Microbiome

Cancer is the second leading cause of death worldwide. A large proportion of cancer deaths is associated with modifiable risk factors, such as tobacco use, or dietary factors. This indicates a major role for prevention to reduce cancer deaths. Prevention has two main facets, identifying individuals at risk and mitigating their risk. Here we focus on the first, using microbiome alterations to stratify individuals into groups of higher and lower cancer risk, leveraging two cohorts of cancer patients and four general population cohorts from different European countries, totaling 16,255 individuals and including 3,341 cancer cases. For that, we first establish existence of common microbiome alterations in several specific cancer types (lung, kidney and breast) and show their presence in groups of individuals with different cancers. These alterations are found in France, the Netherlands, Finland and Latvia. Next, we show that people who are diagnosed with cancer after microbiome sampling, totaling 1,640 individuals, have microbiome alterations like those found in diagnosed cancer cases, albeit to a lower degree, across the four populations studied, indicating that the microbiome may evolve towards the one found in cancer well before cancer is diagnosed. Finally, we show that the extent of microbiome alterations can be used for stratification of both the diagnosed cancer cases and people who may be at increased cancer risk.

Antimicrobial exposure can alter gut resistance reservoirs, but bulk metagenomics alone often cannot distinguish whether observed changes reflect expansion of bacterial hosts, altered abundance of plasmid-derived sequences, or redistribution of mobile elements across host backgrounds. Here, we combined longitudinal bulk short-read metagenomics with selected bulk long-read and single-cell shotgun metagenomic sequencing to analyse faecal samples from six Danish pigs over 11 weeks, including an unplanned tiamulin exposure affecting the three pigs housed on the right side of the stable. We constructed a catalogue of 885 plasmid-derived sequences collapsed into 195 bins. Twenty-eight bins and 212 contigs carried resistance annotations, including ribosomal-target markers relevant to pleuromutilin exposure. Single-cell evidence linked subsets of plasmid-derived bins and contigs to bacterial host taxa, enabling host-resolved inspection of resistance-associated plasmid-derived features in longitudinal bulk metagenomes. The microbiome-wide plasmid-derived-sequence prevalence screen identified two bins with post-event associations, whereas resistance-gene abundance and host-attributed plasmid-derived-sequence abundance screens identified no significant host-resolved associations. Because exposure was unplanned and confounded with pen side and disease signs, treatment-response results are exploratory. The main contribution is a single-cell-informed microbial ecology workflow for linking plasmid-derived resistance features to host backgrounds and longitudinal abundance patterns in complex gut

Microbiome has co-evolved with insects over millions of years, establishing symbiotic relationships that influence their fitness and shape evolutionary trajectories. These long-term associations are shaped by the hosts ecology, diet, and physiology which determines microbial diversity. In turn, external stresses such as pathogenic infections can adversely affect the host microbiome, further compounding the effects of pathogenic infections on the host. Using a computational pipeline and publicly available transcriptomics datasets, we analyzed the effect of viral infections on transcript derived microbiome diversity and composition in various insects including Drosophila melanogaster, Acyrthosiphon pisum, Antheraea pernyi, and Culex pipiens. The meta-transcriptomic reanalysis provides an indirect but comprehensive comparison of infection-associated transcriptional microbiome changes across hosts with distinct life histories. Our study reveals that viral infections influence the host-associated microbial transcripts, leading to shifts in transcript-derived microbial profiles and offering insights into host-microbe dynamics. In Cx. pipiens, microbial transcript profiles remained relatively stable following RVFV exposure and were dominated by the bacterial endosymbiont Wolbachia. Our study highlights viral infections as key drivers of infection-associated shifts in microbial profiles and suggests a potential association between Wolbachia dominance and transcriptional microbiome stability in insects, with age as a contributing factor in shaping microbial transcript profiles in Culex. This cross-species framework uncovers conserved and host-specific patterns of virus-microbiome interactions, expanding our understanding of microbial resilience mechanisms in insects.

Background: Anal squamous cell carcinoma (ASCC) develops from high-grade squamous intraepithelial lesions (HSIL), but only a subset of patients with HSIL progress to invasive cancer, suggesting that factors beyond persistent human papillomavirus (HPV) infection contribute to disease progression. The anal microbiome may be a contributing factor; however, direct comparisons between HSIL and treatment-naive ASCC remain limited. Methods: Anal canal swabs were collected from adults with histologically confirmed HSIL or nonmetastatic ASCC prospectively enrolled at Montefiore Medical Center from September 2022 through October 2024. Shotgun metagenomic sequencing was performed after human-read removal. Microbial diversity and community composition were compared between disease groups. Differentially abundant species were identified using ANCOM-BC2 and validated using adjusted centered log-ratio regression. Longitudinal changes and MetaCyc pathways associated with persistent ASCC-enriched species were also evaluated. Multivariable analyses adjusted for age, sex, tobacco use, HIV status, and HPV status. Results: Among 121 participants, 102 had HSIL and 19 had ASCC. Observed richness was lower in ASCC (adjusted beta = -21.28; p = 0.035), while Shannon and inverse Simpson diversity did not differ. Microbial community composition differed by disease group using Bray-Curtis (R2 = 0.100; p = 0.001), Jaccard (R2 = 0.133; p = 0.002), and Aitchison distances (R2 = 0.172; p = 0.001). Of 60 species identified by ANCOM-BC2, 58 were validated, including six enriched in ASCC: Fusobacterium nucleatum, Parvimonas micra, Peptococcus niger, Porphyromonas asaccharolytica, Porphyromonas levii, and Porphyromonas sp. CAG:1061. All six remained associated with ASCC longitudinally, and five demonstrated greater baseline-to-month-6 decreases in ASCC than HSIL. Twenty-six MetaCyc pathways were linked to the persistent ASCC-enriched species and were also enriched in ASCC, highlighting shared functions in amino acid metabolism, anaerobic degradation, and carbohydrate and energy metabolism. Conclusion: ASCC was characterized by lower observed richness and enrichment of a distinct anaerobic microbial signature that persisted across longitudinal sampling and declined following treatment initiation. These taxa were linked to a shared functional profile dominated by amino acid metabolism and anaerobic metabolic pathways. These findings identify microbial features associated with invasive anal cancer and provide a foundation for evaluating their relationship with disease progression, tumor burden, and treatment response.

Human spaceflight may perturb the gut microbiota, but densely sampled short missions remain poorly characterized. We profiled 27 phase-matched fecal samples from two astronauts during an 18-day International Space Station mission and one ground-based participant following the same daily schedule using Oxford Nanopore full-length 16S sequencing. Participant identity dominated genus-level Bray-Curtis variation (R2 = 0.489, p < 0.001). In astronaut-only community analyses, mission phase explained 23.4% of genus-level (p = 0.035) and 22.3% of species-level (p = 0.021) variation. Astronauts showed greater displacement from personal baselines than B1 (0.331 versus 0.171) and 1.58-fold higher volatility. Astronaut-only taxon models identified 2 of 81 genera and 5 of 139 species; Collinsella increased from quarantine to orbit (coefficient = 2.586, q = 0.037). Thus, the short-duration spaceflight interval was accompanied by individualized, temporally localized community and taxon shifts rather than uniform microbiota restructuring.

Improving environmental management in the offshore Oil & Gas sector requires approaches that capture the ecosystem services (ES) provided by marine sediments, particularly their roles in carbon and nutrient cycling. Microbial communities are central to these processes, and molecular tools offer new opportunities to assess their functional diversity. To explore how different sequencing approaches inform environmental impact assessment, we compared taxonomic and functional prediction based on 16S metabarcoding, shotgun metagenomics and messenger RNA-based metatranscriptomics, targeting prokaryotic communities. Our aims were to evaluate the ability of each approach to detect impact and to determine how well they captured functions relevant to ES. All approaches revealed clear differences in community composition between impacted and non-impacted sediments at both taxonomic and functional levels, with impact significantly associated with hydrocarbon and barium content. Functional inventories showed substantial overlap across the three approaches, and 48-55 ES-related processes were detectable in all datasets. While metagenomics provided the strongest statistical discrimination between impact groups, metatranscriptomics resolved the actively expressed pathways underpinning ES, yielding the most biologically meaningful functional profiles despite its lower statistical power. All approaches indicated that Oil & Gas activity drives shift towards anaerobic, hydrocarbon-degrading, and sulfur-respiring microbial communities, with hydrocarbon degradation, sulfur cycling, and metal detoxification being the dominant ES processes in impacted sediments. Metabarcoding was confirmed as a cost-effective option for impact assessment when focused on taxonomic composition. However, functional prediction from metabarcoding data proved less reliable, as several ES showed contrasting associations to impact category between metabarcoding and shotgun sequencing approaches.

Early-life microbial colonization is critical for shaping host development, yet how different maternal microbial reservoirs contribute to colonization of distinct offspring body sites remains poorly understood. Here, we explored microbial colonization and maturation of oral and rectal microbiota in puppies during the first postnatal week and assessed the contributions of maternal oral, vaginal, rectal, and milk microbiota to early colonization. We collected 505 samples from 33 dams and their litters at day 1 and day 8 postpartum and characterized microbial communities using 16S rRNA gene sequencing. We found that the pup oral microbiome on day 1 closely resembled maternal milk and vaginal microbiota. In contrast, the pup rectal microbiome was initially distinct from all maternal body sites, suggesting contributions from additional unmeasured sources. By day 8, both oral and rectal microbiomes exhibited signs of early maturation, with decreased relative abundance of opportunistic taxa and increased resemblance to maternal profiles. In dams, the vaginal microbiome showed the largest postpartum shift, while the rectal microbiome exhibited a smaller but significant change, and milk and oral microbiomes remained stable. Our results reveal that maternal contributions to early microbiome assembly are body-site specific, with oral and rectal microbiomes following distinct developmental trajectories during the first week of life, consistent with a dynamic colonization process shaped by both maternal and likely additional non-maternal sources. The observed patterns parallel those reported in humans, supporting the value of dogs as a comparative model for studying early-life microbiome colonization and maternal-offspring transmission in mammals.

Omics studies increasingly depend on long, multi-directional workflows, making auditability as important as individual analytical tools. Existing LLM-driven bioinformatics agents automate parts of this work, but few have been tested for conclusion-level reproduction with traceable execution. MetaClaw splits analysis into a deterministic FlowHub upstream tool flow and a customizable OpenClaw downstream skill container, coupled through one YAML pipeline registry. Per-job bundles archive FlowHub specifications, skill scripts, pinned Dockerfiles, parameters and outputs, while downstream containers run without network access. We benchmarked MetaClaw on downsampled cohorts from four published studies, comprising 94 samples in total. Although downsampling likely limited sensitivity to low-prevalence markers, MetaClaw recovered 2/4 sorghum markers, 3/3 RRMS features, 3/5 CRC markers and 5/5 permafrost marker groups. Across 45 model-by-prompt runs, all backends completed upstream processing, whereas downstream validity depended on model and instruction detail; three decoy-tested endpoints showed no significant differences. In 35 ablation sessions, removing the registry, reference scripts, planning loop or manifest caused distinct losses. Relative to registry removal, the intact scaffold reduced time by 32%, tool calls by 37% and token cost by 47%. MetaClaw therefore links modular architecture, secure rerunnable provenance, biological reproduction, perturbation robustness and measurable resource efficiency in an auditable agentic framework.

Megasphaera elsdenii is best known as a prominent lactate consumer within the rumen microbial community in livestock, and its metabolic properties are relatively well studied. In humans, it can be isolated from healthy donors feces and, more often, from patients feces with diverse inflammatory conditions. It is currently unclear whether human and animal gut isolates are genetically related and perform the same metabolic function. In this study, we compared 86 M. elsdenii genomes from human feces (as a proxy for the human gut) to animal gut isolates and contrasted both with phylotypes colonizing the vaginal niche. Phylogenetic analysis showed that human gut samples form a genetically homogeneous branch with animal gut isolates, with samples intermingled and no evident host-specific clustering. Human gut samples shared most of their genes and biochemical pathways with isolates from swine and cattle, despite differences in their digestive tracts. When human and animal samples were labeled, supervised analysis detected only minor differences in pathway content. Genome-scale metabolic modeling suggests that human and animal gut samples likely share identical carbon and energy source requirements. Moreover, the requirements for lactate and acetate were conserved across all studied samples, regardless of the host or niche. Finally, we found no virulence genes, and lactate utilization remains a plausible explanation for M. elsdenii accumulation in different niches. IMPORTANCEMegasphaera elsdenii is considered a commensal in the human gut and animal rumen. However, M. elsdenii tends to be more abundant in patients feces with diverse inflammatory conditions. As we know little about strain diversity and biology in human isolates, comparison with better-studied isolates can be informative. In this study, we compared M. elsdenii genomes from the human gut to those in better-studied isolates from ruminal and non-ruminal animals. Human-derived samples from patients and healthy donors were genetically very similar to animal isolates and may have shared a common origin. We found that human and animal samples have a similar genomic makeup and metabolic potential, and that neither group harbors virulence genes. We hypothesize that M. elsdenii is a benign commensal that grows in response to lactate accumulation in the inflamed gut.

The human gut microbiome plays a central role in host physiology and disease, yet metagenomic analysis pipelines remain fragmented across sample preparation, taxonomic classification, and clinical interpretation stages, complicating reproducibility and translational use. Here we present GTX-GUT, a fully automated, containerized Snakemake pipeline for 16S rRNA gut microbiome profiling that integrates quality control, taxonomic classification (QIIME2/DADA2 against Greengenes 13.8), diversity and compositional metrics benchmarked against a curated healthy reference population, enterotype classification, a clinical association module spanning 11 disease categories, and automated natural-language report generation. We validated the pipeline using the ZymoBIOMICS mock community, showing that BBDuk preprocessing substantially reduced genus-level quantification error (Mean Absolute Error reduced from 7.34 to 1.58 percentage points; Pearsons r improved from 0.576 to 0.833). Application to a human sample from a patient with type 2 Diabetes Mellitus recovered a dysbiotic signature consistent with the literature, including reduced Firmicutes abundance, elevated Bacteroidetes and Proteobacteria, and a predominance of clinical associations within metabolic and gastrointestinal categories. These results demonstrate that GTXGUT provides a reproducible, end-to-end framework linking raw sequencing data to clinically interpretable output, with direct applicability to research and translational microbiome studies.

Lifestyle changes associated with industrialization, including shifts in diet, are linked to extensive remodeling of the human gut microbiome. Investigating the gut microbiomes of traditional populations living non-industrialized lifestyles, such as the Hadza hunter-gatherers of Tanzania, therefore offers insight into bacterial taxa and functions depleted in industrialized microbiomes. Despite pronounced compositional differences that correspond to lifestyle, many commensal gut species are shared between industrialized and non-industrialized populations, raising a fundamental question of whether and how their functional repertoires diverge across contrasting gut environments. Here, by integrating strain-resolved metagenomics with culturomics and in vivo models, we identify phylogenetic and functional divergence within prevalent gut species shared between Hadza and industrialized populations, particularly among members of the Clostridia class. Dorea longicatena, prevalent across diverse human populations independent of lifestyle, is a poorly characterized member of Clostridia. Using strains of this D. longicatena isolated from either Hadza or industrialized human stool samples we uncover metabolic pathways selectively enriched in Hadza strains, including machinery for utilizing plant-derived pectic polysaccharides. We further demonstrate that this Hadza-enriched polysaccharide-utilization machinery can be leveraged with its cognate dietary substrate to promote D. longicatena colonization within a model industrialized microbiota, reshaping intestinal and portal-vein metabolite profiles. Together, our findings reveal substantial functional divergence within gut bacterial species shared across lifestyles, providing a framework for identifying diet-responsive strains and pathways that may complement ecological and metabolic functions underrepresented in industrialized microbiotas.

Functional profiling of anaerobic digestion microbiomes is routinely performed against KEGG or MetaCyc, whose paid commercial licences block fee-for-service analysis for biogas operators, the setting where the results have the most immediate operational value. We present digestome, a curated marker-gene panel for anaerobic digestion built exclusively on sources that are free for commercial use (NCBIfam, public domain; Pfam, CC0; Rhea, CC BY), together with a scorer that reports pathway completeness, branch capability and, for hydrolysis, whether the enzyme is built for export. Benchmarked against 1,401 metagenome-assembled genomes from 134 anaerobic digesters, the panel assigned no methanogenesis route to any of 1,361 non-methanogens, and every acetoclastic call fell within Methanosarcina or Methanothrix. The result held for 3,043 species representatives from the Genome Taxonomy Database, spanning 203 phyla, which are built differently from binned metagenomes: the only background genomes given a route were two archaea carrying mcrA, and none of the 90 anaerobic methane and alkane oxidisers was given one. Specificity against non-methanogens does not show that a methanogen gets the right route: with a family-level check, 59 of 156 acetoclastic calls in that sample fell on methylotrophic genera that do not use acetate, and a lineage policy removed them, with 14 more left unconfirmed in unnamed genera. Testing whether a catalytic domain shares a polypeptide with an export module reduced the genomes credited with cellulolytic capacity from 597 to 50. A causal graph of the measurement shows that a profile reports the community together with three measurement choices (sampling, DNA extraction and sequencing depth, and reference genomes), which fixes what one profile can attribute and what a prospective validation would have to record.

The human microbiome is a complex, multikingdom ecosystem where bacteria and fungi cohabit and interact. Despite their ecological and clinical significance, cross-kingdom dynamics remain poorly characterized due to dominant single-kingdom research approaches. To understand the principles structuring multi-kingdom microbial communities, we applied the sparse inference method SpiecEasi to 45 publicly available samples from the gastrointestinal tract, skin, and oral cavity. Bacterial (16S rRNA) and fungal (ITS) sequencing data were processed using QIIME2, managed in phyloseq, and co-occurrence networks were inferred via SpiecEasi with Meinshausen- Buhlmann estimation. To validate robustness, we employed SparCC as a secondary inference method and performed 100 bootstrap iterations. Body site stratification controlled for environmental confounders. Our analysis revealed a microbial network of 5,023 taxa (5,020 bacterial, 3 fungal) connected by 30,478 significant associations. Crucially, we identified 737 robust bacterial-fungal interkingdom interactions (689 positive, 48 negative) confirmed by both inference methods. The network exhibited sparse connectivity (density = 0.0024) and modular structure (modularity = 0.45). Hub analysis identified 15 keystone taxa, including Bacteroides uniformis and Faecalibacterium prausnitzii. Interaction patterns were body-site-specific (P < 0.001), with the gastrointestinal tract showing the highest interkingdom connectivity (385 edges). This study provides systematic evidence that bacterial-fungal interactions are abundant and integral to human microbiome architecture. The discovery of 737 cross-kingdom associations challenges the prevailing single-kingdom paradigm and advocates for an integrated multikingdom perspective. These interactions, particularly those mediated by keystone hubs, represent novel targets for microbiome-based therapeutics and diagnostics. ImportanceThis study challenges the prevailing single-kingdom paradigm in microbiome research by demonstrating that bacterial-fungal interactions are abundant and integral to human microbiome architecture. The discovery of 737 cross-kingdom associations across three body sites provides a foundational resource for understanding multikingdom microbial ecology. The identification of keystone bacterial hubs--particularly Bacteroides uniformis and Faecalibacterium prausnitzii--as central connectors in interkingdom networks opens new avenues for microbiome-based therapeutics and diagnostics. Our integrated analytical framework, combining SpiecEasi and SparCC with body site stratification, offers a robust methodological template for future cross-kingdom studies.

Zoonotic and other livestock-associated pathogens move readily within animal production systems, yet their dispersal into surrounding environments is poorly characterized. We evaluated whether synanthropic filth flies act as spatially integrating sentinels of livestock-associated pathobiomes. From 30 roadside sites in six Texas counties stratified by livestock production intensity, we collected 3,373 Calliphoridae spp. and 12 Musca domestica, together with co-located soil, water, plant, and fecal samples, and characterized 195 metagenomes (122 insect, 73 environmental). Using diversity, differential-abundance, machine-learning, indicator-species, and network analyses, we found three robust patterns. First, fly metagenomes form a microbial compartment compositionally distinct from co-located environmental matrices (PERMANOVA R{superscript 2} = 0.094, F = 19.81, P = 0.001) and carry markedly higher relative abundances of pathogen-associated taxa, so insect sampling is complementary to, not redundant with, environmental sampling. Second, the dominant landscape signal is categorical rather than graded: cattle presence, not a Low-Medium-High density gradient, most strongly correlated with community structure, with the ruminant-associated anaerobe Peptostreptococcus russellii as the most reproducible biomarker. Third, intensification produced taxon-level enrichments (notably the feedlot pathogen Trueperella pyogenes) and progressive co-occurrence-network fragmentation, without a monotonic dose-response. A curated 12-pathogen panel failed to classify landscape context, whereas whole-community analysis succeeded. Filth flies thus provide a pragmatic, spatially integrated readout that distinguishes low-impact from livestock-impacted landscapes, offering a defensible tool for One Health pathogen surveillance. IMPORTANCEZoonotic and other livestock-associated pathogens emerging from animal production are among the most pressing threats to global health but tracking how they spill into the wider environment is difficult and expensive. We show that common filth flies, abundant, easy to trap, and free ranging across farms, fields, and human spaces, act as living samplers that concentrate and report the microbial signature of nearby livestock. Sampling flies captured information that soil and water sampling missed, and a single binary signal, the presence or absence of cattle, was associated with the microbial community more strongly than any measure of animal density. Narrow tests targeting a short curated list of pathogens were not successful in differentiating sites, while analyzing the whole microbial community provided more discriminating power. These findings support a practical, low-cost strategy in which insects serve as biological sentinels for the detection of livestock-associated pathogens across agricultural landscapes.

The oral cavity contains multiple microbial sub-niches, but which taxa consistently play an ecologically important, health-associated role within each niche, and how conserved they are across populations, remains poorly understood, partly due to the lack of a standardised identification framework. We developed a multi-cohort framework integrating 37,739 oral microbiome profiles (16S rRNA and shotgun sequencing) from 142 cohorts (41 countries) ranking 542 taxa across four oral habitats, supragingival, subgingival, tongue-tonsil, and buccal-palate-mucosa, via a new Health-Associated-Core (HAC) score capturing consistent prevalence, ecological influence, and health-association. For saliva, with available longitudinal sampling, we extended this into a salivary-Health-Associated-Core-Keystone (sHACK) score additionally capturing stability-association, ranking 499 taxa. Using two complementary approaches for identifying ecological modules, high-sHACK salivary taxa concentrated within a single, connected sub-community of 28 members, consistently linked to prevalence, ecological influence, stability, and health. This sub-communitys abundance alone outperformed conventional dysbiosis indices in distinguishing healthy from diseased individuals and tracked stability in an independent cohort of 4,621 microbiomes. Comparable sub-communities emerged across three other subsites, with compositional differences mirroring physicochemical variation between sites. Machine learning linked taxa-specific-genome-encoded functions to their corresponding subsite-specific HAC/sHACK scores, offering a unified framework for prioritizing oral microbes diagnostically and therapeutically.

Diet is an important determinant of gut microbiome composition and host physiology. However, dietary exposure is often represented using categorical groups that may not fully capture variation in nutrient intake or its associations with gut microbiome and host biomarkers. Here, we analyzed diet-microbiome-host physiology relationships in healthy adults with long-term adherence ([&ge;]2 years) to omnivore, vegetarian, or vegan diets. We integrated four-day weighed dietary records, blood biomarkers, and 16S rRNA gene sequencing to characterize dietary intake, host physiology, and gut microbiome composition. We derived nutrient intake gradients by dimensionality reduction of representative nutrients and identified the second nutrient intake gradient (NIG2) as a quantitative axis reflecting variation between animal- and plant-leaning nutrient profiles, broadly recapitulating categorical dietary groups. While NIG2 showed no association with microbiome within-sample diversity, it explained gut microbiome compositional variation that was not detected using categorical dietary groups. Mediation analyses identified nominal candidate microbiome-mediated diet-biomarker relationships involving thyroid-related, hematological, and vitamin-related biomarkers. Network analyses of NIG2-defined animal-leaning and plant-leaning subsets identified subset-specific microbial associations and differences in microbial community structure and potential interactions. Overall, this continuous nutrient-derived dietary gradient provides a quantitative exposure metric for characterizing diet-microbiome-host physiology relationships. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC="FIGDIR/small/740531v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@104a825org.highwire.dtl.DTLVardef@bb2b33org.highwire.dtl.DTLVardef@114171org.highwire.dtl.DTLVardef@df9c38_HPS_FORMAT_FIGEXP M_FIG C_FIG

Fermentation of foods and beverages represents one of humanitys oldest biotechnologies, generating compounds with demonstrated benefits for gut microbiota modulation, immune regulation, and metabolic health. The global rise of non-communicable chronic diseases, including obesity, type 2 diabetes, and chronic inflammation, has intensified the search for microbiome-based interventions, positioning fermented beverages as promising sources of next-generation probiotics and functional microbial consortia. Beverages such as kefir and kombucha, together with traditional Mexican fermented beverages including pozol and pulque, have been subjected to high-depth shotgun metagenomic studies generating high quality genomic resources. Systematic genomic mining efforts aimed at the functional characterization and biotechnological exploitation of these microbial communities, however, remain scarce. Here, we used a bioprospecting pipeline applied to milk-based kefir, kombucha, pozol, and pulque, integrating targeted genomic mining of genes associated with the biosynthesis of B-group vitamins, short-chain fatty acids, natural products, and CAZymes with potential to enhance starch and dietary fiber utilization upon intestinal colonization. Through genome-scale metabolic modeling of metagenome-assembled genomes, we identified microbial candidates predicted as central producers of secondary metabolites involved in pathogen control. We then used these results for the in silico synthetic assembly of a six-member synthetic microbial community predicted to exhibit stable cooperative growth and high metabolic functionality. Cross-feeding analysis revealed iron as one of the most widely shared elements among community members, with Priestia flexa from pozol, serving as a major donor of compounds involved in iron transport and as a stabilizing element within the synthetic community. This approach allows us to design a theoretical highly functional probiotic community, opening new avenues for the systematic exploitation of microbial diversity for biomedical purposes.

Fiber supplements are the first line of treatment for all patients with benign anorectal disease. In addition, fiber has been found to support gut microbiota and reduce inflammation; however, their integrated effect on the colonic mucosal environment is incompletely defined. We evaluated how mixed-fiber formulations may collectively shape the rectal mucosal environment through coordinated effects on microbiota composition, short-chain fatty acid (SCFA) handling, and host transcription. We evaluated multi-compartment responses to a 28-day mixed-fiber intervention in 37 participants with benign anorectal disease (~15 g/day; 7.5g psyllium husk, 8g wheat dextrin) and assessed the microbiota (16S rRNA), the metabolome (GC-MS and LC-MS), and transcriptome (subset n=10) before (PRE) and after (POST) supplementation. Changes in KEGG pathways were tested with gene set enrichment analysis (GSEA). Associations were tested by Spearman correlation, and microbiota-based prediction of SCFA responses by random-forest regression. Microbiota diversity was stable after the fiber intervention, however, 10 mucosal ASVs were differentially abundant including Parabacteroides and Lachnospiraceae NK4A136 group while SCFAs, propionate and isobutyrate, both decreased in rectal tissue and butyrate decreased in serum. To understand these shifts in the metabolome, we proceeded with the transcriptome where we identified 130 differentially expressed genes and enrichment of metabolic and SCFA-related pathways. Random forest regression captured a modest signal for serum butyrate. Taken together, short-term mixed-fiber supplementation produced selective rectal mucosal taxonomic shifts and enriched mucosal metabolic and SCFA-metabolism program despite reduced SCFA pools, suggesting that even brief over-the-counter fiber supplementation may reshape the mucosal metabolic environment.

Malnutrition is a leading cause of child mortality worldwide and has long-lasting health and socio-economic consequences. Studies have established causal links between the gut microbiota and childhood malnutrition, with key microbial signatures including delayed microbiome development and an enrichment of bacterial pathogens. While current dietary interventions improve growth and developmental outcomes, post-therapy regression to an immature microbial state is common. Fecal virome transplants (FVTs) represent a promising approach to reshape gut microbial communities, yet their therapeutic potential in early life remains poorly described. In this work, we established a diet-inducible human microbiota-associated (HMA) mouse model of early-life stunting, where malnourished pups were 35% lighter and 25% shorter than healthy controls. We developed a predictive model to quantify gut bacteriome development, identifying Enterococcus and Clostridium as primary drivers of healthy maturation. Our model revealed that the malnourished HMA mouse gut remains significantly immature compared to healthy controls and decoupled from the mouse's chronological age. While a successful FVT from a healthy donor induced targeted changes in specific bacterial taxa, including a significant increase in Enterococcus species, it did not rescue physical growth or lead to broad community-level shifts. In contrast, a failed FVT from a different healthy donor revealed a significant mismatch between the donor virome and recipient bacteriome, indicating niche incompatibility that limits FVT efficacy. Our work establishes a robust human microbiota-associated mouse model for studying maturation of the gut in early life, suggesting that FVT alone is insufficient to reproducibly reshape the malnourished gut. These findings highlight the need for synergistic strategies, combining viral interventions with nutritional supplementation for maximum therapeutic effect.

Fecal-derived in vitro microbial communities represent promising models for investigating gut microbiome function and facilitating translational applications. However, the factors governing community establishment and the underlying mechanisms remain incompletely understood. We employed large-scale quantitative metaproteomics to investigate the functional and community dynamics of human fecal microbiomes cultured in three media over 25 serial passages. Our results demonstrated that both culture medium and donor-specific microbiome characteristics significantly influence community establishment. Taxon-specific functional analyses revealed nutrient availability in the medium and the microbial stress responses as key determinants of in vitro community establishment. Notably, the metabolism of mucin glycans in RapidAIM medium contributed to the maintenance of Bacteroidaceae species that were poorly supported in other media, resulting in an in vitro community that most closely resembled the uncultured fecal inoculum. Overall, these findings provide mechanistic insights into in vitro gut microbial community establishment, supporting further optimization and application of fecal-derived in vitro microbiome models.