Microbiome

Microbial eukaryotes are prevalent members of host-associated and free-living microbial communities, but are routinely excluded from studies of these communities. Existing methods for eukaryote detection from whole metagenome sequencing are limited by contamination of eukaryotic reference genomes and incomplete taxonomic coverage. Our previously published tool EukDetect addressed these challenges using a curated database of universal BUSCO marker genes, but lacked validated quantitative abundance metrics and was built from a limited number of genomes. Here we present EukDetect2, incorporating a database containing 6,948 microbial eukaryotic genomes representing 6,594 unique species, 2,339 of which are newly added since EukDetect version 1, alongside quantitative metrics for estimating absolute and relative abundance of microbial eukaryotes. Using simulated data, we demonstrate accurate abundance estimation, no false positives from bacterial or host-derived reads, and equivalent or greater sensitivity and specificity than alternative taxonomic profiling tools across a range of microbial abundances and community compositions. Applying EukDetect2 across globally distributed human gut microbiome cohorts, we find that Blastocystis spp. and Dientamoeba fragilis are the most prevalent gut eukaryotes across cohorts, while host-associated fungi are consistently less prevalent than commensal protists. Blastocystis abundance is positively associated with a gut microbial community enriched for fiber-fermenting microbes and depleted for pro-inflammatory and industrialization-associated taxa. EukDetect2 provides sensitive, accurate, and quantitative metrics for investigating microbial eukaryotes from metagenomic samples.

The human gut microbiome harbours a diverse community of microeukaryotes, predominantly fungi, which may potentially play important roles in gut ecology and homeostasis. Despite their potential, the study of gut microeukaryotes has been hampered by the limited sensitivity of standard sequencing approaches, which struggle to capture DNA from low-abundance microorganisms against the overwhelming background of bacterial biomass. To address this, we developed a method to selectively enrich for microeukaryotic cells in human faecal samples by depleting bacterial cells prior to metagenomic sequencing. Through systematic comparison and optimisation at each processing step, we established a robust standard operating procedure (SOP) for microeukaryotic cell enrichment. By benchmarking this SOP across eight human faecal samples with three technical replicates each, we showed that it consistently increased microeukaryote representation in metagenomic libraries, greater microeukaryotic taxonomic diversity, and a reduced proportion of unclassified taxa. Together, these improvements enabled substantially deeper characterisation of the microeukaryotic fraction of the human gut microbiome.

Background: Early-onset colorectal cancer (EOCRC) is increasing globally, particularly among Hispanic/Latino (H/L) populations, yet the contribution of tumor-colonizing microbiota to age-associated colorectal cancer (CRC) biology remains poorly understood. Most microbiome studies have focused on fecal communities or non-Hispanic populations, leaving the intratumoral microbial landscape of H/L patients largely unexplored. Methods: We performed an exploratory characterization of tumor-colonizing microbiota using whole-exome sequencing (WES) data from four primary colorectal tumors obtained from H/L patients treated at City of Hope, including two EOCRC (<50 years) and two late-onset colorectal cancer (LOCRC; [&ge;]50 years) cases. Following removal of host-derived sequences, microbial taxonomic profiling was conducted at the family, genus, and species levels, and microbial metabolic pathways were inferred. Clinical and pathological data were integrated to evaluate age-associated differences in microbial composition and predicted function. Results: Family-, genus-, and species-level analyses consistently demonstrated greater microbial diversity in LOCRC than EOCRC. LOCRC contained more than twice the number of unique bacterial families, nearly three times as many unique genera, and more than twice as many unique bacterial species. A conserved core microbiota, including Fusobacteriaceae, Prevotellaceae, Fusobacterium, and Prevotella, was identified across both age groups, whereas LOCRC was enriched in CRC-associated taxa including Fusobacterium nucleatum, Bacteroides fragilis, Parvimonas micra, Porphyromonas asaccharolytica, and Dialister pneumosintes. Species-level analyses revealed only a single shared bacterial species between EOCRC and LOCRC, indicating progressive microbial divergence with increasing taxonomic resolution. In contrast, functional profiling identified 11 predicted microbial metabolic pathways, of which nine were shared between age groups, two were unique to EOCRC, and none were exclusive to LOCRC. Core metabolic pathways involved in energy metabolism, amino acid biosynthesis, phospholipid metabolism, and central carbon metabolism exhibited comparable abundance across both groups, demonstrating substantial functional conservation despite pronounced taxonomic differences. Conclusions: Tumor-colonizing microbiota differ markedly between EOCRC and LOCRC in H/L patients, with late-onset tumors exhibiting substantially greater microbial richness and taxonomic complexity. Despite these compositional differences, microbial metabolic functions remain largely conserved, supporting the concept of functional redundancy within the colorectal tumor microenvironment (TME). Although exploratory, this proof-of-concept study provides one of the first characterizations of intratumoral microbiota in H/L EOCRC and establishes a foundation for larger multi-omics investigations aimed at identifying microbiome-based biomarkers and therapeutic targets for precision oncology.

The gut microbiota plays a fundamental role in human health, nutrition, immune development, and disease, driving widespread adoption of 16S rRNA gene sequencing for microbial community characterization. Short-read V3-V4 sequencing remains the dominant approach for large-scale microbiome studies; however, interrogation of only a small fraction of the 16S gene limits phylogenetic resolution and frequently restricts biological interpretation at the species level. Although full-length (V1-V9) 16S sequencing has emerged as a promising alternative, comprehensive evaluation of highly multiplexed full-length workflows in complex human gut microbiomes remains limited. Here, we establish and evaluate a full-length 16S framework for species-resolved human gut microbiome profiling. The workflow was assessed using defined microbial communities, technical replicates, and healthy human fecal microbiomes. Full-length sequencing generated highly concordant taxonomic profiles across independent technical workflows and enabled reproducible recovery of complex microbial communities at both genus and species levels. Application to human fecal microbiomes revealed substantial inter-individual heterogeneity together with extensive ASV-level microdiversity, highlighting the ability of full-length sequencing to resolve fine-scale phylogenetic variation within dominant gut-associated taxa. To quantify the analytical gain afforded by full-length sequencing, V3-V4 datasets were computationally reconstructed directly from identical full-length reads, eliminating methodological and biological confounders. While alpha diversity metrics and overall community structure remained highly concordant between approaches, full-length sequencing markedly improved taxonomic resolution, increasing species-level assignment from approximately 20% to 98% and resolving substantial intra-genus diversity within clinically and ecologically relevant genera including Bifidobacterium, Prevotella, Blautia, Enterococcus, and Klebsiella. Collectively, these findings position full-length 16S sequencing as an enabling technology for the next generation of microbiome studies, where species-level resolution can be integrated with large-scale cohort, longitudinal, and population-health investigations.

Metagenomic studies of arthropod-associated microbiomes have generated vast amounts of sequence data, yet the functional and structural organization of these proteins remains largely unexplored. Here, we present ArthroVerse, the first comprehensive database of protein families derived from arthropod-associated metagenomes. Non-redundant protein families were generated after rigorous filtering, deduplication, and clustering. The protein families were further annotated with microbial taxonomy, host associations, protein structural information, and Carbohydrate-active enzymes (CAZyme) predictions. The resulting dataset integrates both metagenomic and reference genome-derived proteins, enabling systematic exploration of functional diversity, evolutionary relationships, and host-microbe interactions in insect microbiomes. ArthroVerse provides a valuable resource for the study of microbial ecology and arthropod physiology, offering unprecedented insight into the protein landscape of insect-associated microbial communities.

Background Colonisation resistance provided by the gut microbiota is a critical barrier to pathogen invasion, yet its study in vivo is constrained by the complexity and cost of vertebrate models. Here, we developed a humanised Galleria mellonella infection model by inoculating wax moth larvae with complex human faecal microbiota. 16S rRNA gene sequencing confirmed stable, reproducible establishment of a diverse human associated community across larvae over four days. Results Humanised larvae exhibited colonisation resistance against Salmonella enterica serovar Typhimurium, with mortality reduced to 20% compared to 90% in non colonised controls. To test whether prophages could overcome this barrier, we infected larvae with isogenic S. Tm strains differing in the presence of prophage P22. Infection with the P22 carrying strain resulted in a threefold higher larval mortality (60% vs. 20%), increased pathogen load, and a significant reduction in the abundance of resident E. coli. Free P22 virions were detected early after infection, indicating extensive prophage activity. Notably, P22 can neither adsorb nor lyse resident E. coli, indicating that prophage mediated invasion success did not rely on direct lysis. Instead, using high throughput metabolic profiling paired with whole genome sequencing of three replicate lineages, we found that phage activation intensified resource partitioning, accelerating functional metabolic adaptations in E. coli that significantly reduced the niche overlap between the invading pathogen and the commensal E. coli. Conclusion Our findings establish the first humanised G. mellonella model supporting complex human microbiota and provide a novel non lytic mechanism by which prophages influence species interactions. This scalable, low cost model offers a new platform to dissect pathogen phage microbiota interactions relevant to human gut ecology.

Microbiome analysis has become a pivotal tool in understanding the role of microbiota in human health and disease. However, the lack of standardized workflows, together with the limitations of proprietary software solutions, hampers reproducibility and flexibility. Here, we present Mbiome, an open-source, user-friendly and automated workflow designed to streamline amplicon-based microbiome analysis. Built upon QIIME2, Mbiome supports both bacterial (16S rRNA) and fungal (ITS) profiling, and is compatible with raw fastq files generated by Ion Torrent (IT) and Illumina (IL) sequencing platforms. The workflow guides users through an interactive setup process via a simple configuration file, enabling researchers with minimal bioinformatics experience to perform comprehensive analyses without writing code. Once configured, Mbiome automates major steps including quality control, taxonomic assignment, and {beta}-diversity analyses, functional predictions (via q2-metnet), and customizable visualizations and statistical analyses. Mbiome has been validated using real-world datasets from multiple sclerosis research projects, performing a comparison between different microbiome analysis approaches, including 16S hypervariable region reconstruction, amplicon-based strategies, and cross-platform sequencing (IT and IL), as well as against results obtained with Ion Reporter (IR) commercial software. This evaluation demonstrated its versatility and effectiveness across different sequencing platforms. Moreover, Mbiome provided improved flexibility, transparency, and taxonomic resolution compared to IR. By combining accessibility, reproducibility, and cross-platform compatibility, Mbiome lowers the barrier to microbiome data analysis and facilitates high-quality, standardized workflows in both research and applied settings. Mbiome is publicly available at https://github.com/MGorostidi/mbiome.

Antimicrobial resistance (AMR) is an ever-increasing threat to population health. Industrial, environmental and societal factors are increasingly recognized as important contributors to AMR within communities. Here, we investigated the spatial distribution of AMR genes (ARGs) across Alberta, Canada and their association with socio-economic, immigration-related, and agro-industrial characteristics using municipal wastewater-based surveillance. We analyzed monthly wastewater metagenomes collected between March 2022 and March 2023 across eleven municipalities, representing 39% of Alberta's population. Integration with census data enabled multivariate analysis, revealing that municipal resistome profiles were strongly structured along income and immigration-related population gradients. ARGs spanning 14 resistance classes exhibited distinct distributional patterns across income and immigration gradients, including contrasting associations among beta-lactam, aminoglycoside, and macrolide-lincosamide-streptogramin ARGs, consistent with heterogeneous selection pressures across sub-populations. These findings demonstrate the capacity of longitudinal wastewater surveillance to identify persistent population-level resistome patterns and highlight the importance of incorporating sociodemographic context into AMR surveillance and mitigation strategies.

Bdellovibrio and like organisms (BALOs) are obligate bacterial predators that shape microbial communities by promoting species diversity, yet they have long been considered irrelevant to the human gut due to their presumed obligate aerobic lifestyle. Here, we challenge this view through a combined meta-analytic, experimental, and conceptual investigation of BALOs in human microbiomes. Reanalyzing 168,000 consistently processed samples from the Human Microbiome Compendium spanning 482 studies, we detected BALOs in more than 80 studies and across multiple body sites worldwide, with a gut prevalence of 2.4%, a finding confirmed by reanalysis of the PRIME database for 16S rRNA microbiome data. Strikingly, BALO presence was consistently associated with higher microbial alpha-diversity across body sites and disease contexts. Biopsy-derived samples showed a substantially higher prevalence than fecal samples, suggesting a mucosa-proximal niche. Our laboratory experiments showed that multiple Bdellovibrio strains can delay the loss of microbial diversity in vitro and remain active under gut-relevant conditions, including 37C, pH 6.5, and in the presence of mucus. Genomic analyses further revealed terminal reductases, including nitrite reductases, in several BALO genomes, indicating the capacity for anaerobic or microaerobic respiration, consistent with persistence in mucosal microenvironments. Notably, the metabolic and ecological profiles of cultured BALOs closely match those of facultative anaerobes, which constitute their preferred prey and are central drivers of dysbiosis in inflammatory bowel disease, diabetes, colorectal cancer, and chronic kidney disease. Building on these findings, we propose a conceptual framework in which BALOs contribute to gut homeostasis by controlling the expansion of facultative anaerobes under inflammatory conditions, thereby facilitating the restoration of fermentative, butyrate-producing communities. Together, our results establish BALOs as consistent, functionally relevant members of the human microbiome and a promising natural candidate for therapeutic strategies targeting chronic gut disease.

Plant-microbe interactions in the rhizosphere are central to nutrient cycling and ecosystem functioning. Sedimentary ancient DNA (sedaDNA) is a promising yet underexplored tool for reconstructing past microbial communities and investigating ecological interactions among plants, animals, and microorganisms. Here, we reanalyse the previously published Kap Kobenhavn Formation (Northern Greenland) sedaDNA dataset to move beyond taxonomic ecosystem reconstruction and test whether ancient sediments preserve structured, rhizosphere-compatible plant-microbe association signals. Our results show that this ancient boreal ecosystem hosted several rhizosphere-associated taxa, comparable to those in modern boreal soils. Several bacterial genera co-occurred repeatedly with specific plant families, forming a rhizosphere-like taxonomic core with predicted plant-growth-promoting traits related to nutrient acquisition, colonisation, and stress tolerance. Although sedaDNA co-occurrence cannot demonstrate direct symbiosis, the consistency of taxonomic, network, and functional signals suggests that ancient sediments preserve interconnected ecological structure. Our findings extend sedaDNA-based ecosystem reconstruction beyond taxonomy and provide a possibility for investigating plant-microbe association signals in deep time.

Biological N2O production from organic nitrogen is generally assumed to require canonical nitrification, which generates oxidized nitrogen that subsequently fuel denitrification. Whether this paradigm universally applies to nitrogen-rich microbial communities remains unclear. Here, we investigated N2O production across an industrial poultry manure composting process and found that substantial N2O formation occurred despite the apparent absence of canonical ammonia oxidation. Neither allylthiourea inhibition nor metagenomic analyses provided evidence for ammonia-oxidizing microorganisms or their activity. Instead, metagenomic analyses identified abundant bacterial nitric oxide synthase (bNos) genes, many of which were phylogenetically affiliated with Bacilli, the dominant bacterial group throughout composting. Physiological experiments with Bacillus isolates demonstrated a nitrification-independent route in which L-arginine was oxidized to NO2-/NO3-, consistent with bNOS-mediated NO formation followed by abiotic oxidation. Recovery of 15N-labelled N2O following 15NO2- addition established NO2- as an immediate precursor of aerobically produced N2O, confirming that the oxidized nitrogen generated through this alternative route subsequently fueled denitrification. Metagenomic analyses further revealed extensive denitrification potential but comparatively low nosZ abundance. Together, these findings identify a previously overlooked route linking organic nitrogen turnover to denitrification independently of canonical nitrification, thereby expanding current models of microbial N2O production in composts and potentially other protein-rich thermophilic environments.

Accurate taxonomic profiling of human microbiomes is essential for advancing research and understanding the complex role microbial communities play in human health. When using shotgun metagenomics, the sequencing data is analyzed through metagenomic pipelines, which incorporate various open-source tools and classify microbes based on matched paired-end DNA reads. However, differences in sequencing and computational approaches can produce substantially different microbiome profiles from the same sample, making validation critical. One approach for validation is benchmarking with realistic mock communities, but this remains relatively rare. Additionally, existing benchmarks often overlook microbiome variability across life stages and body sites, limiting their clinical and research utility. Here, we developed age- and body site-stratified synthetic metagenomes, enabling context-aware benchmarking of microbiome pipelines. Using novelty-based sampling to prioritize microbial diversity and minimize redundancy among selected samples, we selected 300 representative, real biological samples spanning six categories: adult, child, toddler, and infant (>6 months and <6 months) gut samples, as well as adult vaginal samples. We validated three pipelines, Tiny Health's proprietary Metagenomic Classifier v2 (THMCv2), MetaPhlAn4, and Kraken2+Bracken, using precision, recall, F1 score, and area under the precision-recall curve (AUPR) across age groups and sample types. THMCv2 demonstrated higher recall and F1 scores, detecting more taxa across sample types and ages, while MetaPhlAn4 achieved the highest precision. THMCv2 also achieved the highest area under the precision-recall curve, reflecting peak performance across both abundant and rare species. When analyses were weighted by abundance, THMCv2 and MetaPhlAn4 each characterized the mock community nearly perfectly. Errors for THMCv2 were largely restricted to very low-abundance taxa (<0.001%), whereas MetaPhlAn4 occasionally produced false positives for higher-abundance taxa. Species-level analyses of clinically relevant microbes confirmed these patterns, with THMCv2 demonstrating higher sensitivity, MetaPhlAn4 higher specificity, and Kraken2 lower overall performance. These results demonstrate clear precision-recall trade-offs in metagenomic profiling. This benchmarking framework provides a reproducible approach for evaluating pipeline performance across diverse microbiome contexts and life stages.

Wastewater metagenomic sequencing (WW-MGS) enables simultaneous detection of hundreds of pathogens, but its use for quantitative pathogen tracking has not been robustly validated. Like wastewater PCR (WW-PCR), WW-MGS is affected by biases from variable fecal dilution and sample processing, but must additionally contend with the compositional structure of sequencing data, where a taxon's apparent abundance depends on the abundance of every other taxon in the sample. Simple summaries such as a pathogen's fraction of total reads may therefore be poorly suited to quantitative use. We retrospectively evaluated seven normalization approaches that attempt to control for these sources of bias against a baseline of total read relative abundance, using 1,425 samples from the CASPER consortium spanning 25 U.S. sites. Each approach was compared against WW-PCR and clinical data across eight total pathogens. Among the normalization strategies we evaluated, tobamovirus markers, diet-derived plant viruses abundant in human stool, performed best. Normalizing WW-MGS data by tobamovirus-genus counts improved median site concordance for 18 of 19 pathogen and comparison-source combinations. Gains were largest for year-round-circulating SARS-CoV-2 and norovirus and smaller for sharply seasonal pathogens such as influenza and respiratory syncytial virus, where baseline concordance was already high. Tobamovirus normalization rarely degraded concordance, with median gains roughly five times larger than median losses. Tobamovirus-normalized WW-MGS reached clinical concordance comparable to targeted WW-PCR, supporting its use as a quantitative trend-monitoring tool alongside pathogen-agnostic detection.

Environmental resistome comprises diverse antibiotic resistance genes (ARGs) that can play critical roles in "One Health", facilitating the evolution, persistence, and dissemination of microbial resistances. Yet, knowledge gaps exist in resistome structures and ecological connectivity across various ecosystems at a national scale. Here, we combined nationwide extensive short- and long-read sequencing efforts for soils, sediments, waters and wastewater treatment plants across Denmark to resolve resistome composition, habitat specificity and connectivity. From over 7,000 sequenced environmental samples (24 Tb of metagenomic data) that were classified into 21 distinct habitat classifications, resistomes exhibited habitat-specific patterns. We identified core ARGs for establishing environmental baseline of ARGs, and habitat-associated indicator ARGs facilitating source tracking. Using 110 deep long-read metagenomes (9 Tb data), we showed that only a subset of cross-habitat commonly-abundant ARGs showed elevated associations with MGEs and broad host range, suggesting unequal resistome connectivity across ecosystems among environmental ARGs. Additionally, although natural habitats had much lower resistome relative abundance and transferability than human-associated habitats, some mobile environmental ARGs exhibited links to those in human pathogens. These findings establish an ecological framework for interpreting environmental resistomes and prioritizing ARGs for environmental surveillance in the One Health framework.

Genome-scale metabolic models (GSMMs) are important aids towards system-level understanding of the metabolic physiology of the gut microbes and for rational microbiome engineering. While large-scale repositories of GSMMs for gut-associated bacteria are available, strain-level variability and the continuous discovery of novel taxa through metagenomics and culturomics underscore the need for scalable, ab initio reconstruction tools. Here, we present CarveMe-GutMicrobes, a client-side framework for rapid reconstruction of metabolic models directly from (meta)genomic input. Building upon the original CarveMe framework, CarveMe-GutMicrobes incorporates an expanded, gut-microbe-centric biochemical database that includes reactions, metabolites, and gene-protein-reaction (GPR) associations curated specifically for Bacteria and Archaea inhabiting the human gut. The tool supports taxonomic restriction of the reference database to improve context-specific accuracy. To test the CarveMe-GutMicrobes and to address the paucity of experimental data for non-model gut taxa, we generated new experimental datasets on metabolite secretion profiles and gene essentiality. CarveMe-GutMicrobes models demonstrated high predictive performance performance against these as well as previously available datasets. By integrating curated resources, extending reaction coverage, and offering new empirical datasets, CarveMe-GutMicrobes provides a scalable platform for high-resolution metabolic reconstruction towards broader adoption of GSMMs in gut microbiome research.

Industrialization has reshaped human gut microbiomes, but its effects on other human-associated mammals remain poorly understood. Domestic dogs provide an informative comparative system because they have shared human environments and food systems for millennia yet retain distinct host biology. However, most canine microbiome studies have focused on industrialized companion animals, limiting our understanding of the ecological range of the domestic dog gut microbiome. We analyzed fecal 16S rRNA gene profiles from 261 dogs sampled across Nepal, Thailand, the United Arab Emirates, and the United States, spanning forager, agrarian, pastoralist, urban, and industrialized lifestyles; 257 dogs remained after excluding recent antibiotic exposure. Lifestyle was the strongest measured correlate of canine gut microbiome composition, and this structure persisted in restricted analyses of mature, non-shelter dogs sampled from temperate climate regions. Industrialized dogs differed from non-industrialized dogs through directional genus-level turnover, restructuring of VANISH- and BloSSUM-like microbial guilds, and shifts in predicted functional potential. Non-industrialized dogs were not microbiologically uniform: pastoralist dogs carried non-industrialized microbiome profiles but diverged from a simple forager-to-industrialized continuum. Cross-species comparisons with humans sampled across matched lifestyle categories showed parallel lifestyle-associated restructuring in both hosts, but host species remained the dominant axis of variation and the genera responding to industrialization were largely host-specific. These findings expand the ecological baseline for the domestic dog gut microbiome and identify industrialization as a major axis of microbiome restructuring in a long-term human-associated mammal. More broadly, they show that shared lifestyle transitions can impose parallel ecological pressures across host species without overriding host-specific community assembly.

Motivation: Horizontal gene transfer (HGT) shapes bacterial evolution and microbial ecosystems, yet detecting HGT within microbiomes remains a challenge due to fragmented metagenomic assemblies, reference bias, reliance on gene boundaries, and limited ability to model structural mosaicism and patterns across genomes. Methods: We present Kente, a novel pangenome graph-based framework designed for HGT detection that aligns metagenomic assembly contigs to a curated database of >600 genus-level bacterial pangenome graphs constructed using minigraph. Kente infers local taxonomic composition along contigs using alignment evidence and classifies candidate transfers using structured clade-transition topologies (e.g., A-B-A sandwich, open tips, and mosaic patterns). A complementary intra-genus module detects inter-species transfers within a single genus graph using segment-level clade annotations. Results: Across simulated intra- and inter-genus transfer scenarios, Kente achieves higher precision and comparable recall relative to existing gene-centric microbiome HGT detection approaches while reducing false positives from fragmented assemblies. Application to real human gut metagenomes (HMP2, n = 26) demonstrates Kente's ability to detect candidate cross-lineage transfer regions in complex microbial communities. Runtime profiling shows near-linear scaling with input size, enabling efficient analysis of large metagenomic assemblies. Availability and Implementation: https://github.com/treangenlab/Kente

Molecular hydrogen (H2) and hydrogen sulfide (H2S) are central gut metabolites that shape microbial metabolism and affect host health. In Crohns disease (CD), the shift in microbiota composition ( dysbiosis) is associated with intestinal accumulation of these gases, but the responsible microbes remain poorly resolved. Here, we analysed 4,644 bacterial and archaeal species-level genomes from the Unified Human Gastrointestinal Genome Collection to identify H2-cycling microbes, assessed their prevalence in ca. 1,700 stool metagenomes from healthy and diseased individuals, and validated their activity using culture-based incubations of stool isolates and biopsy samples. Approximately half of all species encoded H2-producing abilities, with acetate- and propionate-forming fermenters such as Phocaeicola and Bacteroides dominating healthy cohorts, whereas comparatively few taxa, including Escherichia and Megamonas, encoded H2 consuming abilities. In CD, H2 producers became more abundant but less diverse, favouring species with multiple H2-evolving hydrogenases and more fermentation routes, especially Clostridium and Enterocloster species. Consistently, isolates enriched in CD produced H2 faster and at higher concentrations than health-associated isolates. Increased H2S-producing capacity in CD was driven mainly by these H2-producing fermenters carrying anaerobic sulfite reductases (Asr), rather than sulfate-reducing bacteria, and was supported by elevated H2S production in Asr-positive isolates, likely providing an additional electron sink. These findings provide a species-resolved view of gut gas metabolism and implicate metabolically flexible fermenters in excessive gas and sulfide production in gut disorders.

Temperate bacteriophages are dominant members of the human gut microbiome that can infect and lyse their bacterial hosts or integrate as prophages. During this integrated state, prophages exhibit extensive control over host physiology and lysis via induction. Here, we studied a diverse collection of Bacteroidales isolates, which are amongst the most abundant bacterial orders within the human gut, identifying 902 high-quality prophage genomes present within 305 isolates, 240 of which were poly-lysogens. Despite their prevalence, our understanding of the function and induction triggers of prophages is limited. To predict prophage induction, we employed an iterative profile Hidden Markov Model search across divergent bacterial hosts to identify prophage regulatory components. We found 197 Bacteroidales prophages encoding complete CI-like repressor proteins, which initiate induction upon DNA damage. We selected Bacteroides thetaiotaomicron strain Bt_806 to characterise further as it harboured six diverse prophages, including the prevalent and abundant prophage LoVE, which was the only integrated prophage encoding a complete CI-like repressor. Transcriptomics revealed phage LoVE was routinely induced upon DNA damage, while the five co-habiting prophages remained stably integrated yet exhibited transcriptionally active genes associated with regulation, prophage maintenance, and uncharacterised functions. Finally, we selected an additional eleven Bacteroidales poly-lysogens, confirming that integrated prophages encoding complete CI-like repressors were reliably induced upon DNA damage. Together, we demonstrate that mechanistic understanding of prophage induction linked with identification of regulatory genes enables selective and predictable induction of gut prophage species as a potential tool to modulate the microbiome.

Animal microbiomes are shaped by both environmental exposure and host-associated filtering, but the relative importance of these processes remains poorly understood. Dung-associated insects provide an ideal model because they develop and feed in highly dynamic microbial environments. We investigated the gut microbiomes of six sympatric dung fly species of the genus Sepsis (Diptera: Sepsidae) and compared them with microbial communities in cow dung throughout a growing season in Switzerland. Using full-length 16S rRNA gene sequencing (PacBio), we characterized bacterial communities from 74 fly and 15 dung samples. Seasonal variation was the strongest predictor of microbiome composition, whereas host species exerted weaker effects that persisted after removing dung-associated taxa, indicating that gut communities are not merely passive reflections of environmental exposure. Only few gut microbiome reads were attributable to dung-associated taxa, and environmental overlap differed among fly species rather than season. A highly non-random core microbiome persisted across all six species: 36 bacterial genera (of 469) were shared by all hosts at [~]119-fold enrichment above random expectation and remained after removing dung-associated taxa. These findings support a two-layer model of microbiome assembly, in which seasonal environmental variation determines microbial availability while host-specific processes selectively retain a subset of taxa.