Identification of drug targets for Sjögren’s syndrome: multi-omics Mendelian randomization and colocalization analyses

孟德尔随机化 DNA甲基化 生物 表达数量性状基因座 甲基化 遗传学 基因 计算生物学 全基因组关联研究 基因表达 候选基因 生物信息学 单核苷酸多态性 基因型 遗传变异
作者
Yingjie Bai,Jiayi Wang,Xuefeng Feng,Le Xie,Shengao Qin,Guowu Ma,Shouxin Zhang
出处
期刊:Frontiers in Immunology [Frontiers Media SA]
卷期号:15
标识
DOI:10.3389/fimmu.2024.1419363
摘要

Background Targeted therapy for Sjögren’s syndrome (SS) has become an important focus for clinicians. Multi-omics-wide Mendelian randomization (MR) analyses have provided new ideas for identifying potential drug targets. Methods We conducted summary-data-based Mendelian randomization (SMR) analysis to evaluate therapeutic targets associated with SS by integrating DNA methylation, gene expression and protein quantitative trait loci (mQTL, eQTL, and pQTL, respectively). Genetic associations with SS were derived from the FinnGen study (discovery) and the GWAS catalog (replication). Colocalization analyses were employed to determine whether two potentially relevant phenotypes share the same genetic factors in a given region. Moreover, to delve deeper into potential regulation among DNA methylation, gene expression, and protein abundance, we conducted MR analysis to explore the causal relationship between candidate gene methylation and expression, as well as between gene expression and protein abundance. Drug prediction and molecular docking were further employed to validate the pharmacological activity of the candidate drug targets. Results Upon integrating the multi-omics data, we identified three genes associated with SS risk: TNFAIP3, BTN3A1, and PLAU. The methylation of cg22068371 in BTN3A1 was positively associated with protein levels, consistent with the negative effect of cg22068371 methylation on the risk of SS. Additionally, positive correlations were observed between the gene methylation of PLAU (cg04939496) and expression, as well as between expression and protein levels. This consistency elucidates the promotional effects of PLAU on SS risk at the DNA methylation, gene expression, and protein levels. At the protein level, genetically predicted TNFAIP3 (OR 2.47, 95% CI 1.56–3.92) was positively associated with SS risk, while BTN3A1 (OR 2.96E-03, 95% CI 2.63E-04–3.33E-02) was negatively associated with SS risk. Molecular docking showed stable binding for candidate drugs and target proteins. Conclusion Our study reveals promising therapeutic targets for the treatment of SS, providing valuable insights into targeted therapy for SS. However, further validation through future experiments is warranted.
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