Cellular decisions of self-renewal or differentiation arise from integration and reciprocal titration of numerous regulatory networks. Notch and Wnt/β-Catenin signaling often intersect in stem and progenitor cells and regulate one another transcriptionally. The biological outcome of signaling through each pathway often depends on the context and timing as cells progress through stages of differentiation. Here, we show that membrane-bound Notch physically associates with unphosphorylated (active) β-Catenin in stem and colon cancer cells and negatively regulates post-translational accumulation of active β-Catenin protein. Notch-dependent regulation of β-Catenin protein did not require ligand-dependent membrane cleavage of Notch or the glycogen synthase kinase-3β-dependent activity of the β-catenin destruction complex. It did, however, require the endocytic adaptor protein, Numb, and lysosomal activity. This study reveals a previously unrecognized function of Notch in negatively titrating active β-Catenin protein levels in stem and progenitor cells.

The regulation of multipotent cardiac progenitor cell (CPC) expansion and subsequent differentiation into cardiomyocytes, smooth muscle, or endothelial cells is a fundamental aspect of basic cardiovascular biology and cardiac regenerative medicine. However, the mechanisms governing these decisions remain unclear. Here, we show that Wnt/β-Catenin signaling, which promotes expansion of CPCs1–3, is negatively regulated by Notch1-mediated control of phosphorylated β-Catenin accumulation within CPCs, and that Notch1 activity in CPCs is required for their differentiation. Notch1 positively, and β-Catenin negatively, regulated expression of the cardiac transcription factors, Isl1, Myocd and Smyd1. Surprisingly, disruption of Isl1, normally expressed transiently in CPCs prior to their differentiation4, resulted in expansion of CPCs in vivo and in an embryonic stem (ES) cell system. Furthermore, Isl1 was required for CPC differentiation into cardiomyocyte and smooth muscle cells, but not endothelial cells. These findings reveal a regulatory network controlling CPC expansion and cell fate that involve unanticipated functions of β-Catenin, Notch1 and Isl1 that may be leveraged for regenerative approaches involving CPCs.

Purpose of Review Coronavirus disease of 2019 (COVID-19) is a cause of significant morbidity and mortality worldwide. While cardiac injury has been demonstrated in critically ill COVID-19 patients, the mechanism of injury remains unclear. Here, we review our current knowledge of the biology of SARS-CoV-2 and the potential mechanisms of myocardial injury due to viral toxicities and host immune responses. Recent Findings A number of studies have reported an epidemiological association between history of cardiac disease and worsened outcome during COVID infection. Development of new onset myocardial injury during COVID-19 also increases mortality. While limited data exist, potential mechanisms of cardiac injury include direct viral entry through the angiotensinconverting enzyme 2 (ACE2) receptor and toxicity in host cells, hypoxia-related myocyte injury, and immune-mediated cytokine release syndrome. Potential treatments for reducing viral infection and excessive immune responses are also discussed. Summary COVID patients with cardiac disease history or acquire new cardiac injury are at an increased risk for in-hospital morbidity and mortality. More studies are needed to address the mechanism of cardiotoxicity and the treatments that can minimize permanent damage to the cardiovascular system.

Objective: To delineate temporal and spatial dynamics of vascular smooth muscle cell (SMC) transcriptomic changes during aortic aneurysm development in Marfan syndrome (MFS). Approach and Results: We performed single-cell RNA sequencing to study aortic root/ascending aneurysm tissue from Fbn1 C1041G/ + (MFS) mice and healthy controls, identifying all aortic cell types. A distinct cluster of transcriptomically modulated SMCs (modSMCs) was identified in adult Fbn1 C1041G/ + mouse aortic aneurysm tissue only. Comparison with atherosclerotic aortic data (ApoE −/− mice) revealed similar patterns of SMC modulation but identified an MFS-specific gene signature, including plasminogen activator inhibitor-1 ( Serpine1 ) and Kruppel-like factor 4 ( Klf4 ). We identified 481 differentially expressed genes between modSMC and SMC subsets; functional annotation highlighted extracellular matrix modulation, collagen synthesis, adhesion, and proliferation. Pseudotime trajectory analysis of Fbn1 C1041G/ + SMC/modSMC transcriptomes identified genes activated differentially throughout the course of phenotype modulation. While modSMCs were not present in young Fbn1 C1041G/ + mouse aortas despite small aortic aneurysm, multiple early modSMCs marker genes were enriched, suggesting activation of phenotype modulation. modSMCs were not found in nondilated adult Fbn1 C1041G/ + descending thoracic aortas. Single-cell RNA sequencing from human MFS aortic root aneurysm tissue confirmed analogous SMC modulation in clinical disease. Enhanced expression of TGF-β (transforming growth factor beta)-responsive genes correlated with SMC modulation in mouse and human data sets. Conclusions: Dynamic SMC phenotype modulation promotes extracellular matrix substrate modulation and aortic aneurysm progression in MFS. We characterize the disease-specific signature of modSMCs and provide temporal, transcriptomic context to the current understanding of the role TGF-β plays in MFS aortopathy. Collectively, single-cell RNA sequencing implicates TGF-β signaling and Klf4 overexpression as potential upstream drivers of SMC modulation.

Introduction Smooth muscle cells (SMC) play a critical role in atherosclerosis. The Aryl hydrocarbon receptor (AHR) is an environment-sensing transcription factor that contributes to vascular development, and has been implicated in coronary artery disease (CAD) risk. We hypothesized that AHR can affect atherosclerosis by regulating phenotypic modulation of SMC. MethodsWe combined RNA-Seq, ChIP-Seq, ATAC-Seq and in-vitro assays in human coronary artery SMC (HCASMC), with single-cell RNA-Seq (scRNA-Seq), histology, and RNAscope in an SMC-specific lineage-tracing Ahr knockout mouse model of atherosclerosis to better understand the role of AHR in vascular disease.Results Genomic studies coupled with functional assays in cultured HCASMC revealed that AHR modulates HCASMC phenotype and suppresses ossification in these cells. Lineage tracing and activity tracing studies in the mouse aortic sinus showed that the Ahr pathway is active in modulated SMC in the atherosclerotic lesion cap. Furthermore, scRNA-Seq studies of the SMC-specific Ahr knockout mice showed a significant increase in the proportion of modulated SMC expressing chondrocyte markers such as Col2a1 and Alpl, which localized to the lesion neointima. These cells, which we term "chondromyocytes" (CMC), were also identified in the neointima of human coronary arteries. In histological analyses, these changes manifested as larger lesion size, increased lineage-traced SMC participation in the lesion, decreased lineage-traced SMC in the lesion cap, and increased alkaline phosphatase activity in lesions in the Ahr knockout compared to wild-type mice. We propose that AHR is likely protective based on these data and inference from human genetic analyses. ConclusionOverall, we conclude that AHR promotes maintenance of lesion cap integrity and diminishes the disease related SMC-to-CMC transition in atherosclerotic tissues.

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Background: Smooth muscle cells (SMC) transition into a number of different phenotypes during atherosclerosis, including those that resemble fibroblasts and chondrocytes, and make up the majority of cells in the atherosclerotic plaque. To better understand the epigenetic and transcriptional mechanisms that mediate these cell state changes, and how they relate to risk for coronary artery disease (CAD), we have investigated the causality and function of transcription factors (TFs) at genome wide associated loci. Methods: We employed CRISPR-Cas 9 genome and epigenome editing to identify the causal gene and cell(s) for a complex CAD GWAS signal at 2q22.3. Subsequently, single-cell epigenetic and transcriptomic profiling in murine models and human coronary artery smooth muscle cells were employed to understand the cellular and molecular mechanism by which this CAD risk gene exerts its function. Results: CRISPR-Cas 9 genome and epigenome editing showed that the complex CAD genetic signals within a genomic region at 2q22.3 lie within smooth muscle long-distance enhancers for ZEB2 , a TF extensively studied in the context of epithelial mesenchymal transition (EMT) in development and cancer. ZEB2 regulates SMC phenotypic transition through chromatin remodeling that obviates accessibility and disrupts both Notch and TGFβ signaling, thus altering the epigenetic trajectory of SMC transitions. SMC specific loss of ZEB2 resulted in an inability of transitioning SMCs to turn off contractile programing and take on a fibroblast-like phenotype, but accelerated the formation of chondromyocytes, mirroring features of high-risk atherosclerotic plaques in human coronary arteries. Conclusions: These studies identify ZEB2 as a new CAD GWAS gene that affects features of plaque vulnerability through direct effects on the epigenome, providing a new thereapeutic approach to target vascular disease.

IMPORTANCEEarly detection and characterization of increased left ventricular (LV) wall thickness can markedly impact patient care but is limited by under-recognition of hypertrophy, measurement error and variability, and difficulty differentiating causes of increased wall thickness, such as hypertrophy, cardiomyopathy, and cardiac amyloidosis.OBJECTIVE To assess the accuracy of a deep learning workflow in quantifying ventricular hypertrophy and predicting the cause of increased LV wall thickness. DESIGN, SETTINGS, AND PARTICIPANTSThis cohort study included physician-curated cohorts from the Stanford Amyloid Center and Cedars-Sinai Medical Center (CSMC) Advanced Heart Disease Clinic for cardiac amyloidosis and the Stanford Center for Inherited Cardiovascular Disease and the CSMC Hypertrophic Cardiomyopathy Clinic for hypertrophic cardiomyopathy from January 1, 2008, to December 31, 2020. The deep learning algorithm was trained and tested on retrospectively obtained independent echocardiogram videos from Stanford Healthcare, CSMC, and the Unity Imaging Collaborative. MAIN OUTCOMES AND MEASURES The main outcome was the accuracy of the deep learning algorithm in measuring left ventricular dimensions and identifying patients with increased LV wall thickness diagnosed with hypertrophic cardiomyopathy and cardiac amyloidosis. RESULTS The study included 23 745 patients: 12 001 from Stanford Health Care (6509 [54.2%] female; mean [SD] age, 61.6 [17.4] years) and 1309 from CSMC (808 [61.7%] female; mean [SD] age, 62.8 [17.2] years) with parasternal long-axis videos and 8084 from Stanford Health Care (4201 [54.0%] female; mean [SD] age, 69.1 [16.8] years) and 2351 from CSMS (6509 [54.2%] female; mean [SD] age, 69.6 [14.7] years) with apical 4-chamber videos. The deep learning algorithm accurately measured intraventricular wall thickness (mean absolute error [MAE], 1.2 mm; 95% CI, 1.1-1.3 mm), LV diameter (MAE, 2.4 mm; 95% CI, 2.2-2.6 mm),and posterior wall thickness (MAE, 1.4 mm; 95% CI, 1.2-1.5 mm) and classified cardiac amyloidosis (area under the curve [AUC], 0.83) and hypertrophic cardiomyopathy (AUC, 0.98) separately from other causes of LV hypertrophy. In external data sets from independent domestic and international health care systems, the deep learning algorithm accurately quantified ventricular parameters (domestic: R 2 , 0.96; international: R 2 , 0.90). For the domestic data set, the MAE was 1.7 mm (95% CI, 1.6-1.8 mm) for intraventricular septum thickness, 3.8 mm (95% CI, 3.5-4.0 mm) for LV internal dimension, and 1.8 mm (95% CI, 1.7-2.0 mm) for LV posterior wall thickness. For the international data set, the MAE was 1.7 mm (95% CI, 1.5-2.0 mm) for intraventricular septum thickness, 2.9 mm (95% CI, 2.4-3.3 mm) for LV internal dimension, and 2.3 mm (95% CI, 1.9-2.7 mm) for LV posterior wall thickness. The deep learning algorithm accurately detected cardiac amyloidosis (AUC, 0.79) and hypertrophic cardiomyopathy (AUC, 0.89) in the domestic external validation site. CONCLUSIONS AND RELEVANCEIn this cohort study, t...