Integrative Analyses Identify Potential Key Genes and Calcium-Signaling Pathway in Familial Atrioventricular Nodal Reentrant Tachycardia Using Whole-Exome Sequencing

Abstract

BackgroundAtrioventricular nodal reentrant tachycardia (AVNRT) is a common arrhythmia. Growing evidence suggests that family aggregation and genetic factors are involved in AVNRT. However, in families with a history of AVNRT, disease-causing genes have not been reported.ObjectiveTo investigate the genetic contribution of familial AVNRT using a whole-exome sequencing (WES) approach.MethodsBlood samples were collected from 20 patients from nine families with a history of AVNRT and 100 control participants, and we systematically analyzed mutation profiles using WES. Gene-based burden analysis, integration of previous sporadic AVNRT data, pedigree-based co-segregation, protein-protein interaction network analysis, single-cell RNA sequencing, and confirmation of animal phenotype were performed.ResultsAmong 95 related reference genes, seven candidate pathogenic genes have been identified both in sporadic and familial AVNRT, including CASQ2, AGXT, ANK2, SYNE2, ZFHX3, GJD3, and SCN4A. Among the 37 reference genes from sporadic AVNRT, five candidate pathogenic genes were identified in patients with both familial and sporadic AVNRT: LAMC1, ryanodine receptor 2 (RYR2), COL4A3, NOS1, and ATP2C2. To identify the common pathogenic mechanisms in all AVNRT cases, five pathogenic genes were identified in patients with both familial and sporadic AVNRT: LAMC1, RYR2, COL4A3, NOS1, and ATP2C2. Considering the unique internal candidate pathogenic gene within pedigrees, three genes, TRDN, CASQ2, and WNK1, were likely to be the pathogenic genes in familial AVNRT. Notably, the core calcium-signaling pathway may be closely associated with the occurrence of AVNRT, including CASQ2, RYR2, TRDN, NOS1, ANK2, and ATP2C2.ConclusionOur pedigree-based studies demonstrate that RYR2 and related calcium signaling pathway play a critical role in the pathogenesis of familial AVNRT using the WES approach.