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Sci Rep. 2014 Jan 27;4:3850. doi: 10.1038/srep03850.

Utilizing multiple in silico analyses to identify putative causal SCN5A variants in Brugada syndrome.

Author information

1
1] Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan [2] Graduate Institute of Physiology, College of Medicine, National Taiwan University, Taipei, Taiwan.
2
YongLin Biomedical Engineering Center, National Taiwan University, Taipei, Taiwan.
3
Graduate Institute of Physiology, College of Medicine, National Taiwan University, Taipei, Taiwan.
4
Department of Medicine, Krannert Institute of Cardiology and Division of Cardiology, Indiana University School of Medicine, Indianapolis, Indiana.
5
Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
6
Department of Environmental and Occupational Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
7
Institute of Pharmacology, College of Medicine, National Taiwan University, Taipei, Taiwan.
8
1] YongLin Biomedical Engineering Center, National Taiwan University, Taipei, Taiwan [2] Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.

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.

PMID:
24463578
PMCID:
PMC3902491
DOI:
10.1038/srep03850
[Indexed for MEDLINE]
Free PMC Article
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