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Hypertrophic cardiomyopathy (HCM) is a genetic heart disease that is characterized by unexplained segmental hypertrophy that is usually most pronounced in the septum. While sarcomeric gene mutations are often the genetic basis for HCM, the mechanistic origin for the heterogeneous remodeling remains largely unknown. A better understanding of the gene networks driving the cardiomyocyte (CM) hypertrophy is required to improve therapeutic strategies. Patients suffering from HCM often receive a septal myectomy surgery to relieve outflow tract obstruction due to hypertrophy. Using single-cell RNA sequencing (scRNA-seq) on septal myectomy samples from patients with HCM, we identify functional links between genes, transcription factors, and cell size relevant for HCM. The data show the utility of using scRNA-seq on the human hypertrophic heart, highlight CM heterogeneity, and provide a wealth of insights into molecular events involved in HCM that can eventually contribute to the development of enhanced therapies.

Arrhythmogenic cardiomyopathy (ACM) is an inherited disorder often caused by pathogenic variants in desmosomal genes and characterized by progressive fibrotic and fat tissue accumulation in the heart. The cellular origin and responsible molecular mechanisms of fibro-fatty deposits have been a matter of debate, due to limitations in animal models recapitulating this phenotype. Here, we used human-induced pluripotent stem cell ( hiPSC)-derived cardiac cultures, single-cell RNA sequencing (scRNA-seq), and explanted human ACM hearts to study the epicardial contribution to fibro-fatty remodeling in ACM. hiPSC-epicardial cells generated from patients with ACM showed spontaneous fibro-fatty cellular differentiation that was absent in isogenic controls. This was further corroborated upon siRNA-mediated targeting of desmosomal genes in hiPSC-epicardial cells generated from healthy donors. scRNA-seq analysis identified the transcription factor TFAP2A (activating enhancer-binding protein 2 alpha) as a key trigger promoting this process. Gain- and loss-of-function studies on hiPSC-epicardial cells and primary adult epicardial-derived cells demonstrated that TFAP2A mediated epicardial differentiation through enhancing epithelial-to-mesenchymal transition (EMT). Furthermore, examination of explanted hearts from patients with ACM revealed epicardial activation and expression of TFAP2A in the subepicardial mesenchyme. These data suggest that TFAP2A-mediated epicardial EMT underlies fibro-fatty remodeling in ACM, a process amenable to therapeutic intervention.

Advances in our ability to zoom in on single cells have revealed striking heterogeneity within isogenic populations. Attention has so far focussed predominantly on underlying stochastic variability in regulatory pathways and downstream differentiation events. In contrast, the role of stochasticity in metabolic processes and networks has long remained unaddressed. Here we review recent studies that have begun to overcome key technical challenges in addressing this issue. First findings have already demonstrated that metabolic networks are stochastic in nature, and highlight the plethora of cellular processes that are critically affected by it.

Arrhythmogenic cardiomyopathy (ACM) is an inherited disease mainly caused by desmosomal gene mutations and characterized by myocardial loss, replacement with fibro-fatty tissue, arrhythmias and sudden cardiac death. To date, it is unclear which cell type and molecular mechanisms contribute to the fibro-fatty phenotype. The epicardium is the outer mesothelial layer of the heart which has the capacity to undergo epithelial-to-mesenchymal transition (EMT) and differentiate into various cardiac cell types. The aim of this study is to investigate whether epicardial cells contribute to the excess fibro-fatty infiltration seen in ACM patients. To this end, we differentiated induced pluripotent stem cells (iPSCs) from an ACM patient with a haploinsufficiency-causing mutation in the desmosomal gene plakophilin-2 (PKP2) (c.2013delC), and an isogenic control into epicardial cells. While Western blot, flow cytometry, qPCR and immunofluorescence imaging indicated comparable epicardial differentiation efficiencies, RNA-sequencing revealed a profound increased expression of fibroblast and lipid markers only in mutant epicardial cells. These data were corroborated by the spontaneous accumulation of lipid droplets and adipogenic markers in the PKP2 mutant epicardial cells. Single cell sequencing analysis revealed a significant induction of Activating Enhancer-Binding Protein 2 (AP2) family of transcription factors in a subset of PKP2 mutant epicardial cells, which are known to play roles in EMT and lipid biogenesis. siRNA-mediated knock down of AP2 members in mutant epicardial cells significantly reduced the expression of fat and fibroblast markers, suggesting an AP2-mediated fibro-fatty signalling in epicardial cells. The PKP2-dependence of these findings was further validated in healthy iPSC-epicardial cells treated with siRNAs targeting PKP2 which recapitulated the observations made in the mutant cells. Using this human in vitro model system, we were able to show the epicardial role during fibro-fatty tissue replacement upon PKP2 suppression. Ongoing experiments, including studies on explanted ACM hearts and a PKP2 c.2013delC knock-in mouse model, aim at further elucidating the molecular mechanisms of ACM pathogenesis.

Biochemical reactions typically occur at low copy numbers, but at once in crowded and diverse environments. Space and stochasticity therefore play an essential role in biochemical networks. Spatial-stochastic simulations have become a prominent tool for understanding how stochasticity at the microscopic level influences the macroscopic behavior of such systems. However, while particle-based models guarantee the level of detail necessary to accurately describe the microscopic dynamics at very low copy numbers, the algorithms used to simulate them oftentimes imply trade-offs between computational efficiency and accuracy. eGFRD (enhanced Green's Function Reaction Dynamics) is an exact algorithm that evades such trade-offs by partitioning the N-particle system into M, Comment: 30 pages and 70 pages of supplementary information, 6 figures and 10 supplementary figures

Clustering of molecules on biological membranes is a widely observed phenomenon. In some cases, such as the clustering of Ras proteins on the membranes of mammalian cells, proper cell signaling is critically dependent on the maintenance of these clusters. Yet, the mechanism by which clusters form and are maintained in these systems remains unclear. Recently, it has been discovered that activated Ras promotes further Ras activation. Here we show using particle-based simulation that this positive feedback is sufficient to produce persistent clusters of active Ras molecules at the nanometer scale via a dynamic nucleation mechanism. Furthermore, we find that our cluster statistics are consistent with experimental observations of the Ras system. Interestingly, we show that our model does not support a Turing regime of macroscopic reaction-diffusion patterning, and therefore that the clustering we observe is a purely stochastic effect, arising from the coupling of positive feedback with the discrete nature of individual molecules. These results underscore the importance of stochastic and dynamic properties of reaction diffusion systems for biological behavior.