Bridging inflammatory bowel diseases and hepatobiliary disorders through pathway enrichment and module-based approach

Inflammatory bowel diseases (IBD) including Crohn’s disease (CD) and ulcerative colitis (UC) are associated with various hepatobiliary disorders. Two of the chronic hepatobiliary disorders that may coexist with inflammatory bowel diseases are: primary biliary cholangitis (PBC) and primary sclerosing cholangitis (PSC). Previous studies have hypothesized that IBD, PBC, and PSC might share an underlying mechanism which contributes to the pathogenesis of the three conditions. In this study, a module-based network analysis and pathway enrichment analysis was applied on IBD, PSC, and PBC differentially expressed genes (DEGs). The sample data were obtained from the study by Ostrowski et al. (2019). A network module-based approach was applied to examine generated results where additional information about biological processes, pathways and molecular functions can be inferred. FunRich and Enrichr were utilized as functional enrichment tools. A protein interaction network was constructed for the three conditions using STRING. Functional modules and overlapping modules of IBD, PSC, and PBC were identified using different plug-ins in Cytoscape. Some of the results were consistent with the findings of Ostrowski et al. (2019) such as the ATP synthesis and signal transduction that is shared among the overlapping genes in IBD, PBC, and PSC. ModuLand highlighted nodes that have been previously reported to have a role in the pathogenesis of autoimmune diseases. The proposed approach demonstrated that the module-based approach contributes to similar results regarding biological processes and pathway enrichment of generated modules, compared to enrichment analysis of DEGs. In addition, the utilization of the ModuLand plug-in to find hierarchal layers of disease genes is still poorly researched and would benefit from more in-depth comparison with related tools for module discovery. For instance, implementing ModuLand plug-in can potentially support research in elucidating complex diseases