Production of hiPSC-derived atrial cardiomyocytes to study the contribution of the KCNN3 variants to lone atrial fibrillation

Author: 
Date created: 
2019-05-28
Identifier: 
etd20438
Keywords: 
Atrial Fibrillation
Human pluripotent stem cell derived atrial cardiomyocytes
KCNN3
Abstract: 

Atrial fibrillation (AF), is the most common cardiac arrhythmia worldwide. AF increases the risk of stroke five-fold and heart failure three-fold. Over a quarter of AF patients suffer from lone AF which has been found to have a significant genetic component. Recently, a number of GWAS studies have found KCNN3, the gene expressing a Ca2+-activated K+ channel SK3, to be associated with lone AF. AF is a complex disease that is difficult to study with current experimental models. The advent of pluripotent stem cell (PSC) derived cardiomyocytes (hPSC-CMs) has revolutionized the field of cardiac research. For the first time, we are able to study human disease in human models while avoiding the challenges of obtaining biopsy tissue. Additionally, we are able to study a patient’s disease in a personalized manner by the use the patient-derived induced pluripotent stem cells (hiPSCs). Current differentiation protocols result in a mixed cardiac population that consists of nodal, atrial, and ventricular cells. This makes the study of chamber-specific diseases, like atrial fibrillation (AF), difficult. As such, the development of atrial-specific differentiation protocols is vital. Using retinoic acid, we optimized a protocol to selectively differentiate hiPSC-derived atrial cardiomyocytes (hiPSC-aCMs). We found that the addition of retinoic acid from days 4 – 6 at a concentration of 0.75 µM resulted in a predominantly atrial phenotype at a transcript, protein, and functional level. We then used CRISPR-Cas9 genome editing technology to insert an early stop codon into exon 7 of the KCNN3 gene to knockout its expression. In the future, we hope to differentiate these cells into hiPSC-aCMs to determine the contribution of SK3 to cardiac function and potentially AF.

Document type: 
Thesis
Rights: 
This thesis may be printed or downloaded for non-commercial research and scholarly purposes. Copyright remains with the author.
File(s): 
Supervisor(s): 
Glen Tibbits
Department: 
Science: Department of Biomedical Physiology and Kinesiology
Thesis type: 
(Thesis) M.Sc.
Statistics: