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

Content Provider	PubMed Central
Author	Juang, Jyh-ming Jimmy Lu, Tzu-pin Lai, Liang-chuan Hsueh, Chia-hsiang Liu, Yen-bin Tsai, Chia-ti Lin, Lian-yu Yu, Chih-chieh Hwang, Juey-jen Chiang, Fu-tien Yeh, Sherri Shih-fan Chen, Wen-pin Chuang, Eric Y. Lai, Ling-ping Lin, Jiunn-lee
Copyright Year	2014
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.
Related Links	http://dx.doi.org/10.1038/srep03850
Starting Page	3850
File Format	PDF
ISSN	20452322
e-ISSN	20452322
Journal	Scientific Reports
Volume Number	4
Language	English
Publisher	Nature Publishing Group
Publisher Date	2014-01-01
Access Restriction	Open
Rights Holder	Nature Publishing Group
Subject Keyword	Science and technology Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Multidisciplinary

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