Liu Wei, Wang Jianying, Wang Tengjiao, Xie Hongwei
College of Mechanical & Electronic Engineering and Automation, National University of Defense Technology, Changsha, China.
The Affiliated Hospital of Military Medical Sciences, Beijing, China.
PLoS One. 2014 Dec 16;9(12):e115074. doi: 10.1371/journal.pone.0115074. eCollection 2014.
Construction and analyses of tissue specific networks is crucial to unveil the function and organizational structure of biological systems. As a direct method to detect protein dynamics, human proteome-wide expression data provide an valuable resource to investigate the tissue specificity of proteins and interactions. By integrating protein expression data with large-scale interaction network, we constructed 30 tissue/cell specific networks in human and analyzed their properties and functions. Rather than the tissue specificity of proteins, we mainly focused on the tissue specificity of interactions to distill tissue specific networks. Through comparing our tissue specific networks with those inferred from gene expression data, we found our networks have larger scales and higher reliability. Furthermore, we investigated the similar extent of multiple tissue specific networks, which proved that tissues with similar functions tend to contain more common interactions. Finally, we found that the tissue specific networks differed from the static network in multiple topological properties. The proteins in tissue specific networks are interacting looser and the hubs play more important roles than those in the static network.
构建和分析组织特异性网络对于揭示生物系统的功能和组织结构至关重要。作为检测蛋白质动态变化的直接方法，人类全蛋白质组表达数据为研究蛋白质及其相互作用的组织特异性提供了宝贵资源。通过将蛋白质表达数据与大规模相互作用网络相结合，我们构建了30个人类组织/细胞特异性网络，并分析了它们的特性和功能。我们主要关注相互作用的组织特异性，而非蛋白质的组织特异性，以此来提取组织特异性网络。通过将我们构建的组织特异性网络与从基因表达数据推断出的网络进行比较，我们发现我们构建的网络规模更大且可靠性更高。此外，我们研究了多个组织特异性网络的相似程度，结果表明功能相似的组织往往包含更多共同的相互作用。最后，我们发现组织特异性网络在多个拓扑特性方面与静态网络不同。组织特异性网络中的蛋白质相互作用较为松散，且枢纽蛋白比静态网络中的枢纽蛋白发挥着更重要的作用。
