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Comparative analysis of housekeeping and tissue-specific driver nodes in human protein interaction networks

View Article: PubMed Central - PubMed

ABSTRACT

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.

No MeSH data available.


Distribution of the number of associated (a) biological process, (b) cellular component and (c) molecular function terms of different types of proteins. The distribution is represented by box plots (line = median). In each figure, outliers have been masked for clarity. Only direct GO annotations are taken into account
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Fig5: Distribution of the number of associated (a) biological process, (b) cellular component and (c) molecular function terms of different types of proteins. The distribution is represented by box plots (line = median). In each figure, outliers have been masked for clarity. Only direct GO annotations are taken into account

Mentions: Multifunctional proteins often interact with distinct sets of partners to carry out different biological functions [50–53]. Therefore, they may play important roles in cells. We wonder whether different types of proteins are involved in different number of biological functions. For each protein, the number of associated Gene Ontology (GO) terms is calculated by exploring GO annotations [54]. Here we only consider direct GO annotations. All the three domains (Biological Process (BP), Cellular Component (CC) and Molecular Function (MF)) are considered. From Fig. 5, we observe that MDS proteins are significantly associated with more functions than NMDS proteins (Kolmogrov-Smirnov test, Additional file 6). Moveover, HK-MDS proteins carry out more biological roles than TS-MDS proteins. Similar results are observed when we consider both direct GO annotations and all parent terms (Additional files 6 and 7).Fig. 5


Comparative analysis of housekeeping and tissue-specific driver nodes in human protein interaction networks
Distribution of the number of associated (a) biological process, (b) cellular component and (c) molecular function terms of different types of proteins. The distribution is represented by box plots (line = median). In each figure, outliers have been masked for clarity. Only direct GO annotations are taken into account
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5016887&req=5

Fig5: Distribution of the number of associated (a) biological process, (b) cellular component and (c) molecular function terms of different types of proteins. The distribution is represented by box plots (line = median). In each figure, outliers have been masked for clarity. Only direct GO annotations are taken into account
Mentions: Multifunctional proteins often interact with distinct sets of partners to carry out different biological functions [50–53]. Therefore, they may play important roles in cells. We wonder whether different types of proteins are involved in different number of biological functions. For each protein, the number of associated Gene Ontology (GO) terms is calculated by exploring GO annotations [54]. Here we only consider direct GO annotations. All the three domains (Biological Process (BP), Cellular Component (CC) and Molecular Function (MF)) are considered. From Fig. 5, we observe that MDS proteins are significantly associated with more functions than NMDS proteins (Kolmogrov-Smirnov test, Additional file 6). Moveover, HK-MDS proteins carry out more biological roles than TS-MDS proteins. Similar results are observed when we consider both direct GO annotations and all parent terms (Additional files 6 and 7).Fig. 5

View Article: PubMed Central - PubMed

ABSTRACT

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.

No MeSH data available.