|Ph.D Student||Shinnawi Rami|
|Subject||Induced Pluripotent Stem Cells Derived Cardiomyocytes as|
In Vitro Models of Inherited and Drug-Induced
|Department||Department of Medicine||Supervisor||Professor Lior Gepstein|
In recent years we have witnessed an explosion of knowledge regarding acquired and inherited cardiac arrhythmias responsible for sudden cardiac death (SCD) in otherwise healthy individuals. While the genetic basis for these syndromes is gradually coming into better focus, understanding their pathophysiology and developing novel therapies are hampered by the lack of suitable in vitro patient-specific human models. A promising solution comes from the human-induced pluripotent stem-cell (hiPSC) technology. This technology is based on reprogramming patient’s somatic cells into hiPSCs, which can then be differentiated into hiPSC-derived cardiomyocytes (hiPSC-CMs). The patient/disease-specific hiPSC-CMs can recapitulate the patient-specific clinical phenotype, thereby generating unique “disease-in-a-dish” model providing new tools for disease modeling, drug development, and precision medicine.
To fulfill its unique potential in the cardiovascular field, efficient methods should be developed for high-resolution, large-scale, and long-term functional phenotyping of hiPSC-CMs. To achieve this goal, we combined hiPSC technology with genetically-encoded voltage- and calcium-indicators. Expression of these indicators in hiPSC-CMs allowed short- and long-term monitoring of their action-potential, calcium-handling properties, and arrhythmia development in response to pharmaceutical agents and in hiPSC-CMs-derived from patients with inherited arrhythmogenic syndromes such the long QT syndrome (LQTS) and catecholaminergic polymorphic ventricular tachycardia (CPVT). Our results suggest that the combination of such reporters with hiPSC-CMs may bring a unique value to the study of inherited disorders and drug testing.
Next, we used the hiPSC technology to establish a patient-specific model of the short QT syndrome (SQTS), both at the cellular- and tissue-level. SQTS is a recently discovered inherited arrhythmia characterized by abnormal ion-channel function, life-threatening arrhythmias and SCD. We generated patient-specific-hiPSCs from a healthy volunteer and a symptomatic SQTS patient along with appropriate isogenic control (using CRISPR-based genome-editing). Intracellular patch-clamp action-potential and the relevant IKr current recordings from hiPSC-CMs recapitulated the SQTS cellular phenotype. To take the field of hiPSC-based inherited arrhythmia modeling to the next level, we combined patient-specific-hiPSC-CMs, with two-dimensional tissue modeling and optical mapping to study the SQTS at the tissue-level. We generated hiPSC-derived cardiac-cell-sheets (hiPSC-CCSs), which provided novel mechanistic insights into arrhythmogenicity in SQTS demonstrating the development of reentrant activity (rotors) with distinct biophysical properties. Furthermore, the model demonstrated differential response to various drugs similar to the patient’s clinical profile and suggested the beneficial effect of an alternative drug (disopyramide), highlighting the model’s potential as a platform for drug screening and development.
In the last part, we proposed a novel therapeutic approach for inherited arrhythmias with a dominant-negative mechanism, based on the use of short-hairpin-RNAs (shRNAs) to selectively silence the expression of the mutated allele. We specifically focused on the LQTS2 and SQTS1 (both caused by dominant-negative mutations in KCNH2 gene) and suggested to use allele-specific-shRNAs (as-shRNAs) to silence the mutated allele based on targeting common single-nucleotide-polymorphism (SNP) in the targeted gene. We identified a common SNP in the KCNH2 gene (heterozygous in both lines), designed as-shRNAs allele-specifically targeting the SNP variants, and confirmed their allele-specific silencing ability. Electrophysiological studies demonstrated the potential of our approach to treat inherited arrhythmias in in vitro hiPSC-based models.