Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan.
BMC Bioinformatics. 2010 May 7;11:232. doi: 10.1186/1471-2105-11-232.
Phosphorylation is a ubiquitous and fundamental regulatory mechanism that controls signal transduction in living cells. The number of identified phosphoproteins and their phosphosites is rapidly increasing as a result of recent mass spectrometry-based approaches.
We analyzed time-course phosphoproteome data obtained previously by liquid chromatography mass spectrometry with the stable isotope labeling using amino acids in cell culture (SILAC) method. This provides the relative phosphorylation activities of digested peptides at each of five time points after stimulating HeLa cells with epidermal growth factor (EGF). We initially calculated the correlations between the phosphorylation dynamics patterns of every pair of peptides and connected the strongly correlated pairs to construct a network. We found that peptides extracted from the same intracellular fraction (nucleus vs. cytoplasm) tended to be close together within this phosphorylation dynamics-based network. The network was then analyzed using graph theory and compared with five known signal-transduction pathways. The dynamics-based network was correlated with known signaling pathways in the NetPath and Phospho.ELM databases, and especially with the EGF receptor (EGFR) signaling pathway. Although the phosphorylation patterns of many proteins were drastically changed by the EGF stimulation, our results suggest that only EGFR signaling transduction was both strongly activated and precisely controlled.
The construction of a phosphorylation dynamics-based network provides a useful overview of condition-specific intracellular signal transduction using quantitative time-course phosphoproteome data under specific experimental conditions. Detailed prediction of signal transduction based on phosphoproteome dynamics remains challenging. However, since the phosphorylation profiles of kinase-substrate pairs on the specific pathway were localized in the dynamics-based network, our method will be a complementary strategy to explore new components of protein signaling pathways in combination with previous methods (including software) of predicting direct kinase-substrate relationships.
磷酸化是一种普遍存在且基本的调控机制，控制着活细胞中的信号转导。由于最近基于质谱的方法，已鉴定出的磷酸化蛋白及其磷酸化位点数量迅速增加。
我们分析了先前通过液相色谱-质谱联用（LC-MS）和稳定同位素标记的氨基酸在细胞培养中的应用（SILAC）方法获得的时间过程磷酸蛋白质组数据。这提供了在表皮生长因子（EGF）刺激 HeLa 细胞后五个时间点消化肽的相对磷酸化活性。我们最初计算了每对肽的磷酸化动力学模式之间的相关性，并将强相关对连接起来构建网络。我们发现，从同一细胞内部分（核与细胞质）提取的肽在这个基于磷酸化动力学的网络中彼此之间往往比较接近。然后使用图论对网络进行分析，并与五个已知的信号转导途径进行比较。基于动力学的网络与 NetPath 和 Phospho.ELM 数据库中的已知信号通路相关，特别是与表皮生长因子受体（EGFR）信号通路相关。尽管 EGF 刺激使许多蛋白质的磷酸化模式发生了剧烈变化，但我们的结果表明，只有 EGFR 信号转导既被强烈激活又被精确调控。
使用特定实验条件下的定量时间过程磷酸蛋白质组数据构建基于磷酸化动力学的网络，为特定条件下的细胞内信号转导提供了有用的概述。基于磷酸蛋白质组动力学的详细信号转导预测仍然具有挑战性。然而，由于特定途径上激酶-底物对的磷酸化谱定位在动力学网络中，因此我们的方法将成为一种补充策略，与以前预测直接激酶-底物关系的方法（包括软件）相结合，用于探索蛋白质信号通路的新组成部分。
