Advertisement

EvoPPI 1.0: a Web Platform for Within- and Between-Species Multiple Interactome Comparisons and Application to Nine PolyQ Proteins Determining Neurodegenerative Diseases

  • Noé Vázquez
  • Sara Rocha
  • Hugo López-FernándezEmail author
  • André Torres
  • Rui Camacho
  • Florentino Fdez-Riverola
  • Jorge Vieira
  • Cristina P. Vieira
  • Miguel Reboiro-Jato
Original Research Article

Abstract

Protein–protein interaction (PPI) data is essential to elucidate the complex molecular relationships in living systems, and thus understand the biological functions at cellular and systems levels. The complete map of PPIs that can occur in a living organism is called the interactome. For animals, PPI data is stored in multiple databases (e.g., BioGRID, CCSB, DroID, FlyBase, HIPPIE, HitPredict, HomoMINT, INstruct, Interactome3D, mentha, MINT, and PINA2) with different formats. This makes PPI comparisons difficult to perform, especially between species, since orthologous proteins may have different names. Moreover, there is only a partial overlap between databases, even when considering a single species. The EvoPPI (http://evoppi.i3s.up.pt) web application presented in this paper allows comparison of data from the different databases at the species level, or between species using a BLAST approach. We show its usefulness by performing a comparative study of the interactome of the nine polyglutamine (polyQ) disease proteins, namely androgen receptor (AR), atrophin-1 (ATN1), ataxin 1 (ATXN1), ataxin 2 (ATXN2), ataxin 3 (ATXN3), ataxin 7 (ATXN7), calcium voltage-gated channel subunit alpha1 A (CACNA1A), Huntingtin (HTT), and TATA-binding protein (TBP). Here we show that none of the human interactors of these proteins is common to all nine interactomes. Only 15 proteins are common to at least 4 of these polyQ disease proteins, and 40% of these are involved in ubiquitin protein ligase-binding function. The results obtained in this study suggest that polyQ disease proteins are involved in different functional networks. Comparisons with Mus musculus PPIs are also made for AR and TBP, using EvoPPI BLAST search approach (a unique feature of EvoPPI), with the goal of understanding why there is a significant excess of common interactors for these proteins in humans.

Keywords

Protein–protein interactions databases Inter-specific comparisons PolyQ disease proteins 

Notes

Acknowledgements

This article is a result of the project Norte-01-0145-FEDER-000008—Porto Neurosciences and Neurologic Disease Research Initiative at I3S, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (FEDER). Sara Rocha is also supported by this project. H. López-Fernández is supported by a post-doctoral fellowship from Xunta de Galicia (ED481B 2016/068-0). SING group thanks Centro de Investigación, Transferencia e Innovación (CITI) from University of Vigo for hosting its IT infrastructure. Financial support from the Xunta de Galicia (Centro singular de investigación de Galicia accreditation 2016–2019) and the European Union (European Regional Development Fund—ERDF), is gratefully acknowledged.

Supplementary material

12539_2019_317_MOESM1_ESM.pdf (1.7 mb)
Supplementary material 1 (PDF 1780 KB)

References

  1. 1.
    The UniProt Consortium (2017) UniProt: the universal protein knowledgebase. Nucleic Acids Res 45:D158–D169CrossRefGoogle Scholar
  2. 2.
    Alanis-Lobato G, Andrade-Navarro MA, Schaefer MH (2017) HIPPIE v2.0: enhancing meaningfulness and reliability of protein–protein interaction networks. Nucleic Acids Res 45:D408–D414CrossRefGoogle Scholar
  3. 3.
    Cusick ME, Klitgord N, Vidal M, Hill DE (2005) Interactome: gateway into systems biology. Hum Mol Genet 14:R171–R181CrossRefGoogle Scholar
  4. 4.
    Chiti F, Dobson CM (2017) Protein misfolding, amyloid formation, and human disease: a summary of progress over the last decade. Annu Rev Biochem 86:27–68CrossRefGoogle Scholar
  5. 5.
    Cescatti M, Saverioni D, Capellari S, Tagliavini F, Kitamoto T, Ironside J, Giese A, Parchi P (2016) Analysis of conformational stability of abnormal prion protein aggregates across the spectrum of Creutzfeldt–Jakob disease prions. J Virol 90:6244–6254CrossRefGoogle Scholar
  6. 6.
    Peng X, Wang J, Peng W, Wu F-X, Pan Y (2017) Protein–protein interactions: detection, reliability assessment and applications. Brief Bioinform 18:798–819Google Scholar
  7. 7.
    Folador E, de Oliveira Junior A, Tiwari S, Jamal S, Ferreira R, Barh D, Ghosh P, Silva A, Azevedo V (2015) In silico protein–protein interactions: avoiding data and method biases over sensitivity and specificity. Curr Protein Pept Sci 16:689–700CrossRefGoogle Scholar
  8. 8.
    Chatr-aryamontri A, Oughtred R, Boucher L, Rust J, Chang C, Kolas NK, O’Donnell L, Oster S, Theesfeld C, Sellam A, Stark C, Breitkreutz B-J, Dolinski K, Tyers M (2017) The BioGRID interaction database: 2017 update. Nucleic Acids Res 45:D369–D379CrossRefGoogle Scholar
  9. 9.
    Stark C (2006) BioGRID: a general repository for interaction datasets. Nucleic Acids Res 34:D535–D539CrossRefGoogle Scholar
  10. 10.
    Simonis N, Rual J-F, Carvunis A-R, Tasan M, Lemmens I, Hirozane-Kishikawa T, Hao T, Sahalie JM, Venkatesan K, Gebreab F, Cevik S, Klitgord N, Fan C, Braun P, Li N, Ayivi-Guedehoussou N, Dann E, Bertin N, Szeto D, Dricot A, Yildirim MA, Lin C, de Smet A-S, Kao H-L, Simon C, Smolyar A, Ahn JS, Tewari M, Boxem M, Milstein S, Yu H, Dreze M, Vandenhaute J, Gunsalus KC, Cusick ME, Hill DE, Tavernier J, Roth FP, Vidal M (2009) Empirically controlled mapping of the Caenorhabditis elegans protein–protein interactome network. Nat Methods 6:47–54CrossRefGoogle Scholar
  11. 11.
    Rolland T, Taşan M, Charloteaux B, Pevzner SJ, Zhong Q, Sahni N, Yi S, Lemmens I, Fontanillo C, Mosca R, Kamburov A, Ghiassian SD, Yang X, Ghamsari L, Balcha D, Begg BE, Braun P, Brehme M, Broly MP, Carvunis A-R, Convery-Zupan D, Corominas R, Coulombe-Huntington J, Dann E, Dreze M, Dricot A, Fan C, Franzosa E, Gebreab F, Gutierrez BJ, Hardy MF, Jin M, Kang S, Kiros R, Lin GN, Luck K, MacWilliams A, Menche J, Murray RR, Palagi A, Poulin MM, Rambout X, Rasla J, Reichert P, Romero V, Ruyssinck E, Sahalie JM, Scholz A, Shah AA, Sharma A, Shen Y, Spirohn K, Tam S, Tejeda AO, Trigg SA, Twizere J-C, Vega K, Walsh J, Cusick ME, Xia Y, Barabási A-L, Iakoucheva LM, Aloy P, De Las Rivas, J, Tavernier J, Calderwood MA, Hill DE, Hao T, Roth FP, Vidal M (2014) A proteome-scale map of the human interactome network. Cell 159:1212–1226CrossRefGoogle Scholar
  12. 12.
    Yu H, Tardivo L, Tam S, Weiner E, Gebreab F, Fan C, Svrzikapa N, Hirozane-Kishikawa T, Rietman E, Yang X, Sahalie J, Salehi-Ashtiani K, Hao T, Cusick ME, Hill DE, Roth FP, Braun P, Vidal M (2011) Next-generation sequencing to generate interactome datasets. Nat Methods 8:478–480CrossRefGoogle Scholar
  13. 13.
    Rual J-F, Venkatesan K, Hao T, Hirozane-Kishikawa T, Dricot A, Li N, Berriz GF, Gibbons FD, Dreze M, Ayivi-Guedehoussou N, Klitgord N, Simon C, Boxem M, Milstein S, Rosenberg J, Goldberg DS, Zhang LV, Wong SL, Franklin G, Li S, Albala JS, Lim J, Fraughton C, Llamosas E, Cevik S, Bex C, Lamesch P, Sikorski RS, Vandenhaute J, Zoghbi HY, Smolyar A, Bosak S, Sequerra R, Doucette-Stamm L, Cusick ME, Hill DE, Roth FP, Vidal M (2005) Towards a proteome-scale map of the human protein–protein interaction network. Nature 437:1173–1178CrossRefGoogle Scholar
  14. 14.
    Venkatesan K, Rual J-F, Vazquez A, Stelzl U, Lemmens I, Hirozane-Kishikawa T, Hao T, Zenkner M, Xin X, Goh K-I, Yildirim MA, Simonis N, Heinzmann K, Gebreab F, Sahalie JM, Cevik S, Simon C, de Smet A-S, Dann E, Smolyar A, Vinayagam A, Yu H, Szeto D, Borick H, Dricot A, Klitgord N, Murray RR, Lin C, Lalowski M, Timm J, Rau K, Boone C, Braun P, Cusick ME, Roth FP, Hill DE, Tavernier J, Wanker EE, Barabási A-L, Vidal M (2009) An empirical framework for binary interactome mapping. Nat Methods 6:83–90CrossRefGoogle Scholar
  15. 15.
    Murali T, Pacifico S, Yu J, Guest S, Roberts GG, Finley RL (2011) DroID 2011: a comprehensive, integrated resource for protein, transcription factor, RNA and gene interactions for Drosophila. Nucleic Acids Res 39:D736–D743CrossRefGoogle Scholar
  16. 16.
    Attrill H, Falls K, Goodman JL, Millburn GH, Antonazzo G, Rey AJ, Marygold SJ (2016) FlyBase Consortium: FlyBase: establishing a Gene Group resource for Drosophila melanogaster. Nucleic Acids Res 44:D786–D792CrossRefGoogle Scholar
  17. 17.
    López Y, Nakai K, Patil A (2015) HitPredict version 4: comprehensive reliability scoring of physical protein–protein interactions from more than 100 species. Database 2015:bav117CrossRefGoogle Scholar
  18. 18.
    Persico M, Ceol A, Gavrila C, Hoffmann R, Florio A, Cesareni G (2005) HomoMINT: an inferred human network based on orthology mapping of protein interactions discovered in model organisms. BMC Bioinform 6:S21CrossRefGoogle Scholar
  19. 19.
    Meyer MJ, Das J, Wang X, Yu H (2013) INstruct: a database of high-quality 3D structurally resolved protein interactome networks. Bioinformatics 29:1577–1579CrossRefGoogle Scholar
  20. 20.
    Mosca R, Céol A, Aloy P (2013) Interactome3D: adding structural details to protein networks. Nat Methods 10:47–53CrossRefGoogle Scholar
  21. 21.
    Calderone A, Castagnoli L, Cesareni G (2013) mentha: a resource for browsing integrated protein-interaction networks. Nat Methods 10:690–691CrossRefGoogle Scholar
  22. 22.
    Licata L, Briganti L, Peluso D, Perfetto L, Iannuccelli M, Galeota E, Sacco F, Palma A, Nardozza AP, Santonico E, Castagnoli L, Cesareni G (2012) MINT, the molecular interaction database: 2012 update. Nucleic Acids Res 40:D857–D861CrossRefGoogle Scholar
  23. 23.
    Cowley MJ, Pinese M, Kassahn KS, Waddell N, Pearson JV, Grimmond SM, Biankin AV, Hautaniemi S, Wu J (2012) PINA v2.0: mining interactome modules. Nucleic Acids Res 40:D862–D865CrossRefGoogle Scholar
  24. 24.
    Mendivil Ramos O, Ferrier DEK (2012) Mechanisms of gene duplication and translocation and progress towards understanding their relative contributions to animal genome evolution. Int J Evol Biol 2012:1–10CrossRefGoogle Scholar
  25. 25.
    Fan H-C, Ho L-I, Chi C-S, Chen S-J, Peng G-S, Chan T-M, Lin S-Z, Harn H-J (2014) Polyglutamine (PolyQ) diseases: genetics to treatments. Cell Transpl 23:441–458CrossRefGoogle Scholar
  26. 26.
    Fielding RT (2000) Architectural styles and the design of network-based software architectures. University of California, IrvineGoogle Scholar
  27. 27.
    Schaefer MH, Wanker EE, Andrade-Navarro MA (2012) Evolution and function of CAG/polyglutamine repeats in protein–protein interaction networks. Nucleic Acids Res 40:4273–4287CrossRefGoogle Scholar
  28. 28.
    Petrakis S, Schaefer MH, Wanker EE, Andrade-Navarro MA (2013) Aggregation of polyQ-extended proteins is promoted by interaction with their natural coiled-coil partners. Insights Perspect BioEssays 35:503–507CrossRefGoogle Scholar
  29. 29.
    Fiumara F, Fioriti L, Kandel ER, Hendrickson WA (2010) Essential role of coiled coils for aggregation and activity of Q/N-rich prions and polyQ proteins. Cell 143:1121–1135CrossRefGoogle Scholar
  30. 30.
    Butland SL, Devon RS, Huang Y, Mead C-L, Meynert AM, Neal SJ, Lee S, Wilkinson A, Yang GS, Yuen MM, Hayden MR, Holt RA, Leavitt BR, Ouellette BF (2007) CAG-encoded polyglutamine length polymorphism in the human genome. BMC Genom 8:126CrossRefGoogle Scholar
  31. 31.
    Nath SR, Lieberman AP (2017) The ubiquitination, disaggregation and proteasomal degradation machineries in polyglutamine disease. Front Mol Neurosci 10:78CrossRefGoogle Scholar
  32. 32.
    Pratt WB, Gestwicki JE, Osawa Y, Lieberman AP (2015) Targeting Hsp90/Hsp70-based protein quality control for treatment of adult onset neurodegenerative diseases. Annu Rev Pharmacol Toxicol 55:353–371CrossRefGoogle Scholar
  33. 33.
    Rusmini P, Crippa V, Cristofani R, Rinaldi C, Cicardi ME, Galbiati M, Carra S, Malik B, Greensmith L, Poletti A (2016) The role of the protein quality control system in SBMA. J Mol Neurosci 58:348–364CrossRefGoogle Scholar
  34. 34.
    Ciechanover A, Brundin P (2003) The ubiquitin proteasome system in neurodegenerative diseases. Neuron 40:427–446CrossRefGoogle Scholar

Copyright information

© International Association of Scientists in the Interdisciplinary Areas 2019

Authors and Affiliations

  • Noé Vázquez
    • 1
    • 2
  • Sara Rocha
    • 3
    • 4
  • Hugo López-Fernández
    • 1
    • 2
    • 3
    • 4
    • 5
    Email author
  • André Torres
    • 3
    • 4
  • Rui Camacho
    • 6
  • Florentino Fdez-Riverola
    • 1
    • 2
    • 5
  • Jorge Vieira
    • 3
    • 4
  • Cristina P. Vieira
    • 3
    • 4
  • Miguel Reboiro-Jato
    • 1
    • 2
    • 5
  1. 1.ESEI-Escuela Superior de Ingeniería InformáticaUniversidad de VigoOurenseSpain
  2. 2.Centro de Investigaciones Biomédicas (Centro Singular de Investigación de Galicia)VigoSpain
  3. 3.Instituto de Investigação e Inovação em Saúde (I3S)Universidade do PortoPortoPortugal
  4. 4.Instituto de Biologia Molecular e Celular (IBMC)PortoPortugal
  5. 5.SING Research GroupGalicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGOVigoSpain
  6. 6.LIAAD and DEI and Faculdade de EngenhariaUniversidade do PortoPortoPortugal

Personalised recommendations