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pubmed-article:20973567pubmed:dateCreated2010-12-3lld:pubmed
pubmed-article:20973567pubmed:abstractTextAnalysis 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.lld:pubmed
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pubmed-article:20973567pubmed:year2010lld:pubmed
pubmed-article:20973567pubmed:articleTitleGlobal protein-protein interaction network in the human pathogen Mycobacterium tuberculosis H37Rv.lld:pubmed
pubmed-article:20973567pubmed:affiliationNational Key Laboratory of Agricultural Microbiology, Center for Proteomics Research, College of Life Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.lld:pubmed
pubmed-article:20973567pubmed:publicationTypeJournal Articlelld:pubmed
pubmed-article:20973567pubmed:publicationTypeResearch Support, Non-U.S. Gov'tlld:pubmed