Fecal and colonic microbiota differ in composition, function, diagnostic utility and correlation with inflammatory cytokines in ulcerative colitis

Fecal samples have been frequently used to investigate ulcerative colitis (UC)-related gut microbiota study due to their easy availability. However, whether fecal microbiota accurately represents the mucosal microbiome of UC patients remains controversial. This study aims to analyze the gut microbiota in feces and colonic tissue, and to demonstrate from different perspectives whether the gut microbiota in feces can fully characterize the gut microbiota in colonic tissue. We employed 16S rRNA sequencing and standardized microbiome analysis for fecal and colon tissue samples from 12 UC patients and 16 non-IBD controls (NIC). And we also integrates ELISA, functional analysis, and machine learning techniques to discuss the similarities and differences among differentially identified UC-related microorganisms screened from two sample types. As results, colonic tissue exhibited greater microbial diversity than feces. And some taxa were only detected in colonic tissue. In UC, α-diversity was higher in colonic tissue but lower in feces compared to NIC. Only two microbial genera (Ligilactobacillus and Intestinimonas) showed consistent differential abundance across both sample types. At the functional pathway level, both fecal and colonic microbiota were primarily associated with metabolic pathways; however, colonic microbiota were additionally linked to immune-related pathways, such as the NOD-like receptor signaling pathway. IL-1β, IL-17 and IL-22 were significantly overexpressed in UC. Most fecal differential microbes were negatively correlated with those three cytokines. Regarding the two shared differentially expressed genera, abundance of Ligilactobacillus in tissues was more strongly correlated with the expression of inflammatory cytokines. The correlation between the abundance of Intestinimonas and the expression levels of inflammatory cytokines showed opposite trends in tissues and feces. The combined SHAP and random forest algorithms shown that the UC classification model based on tissue markers was more stable and exhibited weaker overfitting than the model based on fecal markers. In general, fecal microbiota profiles did not adequately represent the microbial landscape of colonic tissue in patients with UC. For studies investigating disease mechanisms, colonic tissue was the more appropriate sample type. However, due to its accessibility, fecal samples remained a promising source for microbial biomarker discovery and the development of diagnostic models.