Metabolomics最新文献

Introduction: Diabetic nephropathy (DN) is a major cause of chronic kidney disease and end-stage renal failure worldwide. The current diagnostic marker, albuminuria, lacks specificity and often detects renal damage only at advanced stages.

Objectives: This study aimed to characterize urinary metabolic alterations associated with DN and explore metabolite panels with diagnostic potential.

Methods: A targeted urinary metabolomics analysis was performed using the validated TMIC Urine MEGA Assay, quantifying 268 metabolites in 60 participants (20 controls, 20 type 2 diabetes mellitus [DM-2], and 20 DN patients). Data were analyzed by Partial Least Squares Discriminant Analysis (PLS-DA) for visualization, and penalized regression algorithms [Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net (EN) with a Genetic Algorithm (GA)] followed by logistic regression (LR) modeling to identify potential discriminative variables.

Results: DN patients showed marked alterations in metabolites related to oxidative stress, mitochondrial dysfunction, and inflammation. Twenty-four of 86 quantified uremic toxins differed significantly between DN and comparison groups. The LASSO-derived model identified β-alanine, kynurenine, glucose and argininic acid as key discriminants (AUC = 0.905, 10-fold CV), while inclusion of GFR and additional metabolites (2-hydroxybutyric acid, shikimic acid) improved performance (AUC = 0.96).

Conclusions: Quantitative urinary metabolomics revealed metabolic perturbations reflective of DN pathophysiology and identified candidate metabolite panels with potential for non-invasive disease characterization. These findings, though preliminary, provide a foundation for validation in larger, longitudinal cohorts and for integrating urinary metabolomics into precision diagnostics for diabetic kidney disease.

Background: This study aimed to analyze the molecular composition and physiological changes in the plasma of patients with convalescent coronavirus disease 2019 (COVID-19).

Methods: Plasma samples from 29 hospitalized patients recovering from COVID-19 and 30 uninfected controls were analyzed using untargeted metabolomics and data-independent acquisition mass spectroscopy proteomic analyses. Integrative metabolomic-proteomic analysis was then conducted to construct a protein-metabolite interaction network.

Results: Untargeted metabolomic profiles revealed 415 differential metabolites, with 28.05% of the metabolites belonging to lipids and lipid-like molecules, most of which were upregulated in patients with convalescent COVID-19, such as sphingolipids. Differential metabolites were involved in taste transduction, thermogenesis, and sphingolipid metabolism. Proteomic analysis identified 947 differentially expressed proteins, which were mainly involved in immunoinflammation-related pathways, such as complement and coagulation cascades, neutrophil extracellular trap formation, and platelet activation. Several significant pathways were influenced by differential metabolites and proteins, such as estrogen signaling, ferroptosis, and neurodegeneration-associated pathways.

Conclusion: This study revealed differential metabolite and protein compositions in the plasma of patients with convalescent COVID-19 compared with uninfected controls. The main physiological changes were associated with the pathology of this disease, suggesting that the phenotype of patients with convalescent COVID-19 did not return to a phenotype similar to that of uninfected controls.

Introduction: Fermentation relies on the interaction of microorganisms to enhance flavor, extend shelf life, and improve health. Lentinus polychrous Lév., a mushroom native to Northern Thailand with prebiotic properties, is commonly used in traditional fermented foods; however, the microbial and nutritional characteristics of fermented mushroom sausages have received limited scientific attention.

Objective: To investigate changes in microbial communities and metabolite profiles during fermentation.

Method: We applied an integrated microbiomic and metabolomic approach to assess the effects of fermentation on Lentinus polychrous Lév. mushrooms. Mushroom samples were fermented for up to three days. Bacterial profiles were analyzed using 16S rRNA gene sequencing, while metabolic changes were characterized by LC-MS/MS.

Results: Microbiomics revealed a clear succession from initial Bacillus dominance to a Weissella-dominated community. Concurrently, metabolomics identified 107 metabolites. Key metabolic shifts included decreases in gentiobiose, phenylalanine, and isoleucine, alongside increases in L-tryptophan, beta-hydroxyisovaleric acid, and specific lipids after fermentation. Epicatechin also increased. Correlation analysis showed strong positive associations between Weissella and most differential metabolites, suggesting a crucial role for Weissella in fermentation.

Conclusion: This study demonstrated dynamic shifts from Bacillus to Weissella during Lentinus polychrous Lév sausage fermentation, accompanied by significant metabolic changes. Weissella dominance correlated with enhanced nutrient bioavailability, underscoring the potential of this traditional fermented food as a functional food for health promotion.

Plants of the Himalayan region produce a wide spectrum of metabolites whose abundance is strongly influenced by species identity, genotype, developmental stage, and environmental factors such as altitude and temperature. These metabolites are of major relevance to the food, cosmetic, and pharmaceutical industries, yet their quality and composition often fluctuate with changing climatic conditions. This review synthesizes available evidence on the influence of environmental gradients particularly altitude and temperature on metabolite production in Himalayan plants, with a special emphasis on seabuckthorn (Hippophae spp.), a species of considerable commercial and pharmacological value. Unlike broader reviews of plant environment interactions, this work focuses specifically on Himalayan taxa, identifies emerging trends across metabolite classes (phenolics, flavonoids, alkaloids, terpenoids, and fatty acids), and highlights the adaptive significance of these compounds under climatic stress. In addition, 16 threatened medicinal plants of the Himalaya are considered, for which information on metabolite responses to environmental variables remains scarce. By integrating findings across species and metabolite groups, this review provides new insights into how Himalayan plants adapt to climatic challenges. Such knowledge is critical for guiding conservation strategies, optimizing cultivation practices, and ensuring the sustainable utilization of these species for nutraceutical and therapeutic applications.

Introduction: Since the early 2000s, metabolomics has grown rapidly, becoming integral to fields like life sciences, health, and environmental research. This expansion has led to the formation of national and international societies, such as Germany's DGMet, to tackle emerging challenges. One of DGMet's goals is to improve measurement quality by assessing community needs for harmonization and standardization. A recent survey within the German-speaking community aimed to identify current practices and gaps in the use of chemical standards and reference materials, to guide future standardization efforts and collaborative initiatives.

Methods: An online survey was conducted between June 2023 and April 2024. The survey consisted of 38 key questions and was open to research institutions from Germany, Austria, and Switzerland.

Results: The survey was accessed by 68 laboratories, with 23 institutes providing complete or partial responses (34% response rate), which is comparable to rates reported in similar surveys within the metabolomics and lipidomics communities. Respondents were mainly experienced researchers from Germany, focusing mainly on health-related ("red") metabolomics, as indicated by 78% of the respondents, followed by microbial ("grey", 48%) and plant ("green", 39%) metabolomics (multiple answers possible). The use of targeted methods was reported more frequently (91%) than that of non-targeted methods (78%), whereas metabolite fractions studied were equally split between polar, midpolar and lipid fractions (83% each). Human (74%), mouse (61%) and Arabidopsis (30%) were the most frequently studied organisms. Most participants used synthetic chemical standards for instrument qualification (83%), calibration (78%), and metabolite identification (74%), while matrix reference materials were mainly applied for quality control (52%) and method validation (44%). There was a strong demand for more standards, especially for metabolite identification and quantification, with cost being a major barrier, particularly for isotopically labelled standards and certified reference materials.

Conclusions: Valuable insights into the use of standards and reference materials within the German-speaking metabolomics community were obtained. Moving forward, the community should address critical gaps in metabolomics standardization. To achieve this, it must share its knowledge, articulate its needs clearly, and actively engage in joint efforts with national metrology institutes and international standardization initiatives.

Introduction: Identifying the phytochemistry underpinning a plant's observed therapeutic benefits is essential for understanding mechanisms of action and developing novel therapeutics. More recent efforts fusing global metabolomics and multivariate predictive modeling have improved compound discovery; however, these models rely on chemical variations between samples, which often necessitates at least one round of fractionation and may result in compound loss or degradation.

Objectives: This study uses multiple whole botanical extracts to explore whether a metabolome-wide association study approach can accurately identify bioactive phytochemicals without prior fractionation.

Methods: We employed 40 Ocimum extracts with a range of IC₅₀ levels against HT-29 cells in an in vitro MTT assay and combined this data with untargeted UPLC-MS/MS metabolomics for biochemometric modeling of the potential bioactives. Multiple chemometric tools and statistical filters were employed to improve feature selection.

Results: The metabolomic profiles resulted in ca. 1600 metabolite features; implementing source-based filters, followed by LASSO dimension reduction, improved the reliability of Partial Least Squares (PLS) bioactivity predictions. The resulting model highlighted four biomarkers positively correlated with activity, one of which was putatively identified as gallic acid. Gallic acid's cytotoxicity against HT-29 cells was confirmed with the purified compound.

Conclusion: This study results demonstrated that predictive modeling of botanicals using a metabolome-wide association study of extracts with no fractionation was capable of identifying biologically active compounds.

Purpose: Fish aquaculture faces sustainable production challenges. Among them are the pathogenic outbreaks that can compromise the health of the stocks from various perspectives, including broodstock reproduction. This study focused on identifying the metabolite alterations produced after a bacterial infection by Vibrio anguillarum in the gonads of European seabass (Dicentrarchus labrax). Sex-related response to the infection challenge was studied using a metabolomics approach.

Method: The metabolome of testes and ovaries of adult fish were extracted and analyzed after 48 h of bacterial exposure by ultra-high-performance liquid chromatography-mass spectrometer using negative-mode electrospray ionization (ESI) (UHPLC-MS, Vanquish Horizon UHPLC coupled to a Thermo Fisher Scientific Q-Exactive HF). To further decipher the molecular events, metabolomic and transcriptomic data were interconnected.

Results: In total, 97 metabolites were identified. In the ovary, uric acid, O-phosphoethanolamine, allantoin, and acetoacetic acid were more represented. By contrast, nine metabolites were altered after the infection in testes, including uridine, N-acetylglucosamine-6-Phosphate, and Gamma-aminobutyric acid (GABA). The most abundant metabolic cascades triggered by infection in ovaries were related to glyoxylate and dicarboxylate metabolism, nitrogen metabolism, and purine metabolism, while in testes, we observed changes in glycerolipid metabolism, glycerophospholipid metabolism, and galactose metabolism.

Conclusion: The present results demonstrate, for the first time in fish, that changes in metabolic pathways induced following infection are sex-dependent. The findings will help develop sex-specific immune therapies, identify resistant phenotypes, and improve aquaculture infection protocols.

Objective: Metabolomic risk factors for dementia are under studied, especially in Latinos. We examined the relationship between plasma metabolomic profiles and a Magnetic-Resonance Imaging (MRI)-based markers of brain aging in a cohort of older adult Puerto Ricans residing in the greater Boston area.

Methods: We used multiple linear regression, adjusted for covariates, to examine the association between metabolite concentration and MRI-derived brain age deviation. Metabolites were measured at baseline with untargeted metabolomic profiling (Metabolon, Inc). Brain age deviation was calculated at wave 4 (~ 9 years from Boston Puerto Rican Health Study (BPRHS) baseline) as chronologic age, minus MRI-estimated brain age, representing the rate of biological brain aging relative to chronologic age. We also examined if metabolites associated with brain age deviation were similarly associated with hippocampal volume and global cognitive function.

Results: Several metabolites, including isobutyrylcarnitine, propionylcarnitine, phenylacetylglutamine, phenylacetylcarnitine (acetylated peptides), p-cresol-glucuronide, phenylacetylglutamate, and trimethylamine N-oxide (TMAO) were associated with worse brain aging. Taurocholate sulfate, a bile salt, was marginally associated with better brain aging. Most metabolites with negative associations with brain age deviation also were inversely, although not significantly, associated with hippocampal volume and cognitive function.

Conclusion: The metabolites associated with brain aging in this study are generally consistent with prior literature and highlight the potential role of TMAO, BCAA and other microbially derived metabolites in dementia.