Key genes and functional coexpression modules involved in the pathogenesis of systemic lupus erythematosus

Abstract

We performed a systematic review of genome-wide gene expression datasets to identify key genes and functional modules involved in the pathogenesis of systemic lupus erythematosus (SLE) at a systems level. Genome-wide gene expression datasets involving SLE patients were searched in Gene Expression Omnibus and ArrayExpress databases. Robust rank aggregation (RRA) analysis was used to integrate those public datasets and identify key genes associated with SLE. The weighted gene coexpression network analysis (WGCNA) was adapted to identify functional modules involved in SLE pathogenesis, and the gene ontology enrichment analysis was utilized to explore their functions. The aberrant expressions of several randomly selected key genes were further validated in SLE patients through quantitative real-time polymerase chain reaction. Fifteen genome-wide gene expression datasets were finally included, which involved a total of 1,778 SLE patients and 408 healthy controls. A large number of significantly upregulated or downregulated genes were identified through RRA analysis, and some of those genes were novel SLE gene signatures and their molecular roles in etiology of SLE remained vague. WGCNA further successfully identified six main functional modules involved in the pathogenesis of SLE. The most important functional module involved in SLE included 182 genes and mainly enriched in biological processes, including defense response to virus, interferon signaling pathway, and cytokine-mediated signaling pathway. This study identifies a number of key genes and functional coexpression modules involved in SLE, which provides deepening insights into the molecular mechanism of SLE at a systems level and also provides some promising therapeutic targets.

An allele-specific functional SNP associated with two autoimmune diseases modulatesIRF5expression by long-range chromatin loop formation

“…Studies on murine models also indicated that Irf −/− mice could result in poor lymphocyte activation, decreased autoantibody levels [41] and significant lower production of IFN-I and key cytokines (IL-12 and IL-23) that link innate immunity to SLE pathogenesis [42]. Consistently, our analysis demonstrated that the risk allele of rs13239597 could augment IRF5 expression, and IRF5 was significantly higher expressed in SLE patients compared with healthy individuals [34].…”

“…(F) Comparison of IRF5 expression between healthy individuals and SLE patients in whole blood samples from three SLE genome-wide gene expression datasets (GSE61635, GSE39088 and GSE65391) [34] are shown.…”

“…To compare the IRF5 expression between SLE patients and healthy individuals, we screened the IRF5 expression in whole blood samples of three SLE genome-wide gene expression datasets (GSE61635, GSE39088 and GSE65391) [34]. Significantly higher IRF5 expression was found in SLE patients compared with healthy individuals in all three datasets ( P = 1.272 × 10 −4 in GSE61635, P = 0.001 in GSE39088 and P = 0.019 in GSE65391, Figure 5F), which was consistent with that the risk allele-A of rs13239597 could augment IRF5 expression.…”

“…We retrieved three SLE genome-wide gene expression datasets in whole blood samples from the Gene Expression Omnibus (GEO) repository, including 30 healthy controls and 99 SLE patients for GSE61635, 46 healthy controls and 96 patients for GSE39088 and 72 healthy controls and 924 patients for GSE65391[34]. We compared the average expression level for all microarray probes on IRF5 between SLE patients and healthy samples in each dataset using two-tailed student’s T test, respectively.…”

Identification of Crucial Genes and Pathways in Human Arrhythmogenic Right Ventricular Cardiomyopathy by Coexpression Analysis

“…Briefly, WGCNA is designed to uncover highly correlated gene modules and to relate gene clusters to one another and to sample traits. In recent studies, it has been verified in previous sample characteristics such as disease status (Yan et al, 2018), gender (Fatima et al, 2018), age (Maffei et al, 2017), and BMI (Wang W. et al, 2017). It was also proved that it was a reliable and promising instrument for cardiovascular diseases’ clinical diagnosis (Chen et al, 2016) and cardiomyocyte differentiation (Liu et al, 2017).…”