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Integrating protein-protein interaction networks with phenotypes reveals signs of interactions

Nature Methods volume 11, pages 94–99 (2014)Cite this article

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An Erratum to this article was published on 27 June 2014

This article has been updated

Abstract

A major objective of systems biology is to organize molecular interactions as networks and to characterize information flow within networks. We describe a computational framework to integrate protein-protein interaction (PPI) networks and genetic screens to predict the 'signs' of interactions (i.e., activation-inhibition relationships). We constructed a Drosophila melanogaster signed PPI network consisting of 6,125 signed PPIs connecting 3,352 proteins that can be used to identify positive and negative regulators of signaling pathways and protein complexes. We identified an unexpected role for the metabolic enzymes enolase and aldo-keto reductase as positive and negative regulators of proteolysis, respectively. Characterization of the activation-inhibition relationships between physically interacting proteins within signaling pathways will affect our understanding of many biological functions, including signal transduction and mechanisms of disease.

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Figure 1: Framework to predict the signs of protein interactions.
Figure 2: Drosophila signed PPI network properties.
Figure 3
Figure 4: Validation of predicted proteasome regulators.

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Change history

  • 17 March 2014

    In the version of this article initially published, there was an error in the expression describing the sign score Ssign. The variable Tn in the denominator should have been Tp. The error has been corrected in the HTML and PDF versions of the article.

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Acknowledgements

We thank B. Housden, Y. Kwon, I.T. Flockhart and S. Rajagopal for helpful suggestions for tool development and manuscript preparation. This work was financially supported by P01-CA120964, R01-GM067761 and R01DK088718. S.E.M. is also supported in part by the Dana-Farber/Harvard Cancer Center (P30-CA06516). N.P. is supported by the Howard Hughes Medical Institute.

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Author notes

  1. Charles Roesel

    Present address: Present address: Marine Science Center, Northeastern University, Nahant, Massachusetts, USA.,

Authors and Affiliations

  1. Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA

    Arunachalam Vinayagam, Jonathan Zirin, Yanhui Hu, Anastasia A Samsonova, Ralph A Neumüller, Stephanie E Mohr & Norbert Perrimon

  2. Department of Genetics, Drosophila RNAi Screening Center, Harvard Medical School, Boston, Massachusetts, USA

    Charles Roesel, Yanhui Hu, Bahar Yilmazel & Stephanie E Mohr

  3. Bioinformatics Program, Northeastern University, Boston, Massachusetts, USA

    Charles Roesel & Bahar Yilmazel

  4. Howard Hughes Medical Institute, Boston, Massachusetts, USA

    Norbert Perrimon

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Contributions

A.V. and N.P. conceived of and designed the project. A.V. developed the computational method and built and analyzed the signed network. J.Z. designed and performed experimental validation. C.R. and B.Y. developed the SignedPPI database. A.V. and S.E.M. coordinated the development of the SignedPPI database. A.V. and Y.H. compiled RNAi screens. A.A.S. analyzed modENCODE expression data. R.A.N. contributed the unpublished RNAi screen. All authors contributed to manuscript preparation.

Corresponding authors

Correspondence to Arunachalam Vinayagam or Norbert Perrimon.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–11, Supplementary Tables 1–6, 8, 9 and 11–14 (PDF 3922 kb)

Supplementary Table 7

Signed PPI network integrated with gene-expression datasets from modENCODE database (XLSX 799 kb)

Supplementary Table 10

List of triad motifs extracted from the signed network (XLSX 184 kb)

Supplementary Table 15

Signed functional interaction network constructed based on STRING interactions. Only STRING interactions with confidence score ≥ 700 is considered for the analysis (XLSX 2275 kb)

Supplementary Data

Images correspond to RNAi screen in Drosophila primary embryonic muscle cells. Each image corresponds to a single gene and a single field out of 24 fields per well. Individual image files are named as follows, gene names followed by the amplicon ID (ZIP 84492 kb)

Supplementary Software

SignPredictor tool to predict signed PPIs. (ZIP 591 kb)

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Vinayagam, A., Zirin, J., Roesel, C. et al. Integrating protein-protein interaction networks with phenotypes reveals signs of interactions. Nat Methods 11, 94–99 (2014). https://doi.org/10.1038/nmeth.2733

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