Abstract
Mutations in SCN5A can alter the cardiac sodium current INa and increase the risk of potentially lethal conditions such as Brugada and long-QT syndromes. The relation between mutations and their clinical phenotypes is complex, and systems to predict clinical severity of unclassified SCN5A variants perform poorly. We investigated if instead we could predict changes to INa, leaving the link from INa to clinical phenotype for mechanistic simulation studies. An exhaustive list of nonsynonymous missense mutations and resulting changes to INa was compiled. We then applied machine-learning methods to this dataset, and found that changes to INa could be predicted with higher sensitivity and specificity than most existing predictors of clinical significance. The substituted residues’ location on the protein correlated with channel function and strongly contributed to predictions, while conservedness and physico-chemical properties did not. However, predictions were not sufficiently accurate to form a basis for mechanistic studies. These results show that changes to INa, the mechanism through which SCN5A mutations create cardiac risk, are already difficult to predict using purely in-silico methods. This partly explains the limited success of systems to predict clinical significance of SCN5A variants, and underscores the need for functional studies of INa in risk assessment.
Highlights
The human gene SCN5A encodes the pore-forming α-subunit of the cardiac sodium channel Nav1.5, which carries INa, the fast sodium current
This was calculated by listing all nucleotide substitutions in the coding region of SCN5A and noting which ones resulted in an amino-acid switch. From this we calculated that there are approximately 11923/2016 ≈ 5.91 possible mutations per position in the gene, so that while Fig. 2 accurately reflects the positions at which variants are known, it should not be taken as an indication of the coverage of our dataset, which it overstates by about six times
Predictors of SCN5A variant pathogenicity suffer from poor performance
Summary
The human gene SCN5A encodes the pore-forming α-subunit of the cardiac sodium channel Nav1.5, which carries INa, the fast sodium current. The proarrhythmic mechanisms associated with a specific SCN5A mutation have been established using a systems approach that follows genetic screening by current measurements in expression systems, and uses mathematical modelling to show how they can lead to the observed cell, tissue, and ECG effects[10,11,12] While this approach has traditionally been applied to investigate single mutations with known clinical phenotypes, a study on long-QT syndrome type 1 by Hoefen et al.[13] clearly demonstrated www.nature.com/scientificreports/. We aimed to establish whether machine-learning methods could be used to predict functional changes to INa from non-synonymous missense mutations in SCN5A If successful, this reduced machine-learning step (from gene to ion current) could be followed by a mechanistic modelling step (from ion current to clinical phenotype) to create a two-stage in-silico predictor.
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