Fibroblasts can be directly reprogrammed into cardiomyocyte-like cells (iCMs) by overexpression of cardiac transcription factors or microRNAs. However, induction of functional cardiomyocytes is inefficient, and molecular mechanisms of direct reprogramming remain undefined. Here, we demonstrate that addition of miR-133a (miR-133) to Gata4, Mef2c, and Tbx5 (GMT) or GMT plus Mesp1 and Myocd improved cardiac reprogramming from mouse or human fibroblasts by directly repressing Snai1, a master regulator of epithelial-to-mesenchymal transition. MiR-133 overexpression with GMT generated sevenfold more beating iCMs from mouse embryonic fibroblasts and shortened the duration to induce beating cells from 30 to 10 days, compared to GMT alone. Snai1 knockdown suppressed fibroblast genes, upregulated cardiac gene expression, and induced more contracting iCMs with GMT transduction, recapitulating the effects of miR-133 overexpression. In contrast, overexpression of Snai1 in GMT/miR-133-transduced cells maintained fibroblast signatures and inhibited generation of beating iCMs. MiR-133-mediated Snai1 repression was also critical for cardiac reprogramming in adult mouse and human cardiac fibroblasts. Thus, silencing fibroblast signatures, mediated by miR-133/ Snai1, is a key molecular roadblock during cardiac reprogramming.

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Mutations in RNA binding motif protein 20 ( RBM20 ) are a common cause of familial dilated cardiomyopathy (DCM). Many RBM20 mutations cluster within an arginine/serine-rich (RS-rich) domain, which mediates nuclear localization. These mutations induce RBM20 mis-localization to form aberrant ribonucleoprotein (RNP) granules in the cytoplasm of cardiomyocytes and abnormal alternative splicing of cardiac genes, contributing to DCM. We used adenine base editing (ABE) and prime editing (PE) to correct pathogenic p.R634Q and p.R636S mutations in the RS-rich domain in human isogenic induced pluripotent stem cell (iPSC)–derived cardiomyocytes. Using ABE to correct RBM20 R634Q human iPSCs, we achieved 92% efficiency of A-to-G editing, which normalized alternative splicing of cardiac genes, restored nuclear localization of RBM20, and eliminated RNP granule formation. In addition, we developed a PE strategy to correct the RBM20 R636S mutation in iPSCs and observed A-to-C editing at 40% efficiency. To evaluate the potential of ABE for DCM treatment, we also created Rbm20 R636Q mutant mice. Homozygous (R636Q/R636Q) mice developed severe cardiac dysfunction, heart failure, and premature death. Systemic delivery of ABE components containing ABEmax-VRQR-SpCas9 and single-guide RNA by adeno-associated virus serotype 9 in these mice restored cardiac function as assessed by echocardiography and extended life span. As seen by RNA sequencing analysis, ABE correction rescued the cardiac transcriptional profile of treated R636Q/R636Q mice, compared to the abnormal gene expression seen in untreated mice. These findings demonstrate the potential of precise correction of genetic mutations as a promising therapeutic approach for DCM.

BackgroundIt has never been possible to immediately evaluate heart rate variability (HRV) during exercise. We aimed to visualize the real‐time changes in the power spectrum of HRV during exercise and to investigate its relationship to the ventilatory threshold (VT).Methods and ResultsThirty healthy subjects (29.1±5.7 years of age) and 35 consecutive patients (59.0±13.2 years of age) with myocardial infarctions underwent cardiopulmonary exercise tests with an RAMP protocol ergometer. The HRV was continuously assessed with power spectral analyses using the maximum entropy method and projected on a screen without delay. During exercise, a significant decrease in the high frequency (HF) was followed by a drastic shift in the power spectrum of the HRV with a periodic augmentation in the low frequency/HF (L/H) and steady low HF. When the HRV threshold (HRVT) was defined as conversion from a predominant high frequency (HF) to a predominant low frequency/HF (L/H), the VO 2 at the HRVT (HRVT‐VO 2) was substantially correlated with the VO 2 at the lactate threshold and VT) in the healthy subjects (r=0.853 and 0.921, respectively). The mean difference between each threshold (0.65 mL/kg per minute for lactate threshold and HRVT, 0.53 mL/kg per minute for VT and HRVT) was nonsignificant (P>0.05). Furthermore, the HRVT‐VO 2 was also correlated with the VT‐VO 2 in these myocardial infarction patients (r=0.867), and the mean difference was −0.72 mL/kg per minute and was nonsignificant (P>0.05).ConclusionsA HRV analysis with our method enabled real‐time visualization of the changes in the power spectrum during exercise. This can provide additional information for detecting the VT.