Predictive model identifies key network regulators of cardiomyocyte mechano-signaling

Abstract

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.

Using both qualitative and quantitative data in parameter identification for systems biology models

“…Examples of assembled qualitative datasets can be found in refs. 6 , 26 . Next, one must define the objective function f tot (Eq.…”

Concise Review: Reduction of Adverse Cardiac Scarring Facilitates Pluripotent Stem Cell-Based Therapy for Myocardial Infarction

“…Integrins have been shown to bind adaptor proteins such as talin and vinculin to regulate both mechanical and electrical coupling of cardiomyocytes through linking the myofibrils to the ECM . A computational pathway model has been established to reveal key signaling regulators and downstream genes of mechanosensors including integrins, angiotensin type 1 receptor, and calcium channels in the cardiac mechano‐signaling network . Therefore, the response of cardiomyocytes to stiffness is dependent on expression of myofibrillar proteins and the assembly and maturation of myofibrils .…”

Fibroblast Mechanotransduction Network Predicts Targets for Mechano-Adaptive Infarct Therapies

“…New nodes and/or edges were included if at least two independent studies contained evidence of either activation of a node or activation of another species by a node in either cardiac or other fibroblast subtypes. As previously described (Tan et al, 2017;Zeigler et al, 2016), the final network was implemented as logic-based ordinary differential equations in which activity levels of all nodes were modeled as a system of Hill equations, and all activity levels and input stimuli were represented as normalized values between 0 and 1. Logical NOT, AND, and OR gates were used for inhibitory and complex signaling interactions by applying logical operations…”

“…Computational approaches for investigating cell signaling have shown promise for discovering mechanisms-of-action underlying pathological states and drug treatments. Previous models representing cardiac cell signaling as a network of interactions have proven successful in generating detailed insight into cardiomyocyte contraction and hypertrophy as well as fibroblast chemotransduction and various other biological systems (Saez-Rodriguez et al, 2015;Saucerman and McCulloch, 2004;Tan et al, 2017;Zeigler et al, 2020). Previous models have simulated cardiac and adventitial fibroblast activation and pro-fibrotic activity in response to biochemical and biomechanical cues, although limited exploration of mechanotransduction pathways in these models still leave unanswered questions about the role of tension in regional matrix turnover (Wang et al, 2020;Zeigler et al, 2016).…”

“…AUCs less than or equal to -5% were categorized as dampening interactions (tension decreases node response), and AUCs with opposite signs between baseline and tension conditions and exceeded the threshold of ±5% were classified as reversal interactions (tension causes biochemical stimulation to exert the opposite effect on node activity). This threshold has been shown previously to distinguish qualitative changes in activity levels with individual node perturbations (Tan et al, 2017;Wang et al, 2020), and while these studies did not use an integrated AUC metric for comparison, our comparison of AUCs requires greater peak activation levels to meet the threshold, thereby imposing a stricter comparison than these previous studies.…”