Protein evolution on a human signaling network.
Protein evolution on a human signaling network.
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DOI：
10.1186/1752-0509-3-21
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发表时间：
2009-02-18
影响因子：
--
通讯作者：
Wang E
中科院分区：
文献类型：
--
作者：
Cui Q;Purisima EO;Wang E
细胞网络的架构结构为创新提供了框架，也为蛋白质进化提供了约束。这个问题以前已经通过分析蛋白质相互作用网络进行了广泛的研究。然而，目前尚不清楚信号网络如何影响和约束蛋白质进化，以及蛋白质进化如何改变和塑造信号网络的功能结果。在这项研究中，我们通过人工管理信号通路构建了一个包含1，600多个节点和5，000多个链接的人类信号网络，并分析了网络上人-小鼠直系同源物的dN/dS值。我们发现，蛋白质dN/dS值沿着信号信息流从细胞外空间到细胞核而降低。在网络中，相邻蛋白质往往具有相似的dN/dS比，表明相邻蛋白质具有相似的进化速率：共快或共慢。然而，蛋白质之间的不同类型的关系（激活，抑制和中性）对蛋白质进化速率有不同的影响，即，物理上相互作用的蛋白质对具有最接近的进化速率。此外，对于定向最短路径，两个蛋白质距离越远，它们共享相似进化速率的机会就越小。然而，这种行为没有观察到中立的最短路径。快速进化的信号蛋白有两种进化模式：免疫蛋白进化更独立，而凋亡蛋白倾向于与其他信号蛋白形成网络组件，并共享更相似的进化速率，可能增强凋亡和其他信号通路之间的快速信息交换。蛋白质相互作用网络中蛋白质进化的主要网络约束已经在信号网络中发现。我们进一步揭示了网络特征如何影响蛋白质的进化和共同进化行为，以及蛋白质进化如何改变信号网络的现有功能。这些新的见解为理解信号网络背景下的蛋白质进化提供了一些一般原则。
The architectural structure of cellular networks provides a framework for innovations as well as constraints for protein evolution. This issue has previously been studied extensively by analyzing protein interaction networks. However, it is unclear how signaling networks influence and constrain protein evolution and conversely, how protein evolution modifies and shapes the functional consequences of signaling networks. In this study, we constructed a human signaling network containing more than 1,600 nodes and 5,000 links through manual curation of signaling pathways, and analyzed the dN/dS values of human-mouse orthologues on the network. We revealed that the protein dN/dS value decreases along the signal information flow from the extracellular space to nucleus. In the network, neighbor proteins tend to have similar dN/dS ratios, indicating neighbor proteins have similar evolutionary rates: co-fast or co-slow. However, different types of relationships (activating, inhibitory and neutral) between proteins have different effects on protein evolutionary rates, i.e., physically interacting protein pairs have the closest evolutionary rates. Furthermore, for directed shortest paths, the more distant two proteins are, the less chance they share similar evolutionary rates. However, such behavior was not observed for neutral shortest paths. Fast evolving signaling proteins have two modes of evolution: immunological proteins evolve more independently, while apoptotic proteins tend to form network components with other signaling proteins and share more similar evolutionary rates, possibly enhancing rapid information exchange between apoptotic and other signaling pathways. Major network constraints on protein evolution in protein interaction networks previously described have been found for signaling networks. We further uncovered how network characteristics affect the evolutionary and co-evolutionary behavior of proteins and how protein evolution can modify the existing functionalities of signaling networks. These new insights provide some general principles for understanding protein evolution in the context of signaling networks.
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