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PubMed2026

Interconnected influences of diet, gut microbiome, and metabolome on cognition across three metabolomics platforms.

Cognitive impairment is increasing with global aging, yet mechanisms linking diet, the gut microbiome, and metabolism to cognitive function remain unclear. To investigate a diet-microbiome-metabolome axis associated with cognition, we integrated fecal metagenomics, diet, and multi-platform plasma metabolomics in 505 older adults from four ADRCs. Several microbes broadly associated with circulating metabolites were also linked to multiple measures of cognitive performance. These taxa exhibited coordinated metabolic signatures, with cognition-positive microbes associated with antioxidant, lipid, and microbial-host co-metabolites, and microbes negatively associated with cognition were linked to inflammatory and aromatic amino acid-derived metabolites. Dietary patterns, particularly the Healthy Eating Index Greens and Beans component, were associated with microbial composition and metabolomic structure. Mediation analyses supported a diet-microbe-metabolite-cognition pathway, while metabolites remained associated with cognition after accounting for microbial features. These findings highlight the metabolome as a central integrator of diet, microbial activity, and cognitive function.

Journal Article↗

PubMed2025

Untargeted metabolomics reveals anion and organ-specific metabolic responses of salinity tolerance in willow.

Willows can alleviate soil salinisation while generating sustainable feedstock for biorefinery, yet the metabolomic adaptations underlying their tolerance remain poorly understood. Salix miyabeana was treated with two environmentally abundant salts, NaCl and Na2SO4, in a 12-week pot trial. Willows tolerated salts across all treatments (up to 9.1 dS m-1 soil ECe), maintaining biomass while selectively partitioning ions, confining Na+ to roots and accumulating Cl- andin the canopy and adapting to osmotic stress via reduced stomatal conductance. Untargeted metabolomics captured >5000 putative compounds, including 278 core willow metabolome compounds constitutively produced across organs. Across all treatments, salinity drove widespread metabolic reprogramming, altering 28% of the overall metabolome, with organ-tailored strategies. Comparing salt forms at equimolar sodium, shared differentially abundant metabolites were limited to 3% of the metabolome, representing the generalised salinity response, predominantly in roots. Anion-specific metabolomic responses were extensive. NaCl reduced carbohydrates and tricarboxylic acid cycle intermediates, suggesting potential carbon and energy resource pressure, and accumulated root structuring compounds, antioxidant flavonoids, and fatty acids. Na2SO4 salinity triggered accumulation of sulphur-containing larger peptides, suggesting excess sulphate incorporation leverages ion toxicity to produce specialised salt-tolerance-associated metabolites. This high-depth picture of the willow metabolome underscores the importance of capturing plant adaptations to salt stress at organ scale and considering ion-specific contributions to soil salinity.

Salix↗

PubMed2026

Longitudinal development of infant oral ecosystem: salivary metabolomic, bacteriome, and virome dynamics in early infancy.

This prospective cohort study investigated the longitudinal development of the salivary bacteriome, virome, and metabolome during early infancy. We assessed the associations between oral bacteria, viruses, and metabolites from 10 mother-infant dyads, with oral samples collected at 1 and 2 years of age. Forty saliva and plaque samples underwent untargeted metabolomic analysis, and infant saliva samples underwent metagenomic sequencing. Maternal salivary and plaque metabolomic profiles remained largely stable, whereas infant profiles were clearly separated from maternal profiles and changed with age. Notably, infant dental plaque metabolism underwent more substantial changes from year 1 to year 2 than saliva, with age-dependent metabolite shifts mainly involving energy, amino acid, nucleotide, and lipid metabolic pathways. Our findings also revealed significant developmental shifts in salivary bacteriome, virome, and functional pathway profiles during early childhood. The most abundant oral bacteria in early life, comprising over 75% of total abundance, included Veillonella, Streptococcus, Rothia, Prevotella, Neisseria, and Actinomyces species. While human viruses like Roseolovirus were detected, bacteriophages constituted the majority of the virome. Comparing infants at year 1 and year 2, we identified differentially abundant bacteria, viruses, metabolic functional pathways, and specific metabolites. We observed associations between bacteria and viruses, noting that these cross-kingdom relationships attenuated as infants grew. The study results underscore the complex and dynamic development of the oral microbiome, virome, and metabolome during early childhood.IMPORTANCEThe human oral cavity undergoes substantial microbial and metabolic development during early childhood, yet the temporal changes in the infant oral ecosystem remain incompletely understood. In this study, we longitudinally profiled the salivary metabolome, bacteriome, and virome of infants at 1 and 2 years of age. We demonstrated that the infant oral metabolome undergoes substantial developmental shifts, particularly in pathways related to energy, amino acid, and lipid metabolism; whereas maternal metabolic profiles remained stable over the same period. Furthermore, our results revealed the dynamic assembly of infant salivary virome and bacteriome and their associations with the functional pathways and metabolites. These findings provide new insights into the complex and dynamic development of the oral microbiome, virome, and metabolome in early infancy.

bacteriome↗

PubMed2026

Metabolomic ageing across mental and behavioural disorders.

BACKGROUND: Individuals with mental disorders face excess morbidity and premature mortality. Accelerated ageing has been proposed as a contributing mechanism but population-scale evidence across diverse diagnoses is limited. OBJECTIVE: To examine whether metabolomic ageing differs across mental disorders and whether associations vary by sex, age group and genetic liability. METHODS: Using plasma metabolomic profiles from UK Biobank participants, we applied a metabolomic ageing clock (MileAge) to estimate disorder-specific differences between metabolite-predicted and chronological age. Mental disorders were ascertained from health records and self-reported physician diagnoses. We analysed nine diagnostic groups and 45 individual disorders and assessed sex and age group differences and associations with polygenic scores. FINDINGS: Among 225 212 participants (54% female; mean age 56.97), 38 524 had a diagnosis preceding baseline. Substance use, psychotic, affective and neurotic disorders were associated with a metabolite-predicted age older than chronological age, largest for psychosis (β=0.556, 95% CI 0.250 to 0.861, p<0.001). Obsessive-compulsive and eating disorders were associated with a metabolite-predicted age younger than chronological age. Several associations were stronger in males and in individuals aged <65 years. Higher genetic liability to depression, autism and attention-deficit/hyperactivity disorder predicted an older metabolomic age (β range=0.020 to 0.047), whereas polygenic scores for psychosis and tobacco use disorder predicted a younger metabolomic age (β range=-0.023 to -0.040). For obsessive-compulsive disorder and anorexia nervosa, clinical and genetic associations indicated younger metabolomic ageing. CONCLUSIONS: Metabolomic ageing in mental disorders is heterogeneous. While many disorders are associated with an older biological age, some are linked to a younger biological age. Divergence between genetic liability and clinical phenotypes suggests that non-genetic factors shape biological ageing differences. CLINICAL IMPLICATIONS: Biological age should not be assumed to uniformly exceed chronological age across mental disorders. Sex and age-specific approaches could improve understanding of biological ageing processes in psychiatry.

Humans↗

PubMed2026

NMR metabolomics and glycomics for cancer detection in patients with non-specific symptoms: a prospective observational cohort study.

BACKGROUND: Early cancer diagnosis in patients with non-specific symptoms is limited by the lack of discriminatory tests. Within the Oxfordshire Suspected CANcer (SCAN) pathway, exploratory biomarker work showed that serum 1H NMR-based metabolomics can identify cancer with high accuracy. SCAN2 evaluated whether integrating metabolomics with glycomics provides complementary molecular information and improves discrimination in a clinically complex, real-world population. METHODS: Serum from 369 SCAN patients (59 cancers) was analysed using AXINON® System-derived NMR metabolomics and HPLC-MS glycomics. Machine-learning models were trained to predict cancer status, with performance assessed by receiver operating characteristic (ROC) analysis of pooled cross-validated predictions. To place cancer risk in a broader clinical context, a second classifier modelling alternative non-cancer diagnosis was incorporated, and mean predicted probabilities from both models were jointly projected into a two-dimensional space, maintaining strict separation of training and test data. FINDINGS: In the full cohort, integration of glycomics with metabolomics achieved an AUC of 0.814 (95% CI 0.808-0.820). In a refined sub-cohort excluding major comorbidities and selected cancer types (32 cancers, 277 non-cancers), performance improved to an AUC of 0.884 (95% CI 0.879-0.890). Discriminatory features included cancer-associated biantennary fucosylated glycans alongside amino acid metabolites (glutamate, histidine) and lipoprotein-related measures. A classifier distinguishing metastatic from non-metastatic disease (n = 29 vs. 30) achieved an AUC of 0.80. Joint probability analysis in the full cohort preserved cancer-associated signatures across comorbidity burden, with projection-based classification achieving an accuracy of 89.2% (95% CI 85.7-92.6). INTERPRETATION: These findings validate the SCAN1 metabolomic signature in a more clinically complex cohort and indicate that integrating glycomics with metabolomics provides complementary biological information for cancer discrimination. Joint probability analysis provides an interpretable framework for cancer risk stratification within multimorbid diagnostic pathways, supporting the clinical potential of scalable multi-omics blood testing. FUNDING: EPSRC, EU Horizon 2020, Wellcome/MLSTF, Novo Nordisk Foundation.

Humans↗

PubMed2026

Network-based integration of metabolomics data from large-scale repositories.

INTRODUCTION: Public metabolomics data repositories such as MetaboLights and Metabolomics Workbench host rapidly growing volumes of raw data, processed results, and metadata. As data deposition becomes a prerequisite for funding and publication, there is an increasing need for tools that enable integration and joint reanalysis of datasets across studies to maximise reuse and reproducibility. OBJECTIVES: This study aims to enable large-scale integrative meta-analysis of public metabolomics data, exploiting harmonised metabolite annotations to identify robust multi-study metabolite and pathway signatures and to provide global visual overviews of repository content. METHODS: We developed a network-based integration framework operating at both the study (dataset) level and the metabolite or pathway level. Metabolite-level meta-networks integrate studies with shared biological context using co-occurrences of differential metabolites represented as bipartite graphs. Study-level networks compare observed metabolites for overall repository exploration. Networks can be explored interactively using a dedicated Python Dash app available at https://github.com/EloisaRL/Metabolomic-data-analysis-app/tree/main . RESULTS: As an example, the approach was applied to six COVID-19 plasma datasets from MetaboLights generated using LC-MS and NMR. Ten metabolites were identified as differential in at least three studies, including consistently up-regulated pyroglutamic acid, in agreement with the literature. Pathway-level networks provided an overview of shared biological processes across studies. A global network of 1,181 studies in Metabolomics Workbench demonstrated clustering by assay coverage and associated metadata, as expected. CONCLUSION: Network-based integration of harmonised metabolomics data enables robust cross-study analyses and highlights the critical importance of standardised annotation pipelines. Such approaches enhance the reuse, reproducibility, and impact of public metabolomics datasets, accelerating biological discovery.

Metabolomics↗

PubMed2018

High-Throughput Metabolomics by 1D NMR.

Metabolomics deals with the whole ensemble of metabolites (the metabolome). As one of the -omic sciences, it relates to biology, physiology, pathology and medicine; but metabolites are chemical entities, small organic molecules or inorganic ions. Therefore, their proper identification and quantitation in complex biological matrices requires a solid chemical ground. With respect to for example, DNA, metabolites are much more prone to oxidation or enzymatic degradation: we can reconstruct large parts of a mammoth's genome from a small specimen, but we are unable to do the same with its metabolome, which was probably largely degraded a few hours after the animal's death. Thus, we need standard operating procedures, good chemical skills in sample preparation for storage and subsequent analysis, accurate analytical procedures, a broad knowledge of chemometrics and advanced statistical tools, and a good knowledge of at least one of the two metabolomic techniques, MS or NMR. All these skills are traditionally cultivated by chemists. Here we focus on metabolomics from the chemical standpoint and restrict ourselves to NMR. From the analytical point of view, NMR has pros and cons but does provide a peculiar holistic perspective that may speak for its future adoption as a population-wide health screening technique.

Animals↗

PubMed2026

Untargeted-targeted metabolomics: energy metabolism characteristics in heart failure staging and discovery of novel biomarkers.

BACKGROUND: Heart Failure represents the severe stage of various heart diseases. Its global morbidity and mortality are on the rise, making it a serious public health issue that imposes a heavy burden on patients' families and society. Currently, there are relatively few systematic studies on the changes in specific metabolites and pathways in different stages of heart failure, such as Stage A, Stage B and Stage C. AIMS: Using untargeted-targeted metabolomics to explore the metabolic characteristics of Heart Failure, and screen out serum metabolic markers with potential diagnostic and prognostic value. METHODS: This study is a cross-sectional study. A total of 210 heart failure patients from Xiyuan Hospital of China Academy of Chinese Medical Sciences were enrolled between October 2023 and October 2024. Among them, 60 patients were selected for targeted metabolomics analysis via stratified sampling. Serum samples of the patients were collected and pretreated with methanol, then metabolites were detected using untargeted and targeted LC-MS respectively. After the raw data were processed with MSDIAL, pattern recognition was performed using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). Differential metabolites with variable importance in projection (VIP) > 1 and P < 0.05 were screened, and relevant pathways were analyzed via enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. RESULTS: Untargeted metabolomics revealed that, compared with patients in Stages A and B, those with heart failure in Stage C had decreased serum levels of alanine, creatine, and branched-chain amino acids, along with increased levels of citric acid, fumaric acid, and malic acid. The differential metabolites were primarily enriched in pathways including the citric acid cycle, central carbon metabolism, and amino acid metabolism, indicating that energy metabolism plays a crucial role in the occurrence and progression of HF. Targeted metabolomics validated the findings from untargeted metabolomics: compared with Stage A, the level of phosphoenolpyruvate in Stage B was reduced; and in comparison with patients in Stage A or B, patients in Stage C showed decreased serum levels of multiple energy metabolites (e.g., glucose-6-phosphate, fructose-6-phosphate, 3-phosphoglyceric acid, AMP, ADP and ATP) as well as increased levels of malic acid, which is consistent with the characteristics of the "hypermetabolism-energy starvation" paradox. CONCLUSION: Stage C of heart failure is characterized by energy metabolism collapse (decreased ATP and TCA compensation), and differential metabolites (such as malic acid) may serve as potential candidate biomarkers pending longitudinal validation.

Humans↗

PubMed2026

Metabolomics in breast cancer: insights into treatment responses, disease progression, and prognostic assessment.

BACKGROUND: Alterations in metabolic pathways are a hallmark of cancer and play a pivotal role in breast cancer development and progression. The inherent metabolic heterogeneity of breast cancer contributes to differences in therapeutic response and patients' prognosis. Clinical metabolomics has emerged as a promising approach for identifying metabolic biomarkers that reflect tumor biology, treatment-related changes after diagnosis, and patients' outcomes. AIMS OF REVIEW: This review summarizes the metabolomic profiles of breast cancer patients, using various biological materials and analytical methods, to assess their potential role as biomarkers for monitoring therapeutic response, adverse treatment effects, tracking disease progression, and predicting prognosis. KEY SCIENTIFIC CONCEPT OF REVIEW: Metabolomic shifts generate unique signatures with promising potential as biomarkers for evaluating treatment response, monitoring therapeutic adverse effects, disease progression, and predicting clinical outcomes in breast cancer patients. Biological matrices, such as serum, plasma, and tumor tissue, were commonly used in both untargeted and targeted metabolomics approaches. Liquid chromatography-mass spectrometry is the most commonly used analytical method in clinical metabolomics studies. Altered metabolites were identified and linked to metabolic pathways, particularly amino acids, glucose, and fatty acids metabolism. When integrated with genomic and transcriptomic data, these metabolic fingerprints offer a multidimensional perspective on disease trajectory, thereby enhancing patient stratification and informing personalized therapeutic strategies.

Humans↗

PubMed2026

Integrated salivary proteomic and metabolomic analyses reveal molecular characterization and novel biomarker panels of chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a respiratory disorder characterized by chronic inflammation, oxidative stress, and metabolic dysregulation. The lack of convenient and easily-accessible non-invasive diagnostic approaches remains a major clinical challenge. This study applied an integrated saliva-based proteomic and untargeted metabolomic strategy to identify potential biomarkers for COPD classification. Comprehensive multi-omics analyses identified 225 differentially abundant proteins and 60 differentially abundant metabolites between patients with COPD and healthy controls, including 24 biologically relevant endogenous metabolites. Functional enrichment analyses revealed pronounced dysregulation of mitochondrial energy metabolism, redox homeostasis, lipid remodeling, and inflammatory-related pathways in COPD. By integrating salivary proteomic and metabolomic biomarkers, a stepwise feature selection combined with LASSO logistic regression was used to construct diagnostic models, yielding an optimized biomarker panel consisting of 11 proteins and 2 endogenous metabolites. This integrated model achieved excellent diagnostic performance, with an area under the ROC curve of 0.96. Collectively, these findings demonstrate that integrated salivary proteomic and metabolomic profiling provides a robust, non-invasive approach for COPD classification and offers a promising foundation for the development of biosensor-based diagnostic platforms and early disease detection. SIGNIFICANCE: Chronic obstructive pulmonary disease (COPD) remains a major global health burden. Current diagnostic approaches rely largely on spirometry and clinical assessment, which are limited in sensitivity for early-stage disease and unsuitable for large-scale screening. This study employs an integrated saliva-based proteomic and metabolomic strategy to identify non-invasive biomarkers for COPD classification. Our findings reveal coordinated dysregulation of mitochondrial energy metabolism, redox homeostasis, and lipid remodeling in COPD, highlighting the interconnected roles of metabolic reprogramming, oxidative stress, and inflammation in disease pathophysiology. Notably, a robust diagnostic panel comprising 11 proteins and 2 endogenous metabolites was established, achieving excellent classification performance (AUC of 0.96). To our knowledge, the integrated application of salivary proteomics and metabolomics for COPD diagnosis remains largely unexplored, underscoring the significance and translational potential of our findings.

Humans↗

PubMed2026

Integration of metabolomics and proteomics reveals the toxicological mechanisms of environmentally relevant concentrations of cadmium on juvenile rockfish (Sebastes schlegelii).

As a highly toxic heavy metal, cadmium (Cd) is widely distributed in the coastal environments of the Bohai Sea, posing significant ecological and health risks. This is of particular concern for Sebastes schlegelii, a rockfish species commonly found along the Bohai coast and consumed by local populations. In this study, juvenile S. schlegelii were randomly assigned to three groups (control, 5 and 50 μg/L Cd) for a 14-day exposure period, followed by analysis of Cd bioaccumulation, as well as metabolomic and proteomic profiling. ICP-MS analysis indicated dose-dependent Cd bioaccumulation in the whole body, with 0.11 ± 0.07 μg/g dry weight in the 5 μg/L group and 0.38 ± 0.09 μg/g dry weight in the 50 μg/L group (9.5-fold higher than the control, p < 0.05). An iTRAQ-based proteomic analysis determined 168 differentially expressed proteins, while 1H NMR-based metabolomic profiling identified 34 metabolites with significant alterations. Integrated analysis of the proteomic and metabolomic data provided insights into the molecular responses of juvenile rockfish to Cd exposure. Specifically, metabolomic results indicated significant alterations in key metabolites, including lactate, phosphocholine, adenosine triphosphate, alanine, and inosine in the Cd-treated groups. Proteomic analysis further suggested that Cd exposure was associated with immune and oxidative stress responses, neurotoxicity, cellular damage, and disruptions in critical metabolic pathways, such as glycolysis, the tricarboxylic acid cycle, amino acid and lipid metabolism. Overall, this study demonstrates the utility of integrating proteomics and metabolomics to characterize molecular responses to Cd stress in juvenile S. schlegelii.

Animals↗

PubMed2026

Integrated landscape of salivary metagenome and multi-biofluid metabolome characterizes a microbial-metabolic axis in upper gastrointestinal cancer progression.

BACKGROUND: Upper gastrointestinal cancer (UGIC) imposes a major global health burden, yet the stage-specific molecular changes along the microbial-metabolic axis remain limited understood. We aimed to delineate this molecular landscape across UGIC progression and evaluate its potential as non-invasive methods for precision screening. RESULTS: Derived from a multi-center population-based UGIC screening program, we enrolled 420 individuals, stratified into normal, low-grade intraepithelial neoplasia (LGIN), high-grade intraepithelial neoplasia (HGIN), and UGIC (n = 105 per group). Integrated salivary metagenomics and paired salivary/plasma metabolomics were performed to capture local and systemic dysregulation. We uncovered distinct stage-specific divergence during UGIC progression: profound remodeling of the salivary microbiota (104 differential species) and salivary metabolomics (80 differential metabolites) initiated early at the LGIN stage, whereas plasma metabolic dysregulation (40 differential metabolites) peaked significantly later at the HGIN stage. Integrative analysis revealed salivary microbiota related more closely with salivary metabolome than plasma metabolome. Moreover, statistical evidence suggested that dysbiotic salivary microbiota was associated with altered lysine- and tryptophan-related catabolic pathways converging on Acetyl-CoA-related metabolic nodes, supporting a potential metabolic mechanism in precancerous lesions. Finally, the discriminative model integrating metagenomic and metabolomic markers demonstrated promising diagnostic performance in distinguishing these precancerous lesions (LGIN: area under the curve [AUC] = 0.83; HGIN: AUC = 0.77) and UGIC (AUC = 0.76) from normal. CONCLUSION: This study characterizes a stage-specific microbial-metabolic axis that facilitates the comprehensive understanding of UGIC pathogenesis. These multi-biofluid signatures offer a promising non-invasive triage strategy for detecting precancerous lesions and optimizing endoscopic resource allocation. Video Abstract.

Female↗

PubMed2026

Plasma Exosome Metabolomics Reveal Stage-Specific Alterations in Elderly Women With Premetabolic and Metabolic Syndrome.

BACKGROUND: Metabolic syndrome (MetS) is a chronic disorder that poses a major threat to global health. Exosomes have emerged as promising biomarkers for diagnosing and monitoring chronic diseases. However, stage-specific alterations in the exosomal metabolome during MetS development remain poorly understood. This study aimed to characterize the plasma exosomal metabolome and explore candidate exosomal biomarkers in individuals with MetS. METHODS: This study included 20 patients with MetS, 23 individuals with pre-MetS, and 45 healthy controls. Plasma exosomes were isolated and analyzed using untargeted liquid chromatography-mass spectrometry-based metabolomics. Differential metabolites were defined by a dual-threshold, that is, p < 0.05 from t-test and variable importance in projection > 1 from partial least squares discriminant analysis, with fold change indicating their expression changes. Further, we employed machine learning algorithms to predict MetS status. RESULTS: We identified 27 differential metabolites between the pre-MetS and control groups, mainly enriched in histidine metabolism and the tricarboxylic acid cycle. Of these, 12 metabolites were upregulated, and 15 were downregulated, with 1-methylhistidine and isocitrate playing central regulatory roles. Comparison between the MetS and control groups revealed 45 differentially expressed metabolites, mainly enriched in thiamine metabolism, including 13 upregulated and 32 downregulated. In the pre-MetS group, cladribine showed the highest area under the curve (AUC) (0.743, p < 0.05), whereas 3-methylxanthine yielded the largest AUC (0.714, p < 0.05) in the MetS group. CONCLUSION: Our study characterized stage-dependent alterations in the plasma exosome-derived metabolome in MetS and suggests that exosomal metabolomics may provide complementary molecular information on early MetS metabolic perturbations.

exosomal features↗

PubMed2026

Metabolomic differences in the Ophiura sarsii complex from the Yellow Sea Cold Water Mass and Bering Sea Cold Pool.

Metabolomics provides a functional readout of cellular physiology and can reveal metabolite-level differences associated with environmental and evolutionary contexts. Here, we used GC-MS- and LC-MS-based metabolomics to characterize metabolic profiles of the Ophiura sarsii complex from the Yellow Sea Cold Water Mass (YSCWM) and the Bering Sea Cold Pool (BSCP). This metabolomics analysis identified 398 LC-MS/MS and 87 GC-MS/MS differential metabolites (DEMs). Marked metabolic differences were observed between the two taxa, involving antioxidant-related metabolites, central carbon-related intermediates, osmolyte-associated compounds, and membrane lipid components. O. sarsii vadicola from the YSCWM showed higher levels of glutathione, glucose, citric acid, D-ribulose 5-phosphate, and unsaturated lipid-related metabolites, indicating differences in antioxidant-related and energy-associated metabolic profiles. By contrast, O. sarsii from the BSCP was characterized by higher levels of sugar alcohols, particularly myo-inositol, together with differences in membrane lipid-associated metabolites. These results provide metabolomics-based evidence for metabolite-level physiological differences between two members of the O. sarsii complex sampled from the Yellow Sea Cold Water Mass and the Bering Sea Cold Pool, while the relative contributions of lineage divergence and site-specific environmental variation remain to be tested experimentally.

Metabolomics↗

PubMed2026

Tryptophan-driven metabolomic shift in Acidobacteriaceae reveals phytohormones and antifungal metabolites.

UNLABELLED: Acidobacteriota is one of the most abundant phyla in soils and has recently attracted attention for its potential role in promoting phytosanitary benefits. The metabolomic capabilities of this phylum remain poorly characterized, with few experimentally confirmed metabolites described. To address these gaps, we combined untargeted metabolomic profiling with comparative genomic analyses to explore the functional potential of newly isolated Acidobacteriaceae strains. Genome mining across the Acidobacteriota phylum revealed the presence and taxon-specific enrichment of genes associated with plant-related traits, including phytohormone biosynthesis. In parallel, metabolomic analyses of OSMAC-derived extracts uncovered pronounced condition-dependent metabolic variation. Tryptophan supplementation was associated with marked metabolomic reprogramming, including changes in indole-derived metabolites, such as indole-3-acetic acid. Subsequent analyses linked these metabolic shifts to the suppression of phytopathogenic fungi and enabled the identification of malassezindoles and pityriacitrins as active compounds, confirmed by structure elucidation using NMR spectroscopy. Overall, these findings shed light on the previously unexplored metabolic potential of the Acidobacteriota phylum, emphasizing its ecological importance for phytosanitary applications. IMPORTANCE: Despite their ubiquity and genomic diversity, the functional metabolism of members of the Acidobacteriota has largely remained uncharacterized. This study links genomic predictions to experimentally verified metabolomic outputs of Acidobacteriaceae, demonstrating tryptophan-responsive metabolic shifts translating to phytohormones and metabolites suppressing fungal growth. Our work underscores the emerging role of Acidobacteriota as important contributors to soil ecosystem functioning and plant-microbe interactions.

Acidobacteriota↗

PubMed2026

Proteomic and metabolomic profiling reveals dysregulation of immune states, mucin-type glycosylation and steroid metabolism in extramammary Paget's disease.

BACKGROUND: Extramammary Paget's disease is a rare cutaneous adenocarcinoma characterized by mucin-rich Paget cells and chronic inflammation, yet its molecular basis remains unclear. OBJECTIVE: To systematically characterize the proteomic and metabolomic landscape of EMPD, uncover immune heterogeneity, and identify molecular pathways underlying tumor progression and microenvironment remodeling. METHODS: We performed integrated proteomic and metabolomic analyses on 92 male tumor patients and 30 healthy controls, identifying 10,217 proteins and 1466 metabolites. RESULTS: Extramammary Paget's disease lesions exhibited broad activation of inflammatory pathways. Immune profiling further uncovered substantial inflammatory heterogeneity, delineating immune-cold and immune-hot subtypes, with the latter associated with stronger invasive potential. Aberrant mucin-type glycosylation was also prominent, featuring Tn-modified MUC1 and MUC5AC accompanied by elevated GALNT7, GALNT6, GALNT4, and ST6GAL1, which correlated with inflammatory intensity. Metabolomic data demonstrated elevated levels of testosterone, dehydroepiandrosterone, and related intermediates in tumor tissues, indicating an androgen-enriched metabolic profile in extramammary Paget's disease. CONCLUSION: These findings reveal immune, glycoproteomic, and metabolomic pathways in extramammary Paget's disease pathogenesis and provide novel insights for molecular classification and therapeutic targeting.

Humans↗

PubMed2025

Stepping out of the dark: how metabolomics shed light on fungal biology.

Metabolomics, a critical tool for analyzing small-molecule metabolites, integrates with genomics, transcriptomics, and proteomics to provide a systems-level understanding of fungal biology. By mapping metabolic networks, it elucidates regulatory mechanisms driving physiological and ecological adaptations. In fungal pathogenesis, metabolomics reveals host-pathogen dynamics, identifying virulence factors like gliotoxin in Aspergillus fumigatus and metabolic shifts, such as glyoxylate cycle upregulation in Candida albicans. Ecologically, it highlights fungal responses to abiotic stressors, including osmolyte production like trehalose, enhancing survival in extreme environments. These insights highlight metabolomics' role in decoding fungal persistence and niche colonization. In drug discovery, it aids target identification by profiling biosynthetic pathways, supporting novel antifungal and nanostructured therapy development. Combined with multi-omics, metabolomics advances insights into fungal pathogenesis, ecological interactions, and therapeutic innovation, offering translational potential for addressing antifungal resistance and improving treatment outcomes for fungal infections. Its progress shed light on complex fungal molecular profiles, advancing discovery and innovation in fungal biology.

Metabolomics↗

PubMed2025

Uncovering potential biomarkers and metabolic pathways in systemic lupus erythematosus and lupus nephritis through integrated microbiome and metabolome analysis.

OBJECTIVE: This study aims to explore the relationship between gut microbiota and fecal metabolomic profiles in patients with systemic lupus erythematosus (SLE), with and without lupus nephritis (LN), in order to identify potentially relevant biomarkers and better understand their association with disease progression. METHODS: Fecal samples from 15 healthy controls (HC) and 36 SLE patients (18 SLE-nonLN and 18 SLE-LN) were analyzed using 16S rRNA gene sequencing and untargeted metabolomics. Differential microbial taxa and metabolites were identified using Linear Discriminant Analysis Effect Size (LEfSe) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Receiver Operating Characteristic (ROC) curve analyses were used to assess the potential clinical relevance of selected metabolites. RESULTS: Beta diversity analysis demonstrated distinct microbial clustering between groups (p < 0.05). SLE-LN samples showed an increased relative abundance of Proteobacteria and decreased Firmicutes compared to SLE-nonLN. Metabolomic profiling identified multiple differentially abundant metabolites, with notable enrichment in primary bile acid biosynthesis pathways (e.g., Glycocholic acid, AUC = 0.951). In the SLE-nonLN group, increased Glycoursodeoxycholic acid levels (AUC = 0.922) were observed in pathways related to taurine and hypotaurine metabolism. Correlation analysis indicated a negative association between Escherichia-Shigella and bile acid levels (p < 0.01). CONCLUSION: This integrative analysis suggests that patients with SLE and LN harbor distinct gut microbiota and metabolomic profiles. The identified microbial taxa and metabolites may have potential as non-invasive biomarkers and could contribute to a better understanding of SLE pathogenesis and progression.

Humans↗