A computational model of cardiac fibroblast signaling predicts context-dependent drivers of myofibroblast differentiation.
A computational model of cardiac fibroblast signaling predicts context-dependent drivers of myofibroblast differentiation.
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DOI：
10.1016/j.yjmcc.2016.03.008
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发表时间：
2016-05
影响因子：
5
通讯作者：
Saucerman JJ
中科院分区：
文献类型：
--
作者：
Zeigler AC;Richardson WJ;Holmes JW;Saucerman JJ
心脏成纤维细胞支持心脏功能，成纤维细胞信号异常可导致纤维化和心功能不全。然而，在心脏损伤的复杂信号环境中，信号分子如何驱动肌成纤维细胞分化和纤维化仍不清楚。我们开发了一个心脏成纤维细胞信号传递的大规模计算模型，以确定不同信号背景下纤维化的调节因素。该模型网络集成了10条信号通路，包括91个节点和134个反应，对80%的独立实验进行了正确预测。该模型预测了关键的纤维化信号调节因子(如活性氧、组织生长因子β(转化生长因子β)受体)，其功能因细胞外环境而异。我们描述了网络结构与功能的关系，确定了功能模块，并预测了转化生长因子β和机械信号之间的串扰，这在成人心脏成纤维细胞中得到了实验验证。这项研究提供了一个系统框架，用于预测不同信号背景下成纤维细胞信号的关键调控因子。
Cardiac fibroblasts support heart function, and aberrant fibroblast signaling can lead to fibrosis and cardiac dysfunction. Yet how signaling molecules drive myofibroblast differentiation and fibrosis in the complex signaling environment of cardiac injury remains unclear. We developed a large-scale computational model of cardiac fibroblast signaling in order to identify regulators of fibrosis under diverse signaling contexts. The model network integrates 10 signaling pathways, including 91 nodes and 134 reactions, and it correctly predicted 80% of independent previous experiments. The model predicted key fibrotic signaling regulators (e.g. reactive oxygen species, tissue growth factor β (TGFβ) receptor), whose function varied depending on the extracellular environment. We characterized how network structure relates to function, identified functional modules, and predicted cross-talk between TGFβ and mechanical signaling, which was validated experimentally in adult cardiac fibroblasts. This study provides a systems framework for predicting key regulators of fibroblast signaling across diverse signaling contexts.