Abstract
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.
Highlights
Cardiac hypertrophy, a putative risk factor of heart failure, is a context-dependent phenomenon [1,2,3] which is regulated by a broad range of biochemical and biomechanical factors interacting in a complex network of signaling pathways [4,5]
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
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
Summary
A putative risk factor of heart failure, is a context-dependent phenomenon [1,2,3] which is regulated by a broad range of biochemical and biomechanical factors interacting in a complex network of signaling pathways [4,5]. Homogenous growth of the heart occurs in the contexts of exercise and pregnancy [6,7]. Recent advances in experimental and computational approaches [22,23,24,25,26,27] facilitate the investigation of whole signaling networks and inference of new causal interactions. Prior knowledge approaches have been implemented successfully by several groups to model large-scale signaling networks [34,35]. Prior knowledge models comprise numerous parameters to train, and only limited experimental data are available for cell signaling networks. Approaches with less required data, e.g. Boolean and Logic-based ODE (LDE), are prevalent for modeling large-scale signaling networks [36,37,38,39]. Researchers prefer to utilize hybrid approaches for modeling large-scale networks [40,41]
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