Chen Bohang, Wang Chuqiao, Li Wenjie
Liaoning University of Traditional Chinese Medicine, Shenyang, Liaoning 110847, China.
Liaoning Health Industry Group Fukuang General Hospital, Fushun, Liaoning 113008, China.
Int J Cardiol. 2025 Aug 15;433:133325. doi: 10.1016/j.ijcard.2025.133325. Epub 2025 Apr 30.
Mitral valve prolapse (MVP), the most prevalent primary valvular disease, serves as a direct risk factor for multiple cardiovascular disorders and exhibits a high prevalence in the general population. As no specific pharmacological therapies currently exist for MVP, the identification of precise therapeutic targets is imperative.
We conducted comprehensive causal genetic inference by integrating genetic data from expression quantitative trait loci (eQTL) and genome-wide association studies (GWAS). Analytical approaches included Mendelian Randomization (MR), colocalization analysis, Summary-data-based Mendelian Randomization (SMR), Linkage Disequilibrium Score Regression (LDSC), and High-Definition Likelihood (HDL) analysis. Protein quantitative trait loci (pQTL) were utilized to validate gene expression. Replication analyses were performed using additional exposure datasets. Methylation quantitative trait loci (mQTL) were employed to elucidate regulatory roles of methylation sites on genes and disease pathogenesis. Phenome-Wide Association Study (PheWAS) was conducted to predict potential adverse effects of gene-targeted therapies. Drug candidates targeting identified genes were predicted via the Drug Signature Database (DSigDB) and validated through molecular docking. Core targets were identified using the STRING database, followed by molecular dynamics simulations.
Two-sample MR analysis showed that genetically predicted 266 genes had positive or negative causal relationships with MVP. Colocalization analysis indicated that 9 genes had a posterior probability greater than 0.75. Subsequent SMR analysis excluded the gene GAPVD1. HDL analysis showed that except for the gene PTPN1, the remaining 7 genes were all significantly genetically associated with MVP, and LDSC analysis further showed that only NMB was associated with MVP. Validation using pQTL data confirmed that increased NMB protein expression reduced the risk of MVP. Replication analysis further verified this conclusion. In addition, SMR analysis of methylation sites for 8 genes indicated that multiple methylation sites played a key role in gene regulation of mitral valve prolapse. PheWAS results showed that targeted therapy for 8 genes did not detect other causal associations at the genome-wide significance level. Molecular docking showed that quercetin had good binding ability with 8 target genes. The STRING database identified 3 core target proteins, and molecular dynamics simulations further verified the binding ability of quercetin with core target proteins.
This study successfully predicted the potential of multiple druggable genes as effective therapeutic targets for MVP through genetic methods, validated the potential of quercetin as a drug, and provided new ideas for drug treatment strategies for MVP.
二尖瓣脱垂（MVP）是最常见的原发性瓣膜疾病，是多种心血管疾病的直接危险因素，在普通人群中患病率较高。由于目前尚无针对MVP的特异性药物治疗方法，因此确定精确的治疗靶点至关重要。
我们通过整合来自表达定量性状基因座（eQTL）和全基因组关联研究（GWAS）的遗传数据进行全面的因果基因推断。分析方法包括孟德尔随机化（MR）、共定位分析、基于汇总数据的孟德尔随机化（SMR）、连锁不平衡评分回归（LDSC）和高分辨率似然（HDL）分析。利用蛋白质定量性状基因座（pQTL）验证基因表达。使用额外的暴露数据集进行复制分析。利用甲基化定量性状基因座（mQTL）阐明甲基化位点对基因和疾病发病机制的调控作用。进行全表型关联研究（PheWAS）以预测基因靶向治疗的潜在不良反应。通过药物特征数据库（DSigDB）预测靶向已鉴定基因的候选药物，并通过分子对接进行验证。使用STRING数据库鉴定核心靶点，随后进行分子动力学模拟。
两样本MR分析表明，遗传预测的266个基因与MVP存在正或负的因果关系。共定位分析表明，9个基因的后验概率大于0.75。随后的SMR分析排除了基因GAPVD1。HDL分析表明除基因PTPN1外，其余7个基因均与MVP存在显著的遗传关联，LDSC分析进一步表明只有NMB与MVP相关。使用pQTL数据进行验证证实，NMB蛋白表达增加可降低MVP风险。复制分析进一步验证了这一结论。此外，对8个基因的甲基化位点进行SMR分析表明，多个甲基化位点在二尖瓣脱垂的基因调控中起关键作用。PheWAS结果表明，对8个基因的靶向治疗在全基因组显著性水平上未检测到其他因果关联。分子对接表明槲皮素与8个靶基因具有良好的结合能力。STRING数据库鉴定出3个核心靶蛋白，分子动力学模拟进一步验证了槲皮素与核心靶蛋白的结合能力。
本研究通过遗传方法成功预测了多个可成药基因作为MVP有效治疗靶点的潜力，验证了槲皮素作为药物的潜力，并为MVP的药物治疗策略提供了新思路。
