Identification of additional proteins in differential proteomics using protein interaction networks

Authors

  • Frederik Gwinner,

    1. Department of Genomes and Genetics, Systems Biology Laboratory, Institut Pasteur, Paris, France
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    • These authors contributed equally to this work.

  • Adelina E Acosta-Martin,

    1. INSERM, U744, IFR142, University of Lille Nord de France, Lille, France
    2. Institut Pasteur de Lille, Lille, France
    Current affiliation:
    1. Acosta-Martin, Biomedical Proteomics Research Group, Faculty of Medicine, Geneva University, Genève, 4, Switzerland
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    • These authors contributed equally to this work.

  • Ludovic Boytard,

    1. INSERM, U744, IFR142, University of Lille Nord de France, Lille, France
    2. Institut Pasteur de Lille, Lille, France
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  • Maggy Chwastyniak,

    1. INSERM, U744, IFR142, University of Lille Nord de France, Lille, France
    2. Institut Pasteur de Lille, Lille, France
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  • Olivia Beseme,

    1. INSERM, U744, IFR142, University of Lille Nord de France, Lille, France
    2. Institut Pasteur de Lille, Lille, France
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  • Hervé Drobecq,

    1. Institut Pasteur de Lille, Lille, France
    2. Centre national de la recherche scientifique (CNRS), Lille, France
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  • Sophie Duban-Deweer,

    1. E.A.2465, IMPRT-IFR114, University of Artois, Lens, France
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  • Francis Juthier,

    1. E.A.2393, IMPRT-IFR114, University of Lille Nord de France, Lille, France
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  • Brigitte Jude,

    1. E.A.2393, IMPRT-IFR114, University of Lille Nord de France, Lille, France
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  • Philippe Amouyel,

    1. INSERM, U744, IFR142, University of Lille Nord de France, Lille, France
    2. Institut Pasteur de Lille, Lille, France
    3. Centre Hospitalier régional et Universitaire de Lille, Lille, France
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  • Florence Pinet,

    Corresponding author
    1. Institut Pasteur de Lille, Lille, France
    2. Centre Hospitalier régional et Universitaire de Lille, Lille, France
    • INSERM, U744, IFR142, University of Lille Nord de France, Lille, France
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  • Benno Schwikowski

    Corresponding author
    • Department of Genomes and Genetics, Systems Biology Laboratory, Institut Pasteur, Paris, France
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  • Colour Online: See the article online to view Figs. 1–4 in colour.

Correspondence: Dr. Benno Schwikowski, Systems Biology Lab, Institut Pasteur, 25-28 rue du Dr. Roux, 75015 Paris, France

E-mail: benno@pasteur.fr

Fax: +33-14061-3704

Additional corresponding author: Dr. Florence Pinet,

E-mail: florence.pinet@pasteur-lille.fr

Abstract

In this study, we developed a novel computational approach based on protein–protein interaction networks to identify a list of proteins that might have remained undetected in differential proteomic profiling experiments. We tested our computational approach on two sets of human smooth muscle cell protein extracts that were affected differently by DNase I treatment. Differential proteomic analysis by saturation DIGE resulted in the identification of 41 human proteins. The application of our approach to these 41 input proteins consisted of four steps: (i) Compilation of a human protein–protein interaction network from public databases; (ii) calculation of interaction scores based on functional similarity; (iii) determination of a set of candidate proteins that are needed to efficiently and confidently connect the 41 input proteins; and (iv) ranking of the resulting 25 candidate proteins. Two of the three highest-ranked proteins, beta-arrestin 1, and beta-arrestin 2, were experimentally tested, revealing that their abundance levels in human smooth muscle cell samples were indeed affected by DNase I treatment. These proteins had not been detected during the experimental proteomic analysis. Our study suggests that our computational approach may represent a simple, universal, and cost-effective means to identify additional proteins that remain elusive for current 2D gel-based proteomic profiling techniques.

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