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

Area of Science:

• Genetics
• Cardiology
• Bioinformatics

Background:

• Brugada syndrome (BrS) is an inherited cardiac condition linked to sudden cardiac death.
• SCN5A gene mutations are a primary cause of BrS.
• In vitro validation of SCN5A variants is resource-intensive.

Purpose of the Study:

• To develop and validate an in silico approach for predicting functional SCN5A variants in BrS patients.
• To reduce the time and cost associated with identifying pathogenic mutations.

Main Methods:

• Direct DNA sequencing identified five SCN5A variants in 14 BrS patients.
• Multiple bioinformatics algorithms were employed for in silico analysis.
• Mass spectrometry and in vitro electrophysiological assays validated computational predictions.

Main Results:

• In silico analysis predicted two variants (1651G>A and 1776C>G) as high-risk SCN5A mutations.
• These predictions were confirmed through subsequent experimental validation.
• Two novel SCN5A mutations associated with BrS were identified.

Conclusions:

• Integrating sequence-based and structural bioinformatics enhances the selection of potential SCN5A variants for BrS.
• This computational strategy streamlines the identification of pathogenic mutations, aiding in BrS diagnosis and research.