PubMed

Adv Sci (Weinh). 2025 Apr 25:e2417355. doi: 10.1002/advs.202417355. Online ahead of print.ABSTRACTMETTL14 dimerizes with METTL3 to install N6-methyladenosine (m6A) on mRNA (m6A writers). Subsequently, m6A readers bind to m6A-marked RNA to influence its metabolism. RNA m6A emerges to critically regulate multiple intracellular processes; however, there is a gap in our understanding of m6A in liver metabolism. Glucose-6-phosphatase catalytic subunit (G6pc) mediates hepatic glucose production (HGP) and serves as the gatekeeper for glycogenolysis and gluconeogenesis; however, G6pc regulation is not fully understood. Here, METTL14 is identified as a posttranscriptional regulator of G6pc. Liver METTL14, METTL3, and m6A-methylated G6pc mRNA are upregulated in mice with diet-induced obesity. Deletion of Mettl14 decreases, whereas overexpression of METTL14 increases, G6pc mRNA m6A in hepatocytes in vitro and in vivo. Five m6A sites are identified, and disruption of them (G6pcΔ 5A) blocks METTL14-induced m6A methylation of G6pcΔ 5A mRNA. METTL14 increases both stability and translation of G6pc but not G6pcΔ 5A mRNA. YTHDF1 and YTHDF3 but not YTHDF2 (m6A readers) bind to m6A-marked G6pc mRNA to increase its synthesis. Deletion of hepatic Mettl14 decreases gluconeogenesis in primary hepatocytes, liver slices, and mice. Hepatocyte-specific restoration of G6pc reverses defective HGP in Mettl14 knockout mice. These results unveil a METTL14/G6pc mRNA m6A/G6pc biosynthesis/HGP axis governing glucose metabolism in health and metabolic disease.PMID:40278833 | DOI:10.1002/advs.202417355

Toxins (Basel). 2025 Apr 3;17(4):178. doi: 10.3390/toxins17040178.ABSTRACTDeoxynivalenol (DON) is a mycotoxin ubiquitously present in the environment. Emerging evidence demonstrated that sulforaphane (SFN) exerts potent protective effects against DON-triggered cytotoxicity through multimodal mechanisms. This study aimed to investigate the protective mechanism of SFN during DON exposure. Untargeted metabolomics of IPEC-J2 cells revealed a total of 399 differential metabolites between the DON and control group and 365 differential metabolites between the SFN + DON and DON group. KEGG enrichment was performed to investigate the potential regulatory pathways. The transcriptome identified a total of 1839 differential expression genes (DEGs) between DON and SFN + DON groups. This result indicated that DON exposure and SFN treatment have a profound impact on cellular metabolism and genes. Integrated analysis of the transcriptome and metabolome showed that spermine was a potential biomarker for SFN treatment. SFN increased spermine abundance by regulating genes in glutathione, beta-alanine, and arginine and proline metabolism pathways. Functional experiments demonstrated that spermine alleviated DON-induced oxidative stress, as evidenced by increased cell viability, reduced ROS levels, restored mitochondrial membrane potential (ΔΨm), and normalized antioxidant enzyme activity. Moreover, spermine significantly decreased the cell apoptosis rate induced by DON, which suggested that spermine significantly alleviated the DON-induced cytotoxicity. Overall, these findings elucidated the protective role of SFN through spermine-related mechanisms against the toxicity of DON.PMID:40278676 | DOI:10.3390/toxins17040178

Toxins (Basel). 2025 Mar 21;17(4):150. doi: 10.3390/toxins17040150.ABSTRACTMembers of the benthic marine dinoflagellate genus Amphidinium produce a variety of bioactive compounds, exhibiting potent cytotoxicity in cell assays. Crude methanolic extracts from three genetically distinct cultured strains of A. eilatiense J.J. Lee were screened for cytotoxicity against three human breast and four lung cancer cell lines to evaluate potential applications in anticancer therapy. A standard tetrazolium cell viability assay demonstrated that the methanolic crude extract (100 µg mL-1) from strain AeSQ181 reduced cell viability by 20-35% in five cancer cell lines. Further bioassay-guided fractionation of these crude extracts yielded non-polar fractions (FNP-5 and FNP-6) with particularly high cytotoxic activity against lung (H1563) and breast (MDA-MB-231) adenocarcinoma cell lines. Untargeted metabolomic analysis of cytotoxic fractions by liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) revealed a much richer chemical diversity profile than previous toxigenicity studies on Amphidinium that exclusively focused on linear and cyclic polyethers and their macrolide analogs as putative cytotoxins. This untargeted metabolomic study showed substantial differences in chemical composition between the biologically active and non-active fractions. Preliminary biological and chemical characterization of these A. eilatiense fractions confirms that this species is a rich source of bioactive natural products with potential applications such as anticancer therapeutics.PMID:40278648 | DOI:10.3390/toxins17040150

Toxics. 2025 Apr 2;13(4):271. doi: 10.3390/toxics13040271.ABSTRACTO3 (ozone) is an environmental pollutant that can exacerbate inflammatory damage and contribute to respiratory diseases. However, the molecular mechanisms and potential targets for intervention in ozone-induced lung inflammatory injury are not yet known. To address this, our study exposed mice to 0.6 ppm and 1.0 ppm of O3 (3 h/d, 14 d), evaluating lung inflammation through histopathological examinations, lung function assessments, and analyses of white blood cells and inflammatory factors in BALF. Furthermore, we employed transcriptomic and non-targeted metabolomic approaches to decipher differentially expressed genes (DEGs) and metabolites in mouse lung tissue from the 1.0 ppm O3 exposure group. A comprehensive integration analysis of these omics data was conducted using Pearson correlation analysis. Finally, our findings show that ozone exposure indeed elicits pulmonary inflammation. Transcriptomic analysis identified 311 differentially expressed genes, predominantly implicated in circadian rhythm, IL-17 signaling pathway, and PPAR signaling. Meanwhile, metabolomic profiling revealed 41 differentially regulated metabolites, mainly associated with riboflavin metabolism, glutathione metabolism, and ABC transporter pathways. Integrated multi-omics analysis through Pearson correlation identified three key components (Pla2g10, O-phosphoethanolamine, and phosphorylcholine) showing significant enrichment in glycerophospholipid metabolism. Collectively, our findings suggest that glycerophospholipid metabolism may serve as potential therapeutic targets and diagnostic biomarkers for ozone-induced pulmonary inflammatory injury.PMID:40278587 | DOI:10.3390/toxics13040271

Toxics. 2025 Mar 21;13(4):232. doi: 10.3390/toxics13040232.ABSTRACT(1) Background: Silica nanoparticles (SiO2 NPs) have a high potential for human exposure and tend to accumulate in the liver. This study aimed to explore the size-dependent cytotoxicity induced by SiO2 NPs and identify key molecular pathways at the in vitro level through proteomics, metabolomics, and a combination of multiple omics methods. (2) Methods: The human hepatoma cells (HepG2) cells were exposed to SiO2 NPs of three different sizes (60, 250, and 400 nm) at doses of 0, 12.5, 25, 50, 100, and 200 μg/mL for 24 h. (3) Results: Exposure to 60 nm SiO2 NPs induced more reduction in cell viability than the other two larger-scale particles. Changes in the metabolomic and proteomic profiles of HepG2 cells induced by SiO2 NPs were also size-dependent. The main pathways that were significantly affected in the 60 nm SiO2 NPs treatment group represented cholesterol metabolism in proteomics and central carbon metabolism in metabolomics. Moreover, common enrichment pathways between differential proteins and metabolites included protein digestion and absorption and vitamin digestion and absorption. (4) Conclusions: Exposure to SiO2 NPs could induce size-dependent cytotoxicity and changes in proteomics and metabolomics, probably mainly by interfering with energy metabolism pathways.PMID:40278548 | DOI:10.3390/toxics13040232

Metabolites. 2025 Apr 17;15(4):279. doi: 10.3390/metabo15040279.ABSTRACTBACKGROUND/OBJECTIVES: Metabolite risk score (MRS), which considers the collective effects of metabolites closely reflecting a phenotype, is a new approach for disease assessment, moving away from focusing solely on individual biomarkers. This study aimed to investigate a metabolite panel for dyslipidemia and verify the diagnostic efficacy of MRS on dyslipidemia.METHODS: Key metabolite identification and MRS establishment were conducted in the discovery set, and MRS validation was performed in the replication set, with 50 healthy individuals and 50 dyslipidemia patients in each set. The MRS was constructed using key metabolites, identified via UPLC-MS/MS analysis, employing a weighted approach based on linear regression analysis.RESULTS: N-acetylisoputreanine-γ-lactam and eicosapentaenoic acid were identified as key metabolites for dyslipidemia and were utilized for establishing the MRS. In addition to the MRS model, a conventional dyslipidemia diagnostic model based on lipid profiles, as well as a combined model (MRS + lipid profiles), were also established. In the discovery set, the MRS model diagnosed dyslipidemia with 85.4% accuracy. When combined with lipid profiles, accuracy improved to 91.8%. In the replication set, the MRS demonstrated diagnostic power with 76.1% accuracy, while the combined model achieved 86.0% accuracy for dyslipidemia assessment.CONCLUSIONS: The MRS alone indicated sufficient assessment power in a real-world setting, despite a slight reduction in assessment ability when validated in the replication set. At this stage, therefore, the MRS serves as an auxiliary tool for disease diagnosis. This first attempt to apply MRS for dyslipidemia may offer a foundational concept for MRS in this disease.PMID:40278408 | DOI:10.3390/metabo15040279

Metabolites. 2025 Apr 15;15(4):275. doi: 10.3390/metabo15040275.ABSTRACT(1) Background: The lack of reliable biomarkers remains a significant barrier to improving outcomes for patients with schizophrenia. While metabolomic analyses of blood, urine, and feces have been explored, results have been inconsistent. Compared to peripheral compartments, cerebrospinal fluid (CSF) more closely reflects the chemical composition of brain extracellular fluid. Given that brain dysregulation may be more pronounced during the first episode of psychosis (FEP), we hypothesized that metabolomic analysis of CSF from FEP patients could reveal disease-associated biomarkers. (2) Methods: We recruited 15 patients within 24 months of psychosis onset (DSM-4 criteria) and 14 control participants through the Johns Hopkins Schizophrenia Center. CSF samples were analyzed using both non-targeted and targeted liquid chromatography-mass spectrometry. (3) Results: The non-targeted analysis identified lower levels of N-acetylneuraminic acid and N-acetyl-L-aspartic acid in the FEP group, while levels of uric acid were elevated. The targeted analysis focused on indolic and phenolic molecules previously linked to neuropsychiatric disorders. Notably, L-phenylalanine and 4-hydroxycinnamic acid levels were lower in the FEP group, and this difference remained significant after adjusting for age and sex. However, none of the significant differences in analyte levels between the groups survived an adjustment for multiple comparisons. (4) Conclusions: Our intriguing but preliminary associations align with results from other investigational approaches and highlight potential CSF analytes that warrant further study in larger samples.PMID:40278404 | DOI:10.3390/metabo15040275

Metabolites. 2025 Apr 14;15(4):273. doi: 10.3390/metabo15040273.ABSTRACTBurnout syndrome, which significantly impacts both individual and societal quality of life, is primarily characterized by three key criteria: depersonalization, emotional exhaustion, and low personal accomplishment, all linked to work-related stress. Purpose: Comparative evaluation of urine metabolite patterns that may discriminate the burnout levels and the effects of night shifts on healthcare professionals. The Maslach Burnout Inventory survey was administered to 64 physicians and nurses working day and night shifts, with scores for each criterion recorded. Methods: Urine samples were collected, and metabolomic patterns were analyzed using UHPLC-QTOF-ESI+-MS technology. This analysis employed both untargeted and semi-targeted metabolomics, coupled with multivariate and ANOVA statistics, utilizing the online Metaboanalyst 6.0 platform. Partial Least Squares Discriminant Analysis (PLSDA) was performed, along with VIP values, Random Forest graphs, and heatmaps based on 79 identified metabolites. These were further complemented by biomarker analysis (AUC ranking) and pathway analysis of metabolic networks. Results: The findings highlighted the biochemical effects of night shifts and their correlation with burnout scores from each dimension. Conclusions: This study demonstrated the involvement of three major metabolic pathways in diagnosing burnout: lipid metabolism, particularly related to steroid hormones (cortisol, cortisone, and androsterone metabolites); energetic metabolism, involving long-chain acylated carnitines as transporters of free fatty acids, which play a role in burnout control; and a third pathway affecting catecholamine metabolism (neurotransmitters derived from tyrosine, such as dopamine, adrenaline, and noradrenaline), as well as tryptophan metabolism (serotonin and melatonin metabolites) and amino acid metabolism (including aspartate, arginine, and valine).PMID:40278402 | DOI:10.3390/metabo15040273

Metabolites. 2025 Apr 12;15(4):269. doi: 10.3390/metabo15040269.ABSTRACTBackground: The composition and metabolic activity of the gut microbiota play a crucial role in various health conditions, including the occurrence and development of chronic constipation. Recent metabolomic advances reveal that gut microbiota-derived metabolites-such as SCFAs, bile acids, neurotransmitters, and microbial gases-play critical roles in regulating intestinal function. Methods: We systematically analyzed the current literature on microbial metabolomics in chronic constipation. This review consolidates findings from high-throughput metabolomic techniques (GC-MS, LC-MS, NMR) comparing metabolic profiles of constipated patients with healthy individuals. It also examines diagnostic improvements and personalized treatments, including fecal microbiota transplantation and neuromodulation, guided by these metabolomic insights. Results: This review shows that reduced SCFA levels impair intestinal motility and promote inflammation. An altered bile acid metabolism-with decreased secondary bile acids like deoxycholic acid-disrupts receptor-mediated signaling, further affecting motility. Additionally, imbalances in amino acid metabolism and neurotransmitter production contribute to neuromuscular dysfunction, while variations in microbial gas production (e.g., methane vs. hydrogen) further modulate gut transit. Conclusions: Integrating metabolomics with gut microbiota research clarifies how specific microbial metabolites regulate gut function. These insights offer promising directions for precision diagnostics and targeted therapies to restore microbial balance and improve intestinal motility.PMID:40278398 | DOI:10.3390/metabo15040269

Metabolites. 2025 Apr 10;15(4):263. doi: 10.3390/metabo15040263.ABSTRACTBackground: Despite the well-established liver-protective efficacy of monoammonium glycyrrhizinate (MONO), diammonium glycyrrhizinate (DIAM), and magnesium isoglycyrrhizinate (MAGN), which has been translated into clinical practice, their clinical differentiation remains elusive owing to their structural similarities and overlapping therapeutic effects. Methods: The present study delves into the pharmacokinetics, cellular-level liver-protective potencies, and underlying mechanisms of action of these three compounds through a comprehensive analysis. Results: The findings reveal that both DIAM and MAGN exhibit superior bioavailability and hepatoprotective profiles compared to MONO. Notably, an investigation of the metabolic pathways mediating liver protection in normal human liver cells (LO2), utilizing an ultra-performance liquid chromatography-time of flight tandem mass spectrometry (UPLC-TOF-MS/MSe) platform, demonstrated that MAGN augments antioxidant components, thereby favoring its application in drug-induced liver injury (DILI). Conversely, DIAM appears to be a more suitable candidate for addressing non-alcoholic fatty liver disease (NAFLD) and viral hepatitis. Conclusion: This study contributes novel perspectives on the mechanisms of action and potential clinical utilities of DIAM and MAGN in liver disease prevention and management.PMID:40278392 | DOI:10.3390/metabo15040263

Metabolites. 2025 Apr 5;15(4):250. doi: 10.3390/metabo15040250.ABSTRACTBackground/Objectives: Millions of new diagnoses of breast cancer are made each year, with many cases having poor prognoses and limited treatment options, particularly for some subtypes such as triple-negative breast cancer. Resveratrol, a naturally occurring polyphenol, has demonstrated many anticancer properties in breast cancer studies. However, the mechanism of action of this compound remains elusive, although prior evidence suggests that this compound may work through altering cancer cell metabolism. Our objective for the current study was to perform untargeted metabolomics analysis on resveratrol-treated breast cancer cells to identify key metabolic targets of this compound. Methods: MCF-7 and MDA-MB-231 breast cancer cells were treated with varying doses of resveratrol and extracted for mass spectrometry-based untargeted metabolomics. Data preprocessing and filtering of metabolomics data from MCF-7 samples yielded 4751 peaks, with 312 peaks matched to an in-house standards library and 3459 peaks matched to public databases. Results: Pathway analysis in MetaboAnalyst identified significant (p < 0.05) metabolic pathways affected by resveratrol treatment, particularly those involving steroid, fatty acid, amino acid, and nucleotide metabolism. Evaluation of standard-matched peaks revealed acylcarnitines as a major target of resveratrol treatment, with long-chain acylcarnitines exhibiting a 2-5-fold increase in MCF-7 cells and a 5-13-fold increase in MDA-MB-231 cells when comparing the 100 µM treated cells to vehicle-treated cells (p < 0.05, VIP > 1). Notably, doses below 10 µM showed an opposite effect, possibly indicating a biphasic effect of resveratrol due to a switch from anti-oxidant to pro-oxidant effects as dose levels increase. Conclusions: These findings suggest that resveratrol induces mitochondrial metabolic reprogramming in breast cancer cells in a dose-dependent manner. The biphasic response indicates a potential optimal dosage for therapeutic effectiveness. Further research is warranted to explore the mechanisms underlying these metabolic alterations and their implications for precision nutrition strategies in cancer treatment.PMID:40278380 | DOI:10.3390/metabo15040250

Metabolites. 2025 Apr 5;15(4):251. doi: 10.3390/metabo15040251.ABSTRACTBackground: The World Health Organization has indicated that airport noise is strongly associated with cardiovascular disease, with vascular inflammation identified as the primary mechanism. Therefore, long-term exposure to airport noise is considered far more harmful than other types of noise. However, there remains a lack of research into the mechanisms underlying long-term exposure to airport noise and harm to the human body. Methods: A mouse model was established and exposed to airport noise at a maximum sound pressure level of 95 dB(A) and an equivalent continuous sound pressure level of 72 dB(A) for 12 h per day over a period of 100 days. Quantitative polymerase chain reaction (qPCR) was used to detect the mRNA expression levels of pro-inflammatory and anti-inflammatory factors. Enzyme-linked immunosorbent assay (ELISA) was used to detect LPS, LTA, TMA, and TMAO levels. Intestinal flora composition was analyzed by 16S rDNA sequencing, and targeted metabolomics was employed to determine the levels of serum short-chain fatty acids. Results: Long-term airport noise exposure significantly increased systolic blood pressure, diastolic blood pressure, and mean blood pressure (p < 0.05); significantly increased the mRNA expression levels of oxidative stress parameters (nuclear matrix protein 2, 3-nitrotyrosine, and monocyte chemoattractant protein-1) (p < 0.05); significantly increased pro-inflammatory factors (interleukin 6 and tumor necrosis factor alpha) (p < 0.05); significantly decreased the mRNA expression level of anti-inflammatory factor interleukin 10 (p < 0.05); and significantly increased the content of LPS and LTA (p < 0.05). The composition of the main flora in the intestinal tract was structurally disordered, and there were significant differences between the noise-exposed and control groups at the levels of the phylum, family, and genus of bacteria. β-diversity of the principal component analysis diagrams was clearly distinguished. Compared with those of the control group, TMA-producing bacteria and levels of TMA and TMAO were significantly reduced, and the serum ethanoic acid and propanoic acid levels of the noise-exposed group were significantly decreased (p < 0.05). Conclusions: Long-term airport noise exposure causes significant elevation of blood pressure and structural disruption in the composition of the intestinal flora in mice, leading to elevated levels of oxidative stress and inflammation, resulting in metabolic disorders that lead to significant changes in the production of metabolites.PMID:40278379 | DOI:10.3390/metabo15040251

Metabolites. 2025 Apr 4;15(4):249. doi: 10.3390/metabo15040249.ABSTRACTBackground: Quantification of metabolites from nuclear magnetic resonance (NMR) spectra in an accurate, high-throughput manner requires effective data processing tools. Neural networks are relatively underexplored in quantitative NMR metabolomics despite impressive speed and throughput compared to more conventional peak-fitting metabolomics software. Methods: This work investigates practices for dataset and model development in the task of metabolite quantification directly from simulated NMR spectra for three neural network models: the multi-layered perceptron, the convolutional neural network, and the transformer. Model architectures, training parameters, and training datasets are optimized before comparing each model on simulated 400-MHz 1H-NMR spectra of complex mixtures with 8, 44, or 86 metabolites to quantify in spectra ranging from simple to highly complex and overlapping peaks. The optimized models were further validated on spectra at 100- and 800-MHz. Results: The transformer was the most effective network for NMR metabolite quantification, especially as the number of metabolites per spectra increased or target concentrations were low or had a large dynamic range. Further, the transformer was able to accurately quantify metabolites in simulated spectra from 100-MHz up to 800-MHz. Conclusions: The methods developed in this work reveal that transformers have the potential to accurately perform fully automated metabolite quantification in real-time and, with further development with experimental data, could be the basis for automated quantitative NMR metabolomics software.PMID:40278378 | DOI:10.3390/metabo15040249

Metabolites. 2025 Apr 4;15(4):248. doi: 10.3390/metabo15040248.ABSTRACTBackground/Objectives: The gut microbiota plays a pivotal role in host physiology through metabolite production, with lipids serving as essential biomolecules for cellular structure, metabolism, and signaling. This review aims to elucidate the interactions between gut microbiota and lipid metabolism and their implications for enhancing swine production. Methods: We systematically analyzed current literature on microbial lipid metabolism, focusing on mechanistic studies on microbiota-lipid interactions, key regulatory pathways in microbial lipid metabolism, and multi-omics evidence (metagenomic/metabolomic) from swine models. Results: This review outlines the structural and functional roles of lipids in bacterial membranes and examines the influence of gut microbiota on the metabolism of key lipid classes, including cholesterol, bile acids, choline, sphingolipids, and fatty acids. Additionally, we explore the potential applications of microbial lipid metabolism in enhancing swine production performance. Conclusions: Our analysis establishes a scientific framework for microbiota-based strategies to optimize lipid metabolism. The findings highlight potential interventions to improve livestock productivity through targeted manipulation of gut microbial communities.PMID:40278377 | DOI:10.3390/metabo15040248

Metabolites. 2025 Apr 3;15(4):247. doi: 10.3390/metabo15040247.ABSTRACTBACKGROUND: Severe acute asthma (SAA) in children can be life-threatening. There has been a significant rise in paediatric intensive care unit (PICU) admissions due to SAA over the past two decades. While asthma is a heterogeneous disease, its underlying pathophysiological pathways remain underexplored. This study aimed to assess the value of non-invasive targeted exhaled breath metabolomics analysis to better characterise SAA.METHODS: Breath samples from 17 children admitted to the PICU with SAA (cases) and 27 children with controlled severe asthma (controls) were analysed using thermal desorption gas chromatography-mass spectrometry (TD-GC-MS).RESULTS: A targeted volatile organic compound (VOC) analysis identified 25 compounds, of which 16 were shared between groups. Four VOCs were significantly more often present in SAA, and nine VOCs exhibited higher concentrations in SAA. Longitudinal analysis of VOCs from follow-up samples of 10 cases showed no significant temporal differences, reinforcing the reproducibility of identified biomarkers.CONCLUSIONS: This study exemplifies the potential of exhaled breath analysis to provide insights into the molecular background of SAA. Breath metabolomics may enable early recognition of severe asthma attacks and preventive therapeutic interventions in children with severe asthma.PMID:40278376 | DOI:10.3390/metabo15040247

Metabolites. 2025 Apr 2;15(4):241. doi: 10.3390/metabo15040241.ABSTRACTBackground: The natural differences in running capacities among rats remain poorly understood, and the mechanisms driving these differences need further investigation. Methods: Twenty male Sprague-Dawley (SD) rats were selected. High and low running capacity rats were identified using Treadmill Exhaustion Tests. Peripheral blood was collected for serum isolation, followed by a metabolomics analysis using LC-MS/MS. Data were preprocessed, and a principal component analysis (PCA) and a partial least squares-discriminant analysis (PLS-DA) were applied to identify metabolic profile differences. Significant metabolites were screened, and a pathway enrichment analysis was conducted using the KEGG database to determine key metabolic pathways. Forty SD rats (equal male and female) were randomly divided into an inosine triphosphate (ITP) group (24.29 mg/kg.bw daily) and a control group. Running capacity was assessed after one week of continuous treatment. Results: Three independent measurements showed consistent differences in running capacity. A total of 519 differential metabolites were identified, with 255 up-regulated and 264 down-regulated. The KEGG pathway analysis revealed a significant enrichment of the Purine Metabolism pathway (ITP-ATP) in the high running capacity group (p < 0.05). The ITP-treated group exhibited a significantly higher running capacity than the controls (p < 0.05), confirming the efficacy of dietary ITP supplementation. Conclusions: The running capacity of rats is influenced by the ITP-ATP pathway, and exogenous ITP administration through dietary intervention significantly improves running ability.PMID:40278370 | DOI:10.3390/metabo15040241

Metabolites. 2025 Mar 28;15(4):232. doi: 10.3390/metabo15040232.ABSTRACTBACKGROUND: Best-corrected visual acuity (BCVA) is the current gold standard of retinal function measurement but is not affected in early and intermediate forms of age-related macular degeneration (AMD). Increasing evidence suggests that microperimetry is a sensitive measure of visual function. This study sought to analyze the associations between plasma and urine metabolites and microperimetry in AMD.METHODS: We included data on 363 eyes (95 controls, 268 AMD). Microperimetry was performed in patients with or without AMD using the Macular Integrity Assessment (MAIA) microperimetry system, employing a 37-point full-threshold protocol. Plasma and urine samples were analyzed via ultra-high-performance liquid chromatography-mass spectrometry. Multilevel mixed-effects linear models were used to assess associations between the metabolites and retinal sensitivity. Statistical significance was determined by considering the number of independent tests that accounted for 80% of the variance (ENT80).RESULTS: We identified two plasma and seven urine metabolites, which were significantly associated with mean retinal sensitivity in AMD, and the key results include metabolites in the lysine metabolism pathway.CONCLUSIONS: To our knowledge, we present the first assessment of the associations between plasma and urinary metabolites and retinal microperimetry sensitivity in AMD. This work can reveal more insight into the pathogenesis of AMD.PMID:40278361 | DOI:10.3390/metabo15040232

Metabolites. 2025 Mar 27;15(4):229. doi: 10.3390/metabo15040229.ABSTRACTBACKGROUND: Global warming poses significant challenges to aquaculture, as elevated water temperatures adversely affect fish health and survival. This study investigated the effects and potential mechanisms of dietary tea polyphenols (TPs) on acute heat stress and survival in hybrid crucian carp HCC2.METHODS: The fish in the control (CON) group and heat stress group (HS group, three replicates, each containing 20 fish, n = 60 per group) were fed diets with 0 mg/kg TPs, and the three experimental groups (HSLTP, HSMTP, and HSHTP, n = 20 × 3 replicates) were fed the diets with 100, 200, or 400 mg/kg TPs for 60 days. Further, fish in the experimental groups (HS, HSLTP, HSMTP, and HSHTP) were exposed at 38 °C for 24 h to induce acute heat stress. Survival data and serum and tissue samples were collected for the analysis. Metabolomics using UPLC-Q-TOF/MS was employed to evaluate the metabolite changes in the fish livers.RESULTS: Notably, dietary TPs significantly improved survival rates and antioxidant enzyme levels and reduced serum ALT, AST, cortisol, glucose, MDA, and liver HSP-70 levels in the heat-stressed fish. Metabolomic analysis revealed that TPs modulated lipid metabolism, particularly glycerophospholipid and arachidonic acid pathways, which may contribute to a higher tolerance to acute heat stress.CONCLUSIONS: These findings suggest that TPs are a promising, eco-friendly feed additive for protecting fish from heat stress and optimizing aquaculture practices.PMID:40278359 | DOI:10.3390/metabo15040229

Metabolites. 2025 Mar 27;15(4):228. doi: 10.3390/metabo15040228.ABSTRACTBackground/Objectives: Severe fever with thrombocytopenia syndrome (SFTS) is a viral infection primarily found in Asia, with a case fatality rate of about 10%. Despite its increasing prevalence, the underlying pathogenic mechanisms remain poorly understood, limiting the development of effective therapeutic interventions. Methods: We employed an untargeted metabolomics approach using liquid chromatography-mass spectrometry (LC-MS) to analyze serum samples from 78 SFTS patients during the acute phase of their illness. Differential metabolic features between survival and fatal cases were identified through multivariate statistical analysis. Furthermore, we constructed a metabolic prognostic model based on these biomarkers to predict disease severity. Results: Significant alterations were observed in four key metabolic pathways: sphingolipid metabolism, biosynthesis of phenylalanine, tyrosine, and tryptophan, primary bile acid biosynthesis, and phenylalanine metabolism. Elevated levels of phenyllactic acid and isocitric acid were strongly associated with adverse outcomes and demonstrated high discriminatory power in distinguishing fatal cases from survivors. The metabolic prognostic model incorporating these biomarkers achieved a sensitivity of 75% and a specificity of 90% in predicting disease severity. Conclusions: Our findings highlight the pivotal role of metabolic dysregulation in the pathogenesis of SFTS and suggest that targeting specific metabolic pathways could open new avenues for therapeutic development. The identification of prognostic biomarkers provides a valuable tool for early risk stratification and timely clinical intervention, potentially improving patient outcomes.PMID:40278357 | DOI:10.3390/metabo15040228

Metabolites. 2025 Mar 26;15(4):226. doi: 10.3390/metabo15040226.ABSTRACTBackground: The dark green coloration of bunching onion leaf blades is a key determinant of market value, nutritional quality, and visual appeal. This trait is regulated by a complex network of pigment interactions, which not only determine coloration but also serve as critical indicators of plant growth dynamics and stress responses. This study aimed to elucidate the mechanisms regulating the dark green trait and develop a predictive model for accurately assessing pigment composition. These advancements enable the efficient selection of dark green varieties and facilitate the establishment of optimal growth environments through plant growth monitoring. Methods: Seven varieties and lines of heat-tolerant bunching onions were analyzed, including two commercial F1 cultivars, along with two purebred varieties and three F1 hybrid lines bred in Yamaguchi Prefecture. The analysis was conducted on visible spectral reflectance data (400-700 nm at 20 nm intervals) and pigment compounds (chlorophyll a, chlorophyll b and pheophytin a, lutein, and β-carotene), whereas primary and secondary metabolites were assessed by using widely targeted metabolomics. In addition, a random forest regression model was constructed by using spectral reflectance data and pigment compound contents. Results: Principal component analysis based on spectral reflectance data and the comparative profiling of 186 metabolites revealed characteristic metabolite accumulation associated with each green color pattern. The "green" group showed greater accumulation of sugars, the "gray green" group was characterized by the accumulation of phenolic compounds, and the "dark green" group exhibited accumulation of cyanidins. These metabolites are suggested to accumulate in response to environmental stress, and these differences are likely to influence green coloration traits. Furthermore, among the regression models for estimating pigment compound contents, the one for chlorophyll a content achieved high accuracy, with an R2 value of 0.88 in the test dataset and 0.78 in Leave-One-Out Cross-Validation, demonstrating its potential for practical application in trait evaluation. However, since the regression model developed in this study is based on data obtained from greenhouse conditions, it is necessary to incorporate field trial results and reconstruct the model to enhance its adaptability. Conclusions: This study revealed that cyanidin is involved in the characteristics of dark green varieties. Additionally, it was demonstrated that chlorophyll a can be predicted using visible spectral reflectance. These findings suggest the potential for developing markers for the dark green trait, selecting high-pigment-accumulating varieties, and facilitating the simple real-time diagnosis of plant growth conditions and stress status, thereby enabling the establishment of optimal environmental conditions. Future studies will aim to elucidate the genetic factors regulating pigment accumulation, facilitating the breeding of dark green varieties with enhanced coloration traits for summer cultivation.PMID:40278355 | DOI:10.3390/metabo15040226