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Mapping of signaling networks through synthetic genetic interaction analysis by RNAi

Nature Methods volume 8, pages 341–346 (2011)Cite this article

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Abstract

The analysis of synthetic genetic interaction networks can reveal how biological systems achieve a high level of complexity with a limited repertoire of components. Studies in yeast and bacteria have taken advantage of collections of deletion strains to construct matrices of quantitative interaction profiles and infer gene function. Yet comparable approaches in higher organisms have been difficult to implement in a robust manner. Here we report a method to identify genetic interactions in tissue culture cells through RNAi. By performing more than 70,000 pairwise perturbations of signaling factors, we identified >600 interactions affecting different quantitative phenotypes of Drosophila melanogaster cells. Computational analysis of this interaction matrix allowed us to reconstruct signaling pathways and identify a conserved regulator of Ras-MAPK signaling. Large-scale genetic interaction mapping by RNAi is a versatile, scalable approach for revealing gene function and the connectivity of cellular networks.

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Figure 1: A multiparametric approach to identify genetic interactions through double-RNAi.
Figure 2: Clustering of genetic interaction profiles predicts gene function.
Figure 3: Phenotype-specific genetic interactions.
Figure 4: Multiparametric detection of interactions.
Figure 5: Cka is a conserved regulator of Ras/MAPK signaling.

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Acknowledgements

We thank A.-C. Gavin, A. Teleman and F. Markowetz for helpful comments on the manuscript, N. Perrimon (Harvard Medical School), W. Du (University of Chicago) and S. Hou (National Cancer Institute–Frederick) for sharing reagents and fly stocks, members of the Drosophila Genomics Resource Center for providing plasmids, members of the Bloomington Drosophila stock center for fly stocks and the information technology Service Units of the European Molecular Biology Laboratory and the German Cancer Research Center, for technical support; and A. Kiger and R. Gentleman for helpful discussions. The work was supported in part by a fellowship of the Studienstiftung (T.H.), the CellNetworks Cluster of Excellence (T.S.), a program grant of the Human Frontiers Sciences Program (W.H. and M.B.) and the EU FP7 project CancerPathways (W.H. and M.B.).

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Author notes

  1. Thomas Horn, Thomas Sandmann and Bernd Fischer: These authors contributed equally to this work.

Authors and Affiliations

  1. Division Signaling and Functional Genomics and Heidelberg University, Department of Cell and Molecular Biology, German Cancer Research Center (Deutsches Krebsforschungszentrum), Faculty of Medicine Mannheim, Heidelberg, Germany

    Thomas Horn, Thomas Sandmann & Michael Boutros

  2. Heidelberg University, Hartmut Hoffmann-Berling International Graduate School for Molecular and Cellular Biology, Heidelberg, Germany

    Thomas Horn

  3. Heidelberg University, CellNetworks Cluster of Excellence, Heidelberg, Germany

    Thomas Sandmann

  4. European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany

    Bernd Fischer, Elin Axelsson & Wolfgang Huber

Authors
  1. Thomas Horn
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  2. Thomas Sandmann
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  3. Bernd Fischer
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  4. Elin Axelsson
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Contributions

T.H., T.S., B.F., W.H. and M.B. designed the study; T.H. and T.S. performed the experiments; B.F. analyzed the data; E.A. performed initial analyses; T.S., T.H., B.F., W.H. and M.B. wrote the manuscript.

Corresponding authors

Correspondence to Wolfgang Huber or Michael Boutros.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–22, Supplementary Tables 2,4,5 (PDF 6273 kb)

Supplementary Table 1

Primer and amplicon sequences for all targeted genes (XLS 122 kb)

Supplementary Table 3

Genetic interaction data based on cell number, nuclear area, fluorescent intensity (XLS 5363 kb)

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Horn, T., Sandmann, T., Fischer, B. et al. Mapping of signaling networks through synthetic genetic interaction analysis by RNAi. Nat Methods 8, 341–346 (2011). https://doi.org/10.1038/nmeth.1581

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