Juang, Jyh-Ming JimmyLu, Tzu-PinLai, Liang-ChuanHsueh, Chia-HsiangLiu, Yen-BinTsai, Chia-TiLin, Lian-YuYu, Chih-ChiehHwang, Juey-JenChiang, Fu-TienYeh, Sherri Shih-FanChen, Wen-PinChuang, Eric Y.Lai, Ling-PingLin, Jiunn-Lee2025-04-232025-04-232014-01-27Juang JM, Lu TP, Lai LC, et al. Utilizing multiple in silico analyses to identify putative causal SCN5A variants in Brugada syndrome. Sci Rep. 2014;4:3850. Published 2014 Jan 27. doi:10.1038/srep03850https://hdl.handle.net/1805/47379Brugada 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.en-USAttribution-NonCommercial-ShareAlike 4.0 InternationalBrugada SyndromeGenetic predisposition to diseaseNAV1.5 voltage-gated sodium channelArticle