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:

CRISPR screen in regulatory T cells reveals modulators of Foxp3

Abstract

Regulatory T (Treg) cells are required to control immune responses and maintain homeostasis, but are a significant barrier to antitumour immunity1. Conversely, Treg instability, characterized by loss of the master transcription factor Foxp3 and acquisition of proinflammatory properties2, can promote autoimmunity and/or facilitate more effective tumour immunity3,4. A comprehensive understanding of the pathways that regulate Foxp3 could lead to more effective Treg therapies for autoimmune disease and cancer. The availability of new functional genetic tools has enabled the possibility of systematic dissection of the gene regulatory programs that modulate Foxp3 expression. Here we developed a CRISPR-based pooled screening platform for phenotypes in primary mouse Treg cells and applied this technology to perform a targeted loss-of-function screen of around 500 nuclear factors to identify gene regulatory programs that promote or disrupt Foxp3 expression. We identified several modulators of Foxp3 expression, including ubiquitin-specific peptidase 22 (Usp22) and ring finger protein 20 (Rnf20). Usp22, a member of the deubiquitination module of the SAGA chromatin-modifying complex, was revealed to be a positive regulator that stabilized Foxp3 expression; whereas the screen suggested that Rnf20, an E3 ubiquitin ligase, can serve as a negative regulator of Foxp3. Treg-specific ablation of Usp22 in mice reduced Foxp3 protein levels and caused defects in their suppressive function that led to spontaneous autoimmunity but protected against tumour growth in multiple cancer models. Foxp3 destabilization in Usp22-deficient Treg cells could be rescued by ablation of Rnf20, revealing a reciprocal ubiquitin switch in Treg cells. These results reveal previously unknown modulators of Foxp3 and demonstrate a screening method that can be broadly applied to discover new targets for Treg immunotherapies for cancer and autoimmune disease.

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

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

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

Fig. 1: Discovery and validation of Foxp3 regulators in primary mouse Treg cells using a targeted pooled CRISPR screen.
Fig. 2: Usp22 is required for Foxp3 maintenance and Treg suppressive function.
Fig. 3: Treg-specific ablation of Usp22 results in autoimmunity and enhances antitumour immunity.

Similar content being viewed by others

Data availability

Data from the screen (Fig. 1) and RNA sequencing (Fig. 2, Extended Data Fig. 5) are provided here in Supplementary Tables 1 and 5. ChIP–seq data that support the findings of this study have been deposited in the Gene Expression Omnibus under the accession code GSE140102. Foxp3 ChIP–seq results are in the Gene Expression Omnibus under accession code GSE40684; ATAC–seq and ChIP–seq for H3K4me, H3K27ac, H3K4me3 are in the Sequence Read Archive under accession number DRP003376. Source data for Figs. 13 and Extended Data Figs. 28 are provided with the paper. All other data are available from the corresponding author upon reasonable request.

Code availability

All code used for data visualization in this manuscript will be made available on request.

References

  1. Sakaguchi, S., Yamaguchi, T., Nomura, T., Ono, M. & Regulatory, T. Regulatory T cells and immune tolerance. Cell 133, 775–787 (2008).

    Article  CAS  PubMed  Google Scholar 

  2. Zhou, X. et al. Instability of the transcription factor Foxp3 leads to the generation of pathogenic memory T cells in vivo. Nat. Immunol. 10, 1000–1007 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Bailey-Bucktrout, S. L. & Bluestone, J. A. Regulatory T cells: stability revisited. Trends Immunol. 32, 301–306 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Overacre-Delgoffe, A. E. & Vignali, D. A. A. Treg fragility: a prerequisite for effective antitumor immunity? Cancer Immunol. Res. 6, 882–887 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Bluestone, J. A. & Tang, Q. Treg cells-the next frontier of cell therapy. Science 362, 154–155 (2018).

    Article  ADS  CAS  PubMed  Google Scholar 

  6. Doench, J. G. et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR–Cas9. Nat. Biotechnol. 34, 184–191 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. 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  CAS  Google Scholar 

  8. Beyer, M. & Schultze, J. L. Plasticity of Treg cells: is reprogramming of Treg cells possible in the presence of FOXP3? Int. Immunopharmacol. 11, 555–560 (2011).

    Article  CAS  PubMed  Google Scholar 

  9. Maruyama, T., Konkel, J. E., Zamarron, B. F. & Chen, W. The molecular mechanisms of Foxp3 gene regulation. Semin. Immunol. 23, 418–423 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Kitoh, A. et al. Indispensable role of the Runx1–Cbfβ transcription complex for in vivo-suppressive function of FoxP3+ regulatory T cells. Immunity 31, 609–620 (2009).

    Article  CAS  PubMed  Google Scholar 

  11. Rudra, D. et al. Runx-CBFβ complexes control expression of the transcription factor Foxp3 in regulatory T cells. Nat. Immunol. 10, 1170–1177 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Ono, M. et al. Foxp3 controls regulatory T-cell function by interacting with AML1/Runx1. Nature 446, 685–689 (2007).

    Article  ADS  CAS  PubMed  Google Scholar 

  13. Yao, Z. et al. Nonredundant roles for Stat5a/b in directly regulating Foxp3. Blood 109, 4368–4375 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Williams, L. M. & Rudensky, A. Y. Maintenance of the Foxp3-dependent developmental program in mature regulatory T cells requires continued expression of Foxp3. Nat. Immunol. 8, 277–284 (2007).

    Article  CAS  PubMed  Google Scholar 

  15. Beyer, M. et al. Repression of the genome organizer SATB1 in regulatory T cells is required for suppressive function and inhibition of effector differentiation. Nat. Immunol. 12, 898–907 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Koutelou, E., Hirsch, C. L. & Dent, S. Y. R. Multiple faces of the SAGA complex. Curr. Opin. Cell Biol. 22, 374–382 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Schumann, K. et al. Generation of knock-in primary human T cells using Cas9 ribonucleoproteins. Proc. Natl Acad. Sci. USA 112, 10437–10442 (2015).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  18. Rubtsov, Y. P. et al. Regulatory T cell-derived interleukin-10 limits inflammation at environmental interfaces. Immunity 28, 546–558 (2008).

    Article  CAS  PubMed  Google Scholar 

  19. da Silva Martins, M. & Piccirillo, C. A. Functional stability of Foxp3+ regulatory T cells. Trends Mol. Med. 18, 454–462 (2012).

    Article  PubMed  CAS  Google Scholar 

  20. Feng, Y. et al. Control of the inheritance of regulatory T cell identity by a cis element in the Foxp3 locus. Cell 158, 749–763 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Li, X., Liang, Y., LeBlanc, M., Benner, C. & Zheng, Y. Function of a Foxp3 cis-element in protecting regulatory T cell identity. Cell 158, 734–748 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Zheng, Y. et al. Role of conserved non-coding DNA elements in the Foxp3 gene in regulatory T-cell fate. Nature 463, 808–812 (2010).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  23. DuPage, M. et al. The chromatin-modifying enzyme Ezh2 is critical for the maintenance of regulatory T cell identity after activation. Immunity 42, 227–238 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Wei, G. et al. Global mapping of H3K4me3 and H3K27me3 reveals specificity and plasticity in lineage fate determination of differentiating CD4+ T cells. Immunity 30, 155–167 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Josefowicz, S. Z. et al. Extrathymically generated regulatory T cells control mucosal TH2 inflammation. Nature 482, 395–399 (2012).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  26. Yu, X. et al. SENP3 maintains the stability and function of regulatory T cells via BACH2 deSUMOylation. Nat. Commun. 9, 3157 (2018).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  27. Chen, Z. et al. The ubiquitin ligase Stub1 negatively modulates regulatory T cell suppressive activity by promoting degradation of the transcription factor Foxp3. Immunity 39, 272–285 (2013).

    Article  CAS  PubMed  Google Scholar 

  28. van Loosdregt, J. et al. Stabilization of the transcription factor Foxp3 by the deubiquitinase USP7 increases Treg-cell-suppressive capacity. Immunity 39, 259–271 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Dang, E. V. et al. Control of TH17/Treg balance by hypoxia-inducible factor 1. Cell 146, 772–784 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Li, Y. et al. USP21 prevents the generation of T-helper-1-like Treg cells. Nat. Commun. 7, 13559 (2016).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  31. Henry, K. W. et al. Transcriptional activation via sequential histone H2B ubiquitylation and deubiquitylation, mediated by SAGA-associated Ubp8. Genes Dev. 17, 2648–2663 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Melo-Cardenas, J., Zhang, Y., Zhang, D. D. & Fang, D. Ubiquitin-specific peptidase 22 functions and its involvement in disease. Oncotarget 7, 44848–44856 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Lin, Z. et al. USP22 antagonizes p53 transcriptional activation by deubiquitinating Sirt1 to suppress cell apoptosis and is required for mouse embryonic development. Mol. Cell 46, 484–494 (2012).

    Article  CAS  PubMed  Google Scholar 

  34. Zhou, X. et al. Selective miRNA disruption in T reg cells leads to uncontrolled autoimmunity. J. Exp. Med. 205, 1983–1991 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Luche, H., Weber, O., Nageswara Rao, T., Blum, C. & Fehling, H. J. Faithful activation of an extra-bright red fluorescent protein in “knock-in” Cre-reporter mice ideally suited for lineage tracing studies. Eur. J. Immunol. 37, 43–53 (2007).

    Article  CAS  PubMed  Google Scholar 

  36. Bailey-Bucktrout, S. L. et al. Self-antigen-driven activation induces instability of regulatory T cells during an inflammatory autoimmune response. Immunity 39, 949–962 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Platt, R. J. et al. CRISPR-Cas9 knockin mice for genome editing and cancer modeling. Cell 159, 440–455 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Haribhai, D. et al. Regulatory T cells dynamically control the primary immune response to foreign antigen. J. Immunol. 178, 2961–2972 (2007).

    Article  CAS  PubMed  Google Scholar 

  39. Joung, J. et al. Genome-scale CRISPR–Cas9 knockout and transcriptional activation screening. Nat. Protoc. 12, 828–863 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Stubbington, M. J. et al. An atlas of mouse CD4+ T cell transcriptomes. Biol. Direct 10, 14 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Gilbert, L. A. et al. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159, 647–661 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Hultquist, J. F. et al. A Cas9 ribonucleoprotein platform for functional genetic studies of HIV–host interactions in primary human T cells. Cell Rep. 17, 1438–1452 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Brinkman, E. K., Chen, T., Amendola, M. & van Steensel, B. Easy quantitative assessment of genome editing by sequence trace decomposition. Nucleic Acids Res. 42, e168 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Melo-Cardenas, J. et al. USP22 deficiency leads to myeloid leukemia upon oncogenic Kras activation through a PU.1 dependent mechanism. Blood 132, 423–434 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Chen, S. et al. Host miR155 promotes tumor growth through a myeloid-derived suppressor cell-dependent mechanism. Cancer Res. 75, 519–531 (2015).

    Article  CAS  PubMed  Google Scholar 

  47. Qiu, J. et al. The aryl hydrocarbon receptor regulates gut immunity through modulation of innate lymphoid cells. Immunity 36, 92–104 (2012).

    Article  CAS  PubMed  Google Scholar 

  48. Lee, S.-M., Gao, B. & Fang, D. FoxP3 maintains Treg unresponsiveness by selectively inhibiting the promoter DNA-binding activity of AP-1. Blood 111, 3599–3606 (2008).

    Article  CAS  PubMed  Google Scholar 

  49. Gao, B., Kong, Q., Kemp, K., Zhao, Y.-S. & Fang, D. Analysis of sirtuin 1 expression reveals a molecular explanation of IL-2-mediated reversal of T-cell tolerance. Proc. Natl Acad. Sci. USA 109, 899–904 (2012).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  50. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  51. Samstein, R. M. et al. Foxp3 exploits a pre-existent enhancer landscape for regulatory T cell lineage specification. Cell 151, 153–166 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Kitagawa, Y. et al. Guidance of regulatory T cell development by Satb1-dependent super-enhancer establishment. Nat. Immunol. 18, 173–183 (2017).

    Article  CAS  PubMed  Google Scholar 

  53. Whyte, W. A. et al. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell 153, 307–319 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Creyghton, M. P. et al. Histone H3K27ac separates active from poised enhancers and predicts developmental state. Proc. Natl Acad. Sci. USA 107, 21931–21936 (2010).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  55. Simeonov, D. R. et al. Discovery of stimulation-responsive immune enhancers with CRISPR activation. Nature 549, 111–115 (2017).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  56. Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).

    Article  CAS  PubMed  Google Scholar 

  57. Lovén, J. et al. Revisiting global gene expression analysis. Cell 151, 476–482 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Szklarczyk, D. et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47 (D1), D607–D613 (2019).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank all members of the Marson lab as well as M. S. Anderson, K. M. Ansel, C. J. Ye, K. Schumann and L. Gilbert for helpful suggestions and technical advice; J. Freimer, S. Raju and E. Guo for helpful advice and assistance with the RNA-seq analysis pipeline; V. Nguyen, V. Tobin, R. Apathy, M. Nguyen, the UCSF Flow Cytometry Core, and N. Hah and G. Chou in the Salk NGS Core Facility for technical assistance; S. Pyle for assistance with graphics; and D. Nguyen for critical reading of the manuscript. D.F. is supported by NIH R01 grants (AI079056, AI108634 and CA232347). E.M. is supported by NIH F31 CA220801-03. J.T.C. is supported by the National Science Foundation Graduate Research Fellowship Program grant 1650113. J.G. was supported by the Salk Institute T32 Cancer Training Grant T32CA009370 and the NIGMS NRSA F32 GM128377-01. D.C.H. is supported by the National Institutes of Health (NIH) (GM128943-01, CA184043-03), the V Foundation for Cancer Research V2016-006, the Pew-Stewart Foundation for Cancer Research and the Leona M. and Harry B. Helmsley Charitable Trust. The Marson lab has received gifts from J. Aronov, G. Hoskin, K. Jordan, B. Bakar, the Caufield family and funds from the Innovative Genomics Institute (IGI), the Northern California JDRF Center of Excellence and the Parker Institute for Cancer Immunotherapy (PICI). A.M. holds a Career Award for Medical Scientists from the Burroughs Wellcome Fund, is an investigator at the Chan–Zuckerberg Biohub and is a recipient of a The Cancer Research Institute (CRI) Lloyd J. Old STAR grant. This work used the Vincent J. Coates Genomics Sequencing Laboratory at UC Berkeley, supported by NIH S10 OD018174 Instrumentation Grant, the UCSF Flow Cytometry Core, supported by the Diabetes Research Center grants NIH P30 DK063720 and NIH S10 1S10OD021822-01, and the Salk NGS Core Facility, supported by the NIH-NCI CCSG: P30 014195, the Chapman Foundation and the Helmsley Charitable Trust.

Author information

Authors and Affiliations

Authors

Contributions

Conceptualization: J.T.C., E.M., E.S., Yusi .Z., Z.S., F.V.G., J.A.B., A.M. and D.F. Methodology: J.T.C., E.S. and T.L.R. Investigation: J.T.C., E.M., E.S., J.G., Yusi Z., O.S., Y.X., T.L.R., D.R.S., Yana Z., S.C., Z.L., J.M.W., J.H., I.A.V., G.Y.P., Y.L. and I.I. Resources: D.C.H., J.A.B., A.M. and D.F. Formal analysis: J.T.C., E.S. and J.G. Software: E.S. Data curation: J.T.C., E.S. and J.G. Supervision: B.Z., Y.L., F.V.G., D.C.H., J.A.B., A.M. and D.F. Funding acquisition: J.T.C., E.M., D.C.H., A.M. and D.F. Writing, original draft preparation: J.T.C., E.M., J.G., Yusi Z., A.M. and D.F. Writing, review and editing: J.T.C., E.M., J.G., Z.S., F.V.G., D.C.H., J.A.B., A.M. and D.F.

Corresponding authors

Correspondence to Alexander Marson or Deyu Fang.

Ethics declarations

Competing interests

T.L.R. is a cofounder of Arsenal Biosciences. A.M. is a cofounder, member of the Boards of Directors and a member of the Scientific Advisory Boards of Spotlight Therapeutics and Arsenal Biosciences. A.M. has served as an advisor to Juno Therapeutics, is a member of the scientific advisory board at PACT Pharma, and is an advisor to Trizell. A.M. owns stock in Arsenal Biosciences, Spotlight Therapeutics and PACT Pharma. The Marson lab has received sponsored research support from Juno Therapeutics, Epinomics and Sanofi, and gifts from Gilead and Anthem. J.A.B. is a cofounder of Sonoma BioTherapeutics; a stock holder and member of the Board of Directors on Rheos Medicines; and a stock holder and member of the Scientific Advisory Boards of Vir Therapeutics, Arcus Biotherapeutics, Solid Biosciences, and Celsius Therapeutics. J.A.B. owns stock in MacroGenics and Kadmon Holdings. A patent application has been filed based on the screen data described here.

Additional information

Peer review information Nature thanks John Doench, William P. Tansey 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 Fig. 1 Design and quality control of targeted pooled CRISPR screen in primary mouse Treg cells.

a, Design strategy for selection of genes for unbiased targeted library of 492 targets, including 489 nuclear factors and 3 control targets (non-targeting (NT), GFP and RFP). Genes were selected on the basis of gene ontology (GO) annotation and then subselected on the basis of highest expression across any CD4 T cell subset for a total of 2,000 sgRNAs. b, Diagram of MSCV expression vector with Thy1.1 reporter used for retroviral transduction of the sgRNA library. c, Detailed time line schematic of the 12-day targeted screen pipeline. Arrows indicate when the cells were split and medium was replenished. d, Retroviral transduction efficiency of the targeted library in primary mouse Treg cells shown by Thy1.1 surface expression measured by flow cytometry. The infection was scaled to achieve a high efficiency multiplicity of infection. e, Foxp3 expression from screen input, output and control cells measured by flow cytometry. Top, Foxp3 expression from input Foxp3+ purified Treg cells as measured by GFP expression on day 0. Middle, Foxp3 expression as measured by endogenous intracellular staining from control Treg cells (not transduced with library) on day 12. Bottom, Foxp3 expression as measured by endogenous intracellular staining from screen Treg cells (transduced with library) on day 12. f, Targeted screen (2,000 guides) shows that sgRNAs targeting Foxp3 and Usp22 were enriched in Foxp3low cells (blue). Non-targeting control (NT ctrl) sgRNAs were evenly distributed across the cell populations (black). g, Distribution of read counts after next-generation sequencing of sgRNAs of sorted cell populations, Foxp3high and Foxp3low. h, Schematic of experimentally determined and predicted protein–protein interactions between top hits, 16 negative regulators (red) and 25 positive regulators (red), generated by STRING-db59. Black lines connect interacting proteins and dotted lines outline selected known protein complexes. All data are presented as mean ± s.e.m. ns, no significant difference. Sample sizes (n), P values, statistical tests and number of times experiments were replicated are listed in Methods, ‘Statistics and reproducibility’.

Extended Data Fig. 2 Validation of Foxp3 modulators in primary mouse and human Treg cells with Cas9 RNP electroporation.

a, Overview of orthogonal validation strategy using arrayed electroporation of Cas9 RNPs in Treg cells. b, Foxp3 expression 4 days after electroporation of Cas9 RNPs in mouse Treg cells as measured by flow cytometry of top screen hits. Each row shows three histograms layered on top of one another (1–2 for controls) with each representing effects of independent gRNAs for each target gene. Percentages shown on the right depict the average frequency of Foxp3+ cells across gRNAs targeting each gene. c, Percentage of Foxp3 cells of live, CD4+ cells 4 days after electroporation of Cas9 RNPs in mouse Treg cells as measured by flow cytometry of top screen hits. Each data point represents an independent sgRNA for each target gene. d, Foxp3 MFI of Foxp3+ mouse Treg cells for 3-4 distinct gRNAs targeting each gene paired with the mean KO efficiency (top) for each guide as determined by TIDE analysis. e, Representative flow plots depicting FOXP3 and CD25 expression 7 days after electroporation of Cas9 RNPs targeting USP22 or NT ctrl in human Treg cells. The subpopulation of cells with the highest expression of FOXP3 and CD25 (FOXP3highCD25high) is highlighted with a red gate. f, Percentage of FOXP3+ cells from human Treg cells electroporated with Cas9 RNPs targeting USP22 or NT ctrl in ten biological replicates. Lines connect paired samples. g, Percentage of FOXP3highCD25high cells from human Treg cells electroporated with Cas9 RNPs targeting USP22 or NT ctrl in 10 biological replicates. h, FOXP3 MFI of human Treg cells for 3–4 distinct gRNAs targeting each gene paired with the mean KO efficiency (top) for each guide as determined by TIDE analysis. i, Simple linear regression of FOXP3 MFI (y axis) by percentage of editing efficiency determined by TIDE analysis (x axis) for 4 gRNAs targeting USP22 in 2–4 biological donors. j, FOXP3 MFI of human Treg cells electroporated with Cas9 RNPs with 2–3 distinct sgRNAs each in 2–4 biological donors; corresponding to h. Data points with less than 60% editing efficiency KO by TIDE analysis were excluded from the graph. All data are presented as mean ± s.e.m. Sample sizes (n), P values, statistical tests and number of times experiments were replicated are listed in Methods, ‘Statistics and reproducibility’

Source Data.

Extended Data Fig. 3 Design and validation of Treg-specific Usp22-knockout mice.

a, Schematic of the mouse Usp22 locus. Targeting vector contains IRES-lacZ and a neo cassette inserted into exon 2. b, Genotyping by PCR showed a 600-bp band for the WT allele and a 400-bp band for mutant allele, simultaneously in the homozygous floxed (fl/fl) mice. c, Western blot analysis of Usp22 in CD4+CD25 conventional T cells (Tconv) and CD4+CD25+ Treg cells isolated from Usp22+/+Foxp3YFP-cre WT and Usp22fl/flFoxp3YFP-cre KO mice. GAPDH was used as a loading control. d, Statistical analysis of CD4+Foxp3+ Treg frequencies, corresponding to Fig. 2c. e, Western blot analysis of Foxp3 protein from Treg cells isolated from spleen and lymph nodes of Usp22+/+Foxp3YFP-cre WT or Usp22fl/flFoxp3YFP-cre KO mice. GAPDH was used as a loading control. f, iTreg differentiation of naive CD4+ T cells from Usp22+/+Foxp3YFP-cre WT or Usp22fl/flFoxp3YFP-cre KO mice with titration of TGF-β (as indicated). g, Summary of iTreg differentiation of naive CD4+ T cells from Usp22+/+Foxp3YFP-cre WT or Usp22fl/flFoxp3YFP-cre KO mice with titration of TGF-β (as indicated). h, In vitro suppressive activity of Treg cells assessed by the division of naive CD4+CD25 T cells. Naive T cells were labelled with cytosolic cell proliferation dye and activated by anti-CD3 and antigen-presenting cells (irradiated splenocytes from WT mice, depleted of CD3+ T cells), then cocultured at various ratios (as indicated above) with YFP+ Treg cells sorted from eight-week-old Usp22+/+Foxp3YFP-cre WT or Usp22fl/flFoxp3YFP-cre KO mice. Numbers indicate the percentage of non-dividing cells for each ratio. i, In vitro suppressive activity of control (pMIG-Control) or Foxp3+ (pMIG-Foxp3) transduced YFP+ Treg cells sorted from Usp22+/+Foxp3YFP-cre WT or Usp22fl/flFoxp3YFP-cre KO mice. Naive T cells were labelled with cytosolic cell proliferation dye and activated then cocultured at 1:4 transduced YFP+ Treg cells to naive T effectors (Teff). Numbers indicate the percentage of non-dividing cells for each ratio. j, Summary of in vitro suppression experiments, corresponding to i. Lines connect paired samples. Ratios indicate the proportion of Treg cells to naive T effectors (Teff). Data are presented as the frequency of non-dividing cells relative to the corresponding WT 0:1 Treg:Teff control. Data are mean ± s.e.m. Sample sizes (n), P values, statistical tests and number of times experiments were replicated are listed in Methods, ‘Statistics and reproducibility’. Source data for gels and blots can be found in Supplementary Fig. 1

Source Data.

Extended Data Fig. 4 Usp22 acts as a deubiquitinase to control post-translational Foxp3 expression.

a, Endogenous interaction of Usp22 and Foxp3 in mouse iTreg cells from WT mice. Rabbit Usp22 antibody was used to perform the immunoprecipitation and mouse Foxp3 antibody was used to detect the bound Foxp3. Normal rabbit IgG was used as control. Whole-cell lysates (WCL) were used as sample processing controls. b, Ubiquitination assay of Foxp3. HEK293 cells were cotransfected with Flag–Foxp3 and HA–ubiquitin (HA–ub) and either Myc-empty vector, Myc–Usp22, or the catalytically inactive mutant Myc–Usp22(C185A) (C>A), and then immunoprecipitated with anti-Flag and immunoblotted for HA-ubiquitin (Foxp3-ub). Whole-cell lysates were used as sample processing controls. c, Splenocytes isolated from Usp22+/+Foxp3YFP-cre WT or Usp22fl/flFoxp3YFP-cre KO mice were treated with 200 μg ml−1 cycloheximide for the indicated times. Inset numbers for each histogram indicate the MFI of Foxp3 in Treg cells (black, WT; blue, KO). d, Foxp3 MFI from splenic CD4+CD25+Foxp3+ Treg population treated with 200 μg ml−1 cycloheximide for the indicated time course (n = 3), corresponding to c. Data are mean ± s.e.m. Sample sizes (n), P values, statistical tests and number of times experiments were replicated are listed in Methods, ‘Statistics and reproducibility’. Source data for blots can be found in Supplementary Fig. 1.

Source Data

Extended Data Fig. 5 Usp22 regulates Foxp3 through transcriptional mechanisms.

a, Representative flow cytometry analysis of the YFP+ Treg population (gated on CD4+ cells) from the spleen and lymph nodes of Usp22+/+Foxp3YFP-cre WT or Usp22fl/flFoxp3YFP-cre KO mice. b, Statistical analysis of YFP MFI in CD4+YFP+ Treg cells from the thymus (Thy), peripheral lymph nodes (pLN) and spleen (Spl) of Usp22+/+Foxp3YFP-cre WT or Usp22fl/flFoxp3YFP-cre KO mice. c, Statistical analysis of CD4+YFP+ Treg frequencies in Usp22+/+Foxp3YFP-cre WT or Usp22fl/flFoxp3YFP-cre KO mice, corresponding to b. d, Volcano plot for RNA sequencing of Usp22 RNP KO Treg cells vs Rnf20 RNP KO Treg cells. The x axis shows LFC; the y axis shows −log10 of the P value as calculated by DESeq2. Genes downregulated in the Usp22 RNP KO compared with Rnf20 RNP KO are shown in red and upregulated genes are shown in blue, defined by P < 5 × 10−3 and LFC > 0.8. Foxp3 (shown in green) trended down but did not reach significance. e, qPCR analysis of FOXP3 mRNA in human Treg cells from 2 donors 8 days post-electroporation with Cas9 RNPs targeting NTC, FOXP3, USP22, RNF20 or both USP22 and RNF20; normalized to the expression of ACTB transcripts. Data are mean ± s.e.m. and are representative of at least two independent experiments. f, qPCR analysis of Foxp3 mRNA levels in mouse Treg cells 4 and 8 days post-electroporation with Cas9 RNPs targeting NTC, Foxp3, Usp22, Rnf20 or both Usp22 and Rnf20; normalized to the expression of Actb transcripts. g, Western blot analysis of ubiquitinated histone 2A (H2AK119Ub; H2A-ub) and ubiquitinated histone 2B (H2BK120Ub; H2B-ub) from iTreg cells from Usp22+/+Foxp3YFP-cre WT or Usp22fl/flFoxp3YFP-cre KO mice. GAPDH was used as a loading control. Source data can be found in Supplementary Fig. 1. h, Schematic of Foxp3 locus depicting PCR products used for ChIP–qPCR in i and j. i, ChIP–qPCR analysis of H2AK119Ub (H2A-ub), where primers amplified across the TSS and the CNS1 enhancer region of the Foxp3 locus. Data are normalized to the input and are presented as mean ± s.d. j, ChIP–qPCR data analysis for H2BK120Ub (H2B-ub) for PCR across the TSS and across the CNS1 enhancer region of the Foxp3 locus. Data are normalized to the input and are presented as mean ± s.d. k, Heat map of ChIP–seq read density for Foxp3, Usp22 and Rnf20 at sites bound by Foxp3 (using previously published Foxp3 ChIP data51), ranked by highest to lowest Foxp3-binding signal. The corresponding LFC for either H2BK120Ub or H2AK119Ub upon Usp22 or Rnf20 deletion at these sites are plotted on the right, with each biological replicate shown as an individual column. l, Average ChIP–seq read density of H2BK120Ub at Treg superenhancers in control versus Usp22-deficient Treg cells. m, Co-occurrence analysis showing the natural log of the ratio of the observed number of overlapping regions over the expected values for sites that either gain or lose H2BK120Ub in Usp22-deficient Treg cells against publicly available histone modification data for H3K4me, H3K4me3 and H3K27ac as well as enhancer classes, as described in Methods. n, Analysis of reciprocal regulation of Foxp3 by deubiquitinase Usp22 and E3 ubiquitin ligase Rnf20. YFP MFI of Treg cells sorted from Usp22+/+Foxp3YFP-cre WT or Usp22fl/flFoxp3YFP-cre KO mice and then electroporated with either NTC or Rnf20 RNP, corresponding with Fig. 2j, where Foxp3 MFI from the same experiment is shown. All data are presented as mean ± s.e.m., unless otherwise stated. Sample sizes (n), P values, statistical tests and number of times experiments were replicated are listed in Methods, ‘Statistics and reproducibility’. Source data for blots can be found in Supplementary Fig. 1.

Source Data

Extended Data Fig. 6 Autoimmune inflammation in Treg-specific Usp22 knockout mice.

a, Body weight differences between 8-week-old, sex-matched C57BL/6 WT (BL6), Usp22+/+Foxp3YFP-cre WT or Usp22fl/flFoxp3YFP-cre KO mice. b, Representative flow cytometry analysis of CD44 and CD62L expression in splenic CD4+ and CD8+ T cells from aged seven-month-old Usp22+/+Foxp3YFP-cre WT and Usp22fl/flFoxp3YFP-cre KO mice. Numbers in quadrants indicate percentage of each cell population. c, The frequency of splenic CD4+ and CD8+ effector T cells (CD44highCD62Llow) and naive T cells (CD44lowCD62Lhigh) of aged seven-month-old Usp22+/+Foxp3YFP-cre WT and Usp22fl/flFoxp3YFP-cre KO mice summarized, corresponding to b. All data are presented as mean ± s.e.m. Sample sizes (n), P values, statistical tests and number of times experiments were replicated are listed in Methods, ‘Statistics and reproducibility’.

Source Data

Extended Data Fig. 7 T cell-specific ablation of Usp22 results in decreased Foxp3 and increased T cell activation.

a, Western blot analysis of Usp22 in CD4+ T cells isolated from spleens of Usp22fl/flLckcre KO and Usp22+/+Lckcre WT mice. GAPDH was used as a loading control. Source data can be found in Supplementary Fig. 1. b, Representative macroscopic images of spleens and peripheral lymph nodes (pLN) from ten-month-old Usp22fl/flLckcre KO and Usp22+/+Lckcre WT mice. c, Representative flow cytometry plots showing CD44 and CD62L expression in CD4+ and CD8+ T cells from spleens of ten-month-old Usp22fl/flLckcre KO and Usp22+/+Lckcre WT mice. d, Frequency of effector memory T cells (CD44highCD62Llow) in peripheral lymph nodes (pLN) and spleens from ten-month-old Usp22fl/flLckcre KO and Usp22+/+Lckcre WT mice. e, Representative flow cytometry plots showing the splenic CD4+Foxp3+ Treg population from ten-month-old Usp22fl/flLckcre KO and Usp22+/+Lckcre WT mice. f, Foxp3 MFI of the CD4+Foxp3+ Treg population in the spleen and pLN from ten-month-old Usp22fl/flLckcre KO and Usp22+/+Lckcre WT mice. g, IL-2 production by CD4+CD25 T cells under various stimulation conditions (as indicated) for three days was assessed by flow cytometry in Usp22fl/flLckcre KO and Usp22+/+Lckcre WT mice. Although the dominant effect of Usp22-deficiency in T cells was increased T cell activation and lymphoproliferation, we found some evidence of impaired IL-2 production in conventional T cells. h, Usp22-deficiency in T cells led to a selective defect in iTreg differentiation. In vitro differentiation of CD4+ naive T cells cultured under TH1, TH2, TH17 or sub-optimal TGF-β (1 ng ml−1) iTreg conditions from Usp22fl/flLckcre KO and Usp22+/+Lckcre WT mice was assessed by flow cytometry. i, Summary of in vitro differentiation experiments showing percent differentiation, corresponding to h. Data are mean ± s.e.m. Sample sizes (n), P values, statistical tests and number of times experiments were replicated are listed in Methods, ‘Statistics and reproducibility’

Source Data.

Extended Data Fig. 8 Tumour growth is inhibited in Treg-specific Usp22 knockout mice in multiple cancer models.

a, Left, representative flow cytometric analysis of splenic IFN-γ in CD8+ T cells from EG7 tumour-bearing Usp22+/+Foxp3YFP-cre WT or Usp22fl/flFoxp3YFP-cre KO mice. Right, statistical analysis of IFN-γ production by splenic CD8+ T cells from EG7 tumour-bearing Usp22+/+Foxp3YFP-cre WT or Usp22fl/flFoxp3YFP-cre KO. b, Left, representative flow cytometric analysis of splenic granzyme B (GrzB) in CD8+ T cells from EG7 tumour-bearing Usp22+/+Foxp3YFP-cre WT or Usp22fl/flFoxp3YFP-cre KO mice. Right, statistical analysis of granzyme B production by splenic CD8+ T cells from EG7 tumour-bearing Usp22+/+Foxp3YFP-cre WT or Usp22fl/flFoxp3YFP-cre KO mice. c, The MFI of various Treg markers (as indicated) from splenic CD4+Foxp3+ Treg cells from Usp22+/+Foxp3YFP-cre WT or Usp22fl/flFoxp3YFP-cre KO EG7 tumour-bearing mice, assessed by flow cytometry. d, qPCR analysis of Ifng, Gzmb and Cd8a mRNA levels in the tumour tissue of Usp22+/+Foxp3YFP-cre WT or Usp22fl/flFoxp3YFP-cre KO EG7 tumour-bearing mice. e, Tumour volumes from Usp22+/+Foxp3YFP-cre WT or Usp22fl/flFoxp3YFP-cre KO mice subcutaneously inoculated with 5 × 104 B16 melanoma cells. For e, h, k, tumour volumes were measured every 2–3 days by scaling along 3 orthogonal axes (x, y and z) and calculated as (xyz)/2. f, The MFI of various Treg markers (as indicated) from splenic CD4+Foxp3+ Treg cells in Usp22+/+Foxp3YFP-cre WT or Usp22fl/flFoxp3YFP-cre KO B16 tumour-bearing mice, assessed by flow cytometry. g, Foxp3 MFI of Foxp3+ cells from tumour-infiltrating Treg cells in Usp22+/+Foxp3YFP-cre WT or Usp22fl/flFoxp3YFP-cre KO B16 tumour-bearing mice. h, Tumour volumes from Usp22+/+Foxp3YFP-cre WT or Usp22fl/flFoxp3YFP-cre KO mice subcutaneously inoculated with 1 × 106 LLC1 Lewis lung carcinoma cells. i, The MFI of various Treg markers (as indicated) from splenic CD4+Foxp3+ Treg cells in Usp22+/+Foxp3YFP-cre WT or Usp22fl/flFoxp3YFP-cre KO LLC1 tumour-bearing mice, assessed by flow cytometry. j, Foxp3 MFI of Foxp3+ cells from tumour-infiltrating Treg cells in Usp22+/+Foxp3YFP-cre WT or Usp22fl/flFoxp3YFP-cre KO LLC1 tumour-bearing mice. k, Tumour volumes from Usp22+/+Foxp3YFP-cre WT or Usp22fl/flFoxp3YFP-cre KO mice subcutaneously inoculated with 1 × 106 MC38 colon adenocarcinoma cells. l, The MFI of various Treg markers (as indicated) from splenic CD4+Foxp3+ Treg cells in Usp22+/+Foxp3YFP-cre WT or Usp22fl/flFoxp3YFP-cre KO MC38 tumour-bearing mice, assessed by flow cytometry. m, Foxp3 MFI of Foxp3+ cells from tumour-infiltrating Treg cells in Usp22+/+Foxp3YFP-cre WT or Usp22fl/flFoxp3YFP-cre KO MC38 tumour-bearing mice. Data are mean ± s.e.m. Sample sizes (n), P values, statistical tests and number of times experiments were replicated are listed in Methods, ‘Statistics and reproducibility’

Source Data.

Supplementary information

Supplementary Figure 1

Uncropped gel source data.

Reporting Summary

Supplementary Data

Statistics and Reproducibility. The exact sample sizes (n), p-values, statistical tests and number of times the experiment was replicated.

Supplementary Table 1

Screen data. This file includes results from MAGeCK analysis for sgRNA and gene level enrichment and normalized and raw count files.

Supplementary Table 2

Tracking of Indels by DEcomposition (TIDE) analysis. This file includes primer sequences used to amply targeted DNA regions and editing efficiency data for RNP electroporations determined by using Sanger sequencing traces to quantify insertions and deletions in the DNA of a targeted cell pool.

Supplementary Table 3

Synthetic oligos used in this study - sgRNA library, primers and crRNA for RNP arrays.

Supplementary Table 4

A list of antibodies used in this study.

Supplementary Table 5

Differentially expressed genes and raw counts from RNA sequencing.

Source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cortez, J.T., Montauti, E., Shifrut, E. et al. CRISPR screen in regulatory T cells reveals modulators of Foxp3. Nature 582, 416–420 (2020). https://doi.org/10.1038/s41586-020-2246-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-020-2246-4

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer