Background: The intestinal mucus layer is comprised of heavily glycosylated mucins, including mucin 2 (MUC2), that serve as a nutrient source for certain bacterial members of the gut microbiota. Only a subset of gut commensals encode the glycoside hydrolases required to degrade mucin glycans. However, mucin-degrading microbes can release glycans and generate compounds that can cross-fed non-mucin degrading microbes, creating complex microbial networks. While pairwise studies have shown that mucin degradation drives cross-feeding and metabolite exchange, the broader impact of mucins on community structure and metabolic output remains poorly understood.
Objective: In this study, we sought to identify how a defined microbial consortium of human commensals with varied mucin-degrading capacities responds to MUC2 to shape community composition and metabolic output.
Methods: A defined consortium of human gut commensals with varied mucin-degrading capacities was cultivated in anaerobic bioreactors in the presence or absence of porcine MUC2. Community composition was assessed, and extracellular metabolites were quantified using targeted and untargeted metabolomic profiling.
Results: MUC2 supplementation significantly altered community structure, promoting the expansion of Akkermansia muciniphila while reducing Prevotella. MUC2 also reshaped microbial metabolism, decreasing acetate levels while increasing propionate, butyrate, and formate. In addition, MUC2 supplementation altered amino acid utilization and vitamin metabolism and reduced several neuroactive compounds, including glutamate, γ-aminobutyric acid (GABA), and anthranilic acid, while increasing tryptamine levels.
Conclusion: These findings demonstrate that mucins exert broad effects on microbial community structure and metabolic output. Collectively, this work highlights the central role of bacterial cross-feeding in shaping gut ecosystem function.
Aim: Immune checkpoint inhibitors (ICIs), particularly anti-programmed cell death protein 1 (PD-1) therapy, have improved cancer treatment outcomes, yet durable benefit is achieved in only a subset of patients. Growing evidence implicates the gut microbiome as a modulator of ICI responsiveness, but defined and experimentally validated microbial strategies remain limited. This study aimed to identify responder-associated gut microbes and to evaluate a defined bacterial consortium for enhancing PD-1 blockade efficacy.
Methods: Publicly available shotgun metagenomic datasets from anti-PD-1-treated cancer patients were re-analyzed to compare gut microbiome profiles between responders and non-responders. Bacterial taxa reproducibly enriched in responders were selected based on consistency across analytical criteria and cultivability and assembled into a four-strain consortium (UJ-04). The immune-adjuvant potential of UJ-04, alone or combined with anti-PD-1 therapy, was evaluated in a B16-F10 melanoma mouse model, with tumor growth and immune responses assessed by flow cytometry.
Results: Metagenomic re-analysis identified four commensal bacterial taxa consistently enriched in responder patients, forming the defined UJ-04 consortium. While UJ-04 alone showed minimal antitumor activity, combination treatment with anti-PD-1 significantly enhanced tumor growth inhibition compared with anti-PD-1 monotherapy. This effect was accompanied by increased intratumoral CD8+ T cells and natural killer cells, with concordant immune trends in peripheral compartments.
Conclusion: A responder-informed, defined microbial consortium functionally translates clinical microbiome associations into in vivo validation and enhances PD-1 blockade efficacy by modulating host antitumor immunity. These findings support defined bacterial consortia as microbiome-based immunomodulatory adjuncts for immunotherapy.
Rheumatoid arthritis (RA) is a chronic autoimmune disease preceded by a prolonged preclinical phase marked by the emergence of autoantibodies and mucosal immune dysregulation. Evidence from human studies and animal models consistently demonstrates that gut microbiota dysbiosis contributes to this transition, particularly through impaired intestinal barrier function, activation of pro-inflammatory pathways, and molecular mimicry. Specific taxa - including Prevotella copri, Collinsella aerofaciens, and reductions in butyrate-producing bacteria - have been linked to heightened systemic inflammation, increased T helper 17 responses, and the generation of RA-associated autoantibodies. Current research also indicates that anti-rheumatic medications such as methotrexate, sulfasalazine, and minocycline produce measurable shifts in gut microbial composition, suggesting that microbiota-drug interactions may influence treatment response. Therapeutic approaches aimed at modifying gut ecology - including dietary interventions, prebiotics, probiotics, and fecal microbiota transplantation - show early potential in restoring microbial balance, improving intestinal barrier integrity, and reducing inflammatory markers, although evidence in the preclinical RA stage remains limited. Additionally, emerging data highlight the importance of intestinal autophagy and microRNA networks in regulating epithelial integrity and systemic immune activation. Taken together, the literature supports a mechanistic link between gut dysbiosis and the onset of RA. It points to microbiota-targeted strategies as promising avenues for delaying or preventing disease progression. Future studies should prioritize longitudinal analyses and interventional trials focusing specifically on individuals at risk for RA.
The objective was to quantify the absolute abundance of the jejunal mucosa-associated microbiota in pigs and develop a standardized spike-in control protocol. The spike-in control, containing bacteria that are Gram-negative (Imtechella halotolerans) and Gram-positive (Allobacillus halotolerans), was diluted 100 times and 10 μL (100,000 bacterial cells of each species) was added to 100 mg of porcine jejunal mucosa samples. The relative abundance of the jejunal mucosa-associated microbiota differed between the diet groups, whereas no difference was observed in their absolute abundance. In conclusion, the spike-in control containing 100,000 cells of Imtechella halotolerans and Allobacillus halotolerans can be used to quantify the absolute abundance of microbiota in 100 mg of pig jejunal mucosa.
Objectives: Targeted metabolomic analysis of faecal samples has been limited by narrow chemical coverage. Here, we established a multiplexed, triple quadrupole mass spectrometry (TQMS)-based targeted metabolomics workflow. This workflow allows accurate detection and semi-quantification of diverse faecal metabolites and provides a methodological platform for studying host-microbiome metabolic interactions.
Methods: Faecal metabolomes from germ-free (GF) mice, ex-germ-free (Ex-GF) mice, and human participants were analysed using TQMS-based targeted metabolomics. The analysis comprised multiple methods targeting amino acids and their derivatives, carbohydrates, short-chain fatty acids, bile acids, lipid mediators, and phospholipids.
Results: In total, 607 low-molecular-weight metabolites in 44 chemical categories were detected and semi-quantified. Faecal metabolomes of GF and Ex-GF mice were analysed, uncovering 341 intestinal microbiome-dependent metabolites. A proof-of-concept analysis using faecal samples from five patients with colorectal cancer demonstrated the successful application of this platform to human clinical material, highlighting its strong potential for future disease-oriented metabolomic investigations.
Conclusion: We developed a multi-targeted faecal metabolomics platform that substantially expands the chemical space accessible to targeted analysis. This workflow provides a methodological foundation for future large-scale and translational studies.
Background: Bifidobacteria are recognized as one of the most influential bacterial groups inhabiting the human gut, capable of modulating the host’s health. To understand how these bacteria interact with their hosts, it is essential to investigate their extracellular structures, such as pili. While the presence of sortase-dependent pili has already been investigated in the Bifidobacterium genus, limited information is available on Type IV pili (T4P), which have previously been identified in a few species as tight adherence (Tad) loci.
Methods: Here, we explored the T4P distribution across the currently 117 (sub)species representing all described taxa of the Bifidobacterium genus, revealing two distinct loci unevenly distributed within the genus through in silico genomic analyses supported by in vitro validation.
Results: Our analysis identified a conserved Type IVc pili (T4cP) structure across all bifidobacterial taxa, with minor predicted structural variations in members of the Bifidobacterium longum and Bifidobacterium boum phylogenetic groups. This T4cP structure, also known as Tad, exhibited an ancestral, non-retractile architecture typically associated with stable colonization and long-term persistence. In addition, a secondary Type IVa pili (T4aP) structure was detected in 13 bifidobacterial species. These species are associated with specific ecological niches, including primate, bovine, and porcine hosts, suggesting a link between this locus and host-associated adaptation.
Conclusion: Notably, twitching motility assays demonstrated that Bifidobacterium adolescentis strains harboring the T4aP locus exhibit motility in response to specific environmental signals, observed upon starch supplementation of the growth medium, thereby challenging the traditional view of bifidobacteria as a strictly non-motile bacterial genus.
Background: Inflammatory bowel diseases (IBDs) are complex conditions marked by chronic inflammation in the gastrointestinal tract. Traditional classification separates IBD into Crohn’s disease and ulcerative colitis, but this division may not fully capture disease heterogeneity. Here, we examine whether microbiome-driven subtyping can describe novel clinical IBD phenotypes. To achieve this, we applied unsupervised clustering to fecal microbiota profiles from the population-based Inflammatory Bowel Disease in South-Eastern Norway III (IBSEN III) cohort.
Methods: A Gaussian Mixture Model (GMM) was used to cluster participants with IBD based on microbiome composition and examine associations between clusters and clinical outcomes, including inflammatory markers and disease severity during the first year after inclusion.
Results: Three microbiome-based clusters were identified: CLO (dominated by Clostridia UCG-014), ALF (Agathobacter, Lachnoclostridium, and Faecalibacterium), and RUM (Ruminococcus gnavus). Participants in the RUM cluster had a higher risk of future severe disease than those in the CLO cluster, even among participants with remission-to-mild disease at inclusion (21% vs. 6%, P < 0.00001). This association could not be explained by antibiotic use or baseline disease severity. Cluster membership alone performed comparably to fecal calprotectin in distinguishing severe disease, and a combined model significantly improved accuracy (P < 0.0001).
Conclusion: Our findings demonstrate a connection between microbiome composition and the risk of severe disease development, which is partly independent of inflammation levels at the time of sampling. Microbiome-informed subgrouping could lead to more personalized treatment strategies. Further validation is needed to determine the clinical utility of these clusters.