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.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM083172-04
Application #
8055548
Study Section
Modeling and Analysis of Biological Systems Study Section (MABS)
Program Officer
Lyster, Peter
Project Start
2008-04-01
Project End
2013-03-31
Budget Start
2011-04-01
Budget End
2013-03-31
Support Year
4
Fiscal Year
2011
Total Cost
$255,592
Indirect Cost
Name
University of Colorado at Boulder
Department
Chemistry
Type
Schools of Arts and Sciences
DUNS #
007431505
City
Boulder
State
CO
Country
United States
Zip Code
80309
Chapnick, Douglas A; Liu, Xuedong (2014) Leader cell positioning drives wound-directed collective migration in TGF?-stimulated epithelial sheets. Mol Biol Cell 25:1586-93
Chapnick, Douglas A; Jacobsen, Jeremy; Liu, Xuedong (2013) The development of a novel high throughput computational tool for studying individual and collective cellular migration. PLoS One 8:e82444
Zi, Zhike; Chapnick, Douglas A; Liu, Xuedong (2012) Dynamics of TGF-?/Smad signaling. FEBS Lett 586:1921-8
Liu, Xuedong; Winey, Mark (2012) The MPS1 family of protein kinases. Annu Rev Biochem 81:561-85
He, Ju; Ye, Jun; Cai, Yongfei et al. (2011) Structure of p300 bound to MEF2 on DNA reveals a mechanism of enhanceosome assembly. Nucleic Acids Res 39:4464-74
Zi, Zhike; Feng, Zipei; Chapnick, Douglas A et al. (2011) Quantitative analysis of transient and sustained transforming growth factor-? signaling dynamics. Mol Syst Biol 7:492
Zhang, Xiaojuan; Yin, Qingqing; Ling, Youguo et al. (2011) Two LXXLL motifs in the N terminus of Mps1 are required for Mps1 nuclear import during G(2)/M transition and sustained spindle checkpoint responses. Cell Cycle 10:2742-50
Chapnick, Douglas A; Liu, Xuedong (2010) Analysis of ligand-dependent nuclear accumulation of Smads in TGF-beta signaling. Methods Mol Biol 647:95-111
Clarke, David C; Liu, Xuedong (2010) Measuring the absolute abundance of the Smad transcription factors using quantitative immunoblotting. Methods Mol Biol 647:357-76
Clarke, David C; Brown, Meredith L; Erickson, Richard A et al. (2009) Transforming growth factor beta depletion is the primary determinant of Smad signaling kinetics. Mol Cell Biol 29:2443-55

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