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PubMed2026

Integrative multi-omics analyses suggest a candidate microbial metabolite-associated host gene network in ulcerative colitis.

Ulcerative colitis (UC) is associated with gut microbial dysbiosis, but the host molecular alterations potentially linked to microbially derived metabolites remain incompletely understood. We integrated Mendelian randomization (MR), microbial metabolite annotation, computational target prediction, colonic transcriptomics, network analysis, and machine learning. MiBioGen microbiome GWAS data were used as exposures and FinnGen Release 12 ULCERENTER as the outcome. Metabolites linked to MR-prioritized taxa were retrieved from GutMGene, and human targets were predicted using SwissTargetPrediction and SEA. UC-related genes were defined by integrating differential expression analysis and WGCNA and then intersected with predicted metabolite targets. MR prioritized one family and eight genera showing nominal genetically supported associations with UC, but none remained significant after Benjamini-Hochberg FDR correction. Three prioritized genera were linked to 15 microbe-metabolite records, corresponding to 13 unique metabolites; nine were retained for target prediction, yielding 277 unique predicted human targets. Transcriptomic analysis identified 1,530 DEGs and a 312-gene MEgrey60 module, with 273 overlapping genes, producing 1,569 unique UC-related genes. Their intersection with the 277 predicted targets yielded 47 candidate genes. Enrichment analyses highlighted mainly metabolic and lipid-related processes. Random Forest showed the highest mean AUC across the two independent external benchmarking cohorts, and SHAP prioritized EPHX1, HSD17B2, IGFBP5, and MMP10. IBDome analysis showed inflammation-associated expression differences in these genes. This study provides a genomics-informed, hypothesis-generating framework that prioritizes candidate microbe-metabolite-host relationships in UC for future experimental validation.

Humans↗

PubMed2026

A geroprotective probiotic and its functional metabolite counteract inflammaging to extend healthspan.

The gut microbiome profoundly influences host aging, yet the specific microbes and mechanisms governing divergent aging trajectories remain elusive. In this study, we delineated enterotype-specific gut microbial remodeling during aging and developed a microbiome-based aging clock (MicroAge) to track biological aging trajectories. We identified Bifidobacterium pseudocatenulatum (B. pseudocatenulatum) as a candidate geroprotective species consistently depleted during aging across both sexes and multiple Chinese cohorts. In naturally aged mice, oral B. pseudocatenulatum monotherapy rescued intestinal homeostasis, mitigated multiorgan inflammaging, enhanced cognitive-motor performance and extended healthspan. Mechanistically, we characterized 5-aminovaleric acid betaine (5-AVAB) as a key B. pseudocatenulatum-derived metabolite whose levels decline physiologically in aging humans. 5-AVAB supplementation partially recapitulated a broad spectrum of the systemic benefits observed with B. pseudocatenulatum treatment, including improved cognitive and motor function and suppressed multiorgan inflammaging. Our findings identify the B. pseudocatenulatum-5-AVAB axis as a promising target for microbiome-based interventions to promote healthy aging.

Animals↗

PubMed2025

The role of mobile genetic elements in adaptation of the microbiota to the dynamic human gut ecosystem.

The human intestinal microbiota is a dynamic ecosystem shaped by extensive horizontal gene transfer, particularly in individuals from industrialized populations. In this review, we discuss recent advances in our understanding of how mobile genetic elements (MGEs) contribute to microbial ecology and evolution in this diverse community, focusing on MGEs carrying fitness-conferring genes. Bacteroidales species can colonize individuals for decades and serve as major hubs for MGE exchange. Most MGEs are highly variable across individuals and geographies. Occasionally, conserved MGEs can spread across geography and lifestyles. Functional characterizations of MGEs reveal their roles in antibiotic resistance, interbacterial antagonism, biofilm formation, immune evasion, and nutrient acquisition, among others. Substantive progress in our understanding of MGEs in the gut microbiome offers promising avenues for therapeutic microbiome interventions. However, major challenges remain in functional prediction, host-MGE linkage, and experimental characterization.

Humans↗

PubMed2026

Challenges and future directions in AI-driven biomaterials for microbiome-associated oral infectious diseases: A systematic review.

Oral biofilm-induced antimicrobial resistance is the core pathogenic mechanism of microbiome-associated oral infectious diseases (dental caries, periodontitis, peri-implantitis, and endodontic infection). Traditional therapies and biomaterials are limited by poor biofilm penetration, drug resistance induction, single functionality, and inadequate adaptation to dynamic oral microenvironmental changes (e.g., pH fluctuations, salivary rinsing, masticatory stimulation). Artificial intelligence (AI) has transformed the field by integrating materials science, microbiology, and stomatology data. Via machine learning, deep learning, and multi-physics simulation, AI optimizes biomaterial physicochemical properties, decodes microenvironmental signals, constructs precise sensing-response loops, and supports the full chain of material design, performance prediction, and action simulation, advancing treatment from empirical intervention to precision regulation. This systematic review retrieved literature from PubMed, Embase, and Web of Science (January 2016-January 2026) using keywords across three dimensions: AI, biomaterials, and oral microbiome. Following inclusion/exclusion criteria, 99 articles were included. It elaborates on five core mechanisms of AI-driven oral biomaterials (precise oral microbiome analysis, targeted material design/optimization, performance prediction/simulation, targeted delivery/intervention, effect evaluation/dynamic regulation), analyzes their applications in microbiome-targeted biomaterial research and development (R&D) and clinical practice for the four major oral infectious diseases, addresses technical bottlenecks (insufficient targeting specificity and precision of biomaterials, poor stability and durability in complex oral microenvironments, inadequate biofilm disruption capacity, and clinical translation obstacles), and proposes future directions (multimodal design to enhance targeting specificity, structural and component optimization to improve stability/durability, development of multi-mechanism synergistic biofilm disruption strategies, strengthening translational research for clinical application, and deep integration of AI in the full chain of biomaterial R&D). This work provides comprehensive theoretical and practical support for the R&D, optimization, and clinical translation of AI-driven microbiome-targeted oral biomaterials.

Humans↗

PubMed2026

Deciphering the Microbiome-Gut-Eye Axis: A Mendelian Randomization Analysis of the Causal Influence of Gut Microbiota on Myopia.

INTRODUCTION: The intricate relationship between the gut microbiome and myopia is increasingly recognized, underscoring the need to explore its causal dynamics. Despite emerging evidence, the influence of Gut Microbiota (GM) on ocular development remains underexplored. METHODS: This study utilized Mendelian Randomization (MR) to investigate the causal impact of GM on the development of myopia. Instrumental variables (IVs) were identified from Genome-Wide Association Studies (GWAS), focusing on genetic variants significantly associated with microbiome composition. A comprehensive array of MR techniques was applied to ensure a robust estimation of causal effects and to adjust for potential confounders and pleiotropy. RESULTS: The Inverse-Variance Weighted (IVW) method was used to identify significant associations between GM and myopia. Increased risk of myopia was linked to the class Betaproteobacteria (OR=1.01, 95% CI 1.004-1.017, P=0.003), the order Burkholderiales (OR=1.009, 95% CI 1.001-1.016, P=0.02), the family Oxalobacteraceae (OR=1.005, 95% CI 1.001-1.01, P=0.023), and several genera including Eubacterium xylanophilum group (OR=1.007, 95% CI 1.001-1.013, P=0.033), and Bifidobacterium (OR=1.005, 95% CI 1-1.01, P=0.038). Protective effects were noted for the order Mollicutes RF9 (OR=0.994, 95% CI 0.99-0.999, P=0.014), the genus Allisonella (OR=0.996, 95% CI 0.993-0.999, P=0.019), the genus Lachnospiraceae UCG001 (OR=0.994, 95% CI 0.989-1, P=0.045), and the family Enterobacteraceae (OR=0.991, 95% CI 0.982-1, P=0.047) and order Enterobacteriales (OR=0.991, 95% CI 0.982-1, P=0.047). Sensitivity analyses further confirmed the robustness of these findings. DISCUSSION: This study provides causal evidence for the "Microbiome-Gut-Eye Axis" in myopia development, identifying specific gut microbiota that influence myopia risk. These findings suggest potential for microbiota-targeted interventions, warranting further research in diverse populations. CONCLUSIONS: The findings support the "Microbiome-Gut-Eye Axis" as a potential factor in myopia pathogenesis and highlight microbiota-targeted interventions as novel therapeutic strategies for managing myopia. This study lays the groundwork for further research on how modifying GM can influence eye health and offers new perspectives on preventive health strategies.

Humans↗

PubMed2026

Metagenomic analysis of the midgut microbiome in Dermacentor abaensis ticks at different feeding states.

Ticks are blood-sucking ectoparasites of humans and animals, ranking second only to mosquitoes as vectors of diseases. Dermacentor abaensis is distributed in Sichuan, Qinghai, and Gansu, China. Because D. abaensis harbors several pathogens, it poses a threat to public health and livestock production. However, the midgut microbiota of D. abaensis at distinct feeding states remains poorly characterized. Adult D. abaensis ticks at various feeding states were collected from yaks in Gansu Province, China. Genomic DNA was extracted from midguts and midgut contents of unfed, partially fed, and fully engorged female D. abaensis. A metagenomic sequencing approach was employed to profile the midgut microflora among three groups. A total of 83 phyla, 908 genera, and 1857 species were annotated across the three groups. At the phylum level, Pseudomonadota, Mucoromycota, and Ascomycota were the most abundant. At the species level, common bacterial species such as Klebsiella pneumoniae and Anaplasma phagocytophilum, alongside viruses and eukaryotes, were detected in all three groups. Unique microorganisms were also observed in each group: unfed (n = 305), partially fed (n = 59), and fully engorged (n = 20). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis suggested that the D. abaensis microbiome contains a relatively high abundance of functional genes involved in lipid and amino acid metabolism across the three different feeding states. These findings indicate that while core microbial taxa are shared in the midgut of female D. abaensis, observable trends suggest variations in microbial diversity and composition as blood-feeding progresses. The present study provides a descriptive baseline of the midgut microbial composition of D. abaensis, which may inform future studies on tick biology and the ecology of tick-borne pathogens.

Animals↗

PubMed2025

Gut microbial and functional alterations lead to metagenomic signatures for midgut neuroendocrine tumor patients and for carcinoid syndrome.

Midgut neuroendocrine tumors (NET) derive from enterochromaffin cells, which have a close interrelationship with intestinal microbiota. Recently, we have utilized 16S rRNA sequencing to uncover that midgut NET patients have a depleted gut microbiome and a specific fecal microbial signature. This study aims to validate these findings and to further characterize the role of microbes and microbial metabolic pathways in midgut NET patients with and without carcinoid syndrome (CS). Fecal samples from 60 midgut NET patients and 20 household-matched controls were subjected to whole metagenome sequencing. The gut microbial community composition of midgut NET patients differed from that of controls, with 2 genera, 17 species and 9 microbial pathways showing differential abundance (P < 0.001). No differences in the microbial composition were observed between midgut NET patients with and without CS (P > 0.05). However, we did observe changes in inter-genus correlations of Bacteroides, Odoribacter, Parasutterella, Klebsiella, Ruminococcus and Proteobacteria when comparing these two patient groups. A signature of 16 microbial species (area under the receiver operating characteristics (AUROC) curve 0.892) or 18 microbial pathways (AUROC 0.909) accurately predicted the presence of a midgut NET. Furthermore, a microbial signature consisting of 14 functional microbial pathways distinguished CS patients from non-CS patients (AUROC 0.807). Thus, this study confirms that the gut microbiome of midgut NET patients is altered at the metagenomic level, which is not related to the presence of CS. A fecal microbial signature could constitute a novel biomarker for the diagnosis of midgut NET or CS.

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PubMed2025

MetaflowX: a scalable and resource-efficient workflow for multi-strategy metagenomic analysis.

Microbiomes play crucial roles in diverse ecosystems, spanning environmental, agricultural, and human health domains. However, in-depth metagenomic data analysis presents significant technical and resource challenges, particularly at scale. Existing computational pipelines are typically limited to either reference-based or reference-free approaches and exhibit inefficiencies in process large datasets. Here, we introduce MetaflowX (https://github.com/01life/MetaflowX), an open-resource workflow integrating both analytical paradigms for enhanced metagenomic investigations. This modular framework encompasses short-read quality control, rapid microbial profiling, hybrid contig assembly and binning, high-quality metagenome-assembled genome (MAG) identification, as well as bin refinement and reassembly. Benchmarking tests showed that MetaflowX completed full metagenomic analyses up to 14-fold faster and with 38% less disk usage than existing workflows. It also recovered the highest number of high-quality and taxonomically diverse MAGs. A dedicated reassembly module further improved MAG quality, increasing completeness by 5.6% and reducing contamination by 53% on average. Functional annotation modules enable detection of key features, including virulence and antibiotic resistance genes. Designed for extensibility, MetaflowX provides an efficient solution addressing current and emerging demands in large-scale metagenomic research.

Metagenomics↗

PubMed2025

Characterization of gut microbiota and metabolites in renal transplant recipients during COVID-19 and prediction of one-year allograft function.

BACKGROUND: The gut-lung-kidney axis is pivotal in immune-related kidney diseases, with gut dysbiosis potentially exacerbating the severity of Coronavirus disease 2019 (COVID-19) in recipients of kidney transplant. This study aimed to characterize the gut microbiome and metabolome in renal transplant recipients with COVID-19 pneumonia over a one-year follow-up period. METHODS: A total of 30 renal transplant recipients were enrolled, comprising 17 with COVID-19 pneumonia, six with mild COVID-19, and seven without COVID-19. Fecal samples were collected at the onset of infection for gut microbiome and metabolome analysis. Generalized Estimating Equations (GEE) model and Latent Class Growth Mixed Model (LCGMM) were employed to dissect the relationships among clinical characteristics, laboratory tests, and gut microbiota and metabolites. RESULTS: Four microbial phyla (Deferribacteres, TM7, Fusobacteria, and Gemmatimonadetes) and 13 genera were significantly enriched across three recipients groups, correlating with baseline inflammatory response and allograft function. Additionally, 52 differentially expressed metabolites were identified, with seven significantly correlating with eight altered microbiota genera. LCGMM revealed two distinct classes of recipients, with those suffering from COVID-19 pneumonia exhibiting significantly elevated serum creatinine (Scr) trajectories over the one-year period. GEE further identified 12 genera and 181 metabolites closely associated with these trajectories; a multivariable model incorporating gut metabolites of 1-Caffeoylquinic Acid and PMK was found to effectively predict one-year allograft function. CONCLUSIONS: Our study indicates a possible interaction between the composition of the gut microbiota and metabolites community and COVID-19 in renal transplant recipients, particularly in relation to disease severity and the prediction of one-year allograft function.

Humans↗

PubMed2026

In vivo porcine multi-omics integration identifies microbiome-driven histamine elevation and lasting gut perturbations following Ascaris suum infection and fenbendazole treatment.

Ascaris roundworms impair human and swine health. While treatments using anthelmintic drugs are generally effective in eliminating worms, their effects on the gut microenvironment remain poorly understood. Here we applied integrated multi-omics to characterize infection- and treatment-associated alterations in the pig-Ascaris system. In vitro anaerobic cultures were conducted as supportive validation of selected observations. Ascaris suum infection altered microbial composition and dysregulated 182 serum and fecal metabolites, including histamine and p-cresol sulfate. Compared with time-matched uninfected controls, infected pigs treated with fenbendazole showed marked differences in gut microbial composition 13 days after confirmed worm clearance. Eleven microbial pathways were enriched in successfully treated pigs, including peptidoglycan biosynthesis and histidine metabolism, indicating that infection-associated alterations may persist after treatment. In vitro co-exposure of Lactobacillus reuteri to fenbendazole and A. suum proteins increased histamine production by approximately 79% at 48 h (p < 0.05), serving as supportive evidence of a microbiome contribution. Collectively, our in vivo findings support that host-microbiota-parasite interactions are multifaceted. Microbiota-derived metabolites were associated with regulation of host gene expression, such as TFF2 and IL8. Microbiota plasticity allows the exploitation of the niche differentiated upon infection, resulting in the proliferation of certain Lactobacillus strains in treated animals. Nevertheless, interpretations of treatment effects are made cautiously given the absence of an uninfected drug-only group and the cross-sectional design. Understanding these complex interactions will be important for the design of next-generation functional anthelmintics.

Animals↗

PubMed2025

Novel insights into the genetic architecture and mechanisms of host/microbiome interactions from a multi-cohort analysis of outbred laboratory rats.

The intestinal microbiome influences health and disease. Its composition is affected by host genetics and environmental exposures. Understanding host genetic effects is critical but challenging in humans, due to the difficulty of detecting, mapping and interpreting them. To address this, we analysed host genetic effects in four cohorts of outbred laboratory rats exposed to distinct but controlled environments. We found that polygenic host genetic effects were consistent across environments. We identified three replicated microbiome-associated loci, one of which involved a sialyltransferase gene and Paraprevotella. We found a similar association in a human cohort, between ST6GAL1 and Paraprevotella, both of which have been linked with immune and infectious diseases. Moreover, we found evidence of indirect genetic effects on microbiome phenotypes, which substantially increased their total genetic variance. Finally, we identified a novel mechanism whereby indirect genetic effects can contribute to "missing heritability".

Journal Article↗

PubMed2026

Immunological, Inflammatory, and Microbiota Determinants of Carpal Tunnel Syndrome: Evidence from Mendelian Randomization.

INTRODUCTION: Carpal Tunnel Syndrome (CTS) is a common peripheral neuropathy, and immune dysregulation and microbial dysbiosis are believed to play a role in its development. However, the cause-and-effect relationships have yet to be clarified. METHODS: Using publicly available Genome-Wide Association Study (GWAS), there are 731 immune cell phenotypes, 91 inflammatory proteins, 150 skin microbiota taxon, and 473 gut microbiota taxon based on two-sample Mendelian Randomization (MR) analysis to test whether there is a causal relationship between them and CTS. The results from the study were shown to have some degree of stability as demonstrated by various sensitivity analyses, which included running heterogeneity tests, performing MR -PRESSO, and running MR-Egger regressions. On the other hand, reverse MR was performed to verify the direction of the association. In addition, a two-step MR mediation analysis was conducted to explore whether there was a mediation effect of gut microbiota and skin microbiota, respectively, of immune and inflammatory traits on CTS. RESULTS: 22 Immune cell traits, 4 Inflammatory proteins, 18 gut microbiota taxa, and 3 skin microbiota taxa are causally associated with CTS. Reverse MR suggested feedback effects of CTS on select immune traits and gut microbiota. Mediation analysis revealed 4 gut microbiota taxa that substantially mediated immune/inflammatory effects upon CTS, with mediation rates as high as 44%; however, skin microbiota did not demonstrate any mediation. DISCUSSION: The immune dysregulation, inflammation, and the gut microbiota that cause CTS are all revealed through this research. Mendelian randomization analysis suggests that traits and inflammatory proteins of immune cells directly increase the risk of CTS, and certain types of gut microbes partially mediate these effects. Therefore, the results show a central role of the immune-gut axis in CTS pathogenesis, and suggest a systemic, rather than a local, immune-microbial interaction in disease development. CONCLUSION: We provided the first causal evidence that immune cells, inflammatory proteins, and CTS risk are causally associated with some specific taxa of gut microbiota. This contributes to a better understanding of the immune-microbiome interactions in the process of occurrence and development of CTS, and also provides theoretical support for precision prevention and treatment.

Humans↗

PubMed2024

Soil keystone viruses are regulators of ecosystem multifunctionality.

Ecosystem multifunctionality reflects the capacity of ecosystems to simultaneously maintain multiple functions which are essential bases for human sustainable development. Whereas viruses are a major component of the soil microbiome that drive ecosystem functions across biomes, the relationships between soil viral diversity and ecosystem multifunctionality remain under-studied. To address this critical knowledge gap, we employed a combination of amplicon and metagenomic sequencing to assess prokaryotic, fungal and viral diversity, and to link viruses to putative hosts. We described the features of viruses and their potential hosts in 154 soil samples from 29 farmlands and 25 forests distributed across China. Although 4,460 and 5,207 viral populations (vOTUs) were found in the farmlands and forests respectively, the diversity of specific vOTUs rather than overall soil viral diversity was positively correlated with ecosystem multifunctionality in both ecosystem types. Furthermore, the diversity of these keystone vOTUs, despite being 10-100 times lower than prokaryotic or fungal diversity, was a better predictor of ecosystem multifunctionality and more strongly associated with the relative abundances of prokaryotic genes related to soil nutrient cycling. Gemmatimonadota and Actinobacteria dominated the host community of soil keystone viruses in the farmlands and forests respectively, but were either absent or showed a significantly lower relative abundance in that of soil non-keystone viruses. These findings provide novel insights into the regulators of ecosystem multifunctionality and have important implications for the management of ecosystem functioning.

Soil Microbiology↗

PubMed2026

Gut microbiota and metabolic alterations in participants with flatulence identify Faecalibacterium prausnitzii as a key microbial target for clinical intervention.

Flatulence is closely associated with gut dysbiosis, yet the characteristic microbial signatures, metabolic alterations, and actionable intervention targets remain unclear. This limited mechanistic understanding has hindered the development of precise microbiota-based strategies for managing flatulence. Here, we found that participants with flatulence exhibited marked shifts in gut microbial functions and fecal metabolic profiles compared with healthy controls, characterized by enhanced abnormal fermentation, enrichment of oxidative stress-related functions, elevated low-grade inflammatory signatures, and reduced anti-inflammatory and mucosal-protective metabolic features. Faecalibacterium prausnitzii was significantly negatively associated with the high-gas-producing phenotype. In vitro replenishment experiments further validated the role of F. prausnitzii in reducing gas production, promoting butyrate generation, and remodeling butyrate-associated microbial communities. Based on microbial interaction analysis, we identified Bifidobacterium longum CCFM1319 as a candidate strain for targeting F. prausnitzii. In a double-blind, randomized, placebo-controlled clinical trial, supplementation with B. longum CCFM1319 significantly increased intestinal F. prausnitzii abundance and improved flatulence-related symptoms. Collectively, these findings reveal the microbiota and metabolic dysbiosis underlying flatulence, highlight the key regulatory role of F. prausnitzii, and lays the foundation for targeted microbiota-based intervention strategies for flatulence.

Humans↗

PubMed2026

Plasmacytoid dendritic cell-mediated L-glutamate catabolism links gut microbiota to male infertility.

Emerging evidence suggests that gut microbiota composition influences male reproductive health; however, the immunometabolic mechanisms underlying this association remain insufficiently characterized. We investigated whether specific immune cell-mediated metabolic pathways, particularly plasmacytoid dendritic cell (pDC)-driven L-glutamate catabolism via the hydroxyglutarate pathway, contribute to the causal link between gut microbiota and male infertility. We conducted a 2-sample, 2-step Mendelian randomization (MR) analysis using inverse-variance weighting as the primary estimator and Bayesian weighted MR for robustness. Exposure data comprised 412 gut microbial taxa/metabolic pathways and 731 immune cell phenotypes from large European-ancestry genome-wide association studies. Male infertility genome-wide association studies data (1429 cases; 128,710 controls) were obtained from FinnGen R10. Only exposure-mediator-outcome pairs meeting stringent pleiotropy, heterogeneity, and reverse-causality criteria were retained for mediation analysis. Nine microbial taxa/metabolic pathways and 18 immune traits exhibited putative causal associations with male infertility. The L-glutamate degradation V pathway via hydroxyglutarate was linked to reduced infertility risk (inverse-variance weighting odds ratio [OR] = 0.68; 95% confidence interval, 0.52-0.89; P = .005). Two-step MR suggested that forward scatter area on pDCs may mediate this association, although the mediation effect was imprecise (effect = 0.0277; 95% confidence interval, -0.0348 to 0.0903). This study provides suggestive genetic evidence that pDC-mediated glutamate catabolism may connect gut microbial metabolic activity to male infertility. These findings highlight immunometabolic pathways as testable targets for mechanistic validation and microbiota-directed interventions.

Male↗

PubMed2025

Dual-approach analysis of gut microbiome in patients with type 1 diabetes and diabetic kidney disease.

BACKGROUND: Type 1 diabetes (T1D) is a multifactorial autoimmune disease mediated by genetic, epigenetic, and environmental factors. Diabetic kidney disease (DKD) is a major complication of diabetes mellitus which affects 30-40% of T1D patients. Increasing evidence suggests the significant role of the microbiome in the progression of both T1D and DKD. MATERIALS AND METHODS: Here we recruited 76 T1D patients and 22 healthy controls and combined data from sigmoid colon biopsy samples analysed with V3-V4 region amplification of 16S rRNA gene and shotgun metagenomics data obtained from faecal samples. Additionally, we compared T1D patients with and without progression of DKD. RESULTS: We observed significant differences within both sample types at various taxonomic and functional levels. T1D patient microbiota detected using biopsy samples had a lower abundance of the Bacteroides genus when compared to healthy controls. Significantly, despite only a few taxonomic differences patients with and without DKD progression were vastly different at the functional pathway level within the faecal samples - we observed 2 and 61 enriched pathways in these groups. respectively, with several of these pathways linked to the mediation of renal function. CONCLUSION: Altogether, we present novel data about microbial signatures relevant to T1D and DKD progression, which partly supports previous data and also presents possible tissue type or population-specific elements. DKD progression is characterized with significant differences within the functional level of the gut microbiome.

Humans↗

PubMed2026

Oral and gut microbiota profiles in patients with locally advanced rectal cancer with varying responses to neoadjuvant chemoradiotherapy.

Recent research has focused on gut bacteria in colorectal cancer, but the influence of other microbiota, including oral and nonbacterial gut microbiota, on treatment efficacy remains insufficiently explored. This study aimed to investigate their relationship with the efficacy of neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC). Saliva and fecal samples were collected from patients with LARC before treatment. Shotgun metagenomic sequencing was used to profile bacterial, archaeal, eukaryotic, and viral taxonomic groups and to examine oral and gut microbial functions. An artificial intelligence-based prediction model was developed by integrating oral and gut microbiome data with clinical information. Statistical analyses compared diversity and response-associated microbial features between responders and non-responders to nCRT. Response-associated differences were observed in bacterial and nonbacterial taxonomic profiles and in oral and gut microbial functional profiles. In the internal test subset, the integrated analysis yielded an observed AUC of 0.917. Given the small cohort and the exploratory comparison of candidate classifiers, this estimate requires confirmation in larger, independent cohorts. Baseline oral and gut microbiome profiles were associated with response to nCRT. Integrating microbiome and clinical features showed potential for response prediction, but the model remains exploratory and requires validation in larger, independent cohorts before clinical application. Retrospectively registered on 01/08/2026, NCT07346729.

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PubMed2025

Gut Colonization With Vancomycin-Resistant Enterococcus Shapes the Gut Microbiome in the Intensive Care Unit.

BACKGROUND: Gut pathogen colonization with vancomycin-resistant Enterococcus (VRE) is common in the intensive care unit (ICU) and is associated with worse clinical outcomes; however, the timing of VRE colonization and its collateral effects on the gut microbiome are incompletely understood. METHODS: Medical ICU patients admitted with sepsis and receiving broad-spectrum antibiotics were sampled via deep rectal swabs at ICU admission and on ICU day 3, 7, 14, and 30. Rectal swabs were cultured for VRE on selective media and analyzed via 16S ribosomal RNA gene sequencing. RESULTS: Ninety patients were sampled (340 longitudinal swabs). VRE positivity rose from 20% at ICU admission to a peak of 33% by ICU day 14 and then modestly declined to 31% by ICU day 30. Paralleling this, alpha diversity fell while Enterococcus relative abundance rose through ICU day 14 with both returning to baseline by ICU day 30. The median relative abundance of Enterococcus was 38% (interquartile range [IQR], 7.4%-75%) for VRE-positive samples compared to 0.01% (IQR, 0%-19%) for VRE-negative samples (rank-sum P < .01); 38 samples had ≥90% Enterococcus and 8 samples were 100% Enterococcus by sequencing. VRE was associated with lower alpha diversity (median Shannon index 1.90 [IQR, 0.89-2.66] if VRE positive versus 2.64 [IQR, 1.58-3.22] if VRE negative; P < .01). CONCLUSIONS: VRE gut colonization peaked at ICU day 14 followed by a modest decline and was associated with low alpha diversity. Improved understanding of dynamic changes in the gut microbiome may facilitate successful future ICU interventions. CLINICAL TRIALS REGISTRATION: NCT03865706.

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