Background:
Congenital long QT syndrome (LQTS) and Brugada syndrome (BrS) are inherited predispositions to sudden cardiac death due to lethal ventricular arrhythmia, attributable mainly to mutations in cardiac ion channel genes. As with many other inherited disorders, clinical genetic testing has become standard-of-care. The widespread use of next generation sequencing (NGS) in the genetic diagnosis of LQTS and BrS has led to explosive growth in the number of gene variants and emerging challenge to classify variants accurately with respect to potential pathogenicity. The interpretation of clinical genetic tests is often confounded by variants of unknown significance (VUS). Several in silico tools have been developed to predict the pathogenicity, but these methods are regarded to have a lower value than experimental evidence for classifying variants in the clinical setting. The standard approach for determining the functional properties of an ion channel variant is cellular electrophysiology using patch-clamp recording of heterologously expressed recombinant channels. However, the classical manual patch-clamp is time-consuming and labor intensive, making it too low throughput for determining the functional consequences of more than a few variants at a time.

Objectives:
The objectives of this study are to develop an efficient and high throughput paradigm linking genotype to function for a human cardiac ion channel that will enable data-driven classification of large numbers of variants.

Methods and future perspectives:
We will focus on 3 major cardiac ion channel genes, KCNQ1, KCNH2, and SCN5A, responsible for LQTS and BrS, and demonstrate missense VUSs of minor allele frequency<0.1% from our NGS databases consisting of LQTS, BrS and controls. To overcome the challenge of determining the function of hundreds of ion channel variants, we will implement two advanced technologies: high-efficiency cell electroporation and automated planar patch-clamp recording developed previously for drug discovery. VUS will be introduced into the channel cDNA cloned in a bicistronic plasmid pIRES2-EGFP, and transfected into cultured cells by electroporation. Forty-eight hours after transfection, GFP-positive cells will be collected by flow cytometry and subjected to a 16-well automated patch-clamp Patchliner® (planned to be installed in National Cerebral and Cardiovascular Center), which enables recording from 48 cells automatically without operators on-site. Each ion channel VUS will be functionally annotated and classified based on the electrophysiological validation, and the data will be released publicly. Data-driven classification of large numbers of variants associated with lethal arrythmia is expected to create new opportunities for precision medicine.