Differentiation of Gut Microbiome Profiles Between Healthy and IBD Individuals Using 16S rRNA Sequencing and AI-Enhanced Analysis: Insights from Indian Cohorts

Gut microbiome plays a major role in human health, and its dysbiosis is associated with diseases i.e. inflammatory bowel disease (IBD). This study investigates microbial differences between healthy individuals and IBD patients using 16S rRNA sequencing data from various BioProjects within the Indian Microbiome Database. Data from projects PRJNA397112, PRJNA422434, PRJNA515526, PRJNA559123, and PRJNA637482 were analyzed and taxonomic classification was performed using Qiita (Gonzalez et al. 2018) with the picking method of closed reference Operational Taxonomic Units (OTUs) through SortMeRNA (Kopylova et al. 2012). Additionally, 16S PIP (Miao et al. 2017) was used for pathogenic bacteria identification. To enhance the analysis, Artificial Intelligence (AI)-based methods such as machine learning (ML) clustering and predictive modeling were employed to identify microbial patterns linked to health outcomes. Significant differences were observed in microbial diversity and composition between healthy and IBD samples, with an increase in pathogenic species in IBD patients. These findings, combined with AI analysis, provide insights into microbial markers of gut health and disease, offering potential applications in personalized medicine. This research underscores the potential of AI in identifying microbial markers for personalized treatment strategies.