Evidence map›Paper›PMID 24901250›Full record

ArticlePLoS computational biology2014

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

Junjie Wang, Lisa Tucker-Kellogg, Inn Chuan Ng, Ruirui Jia, P S Thiagarajan, Jacob K White, Hanry Yu

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.7field-weighted citation impact, top 31% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 13 citations in OpenAlex.

  1. Review
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  3. PyCoTools: a Python toolbox for COPASI.Bioinformatics (Oxford, England) · 2018
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 2 countries.

Junjie WangComputational and Systems Biology, Singapore-MIT Alliance, Singapore; Mechanobiology Institute, Singapore.
Lisa Tucker-KelloggComputational and Systems Biology, Singapore-MIT Alliance, Singapore; Mechanobiology Institute, Singapore; Centre for Computational Biology, Duke-NUS Graduate Medical School, Singapore.
Inn Chuan NgNUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore.
Ruirui JiaDepartment of Physiology, National University of Singapore, Singapore.
P S ThiagarajanNUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore; School of Computing, National University of Singapore, Singapore.
Jacob K WhiteComputational and Systems Biology, Singapore-MIT Alliance, Singapore; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
Hanry YuComputational and Systems Biology, Singapore-MIT Alliance, Singapore; Mechanobiology Institute, Singapore; NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore; Department of Physiology, National University of Singapore, Singapore; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
National University of Singapore · SGSingapore-MIT Alliance for Research and Technology · SGDuke-NUS Medical School · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Cell LineComputational BiologyComputer SimulationFeedback, PhysiologicalHumansIntracellular Signaling Peptides and ProteinsIn Vitro TechniquesModels, TheoreticalPhosphoprotein PhosphatasesPhosphorylationProtein Phosphatase 2CProteolysisProto-Oncogene ProteinsSignal TransductionSmad Proteins, Receptor-RegulatedTransforming Growth Factor betaIntracellular Signaling Peptides and ProteinsPhosphoprotein PhosphatasesPPM1A protein, humanProtein Phosphatase 2CProto-Oncogene ProteinsSKIL protein, humanSmad Proteins, Receptor-RegulatedTransforming Growth Factor beta

Identifiers

PMID24901250
PMCPMC4105941
OpenAlexW2013966186

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.