SEARCH · Search PubMed

Results for “Microbiome Data”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3Linked to original sources

PubMed2025

Establishing the ELIXIR Microbiome Community.

Microbiome research has grown substantially over the past decade in terms of the range of biomes sampled, identified taxa, and the volume of data derived from the samples. In particular, experimental approaches such as metagenomics, metabarcoding, metatranscriptomics and metaproteomics have provided profound insights into the vast, hitherto unknown, microbial biodiversity. The ELIXIR Marine Metagenomics Community, initiated amongst researchers focusing on marine microbiomes, has concentrated on promoting standards around microbiome-derived sequence analysis, as well as understanding the gaps in methods and reference databases, and identifying solutions to the computational overheads of performing such analyses. Nevertheless, the methods used and the challenges faced are not confined to marine microbiome studies, but are broadly applicable to other biomes. Thus, expanding this Marine Metagenomics Community to a more inclusive ELIXIR Microbiome Community will enable it to encompass a broader range of biomes and link expertise across 'omics technologies. Furthermore, engaging with a large number of researchers will improve the efficiency and sustainability of bioinformatics infrastructure and resources for microbiome research (standards, data, tools, workflows, training), which will enable a deeper understanding of the function and taxonomic composition of the different microbial communities.

Computational Biology↗

PubMed2026

KG-Microbe: Building modular and scalable knowledge graphs for microbiome and microbial sciences.

BACKGROUND: The integration of many disparate forms of data is essential for understanding the microbial world and its interaction with the environment and human health. Doing so is particularly challenging in the context of microbe-host and microbe-microbe interactions that contribute to health or environmental outcomes. There are thousands of relevant microbial species, and millions of interactions among those microbes and with their environment or host. Integrated information (e.g., about host and microbial physiology, genetics, and metabolism) facilitates deeper understanding of complex mechanisms and helps interpret correlative results. RESULTS: The KG-Microbe construction framework is a novel approach to harmonizing bacterial and archaeal data in the form of a findable, accessible, interoperable, reusable and AI-ready knowledge graph (KG). Starting from a core KG with organismal traits, environments, and growth preferences and the integration of established ontologies, the framework generates a hierarchy of related KGs targeting specific use cases, including the human microbiome in the context of disease, or environmental microbiomes. The framework supports customizable taxa subsets representing communities or clades of interest. Evaluations of the KG-Microbe KGs through a series of competency questions demonstrate the accuracy and effectiveness of the data harmonization, and the utility of the resulting KGs in studies of inflammatory bowel disease and Parkinson's disease. Finally, the predictive and environmental capabilities of the KGs are demonstrated by predicting growth preferences using graph features. CONCLUSIONS: The KG-Microbe framework unifies microbial contexts in a single resource to support integrative analyses across biomedical, host, and environmental domains. KG-Microbe is a flexible, modular enabling technology for humans and machine learning methods to uncover candidate mechanistic explanations of microbial associations.

Microbiota↗

PubMed2026

Gut microbiome in advanced non-small cell lung cancer: effect of chemotherapy and impact on efficacy.

BACKGROUND: While evidence linking the gut microbiome (GM) to cancer immunotherapy is growing, data regarding its role in chemotherapy remains limited. This study aims to investigate the effect of chemotherapy on GM composition and its potential as a predictive biomarker for treatment outcomes in advanced non-small cell lung cancer (NSCLC). METHODS: Advanced NSCLC patients treated with chemotherapy at Ramathibodi Hospital were prospectively enrolled. Clinical data and stool samples were collected at three time points: baseline, post-evaluation, and at progression of disease (PD). Fecal bacterial DNA was extracted, followed by PacBio Sequel II sequencing and comprehensive bioinformatic analysis. Clinical data were summarized using descriptive statistics. Progression-free survival (PFS) and overall survival (OS) were estimated by the Kaplan-Meier method, and predictive factors were identified using Cox-regression analysis. RESULTS: This study analyzed 54 stool samples from 27 NSCLC patients treated with platinum-doublet chemotherapy. The median PFS and OS were 5.3 months [95% confidence interval (CI): 2.4-8.4] and 13.8 months (95% CI: 5.2-not reached), respectively. Post-chemotherapy changes (n=20 paired samples) showed a significant decrease in microbial richness, as evidenced by reduced abundance-based coverage estimator (ACE) (P=0.02) and Chao1 (P=0.03) alpha diversity indices. Taxonomically, the relative abundance of Enterobacter was significantly decreased post-chemotherapy (P=0.03). Regarding treatment response (n=26 evaluable patients; 13 PD, 13 clinical benefit), baseline alpha diversity was not predictive of outcome. However, the relative abundance of Akkermansia was notably higher in the clinical benefit group, approaching statistical significance (P=0.07). CONCLUSIONS: Chemotherapy significantly reduced GM by decreasing species richness (as measured by the ACE and Chao1 index), while species diversity (as measured by the Shannon and Simpson index) remained unchanged. Therefore, confirming the definitive role of the GM as a predictive biomarker in chemotherapy-treated NSCLC patients necessitates further investigation in a larger, more robustly powered cohort.

Gut microbiome (GM)↗

PubMed2024

Exclusive enteral nutrition initiates individual protective microbiome changes to induce remission in pediatric Crohn's disease.

Exclusive enteral nutrition (EEN) is a first-line therapy for pediatric Crohn's disease (CD), but protective mechanisms remain unknown. We established a prospective pediatric cohort to characterize the function of fecal microbiota and metabolite changes of treatment-naive CD patients in response to EEN (German Clinical Trials DRKS00013306). Integrated multi-omics analysis identified network clusters from individually variable microbiome profiles, with Lachnospiraceae and medium-chain fatty acids as protective features. Bioorthogonal non-canonical amino acid tagging selectively identified bacterial species in response to medium-chain fatty acids. Metagenomic analysis identified high strain-level dynamics in response to EEN. Functional changes in diet-exposed fecal microbiota were further validated using gut chemostat cultures and microbiota transfer into germ-free Il10-deficient mice. Dietary model conditions induced individual patient-specific strain signatures to prevent or cause inflammatory bowel disease (IBD)-like inflammation in gnotobiotic mice. Hence, we provide evidence that EEN therapy operates through explicit functional changes of temporally and individually variable microbiome profiles.

Crohn Disease↗

PubMed2026

Bacteria and phage consortia modulate cecal SCFA production and host metabolism to enhance feed efficiency in ducks.

BACKGROUND: The gut microbiota influences poultry health, nutrition, feed efficiency (FE), and overall productivity. However, the relationship between gut microbes, including bacteria and phages, and FE in ducks remains underexplored. To address this, we integrated cecal 16S amplicon, metagenome, microbiota-derived short-chain fatty acids (SCFAs) profiling, liver transcriptome, and serum metabolome data to illustrate the contribution of the gut microbiome (bacteria and viruses) to duck FE. RESULTS: We reconstructed viral genomes and prokaryotic metagenome-assembled genomes (MAGs) and annotated their genes using comprehensive databases. Prokaryotic hosts of viruses were also predicted to understand virus-host dynamics within the gut ecosystem. Our results revealed that high-FE ducks have higher concentration of propionate and butyrate in cecum compared with low-FE ducks. The metagenome sequencing revealed distinct cecal microbiota profiles between two groups, with increased relative abundance of representative SCFA producers, especially Paraprevotella sp905215575 and Bacteroides sp944322345, and enhanced SCFA-biosynthesis pathways in high-FE ducks. Virome genome assembly identified two phages encoding auxiliary metabolic genes (AMGs) involved in pyruvate metabolism, enhancing nutrient availability for host bacteria to produce SCFAs (e.g., temperate phage-encoded pyruvate phosphate dikinase) or exploiting host central metabolic pathways for viral replication (e.g., lytic phage-encoded formate C-acetyltransferase). Furthermore, these representative SCFA-producing bacteria and phage consortia were associated with serum metabolites (including L-histidine and 4-hydroxydecanedioylcarnitine) linked to duck FE. CONCLUSION: Collectively, these findings provide novel insights into the gut microbial factors regulating FE in ducks, offering potential strategies to optimize poultry nutrition and productivity. Video Abstract.

Animals↗

PubMed2026

Integrative machine learning models to unravel gut microbial dysbiosis and functional disruption in polycystic ovary syndrome.

OBJECTIVE: To study gut microbial diversity and metabolic pathway disruptions in women with PolyCystic Ovary Syndrome (PCOS) compared with healthy controls, and to evaluate the diagnostic potential of microbiome-driven machine learning models. DESIGN: Case-controlled metagenomic data analysis SUBJECTS: Gut metagenomic data from women diagnosed with PCOS and age-matched healthy female controls EXPOSURE: Presence of PCOS MAIN OUTCOME MEASURES: The primary outcome measures will include gut microbial alpha and beta diversity indices, microbial taxon abundance, functional pathway profiles, predicted metabolite levels, microbe-functional pathway-metabolite interaction networks, and the diagnostic accuracy of microbiome-based machine learning models. RESULTS: Alpha and beta diversity analyses revealed marked gut microbial dysbiosis in women with PCOS, despite comparable species richness to healthy controls. Differential abundance analysis identified 41 significantly altered microbial species, including enrichment of proinflammatory taxa, such as Bacteroides vulgatus and Ruminococcus gnavus, and depletion of beneficial commensals, including Roseburia hominis and Prevotella copri. These compositional shifts indicate a proinflammatory microbial community structure in PCOS. Functional profiling demonstrated the upregulation of pathways involved in nucleotide turnover, lipid and carbohydrate metabolism, and neurotransmitter synthesis, potentially contributing to metabolic and neuroendocrine disruption. Network analysis revealed fragmented and unstable microbial-metabolite associations in PCOS compared with cohesive networks in controls. Microbiome-based machine learning models achieved a diagnostic accuracy of 84.25% (area under the curve 0.93), underscoring their predictive potential. CONCLUSION: The gut microbiome in PCOS is characterized by a proinflammatory community structure and disrupted metabolic pathways. These findings demonstrate the diagnostic potential of microbiome-based models and underscore the gut microbiome as a promising target for therapeutic interventions in the management of PCOS.

Polycystic Ovary Syndrome↗

PubMed2026

Longitudinal Clinical, Physiological, and Molecular Profiling of Female Patients With Metastatic Cancer: Protocol and Feasibility of a Multicenter High-Definition Oncology Study.

PURPOSE: A substantial proportion of patients receiving genomically matched therapies do not achieve clinical benefit, underscoring the influence of nongenetic factors on cancer outcomes. High-Definition Oncology (HDO) proposes integrating longitudinal, multimodal patient data-spanning clinical, molecular, physiological, and behavioral domains-to enable truly individualized cancer care. This manuscript describes the HDO study design, framework, and feasibility results in women with metastatic cancer. METHODS: We initiated a prospective, multicenter observational study (HDO study; ClinicalTrials.gov identifier: NCT06590506) enrolling 300 female patients with newly diagnosed metastatic breast, lung, or colorectal cancer. Here, we report the study design, standardized workflows, prespecified feasibility criteria, and early internal pilot results. Eleven data modalities are collected longitudinally, including tumor and germline genomics, germline epigenomics, gut microbiome, blood and stool metabolomics and proteomics, exposome characterization, wearable-derived physiological monitoring, digital footprint assessment, medical imaging, and patient-reported outcomes. Standardized workflows govern clinical procedures, data acquisition, biospecimen processing, and quality control across all participating sites. RESULTS: Feasibility was evaluated in the first 30 participants (10% of planned accrual). Patients completed 100% of scheduled clinical visits, 97.4% of planned plasma collections, 80.7% of stool samples, and all tumor biopsies. Wearable devices captured activity, heart rate, sleep, and blood oxygen saturation data during 95.0%, 84.2%, 90.6%, and 70.7% of total patient-days, respectively. Biospecimens met predefined quality control metrics across all molecular modalities. Engagement with mobile applications for pain and emotion reporting exceeded 80%. CONCLUSION: The HDO study demonstrates the feasibility of comprehensive, longitudinal, multimodal data collection in women with metastatic cancer. This internal pilot establishes an integrated framework for future analyses aimed at characterizing disease trajectories, defining molecular and physiological determinants of outcomes, and developing patient-specific computational models.

Humans↗

PubMed2026

Resistome and microbiome-immune interactions in an Eastern European population with high antibiotic use.

The gut microbiome influences host health, affecting gastrointestinal, metabolic, immune, cardiovascular, and neurological functions. A balanced microbiome is associated with favorable health outcomes. However, excessive antibiotic use and dietary habits can disrupt this ecosystem, leading to dysbiosis and affecting body homeostasis. This first comprehensive metagenomic analysis of the gut microbiome in a healthy Romanian cohort, a population underrepresented in microbiome studies and characterized by high antibiotic consumption, addresses a gap in current microbiome research. We report an enrichment of Enterobacteriaceae although overall composition is more comparable to other European than non-European cohorts. Community configurations align with established enterotype patterns, and our analysis provides insight into their relationship with within-phylum diversity. The analysis of antimicrobial resistance provides insight into the prevalence of resistance genes within this reservoir. We specifically report the presence of cfr(E), a Clostridioides difficile gene, and tet(X5), a variant from the ubiquitous tet family, genes not previously reported in healthy European populations. Integration with data from the European Centre for Disease Prevention and Control links the overall prevalence of resistance genes in this reservoir to antibiotic classes with higher community consumption in this population, notably beta-lactams and quinolones, highlighting potential targets for antibiotic stewardship programs. Finally, we investigate the relationship between the microbial profile and the systemic immune responses, inferred from correlations with in vitro cytokine production. Notably, we identify potential immune-priming roles for Collinsella, Flavonifractor, and Bifidobacterium species.IMPORTANCEThis first comprehensive study of the healthy gut microbiome in a Romanian cohort addresses a gap in current microbiome research, dominated by data sets from a limited number of regions. It sets a baseline for the microbiome and resistome composition of this population, and, while definitions of "healthy" microbiomes, or baseline resistomes, remain lacking, such study helps contextualize future studies and support the monitoring of dynamics. The Enterobacteriaceae abundance suggests a microbiome composition potentially influenced by antimicrobial consumption, a relevant pattern in a region with a high burden of nosocomial infections. In addition, the prevalence of antimicrobial resistance genes and the concordance with commonly used antibiotics in the community reinforce the need to address antibiotic use in public health strategies. Although gut microbiome-immunity relationships remain incompletely understood, our findings support a role for microbiome composition in immune-related traits and provide a valuable resource for future studies.

Humans↗

PubMed2026

PREVENT 1, a nationwide Swedish infant cohort for longitudinal gut microbiome profiling and early-life health outcomes: cohort profile.

PURPOSE: PREVENT 1 is a nationwide, prospective Swedish infant cohort established to characterise gut microbiome development during the first 2 years of life and to relate microbial trajectories to feeding, infections, growth and everyday well-being. The study integrates repeated infant stool sampling with shotgun metagenomics analysis with aligned parental questionnaires, stool photographs and infant cry recordings collected at three approximately 3-month intervals for each infant. PARTICIPANTS: Families were recruited nationwide in Sweden from September 2023 through targeted digital channels. Eligible participants were term-born infants residing in Sweden and aged <1 year at enrolment. Baseline questionnaire data and stool samples were collected from 253 infants. Parents completed questionnaires covering socio-demographic characteristics and health, pregnancy and delivery, postnatal factors, infant environment, feeding and growth, infections and other health outcomes, gastrointestinal symptoms and everyday well-being. FINDINGS TO DATE: Retention was high, with 248 families completing at least one follow-up questionnaire at Phase 2 and 243 at Phase 3. For stool samples, 250 infants provided at least two samples and 241 provided all three. At enrolment, 42.3% of infants were older than 7 months, 73.9% had weight-for-length z-scores in the normal range and exclusive breastfeeding at 4 months was reported for 58.9%. FUTURE PLANS: Three-phase sample and questionnaire data collection was completed in December 2024. Future analyses will examine microbiome features, resistome profiles and functional pathways in relation to antibiotic exposure, feeding, growth and infant health outcomes. Subject to ethical approval and participant consent, follow-up may include further stool collection and Swedish register linkage. TRIAL REGISTRATION NUMBER: NCT06285630.

Female↗

PubMed2026

Gut Microbiome Composition Is Associated With Response to CD38 Antibody (Daratumumab) Treatment Among Relapsed Multiple Myeloma Patients.

INTRODUCTION: Growing data support interactions between host-gut microbes and treatment responses in multiple myeloma (MM), where a higher abundance of Eubacterium hallii in stool samples has been found among MM patients with negative minimal residual disease after induction therapy. Here, we evaluated changes in the gut microbiome associated with daratumumab (dara) based therapy in 40 MM patients, before and after therapy. PATIENTS AND METHODS: Patients with relapsed MM and prior autologous transplantation who had received 1 to 4 prior lines of therapy were eligible. Two stool samples were collected, one within 1 week prior to dara (predara) and one immediately after 4 doses of dara (postdara). Metagenomics sequencing was conducted. Microbiome taxonomic analyses were performed using MetaPhlAn4, and microbial functional pathway analyses were conducted using HUMAnN3.6. QIIME2 was used for compositional and statistical analyses. RESULTS: Of 40 participants enrolled, there were 5 nonresponders; 35 patients achieved partial response (PR) or better (responders). Among responders, 10 patients achieved complete remission (CR), and 25 patients achieved either very good partial response (VGPR) or PR. There were no statistically significant differences between overall pre and postdara gut microbiomes. Differential abundance analysis (ANCOM-BC) showed statistically significant (q ≤ 0.05) overgrowth of Alistipes finegoldii and Acidaminococcus intestini species in responders and Ruminococcus torques, Sellimonas intestinalis and Clostridium symbiosum in nonresponders. Compared to non-CR, CR samples showed enrichment of Faecalibacterium prausnitzii; non-CR samples were enriched in Segatella copri and Faecalimonas umbilicata. DISCUSSION/CONCLUSION: Our results suggest differences in species between clinical responders and nonresponders, but larger prospective studies are needed to confirm these results.

Clinical response↗

PubMed2026

Toxoplasma gondii infection disrupts secondary bile acid transformation in feline gut microbiota.

UNLABELLED: Bile acid (BA) transformation relies on gut microbiota and is vulnerable to Toxoplasma gondii infection, yet feline microbial BA-transforming capacity upon toxoplasmosis remains unclear. Here, we constructed a catalog of 2,474 nonredundant feline gut microbial genomes and integrated serum metabolomic data to verify BA transformation alterations. The results revealed that the feline gut microbiome harbored widespread genetic potential for BA transformation but lacked a complete 7α-dehydroxylation pathway due to the absence of the key gene baiE. The BA transformation-related genomes (2,045 in total) were predominantly from the phyla Bacillota_A and Actinomycetota, among which only 37 encoded baiB, all belonging to Bacillota_A. The distribution of BA transformation-related genes varied across intestinal regions: genes encoding 7α-HSDH were primarily enriched in the small intestine, whereas genes encoding 3α-HSDH, baiCD, and baiH were more abundant in the large intestine. Additionally, the abundance of genes encoding BSH and 3α-HSDH increased significantly in the small intestine on day 3 post-infection, accompanied by increases in the phylum Bacillota_C and genera such as Blautia_A, Enterococcus_E, and Ligilactobacillus. Serum metabolomics revealed a significant increase in cholesterol levels post-infection, supporting the impact of T. gondii infection on intestinal BA transformation. These findings illustrated that the feline gut microbiota played an important role in BA transformation and that T. gondii infection disrupted the microbial potential for secondary BA transformation. This study provided new insights into gut microbiota-associated metabolic perturbations during feline toxoplasmosis. IMPORTANCE: Bile acid (BA) transformation plays a critical role in host metabolism and immune regulation. Although studies on BA transformation are increasing, the capacity for BA transformation within the feline gut microbiota and the impact of Toxoplasma gondii infection on this capacity remain unclear. To bridge this gap, we constructed a catalog of 2,474 nonredundant feline gut microbial genomes and integrated serum metabolomic data to verify BA transformation alterations. Our findings revealed that the feline gut microbiome lacked a complete 7α-dehydroxylation pathway, and the specific functions involved in BA transformation may differ between the small and large intestines. Furthermore, integrated metagenomic and serum metabolomic analyses suggested that T. gondii infection disrupted BA transformation capacity in the small intestine. This study provided new insights into gut microbiota-associated metabolic perturbations during feline toxoplasmosis.

Toxoplasma gondii↗

PubMed2024

Predictions of rhizosphere microbiome dynamics with a genome-informed and trait-based energy budget model.

Soil microbiomes are highly diverse, and to improve their representation in biogeochemical models, microbial genome data can be leveraged to infer key functional traits. By integrating genome-inferred traits into a theory-based hierarchical framework, emergent behaviour arising from interactions of individual traits can be predicted. Here we combine theory-driven predictions of substrate uptake kinetics with a genome-informed trait-based dynamic energy budget model to predict emergent life-history traits and trade-offs in soil bacteria. When applied to a plant microbiome system, the model accurately predicted distinct substrate-acquisition strategies that aligned with observations, uncovering resource-dependent trade-offs between microbial growth rate and efficiency. For instance, inherently slower-growing microorganisms, favoured by organic acid exudation at later plant growth stages, exhibited enhanced carbon use efficiency (yield) without sacrificing growth rate (power). This insight has implications for retaining plant root-derived carbon in soils and highlights the power of data-driven, trait-based approaches for improving microbial representation in biogeochemical models.

Rhizosphere↗

PubMed2025

HoloFoodR: a statistical programming framework for holo-omics data integration workflows.

SUMMARY: Holo-omics is an emerging research area that integrates multi-omic datasets from the host organism and its microbiome to study their interactions. Recently, curated and openly accessible holo-omic databases have been developed. The HoloFood database, for instance, provides nearly 10 000 holo-omic profiles for salmon and chicken under controlled treatments. However, bridging the gap between holo-omic data resources and algorithmic frameworks remains a challenge. Combining the latest advances in statistical programming with curated holo-omic data sets can facilitate the design of open and reproducible research workflows in the emerging field of holo-omics. AVAILABILITY AND IMPLEMENTATION: HoloFoodR R/Bioconductor package and the source code are available under the open-source Artistic License 2.0 at the package homepage https://doi.org/10.18129/B9.bioc.HoloFoodR.

Software↗

PubMed2025

Dual-approach analysis of gut microbiome in patients with type 1 diabetes and diabetic kidney disease.

BACKGROUND: Type 1 diabetes (T1D) is a multifactorial autoimmune disease mediated by genetic, epigenetic, and environmental factors. Diabetic kidney disease (DKD) is a major complication of diabetes mellitus which affects 30-40% of T1D patients. Increasing evidence suggests the significant role of the microbiome in the progression of both T1D and DKD. MATERIALS AND METHODS: Here we recruited 76 T1D patients and 22 healthy controls and combined data from sigmoid colon biopsy samples analysed with V3-V4 region amplification of 16S rRNA gene and shotgun metagenomics data obtained from faecal samples. Additionally, we compared T1D patients with and without progression of DKD. RESULTS: We observed significant differences within both sample types at various taxonomic and functional levels. T1D patient microbiota detected using biopsy samples had a lower abundance of the Bacteroides genus when compared to healthy controls. Significantly, despite only a few taxonomic differences patients with and without DKD progression were vastly different at the functional pathway level within the faecal samples - we observed 2 and 61 enriched pathways in these groups. respectively, with several of these pathways linked to the mediation of renal function. CONCLUSION: Altogether, we present novel data about microbial signatures relevant to T1D and DKD progression, which partly supports previous data and also presents possible tissue type or population-specific elements. DKD progression is characterized with significant differences within the functional level of the gut microbiome.

Humans↗

PubMed2025

Plasma metabolites mediate the causal relationship between gut microbiota and erectile dysfunction: insights from Mendelian randomization study.

BACKGROUND: While the relationship between gut microbiota and erectile dysfunction (ED) has been reported, the specific pathways involved remain unclear. AIM: This study aims to investigate the causal relationship between gut microbiota and ED, and to identify the potential role of plasma metabolites as mediators. METHODS: Utilizing aggregated genome-wide association study (GWAS) data, a comprehensive two-sample Mendelian randomization (MR) analysis was performed involving 196 gut microbiota taxa, 1400 plasma metabolites and ED. Causal relationships between gut microbiota, plasma metabolites and ED were explored. In addition, mediation analysis was applied to identify the pathway from gut microbiota to ED mediated by plasma metabolites. OUTCOMES: This study reveals that plasma metabolites act as mediators regulating the influence of gut microbiota on ED. RESULTS: MR analysis identified causal relationships between six gut microbial taxa and ED, with Butyrivibrio increasing the risk of ED, while Alistipes, Prevotella 9, Dialister, Marvinbryantia, and LachnospiraceaeUCG010 exhibited protective effects. Additionally, 45 plasma metabolites demonstrated causal associations with ED. Finally, mediation analysis revealed four mediation relationships. Sensitivity analysis indicated no heterogeneity or pleiotropy in this study. CLINICAL IMPLICATIONS: Modulating gut microbiota or targeting specific metabolites may offer new therapeutic approaches for ED, highlighting the potential for microbiome-based interventions. STRENGTHS AND LIMITATIONS: The MR approach and large-scale GWAS data provide robust causal evidence, but the findings are limited by their focus on European populations and lack of experimental validation. Further studies are needed to confirm these mechanisms in diverse cohorts and functional models. CONCLUSION: This study establishes a causal link between gut microbiota, plasma metabolites, and ED, identifying specific microbial taxa and metabolites as key contributors to ED risk. The mediating role of plasma metabolites highlights potential therapeutic strategies, such as probiotics or dietary interventions targeting harmful metabolites.

Mendelian randomization↗

PubMed2026

Benchmarking of Reference-Based Tools for Strain-Level Resolution of Plant Microbiome.

Strain-level identification of each microbe is crucial for understanding its role in the host. Most of the existing tools have primarily been evaluated on human metagenomic datasets, whereas the plant microbiome exhibits greater diversity and complexity and thus poses a challenge in the strain-level resolution of individual microbes. In this study, we conducted a comprehensive benchmarking of available reference-based tools for strain-level resolution of the plant microbiome. We evaluated seven tools on various performance parameters, like computational requirements, F1-score and relative abundances using synthetic datasets comprising microbes known to have strong associations with plants as well as real plant microbiome datasets. Our results demonstrated a better performance of StrainScan on the synthetic data, achieving higher F1-score and more accurate relative abundance estimates as compared to other tools, but its performance declined gradually with increasing strain diversity. However, StrainGE and StrainScan exhibited competitive performance on real plant metagenome data. Overall, though StrainGE exhibited better performance, it was more computationally expensive. However, StrainScan performed better in detecting low-abundance strains. Our findings suggest the comparative suitability of the available tools for the strain-level analysis of plant metagenome data and highlight the need for the development of more efficient and accurate taxonomic classifiers capable of handling the complex plant metagenome data while maintaining computational efficiency.

Microbiota↗

PubMed2025

Improved detection of microbiome-disease associations via population structure-aware generalized linear mixed effects models (microSLAM).

Microbiome association studies typically link host disease or other traits to summary statistics measured in metagenomics data, such as diversity or taxonomic composition. But identifying disease-associated species based on their relative abundance does not provide insight into why these microbes act as disease markers, and it overlooks cases where disease risk is related to specific strains with unique biological functions. To bridge this knowledge gap, we developed microSLAM, a mixed-effects model and an R package that performs association tests that connect host traits to the presence/absence of genes within each microbiome species, while accounting for strain genetic relatedness across hosts. Traits can be quantitative or binary (such as case/control). MicroSLAM is fit in three steps for each species. The first step estimates population structure across hosts. Step two calculates the association between population structure and the trait, enabling detection of species for which a subset of related strains confer risk. To identify specific genes whose presence/absence across diverse strains is associated with the trait, step three models the trait as a function of gene occurrence plus random effects estimated from step two. Applying microSLAM to 710 gut metagenomes from inflammatory bowel disease (IBD) samples, we discovered 56 species whose population structure correlates with IBD, meaning that different lineages are found in cases versus controls. After controlling for population structure, 20 species had genes significantly associated with IBD. Twenty-one of these genes were more common in IBD patients, while 32 genes were enriched in healthy controls, including a seven-gene operon in Faecalibacterium prausnitzii that is involved in utilization of fructoselysine from the gut environment. The vast majority of species detected by microSLAM were not significantly associated with IBD using standard relative abundance tests. These findings highlight the importance of accounting for within-species genetic variation in microbiome studies.

Humans↗

PubMed2026

Genome-resolved analysis reveals disruption of gut microbial vitamin B and K2 biosynthesis during Toxoplasma gondii infection in mice.

UNLABELLED: Toxoplasma gondii infection remodels the gut microbiome, yet its impact on microbial vitamin biosynthetic potential and host redox metabolism remains unclear. Here, we integrated mouse gut metagenomes with publicly available metagenome-assembled genomes (MAGs) to construct a genome-resolved atlas of B-vitamin and vitamin K2 biosynthesis. From 45,697 MAGs, we curated 4,771 representative genomes, of which 2,682 met high-quality criteria (completeness ≥90%, contamination <5%). Functional annotation identified 229,717 vitamin-related genes corresponding to 177 Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologs across de novo pathways for eight B vitamins, thiamine (B1), riboflavin (B2), niacin (B3), pantothenate (B5), pyridoxine (B6), biotin (B7), folate (B9), cobalamin (B12), and vitamin K2. Among the high-quality genomes, 1,665 encoded complete de novo pathways for at least one vitamin, highlighting functional specialization and community-level complementarity. Transcripts per million-normalized metagenomic read counts revealed significant differences in KEGG ortholog abundances across six of the nine vitamin pathways. Reanalysis of metagenomic data from infected mice (acute, chronic, and control; n = 10 per group) revealed a stage-dependent reduction in α-diversity of vitamin biosynthesis pathways during acute infection, and a clear β-diversity separation from chronic and control groups. Core niacin biosynthesis genes (nadB, nadA, nadC) displayed phylum-specific redistribution, indicating selective remodeling of microbial NAD+ precursor production under infection-induced metabolic stress. These results suggest that T. gondii infection disrupts cooperative vitamin biosynthetic networks while specifically modulating niacin pathways linked to host NAD+ metabolism. IMPORTANCE: Gut microbes can synthesize essential vitamins, but how infection alters this function is poorly understood. By integrating mouse gut metagenomes with genome-resolved microbial data, we show that Toxoplasma gondii infection reshapes the vitamin biosynthetic potential of the gut microbiome in a stage-dependent manner. Acute infection reduces the diversity of vitamin biosynthesis pathways and shifts the taxonomic distribution of key niacin biosynthesis genes involved in microbial NAD+ precursor production. These findings identify vitamin metabolism, especially niacin-related pathways, as a sensitive functional axis of microbiome remodeling during infection. Our work links microbial taxonomic changes to functional metabolic consequences and suggests that microbiome-mediated regulation of NAD+-related metabolism may contribute to host redox adaptation during T. gondii infection.

B vitamins↗