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

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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.

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Notes

  1. https://www.uniprot.org/help/api_idmapping.

  2. https://angular.io/.

  3. https://material.angular.io/.

  4. https://www.creative-tim.com/product/material-dashboard-angular2/.

  5. https://www.oracle.com/es/java/technologies/java-ee.html.

  6. https://www.docker.com/.

  7. https://github.com/docker-java/docker-java.

  8. http://wildfly.org/.

  9. https://www.mysql.com/.

  10. http://pantherdb.org.

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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.

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Authors and Affiliations

  1. ESEI-Escuela Superior de Ingeniería Informática, Universidad de Vigo, Edificio Politécnico, Campus Universitario As Lagoas s/n, 32004, Ourense, Spain

    Noé Vázquez, Hugo López-Fernández, Florentino Fdez-Riverola & Miguel Reboiro-Jato

  2. Centro de Investigaciones Biomédicas (Centro Singular de Investigación de Galicia), Vigo, Spain

    Noé Vázquez, Hugo López-Fernández, Florentino Fdez-Riverola & Miguel Reboiro-Jato

  3. Instituto de Investigação e Inovação em Saúde (I3S), Universidade do Porto, Rua Alfredo Allen, 208, 4200-135, Porto, Portugal

    Sara Rocha, Hugo López-Fernández, André Torres, Jorge Vieira & Cristina P. Vieira

  4. Instituto de Biologia Molecular e Celular (IBMC), Rua Alfredo Allen, 208, 4200-135, Porto, Portugal

    Sara Rocha, Hugo López-Fernández, André Torres, Jorge Vieira & Cristina P. Vieira

  5. SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain

    Hugo López-Fernández, Florentino Fdez-Riverola & Miguel Reboiro-Jato

  6. LIAAD and DEI and Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

    Rui Camacho

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  1. Noé Vázquez
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Correspondence to Hugo López-Fernández.

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Vázquez, N., Rocha, S., López-Fernández, H. et al. EvoPPI 1.0: a Web Platform for Within- and Between-Species Multiple Interactome Comparisons and Application to Nine PolyQ Proteins Determining Neurodegenerative Diseases. Interdiscip Sci Comput Life Sci 11, 45–56 (2019). https://doi.org/10.1007/s12539-019-00317-y

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