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Genetic wiring maps of single-cell protein states reveal an off-switch for GPCR signalling


As key executers of biological functions, the activity and abundance of proteins are subjected to extensive regulation. Deciphering the genetic architecture underlying this regulation is critical for understanding cellular signalling events and responses to environmental cues. Using random mutagenesis in haploid human cells, we apply a sensitive approach to directly couple genomic mutations to protein measurements in individual cells. Here we use this to examine a suite of cellular processes, such as transcriptional induction, regulation of protein abundance and splicing, signalling cascades (mitogen-activated protein kinase (MAPK), G-protein-coupled receptor (GPCR), protein kinase B (AKT), interferon, and Wingless and Int-related protein (WNT) pathways) and epigenetic modifications (histone crotonylation and methylation). This scalable, sequencing-based procedure elucidates the genetic landscapes that control protein states, identifying genes that cause very narrow phenotypic effects and genes that lead to broad phenotypic consequences. The resulting genetic wiring map identifies the E3-ligase substrate adaptor KCTD5 (ref. 1) as a negative regulator of the AKT pathway, a key signalling cascade frequently deregulated in cancer. KCTD5-deficient cells show elevated levels of phospho-AKT at S473 that could not be attributed to effects on canonical pathway components. To reveal the genetic requirements for this phenotype, we iteratively analysed the regulatory network linked to AKT activity in the knockout background. This genetic modifier screen exposes suppressors of the KCTD5 phenotype and mechanistically demonstrates that KCTD5 acts as an off-switch for GPCR signalling by triggering proteolysis of Gβγ heterodimers dissociated from the Gα subunit. Although biological networks have previously been constructed on the basis of gene expression2,3, protein–protein associations4,5,6, or genetic interaction profiles7,8, we foresee that the approach described here will enable the generation of a comprehensive genetic wiring map for human cells on the basis of quantitative protein states.

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Figure 1: Genetic wiring maps for protein phenotypes measured in cultured human cells.
Figure 2: Protein phenotypes are regulated by extensive genetic networks and can be influenced by suppressor interactions.
Figure 3: KCTD5 acts as off-switch for GPCR Gβγ signalling.

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  1. Bayón, Y. et al. KCTD5, a putative substrate adaptor for cullin3 ubiquitin ligases. FEBS J. 275, 3900–3910 (2008)

    PubMed  Google Scholar 

  2. Eisen, M. B., Spellman, P. T., Brown, P. O. & Botstein, D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl Acad. Sci. USA 95, 14863–14868 (1998)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  3. Hughes, T. R. et al. Functional discovery via a compendium of expression profiles. Cell 102, 109–126 (2000)

    CAS  PubMed  Google Scholar 

  4. Huttlin, E. L. et al. The BioPlex network: a systematic exploration of the human interactome resource. Cell 162, 425–440 (2015)

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Havugimana, P. C. et al. A census of human soluble protein complexes. Cell 150, 1068–1081 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Hein, M. Y. et al. A human interactome in three quantitative dimensions organized by stoichiometries and abundances. Cell 163, 712–723 (2015)

    CAS  PubMed  Google Scholar 

  7. Costanzo, M . et al. A global genetic interaction network maps a wiring diagram of cellular function. Science 353, aaf1420 (2016)

    PubMed  PubMed Central  Google Scholar 

  8. Tong, A. H. Y. et al. Global mapping of the yeast genetic interaction network. Science 303, 808–813 (2004)

    ADS  CAS  PubMed  Google Scholar 

  9. Parts, L. Genome-wide mapping of cellular traits using yeast. Yeast 31, 197–205 (2014)

    CAS  PubMed  Google Scholar 

  10. Jaitin, D. A. et al. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-seq. Cell 167, 1883–1896.e15 (2016)

    CAS  PubMed  Google Scholar 

  11. Dixit, A. et al. Perturb-seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic resource. Cell 167, 1853–1866.e17 (2016)

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Parnas, O. et al. A genome-wide CRISPR screen in primary immune cells to dissect regulatory networks. Cell 162, 675–686 (2015)

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Blomen, V. A. et al. Gene essentiality and synthetic lethality in haploid human cells. Science 350, 1092–1096 (2015)

    ADS  CAS  PubMed  Google Scholar 

  14. Albert, F. W., Treusch, S., Shockley, A. H., Bloom, J. S. & Kruglyak, L. Genetics of single-cell protein abundance variation in large yeast populations. Nature 506, 494–497 (2014)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  15. Moffat, J. et al. A lentiviral RNAi library for human and mouse genes applied to an arrayed viral high-content screen. Cell 124, 1283–1298 (2006)

    CAS  PubMed  Google Scholar 

  16. Carette, J. E. et al. Haploid genetic screens in human cells identify host factors used by pathogens. Science 326, 1231–1235 (2009)

    ADS  CAS  PubMed  Google Scholar 

  17. Carette, J. E. et al. Ebola virus entry requires the cholesterol transporter Niemann–Pick C1. Nature 477, 340–343 (2011)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  18. Platanias, L. C. Mechanisms of type-I- and type-II-interferon-mediated signalling. Nat. Rev. Immunol. 5, 375–386 (2005)

    CAS  PubMed  Google Scholar 

  19. Lund, A. H. & van Lohuizen, M. Polycomb complexes and silencing mechanisms. Curr. Opin. Cell Biol. 16, 239–246 (2004)

    CAS  PubMed  Google Scholar 

  20. Sabari, B. R. et al. Intracellular crotonyl-CoA stimulates transcription through p300-catalyzed histone crotonylation. Mol. Cell 58, 203–215 (2015)

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Meikle, P. J. et al. Diagnosis of lysosomal storage disorders: evaluation of lysosome-associated membrane protein LAMP-1 as a diagnostic marker. Clin. Chem. 43, 1325–1335 (1997)

    CAS  PubMed  Google Scholar 

  22. Sarbassov, D. D., Guertin, D. A., Ali, S. M. & Sabatini, D. M. Phosphorylation and regulation of Akt/PKB by the rictor-mTOR complex. Science 307, 1098–1101 (2005)

    ADS  CAS  PubMed  Google Scholar 

  23. Lukov, G. L., Hu, T., McLaughlin, J. N., Hamm, H. E. & Willardson, B. M. Phosducin-like protein acts as a molecular chaperone for G protein βγ dimer assembly. EMBO J. 24, 1965–1975 (2005)

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Stephens, L. et al. A novel phosphoinositide 3 kinase activity in myeloid-derived cells is activated by G protein βγ subunits. Cell 77, 83–93 (1994)

    CAS  PubMed  Google Scholar 

  25. Leopoldt, D. et al. Gβγ stimulates phosphoinositide 3-kinase-γ by direct interaction with two domains of the catalytic p110 subunit. J. Biol. Chem. 273, 7024–7029 (1998)

    CAS  PubMed  Google Scholar 

  26. Oner, S. S. et al. Regulation of the G-protein regulatory-Gαi signaling complex by nonreceptor guanine nucleotide exchange factors. J. Biol. Chem. 288, 3003–3015 (2013)

    CAS  PubMed  Google Scholar 

  27. Yoda, A. et al. Mutations in G protein β subunits promote transformation and kinase inhibitor resistance. Nat. Med. 21, 71–75 (2015)

    CAS  PubMed  Google Scholar 

  28. Schwenk, J. et al. Native GABAB receptors are heteromultimers with a family of auxiliary subunits. Nature 465, 231–235 (2010)

    ADS  CAS  PubMed  Google Scholar 

  29. Smaldone, G. et al. Cullin 3 recognition is not a universal property among KCTD proteins. PLoS ONE 10, e0126808 (2015)

    PubMed  PubMed Central  Google Scholar 

  30. Turecek, R. et al. Auxiliary GABAB receptor subunits uncouple G protein βγ subunits from effector channels to induce desensitization. Neuron 82, 1032–1044 (2014)

    CAS  PubMed  Google Scholar 

  31. Lackner, D. H. et al. A generic strategy for CRISPR-Cas9-mediated gene tagging. Nat. Commun. 6, 10237 (2015)

    ADS  CAS  PubMed  Google Scholar 

  32. Jae, L. T. et al. Deciphering the glycosylome of dystroglycanopathies using haploid screens for Lassa virus entry. Science 340, 479–483 (2013)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  33. Blondal, T. et al. Isolation and characterization of a thermostable RNA ligase 1 from a Thermus scotoductus bacteriophage TS2126 with good single-stranded DNA ligation properties. Nucleic Acids Res. 33, 135–142 (2005)

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009)

    Article  PubMed  PubMed Central  Google Scholar 

  35. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Joshi-Tope, G. et al. Reactome: a knowledgebase of biological pathways. Nucleic Acids Res. 33, D428–D432 (2005)

    CAS  PubMed  Google Scholar 

  37. Kamburov, A., Wierling, C., Lehrach, H. & Herwig, R. ConsensusPathDB–a database for integrating human functional interaction networks. Nucleic Acids Res. 37, D623–D628 (2009)

    CAS  PubMed  Google Scholar 

  38. Trapnell, C., Pachter, L. & Salzberg, S. L. TopHat: discovering splice junctions with RNA-seq. Bioinformatics 25, 1105–1111 (2009)

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015)

    CAS  PubMed  Google Scholar 

  40. Luna-Vargas, M. P. A. et al. Enabling high-throughput ligation-independent cloning and protein expression for the family of ubiquitin specific proteases. J. Struct. Biol. 175, 113–119 (2011)

    CAS  PubMed  Google Scholar 

  41. Fessler, E. et al. TGFβ signaling directs serrated adenomas to the mesenchymal colorectal cancer subtype. EMBO Mol. Med. 8, 745–760 (2016)

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Xu, G., Paige, J. S. & Jaffrey, S. R. Global analysis of lysine ubiquitination by ubiquitin remnant immunoaffinity profiling. Nat. Biotechnol. 28, 868–873 (2010)

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Udeshi, N. D., Mertins, P., Svinkina, T. & Carr, S. A. Large-scale identification of ubiquitination sites by mass spectrometry. Nat. Protocols 8, 1950–1960 (2013)

    CAS  PubMed  Google Scholar 

  44. Ameziane, N. et al. A novel Fanconi anaemia subtype associated with a dominant-negative mutation in RAD51. Nat. Commun. 6, 8829 (2015)

    ADS  CAS  PubMed  Google Scholar 

  45. Cox, J. et al. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol. Cell Proteomics 13, 2513–2526 (2014)

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Stark, C. et al. BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 34, 535–539 (2006)

    Google Scholar 

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We thank J. Goedhart, L. Wessels, B. van Steensel, S. Nijman, and members of the Brummelkamp, Perrakis, and Sixma laboratories for discussions. We thank R. Spaapen for providing CUL3 knockout cells, P. Celie and M. Stadnik for assistance with the recombinant protein expression, as well as E. Fessler and J. P. Medema for generation of WNT3A/R-spondin-conditioned medium. This work was supported by the Dutch Cancer Society (NKI 2015-7609), the Cancer Genomics Center, an Ammodo KNAW Award 2015 for Biomedical Sciences to T.R.B., by the Netherlands Organization for Scientific Research (NWO) as part of the National Roadmap Large-scale Research Facilities of the Netherlands, Proteins@Work (project number 184.032.201) to O.B.B. and A.F.M.A., and by a Vidi grant (723.012.102) to A.F.M.A.

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Authors and Affiliations



M.B., V.A.B., J.N., M.R. L.T.J., and T.R.B. were responsible for the overall design of the study. V.A.B. and E.S. performed the bioinformatics. E.S. developed the Phenosaurus platform. O.B.B. and A.F.M.A. designed, performed, and analysed the proteomics experiments. M.B., V.A.B., L.T.J., and T.R.B. wrote the manuscript; all authors commented on it.

Corresponding authors

Correspondence to Lucas T. Jae or Thijn R. Brummelkamp.

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Competing interests

T.R.B. is co-founder and shareholder of Haplogen GmbH and Scenic Biotech BV, and M.B., V.B; J.N., M.R., L.T.J., and T.R.B. are listed as inventors on a patent application related to the technology.

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Reviewer Information Nature thanks J. Moffat and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Figure 1 Validation of selected identified regulators.

a, Wild-type, JAK1-, LMNB1-, and LMNA1-deficient HAP1 cells were treated with IFN-γ for the indicated amount of time, lysates were prepared and analysed by immunoblotting. b, Wild-type, PRPF39-deficient, and PRPF39-deficient HAP1 cells reconstituted with Flag-tagged PRPF39 were treated with the protein synthesis inhibitor anisomycin for 4 h; lysates were prepared and analysed by immunoblotting.

Extended Data Figure 2 Gene expression is a requirement for phenotypic contribution.

a, The datasets for the two screens were filtered to display only the genes falling within the top 25% (4,200 genes) highest and non- or lowest-expressed genes in HAP1 cells. b, Bar plot representing the quantification of all screens (analysed as in a).

Extended Data Figure 3 Analysis of genes linked to few or many phenotypes.

a, Number of reported physical protein–protein interactions as a function of the number of phenotypes analysed in this study affected by a gene. b, As in a but with genes categorized as affecting either few (one or two; 1,478 genes) or many (three or more; 510 genes) traits. Two-sided unpaired t-test shows a modest but significant difference in the average number of protein–protein interactions between both groups. The y axis is cropped at 256 protein–protein interactions for better visibility and the median number of protein–protein interactions in each group is indicated. Box plots and error bars drawn according to Tukey’s representation. c, Comparison of fitness contribution for genes affecting few (one or two) versus many (three to ten) phenotypes. Genes specifically required for fitness in HAP1 cells13 were intersected with the genes contributing to phenotype-affecting genes and the proportion occurring in either group was tested using a two-sided χ2 test.

Extended Data Figure 4 Expression levels relate to phenotypic contribution.

a, A total of 16,800 interrogatable genes were ranked on expression levels in HAP1 cells and binned into 17 bins containing approximately 1,000 genes per bin. In each bin, the number of genes identified as a regulator of at least one phenotypic trait was counted. b, To account for differences between screens, the same binned approach as in a was applied for the number of genes contributing to each individual phenotypic trait additionally. c, To analyse the relationship between expression levels and mutation frequencies the number of sense insertions per gene in the glycosylated LAMP1 screen is plotted per bin, demonstrating that the observed increase in phenotypic contribution from bins 8–17 is not due to a higher average mutation frequency. Box plots and error bars drawn according to Tukey’s representation.

Extended Data Figure 5 Genetic wiring map for phosphorylation of AKT at S473 identifies known regulators of this process.

Outcome of genetic screen for AKT phosphorylation at S473. Data were generated and analysed as in Fig. 1. Selected known factors affecting AKT phosphorylation are labelled and their role in the signalling cascade is indicated in the cartoon. Individual gene-trap insertions (black dots) and their distribution across the gene bodies in the high and low channels (pAKT staining intensity) are shown for INPP4A and RICTOR.

Extended Data Figure 6 Effect of different KCTD family members on AKT phosphorylation at S473.

KCTD family members are highlighted in the dataset described previously.

Extended Data Figure 7 KCTD5 regulates phosphorylation of AKT in different human cell lines without affecting levels of common regulators.

a, Immunoblot confirming the effect of KCTD5 and CUL3 on AKT phosphorylation (S473) as detected in the genetic screen. Wild-type HAP1 cells and HAP1 cells deficient in KCTD5 or CUL3 were lysed and probed with specific antibodies by immunoblotting. b, Indicated wild-type and KCTD5-deficient HEK293 cells (two independent clones) were lysed and probed with specific antibodies by immunoblotting. Three additional cell lines (SKBR3, A549, and U2OS) were infected with a mix of two different lentiviral gRNAs targeting KCTD5 (RFP–CRISPR backbone). RFP-positive cells were sorted after 4 days and immunoblotted with the indicated antibodies. c, Wild-type or KCTD5-deficient HAP1 cells (three independent clones) were lysed and analysed with specific antibodies by immunoblotting.

Extended Data Figure 8 The Gβγ dimer is destabilized in the presence of KCTD5.

a, Wild-type HAP1 cells and HAP1 cells deficient in KCTD5 or CUL3 were lysed and probed with specific antibodies by immunoblotting. Increased levels of GNB1 and GNG5, as well as increased phosphorylation of AKT at S473, are comparable in cells deficient for KCTD5 or Cullin3. b, For RIC8A*, transcript uc001lof.3 was considered because the longer 5′ UTR in Refseq reduced the observed effect size.

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Brockmann, M., Blomen, V., Nieuwenhuis, J. et al. Genetic wiring maps of single-cell protein states reveal an off-switch for GPCR signalling. Nature 546, 307–311 (2017).

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