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Comparative analysis of housekeeping and tissue-specific driver nodes in human protein interaction networks
BACKGROUND: Several recent studies have used the Minimum Dominating Set (MDS) model to identify driver nodes, which provide the control of the underlying networks, in protein interaction networks. There may exist multiple MDS configurations in a given network, thus it is difficult to determine which...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5016887/ https://www.ncbi.nlm.nih.gov/pubmed/27612563 http://dx.doi.org/10.1186/s12859-016-1233-0 |
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author | Zhang, Xiao-Fei Ou-Yang, Le Dai, Dao-Qing Wu, Meng-Yun Zhu, Yuan Yan, Hong |
author_facet | Zhang, Xiao-Fei Ou-Yang, Le Dai, Dao-Qing Wu, Meng-Yun Zhu, Yuan Yan, Hong |
author_sort | Zhang, Xiao-Fei |
collection | PubMed |
description | BACKGROUND: Several recent studies have used the Minimum Dominating Set (MDS) model to identify driver nodes, which provide the control of the underlying networks, in protein interaction networks. There may exist multiple MDS configurations in a given network, thus it is difficult to determine which one represents the real set of driver nodes. Because these previous studies only focus on static networks and ignore the contextual information on particular tissues, their findings could be insufficient or even be misleading. RESULTS: In this study, we develop a Collective-Influence-corrected Minimum Dominating Set (CI-MDS) model which takes into account the collective influence of proteins. By integrating molecular expression profiles and static protein interactions, 16 tissue-specific networks are established as well. We then apply the CI-MDS model to each tissue-specific network to detect MDS proteins. It generates almost the same MDSs when it is solved using different optimization algorithms. In addition, we classify MDS proteins into Tissue-Specific MDS (TS-MDS) proteins and HouseKeeping MDS (HK-MDS) proteins based on the number of tissues in which they are expressed and identified as MDS proteins. Notably, we find that TS-MDS proteins and HK-MDS proteins have significantly different topological and functional properties. HK-MDS proteins are more central in protein interaction networks, associated with more functions, evolving more slowly and subjected to a greater number of post-translational modifications than TS-MDS proteins. Unlike TS-MDS proteins, HK-MDS proteins significantly correspond to essential genes, ageing genes, virus-targeted proteins, transcription factors and protein kinases. Moreover, we find that besides HK-MDS proteins, many TS-MDS proteins are also linked to disease related genes, suggesting the tissue specificity of human diseases. Furthermore, functional enrichment analysis reveals that HK-MDS proteins carry out universally necessary biological processes and TS-MDS proteins usually involve in tissue-dependent functions. CONCLUSIONS: Our study uncovers key features of TS-MDS proteins and HK-MDS proteins, and is a step forward towards a better understanding of the controllability of human interactomes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1233-0) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5016887 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-50168872016-09-19 Comparative analysis of housekeeping and tissue-specific driver nodes in human protein interaction networks Zhang, Xiao-Fei Ou-Yang, Le Dai, Dao-Qing Wu, Meng-Yun Zhu, Yuan Yan, Hong BMC Bioinformatics Research Article BACKGROUND: Several recent studies have used the Minimum Dominating Set (MDS) model to identify driver nodes, which provide the control of the underlying networks, in protein interaction networks. There may exist multiple MDS configurations in a given network, thus it is difficult to determine which one represents the real set of driver nodes. Because these previous studies only focus on static networks and ignore the contextual information on particular tissues, their findings could be insufficient or even be misleading. RESULTS: In this study, we develop a Collective-Influence-corrected Minimum Dominating Set (CI-MDS) model which takes into account the collective influence of proteins. By integrating molecular expression profiles and static protein interactions, 16 tissue-specific networks are established as well. We then apply the CI-MDS model to each tissue-specific network to detect MDS proteins. It generates almost the same MDSs when it is solved using different optimization algorithms. In addition, we classify MDS proteins into Tissue-Specific MDS (TS-MDS) proteins and HouseKeeping MDS (HK-MDS) proteins based on the number of tissues in which they are expressed and identified as MDS proteins. Notably, we find that TS-MDS proteins and HK-MDS proteins have significantly different topological and functional properties. HK-MDS proteins are more central in protein interaction networks, associated with more functions, evolving more slowly and subjected to a greater number of post-translational modifications than TS-MDS proteins. Unlike TS-MDS proteins, HK-MDS proteins significantly correspond to essential genes, ageing genes, virus-targeted proteins, transcription factors and protein kinases. Moreover, we find that besides HK-MDS proteins, many TS-MDS proteins are also linked to disease related genes, suggesting the tissue specificity of human diseases. Furthermore, functional enrichment analysis reveals that HK-MDS proteins carry out universally necessary biological processes and TS-MDS proteins usually involve in tissue-dependent functions. CONCLUSIONS: Our study uncovers key features of TS-MDS proteins and HK-MDS proteins, and is a step forward towards a better understanding of the controllability of human interactomes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1233-0) contains supplementary material, which is available to authorized users. BioMed Central 2016-09-09 /pmc/articles/PMC5016887/ /pubmed/27612563 http://dx.doi.org/10.1186/s12859-016-1233-0 Text en © The Author(s) 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Zhang, Xiao-Fei Ou-Yang, Le Dai, Dao-Qing Wu, Meng-Yun Zhu, Yuan Yan, Hong Comparative analysis of housekeeping and tissue-specific driver nodes in human protein interaction networks |
title | Comparative analysis of housekeeping and tissue-specific driver nodes in human protein interaction networks |
title_full | Comparative analysis of housekeeping and tissue-specific driver nodes in human protein interaction networks |
title_fullStr | Comparative analysis of housekeeping and tissue-specific driver nodes in human protein interaction networks |
title_full_unstemmed | Comparative analysis of housekeeping and tissue-specific driver nodes in human protein interaction networks |
title_short | Comparative analysis of housekeeping and tissue-specific driver nodes in human protein interaction networks |
title_sort | comparative analysis of housekeeping and tissue-specific driver nodes in human protein interaction networks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5016887/ https://www.ncbi.nlm.nih.gov/pubmed/27612563 http://dx.doi.org/10.1186/s12859-016-1233-0 |
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