β-Arrestin-mediated Regulation of the Human Ether-a-go-go-Related Gene (hERG) Potassium Channel

The human ether-a-go-go-related gene (hERG) encodes the voltage-gated K+ channel, hERG (Kv11.1). This channel passes the rapidly-activating delayed rectifier K+ current (IKr), which is important for cardiac repolarization. A reduction in IKr due to loss-of-function mutations or drug interactions causes long QT syndrome (LQTS), which can lead to cardiac arrhythmias and sudden cardiac death. The density of hERG channels in the plasma membrane is a key determinant of normal physiological function, and is balanced by trafficking to and from the cell surface. Many LQTS-associated hERG mutations result in a trafficking deficiency of otherwise functional channels. Thus, elucidating mechanisms of hERG regulation at the plasma membrane is useful for the prevention and treatment of LQTS. We previously demonstrated that M3 muscarinic receptor activation increases mature hERG expression through a Gq protein-dependent protein kinase C (PKC) pathway. In addition to conventional Gq protein-coupling, M3 receptors recruit β-arrestins upon agonist binding. Traditionally known for their role in receptor desensitization and internalization, β-arrestins also act as adaptor proteins to facilitate G protein-independent signaling. In the present work, I investigated the exclusive effect of β-arrestin signaling on hERG expression by utilizing an arrestin-biased M3 designer receptor (M3D-arr) exclusively activated by clozapine-N-oxide (CNO). By expressing M3D-arr in hERG-HEK cells and treating with CNO under various conditions, I found that M3D-arr activation increased mature hERG expression and current. Within this paradigm, M3D-arr recruited β-arrestin to the plasma membrane, and promoted the PI3K-dependent activation of Akt. I further found that the activated Akt acted through phosphatidylinositol 3-phosphate 5-kinase (PIKfyve) and Rab11 to facilitate endosomal recycling of hERG channels to the plasma membrane.

Computational Methods for Identifying Hypertrophic Cardiomyopathy using 12-lead ECG

Hypertrophic cardiomyopathy (HCM) is a cardiovascular disease where the heart muscle is partially thickened and blood flow is - potentially fatally - obstructed. It is one of the leading causes of sudden cardiac death in young people. Electrocardiography (ECG) and Echocardiography (Echo) are the standard tests for identifying HCM and other cardiac abnormalities. The American Heart Association has recommended using a pre-participation questionnaire for young athletes instead of ECG or Echo tests due to considerations of cost and time involved in interpreting the results of these tests by an expert cardiologist. Initially we set out to develop a classifier for automated prediction of young athletes’ heart conditions based on the answers to the questionnaire. Classification results and further in-depth analysis using computational and statistical methods indicated significant shortcomings of the questionnaire in predicting cardiac abnormalities. Automated methods for analyzing ECG signals can help reduce cost and save time in the pre-participation screening process by detecting HCM and other cardiac abnormalities. Therefore, the main goal of this dissertation work is to identify HCM through computational analysis of 12-lead ECG. ECG signals recorded on one or two leads have been analyzed in the past for classifying individual heartbeats into different types of arrhythmia as annotated primarily in the MIT-BIH database. In contrast, we classify complete sequences of 12-lead ECGs to assign patients into two groups: HCM vs. non-HCM. The challenges and issues we address include missing ECG waves in one or more leads and the dimensionality of a large feature-set. We address these by proposing imputation and feature-selection methods. We develop heartbeat-classifiers by employing Random Forests and Support Vector Machines, and propose a method to classify full 12-lead ECGs based on the proportion of heartbeats classified as HCM. The results from our experiments show that the classifiers developed using our methods perform well in identifying HCM. Thus the two contributions of this thesis are the utilization of computational and statistical methods for discovering shortcomings in a current screening procedure and the development of methods to identify HCM through computational analysis of 12-lead ECG signals.