A protein interaction atlas for the nuclear receptors: Properties and quality of a hub-based dimerisation network

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Abstract

Background: The nuclear receptors are a large family of eukaryotic transcription factors that constitute major pharmacological targets. They exert their combinatorial control through homotypic heterodimerisation. Elucidation of this dimerisation network is vital in order to understand the complex dynamics and potential cross-talk involved. Results: Phylogeny, protein-protein interactions, protein-DNA interactions and gene expression data have been integrated to provide a comprehensive and up-to-date description of the topology and properties of the nuclear receptor interaction network in humans. We discriminate between DNA-binding and non-DNA-binding dimers, and provide a comprehensive interaction map, that identifies potential cross-talk between the various pathways of nuclear receptors. Conclusion: We infer that the topology of this network is hub-based, and much more connected than previously thought. The hub-based topology of the network and the wide tissue expression pattern of NRs create a highly competitive environment for the common heterodimerising partners. Furthermore, a significant number of negative feedback loops is present, with the hub protein SHP [NR0B2] playing a major role. We also compare the evolution, topology and properties of the nuclear receptor network with the hub-based dimerisation network of the bHLH transcription factors in order to identify both unique themes and ubiquitous properties in gene regulation. In terms of methodology, we conclude that such a comprehensive picture can only be assembled by semi-automated text-mining, manual curation and integration of data from various sources. © 2007 Amoutzias et al; licensee BioMed Central Ltd.

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Amoutzias, G. D., Pichler, E. E., Mian, N., De Graaf, D., Imsiridou, A., Robinson-Rechavi, M., … Oliver, S. G. (2007). A protein interaction atlas for the nuclear receptors: Properties and quality of a hub-based dimerisation network. BMC Systems Biology, 1. https://doi.org/10.1186/1752-0509-1-34

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