bioRxivBioinformaticsPreprint
Bayesian Network Analysis Identifies PREX1 as a Master Regulator of Cytoskeletal Disruption in Inflammatory Bowel Disease
Snezhanna S. MedvedevaiD, Julia Kulygina, Ksenia N. MorozovaiD, Julia V. PopovaiD, Maksim Struchalin, M F Osipenko, Elena N. KozhevnikovaiD
Abstract
BackgroundInflammatory bowel diseases are complex multifactorial and polygenic conditions with incompletely understood etiology. Transcriptomic and metabolomic studies reveal hundreds of genes and metabolites associated with the inflammatory dowel diseases (IBD), while about 240 genetic loci are associated with these diseases. IBD are accompanied by numerous epithelial lesions including epithelial cells damage and barrier dysfunction. Recent studies suggest that cytoskeleton dysregulation underlies these aspects of IBD. We have previously shown that the disruption of cortical actin filaments and microvilli degeneration are characteristic of Muc2 mouse IBD model. MethodsWe used transmission electron microscopy to evaluate ultrastructural defects in the intestinal epithelium of IBD patients. A probabilistic graphical model (Bayesian network) was employed in order to identify the key gene network hierarchy that might define cytoskeletal architecture among other regulatory events in IBD. The Bayesian network was based on preselected key node genes from open access transcriptomic data obtained in patients with Crohns disease. ResultsElectron microscopy of IBD patients demonstrated disruption of the brush border ultrastructure, similar to the microvillar defects previously described in Muc2 colitis model mice. We highlighted a list of genes contributing to actin polymerization and bundling, of which PREX1 were further confirmed using the transcriptome data obtained from Muc2 mice. ConclusionOur study underscores the utility of Bayesian network analysis in complex cellular phenotypes that helped to identify potential target genes responsible for cortical cytoskeleton and microvilli disruption upon chronic intestinal inflammation.
- organism: human
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- approach: Bayesian inference
- Citations
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