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Differentiation proceeds along a continuum of increasingly fate-restricted intermediates, referred to as canalization1,2. Canalization is essential for stabilizing cell fate, but the mechanisms that underlie robust canalization are unclear. Here we show that the BRG1/BRM-associated factor (BAF) chromatin-remodelling complex ATPase gene Brm safeguards cell identity during directed cardiogenesis of mouse embryonic stem cells. Despite the establishment of a well-differentiated precardiac mesoderm, Brm-/- cells predominantly became neural precursors, violating germ layer assignment. Trajectory inference showed a sudden acquisition of a non-mesodermal identity in Brm-/- cells. Mechanistically, the loss of Brm prevented de novo accessibility of primed cardiac enhancers while increasing the expression of neurogenic factor POU3F1, preventing the binding of the neural suppressor REST and shifting the composition of BRG1 complexes. The identity switch caused by the Brm mutation was overcome by increasing BMP4 levels during mesoderm induction. Mathematical modelling supports these observations and demonstrates that Brm deletion affects cell fate trajectory by modifying saddle-node bifurcations2. In the mouse embryo, Brm deletion exacerbated mesoderm-deleted Brg1-mutant phenotypes, severely compromising cardiogenesis, and reveals an in vivo role for Brm. Our results show that Brm is a compensable safeguard of the fidelity of mesoderm chromatin states, and support a model in which developmental canalization is not a rigid irreversible path, but a highly plastic trajectory.

Cardiac myocytes transduce changes in mechanical loading into cellular responses via interacting cell signalling pathways. We previously reported a logic-based ordinary differential equation model of the myocyte mechanosignalling network that correctly predicts 78% of independent experimental results not used to formulate the original model. Here, we use Monte Carlo and polynomial chaos expansion simulations to examine the effects of uncertainty in parameter values, model logic and experimental validation data on the assessed accuracy of that model. The prediction accuracy of the model was robust to parameter changes over a wide range being least sensitive to uncertainty in time constants and most affected by uncertainty in reaction weights. Quantifying epistemic uncertainty in the reaction logic of the model showed that while replacing 'OR' with 'AND' reactions greatly reduced model accuracy, replacing 'AND' with 'OR' reactions was more likely to maintain or even improve accuracy. Finally, data uncertainty had a modest effect on assessment of model accuracy. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.

Protein interaction databases are critical resources for network bioinformatics and integrating molecular experimental data. Interaction databases may also enable construction of predictive computational models of biological networks, although their fidelity for this purpose is not clear. Here, we benchmark protein interaction databases X2K, Reactome, Pathway Commons, Omnipath and Signor for their ability to recover manually curated edges from three logic‐based network models of cardiac hypertrophy, mechano‐signalling and fibrosis. Pathway Commons performed best at recovering interactions from manually reconstructed hypertrophy (137 of 193 interactions, 71%), mechano‐signalling (85 of 125 interactions, 68%) and fibroblast networks (98 of 142 interactions, 69%). While protein interaction databases successfully recovered central, well‐conserved pathways, they performed worse at recovering tissue‐specific and transcriptional regulation. This highlights a knowledge gap where manual curation is critical. Finally, we tested the ability of Signor and Pathway Commons to identify new edges that improve model predictions, revealing important roles of protein kinase C autophosphorylation and Ca2+/calmodulin‐dependent protein kinase II phosphorylation of CREB in cardiomyocyte hypertrophy. This study provides a platform for benchmarking protein interaction databases for their utility in network model construction, as well as providing new insights into cardiac hypertrophy signalling. Key points: Protein interaction databases are used to recover signalling interactions from previously developed network models.The five protein interaction databases benchmarked recovered well‐conserved pathways, but did poorly at recovering tissue‐specific pathways and transcriptional regulation, indicating the importance of manual curation.We identify new signalling interactions not previously used in the network models, including a role for Ca2+/calmodulin‐dependent protein kinase II phosphorylation of CREB in cardiomyocyte hypertrophy. [ABSTRACT FROM AUTHOR]

A traditional approach to investigate the genetic basis of complex diseases is to identify genes with a global change in expression between diseased and healthy individuals. However, population heterogeneity may undermine the effort to uncover genes with significant but individual contribution to the spectrum of disease phenotypes within a population. Here we investigate individual changes of gene expression when inducing hypertrophy and heart failure in 100 + strains of genetically distinct mice from the Hybrid Mouse Diversity Panel (HMDP). We find that genes whose expression fold-change correlates in a statistically significant way with the severity of the disease are either up or down-regulated across strains, and therefore missed by a traditional population-wide analysis of differential gene expression. Furthermore, those "fold-change" genes are enriched in human cardiac disease genes and form a dense co-regulated module strongly interacting with the cardiac hypertrophic signaling network in the human interactome. We validate our approach by showing that the knockdown of Hes1, predicted as a strong candidate, induces a dramatic reduction of hypertrophy by 80-90% in neonatal rat ventricular myocytes. Our results demonstrate that individualized approaches are crucial to identify genes underlying complex diseases as well as to develop personalized therapies.

Dysregulation of oxidative phosphorylation is associated with increased mitochondrial reactive oxygen species production and some of the most prevalent human diseases including obesity, cancer, diabetes, neurodegeneration, and heart disease. Chemical 'mitochondrial uncouplers' are lipophilic weak acids that transport protons into the mitochondrial matrix via a pathway that is independent of ATP synthase, thereby uncoupling nutrient oxidation from ATP production. Mitochondrial uncouplers also lessen the proton motive force across the mitochondrial inner membrane and thereby increase the rate of mitochondrial respiration while decreasing production of reactive oxygen species. Thus, mitochondrial uncouplers are valuable chemical tools that enable the measurement of maximal mitochondrial respiration and they have been used therapeutically to decrease mitochondrial reactive oxygen species production. However, the most widely used protonophore uncouplers such as carbonyl cyanide p-trifluoromethoxyphenylhydrazone (FCCP) and 2,4-dinitrophenol have off-target activity at other membranes that lead to a range of undesired effects including plasma membrane depolarization, mitochondrial inhibition, and cytotoxicity. These unwanted properties interfere with the measurement of mitochondrial function and result in a narrow therapeutic index that limits their usefulness in the clinic. To identify new mitochondrial uncouplers that lack off-target activity at the plasma membrane we screened a small molecule chemical library. Herein we report the identification and validation of a novel mitochondrial protonophore uncoupler (2-fluorophenyl){6-[(2-fluorophenyl)amino](1,2,5-oxadiazolo[3,4-e]pyrazin-5-yl)}amine, named BAM15, that does not depolarize the plasma membrane. Compared to FCCP, an uncoupler of equal potency, BAM15 treatment of cultured cells stimulates a higher maximum rate of mitochondrial respiration and is less cytotoxic. Furthermore, BAM15 is bioactive in vivo and dose-dependently protects mice from acute renal ischemic-reperfusion injury. From a technical standpoint, BAM15 represents an effective new tool that allows the study of mitochondrial function in the absence of off-target effects that can confound data interpretation. From a therapeutic perspective, BAM15-mediated protection from ischemia-reperfusion injury and its reduced toxicity will hopefully reignite interest in pharmacological uncoupling for the treatment of the myriad of diseases that are associated with altered mitochondrial function.

To identify how cardiomyocyte mechanosensitive signaling pathways are regulated by anisotropic stretch, micropatterned mouse neonatal cardiomyocytes were stretched primarily longitudinally or transversely to the myofiber axis. Four hours of static, longitudinal stretch induced differential expression of 557 genes, compared with 30 induced by transverse stretch, measured using RNA-seq. A logic-based ordinary differential equation model of the cardiac myocyte mechanosignaling network, extended to include the transcriptional regulation and expression of 784 genes, correctly predicted measured expression changes due to anisotropic stretch with 69% accuracy. The model also predicted published transcriptional responses to mechanical load in vitro or in vivo with 63-91% accuracy. The observed differences between transverse and longitudinal stretch responses were not explained by differential activation of specific pathways but rather by an approximately twofold greater sensitivity to longitudinal stretch than transverse stretch. In vitro experiments confirmed model predictions that stretch-induced gene expression is more sensitive to angiotensin II and endothelin-1, via RhoA and MAP kinases, than to the three membrane ion channels upstream of calcium signaling in the network. Quantitative cardiomyocyte gene expression differs substantially with the axis of maximum principal stretch relative to the myofilament axis, but this difference is due primarily to differences in stretch sensitivity rather than to selective activation of mechanosignaling pathways. [ABSTRACT FROM AUTHOR]

After myocardial infarction (MI), cardiac cells work together to regulate wound healing of the infarct. The pathological response to MI yields cardiac remodelling comprising inflammatory and fibrosis phases, and the interplay of cellular dynamics that underlies these phases has not been elucidated. This study developed a computational model to identify cytokine and cellular dynamics post‐MI to predict mechanisms driving post‐MI inflammation, resolution of inflammation, and scar formation. Additionally, this study evaluated the interdependence between inflammation and fibrosis. Our model bypassed limitations of in vivo approaches in achieving cellular specificity and performing specific perturbations such as global knockouts of chemical factors. The model predicted that inflammation is a graded response to initial infarct size that is amplified by a positive feedback loop between neutrophils and interleukin 1β (IL‐1β). Resolution of inflammation was driven by degradation of IL‐1β, matrix metalloproteinase 9, and transforming growth factor β (TGF‐β), as well as apoptosis of neutrophils. Inflammation regulated TGFβ secretion directly through immune cell recruitment and indirectly through upregulation of macrophage phagocytosis. Lastly, we found that mature collagen deposition was an ultrasensitive switch in response to inflammation, which was amplified primarily by cardiac fibroblast proliferation. These findings describe the relationship between inflammation and fibrosis and highlight how the two responses work together post‐MI. This model revealed that post‐MI inflammation and fibrosis are dynamically coupled, which provides rationale for designing novel anti‐inflammatory, pro‐resolving or anti‐fibrotic therapies that may improve the response to MI. Key points: Inflammation and matrix remodelling are two processes involved in wound healing after a heart attack.Cardiac cells work together to facilitate these processes; this is done by secreting cytokines that then regulate the cells themselves or other cells surrounding them.This study developed a computational model of the dynamics of cardiac cells and cytokines to predict mechanisms through which inflammation and matrix remodelling is regulated.We show the roles of various cytokines and signalling motifs in driving inflammation, resolution of inflammation and fibrosis.The novel concept of inflammation–fibrosis coupling, based on the model prediction that inflammation and fibrosis are dynamically coupled, provides rationale for future studies and for designing therapeutics to improve the response after a heart attack. [ABSTRACT FROM AUTHOR]