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PubMed2026

Plasmacytoid dendritic cell-mediated L-glutamate catabolism links gut microbiota to male infertility.

Emerging evidence suggests that gut microbiota composition influences male reproductive health; however, the immunometabolic mechanisms underlying this association remain insufficiently characterized. We investigated whether specific immune cell-mediated metabolic pathways, particularly plasmacytoid dendritic cell (pDC)-driven L-glutamate catabolism via the hydroxyglutarate pathway, contribute to the causal link between gut microbiota and male infertility. We conducted a 2-sample, 2-step Mendelian randomization (MR) analysis using inverse-variance weighting as the primary estimator and Bayesian weighted MR for robustness. Exposure data comprised 412 gut microbial taxa/metabolic pathways and 731 immune cell phenotypes from large European-ancestry genome-wide association studies. Male infertility genome-wide association studies data (1429 cases; 128,710 controls) were obtained from FinnGen R10. Only exposure-mediator-outcome pairs meeting stringent pleiotropy, heterogeneity, and reverse-causality criteria were retained for mediation analysis. Nine microbial taxa/metabolic pathways and 18 immune traits exhibited putative causal associations with male infertility. The L-glutamate degradation V pathway via hydroxyglutarate was linked to reduced infertility risk (inverse-variance weighting odds ratio [OR] = 0.68; 95% confidence interval, 0.52-0.89; P = .005). Two-step MR suggested that forward scatter area on pDCs may mediate this association, although the mediation effect was imprecise (effect = 0.0277; 95% confidence interval, -0.0348 to 0.0903). This study provides suggestive genetic evidence that pDC-mediated glutamate catabolism may connect gut microbial metabolic activity to male infertility. These findings highlight immunometabolic pathways as testable targets for mechanistic validation and microbiota-directed interventions.

Male↗

PubMed2026

Early-Onset Colorectal Cancer: Clinical and Molecular Features with Emerging Insights from Comprehensive Genomic Profiling.

Early‑onset colorectal cancer (EOCRC), defined as colorectal cancer (CRC) diagnosed before 50 years of age, is increasing globally. Colorectal cancer is currently the third most commonly diagnosed cancer and the second leading cause of cancer-related death worldwide, with GLOBOCAN 2024 estimating approximately 2.04 million new cases and 917,895 deaths in 2024. Recent studies indicate a sustained rise in EOCRC incidence across multiple regions and birth cohorts, with the greatest increases observed among younger adults. Although hereditary cancer syndromes account for 20-25% of EOCRC cases, most occur in the absence of known genetic predispositions or established risk factors. Emerging evidence implicates the gut microbiome as a potential contributor to EOCRC, with distinct microbial signatures differentiating it from late‑onset colorectal cancer (LOCRC) diagnosed after 50 years of age. This review synthesizes current evidence on clinical, molecular, and diagnostic features distinguishing EOCRC from LOCRC, including differences in anatomical distribution, histopathology, genomic and epigenetic alterations, microbiome composition, and immune landscape, and discusses their implications for personalised screening and therapeutic strategies. We performed a retrospective secondary analysis of comprehensive genomic and immune profiling data from 1737 patients with colorectal cancer tested between June 2021 and June 2023. The analysis showed that tumours arising in patients with EOCRC had lower tumour mutational burden than tumours diagnosed as LOCRC, whereas other immune-related biomarkers, including tumour immunogenicity score, did not remain significantly different after correction for multiple testing. Despite these emerging biological differences, current screening strategies remain largely dependent on an age threshold of 50 years, and EOCRC is not addressed by age‑specific treatment approaches. We therefore review the translational potential of emerging biomarkers, including microbial signatures and liquid biopsy approaches, and propose a framework for integrating molecular profiling into clinical practice. Finally, we highlight the unmet need for coordinated efforts to improve screening in younger populations, address fertility preservation considerations, and ensure adequate psychosocial support for patients with EOCRC.

Early-onset colorectal cancer↗

PubMed2025

A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites.

Genome-scale modeling of microbiome metabolism enables the simulation of diet-host-microbiome-disease interactions. However, current genome-scale reconstruction resources are limited in scope by computational challenges. We developed an optimized and highly parallelized reconstruction and analysis pipeline to build a resource of 247,092 microbial genome-scale metabolic reconstructions, deemed APOLLO. APOLLO spans 19 phyla, contains >60% of uncharacterized strains, and accounts for strains from 34 countries, all age groups, and multiple body sites. Using machine learning, we predicted with high accuracy the taxonomic assignment of strains based on the computed metabolic features. We then built 14,451 metagenomic sample-specific microbiome community models to systematically interrogate their community-level metabolic capabilities. We show that sample-specific metabolic pathways accurately stratify microbiomes by body site, age, and disease state. APOLLO is freely available, enables the systematic interrogation of the metabolic capabilities of largely still uncultured and unclassified species, and provides unprecedented opportunities for systems-level modeling of personalized host-microbiome co-metabolism.

Humans↗

PubMed2026

Integrative multi-omics profiling of insomnia-related molecular features reveals microbiome, immune, and therapy-relevant heterogeneity in colorectal cancer.

Emerging evidence implicates insomnia as a potential risk factor in carcinogenesis, potentially involving systemic inflammation, circadian disruption, and microbiome alterations. However, the molecular associations linking insomnia-related features to colorectal cancer (CRC), particularly with respect to tumor biology, immune microenvironmental states, and therapy-relevant phenotypes, remain largely unexplored. Multi-omics integration of genomic, transcriptomic, and microbiome data from 3,026 CRC patients across seven independent cohorts, including a large, well-annotated Clinical Omics study of Colorectal Cancer in China (COCC) cohort, enabled insomnia-based molecular classification through unsupervised non-negative matrix factorization (NMF) clustering. The insomnia subtype (IS) was biologically characterized via pathway enrichment, immune deconvolution, microbial profiling, and single-cell transcriptomics. Furthermore, an insomnia score (ISscore) was developed and validated in multiple cohorts for risk stratification and assessment of treatment-response-related indicators in CRC. Unsupervised clustering revealed two distinct molecular subtypes (IS1/IS2), with IS2 demonstrating significantly poorer survival. IS2 exhibited marked activation of EMT/angiogenesis pathways versus cell cycle activation in IS1. The IS2 microenvironment showed increased immunosuppression-related infiltration and exhausted T cell signatures, together with intratumoral microbiome variation characterized by depletion of Ruminococcaceae UCG-002 and enrichment of Hungatella/Selenomonas. The ISscore system stratified survival risk and was associated with computational indicators of immunotherapy response. Single-cell analysis nominated PPIA-BSG as a potential cell-cell communication signal involving high-ISscore tumor cells, CXCL12+ endothelial cells, and CLEC9A+ dendritic cell subsets. This multi-omics characterization of insomnia-CRC interplay suggests that insomnia-related molecular features are associated with an immunologically distinct and microbiome-altered tumor ecosystem. The ISscore provides a reproducible framework for capturing insomnia-related molecular heterogeneity, supporting risk stratification and future evaluation of therapy-relevant phenotypes.IMPORTANCEChronic insomnia affects millions, but it is not typically considered a cancer risk factor. Our study, analyzing vast biological data from over 3,000 colorectal cancer patients, uncovers a potential link between a person's predisposition to insomnia and their risk of developing this disease. This suggests that the biological pathways related to sleep may play a role in cancer development. Understanding this connection opens up new avenues for identifying individuals at higher risk and developing novel prevention strategies for colorectal cancer.

colorectal cancer↗

PubMed2025

Systematic evaluation of metatranscriptomic differential gene expression in silico, in vitro, and in vivo enables elucidation of inter-species cross-feeding.

Metatranscriptomic (MTX) sequencing quantifies gene expression from the collective genomes of microbial communities (microbiomes), enabling assessment of functional activity rather than functional potential. While differential expression testing is instrumental to RNA-sequencing analysis, current metatranscriptomic approaches have been benchmarked only on simulated data and not under real operating conditions, resulting in a lack of standard practices. Here, we evaluate the performance of statistical differential expression methods on both simulated datasets and data collected from real bacterial 'mock communities' designed for this purpose. We assess the robustness of individual methods to organisms' low relative abundance, differential abundance, low prevalence, and transcription rate changes, showing that no existing methods perform adequately across all confounding conditions. We then apply the same approaches to metatranscriptomic datasets generated from gnotobiotic mice colonized with defined consortia of human bacterial strains and show that the method nominated by our mock community comparisons successfully inferred cross-feeding dynamics which were validated in vitro. We conclude that MTX method benchmarking on real, not simulated, datasets can and should optimize model implementation, enabling inference and validation of cross-feeding and other inter-species and host-microbe dynamics from in vivo studies.

Journal Article↗

PubMed2025

Metagenomic characterization of oral microbiome signatures to predict upper gastrointestinal and pancreaticobiliary cancers: a case-control study.

BACKGROUND: This study investigated the oral microbiome signatures associated with upper gastrointestinal (GI) and pancreaticobiliary cancers. METHODS: Saliva samples from cancer patients and age- and sex-matched healthy controls were analyzed using 16S rRNA-targeted sequencing, followed by comprehensive bioinformatics analysis. RESULTS: Significant dissimilarities in microbial composition were observed between cancer patients and controls across esophageal cancer (EC), gastric cancer (GC), biliary tract cancer (BC), and pancreatic cancer (PC) groups (R2 = 0.067, = 0.075, = 0.068, and = 0.044; p = 0.001, = 0.001, = 0.002, and = 0.004, respectively). Additionally, the oral microbiome composition significantly differed by the four cancer sites (p = 0.001 for EC vs. GC, EC vs. BC, EC vs. PC, GC vs. BC, and GC vs. PC; p = 0.013 for BC vs. PC). We built oral metagenomic classifiers to predict cancer and selected specific microbial taxa with diagnostic properties. For EC, the classifier differentiated cancer patients and controls with good accuracy (area under the curve [AUC] = 0.791) and included three genera: Akkermansia, Escherichia-Shigella, and Subdoligranulum. For GC, the classifier exhibited high discriminative power (AUC = 0.961); it included five genera (Escherichia-Shigella, Gemella, Holdemanella, Actinomyces, and Stomatobaculum) and three species (Eubacterium sp. oral clone EI074, Ruminococcus sp. Marseille-P328, and Leptotrichia wadei F0279). However, microbial taxa with diagnostic features for BC and PC were not identified. CONCLUSIONS: These findings suggested that the oral microbiome composition may serve as an indicator of tumorigenesis in upper GI and pancreaticobiliary cancers. The development of oral metagenomic classifiers for EC and GC demonstrates the potential value of microbial biomarkers in cancer screening.

Humans↗

PubMed2026

Cleanifier: contamination removal from microbial sequences using spaced seeds of a human pangenome index.

MOTIVATION: The first step when working with DNA data of human-derived microbiomes is to remove human contamination for two reasons. First, many countries have strict privacy and data protection guidelines for human sequence data, so microbiome data containing partly human data cannot be easily further processed or published. Second, human contamination may cause problems in downstream analysis, such as metagenomic binning or genome assembly. For large-scale metagenomics projects, fast and accurate removal of human contamination is therefore critical. RESULTS: We introduce Cleanifier, a fast and memory frugal alignment-free tool for detecting and removing human contamination based on gapped k-mers, or spaced seeds. Cleanifier uses a pangenome index of known human gapped k-mers, and the creation and use of alternative references is also possible. Reads are classified and filtered according to their gapped k-mer content. Cleanifier supports two filtering modes: one that queries all gapped k-mers and one that queries only a sample of them. A comparison of Cleanifier with other state-of-the-art tools shows that the sampling mode makes Cleanifier the fastest method with comparable accuracy. When using a probabilistic Cuckoo filter to store the complete k-mer set, Cleanifier has similar memory requirements to methods that use a sampled minimizer index. At the same time, Cleanifier is more flexible, because it can use different sampling methods on the same index. AVAILABILITY AND IMPLEMENTATION: Cleanifier is available via gitlab (https://gitlab.com/rahmannlab/cleanifier), PyPi (https://pypi.org/project/cleanifier/), and Bioconda (https://anaconda.org/bioconda/cleanifier). The pre-computed human pangenome index is available at Zenodo (https://doi.org/10.5281/zenodo.15639519).

Humans↗

PubMed2025

Metagenomic Analysis of the Tonsil Virome Highlights Its Diagnostic Potential for Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is a chronic autoimmune disease whose exact pathogenesis remains unclear, despite links to genetics, environmental factors, and microbial dysbiosis. Recent studies have highlighted the role of the microbiome in RA, yet the contribution of the tonsil virome remains unexplored. This study aims to investigate whether changes in the tonsil virome are associated with RA progression and assess its diagnostic potential. Using metagenomic data from 32 RA patients and 30 healthy controls (HCs), we identified 45 782 viral operational taxonomic units (vOTUs), with 14 341 classified as core vOTUs. RA patients exhibited significantly reduced virome richness and diversity, whereas Siphoviridae and Microviridae dominated both groups. Statistical analysis identified 235 RA-associated viral markers, including 13 enriched in RA and 222 in HCs. RA-enriched markers were primarily bacteriophages infecting Streptococcaceae, whereas HCs displayed more diverse viral-host interactions. Random forest models demonstrated strong discriminatory power of viral markers in distinguishing RA patients from HCs, achieving an AUC of 0.960, outperforming bacterial markers. Correlation analyses further linked viral markers to immune cell subsets, suggesting that tonsil virome alterations may influence immune dysregulation in RA. This study reveals significant changes in the tonsil virome of RA patients, highlighting its potential as a diagnostic tool and offering new insights into RA pathogenesis. These findings pave the way for future research into the virome's role in autoimmune diseases and therapeutic development.

Humans↗

PubMed2026

Amplicon and metagenomic sequencing reveal thifluzamide drive rhizosphere microbial structural shifts and functional adaption.

Thifluzamide (TF) is a widely used phenyl urea fungicide in rice production; however, its impacts on the structural composition and functional dynamics of the rhizosphere microbiome remain poorly understood. Here, we systematically investigated the effects of TF on the structure, interactions, and functional potential of the rice (Oryza sativa L.) rhizosphere microbiome using integrated amplicon sequencing and metagenomic approaches. TF application significantly altered both bacterial and fungal community composition, bacterial diversity was markedly reduced, whereas fungal diversity increased. With bacterial diversity markedly reduced while fungal diversity increased. Beta-diversity analyses revealed strong treatment-driven community separation, indicating pronounced TF-induced microbial restructuring. Co-occurrence network analysis demonstrated reduced complexity and connectivity in bacterial networks but increased negative co-occurrence patterns within fungal communities, suggesting contrasting stability responses between microbial kingdoms. Metagenomic profiling further revealed substantial functional shifts, including the differential enrichment of KEGG and COG pathways associated with xenobiotic metabolism. Notably, while total ARG abundance remained stable, TF exposure altered the resistome profile by selectively enriching specific classes of antibiotic resistance genes (ARGs), biocide resistance genes (BRGs), and mobile genetic elements (MGEs). Strong positive correlations between MGEs and ARGs highlighted an elevated potential for horizontal gene transfer. Metagenome-assembled genome (MAG) analysis identified specific TF-enriched bacterial taxa, including Methylophilus, Sulfurospirillum, and Azospirillum, which harbored genes involved in pesticide degradation and xenobiotic transformation. Collectively, these findings demonstrate that TF profoundly reshapes the rice rhizosphere microbiome by altering microbial diversity, interaction networks, resistance gene profiles, and functional capacities. This study provides genomic insights into fungicide-microbiome interactions, underscoring the potential ecological implications associated with TF application, while identifying candidate microbial taxa that may contribute to pesticide degradation and rhizosphere microecology resilience.

Rhizosphere↗

PubMed2025

Metagenomic analysis of microbial community dynamics in konjac rhizosphere during soft rot disease progression.

Amorphophallus konjac, the sole glucomannan-rich species in the Araceae family, faces significant yield and quality losses due to soft rot disease. Understanding the relationship between soil microbial communities and soft rot incidence is critical for sustainable konjac production. Metagenomic profiling was employed to systematically characterize the spatiotemporal dynamics of rhizosphere microbiomes during disease progression. Microbial alpha diversity (Chao1 index) exhibited a significant peak in the rhizosphere of diseased plants at the mature stage, contrasting with stable diversity patterns in healthy and latently infected groups, indicating dysbiosis-associated richness inflation during disease progression. Principal coordinate analysis (PCoA) revealed significant divergence in rhizosphere microbial structures between diseased and healthy/latently infected groups, with higher compositional variability observed in diseased samples. At the phylum level, Chloroflexi and Acidobacteria abundances in healthy mature plants exceeded those in diseased plants by 11.54% and 4.6%, respectively, while pathogenic Rhizopus arrhizus and Rhizopus microsporus were significantly enriched in diseased mature plants. Correlation analyses demonstrated predominantly negative associations between bacterial species and soil factors, contrasting with positive fungal correlations. KEGG pathway annotation identified carbohydrate metabolism and amino acid synthesis as core microbial functions in the konjac rhizosphere. Collectively, Chloroflexi and Acidobacteria were validated as putative biocontrol agents, while Rhizopus spp. emerged as key drivers of soft rot development. These findings provide mechanistic insights for designing microbiome-based biocontrol strategies to mitigate konjac soft rot, offering a sustainable alternative to conventional agrochemical reliance. KEY POINTS: • Diseased konjac microbial richness peaks; healthy plants enrich Chloroflexi/Acidobacteria. • Rhizopus pathogens drive soft rot; bacteria and fungi show opposing soil factor links. • Lays groundwork for microbiome approaches to cut agrochemicals in konjac rot control.

Rhizosphere↗

PubMed2025

Potential biomarkers for human Ascending aortic aneurysm identified through metagenomic and metabolomic analyses: A case-control study.

INTRODUCTION: Ascending aortic aneurysm (AsAA) is a high-risk cardiovascular condition; recent research indicates a possible association between gut microbiota, plasma metabolites, and the pathogenesis of AsAA. OBJECTIVE: This study aims to investigate the compositional and metabolic alterations in the gut microbiota of AsAA patients to identify potential biomarkers for AsAA. METHODS: This study enlisted 72 participants, comprising 44 individuals with AsAA and 28 healthy controls. All participants underwent examination for clinical features, and fecal and plasma samples were obtained for metagenomic and metabolomic studies. RESULTS: Metagenomic analysis revealed a significant reduction of 23 bacterial species in AsAA patients, including Bifidobacterium adolescentis, Bifidobacterium longum, Lactiplantibacillus plantarum, Enterococcus faecalis, and Streptococcus thermophilus, while 52 bacterial species, such as Prevotella copri, Phascolarctobacterium faecium, and Eubacterium ventriosum, were found to be enriched. Furthermore, we identified seven microbial co-abundance groups (CAGs), of which three (predominantly comprising Roseburia, Agathobacter, and Prevotella) were significantly elevated in AsAA patients, whereas one (predominantly comprising Escherichia) was substantially diminished. KEGG pathway enrichment analysis indicated that the biosynthesis of unsaturated fatty acids pathway displayed the most pronounced differences between groups. Metabolomics data revealed that 22 metabolites, including ceramides, were significantly elevated, while 8 metabolites, such as threonine, were notably downregulated. Moreover, clinical indicators like C-reactive protein (CRP) and complement components C3 and C4 have shown strong correlations with specific gut microbiota (Streptococcus, Prevotella) and plasma metabolites (threonine, ceramides). These findings indicate that inflammatory responses, metabolic dysregulation, and gut microbiota imbalance are pivotal in the etiology of AsAA. CONCLUSION: This study demonstrates substantial alterations in gut microbiota composition and plasma metabolites in patients with AsAA. Prevotella and ceramides exhibit potential as biomarkers for AsAA diagnosis. Furthermore, a synergy of Prevotella and ceramides may function as a potent disease prediction classifier, offering novel perspectives on the early diagnosis and targeted treatment of AsAA.

Humans↗

PubMed2026

Comparative metagenomic assessment of Illumina-compatible library preparation methods, short-read lengths, and PacBio HiFi sequencing reveals differences in microbial and functional diversity recovery from a complex environmental sample.

UNLABELLED: Metagenomics enables comprehensive exploration of microbial communities but is influenced by library preparation and sequencing technologies, affecting recovery of microbial genomes and proteins. Here, we benchmarked six Illumina-compatible short-read library preparation conditions in triplicate at 2 × 150 bp and 2 × 250 bp read lengths alongside PacBio HiFi long-read sequencing using a composite environmental sample of marine mangrove sediment and terrestrial palm tree soil. Longer short reads (2 × 250 bp) combined with optimal library preparation approaches improved assembly quality, protein detection, and metagenome-assembled genome (MAG) recovery, achieving results approaching those of long-read sequencing. TruSeq libraries at 2 × 250 bp recovered more than sevenfold more unique proteins than the same kit at 2 × 150 bp (811,701 vs 110,108) using the same number of sequencing reads, while recovering a comparable number of high-quality MAGs to PacBio HiFi long-read sequencing (11 vs 18) and surpassing it in protein discovery by almost 10-fold (811,701 vs 87,745) at less than half of the sequencing cost. Furthermore, biosynthetic gene cluster analysis identified 46 biosynthetic gene clusters in TruSeq-250PE assemblies compared to 38 in PacBio HiFi, with several showing no close match in the MIBiG database. Although long reads yield more contiguity and complete genomes, longer short reads offer a cost-effective, scalable alternative for uncovering microbial and functional diversity. These findings provide critical guidance for metagenomic experimental design, demonstrating that strategic selection of library preparation chemistry and sequencing parameters can reveal more unknown microbial information in complex biomes without requiring additional sequencing depth. IMPORTANCE: Metagenomic outcomes are strongly influenced by library preparation and sequencing strategies, yet their combined effects in complex environmental samples remain poorly defined. Here, we provide the first direct comparison of Illumina NovaSeq short-read metagenomic sequencing at 2 × 150 bp and 2 × 250 bp across multiple library preparation kits, alongside PacBio HiFi long-read sequencing. We show that sequencing read length and library preparation critically shape assembly quality, protein recovery, and metagenome-assembled genome (MAG) reconstruction. These findings demonstrate that short-read sequencing at 2 × 250 bp, with appropriate library preparation, can match long-read technologies in MAG recovery while substantially surpassing them in protein discovery. With less than half of the sequencing price and a 3.5-fold reduction in cost per gigabase of usable data, this method facilitates more accessible large-scale metagenomic analysis within complex environmental systems.

Metagenomics↗

PubMed2025

Exploring Potential Causality and Molecular Mechanisms between Heart Failure and Renal Failure: Insights from Mendelian Randomization Studies, the MIMIC-IV Database and the Gene Expression Omnibus Database.

UNLABELLED: Introduction: Heart failure (HF) and renal failure (RF) frequently coexist as cardiorenal syndrome, but their underlying causal mechanisms remain poorly defined. METHODS: This study applied Mendelian randomization (MR) using genome-wide association study (GWAS) datasets to investigate the causal effect of HF on RF. The inverse variance weighted method assessed causality, and summary-data-based MR (SMR) was used to identify therapeutic targets. Additional analyses included 211 gut microbiota traits and 1,400 serum metabolites. Validation was performed using the MIMIC-IV database. Transcriptomic data were analyzed to identify differentially expressed genes (DEGs) and key transcription factors (TFs). RESULTS: This study found that HF significantly increases the risk of RF (OR = 1.54, 95% CI: 1.07-2.23, p = 0.020). SMR analysis identified SURF1 and MAP3K11 as potential therapeutic targets for HF and RF. One gut microbiota genus and one serum metabolite showed causal associations with both diseases. MIMIC-IV data supported the HF-RF association (OR = 2.94, 95% CI: 2.81-3.07, p < 0.001). A total of 11 overlapping DEGs were enriched in the MAPK cascade, with RELA identified as a key TF. CONCLUSION: This study provides genetic and molecular evidence supporting a causal role of HF in RF, highlighting microbial, metabolic, and immune mechanisms as potential therapeutic targets. .

Humans↗

PubMed2025

Microbiology Galaxy Lab: The first community-driven gateway for reproducible and FAIR analysis of microbial data.

The explosion of microbial omics data has outpaced the ability of many researchers to analyze it, with complex tools and limited computational resources creating barriers to discovery. To address this gap, we present the Microbiology Galaxy Lab: a free, globally accessible, community-supported platform that combines state-of-the-art analytical power with user-friendly accessibility. Supported by the Galaxy and global microbiology communities, this platform integrates over 315 tool suites and 115 curated workflows, enabling comprehensive metabarcoding, (meta)genomic, (meta)transcriptomic, and (meta)proteomic data analysis within a FAIR-aligned environment. It also supports research in the health and infectious disease sectors, as well as in environmental microbiology. The platform's utility is exemplified through various use cases, including antimicrobial resistance tracking, biomarker prediction, microbiome classification, and functional annotation of key microbes. Built on reproducibility and community engagement, it supports creation, sharing, and updating of best-practice workflows. Over 35 tutorials and learning paths empower scientists, fostering an ecosystem that keeps resources at the forefront of microbial science. The Microbiology Galaxy Lab enables collective analysis, democratising research, thereby accelerating discovery across the global microbiology community (microbiology.usegalaxy.org, .eu, .org.au, .fr).

Journal Article↗

PubMed2026

Commensal Dysbiosis Alters Primary Bile Acid Signaling to Drive Mammary Gland Inflammation and Breast Tumor Dissemination.

UNLABELLED: Breast cancer is the most commonly diagnosed malignancy and a leading cause of cancer-related mortality. Hormone receptor-positive (HR+) tumors represent the most prevalent metastatic subtype, and early dissemination remains a major clinical challenge. Commensal dysbiosis, defined as an inflammatory gut microbiome with low biodiversity, promotes metastasis by inducing mammary gland inflammation. In this study, we investigated systemic mechanisms governing dysbiosis-induced metastasis. Metabolomic profiling revealed elevated primary bile acids (BA) in the dysbiotic fecal microbiome. Sequestration and supplementation approaches demonstrated that beyond driving metabolic disease and mammary gland inflammation, primary BAs orchestrated enhanced HR+ tumor dissemination via a prostaglandin E2 (PGE2)-dependent pathway. Analysis of The Cancer Genome Atlas showed that BA, insulin resistance, and PGE2 gene signatures are associated with reduced survival in patients with HR+ tumors. In complementary analyses using the Epic Cosmos electronic health record database, BA sequestrant use was associated with longer restricted mean survival time among patients with metastatic disease. Together, these findings reveal that commensal dysbiosis-associated loss of microbial BA metabolism elevates primary BAs and promotes HR+ metastatic progression through PGE2 signaling. SIGNIFICANCE: Dysbiosis-induced bile acids drive systemic and mammary tissue-specific inflammation that promotes HR+ breast tumor metastasis, supporting the development of strategies targeting microbiome-derived metabolites to reduce metastatic risk in vulnerable populations.

Female↗

PubMed2026

Building biofilms for saline hydrogenotrophic denitrification from contrasting origins: Convergent acclimation, divergent performance.

Hydrogenotrophic denitrification is promising for deep nitrogen removal from saline, low-C/N wastewaters, but rapid establishment of stable biofilms at high salinity remains challenging. Here, two saline-adapted inocula from two representative, functionally contrasting habitats-a functionally-diversified inoculum from mangrove sediment and a functionally-focused inoculum from seabed sediment-were acclimated in parallel H2-based membrane biofilm reactors at constant 3.5% salinity. The Diverse-derived biofilm required 80 d to reach steady state and achieved only partial denitrification with 61.1% nitrate removal and considerable nitrite accumulation. In contrast, the Focus-derived biofilm rapidly established complete denitrification within ∼40 d, which was maintained for >50 d, with effluent NOx- below 1 mg-N·L-1 and 98.7% nitrate removal. Microbiome analyses showed that identical operation promoted convergence in community structure and enriched similar community-level functional potentials. However, genome-resolved analysis revealed distinct source-dependent functional organization among dominant microbial populations. Complete denitrifiers co-encoding denitrifying, hydrogenotrophic, and autotrophic functions were preferentially enriched in the Focus-derived biofilm, whereas these functions remained partitioned among different dominant populations in the Diverse-derived biofilm, coinciding with less complete denitrification. These findings indicate that saline hydrogenotrophic denitrification performance depends not only on which functions are enriched at the community level, but also on how key functions become organized among microbial populations, providing a previously overlooked criterion for inoculum selection in saline biological nitrogen control.

Complete denitrification↗

PubMed2026

Enhancing the fiber degradation efficiency in dairy cattle rumen through engineered bacterial communities.

BACKGROUND: The rumen functions as an anaerobic fermentation chamber, housing microorganisms with cellulolytic and proteolytic capabilities that facilitate feed utilization. Fiber-degrading bacteria possess the capability to enhance the productivity of cellulolytic feed. The application of omics technologies has greatly improved our understanding of the rumen microbiome. Determining microbial composition and functional patterns in the rumen does not equate to a comprehensive exploration of rumen microbial resources and their mechanisms of action. This study seeks to integrate high throughput 16S rRNA data with information on culturomics, cellulolytic activities, nutrition, and synthetic microbial communities (SynCom) engineering. The objective is to evaluate the relationship between rumen microbial activity and fiber utilization efficiency in cattle, ultimately aiming to develop a more powerful intervention strategy for the ruminant industry. RESULTS: The enrichment culture with various carbon sources led to significant alterations in the composition and structure of rumen microbiota, particularly enhancing those associated with carbohydrate metabolism. Employing the culturomics methodology, 896 strains from 78 species (including 8 novel species) were isolated, resulting in a 10.1% isolation rate relative to the rumen bacterial community. Among them, 35 strains demonstrated boosted cellulose-degrading capability on plates, while 25 exhibited the ability to degrade hemicellulose as well. SynComs of these candidates were prepared based on the ratio observed in rumen microbiota exhibiting high cellulolytic performance. SynCom 3 improved the neutral detergent fiber degradation (NDFD) by 20.39% averagely. Additionally, both in vitro and in situ assessments indicated that the optimization of dose/strain in SynCom 3 significantly improved the in vitro NDFD by 20.56% and increased the in situ NDFD by 7.81%, along with the acidic detergent fiber (ADF, + 11.47%). Genomic analysis revealed that the SynCom 3 functioned well in fiber degradation through the synergistic action of key carbohydrate-active enzymes. CONCLUSIONS: This study strengthens rumen microbiome research by integrating omics and SynCom engineering within a microbiota-bacteria-enzymes-genes framework, revealing the significance of enzymatic synergy in carbohydrate metabolism. The findings establish a framework for utilizing low-abundance microbes and engineering functional consortia, which are crucial for improving ruminant feed utilization and biomass conversion. Future research should investigate the transcriptomic profiles and the metabolic cross-feeding mechanisms of fiber-degrading strains in the rumen. Video Abstract.

Animals↗

PubMed2026

Host-independent metagenomics reveal gut bacteria contribution to Delia antiqua growth by vitamin B6 provision.

Insect guts host a diverse and abundant array of microorganisms. These microbes improve host fitness by extensively involving in a range of crucial physiological processes, which have mainly been revealed by high-throughput sequencing, particularly metagenomics. However, it is almost impossible to make an accurate and complete distinction between the genetic functions of microbial symbionts and insect hosts without host genome data. By comparing metagenomic data from gut germ-free and nonaxenic larvae, we accurately identified the data belonging to the gut microbiome of the onion maggot Delia antiqua (Diptera: Anthomyiidae). Besides, a correlation between bacteria of the genus Wohlfahrtiimonas (Gammaproteobacteria: Pseudomonadaceae) and vitamin B6 metabolism was detected through collinearity analysis. Furthermore, in vitro tests confirmed that the gut bacterium Wohlfahrtiimonas larvae contributed to the growth of D. antiqua larvae via the independent synthesis of vitamin B6. This study provides a comprehensive view of the gut bacterial diversity in D. antiqua and reveals a functional profile that is strictly specific to the gut microbiota of this species. It has preliminarily revealed the functional differentiation between insect hosts and their symbiotic microorganisms. This study also offers a technical reference for the study of microbial symbiotic functions in other insect-microbe symbioses without host genomic data.

Animals↗