KvDB; mining and mapping sequence variants in voltage-gated potassium channels

Express with caution: Epitope tags and cDNA variants effects on hERG channel trafficking, half-life and function

INTRODUCTION

Genetic mutations in KCNH2, which encodes hERG, the alpha subunit of the potassium channel responsible for the IKr current, cause long QT syndrome (LQTS), an inherited cardiac arrhythmia disorder. Electrophysiology techniques are used to correlate genotype with molecular phenotype to determine which mutations identified in patients diagnosed with LQTS are disease causing, and which are benign. These investigations are usually done using heterologous expression in cell lines, and often, epitope fusion tags are used to enable isolation and identification of the protein of interest.

METHODS AND RESULTS

Here, we demonstrate through electrophysiology techniques and immunohistochemistry, that both N-terminal and C-terminal myc fusion tags may perturb hERG protein channel expression and kinetics of the IKr current. We also characterize the impact of 2 previously reported inadvertent cDNA variants on hERG channel expression and half-life.

CONCLUSION

Our results underscore the importance of careful characterization of the impact of epitope fusion tags and of confirming complete sequence accuracy prior to genotype-phenotype studies for ion channel proteins such as hERG.

KvSNP: accurately predicting the effect of genetic variants in voltage-gated potassium channels

MOTIVATION

Non-synonymous single nucleotide polymorphisms (nsSNPs) in voltage-gated potassium (Kv) channels cause diseases with potentially fatal consequences in seemingly healthy individuals. Identifying disease-causing genetic variation will aid presymptomatic diagnosis and treatment of such disorders. NsSNP-effect predictors are hypothesized to perform best when developed for specific gene families. We, thus, created KvSNP: a method that assigns a disease-causing probability to Kv-channel nsSNPs.

RESULTS

KvSNP outperforms popular non gene-family-specific methods (SNPs&GO, SIFT and Polyphen) in predicting the disease potential of Kv-channel variants, according to all tested metrics (accuracy, Matthews correlation coefficient and area under receiver operator characteristic curve). Most significantly, it increases the separation of the median predicted disease probabilities between benign and disease-causing SNPs by 26% on the next-best competitor. KvSNP has ranked 172 uncharacterized Kv-channel nsSNPs by disease-causing probability.

AVAILABILITY AND IMPLEMENTATION

KvSNP, a WEKA implementation is available at www.bioinformatics.leeds.ac.uk/KvDB/KvSNP.html.

CONTACT

d.r.westhead@leeds.ac.uk

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.