Controllability analysis of molecular pathways points to proteins that control the entire interaction network.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
19 02 2020
19 02 2020
Historique:
received:
29
03
2019
accepted:
20
01
2020
entrez:
21
2
2020
pubmed:
23
2
2020
medline:
13
11
2020
Statut:
epublish
Résumé
Inputs to molecular pathways that are the backbone of cellular activity drive the cell to certain outcomes and phenotypes. Here, we investigated proteins that topologically controlled different human pathways represented as independent molecular interaction networks, suggesting that a minority of proteins control a high number of pathways and vice versa. Transcending different topological levels, proteins that controlled a large number of pathways also controlled a network of interactions when all pathways were combined. Furthermore, control proteins that were robust when interactions were rewired or inverted also increasingly controlled an increasing number of pathways. As for functional characteristics, such control proteins were enriched with regulatory and signaling genes, disease genes and drug targets. Focusing on evolutionary characteristics, proteins that controlled different pathways had a penchant to be evolutionarily conserved as equal counterparts in other organisms, indicating the fundamental role that control analysis of pathways plays for our understanding of regulation, disease and evolution.
Identifiants
pubmed: 32076007
doi: 10.1038/s41598-020-59717-6
pii: 10.1038/s41598-020-59717-6
pmc: PMC7031241
doi:
Substances chimiques
Proteins
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2943Références
Liu, Y. Y., Slotine, J. J. & Barabasi, A. L. Controllability of complex networks. Nat. 473, 167–173 (2011).
doi: 10.1038/nature10011
Vinayagam, A. et al. Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets. Proc. Natl Acad. Sci. USA 113, 4976–4981 (2016).
doi: 10.1073/pnas.1603992113
Basler, G., Nikoloski, Z., Larhlimi, A., Barabasi, A. L. & Liu, Y. Y. Control of fluxes in metabolic networks. Genome Res. 26, 956–968 (2016).
doi: 10.1101/gr.202648.115
Yan, G. et al. Network control principles predict neuron function in the Caenorhabditis elegans connectome. Nat. 550, 519–523 (2017).
doi: 10.1038/nature24056
Gao, J., Liu, Y. Y., D’Souza, R. M. & Barabási, A. L. Target control of complex networks. Nat. Commun. 5, 5415 (2014).
doi: 10.1038/ncomms6415
Ogata, H. et al. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 27, 29–34 (1999).
doi: 10.1093/nar/27.1.29
Jupe, S., Akkerman, J. W., Soranzo, N. & Ouwehand, W. H. Reactome - a curated knowledgebase of biological pathways: megakaryocytes and platelets. J. Thromb. Haemost. 10, 2399–2402 (2012).
doi: 10.1111/j.1538-7836.2012.04930.x
Chen, W. H., Minguez, P., Lercher, M. J. & Bork, P. OGEE: an online gene essentiality database. Nucleic Acids Res. 40, D901–906 (2012).
doi: 10.1093/nar/gkr986
Luo, H., Lin, Y., Gao, F., Zhang, C. T. & Zhang, R. DEG 10, an update of the database of essential genes that includes both protein-coding genes and noncoding genomic elements. Nucleic Acids Res. 42, D574–580 (2014).
doi: 10.1093/nar/gkt1131
Vaquerizas, J. M., Kummerfeld, S. K., Teichmann, S. A. & Luscombe, N. M. A census of human transcription factors: function, expression and evolution. Nat. Rev. Genet. 10, 252–263 (2009).
doi: 10.1038/nrg2538
Wilson, D., Charoensawan, V., Kummerfeld, S. K. & Teichmann, S. A. DBD–taxonomically broad transcription factor predictions: new content and functionality. Nucleic Acids Res. 36, D88–92 (2008).
doi: 10.1093/nar/gkm964
Cheng, F., Jia, P., Wang, Q. & Zhao, Z. Quantitative network mapping of the human kinome interactome reveals new clues for rational kinase inhibitor discovery and individualized cancer therapy. Oncotarget 5, 3697–3710 (2014).
pubmed: 25003367
pmcid: 25003367
Futreal, P. A. et al. A census of human cancer genes. Nat. Rev. Cancer 4, 177–183 (2004).
doi: 10.1038/nrc1299
Higgins, M. E., Claremont, M., Major, J. E., Sander, C. & Lash, A. E. CancerGenes: a gene selection resource for cancer genome projects. Nucleic Acids Res. 35, D721–726 (2007).
doi: 10.1093/nar/gkl811
Ako-Adjei, D. et al. HIV-1, human interaction database: current status and new features. Nucleic Acids Res. 43, D566–570 (2015).
doi: 10.1093/nar/gku1126
Robinson, P. N. et al. The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease. Am. J. Hum. Genet. 83, 610–615 (2008).
doi: 10.1016/j.ajhg.2008.09.017
Amberger, J., Bocchini, C. & Hamosh, A. A new face and new challenges for Online Mendelian Inheritance in Man (OMIM(R)). Hum. Mutat. 32, 564–567 (2011).
doi: 10.1002/humu.21466
Hindorff, L. A. et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl Acad. Sci. USA 106, 9362–9367 (2009).
doi: 10.1073/pnas.0903103106
Knox, C. et al. DrugBank 3.0: a comprehensive resource for ‘omics’ research on drugs. Nucleic Acids Res. 39, D1035–1041 (2011).
doi: 10.1093/nar/gkq1126
Wuchty, S. Controllability in protein interaction networks. Proc. Natl Acad. Sci. USA 111, 7156–7160 (2014).
doi: 10.1073/pnas.1311231111
Wuchty, S., Boltz, T. & Küçük-McGinty, H. Links between critical proteins drive the controllability of protein interaction networks. Proteomics (2017).
Nacher, J. C. & Akutsu, T. Analysis of critical and redundant nodes in controlling directed and undirected complex networks using dominating sets. J. Compl. Netw. 2, 394–412 (2014).
doi: 10.1093/comnet/cnu029
Barabasi, A. L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011).
doi: 10.1038/nrg2918
Sales, G., Calura, E., Cavalieri, D. & Romualdi, C. graphite - a Bioconductor package to convert pathway topology to gene network. BMC Bioinforma. 13, 20 (2012).
doi: 10.1186/1471-2105-13-20
Hornbeck, P. V. et al. PhosphoSitePlus, 2014: mutations, PTMs and recalibrations. Nucleic Acids Res. 43, D512–520 (2015).
doi: 10.1093/nar/gku1267
Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).
doi: 10.1038/75556
Almen, M. S., Nordstrom, K. J., Fredriksson, R. & Schioth, H. B. Mapping the human membrane proteome: a majority of the human membrane proteins can be classified according to function and evolutionary origin. BMC Biol. 7, 50 (2009).
doi: 10.1186/1741-7007-7-50
Hopkins, A. L. & Groom, C. R. The druggable genome. Nat. Rev. Drug. Discov. 1, 727–730 (2002).
doi: 10.1038/nrd892
Hopcroft, J. E. & Karp, R. M. An n
doi: 10.1137/0202019