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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.


A graphical example that illustrates the CI-MDS model. A minimum dominating set (MDS) is defined as an optimized subset of proteins (red nodes) from which each remaining (i.e., NMDS) protein (white nodes) can be reached by at least one interaction. For the given toy network, there exists three different MDS configurations : (a) {3, 4}, (b) {3, 5} and (c) {3, 6}. Therefore, it is difficult to determine which one is the real set of controller nodes according to the standard MDS model. To overcome this problem, we introduce a CI-MDS model which takes into account the collective influence of proteins. Here we compute the collective influence of each protein with ℓ=1 (above the nodes). The collective influence of protein 4 is higher than those of proteins 5 and 6. According to the CI-MDS model, proteins {3, 4} are determined as an optimal MDS because its members have the highest collective influence among all the three possible MDS configurations
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Fig3: A graphical example that illustrates the CI-MDS model. A minimum dominating set (MDS) is defined as an optimized subset of proteins (red nodes) from which each remaining (i.e., NMDS) protein (white nodes) can be reached by at least one interaction. For the given toy network, there exists three different MDS configurations : (a) {3, 4}, (b) {3, 5} and (c) {3, 6}. Therefore, it is difficult to determine which one is the real set of controller nodes according to the standard MDS model. To overcome this problem, we introduce a CI-MDS model which takes into account the collective influence of proteins. Here we compute the collective influence of each protein with ℓ=1 (above the nodes). The collective influence of protein 4 is higher than those of proteins 5 and 6. According to the CI-MDS model, proteins {3, 4} are determined as an optimal MDS because its members have the highest collective influence among all the three possible MDS configurations

Mentions: In a protein interaction network, we define a Minimum Dominating Set (MDS) as the smallest subset of proteins from which each Non-MDS (NMDS) protein can be reached by one interaction (Fig. 3) (see “Methods”). In other words, each NMDS protein must be connected to at least one MDS protein. As mentioned in [12, 16], there may exist more than one MDS configuration in a given network (Fig. 3). Therefore, different results may be generated by using different optimization algorithms to solve the standard MDS model [6, 14]. To overcome this problem, we develop a Collective-Influence-corrected Minimum Dominating Set (CI-MDS) model by taking into account the collective influence of proteins (see “Methods”). We apply the standard MDS model and the CI-MDS model on each tissue-specific network to detect tissue-dependent MDS proteins. We solve the two models by using two different optimization methods: “lp_solve” [43] and “intlinprog” [44]. There is a distance parameter ℓ in the proposed CI-MDS model. To investigate the effect of ℓ, we try several different values (e.g., ℓ=0,1,2,3). The standard MDS model produces quite different MDSs by using different optimization algorithms, but the CI-MDS model (with ℓ≥1) generates almost the same MDSs (Additional file 2).Fig. 3


Comparative analysis of housekeeping and tissue-specific driver nodes in human protein interaction networks
A graphical example that illustrates the CI-MDS model. A minimum dominating set (MDS) is defined as an optimized subset of proteins (red nodes) from which each remaining (i.e., NMDS) protein (white nodes) can be reached by at least one interaction. For the given toy network, there exists three different MDS configurations : (a) {3, 4}, (b) {3, 5} and (c) {3, 6}. Therefore, it is difficult to determine which one is the real set of controller nodes according to the standard MDS model. To overcome this problem, we introduce a CI-MDS model which takes into account the collective influence of proteins. Here we compute the collective influence of each protein with ℓ=1 (above the nodes). The collective influence of protein 4 is higher than those of proteins 5 and 6. According to the CI-MDS model, proteins {3, 4} are determined as an optimal MDS because its members have the highest collective influence among all the three possible MDS configurations
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig3: A graphical example that illustrates the CI-MDS model. A minimum dominating set (MDS) is defined as an optimized subset of proteins (red nodes) from which each remaining (i.e., NMDS) protein (white nodes) can be reached by at least one interaction. For the given toy network, there exists three different MDS configurations : (a) {3, 4}, (b) {3, 5} and (c) {3, 6}. Therefore, it is difficult to determine which one is the real set of controller nodes according to the standard MDS model. To overcome this problem, we introduce a CI-MDS model which takes into account the collective influence of proteins. Here we compute the collective influence of each protein with ℓ=1 (above the nodes). The collective influence of protein 4 is higher than those of proteins 5 and 6. According to the CI-MDS model, proteins {3, 4} are determined as an optimal MDS because its members have the highest collective influence among all the three possible MDS configurations
Mentions: In a protein interaction network, we define a Minimum Dominating Set (MDS) as the smallest subset of proteins from which each Non-MDS (NMDS) protein can be reached by one interaction (Fig. 3) (see “Methods”). In other words, each NMDS protein must be connected to at least one MDS protein. As mentioned in [12, 16], there may exist more than one MDS configuration in a given network (Fig. 3). Therefore, different results may be generated by using different optimization algorithms to solve the standard MDS model [6, 14]. To overcome this problem, we develop a Collective-Influence-corrected Minimum Dominating Set (CI-MDS) model by taking into account the collective influence of proteins (see “Methods”). We apply the standard MDS model and the CI-MDS model on each tissue-specific network to detect tissue-dependent MDS proteins. We solve the two models by using two different optimization methods: “lp_solve” [43] and “intlinprog” [44]. There is a distance parameter ℓ in the proposed CI-MDS model. To investigate the effect of ℓ, we try several different values (e.g., ℓ=0,1,2,3). The standard MDS model produces quite different MDSs by using different optimization algorithms, but the CI-MDS model (with ℓ≥1) generates almost the same MDSs (Additional file 2).Fig. 3

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.