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

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.

Virtual drug screen reveals context-dependent inhibition of cardiomyocyte hypertrophy

“…To simulate the effects of drugs on cardiac hypertrophy, we implemented drug activity into a logic-based signaling network. This network model was previously manually curated to represent signaling molecules as nodes and directed interactions as edges 13,23 . Here, drugs that target the nodes of this network were identified from the DrugBank database ( Figure 1A ).…”

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

“…To answer this question, we expanded the hypertrophy signaling network model by incorporating the candidate edges in Table 4 . At first, candidate edges were added individually, evaluating the performance of the network against a battery of 450 experiments from the prior literature (14). Out of the fifteen candidate edges added to the network, only three individually improved the validation of the model ( Figure 6a ).…”

“…At first, candidate edges were added individually, evaluating the performance of the network against a battery of 450 experiments from the prior literature(14). Out of the…”

“…Edges were added individually to create model variations. These variations were used to compare simulations against 450 known experimental outcomes (14), as performed previously. If the validation of the model variations were at least as good as the validation of the original network model then those corresponding edges were considered high performing edges.…”

Mechanisms underlying divergent relationships between Ca2+ and YAP/TAZ signalling

“…We calculated the Morris sensitivity measures including the Morris index (μ * ) and standard deviation (σ ) for each parameter in the model. The Morris elementary effects method is a proper screening method used to determine model output sensitivity to variations in its parameter in the case of large-scale network models with numerous parameters (Khalilimeybodi et al, 2020;. To conduct the sensitivity analysis, we first generated Morris sampling data through the EE sensitivity package developed by Khare et al (2015) with the 'Sampling for Uniformity' strategy, oversampling size of 300, input factor level of 16 and trajectory number of 16.…”