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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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.
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.
Reviewer Information Nature thanks J. Moffat and the other anonymous reviewer(s) for their contribution to the peer review of this work.
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Extended data figures and tables
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.
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).
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.
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.
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.
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). https://doi.org/10.1038/nature22376
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