Zhao Peng, Meng Li, Han Feiyuan, Yu Zhongzhi, Wang Yidan, Wu Yunfei, Wang Yan, Yu Bo, Liu Xinxin, Tian Jinwei
Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, 150086, Xuefu Road 246, Harbin, Province Heilongjiang, China; Key Laboratory of Myocardial Ischemia, Ministry of Education, 150086, Xuefu Road 246, Harbin, Province Heilongjiang, China; Heilongjiang Provincial Key Laboratory of Panvascular Disease, China.
Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, 150086, Xuefu Road 246, Harbin, Province Heilongjiang, China; Heilongjiang Provincial Key Laboratory of Panvascular Disease, China.
Int Immunopharmacol. 2024 Dec 25;143(Pt 3):113520. doi: 10.1016/j.intimp.2024.113520. Epub 2024 Oct 31.
Cardiac conduction disorders predispose individuals to arrhythmias, currently but the exact mechanisms of cardiac conduction remain elusive. The study sought to identify the causal association between circulating plasma proteins and electrocardiogram (ECG) traits, offer valuable biological insights and clinical guidance into cardiac conduction.
Proteome-wide Mendelian randomization (MR) analysis was firstly conducted to assess causal associations between plasma proteins and five ECG traits, including P wave duration (PWD), QRS duration, PR, QT and RR intervals. Multiple sensitivity analyses were implemented. The reverse MR analysis, colocalization analysis and replication analysis were used to consolidate the reliability of our results. Then, we conducted mediation analysis to explore potential mechanism between plasma proteins and ECG traits. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were applied to clarify the biological functions of target proteins. Finally, phenome-wide MR (Phe-MR) and drug databases were searched.
We identified 3 proteins (FAM151A, VEGF165, VEGF121) associated with PWD, 12 proteins (ABHD10, ADK, Cathepsin_S, DUSP13, Ephrin_A3, MAPRE2, OMG, PAM, PMM1, SH3BGRL3, TCP4, SYT11) linked to PR interval, 1 protein (PKC_A) related to QRS duration, and 2 proteins (MXRA7, SVEP1) associated with QT interval. A significant causal effects of ECG traits on them was not found in reverse MR. Colocalization and replication analyses strengthened our findings further. The impacts were partly mediated by anthropometric measures. Enrichment analysis of target proteins mainly enriched for multiple key pathways such as regulation of hydrolase activity and fibronectin binding. Through drug databases searching, 5 identified proteins (VEGFA, ADK, PAM, Cathepsin_S, PKC_A) were considered druggable.
We discovered significant causal associations between genetically predicted levels of 18 plasma proteins and ECG traits. These results highlight the importance of circulating plasma proteins in cardiac conduction and open up the possibility of novel arrhythmia drug development.
心脏传导障碍使个体易患心律失常，但目前心脏传导的确切机制仍不清楚。本研究旨在确定循环血浆蛋白与心电图（ECG）特征之间的因果关系，为心脏传导提供有价值的生物学见解和临床指导。
首先进行全蛋白质组孟德尔随机化（MR）分析，以评估血浆蛋白与五种ECG特征之间的因果关系，这五种特征包括P波时限（PWD）、QRS时限、PR间期、QT间期和RR间期。进行了多项敏感性分析。采用反向MR分析、共定位分析和重复分析来巩固我们结果的可靠性。然后，我们进行中介分析以探索血浆蛋白与ECG特征之间的潜在机制。应用基因本体（GO）和京都基因与基因组百科全书（KEGG）富集分析来阐明靶蛋白的生物学功能。最后，搜索了全表型MR（Phe-MR）和药物数据库。
我们鉴定出3种与PWD相关的蛋白（FAM151A、VEGF165、VEGF121），12种与PR间期相关的蛋白（ABHD10、ADK、组织蛋白酶_S、DUSP13、Ephrin_A3、MAPRE2、OMG、PAM、PMM1、SH3BGRL3、TCP4、SYT11），1种与QRS时限相关的蛋白（PKC_A），以及2种与QT间期相关的蛋白（MXRA7、SVEP1）。反向MR分析未发现ECG特征对它们有显著的因果效应。共定位和重复分析进一步强化了我们的发现。这些影响部分由人体测量指标介导。靶蛋白的富集分析主要富集于多种关键途径，如水解酶活性调节和纤连蛋白结合。通过搜索药物数据库，5种鉴定出的蛋白（VEGFA、ADK、PAM、组织蛋白酶_S、PKC_A）被认为是可成药的。
我们发现18种血浆蛋白的遗传预测水平与ECG特征之间存在显著的因果关系。这些结果突出了循环血浆蛋白在心脏传导中的重要性，并为新型心律失常药物的开发开辟了可能性。
