In silico prediction of protein-protein interactions in human macrophages

Abstract : BACKGROUND: Protein-protein interaction (PPI) network analyses are highly valuable in deciphering and understanding the intricate organisation of cellular functions. Nevertheless, the majority of available protein-protein interaction networks are context-less, i.e. without any reference to the spatial, temporal or physiological conditions in which the interactions may occur. In this work, we are proposing a protocol to infer the most likely protein-protein interaction (PPI) network in human macrophages. RESULTS: We integrated the PPI dataset from the Agile Protein Interaction DataAnalyzer (APID) with different meta-data to infer a contextualized macrophage-specific interactome using a combination of statistical methods. The obtained interactome is enriched in experimentally verified interactions and in proteins involved in macrophage-related biological processes (i.e. immune response activation, regulation of apoptosis). As a case study, we used the contextualized interactome to highlight the cellular processes induced upon Mycobacterium tuberculosis infection. CONCLUSION: Our work confirms that contextualizing interactomes improves the biological significance of bioinformatic analyses. More specifically, studying such inferred network rather than focusing at the gene expression level only, is informative on the processes involved in the host response. Indeed, important immune features such as apoptosis are solely highlighted when the spotlight is on the protein interaction level.
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BMC Research Notes, BioMed Central, 2014, 7 (1), pp.157. 〈10.1186/1756-0500-7-157〉
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http://www.hal.inserm.fr/inserm-00981319
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Soumis le : mardi 22 avril 2014 - 05:36:29
Dernière modification le : mardi 1 mai 2018 - 01:17:04

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Oussema Souiai, Fatma Guerfali, Slimane Ben Miled, Christine Brun, Alia Benkahla. In silico prediction of protein-protein interactions in human macrophages. BMC Research Notes, BioMed Central, 2014, 7 (1), pp.157. 〈10.1186/1756-0500-7-157〉. 〈inserm-00981319〉

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