Global Protein-Protein Interaction Network in the Human Pathogen Mycobacterium tuberculosis H37Rv
Global Protein-Protein Interaction Network in the Human Pathogen Mycobacterium tuberculosis H37Rv
复制标题
人类病原体结核分枝杆菌 H37Rv 中的全局蛋白质-蛋白质相互作用网络
DOI：
10.1021/pr100808n
复制
发表时间：
2010-12-01
影响因子：
4.4
通讯作者：
He, Zheng-Guo
中科院分区：
文献类型：
--
作者：
Wang, Yi;Cui, Tao;He, Zheng-Guo
分析病原体的蛋白质-蛋白质相互作用网络是剖析基因功能、潜在信号转导和毒力途径的有力方法。本研究基于高通量细菌双杂交方法，着眼于构建人类病原体结核分枝杆菌H37 Rv的全局蛋白质-蛋白质相互作用（PPI）网络。并且鉴定了超过8000种新的相互作用。通过两种独立的方法验证了PPI网络的总体质量，成功率高达60%以上计算了PPI网络的参数，平均最短路长为4.31，结核分枝杆菌B2 H网络的拓扑系数完全服从幂律分布（相关系数= 0 999，R平方= 0 999）跨物种PPI网络比较揭示了结核分枝杆菌和几种原核生物PPI网络之间的94个保守子网络。全球网络与蛋白质分泌途径相关。发现两个WhiB样调节因子是全球网络中高度连接的蛋白质。第一个系统的非计算PPI数据的人类病原体，它提供了一个有用的资源，研究感染机制，新的信号通路，和新的抗结核药物的开发
Analysis of the protein-protein interaction network of a pathogen is a powerful approach for dissecting gene function, potential signal transduction, and virulence pathways This study looks at the construction of a global protein-protein interaction (PPI) network for the human pathogen Mycobacterium tuberculosis H37Rv, based on a high throughput bacterial two-hybrid method Almost the entire ORFeome was cloned, and more than 8000 novel interactions were identified The overall quality of the PPI network was validated through two independent methods, and a high success rate of more than 60% was obtained The parameters of PPI networks were calculated The average shortest path length was 4 31 The topological coefficient of the M tuberculosis B2H network perfectly followed a power law distribution (correlation = 0 999 R-squared = 0 999) and represented the best fit in all currently available PPI networks A cross species PPI network comparison revealed 94 conserved subnetworks between M tuberculosis and several prokaryotic organism PPI networks The global network was linked to the protein secretion pathway Two WhiB like regulators were found to be highly connected proteins in the global network This is the first systematic noncomputational PPI data for the human pathogen and it provides a useful resource for studies of infection mechanisms new signaling pathways, and novel antituberculosis drug development