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Whole-genome profiling of DNA methylation and hydroxymethylation identifies distinct regulatory programs among innate lymphocytes

Nature Immunology volume 23, pages 619–631 (2022)Cite this article

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Abstract

Innate lymphocytes encompass a diverse array of phenotypic identities with specialized functions. DNA methylation and hydroxymethylation are essential for epigenetic fidelity and fate commitment. The landscapes of these modifications are unknown in innate lymphocytes. Here, we characterized the whole-genome distribution of methyl-CpG and 5-hydroxymethylcytosine (5hmC) in mouse innate lymphoid cell 3 (ILC3), ILC2 and natural killer (NK) cells. We identified differentially methylated regions (DMRs) and differentially hydroxymethylated regions (DHMRs) between ILC and NK cell subsets and correlated them with transcriptional signatures. We associated lineage-determining transcription factors (LDTFs) with demethylation and demonstrated unique patterns of DNA methylation/hydroxymethylation in relationship to open chromatin regions (OCRs), histone modifications and TF-binding sites. We further identified an association between hydroxymethylation and NK cell superenhancers (SEs). Using mice lacking the DNA hydroxymethylase TET2, we showed the requirement for TET2 in optimal production of hallmark cytokines by ILC3s and interleukin-17A (IL-17A) by inflammatory ILC2s. These findings provide a powerful resource for studying innate lymphocyte epigenetic regulation and decode the regulatory logic governing their identity.

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Fig. 1: Genome-wide profiling of DNA methylation in ILC–NK cell subsets.
Fig. 2: Genome-wide profiling of DNA hydroxymethylation in ILC–NK cell subsets.
Fig. 3: DNA methylation and hydroxymethylation predict differential OCR activity.
Fig. 4: Differential DNA methylation and hydroxymethylation are associated with specific histone modifications and T-BET-binding sites.
Fig. 5: Hydroxymethylation demarcates SEs in NK cells.
Fig. 6: TET2 is required for IL-17A production by iILC2s.
Fig. 7: TET2 is required for optimal ILC3 effector function.

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Data availability

All next-generation sequencing data generated in this study were deposited in the Gene Expression Omnibus (GSE190944). Innate lymphocyte RNA-sequencing, ATAC-seq and NK cell ChIP–seq data were previously published (GSE109125, GSE100738, GSE145299 and GSE112813). ELF1 ChIP–seq data were previously published (GSE40686).

References

  1. Vivier, E. et al. Innate lymphoid cells: 10 years on. Cell 174, 1054–1066 (2018).

    Article  CAS  PubMed  Google Scholar 

  2. Bando, J. K. & Colonna, M. Innate lymphoid cell function in the context of adaptive immunity. Nat. Immunol. 17, 783–789 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Serafini, N., Vosshenrich, C. A. J. & Di Santo, J. P. Transcriptional regulation of innate lymphoid cell fate. Nat. Rev. Immunol. 15, 415–428 (2015).

    Article  CAS  PubMed  Google Scholar 

  4. Sun, J. C. Transcriptional control of NK cells. in Natural Killer Cells (eds Vivier, E., Di Santo, J. & Moretta, A.) 1–36 (Springer International Publishing, 2016).

  5. Robinette, M. L. et al. Transcriptional programs define molecular characteristics of innate lymphoid cell classes and subsets. Nat. Immunol. 16, 306–317 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Yoshida, H. et al. The cis-regulatory atlas of the mouse immune system. Cell 176, 897–912 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Shih, H.-Y. et al. Developmental acquisition of regulomes underlies innate lymphoid cell functionality. Cell 165, 1120–1133 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Collins, P. L. et al. Gene regulatory programs conferring phenotypic identities to human NK cells. Cell 176, 348–360 (2019).

    Article  CAS  PubMed  Google Scholar 

  9. Koues, O. I. et al. Distinct gene regulatory pathways for human innate versus adaptive lymphoid Cells. Cell 165, 1134–1146 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Lau, C. M. et al. Epigenetic control of innate and adaptive immune memory. Nat. Immunol. 19, 963–972 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Ziller, M. J. et al. Charting a dynamic DNA methylation landscape of the human genome. Nature 500, 477–481 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Stadler, M. B. et al. DNA-binding factors shape the mouse methylome at distal regulatory regions. Nature 480, 490–495 (2011).

    Article  CAS  PubMed  Google Scholar 

  13. Bachman, M. et al. 5-Hydroxymethylcytosine is a predominantly stable DNA modification. Nat. Chem. 6, 1049–1055 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Song, C.-X. et al. Selective chemical labeling reveals the genome-wide distribution of 5-hydroxymethylcytosine. Nat. Biotechnol. 29, 68–72 (2011).

    Article  CAS  PubMed  Google Scholar 

  15. Wu, X. & Zhang, Y. TET-mediated active DNA demethylation: mechanism, function and beyond. Nat. Rev. Genet. 18, 517–534 (2017).

    Article  CAS  PubMed  Google Scholar 

  16. Lio, C.-W. J. & Rao, A. TET enzymes and 5hmC in adaptive and innate immune systems. Front Immunol. 10, 210 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Lio, C.-W. et al. Tet2 and Tet3 cooperate with B-lineage transcription factors to regulate DNA modification and chromatin accessibility. eLife 5, e18290 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Lio, C.-W. J. et al. TET enzymes augment activation-induced deaminase (AID) expression via 5-hydroxymethylcytosine modifications at the Aicda superenhancer. Sci. Immunol. 4, eaau7523 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Lee, P. P. et al. A critical role for Dnmt1 and DNA methylation in T cell development, function, and survival. Immunity 15, 763–774 (2001).

    Article  CAS  PubMed  Google Scholar 

  20. Makar, K. W. et al. Active recruitment of DNA methyltransferases regulates interleukin 4 in thymocytes and T cells. Nat. Immunol. 4, 1183–1190 (2003).

    Article  CAS  PubMed  Google Scholar 

  21. 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  CAS  PubMed  PubMed Central  Google Scholar 

  22. Ichiyama, K. et al. The methylcytosine dioxygenase Tet2 promotes DNA demethylation and activation of cytokine gene expression in T cells. Immunity 42, 613–626 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Tsagaratou, A. et al. TET proteins regulate the lineage specification and TCR-mediated expansion of i NKT cells. Nat. Immunol. 18, 45–53 (2017).

    Article  CAS  PubMed  Google Scholar 

  24. Ji, H. et al. Comprehensive methylome map of lineage commitment from haematopoietic progenitors. Nature 467, 338–342 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Barwick, B. G., Scharer, C. D., Bally, A. P. R. & Boss, J. M. Plasma cell differentiation is coupled to division-dependent DNA hypomethylation and gene regulation. Nat. Immunol. 17, 1216–1225 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Ageliki, T. et al. Dissecting the dynamic changes of 5-hydroxymethylcytosine in T-cell development and differentiation. Proc. Natl Acad. Sci. USA. 111(32), https://doi.org/10.1073/pnas.1412327111 (2014).

  27. Ebihara, T. et al. Runx3 specifies lineage commitment of innate lymphoid cells. Nat. Immunol. 16, 1124–1133 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Smith, Z. D. & Meissner, A. DNA methylation: roles in mammalian development. Nat. Rev. Genet. 14, 204–220 (2013).

    Article  CAS  PubMed  Google Scholar 

  29. Yagi, R. et al. The transcription factor GATA3 is critical for the development of all IL-7Rα-expressing innate lymphoid cells. Immunity 40, 378–388 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Lorincz, M. C., Dickerson, D. R., Schmitt, M. & Groudine, M. Intragenic DNA methylation alters chromatin structure and elongation efficiency in mammalian cells. Nat. Struct. Mol. Biol. 11, 1068–1075 (2004).

    Article  CAS  PubMed  Google Scholar 

  31. Califano, D. et al. Transcription factor Bcl11b controls identity and function of mature type 2 innate lymphoid cells. Immunity 43, 354–368 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Walker, J. A. et al. Bcl11b is essential for group 2 innate lymphoid cell development. J. Exp. Med. 212, 875–882 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Pastor, W. A. et al. Genome-wide mapping of 5-hydroxymethylcytosine in embryonic stem cells. Nature 473, 394–397 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Yu, M. et al. Base-resolution analysis of 5-hydroxymethylcytosine in the mammalian genome. Cell 149, 1368–1380 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Zook, E. C. et al. The ETS1 transcription factor is required for the development and cytokine-induced expansion of ILC2. J. Exp. Med. 213, 687–696 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Ramirez, K. et al. Gene deregulation and chronic activation in natural killer cells deficient in the transcription factor ETS1. Immunity 36, 921–932 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Xue, H.-H. et al. GA binding protein regulates interleukin 7 receptor α-chain gene expression in T cells. Nat. Immunol. 5, 1036–1044 (2004).

    Article  CAS  PubMed  Google Scholar 

  38. 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 

  39. Dorner, B. G. et al. Coordinate expression of cytokines and chemokines by NK cells during murine cytomegalovirus infection. J. Immunol. 172, 3119–3131 (2004).

    Article  CAS  PubMed  Google Scholar 

  40. Orange, J. S. & Biron, C. A. An absolute and restricted requirement for IL-12 in natural killer cell IFN-γ production and antiviral defense. Studies of natural killer and T cell responses in contrasting viral infections. J. Immunol. 156, 1138–1142 (1996).

    CAS  PubMed  Google Scholar 

  41. Sciumè, G. et al. Rapid enhancer remodeling and transcription factor repurposing enable high magnitude gene induction upon acute activation of NK cells. Immunity 53, 745–758 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Lindroth, A. M. et al. Antagonism between DNA and H3K27 methylation at the imprinted Rasgrf1 locus. PLoS Genet. 4, e1000145 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Lister, R. et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 462, 315–322 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Neri, F. et al. Intragenic DNA methylation prevents spurious transcription initiation. Nature 543, 72–77 (2017).

    Article  CAS  PubMed  Google Scholar 

  45. Wagner, E. J. & Carpenter, P. B. Understanding the language of Lys36 methylation at histone H3. Nat. Rev. Mol. Cell Biol. 13, 115–126 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Hnisz, D. et al. Super-enhancers in the control of cell identity and disease. Cell 155, 934–947 (2013).

    Article  CAS  PubMed  Google Scholar 

  47. Vahedi, G. et al. Stretch-enhancers delineate disease-associated regulatory nodes in T cells. Nature 520, 558–562 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Adams, N. M. et al. Transcription factor IRF8 orchestrates the adaptive natural killer cell response. Immunity 48, 1172–1182 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Veillette, A. SLAM-family receptors: immune regulators with or without SAP-family adaptors. Cold Spring Harb. Perspect. Biol. 2, a002469 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Huang, Y. et al. IL-25-responsive, lineage-negative KLRG1hi cells are multipotential ‘inflammatory’ type 2 innate lymphoid cells. Nat. Immunol. 16, 161–169 (2015).

    Article  CAS  PubMed  Google Scholar 

  51. Colonna, M. Innate lymphoid cells: diversity, plasticity, and unique functions in immunity. Immunity 48, 1104–1117 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Deaton, A. M. et al. Cell type–specific DNA methylation at intragenic CpG islands in the immune system. Genome Res. 21, 1074–1086 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Shukla, S. et al. CTCF-promoted RNA polymerase II pausing links DNA methylation to splicing. Nature 479, 74–79 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Maunakea, A. K. et al. Conserved role of intragenic DNA methylation in regulating alternative promoters. Nature 466, 253–257 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Asnagli, H., Afkarian, M. & Murphy, K. M. Cutting edge: identification of an alternative GATA-3 promoter directing tissue-specific gene expression in mouse and human. J. Immunol. 168, 4268–4271 (2002).

    Article  CAS  PubMed  Google Scholar 

  56. Isoda, T. et al. Non-coding transcription instructs cohesin-dependent chromatin folding and compartmentalization to dictate enhancer–promoter communication and T cell fate. Cell 171, 103–119 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Spruijt, C. G. et al. Dynamic readers for 5-(hydroxy)methylcytosine and its oxidized derivatives. Cell 152, 1146–1159 (2013).

    Article  CAS  PubMed  Google Scholar 

  58. Bal, S. M., Golebski, K. & Spits, H. Plasticity of innate lymphoid cell subsets. Nat. Rev. Immunol. 552–565 (2020).

  59. Sun, J. C., Beilke, J. N. & Lanier, L. L. Adaptive immune features of natural killer cells. Nature 457, 557–561 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Ko, M. et al. Ten-eleven-translocation 2 (TET2) negatively regulates homeostasis and differentiation of hematopoietic stem cells in mice. Proc. Natl Acad. Sci. USA 108, 14566–14571 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Moran-Crusio, K. et al. Tet2 loss leads to increased hematopoietic stem cell self-renewal and myeloid transformation. Cancer Cell 20, 11–24 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Eberl, G. & Littman, D. R. Thymic origin of intestinal αß T cells revealed by fate mapping of RORγt+ cells. Science 305, 248–251 (2004).

    Article  CAS  PubMed  Google Scholar 

  63. Schlenner, S. M. et al. Fate mapping reveals separate origins of T cells and myeloid lineages in the thymus. Immunity 32, 426–436 (2010).

    Article  CAS  PubMed  Google Scholar 

  64. Wang, Q. et al. Circadian rhythm-dependent and circadian rhythm-independent impacts of the molecular clock on type 3 innate lymphoid cells. Sci. Immunol. 4, eaay7501 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Li, D., Zhang, B., Xing, X. & Wang, T. Combining MeDIP–seq and MRE–seq to investigate genome-wide CpG methylation. Methods 72, 29–40 (2015).

    Article  CAS  PubMed  Google Scholar 

  66. Han, D. et al. A highly sensitive and robust method for genome-wide 5hmC profiling of rare cell populations. Mol. Cell 63, 711–719 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  69. Stark, R. & Brown, G. DiffBind: differential binding analysis of ChIP-Seq peak data. Bioconductor http://bioconductor.org/packages/release/bioc/vignettes/DiffBind/inst/doc/DiffBind.pdf (2011).

  70. Zhang, B. et al. Functional DNA methylation differences between tissues, cell types, and across individuals discovered using the M&M algorithm. Genome Res. 23, 1522–1540 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Amemiya, H. M., Kundaje, A. & Boyle, A. P. The ENCODE blacklist: identification of problematic regions of the genome. Sci. Rep. 9, 9354 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Ramírez, F. et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44, W160–W165 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  73. 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  Google Scholar 

  74. Li, D., Hsu, S., Purushotham, D., Sears, R. L. & Wang, T. WashU Epigenome Browser update 2019. Nucleic Acids Res. 47, W158–W165 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Neph, S. et al. BEDOPS: high-performance genomic feature operations. Bioinformatics 28, 1919–1920 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank the Flow Cytometry core facility at the Department of Pathology and Immunology (Washington University in St. Louis). This research was supported by NIH grants F30 DK127540-01, T32 DK 77653-28 (to V.P.), R00 DK118110 (to J.K.B.), R21 AI156411 (to P.L.C.), R01HG007175, U24ES026699, U01HG009391, U24HG012070 (to X.X., D.L. and T.W.), R01 AI134035 (to M.C. and E.O.M.), R01 DE025884, 1R01 AI134236-01 and R01 DK124699 (to M.C.).

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  1. Jennifer K. Bando

    Present address: Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA

Authors and Affiliations

  1. Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA

    Vincent Peng, Jennifer K. Bando, Tihana Trsan, Blanda Di Luccia, Wei-Le Wang & Marco Colonna

  2. Department of Genetics, The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA

    Xiaoyun Xing, Daofeng Li, Hyung Joo Lee & Ting Wang

  3. Department of Microbial Infection and Immunity, The Ohio State University, Columbus, OH, USA

    Patrick L. Collins & Eugene M. Oltz

  4. McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA

    Ting Wang

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Contributions

V.P., T.W. and M.C. designed experiments. V.P., J.K.B., X.X., T.T. and P.L.C. performed experiments. W.-L.W. assisted in generating BM chimeras. B.D.L. assisted in C. rodentium infections. H.J.L., P.L.C. and D.L. provided bioinformatic support. V.P., E.M.O., T.W. and M.C. wrote the manuscript.

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Correspondence to Ting Wang or Marco Colonna.

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Competing interests

M.C. receives research support from Aclaris, Pfizer, Oncorus, Ono and NGM Biopharmaceuticals, is a scientific advisory board member of Vigil Neuroscience and NGM Biopharmaceuticals and is a consultant of Cell Signaling Technologies. The remaining authors declare no competing interests.

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

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Extended data

Extended Data Fig. 1 DMR characteristics.

DMR characteristics. a, number of DMRs plotted against FDR threshold for each pairwise comparison. b, PCA plot calculated from MeDIP-seq signal for each replicate. PCs were calculated from the top 1000 most variable DMRs. c, Venn diagrams showing overlapping DMRs between each cell type comparison.

Extended Data Fig. 2 DHMR characteristics.

DHMR characteristics. a, Distribution of DHMR width. b, PCA plot calculated from hmC-seal signal for each replicate. PCs were calculated from the top 1000 most variable DHMRs. c, Venn diagram showing overlapping DHMRs between each cell type comparison. d, Differential enrichment of hydroxymethylation for each DHMR plotted against differentially expressed genes between each pair of cell types. e, Gene ontology enrichment from hypomethylated DMRs and hyper-hydroxymethylated DHMRs for each cell type.

Extended Data Fig. 3 DMRs highlight novel genes in ILC biology.

DMRs highlight novel genes in ILC biology. Genome browser tracks showing the genomic distribution of 5hmC (red), methyl-CpG (blue) and unmethylated CpG (blue) at loci of Epas1. Gene expression for each NK-ILC type is plotted in the top-left corner. CGI elements are annotated as the following: shelf (light blue), shore (yellow), islands (green).

Extended Data Fig. 4 Association between DHMRs and distal enhancers.

Association between DHMRs and distal enhancers. a, Distribution of hmC signal around OCRs for each cell type. Rows are ordered from most active OCRs to least active OCRs (top-bottom) for each cell type. b, Genome browser tracks showing the genomic distribution 5hmC (red), methyl-CpG (blue), unmethylated CpG (blue), chromatin accessibility (green), and ELF-1 ChIP-seq (yellow) at the Lif locus. Overlapping DMRs with ELF-1 binding sites are highlighted in red.

Extended Data Fig. 5 Correlation between hmC, H3K4Me3, and H3K36Me3.

Correlation between hmC, H3K4Me3, and H3K36Me3. a, Enrichment profiles for DNA methylation, H3K4Me3, and H3K36Me3 in NK cells. b, Comparison of 5mC and 5hmC at 5kb windows centered on super-enhancers and control enhancers (n=877). Boxes extend from the 25th to 75th percentiles, whiskers extend to 1.5 times the IQR, and the center line is the median. Statistical significance calculated using unpaired two-tailed Student’s t-test. c, Scatterplots comparing RNA, H3K27Ac, or p300 signal with 5hmC abundance at NK super-enhancers. Statistical significance calculated with the Pearson correlation coefficient. Trendline indicates linear regression fitting with shaded areas corresponding to 95% confidence intervals.

Extended Data Fig. 6 TET2 supports ILC effector functions in a cell-intrinsic manner.

TET2 supports ILC effector functions in a cell-intrinsic manner. a, Diagram of mixed BM chimera generation. b, Donor-derived chimerism of NK, ILC2, and ILC3 (n=3). c-e, IFNγ production by splenic NK cells in response to c, IL-12 (10ng/mL) and IL-18 (1ng/mL) d, NK1.1 cross-linking and e, Yac-1 target cells. f, Granzyme-B production by splenic NK cells in response to Yac-1 cells. g, IL-5 and h, IL-13 production by ILC2 following stimulation with PMA/Ionomycin. i, Production of IL-17A by ILC3 following stimulation with IL-23 (10ng/mL) (c-i; n=6). Statistical significance calculated using paired two-tailed Student’s t-test. Each symbol represents an individual mouse. Data are representative of three independent experiments (b) or pooled from two independent experiments (c-i).

Extended Data Fig. 7 TET2 is not required for NK cell responses to MCMV.

TET2 is not required for NK cell responses to MCMV. a, Diagram of single BM chimera generation. b, Splenic NK maturation on day 4 post-infection (n=3). c, Frequency of Ly49H+ NK cells on day 4 post-infection (n=3). Production of d, IFNγ (n=3), e, TNFα (n=5), and f, Granzyme-B (n=3) by splenic NK cells after 4 hours of incubation with brefeldin A. Frequency of g, KLRG1+ and h, CD69+ NK cells (g-h;n=3). Frequency of i, Ly49D+ Ly49A−, j, Ly49A+ Ly49D+, and k, Ly49A+ Ly49D− NK cells (i-k;n=5). Statistical significance calculated using unpaired two-tailed Student’s t-test. Each symbol represents an individual mouse; small horizontal lines indicate the mean (± s.e.). Data are representative of two independent experiments (b-d, f, g-h) or pooled from two independent experiments (e, i-k).

Extended Data Fig. 8 TET2-deficient ILC3 do not have significant changes in 5hmC.

TET2-deficient ILC3 do not have significant changes in 5hmC. a, MA plot showing total number of DHMRs identified. b-c, Genome browser tracks showing the genomic distribution 5hmC from WT (blue) and Tet2−/−(red) ILC3 at the b Il17a/Il17f and c Il22 loci.

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Supplementary Table 1

Table of identified DMRs and DHMRs in all pairwise comparisons.

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Peng, V., Xing, X., Bando, J.K. et al. Whole-genome profiling of DNA methylation and hydroxymethylation identifies distinct regulatory programs among innate lymphocytes. Nat Immunol 23, 619–631 (2022). https://doi.org/10.1038/s41590-022-01164-8

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