Polymorphisms of STAT4 and the risk of inflammatory bowel disease: A case-control study in Chinese Han population

Effector T Helper Cell Subsets in Inflammatory Bowel Diseases

The gastrointestinal tract is a site of high immune challenge, as it must maintain a delicate balance between tolerating luminal contents and generating an immune response toward pathogens. CD4⁺ T cells are key in mediating the host protective and homeostatic responses. Yet, CD4⁺ T cells are also known to be the main drivers of inflammatory bowel disease (IBD) when this balance is perturbed. Many subsets of CD4⁺ T cells have been identified as players in perpetuating chronic intestinal inflammation. Over the last few decades, understanding of how each subset of Th cells plays a role has dramatically increased. Simultaneously, this has allowed development of therapeutic innovation targeting specific molecules rather than broad immunosuppressive agents. Here, we review the emerging evidence of how each subset functions in promoting and sustaining the chronic inflammation that characterizes IBD.

Association of STAT4 rs7574865 polymorphism with susceptibility to inflammatory bowel disease: A systematic review and meta-analysis

OBJECTIVE

Association of Signal transducers and activators of transcription-4 (STAT4) gene polymorphism with susceptibility to inflammatory bowel disease have been investigated in a number of epidemiological studies, but the results are inclusive. The aim of this meta-analysis was to more precisely estimate the relationship.

METHODS

The databases of Pubmed and CBM updated to October, 2014 were retrieved. Random- or fixed-effect model was used to estimate odd radio (OR) and corresponding 95% confidence interval (95%CI) on the basis of heterogeneity.

RESULTS

Seven articles containing 2196 Crohn's disease (CD) cases, 1588 ulcerative colitis (UC) cases and 4126 controls were identified. We detected a significant association between STAT4 rs7574865 polymorphism and IBD susceptibility in overall population (GG vs. GT+TT, OR=0.855, 95% CI=0.760-0.962, P=0.009), but not in Caucasian and Asian population, respectively. No association was detected between rs7574865 polymorphism and CD susceptibility in overall, Asian and Caucasian population, respectively. Interestingly, a significant association was detected between rs7574865 with UC susceptibility in overall population (G vs. T, OR=0.881, 95% CI=0.798-0.972, P=0.012; GG vs. GT+TT, OR=0.788, 95% CI=0.679-0.914, P=0.002; GG vs. TT, OR=0.683, 95% CI=0.498-0.937, P=0.018) and Caucasians (GG vs. GT+TT, OR=0.833, 95% CI=0.701-0.990, P=0.038; GG+GT vs. TT, OR=0.667, 95% CI=0.456-0.975, P=0.037; GG vs. TT, OR=0.636, 95% CI=0.433-0.934, P=0.021), respectively, and a possible association was found in Asian population (GG vs. GT+TT, OR=0.709, 95% CI=0.503-0.998, P=0.049).

CONCLUSIONS

STAT4 rs7574865 gene is IBD risk factor, and this gene polymorphism is associated with UC susceptibility, especially in Caucasians. To confirm these findings, further studies with more sample size are required for a definitive conclusion.

Identification of inflammatory bowel disease-related proteins using a reverse k-nearest neighbor search

Inflammatory bowel disease (IBD) is a chronic disease whose incidence and prevalence increase every year; however, the pathogenesis of IBD is still unclear. Thus, identifying IBD-related proteins is important for understanding its complex disease mechanism. Here, we propose a new and simple network-based approach using a reverse k-nearest neighbor ( R k NN ) search to identify novel IBD-related proteins. Protein–protein interactions (PPI) and Genome-Wide Association Studies (GWAS) were used in this study. After constructing the PPI network, the R k NN search was applied to all of the proteins to identify sets of influenced proteins among their k-nearest neighbors ( R k NNs ). An observed protein whose influenced proteins were mostly known IBD-related proteins was statistically identified as a novel IBD-related protein. Our method outperformed a random aspect, k NN search, and centrality measures based on the network topology. A total of 39 proteins were identified as IBD-related proteins. Of these proteins, 71% were reported at least once in the literature as related to IBD. Additionally, these proteins were found over-represented in the IBD pathway and enriched in importantly functional pathways in IBD. In conclusion, the R k NN search with the statistical enrichment test is a great tool to identify IBD-related proteins to better understand its complex disease mechanism.