Multi-omics analysis of Inflammatory Bowel Disease patients
Integrating multiple -omic datasets from IBD patients to facilitate precision medicine and treatment.
Led by: Korcsmáros Group
Start date: June 2018
Duration: Aug 2021
Grants: Collaboration in connection to the ERC Advanced Grant
- CrUCCial of Prof. Séverine Vermeire (Leuven, Belgium)
Inflammatory Bowel Disease (IBD) is a disorder of the human gastrointestinal tract (GI) characterised by inflammation of the gut mucosal layer and dysbiosis of the microbiome. IBD can broadly be classified into two well-studied diseases - Ulcerative Colitis (UC) and Crohn’s Disease (CD) - which are increasingly prevalent in the global population. However, IBD is a complex disease influenced by a variety of extrinsic and intrinsic factors such as genetics, microbiome, life-style (diet, smoking etc), xenobiotic exposures etc.
The inherent complexity of the disease brings with it a plethora of challenges in understanding disease pathogenesis which has implications in classifying disease subtypes, discovering drug-targets and predicting drug responses. One of the suggested approaches to tackle these challenges is to make use of data integration methodologies.
Stemming from our strategic collaboration with the IBD unit headed by Prof. Séverine Vermeire at KU Leuven, we have access to patient -omic datasets (such as genotyping, tissue and immune cell transcriptomics, blood proteomics, gut microbiome etc). We use data integration approaches aided by systems biology and machine learning to disentangle the complexity of IBD by exploiting the benefits delivered by the cumulative insights rendered by multiple -omic layers.
We harness the advantages of various integrative approaches (data driven or hypothesis driven) to further our understanding of the IBD disease drivers in a patient specific manner so that therapeutic approaches with minimal side-effects and maximal benefits can be tailored to fit the patient or groups of patients.
Such approaches are also expected to result in improved diagnosis and prognosis of patients.