Book Description

Cardiac arrhythmias are diseases of electrical rhythm that can lead to sudden cardiac death. Inherited arrhythmias can strike any age group, and are a leading cause of sudden death among young people. Many of these are caused by mutations in ion channels, proteins which govern the flow of electricity through the heart. As these are genetic mutations, we are able to use genetic testing to determine who may be at risk, and to prophylactically treat them accordingly. However, a common feature of cardiac arrhythmias is genotype-phenotype discordance. This means that, for various reasons, carrying a disease-causing mutation does not ensure a disease phenotype. When one considers genotype-phenotype discordance in the light of the fact that treatments such as the implantation of a cardiodefibrillator carry significant cost and risk, it is clear that a difficult proposition faces medical professionals in balancing over- and under-treatment. Thus, the overall aim of my dissertation is to improve the diagnostic ability of clinicians when presented with an individual who has a mutation in a cardiac ion channel. My first project approaches this by examining the fidelity of clinical genetic testing in calling pathogenic mutations in the context of Long QT Syndrome. I present an interesting case of a family which carries three potential mutations and show via electrophysiology and computer modeling that it is likely a combination of all three that causes disease. For my second project, I look more closely at potential mechanisms of genotype-phenotype discordance in the context of another arrhythmia, Brugada Syndrome. Specifically, I characterize a novel mechanism using electrophysiology and biochemistry whereby apparently benign mutations in NaV1.5 can lead to a loss-of-function. These mutations do not show any defects when expressed alone, but by interaction with a wild-type protein lead to a loss-of-function. In summary, my work overall shows that biophysical characterization of mutations remains the best tool for predicting risk in genetic arrhythmias, and I also present a novel mechanism to help explain the high degree of variability seen in inherited arrhythmias.