Utilizing Multiple in Silico Analyses to Identify Putative Causal SCN5A Variants in Brugada Syndrome

Overview

Journal Sci Rep

Specialty Science

Date 2014 Jan 28

PMID 24463578

Citations 13

Authors

Jyh-Ming Jimmy Juang

Tzu-Pin Lu

Liang-Chuan Lai

Chia-Hsiang Hsueh

Yen-Bin Liu

Chia-Ti Tsai

Lian-Yu Lin

Chih-Chieh Yu

Juey-Jen Hwang

Fu-Tien Chiang

Sherri Shih-Fan Yeh

Wen-Pin Chen

Eric Y Chuang

Ling-Ping Lai

Jiunn-Lee Lin

Affiliations

Soon will be listed here.

Abstract

Brugada syndrome (BrS) is an inheritable sudden cardiac death disease mainly caused by SCN5A mutations. Traditional approaches can be costly and time-consuming if all candidate variants need to be validated through in vitro studies. Therefore, we developed a new approach by combining multiple in silico analyses to predict functional and structural changes of candidate SCN5A variants in BrS before conducting in vitro studies. Five SCN5A non-synonymous variants (1651G>A, 1776C>G, 1673A>G, 3269C>T and 3578G>A) were identified in 14 BrS patients using direct DNA sequencing. Several bioinformatics algorithms were applied and predicted that 1651G>A (A551T) and 1776C>G (N592K) were high-risk SCN5A variants (odds ratio 59.59 and 23.93). The results were validated by Mass spectrometry and in vitro electrophysiological assays. We concluded that integrating sequence-based information and secondary protein structures elements may help select highly potential variants in BrS before conducting time-consuming electrophysiological studies and two novel SCN5A mutations were validated.

Citing Articles

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