Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America.
PLoS Comput Biol. 2010 Jan 29;6(1):e1000654. doi: 10.1371/journal.pcbi.1000654.
Signaling pathways mediate the effect of external stimuli on gene expression in cells. The signaling proteins in these pathways interact with each other and their phosphorylation levels often serve as indicators for the activity of signaling pathways. Several signaling pathways have been identified in mammalian cells but the crosstalk between them is not well understood. Alliance for Cellular Signaling (AfCS) has measured time-course data in RAW 264.7 macrophage cells on important phosphoproteins, such as the mitogen-activated protein kinases (MAPKs) and signal transducer and activator of transcription (STATs), in single- and double-ligand stimulation experiments for 22 ligands. In the present work, we have used a data-driven approach to analyze the AfCS data to decipher the interactions and crosstalk between signaling pathways in stimulated macrophage cells. We have used dynamic mapping to develop a predictive model using a partial least squares approach. Significant interactions were selected through statistical hypothesis testing and were used to reconstruct the phosphoprotein signaling network. The proposed data-driven approach is able to identify most of the known signaling interactions such as protein kinase B (Akt) --> glycogen synthase kinase 3alpha/beta (GSKalpha/beta) etc., and predicts potential novel interactions such as P38 --> RSK and GSK --> ezrin/radixin/moesin. We have also shown that the model has good predictive power for extrapolation. Our novel approach captures the temporal causality and directionality in intracellular signaling pathways. Further, case specific analysis of the phosphoproteins in the network has led us to propose hypothesis about inhibition (phosphorylation) of GSKalpha/beta via P38.
信号通路介导细胞中外源刺激对基因表达的影响。这些信号通路中的信号蛋白相互作用，其磷酸化水平通常作为信号通路活性的指标。在哺乳动物细胞中已经鉴定出几种信号通路，但它们之间的串扰尚不清楚。细胞信号联盟（AfCS）已经在 RAW 264.7 巨噬细胞中测量了重要磷酸蛋白（如丝裂原活化蛋白激酶（MAPKs）和信号转导和转录激活因子（STATs））的时间过程数据，这些数据来自于 22 种配体的单配体和双配体刺激实验。在本工作中，我们使用数据驱动的方法来分析 AfCS 数据，以破译受刺激的巨噬细胞中信号通路之间的相互作用和串扰。我们使用动态映射通过偏最小二乘方法开发了一个预测模型。通过统计假设检验选择显著的相互作用，并用于重建磷酸蛋白信号网络。所提出的数据驱动方法能够识别大多数已知的信号相互作用，如蛋白激酶 B（Akt）-->糖原合成酶激酶 3alpha/beta（GSKalpha/beta）等，并预测潜在的新相互作用，如 P38 --> RSK 和 GSK --> ezrin/radixin/moesin。我们还表明，该模型具有良好的外推预测能力。我们的新方法捕捉到了细胞内信号通路中的时间因果关系和方向性。此外，对网络中磷酸蛋白的具体案例分析使我们提出了关于 P38 通过磷酸化抑制 GSKalpha/beta 的假设。
