Integrated 16S rRNA Bioinformatics Analysis of Gut Microbiota, Predicted Pathways, and Candidate Biomarkers in Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) has been linked to alterations in gut microbiota composition and predicted microbial functions that could be connected to intestinal immune processes. Researchers conducted a study to compare gut microbiota profiles between patients with IBD and healthy individuals, and to investigate the relationships between predicted microbial pathways and specific bacterial genera.

The team utilized publicly available 16S rRNA amplicon sequencing data from 22 stool samples - 11 from IBD patients and 11 from healthy controls.

The analysis involved taxonomic profiling, diversity assessment, differential abundance testing, and functional prediction using the PICRUSt2 tool. Microbe-pathway associations were examined using Spearman correlation and Cytoscape to explore potential connections. Additional analyses integrated differential abundance with machine learning feature importance to prioritize candidate genera, assessed a three-genus signature, evaluated selected findings in an independent dataset, and compared predicted fermentation- and acetate-related pathways.

The results demonstrated that alpha diversity was significantly lower in the IBD group, as indicated by the Shannon index (p = 0.003). Furthermore, Bray-Curtis PCoA revealed a separation between the two groups primarily along PC1 (PERMANOVA pseudo-F = 6.125, p value not provided).