Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Post-translational modification-centric base editor screens to assess phosphorylation site functionality in high throughput

Abstract

Signaling pathways that drive gene expression are typically depicted as having a dozen or so landmark phosphorylation and transcriptional events. In reality, thousands of dynamic post-translational modifications (PTMs) orchestrate nearly every cellular function, and we lack technologies to find causal links between these vast biochemical pathways and genetic circuits at scale. Here we describe the high-throughput, functional assessment of phosphorylation sites through the development of PTM-centric base editing coupled to phenotypic screens, directed by temporally resolved phosphoproteomics. Using T cell activation as a model, we observe hundreds of unstudied phosphorylation sites that modulate NFAT transcriptional activity. We identify the phosphorylation-mediated nuclear localization of PHLPP1, which promotes NFAT but inhibits NFκB activity. We also find that specific phosphosite mutants can alter gene expression in subtle yet distinct patterns, demonstrating the potential for fine-tuning transcriptional responses. Overall, base editor screening of PTM sites provides a powerful platform to dissect PTM function within signaling pathways.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Signaling dynamics of early T cell activation.
Fig. 2: Base editing capabilities of empirically derived phosphorylation sites.
Fig. 3: Base editing screening reveals phosphosites involved in proliferation or survival.
Fig. 4: Proteome-wide base editing of phosphosites modulating NFAT transcriptional activity.
Fig. 5: Phosphorylation-induced nuclear translocation of PHLPP1 promotes NFAT and represses NFκB transcriptional responses.
Fig. 6: Dissecting T cell activation transcriptional responses.

Similar content being viewed by others

Data availability

Raw mass spectrometry data and metadata can be accessed at ftp://MSV000092965@massive.ucsd.edu. Raw RNA sequencing data can be accessed at GEO accession ID GSE244164.

Code availability

The code for the base editor design tool is available at https://github.com/mhegde/base-editor-design-tool.

References

  1. Hunter, T. Why nature chose phosphate to modify proteins. Philos. Trans. R. Soc. Lond. B Biol. Sci. 367, 2513–2516 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Manning, G., Whyte, D. B., Martinez, R., Hunter, T. & Sudarsanam, S. The protein kinase complement of the human genome. Science 298, 1912–1934 (2002).

    Article  CAS  PubMed  Google Scholar 

  3. Chen, M. J., Dixon, J. E. & Manning, G. Genomics and evolution of protein phosphatases. Sci. Signal 10, eaag1796 (2017).

    Article  PubMed  Google Scholar 

  4. Katrancha, S. M. et al. Trio haploinsufficiency causes neurodevelopmental disease-associated deficits. Cell Rep. 26, 2805–2817.e9 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Martinez-Val, A. et al. Spatial-proteomics reveals phospho-signaling dynamics at subcellular resolution. Nat. Commun. 12, 7113 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Koch, H. et al. Phosphoproteome profiling reveals molecular mechanisms of growth-factor-mediated kinase inhibitor resistance in EGFR-overexpressing cancer cells. J. Proteome Res 15, 4490–4504 (2016).

    Article  CAS  PubMed  Google Scholar 

  7. Paulo, J. A. & Gygi, S. P. A comprehensive proteomic and phosphoproteomic analysis of yeast deletion mutants of 14-3-3 orthologs and associated effects of rapamycin. Proteomics 15, 474–486 (2015).

    Article  CAS  PubMed  Google Scholar 

  8. Needham, E. J., Parker, B. L., Burykin, T., James, D. E. & Humphrey, S. J. Illuminating the dark phosphoproteome. Sci. Signal 12, eaau8645 (2019).

    Article  CAS  PubMed  Google Scholar 

  9. Hornbeck, P. V. et al. PhosphoSitePlus, 2014: mutations, PTMs and recalibrations. Nucleic Acids Res. 43, D512–D520 (2015).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Shifrut, E. et al. Genome-wide CRISPR screens in primary human T cells reveal key regulators of immune function. Cell 175, 1958–1971.e15 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Li, W. et al. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. 15, 554 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Meyers, R. M. et al. Computational correction of copy number effect improves specificity of CRISPR–Cas9 essentiality screens in cancer cells. Nat. Genet. 49, 1779–1784 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Rees, H. A. & Liu, D. R. Base editing: precision chemistry on the genome and transcriptome of living cells. Nat. Rev. Genet. 19, 770–788 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Hanna, R. E. et al. Massively parallel assessment of human variants with base editor screens. Cell 184, 1064–1080.e20 (2021).

    Article  CAS  PubMed  Google Scholar 

  17. Lue, N. Z. et al. Base editor scanning charts the DNMT3A activity landscape. Nat. Chem. Biol. 19, 176–186 (2023).

    Article  CAS  PubMed  Google Scholar 

  18. Li, H. et al. Assigning functionality to cysteines by base editing of cancer dependency genes. Nat. Chem. Biol. 19, 1320–1330 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Schmidt, R. et al. Base-editing mutagenesis maps alleles to tune human T cell functions. Nature 625, 805–812 (2024).

    Article  CAS  PubMed  Google Scholar 

  20. Yeh, W.-H., Chiang, H., Rees, H. A., Edge, A. S. B. & Liu, D. R. In vivo base editing of post-mitotic sensory cells. Nat. Commun. 9, 2184 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Abraham, R. T. & Weiss, A. Jurkat T cells and development of the T-cell receptor signalling paradigm. Nat. Rev. Immunol. 4, 301–308 (2004).

    Article  CAS  PubMed  Google Scholar 

  22. Larange, A. et al. A regulatory circuit controlled by extranuclear and nuclear retinoic acid receptor α determines T cell activation and function. Immunity 56, 2054–2069 (2023).

    Article  CAS  PubMed  Google Scholar 

  23. Abelin, J. G. et al. Workflow enabling deepscale immunopeptidome, proteome, ubiquitylome, phosphoproteome, and acetylome analyses of sample-limited tissues. Nat. Commun. 14, 1851 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Krug, K. et al. A curated resource for phosphosite-specific signature analysis. Mol. Cell Proteom. 18, 576–593 (2019).

    Article  CAS  Google Scholar 

  25. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Richter, M. F. et al. Phage-assisted evolution of an adenine base editor with improved Cas domain compatibility and activity. Nat. Biotechnol. 38, 883–891 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Huang, T. P., Newby, G. A. & Liu, D. R. Precision genome editing using cytosine and adenine base editors in mammalian cells. Nat. Protoc. 16, 1089–1128 (2021).

    Article  CAS  PubMed  Google Scholar 

  28. Kluesner, M. G. et al. EditR: a method to quantify base editing from Sanger sequencing. CRISPR J. 1, 239–250 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Helou, Y. A., Nguyen, V., Beik, S. P. & Salomon, A. R. ERK positive feedback regulates a widespread network of tyrosine phosphorylation sites across canonical T cell signaling and actin cytoskeletal proteins in Jurkat T cells. PLoS ONE 8, e69641 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Bottini, N. et al. Activation of ZAP-70 through specific dephosphorylation at the inhibitory Tyr-292 by the low molecular weight phosphotyrosine phosphatase (LMPTP). J. Biol. Chem. 277, 24220–24224 (2002).

    Article  CAS  PubMed  Google Scholar 

  31. Di Bartolo, V. et al. Tyrosine 319, a newly identified phosphorylation site of ZAP-70, plays a critical role in T cell antigen receptor signaling. J. Biol. Chem. 274, 6285–6294 (1999).

    Article  PubMed  Google Scholar 

  32. Jutz, S. et al. Assessment of costimulation and coinhibition in a triple parameter T cell reporter line: Simultaneous measurement of NF-κB, NFAT and AP-1. J. Immunol. Methods 430, 10–20 (2016).

    Article  CAS  PubMed  Google Scholar 

  33. Li, J. et al. Functional phosphoproteomics in cancer chemoresistance using CRISPR-mediated base editors. Adv. Sci. 9, e2200717 (2022).

    Article  Google Scholar 

  34. Pihlajamaa, P., Kauko, O., Sahu, B., Kivioja, T. & Taipale, J. A competitive precision CRISPR method to identify the fitness effects of transcription factor binding sites. Nat. Biotechnol. 41, 197–203 (2023).

    Article  CAS  PubMed  Google Scholar 

  35. Quesada, A. E. et al. Clinico-pathologic characteristics and outcomes of the World Health Organization (WHO) provisional entity de novo acute myeloid leukemia with mutated RUNX1. Mod. Pathol. 33, 1678–1689 (2020).

    Article  CAS  PubMed  Google Scholar 

  36. Huang, K. et al. Genome-wide CRISPR-Cas9 screening identifies NF-κB/E2F6 responsible for EGFRvIII-associated temozolomide resistance in Glioblastoma. Adv. Sci. 6, 1900782 (2019).

    Article  Google Scholar 

  37. Cheng, F. H. C. et al. E2F6 functions as a competing endogenous RNA, and transcriptional repressor, to promote ovarian cancer stemness. Cancer Sci. 110, 1085–1095 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Johnson, J. L. et al. An atlas of substrate specificities for the human serine/threonine kinome. Nature 613, 759–766 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Mognol, G. P. et al. Targeting the NFAT:AP-1 transcriptional complex on DNA with a small-molecule inhibitor. Proc. Natl Acad. Sci. USA 116, 9959–9968 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Wang, B. et al. Integrative analysis of pooled CRISPR genetic screens using MAGeCKFlute. Nat. Protoc. 14, 756–780 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Raudvere, U. et al. g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res. 47, W191–W198 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Hogan, P. G., Chen, L., Nardone, J. & Rao, A. Transcriptional regulation by calcium, calcineurin, and NFAT. Genes Dev. 17, 2205–2232 (2003).

    Article  CAS  PubMed  Google Scholar 

  43. Ortega-Pérez, I. et al. c-Jun N-terminal kinase (JNK) positively regulates NFATc2 transactivation through phosphorylation within the N-terminal regulatory domain. J. Biol. Chem. 280, 20867–20878 (2005).

    Article  PubMed  Google Scholar 

  44. Ishitani, T. et al. The TAK1-NLK mitogen-activated protein kinase cascade functions in the Wnt-5a/Ca(2+) pathway to antagonize Wnt/beta-catenin signaling. Mol. Cell. Biol. 23, 131–139 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. MacDonnell, S. M. et al. CaMKII negatively regulates calcineurin-NFAT signaling in cardiac myocytes. Circ. Res. 105, 316–325 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Anshabo, A. T., Milne, R., Wang, S. & Albrecht, H. CDK9: a comprehensive review of its biology, and its role as a potential target for anti-cancer agents. Front Oncol. 11, 678559 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Phee, H. et al. Pak2 is required for actin cytoskeleton remodeling, TCR signaling, and normal thymocyte development and maturation. eLife 3, e02270 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Pareek, T. K. et al. Cyclin-dependent kinase 5 activity is required for T cell activation and induction of experimental autoimmune encephalomyelitis. J. Exp. Med. 207, 2507–2519 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Ochoa, D. et al. The functional landscape of the human phosphoproteome. Nat. Biotechnol. 38, 365–373 (2020).

    Article  CAS  PubMed  Google Scholar 

  50. Chen, M. et al. Identification of PHLPP1 as a tumor suppressor reveals the role of feedback activation in PTEN-mutant prostate cancer progression. Cancer Cell 20, 173–186 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Nitsche, C. et al. The phosphatase PHLPP1 regulates Akt2, promotes pancreatic cancer cell death, and inhibits tumor formation. Gastroenterology 142, 377–87.e1–5 (2012).

    Article  CAS  PubMed  Google Scholar 

  52. Cohen Katsenelson, K. et al. PHLPP1 counter-regulates STAT1-mediated inflammatory signaling. eLife 8, e48609 (2019).

  53. Patterson, S. J. et al. Cutting edge: PHLPP regulates the development, function, and molecular signaling pathways of regulatory T cells. J. Immunol. 186, 5533–5537 (2011).

    Article  CAS  PubMed  Google Scholar 

  54. Balasuriya, N. et al. Genetic code expansion and live cell imaging reveal that Thr-308 phosphorylation is irreplaceable and sufficient for Akt1 activity. J. Biol. Chem. 293, 10744–10756 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Feske, S. Calcium signalling in lymphocyte activation and disease. Nat. Rev. Immunol. 7, 690–702 (2007).

    Article  CAS  PubMed  Google Scholar 

  56. Gwack, Y. et al. A genome-wide Drosophila RNAi screen identifies DYRK-family kinases as regulators of NFAT. Nature 441, 646–650 (2006).

    Article  CAS  PubMed  Google Scholar 

  57. Liu, H. et al. NFATc1 phosphorylation by DYRK1A increases its protein stability. PLoS ONE 12, e0172985 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Thompson, B. J. et al. DYRK1A controls the transition from proliferation to quiescence during lymphoid development by destabilizing Cyclin D3. J. Exp. Med. 212, 953–970 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Chen, J. et al. NR4A transcription factors limit CAR T cell function in solid tumours. Nature 567, 530–534 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Beltrao, P. et al. Systematic functional prioritization of protein posttranslational modifications. Cell 150, 413–425 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Beltrao, P., Bork, P., Krogan, N. J. & van Noort, V. Evolution and functional cross-talk of protein post-translational modifications. Mol. Syst. Biol. 9, 714 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Liu, N., Guo, Y., Ning, S. & Duan, M. Phosphorylation regulates the binding of intrinsically disordered proteins via a flexible conformation selection mechanism. Commun. Chem. 3, 1–9 (2020).

    Article  Google Scholar 

  63. Nicolaou, S. T., Hebditch, M., Jonathan, O. J., Verma, C. S. & Warwicker, J. PhosIDP: a web tool to visualize the location of phosphorylation sites in disordered regions. Sci. Rep. 11, 9930 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Trinidad, J. C. et al. Global identification and characterization of both O-GlcNAcylation and phosphorylation at the murine synapse. Mol. Cell Proteom. 11, 215–229 (2012).

    Article  Google Scholar 

  65. Ren, X. et al. High-throughput PRIME-editing screens identify functional DNA variants in the human genome. Mol. Cell 83, 4633–4645.e9 (2023).

    Article  CAS  PubMed  Google Scholar 

  66. Hiatt, J. et al. Efficient generation of isogenic primary human myeloid cells using CRISPR-Cas9 ribonucleoproteins. Cell Rep. 35, 109105 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Mari, T. et al. In vitro Kinase-to-Phosphosite database (iKiP-DB) predicts kinase activity in phosphoproteomic fatasets. J. Proteome Res 21, 1575–1587 (2022).

  68. Hwang, G.-H. et al. Web-based design and analysis tools for CRISPR base editing. BMC Bioinform. 19, 542 (2018).

    Article  CAS  Google Scholar 

  69. Chen, P. J. et al. Enhanced prime editing systems by manipulating cellular determinants of editing outcomes. Cell 184, 5635–5652.e29 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank A. Haber, P. Vijayanand, E. Kvedaraite, B. Hamilton, A. Rubin, T.M. Yaron and M. Gentili for useful discussion. We also thank G. and S. Clouse and S. Carr for support, as well as P. Guo and the Nikon Imaging Center at the University of California San Diego for the support on microscopy experiments. This work was supported by National Institutes of Health (NIH) grant nos. R35GM147554 and R01CA279795 (S.A.M.); NIH grant no. R35GM122523 (A.C.N.); NIH grant nos. U01AI142756, R35GM118062, RM1HG009490 and HHMI (D.R.L.); NIH grant nos. R01AI040127 and R01AI109842 (P.G.H.); Stem Cell Network Jump Start Award (no. ECR-C4R1-7) for C.G.d.B. who is a Michael Smith Health Research BC Scholar; and the University of California San Diego Graduate Training Program in Cellular and Molecular Pharmacology (grant no. T32 GM007752) and the National Science Foundation Graduate Research Fellowship Program (no. DGE-1650112) (A.C.J.). The NovaSeq 6000 was acquired through the Shared Instrumentation Grant Program (S10) S10OD025052; La Jolla Institute for Immunology Next-Generation Sequencing Core Facility RRID:SCR_023107. FACSAria-3 was acquired through the Shared Instrumentation Grant Program (S10): RR027366; La Jolla Institute for Immunology Flow Cytometry Core RRID:SCR_014832.

Author information

Authors and Affiliations

Authors

Contributions

S.A.M. conceptualized the study. P.H.K., A.C.J., M.H., C.G.d.B., G.A.N. and S.A.M. developed the methodology. M.H. and J.G.D. wrote the software. P.H.K., A.A.D.S., A.C.J. and S.A.M. validated the results. A.A.D.S., M.E.O., R.B., S.A., R.A.G., G.A.N. and S.A.M. performed the formal analyses. P.H.K., A.A.D.S., M.B., A.C.J., M.E.O., M.I.M., N.P., P.G.H., R.B., A.C.N., S.A., R.A.G., C.G.d.B., G.A.N. and S.A.M. performed the investigations. M.B., N.P., J.L., T.L., P.G.H., D.R.L., J.G.D., G.A.N., C.G.d.B. and S.A.M. provided resources. A.A.D.S., M.E.O., M.H., M.I.M., R.B., J.G.D., S.A., R.A.G. and S.A.M. curated the data. All authors wrote the paper. P.H.K., A.A.D.S., A.C.J., M.E.O., S.A. and S.A.M. visualized the findings. S.A.M. supervised the project and was the project administrator. S.A.M. acquired funding.

Corresponding author

Correspondence to Samuel A. Myers.

Ethics declarations

Competing interests

D.R.L. is a consultant and/or equity owner for Prime Medicine, Beam Therapeutics, Pairwise Plants, Chroma Medicine and Nvelop Therapeutics—companies that use or deliver genome editing or epigenome engineering agents. The other authors declare no competing interests.

Peer review

Peer review information

Nature Methods thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: Rita Strack, in collaboration with the Nature Methods team.

Additional information

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

Extended data

Extended Data Fig. 1 associated with Fig. 1 Kennedy et al.

Phosphoproteomics quality controls. a) Principal component analysis of all phosphoproteomics samples prior to differential expression analysis. b) Multi-scatter plot comparing all samples to each other pairwise. Pearson’s r is shown. Colors of samples are the same as in Extended Data Fig. 1a.

Extended Data Fig. 2 associated with Fig. 2 Kennedy et al.

Gating strategy for CD69 staining analyzed by flow cytometry.

Extended Data Fig. 3 associated with Fig. 3 Kennedy et al.

Gene ontology analysis of the genes targeted by the sgRNAs depicted in Fig. 3d. Colors coordinate with Fig. 3d. Dotted line is the hypergeometric distribution test FDR threshold.

Extended Data Fig. 4 associated with Fig. 4 Kennedy et al.

Quality control and characterization of phosphosite base editing coupled to NFAT activity reporters. a) Pairwise Spearman correlations between all normalized log transformed read counts across replicates and experimental conditions. 0.4 is the lower limit cut off in black. b) Mean (across replicates) sgRNA counts for individual sgRNAs prior to collapsing redundant phosphosite targets in the GFP high and low bins. Regression line is shown. c) Percentage of phosphosite targets with one or more protospacer sequences. d) g:Profiler (gene-centric) analysis of genes with phosphosite mutations enriched in the GFP low or GFP high bins. For the x-axis the normalized enrichment score (NES) was multiplied by the -log10 FDR. e) GSEA (gene-centric) analysis of gene sets enriched in the GFP high bin. TCR Calcium Pathway is bolded. f) Proportion of phosphosite targets that contain a putative bystander edit in the library as a whole and in the sorted GFP bins. Student’s two sample T test P value is shown. g) Scatterplot comparing the F statistic from the phosphoproteomic analysis, a proxy for magnitude and reproducibility of abundance changes across the four time points, and the log2 fold change GFPhigh/low bins calculated by MAGeCK. Horizontal red dashed line delineates nominal p value of < 0.05 from the moderated F test of the phosphoproteomics data. h) Scatterplot comparing the log2 fold change GFPhigh/low bins calculated by MAGeCK to the predicted functional score from the machine learning analysis in Ochoa et al.49. Inset shows the full data structure while the scatter plot is a zoom of points above a predicted functional score of 0.5. Horizontal red dashed line delineates a score threshold determined in Ochoa et al.49. i) Distribution of predicted functional scores from Ochoa et al.49 for all data points in the GFP screen, the phosphosite mutants that increased (‘up’ in red) or decreased GFP levels (‘down’ in purple). P values for comparison to the whole data set are shown. Data points represent the mean log2 FC (GFPhigh/GFP low) of four transduction replicates. P values for an ANOVA test followed by uncorrected Fisher’s least significant difference for multiple comparisons.

Extended Data Fig. 5 associated with Fig. 5, Kennedy et al.

EditR software analysis28 plots outlining bystander base editing levels for PHLPP1 S118P and MAPK1 Y187C prior to single cell clone isolation.

Extended Data Fig. 6 associated with Fig. 6, Kennedy et al.

a) Log2 fold change of select T cell genes differentially expressed between PHLPP1 S118P and MAPK1 Y187C mutant cells, compared to HEK3 control cells. b) Gating strategy for intracellular GZMB staining and analysis by flow cytometry.

Supplementary information

Reporting Summary

Supplementary Data 1

The .json file of phosphoproteomic data in Fig. 1b, which can be explored using Morpheus https://software.broadinstitute.org/morpheus/.

Supplementary Data 2

The .json file of the transcriptional data in Fig. 5. Differentially expressed genes of phosphosite mutants at 0 and 6 h post T cell activation, where values are in log2 fold changes to the mean. Only statistically significant genes are plotted. These data can be explored using https://software.broadinstitute.org/morpheus/.

Supplementary Tables

Supplementary Table 1. Phosphoproteomics analyses processed by Spectrum Mill and statistically tested by Protigy using the moderated F-test. ‘modF’ provides all analysis results and measurement values. ‘Class vector’ provides sample key for TMT channels. ‘Description of table header’ refers to modF and describes where which analysis comes from. Supplemental Table 2A. ABE8e-targetable phosphosites. Supplementary Table 2B. BE4-targetable phosphosites. Supplementary Table 3. Differential analysis of sgRNA abundances between pre- and post-ABE8e protein introduction. MAGeCK P value is shown. Supplementary Table 4. Differential analysis of sgRNA abundances between GFP high and GFP low bins–NFAT activity screen. MAGeCK P value is shown. Supplementary Table 5. RNA sequencing analysis of activated Jurkat T cells with various phosphosite mutations. P values were determined in using a variant of the negative binomial exact test in Cell Ranger (10x Genomics). Supplementary Table 6. Oligonucleotide sequences for sgRNA in vitro transcription and cloning into pRDA_118 vector for lentivirus production.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kennedy, P.H., Alborzian Deh Sheikh, A., Balakar, M. et al. Post-translational modification-centric base editor screens to assess phosphorylation site functionality in high throughput. Nat Methods 21, 1033–1043 (2024). https://doi.org/10.1038/s41592-024-02256-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41592-024-02256-z

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing