Lung Tissue Research Consortium (LTRC). Logistic regression identified metabolites associated with COPD and IPF, and results were replicated in an external cohort, COPDGene. We applied Weighted Gene Co-expression Network Analysis (WGCNA) to explore disease-associated metabolite modules. We further evaluated relationships between significant metabolites and risk genes of interest.

Results: Of 1131 metabolites in LTRC, 246 (21.8%) differed between COPD and controls, and 136 (12.0%) between IPF and controls (FDR < 0.05). Among 80 shared significant metabolites in COPD and IPF, 77 showed concordant directions of effect. Shared metabolomic changes included reduced levels of steroids, triglycerides, diglycerides, phosphatidylcholines, and increased levels of carnitines. In contrast, polyunsaturated fatty acids, nicotine, and thyroxine metabolites differed between COPD and IPF; these findings were further explored by the WGCNA. External replication was performed for 120 metabolites measured in both cohorts, of which 49 (40.8%) replicated in COPD vs. control. In exploratory analyses leveraging quantitative imaging abnormalities (QIA) as a surrogate for IPF; only 4 (3.3%) metabolites replicated in the QIA vs. control model, and only 9 (7.5%) for COPD vs. QIA.

Conclusion: Alterations in metabolomic profiles of COPD and IPF suggested shared dysregulation of several lipids. However, some metabolites pointed to disease-specific differences.

Introduction: Glutamine, the most abundant amino acid in the body, is a key metabolic substrate for endothelial cells. Glutamine supplementation protects against cardiovascular disease in animal models and in humans; however, glutamine in vitro has inconsistent effects on endothelial function. Furthermore, little is known about how altered metabolite concentrations, for example excess glucose in hyperglycemia or excess glutamine in cell culture media, affect endothelial cell metabolism.

Objectives: The objective of this study was to determine how physiological and supplemented glutamine affect endothelial metabolism in normal and high glucose conditions.

Methods: Primary human coronary artery endothelial cells were cultured in varied glutamine concentrations and in normal and high glucose. Glutamine uptake and glutamate secretion were measured using a YSI bioanalyzer; oxidative respiration was assessed using a Seahorse Metabolic Analyzer; and glutamine carbon incorporation into the TCA cycle, amino acids, antioxidants, and other pathways was evaluated via liquid chromatography-mass spectrometry.

Results: As extracellular glutamine increased, endothelial cells took up more glutamine, but glutamate secretion saturated above 2 mM glutamine. Excess glutamine was primarily stored intracellularly, although increasing extracellular glutamine concentration did increase oxidative respiration and TCA cycle isotope enrichment. We also observed increased glutamine incorporation into glutathione, UDP-GlcNAc, and amino acids. When total metabolite abundance was examined, intracellular succinate, unsaturated fatty acids, and one-carbon metabolism-related metabolites decreased with increasing glutamine.

Conclusion: These findings demonstrate that excess extracellular glutamine reprograms endothelial metabolism, suggesting that glutamine supplementation should be used with caution in cardiovascular therapies and endothelial cell culture.

Introduction: Bisphenol A (BPA) is a widely distributed endocrine-disrupting chemical with documented metabolic effects in experimental models. Although hepatic transcriptional alterations following BPA exposure have been reported, the extent to which these changes translate into functional metabolic remodeling remains unclear.

Objectives: This study aimed to characterize global hepatic metabolomic alterations following chronic exposure to BPA at the lowest observed adverse effect level (LOAEL) in mice and to determine whether the affected metabolic networks overlap with pathways implicated in non-alcoholic fatty liver disease (NAFLD) progression and early events associated with hepatocarcinogenic susceptibility.

Methods: Untargeted liquid chromatography-mass spectrometry (LC-MS)-based metabolomics was performed on liver samples from BPA-exposed (n = 8) and control (n = 6) mice. Differential metabolite analysis and pathway enrichment analysis were conducted to identify significantly altered metabolites and metabolic pathways.

Results: BPA exposure induced marked hepatic metabolic remodeling involving polyunsaturated fatty acids, arachidonic acid-derived eicosanoids, lysophospholipids, retinoid metabolism, and phase II detoxification pathways. Dysregulation of omega-3 and omega-6 fatty acids and altered prostaglandin and thromboxane derivatives indicated disruption of inflammatory lipid mediator balance. Changes in retinol- and retinoic acid-related metabolites suggested impaired differentiation-associated signaling, while increased sulfated and glucuronidated metabolites reflected enhanced xenobiotic metabolism. Pathway enrichment analysis highlighted biosynthesis of unsaturated fatty acids, arachidonic acid metabolism, and retinol metabolism as significantly affected pathways.

Conclusion: Chronic BPA exposure at a LOAEL dose induces coordinated hepatic metabolic reprogramming characterized by pro-inflammatory lipid remodeling, disruption of retinoid signaling, and activation of detoxification mechanisms. These alterations may represent metabolic features associated with pathways relevant to NAFLD progression and hepatocarcinogenic susceptibility.

Pathogenic mitochondrial DNA (mtDNA) mutations contribute to a broad spectrum of both common and rare metabolic diseases. However, clinical presentation is highly variable and only partially explained by the proportion of mutant mtDNA or heteroplasmy. With the relationship between mutation burden and clinical manifestation poorly defined, controlled models are required to uncover underlying mechanisms. Here, we explore the metabolic consequences of increasing heteroplasmy in a well-characterised mouse model harbouring a pathogenic mtDNA deletion. Untargeted urinary metabolomics reveals distinct mutation load-dependent metabolic shifts with some metabolites declining early on, while others exhibit threshold-like increases beyond ~ 60% mutation load - the level at which lactic acidemia and OXPHOS defects become apparent in this model. To assess translational relevance, we examined these heteroplasmy-associated metabolites in urine from patients carrying the most common mtDNA mutation, m.3243 A > G. Several of these metabolites were differentially expressed in patients relative to controls, with conserved directionality across species. Among these, 2-hydroxyisovalerate (2-HIVA), which was most strongly affected in the mouse model, also emerged as the top discriminator in patients. Receiver operating characteristic analysis indicated that urinary 2-HIVA has strong discriminatory power, supporting its potential utility as a biomarker for mtDNA-based disorders. These findings enhance our understanding of mtDNA-related disease pathophysiology and establish a foundation for further validation studies.