Comprehensive genetic analysis based on multi - omics reveals novel therapeutic targets for mitral valve prolapse and drug molecular dynamics simulation.

OBJECTIVE

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.

METHOD

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.

RESULT

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.

CONCLUSION

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.