Zhang Xiao-Fei, Ou-Yang Le, Dai Dao-Qing, Wu Meng-Yun, Zhu Yuan, Yan Hong
School of Mathematics and Statistics & Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Luoyu Road, Wuhan, 430079, China.
College of Information Engineering, Shenzhen University, Nanhai Ave 3688, Shenzhen, 518060, China.
BMC Bioinformatics. 2016 Sep 9;17(1):358. doi: 10.1186/s12859-016-1233-0.
Several recent studies have used the Minimum Dominating Set (MDS) model to identify driver nodes, which provide the control of the underlying networks, in protein interaction networks. There may exist multiple MDS configurations in a given network, thus it is difficult to determine which one represents the real set of driver nodes. Because these previous studies only focus on static networks and ignore the contextual information on particular tissues, their findings could be insufficient or even be misleading.
In this study, we develop a Collective-Influence-corrected Minimum Dominating Set (CI-MDS) model which takes into account the collective influence of proteins. By integrating molecular expression profiles and static protein interactions, 16 tissue-specific networks are established as well. We then apply the CI-MDS model to each tissue-specific network to detect MDS proteins. It generates almost the same MDSs when it is solved using different optimization algorithms. In addition, we classify MDS proteins into Tissue-Specific MDS (TS-MDS) proteins and HouseKeeping MDS (HK-MDS) proteins based on the number of tissues in which they are expressed and identified as MDS proteins. Notably, we find that TS-MDS proteins and HK-MDS proteins have significantly different topological and functional properties. HK-MDS proteins are more central in protein interaction networks, associated with more functions, evolving more slowly and subjected to a greater number of post-translational modifications than TS-MDS proteins. Unlike TS-MDS proteins, HK-MDS proteins significantly correspond to essential genes, ageing genes, virus-targeted proteins, transcription factors and protein kinases. Moreover, we find that besides HK-MDS proteins, many TS-MDS proteins are also linked to disease related genes, suggesting the tissue specificity of human diseases. Furthermore, functional enrichment analysis reveals that HK-MDS proteins carry out universally necessary biological processes and TS-MDS proteins usually involve in tissue-dependent functions.
Our study uncovers key features of TS-MDS proteins and HK-MDS proteins, and is a step forward towards a better understanding of the controllability of human interactomes.
最近的几项研究使用最小支配集（MDS）模型来识别蛋白质相互作用网络中的驱动节点，这些节点控制着底层网络。给定网络中可能存在多个MDS配置，因此很难确定哪一个代表真正的驱动节点集。由于这些先前的研究仅关注静态网络而忽略了特定组织的上下文信息，其研究结果可能不充分甚至具有误导性。
在本研究中，我们开发了一种考虑蛋白质集体影响的集体影响校正最小支配集（CI-MDS）模型。通过整合分子表达谱和静态蛋白质相互作用，还建立了16个组织特异性网络。然后，我们将CI-MDS模型应用于每个组织特异性网络以检测MDS蛋白。使用不同的优化算法求解时，它会生成几乎相同的MDS。此外，我们根据MDS蛋白在其中表达和被识别的组织数量，将MDS蛋白分为组织特异性MDS（TS-MDS）蛋白和管家MDS（HK-MDS）蛋白。值得注意 的是，我们发现TS-MDS蛋白和HK-MDS蛋白具有明显不同的拓扑和功能特性。与TS-MDS蛋白相比，HK-MDS蛋白在蛋白质相互作用网络中更具中心性，与更多功能相关，进化更慢且经历更多的翻译后修饰。与TS-MDS蛋白不同，HK-MDS蛋白与必需基因、衰老基因、病毒靶向蛋白、转录因子和蛋白激酶显著对应。此外，我们发现除了HK-MDS蛋白外，许多TS-MDS蛋白也与疾病相关基因有关，这表明人类疾病的组织特异性。此外，功能富集分析表明，HK-MDS蛋白执行普遍必需的生物学过程，而TS-MDS蛋白通常参与组织依赖的功能。
我们的研究揭示了TS-MDS蛋白和HK-MDS蛋白的关键特征，朝着更好地理解人类相互作用组的可控性迈出了一步。
