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.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Signal Transduction and Targeted Therapy Open Access 25 August 2023
Clinical Epigenetics Open Access 15 December 2022
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Rent or buy this article
Prices vary by article type
Prices may be subject to local taxes which are calculated during checkout
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).
Vivier, E. et al. Innate lymphoid cells: 10 years on. Cell 174, 1054–1066 (2018).
Bando, J. K. & Colonna, M. Innate lymphoid cell function in the context of adaptive immunity. Nat. Immunol. 17, 783–789 (2016).
Serafini, N., Vosshenrich, C. A. J. & Di Santo, J. P. Transcriptional regulation of innate lymphoid cell fate. Nat. Rev. Immunol. 15, 415–428 (2015).
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).
Robinette, M. L. et al. Transcriptional programs define molecular characteristics of innate lymphoid cell classes and subsets. Nat. Immunol. 16, 306–317 (2015).
Yoshida, H. et al. The cis-regulatory atlas of the mouse immune system. Cell 176, 897–912 (2019).
Shih, H.-Y. et al. Developmental acquisition of regulomes underlies innate lymphoid cell functionality. Cell 165, 1120–1133 (2016).
Collins, P. L. et al. Gene regulatory programs conferring phenotypic identities to human NK cells. Cell 176, 348–360 (2019).
Koues, O. I. et al. Distinct gene regulatory pathways for human innate versus adaptive lymphoid Cells. Cell 165, 1134–1146 (2016).
Lau, C. M. et al. Epigenetic control of innate and adaptive immune memory. Nat. Immunol. 19, 963–972 (2018).
Ziller, M. J. et al. Charting a dynamic DNA methylation landscape of the human genome. Nature 500, 477–481 (2013).
Stadler, M. B. et al. DNA-binding factors shape the mouse methylome at distal regulatory regions. Nature 480, 490–495 (2011).
Bachman, M. et al. 5-Hydroxymethylcytosine is a predominantly stable DNA modification. Nat. Chem. 6, 1049–1055 (2014).
Song, C.-X. et al. Selective chemical labeling reveals the genome-wide distribution of 5-hydroxymethylcytosine. Nat. Biotechnol. 29, 68–72 (2011).
Wu, X. & Zhang, Y. TET-mediated active DNA demethylation: mechanism, function and beyond. Nat. Rev. Genet. 18, 517–534 (2017).
Lio, C.-W. J. & Rao, A. TET enzymes and 5hmC in adaptive and innate immune systems. Front Immunol. 10, 210 (2019).
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).
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).
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).
Makar, K. W. et al. Active recruitment of DNA methyltransferases regulates interleukin 4 in thymocytes and T cells. Nat. Immunol. 4, 1183–1190 (2003).
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).
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).
Tsagaratou, A. et al. TET proteins regulate the lineage specification and TCR-mediated expansion of i NKT cells. Nat. Immunol. 18, 45–53 (2017).
Ji, H. et al. Comprehensive methylome map of lineage commitment from haematopoietic progenitors. Nature 467, 338–342 (2010).
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).
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).
Ebihara, T. et al. Runx3 specifies lineage commitment of innate lymphoid cells. Nat. Immunol. 16, 1124–1133 (2015).
Smith, Z. D. & Meissner, A. DNA methylation: roles in mammalian development. Nat. Rev. Genet. 14, 204–220 (2013).
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).
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).
Califano, D. et al. Transcription factor Bcl11b controls identity and function of mature type 2 innate lymphoid cells. Immunity 43, 354–368 (2015).
Walker, J. A. et al. Bcl11b is essential for group 2 innate lymphoid cell development. J. Exp. Med. 212, 875–882 (2015).
Pastor, W. A. et al. Genome-wide mapping of 5-hydroxymethylcytosine in embryonic stem cells. Nature 473, 394–397 (2011).
Yu, M. et al. Base-resolution analysis of 5-hydroxymethylcytosine in the mammalian genome. Cell 149, 1368–1380 (2012).
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).
Ramirez, K. et al. Gene deregulation and chronic activation in natural killer cells deficient in the transcription factor ETS1. Immunity 36, 921–932 (2012).
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).
Samstein, R. M. et al. Foxp3 exploits a pre-existent enhancer landscape for regulatory T cell lineage specification. Cell 151, 153–166 (2012).
Dorner, B. G. et al. Coordinate expression of cytokines and chemokines by NK cells during murine cytomegalovirus infection. J. Immunol. 172, 3119–3131 (2004).
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).
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).
Lindroth, A. M. et al. Antagonism between DNA and H3K27 methylation at the imprinted Rasgrf1 locus. PLoS Genet. 4, e1000145 (2008).
Lister, R. et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 462, 315–322 (2009).
Neri, F. et al. Intragenic DNA methylation prevents spurious transcription initiation. Nature 543, 72–77 (2017).
Wagner, E. J. & Carpenter, P. B. Understanding the language of Lys36 methylation at histone H3. Nat. Rev. Mol. Cell Biol. 13, 115–126 (2012).
Hnisz, D. et al. Super-enhancers in the control of cell identity and disease. Cell 155, 934–947 (2013).
Vahedi, G. et al. Stretch-enhancers delineate disease-associated regulatory nodes in T cells. Nature 520, 558–562 (2015).
Adams, N. M. et al. Transcription factor IRF8 orchestrates the adaptive natural killer cell response. Immunity 48, 1172–1182 (2018).
Veillette, A. SLAM-family receptors: immune regulators with or without SAP-family adaptors. Cold Spring Harb. Perspect. Biol. 2, a002469 (2010).
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).
Colonna, M. Innate lymphoid cells: diversity, plasticity, and unique functions in immunity. Immunity 48, 1104–1117 (2018).
Deaton, A. M. et al. Cell type–specific DNA methylation at intragenic CpG islands in the immune system. Genome Res. 21, 1074–1086 (2011).
Shukla, S. et al. CTCF-promoted RNA polymerase II pausing links DNA methylation to splicing. Nature 479, 74–79 (2011).
Maunakea, A. K. et al. Conserved role of intragenic DNA methylation in regulating alternative promoters. Nature 466, 253–257 (2010).
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).
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).
Spruijt, C. G. et al. Dynamic readers for 5-(hydroxy)methylcytosine and its oxidized derivatives. Cell 152, 1146–1159 (2013).
Bal, S. M., Golebski, K. & Spits, H. Plasticity of innate lymphoid cell subsets. Nat. Rev. Immunol. 552–565 (2020).
Sun, J. C., Beilke, J. N. & Lanier, L. L. Adaptive immune features of natural killer cells. Nature 457, 557–561 (2009).
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).
Moran-Crusio, K. et al. Tet2 loss leads to increased hematopoietic stem cell self-renewal and myeloid transformation. Cancer Cell 20, 11–24 (2011).
Eberl, G. & Littman, D. R. Thymic origin of intestinal αß T cells revealed by fate mapping of RORγt+ cells. Science 305, 248–251 (2004).
Schlenner, S. M. et al. Fate mapping reveals separate origins of T cells and myeloid lineages in the thymus. Immunity 32, 426–436 (2010).
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).
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).
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).
Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010).
Zhang, Y. et al. Model-based analysis of ChIP–seq (MACS). Genome Biol. 9, R137 (2008).
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).
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).
Amemiya, H. M., Kundaje, A. & Boyle, A. P. The ENCODE blacklist: identification of problematic regions of the genome. Sci. Rep. 9, 9354 (2019).
Ramírez, F. et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44, W160–W165 (2016).
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).
Li, D., Hsu, S., Purushotham, D., Sears, R. L. & Wang, T. WashU Epigenome Browser update 2019. Nucleic Acids Res. 47, W158–W165 (2019).
McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).
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).
Neph, S. et al. BEDOPS: high-performance genomic feature operations. Bioinformatics 28, 1919–1920 (2012).
Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).
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.).
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.
Peer review information
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.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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.
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.
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).
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.
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.
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).
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).
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.
About this article
Cite this article
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
This article is cited by
Nature Reviews Immunology (2023)
Signal Transduction and Targeted Therapy (2023)
Clinical Epigenetics (2022)