Altered Electrical, Biomolecular, and Immunologic Phenotypes in a Novel Patient-Derived Stem Cell Model of Desmoglein-2 Mutant ARVC
Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a progressive heart condition which causes fibro-fatty myocardial scarring, ventricular arrhythmias, and sudden cardiac death. Most cases of ARVC can be linked to pathogenic mutations in the cardiac desmosome, but the pathophysiology is not well understood, particularly in early phases when arrhythmias can develop prior to structural changes. Here, we created a novel human induced pluripotent stem cell-derived cardiomyocyte (hiPSC-CM) model of ARVC from a patient with a c.2358delA variant in desmoglein-2 (DSG2). These DSG2-mutant (DSG2Mut) hiPSC-CMs were compared against two wildtype hiPSC-CM lines via immunostaining, RT-qPCR, Western blot, RNA-Seq, cytokine expression and optical mapping. Mutant cells expressed reduced DSG2 mRNA and had altered localization of desmoglein-2 protein alongside thinner, more disorganized myofibrils. No major changes in other desmosomal proteins were noted. There was increased pro-inflammatory cytokine expression that may be linked to canonical and non-canonical NFκB signaling. Action potentials in DSG2Mut CMs were shorter with increased upstroke heterogeneity, while time-to-peak calcium and calcium decay rate were reduced. These were accompanied by changes in ion channel and calcium handling gene expression. Lastly, suppressing DSG2 in control lines via siRNA allowed partial recapitulation of electrical anomalies noted in DSG2Mut cells. In conclusion, the aberrant cytoskeletal organization, cytokine expression, and electrophysiology found DSG2Mut hiPSC-CMs could underlie early mechanisms of disease manifestation in ARVC patients.Keywords:ARVC; NFκB signaling; arrhythmogenic cardiomyopathy; arrhythmogenic right ventricular cardiomyopathy; desmoglein-2; induced pluripotent stem cells; patient-derived stem cells.
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基因水平:PCR Array、RT-PCR、PCR、单细胞测序
蛋白水平:MSD、Luminex、CBA、Elispot、Antibody Array、ELISA、Sengenics
细胞水平:细胞染色、细胞分选、细胞培养、细胞功能
组织水平:空间多组学、多重荧光免疫组化、免疫组化、免疫荧光
数据分析:流式数据分析、组化数据分析、多因子数据分析
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