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Modularity analysis based on predicted protein-protein interactions provides new insights into pathogenicity and cellular process of <it>Escherichia coli </it>O157:H7

By Wang Xia, Yue Junjie, Ren Xianwen, Wang Yuelan, Tan Mingfeng, Li Beiping and Liang Long

Abstract

<p>Abstract</p> <p>Background</p> <p>With the development of experimental techniques and bioinformatics, the quantity of data available from protein-protein interactions (PPIs) is increasing exponentially. Functional modules can be identified from protein interaction networks. It follows that the investigation of functional modules will generate a better understanding of cellular organization, processes, and functions. However, experimental PPI data are still limited, and no modularity analysis of PPIs in pathogens has been published to date.</p> <p>Results</p> <p>In this study, we predict and analyze the functional modules of <it>E. coli </it>O157:H7 systemically by integrating several bioinformatics methods. After evaluation, most of the predicted modules are found to be biologically significant and functionally homogeneous. Six pathogenicity-related modules were discovered and analyzed, including novel modules. These modules provided new information on the pathogenicity of O157:H7. The modularity of cellular function and cooperativity between modules are also discussed. Moreover, modularity analysis of O157:H7 can provide possible candidates for biological pathway extension and clues for discovering new pathways of cross-talk.</p> <p>Conclusions</p> <p>This article provides the first modularity analysis of a pathogen and sheds new light on the study of pathogens and cellular processes. Our study also provides a strategy for applying modularity analysis to any sequenced organism.</p

Topics: Biology (General), QH301-705.5, Science, Q, DOAJ:Biology, DOAJ:Biology and Life Sciences, Computer applications to medicine. Medical informatics, R858-859.7
Publisher: BioMed Central
Year: 2011
DOI identifier: 10.1186/1742-4682-8-47
OAI identifier: oai:doaj.org/article:1654332a793044d4bcaec8e30216794d
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