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 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.
School of Mathematics and Statistics & Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Luoyu Road, Wuhan, 430079, China.
Xiao-Fei
XF
Zhang
College of Information Engineering, Shenzhen University, Nanhai Ave 3688, Shenzhen, 518060, China.
Le
L
Ou-Yang
Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Xingang West Road, Guangzhou, 510275, China. stsddq@mail.sysu.edu.cn.
Dao-Qing
DQ
Dai
School of Statistics and Management, Shanghai University of Finance and Economics, Guoding Road, Shanghai, 200433, China.
Meng-Yun
MY
Wu
School of Automation, China University of Geosciences, Lumo Road, Wuhan, 430074, China.
Yuan
Y
Zhu
Department of Electronic and Engineering, City University of Hong Kong, Tat Chee Avenue, Hong Kong, China.
Hong
H
Yan
D000375:Aging / Q000235:genetics; D000465:Algorithms; D019143:Evolution, Molecular; D063990:Gene Ontology; D020043:Genes, Essential; D006801:Humans; D008962:Models, Theoretical; D009369:Neoplasms / Q000235:genetics; D009928:Organ Specificity / Q000235:genetics*; D060066:Protein Interaction Maps; D011499:Protein Processing, Post-Translational / Q000235:genetics; D014780:Viruses / Q000378:metabolism
Collective influence; Driver proteins; Minimum dominating set; Protein interaction network; Tissue specificity