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.

Dynamic transcriptional activity and chromatin remodeling of regulatory T cells after varied duration of interleukin-2 receptor signaling

Abstract

Regulatory T (Treg) cells require (interleukin-2) IL-2 for their homeostasis by affecting their proliferation, survival and activation. Here we investigated transcriptional and epigenetic changes after acute, periodic and persistent IL-2 receptor (IL-2R) signaling in mouse peripheral Treg cells in vivo using IL-2 or the long-acting IL-2-based biologic mouse IL-2–CD25. We show that initially IL-2R-dependent STAT5 transcription factor-dependent pathways enhanced gene activation, chromatin accessibility and metabolic reprogramming to support Treg cell proliferation. Unexpectedly, at peak proliferation, less accessible chromatin prevailed and was associated with Treg cell contraction. Restimulation of IL-2R signaling after contraction activated signature IL-2-dependent genes and others associated with effector Treg cells, whereas genes associated with signal transduction were downregulated to somewhat temper expansion. Thus, IL-2R-dependent Treg cell homeostasis depends in part on a shift from more accessible chromatin and expansion to less accessible chromatin and contraction. Mouse IL-2–CD25 supported greater expansion and a more extensive transcriptional state than IL-2 in Treg cells, consistent with greater efficacy to control autoimmunity.

Your institute does not have access to this article

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Sustained IL-2R signaling and extensive gene activation drives Treg cell expansion in vivo.
Fig. 2: Regulatory transcriptional changes in response to persistent IL-2R signaling support Treg cell expansion.
Fig. 3: Sustained IL-2R signaling reprograms Treg cell energetic metabolism.
Fig. 4: Repeated administration of high doses of mIL-2 does not fully recapitulate the response of Treg cells to mIL-2–CD25.
Fig. 5: Chromatin accessibility in Treg cells is dynamically regulated by mIL-2–CD25.
Fig. 6: ATAC–seq/RNA-seq integration uncovers new mediators induced by a persistent IL-2R signaling leading to Treg cell expansion.
Fig. 7: mIL-2–CD25-dependent increase in less accessible chromatin regions does not limit STAT5 activation or IL-2-dependent gene transcription after restimulation with mIL-2–CD25.

Data availability

Data are deposited in the Gene Expression Omnibus under accession codes GSE163946 (RNA-seq) and GSE162030 (ATAC–seq). Source data are provided with this paper. Other data will be made available upon reasonable request.

References

  1. Buszko, M. & Shevach, E. M. Control of regulatory T cell homeostasis. Curr. Opin. Immunol. 67, 18–26 (2020).

    CAS  PubMed  Article  Google Scholar 

  2. Abbas, A. K., Trotta, E., Simeonov, D. R., Marson, A. & Bluestone, J. A. Revisiting IL-2: biology and therapeutic prospects. Sci. Immunol. 3, eaat1482 (2018).

  3. Sharabi, A. et al. Regulatory T cells in the treatment of disease. Nat. Rev. Drug Discov. 17, 823–844 (2018).

    CAS  PubMed  Article  Google Scholar 

  4. Yu, A., Zhu, L., Altman, N. H. & Malek, T. R. A low interleukin-2 receptor signaling threshold supports the development and homeostasis of T regulatory cells. Immunity 30, 204–217 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. Yu, A. et al. Selective IL-2 responsiveness of regulatory T cells through multiple intrinsic mechanisms supports the use of low-dose IL-2 therapy in type 1 diabetes. Diabetes 64, 2172–2183 (2015).

    CAS  PubMed  Article  Google Scholar 

  6. Donohue, J. H. & Rosenberg, S. A. The fate of interleukin-2 after in vivo administration. J. Immunol. 130, 2203–2208 (1983).

    CAS  PubMed  Google Scholar 

  7. Pol, J. G., Caudana, P., Paillet, J., Piaggio, E. & Kroemer, G. Effects of interleukin-2 in immunostimulation and immunosuppression. J. Exp. Med. 217, e20191247 (2020).

  8. Ward, N. C. et al. IL-2/CD25: a long-acting fusion protein that promotes immune tolerance by selectively targeting the IL-2 receptor on regulatory T cells. J. Immunol. 201, 2579–2592 (2018).

    CAS  PubMed  Article  Google Scholar 

  9. Chorro, L. et al. Interleukin 2 modulates thymic-derived regulatory T cell epigenetic landscape. Nat. Commun. 9, 5368 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. Fontenot, J. D., Rasmussen, J. P., Gavin, M. A. & Rudensky, A. Y. A function for interleukin 2 in Foxp3-expressing regulatory T cells. Nat. Immunol. 6, 1142–1151 (2005).

    CAS  PubMed  Article  Google Scholar 

  11. Toomer, K. H. et al. Essential and non-overlapping IL-2Rα-dependent processes for thymic development and peripheral homeostasis of regulatory T cells. Nat. Commun. 10, 1037 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  12. Ward, N. C. et al. Persistent IL-2 receptor signaling by IL-2/CD25 fusion protein controls diabetes in NOD mice by multiple mechanismss. Diabetes 69, 2400–2413 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. Grasshoff, H. et al. Low-dose IL-2 therapy in autoimmune and rheumatic diseases. Front. Immunol. 12, 648408 (2021).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14, 128 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  15. Gregory, M. A., Qi, Y. & Hann, S. R. Phosphorylation by glycogen synthase kinase-3 controls c-Myc proteolysis and subnuclear localization. J. Biol. Chem. 278, 51606–51612 (2003).

    CAS  PubMed  Article  Google Scholar 

  16. Loftus, R. M. et al. Amino acid-dependent c-Myc expression is essential for NK cell metabolic and functional responses in mice. Nat. Commun. 9, 2341 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  17. Oh, H. et al. An NF-κB transcription-factor-dependent lineage-specific transcriptional program promotes regulatory T cell identity and function. Immunity 47, 450–465 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. Rovira-Clave, X., Angulo-Ibanez, M., Tournier, C., Reina, M. & Espel, E. Dual role of ERK5 in the regulation of T cell receptor expression at the T cell surface. J. Leukoc. Biol. 99, 143–152 (2016).

    CAS  PubMed  Article  Google Scholar 

  19. Geiman, T. M. & Muegge, K. Lsh, an SNF2/helicase family member, is required for proliferation of mature T lymphocytes. Proc. Natl Acad. Sci. USA 97, 4772–4777 (2000).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. Tameni, A. et al. The DNA-helicase HELLS drives ALK ALCL proliferation by the transcriptional control of a cytokinesis-related program. Cell Death Dis. 12, 130 (2021).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. Nevins, J. R. The Rb/E2F pathway and cancer. Hum. Mol. Genet 10, 699–703 (2001).

    CAS  PubMed  Article  Google Scholar 

  22. Saravia, J. et al. Homeostasis and transitional activation of regulatory T cells require c-Myc. Sci. Adv. 6, eaaw6443 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. Rath, S. et al. MitoCarta3.0: an updated mitochondrial proteome now with sub-organelle localization and pathway annotations. Nucleic Acids Res. 49, D1541–D1547 (2021).

    CAS  PubMed  Article  Google Scholar 

  24. Dias, S. et al. Effector regulatory T cell differentiation and immune homeostasis depend on the transcription factor Myb. Immunity 46, 78–91 (2017).

    CAS  PubMed  Article  Google Scholar 

  25. Choy, J. S. et al. DNA methylation increases nucleosome compaction and rigidity. J. Am. Chem. Soc. 132, 1782–1783 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. Lobanenkov, V. V. et al. A novel sequence-specific DNA binding protein which interacts with three regularly spaced direct repeats of the CCCTC-motif in the 5′-flanking sequence of the chicken c-myc gene. Oncogene 5, 1743–1753 (1990).

    CAS  PubMed  Google Scholar 

  27. Nora, E. P. et al. Molecular basis of CTCF binding polarity in genome folding. Nat. Commun. 11, 5612 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  28. Nagraj, V. P., Magee, N. E. & Sheffield, N. C. LOLAweb: a containerized web server for interactive genomic locus overlap enrichment analysis. Nucleic Acids Res. 46, W194–W199 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. Nasmyth, K. & Haering, C. H. Cohesin: its roles and mechanisms. Annu. Rev. Genet. 43, 525–558 (2009).

    CAS  PubMed  Article  Google Scholar 

  30. Margueron, R. & Reinberg, D. The Polycomb complex PRC2 and its mark in life. Nature 469, 343–349 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. Pasini, D. et al. JARID2 regulates binding of the Polycomb repressive complex 2 to target genes in ES cells. Nature 464, 306–310 (2010).

    CAS  PubMed  Article  Google Scholar 

  32. Assenov, Y. et al. Comprehensive analysis of DNA methylation data with RnBeads. Nat. Methods 11, 1138–1140 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. Parelho, V. et al. Cohesins functionally associate with CTCF on mammalian chromosome arms. Cell 132, 422–433 (2008).

    CAS  PubMed  Article  Google Scholar 

  34. Wendt, K. S. & Peters, J. M. How cohesin and CTCF cooperate in regulating gene expression. Chromosome Res. 17, 201–214 (2009).

    CAS  PubMed  Article  Google Scholar 

  35. Bronner, C., Alhosin, M., Hamiche, A. & Mousli, M. Coordinated dialogue between UHRF1 and DNMT1 to ensure faithful inheritance of methylated DNA patterns. Genes 10, 65 (2019).

  36. Han, M. et al. A role for LSH in facilitating DNA methylation by DNMT1 through enhancing UHRF1 chromatin association. Nucleic Acids Res. 48, 12116–12134 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. Petracovici, A. & Bonasio, R. Distinct PRC2 subunits regulate maintenance and establishment of Polycomb repression during differentiation. Mol. Cell 81, 2625–2639 (2021).

    CAS  PubMed  Article  Google Scholar 

  38. Haring, J. S., Badovinac, V. P. & Harty, J. T. Inflaming the CD8+ T cell response. Immunity 25, 19–29 (2006).

    CAS  PubMed  Article  Google Scholar 

  39. Prlic, M. & Bevan, M. J. Exploring regulatory mechanisms of CD8+ T cell contraction. Proc. Natl Acad. Sci. USA 105, 16689–16694 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. Martin, M. D. & Badovinac, V. P. Antigen-dependent and -independent contributions to primary memory CD8+ T cell activation and protection following infection. Sci. Rep. 5, 18022 (2015).

  41. Sakaguchi, S., Vignali, D. A., Rudensky, A. Y., Niec, R. E. & Waldmann, H. The plasticity and stability of regulatory T cells. Nat. Rev. Immunol. 13, 461–467 (2013).

    CAS  PubMed  Article  Google Scholar 

  42. Geginat, J. et al. Plasticity of human CD4+ T cell subsets. Front. Immunol. 5, 630 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  43. Wan, Y. Y. & Flavell, R. A. Regulatory T cell functions are subverted and converted owing to attenuated Foxp3 expression. Nature 445, 766–770 (2007).

    CAS  PubMed  Article  Google Scholar 

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

    CAS  PubMed  Article  Google Scholar 

  45. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    CAS  PubMed  Article  Google Scholar 

  46. 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).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  47. Buenrostro, J. D., Wu, B., Chang, H. Y. & Greenleaf, W. J. ATAC–seq: a method for assaying chromatin accessibility genome wide. Curr. Protoc. Mol. Biol. 109, 21.29.1–21.29.9 (2015).

    Article  Google Scholar 

  48. Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. Stowers, R. S. et al. Matrix stiffness induces a tumorigenic phenotype in mammary epithelium through changes in chromatin accessibility. Nat. Biomed. Eng. 3, 1009–1019 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  50. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. Zhang, Y. et al. Model-based analysis of ChIP–seq (MACS). Genome Biol. 9, R137 (2008).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  52. Li, Q. H., Brown, J. B., Huang, H. Y. & Bickel, P. J. Measuring reproducibility of high-throughput experiments. Ann. Appl Stat. 5, 1752–1779 (2011).

    Google Scholar 

  53. Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS Comput. Biol. 9, e1003118 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  54. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9, 215–216 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  56. Bhasin, J. M. & Ting, A. H. Goldmine integrates information placing genomic ranges into meaningful biological contexts. Nucleic Acids Res. 44, 5550–5556 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  57. Oliveros, J. C. Venny. An interactive tool for comparing lists with Venn’s diagrams. https://bioinfogp.cnb.csic.es/tools/venny/index.html (2015).

  58. Kramer, A., Green, J., Pollard, J. Jr. & Tugendreich, S. Causal analysis approaches in ingenuity pathway analysis. Bioinformatics 30, 523–530 (2014).

    PubMed  Article  CAS  Google Scholar 

  59. Mookerjee, S. A., Gerencser, A. A., Nicholls, D. G. & Brand, M. D. Quantifying intracellular rates of glycolytic and oxidative ATP production and consumption using extracellular flux measurements. J. Biol. Chem. 293, 12649–12652 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

Download references

Acknowledgements

We thank A. S. Savio for technical assistance. J. Enten, P. Guevara, S. Saigh and N. Ward from the Flow Cytometry Core and M. Brooks, Y. Cardentey and J. Kemper from the Oncogenomics Core of the Sylvester Comprehensive Cancer Center (supported by National Institutes of Health (NIH) P30CA240139); and M. Struthers and F. Ramirez-Valle at Bristol Myers Squibb and A. Villarino at the University of Miami for critically reading the manuscript. This research was supported by funding to T.R.M. from Bristol Myers Squibb and the NIH (R01AI148675).

Author information

Authors and Affiliations

Authors

Contributions

Conception and design: A.M. and T.R.M. Acquisition of data: A.M., A. Yu, S.H. and C.M.S. Analysis and interpretation of data: A.M., Z.G., L.W., S.H., Y.B., A. Yan, X.S.C. and T.R.M. Manuscript writing: A.M. and T.R.M. All authors edited and approved the manuscript.

Corresponding author

Correspondence to Thomas R. Malek.

Ethics declarations

Competing interests

The University of Miami and T.R.M. have patents pending on IL-2–CD25 fusion proteins (WO2016022671A1) and their use (PCT/US20/13152) that have been licensed exclusively to Bristol Myers Squibb, and this research has been supported in part by a collaboration and sponsored research and licensing agreement with Bristol Myers Squibb. The other authors declare no competing interests.

Peer review

Peer review information

Nature Immunology thanks Andrew Wells and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Jamie D. K. Wilson, in collaboration with the Nature Immunology 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 Sustained pSTAT5 activation of Treg by single injection of mIL-2/CD25 but not of mIL-2One sentence only.

Unfractionated splenocytes from mice (n = 3/group) were injected with mIL-2/CD25, mIL-2 or control PBS and stained with indicated antibodies. (a) Representative flow cytometry plots showing gating for Treg and (b) expression of pSTAT5+ of Tregs at each experimental condition.

Extended Data Fig. 2 Representative FACS gating strategy for sorted Treg at 72 hr post single injection of mIL-2/CD25.

CD4 + T cells from the spleen of CD4 + Foxp3 + -RFP + reporter mice were enriched with anti-CD4 magnetic beads and stained with FITC - anti-CD4 antibody. RFP + Treg were sorted as shown. Treg purity was typically greater than 98%.

Extended Data Fig. 3 Expression of DEGs (FDR < 0.01) at 4 hr after single injection of PBS, mIL2, or mIL2/CD25 using the same RNAseq data set as (Fig. 1d).

Heat map of K-means clustering of 789 differentially expressed genes in response to single injection or re-stimulation with mIL-2/CD25 versus control PBS. Clustering was done using Morpheus software (clustering type: K-means clustering, distance metric: 1-Pearson correlation). The colors in the map display the relative values within indicated experimental conditions. Blue indicates the lowest expression, white indicates intermediate expression, and red indicates the highest expression. Genes were grouped into three clusters on the basis of the expression similarity.

Extended Data Fig. 4 Significant upstream regulators (p value of overlap < 0.05; -2 ≤ z-score ≥ 2) at 1.5 hr post injection of mIL-2/CD25.

The horizontal bars denote the different regulators based on the activation z-score. Red color indicates activation, while blue color indicates inhibition. (b) Top gene network related to ‘Cellular Development, Cellular Growth and Proliferation, Lymphoid Tissue Structure and Development’ in Tregs 1.5 hrs post injection. TCR and STAT5 were identified as key hubs.

Extended Data Fig. 5 Significant modulated canonical pathways predicted by GSEA (a) or Ingenuity Pathway Analysis (IPA) (b) post single injection of mIL-2/CD25.

Cutoffs: GSEA FDR < 0.25; IPA p < 0.05; z-score of activation (orange: active ≥2; blue: inhibited ≤ -2). NES: normalized enrichment score.

Extended Data Fig. 6 Significant modulated upstream regulators predicted by IPA at each time post single administration of mIL-2/CD25.

p < 0.05; z-score of activation (orange: active ≥2; blue: inhibited ≤ -2).

Extended Data Fig. 7 (a) Western blot and (b) normalized (MYC/β-Actin) expression after densitometry analysis (n = 3) showing that MYC protein in Treg was significatively increased at 2 hr and persisted for up to 48 hr post single injection of mIL-2/CD25.

Protein extracts were obtained from FACS-sorted CD4 + Foxp3 + Tregs (>95% Foxp3 + ) isolated from C57BL/6J-Foxp3 + RFP mice at the indicated times after PBS, or mIL-2/CD25 (20 µg) injection. Expression levels of MYC and β-actin were analyzed by Western blotting with anti–MYC (10828-1-AP), or anti-β Actin (20536-1-AP) and revealed with a goat anti-rabbit polyclonal (G-21234). (b) One-way ANOVA with Tukey’s multiple comparisons test, ****p < 0.0001; ***p = 0.0002; n.s p = 0.1131. (c) RNA-seq time course expression of Myc or Slc7a5 in response to mIL-2/CD25. Data shown in (a) is from one representative gel where lanes were spliced to move relevant data neighboring to each other.

Source data

Extended Data Fig. 8 Sustained IL-2R signaling reprograms Treg energetic metabolism.

(a) Overlap between DEGs upregulated by mIL-2/CD25 at 72 hr post single injection (FDR < 0.01; fold change ≥ 1.5 X) and a set of mouse mitochondrial genes from the database Mitocarta 3.0. (b) Significant (upregulated, red; down-regulated, green) DEGs in the oxidative phosphorylation (OXPHOS) pathway in response to single injection of mIL-2/CD25 shown as components within each electron transport chain complex.

Extended Data Fig. 9 Enrichment analysis was performed on significantly over-represented genes and ten most significant groups are represented according to GO molecular function.

Reference dotted lines indicate p < 0.05 fold change cutoff.

Supplementary information

Reporting Summary

Supplementary Table 1

Significant differentially expressed genes from RNA-seq.

Supplementary Table 2

GSEA results.

Supplementary Table 3

Significant differentially accessible regions from ATAC–seq.

Supplementary Table 4

LOLA results.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 7

Statistical source data.

Source Data Extended Data Fig. 7

Unprocessed western blot.

Source Data Extended Data Fig. 7

Statistical source data.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Moro, A., Gao, Z., Wang, L. et al. Dynamic transcriptional activity and chromatin remodeling of regulatory T cells after varied duration of interleukin-2 receptor signaling. Nat Immunol 23, 802–813 (2022). https://doi.org/10.1038/s41590-022-01179-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41590-022-01179-1

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