Quantitative Analysis of TGF-b/Smad Signaling Dynamics

TGF-b/Smad 信号传导动力学的定量分析

• 批准号: 7467828
• 负责人: XUEDONG LIU
• 金额: $ 25.44万
• 依托单位: UNIVERSITY OF COLORADO
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-04-01 至 2012-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7467828
• 关键词: Adverse effects; Biological; Biology; Cells; Cessation of life; Data; Diabetes Mellitus; Disease; Dose; Environment; Event; Gene Expression; Gene Expression Profile; Generations; Goals; Health; Homeostasis; Human; Kinetics; Knowledge; Lead; Ligands; Malignant Neoplasms; Measures; Mediator of activation protein; Modeling; Molecular; Pathway interactions; Phosphorylation; Positioning Attribute; Process; Property; Protein Dephosphorylation; Public Health; Rate; Reaction; Relative (related person); Signal Pathway; Signal Transduction; Signal Transduction Pathway; Smad Proteins; Smad protein; Specificity; System; Systems Biology; Testing; Therapeutic Intervention; Transforming Growth Factors; Variant; antitumor drug; base; cancer cell; cellular transduction; cytokine; human disease; improved; innovation; mathematical model; migration; novel; receptor; research study; response; tool; trafficking

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.

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其环境。正常信号网络的异常可能导致人类疾病，如癌症和糖尿病。转化生长因子（TGF-β）是一种重要的信号通路，调节细胞内稳态的各个方面，包括增殖、分化、迁移和死亡。单个细胞因子如TGF-β如何以细胞环境依赖性方式发挥如此多样的生物学效应是生物学中的一个突出问题。虽然很清楚TGF-β通过细胞内介体Smad蛋白质发出信号以调节基因表达，但关于细胞如何响应不同配体剂量以及配体暴露的变化如何影响Smad信号传导动力学和随后的基因表达的知识相对较少。我们的长期目标是基于分子机制预测细胞对TGF-β信号的反应。本申请的目的是定量评估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性Smad信号动力学。我们假设了TGF-β信号转导的以下原理，据此我们配置了该提案：1）细胞解码配体剂量2）细胞通过设定R-Smad磷酸化速率相对于去磷酸化速率来调节细胞内的信号，和3）Smad寡聚化精细调节信号动力学性质，并作为信号特异性和靶标多样性的机制。我们的提议评估了TGF-β信号传导中不同事件对确定总体信号的贡献，这反过来又决定了所得的基因表达谱和生物学应答。我们将使用整合动力学实验和数学建模的系统生物学方法来研究我们的假设，如以下具体目标所述：1）确定细胞解码TGF-β配体剂量的机制。2)确定R-Smad磷酸化和去磷酸化的速率如何调节Smad信号转导。3)评价Smad低聚反应的动力学性质。TGF-β信号转导是一个动态的过程，在全球细胞调节网络的背景下运作。该网络的系统属性和定量方面的定义很差。我们开发了TGF-_/Smad信号传导的初始数学模型，并且我们完全有能力通过实验和进一步的建模分析来验证这些预测和模型假设。我们期望应用创新的系统生物学方法来研究TGF-β/Smad信号传导将从根本上推进我们在这个主要信号传导网络中的知识。特别是，我们预见使用该模型来预测健康和疾病中对TGF-β的生物反应。鉴于TGF-β信号转导途径经常被靶向用于人类癌细胞中的畸变，对该途径的定量理解对于评估抗肿瘤药物的功效和减轻治疗干预中的不良副作用将是必不可少的。公共卫生相关性： 转化生长因子（TGF-β）是一种重要的信号通路，调节细胞内稳态的各个方面，包括增殖、分化、迁移和死亡。本申请的目的是定量评估TGF-β信号转导动力学，并开发一个全面的数学模型，能够预测健康和疾病中对TGF-β的生物反应。鉴于TGF-β信号转导途径经常被靶向用于人类癌细胞中的畸变，对该途径的定量理解对于评估抗肿瘤药物的功效和减轻治疗干预中的不良副作用将是必不可少的。