The self-limiting dynamics of TGF-β signaling in silico and in vitro, with negative feedback through PPM1A upregulation.

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...

Full description

Bibliographic Details
Main Authors: Junjie Wang, Lisa Tucker-Kellogg, Inn Chuan Ng, Ruirui Jia, P S Thiagarajan, Jacob K White, Hanry Yu
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2014-06-01
Series:PLoS Computational Biology
Online Access:http://europepmc.org/articles/PMC4105941?pdf=render
id doaj-8614980317a34d449f8021a920999167
record_format Article
spelling doaj-8614980317a34d449f8021a9209991672020-11-25T01:42:34ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582014-06-01106e100357310.1371/journal.pcbi.1003573The self-limiting dynamics of TGF-β signaling in silico and in vitro, with negative feedback through PPM1A upregulation.Junjie WangLisa Tucker-KelloggInn Chuan NgRuirui JiaP S ThiagarajanJacob K WhiteHanry YuThe 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.http://europepmc.org/articles/PMC4105941?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Junjie Wang
Lisa Tucker-Kellogg
Inn Chuan Ng
Ruirui Jia
P S Thiagarajan
Jacob K White
Hanry Yu
spellingShingle Junjie Wang
Lisa Tucker-Kellogg
Inn Chuan Ng
Ruirui Jia
P S Thiagarajan
Jacob K White
Hanry Yu
The self-limiting dynamics of TGF-β signaling in silico and in vitro, with negative feedback through PPM1A upregulation.
PLoS Computational Biology
author_facet Junjie Wang
Lisa Tucker-Kellogg
Inn Chuan Ng
Ruirui Jia
P S Thiagarajan
Jacob K White
Hanry Yu
author_sort Junjie Wang
title The self-limiting dynamics of TGF-β signaling in silico and in vitro, with negative feedback through PPM1A upregulation.
title_short The self-limiting dynamics of TGF-β signaling in silico and in vitro, with negative feedback through PPM1A upregulation.
title_full The self-limiting dynamics of TGF-β signaling in silico and in vitro, with negative feedback through PPM1A upregulation.
title_fullStr The self-limiting dynamics of TGF-β signaling in silico and in vitro, with negative feedback through PPM1A upregulation.
title_full_unstemmed The self-limiting dynamics of TGF-β signaling in silico and in vitro, with negative feedback through PPM1A upregulation.
title_sort self-limiting dynamics of tgf-β signaling in silico and in vitro, with negative feedback through ppm1a upregulation.
publisher Public Library of Science (PLoS)
series PLoS Computational Biology
issn 1553-734X
1553-7358
publishDate 2014-06-01
description 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.
url http://europepmc.org/articles/PMC4105941?pdf=render
work_keys_str_mv AT junjiewang theselflimitingdynamicsoftgfbsignalinginsilicoandinvitrowithnegativefeedbackthroughppm1aupregulation
AT lisatuckerkellogg theselflimitingdynamicsoftgfbsignalinginsilicoandinvitrowithnegativefeedbackthroughppm1aupregulation
AT innchuanng theselflimitingdynamicsoftgfbsignalinginsilicoandinvitrowithnegativefeedbackthroughppm1aupregulation
AT ruiruijia theselflimitingdynamicsoftgfbsignalinginsilicoandinvitrowithnegativefeedbackthroughppm1aupregulation
AT psthiagarajan theselflimitingdynamicsoftgfbsignalinginsilicoandinvitrowithnegativefeedbackthroughppm1aupregulation
AT jacobkwhite theselflimitingdynamicsoftgfbsignalinginsilicoandinvitrowithnegativefeedbackthroughppm1aupregulation
AT hanryyu theselflimitingdynamicsoftgfbsignalinginsilicoandinvitrowithnegativefeedbackthroughppm1aupregulation
AT junjiewang selflimitingdynamicsoftgfbsignalinginsilicoandinvitrowithnegativefeedbackthroughppm1aupregulation
AT lisatuckerkellogg selflimitingdynamicsoftgfbsignalinginsilicoandinvitrowithnegativefeedbackthroughppm1aupregulation
AT innchuanng selflimitingdynamicsoftgfbsignalinginsilicoandinvitrowithnegativefeedbackthroughppm1aupregulation
AT ruiruijia selflimitingdynamicsoftgfbsignalinginsilicoandinvitrowithnegativefeedbackthroughppm1aupregulation
AT psthiagarajan selflimitingdynamicsoftgfbsignalinginsilicoandinvitrowithnegativefeedbackthroughppm1aupregulation
AT jacobkwhite selflimitingdynamicsoftgfbsignalinginsilicoandinvitrowithnegativefeedbackthroughppm1aupregulation
AT hanryyu selflimitingdynamicsoftgfbsignalinginsilicoandinvitrowithnegativefeedbackthroughppm1aupregulation
_version_ 1725035412855980032