CONTRIBUTION OF TOLL-LIKE RECEPTORS AND CYTOKINE GENES POLYMORPHISMS TO THE RISK OF DEVELOPING ULCERATIVE COLITIS

Abstract

Introduction. Inflammatory bowel disease (IBD), the incidence of which continues to rise in urbanized populations, remains a common problem. Over 260 genetic loci associated with IBD have been identified, many of which are involved in immune sensing and signaling pathways [3]. Ulcerative colitis (UC) is a multifactorial disorder, so it is necessary to study not only the influence of certain gene polymorphisms, but also to identify intergenic interactions for a better understanding of pathogenesis and the identification of genetic risk factors. Objective. To evaluate the contribution of single nucleotide polymorphisms in the TLR1, TLR2, TLR4, TLR6, TNFA, and IL6 genes to the predisposition to UC using the multifactor dimensionality reduction (MDR) method. Materials and methods. This study included 137 individuals with UC and 166 healthy controls. The contribution of SNPs was assessed using predictive models of UC. Modeling was performed using MDR based on allelic variant data for 10 polymorphic sites. Several predictive models were derived using exhaustive and forced search algorithms, as well as information analysis. Results. The final model includes SNPs in the Toll-like receptor genes TLR1 rs5743618, TLR2 rs5743708, TLR6 rs5743810, TLR4 rs4986790, TLR4 rs4986791, and the cytokine TNFA rs361525 and rs1800630. The model demonstrated high cross-validation consistency (10/10), statistical significance (P < 0.0001), sensitivity (87.6 %), as well as satisfactory specificity (67.5 %) and diagnostic accuracy (77.5 %). The analysis revealed the nature of gene-gene interactions between TLRs and cytokines associated with UC risk. A pronounced epistatic interaction was detected between polymorphic variants of TLR1 and TLR6, as well as a moderate epistatic interaction between the two TLR4 polymorphisms. Conclusion. The analysis of gene-gene interactions using the MDR method made it possible to evaluate the contribution of SNPs in TLRs and TNFA to the development of UC predisposition and to build a predictive model.