Predicting novel disease mutations in the cardiac sodium channel

Svetlana I. Tarnovskaya, Vyacheslav S. Korkosh, Boris S. Zhorov, Dmitrij Frishman

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Background: Voltage-gated sodium channels Nav1.x mediate the rising phase of action potential in excitable cells. Variations in gene SCN5A, which encodes the hNav1.5 channel, are associated with arrhythmias and other heart diseases. About 1,400 SCN5A variants are listed in public databases, but for more than 30% of these the clinical significance is unknown and can currently only be derived by bioinformatics approaches. Methods and results: We used the ClinVar, SwissVar, Humsavar, gnomAD, and Ensembl databases to assemble a dataset of 1392 hNav1.5 variants (370 pathogenic variants, 602 benign variants and 420 variants of uncertain significance) as well as a dataset of 1766 damaging variants in 20 human sodium and calcium channel paralogs. Twelve in silico tools were tested for their ability to predict damaging mutations in hNav1.5. The best performing tool, MutPred, correctly predicted 93% of damaging variants in our hNav1.5 dataset. Among the 86 hNav1.5 variants for which electrophysiological data are also available, MutPred correctly predicted 82% of damaging variants. In the subset of 420 uncharacterized hNav1.5 variants MutPred predicted 196 new pathogenic variants. Among these, 74 variants are also annotated as damaging in at least one hNav1.5 paralog. Conclusions: Using a combination of sequence-based bioinformatics techniques and paralogous annotation we have substantially expanded the knowledge on disease variants in the cardiac sodium channel and assigned a pathogenic status to a number of mutations that so far have been described as variants of uncertain significance. A list of reclassified hNav1.5 variants and their properties is provided.

Original languageEnglish
Pages (from-to)603-611
Number of pages9
JournalBiochemical and Biophysical Research Communications
Volume521
Issue number3
DOIs
StatePublished - 15 Jan 2020

Keywords

  • Disease informatics
  • Molecular evolution
  • Protein structure
  • Sequence analysis

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