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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.

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).

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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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