AAV9:PKP2 improves heart function and survival in a Pkp2-deficient mouse model of arrhythmogenic right ventricular cardiomyopathy
Abstract Summary: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a heart disease that leads to abnormal heartbeats and a higher risk of sudden cardiac death. ARVC is often caused by changes in a gene called PKP2, that then makes less PKP2 protein. PKP2 protein is important for the normal structure and function of the heart. Human ARVC characteristics can be mimicked in a mouse model missing this gene. Given no therapeutic option, our goal was to test if adding a working copy of PKP2 gene in the heart of this mouse model, using a technique called gene therapy that can deliver genes to cells, could improve heart function. Here, we show that a single dose of PKP2 gene therapy can improve heart function and heartbeats as well as extend lifespan in mice. PKP2 gene therapy may be a promising approach to treat ARVC patients with PKP2 mutations.
Originally Published in: Nature (March 2024) (Link to Paper)
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Deep learning detects cardiotoxicity in a high-content screen with induced pluripotent stem cell-derived cardiomyocytes
Drug-induced cardiotoxicity and hepatotoxicity are major causes of drug attrition. To decrease late-stage drug attrition, pharmaceutical and biotechnology industries need to establish biologically relevant models that use phenotypic screening to detect drug-induced toxicity in vitro. In this study, we sought to rapidly detect patterns of cardiotoxicity using high-content image analysis with deep learning and induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). We screened a library of 1280 bioactive compounds and identified those with potential cardiotoxic liabilities in iPSC-CMs using a single-parameter score based on deep learning. Compounds demonstrating cardiotoxicity in iPSC-CMs included DNA intercalators, ion channel blockers, epidermal growth factor receptor, cyclin-dependent kinase, and multi-kinase inhibitors. We also screened a diverse library of molecules with unknown targets and identified chemical frameworks that show cardiotoxic signal in iPSC-CMs. By using this screening approach during target discovery and lead optimization, we can de-risk early-stage drug discovery. We show that the broad applicability of combining deep learning with iPSC technology is an effective way to interrogate cellular phenotypes and identify drugs that may protect against diseased phenotypes and deleterious mutations.
Originally Published in: eLife (2021) (Link to Paper)
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