Tu12 - An In Silico Analysis to Predict the Function of Genes Associated with Type 1 Diabetes, Systemic Lupus Erythematosus, and Juvenile Idiopathic Arthritis in Colombian Patients

Abstract Text:

Background: Autoimmune diseases are complex-heterogeneous. There are clinical and genetic grounds for assuming similar genetic mechanisms in these diseases

Aim: Predict the function and interactions of genes associated (MGAT5, RUNX1, PSD3, GRB2, GABRA2, CD86, KSR2, and EDEM3) with T1D, SLE, and JIA.

Methods: We analysed whole-exome-sequences of 75 patient diagnosed with T1D (n=25), SLE (n=25) and JIA (n=25) recruited in Barranquilla-Colombia. Single and multi-locus linear mixed-effects-models were used to identify genomic-variants associated with SLE, JIA, and/or T1D. We prioritize the analysis to the following genes MGAT5, RUNX1, PSD3, GRB2, GABRA2, CD86, KSR2, and EDEM3. We use biological network predictor server such as GeneMANIA, STRING, and Phenolyzer to predict Genetic and Protein-interactions.

Results: GenMANIA reported a genetic-interaction between CD86 and MGAT5; Co-expression of CD86, MGAT5, PSD3, GRB2 and EDEM3; and the physical-interaction between CD86 and GRB2 in the T cell activation pathway. Phenolyzer displayed three seed-genes (GRB2, RUNX1, EDEM3) that interact with other genes; GRB2 associate to SLE and interact with KSR2 and CD86; CD86 is a bridge with RUNX1 that relate to JIA and interact with MGAT5, EDEM3 and GABRA2. k-means-clustering of STRING showed 3 cluster of protein-protein interactions (C1-GABRA2, PSD3, RUNX1; C2-EDEM3, KSR2, MGAT5; C3-CD86, GRB2).

Conclusions: Taking together these in silico results, we found that these eight genes are involved in different pathways; however, the role of these genes should be analyzed particularly in the pathophysiology of each disease. Some genes confirm their association with SLE, and JIA but other are reported for the first-time associated with T1D.