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Metabolites

Metabolites is an international, peer-reviewed, open access journal of metabolism and metabolomics, published monthly online by MDPI.

Indexed in PubMed | Quartile Ranking JCR - Q2 (Biochemistry and Molecular Biology)

All Articles (6,289)

Background: Traditional machine-learning approaches face challenges when attempting to integrate diverse biological information for predicting metabolite–disease relationships. The intricate connections linking metabolites, diseases, proteins, and Gene Ontology (GO) annotations present substantial obstacles for conventional pairwise graph representations, which prove inadequate for modeling such complex multi-way interactions. Methods: An innovative hypergraph-based framework (DHG-LGB) was developed to exploit this complexity through conceptualizing diseases as hyperedges. Within this architecture, individual hyperedges link multiple vertices including metabolites, proteins, and GO annotations, thereby enabling richer representation of the biological networks underlying metabolite–disease relationships. Metabolite–disease relationships were encoded as low-dimensional vectors through hypergraph neural network (HGNN) operations incorporating Laplacian smoothing and message propagation mechanisms. LightGBM (LGB) was used to construct a model for identifying the potential metabolite–disease associations. Results: Under 5-fold cross-validation, DHG-LGB achieved 98.87% accuracy, 91.77% sensitivity, 99.58% specificity, 95.60% precision, Matthews correlation coefficient (MCC) of 0.9305, receiver operating characteristic area under curve (AUC) of 0.9983, and precision-recall area under curve (AUPRC) of 0.9860. The framework maintained strong performance when tested with varying positive-to-negative ratios (spanning 1:1 through 1:10), consistently achieving AUC values exceeding 0.9954 and AUPRC values above 0.9820, thereby confirming excellent robustness and generalization capability. Comparative evaluations against existing methodologies verified the superiority of DHG-LGB. Conclusions: The DHG-LGB framework delivers more comprehensive modeling of biological interactions relative to conventional approaches and substantially enhances predictive accuracy for metabolite–disease relationships. It is foreseeable that it will be a valuable computational tool for biomarker identification and precision medicine initiatives.

9 February 2026

Hypergraph Bipartite Representation.

Background: This study investigated the hypolipidemic and hepatoprotective effects of refined soybean oil supplemented with an Ocimum basilicum L. extract, characterized by HPLC and found to be rich in caftaric, caffeic, chicoric, and rosmarinic acids. Methods: After a 12-week model of diet-induced hyperlipidemia, we examined the plasma levels of TC, TG, Glucose, HDL-C, and LDL-C and the LDL-C/HDL-C ratio using enzymatic kits. The Plasma Hepatic and Biliary Marker Analysis was analysed following standardized hospital protocols with quality-controlled instrumentation. Results: The supplementation with Basil-Enriched Oil (BEO) resulted in a notable redistribution of lipids, significantly reducing the plasma total cholesterol (−75%), triglycerides (−96%), and glucose (−22%), while enhancing their hepatic sequestration. This was accompanied by a marked improvement in the LDL-C/HDL-C ratio and a reduction in hepatic oxidative stress (measured by MDA). Importantly, BEO preserved liver structure and prevented steatosis, despite inducing an increase in adaptive hepatomegaly. Conclusions: The results reveal a dual mechanism whereby the antioxidant properties of BEO collaborate with reprogrammed lipid metabolism, promoting safe hepatic storage rather than harmful circulating levels. These findings strongly advocate for the extract’s potential as a nutraceutical for addressing hyperlipidemia and related metabolic disorders by targeting both oxidative stress and lipid imbalance. Further research is required to confirm these effects in clinical settings and to confirm its long-term efficacy.

5 February 2026

HPLC phenolic profile of BE; 1: caftaric acid; 2: gallic acid; 3: chlorogenic acid; 4: caffeic acid; 5: chicoric acid; 6: rosmarinic acid.

Objectives: This study explored the antidepressant mechanisms of aerobic exercise in CUMS rats by analyzing urinary metabolomics (LC-MS and NMR), with the aim of providing both theoretical and practical support for exercise-based depression interventions. Methods: (1) Thirty-two Sprague-Dawley rats were acclimatized for one week and then randomly assigned to four groups (n = 8 per group): control (C), control + aerobic exercise group (E), CUMS model (D), and CUMS + exercise (DE). Groups D and DE were subjected to nine types of CUMS stimuli. Behavioral indicators were assessed weekly, and the successful establishment of the CUMS model was confirmed at week 3. Following successful modeling, rats in groups E and DE underwent four weeks of aerobic exercise training. Throughout this period, groups D and DE continued to receive CUMS exposure, while groups C and E were maintained under standard control conditions. (2) At the end of week 7, behavioral tests were repeated. Twelve-hour urine samples were collected for metabolomic analysis using liquid chromatography–mass spectrometry (LC-MS) and 1H-NMR spectroscopy. The following morning, rats were euthanized under anesthesia. Whole blood was collected from the abdominal aorta, and serum was separated for subsequent biochemical assays. Bioinformatics approaches were employed to identify potential targets and signaling pathways associated with the antidepressant effects of aerobic exercise. (3) For statistical analysis, one-way or two-way analysis of variance (ANOVA) was applied to behavioral, physiological, and biochemical data, whereas multivariate statistical analysis was used for metabolomic data. Results: (1) By week 3, body mass, sucrose preference, rearing frequency, and the number of grid crossings were significantly lower in groups D and DE than in groups C and E (p < 0.05 or p < 0.01). These findings confirmed the successful establishment of the depression model. At week 7, all behavioral indicators in group DE showed significant recovery relative to group D (p < 0.05 or p < 0.01). (2) Compared with group C, corticosterone and blood ammonia levels were significantly elevated in group D (p < 0.01). In contrast, these levels were markedly reduced in group DE compared with group D (p < 0.01). (3) LC-MS analysis identified 25 urinary metabolites associated with depression in group D relative to group C. Among these, 21 were significantly downregulated and 4 were upregulated (p < 0.05 or p < 0.01), involving seven metabolic pathways. Following aerobic exercise intervention, six of these depression-related metabolites in group DE showed significant recovery (p < 0.05 or p < 0.01), which were associated with two metabolic pathways. (4) Integrated analysis of LC-MS and 1H-NMR data revealed glutamine as a common differential metabolite, linked to three metabolic pathways. All metabolic pathways modulated by aerobic exercise were related to amino acid metabolism. (5) Bioinformatics analysis indicated that AKT1, MTOR, IL6, RAF1, and TNF were core targets through which aerobic exercise regulated urinary metabolism in CUMS rats. Conclusions: A four-week regimen of aerobic exercise significantly improved depressive-like behaviors and enhanced anti-fatigue capacity in CUMS rats. This exercise regimen promoted urinary metabolic remodeling, primarily through the modulation of amino acid metabolism. Furthermore, its antidepressant effect is likely mediated through the regulation of core tissue targets—including AKT1, mTOR, IL-6, RAF1, and TNF—thereby influencing key pathways such as PI3K-AKT, MAPK/ERK, and neuroinflammatory signaling.

5 February 2026

Design and timeline of animal experiment.

Background/Objectives: The stability of metabolites and lipids in feces varies depending on the storage temperature and duration. Methods: We examined the stability of various metabolites and lipids in human feces under 10 different storage conditions (room temperature for 2, 6, 24, and 48 h, 4 °C for 6, 24, and 48 h, −20 °C for 1 week, 2 weeks and 1 month) and explored markers useful for quality control of fecal samples, using metabolites and lipids that vary depending on temperature and time. Results: There was generally more variation at 4 °C than at −20 °C, and more at room temperature than at 4 °C, and variation also increased as the storage duration was extended under each temperature condition. Some metabolites and lipids were found to be unstable, even over short periods (2 or 6 h) at room temperature or 4 °C storage. However, storage at −20 °C generally maintained the stability of most of them for up to two weeks. Our results suggest that the following ratios can serve as useful quality control markers: methionine to S-methyl-5-thioadenosine, xanthine to inosine and N-linoleoyl leucine to 1,2-dilinoleoyl-sn-glycerol. Conclusions: For comprehensive metabolite and lipid analysis, we recommend promptly transferring samples to −80 °C storage, except when stored at −20 °C for no longer than two weeks, with checks on markers for quality control. When measuring specific metabolites or lipids, our catalog data can be consulted to determine acceptable storage conditions.

4 February 2026

Temperature- and time-dependent changes in metabolite quantity: the values obtained following storage at −80 °C were considered control values, and those obtained under each condition were compared with the control. The metabolites with p-values less than 0.05 and a fold change greater than 2 or less than 0.5 were counted under each condition, and the number was highlighted according to the fold change (FC) ratio.

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Metabolites - ISSN 2218-1989