Search Results (2,331)

Background/Objectives: The breast cancer susceptibility gene 1 (BRCA1) is a tumor suppressor gene whose mutations are associated with increased susceptibility to develop breast or ovarian cancer. BRCA1 mainly exerts its protective effects through DNA double-strand break repair. Although not itself a transcriptional factor, BRCA1, through its multiple protein interaction domains, exerts transcriptional coregulation. In addition, BRCA1 expression alters cellular metabolism including inhibition of de novo fatty acid synthesis, changes in cellular bioenergetics, and activation of antioxidant defenses. Some of these actions may contribute to its global oncosuppressive effects. However, the breadth of metabolic pathways reprogrammed by BRCA1 is not fully elucidated. Methods: Breast cancer cells expressing BRCA1 were investigated by multiplatform metabolomics, metabolism-related transcriptomics, and joint metabolomics/transcriptomics data processing techniques, namely two-way orthogonal partial least squares and pathway analysis. Results: Joint analyses revealed the most important metabolites, genes, and pathways of metabolic reprogramming in BRCA1-expressing breast cancer cells. The breadth of metabolic reprogramming included fatty acid synthesis, bioenergetics, HIF-1 signaling pathway, antioxidation, nucleic acid synthesis, and other pathways. Among them, rewiring of glycerophospholipid (including phosphatidylcholine, -serine and -inositol) metabolism and increased arginine metabolism have not been reported yet. Conclusions: Rewired glycerophospholipid and arginine metabolism were identified as components of BRCA1-induced metabolic reprogramming in breast cancer cells. The study helps to identify metabolites that are candidate biomarkers of the BRCA1 genotype and metabolic pathways that can be exploited in targeted therapies. Full article

Diabetes mellites (DM) is a chronic disease with increasing prevalence worldwide and multiple health implications. Among them, sarcopenia is a metabolic disorder characterized by loss of muscle mass and function. The two age-related diseases, DM and sarcopenia, share underlying pathophysiological pathways. This narrative literature review aims to provide an overview of the existing evidence on metabolomic studies evaluating DM associated with sarcopenia. Advancements in targeted and untargeted metabolomics techniques could provide better insight into the pathogenesis of sarcopenia in DM and describe their entangled and fluctuating interrelationship. Recent evidence showed that sarcopenia in DM induced significant changes in protein, lipid, carbohydrate, and in energy metabolisms in humans, animal models of DM, and cell cultures. Newer metabolites were reported, known metabolites were also found significantly modified, while few amino acids and lipids displayed a dual behavior. In addition, several therapeutic approaches proved to be promising interventions for slowing the progression of sarcopenia in DM, including physical activity, newer antihyperglycemic classes, D-pinitol, and genetic USP21 ablation, although none of them were yet validated for clinical use. Conversely, ceramides had a negative impact. Further research is needed to confirm the utility of these findings and to provide potential metabolomic biomarkers that might be relevant for the pathogenesis and treatment of sarcopenia in DM. Full article

Background/Objectives: COVID-19 is a systemic viral infection that may lead to serious complications including acute kidney injury that requires kidney replacement therapy. The primary aim of this study was to evaluate urinary SerpinA3 (uSerpinA3) excretion as a biomarker of kidney recovery at 90 days, and the mortality in patients with critical COVID-19 and AKI requiring kidney replacement therapy (KRT). Methods: The study included patients with critical COVID-19 on invasive mechanical ventilation (IMV) requiring KRT. Blood and urine samples were obtained when KRT was initiated (day zero), and thereafter on days 1, 3, 7, and 14 post-replacement. uSerpinA3, kidney injury molecule-1 (uKIM-1), and neutrophil gelatinase-associated lipocalin (uNGAL) were measured in urine, and interleukin-6 (IL-6), interleukin-10 (IL-10), and tumor necrosis factor alpha (TNF-α) in peripheral blood. In addition, metabolomics in sample days zero and 3, and in the survivors on sample day 90 was performed by employing gas chromatography coupled with mass spectrometry. Results: A total of 60 patients were recruited, of whom 29 (48%) survived hospitalization and recovered kidney function by day 90. In the survivors, 79% presented complete recovery (CRR) and the remaining (21%) recovered partially (PRR). In terms of uSerpinA3, levels on days 7 and 14 predicted CRR, with AUC values of 0.68 (p = 0.041) and 0.71 (p = 0.030), respectively, as well as mortality, with AUC values of 0.75 (p = 0.007) and 0.76 (p = 0.015), respectively. Among the other biomarkers, the excretion of uKIM-1 on day zero of KRT had a superior performance as a CRR predictor [(AUC, 0.71 (p = 0.017)], and as a mortality predictor [AUC, 0.68 (p = 0.028)]. In the metabolomics analysis, we identified four distinct profiles; the metabolite that maintained statistical significance in predicting mortality was p-cresol glucuronide. Conclusions: This study strongly suggests that uSerpinA3 and uKIM-1 can predict CRR and mortality in patients with critical COVID-19 and AKI requiring KRT. Metabolic analysis appears promising for identifying affected pathways and their clinical impact in this population. Full article

High semen quality is vital for reproductive success in the swine industry; however, seasonal fluctuations often compromise this quality. The molecular mechanism underlying these seasonal effects on semen quality remains largely unclear. This study employed untargeted metabolomic profiling of boar seminal plasma (SP) to identify metabolites and metabolic pathways associated with semen quality during the summer and winter months. Semen samples were collected from mature Duroc boars at a commercial boar stud and classified as Passed or Failed based on motility and morphology. SP from five samples per group was analyzed using ultra-high-performance liquid chromatography–mass spectrometry (UHPLC-MS). In total, 373 metabolites were detected in positive ion mode and 478 in negative ion mode. Several differentially expressed metabolites (DEMs) were identified, including ergothioneine, indole-3-methyl acetate, and avocadyne in the summer, as well as LysoPC, dopamine, and betaine in the winter. These metabolites are associated with key sperm functions, including energy metabolism, antioxidant defense, and capacitation. KEGG pathway analysis indicated enrichment in starch and sucrose metabolism, pyrimidine metabolism, and amino acid metabolism across the seasons. Overall, the results reveal that SP metabolomic profiles vary with the season, thereby influencing semen quality. The identified metabolites may serve as potential biomarkers for assessing semen quality and enhancing reproductive efficiency in swine production. Full article

Cancer is a systemic disease rather than a localized pathology and is characterized by widespread effects, including whole-body exhaustion and chronic inflammation. A thorough understanding of cancer pathophysiology requires a systemic approach that accounts for the complex interactions between cancer cells and host tissues. To explore these dynamics, we employed a comprehensive metabolomic analysis of plasma samples from patients with either esophageal or head and neck squamous cell carcinoma (SCC). Plasma samples from 149 patients were metabolically profiled and correlated with clinical data. Among the metabolites identified, lysophosphatidylcholine (LPC) emerged as the sole biomarker strongly correlated with prognosis. A significant reduction in plasma LPC levels was linked to poorer overall survival. Plasma LPC levels demonstrated minimal correlation with patient-specific factors, such as tumor size and general condition, but showed significant association with the response to immune checkpoint inhibitor therapy. Proteomic and cytokine analyses revealed that low plasma LPC levels reflected systemic chronic inflammation, characterized by high levels of inflammatory proteins, the cytokines interleukin-6 and tumor necrosis factor-α, and coagulation-related proteins. These findings indicate that plasma LPC levels may be used as reliable biomarkers for predicting prognosis and evaluating the efficacy of immunotherapy in patients with SCC. Full article

Background: Type 2 diabetes (T2D) is a global health epidemic with rising prevalence within Asian populations, particularly amongst individuals with high visceral adiposity and ectopic organ fat, the so-called Thin-Outside, Fat-Inside phenotype. Metabolomic and microbiome shifts may herald T2D onset, presenting potential biomarkers and mechanistic insight into metabolic dysregulation. However, multi-omics datasets across ethnicities remain limited. Methods: We performed cross-sectional multi-omics analyses on 171 adults (99 Asian Chinese, 72 European Caucasian) from the New Zealand-based TOFI_Asia cohort at 4-years follow-up. Paired plasma and faecal samples were analysed using untargeted metabolomic profiling (polar/lipid fractions) and shotgun metagenomic sequencing, respectively. Sparse multi-block partial least squares regression and discriminant analysis (DIABLO) unveiled signatures associated with ethnicity, glycaemic status, and sex. Results: Ethnicity-based DIABLO modelling achieved a balanced error rate of 0.22, correctly classifying 76.54% of test samples. Polar metabolites had the highest discriminatory power (AUC = 0.96), with trigonelline enriched in European Caucasians and carnitine in Asian Chinese. Lipid profiles highlighted ethnicity-specific signatures: Asian Chinese showed enrichment of polyunsaturated triglycerides (TG.16:0_18:2_22:6, TG.18:1_18:2_22:6) and ether-linked phospholipids, while European Caucasians exhibited higher levels of saturated species (TG.16:0_16:0_14:1, TG.15:0_15:0_17:1). The bacteria Bifidobacterium pseudocatenulatum, Erysipelatoclostridium ramosum, and Enterocloster bolteae characterised Asian Chinese participants, while Oscillibacter sp. and Clostridium innocuum characterised European Caucasians. Cross-omic correlations highlighted negative correlations of Phocaeicola vulgatus with amino acids (r = −0.84 to −0.76), while E. ramosum and C. innocuum positively correlated with long-chain triglycerides (r = 0.55–0.62). Conclusions: Ethnicity drove robust multi-omic differentiation, revealing distinctive metabolic and microbial profiles potentially underlying the differential T2D risk between Asian Chinese and European Caucasians. Full article

Background: Charcot–Marie–Tooth (CMT) is a group of inherited diseases impairing the peripheral nervous system. CMT originates from genetic variants that affect proteins fundamental for the myelination of peripheral nerves and survival. Moreover, environmental and humoral factors can impact disease development and evolution. Currently, no therapy is available. Metabolomics is an emerging field of biomedical research that enables the development of novel biomarkers for neurodegenerative diseases by targeting metabolic pathways or metabolites. This study aimed to evaluate the metabolomics profile of CMT disease by comparing patients with healthy individuals. Methods: A total of 22 CMT patients (CMT) were included in this study and were demographically matched with 26 healthy individuals (C). Serum samples were analyzed through Nuclear Magnetic Resonance spectroscopy, and multivariate and univariate statistical analyses were subsequently applied. Results: A supervised model showed a clear separation (R²X = 0.3; R²Y = 0.7; Q² = 0.4; p-value = 0.0004) between the two classes of subjects, and nine metabolites were found to be significantly different (2-hydroxybutyrate, 3-hydroxybutyrate, 3-methyl-2-oxovalerate, choline, citrate, glutamate, isoleucine, lysine, and methyl succinate). The combined ROC curve showed an AUC of 0.94 (CI: 0.9–1). Additional altered metabolic pathways were also identified within the disease context. Conclusion: This study represents a promising starting point, demonstrating the efficacy of metabolomics in evaluating CMT patients and identifying novel potential disease biomarkers. Full article

Cancer metabolic reprogramming plays a critical role in tumor progression and therapeutic resistance, underscoring the need for advanced analytical strategies. Metabolomics, leveraging mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, offers a comprehensive and functional readout of tumor biochemistry. By enabling both targeted metabolite quantification and untargeted profiling, metabolomics captures the dynamic metabolic alterations associated with cancer. The integration of metabolomics with machine learning (ML) approaches further enhances the interpretation of these complex, high-dimensional datasets, providing powerful insights into cancer biology from biomarker discovery to therapeutic targeting. This review systematically examines the transformative role of ML in cancer metabolomics. We discuss how various ML methodologies—including supervised algorithms (e.g., Support Vector Machine, Random Forest), unsupervised techniques (e.g., Principal Component Analysis, t-SNE), and deep learning frameworks—are advancing cancer research. Specifically, we highlight three major applications of ML–metabolomics integration: (1) cancer subtyping, exemplified by the use of Similarity Network Fusion (SNF) and LASSO regression to classify triple-negative breast cancer into subtypes with distinct survival outcomes; (2) biomarker discovery, where Random Forest and Partial Least Squares Discriminant Analysis (PLS-DA) models have achieved >90% accuracy in detecting breast and colorectal cancers through biofluid metabolomics; and (3) prognostic modeling, demonstrated by the identification of race-specific metabolic signatures in breast cancer and the prediction of clinical outcomes in lung and ovarian cancers. Beyond these areas, we explore applications across prostate, thyroid, and pancreatic cancers, where ML-driven metabolomics is contributing to earlier detection, improved risk stratification, and personalized treatment planning. We also address critical challenges, including issues of data quality (e.g., batch effects, missing values), model interpretability, and barriers to clinical translation. Emerging solutions, such as explainable artificial intelligence (XAI) approaches and standardized multi-omics integration pipelines, are discussed as pathways to overcome these hurdles. By synthesizing recent advances, this review illustrates how ML-enhanced metabolomics bridges the gap between fundamental cancer metabolism research and clinical application, offering new avenues for precision oncology through improved diagnosis, prognosis, and tailored therapeutic strategies. Full article

(1) Background: Epidemiological studies link ozone (O₃) exposure to diabetes risk, but mechanisms and early biomarkers remain unclear. (2) Methods: Female mice exposed to 0.5/1.0 ppm O₃ were assessed for glucose tolerance and HOMA (homeostasis model assessment) index. Genes related to impaired glucose tolerance and insulin resistance were screened through the Comparative Toxicogenomics Database (CTD), and verified using quantitative real-time PCR. In addition, liver histopathological observations and the determination of basic biochemical indicators were conducted, and targeted metabolomics analysis was performed on the liver to verify glycogen levels and gene expression. In vitro validation was conducted with HepG2 and Min6 cell lines. (3) Results: Fasting blood glucose and insulin resistance were elevated following O₃ exposure. Given that the liver plays a critical role in glucose metabolism, we further investigated hepatocyte apoptosis and alterations in glycogen metabolism, including reduced glycogen levels and genetic dysregulation. Metabolomics analysis revealed abnormalities in fructose metabolism and glycogen synthesis in the livers of the O₃-exposed group. In vitro studies demonstrated that oxidative stress enhances both liver cell apoptosis and insulin resistance in pancreatic islet β cells. (4) Conclusions: O₃ triggers prediabetes symptoms via hepatic metabolic dysfunction and hepatocyte apoptosis. The identified metabolites and genes offer potential as early biomarkers and therapeutic targets. Full article

Pancreatic cancer is frequently accompanied by cancer-associated cachexia, a debilitating metabolic syndrome marked by progressive skeletal muscle wasting and systemic metabolic dysfunction. This study presents a systems biology framework to simultaneously identify therapeutic targets for both pancreatic ductal adenocarcinoma (PDAC) and its associated cachexia (PDAC-CX), using cell-specific genome-scale metabolic models (GSMMs). The human metabolic network Recon3D was extended to include protein synthesis, degradation, and recycling pathways for key inflammatory and structural proteins. These enhancements enabled the reconstruction of cell-specific GSMMs for PDAC and PDAC-CX, and their respective healthy counterparts, based on transcriptomic datasets. Medium-independent metabolic biomarkers were identified through Parsimonious Metabolite Flow Variability Analysis and differential expression analysis across five nutritional conditions. A fuzzy multi-objective optimization framework was employed within the anticancer target discovery platform to evaluate cell viability and metabolic deviation as dual criteria for assessing therapeutic efficacy and potential side effects. While single-enzyme targets were found to be context-specific and medium-dependent, eight combinatorial targets demonstrated robust, medium-independent effects in both PDAC and PDAC-CX cells. These include the knockout of SLC29A2, SGMS1, CRLS1, and the RNF20–RNF40 complex, alongside upregulation of CERK and PIKFYVE. The proposed integrative strategy offers novel therapeutic avenues that address both tumor progression and cancer-associated cachexia, with improved specificity and reduced off-target effects, thereby contributing to translational oncology. Full article

Background/Objective: The hepatitis B virus (HBV) can cause chronic hepatitis B (CHB), which can rapidly progress into fatal liver cirrhosis (CHB-LC) and hepatocellular carcinoma (CHB-HCC). Methods: In this study, we investigated metabolites associated with distinct clinical stages of HBV infection for the identification of stage-specific serum metabolite biomarkers using ¹H-NMR-based metabolomics. Results: A total of 64 serum metabolites were identified, among which six core discriminatory metabolites, namely isoleucine, tryptophan, histamine (for CHB), and pyruvate, TMAO, lactate (for CHB-HCC), were consistently significant across univariate and multivariate statistical analyses, including ANOVA with FDR, OPLS-DA, and VIP scoring. These metabolites were closely linked to key metabolic pathways, such as propanoate metabolism, pyruvate metabolism, and the Warburg effect. Conclusions: The findings suggest that these six core metabolites serve as potential stage-specific biomarkers for CHB, CHB-LC, and CHB-HCC, respectively, and offer a foundation for the future development of metabolomics-based diagnostic and therapeutic strategies. Full article

Background: Myeloproliferative neoplasms (MPN), a group of chronic hematologic neoplasms, are driven by inflammatory mechanisms that influence disease initiation and progression. Emerging evidence highlights the gut microbiome and plasma metabolome as pivotal immunomodulators, yet their causal roles in MPN pathogenesis remain uncharacterized. Methods: We conducted a two-sample Mendelian randomization (MR) analysis to systematically evaluate causal relationships between 196 gut microbial taxa, 526 plasma metabolites, and MPN risk. Instrumental variables were derived from genome-wide association studies (GWASs) of microbial/metabolite traits. Validation utilized 16S rRNA sequencing data from NCBI Bioproject PRJNA376506. Mediation and multivariable MR analyses elucidated metabolite-mediated pathways linking microbial taxa to MPN. Results: Our MR analysis revealed that 7 intestinal taxa and 17 plasma metabolites are causally linked to MPN. External validation confirmed the three taxa’s differential abundance in MPN cohorts. Mediation analysis revealed two mediated relationships, of which succinylcarnitine mediated 14.5% of the effect, and lysine 27.9%, linking the Eubacterium xylanophilum group to MPN. Multivariate MR analysis showed that both succinylcarnitine (p = 0.004) and lysine (p = 0.040) had a significant causal effect on MPN. Conclusions: This study identifies novel gut microbiota–metabolite axes driving MPN pathogenesis through immunometabolic mechanisms. The validated biomarkers provide potential therapeutic targets for modulating inflammation in myeloproliferative disorders. Full article

Ecological restoration in the cold and high-altitude mining areas of the Qinghai–Tibet Plateau is faced with dual challenges of extreme environments and insufficient microbial adaptability. This study aimed to screen local microbial resources with both extreme environmental adaptability and plant-growth-promoting functions. Local fungi (DK; F18-3) and commercially available bacteria (B0) were used as materials to explore their regulatory mechanisms for plant growth, soil physicochemical factors, microbial communities, and metabolic profiles in the field. Compared to bacterial treatments, local fungi treatments exhibited stronger ecological restoration efficacy. In addition, the DK and F18-3 strains, respectively, increased shoot and root biomass by 23.43% and 195.58% and significantly enhanced soil nutrient content and enzyme activity. Microbiome analysis further implied that, compared with the CK, DK treatment could significantly improve the α-diversity of fungi in the rhizosphere soil (the Shannon index increased by 14.27%) and increased the amount of unique bacterial genera in the rhizosphere soil of plants, totaling fourteen genera. Meanwhile, this aggregated the most biomarkers and beneficial microorganisms and strengthened the interactions among beneficial microorganisms. After DK treatment, twenty of the positively accumulated differential metabolites (DMs) in the plant rhizosphere were highly positively associated with six plant traits such as shoot length and root length, as well as beneficial microorganisms (e.g., Apodus and Pseudogymnoascus), but two DMs were highly negatively related to plant pathogenic fungi (including Cistella and Alternaria). Specifically, DK mainly inhibited the growth of pathogenic fungi through regulating the accumulation of D-(+)-Malic acid and Gamma-Aminobutyric acid (Cistella and Alternaria decreased by 84.20% and 58.53%, respectively). In contrast, the F18-3 strain mainly exerted its antibacterial effect by enriching Acidovorax genus microorganisms. This study verified the core role of local fungi in the restoration of mining areas in the Qinghai–Tibet Plateau and provided a new direction for the development of microbial agents for ecological restoration in the Qinghai–Tibet Plateau. Full article

Adenosine receptors (ARs) are G protein-coupled receptors that are widely expressed across tissues, traditionally associated with cardiovascular, neurological, and immune regulation. Recent studies, however, have highlighted their non-canonical functions, revealing critical roles in metabolism, immunometabolism, and epigenetic regulation. AR subtypes, particularly A2A and A2B, modulate glucose and lipid metabolism, mitochondrial activity, and energy homeostasis. In immune cells, AR signaling influences metabolic reprogramming and polarization through key regulators such as mTOR, AMPK, and HIF-1α, contributing to immune tolerance or activation depending on the context. Additionally, ARs have been implicated in epigenetic modulation, affecting DNA methylation, histone acetylation, and non-coding RNA expression via metabolite-sensitive mechanisms. Therapeutically, AR-targeting agents are being explored for cancer and chronic inflammatory diseases. While clinical trials with A2A antagonists in oncology show encouraging results, challenges remain due to receptor redundancy, systemic effects, and the need for tissue-specific selectivity. Future strategies involve biased agonism, allosteric modulators, and combination therapies guided by biomarker-based patient stratification. Overall, ARs are emerging as integrative hubs connecting extracellular signals with cellular metabolic and epigenetic machinery. Understanding these non-canonical roles may unlock novel therapeutic opportunities across diverse disease landscapes. Full article

Cardiac surgery, particularly procedures involving cardiopulmonary bypass (CPB), is associated with a high risk of postoperative complications, including systemic inflammatory response syndrome (SIRS), postoperative atrial fibrillation (POAF), and infection. Growing evidence suggests that the gut–heart axis, through mechanisms involving intestinal barrier integrity and gut microbiota homeostasis, may influence these outcomes. This review summarizes the relationship between gut microbiota composition and the inflammatory response in patients undergoing cardiac surgery and the extent to which these alterations impact clinical outcomes. The reviewed studies consistently show that cardiac surgery induces notable alterations in microbial diversity and composition during the perioperative period. These changes, indicative of dysbiosis, are characterized by a reduction in health-associated bacteria such as Blautia, Faecalibacterium, and Bifidobacterium and an increase in opportunistic pathogens. Inflammatory biomarkers were frequently elevated postoperatively, even in patients without evident complications. Key microbial metabolites and biomarkers, including short-chain fatty acids (SCFAs), trimethylamine N-oxide (TMAO), and bile acids (BAs), were implicated in modulating inflammation and clinical outcomes. Additionally, vitamin D deficiency emerged as a contributing factor, correlating with increased systemic inflammation and a higher incidence of POAF. The findings suggest that gut microbiota composition prior to surgery may influence the severity of the postoperative inflammatory response and that perioperative modulation of the gut microbiota could represent a novel approach to improving surgical outcomes. However, the relationship between dysbiosis and acute illness in surgical patients is confounded by factors such as antibiotic use and other perioperative interventions. Large-scale, standardized clinical studies are needed to better define these interactions and guide future therapeutic strategies in cardiac surgery. Full article