Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America.
Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, Virginia, United States of America.
PLoS Comput Biol. 2020 Dec 18;16(12):e1008490. doi: 10.1371/journal.pcbi.1008490. eCollection 2020 Dec.
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.
心肌肥厚是一种依赖于情境的现象,其中许多生化和生物力学因素通过复杂的大规模信号网络来调节心肌生长。尽管许多研究已经研究了肥厚的信号通路,但对于整个网络的肥厚信号以及该网络如何以依赖于情境的方式发挥作用,人们的了解较少。在这里,我们开发了一种系统方法,CLASSED(基于情境的逻辑信号网络开发),根据情境特定数据修改大规模信号模型,并确定调节情境特异性反应的主要反应和新的串扰。CLASSED 涉及四个连续阶段,其中自动化验证模块作为核心,该模块根据交互图构建基于逻辑的 ODE 模型,并输出模型验证百分比。通过默认参数的估计、分类定性验证、混合 Morris-Sobol 全局敏感性分析以及发现缺失的依赖于情境的串扰来开发情境特定模型。将该管道应用于具有情境特定数据的我们先前知识的肥厚网络中,揭示了关键的信号反应,这些反应明显调节了细胞对异丙肾上腺素、苯肾上腺素、血管紧张素 II 和拉伸的反应。此外,我们使用 CLASSED 开发了一种β-肾上腺素能心脏肥厚的情境特定模型。该模型预测了 ISO 特异性情境中钙/钙调蛋白依赖性途径和 Ras 上游信号之间的新串扰。心肌细胞中的实验验证了模型对 CaMKII-Gβγ 和 CaN-Gβγ 相互作用在介导 ISO 特异性情境中肥厚信号中的作用的预测,并揭示了心脏成肌细胞和成纤维细胞之间 ERK1/2 磷酸化幅度和易位的差异。CLASSED 是一种开发情境特定的大规模信号网络的系统方法,深入了解了β-肾上腺素能心脏肥厚中的新发现的串扰。