Quantitative genetic-interaction mapping in mammalian cells

Journal name:
Nature Methods
Volume:
10,
Pages:
432–437
Year published:
DOI:
doi:10.1038/nmeth.2398
Received
Accepted
Published online

Abstract

Mapping genetic interactions (GIs) by simultaneously perturbing pairs of genes is a powerful tool for understanding complex biological phenomena. Here we describe an experimental platform for generating quantitative GI maps in mammalian cells using a combinatorial RNA interference strategy. We performed ~11,000 pairwise knockdowns in mouse fibroblasts, focusing on 130 factors involved in chromatin regulation to create a GI map. Comparison of the GI and protein-protein interaction (PPI) data revealed that pairs of genes exhibiting positive GIs and/or similar genetic profiles were predictive of the corresponding proteins being physically associated. The mammalian GI map identified pathways and complexes but also resolved functionally distinct submodules within larger protein complexes. By integrating GI and PPI data, we created a functional map of chromatin complexes in mouse fibroblasts, revealing that the PAF complex is a central player in the mammalian chromatin landscape.

At a glance

Figures

  1. An overview of the mammalian E-MAP pipeline.
    Figure 1: An overview of the mammalian E-MAP pipeline.

    Flowchart of the experimental setup: esiRNAs to a set of genes are arrayed in a pairwise fashion (in quadruplicate) in tissue culture plates. Reverse transfection is then performed, and the resulting fitness defects are observed using high-content imaging. Raw data are scored and phenotypic signatures are derived for each gene.

  2. Characteristics and quality control of GI data set.
    Figure 2: Characteristics and quality control of GI data set.

    (a) GI scores derived from independent biological replicate experiments. Each data point represents a GI score (S score) for the same pairwise knockdown derived from independent experiments. (b) Knockdown efficiencies as measured by quantitative reverse transcription PCR. (c) Relationship between knockdown efficiency and expression: data were split into five groups and plotted against gene expression relative to that of a housekeeping gene.

  3. Comparison of GI and PPI data sets.
    Figure 3: Comparison of GI and PPI data sets.

    (a,b) Comparison of individual S scores (a) and GI profiles (b) to the likelihood of the corresponding pairs of proteins being physically associated. (c) Fitness after single gene knockdown versus the number of genetic interactions associated with the same gene. Raw cell counts were used as a proxy for fitness, and a cutoff of S ≥ 2 and S ≤ −2 was used to define informative GIs on the y axis.

  4. A module map of chromatin-related genes.
    Figure 4: A module map of chromatin-related genes.

    (a) GIs (left) and profile Pearson correlations (right) for members of the PAF transcriptional elongation complex. (b) GIs (left) and profile Pearson correlations (right) for members of the CNOT complex. S scores and profile Pearson correlations were used in a and b. (c) A module map based on a manually curated set of protein complexes (Supplementary Table 4). Modules and inter-module GI bundles are colored according to the enrichment of the observed GIs, with gray signifying no enrichment of a particular interaction type was observed (Supplementary Table 5). (d) Examples of positive and negative GI bundles corresponding to particular edges on the module map.

  5. Validation of observed GIs with an orthogonal phenotypic readout.
    Figure 5: Validation of observed GIs with an orthogonal phenotypic readout.

    (a) Micrographs derived from mouse embryo fibroblast cells depleted for Ctr9 in combination with knockdowns of Ruvbl1, Ruvbl2, Actl6a or Morfl1. Scale bars, 20 μm. (b) Phenotype strength represented as a fraction of cells with elongated nuclei (longer bars represent more extreme phenotype). Error bars, s.d. based on 100 random samples of 10% of the data in each experiment.

References

  1. Beltrao, P., Cagney, G. & Krogan, N.J. Quantitative genetic interactions reveal biological modularity. Cell 141, 739745 (2010).
  2. Collins, S.R. et al. Functional dissection of protein complexes involved in yeast chromosome biology using a genetic interaction map. Nature 446, 806810 (2007).
  3. Wilmes, G.M. et al. A genetic interaction map of RNA-processing factors reveals links between Sem1/Dss1-containing complexes and mRNA export and splicing. Mol. Cell 32, 735746 (2008).
  4. Lehner, B. Modelling genotype-phenotype relationships and human disease with genetic interaction networks. J. Exp. Biol. 210, 15591566 (2007).
  5. Costanzo, M. et al. The genetic landscape of a cell. Science 327, 425431 (2010).
  6. Ryan, C.J. et al. Hierarchical modularity and the evolution of genetic interactomes across species. Mol. Cell 46, 691704 (2012).
  7. Typas, A. et al. High-throughput, quantitative analyses of genetic interactions in E. coli. Nat. Methods 5, 781787 (2008).
  8. Lehner, B., Crombie, C., Tischler, J., Fortunato, A. & Fraser, A.G. Systematic mapping of genetic interactions in Caenorhabditis elegans identifies common modifiers of diverse signaling pathways. Nat. Genet. 38, 896903 (2006).
  9. Horn, T. et al. Mapping of signaling networks through synthetic genetic interaction analysis by RNAi. Nat. Methods 8, 341346 (2011).
  10. Tong, A.H. et al. Global mapping of the yeast genetic interaction network. Science 303, 808813 (2004).
  11. Baryshnikova, A. et al. Quantitative analysis of fitness and genetic interactions in yeast on a genome scale. Nat. Methods 7, 10171024 (2010).
  12. Schuldiner, M. et al. Exploration of the function and organization of the yeast early secretory pathway through an epistatic miniarray profile. Cell 123, 507519 (2005).
  13. Mohr, S., Bakal, C. & Perrimon, N. Genomic screening with RNAi: results and challenges. Annu. Rev. Biochem. 79, 3764 (2010).
  14. Sims, D. et al. High-throughput RNA interference screening using pooled shRNA libraries and next generation sequencing. Genome Biol. 12, R104 (2011).
  15. Buchholz, F., Kittler, R., Slabicki, M. & Theis, M. Enzymatically prepared RNAi libraries. Nat. Methods 3, 696700 (2006).
  16. Kittler, R. et al. Genome-wide resources of endoribonuclease-prepared short interfering RNAs for specific loss-of-function studies. Nat. Methods 4, 337344 (2007).
  17. Collins, S.R., Roguev, A. & Krogan, N.J. Quantitative genetic interaction mapping using the E-MAP approach. Methods Enzymol. 470, 205231 (2010).
  18. Rantala, J.K. et al. A cell spot microarray method for production of high density siRNA transfection microarrays. BMC Genomics 12, 162 (2011).
  19. Zhang, J.H., Chung, T.D. & Oldenburg, K.R. A simple statistical parameter for use in evaluation and validation of high throughput screening assays. J. Biomol. Screen. 4, 6773 (1999).
  20. Collins, S.R., Schuldiner, M., Krogan, N.J. & Weissman, J.S. A strategy for extracting and analyzing large-scale quantitative epistatic interaction data. Genome Biol. 7, R63 (2006).
  21. Breslow, D.K. et al. A comprehensive strategy enabling high-resolution functional analysis of the yeast genome. Nat. Methods 5, 711718 (2008).
  22. Lee, I., Blom, U.M., Wang, P.I., Shim, J.E. & Marcotte, E.M. Prioritizing candidate disease genes by network-based boosting of genome-wide association data. Genome Res. 21, 11091121 (2011).
  23. Bandyopadhyay, S., Kelley, R., Krogan, N.J. & Ideker, T. Functional maps of protein complexes from quantitative genetic interaction data. PLOS Comput. Biol. 4, e1000065 (2008).
  24. Bandyopadhyay, S. et al. Rewiring of genetic networks in response to DNA damage. Science 330, 13851389 (2010).
  25. Koch, E.N. et al. Conserved rules govern genetic interaction degree across species. Genome Biol. 13, R57 (2012).
  26. Ding, L. et al. A genome-scale RNAi screen for Oct4 modulators defines a role of the Paf1 complex for embryonic stem cell identity. Cell Stem Cell 4, 403415 (2009).
  27. Krogan, N.J. et al. RNA polymerase II elongation factors of Saccharomyces cerevisiae: a targeted proteomics approach. Mol. Cell. Biol. 22, 69796992 (2002).
  28. Belotserkovskaya, R. & Reinberg, D. Facts about FACT and transcript elongation through chromatin. Curr. Opin. Genet. Dev. 14, 139146 (2004).
  29. Collart, M.A. & Panasenko, O.O. The Ccr4–not complex. Gene 492, 4253 (2012).
  30. Lau, N.C. et al. Human Ccr4-Not complexes contain variable deadenylase subunits. Biochem. J. 422, 443453 (2009).
  31. Conaway, R.C. & Conaway, J.W. The INO80 chromatin remodeling complex in transcription, replication and repair. Trends Biochem. Sci. 34, 7177 (2009).
  32. Chen, J. & Wagner, E.J. snRNA 3′ end formation: the dawn of the Integrator complex. Biochem. Soc. Trans. 38, 10821087 (2010).
  33. Blow, J.J. & Dutta, A. Preventing re-replication of chromosomal DNA. Nat. Rev. Mol. Cell Biol. 6, 476486 (2005).
  34. Nasmyth, K. & Haering, C.H. Cohesin: its roles and mechanisms. Annu. Rev. Genet. 43, 525558 (2009).
  35. Cuylen, S. & Haering, C.H. Deciphering condensin action during chromosome segregation. Trends Cell Biol. 21, 552559 (2011).
  36. Gallant, P. Control of transcription by Pontin and Reptin. Trends Cell Biol. 17, 187192 (2007).
  37. Murai, J. et al. Trapping of PARP1 and PARP2 by Clinical PARP Inhibitors. Cancer Res. 72, 55885599 (2012).
  38. Michaut, M. & Bader, G.D. Multiple genetic interaction experiments provide complementary information useful for gene function prediction. PLOS Comput. Biol. 8, e1002559 (2012).
  39. Jaehning, J.A. The Paf1 complex: platform or player in RNA polymerase II transcription? Biochim. Biophys. Acta 1799, 379388 (2010).
  40. Loo, L.H., Wu, L.F. & Altschuler, S.J. Image-based multivariate profiling of drug responses from single cells. Nat. Methods 4, 445453 (2007).
  41. Marciniak, R.A., Lombard, D.B., Johnson, F.B. & Guarente, L. Nucleolar localization of the Werner syndrome protein in human cells. Proc. Natl. Acad. Sci. USA 95, 68876892 (1998).
  42. Nusinow, D.A. et al. Poly(ADP-ribose) polymerase 1 is inhibited by a histone H2A variant, MacroH2A, and contributes to silencing of the inactive X chromosome. J. Biol. Chem. 282, 1285112859 (2007).

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Author information

  1. These authors contributed equally to this work.

    • Assen Roguev &
    • Dale Talbot

Affiliations

  1. Department of Cellular and Molecular Pharmacology, University of California, San Francisco, California, USA.

    • Assen Roguev,
    • Michael Shales &
    • Nevan J Krogan
  2. California Institute for Quantitative Biosciences, San Francisco, California, USA.

    • Assen Roguev,
    • Michael Shales &
    • Nevan J Krogan
  3. Department of Biochemistry and Biophysics, University of California, San Francisco, California, USA.

    • Dale Talbot &
    • Barbara Panning
  4. School of Computer Science and Informatics, Belfield, Dublin, Ireland.

    • Gian Luca Negri
  5. University College Dublin, Conway Institute of Biomedical Research, Dublin, Ireland.

    • Gerard Cagney
  6. University of California San Francisco Helen Diller Family Comprehensive Cancer Center, San Francisco, California, USA.

    • Sourav Bandyopadhyay
  7. J. David Gladstone Institutes, San Francisco, California, USA.

    • Nevan J Krogan

Contributions

A.R., D.T., G.C., S.B., B.P. and N.J.K. designed the project; A.R. and D.T. performed the experiments; A.R., D.T., M.S., G.L.N. and S.B. analyzed the data; and A.R., D.T., M.S., G.C., S.B., B.P. and N.J.K. wrote the paper.

Competing financial interests

The authors declare no competing financial interests.

Corresponding authors

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Author details

Supplementary information

PDF files

  1. Supplementary Text and Figures (13.5 MB)

    Supplementary Figures 1–9, Supplementary Tables 1,4,5

Excel files

  1. Supplementary Table 2 (37 KB)

    Significant GIs observed (−2 ≤ S ≥ 2).

  2. Supplementary Table 3 (74 KB)

    Oligonucleotides used to generate esiRNAs.

Zip files

  1. Supplementary Dataset 1 (287 KB)

    Raw data before scoring GIs.

  2. Supplementary Dataset 2 (221 KB)

    Scored and clustered GI data in Treeview format.

Additional data