Modeling cardiomyocyte signaling and metabolism predicts genotype-to-phenotype mechanisms in hypertrophic cardiomyopathy

Familial hypertrophic cardiomyopathy (HCM) is a significant precursor of heart failure and sudden cardiac death, primarily caused by mutations in sarcomeric and structural proteins. Despite the extensive research on the HCM genotype, the complex and context-specific nature of many signaling and metabolic pathways linking the HCM genotype to phenotype has hindered therapeutic advancements for patients. Here, we have developed a computational model of HCM encompassing cardiomyocyte signaling and metabolic networks and their associated interactions. Utilizing a stochastic logic-based ODE approach, we linked cardiomyocyte signaling to the metabolic network through a gene regulatory network and post-translational modifications. We validated the model against published data on activities of signaling species in the HCM context and transcriptomes of two HCM mouse models (i.e., R403Q-αMyHC and R92W-TnT). Our model predicts that HCM mutation induces changes in metabolic functions such as ATP synthase deficiency and a transition from fatty acids to carbohydrate metabolism. The model indicated major shifts in glutamine-related metabolism and increased apoptosis after HCM-induced ATP synthase deficiency. We predicted that the transcription factors STAT, SRF, GATA4, TP53, and FoxO are the key regulators of cardiomyocyte hypertrophy and apoptosis in HCM in alignment with experiments. Moreover, we identified shared (e.g., activation of PGC1α by AMPK, and FHL1 by titin) and context-specific mechanisms (e.g., regulation of Ca2+ sensitivity by titin in HCM patients) that may control genotype-to-phenotype transition in HCM across different species or mutations. We also predicted potential combination drug targets for HCM (e.g., mavacamten plus ROS inhibitors) preventing or reversing HCM phenotype (i.e., hypertrophic growth, apoptosis, and metabolic remodeling) in cardiomyocytes. This study provides new insights into mechanisms linking genotype to phenotype in familial hypertrophic cardiomyopathy and offers a framework for assessing new treatments and exploring variations in HCM experimental models.

Differential sensitivity to longitudinal and transverse stretch mediates transcriptional responses in mouse neonatal ventricular myocytes

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.NEW & NOTEWORTHY Anisotropic stretch applied to micropatterned neonatal mouse ventricular myocytes induced markedly greater acute transcriptional responses when the major axis of stretch was parallel to the myofilament axis than when it was transverse. Analysis with a novel quantitative network model of mechanoregulated cardiomyocyte gene expression suggests that this difference is explained by higher cell sensitivity to longitudinal loading than transverse loading than by the activation of differential signaling pathways.

Identifying metabolic adaptations characteristic of cardiotoxicity using paired transcriptomics and metabolomics data integrated with a computational model of heart metabolism

Improvements in the diagnosis and treatment of cancer have revealed long-term side effects of chemotherapeutics, particularly cardiotoxicity. Here, we present paired transcriptomics and metabolomics data characterizing in vitro cardiotoxicity to three compounds: 5-fluorouracil, acetaminophen, and doxorubicin. Standard gene enrichment and metabolomics approaches identify some commonly affected pathways and metabolites but are not able to readily identify metabolic adaptations in response to cardiotoxicity. The paired data was integrated with a genome-scale metabolic network reconstruction of the heart to identify shifted metabolic functions, unique metabolic reactions, and changes in flux in metabolic reactions in response to these compounds. Using this approach, we confirm previously seen changes in the p53 pathway by doxorubicin and RNA synthesis by 5-fluorouracil, we find evidence for an increase in phospholipid metabolism in response to acetaminophen, and we see a shift in central carbon metabolism suggesting an increase in metabolic demand after treatment with doxorubicin and 5-fluorouracil.

Logic-based mechanistic machine learning on high-content images reveals how drugs differentially regulate cardiac fibroblasts

Fibroblasts are essential regulators of extracellular matrix deposition following cardiac injury. These cells exhibit highly plastic responses in phenotype during fibrosis in response to environmental stimuli. Here, we test whether and how candidate anti-fibrotic drugs differentially regulate measures of cardiac fibroblast phenotype, which may help identify treatments for cardiac fibrosis. We conducted a high-content microscopy screen of human cardiac fibroblasts treated with 13 clinically relevant drugs in the context of TGFβ and/or IL-1β, measuring phenotype across 137 single-cell features. We used the phenotypic data from our high-content imaging to train a logic-based mechanistic machine learning model (LogiMML) for fibroblast signaling. The model predicted how pirfenidone and Src inhibitor WH-4-023 reduce actin filament assembly and actin-myosin stress fiber formation, respectively. Validating the LogiMML model prediction that PI3K partially mediates the effects of Src inhibition, we found that PI3K inhibition reduces actin-myosin stress fiber formation and procollagen I production in human cardiac fibroblasts. In this study, we establish a modeling approach combining the strengths of logic-based network models and regularized regression models. We apply this approach to predict mechanisms that mediate the differential effects of drugs on fibroblasts, revealing Src inhibition acting via PI3K as a potential therapy for cardiac fibrosis.

Logic-based modeling of biological networks with Netflux

Molecular signaling networks drive a diverse range of cellular decisions, including whether to proliferate, how and when to die, and many processes in between. Such networks often connect hundreds of proteins, genes, and processes. Understanding these complex networks is greatly aided by computational modeling, but these tools require extensive programming knowledge. In this article, we describe a user-friendly, programming-free network simulation tool called Netflux (https://github.com/saucermanlab/Netflux). Over the last decade, Netflux has been used to construct numerous predictive network models that have deepened our understanding of how complex biological networks make cell decisions. Here, we provide a Netflux tutorial that covers how to construct a network model and then simulate network responses to perturbations. Upon completion of this tutorial, you will be able to construct your own model in Netflux and simulate how perturbations to proteins and genes propagate through signaling and gene-regulatory networks.

Virtual drug screen reveals context-dependent inhibition of cardiomyocyte hypertrophy

BACKGROUND AND PURPOSE

Pathological cardiomyocyte hypertrophy is a response to cardiac stress that typically leads to heart failure. Despite being a primary contributor to pathological cardiac remodelling, the therapeutic space that targets hypertrophy is limited. Here, we apply a network model to virtually screen for FDA-approved drugs that induce or suppress cardiomyocyte hypertrophy.

EXPERIMENTAL APPROACH

A logic-based differential equation model of cardiomyocyte signalling was used to predict drugs that modulate hypertrophy. These predictions were validated against curated experiments from the prior literature. The actions of midostaurin were validated in new experiments using TGFβ- and noradrenaline (NE)-induced hypertrophy in neonatal rat cardiomyocytes.

KEY RESULTS

Model predictions were validated in 60 out of 70 independent experiments from the literature and identify 38 inhibitors of hypertrophy. We additionally predict that the efficacy of drugs that inhibit cardiomyocyte hypertrophy is often context dependent. We predicted that midostaurin inhibits cardiomyocyte hypertrophy induced by TGFβ, but not noradrenaline, exhibiting context dependence. We further validated this prediction by cellular experiments. Network analysis predicted critical roles for the PI3K and RAS pathways in the activity of celecoxib and midostaurin, respectively. We further investigated the polypharmacology and combinatorial pharmacology of drugs. Brigatinib and irbesartan in combination were predicted to synergistically inhibit cardiomyocyte hypertrophy.

CONCLUSION AND IMPLICATIONS

This study provides a well-validated platform for investigating the efficacy of drugs on cardiomyocyte hypertrophy and identifies midostaurin for consideration as an antihypertrophic drug.

Logic-based mechanistic machine learning on high-content images reveals how drugs differentially regulate cardiac fibroblasts

Fibroblasts are essential regulators of extracellular matrix deposition following cardiac injury. These cells exhibit highly plastic responses in phenotype during fibrosis in response to environmental stimuli. Here, we test whether and how candidate anti-fibrotic drugs differentially regulate measures of cardiac fibroblast phenotype, which may help identify treatments for cardiac fibrosis. We conducted a high content microscopy screen of human cardiac fibroblasts treated with 13 clinically relevant drugs in the context of TGFβ and/or IL-1β, measuring phenotype across 137 single-cell features. We used the phenotypic data from our high content imaging to train a logic-based mechanistic machine learning model (LogiMML) for fibroblast signaling. The model predicted how pirfenidone and Src inhibitor WH-4-023 reduce actin filament assembly and actin-myosin stress fiber formation, respectively. Validating the LogiMML model prediction that PI3K partially mediates the effects of Src inhibition, we found that PI3K inhibition reduces actin-myosin stress fiber formation and procollagen I production in human cardiac fibroblasts. In this study, we establish a modeling approach combining the strengths of logic-based network models and regularized regression models, apply this approach to predict mechanisms that mediate the differential effects of drugs on fibroblasts, revealing Src inhibition acting via PI3K as a potential therapy for cardiac fibrosis.

Diversity of cells and signals in the cardiovascular system

This white paper is the outcome of the seventh UC Davis Cardiovascular Research Symposium on Systems Approach to Understanding Cardiovascular Disease and Arrhythmia. This biannual meeting aims to bring together leading experts in subfields of cardiovascular biomedicine to focus on topics of importance to the field. The theme of the 2022 Symposium was 'Cell Diversity in the Cardiovascular System, cell-autonomous and cell-cell signalling'. Experts in the field contributed their experimental and mathematical modelling perspectives and discussed emerging questions, controversies, and challenges in examining cell and signal diversity, co-ordination and interrelationships involved in cardiovascular function. This paper originates from the topics of formal presentations and informal discussions from the Symposium, which aimed to develop a holistic view of how the multiple cell types in the cardiovascular system integrate to influence cardiovascular function, disease progression and therapeutic strategies. The first section describes the major cell types (e.g. cardiomyocytes, vascular smooth muscle and endothelial cells, fibroblasts, neurons, immune cells, etc.) and the signals involved in cardiovascular function. The second section emphasizes the complexity at the subcellular, cellular and system levels in the context of cardiovascular development, ageing and disease. Finally, the third section surveys the technological innovations that allow the interrogation of this diversity and advancing our understanding of the integrated cardiovascular function and dysfunction.

Benchmarking of protein interaction databases for integration with manually reconstructed signalling network models

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.

Network model integrated with multi-omic data predicts MBNL1 signals that drive myofibroblast activation

RNA-binding protein muscleblind-like1 (MBNL1) was recently identified as a central regulator of cardiac wound healing and myofibroblast activation. To identify putative MBNL1 targets, we integrated multiple genome-wide screens with a fibroblast network model. We expanded the model to include putative MBNL1-target interactions and recapitulated published experimental results to validate new signaling modules. We prioritized 14 MBNL1 targets and developed novel fibroblast signaling modules for p38 MAPK, Hippo, Runx1, and Sox9 pathways. We experimentally validated MBNL1 regulation of p38 expression in mouse cardiac fibroblasts. Using the expanded fibroblast model, we predicted a hierarchy of MBNL1 regulated pathways with strong influence on αSMA expression. This study lays a foundation to explore the network mechanisms of MBNL1 signaling central to fibrosis.

Signaling network model of cardiomyocyte morphological changes in familial cardiomyopathy

Familial cardiomyopathy is a precursor of heart failure and sudden cardiac death. Over the past several decades, researchers have discovered numerous gene mutations primarily in sarcomeric and cytoskeletal proteins causing two different disease phenotypes: hypertrophic (HCM) and dilated (DCM) cardiomyopathies. However, molecular mechanisms linking genotype to phenotype remain unclear. Here, we employ a systems approach by integrating experimental findings from preclinical studies (e.g., murine data) into a cohesive signaling network to scrutinize genotype to phenotype mechanisms. We developed an HCM/DCM signaling network model utilizing a logic-based differential equations approach and evaluated model performance in predicting experimental data from four contexts (HCM, DCM, pressure overload, and volume overload). The model has an overall prediction accuracy of 83.8%, with higher accuracy in the HCM context (90%) than DCM (75%). Global sensitivity analysis identifies key signaling reactions, with calcium-mediated myofilament force development and calcium-calmodulin kinase signaling ranking the highest. A structural revision analysis indicates potential missing interactions that primarily control calcium regulatory proteins, increasing model prediction accuracy. Combination pharmacotherapy analysis suggests that downregulation of signaling components such as calcium, titin and its associated proteins, growth factor receptors, ERK1/2, and PI3K-AKT could inhibit myocyte growth in HCM. In experiments with patient-specific iPSC-derived cardiomyocytes (MLP-W4R;MYH7-R723C iPSC-CMs), combined inhibition of ERK1/2 and PI3K-AKT rescued the HCM phenotype, as predicted by the model. In DCM, PI3K-AKT-NFAT downregulation combined with upregulation of Ras/ERK1/2 or titin or Gq protein could ameliorate cardiomyocyte morphology. The model results suggest that HCM mutations that increase active force through elevated calcium sensitivity could increase ERK activity and decrease eccentricity through parallel growth factors, Gq-mediated, and titin pathways. Moreover, the model simulated the influence of existing medications on cardiac growth in HCM and DCM contexts. This HCM/DCM signaling model demonstrates utility in investigating genotype to phenotype mechanisms in familial cardiomyopathy.

Redistribution of lamina-associated domains reshapes binding of pioneer factor FOXA2 in development of nonalcoholic fatty liver disease

Nonalcoholic fatty liver disease (NAFLD) is highly prevalent in type 2 diabetes mellitus and the elderly, impacting 40% of individuals over 70. Regulation of heterochromatin at the nuclear lamina has been associated with aging and age-dependent metabolic changes. We previously showed that changes at the lamina in aged hepatocytes and laminopathy models lead to redistribution of lamina-associated domains (LADs), opening of repressed chromatin, and up-regulation of genes regulating lipid synthesis and storage, culminating in fatty liver. Here, we test the hypothesis that change in the expression of lamina-associated proteins and nuclear shape leads to redistribution of LADs, followed by altered binding of pioneer factor FOXA2 and by up-regulation of lipid synthesis and storage, culminating in steatosis in younger NAFLD patients (aged 21-51). Changes in nuclear morphology alter LAD partitioning and reduced lamin B1 signal correlate with increased FOXA2 binding before severe steatosis in young mice placed on a western diet. Nuclear shape is also changed in younger NAFLD patients. LADs are redistrubted and lamin B1 signal decreases similarly in mild and severe steatosis. In contrast, FOXA2 binding is similar in normal and NAFLD patients with moderate steatosis and is repositioned only in NAFLD patients with more severe lipid accumulation. Hence, changes at the nuclear lamina reshape FOXA2 binding with progression of the disease. Our results suggest a role for nuclear lamina in etiology of NAFLD, irrespective of aging, with potential for improved stratification of patients and novel treatments aimed at restoring nuclear lamina function.

Multiscale model of heart growth during pregnancy: integrating mechanical and hormonal signaling

Pregnancy stands at the interface of mechanics and biology. The growing fetus continuously loads the maternal organs as circulating hormone levels surge, leading to significant changes in mechanical and hormonal cues during pregnancy. In response, maternal soft tissues undergo remarkable growth and remodeling to support the mother and baby for a healthy pregnancy. We focus on the maternal left ventricle, which increases its cardiac output and mass during pregnancy. This study develops a multiscale cardiac growth model for pregnancy to understand how mechanical and hormonal cues interact to drive this growth process. We coupled a cell signaling network model that predicts cell-level hypertrophy in response to hormones and stretch to a compartmental model of the rat heart and circulation that predicts organ-level growth in response to hemodynamic changes. We calibrated this multiscale model to data from experimental volume overload and hormonal infusions of angiotensin 2 (AngII), estrogen (E2), and progesterone (P4). We then validated the model's ability to capture interactions between inputs by comparing model predictions against published observations for the combinations of VO + E2 and AngII + E2. Finally, we simulated pregnancy-induced changes in hormones and hemodynamics to predict heart growth during pregnancy. Our model produced growth consistent with experimental data. Overall, our analysis suggests that the rise in P4 during the first half of gestation is an important contributor to heart growth during pregnancy. We conclude with suggestions for future experimental studies that will provide a better understanding of how hormonal and mechanical cues interact to drive pregnancy-induced heart growth.

Intercellular model predicts mechanisms of inflammation-fibrosis coupling after myocardial infarction

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.

Inhibition of DYRK1a Enhances Cardiomyocyte Cycling After Myocardial Infarction

BACKGROUND

DYRK1a (dual-specificity tyrosine phosphorylation-regulated kinase 1a) contributes to the control of cycling cells, including cardiomyocytes. However, the effects of inhibition of DYRK1a on cardiac function and cycling cardiomyocytes after myocardial infarction (MI) remain unknown.

METHODS

We investigated the impacts of pharmacological inhibition and conditional genetic ablation of DYRK1a on endogenous cardiomyocyte cycling and left ventricular systolic function in ischemia-reperfusion (I/R) MI using αMHC-MerDreMer-Ki67p-RoxedCre::Rox-Lox-tdTomato-eGFP (RLTG) (denoted αDKRC::RLTG) and αMHC-Cre::Fucci2aR::DYRK1aflox/flox mice.

RESULTS

We observed that harmine, an inhibitor of DYRK1a, improved left ventricular ejection fraction (39.5±1.6% and 29.1±1.6%, harmine versus placebo, respectively), 2 weeks after I/R MI. Harmine also increased cardiomyocyte cycling after I/R MI in αDKRC::RLTG mice, 10.8±1.5 versus 24.3±2.6 enhanced Green Fluorescent Protein (eGFP)+ cardiomyocytes, placebo versus harmine, respectively, P=1.0×10-3. The effects of harmine on left ventricular ejection fraction were attenuated in αDKRC::DTA mice that expressed an inducible diphtheria toxin in adult cycling cardiomyocytes. The conditional cardiomyocyte-specific genetic ablation of DYRK1a in αMHC-Cre::Fucci2aR::DYRK1aflox/flox (denoted DYRK1a k/o) mice caused cardiomyocyte hyperplasia at baseline (210±28 versus 126±5 cardiomyocytes per 40× field, DYRK1a k/o versus controls, respectively, P=1.7×10-2) without changes in cardiac function compared with controls, or compensatory changes in the expression of other DYRK isoforms. After I/R MI, DYRK1a k/o mice had improved left ventricular function (left ventricular ejection fraction 41.8±2.2% and 26.4±0.8%, DYRK1a k/o versus control, respectively, P=3.7×10-2). RNAseq of cardiomyocytes isolated from αMHC-Cre::Fucci2aR::DYRK1aflox/flox and αMHC-Cre::Fucci2aR mice after I/R MI or Sham surgeries identified enrichment in mitotic cell cycle genes in αMHC-Cre::Fucci2aR::DYRK1aflox/flox compared with αMHC-Cre::Fucci2aR.

CONCLUSIONS

The pharmacological inhibition or cardiomyocyte-specific ablation of DYRK1a caused baseline hyperplasia and improved cardiac function after I/R MI, with an increase in cell cycle gene expression, suggesting the inhibition of DYRK1a may serve as a therapeutic target to treat MI.

Computational model of brain endothelial cell signaling pathways predicts therapeutic targets for cerebral pathologies

Brain endothelial cells serve many critical homeostatic functions. In addition to sensing and regulating blood flow, they maintain blood-brain barrier function, including precise control of nutrient exchange and efflux of xenobiotics. Many signaling pathways in brain endothelial cells have been implicated in both health and disease; however, our understanding of how these signaling pathways functionally integrate is limited. A model capable of integrating these signaling pathways could both advance our understanding of brain endothelial cell signaling networks and potentially identify promising molecular targets for endothelial cell-based drug or gene therapies. To this end, we developed a large-scale computational model, wherein brain endothelial cell signaling pathways were reconstructed from the literature and converted into a network of logic-based differential equations. The model integrates 63 nodes (including proteins, mRNA, small molecules, and cell phenotypes) and 82 reactions connecting these nodes. Specifically, our model combines signaling pathways relating to VEGF-A, BDNF, NGF, and Wnt signaling, in addition to incorporating pathways relating to focused ultrasound as a therapeutic delivery tool. To validate the model, independently established relationships between selected inputs and outputs were simulated, with the model yielding correct predictions 73% of the time. We identified influential and sensitive nodes under different physiological or pathological contexts, including altered brain endothelial cell conditions during glioma, Alzheimer's disease, and ischemic stroke. Nodes with the greatest influence over combinations of desired model outputs were identified as potential druggable targets for these disease conditions. For example, the model predicts therapeutic benefits from inhibiting AKT, Hif-1α, or cathepsin D in the context of glioma - each of which are currently being studied in clinical or pre-clinical trials. Notably, the model also permits testing multiple combinations of node alterations for their effects on the network and the desired outputs (such as inhibiting AKT and overexpressing the P75 neurotrophin receptor simultaneously in the context of glioma), allowing for the prediction of optimal combination therapies. In all, our approach integrates results from over 100 past studies into a coherent and powerful model, capable of both revealing network interactions unapparent from studying any one pathway in isolation and predicting therapeutic targets for treating devastating brain pathologies.

Abstract MP255: Intercellular Model Predicts Macrophage Signaling As A Driver Of Cardiac Fibroblast Response Post Myocardial Infarction

Introduction: Post-myocardial infarction (MI), cardiac fibroblasts and macrophages work together to regulate tissue homeostasis and infarct repair. Macrophage-fibroblast interactions in healthy tissue are stable and resistant to perturbations. However, this robustness post-MI has not been assessed. This study designs and implements an intercellular communication model of macrophage-fibroblast crosstalk to determine drivers of infarct repair.

Methods: An ordinary differential equation model of post-MI cellular dynamics was developed ( Figure 1A ). Model inputs are time courses of cardiomyocytes, neutrophils, and monocytes. These cells, along with simulated macrophages and fibroblasts, secrete chemokines and cytokines which directed cell proliferation, removal, and chemical secretion. The outputs are macrophage and fibroblast densities, secreted factor dynamics, and produced collagen. Model validation was done using published data in post-MI mice. A sensitivity analysis was conducted by knocking down individual parameters to identify key drivers of fibroblast collagen production.

Results: The simulated trends matched the validation time courses. Of the 28 validation relationships, 12 were input-output, 7 were knockouts, and 9 were inhibitor relationships. The validation passed at 78.5%; the 6 failed validations were due to the independent nature of the input curves. Sensitivity analysis ( Figure 1B ) identified macrophage differentiation rate, removal rate, and transforming growth factor-beta (TGFB) secretion rate as pro-fibrotic. Prolonged exposure to TGFB and granulocyte-macrophage colony stimulating factor was anti-fibrotic. Several clustered parameters differentially regulated macrophages and collagen production.

Conclusions: The multicellular model identified macrophage density as a pro-fibrotic driver in the healing infarct. Differential regulation of macrophages and collagen production was predicted by the model.

Abstract P433: A Pharmacological Model Predicts Drug Activity In Cardiomyocyte Hypertrophy

Introduction: Cardiomyocyte (CM) hypertrophy is predictive of heart failure, however there are no clinical therapies that target its intracellular pathways. Hypertrophy is a complex process involving numerous neurohormonal and cytokine inputs, resulting in context-dependent responses that determine CM growth. In the face of this complexity, it is critical that computational models are developed. Accurate predictions of drug activity in CM hypertrophy will require a pharmacological model that is developed with and validated against experimental data.

Hypothesis: We test the hypothesis that our in silico pharmacological model accurately predicts drugs that inhibit cardiac hypertrophy as well as the context-dependent mechanisms by which they work.

Methods: Here we employ a previously published computational model of cardiac hypertrophy signaling. This model utilizes logic-based ordinary differential equations to simulate a network of 106 nodes. Using the DrugBank database, we constructed a pipeline for simulating FDA-approved drugs within this hypertrophy network under multiple environmental contexts. The predicted outcomes of the model were then compared to measured phenotypes from experimental findings in literature.

Results: Predicted outcomes of our model were successfully validated against 29 out of 36 distinct experiments described in literature. These simulations identify the optimal drug types that inhibit hypertrophy for each of 17 different stimuli. Sensitivity analyses performed by simulating knockdowns in our model reveals context-dependent mechanisms predicted for 51 drug types. These predictions confirmed, for example, the role of celecoxib in inhibiting CM hypertrophy induced by isoproterenol. Mechanistic analysis suggests celecoxib prevents protein kinase B (Akt) inhibition of glycogen synthase kinase 3 beta (GSK3β), consistent with literature.

Conclusions: Our pharmacological model accurately predicts FDA-approved drugs that show in vitro inhibition of CM hypertrophy.

Abstract P457: Systems Analysis To Understand The Impact Of The CDK Module On Cardiomyocyte Proliferation

Rationale: Heart failure is caused by the inability of adult mammalian hearts to overcome the loss of cardiomyocytes (CMs). This is due partly to the limited proliferative capacity of CMs, which exit the cell cycle and do not undergo cell division. Current knowledge in cardiac regeneration lacks an understanding of the molecular regulatory networks that determine whether CMs will progress through the cell cycle to proliferate. Our goal is to use computational modeling to understand the expression and activation levels of the core cell cycle network, specifically cyclins and cyclin-cyclin-dependent kinase (CDK) complexes.

Methods: A model of core cell cycle dynamics was curated using previously published studies of CM proliferation regulators. This model incorporates those regulators known to stimulate G1/S and G2/M transitions through the core CDKs. The activity of each of the 22 network nodes (22 reactions) was predicted using a logic-based differential equation approach. The CDK model was then coupled with a minimal ODE model of cell cycle phase distributions and validated based on descriptions and experimental data from the literature. To prioritize key nodes for experimental validation, we performed a sensitivity analysis by stimulating individual knockdown for every node in the network, measuring the fractional activity of all nodes.

Results: Our model confirmed that the knockdown of p21 and Rb protein and the overexpression of E2F transcription factor and cyclinD-cdk4 showed an increase in cells going through DNA synthesis and entering mitosis. A combined knockdown of p21 and p27 showed an increase of cells entering mitosis. Cyclin D-cdk4 and p21 overexpression showed a decrease and increase of Rb expression, respectively. Of the 14 model predictions, 12 agreed with experimental data in the literature. A comprehensive knockdown of the model nodes suggests that E2F (a key transcription factor driving DNA synthesis) is positively regulated by cyclin D while negatively regulated by GSK3B, SMAD3, and pRB.

Conclusion: This model enables us to predict how cardiomyocytes respond to stimuli in the CDK network and identify potential therapeutic regulators that induce cardiomyocyte proliferation.

The Cell Surface Receptors Ror1/2 Control Cardiac Myofibroblast Differentiation

Background A hallmark of heart failure is cardiac fibrosis, which results from the injury-induced differentiation response of resident fibroblasts to myofibroblasts that deposit extracellular matrix. During myofibroblast differentiation, fibroblasts progress through polarization stages of early proinflammation, intermediate proliferation, and late maturation, but the regulators of this progression are poorly understood. Planar cell polarity receptors, receptor tyrosine kinase-like orphan receptor 1 and 2 (Ror1/2), can function to promote cell differentiation and transformation. In this study, we investigated the role of the Ror1/2 in a model of heart failure with emphasis on myofibroblast differentiation. Methods and Results The role of Ror1/2 during cardiac myofibroblast differentiation was studied in cell culture models of primary murine cardiac fibroblast activation and in knockout mouse models that underwent transverse aortic constriction surgery to induce cardiac injury by pressure overload. Expression of Ror1 and Ror2 were robustly and exclusively induced in fibroblasts in hearts after transverse aortic constriction surgery, and both were rapidly upregulated after early activation of primary murine cardiac fibroblasts in culture. Cultured fibroblasts isolated from Ror1/2 knockout mice displayed a proinflammatory phenotype indicative of impaired myofibroblast differentiation. Although the combined ablation of Ror1/2 in mice did not result in a detectable baseline phenotype, transverse aortic constriction surgery led to the death of all mice by day 6 that was associated with myocardial hyperinflammation and vascular leakage. Conclusions Together, these results show that Ror1/2 are essential for the progression of myofibroblast differentiation and for the adaptive remodeling of the heart in response to pressure overload.

Mechano-chemo signaling interactions modulate matrix production by cardiac fibroblasts

Extracellular matrix remodeling after myocardial infarction occurs in a dynamic environment in which local mechanical stresses and biochemical signaling species stimulate the accumulation of collagen-rich scar tissue. It is well-known that cardiac fibroblasts regulate post-infarction matrix turnover by secreting matrix proteins, proteases, and protease inhibitors in response to both biochemical stimuli and mechanical stretch, but how these stimuli act together to dictate cellular responses is still unclear. We developed a screen of cardiac fibroblast-secreted proteins in response to combinations of biochemical agonists and cyclic uniaxial stretch in order to elucidate the relationships between stretch, biochemical signaling, and cardiac matrix turnover. We found that stretch significantly synergized with biochemical agonists to inhibit the secretion of matrix metalloproteinases, with stretch either amplifying protease suppression by individual agonists or antagonizing agonist-driven upregulation of protease expression. Stretch also modulated fibroblast sensitivity towards biochemical agonists by either sensitizing cells towards agonists that suppress protease secretion or de-sensitizing cells towards agonists that upregulate protease secretion. These findings suggest that the mechanical environment can significantly alter fibrosis-related signaling in cardiac fibroblasts, suggesting caution when extrapolating in vitro data to predict effects of fibrosis-related cytokines in situations like myocardial infarction where mechanical stretch occurs.

Network model-based screen for FDA-approved drugs affecting cardiac fibrosis

Cardiac fibrosis is a significant component of pathological heart remodeling, yet it is not directly targeted by existing drugs. Systems pharmacology approaches have the potential to provide mechanistic frameworks with which to predict and understand how drugs modulate biological systems. Here, we combine network modeling of the fibroblast signaling network with 36 unique drug-target interactions from DrugBank to predict drugs that modulate fibroblast phenotype and fibrosis. Galunisertib was predicted to decrease collagen and α-SMA expression, which we validated in human cardiac fibroblasts. In vivo fibrosis data from the literature validated predictions for 10 drugs. Further, the model was used to identify network mechanisms by which these drugs work. Arsenic trioxide was predicted to induce fibrosis by AP1-driven TGFβ expression and MMP2-driven TGFβ activation. Entresto (valsartan/sacubitril) was predicted to suppress fibrosis by valsartan suppression of ERK signaling and sacubitril enhancement of PKG activity, both of which decreased Smad3 activity. Overall, this study provides a framework for integrating drug-target mechanisms with logic-based network models, which can drive further studies both in cardiac fibrosis and other conditions.

Computational model of cardiomyocyte apoptosis identifies mechanisms of tyrosine kinase inhibitor-induced cardiotoxicity

Despite clinical observations of cardiotoxicity among cancer patients treated with tyrosine kinase inhibitors (TKIs), the molecular mechanisms by which these drugs affect the heart remain largely unknown. Mechanistic understanding of TKI-induced cardiotoxicity has been limited in part due to the complexity of tyrosine kinase signaling pathways and the multi-targeted nature of many of these drugs. TKI treatment has been associated with reactive oxygen species generation, mitochondrial dysfunction, and apoptosis in cardiomyocytes. To gain insight into the mechanisms mediating TKI-induced cardiotoxicity, this study constructs and validates a computational model of cardiomyocyte apoptosis, integrating intrinsic apoptotic and tyrosine kinase signaling pathways. The model predicts high levels of apoptosis in response to sorafenib, sunitinib, ponatinib, trastuzumab, and gefitinib, and lower levels of apoptosis in response to nilotinib and erlotinib, with the highest level of apoptosis induced by sorafenib. Knockdown simulations identified AP1, ASK1, JNK, MEK47, p53, and ROS as positive functional regulators of sorafenib-induced apoptosis of cardiomyocytes. Overexpression simulations identified Akt, IGF1, PDK1, and PI3K among the negative functional regulators of sorafenib-induced cardiomyocyte apoptosis. A combinatorial screen of the positive and negative regulators of sorafenib-induced apoptosis revealed ROS knockdown coupled with overexpression of FLT3, FGFR, PDGFR, VEGFR, or KIT as a particularly potent combination in reducing sorafenib-induced apoptosis. Network simulations of combinatorial treatment with sorafenib and the antioxidant N-acetyl cysteine (NAC) suggest that NAC may protect cardiomyocytes from sorafenib-induced apoptosis.

Network Analysis Reveals a Distinct Axis of Macrophage Activation in Response to Conflicting Inflammatory Cues

Macrophages are subject to a wide range of cytokine and pathogen signals in vivo, which contribute to differential activation and modulation of inflammation. Understanding the response to multiple, often-conflicting cues that macrophages experience requires a network perspective. In this study, we integrate data from literature curation and mRNA expression profiles obtained from wild type C57/BL6J mice macrophages to develop a large-scale computational model of the macrophage signaling network. In response to stimulation across all pairs of nine cytokine inputs, the model predicted activation along the classic M1-M2 polarization axis but also a second axis of macrophage activation that distinguishes unstimulated macrophages from a mixed phenotype induced by conflicting cues. Along this second axis, combinations of conflicting stimuli, IL-4 with LPS, IFN-γ, IFN-β, or TNF-α, produced mutual inhibition of several signaling pathways, e.g., NF-κB and STAT6, but also mutual activation of the PI3K signaling module. In response to combined IFN-γ and IL-4, the model predicted genes whose expression was mutually inhibited, e.g., iNOS or Nos2 and Arg1, or mutually enhanced, e.g., Il4rα and Socs1, validated by independent experimental data. Knockdown simulations further predicted network mechanisms underlying functional cross-talk, such as mutual STAT3/STAT6-mediated enhancement of Il4rα expression. In summary, the computational model predicts that network cross-talk mediates a broadened spectrum of macrophage activation in response to mixed pro- and anti-inflammatory cytokine cues, making it useful for modeling in vivo scenarios.

A multiscale model of cardiac concentric hypertrophy incorporating both mechanical and hormonal drivers of growth

Growth and remodeling in the heart is driven by a combination of mechanical and hormonal signals that produce different patterns of growth in response to exercise, pregnancy, and various pathologies. In particular, increases in afterload lead to concentric hypertrophy, a thickening of the walls that increases the contractile ability of the heart while reducing wall stress. In the current study, we constructed a multiscale model of cardiac hypertrophy that connects a finite-element model representing the mechanics of the growing left ventricle to a cell-level network model of hypertrophic signaling pathways that accounts for changes in both mechanics and hormones. We first tuned our model to capture published in vivo growth trends for isoproterenol infusion, which stimulates β-adrenergic signaling pathways without altering mechanics, and for transverse aortic constriction (TAC), which involves both elevated mechanics and altered hormone levels. We then predicted the attenuation of TAC-induced hypertrophy by two distinct genetic interventions (transgenic Gq-coupled receptor inhibitor overexpression and norepinephrine knock-out) and by two pharmacologic interventions (angiotensin receptor blocker losartan and β-blocker propranolol) and compared our predictions to published in vivo data for each intervention. Our multiscale model captured the experimental data trends reasonably well for all conditions simulated. We also found that when prescribing realistic changes in mechanics and hormones associated with TAC, the hormonal inputs were responsible for the majority of the growth predicted by the multiscale model and were necessary in order to capture the effect of the interventions for TAC.

Context-specific network modeling identifies new crosstalk in β-adrenergic cardiac hypertrophy

Cardiac hypertrophy is a context-dependent phenomenon wherein a myriad of biochemical and biomechanical factors regulate myocardial growth through a complex large-scale signaling network. Although numerous studies have investigated hypertrophic signaling pathways, less is known about hypertrophy signaling as a whole network and how this network acts in a context-dependent manner. Here, we developed a systematic approach, CLASSED (Context-specific Logic-bASed Signaling nEtwork Development), to revise a large-scale signaling model based on context-specific data and identify main reactions and new crosstalks regulating context-specific response. CLASSED involves four sequential stages with an automated validation module as a core which builds a logic-based ODE model from the interaction graph and outputs the model validation percent. The context-specific model is developed by estimation of default parameters, classified qualitative validation, hybrid Morris-Sobol global sensitivity analysis, and discovery of missing context-dependent crosstalks. Applying this pipeline to our prior-knowledge hypertrophy network with context-specific data revealed key signaling reactions which distinctly regulate cell response to isoproterenol, phenylephrine, angiotensin II and stretch. Furthermore, with CLASSED we developed a context-specific model of β-adrenergic cardiac hypertrophy. The model predicted new crosstalks between calcium/calmodulin-dependent pathways and upstream signaling of Ras in the ISO-specific context. Experiments in cardiomyocytes validated the model’s predictions on the role of CaMKII-Gβγ and CaN-Gβγ interactions in mediating hypertrophic signals in ISO-specific context and revealed a difference in the phosphorylation magnitude and translocation of ERK1/2 between cardiac myocytes and fibroblasts. CLASSED is a systematic approach for developing context-specific large-scale signaling networks, yielding insights into new-found crosstalks in β-adrenergic cardiac hypertrophy.

Pathological cardiac hypertrophy is a disease in which the heart grows abnormally in response to different motivators such as high blood pressure or variations in hormones and growth factors. The shape of the heart after its growth depends on the context in which it grows. Since cell signaling in the cardiac cells plays a key role in the determination of heart shape, a thorough understanding of cardiac cells signaling in each context enlightens the mechanisms which control response of cardiac cells. However, cell signaling in cardiac hypertrophy comprises a complex web of pathways with numerous interactions, and predicting how these interactions control the hypertrophic signal in each context is not achievable by only experiments or general computational models. To address this need, we developed an approach to bring together the experimental data of each context with a signaling network curated from literature to identify the main players of cardiac cells response in each context and attain the context-specific models of cardiac hypertrophy. By utilizing our approach, we identified the main regulators of cardiac hypertrophy in four important contexts. We developed a network model of β-adrenergic cardiac hypertrophy, and predicted and validated new interactions that regulate cardiac cells response in this context.

Computational model predicts paracrine and intracellular drivers of fibroblast phenotype after myocardial infarction

The fibroblast is a key mediator of wound healing in the heart and other organs, yet how it integrates multiple time-dependent paracrine signals to control extracellular matrix synthesis has been difficult to study in vivo. Here, we extended a computational model to simulate the dynamics of fibroblast signaling and fibrosis after myocardial infarction (MI) in response to time-dependent data for nine paracrine stimuli. This computational model was validated against dynamic collagen expression and collagen area fraction data from post-infarction rat hearts. The model predicted that while many features of the fibroblast phenotype at inflammatory or maturation phases of healing could be recapitulated by single static paracrine stimuli (interleukin-1 and angiotensin-II, respectively), mimicking the reparative phase required paired stimuli (e.g. TGFβ and endothelin-1). Virtual overexpression screens simulated with either static cytokine pairs or post-MI paracrine dynamic predicted phase-specific regulators of collagen expression. Several regulators increased (Smad3) or decreased (Smad7, protein kinase G) collagen expression specifically in the reparative phase. NADPH oxidase (NOX) overexpression sustained collagen expression from reparative to maturation phases, driven by TGFβ and endothelin positive feedback loops. Interleukin-1 overexpression had mixed effects, both enhancing collagen via the TGFβ positive feedback loop and suppressing collagen via NFκB and BAMBI (BMP and activin membrane-bound inhibitor) incoherent feed-forward loops. These model-based predictions reveal network mechanisms by which the dynamics of paracrine stimuli and interacting signaling pathways drive the progression of fibroblast phenotypes and fibrosis after myocardial infarction.

Quantification of model and data uncertainty in a network analysis of cardiac myocyte mechanosignalling

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'.

Multiscale Coupling of an Agent-Based Model of Tissue Fibrosis and a Logic-Based Model of Intracellular Signaling

Wound healing and fibrosis following myocardial infarction (MI) is a dynamic process involving many cell types, extracellular matrix (ECM), and inflammatory cues. As both incidence and survival rates for MI increase, management of post-MI recovery and associated complications are an increasingly important focus. Complexity of the wound healing process and the need for improved therapeutics necessitate a better understanding of the biochemical cues that drive fibrosis. To study the progression of cardiac fibrosis across spatial and temporal scales, we developed a novel hybrid multiscale model that couples a logic-based differential equation (LDE) model of the fibroblast intracellular signaling network with an agent-based model (ABM) of multi-cellular tissue remodeling. The ABM computes information about cytokine and growth factor levels in the environment including TGFβ, TNFα, IL-1β, and IL-6, which are passed as inputs to the LDE model. The LDE model then computes the network signaling state of individual cardiac fibroblasts within the ABM. Based on the current network state, fibroblasts make decisions regarding cytokine secretion and deposition and degradation of collagen. Simulated fibroblasts respond dynamically to rapidly changing extracellular environments and contribute to spatial heterogeneity in model predicted fibrosis, which is governed by many parameters including cell density, cell migration speeds, and cytokine levels. Verification tests confirmed that predictions of the coupled model and network model alone were consistent in response to constant cytokine inputs and furthermore, a subset of coupled model predictions were validated with in vitro experiments with human cardiac fibroblasts. This multiscale framework for cardiac fibrosis will allow for systematic screening of the effects of molecular perturbations in fibroblast signaling on tissue-scale extracellular matrix composition and organization.

Mechanical regulation of gene expression in cardiac myocytes and fibroblasts

The intact heart undergoes complex and multiscale remodelling processes in response to altered mechanical cues. Remodelling of the myocardium is regulated by a combination of myocyte and non-myocyte responses to mechanosensitive pathways, which can alter gene expression and therefore function in these cells. Cellular mechanotransduction and its downstream effects on gene expression are initially compensatory mechanisms during adaptations to the altered mechanical environment, but under prolonged and abnormal loading conditions, they can become maladaptive, leading to impaired function and cardiac pathologies. In this Review, we summarize mechanoregulated pathways in cardiac myocytes and fibroblasts that lead to altered gene expression and cell remodelling under physiological and pathophysiological conditions. Developments in systems modelling of the networks that regulate gene expression in response to mechanical stimuli should improve integrative understanding of their roles in vivo and help to discover new combinations of drugs and device therapies targeting mechanosignalling in heart disease.

High-content phenotypic assay for proliferation of human iPSC-derived cardiomyocytes identifies L-type calcium channels as targets

Over 5 million people in the United States suffer from heart failure, due to the limited ability to regenerate functional cardiac tissue. One potential therapeutic strategy is to enhance proliferation of resident cardiomyocytes. However, phenotypic screening for therapeutic agents is challenged by the limited ability of conventional markers to discriminate between cardiomyocyte proliferation and endoreplication (e.g. polyploidy and multinucleation). Here, we developed a novel assay that combines automated live-cell microscopy and image processing algorithms to discriminate between proliferation and endoreplication by quantifying changes in the number of nuclei, changes in the number of cells, binucleation, and nuclear DNA content. We applied this assay to further prioritize hits from a primary screen for DNA synthesis, identifying 30 compounds that enhance proliferation of human induced pluripotent stem cell-derived cardiomyocytes. Among the most active compounds from the phenotypic screen are clinically approved L-type calcium channel blockers from multiple chemical classes whose activities were confirmed across different sources of human induced pluripotent stem cell-derived cardiomyocytes. Identification of compounds that stimulate human cardiomyocyte proliferation may provide new therapeutic strategies for heart failure.

Atg2, Atg9 and Atg18 in mitochondrial integrity, cardiac function and healthspan in Drosophila

In yeast, the Atg2-Atg18 complex regulates Atg9 recycling from phagophore assembly site during autophagy; their function in higher eukaryotes remains largely unknown. In a targeted screening in Drosophila melanogaster, we show that Mef2-GAL4-RNAi-mediated knockdown of Atg2, Atg9 or Atg18 in the heart and indirect flight muscles led to shortened healthspan (declined locomotive function) and lifespan. These flies displayed an accelerated age-dependent loss of cardiac function along with cardiac hypertrophy (increased heart tube wall thickness) and structural abnormality (distortion of the lumen surface). Using the Mef2-GAL4-MitoTimer mitochondrial reporter system and transmission electron microscopy, we observed significant elongation of mitochondria and reduced number of lysosome-targeted autophagosomes containing mitochondria in the heart tube but exaggerated mitochondrial fragmentation and reduced mitochondrial density in indirect flight muscles. These findings provide the first direct evidence of the importance of Atg2-Atg18/Atg9 autophagy complex in the maintenance of mitochondrial integrity and, regulation of heart and muscle functions in Drosophila, raising the possibility of augmenting Atg2-Atg18/Atg9 activity in promoting mitochondrial health and, muscle and heart function.

Network-based predictions of in vivo cardiac hypertrophy

Cardiac hypertrophy is a common response of cardiac myocytes to stress and a predictor of heart failure. While in vitro cell culture studies have identified numerous molecular mechanisms driving hypertrophy, it is unclear to what extent these mechanisms can be integrated into a consistent framework predictive of in vivo phenotypes. To address this question, we investigate the degree to which an in vitro-based, manually curated computational model of the hypertrophy signaling network is able to predict in vivo hypertrophy of 52 cardiac-specific transgenic mice. After minor revisions motivated by in vivo literature, the model concordantly predicts the qualitative responses of 78% of output species and 69% of signaling intermediates within the network model. Analysis of four double-transgenic mouse models reveals that the computational model robustly predicts hypertrophic responses in mice subjected to multiple, simultaneous perturbations. Thus the model provides a framework with which to mechanistically integrate data from multiple laboratories and experimental systems to predict molecular regulation of cardiac hypertrophy.

Mapping macrophage polarization over the myocardial infarction time continuum

In response to myocardial infarction (MI), cardiac macrophages regulate inflammation and scar formation. We hypothesized that macrophages undergo polarization state changes over the MI time course and assessed macrophage polarization transcriptomic signatures over the first week of MI. C57BL/6 J male mice (3–6 months old) were subjected to permanent coronary artery ligation to induce MI, and macrophages were isolated from the infarct region at days 1, 3, and 7 post-MI. Day 0, no MI resident cardiac macrophages served as the negative MI control. Whole transcriptome analysis was performed using RNA-sequencing on n = 4 pooled sets for each time. Day 1 macrophages displayed a unique pro-inflammatory, extracellular matrix (ECM)-degrading signature. By flow cytometry, day 0 macrophages were largely F4/80^highLy6C^low resident macrophages, whereas day 1 macrophages were largely F4/80^lowLy6C^high infiltrating monocytes. Day 3 macrophages exhibited increased proliferation and phagocytosis, and expression of genes related to mitochondrial function and oxidative phosphorylation, indicative of metabolic reprogramming. Day 7 macrophages displayed a pro-reparative signature enriched for genes involved in ECM remodeling and scar formation. By triple in situ hybridization, day 7 infarct macrophages in vivo expressed collagen I and periostin mRNA. Our results indicate macrophages show distinct gene expression profiles over the first week of MI, with metabolic reprogramming important for polarization. In addition to serving as indirect mediators of ECM remodeling, macrophages are a direct source of ECM components. Our study is the first to report the detailed changes in the macrophage transcriptome over the first week of MI.

An engineering design approach to systems biology

Measuring and modeling the integrated behavior of biomolecular-cellular networks is central to systems biology. Over several decades, systems biology has been shaped by quantitative biologists, physicists, mathematicians, and engineers in different ways. However, the basic and applied versions of systems biology are not typically distinguished, which blurs the separate aspirations of the field and its potential for real-world impact. Here, we articulate an engineering approach to systems biology, which applies educational philosophy, engineering design, and predictive models to solve contemporary problems in an age of biomedical Big Data. A concerted effort to train systems bioengineers will provide a versatile workforce capable of tackling the diverse challenges faced by the biotechnological and pharmaceutical sectors in a modern, information-dense economy.

High content analysis identifies unique morphological features of reprogrammed cardiomyocytes

Direct reprogramming of fibroblasts into cardiomyocytes is a promising approach for cardiac regeneration but still faces challenges in efficiently generating mature cardiomyocytes. Systematic optimization of reprogramming protocols requires scalable, objective methods to assess cellular phenotype beyond what is captured by transcriptional signatures alone. To address this question, we automatically segmented reprogrammed cardiomyocytes from immunofluorescence images and analyzed cell morphology. We also introduce a method to quantify sarcomere structure using Haralick texture features, called SarcOmere Texture Analysis (SOTA). We show that induced cardiac-like myocytes (iCLMs) are highly variable in expression of cardiomyocyte markers, producing subtypes that are not typically seen in vivo. Compared to neonatal mouse cardiomyocytes, iCLMs have more variable cell size and shape, have less organized sarcomere structure, and demonstrate reduced sarcomere length. Taken together, these results indicate that traditional methods of assessing cardiomyocyte reprogramming by quantifying induction of cardiomyocyte marker proteins may not be sufficient to predict functionality. The automated image analysis methods described in this study may enable more systematic approaches for improving reprogramming techniques above and beyond existing algorithms that rely heavily on transcriptome profiling.

Predictive model identifies key network regulators of cardiomyocyte mechano-signaling

Mechanical strain is a potent stimulus for growth and remodeling in cells. Although many pathways have been implicated in stretch-induced remodeling, the control structures by which signals from distinct mechano-sensors are integrated to modulate hypertrophy and gene expression in cardiomyocytes remain unclear. Here, we constructed and validated a predictive computational model of the cardiac mechano-signaling network in order to elucidate the mechanisms underlying signal integration. The model identifies calcium, actin, Ras, Raf1, PI3K, and JAK as key regulators of cardiac mechano-signaling and characterizes crosstalk logic imparting differential control of transcription by AT1R, integrins, and calcium channels. We find that while these regulators maintain mostly independent control over distinct groups of transcription factors, synergy between multiple pathways is necessary to activate all the transcription factors necessary for gene transcription and hypertrophy. We also identify a PKG-dependent mechanism by which valsartan/sacubitril, a combination drug recently approved for treating heart failure, inhibits stretch-induced hypertrophy, and predict further efficacious pairs of drug targets in the network through a network-wide combinatorial search.

Common stresses such as high blood pressure or heart attack can lead to heart failure, which afflicts over 25 million people worldwide. These stresses cause cardiomyocytes to grow and remodel, which may initially be beneficial but ultimately worsen heart function. Current heart failure drugs such as beta-blockers counteract biochemical cues prompting cardiomyocyte growth, yet mechanical cues to cardiomyocytes such as stretch are just as important in driving cardiac dysfunction. However, no pharmacological treatments have yet been approved that specifically target mechano-signaling, in part because it is not clear how cardiomyocytes integrate signals from multiple mechano-responsive sensors and pathways into their decision to grow. To address this challenge, we built a systems-level computational model that represents 125 interactions between 94 stretch-responsive signaling molecules. The model correctly predicts 134 of 172 previous independent experimental observations, and identifies the key regulators of stretch-induced cardiomyocyte remodeling. Although cardiomyocytes have many mechano-signaling pathways that function largely independently, we find that cooperation between them is necessary to cause growth and remodeling. We identify mechanisms by which a recently approved heart failure drug pair affects mechano-signaling, and we further predict additional pairs of drug targets that could be used to help reverse heart failure.

Ampk phosphorylation of Ulk1 is required for targeting of mitochondria to lysosomes in exercise-induced mitophagy

Mitochondrial health is critical for skeletal muscle function and is improved by exercise training through both mitochondrial biogenesis and removal of damaged/dysfunctional mitochondria via mitophagy. The mechanisms underlying exercise-induced mitophagy have not been fully elucidated. Here, we show that acute treadmill running in mice causes mitochondrial oxidative stress at 3-12 h and mitophagy at 6 h post-exercise in skeletal muscle. These changes were monitored using a novel fluorescent reporter gene, pMitoTimer, that allows assessment of mitochondrial oxidative stress and mitophagy in vivo, and were preceded by increased phosphorylation of AMP activated protein kinase (Ampk) at tyrosine 172 and of unc-51 like autophagy activating kinase 1 (Ulk1) at serine 555. Using mice expressing dominant negative and constitutively active Ampk in skeletal muscle, we demonstrate that Ulk1 activation is dependent on Ampk. Furthermore, exercise-induced metabolic adaptation requires Ulk1. These findings provide direct evidence of exercise-induced mitophagy and demonstrate the importance of Ampk-Ulk1 signaling in skeletal muscle.Exercise is associated with biogenesis and removal of dysfunctional mitochondria. Here the authors use a mitochondrial reporter gene to demonstrate the occurrence of mitophagy following exercise in mice, and show this is dependent on AMPK and ULK1 signaling.

Mechanistic Systems Modeling to Improve Understanding and Prediction of Cardiotoxicity Caused by Targeted Cancer Therapeutics

Tyrosine kinase inhibitors (TKIs) are highly potent cancer therapeutics that have been linked with serious cardiotoxicity, including left ventricular dysfunction, heart failure, and QT prolongation. TKI-induced cardiotoxicity is thought to result from interference with tyrosine kinase activity in cardiomyocytes, where these signaling pathways help to control critical processes such as survival signaling, energy homeostasis, and excitation–contraction coupling. However, mechanistic understanding is limited at present due to the complexities of tyrosine kinase signaling, and the wide range of targets inhibited by TKIs. Here, we review the use of TKIs in cancer and the cardiotoxicities that have been reported, discuss potential mechanisms underlying cardiotoxicity, and describe recent progress in achieving a more systematic understanding of cardiotoxicity via the use of mechanistic models. In particular, we argue that future advances are likely to be enabled by studies that combine large-scale experimental measurements with Quantitative Systems Pharmacology (QSP) models describing biological mechanisms and dynamics. As such approaches have proven extremely valuable for understanding and predicting other drug toxicities, it is likely that QSP modeling can be successfully applied to cardiotoxicity induced by TKIs. We conclude by discussing a potential strategy for integrating genome-wide expression measurements with models, illustrate initial advances in applying this approach to cardiotoxicity, and describe challenges that must be overcome to truly develop a mechanistic and systematic understanding of cardiotoxicity caused by TKIs.

Knowledge gaps to understanding cardiac macrophage polarization following myocardial infarction

Following myocardial infarction (MI), macrophages coordinate both pro-inflammatory and reparative responses of the left ventricle (LV) by reacting to and secreting cytokines, chemokines, and growth factors and by stimulating endothelial cells and fibroblasts to modulate neovascularization and scar formation. Healing of the infarcted LV can be divided into three distinct, but overlapping phases: inflammatory, proliferative, and maturation. Macrophages are involved in all phases. Despite macrophages being a major leukocyte cell type in the post-MI LV, how this cell type regulates LV remodeling over the post-MI time continuum is not completely understood. In this review, we summarize the current literature as a foundation to discuss the major knowledge gaps that remain. Defining the post-MI temporal macrophage phenotypes to establish a classification system is the first step in exploring how macrophage phenotypes are regulated, how temporal stimulation and secretion profiles evolve, and how best to modify stimuli to yield predictable cell responses. This article is part of a Special Issue entitled: The role of post-translational protein modifications on heart and vascular metabolism edited by Jason R.B. Dyck & Jan F.C. Glatz.

Computational modeling of cardiac fibroblasts and fibrosis

Altered fibroblast behavior can lead to pathologic changes in the heart such as arrhythmia, diastolic dysfunction, and systolic dysfunction. Computational models are increasingly used as a tool to identify potential mechanisms driving a phenotype or potential therapeutic targets against an unwanted phenotype. Here we review how computational models incorporating cardiac fibroblasts have clarified the role for these cells in electrical conduction and tissue remodeling in the heart. Models of fibroblast signaling networks have primarily focused on fibroblast cell lines or fibroblasts from other tissues rather than cardiac fibroblasts, specifically, but they are useful for understanding how fundamental signaling pathways control fibroblast phenotype. In the future, modeling cardiac fibroblast signaling, incorporating -omics and drug-interaction data into signaling network models, and utilizing multi-scale models will improve the ability of in silico studies to predict potential therapeutic targets against adverse cardiac fibroblast activity.

A computational model of cardiac fibroblast signaling predicts context-dependent drivers of myofibroblast differentiation

Cardiac fibroblasts support heart function, and aberrant fibroblast signaling can lead to fibrosis and cardiac dysfunction. Yet how signaling molecules drive myofibroblast differentiation and fibrosis in the complex signaling environment of cardiac injury remains unclear. We developed a large-scale computational model of cardiac fibroblast signaling in order to identify regulators of fibrosis under diverse signaling contexts. The model network integrates 10 signaling pathways, including 91 nodes and 134 reactions, and it correctly predicted 80% of independent previous experiments. The model predicted key fibrotic signaling regulators (e.g. reactive oxygen species, tissue growth factor β (TGFβ) receptor), whose function varied depending on the extracellular environment. We characterized how network structure relates to function, identified functional modules, and predicted cross-talk between TGFβ and mechanical signaling, which was validated experimentally in adult cardiac fibroblasts. This study provides a systems framework for predicting key regulators of fibroblast signaling across diverse signaling contexts.

Automated microscopy of cardiac myocyte hypertrophy: a case study on the role of intracellular α-adrenergic receptors

Traditional approaches for measuring cardiac myocyte hypertrophy have been of low throughput and subjective, limiting the scope of experimental studies designed to understand it. Here, we describe an automated image acquisition and analysis platform for studying the dynamics of cardiac myocyte hypertrophy in vitro. Image acquisition scripts record 5 × 5 mosaic images of fluorescent protein-labeled neonatal rat ventricular myocytes from each well of a 96-well plate using the microscope's automated stage and focus. Image analysis algorithms automatically segment myocyte boundaries, track myocytes, and quantify changes in shape. We describe each step of the image acquisition and analysis algorithms and provide specific examples of how to implement them using Metamorph and CellProfiler software. With this system, shape dynamics of thousands of individual cardiac myocytes can be tracked for up to a week. This imaging platform was recently applied to study reversal of cardiac myocyte hypertrophy following withdrawal of the α-adrenergic agonist phenylephrine. Hypertrophy readily reversed at low but not high levels of α-adrenergic signaling, leading to identification of an intracellular population of α-adrenergic receptors responsible for this reversibility delay.

Modeling the effects of β1-adrenergic receptor blockers and polymorphisms on cardiac myocyte Ca2+ handling

β-Adrenergic receptor blockers (β-blockers) are commonly used to treat heart failure, but the biologic mechanisms governing their efficacy are still poorly understood. The complexity of β-adrenergic signaling coupled with the influence of receptor polymorphisms makes it difficult to intuit the effect of β-blockers on cardiac physiology. While some studies indicate that β-blockers are efficacious by inhibiting β-adrenergic signaling, other studies suggest that they work by maintaining β-adrenergic responsiveness. Here, we use a systems pharmacology approach to test the hypothesis that in ventricular myocytes, these two apparently conflicting mechanisms for β-blocker efficacy can occur concurrently. We extended a computational model of the β(1)-adrenergic pathway and excitation-contraction coupling to include detailed receptor interactions for 19 ligands. Model predictions, validated with Ca(2+) and Förster resonance energy transfer imaging of adult rat ventricular myocytes, surprisingly suggest that β-blockers can both inhibit and maintain signaling depending on the magnitude of receptor stimulation. The balance of inhibition and maintenance of β(1)-adrenergic signaling is predicted to depend on the specific β-blocker (with greater responsiveness for metoprolol than carvedilol) and β(1)-adrenergic receptor Arg389Gly polymorphisms.

A novel MitoTimer reporter gene for mitochondrial content, structure, stress, and damage in vivo

Mitochondrial dysfunction plays important roles in many diseases, but there is no satisfactory method to assess mitochondrial health in vivo. Here, we engineered a MitoTimer reporter gene from the existing Timer reporter gene. MitoTimer encodes a mitochondria-targeted green fluorescent protein when newly synthesized, which shifts irreversibly to red fluorescence when oxidized. Confocal microscopy confirmed targeting of the MitoTimer protein to mitochondria in cultured cells, Caenorhabditis elegans touch receptor neurons, Drosophila melanogaster heart and indirect flight muscle, and mouse skeletal muscle. A ratiometric algorithm revealed that conditions that cause mitochondrial stress led to a significant shift toward red fluorescence as well as accumulation of pure red fluorescent puncta of damaged mitochondria targeted for mitophagy. Long term voluntary exercise resulted in a significant fluorescence shift toward green, in mice and D. melanogaster, as well as significantly improved structure and increased content in mouse FDB muscle. In contrast, high-fat feeding in mice resulted in a significant shift toward red fluorescence and accumulation of pure red puncta in skeletal muscle, which were completely ameliorated by voluntary wheel running. Hence, MitoTimer allows for robust analysis of multiple parameters of mitochondrial health under both physiological and pathological conditions and will be highly useful for future research of mitochondrial health in multiple disciplines in vivo.

Phenotypic screen quantifying differential regulation of cardiac myocyte hypertrophy identifies CITED4 regulation of myocyte elongation

Cardiac hypertrophy is controlled by a highly connected signaling network with many effectors of cardiac myocyte size. Quantification of the contribution of individual pathways to specific changes in shape and transcript abundance is needed to better understand hypertrophy signaling and to improve heart failure therapies. We stimulated cardiac myocytes with 15 hypertrophic agonists and quantitatively characterized differential regulation of 5 shape features using high-throughput microscopy and transcript levels of 12 genes using qPCR. Transcripts measured were associated with phenotypes including fibrosis, cell death, contractility, proliferation, angiogenesis, inflammation, and the fetal cardiac gene program. While hypertrophy pathways are highly connected, the agonist screen revealed distinct hypertrophy phenotypic signatures for the 15 receptor agonists. We then used k-means clustering of inputs and outputs to identify a network map linking input modules to output modules. Five modules were identified within inputs and outputs with many maladaptive outputs grouping together in one module: Bax, C/EBPβ, Serca2a, TNFα, and CTGF. Subsequently, we identified mechanisms underlying two correlations revealed in the agonist screen: correlation between regulators of fibrosis and cell death signaling (CTGF and Bax mRNA) caused by AngII; and myocyte proliferation (CITED4 mRNA) and elongation caused by Nrg1. Follow-up experiments revealed positive regulation of Bax mRNA level by CTGF and an incoherent feedforward loop linking Nrg1, CITED4 and elongation. With this agonist screen, we identified the most influential inputs in the cardiac hypertrophy signaling network for a variety of features related to pathological and protective hypertrophy signaling and shared regulation among cardiac myocyte phenotypes.