Quantitative Analysis of TGF-b/Smad Signaling Dynamics

描述(由申请人提供)：细胞主要通过信号转导网络的活动来适应环境。正常信号网络的异常可能导致癌症和糖尿病等人类疾病。转化生长因子-β是一条重要的信号通路，调节细胞内稳态的多个方面，包括增殖、分化、迁移和死亡。像转化生长因子-β这样的单一细胞因子如何以依赖于细胞的方式发挥如此多样化的生物学效应是生物学中的一个突出问题。虽然很明显，转化生长因子-β通过细胞内介质Smad蛋白来调节基因表达，但对于细胞如何对不同的配体剂量做出反应，以及配体暴露的变化如何影响Smad信号动力学和随后的基因表达，人们知之甚少。我们的长期目标是基于分子机制预测细胞对转化生长因子-β信号的反应。这项应用的目的是定量评估Smad信号动力学，并开发一个全面的数学模型，能够预测系统水平的配体剂量依赖的Smad信号动力学。我们假设转化生长因子-β信号转导的原理如下：1)细胞通过T_RII受体转运依赖的机制解码配基剂量(每个细胞的转化生长因子-α分子)，2)细胞通过设定R-Smad磷酸化速率相对于去磷酸化速率在细胞内转导信号，3)Smad寡聚微调信号动力学特性，作为信号特异性和靶点多样性的机制。我们的建议评估了转化生长因子-β信号中的不同事件对决定整体信号的贡献，而总信号又决定了所产生的基因表达谱和生物反应。我们将使用系统生物学的方法来研究我们的假设，该方法结合了动力学实验和数学建模，具体目标如下：1)确定细胞解码转化生长因子配体剂量的机制。2)确定R-Smad的磷酸化和去磷酸化速率如何调节Smad信号转导。3)评价Smad齐聚反应的动力学性质。转化生长因子-β信号转导是一个在全球细胞调控网络中运行的动态过程。这个网络的系统属性和量化方面的定义很差。我们建立了一个关于转化生长因子-β/Smad信号的初步数学模型，并通过实验和进一步的模型分析，很好地验证了这些预测和模型假设。我们希望应用创新的系统生物学方法来研究转化生长因子-β/Smad信号，将从根本上提高我们对这一主要信号网络的了解。特别是，我们预计将使用该模型来预测在健康和疾病中对转化生长因子-β的生物学反应。鉴于转化生长因子-β信号转导通路经常针对人类癌细胞的异常，对该通路的定量了解对于评估抗肿瘤药物的疗效和减轻治疗干预中的不良副作用至关重要。公共卫生相关性： 转化生长因子-β是一条重要的信号通路，调节细胞内稳态的多个方面，包括增殖、分化、迁移和死亡。这项应用的目的是定量评估转化生长因子-β信号动力学，并开发一个全面的数学模型，能够预测健康和疾病中对转化生长因子-β的生物反应。鉴于转化生长因子-β信号转导通路经常针对人类癌细胞的异常，对该通路的定量了解对于评估抗肿瘤药物的疗效和减轻治疗干预中的不良副作用至关重要。

DESCRIPTION (provided by applicant): Cells adapt to their environment largely through the activities of signal transduction networks. Aberrations of normal signaling networks can lead to human diseases such as cancer and diabetes. Transforming Growth Factor-_ (TGF-_) is a prominent signaling pathway that regulates diverse aspects of cellular homeostasis including proliferation, differentiation, migration, and death. How a single cytokine like TGF-_ can exert such diverse biological effects in a cell context- dependent manner is an outstanding question in biology. While it is clear that TGF-_ signals through the intracellular mediator Smad proteins to regulate gene expression, relatively little is known about how cells respond to different ligand doses and how variations in ligand exposure impact Smad signaling dynamics and subsequent gene expression. Our long-term goal is to predict cellular responses to TGF-_ signaling based on molecular mechanisms. The objective of this application is to quantitatively assess Smad signaling dynamics and develop a comprehensive mathematical model that is able to predict systems-level ligand dose-dependent Smad signaling dynamics. We hypothesize the following principles of TGF-_ signal transduction, upon which we have configured the proposal: 1) Cells decode the ligand dose (TGF-_ molecules per cell) through a T_RII receptor trafficking-dependent mechanism, 2) Cells transduce the signal inside the cell by setting the rates of R-Smad phosphorylation relative to the rate of dephosphorylation, and 3) Smad oligomerization fine-tunes the signal dynamic properties and serves as a mechanism for signal specificity and target diversity. Our proposal evaluates the contribution of the diverse events in TGF-_ signaling to determining the overall signal, which in turn determines the resulting gene expression profile and biological response. We will investigate our hypothesis using a systems biology approach that integrates kinetic experiments and mathematical modeling, as described in the following specific aims:1) Determine the mechanism by which cells decode the TGF-_ ligand dose. 2) Determine how the rates of R-Smad phosphorylation and dephosphorylation regulate Smad signal transduction. 3) Evaluate the dynamic properties of Smad oligomerization. TGF-_ signaling is a dynamic process that operates in the context of global cellular regulatory network. The system properties and quantitative aspects of this network are poorly defined. We developed an initial mathematical model for TGF-_/Smad signaling and we are well positioned to verify these predictions and the model assumptions through experiment and further modeling analysis. We expect that applying the innovative systems biology approach to study TGF-_/Smad signaling will fundamentally advance our knowledge in this major signaling network. In particular, we foresee using this model to predict biological responses to TGF-_ in health and disease. Given that the TGF-_ signal transduction pathway is frequently targeted for aberrations in human cancer cells, a quantitative understanding of the pathway will be essential for evaluating the efficacy of antitumor drugs and mitigating undesirable side effects in therapeutic interventions. PUBLIC HEALTH RELEVANCE: Transforming Growth Factor-_ (TGF-_) is a prominent signaling pathway that regulates diverse aspects of cellular homeostasis including proliferation, differentiation, migration, and death. The objective of this application is to quantitatively assess TGF-_ signaling dynamics and develop a comprehensive mathematical model that is able to predict biological responses to TGF-_ in health and disease. Given that the TGF-_ signal transduction pathway is frequently targeted for aberrations in human cancer cells, a quantitative understanding of the pathway will be essential for evaluating the efficacy of antitumor drugs and mitigating undesirable side effects in therapeutic interventions.