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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. [Display omitted] • Cerebrovascular endothelial cells play a central role in many CNS pathologies. • A computational model of cerebrovascular endothelial signaling was implemented. • Highly influential molecular signaling nodes were identified for brain endothelium. • Therapeutic targets were predicted for stroke, glioma, and Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Abstract: Cardiac hypertrophy is controlled by a dense signaling network with many pathways associated with cardiac myocyte growth. New large scale methodology is required to quantitatively characterize the pathways that distinguish reversible forms of hypertrophy from irreversible forms that lead to heart failure. Our automated image acquisition method records 5×5 mosaic images of fluorescent protein-labeled cardiac myocytes within each well of a 96-well plate using an automated stage and focus. Post-processing algorithms automatically identify cell edges, quantify cell phenotypes, and track cells. We uniquely applied our imaging platform to study hypertrophy reversibility in a scalable cell model. Cell area changes after washout of a dose response to the α-adrenergic receptor (αAR) agonist phenylephrine (PE) showed that hypertrophy reverses at low but not high levels of α-adrenergic signaling: a reversibility delay. Perturbations with specialized αAR antagonists, a mathematical model, and live imaging of αAR localization identify the mechanism for this reversibility delay: ligand trapping with internalized PE acting on intracellular αAR''s. [Copyright &y& Elsevier]

Abstract: Cardiac hypertrophy is controlled by a dense signaling network with many pathways associated with cardiac myocyte growth. New large scale methodology is required to quantitatively characterize the pathways that distinguish reversible forms of hypertrophy from irreversible forms that lead to heart failure. Our automated image acquisition method records 5×5 mosaic images of fluorescent protein-labeled cardiac myocytes within each well of a 96-well plate using an automated stage and focus. Post-processing algorithms automatically identify cell edges, quantify cell phenotypes, and track cells. We uniquely applied our imaging platform to study hypertrophy reversibility in a scalable cell model. Cell area changes after washout of a dose response to the α-adrenergic receptor (αAR) agonist phenylephrine (PE) showed that hypertrophy reverses at low but not high levels of α-adrenergic signaling: a reversibility delay. Perturbations with specialized αAR antagonists, a mathematical model, and live imaging of αAR localization identify the mechanism for this reversibility delay: ligand trapping with internalized PE acting on intracellular αAR''s. [Copyright &y& Elsevier]

Abstract: Sympathetic stimulation enhances cardiac contractility by stimulating β-adrenergic signaling and protein kinase A (PKA). Recently, phospholemman (PLM) has emerged as an important PKA substrate capable of regulating cytosolic Ca2+ transients. However, it remains unclear how PLM contributes to β-adrenergic inotropy. Here we developed a computational model to clarify PLM''s role in the β-adrenergic signaling response. Simulating Na+ and sarcoplasmic reticulum (SR) Ca2+ clamps, we identify an effect of PLM phosphorylation on SR unloading as the key mechanism by which PLM confers cytosolic Ca2+ adaptation to long-term β-adrenergic receptor (β-AR) stimulation. Moreover, we show that phospholamban (PLB) opposes and overtakes these actions on SR load, forming a negative feed-forward loop in the β-adrenergic signaling cascade. This network motif dominates the negative feedback conferred by β-AR desensitization and accelerates β-AR-induced inotropy. Model analysis therefore unmasks key actions of PLM phosphorylation during β-adrenergic signaling, indicating that PLM is a critical component of the fight-or-flight response. [Copyright &y& Elsevier]

Abstract: Calmodulin (CaM) acts as a common Ca2+ sensor for many signaling pathways, transducing local Ca2+ signals into specific cellular outcomes. Many of CaM''s signaling functions can be explained by its unique biochemical properties, including high and low affinity Ca2+-binding sites with slow and fast kinetics, respectively. CaM is expected to have a limited spatial range of action, emphasizing its role in local Ca2+ signaling. Interactions with target proteins further fine-tune CaM signal transduction. Here, we focus on only three specific cellular targets for CaM signaling in cardiac myocytes: the L-type Ca2+ channel, the ryanodine receptor, and the IP3 receptor. We elaborate a working hypothesis that each channel is regulated by two distinct functional populations of CaM: dedicated CaM and promiscuous CaM. Dedicated CaM is typically tethered to each channel and directly regulates channel activity. In addition, a local pool of promiscuous CaM appears poised to sense local Ca2+ signals and trigger downstream pathways such as Ca2+/CaM dependent-protein kinase II and calcineurin. Understanding how promiscuous CaM coordinates multiple distinct signaling pathways remains a challenge, but is aided by the use of mathematical modeling and a new generation of fluorescent biosensors. This article is part of a special issue entitled "Local Signaling in Myocytes." [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

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. [Display omitted] • HCM/DCM signaling network model links mutations to cardiomyocyte morphological changes. • The computational model predicts higher efficiency for combination pharmacotherapies in familial cardiomyopathy. • Experiments on iPSC-CMs showed rescue of HCM phenotype by combined inhibition of ERK1/2 and PI3K-AKT predicted by the model. [ABSTRACT FROM AUTHOR]

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. • Computational model of cardiomyocyte apoptosis signaling network. • The model is validated against a wide range of experimental data. • Virtual screen predicts regulators of tyrosine kinase inhibitor-induced apoptosis. • Inhibition of ROS is predicted to block apoptosis from tyrosine kinase inhibitors. [ABSTRACT FROM AUTHOR]

Abstract 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. Graphical abstract Unlabelled Image Highlights • Novel automated hybrid live/fixed assay for cardiomyocyte proliferation. • Assay distinguishes proliferation from multinucleation and polyploidization. • Assay applied for high-content screening of compounds. • L-type calcium channel blockers induce cardiomyocyte proliferation. [ABSTRACT FROM AUTHOR]

Abstract 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. Highlights • Knockdown of Atg2, Atg9 or Atg18 in muscle and heart leads to impaired negative geotaxis and a short lifespan in Drosophila. • Knockdown of Atg2, Atg18 or Atg9 causes age-dependent impairment of cardiac structure and function. • Atg2, Atg18 and Atg9 are required in the heart to maintain mitochondrial structure and Drp1 and Mfn2 mRNA expression. • Atg2, Atg18 and Atg9 are required for maintenance of mitochondrial structure and density in IFM. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

β-Adrenergic signaling is spatiotemporally heterogeneous in the cardiac myocyte, conferring exquisite control to sympathetic stimulation. Such heterogeneity drives the formation of protein kinase A (PKA) signaling microdomains, which regulate Ca2+ handling and contractility. Here, we test the hypothesis that the nucleus independently comprises a PKA signaling microdomain regulating myocyte hypertrophy. Spatially-targeted FRET reporters for PKA activity identified slower PKA activation and lower isoproterenol sensitivity in the nucleus (t50 =10.6±0.7min; EC50 =89.0nmol/L) than in the cytosol (t50 =3.71±0.25min; EC50 =1.22nmol/L). These differences were not explained by cAMP or AKAP-based compartmentation. A computational model of cytosolic and nuclear PKA activity was developed and predicted that differences in nuclear PKA dynamics and magnitude are regulated by slow PKA catalytic subunit diffusion, while differences in isoproterenol sensitivity are regulated by nuclear expression of protein kinase inhibitor (PKI). These were validated by FRET and immunofluorescence. The model also predicted differential phosphorylation of PKA substrates regulating cell contractility and hypertrophy. Ca2+ and cell hypertrophy measurements validated these predictions and identified higher isoproterenol sensitivity for contractile enhancements (EC50 =1.84nmol/L) over cell hypertrophy (EC50 =85.9nmol/L). Over-expression of spatially targeted PKA catalytic subunit to the cytosol or nucleus enhanced contractile and hypertrophic responses, respectively. We conclude that restricted PKA catalytic subunit diffusion is an important PKA compartmentation mechanism and the nucleus comprises a novel PKA signaling microdomain, insulating hypertrophic from contractile β-adrenergic signaling responses. [ABSTRACT FROM AUTHOR]

Abstract: Cardiac hypertrophy is controlled by a complex signal transduction and gene regulatory network, containing multiple layers of crosstalk and feedback. While numerous individual components of this network have been identified, understanding how these elements are coordinated to regulate heart growth remains a challenge. Past approaches to measure cardiac myocyte hypertrophy have been manual and often qualitative, hindering the ability to systematically characterize the network''s higher-order control structure and identify therapeutic targets. Here, we develop and validate an automated image analysis approach for objectively quantifying multiple hypertrophic phenotypes from immunofluorescence images. This approach incorporates cardiac myocyte-specific optimizations and provides quantitative measures of myocyte size, elongation, circularity, sarcomeric organization, and cell–cell contact. As a proof-of-concept, we examined the hypertrophic response to α-adrenergic, β-adrenergic, tumor necrosis factor (TNFα), insulin-like growth factor-1 (IGF-1), and fetal bovine serum pathways. While all five hypertrophic pathways increased myocyte size, other hypertrophic metrics were differentially regulated, forming a distinct phenotype signature for each pathway. Sarcomeric organization was uniquely enhanced by α-adrenergic signaling. TNFα and α-adrenergic pathways markedly decreased cell circularity due to increased myocyte protrusion. Surprisingly, adrenergic and IGF-1 pathways differentially regulated myocyte–myocyte contact, potentially forming a feed-forward loop that regulates hypertrophy. Automated image analysis unlocks a range of new quantitative phenotypic data, aiding dissection of the complex hypertrophic signaling network and enabling myocyte-based high-content drug screening. [Copyright &y& Elsevier]