Receptor Tyrosine Kinase Signaling in the Liver

描述(申请人提供)：受体酪氨酸激酶(RTK)信号对许多细胞过程至关重要，包括增殖、分化、细胞代谢、生存和凋亡。生长因子信号网络的改变可能在癌症的发生中发挥关键作用。尽管我们对参与表皮生长因子受体(EGFR)和胰岛素受体(IR)信号传递的蛋白质和脂肪成分的了解呈爆炸性增长，但对信号网络的反应及其在细胞水平上的控制的完整、定量的图景仍然知之甚少。肝细胞对生长因子的反应是多种蛋白质-蛋白质和蛋白质-脂相互作用、信号蛋白亚细胞定位和磷酸化/去磷酸化反应复杂相互作用的结果。在之前的支持期间，我们的工作展示了一种新的跨学科方法的相当大的潜力，该方法将实验研究与非线性系统分析和交互计算模型相结合，以实现对肝细胞中EGFR和IR信号网络的定量了解。在这项应用中，我们寻求持续的支持，以验证通过计算建模提出的可行假设，确定控制对EGF和胰岛素的不同反应模式的分子和动力学因素，并了解细胞如何以上下文相关的方式解释信号。这一知识将提供一个强大的工具来预测和操纵关键的细胞决定，这些决定决定了细胞的命运，从而可能有助于改进药物治疗的发展。具体目标是：(1)将EGFR信号网络的定量分析扩展到新鲜分离的肝细胞中的多个下游分支，包括RAS/Raf/MEK/ERK通路；(2)通过有条件地表达关键信号蛋白来修饰EGFR网络，使其达到所需的水平，并在模型细胞中系统地量化对EGF诱导的信号转导的影响；(3)将胰岛素受体(IR)信号网络整合到实验和计算分析中，并表征其与肝细胞中EGFR通路的相互作用。

DESCRIPTION (provided by applicant): Receptor tyrosine kinase (RTK) signaling is crucial for many cellular processes, including proliferation, differentiation, cell metabolism, survival and apoptosis. Alterations in growth factor signaling networks can play a pivotal role in carcinogenesis. Despite an explosive growth of our knowledge of protein and lipid components involved in the epidermal growth factor receptor (EGFR) and insulin receptor (IR) signaling, an integrative, quantitative picture of responses of the signaling networks and their control at the cellular level remains poorly understood. The response of hepatocytes to growth factors is the consequence of a complex interplay of multiple protein-protein and protein-lipid interactions, subcellular relocation of signaling proteins and phosphorylation/dephosphorylation reactions. During the previous period of support, our work has demonstrated the considerable potential of a novel cross-disciplinary approach that combines experimental studies with nonlinear systems analysis and interacting computational models to achieve a quantitative understanding of the EGFR and IR signaling networks in hepatocytes. In this application we seek continued support to validate feasible hypotheses suggested by computational modeling, determine the molecular and kinetic factors controlling the different response patterns to EGF and insulin, and understand how cells interpret signals in a context-dependent manner. This knowledge will provide a powerful tool to predict and manipulate critical cellular decisions, which determine cell fate and may, thereby, assist in the development of improved drug therapies. The Specific Aims are: (1) To extend the quantitative analysis of the EGFR signaling network to multiple downstream branches including the Ras/Raf/MEK/ERK pathway in freshly isolated hepatocytes; (2) To modify the EGFR network by the conditional expression of key signaling proteins to desired levels and systematically quantify the impact on EGF-induced signaling in a model cell line; (3) To integrate the insulin receptor (IR) signaling network in the experimental and computational analysis and characterize its interaction with the EGFR pathways in hepatocytes.