The Self-Limiting Dynamics of TGF-β Signaling In Silico and In Vitro, with Negative Feedback through PPM1A Upregulation
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Wang-2014-The Self-Limiting Dy.pdf
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Author(s)
Wang, Junjie
Tucker-Kellogg, Lisa
Ng, Inn Chuan
Jia, Ruirui
Thiagarajan, P. S.
White, Jacob K.
Yu, Hanry
Date Issued
June 2014
Journal
PLoS Computational Biology
Publisher
Public Library of Science
Citation
Wang, Junjie, Lisa Tucker-Kellogg, Inn Chuan Ng, Ruirui Jia, P. S. Thiagarajan, Jacob K. White, and Hanry Yu. “The Self-Limiting Dynamics of TGF-β Signaling In Silico and In Vitro, with Negative Feedback through PPM1A Upregulation.” Edited by Christos A. Ouzounis. PLoS Comput Biol 10, no. 6 (June 5, 2014): e1003573.
Version
Final published version
Abstract
The TGF-β/Smad signaling system decreases its activity through strong negative regulation. Several molecular mechanisms of negative regulation have been published, but the relative impact of each mechanism on the overall system is unknown. In this work, we used computational and experimental methods to assess multiple negative regulatory effects on Smad signaling in HaCaT cells. Previously reported negative regulatory effects were classified by time-scale: degradation of phosphorylated R-Smad and I-Smad-induced receptor degradation were slow-mode effects, and dephosphorylation of R-Smad was a fast-mode effect. We modeled combinations of these effects, but found no combination capable of explaining the observed dynamics of TGF-β/Smad signaling. We then proposed a negative feedback loop with upregulation of the phosphatase PPM1A. The resulting model was able to explain the dynamics of Smad signaling, under both short and long exposures to TGF-β. Consistent with this model, immuno-blots showed PPM1A levels to be significantly increased within 30 min after TGF-β stimulation. Lastly, our model was able to resolve an apparent contradiction in the published literature, concerning the dynamics of phosphorylated R-Smad degradation. We conclude that the dynamics of Smad negative regulation cannot be explained by the negative regulatory effects that had previously been modeled, and we provide evidence for a new negative feedback loop through PPM1A upregulation. This work shows that tight coupling of computational and experiments approaches can yield improved understanding of complex pathways.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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DOI of Published Version
https://doi.org/10.1371/journal.pcbi.1003573
