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PubMed2025

Systematically investigating and identifying bacteriocins in the human gut microbiome.

Human gut microbiota produces unmodified bacteriocins, natural antimicrobial peptides that protect against pathogens and regulate host physiology. However, current bioinformatic tools limit the comprehensive investigation of bacteriocins' biosynthesis, obstructing research into their biological functions. Here, we introduce IIBacFinder, a superior analysis pipeline for identifying unmodified class II bacteriocins. Through large-scale bioinformatic analysis and experimental validation, we demonstrate their widespread distribution across the bacterial kingdom, with most being habitat specific. Analyzing over 280,000 bacterial genomes, we reveal the diverse potential of human gut bacteria to produce these bacteriocins. Guided by meta-omics analysis, we synthesized 26 hypothetical bacteriocins from gut commensal species, with 16 showing antibacterial activities. Further ex vivo tests show minimal impact of narrow-spectrum bacteriocins on human fecal microbiota. Our study highlights the huge biosynthetic potential of unmodified bacteriocins in the human gut, paving the way for understanding their biological functions and health implications.

Humans↗

PubMed2025

Metaproteomic Analysis to Assess the Impact of Storage Media on Human Gut Microbiome in Fecal Samples.

The human gut microbiome is a diverse community of microorganisms residing in the gastrointestinal tract. The storage condition of fecal samples may impact the taxonomic and protein compositions of microbiomes in these samples. Here, we performed a mass spectrometry-based metaproteomic study to assess the impact of storage media on human gut microbiome in fecal samples. We evaluated FDA-authorized OMNIgene·GUT (OG), phosphate-buffered saline (PBS), and RNALater (RNAL) buffers and identified 38,185 microbial peptides corresponding to 7348 microbial proteins, which matched 16 phyla, 20 classes, 50 orders, 104 families, 332 genera, and 453 species. We found a high similarity among the fecal microbiomes preserved in OG, PBS, and RNAL in terms of the identification of proteins, taxa, and functional annotations. Both alpha and beta diversity suggested the high similarity among samples stored in the three media. Nonetheless, we also found some notable differences among buffers regarding the abundances of a few taxon groups. A partial human proteome (over 400 proteins) was identified in the fecal samples, with most of these proteins associated with the membrane and extracellular regions. The findings indicate the similarity among microbiomes in the fecal samples stored in OG, PBS, and RNAL regarding proteome profile, taxa, and functional capacity. SUMMARY: This study thoroughly analyzed and compared the metaproteomes of fecal samples preserved at -80°C in PBS, RNALater, and OMNIgene·GUT Dx buffers, offering novel insights into the effectiveness of these buffers in maintaining the stability and composition of the human gut microbiome. We found a high similarity in the identification and quantification of proteins, taxa, and functional annotations across the three buffers, with notable quantitative differences highlighting subtle yet important variations in preservation efficacy. The unique datasets and findings could offer valuable revelations into the impact of fecal sample preservation on translational and clinical analyses of the human gut microbiome.

Humans↗

PubMed2026

Resistant starch types 2 and 4 induce distinct and reversible changes in the human gut microbiome.

Resistant starch (RS) can confer benefits for the gut microbiome and host cardiometabolic health. However, different types of resistant starch can differentially affect gut microbiome composition and functional capacity, especially given interindividual variability in responses, thus limiting the application of resistant starch in dietary strategies. We used shotgun metagenomics to perform a secondary analysis of samples collected during a previously reported randomized clinical trial to determine the effects of dietary supplementation with two types of resistant starch (RS2 and RS4) and a digestible starch (control) on the gut microbiome. Both resistant starch types induced distinct but transient alterations in the gut microbial community. RS2 enriched the keystone degrader, Ruminococcus bromii, and Blautia glucerasea, whereas RS4 favored Parabacteroides distasonis and known but uncharacterized microbial species such as a Lachnospiraceae bacterium. Moreover, we detected strain-level differences in the response of Bifidobacterium adolescentis to resistant starch. Microbial functional profiling revealed an enhanced capacity for complex carbohydrate utilization following resistant starch intake, including increased abundance of specific α-amylases, glycoside hydrolases, starch utilization systems, and other currently uncharacterized genes. Identifying the bacterial strains and genes that respond to different RS types will help to more accurately predict who will benefit from a given RS type. Our findings demonstrate that RS2 and RS4 differentially shape microbial ecology and metabolic capacity and provide a foundation for microbiome-informed personalization of resistant starch-based dietary interventions.IMPORTANCEDietary intake influences human health by modulating metabolism, partly by shaping the microbiota inhabiting the gut. Resistant starch (RS), a dietary fiber, is associated with metabolic improvements. While previous research has explored how RS alters the gut microbiome, RS comprises five types with differing physical and chemical characteristics, and the distinct impacts of each type on the microbiome and host health have not been fully characterized, particularly using high-resolution approaches such as shotgun metagenomics. In this secondary analysis of samples from a longitudinal crossover intervention study, we link dietary supplementation with RS2 and RS4 with distinct and transient changes in the composition and functional potential of the human gut microbiome. Specifically, we identify species that increase in abundance with each RS type, accompanied by increases in genes and pathways involved in complex carbohydrate utilization. The findings support the development of precision nutrition strategies utilizing RS supplementation to improve metabolic health.This study is registered with ClinicalTrials.gov as NCT05743790.

Humans↗

PubMed2026

Identification and Classification of Expressed Orphan Genes, Spurious Orphan Genes, and Conserved Genes in the Human Gut Microbiome.

Orphan genes (OGs)-genes lacking detectable homologs outside a species-are widespread in microbial genomes and are thought to contribute to their adaptation and molecular innovation. However, not all predicted OGs may represent novel functional coding sequences. False positive OGs, also called spurious OGs, can arise from gene prediction errors. We reason that OGs lacking detectable expression are more likely to be spurious. To test this, we combined large-scale metatranscriptomic profiling of the human gut microbiome with machine learning to distinguish expressed OGs from spurious ones and compare them with conserved genes (CGs) found in multiple species. Using nearly 5,000 metatranscriptome libraries, we identified ∼218,000 OGs supported by expression evidence, while ∼330,000 predicted OGs lacked detectable expression and were classified as spurious. We extracted 154 features for sequence, structural, and evolutionary properties for each gene and trained XGBoost classifiers while accounting for genomic representation. The models achieved an area under the receiver operating characteristic curve (AUC) of 0.82 in distinguishing expressed OGs from spurious OGs and an AUC of 0.93 in distinguishing expressed OGs from CGs. Interpretation based on SHAP (SHapley Additive exPlanations) revealed clear biological signals. Particularly, expressed orphans were present in more genomes than spurious ones, and expressed OGs were shorter than CGs. This work improves OG discovery and suggests that expressed OGs differ systematically from CGs and spurious OGs in sequence composition, structural constraints, and evolutionary signals.

Humans↗

PubMed2025

Coarse-grained model of serial dilution dynamics in synthetic human gut microbiome.

Many microbial communities in nature are complex, with hundreds of coexisting strains and the resources they consume. We currently lack the ability to assemble and manipulate such communities in a predictable manner in the lab. Here, we take a first step in this direction by introducing and studying a simplified consumer resource model of such complex communities in serial dilution experiments. The main assumption of our model is that during the growth phase of the cycle, strains share resources and produce metabolic byproducts in proportion to their average abundances and strain-specific consumption/production fluxes. We fit the model to describe serial dilution experiments in hCom2, a defined synthetic human gut microbiome with a steady-state diversity of 63 species growing on a rich media, using consumption and production fluxes inferred from metabolomics experiments. The model predicts serial dilution dynamics reasonably well, with a correlation coefficient between predicted and observed strain abundances as high as 0.8. We applied our model to: (i) calculate steady-state abundances of leave-one-out communities and use these results to infer the interaction network between strains; (ii) explore direct and indirect interactions between strains and resources by increasing concentrations of individual resources and monitoring changes in strain abundances; (iii) construct a resource supplementation protocol to maximally equalize steady-state strain abundances.

Gastrointestinal Microbiome↗

PubMed2026

The antimicrobial gut resistome of the Wayampi reveals a shared background of antibiotic and metal resistance genes with industrialized populations, underscoring the "robust-yet-fragile" architecture of human gut microbiomes.

BACKGROUND: Metagenomics enables detailed profiling of genes encoding antimicrobial resistance. However, most studies focus exclusively on antibiotic resistance genes (ARGs), excluding those associated with non-antibiotic antimicrobials (metals, biocides), and often rely on methods with low-sensitivity and low-specificity. Furthermore, they rarely examine populations exposed to minimal anthropogenic pollution. We analyzed fecal resistomes of 95 Wayampi individuals, an Indigenous community in remote French Guiana, using a targeted metagenomic capture platform covering 8667 genes, including ARGs, metal resistance genes (MRGs) and biocide resistance genes (BRGs) (PMID: 29335005). Resistome profiles were compared with those of Europeans to assess population-level differences. RESULTS: ARG richness was similar between groups (259 in Wayampi vs. 264 in Europeans, 159 shared), but MRGs + BRGs gene richness was significantly higher in Wayampi (11,930 vs. 7419). Most genes appeared in a minority of individuals (mean 5% for ARGs, 2% for MRGs + BRGs), but several ARGs for tetracyclines [tet(32), tet(40), tet(O), tet(Q), tet(W), tet(X), tetAB(P)], aminoglycosides (ant6'-I, aph3-III), macrolides (ermB, ermF, mefA), and sulfonamides (sul2) were present in all individuals. Tetracycline resistance genes predominated overall, while beta-lactam resistance genes were more common in Wayampi, and genes conferring resistance to aminoglycosides, amphenicols, and folate inhibitors were more frequent in Europeans. Among MRGs, copper and arsenic resistance genes prevailed in both groups, followed by those for zinc, iron, cobalt, and nickel. Up to 76% of Wayampiis carried acquired MRGs for copper (pcoABCDRS and tcrB), silver (silACFPRS), arsenic (ars), and mercury (mer) detoxification. Shannon diversity indices were similar for ARGs, MRGs, and BRGs, but composition and evenness differed significantly. UMAP and ADONIS analyses distinguished cohorts based on ARG profiles (p < 0.001), but not on MRGs or BRGs. Correlation analysis revealed conserved gene-sharing networks and introgression of acquired ARGs and MRGs within both gut microbiomes. CONCLUSIONS: The diverse and balanced Wayampi resistome reflects a less perturbed microbiome compared to industrialized populations, and reveals a background of "core" and "shell" acquired ARGs and MRGs, consistent with the "robust-yet-fragile" architecture of scale-free networks. The patchy yet resilient gene distribution suggests varying levels of conserved gene sharing highways among populations, likely shaped by long-term microbial-human evolution, and supports a broader view on acquired antimicrobial resistance. Video Abstract.

Humans↗

PubMed2026

Multi-level aggregation analysis of microbiome composition and host gene expression reveals associations with systemic and local immunity.

The human gut microbiome plays a critical role in immune regulation, yet the molecular links between microbiome composition and host gene expression remain incompletely understood. We analyzed associations between host gene expression and microbiome composition in a cohort of 315 healthy individuals, integrating microarray-based gene expression data from three intestinal sites (ileum, transverse colon, and rectum) and six immune cell types with microbiome sequencing data. Using a hierarchical feature aggregation strategy combining principal component analysis, clustering, and covariate correction, we discovered significant associations primarily related to immunity. While microbial profiles were similar across the three intestinal sites, the transverse colon yielded the most "microbiome-host gene expression" associations. Among the immune cell types, CD8+ cells showed the highest number of associations. The first principal component of microbiome composition, reflecting a gradient from commensals (e.g., Ruminococcaceae and Christensenellaceae) to proinflammatory taxa ([Ruminococcus] gnavus and Lachnoclostridium), correlated with the expression of TNF-α-linked genes (HMOX1, CPI17, HSD3B2, and SLC5A1). Among individual genera, Catenibacterium abundance was associated with gene expression in both intestinal and immune cells, including negative associations with MRPS21 (related to mitochondrial function) in the transverse colon and with CD8+ gene programs related to T cell differentiation. These findings align with emerging evidence implicating mitochondrial dysfunction in intestinal inflammation. Our results identify multi-level associations between the gut microbiome and host gene expression, suggesting potential mechanisms by which microbiota shape local and systemic immunity and vice versa. The implicated genes and taxa represent candidates for experimental validation to improve understanding of host-microbiome homeostasis and its disruption in disease.IMPORTANCEThe gut microbiome and immune system are engaged in a complex interplay throughout human life. While most associative studies focus on case-control comparisons-typically examining patients with conditions such as inflammatory bowel disease or metabolic diseases-less is known about the molecular links between the microbiome and immune system in healthy individuals. In this study of a large cohort of healthy individuals, we addressed this gap by applying multiscale modeling to tackle the high dimensionality of host-microbiome data. We identified multi-level associations between microbiome composition and host gene expression in both intestinal tissues and immune cells. These findings offer a valuable reference for understanding baseline host-microbiome communication and highlight molecular candidates-such as TNF-α-related genes and mitochondrial pathways-for future experimental validation.

Humans↗

PubMed2024

Comprehensive analyses of a large human gut Bacteroidales culture collection reveal species- and strain-level diversity and evolution.

Species of the Bacteroidales order are among the most abundant and stable bacterial members of the human gut microbiome, with diverse impacts on human health. We cultured and sequenced the genomes of 408 Bacteroidales isolates from healthy human donors representing nine genera and 35 species and performed comparative genomic, gene-specific, metabolomic, and horizontal gene transfer analyses. Families, genera, and species could be grouped based on many distinctive features. We also observed extensive DNA transfer between diverse families, allowing for shared traits and strain evolution. Inter- and intra-species diversity is also apparent in the metabolomic profiling studies. This highly characterized and diverse Bacteroidales culture collection with strain-resolved genomic and metabolomic analyses represents a valuable resource to facilitate informed selection of strains for microbiome reconstitution.

Humans↗

PubMed2024

Comprehensive analyses of a large human gut Bacteroidales culture collection reveal species and strain level diversity and evolution.

Species of the Bacteroidales order are among the most abundant and stable bacterial members of the human gut microbiome with diverse impacts on human health. While Bacteroidales strains and species are genomically and functionally diverse, order-wide comparative analyses are lacking. We cultured and sequenced the genomes of 408 Bacteroidales isolates from healthy human donors representing nine genera and 35 species and performed comparative genomic, gene-specific, mobile gene, and metabolomic analyses. Families, genera, and species could be grouped based on many distinctive features. However, we also show extensive DNA transfer between diverse families, allowing for shared traits and strain evolution. Inter- and intra-specific diversity is also apparent in the metabolomic profiling studies. This highly characterized and diverse Bacteroidales culture collection with strain-resolved genomic and metabolomic analyses can serve as a resource to facilitate informed selection of strains for microbiome reconstitution.

Preprint↗

PubMed2025

Temporal stability and lack of variance in microbiome composition and functionality in fit recreational athletes.

Human gut microbiome composition and function is influenced by environmental and lifestyle factors, including exercise and fitness. We studied the composition and functionality of the faecal microbiome of recreational (non-elite) runners (n = 62) with serial shotgun metagenomics, at 4 time points over a 7-week period. Gut microbiome composition and function was stable over time. Grouping of samples on the basis of their fitness level (fair, good, excellent, and superior) or habitual training (low (4-6 h/week), medium (7-9 h/week), high (10-12 h/week), and extreme (13 + hours/week)) revealed no significant microbiome-related differences. Overall, the species Faecalibacterium prausnitzii, Blautia wexlerae, and Prevotella copri were the most abundant members of the gut microbiome. Analysis of co-abundance groups (CAGs) revealed no significant relationship between CAGs and fitness levels or training subgroups. Functional pathways were similar across all samples and timepoints with no clustering based on associated metadata. The most abundant genes identified within samples corresponded to pathways for nucleoside and nucleotide biosynthesis, amino acid biosynthesis, and cell wall biosynthesis. Collectively, these results describe the microbiome of active recreational runners and note temporal stability amongst participants.

Humans↗

PubMed2026

Metagenomic polymorphic toxin effector and immunity profiling predicts microbiome development and disease-related dysbiosis.

Bacteria use antagonistic interbacterial weapons, such as polymorphic toxin secretion systems (TSS), to compete for niches in the human gut microbiome. We hypothesized that TSS influence gut microbiome development and disease-related dysbiosis. We developed a bioinformatic marker gene approach (PolyProf) to quantify TSS including ~200 effector and immunity genes and applied it to ~15,000 publicly available human metagenomes. PolyProf alpha and beta diversity readily distinguished 12 different human disease states and enabled the construction of highly accurate linear regression classifier machine learning models. Elastic net machine learning models integrating bacterial taxonomy with PolyProf had strong predictive value for 12 disease states, outperforming models utilizing taxonomy alone. During microbiome development in the first year of life, PolyProf alpha diversity increases, and beta diversity becomes increasingly like the maternal microbiome, influenced by vertical transfer, delivery mode, and breastfeeding. PolyProf is related to strain sharing among adults through social interactions. In summary, TSS genes strongly correlate with microbiome development and interpersonal strain sharing, suggesting roles for interbacterial antagonism. Since PolyProf distinguishes diverse adult disease statuses, these dynamics may contribute to non-genetic inheritance.IMPORTANCEPrevious research has demonstrated that bacteria compete within the gut microbiome using toxin secretion systems (TSS). How TSS contribute to human microbiome development and the microbiome alterations observed in human diseases is not known. This study develops a new bioinformatic tool for profiling TSS-related genes in metagenomic data. Application of this approach to large-scale human fecal metagenomic data demonstrates the dynamic association of TSS during microbiome development, including the exchange of strains among social contacts. TSS gene abundance patterns are highly predictive of 12 disease states. This study advances the field by enabling TSS profiling in metagenomes and by identifying disease and microbiome development biomarkers that provide hypotheses for future mechanistic studies and may be useful for disease diagnosis.

Dysbiosis↗

PubMed2025

Expansion of a bacterial operon during cancer treatment ameliorates fluoropyrimidine toxicity.

Dose-limiting toxicities remain a major barrier to drug development and therapy, revealing the limited predictive power of human genetics. Here, we demonstrate the utility of a more comprehensive approach to studying drug toxicity through longitudinal profiling of the human gut microbiome during colorectal cancer (CRC) treatment (NCT04054908) coupled to cell culture and mouse experiments. Substantial shifts in gut microbial community structure during oral fluoropyrimidine treatment across multiple patient cohorts, in mouse small and large intestinal contents, and in patient-derived ex vivo communities were revealed by 16S rRNA gene sequencing. Metagenomic sequencing revealed marked shifts in pyrimidine-related gene abundance during oral fluoropyrimidine treatment, including enrichment of the preTA operon, which was sufficient for the inactivation of active metabolite 5-fluorouracil (5-FU). preTA+ bacteria depleted 5-FU in gut microbiota grown ex vivo and in the mouse distal gut. Germ-free and antibiotic-treated mice experienced increased fluoropyrimidine toxicity, which was rescued by colonization with the mouse gut microbiota, preTA+ Escherichia coli, or preTA-high stool from patients with CRC. Last, preTA abundance was negatively associated with fluoropyrimidine toxicity in patients. Together, these data support a causal, clinically relevant interaction between a human gut bacterial operon and the dose-limiting side effects of cancer treatment. Our approach may be generalizable to other drugs, including cancer immunotherapies, and provides valuable insights into host-microbiome interactions in the context of disease.

Animals↗

PubMed2024

Major depletion of insulin sensitivity-associated taxa in the gut microbiome of persons living with HIV controlled by antiretroviral drugs.

BACKGROUND: Persons living with HIV (PWH) harbor an altered gut microbiome (higher abundance of Prevotella and lower abundance of Bacillota and Ruminococcus lineages) compared to non-infected individuals. Some of these alterations are linked to sexual preference and others to the HIV infection. The relationship between these lineages and metabolic alterations, often present in aging PWH, has been poorly investigated. METHODS: In this study, we compared fecal metagenomes of 25 antiretroviral-treatment (ART)-controlled PWH to three independent control groups of 25 non-infected matched individuals by means of univariate analyses and machine learning methods. Moreover, we used two external datasets to validate predictive models of PWH classification. Next, we searched for associations between clinical and biological metabolic parameters with taxonomic and functional microbiome profiles. Finally, we compare the gut microbiome in 7 PWH after a 17-week ART switch to raltegravir/maraviroc. RESULTS: Three major enterotypes (Prevotella, Bacteroides and Ruminococcaceae) were present in all groups. The first Prevotella enterotype was enriched in PWH, with several of characteristic lineages associated with poor metabolic profiles (low HDL and adiponectin, high insulin resistance (HOMA-IR)). Conversely butyrate-producing lineages were markedly depleted in PWH independently of sexual preference and were associated with a better metabolic profile (higher HDL and adiponectin and lower HOMA-IR). Accordingly with the worst metabolic status of PWH, butyrate production and amino-acid degradation modules were associated with high HDL and adiponectin and low HOMA-IR. Random Forest models trained to classify PWH vs. control on taxonomic abundances displayed high generalization performance on two external holdout datasets (ROC AUC of 80-82%). Finally, no significant alterations in microbiome composition were observed after switching to raltegravir/maraviroc. CONCLUSION: High resolution metagenomic analyses revealed major differences in the gut microbiome of ART-controlled PWH when compared with three independent matched cohorts of controls. The observed marked insulin resistance could result both from enrichment in Prevotella lineages, and from the depletion in species producing butyrate and involved into amino-acid degradation, which depletion is linked with the HIV infection.

Humans↗

PubMed2025

Human xenobiotic metabolism proteins have full-length and split homologs in the gut microbiome.

Xenobiotics, including pharmaceutical drugs, can be metabolized by both host and microbiota, in some cases by homologous enzymes. We conducted a systematic search for all known human proteins with gut microbial homologs. Because gene fusion and fission can obscure homology detection, we built a pipeline to identify not only full-length homologs, but also cases where microbial homologs were split across multiple adjacent genes in the same neighborhood or operon ("split homologs"). We found that human proteins with full-length gut microbial homologs disproportionately participate in xenobiotic metabolism. While this included many different enzyme classes, short-chain and aldo-keto reductases were the most frequently detected, especially in prevalent gut microbes, while cytochrome P450 homologs were largely restricted to lower-prevalence facultative anaerobes. In contrast, human proteins with split homologs tended to play roles in central metabolism, especially of nucleobase-containing compounds. We identify twelve specific drugs that gut microbial split homologs may metabolize; 2 of these, 6-mercaptopurine by xanthine dehydrogenase and 5-fluorouracil by dihydropyrimidine dehydrogenase, have been recently confirmed in mouse models. This work provides a comprehensive map of homology between the human and gut microbial proteomes, indicates which human xenobiotic enzyme classes are most likely to be shared by gut microorganisms, and finally demonstrates that split homology may be an underappreciated explanation for microbial contributions to drug metabolism.

Humans↗

PubMed2026

Farming reshapes the gut resistome, virulome, and mobilome of Cervidae.

The rapid expansion of cervid farming raises concerns about antimicrobial resistance (AMR) dissemination, yet its impact on the Cervidae gut microbiome remains poorly characterized. We integrated 89 newly sequenced fecal metagenomes with 599 publicly available datasets, comprising 285 metagenomes from farmed cervids and 370 from wild cervids, to construct a catalog of 15,494 non-redundant metagenome-assembled genomes (MAGs) representing 2,401 species. Our analysis demonstrates that farming profoundly reshapes the gut microbiome's functional composition. Specifically, farmed cervids exhibited significantly higher relative abundance, diversity, and heterogeneity of antimicrobial resistance genes (ARGs) compared to wild counterparts. We observed a robust synergistic relationship between ARGs, virulence factor genes, and mobile genetic element (MGE)-associated genes, identifying 70 ARG-MGE combinations as evidence of potential horizontal gene transfer. Plasmid profiling further suggested that a subset of ARGs may be associated with conjugative plasmids, with plasmid-associated ARGs being significantly more abundant in farmed than in wild cervids. Virome analyses indicated that bacteriophages, particularly Siphoviridae, may serve as mobile reservoirs for ARGs. Notably, Cervidae shared 268 ARG types with humans, including 23 high-risk genes associated with resistance to clinically important antibiotics (e.g. tetX1, vanRD, and bla-CTX-M-178), with Escherichia coli as a key cross-host carrier. These findings highlight that human-impacted cervid gut microbiomes are significant environmental reservoirs of clinically relevant AMR, underscoring the necessity for enhanced antibiotic stewardship and resistance surveillance in managed wildlife within a One Health framework.

Animals↗

PubMed2026

Causal relationships between oral-gut microbiome and bone neoplasm-related phenotypes: Insights from bidirectional Mendelian randomization.

The human oral and gut microbiota are the 4 largest microbial communities in the body and play crucial roles in maintaining homeostasis and influencing disease. Observational studies have suggested links between these microbiota and bone neoplasm-related phenotypes, but establishing causality has been challenging due to confounding factors and reverse causality. We conducted a bidirectional, 2-sample Mendelian randomization (MR) study to investigate evidence consistent with a potential causal association between the saliva and gut microbiota and various bone neoplasm-related phenotypes. Genetic instruments for saliva and gut microbiota were sourced from large genome-wide association studies. Inverse variance weighted was the primary MR method, supplemented by 4 other MR techniques. Sensitivity analyses, including MR-Egger regression, were performed to assess pleiotropy and heterogeneity. In the forward MR analysis, Veillonella parvula from the saliva microbiota was associated with a decreased risk of bone and connective tissue neoplasms (β: -0.236, 95% CI: [-0.275, -0.197], P = 8.20E-33). MR analyses identified genetically predicted associations between several microbial taxa and bone neoplasm-related phenotypes. Reverse MR analyses showed that genetic liability to bone neoplasm-related phenotypes was associated with variation in the composition of the oral (e.g., Order Bacteroidales, Rothia mucilaginosa) and gut microbiota (e.g., Class Methanobacteria, Genus Eubacterium oxidoreducens group). Sensitivity analyses confirmed the robustness of these findings, as no statistical evidence of substantial heterogeneity or directional horizontal pleiotropy was detected. This study provides genetic evidence supporting a bidirectional causal relationship between specific saliva and gut microbiota and bone neoplasm-related phenotypes. Our findings identify several microbial taxa as potential candidates for future biomarker development and therapeutic investigation in bone neoplasm-related phenotypes. However, these genetically informed associations require further mechanistic, experimental, and prospective clinical validation before clinical application.

Humans↗

PubMed2025

Metabolism and gene expression models for the microbiome reveal how diet and metabolic dysbiosis impact disease.

The gut microbiome plays a critical role in human health, spurring extensive research using multi-omic technologies. Although these tools offer valuable insights, they often fall short in capturing the complexity of microbial interactions that associate with disease onset, progression, and treatment. Thus, integration of multi-omics datasets with metabolic models is needed to predict associations between microbial activity and disease. Here, we automated the reconstruction of 495 metabolic and gene expression models (ME-models), overcoming the main limitation preventing the wide use of this approach. We integrated them with multi-omics data from patients with inflammatory bowel disease (IBD), identifying taxa associated with variations in amino acids, short-chain fatty acids, and pH in the gut of IBD patients. In general, this approach provides testable hypotheses of the metabolic activity of the gut microbiota, and the automated pipeline opens the opportunity to study microbial interactions in other biologically relevant settings using ME-models.

Humans↗

PubMed2025

Specificities of chemosensory receptors in the human gut microbiota.

The human gut is rich in metabolites and harbors a complex microbial community, yet surprisingly little is known about the spectrum of chemical signals detected by the large variety of sensory receptors present in the gut microbiome. Here, we systematically mapped the ligand specificities of selected extracytoplasmic sensory domains from twenty members of the human gut microbiota, with a primary focus on the abundant and physiologically important class of Clostridia. Twenty-five metabolites from different chemical classes-including amino acids, nucleobase derivatives, amines, indole, and carboxylates-were identified as specific ligands for fifteen sensory domains from nine bacterial species, which represent all three major functional classes of transmembrane receptors: chemotaxis receptors, histidine kinases, and enzymatic sensors. We have further characterized the specificity and evolution of ligand binding to Cache superfamily sensors specific for lactate, dicarboxylic acids, and for uracil and short-chain fatty acids (SCFAs). Structural and biochemical analysis of the dCache sensor of uracil and SCFAs revealed that its two different ligand types bind at distinct sensory modules. Overall, combining experimental identification with computational analyses, we were able to assign ligands to approximately half of the Cache-type chemotaxis receptors found in the eleven gut commensal genomes from our set, with carboxylic acids representing the largest ligand class. Among these, the most commonly found ligand specificities were for lactate and formate, indicating a particular importance of these metabolites in the human gut microbiota and consistent with their observed growth-promoting effects on selected bacterial commensals.

Humans↗