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access icon openaccess SMILE: a novel procedure for subcellular module identification with localisation expansion

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 09/12/2017, Accepted 12/12/2017, Published 18/12/2017

Computational clustering methods help identify functional modules in protein–protein interaction (PPI) network, in which proteins participate in the same biological pathways or specific functions. Subcellular localisation is crucial for proteins to implement biological functions and each compartment accommodates specific portions of the protein interaction structure. However, the importance of protein subcellular localisation is often neglected in the studies of module identification. In this study, the authors propose a novel procedure, subcellular module identification with localisation expansion (SMILE), to identify super modules that consist of several subcellular modules performing specific biological functions among cell compartments. These super modules identified by SMILE are more functionally diverse and have been verified to be more associated with known protein complexes and biological pathways compared with the modules identified from the global PPI networks in both the compartmentalised PPI and InWeb_InBioMap datasets. The authors’ results reveal that subcellular localisation is a principal feature of functional modules and offers important guidance in detecting biologically meaningful results.

Inspec keywords: proteins; cellular biophysics; molecular biophysics

Other keywords: computational clustering methods; protein subcellular localisation; subcellular modules; protein-protein interaction network; localisation expansion; subcellular module identification; subcellular localisation; biological functions; protein interaction structure; InWeb-InBioMap datasets

Subjects: Biomolecular interactions, charge transfer complexes; Cellular biophysics

References

    1. 1)
      • 53. Gray, K.A., Yates, B., Seal, R.L., et al: ‘Genenames.org: the HGNC resources in 2015’, Nucleic Acids Res., 2015, 43, (Database issue), pp. D1079–D1085.
    2. 2)
      • 1. Robinson, C.V., Sali, A., Baumeister, W.: ‘The molecular sociology of the cell’, Nature, 2007, 450, (7172), pp. 973–982.
    3. 3)
      • 38. Kanehisa, M., Furumichi, M., Tanabe, M., et al: ‘KEGG: new perspectives on genomes, pathways, diseases and drugs’, Nucleic Acids Res., 2017, 45, (D1), pp. D353–D361.
    4. 4)
      • 22. Park, S., Yang, J.S., Shin, Y.E., et al: ‘Protein localization as a principal feature of the etiology and comorbidity of genetic diseases’, Mol. Syst. Biol., 2011, 7, p. 494.
    5. 5)
      • 24. Veres, D.V., Gyurko, D.M., Thaler, B., et al: ‘ComPPI: a cellular compartment-specific database for protein-protein interaction network analysis’, Nucleic Acids Res., 2015, 43, (Database issue), pp. D485–D493.
    6. 6)
      • 31. Yousefi, S., Perozzo, R., Schmid, I., et al: ‘Calpain-mediated cleavage of Atg5 switches autophagy to apoptosis’, Nat. Cell Biol., 2006, 8, (10), pp. 1124–1132.
    7. 7)
      • 47. Dong, J., Horvath, S.: ‘Understanding network concepts in modules’, BMC Syst. Biol., 2007, 1, p. 24.
    8. 8)
      • 17. Rhee, D.Y., Cho, D.Y., Zhai, B., et al: ‘Transcription factor networks in drosophila melanogaster’, Cell Rep., 2014, 8, (6), pp. 2031–2043.
    9. 9)
      • 10. Dittrich, M.T., Klau, G.W., Rosenwald, A., et al: ‘Identifying functional modules in protein-protein interaction networks: an integrated exact approach’, Bioinformatics, 2008, 24, (13), pp. i223–i231.
    10. 10)
      • 5. Jiang, W., Zhang, Y., Meng, F., et al: ‘Identification of active transcription factor and mirna regulatory pathways in Alzheimer's disease’, Bioinformatics, 2013, 29, (20), pp. 2596–2602.
    11. 11)
      • 11. Li, X., Wu, M., Kwoh, C.K., et al: ‘Computational approaches for detecting protein complexes from protein interaction networks: a survey’, BMC Genomics, 2010, 11, (Suppl 1), p. S3.
    12. 12)
      • 45. Liberzon, A., Birger, C., Thorvaldsdottir, H., et al: ‘The molecular signatures database (MSigDB) hallmark gene set collection’, Cell Syst., 2015, 1, (6), pp. 417–425.
    13. 13)
      • 40. Mi, H., Huang, X., Muruganujan, A., et al: ‘Panther version 11: expanded annotation data from gene ontology and reactome pathways, and data analysis tool enhancements’, Nucleic Acids Res., 2017, 45, (D1), pp. D183–D189.
    14. 14)
      • 6. Hao, D., Li, C., Zhang, S., et al: ‘Network-based analysis of genotype-phenotype correlations between different inheritance modes’, Bioinformatics, 2014, 30, (22), pp. 3223–3231.
    15. 15)
      • 13. Wang, Y., Qian, X.: ‘Functional module identification in protein interaction networks by interaction patterns’, Bioinformatics, 2014, 30, (1), pp. 81–93.
    16. 16)
      • 12. Shih, Y.K., Parthasarathy, S.: ‘Identifying functional modules in interaction networks through overlapping Markov clustering’, Bioinformatics, 2012, 28, (18), pp. i473–i479.
    17. 17)
      • 51. Sims, R.J.3rd, Mandal, S.S., Reinberg, D.: ‘Recent highlights of RNA-polymerase-II-mediated transcription’, Curr. Opin. Cell Biol., 2004, 16, (3), pp. 263–271.
    18. 18)
      • 4. Koh, G.C., Porras, P., Aranda, B., et al: ‘Analyzing protein-protein interaction networks’, J. Proteome Res., 2012, 11, (4), pp. 2014–2031.
    19. 19)
      • 23. Barabasi, A.L., Oltvai, Z.N.: ‘Network biology: understanding the cell's functional organization’, Nat. Rev. Genet., 2004, 5, (2), pp. 101–113.
    20. 20)
      • 32. Wang, Y., Qian, X.: ‘Finding low-conductance sets with dense interactions (FLCD) for better protein complex prediction’, BMC Syst. Biol., 2017, 11, (Suppl 3), p. 22.
    21. 21)
      • 50. Huang, L., Guan, R.J., Pardee, A.B.: ‘Evolution of transcriptional control from prokaryotic beginnings to eukaryotic complexities’, Crit. Rev. Eukaryot Gene. Expr., 1999, 9, (3–4), pp. 175–182.
    22. 22)
      • 15. Li, T., Wernersson, R., Hansen, R.B., et al: ‘A scored human protein-protein interaction network to catalyze genomic interpretation’, Nat. Methods, 2017, 14, (1), pp. 61–64.
    23. 23)
      • 48. Marquez, R.T., Xu, L.: ‘Bcl-2:Beclin 1 complex: multiple, mechanisms regulating autophagy/apoptosis toggle switch’, Am. J. Cancer Res., 2012, 2, (2), pp. 214–221.
    24. 24)
      • 29. Tasdemir, E., Chiara Maiuri, M., Morselli, E., et al: ‘A dual role of P53 in the control of autophagy’, Autophagy, 2008, 4, (6), pp. 810–814.
    25. 25)
      • 27. Maiuri, M.C., Zalckvar, E., Kimchi, A., et al: ‘Self-eating and self-killing: crosstalk between autophagy and apoptosis’, Nat. Rev. Mol. Cell Biol., 2007, 8, (9), pp. 741–752.
    26. 26)
      • 20. Hao, N., O'Shea, E.K.: ‘Signal-dependent dynamics of transcription factor translocation controls gene expression’, Nat. Struct. Mol. Biol., 2011, 19, (1), pp. 31–39.
    27. 27)
      • 30. Tasdemir, E., Maiuri, M.C., Galluzzi, L., et al: ‘Regulation of autophagy by cytoplasmic P53’, Nat. Cell Biol., 2008, 10, (6), pp. 676–687.
    28. 28)
      • 44. Liberzon, A.: ‘A description of the molecular signatures database (MSigDB) web site’, Methods Mol. Biol., 2014, 1150, pp. 153–160.
    29. 29)
      • 7. Qin, J., Li, M.J., Wang, P., et al: ‘Proteomirexpress: inferring microrna and protein-centered regulatory networks from high-throughput proteomic and mRNA expression data’, Mol. Cell Proteomics, 2013, 12, (11), pp. 3379–3387.
    30. 30)
      • 36. Ruepp, A., Waegele, B., Lechner, M., et al: ‘CORUM: the comprehensive resource of mammalian protein complexes--2009’, Nucleic Acids Res., 2010, 38, (Database issue), pp. D497–D501.
    31. 31)
      • 39. Kanehisa, M., Goto, S.: ‘KEGG: kyoto encyclopedia of genes and genomes’, Nucleic Acids Res., 2000, 28, (1), pp. 27–30.
    32. 32)
      • 56. Yi, Y., Zhao, Y., Li, C., et al: ‘Raid V2.0: an updated resource of RNA-associated interactions across organisms’, Nucleic Acids Res., 2017, 45, (D1), pp. D115–D118.
    33. 33)
      • 18. Zhang, T., Tan, P., Wang, L., et al: ‘Rnalocate: a resource for RNA subcellular localizations’, Nucleic Acids Res., 2016, 45, (D1), pp. D135–D138.
    34. 34)
      • 8. Lievens, S., Van der Heyden, J., Masschaele, D., et al: ‘Proteome-scale binary interactomics in human cells’, Mol. Cell Proteomics, 2016, 15, (12), pp. 3624–3639.
    35. 35)
      • 41. Thomas, P.D., Campbell, M.J., Kejariwal, A., et al: ‘Panther: a library of protein families and subfamilies indexed by function’, Genome Res., 2003, 13, (9), pp. 2129–2141.
    36. 36)
      • 9. Cheng, L., Fan, K., Huang, Y., et al: ‘Full characterization of localization diversity in the human protein interactome’, J. Proteome Res., 2017, 16, (8), pp. 3019–3029.
    37. 37)
      • 52. Baillat, D., Hakimi, M.A., Naar, A.M., et al: ‘Integrator, a multiprotein mediator of small nuclear RNA processing, associates with the C-terminal repeat of RNA polymerase II’, Cell, 2005, 123, (2), pp. 265–276.
    38. 38)
      • 57. Hu, X., Wu, Y., Lu, Z.J., et al: ‘Analysis of sequencing data for probing RNA secondary structures and protein-RNA binding in studying posttranscriptional regulations’, Brief Bioinf., 2016, 17, (6), pp. 1032–1043.
    39. 39)
      • 28. Marino, G., Niso-Santano, M., Baehrecke, E.H., et al: ‘Self-consumption: the interplay of autophagy and apoptosis’, Nat. Rev. Mol. Cell Biol., 2014, 15, (2), pp. 81–94.
    40. 40)
      • 35. Ruepp, A., Brauner, B., Dunger-Kaltenbach, I., et al: ‘CORUM: the comprehensive resource of mammalian protein complexes’, Nucleic Acids Res., 2008, 36, (Database issue), pp. D646–D650.
    41. 41)
      • 54. Cheng, L., Lo, L.Y., Tang, N.L., et al: ‘CrossNorm: a novel normalization strategy for microarray data in cancers’, Sci. Rep., 2016, 6, pp. 18898.
    42. 42)
      • 49. Pattingre, S., Tassa, A., Qu, X., et al: ‘Bcl-2 antiapoptotic proteins inhibit Beclin 1-dependent autophagy’, Cell, 2005, 122, (6), pp. 927–939.
    43. 43)
      • 33. Enright, A.J., Van Dongen, S., Ouzounis, C.A.: ‘An efficient algorithm for large-scale detection of protein families’, Nucleic Acids Res., 2002, 30, (7), pp. 1575–1584.
    44. 44)
      • 19. An, S., Kumar, R., Sheets, E.D., et al: ‘Reversible compartmentalization of de novo purine biosynthetic complexes in living cells’, Science, 2008, 320, (5872), pp. 103–106.
    45. 45)
      • 34. Papin, J.A., Hunter, T., Palsson, B.O., et al: ‘Reconstruction of cellular signalling networks and analysis of their properties’, Nat. Rev. Mol. Cell Biol., 2005, 6, (2), pp. 99–111.
    46. 46)
      • 26. Li, Y., Zhuang, L., Wang, Y., et al: ‘Connect the dots: a systems level approach for analyzing the mirna-mediated cell death network’, Autophagy, 2013, 9, (3), pp. 436–439.
    47. 47)
      • 2. Yu, H., Braun, P., Yildirim, M.A., et al: ‘High-quality binary protein interaction map of the yeast interactome network’, Science, 2008, 322, (5898), pp. 104–110.
    48. 48)
      • 3. Vidal, M., Cusick, M.E., Barabasi, A.L.: ‘Interactome networks and human disease’, Cell, 2011, 144, (6), pp. 986–998.
    49. 49)
      • 21. Casey, J.R., Grinstein, S., Orlowski, J.: ‘Sensors and regulators of intracellular pH’, Nat. Rev. Mol. Cell Biol., 2010, 11, (1), pp. 50–61.
    50. 50)
      • 14. Pizzuti, C., Rombo, S.E.: ‘Algorithms and tools for protein-protein interaction networks clustering, with a special focus on population-based stochastic methods’, Bioinformatics, 2014, 30, (10), pp. 1343–1352.
    51. 51)
      • 42. Croft, D., O'Kelly, G., Wu, G., et al: ‘Reactome: a database of reactions, pathways and biological processes’, Nucleic Acids Res., 2011, 39, (Database issue), pp. D691–D697.
    52. 52)
      • 55. Cheng, L., Wang, X., Wong, P.K., et al: ‘ICN: a normalization method for gene expression data considering the over-expression of informative genes’, Mol. Biosyst., 2016, 12, (10), pp. 3057–3066.
    53. 53)
      • 37. Kikugawa, S., Nishikata, K., Murakami, K., et al: ‘PCDq: human protein complex database with quality index which summarizes different levels of evidences of protein complexes predicted from H-invitational protein-protein interactions integrative dataset’, BMC Syst. Biol., 2012, 6, (Suppl 2), p. S7.
    54. 54)
      • 25. Fuchs, Y., Steller, H.: ‘Programmed cell death in animal development and disease’, Cell, 2011, 147, (4), pp. 742–758.
    55. 55)
      • 43. Joshi-Tope, G., Gillespie, M., Vastrik, I., et al: ‘Reactome: a knowledgebase of biological pathways’, Nucleic Acids Res., 2005, 33, (Database issue), pp. D428–D432.
    56. 56)
      • 16. Nepusz, T., Yu, H., Paccanaro, A.: ‘Detecting overlapping protein complexes in protein-protein interaction networks’, Nat. Methods, 2012, 9, (5), pp. 471–472.
    57. 57)
      • 46. Shannon, P., Markiel, A., Ozier, O., et al: ‘Cytoscape: a software environment for integrated models of biomolecular interaction networks’, Genome Res., 2003, 13, (11), pp. 2498–2504.
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