Multiomics and Functional Characterization Establish Druggable Targets for PVC-Driven Idiopathic VF

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PROJECT ABSTRACT Sudden cardiac death (SCD) claims >300,000 lives yearly in the US and despite aggressive attempts at phenotype-genotype correlations, ~50% of patients with primary electrical SCD do not meet diagnostic criteria for any SCD syndrome and are labeled Idiopathic Ventricular Fibrillation (IVF). This “catch-all” diagnosis of exclusion encompasses a cohort of individuals that are undefined phenotypically with arrhythmia that is mechanistically unexplored. Moreover, without defined genotype-phenotype characterization, clinical practice guidelines for IVF suggest homogenous treatment for a heterogenous disorder. Recognizing that genetic linkage studies have failed for IVF, we propose a paradigm shift to address these challenging gaps in knowledge. We propose to integrate computational modeling of comprehensive electrophysiologic and multiomic outputs to identify the mechanistic underpinnings of an emerging IVF subphenotype related to Purkinje-triggered VF (PVC- IVF). In collaboration with the Bordeaux group Deep Phenotype efforts, who originally described these emerging subphenotypes of IVF, PVC-IVF patients have been recruited to create induced pluripotent stem cells (iPSCs) from UW and Bordeaux. Our iPSC experimental system has distinct advantages including integration of PVC- IVF iPS-cardiomyocytes (iPS-CMs) with a mixed (ventricular myocyte and Purkinje) cell population with computational myocyte models reflecting region-specific phenotypes. Our group’s design for iPSCs experiments combine innovation of platforms that promote electrical and functional maturity, including incorporation of iPS- cardiac fibroblasts (iPS-CFs), and analysis by computational modeling of iPS-CMs and in silico adult human myocytes and tissue. In our pilot data we demonstrate the effectiveness of using iPSCs to differentiate IVF with experimental evidence and integrate this data into computational modeling approaches to gain mechanistic insight into cellular arrhythmic perturbations. Our overarching goal is to examine the mechanistic underpinning of PVC-IVF and identify specific complementary and synergistic therapeutic targets. In Aim 1 we will integrate experimental functional readouts from our advanced model system designed to recapitulate native cardiac milieu with a combination of micro and nanoscale cues with “bottom-up” computational modeling to unravel the specific cellular perturbations that cause the observed functional PVC-IVF readout. The focus of Aim 2 is to incorporate a broad, unbiased data-driven dataset from multiomic characterization of PVC-IVF patient-specific iPS-CMs into a computational systems pharmacology framework to define synergistic arrhythmogenic pathways and antiarrhythmic polytherapy, which we predict to be safer and more effective than monotherapy approaches. Finally, computational cross-cell translators will predict responses in the adult heart. With completion of our aims, we will define the cellular arrhythmic signature; unravel the down-stream transcriptome, protein expression changes and post-translational modifications; and integrate experimental readouts with computational modeling to create actionable data and find druggable targets for PVC-IVF arrhythmia prevention.