Evidence map›Paper›PMID 27612563›Full record

ArticleBMC bioinformatics2016

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

Xiao-Fei Zhang, Le Ou-Yang, Dao-Qing Dai, Meng-Yun Wu, Yuan Zhu, Hong Yan

Open access · goldAbstract readComparative Study
In one paragraph

Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.4field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 14 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 6 institutions in 2 countries.

Xiao-Fei ZhangSchool of Mathematics and Statistics & Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Luoyu Road, Wuhan, 430079, China.
Le Ou-YangCollege of Information Engineering, Shenzhen University, Nanhai Ave 3688, Shenzhen, 518060, China.
Dao-Qing DaiIntelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Xingang West Road, Guangzhou, 510275, China. stsddq@mail.sysu.edu.cn.
Meng-Yun WuSchool of Statistics and Management, Shanghai University of Finance and Economics, Guoding Road, Shanghai, 200433, China.
Yuan ZhuSchool of Automation, China University of Geosciences, Lumo Road, Wuhan, 430074, China.
Hong YanDepartment of Electronic and Engineering, City University of Hong Kong, Tat Chee Avenue, Hong Kong, China.
Central China Normal University · CNChina University of Geosciences · CNCity University of Hong Kong · HKShanghai University of Finance and Economics · CNShenzhen University · CNSun Yat-sen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSeveral 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.

resultsIn 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.

conclusionsOur 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.

Indexed as

Genes, EssentialProtein Interaction MapsAgingAlgorithmsEvolution, MolecularGene OntologyHumansModels, TheoreticalNeoplasmsOrgan SpecificityProtein Processing, Post-TranslationalVirusesCollective influenceDriver proteinsMinimum dominating setProtein interaction networkTissue specificity

Identifiers

PMID27612563
PMCPMC5016887
OpenAlexW2516918633

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.