Population-Based Mathematical Modeling to Deduce Disease-Causing Cardiac Na+ Channel Gating Defects

Abstract

Mutations in the SCN5A gene, which encodes the primary cardiac Na+ channel (Nav1.5), can cause arrhythmic disorders such as Type 3 Long QT Syndrome (LQT3). A pediatric patient with severe ventricular arrhythmias was recently determined to carry a missense mutation in SCN5A that putatively leads to a replacement of glutamine by proline at residue 1475 (Q1475P). We sought to uncover, through population-based mathematical modeling of ion channel gating: (1) what defects in gating at the molecular level could account for cellular electrophysiological recordings; and (2) whether these alterations to channel gating could reproduce the patient's LQT3 phenotype. Electrophysiology experiments, performed in HEK293 cells, demonstrated that, compared with Wild Type (WT) Nav1.5 channels, Q1475P channels exhibited: (1) a positive shift in the activation curve; (2) a positive shift in the inactivation curve; (3) faster inactivation; and (4) faster recovery from inactivation. Simulations attempted to recapitulate these results by randomizing parameters in a published Markov model of Nav1.5 gating, generating a large (n=10,000) population of channel variants, filtering to match WT and Q1475P channel behaviors. This produced subpopulations of channel variants describing the gating of either genotype. The analysis suggested that the channel gating steps most likely to be affected by the mutation include transitions from: (1) open to closed, (2) inactivated to closed; and (3) closed to inactivated. In addition, when simulated Nav1.5 channels were incorporated into a model of the human ventricular action potential, Q1475P channels were more likely than WT channels to produce arrhythmogenic early afterdepolarizations (EADs). The results provide important insight into this patient's LQT3 phenotype and demonstrate how population-based modeling can generate mechanistic hypotheses about alterations in ion channel gating that result from mutations.