The Self-Limiting Dynamics of TGF-β Signaling In Silico and In Vitro, with Negative Feedback through PPM1A Upregulation
Publication Date
June 05, 2014
Journal
PLOS Computational Biology
Authors
Junjie Wang, Lisa Tucker Kellogg, Inn Chuan Ng, Ruirui Jia, et al
Volume
10
Issue
6
Pages
e1003573
DOI
https://dx.plos.org/10.1371/journal.pcbi.1003573
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1003573
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/24901250
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4105941
Europe PMC
http://europepmc.org/abstract/MED/24901250
Web of Science
000338848100001
Scopus
84903398760
Mendeley
http://www.mendeley.com/research/selflimiting-dynamics-tgf%CE%B2-signaling-silico-vitro-negative-feedback-through-ppm1a-upregulation
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Mendeley 2427 Apr 00:19 UTC

Mendeley | Further Information

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Scopus 930 Jan 22:12 UTC

Scopus | Further Information

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Figshare 21602 Apr 20:43 UTC

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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.</p></div>", "links"=>[], "tags"=>["Biochemistry", "Biochemical simulations", "Computational biology", "systems biology", "Network Analysis", "Regulatory networks", "Signaling networks", "self-limiting", "ppm1a", "upregulation"], "article_id"=>1047605, "categories"=>["Biological Sciences"], "users"=>["Junjie Wang", "Lisa Tucker-Kellogg", "Inn Chuan Ng", "Ruirui Jia", "P. S. Thiagarajan", "Jacob K. 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  • {"files"=>["https://ndownloader.figshare.com/files/1522895"], "description"=>"<p>The + sign indicates that a negative regulatory effect is included in a model, and - indicates a negative regulatory effect is NOT included.</p><p>pS: phospho-R-Smad time series.</p><p>T1R: type I receptor time series.</p><p>tS: total R-Smad time series.</p>", "links"=>[], "tags"=>["Biochemistry", "Biochemical simulations", "Computational biology", "systems biology", "Network Analysis", "Regulatory networks", "Signaling networks"], "article_id"=>1047604, "categories"=>["Biological Sciences"], "users"=>["Junjie Wang", "Lisa Tucker-Kellogg", "Inn Chuan Ng", "Ruirui Jia", "P. S. Thiagarajan", "Jacob K. White", "Hanry Yu"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003573.t001", "stats"=>{"downloads"=>2, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_summary_of_the_8_models_showing_the_models_with_negative_regulatory_effects_/1047604", "title"=>"A summary of the 8 models, showing the models with negative regulatory effects.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-06-05 03:40:03"}
  • {"files"=>["https://ndownloader.figshare.com/files/1522892"], "description"=>"<p>(<b>A</b>) Model 6 with P-Smad Degradation and R-Smad Dephosphorylation (but no Receptor Degradation) was fitted to both the short-exposure (red) and long-exposure (blue) experimental data. (<b>B</b>) Model 6 predicted significant loss of total R-Smad (green curve), while ELISA measurements showed insignificant change (P>0.05, comparing the first and last data points) in total R-Smad concentration (green dots). (<b>C</b>) ELISA measurements of phospho-R-Smad are consistent with previous measurements performed by Western blot <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003573#pcbi.1003573-Lin1\" target=\"_blank\">[9]</a>. Cell lysates were from the same samples as panel B. (<b>D</b>) Model 7 was fitted to the phospho-R-Smad data while constraining the total R-Smad level to be constant.</p>", "links"=>[], "tags"=>["Biochemistry", "Biochemical simulations", "Computational biology", "systems biology", "Network Analysis", "Regulatory networks", "Signaling networks", "experiments"], "article_id"=>1047601, "categories"=>["Biological Sciences"], "users"=>["Junjie Wang", "Lisa Tucker-Kellogg", "Inn Chuan Ng", "Ruirui Jia", "P. S. Thiagarajan", "Jacob K. White", "Hanry Yu"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003573.g004", "stats"=>{"downloads"=>0, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Simulations_and_experiments_for_P_S_mad_D_egradation_/1047601", "title"=>"Simulations and experiments for P-Smad Degradation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-05 03:40:03"}
  • {"files"=>["https://ndownloader.figshare.com/files/1522890"], "description"=>"<p>(<b>A–E</b>) Dots: experimental data from Lin <i>et al</i>. <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003573#pcbi.1003573-Lin1\" target=\"_blank\">[9]</a>. All P-Smad2 measurements used total cell lysate. Curves: the model simulations were fitted to the two sets of data simultaneously. (<b>A</b>) Model 1: R-Smad Dephosphorylation; (<b>B</b>) Model 2: R-Smad Dephosphorylation and Receptor Degradation; (<b>C</b>) Model 3: Receptor Degradation; (<b>D</b>) Model 4: P-Smad Degradation; (<b>E</b>) Model 5: R-Smad Dephosphorylation, Receptor Degradation and P-Smad Degradation. The reactions of each model are listed in the Supporting Information. (<b>F–H</b>) Predictions of the best-fit model (Model 5) in MG132 pre-treated cells. Simulation of MG132 treatment was performed by turning off the Smurf2-induced P-Smad Degradation (setting <i>kdeg<sub>pSmad2</sub></i> = 0) in Model 5. (<b>F</b>) Comparison of the model prediction and experimental data from Lin <i>et al</i>. <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003573#pcbi.1003573-Lin1\" target=\"_blank\">[9]</a> in the short-exposure experiment. (<b>G</b>) Model prediction in the long-exposure experiment. The green shaded area shows the cumulative difference between +MG132 and -MG132. (<b>H</b>) A histogram plots the cumulative differences seen in the short-exposure experiment (red) and the long-exposure experiment (blue).</p>", "links"=>[], "tags"=>["Biochemistry", "Biochemical simulations", "Computational biology", "systems biology", "Network Analysis", "Regulatory networks", "Signaling networks", "fitting", "combinations"], "article_id"=>1047599, "categories"=>["Biological Sciences"], "users"=>["Junjie Wang", "Lisa Tucker-Kellogg", "Inn Chuan Ng", "Ruirui Jia", "P. S. Thiagarajan", "Jacob K. White", "Hanry Yu"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003573.g002", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_fitting_results_with_different_combinations_of_known_negative_regulatory_effects_/1047599", "title"=>"Model fitting results with different combinations of known negative regulatory effects.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-05 03:40:03"}
  • {"files"=>["https://ndownloader.figshare.com/files/1522891"], "description"=>"<p>(<b>A</b>) Different rates of I-Smad-induced Receptor Degradation (<i>klid</i> = 10<sup>−6</sup>∼10<sup>−2</sup>) were applied to Model 5, and the rate of Smurf-induced P-Smad Degradation (<i>kdeg<sub>pSmad2</sub></i>) was fitted to the short-exposure experimental data (red dots) and the long-exposure experimental data (blue dots). All the other parameters were kept the same as those in Model 5 (<b>B</b>) Different Receptor Degradation rates led to different levels of the type I receptor (T1R). Green curves were generated from all models in panel (A) with <i>klid</i> = 10<sup>−6</sup>∼10<sup>−2</sup> and <i>kdeg<sub>pSmad2</sub></i> estimated. (<b>C</b>) In the fitted models in panel (A), the T1R level has negative correlation with the Receptor Degradation rate (<i>klid</i>) but positive correlation with the P-Smad Degradation rate (<i>kdeg<sub>pSmad2</sub></i>). (<b>D</b>) Quantified data from 3 replicates of the western blot in (E). There is no significant loss of the T1R comparing the first and last data points (P>0.05). (<b>E</b>) Western blot of the T1R from whole cell lysates of HaCaT cells treated with TGF-β for 24 hrs (representative of 3 replicates). (<b>F</b>) In the fitted models in panel (A), the rates of Receptor Degradation (<i>klid</i>) and P-Smad Degradation (<i>kdeg<sub>pSmad2</sub></i>) have negative correlation.</p>", "links"=>[], "tags"=>["Biochemistry", "Biochemical simulations", "Computational biology", "systems biology", "Network Analysis", "Regulatory networks", "Signaling networks", "validations"], "article_id"=>1047600, "categories"=>["Biological Sciences"], "users"=>["Junjie Wang", "Lisa Tucker-Kellogg", "Inn Chuan Ng", "Ruirui Jia", "P. S. Thiagarajan", "Jacob K. White", "Hanry Yu"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003573.g003", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predictions_and_validations_of_R_eceptor_D_egradation_/1047600", "title"=>"Predictions and validations of Receptor Degradation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-05 03:40:03"}
  • {"files"=>["https://ndownloader.figshare.com/files/1522889"], "description"=>"<p>The dashed arrows indicate those reactions which are modeled in black box. The red arrows indicate the negative regulatory effects: (1) PPM1A dephosphorylating phospho-R-Smad; (2) Smurf2 induced proteasome degradation of phospho-R-Smad; (3) I-Smad induced receptor inhibition; (4) PPM1A upregulation by Smad signaling.</p>", "links"=>[], "tags"=>["Biochemistry", "Biochemical simulations", "Computational biology", "systems biology", "Network Analysis", "Regulatory networks", "Signaling networks", "pathway", "diagram", "smad", "symbols"], "article_id"=>1047598, "categories"=>["Biological Sciences"], "users"=>["Junjie Wang", "Lisa Tucker-Kellogg", "Inn Chuan Ng", "Ruirui Jia", "P. S. Thiagarajan", "Jacob K. White", "Hanry Yu"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003573.g001", "stats"=>{"downloads"=>1, "page_views"=>44, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_pathway_diagram_of_Smad_signaling_using_symbols_from_BioCarta_/1047598", "title"=>"The pathway diagram of Smad signaling (using symbols from BioCarta).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-05 03:40:03"}
  • {"files"=>["https://ndownloader.figshare.com/files/1522894"], "description"=>"<p>The effects of R-Smad Dephosphorylation, PPM1A Stabilization and P-Smad Degradation were removed one after another from Model 8, and the dynamics of P-Smad2 were simulated after 24 hr TGF-β treatment. In the absence of any negative regulatory effect (red curve), P-Smad2 levels climbed rapidly beyond the scale of the plot; and are not shown for time points after 1 hr.</p>", "links"=>[], "tags"=>["Biochemistry", "Biochemical simulations", "Computational biology", "systems biology", "Network Analysis", "Regulatory networks", "Signaling networks", "contributions"], "article_id"=>1047603, "categories"=>["Biological Sciences"], "users"=>["Junjie Wang", "Lisa Tucker-Kellogg", "Inn Chuan Ng", "Ruirui Jia", "P. S. Thiagarajan", "Jacob K. White", "Hanry Yu"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003573.g006", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relative_contributions_of_different_negative_regulatory_effects_in_our_final_model_Model_8_/1047603", "title"=>"Relative contributions of different negative regulatory effects in our final model (Model 8).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-05 03:40:03"}
  • {"files"=>["https://ndownloader.figshare.com/files/1522893"], "description"=>"<p>(<b>A</b>) Western blot of PPM1A in HaCaT cells with 2 ng/ml TGF-β treatment, representative of 3 replicates. (<b>B</b>) Model 8 predicted PPM1A upregulation under long-exposure of TGF-β (green curve). Our experimental validation showed significant upregulation of PPM1A (green dots, quantification from 3 Western blots, P<0.05 comparing the untreated data point and the 1 hr data point). (<b>C</b>) Model 8 was fitted to the long-exposure and the short-exposure phospho-R-Smad experimental data. (<b>D</b>) Model 8 predicted unchanged T1R levels (green curve), in agreement with our experimental results (green dots). (<b>E</b>) Model 8 predicted unchanged total R-Smad levels (green curve), in agreement with our experimental results (green dots). (<b>F</b>) Red solid curve shows simulation of Model 8 with short-exposure (30 min) of TGF-β, while the yellow dotted curve shows the same simulation except with MG132 pre-treatment. MG132 was simulated as turning off Smurf2-induced P-Smad Degradation (<i>kdeg<sub>pSmad2</sub></i> = 0), but having no impact on basal degradation of Receptors, unphosphorylated R-SMAD, or PPM1A. (<b>G</b>) The blue solid curve shows simulation of Model 8 with long-exposure (8 hr) of TGF-β, and the green dotted curve shows the same simulated except with MG132 pre-treatment. (<b>H</b>) The relative change in P-Smad2 levels after MG132 treatment, calculated from Eq. 1 and simulations of Model 8. The P-Smad2 change simulated using Model 8 in both short-exposure (30 min, red curve) and long-exposure (8 hr, blue curve) simulations was compared with the P-Smad2 changes in the experimental results of Lin <i>et al</i>. <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003573#pcbi.1003573-Lin1\" target=\"_blank\">[9]</a> (30 min-exposure, red dots) and Alarcon <i>et al</i>. <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003573#pcbi.1003573-Alarcon1\" target=\"_blank\">[21]</a> (6 hr-exposure, blue dots). Data points from Alarcon <i>et al</i>. <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003573#pcbi.1003573-Alarcon1\" target=\"_blank\">[21]</a> were quantified from one published image. The discrepancy between our simulations and Alarcon <i>et al</i>. for the 7 hr measurement may be partially explained by MG132-independent differences. Their -MG132 control decreases much faster than that from Lin <i>et al</i>. <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003573#pcbi.1003573-Lin1\" target=\"_blank\">[9]</a> and from our experiments.</p>", "links"=>[], "tags"=>["Biochemistry", "Biochemical simulations", "Computational biology", "systems biology", "Network Analysis", "Regulatory networks", "Signaling networks", "validation", "ppm1a"], "article_id"=>1047602, "categories"=>["Biological Sciences"], "users"=>["Junjie Wang", "Lisa Tucker-Kellogg", "Inn Chuan Ng", "Ruirui Jia", "P. S. Thiagarajan", "Jacob K. White", "Hanry Yu"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003573.g005", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predictions_and_validation_of_PPM1A_U_pregulation_/1047602", "title"=>"Predictions and validation of PPM1A Upregulation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-05 03:40:03"}
PMC Usage Stats 68317 Feb 21:54 UTC

PMC Usage Stats | Further Information

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Relative Metric 212212 Apr 18:15 UTC

Relative Metric

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