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.
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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.
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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.
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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.
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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.
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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.
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
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.
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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 (2024). https://doi.org/10.1038/s41592-024-02256-z
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DOI: https://doi.org/10.1038/s41592-024-02256-z
