A computational model of induced pluripotent stem-cell derived cardiomyocytes for high throughput risk stratification of KCNQ1 genetic variants

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 cellderived 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 I Ks 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.

In Silico Assessment of Class I Antiarrhythmic Drug Effects on Pitx2-Induced Atrial Fibrillation: Insights from Populations of Electrophysiological Models of Human Atrial Cells and Tissues

“…Additionally, both clinical [ 34 ] and theoretical [ 36 ] studies of pharmacotherapy for Pitx2 -induced AF often ignored inter-subject variability in atrial electrophysiology properties. Population-based computational approaches that can capture key disease conditions have proven valuable for understanding inter-subject variability in electrophysiological properties [ 38 , 39 , 40 ] and cardiotoxicity [ 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ]. Similarly, these methods were useful in the study of AF [ 53 , 54 , 55 , 56 , 57 ].…”

A deep learning algorithm to translate and classify cardiac electrophysiology: From induced pluripotent stem cell-derived cardiomyocytes to adult cardiac cells

“…The multitask network presented here performed well in the setting of the noted variability in measurements from iPSC-CM APs. As described in Figure 1, we utilized a modeling and simulation approach from our recent study [52, 69] to generate a population of iPSC-CM action potentials that incorporate variability comparable to that in experimental measurements. Utilizing simulated data presented a unique opportunity: We were able to generate large amounts of data that were used both to train and optimize the network and then to test the network with specifically designated distinct simulated data sets.…”

Building Multi-Dimensional Induced Pluripotent Stem Cells-Based Model Platforms to Assess Cardiotoxicity in Cancer Therapies

“…Such changes can be overcome by adopting uniform reprogramming methodology to reduce epigenetic alterations ( Bar and Benvenisty, 2019 ). In addition to higher reproducibility, iPSC-CM drug responses can be validated by generation of artificial intelligence (AI) algorithms that can systematically compare endpoint measurements such as action potential (AP) or calcium handling parameters to extrapolate experimental outcome for a given class of drug ( Juhola et al, 2018 ; Kernik et al, 2020 ). However, the full potential of such tools can only be exploited with data generated using iPSC-CMs of comparable quality.…”