OP15 Multi-omic data integration with network analysis reveals underlying molecular mechanisms driving Crohn’s disease heterogeneity
P. Sudhakar1,2,3, B. Verstockt1,4, J. Cremer5, S. Verstockt1, T. Korcsmaros2,3, M. Ferrante1,4, S. Vermeire1,4
1Department of Chronic Diseases, Metabolism and Ageing - TARGID, KU Leuven, Leuven, Belgium, 2Gut Microbes and Health, Quadram Institute, Norwich, UK, 3Korcsmaros Group, Organisms and Ecosystems, Earlham Institute, Norwich, UK, 4Department of Gastroenterology and Hepatology, KU Leuven, University Hospitals Leuven, Leuven, Belgium, 5Laboratory of Clinical Immunology, Department of Microbiology and Immunology, KU Leuven, Leuven, Belgium
Background
Crohn’s disease (CD) is a heterogeneous disease characterised by clinical phenotypes including differences in disease behaviour, disease location and extraintestinal manifestations. However, the molecular mechanisms which orchestrate CD heterogeneity are relatively unexplored. We tried to infer such mechanisms by integrating two -omic datasets (genomics and blood proteomics) generated from CD patients.
Methods
576 unique proteins were measured from blood isolated from CD patients (
Results
From the MOFA analysis, we identified five LFs associated with at least one clinical phenotype. Clustering patients along the explanatory LFs achieved meaningful separation of clinical phenotypes such as perianal penetrating disease. The top-ranking proteins associated with perianal-disease included those involved in inflammatory pathways, autophagy or already known to be involved in CD such as IL-8, Rho-GTPase activators, MIF, Caspase 8, TRIM5 and SNAP29. The networks corresponding to the top ranking proteins associated with the perianal phenotype could be broken down into 102 local network motifs. These local motifs pointed out control mechanisms by which a total of 7 mutations mapped to transcription factors (SMAD3, BACH2) and post-translational regulators (such as IFNGR2, IL10, IL2RA, SLC2A4RG and ZMIZ1) could potentially regulate perianal disease‘s pathophysiology and could, therefore, be considered novel drug targets.
Conclusion
By using integrated signature profiles generated from multiple -omic datasets, we identified molecular mechanisms which could potentially describe CD phenotypes such as the occurrence of perianal disease.