Ulcerative Colitis Is Associated with Distinct Colonic Mucosa-Associated Microbial Profiles in a North Indian Cohort

Technical Summary: Ulcerative Colitis and Distinct Colonic Mucosa-Associated Microbial Profiles in a North Indian Cohort

Problem Statement
Ulcerative colitis (UC) is a chronic inflammatory bowel disease (IBD) driven by complex interactions between genetics, environment, and the gut microbiota. While India is currently in stage 2 of the IBD epidemiological transition with a rising case burden, the specific microbial signatures driving UC pathogenesis in this population remain underexplored. Existing literature predominantly focuses on fecal microbiota, which may not accurately reflect the mucosa-associated microbiome (MAM) that resides in direct contact with the intestinal epithelium. Furthermore, global microbial patterns may not apply to the Indian context, where high fecal-oral transmission rates and persistent infections may uniquely shape microbial dynamics. There is a lack of documented signature mucosal microbial profiles for Indian UC patients, necessitating a focused investigation into the MAM to understand disease mechanisms and inform personalized treatment strategies.

Methodology
This cross-sectional study recruited 60 participants from North India: 30 patients diagnosed with UC (confirmed by clinical, endoscopic, and histological data) and 30 non-IBD controls with macroscopically normal colonic mucosa. Participants were excluded if they had used antibiotics, probiotics, or NSAIDs within the previous four weeks.

• Sample Collection: Colonic mucosal biopsies were obtained from the distal colon during colonoscopy or sigmoidoscopy. Samples were immediately preserved in RNAlater and stored at -80°C.
• Sequencing: Bacterial DNA was extracted using a mechanical-chemical lysis protocol optimized for low-biomass specimens. The V3-V4 region of the 16S rRNA gene was amplified and sequenced on the Illumina MiSeq platform.
• Bioinformatics: Raw reads were processed using the DADA2 pipeline for denoising and chimera removal, generating Amplicon Sequence Variants (ASVs). Taxonomic assignment was performed using the SILVA v138 database.
• Statistical Analysis:
  • Diversity: Alpha diversity (Observed richness, Shannon, Simpson, Chao1) and Beta diversity (Bray-Curtis dissimilarity) were assessed.
  • Differential Abundance: Phylum and genus-level differences were analyzed using the Mann-Whitney U test with Benjamini-Hochberg correction. Linear Discriminant Analysis Effect Size (LEfSe) was used to identify taxa with an LDA score > 2.5.
  • Network Analysis: Spearman correlation-based co-occurrence networks were constructed for taxa identified by LEfSe to explore microbial interaction patterns.

Key Results

• Diversity Metrics: No significant differences were observed in alpha diversity (richness or Shannon index) between UC patients and controls. However, beta diversity analysis (PERMANOVA, p < 0.01) revealed distinct clustering between the two groups, indicating a shift in community composition rather than a simple loss of diversity. The UC group exhibited greater inter-individual variability compared to the tighter clustering of controls.
• Phylum-Level Shifts: The UC cohort displayed a significantly elevated Firmicutes-to-Bacteroidota (F/B) ratio (mean 1.91 vs. 1.11 in controls; p = 0.007). Specifically, Firmicutes were enriched in UC, while Fusobacteriota, Actinobacteriota, Patescibacteria, Campylobacterota, and Spirochaetota were significantly reduced.
• Taxonomic Enrichment (LEfSe):
  • UC-Associated: Families such as Campylobacteraceae, Leptotrichiaceae, Lachnospiraceae, and Cardiobacteriaceae were significantly enriched in UC.
  • Control-Associated: Families including Actinomycetaceae, Porphyromonadaceae, and Spirochaetaceae were enriched in the control group.
• Genus and Species Level: At the genus level, Bacteroides, Blautia, Faecalibacterium, and Sutterella were more abundant in UC, whereas Fusobacterium, Leptotrichia, Campylobacter, and Prevotella were reduced. Species-level analysis (with caution regarding resolution) indicated an increase in mucin-degrading taxa (e.g., Ruminococcus torques) and potential pathogens (e.g., Bacteroides fragilis), alongside a depletion of beneficial lactic acid bacteria.
• Microbial Networks: Network analysis revealed a dense, positively correlated cluster of taxa in UC, centered around Leptotrichiaceae and Campylobacteraceae. In contrast, the control network displayed fewer and weaker cross-family associations, suggesting a shift toward coordinated co-occurrence patterns in the diseased mucosal ecosystem.

Significance and Claims
The authors claim that this study provides the first detailed analysis of the colonic mucosa-associated microbiome in UC patients from India. The findings challenge the notion that UC is characterized solely by a global loss of microbial diversity; instead, the study demonstrates that the disease is associated with a complex re-organization of the mucosal community. Key contributions include:

1. Population Specificity: The study highlights that UC in North India presents with distinct microbial signatures (e.g., elevated F/B ratio, specific family enrichments) that may differ from Western cohorts, underscoring the need for population-specific studies.
2. Mucosal Focus: By focusing on the MAM rather than fecal samples, the research captures taxa closely associated with the epithelium, which are often low or inconsistent in stool samples.
3. Ecological Shift: The identification of altered microbial interaction networks suggests that UC pathogenesis involves a shift in community dynamics and coordinated expansion of specific taxa, rather than just the depletion of commensals.

The paper concludes that these taxonomic shifts and altered interaction patterns reinforce the importance of profiling the mucosal niche to understand UC-associated dysbiosis. The authors suggest that these insights may eventually inform personalized treatment strategies, though they acknowledge limitations such as the modest sample size, cross-sectional design, and the lack of functional/metabolomic data.