Hypertrophic cardiomyopathy (HCM) is considered a primary disorder of the sarcomere resulting in unexplained left ventricular hypertrophy but the paradoxical association of nonmyocyte phenotypes such as fibrosis, mitral valve anomalies and microvascular occlusion is unexplained. To understand the interplay between cardiomyocyte and nonmyocyte cell types in human HCM, single nuclei RNA-sequencing was performed on myectomy specimens from HCM patients with left ventricular outflow tract obstruction and control samples from donor hearts free of cardiovascular disease. Clustering analysis based on gene expression patterns identified a total of 34 distinct cell populations, which were classified into 10 different cell types based on marker gene expression. Differential gene expression analysis comparing HCM to Normal datasets revealed differences in sarcomere and extracellular matrix gene expression. Analysis of expressed ligand-receptor pairs across multiple cell types indicated profound alteration in HCM intercellular communication, particularly between cardiomyocytes and fibroblasts, fibroblasts and lymphocytes and involving integrin β1 and its multiple extracellular matrix (ECM) cognate ligands. These findings provide a paradigm for how sarcomere dysfunction is associated with reduced cardiomyocyte secretion of ECM ligands, altered fibroblast ligand-receptor interactions with other cell types and increased fibroblast to lymphocyte signaling, which can further alter the ECM composition and promote nonmyocyte phenotypes.

End stage, nonobstructive hypertrophic cardiomyopathy (HCM) is an intractable condition with no disease-specific therapies. To gain insights into the pathogenesis of nonobstructive HCM, we performed single nucleus RNA-sequencing (snRNA-seq) on human HCM hearts explanted at the time of cardiac transplantation and organ donor hearts serving as controls. Differential gene expression analysis revealed 64 differentially expressed genes linked to specific cell types and molecular functions. Analysis of ligand-receptor pair gene expression to delineate potential intercellular communication revealed significant reductions in expressed ligand-receptor pairs affecting the extracellular matrix, growth factor binding, peptidase regulator activity, platelet-derived growth factor binding and protease binding in the HCM tissue. Changes in Integrin-beta1 receptor expression were responsible for many changes related to extracellular matrix interactions, by increasing in dendritic, smooth muscle and pericyte cells while decreasing in endothelial and fibroblast cells, suggesting potential mechanisms for fibrosis and microvascular disease in HCM and a potential role for dendritic cells. In contrast, there was an increase in ligand-receptor pair expression associated with adenylate cyclase binding, calcium channel molecular functions, channel inhibitor activity, ion channel inhibitor activity, phosphatase activator activity, protein kinase activator activity and titin binding, suggesting important shifts in various signaling cascades in nonobstructive, end stage HCM.

Objectives: To understand Hypertrophic Cardiomyopathy-associated alterations in gene expression and intercellular communication at the single cell level in left ventricular outflow tract lesions. Background: Human hypertrophic cardiomyopathy (HCM) is considered a disorder of the sarcomere (i.e., cardiomyocytes) but the paradoxical association of nonmyocyte phenotypes such as fibrosis, mitral valve anomalies and microvascular occlusion is unexplained. Methods: To understand the interplay between cardiomyocyte and nonmyocyte cell types in human HCM, single nuclei RNA-sequencing (snRNA-seq) was performed on myectomy specimens from HCM patients with left ventricular outflow tract obstruction and control samples from donor hearts free of cardiovascular disease. Results: Clustering analysis identified a total of 34 distinct cell populations, which were classified into 10 different cell types based on marker gene expression. Differential gene expression analysis comparing HCM to Normal datasets revealed differences in sarcomere and extracellular matrix gene expression. Analysis of expressed ligand-receptor pairs across multiple cell types indicated profound disruption in HCM intercellular communication, particularly between cardiomyocytes and fibroblasts, fibroblasts and lymphocytes and involving integrin β1 and its multiple extracellular matrix (ECM) cognate ligands. Conclusions: These findings provide evidence for intercellular interactions in HCM that link sarcomere dysfunction with altered cardiomyocyte secretion of ECM ligands, altered fibroblast ligand-receptor interactions with a variety of cell types and increased fibroblast to lymphocyte signaling, which can further alter the ECM composition, disrupt cellular function and promote nonmyocyte phenotypes.

Hypertrophic Cardiomyopathy (HCM) is a common inherited disorder characterized by unexplained left ventricular hypertrophy with or without left ventricular outflow tract (LVOT) obstruction. Single-nuclei RNA-sequencing (snRNA-seq) of both obstructive and nonobstructive HCM patient samples has revealed alterations in communication between various cell types, but no direct and integrated comparison between the two HCM phenotypes has been reported. We performed a bioinformatic analysis of HCM snRNA-seq datasets from obstructive and nonobstructive patient samples to identify differentially expressed genes and distinctive patterns of intercellular communication. Differential gene expression analysis revealed 37 differentially expressed genes, predominantly in cardiomyocytes but also in other cell types, relevant to aging, muscle contraction, cell motility, and the extracellular matrix. Intercellular communication was generally reduced in HCM, affecting the extracellular matrix, growth factor binding, integrin binding, PDGF binding, and SMAD binding, but with increases in adenylate cyclase binding, calcium channel inhibitor activity, and serine-threonine kinase activity in nonobstructive HCM. Increases in neuron to leukocyte and dendritic cell communication, in fibroblast to leukocyte and dendritic cell communication, and in endothelial cell communication to other cell types, largely through changes in the expression of integrin-β1 and its cognate ligands, were also noted. These findings indicate both common and distinct physiological mechanisms affecting the pathogenesis of obstructive and nonobstructive HCM and provide opportunities for the personalized management of different HCM phenotypes.

Different species of water striders match leg speeds to their body sizes to maximize their jump take off velocity without breaking the water surface, which might have aided evolution of leg structures optimized for exploitation of the water surface tension. It is not understood how water striders achieve this match. Can individuals modify their leg movements based on their body mass and locomotor experience? Here we tested if water striders, Gerris latiabdominis, adjust jumping behaviour based on their personal experience and how an experimentally added body weight affects this process. Females, but not males, modified their jumping behaviour in weight-dependent manner, but only when they experienced frequent jumping. They did so within the environmental constraint set by the physics of water surface tension. Females’ ability to adjust jumping may represent their adaptation to frequent increases or decreases of the weight that they support as mating bouts, during which males ride on top of females, start or end, respectively. This suggests that natural selection for optimized biomechanics combined with sexual selection for mating adaptations shapes this ability to optimally exploit water surface tension, which might have aided adaptive radiation of Gerromorpha into a diversity of semiaquatic niches.

End stage, nonobstructive hypertrophic cardiomyopathy (HCM) is an intractable condition with no disease-specific therapies. To gain insights into the pathogenesis of nonobstructive HCM, we performed single nucleus RNA-sequencing (snRNA-seq) on human HCM hearts explanted at the time of cardiac transplantation and organ donor hearts serving as controls. Differential gene expression analysis revealed 64 differentially expressed genes linked to specific cell types and molecular functions. Analysis of ligand-receptor pair gene expression to delineate potential intercellular communication revealed significant reductions in expressed ligand-receptor pairs likely affecting the extracellular matrix, growth factor binding, peptidase regulator activity, platelet-derived growth factor binding and protease binding in the HCM tissue. Changes in Integrin-β1 receptor expression were responsible for many observed changes related to extracellular matrix interactions, by increasing in dendritic, smooth muscle and pericyte cells while decreasing in endothelial and fibroblast cells, suggesting potential mechanisms for fibrosis and microvascular disease in HCM and a potential role for dendritic cells. In contrast, there was an increase in ligand-receptor pair expression associated with adenylate cyclase binding, calcium channel molecular functions, channel inhibitor activity, ion channel inhibitor activity, phosphatase activator activity, protein kinase activator activity and titin binding, suggesting important shifts in various signaling cascades in nonobstructive, end stage HCM.

Dissolved organic carbon (DOC) is a master variable in aquatic systems. Resolving DOC dynamics requires high‐temporal resolution data. However, DOC concentration cannot be directly measured in situ, and discrete sample collection and analysis becomes expensive as temporal resolution increases. To surmount this problem, an option is to predict site‐specific DOC concentration with linear modeling and optical data predictors collected from high‐cost, high‐maintenance in situ spectrophotometers. This study sought to improve upon the accuracy and field costs of linear predictive DOC methods by using machine learning modeling coupled to low‐to‐zero cost predictors. To do this, we collected 16 months of in situ data (e.g., spectrophotometer attenuation, salinity, temperature), assembled freely available predictors (e.g., point in year, rainfall), and collected samples for DOC analysis, all in a salt marsh creek. At seasonal timescales, machine learning (coefficient of determination [R2] = 0.90) modestly improved upon the accuracy of linear methods (R2 = 0.80) but offered substantial instrumentation cost reductions (~ 90%) by requiring only cost‐free predictors (online data) or cost‐free predictors paired with low‐cost in situ predictors (temperature, salinity, depth). At intertidal timescales, linear methods proved ill‐equipped to predict DOC concentration compared to machine learning, and again, machine learning offered a substantial instrumentation cost reduction (~ 90%). Although our models were developed for and applicable to a single site, the use of machine learning with low‐to‐zero cost predictors provides a blueprint for others trying to model DOC dynamics and other analytes in any complex aquatic system.

No abstract