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 employ different strategies (top-down and bottom-up) of data integration to tackle some of the key challenges existing in the IBD field. We use a top-down approach starting with the statistical interpretation of -omic datasets and using systems biology/machine learning to understand mechanisms/predict biomarkers respectively.
We also harness the utility of the bottom-up approach with particular types of data such as mutations to explore how these potential genetic drivers can result in manifestations such as progression of disease pathogenesis. Thus, by using both the top-down and bottom-up approaches, we can answer different questions pertaining to the existing needs of the IBD clinical community.
The disease behaviour of Crohn’s disease is heterogeneous as evidenced by inflammatory, fibrostenotic or penetrating sub-types. Biomarkers that predict these sub-types at diagnosis and biological mechanisms explaining the difference between them are lacking. Dysregulated CD4+ cell populations in CD patients have been associated with disease activity variation.
As examples of the top-down data integration approach, we aim to identify discriminative features from the integrative analysis of gene expression from immune cells and the genetic risk burden, which explain CD behavioural heterogeneity. A similar top-down approach is also being used to identify biomarkers predictive of responses to anti-inflammatory drugs used in IBD.
As an example of the bottom-up approach, we are using our integrative pipeline called iSNP, which tracks the effects of patient genotypes on patient phenotypes by integrating biological interaction networks, mutation data and contextual expression data (transcriptomics and proteomics).
We have employed this pipeline to identify common patterns of ulcerative colitis (UC) patients clustered together based on their mutation-affected networks. Such patterns help to explain UC disease pathogenesis and bridge the genotype-phenotype gap for improved therapies.
Multi -omic data generated from patients by the IBD unit, KU Leuven
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.