Artículo

Estamos trabajando para incorporar este artículo al repositorio
Consulte el artículo en la página del editor
Consulte la política de Acceso Abierto del editor

Abstract:

Characterizing the behavior of disease genes in the context of biological networks has the potential to shed light on disease mechanisms, and to reveal both new candidate disease genes and therapeutic targets. Previous studies addressing the network properties of disease genes have produced contradictory results. Here we have explored the causes of these discrepancies and assessed the relationship between the network roles of disease genes and their tolerance to deleterious germline variants in human populations leveraging on: the abundance of interactome resources, a comprehensive catalog of disease genes and exome variation data. We found that the most salient network features of disease genes are driven by cancer genes and that genes related to different types of diseases play network roles whose centrality is inversely correlated to their tolerance to likely deleterious germline mutations. This proved to be a multiscale signature, including global, mesoscopic and local network centrality features. Cancer driver genes, the most sensitive to deleterious variants, occupy the most central positions, followed by dominant disease genes and then by recessive disease genes, which are tolerant to variants and isolated within their network modules.

Registro:

Documento: Artículo
Título:Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing
Autor:Piñero, J.; Berenstein, A.; Gonzalez-Perez, A.; Chernomoretz, A.; Furlong, L.I.
Filiación:Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), DCEXS, Pompeu Fabra University (UPF), C/Dr. Aiguader, 88, Barcelona, 08003, Spain
Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 1, Buenos Aires, Argentina
Instituto de Física de Buenos Aires, Consejo Nacional de Investigaciones Científicas y Técnicas, Ciudad Universitaria, Pabellón 1, Buenos Aires, Argentina
Laboratorio de Biología de Sistemas Integrativa, Fundación Instituto Leloir, Buenos Aires, Argentina
Palabras clave:biology; genetics; high throughput sequencing; human; neoplasm; procedures; Computational Biology; High-Throughput Nucleotide Sequencing; Humans; Neoplasms
Año:2016
Volumen:6
DOI: http://dx.doi.org/10.1038/srep24570
Título revista:Scientific Reports
Título revista abreviado:Sci. Rep.
ISSN:20452322
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_20452322_v6_n_p_Pinero

Referencias:

  • Durbin, R.M., A map of human genome variation from population-scale sequencing (2010) Nature, 467, pp. 1061-1073
  • Lawrence, M.S., Discovery and saturation analysis of cancer genes across 21 tumour types (2014) Nature, 505, pp. 495-501
  • Tamborero, D., Comprehensive identification of mutational cancer driver genes across 12 tumor types (2013) Sci. Rep., 3, p. 2650
  • Kandoth, C., Mutational landscape and significance across 12 major cancer types (2013) Nature, 502, pp. 333-339
  • Chuang, H.-Y., Lee, E., Liu, Y.-T., Lee, D., Ideker, T., Network-based classification of breast cancer metastasis (2007) Mol. Syst. Biol., 3, p. 140
  • Jonsson, P.F., Bates, P.A., Global topological features of cancer proteins in the human interactome (2006) Bioinformatics, 22, pp. 2291-2297
  • Zhong, Q., Edgetic perturbation models of human inherited disorders (2009) Mol. Syst. Biol., 5, p. 321
  • Köhler, S., Bauer, S., Horn, D., Robinson, P.N., Walking the interactome for prioritization of candidate disease genes (2008) Am. J. Hum. Genet., 82, pp. 949-958
  • Lage, K., A human phenome-interactome network of protein complexes implicated in genetic disorders (2007) Nat. Biotechnol., 25, pp. 309-316
  • Guney, E., Oliva, B., Exploiting protein-protein interaction networks for genome-wide disease-gene prioritization (2012) Plos One, 7, p. e43557
  • Lim, J., A protein-protein interaction network for human inherited ataxias and disorders of Purkinje cell degeneration (2006) Cell, 125, pp. 801-814
  • Xu, J., Li, Y., Discovering disease-genes by topological features in human protein-protein interaction network (2006) Bioinformatics, 22, pp. 2800-2805
  • Cai, J.J., Borenstein, E., Petrov, D.A., Broker genes in human disease (2010) Genome Biol. Evol., 2, pp. 815-825
  • Goh, K.-I., The human disease network (2007) Proc. Natl. Acad. Sci. USA, 104, pp. 8685-8690
  • Furlong, L.I., Human diseases through the lens of network biology (2012) Trends Genet. Null
  • Pinero, J., Dis genet: A discovery platform for the dynamical exploration of human diseases and their genes (2015) Database, 2015, pp. bav028-bav028
  • Garcia-Alonso, L., The role of the interactome in the maintenance of deleterious variability in human populations (2014) Mol. Syst. Biol., 10, p. 752
  • Schaefer, M.H., HIPPIE: Integrating protein interaction networks with experiment based quality scores (2012) Plos One, 7, p. e31826
  • Rual, J.-F., Towards a proteome-scale map of the human protein-protein interaction network (2005) Nature, 437, pp. 1173-1178
  • Rosvall, M., Bergstrom, C.T., Maps of random walks on complex networks reveal community structure (2008) Proc. Natl. Acad. Sci. USA, 105, pp. 1118-1123
  • Lancichinetti, A., Fortunato, S., Community detection algorithms: A comparative analysis (2009) Phys. Rev. E, 80, p. 056117
  • Berenstein, A.J., Pinero, J., Furlong, L.I., Chernomoretz, A., Mining the modular structure of protein interaction networks (2015) Plos One, 10, p. e0122477
  • Liu, W., Pellegrini, M., Wang, X., Detecting communities based on network topology (2014) Sci. Rep., 4, p. 5739
  • Guimera, R., Amaral, L.A.N., Cartography of complex networks: Modules and universal roles (2005) J. Stat. Mech. Online, 2005, p. nihpa35573
  • Guimera, R., Amaral, L.A.N., Functional cartography of complex metabolic networks (2005) Nature, 433, pp. 895-900
  • Thomas, P.D., PANTHER: A browsable database of gene products organized by biological function, using curated protein family and subfamily classification (2003) Nucleic Acids Res., 31, pp. 334-341
  • Zhu, X., Need, A.C., Petrovski, S., Goldstein, D.B., One gene, many neuropsychiatric disorders: Lessons from Mendelian diseases (2014) Nat. Neurosci., 17, pp. 773-781
  • Kircher, M., A general framework for estimating the relative pathogenicity of human genetic variants (2014) Nat. Genet., 46, pp. 310-315
  • Fernández-Medarde, A., Santos, E., Ras in cancer and developmental diseases (2011) Genes Cancer, 2, pp. 344-358
  • Menche, J., Disease networks. Uncovering disease-disease relationships through the incomplete interactome (2015) Science, 347, p. 1257601
  • Hanahan, D., Weinberg, R.A., The hallmarks of cancer (2000) Cell, 100, pp. 57-70
  • Hanahan, D., Weinberg, R.A., Hallmarks of cancer: The next generation (2011) Cell, 144, pp. 646-674
  • Vogelstein, B., Cancer genome landscapes (2013) Science, 339, pp. 1546-1558
  • Hao, D., Systematic large-scale study of the inheritance mode of Mendelian disorders provides new insight into human diseasome (2014) Eur. J. Hum. Genet.
  • Hao, D., Network-based analysis of genotype-phenotype correlations between different inheritance modes (2014) Bioinformatics, 30, pp. 3223-3231
  • Veitia, R.A., Exploring the etiology of haploinsufficiency (2002) Bioessays, 24, pp. 175-184
  • Wilkie, A.O., The molecular basis of genetic dominance (1994) J. Med. Genet., 31, pp. 89-98
  • Petrovski, S., Wang, Q., Heinzen, E.L., Allen, A.S., Goldstein, D.B., Genic intolerance to functional variation and the interpretation of personal genomes (2013) PLoS Genet, 9, p. e1003709
  • Shyr, C., FLAGS, frequently mutated genes in public exomes (2014) BMC Med. Genomics, 7, p. 64
  • Garcia-Garcia, J., Guney, E., Aragues, R., Planas-Iglesias, J., Oliva, B., Biana: A software framework for compiling biological interactions and analyzing networks (2010) BMC Bioinformatics, 11, p. 56
  • Stark, C., Bio GRID: A general repository for interaction datasets (2006) Nucleic Acids Res, 34, pp. D535-D539
  • Orchard, S., The mint act project-int act as a common curation platform for 11 molecular interaction databases (2014) Nucleic Acids Res, 42, pp. D358-D363
  • Razick, S., Magklaras, G., Donaldson, I.M., IRefIndex: A consolidated protein interaction database with provenance (2008) BMC Bioinformatics, 9, p. 405
  • Rolland, T., A proteome-scale map of the human interactome network (2014) Cell, 159, pp. 1212-1226
  • Janjić, V., Pržulj, N., Biological function through network topology: A survey of the human diseasome (2012) Brief. Funct. Genomics, 11, pp. 522-532
  • Wodak, S.J., Vlasblom, J., Turinsky, A.L., Pu, S., Protein-protein interaction networks: The puzzling riches (2013) Curr. Opin. Struct. Biol., 23, pp. 941-953
  • Lopes, T.J.S., Tissue-specific subnetworks and characteristics of publicly available human protein interaction databases (2011) Bioinformatics, 27, pp. 2414-2421
  • Jensen, L.J., Bork, P., Biochemistry. Not comparable, but complementary (2008) Science, 322, pp. 56-57
  • Stelzl, U., A human protein-protein interaction network: A resource for annotating the proteome (2005) Cell, 122, pp. 957-968
  • Ewing, R.M., Large-scale mapping of human protein-protein interactions by mass spectrometry (2007) Mol. Syst. Biol., 3, p. 89
  • Venkatesan, K., An empirical framework for binary interactome mapping (2009) Nat. Methods, 6, pp. 83-90
  • Wang, J., Toward an understanding of the protein interaction network of the human liver (2011) Mol. Syst. Biol., 7, p. 536
  • Kristensen, A.R., Gsponer, J., Foster, L.J., A high-throughput approach for measuring temporal changes in the interactome (2012) Nat. Methods, 9, pp. 907-909
  • Havugimana, P.C., A census of human soluble protein complexes (2012) Cell, 150, pp. 1068-1081
  • Wagner, S.A., A proteome-wide, quantitative survey of in vivo ubiquitylation sites reveals widespread regulatory roles (2011) Mol. Cell. Proteomics, 10, p. M111013284
  • Stes, E., A COFRADIC protocol to study protein ubiquitination (2014) J. Proteome Res., 13, pp. 3107-3113
  • Povlsen, L.K., Systems-wide analysis of ubiquitylation dynamics reveals a key role for PAF15 ubiquitylation in DNA-damage bypass (2012) Nat. Cell Biol., 14, pp. 1089-1098
  • Bandyopadhyay, S., A human MAP kinase interactome (2010) Nat. Methods, 7, pp. 801-805
  • Vinayagam, A., A directed protein interaction network for investigating intracellular signal transduction (2011) Sci. Signal., 4, p. rs8
  • Futreal, P.A., A census of human cancer genes (2004) Nat. Rev. Cancer, 4, pp. 177-183
  • Rubio-Perez, C., In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals targeting opportunities (2015) Cancer Cell, 27, pp. 382-396
  • Amberger, J., Bocchini, C.A., Scott, A.F., Hamosh, A., McKusick's online mendelian inheritance in man (OMIM) (2009) Nucleic Acids Res, 37, pp. D793-D796
  • Singh, P.P., Affeldt, S., Malaguti, G., Isambert, H., Human dominant disease genes are enriched in paralogs originating from whole genome duplication (2014) PLoS Comput. Biol., 10, p. e1003754
  • Blekhman, R., Natural selection on genes that underlie human disease susceptibility (2008) Curr. Biol., 18, pp. 883-889
  • Activities at the universal protein resource (Uni Prot) (2014) Nucleic Acids Res, 42, pp. D191-D198
  • Landrum, M.J., Clin var: Public archive of relationships among sequence variation and human phenotype (2014) Nucleic Acids Res, 42, pp. D980-D985
  • Mi, H., Thomas, P., PANTHER pathway: An ontology-based pathway database coupled with data analysis tools (2009) Methods Mol. Biol. Clift. Nj, 563, pp. 123-140
  • R: A Language and Environment for Statistical Computing, , http://www.R-project.org/, R Foundation for Statistical Computing, Vienna, Austria
  • Csardi, G., Nepusz, T., The igraph software package for complex network research (2006) Inter Journal Complex Systems, p. 1695. , http://igraph.org/
  • Falcon, S., Gentleman, R., Using GOstats to test gene lists for GO term association (2007) Bioinformatics, 23, pp. 257-258

Citas:

---------- APA ----------
Piñero, J., Berenstein, A., Gonzalez-Perez, A., Chernomoretz, A. & Furlong, L.I. (2016) . Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing. Scientific Reports, 6.
http://dx.doi.org/10.1038/srep24570
---------- CHICAGO ----------
Piñero, J., Berenstein, A., Gonzalez-Perez, A., Chernomoretz, A., Furlong, L.I. "Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing" . Scientific Reports 6 (2016).
http://dx.doi.org/10.1038/srep24570
---------- MLA ----------
Piñero, J., Berenstein, A., Gonzalez-Perez, A., Chernomoretz, A., Furlong, L.I. "Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing" . Scientific Reports, vol. 6, 2016.
http://dx.doi.org/10.1038/srep24570
---------- VANCOUVER ----------
Piñero, J., Berenstein, A., Gonzalez-Perez, A., Chernomoretz, A., Furlong, L.I. Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing. Sci. Rep. 2016;6.
http://dx.doi.org/10.1038/srep24570