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

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.

Bioprinting of Perfusable, Biocompatible Vessel-like Channels with dECM-Based Bioinks and Living Cells

There is a growing interest in the production of bioinks that on the one hand, are biocompatible and, on the other hand, have mechanical properties that allow for the production of stable constructs that can survive for a long time after transplantation. While the selection of the right material is crucial for bioprinting, there is another equally important issue that is currently being extensively researched-the incorporation of the vascular system into the fabricated scaffolds. Therefore, in the following manuscript, we present the results of research on bioink with unique physico-chemical and biological properties. In this article, two methods of seeding cells were tested using bioink B and seeding after bioprinting the whole model. After 2, 5, 8, or 24 h of incubation, the flow medium was used in the tested systems. At the end of the experimental trial, for each time variant, the canals were stored in formaldehyde, and immunohistochemical staining was performed to examine the presence of cells on the canal walls and roof. Cells adhered to both ways of fiber arrangement; however, a parallel bioprint with the 5 h incubation and the intermediate plating of cells resulted in better adhesion efficiency. For this test variant, the percentage of cells that adhered was at least 20% higher than in the other analyzed variants. In addition, it was for this variant that the lowest percentage of viable cells was found that were washed out of the tested model. Importantly, hematoxylin and eosin staining showed that after 8 days of culture, the cells were evenly distributed throughout the canal roof. Our study clearly shows that neovascularization-promoting cells effectively adhere to ECM-based pancreatic bioink. Summarizing the presented results, it was demonstrated that the proposed bioink compositions can be used for bioprinting bionic organs with a vascular system formed by endothelial cells and fibroblasts.

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.

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.

Experiment-based computational model predicts that IL-6 classic and trans-signaling exhibit similar potency in inducing downstream signaling in endothelial cells

Inflammatory cytokine mediated responses are important in the development of many diseases that are associated with angiogenesis. Targeting angiogenesis as a prominent strategy has shown limited effects in many contexts such as cardiovascular diseases and cancer. One potential reason for the unsuccessful outcome is the mutual dependent role between inflammation and angiogenesis. Inflammation-based therapies primarily target inflammatory cytokines such as interleukin-6 (IL-6) in T cells, macrophages, cancer cells, and muscle cells, and there is a limited understanding of how these cytokines act on endothelial cells. Thus, we focus on one of the major inflammatory cytokines, IL-6, mediated intracellular signaling in endothelial cells by developing a detailed computational model. Our model quantitatively characterized the effects of IL-6 classic and trans-signaling in activating the signal transducer and activator of transcription 3 (STAT3), phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt), and mitogen-activated protein kinase (MAPK) signaling to phosphorylate STAT3, extracellular regulated kinase (ERK) and Akt, respectively. We applied the trained and validated experiment-based computational model to characterize the dynamics of phosphorylated STAT3 (pSTAT3), Akt (pAkt), and ERK (pERK) in response to IL-6 classic and/or trans-signaling. The model predicts that IL-6 classic and trans-signaling induced responses are IL-6 and soluble IL-6 receptor (sIL-6R) dose-dependent. Also, IL-6 classic and trans-signaling showed similar potency in inducing downstream signaling; however, trans-signaling induces stronger downstream responses and plays a dominant role in the overall effects from IL-6 due to the in vitro experimental setting of abundant sIL-6R. In addition, both IL-6 and sIL-6R levels regulate signaling strength. Moreover, our model identifies the influential species and kinetic parameters that specifically modulate the downstream inflammatory and/or angiogenic signals, pSTAT3, pAkt, and pERK responses. Overall, the model predicts the effects of IL-6 classic and/or trans-signaling stimulation quantitatively and provides a framework for analyzing and integrating experimental data. More broadly, this model can be utilized to identify potential targets that influence IL-6 mediated signaling in endothelial cells and to study their effects quantitatively in modulating STAT3, Akt, and ERK activation.

The Microenvironment of the Pathogenesis of Cardiac Hypertrophy

Pathological cardiac hypertrophy is a key risk factor for the development of heart failure and predisposes individuals to cardiac arrhythmia and sudden death. While physiological cardiac hypertrophy is adaptive, hypertrophy resulting from conditions comprising hypertension, aortic stenosis, or genetic mutations, such as hypertrophic cardiomyopathy, is maladaptive. Here, we highlight the essential role and reciprocal interactions involving both cardiomyocytes and non-myocardial cells in response to pathological conditions. Prolonged cardiovascular stress causes cardiomyocytes and non-myocardial cells to enter an activated state releasing numerous pro-hypertrophic, pro-fibrotic, and pro-inflammatory mediators such as vasoactive hormones, growth factors, and cytokines, i.e., commencing signaling events that collectively cause cardiac hypertrophy. Fibrotic remodeling is mediated by cardiac fibroblasts as the central players, but also endothelial cells and resident and infiltrating immune cells enhance these processes. Many of these hypertrophic mediators are now being integrated into computational models that provide system-level insights and will help to translate our knowledge into new pharmacological targets. This perspective article summarizes the last decades' advances in cardiac hypertrophy research and discusses the herein-involved complex myocardial microenvironment and signaling components.

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.

User-Controlled 4D Biomaterial Degradation with Substrate-Selective Sortase Transpeptidases for Single-Cell Biology

Stimuli-responsive biomaterials show great promise for modeling disease dynamics ex vivo with spatiotemporal control over the cellular microenvironment. However, harvesting cells from such materials for downstream analysis without perturbing their state remains an outstanding challenge in 3/4-dimensional (3D/4D) culture and tissue engineering. In this manuscript, a fully enzymatic strategy for hydrogel degradation that affords spatiotemporal control over cell release while maintaining cytocompatibility is introduced. Exploiting engineered variants of the sortase transpeptidase evolved to recognize and selectively cleave distinct peptide sequences largely absent from the mammalian proteome, many limitations implicit to state-of-the-art methods to liberate cells from gels are sidestepped. It is demonstrated that evolved sortase exposure has minimal impact on the global transcriptome of primary mammalian cells and that proteolytic cleavage proceeds with high specificity; incorporation of substrate sequences within hydrogel crosslinkers permits rapid and selective cell recovery with high viability. In composite multimaterial hydrogels, it is shown that sequential degradation of hydrogel layers enables highly specific retrieval of single-cell suspensions for phenotypic analysis. It is expected that the high bioorthogonality and substrate selectivity of the evolved sortases will lead to their broad adoption as an enzymatic material dissociation cue and that their multiplexed use will enable newfound studies in 4D cell culture.

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.

Post-myocardial infarction fibrosis: Pathophysiology, examination, and intervention

Cardiac fibrosis plays an indispensable role in cardiac tissue homeostasis and repair after myocardial infarction (MI). The cardiac fibroblast-to-myofibroblast differentiation and extracellular matrix collagen deposition are the hallmarks of cardiac fibrosis, which are modulated by multiple signaling pathways and various types of cells in time-dependent manners. Our understanding of the development of cardiac fibrosis after MI has evolved in basic and clinical researches, and the regulation of fibrotic remodeling may facilitate novel diagnostic and therapeutic strategies, and finally improve outcomes. Here, we aim to elaborate pathophysiology, examination and intervention of cardiac fibrosis after MI.

KLF9 deficiency protects the heart from inflammatory injury triggered by myocardial infarction

The excessive inflammatory response induced by myocardial infarction exacerbates heart injury and leads to the development of heart failure. Recent studies have confirmed the involvement of multiple transcription factors in the modulation of cardiovascular disease processes. However, the role of KLF9 in the inflammatory response induced by cardiovascular diseases including myocardial infarction remains unclear. Here, we found that the expression of KLF9 significantly increased during myocardial infarction. Besides, we also detected high expression of KLF9 in infiltrated macrophages after myocardial infarction. Our functional studies revealed that KLF9 deficiency prevented cardiac function and adverse cardiac remodeling. Furthermore, the downregulation of KLF9 inhibited the activation of NF-κB and MAPK signaling, leading to the suppression of inflammatory responses of macrophages triggered by myocardial infarction. Mechanistically, KLF9 was directly bound to the TLR2 promoter to enhance its expression, subsequently promoting the activation of inflammation-related signaling pathways. Our results suggested that KLF9 is a pro-inflammatory transcription factor in macrophages and targeting KLF9 may be a novel therapeutic strategy for ischemic heart disease.

NetAct: a computational platform to construct core transcription factor regulatory networks using gene activity

A major question in systems biology is how to identify the core gene regulatory circuit that governs the decision-making of a biological process. Here, we develop a computational platform, named NetAct, for constructing core transcription factor regulatory networks using both transcriptomics data and literature-based transcription factor-target databases. NetAct robustly infers regulators' activity using target expression, constructs networks based on transcriptional activity, and integrates mathematical modeling for validation. Our in silico benchmark test shows that NetAct outperforms existing algorithms in inferring transcriptional activity and gene networks. We illustrate the application of NetAct to model networks driving TGF-β-induced epithelial-mesenchymal transition and macrophage polarization.

On modeling the multiscale mechanobiology of soft tissues: Challenges and progress

Tissues grow and remodel in response to mechanical cues, extracellular and intracellular signals experienced through various biological events, from the developing embryo to disease and aging. The macroscale response of soft tissues is typically nonlinear, viscoelastic anisotropic, and often emerges from the hierarchical structure of tissues, primarily their biopolymer fiber networks at the microscale. The adaptation to mechanical cues is likewise a multiscale phenomenon. Cell mechanobiology, the ability of cells to transform mechanical inputs into chemical signaling inside the cell, and subsequent regulation of cellular behavior through intra- and inter-cellular signaling networks, is the key coupling at the microscale between the mechanical cues and the mechanical adaptation seen macroscopically. To fully understand mechanics of tissues in growth and remodeling as observed at the tissue level, multiscale models of tissue mechanobiology are essential. In this review, we summarize the state-of-the art modeling tools of soft tissues at both scales, the tissue level response, and the cell scale mechanobiology models. To help the interested reader become more familiar with these modeling frameworks, we also show representative examples. Our aim here is to bring together scientists from different disciplines and enable the future leap in multiscale modeling of tissue mechanobiology.

Properties and Functions of Fibroblasts and Myofibroblasts in Myocardial Infarction

The adult mammalian heart contains abundant interstitial and perivascular fibroblasts that expand following injury and play a reparative role but also contribute to maladaptive fibrotic remodeling. Following myocardial infarction, cardiac fibroblasts undergo dynamic phenotypic transitions, contributing to the regulation of inflammatory, reparative, and angiogenic responses. This review manuscript discusses the mechanisms of regulation, roles and fate of fibroblasts in the infarcted heart. During the inflammatory phase of infarct healing, the release of alarmins by necrotic cells promotes a pro-inflammatory and matrix-degrading fibroblast phenotype that may contribute to leukocyte recruitment. The clearance of dead cells and matrix debris from the infarct stimulates anti-inflammatory pathways and activates transforming growth factor (TGF)-β cascades, resulting in the conversion of fibroblasts to α-smooth muscle actin (α-SMA)-expressing myofibroblasts. Activated myofibroblasts secrete large amounts of matrix proteins and form a collagen-based scar that protects the infarcted ventricle from catastrophic complications, such as cardiac rupture. Moreover, infarct fibroblasts may also contribute to cardiac repair by stimulating angiogenesis. During scar maturation, fibroblasts disassemble α-SMA+ stress fibers and convert to specialized cells that may serve in scar maintenance. The prolonged activation of fibroblasts and myofibroblasts in the infarct border zone and in the remote remodeling myocardium may contribute to adverse remodeling and to the pathogenesis of heart failure. In addition to their phenotypic plasticity, fibroblasts exhibit remarkable heterogeneity. Subsets with distinct phenotypic profiles may be responsible for the wide range of functions of fibroblast populations in infarcted and remodeling hearts.

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.

Network modeling predicts personalized gene expression and drug responses in valve myofibroblasts cultured with patient sera

Aortic valve stenosis (AVS) patients experience pathogenic valve leaflet stiffening due to excessive extracellular matrix (ECM) remodeling. Numerous microenvironmental cues influence pathogenic expression of ECM remodeling genes in tissue-resident valvular myofibroblasts, and the regulation of complex myofibroblast signaling networks depends on patient-specific extracellular factors. Here, we combined a manually curated myofibroblast signaling network with a data-driven transcription factor network to predict patient-specific myofibroblast gene expression signatures and drug responses. Using transcriptomic data from myofibroblasts cultured with AVS patient sera, we produced a large-scale, logic-gated differential equation model in which 11 biochemical and biomechanical signals were transduced via a network of 334 signaling and transcription reactions to accurately predict the expression of 27 fibrosis-related genes. Correlations were found between personalized model-predicted gene expression and AVS patient echocardiography data, suggesting links between fibrosis-related signaling and patient-specific AVS severity. Further, global network perturbation analyses revealed signaling molecules with the most influence over network-wide activity, including endothelin 1 (ET1), interleukin 6 (IL6), and transforming growth factor β (TGFβ), along with downstream mediators c-Jun N-terminal kinase (JNK), signal transducer and activator of transcription (STAT), and reactive oxygen species (ROS). Lastly, we performed virtual drug screening to identify patient-specific drug responses, which were experimentally validated via fibrotic gene expression measurements in valvular interstitial cells cultured with AVS patient sera and treated with or without bosentan-a clinically approved ET1 receptor inhibitor. In sum, our work advances the ability of computational approaches to provide a mechanistic basis for clinical decisions including patient stratification and personalized drug screening.

Fibroblast mechanotransduction network predicts targets for mechano-adaptive infarct therapies

Regional control of fibrosis after myocardial infarction is critical for maintaining structural integrity in the infarct while preventing collagen accumulation in non-infarcted areas. Cardiac fibroblasts modulate matrix turnover in response to biochemical and biomechanical cues, but the complex interactions between signaling pathways confound efforts to develop therapies for regional scar formation. We employed a logic-based ordinary differential equation model of fibroblast mechano-chemo signal transduction to predict matrix protein expression in response to canonical biochemical stimuli and mechanical tension. Functional analysis of mechano-chemo interactions showed extensive pathway crosstalk with tension amplifying, dampening, or reversing responses to biochemical stimuli. Comprehensive drug target screens identified 13 mechano-adaptive therapies that promote matrix accumulation in regions where it is needed and reduce matrix levels in regions where it is not needed. Our predictions suggest that mechano-chemo interactions likely mediate cell behavior across many tissues and demonstrate the utility of multi-pathway signaling networks in discovering therapies for context-specific disease states.

Systems biology of angiogenesis signaling: Computational models and omics

Angiogenesis is a highly regulated multiscale process that involves a plethora of cells, their cellular signal transduction, activation, proliferation, differentiation, as well as their intercellular communication. The coordinated execution and integration of such complex signaling programs is critical for physiological angiogenesis to take place in normal growth, development, exercise, and wound healing, while its dysregulation is critically linked to many major human diseases such as cancer, cardiovascular diseases, and ocular disorders; it is also crucial in regenerative medicine. Although huge efforts have been devoted to drug development for these diseases by investigation of angiogenesis‐targeted therapies, only a few therapeutics and targets have proved effective in humans due to the innate multiscale complexity and nonlinearity in the process of angiogenic signaling. As a promising approach that can help better address this challenge, systems biology modeling allows the integration of knowledge across studies and scales and provides a powerful means to mechanistically elucidate and connect the individual molecular and cellular signaling components that function in concert to regulate angiogenesis. In this review, we summarize and discuss how systems biology modeling studies, at the pathway‐, cell‐, tissue‐, and whole body‐levels, have advanced our understanding of signaling in angiogenesis and thereby delivered new translational insights for human diseases.

This article is categorized under:

Cardiovascular Diseases > Computational Models

Cancer > Computational Models

Does the Heart Want What It Wants? A Case for Self-Adapting, Mechano-Sensitive Therapies After Infarction

There is a critical need for interventions to control the development and remodeling of scar tissue after myocardial infarction. A significant hurdle to fibrosis-related therapy is presented by the complex spatial needs of the infarcted ventricle, namely that collagenous buildup is beneficial in the ischemic zone but detrimental in the border and remote zones. As a new, alternative approach, we present a case to develop self-adapting, mechano-sensitive drug targets in order to leverage local, microenvironmental mechanics to modulate a therapy's pharmacologic effect. Such approaches could provide self-tuning control to either promote fibrosis or reduce fibrosis only when and where it is beneficial to do so.

Using models to advance medicine: mathematical modeling of post-myocardial infarction left ventricular remodeling

The heart is an organ with limited capacity for regeneration and repair. The irreversible cell death and corresponding diminished ability of the heart to repair after myocardial infarction (MI), is a leading cause of morbidity and mortality worldwide. In this paper, a new mathematical model is presented to study the left ventricular (LV) remodeling and associated events after MI. The model accurately describes and predicts the interactions among heart cells and the immune system post-MI in the absence of medical interventions. The resulting system of nonlinear ordinary differential equations is studied both analytically and numerically in order to demonstrate the functionality and performance of the new model. To the best of our knowledge, this model is the only one of its kind to consider and correctly apply all of the known factors in diseased heart LV modeling. This model has the potential to provide researchers with a predictive computational tool to better understand the MI pathology and develop various cell-based treatment options, with benefits of lowering the cost and reducing the development time.

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.

Fibroblasts: The arbiters of extracellular matrix remodeling

Extracellular matrix (ECM) is the foundation on which all cells and organs converge to orchestrate normal physiological functions. In the setting of pathology, the ECM is modified to incorporate additional roles, with modifications including turnover of existing ECM and deposition of new ECM. The fibroblast is center stage in coordinating both normal tissue homeostasis and response to disease. Understanding how fibroblasts work under normal conditions and are activated in response to injury or stress will provide mechanistic insight that triggers discovery of new therapeutic treatments for a wide range of disease. We highlight here fibroblast roles in the cancer, lung, and heart as example systems where fibroblasts are major contributors to homeostasis and pathology.

Regulators of cardiac fibroblast cell state

Fibroblasts are the primary regulator of cardiac extracellular matrix (ECM). In response to disease stimuli cardiac fibroblasts undergo cell state transitions to a myofibroblast phenotype, which underlies the fibrotic response in the heart and other organs. Identifying regulators of fibroblast state transitions would inform which pathways could be therapeutically modulated to tactically control maladaptive extracellular matrix remodeling. Indeed, a deeper understanding of fibroblast cell state and plasticity is necessary for controlling its fate for therapeutic benefit. p38 mitogen activated protein kinase (MAPK), which is part of the noncanonical transforming growth factor β (TGFβ) pathway, is a central regulator of fibroblast to myofibroblast cell state transitions that is activated by chemical and mechanical stress signals. Fibroblast intrinsic signaling, local and global cardiac mechanics, and multicellular interactions individually and synergistically impact these state transitions and hence the ECM, which will be reviewed here in the context of cardiac fibrosis.

Cardiac fibroblast activation during myocardial infarction wound healing: Fibroblast polarization after MI

Cardiac wound healing after myocardial infarction (MI) evolves from pro-inflammatory to anti-inflammatory to reparative responses, and the cardiac fibroblast is a central player during the entire transition. The fibroblast mirrors changes seen in the left ventricle infarct by undergoing a continuum of polarization phenotypes that follow pro-inflammatory, anti-inflammatory, and pro-scar producing profiles. The development of each phenotype transition is contingent upon the MI environment into which the fibroblast enters. In this mini-review, we summarize our current knowledge regarding cardiac fibroblast activation during MI and highlight key areas where gaps remain.