Identifying therapeutic target genes for diabetic retinopathy using systematic druggable genome-wide Mendelian
Long Xie1, Yu Qin Peng2, Xiang Shen3
1Department of Orthopedics, The Fourth Hospital of Changsha (Integrated Traditional Chinese and Western Medicine Hospital of Changsha), Hunan Normal University, Changsha, Hunan Province, 410006, China. longxie_2020@163.com.
Introduction:
The treatment and prevention of diabetic retinopathy (DR) remain significant challenges. Mendelian randomization (MR) has been widely used to explore novel therapeutic targets. In this study, we conducted a systematic druggable genome-wide MR analysis to explore potential therapeutic targets for DR.
Methods:
We obtained data on druggable genes and screened for genes within blood expression quantitative trait loci (eQTL), which were then subjected to MR analysis and colocalization analysis with DR genome-wide association studies data to identify genes strongly associated with DR. Additionally, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, protein-protein interaction (PPI) network construction, drug candidate prediction, and molecular docking were performed to provide valuable insights for the development of more effective and targeted therapeutic drugs.
Results:
MR analysis of blood eQTLs revealed 30 significant DR-associated druggable genes, with PRKAB1 (OR = 0.935, 95% CI: 0.892 to 0.980) and CNR1 (OR = 0.814, 95% CI: 0.696 to 0.951) being protective genes, whereas CACNA1E (OR = 1.282, 95% CI: 1.050 to 1.565), NME1 (OR = 1.198, 95% CI: 1.028 to 1.397), and CHRNA2 (OR = 1.192, 95% CI: 1.025 to 1.386) were associated with increased risk. KEGG analysis highlighted significant pathways, including adrenergic signaling in cardiomyocytes (hsa04261), the oxytocin signaling pathway (hsa04921), and arrhythmogenic right ventricular cardiomyopathy (hsa05412). PPI network analysis identified two key modules: one comprising BIN1, CDH2, ACTN1, EPAS1, CEBPA, and CTSD nodes, and the other consisting of CACNG6, CACNA1E, CACNA2D3, and RASGRP3 nodes. Drug candidate prediction suggested ethanol and isoflupredone as potential therapeutic interventions, and molecular docking revealed C5's strong protein binding affinity.
Conclusions:
This study utilized MR and colocalization analysis to identify potential drug targets for DR. The findings provide promising leads for the treatments of DR, potentially reducing drug development costs.
