Wang Junjie, Tucker-Kellogg Lisa, Ng Inn Chuan, Jia Ruirui, Thiagarajan P S, White Jacob K, Yu Hanry
Computational and Systems Biology, Singapore-MIT Alliance, Singapore; Mechanobiology Institute, Singapore.
Computational and Systems Biology, Singapore-MIT Alliance, Singapore; Mechanobiology Institute, Singapore; Centre for Computational Biology, Duke-NUS Graduate Medical School, Singapore.
PLoS Comput Biol. 2014 Jun 5;10(6):e1003573. doi: 10.1371/journal.pcbi.1003573. eCollection 2014 Jun.
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.
转化生长因子-β(TGF-β)/Smad信号系统通过强大的负调控降低其活性。已经发表了几种负调控的分子机制,但每种机制对整个系统的相对影响尚不清楚。在这项工作中,我们使用计算和实验方法评估了对HaCaT细胞中Smad信号的多种负调控作用。先前报道的负调控作用按时间尺度分类:磷酸化R-Smad的降解和I-Smad诱导的受体降解是慢模式效应,而R-Smad的去磷酸化是快模式效应。我们对这些效应的组合进行了建模,但没有发现能够解释观察到的TGF-β/Smad信号动态的组合。然后,我们提出了一个磷酸酶PPM1A上调的负反馈回路。由此产生的模型能够解释在短期和长期暴露于TGF-β的情况下Smad信号的动态。与该模型一致,免疫印迹显示在TGF-β刺激后30分钟内PPM1A水平显著升高。最后,我们的模型能够解决已发表文献中关于磷酸化R-Smad降解动态的一个明显矛盾。我们得出结论,Smad负调控的动态不能用先前建模的负调控作用来解释,并且我们提供了通过PPM1A上调形成新的负反馈回路的证据。这项工作表明,计算方法和实验方法的紧密结合可以提高对复杂通路的理解。