Abstract
In the last decade, there has been tremendous progress in identifying genetic anomalies linked to clinical disease. New experimental platforms have connected genetic variants to mechanisms underlying disruption of cellular and organ behavior and the emergence of proarrhythmic cardiac phenotypes. The development of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) signifies an important advance in the study of genetic disease in a patient-specific context. However, considerable limitations of iPSC-CM technologies have not been addressed: 1) phenotypic variability in apparently identical genotype perturbations, 2) low-throughput electrophysiological measurements, and 3) an immature phenotype which may impact translation to adult cardiac response. We have developed a computational approach intended to address these problems. We applied our recent iPSC-CM computational model to predict the proarrhythmic risk of 40 KCNQ1 genetic variants. An IKs computational model was fit to experimental data for each mutation, and the impact of each mutation was simulated in a population of iPSC-CM models. Using a test set of 15 KCNQ1 mutations with known clinical long QT phenotypes, we developed a method to stratify the effects of KCNQ1 mutations based on proarrhythmic markers. We utilized this method to predict the severity of the remaining 25 KCNQ1 mutations with unknown clinical significance. Tremendous phenotypic variability was observed in the iPSC-CM model population following mutant perturbations. A key novelty is our reporting of the impact of individual KCNQ1 mutant models on adult ventricular cardiomyocyte electrophysiology, allowing for prediction of mutant impact across the continuum of aging. This serves as a first step toward translating predicted response in the iPSC-CM model to predicted response of the adult ventricular myocyte given the same genetic mutation. As a whole, this study presents a new computational framework that serves as a high throughput method to evaluate risk of genetic mutations based-on proarrhythmic behavior in phenotypically variable populations.
Highlights
The impact of genetic variation on cardiac electrical activity is increasingly understood through identification and characterization of genetic anomalies in cardiac ion channel encoding genes, and their causal relationship to patient phenotype [1,2,3]
There has been tremendous progress in identifying genetic mutations linked to clinical diseases, such as cardiac arrhythmia
Induced pluripotent stem-cell derived cardiomyocytes have been utilized as a novel in vitro tool to reveal insights into patient-specific disease mechanisms [8,9,10]. iPSC-CMs constitute a powerful approach because they are patient-derived cells that retain the genetic information of the donor patients or cell line and can show patient-specific genotypephenotype relationships, including genetic disease phenotypes such as LQT1 [11,12,13,14,15,16]. iPSC-CMs have unique potential to provide a human physiological context to evaluate the impact of a genetic mutation in an in vitro human cardiac environment [17]. iPSC-CMs have further proven to be a powerful tool in evaluating variants of unknown significance (VUS) and linking genetic variants to their clinical outcomes [18, 19]
Summary
The impact of genetic variation on cardiac electrical activity is increasingly understood through identification and characterization of genetic anomalies in cardiac ion channel encoding genes, and their causal relationship to patient phenotype [1,2,3]. Understanding how variation in cardiac genes impacts cardiac function is important for treating and understanding complex genetic and inherited disorders, distinguishing between benign and hazardous variants of unknown significance (VUS), and revealing differential responses to drug interventions [4, 5]. IPSC-CMs constitute a powerful approach because they are patient-derived cells that retain the genetic information of the donor patients or cell line and can show patient-specific genotypephenotype relationships, including genetic disease phenotypes such as LQT1 [11,12,13,14,15,16]. IPSC-CMs have further proven to be a powerful tool in evaluating VUS and linking genetic variants to their clinical outcomes [18, 19]. Understanding how variants differentially impact the diverse range of patient genetics across a population will be critical to understanding the clinical significance and treatment of genetic disorders
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