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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↗

PubMed2025

A high-quality genomic catalog of the human oral microbiome broadens its phylogeny and clinical insights.

The oral microbiome is increasingly linked to human health. To further examine this microbial community, we present the human reference oral microbiome (HROM), with 72,641 high-quality genomes from 3,426 species, including 2,019 previously unidentified species, improving metagenomic sequence read classification over existing catalogs. Notably, HROM unveils 1,137 previously uncharacterized candidate phyla radiation (CPR) species, establishing Patescibacteria as the most prevalent phylum in the oral microbiota and distinct from environmental Patescibacteria. Additionally, an oral CPR subclade is associated with periodontitis, complementing Porphyromonas gingivalis in predicting disease. Finally, comparing HROM with reference genomes of the gut microbiome reveals taxonomic and functional divergence between these microbiomes. HROM contains 42 ectopic oral species, and their relative abundance in gut microbiota is predictive of intestinal, cardiovascular, and liver diseases. Thus, HROM offers an expanded view of the oral microbiome and highlights the clinical importance of further examining the links between oral microbes and systemic disorders.

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

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↗

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↗

PubMed2025

Animal farming and the oral microbiome in the Agricultural Health Study.

BACKGROUND: Raising farm animals imparts various exposures that may shape the human microbiome. The oral microbiome has been increasingly implicated in disease development. Animal farming has also been associated with certain chronic diseases such as cancer; however, underlying biological mechanisms are unclear. We investigated associations between raising farm animals and the oral microbiome in the Agricultural Health Study. METHODS: This analysis included 1,245 participants (865 farmers and 380 spouses) who provided oral wash specimens and information on types and numbers of specific animals raised on their farms within 2 years before sample collection. The oral microbiome was measured by sequencing the V4 region of the 16S ribosomal RNA gene. We evaluated associations of farm animal exposures with alpha and beta diversity metrics (within- and between-sample diversity, respectively), as well as presence and relative abundance of specific bacterial genera. All analyses adjusted for potential confounders (e.g., age, sex, smoking, alcohol consumption). RESULTS: Overall, 63 % of participants raised farm animals, most commonly cattle (46 %) and hogs (20 %). Those who raised a large number of hogs (≥2,000 vs. no hogs) had higher alpha diversity. Conversely, raising sheep/goats and raising larger numbers of poultry were associated with lower alpha diversity. Beta diversity was not significantly different between participants with and without any farm animals. Participants raising any farm animals had higher relative abundance of Porphyromonas and lower relative abundances of Prevotella and Ruminococcaceae UCG-014. Several genera were more likely to be absent with specific animal exposures (e.g., Capnocytophaga for cattle and sheep/goats; Corynebacterium, Dialister, Stomatobaculum, and Solobacterium for sheep/goats and poultry). CONCLUSIONS: This was the largest study of farm animal exposures and the human microbiome to date. Findings suggest that raising specific farm animals may influence the oral microbiome, supporting the need to further investigate the potential role of animal farming in disease etiology.

Microbiota↗

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

MetagenomicKG: a knowledge graph for metagenomic applications.

MOTIVATION: The sheer volume and variety of genomic content within microbial communities makes metagenomics a field rich in biomedical knowledge. To traverse these complex communities and their vast unknowns, metagenomic studies often depend on distinct reference databases, such as the Genome Taxonomy Database (GTDB), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Bacterial and Viral Bioinformatics Resource Center (BV-BRC), for various analytical purposes. These databases are crucial for the genetic and functional annotation of microbial communities. Nevertheless, the inconsistent nomenclature or identifiers of these databases present challenges for effective integration, representation, and utilization. Knowledge graphs (KGs) offer an appropriate solution by organizing biological entities from different databases to standardized identifiers, allowing their interrelations to be captured into a cohesive network regardless of the naming conventions used in each source. The graph structure not only facilitates the unveiling of hidden patterns but also enriches our biological understanding with deeper insights. Despite KGs having shown potential in various biomedical fields, their application in metagenomics remains underexplored. RESULTS: We present MetagenomicKG, a novel knowledge graph specifically tailored for metagenomic analysis. MetagenomicKG integrates taxonomic, functional, and pathogenesis-related information on the human microbiome sourced from various databases, and further connects these with existing biomedical KGs to expand the biological network. Through various case studies involving the human microbiome, we demonstrate its utility in enabling hypothesis generation regarding the relationships between microbes and diseases, generating sample-specific graph embeddings, and providing robust pathogen prediction. CODE AVAILABILITY: The source code and technical details for constructing the MetagenomicKG and reproducing all analyses are available on GitHub at https://github.com/KoslickiLab/MetagenomicKG. The data used in this manuscript, including the pre-built files and use case input data, are archived on Zenodo with DOI: 10.5281/zenodo.17546861.

Metagenomics↗

PubMed2026

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

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

Microbiota↗

PubMed2026

Reframing the asthma microbiome: Multikingdom, multisite, and multiomic perspectives.

The field of asthma microbiome research has shifted rapidly in recent years. Advances in sequencing technology have led to an increased ability to characterize multikingdom microbial species and integration with host -omics profiling to enhance future translational applications. Traditional bacteria-centric, cross-sectional studies are giving way to mechanistic frameworks that incorporate fungi, viruses, and host-immune interactions. In this state-of-the-art review of emerging concepts in microbiome asthma research, we first propose a structured framework to consider microbiome studies across 5 major domains-microbial kingdom, site of sampling, integration with host -omics, clinical outcome domain, and translational relevance-in order to synthesize recent high-impact human microbiome studies in asthma. We highlight emerging evidence that fungal and viral communities contribute independently to asthma risk and that human microbial communities are linked to distinct inflammatory and immune pathways shaped by host genetic susceptibility.

Asthma↗

PubMed2025

A global survey of taxa-metabolic associations across mouse microbiome communities.

Host-microbiota mutualism is rooted in the exchange of dietary and metabolic molecules. Microbial diversity broadens the metabolite pool, with each taxon contributing distinct compounds in varying proportions. In the human microbiome, high variability in consortial composition is largely compensated by similar metabolic functions across different taxa. However, the extent of compensation in lower diversity mouse models, and whether vivaria are metabolically equivalent, is unknown. We provide a searchable resource of microbiome composition variability across 51 murine vivaria and 12 wild mouse colonies worldwide, with vivarium-specific variants mapped according to predicted 3D structures for each microbial species. Our matched metabolomics data show that realized metabolic potential has relatively low variability, providing functional evidence for metabolic compensation. Additionally, variability is related to taxonomic composition rather than vivarium, revealing taxa-metabolite associations that are potentially relevant to phenotypic differences between vivaria. Collectively, this resource offers tools to strengthen microbiome studies and collaborative science.

Animals↗

PubMed2026

Genome-Resolved Functional Profiling of Osteoporosis-Associated Gut Bacteria Highlights Putative Metabolic and Immunogenic Signatures of the Gut-Bone Axis.

The gut microbiota has emerged as a potential regulator of bone metabolism, but the genome-encoded functional repertoire of osteoporosis-associated gut bacteria remains insufficiently characterized. This study performed in silico functional profiling of gut bacterial taxa associated with osteoporosis, low bone mineral density, or comparator bone-related phenotypes. Twenty candidate taxa were selected from evidence in the human microbiome and represented by 26 curated bacterial reference genomes. Genome-wide annotations were used to map predicted gut-bone axis signatures, carbohydrate-active enzyme (CAZyme) repertoires, selected Kyoto Encyclopedia of Genes and Genomes pathways, and gutSMASH-predicted metabolic gene clusters. Functional burdens were normalized as hits per 1000 annotated proteins and integrated into metabolic, immunogenic, CAZyme, KEGG, and metabolic gene cluster profiles. Twelve predicted gut-bone axis signatures were identified, comprising 3337 primary candidate protein hits and a strict high-confidence subset of 2497 hits. Dominant signatures included vitamin B12/cobalamin metabolism, folate/one-carbon metabolism, peptidoglycan/cell-wall biosynthesis, and short-chain fatty acid-related functions. Dialister invisus, Dialister succinatiphilus, Megamonas funiformis, and Megamonas hypermegale showed the strongest normalized predicted gut-bone axis signal. These hypothesis-generating findings prioritize microbial metabolic and immunogenic features for future metagenomic, metabolomic, and experimental validation studies.

Osteoporosis↗

PubMed2025

Function-based selection of synthetic communities enables mechanistic microbiome studies.

Understanding the complex interactions between microbes and their environment requires robust model systems such as synthetic communities (SynComs). We developed a functionally directed approach to generate SynComs by selecting strains that encode key functions identified in metagenomes. This approach enables the rapid construction of SynComs tailored to any ecosystem. To optimize community design, we implemented genome-scale metabolic models, providing in silico evidence for cooperative strain coexistence prior to experimental validation. Using this strategy, we designed multiple host-specific SynComs, including those for the rumen, mouse, and human microbiomes. By weighting functions differentially enriched in diseased versus healthy individuals, we constructed SynComs that capture complex host-microbe interactions. We designed an inflammatory bowel disease SynCom of 10 members that successfully induced colitis in gnotobiotic IL10-/- mice, demonstrating the potential of this method to model disease-associated microbiomes. Our study establishes a framework for designing functionally representative SynComs of any microbial ecosystem, facilitating mechanistic study.

Animals↗

PubMed2022

Identification of shared bacterial strains in the vaginal microbiota of related and unrelated reproductive-age mothers and daughters using genome-resolved metagenomics.

It has been suggested that the human microbiome might be vertically transmitted from mother to offspring and that early colonizers may play a critical role in development of the immune system. Studies have shown limited support for the vertical transmission of the intestinal microbiota but the derivation of the vaginal microbiota remains largely unknown. Although the vaginal microbiota of children and reproductive age women differ in composition, the vaginal microbiota could be vertically transmitted. To determine whether there was any support for this hypothesis, we examined the vaginal microbiota of daughter-mother pairs from the Baltimore metropolitan area (ages 14-27, 32-51; n = 39). We assessed whether the daughter's microbiota was similar in composition to their mother's using metataxonomics. Permutation tests revealed that while some pairs did have similar vaginal microbiota, the degree of similarity did not exceed that expected by chance. Genome-resolved metagenomics was used to identify shared bacterial strains in a subset of the families (n = 22). We found a small number of bacterial strains that were shared between mother-daughter pairs but identified more shared strains between individuals from different families, indicating that vaginal bacteria may display biogeographic patterns. Earlier-in-life studies are needed to demonstrate vertical transmission of the vaginal microbiota.

Child↗

PubMed2026

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

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

Humans↗

PubMed2026

Dual-transcriptomic analysis of human nasal transcriptome and microbiome reveals host-bacteria associations in symptomatic respiratory infection.

BACKGROUND: The human nasopharynx is colonized by a diverse community of commensal microbiota linked to many respiratory diseases, yet their associations with the host remain unclear. RESULTS: In this study, we introduced a dual-transcriptomics analysis strategy, which can characterize the host transcriptome and microbiome from nasal samples simultaneously. We applied this workflow to a local SARS-CoV-2 cohort with 76 asymptomatic infected patients, among whom 52 (68.42%) developed symptomatic infection during a 1-week follow-up period. Nasal swabs were collected from all 76 patients at enrollment and from 73 patients at one-week later follow-up. We detected a median of 8.94% reads that did not map to the human genome across all 149 samples, among which around half (median 49.68%) were successfully mapped to microbiome genome. Meta-transcriptomic analysis detected significantly higher SARS-related coronavirus loads in samples from the symptomatic group at enrollment (P = 0.004), and both groups showed decreased loads one week later (symptomatic, P = 0.001; asymptomatic, P = 0.035). Compared with benchmarking 16 S rRNA sequencing on 53 samples, our computational strategy showed high correlation of relative abundance in all top 20 genera (median Rho = 0.90, Pmax < 0.001). A total of 670 bacteria species were identified to show a relative abundance ≥ 0.01% in at least 10% samples. Differential abundance analysis identified 76 species (DASs) from six phyla with significantly decreased abundance in samples from the symptomatic group (log2(fold change or FC) < -1 and adjusted P < 0.05) compared to the asymptomatic group at enrollment. Integrating these symptom-associated DASs with host's gene expression using an expression quantitative trait bacteria (eQTB) model, we found 45 symptom-associated DASs identified at enrollment were significantly associated with one to 14 genes (adjusted P < 0.05). GSEA showed a series of symptom-associated DASs were significantly correlated with pathways related to olfactory function, keratinocyte differentiation, and DNA methylation. CONCLUSIONS: In summary, our dual-transcriptomic analysis strategy effectively characterized host-microbiome associations, offering insights into microbial contributions to respiratory diseases.

Humans↗

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↗