Unraveling the causative genes – and pathways - for autoimmune diseases

pathway analysis celiac disease autoimmune disease gene enrichment T cell genes

V. Kumar
dr. S. Withoff
prof. dr. C. Wijmenga

Type of project:
Stage Wetenschap / Research project

Nature of the research:
Systems/functional genetics. Interested students will be involved in ongoing research to identify genetic susceptibility genes that predispose for autoimmune disease. For this we will need to integrate pathway analysis and functional genomics data.

Fields of study:
immunology molecular biology genetics

Background / introduction
Single nucleotide polymorphisms (SNP) based genome wide association studies (GWAS) have been very successful in identifying genetic susceptibility factors to several complex autoimmune diseases, such as type 1 diabetes (T1D), rheumatoid arthritis (RA), celiac disease (CeD), and multiple sclerosis (MS). One of the biggest surprises coming from GWAS studies was the observation that a variety of ‘immune-related’ diseases share disease susceptibility genes. This finding implies that research on the mechanism of any of these diseases may identify pathways affected in many of these diseases.
Our lab focuses on CeD. CeD is an autoimmune-mediated chronic inflammation of the small intestines that results from a T-cell mediated immune response to ingested gluten (explain?). Characteristic features of CeD include gluten-dependent damage of the mucosa of the small bowel (villous atrophy, crypt hyperplasia, symptoms of malabsorption). The genetic make-up of an individual contributes to disease susceptibility. It has been shown that siblings of CeD patients (who share 50% of their genome) have a 30 fold higher risk of developing CeD and that besides specific HLA alleles (DQ2 and DQ8) several other genetic factors are involved in the pathogenesis of this complex disorder. In a recent GWAS, aimed at identification of susceptibility genes for CeD, we have identified 57 independent (non-HLA) SNPs to be strongly associated with CeD. Although, the loci identified so far provide important clues to the pathogenesis of and immunological pathways associated with CeD, we still lack important information on the disease mechanism. One of the underlying problems is that most of the regions identified by GWAS are relatively large and may contain multiple genes. This makes it difficult to pinpoint the causal gene associated with the disease.
Therefore, we are employing powerful bioinformatic algorithms and pathway analysis tools to integrate evidence from co-expression studies, reported physical interactions, gene regulation databases, tissue specific expression profiles, and data mined from literature. This analysis was not only successful in pinpointing the most probable causative gene in each locus but also associated CeD susceptibility genes with the physiologic function of different tissues. Currently we are validating a subgroup of genes involved in epithelial function, by performing different wet-lab experiments (Membrane barrier function assays, confocal microscopy, siRNA approaches, RT-PCR etc.).
Research question / problem definition
We would like to explore the key pathways involved in autoimmune diseases by pathway/gene network analysis, starting from the data which we obtained for CeD. Furthermore, we would like to validate those results by different molecular techniques in lab.
We would like to extract all susceptibility genes identified so far from genetic studies to different autoimmune diseases. These genes will be analyzed through our pathway analysis pipeline to uncover key genes and pathways for each diseases. Validation of work hypotheses will be done by wet-lab experiments.
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Meta-analysis identifies 29 additional ulcerative colitis risk loci, increasing the number of confirmed associations to 47.
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A genetic perspective on coeliac disease.
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Genome-wide association study meta-analysis identifies seven new rheumatoid arthritis risk loci.
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Multiple common variants for celiac disease influencing immune gene expression.
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Using genome-wide pathway analysis to unravel the etiology of complex diseases.
Elbers CC, et al. Genet Epidemiol. 2009 Jul;33(5):419-31.
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