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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.


The distribution of proteins, interactions and MDS proteins across 16 tissues. For proteins and interactions, the x-axis denotes the number of tissue in which they are expressed; for MDS proteins, the x-axis denotes the number of tissue in which they are identified as MDS proteins. The y-axis denotes the frequency. The distribution of proteins, interactions and MDS proteins by the number of tissues in which they are expressed (or selected as MDS proteins) is bi-modal, with most of them being globally (14 – 16 tissues) or tissue-specific (1 – 3 tissues)
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Fig2: The distribution of proteins, interactions and MDS proteins across 16 tissues. For proteins and interactions, the x-axis denotes the number of tissue in which they are expressed; for MDS proteins, the x-axis denotes the number of tissue in which they are identified as MDS proteins. The y-axis denotes the frequency. The distribution of proteins, interactions and MDS proteins by the number of tissues in which they are expressed (or selected as MDS proteins) is bi-modal, with most of them being globally (14 – 16 tissues) or tissue-specific (1 – 3 tissues)

Mentions: We find that 42,290 interactions involving 9834 proteins can occur in at least one of the 16 main tissues, and each tissue-specific network covers only a part of proteins (66.51 – 89.06 %) and interactions (61.45 – 88.41 %) (Table 1). We also observe a bi-modal distribution of expressed proteins across tissues (Fig. 2): 65.9 % of proteins are expressed in 14 – 16 tissues (housekeeping proteins), and 10.7 % of proteins are expressed in 1 – 3 tissues (tissue-specific proteins), which is in agreement with previous observations [42]. Several studies have performed a comprehensive analysis of housekeeping proteins and tissue-specific proteins [31, 32, 34, 42]. Thus, we do not repeat the analysis below.Fig. 2


Comparative analysis of housekeeping and tissue-specific driver nodes in human protein interaction networks
The distribution of proteins, interactions and MDS proteins across 16 tissues. For proteins and interactions, the x-axis denotes the number of tissue in which they are expressed; for MDS proteins, the x-axis denotes the number of tissue in which they are identified as MDS proteins. The y-axis denotes the frequency. The distribution of proteins, interactions and MDS proteins by the number of tissues in which they are expressed (or selected as MDS proteins) is bi-modal, with most of them being globally (14 – 16 tissues) or tissue-specific (1 – 3 tissues)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig2: The distribution of proteins, interactions and MDS proteins across 16 tissues. For proteins and interactions, the x-axis denotes the number of tissue in which they are expressed; for MDS proteins, the x-axis denotes the number of tissue in which they are identified as MDS proteins. The y-axis denotes the frequency. The distribution of proteins, interactions and MDS proteins by the number of tissues in which they are expressed (or selected as MDS proteins) is bi-modal, with most of them being globally (14 – 16 tissues) or tissue-specific (1 – 3 tissues)
Mentions: We find that 42,290 interactions involving 9834 proteins can occur in at least one of the 16 main tissues, and each tissue-specific network covers only a part of proteins (66.51 – 89.06 %) and interactions (61.45 – 88.41 %) (Table 1). We also observe a bi-modal distribution of expressed proteins across tissues (Fig. 2): 65.9 % of proteins are expressed in 14 – 16 tissues (housekeeping proteins), and 10.7 % of proteins are expressed in 1 – 3 tissues (tissue-specific proteins), which is in agreement with previous observations [42]. Several studies have performed a comprehensive analysis of housekeeping proteins and tissue-specific proteins [31, 32, 34, 42]. Thus, we do not repeat the analysis below.Fig. 2

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.