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PubMed2026

Effects of Lactiplantibacillus plantarum KABP051 Probiotic on Body Composition, Microbiome and Mood in Healthy Overweight Adults.

Obesity and mental health disorders are among the greatest public health challenges of the 21st century. Interestingly, an altered microbiome profile has been associated with both conditions. The aim of this randomized, double-blind, placebo-controlled clinical trial was to evaluate the effects of dietary supplementation with a specific probiotic strain (Lactiplantibacillus plantarum KABP051) on body composition and gut microbiome balance, together with measures of mood state, in a population of healthy overweight subjects. Sixty healthy, moderately stressed, nondepressed and overweight or obese volunteers were supplemented for 12 weeks with probiotic (L. plantarum KABP051; 1 billion colony forming units/day) or placebo (microcrystalline cellulose). The KABP051 group experienced significantly greater improvements compared with placebo on body composition measurements, including a reduction in body weight and waist circumference, which decreased in 1.97 ± 0.77 (mean ± SE) kg and 2.15 ± 0.81 (mean ± SE) cm versus placebo at the end of the intervention (both P < .05, mixed model for repeated measures [MMRM] and post-hoc analysis). Microbiome composition improved in KABP051 group, with significant increase in the relative abundance of Lactiplantibacillus spp. versus placebo. Body fat percentage, profile of mood states fatigue, and confusion sub-scores showed a global trend toward improvement compared with placebo, with the change at 12 weeks being significant in the three measurements in post-hoc analysis (P = .015, P = .014, and P = .016, respectively). No serious adverse events were registered during the intervention period. These results suggest that a specific strain of probiotic bacteria (L. plantarum KABP051) may have both metabolic and psychobiotic effects and may be beneficial for enhancing weight loss and body composition, improving energy (less fatigue) and mood levels while embarking on a healthy lifestyle regimen. ClinicalTrials.gov identifier: NCT06808061.

Humans↗

PubMed2025

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

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

Humans↗

PubMed2025

Improved detection of microbiome-disease associations via population structure-aware generalized linear mixed effects models (microSLAM).

Microbiome association studies typically link host disease or other traits to summary statistics measured in metagenomics data, such as diversity or taxonomic composition. But identifying disease-associated species based on their relative abundance does not provide insight into why these microbes act as disease markers, and it overlooks cases where disease risk is related to specific strains with unique biological functions. To bridge this knowledge gap, we developed microSLAM, a mixed-effects model and an R package that performs association tests that connect host traits to the presence/absence of genes within each microbiome species, while accounting for strain genetic relatedness across hosts. Traits can be quantitative or binary (such as case/control). MicroSLAM is fit in three steps for each species. The first step estimates population structure across hosts. Step two calculates the association between population structure and the trait, enabling detection of species for which a subset of related strains confer risk. To identify specific genes whose presence/absence across diverse strains is associated with the trait, step three models the trait as a function of gene occurrence plus random effects estimated from step two. Applying microSLAM to 710 gut metagenomes from inflammatory bowel disease (IBD) samples, we discovered 56 species whose population structure correlates with IBD, meaning that different lineages are found in cases versus controls. After controlling for population structure, 20 species had genes significantly associated with IBD. Twenty-one of these genes were more common in IBD patients, while 32 genes were enriched in healthy controls, including a seven-gene operon in Faecalibacterium prausnitzii that is involved in utilization of fructoselysine from the gut environment. The vast majority of species detected by microSLAM were not significantly associated with IBD using standard relative abundance tests. These findings highlight the importance of accounting for within-species genetic variation in microbiome studies.

Humans↗

PubMed2025

Duration of Hospitalization is Associated with the Gut Microbiome in Patients Undergoing Hematopoietic Stem Cell Transplantation: Early Results from a Randomized Trial of Home Versus Hospital Transplantation.

Home-based hematopoietic stem cell transplantation (HCT) is an innovative care model with growing interest, but its impact on the gut microbiome remains unexplored in a randomized setting. We present interim results from the first randomized controlled trials (RCT) evaluating the effect of HCT location-home versus hospital-on gut microbial diversity and antimicrobial resistance (AMR) gene carriage. We hypothesize that patients randomized to undergo home HCT would have higher gut taxonomic diversity and lower AMR gene abundance compared to those undergoing standard hospital HCT. We analyzed stool samples from the first 28 patients enrolled in ongoing Phase II RCTs comparing home (n = 16) and hospital (n = 12) HCT at Duke University using shotgun metagenomic sequencing to compare taxa and AMR gene composition between groups. We also performed a secondary analysis comparing patients who received transplants at outpatient infusion clinics versus inpatient standard HCT to evaluate the influence of hospitalization duration. In the primary RCT analysis, taxonomic and AMR gene α- and β-diversity were comparable between home and hospital groups, reflecting similar durations of hospitalization despite group allocation. In contrast, secondary analyses demonstrated that patients transplanted in outpatient infusion clinics who experienced significantly reduced hospitalization had higher gut taxonomic α-diversity and differential β-diversity, although AMR gene diversity remained unchanged. In summary, randomization by transplant location did not impact the gut microbiota to the same extent as the duration of hospitalization, although secondary analyses were heavily confounded. Even when taxonomic differences were observed, AMR genes were similar between groups. This RCT represents a novel investigation into how care setting influences the gut microbiome during HCT. Our findings suggest that hospital duration, rather than randomization allocation alone, is the primary driver of microbial disruption. These results underscore the potential for reducing hospital duration to mitigate microbiome injury, thereby informing future interventions to reduce infection risk and improve patient outcomes.

Microbiome↗

PubMed2026

Enteral Nutrition Is Associated with a Distinct Gut Microbiome Composition and Fermentation Capacity Profile After Acute Colonic Injury in Rats.

Enteral nutrition (EN) is known to promote mucosal healing in inflammatory bowel disease, and multi-omics data suggest that the gut microbiome mediates its therapeutic effects. However, the impact of EN and its components on the gut community during recovery from acute epithelial injury remains incompletely understood. We used whole-genome metagenomic sequencing to investigate the effect of an EN formula based on extruded amaranth flour and pea protein on the gut microbiome in a dextran sulfate sodium (DSS) rat model of acute colonic injury. Three groups were compared, as follows: an unchallenged control (n = 9) with standard chow, a colonic injury (5% DSS; n = 9) group with standard chow, and a colonic injury (5% DSS; n = 9) group with EN. Injury was confirmed histologically (median MCHI score was 2, indicating epithelial damage without inflammation). DSS caused significant weight loss. Animals receiving EN regained baseline weight faster, by day 14, whereas animals on standard chow achieved recovery only by day 21. Differences in energy intake should be further investigated to validate the effect of EN on body weight recovery. At day 21, both injury groups demonstrated higher relative abundances of Bacteroidaceae and Erysipelotrichaceae, including the mucin-degrader Allobaculum mucilyticum, compared with the control group. Conversely, Lactobacillus abundance, notably Lactobacillus acidophilus, was higher in the EN group than in both other groups, as was the inferred capacity for lactate-producing fermentation. These findings suggest that EN is associated with a distinct microbial composition and inferred metabolic profile during the post-injury period, with lactobacilli as one of the potential mediators of its effects.

Animals↗

PubMed2026

Genomic and biosynthetic landscape of high-temperature Daqu microbiome.

As the core starter for Chinese Baijiu, high-temperature Daqu is produced through open solid-state fermentation with recurrent inoculation by mature Daqu, forming a rich yet largely untapped reservoir of genomes and bioactive compounds. This study constructs the High-temperature Daqu Fermentation Microbiome catalog using 463 metagenomes spanning the full fermentation cycle. The catalog comprises 4,264 metagenome-assembled genomes that are dereplicated into 252 representative genome-based species, 82 % of which are absent from current global food microbiome databases. It further contains 14.3 million non-redundant genes, of which 17.3 % are novel, and 17,031 biosynthetic gene clusters, of which 62.63 % are novel, thereby substantially expanding the known genomic and biosynthetic space of food microbiomes. Genome-resolved analyses revealed a U-shaped ecological trajectory, shifting from early Bacillus velezensis-enriched assemblages to transient dominance of lactic acid bacteria during peak thermogenesis, before returning in late fermentation to thermotolerant, spore-forming Bacillota and Actinomycetota. In parallel, biosynthetic potential was further organized into four recurrent, stage-enriched profiles, from RiPP-rich thermogenic states to mature-state assemblages enriched in PKS-, NRPS-, and terpene-related capacities, with Bacillus, Kroppenstedtia, and Saccharopolyspora constituting the principal biosynthetic reservoir. Together, this work uncovers a largely unexplored genomic and biosynthetic reservoir in high-temperature Daqu fermentation, providing a target resource for mining thermotolerant industrial enzymes, flavor-related genes, and bioactive metabolites with biotechnological potential.

Microbiota↗

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

Gut Microbiome Composition Is Associated With Response to CD38 Antibody (Daratumumab) Treatment Among Relapsed Multiple Myeloma Patients.

INTRODUCTION: Growing data support interactions between host-gut microbes and treatment responses in multiple myeloma (MM), where a higher abundance of Eubacterium hallii in stool samples has been found among MM patients with negative minimal residual disease after induction therapy. Here, we evaluated changes in the gut microbiome associated with daratumumab (dara) based therapy in 40 MM patients, before and after therapy. PATIENTS AND METHODS: Patients with relapsed MM and prior autologous transplantation who had received 1 to 4 prior lines of therapy were eligible. Two stool samples were collected, one within 1 week prior to dara (predara) and one immediately after 4 doses of dara (postdara). Metagenomics sequencing was conducted. Microbiome taxonomic analyses were performed using MetaPhlAn4, and microbial functional pathway analyses were conducted using HUMAnN3.6. QIIME2 was used for compositional and statistical analyses. RESULTS: Of 40 participants enrolled, there were 5 nonresponders; 35 patients achieved partial response (PR) or better (responders). Among responders, 10 patients achieved complete remission (CR), and 25 patients achieved either very good partial response (VGPR) or PR. There were no statistically significant differences between overall pre and postdara gut microbiomes. Differential abundance analysis (ANCOM-BC) showed statistically significant (q ≤ 0.05) overgrowth of Alistipes finegoldii and Acidaminococcus intestini species in responders and Ruminococcus torques, Sellimonas intestinalis and Clostridium symbiosum in nonresponders. Compared to non-CR, CR samples showed enrichment of Faecalibacterium prausnitzii; non-CR samples were enriched in Segatella copri and Faecalimonas umbilicata. DISCUSSION/CONCLUSION: Our results suggest differences in species between clinical responders and nonresponders, but larger prospective studies are needed to confirm these results.

Clinical response↗

PubMed2026

MHASS: Microbiome HiFi Amplicon Sequencing Simulator.

SUMMARY: Microbiome HiFi Amplicon Sequence Simulator (MHASS) creates realistic synthetic PacBio HiFi amplicon sequencing datasets for microbiome studies, by integrating genome-aware abundance modeling, realistic dual-barcoding strategies, and empirically derived pass-number distributions from actual sequencing runs. MHASS generates datasets tailored for rigorous benchmarking and validation of long-read microbiome analysis workflows, including ASV clustering and taxonomic assignment. AVAILABILITY AND IMPLEMENTATION: Implemented in Python with automated dependency management, the source code for MHASS is freely available at https://github.com/rhowardstone/MHASS along with installation instructions. Our code is also published on Zenodo at https://doi.org/10.5281/zenodo.17486364. The data underlying this article are available on GitHub at https://github.com/rhowardstone/MHASS_evaluation/.

Software↗

PubMed2025

Diversity of leaf- and root microbiomes among genotypes and market classes of desert-grown lettuce (Lactuca sativa L.).

Plant microbiomes are increasingly acknowledged both as extensions of plant characteristics and as biological factors that influence plant traits important for nutrition and resilience. In the context of global change, manipulation of microbiomes has the potential to complement genetic approaches to enhance crop health and productivity under rising heat and drought stress. Understanding the factors that influence microbial communities and their variation across plant genotypes is essential for developing such capabilities. We employed metabarcoding via the Illumina sequencing platform to investigate microbial communities that occur within healthy leaves and roots of 12 lettuce genotypes (Lactuca sativa L.) grown in a desert agriculture environment. We detected diverse foliar- and root-endophytic fungi and bacteria in field-grown lettuce at the Maricopa Agricultural Center (Arizona, USA). The composition of microbial community structure varied with foliar chemistry and root traits. Notably, levels of zinc and other beneficial nutrients in the leaves were strongly linked with specific endophytes. These results document the lettuce microbiome in desert farming and provide insights into endophytes in lettuce leaves, which are noteworthy because they remain after washing and are regularly ingested.

Lactuca↗

PubMed2026

Symbiotic interactions and climate change implications of the octocoral microbiome.

Octocorals are vital components of tropical, temperate, and cold-water benthic marine ecosystems. Their associated microbiomes, comprising microeukaryotes, prokaryotes, and viruses, are increasingly recognised as central to host health, nutrient cycling, and chemical defence. Metagenomics and amplicon sequencing have uncovered taxonomic and functional complexity within these microbial communities, revealing patterns of host specificity and health status, along with seasonality and geographic structuring. However, anthropogenic stressors, particularly those associated with global climate change, exert intense pressure on coral-dominated ecosystems, leading to complex and poorly understood local and regional patterns of octocoral expansion and mortality. Microbial interactions may be a main driver of these contrasting outcomes by mediating the ecological resilience of octocorals to environmental stress. We synthesise the current state of research on the diversity, organisation, and function of the octocoral microbiome, and identify critical knowledge gaps on octocoral holobionts relative to scleractinian corals. Our meta-analysis of 79 publicly available bacterial genomes from octocorals reveals group-specific specialisation in denitrification and nitrate assimilation, along with widespread capacities for essential amino acid, cofactor, and vitamin production, suggesting important contributions to nutrient cycling in the holobiont. While sampling efforts between cultured and uncultured lineages are even, our genomic survey reveals strong sampling bias toward the Atlantic Ocean, temperate gorgonians, and healthy host states, whereas bacterial genomes representing the pathobiome, tropical and/or deep-sea regions, and other octocoral taxa remain underrepresented. Accordingly, we propose future research directions to advance understanding of octocoral microbiome ecology and its role in the resilience of tropical, temperate and cold-water coral reefs.

Endozoicomonadaceae↗

PubMed2026

Benchmarking of Reference-Based Tools for Strain-Level Resolution of Plant Microbiome.

Strain-level identification of each microbe is crucial for understanding its role in the host. Most of the existing tools have primarily been evaluated on human metagenomic datasets, whereas the plant microbiome exhibits greater diversity and complexity and thus poses a challenge in the strain-level resolution of individual microbes. In this study, we conducted a comprehensive benchmarking of available reference-based tools for strain-level resolution of the plant microbiome. We evaluated seven tools on various performance parameters, like computational requirements, F1-score and relative abundances using synthetic datasets comprising microbes known to have strong associations with plants as well as real plant microbiome datasets. Our results demonstrated a better performance of StrainScan on the synthetic data, achieving higher F1-score and more accurate relative abundance estimates as compared to other tools, but its performance declined gradually with increasing strain diversity. However, StrainGE and StrainScan exhibited competitive performance on real plant metagenome data. Overall, though StrainGE exhibited better performance, it was more computationally expensive. However, StrainScan performed better in detecting low-abundance strains. Our findings suggest the comparative suitability of the available tools for the strain-level analysis of plant metagenome data and highlight the need for the development of more efficient and accurate taxonomic classifiers capable of handling the complex plant metagenome data while maintaining computational efficiency.

Microbiota↗

PubMed2026

Longitudinal dynamics of respiratory microbiome composition in infants after new tracheostomy placement.

OBJECTIVES: This prospective longitudinal study characterised respiratory microbiome dynamics following new tracheostomy placement among infants. SETTING: A tertiary care paediatric hospital system in the United States. PARTICIPANTS: Fifteen infants ≤12 months of age contributed 84 tracheal aspirate samples collected from day 1 through 3-4 months post-placement. PRIMARY AND SECONDARY OUTCOME MEASURES: Bacterial composition, including abundance, from 16S rRNA gene sequencing; alpha and beta diversity measures over time. RESULTS: 16S rRNA gene sequencing revealed immediate and sustained bacterial community shifts. Staphylococcus abundance increased and alpha diversity decreased in the first 30 days post-tracheostomy (p<0.05) before returning to baseline. Beta diversity demonstrated compositional changes immediately and with ongoing divergence through 3-4 months. Time and clinical factors (prematurity, ventilation and neurologic impairment) were significantly associated with microbiome structure (p=0.001). CONCLUSIONS: This study provides novel evidence that new tracheostomy placement induces rapid and prolonged airway microbiome disruption in infants, highlighting a previously uncharacterised window of vulnerability with implications for respiratory health.

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↗

PubMed2026

Scale reliant mixed effects models enhance microbiome data analysis.

Linear models, including those used for differential abundance analyses, are frequently used in microbiome research to assess how experimental conditions (e.g., disease state or age) affect microbial abundance. Linear mixed-effects models (MEMs) extend linear models to accommodate complex designs, such as longitudinal sampling or hierarchical study structures. However, when applied to microbiome data, existing MEM approaches suffer from high false positive and false negative rates because sequence counts are compositional - they reflect relative rather than absolute abundances. Current methods attempt to overcome this limitation through normalization, but these approaches rely on strong, often unrealistic assumptions about the unmeasured biological scale (e.g., total microbial load). Here we introduce scale-reliant mixed-effects models (SR-MEM), which extend our earlier scale-reliant inference framework by explicitly modeling uncertainty in the unmeasured scale via user-defined probability distributions. By treating scale as a latent variable rather than fixing it through normalization, SR-MEM enables robust inference for complex experimental designs. SR-MEM can incorporate external scale measurements (e.g., flow cytometry, qPCR) or leverage scale information from independent studies to further improve inference. Across simulations and multiple real-world case studies, SR-MEM consistently controls the false discovery rate while maintaining comparable or higher power than standard approaches relying on normalization or bias correction. In reanalyses of published datasets, SR-MEM yields results that are more reproducible across studies and more consistent with known biological and pharmacological effects. SR-MEM provides a principled and practical framework for mixed-effects modeling of microbiome sequence count data in the presence of unmeasured biological scale. By avoiding normalization-based assumptions and instead propagating scale uncertainty through inference, SR-MEM improves error control and reproducibility in longitudinal and hierarchical studies. An accessible implementation is provided in the ALDEx3 R package.

Microbiota↗

PubMed2025

Bacterial immune systems as causes and consequences of microbiome structure.

Attacks from molecular parasites such as mobile genetic elements (MGEs) have driven the evolution of defense systems in bacterial genomes. Yet, despite significant advances in understanding the molecular mechanisms of these bacterial immune systems, we have only a rudimentary understanding of their ecology and evolution. Bacteria exist as part of complex microbiomes, but community ecology and microbiome research has yet to characterize the impacts of interactions between MGEs and defense mechanisms upon the structure, dynamics and evolution of microbiomes. This Essay introduces and discusses the interplay between bacterial community dynamics and bacterial immune systems, speculating about how these reciprocal interactions may shape microbial community structure and function.

Bacteria↗

PubMed2024

Exclusive enteral nutrition initiates individual protective microbiome changes to induce remission in pediatric Crohn's disease.

Exclusive enteral nutrition (EEN) is a first-line therapy for pediatric Crohn's disease (CD), but protective mechanisms remain unknown. We established a prospective pediatric cohort to characterize the function of fecal microbiota and metabolite changes of treatment-naive CD patients in response to EEN (German Clinical Trials DRKS00013306). Integrated multi-omics analysis identified network clusters from individually variable microbiome profiles, with Lachnospiraceae and medium-chain fatty acids as protective features. Bioorthogonal non-canonical amino acid tagging selectively identified bacterial species in response to medium-chain fatty acids. Metagenomic analysis identified high strain-level dynamics in response to EEN. Functional changes in diet-exposed fecal microbiota were further validated using gut chemostat cultures and microbiota transfer into germ-free Il10-deficient mice. Dietary model conditions induced individual patient-specific strain signatures to prevent or cause inflammatory bowel disease (IBD)-like inflammation in gnotobiotic mice. Hence, we provide evidence that EEN therapy operates through explicit functional changes of temporally and individually variable microbiome profiles.

Crohn Disease↗

PubMed2026

Global gut microbiome atlas identifies epidemiologic-stage-specific signatures in inflammatory bowel disease.

The global rise of inflammatory bowel disease (IBD) reflects environmental shifts, yet how these changes are embedded in the gut microbial ecology remains unclear. We construct a microbiome atlas comprising 245,627 profiles. By classifying countries into three epidemiologic stages, we establish a framework. As the IBD burden increases, the gut microbial alpha diversity declines, and community structures form distinct clusters. This transition is characterized by a gradient of core genera. Integrating six shotgun metagenomic cohorts, we identify the depletion of anabolic pathways in IBD patients. Strain-level analysis reveals that epidemiologic staging shapes genetic architecture within species, identifying an IBD-enriched subclade of Eisenbergiella associated with elevated fecal cholic acid. We develop a microbial inflammatory risk score (MIRS), based on 19 genera, that discriminates IBD from controls (area under the curve [AUC] = 0.92). MIRS correlates with IBD prevalence. Our study provides an atlas linking epidemiology to microbiome ecology and strain evolution, offering a foundation for population-level surveillance and interventions in IBD.

Humans↗