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Epigenomic analysis of primary human T cells reveals enhancers associated with TH2 memory cell differentiation and asthma susceptibility

Abstract

A characteristic feature of asthma is the aberrant accumulation, differentiation or function of memory CD4+ T cells that produce type 2 cytokines (TH2 cells). By mapping genome-wide histone modification profiles for subsets of T cells isolated from peripheral blood of healthy and asthmatic individuals, we identified enhancers with known and potential roles in the normal differentiation of human TH1 cells and TH2 cells. We discovered disease-specific enhancers in T cells that differ between healthy and asthmatic individuals. Enhancers that gained the histone H3 Lys4 dimethyl (H3K4me2) mark during TH2 cell development showed the highest enrichment for asthma-associated single nucleotide polymorphisms (SNPs), which supported a pathogenic role for TH2 cells in asthma. In silico analysis of cell-specific enhancers revealed transcription factors, microRNAs and genes potentially linked to human TH2 cell differentiation. Our results establish the feasibility and utility of enhancer profiling in well-defined populations of specialized cell types involved in disease pathogenesis.

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Figure 1: Reproducibility, microscaling and sensitivity of the H3K4me2 ChIP-seq assay.
Figure 2: Changes in enhancer strength among TH cell subsets.
Figure 3: Genes and pathways linked to differentiation of CD4+ memory cells.
Figure 4: Upstream regulators of TH2 cell genes.
Figure 5: Enrichment of transcription factor binding motifs and sites in enhancers linked to CD4 memory differentiation.
Figure 6: Asthma GWAS SNPs are enriched in TH2 cell enhancers.
Figure 7: Identification of asthma-associated enhancers.

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References

  1. Ahmed, R. & Gray, D. Immunological memory and protective immunity: understanding their relation. Science 272, 54–60 (1996).

    CAS  PubMed  Google Scholar 

  2. Ansel, K.M., Lee, D.U. & Rao, A. An epigenetic view of helper T cell differentiation. Nat. Immunol. 4, 616–623 (2003).

    CAS  PubMed  Google Scholar 

  3. Kay, A.B. Allergy and allergic diseases. Second of two parts. N. Engl. J. Med. 344, 109–113 (2001).

    CAS  PubMed  Google Scholar 

  4. Ober, C. & Yao, T.C. The genetics of asthma and allergic disease: a 21st century perspective. Immunol. Rev. 242, 10–30 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Gregersen, P.K. & Olsson, L.M. Recent advances in the genetics of autoimmune disease. Annu. Rev. Immunol. 27, 363–391 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. WHO fact sheets N206 and N307 http://www.who.int/mediacentre/factsheets/fs206/en/; http://www.who.int/mediacentre/factsheets/fs307/en/index.html (accessed November 2013).

  7. Holgate, S.T. & Polosa, R. Treatment strategies for allergy and asthma. Nat. Rev. Immunol. 8, 218–230 (2008).

    CAS  PubMed  Google Scholar 

  8. Wenzel, S.E., Wang, L. & Pirozzi, G. Dupilumab in persistent asthma. N. Engl. J. Med. 369, 1276 (2013).

    CAS  PubMed  Google Scholar 

  9. Ansel, K.M., Djuretic, I., Tanasa, B. & Rao, A. Regulation of TH2 differentiation and Il4 locus accessibility. Annu. Rev. Immunol. 24, 607–656 (2006).

    CAS  PubMed  Google Scholar 

  10. Vijayanand, P. et al. Interleukin-4 production by follicular helper T cells requires the conserved Il4 enhancer hypersensitivity site V. Immunity 36, 175–187 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Loots, G.G. et al. Identification of a coordinate regulator of interleukins 4, 13, and 5 by cross-species sequence comparisons. Science 288, 136–140 (2000).

    CAS  PubMed  Google Scholar 

  12. Ernst, J. et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473, 43–49 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Zhang, J.A., Mortazavi, A., Williams, B.A., Wold, B.J. & Rothenberg, E.V. Dynamic transformations of genome-wide epigenetic marking and transcriptional control establish T cell identity. Cell 149, 467–482 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Koche, R.P. et al. Reprogramming factor expression initiates widespread targeted chromatin remodeling. Cell Stem Cell 8, 96–105 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Vijayanand, P. et al. Chemokine receptor 4 plays a key role in T cell recruitment into the airways of asthmatic patients. J. Immunol. 184, 4568–4574 (2010).

    CAS  PubMed  Google Scholar 

  17. Mikhak, Z., Strassner, J.P. & Luster, A.D. Lung dendritic cells imprint T cell lung homing and promote lung immunity through the chemokine receptor CCR4. J. Exp. Med. 210, 1855–1869 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Zielinski, C.E. et al. Pathogen-induced human TH17 cells produce IFN-gamma or IL-10 and are regulated by IL-1beta. Nature 484, 514–518 (2012).

    CAS  PubMed  Google Scholar 

  19. Chavez, L. et al. Computational analysis of genome-wide DNA methylation during the differentiation of human embryonic stem cells along the endodermal lineage. Genome Res. 20, 1441–1450 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Lienhard, M., Grimm, C., Morkel, M., Herwig, R. & Chavez, L. MEDIPS: genome wide differential coverage analysis of sequencing data derived from DNA enrichment experiments. Bioinformatics 30, 284–286 (2014).

    CAS  PubMed  Google Scholar 

  21. 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).

    PubMed  PubMed Central  Google Scholar 

  22. Ansel, K.M. et al. Deletion of a conserved Il4 silencer impairs T helper type 1-mediated immunity. Nat. Immunol. 5, 1251–1259 (2004).

    CAS  PubMed  Google Scholar 

  23. Wilson, C.B., Rowell, E. & Sekimata, M. Epigenetic control of T-helper-cell differentiation. Nat. Rev. Immunol. 9, 91–105 (2009).

    CAS  PubMed  Google Scholar 

  24. Seumois, G. et al. An integrated nano-scale approach to profile miRNAs in limited clinical samples. Am. J. Clin. Exp. Immunol. 1, 70–89 (2012).

    PubMed  PubMed Central  Google Scholar 

  25. Douglas, N.C., Jacobs, H., Bothwell, A.L. & Hayday, A.C. Defining the specific physiological requirements for c-Myc in T cell development. Nat. Immunol. 2, 307–315 (2001).

    CAS  PubMed  Google Scholar 

  26. Wang, R. et al. The transcription factor Myc controls metabolic reprogramming upon T lymphocyte activation. Immunity 35, 871–882 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Zhu, J.W. et al. E2F1 and E2F2 determine thresholds for antigen-induced T-cell proliferation and suppress tumorigenesis. Mol. Cell. Biol. 21, 8547–8564 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Pandiyan, P. et al. CD152 (CTLA-4) determines the unequal resistance of Th1 and Th2 cells against activation-induced cell death by a mechanism requiring PI3 kinase function. J. Exp. Med. 199, 831–842 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Kim, T.H. et al. Analysis of the vertebrate insulator protein CTCF-binding sites in the human genome. Cell 128, 1231–1245 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Hawkins, R.D. et al. Global chromatin state analysis reveals lineage-specific enhancers during the initiation of human T helper 1 and T helper 2 cell polarization. Immunity 38, 1271–1284 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Rockwell, C.E., Zhang, M., Fields, P.E. & Klaassen, C.D. Th2 skewing by activation of Nrf2 in CD4(+) T cells. J. Immunol. 188, 1630–1637 (2012).

    CAS  PubMed  Google Scholar 

  32. Gerstein, M.B. et al. Architecture of the human regulatory network derived from ENCODE data. Nature 489, 91–100 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Maurano, M.T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Gerasimova, A. et al. Predicting cell types and genetic variations contributing to disease by combining GWAS and epigenetic data. PLoS ONE 8, e54359 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Moffatt, M.F. et al. A large-scale, consortium-based genomewide association study of asthma. N. Engl. J. Med. 363, 1211–1221 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Dolan, M.J. et al. CCL3L1 and CCR5 influence cell-mediated immunity and affect HIV-AIDS pathogenesis via viral entry-independent mechanisms. Nat. Immunol. 8, 1324–1336 (2007).

    CAS  PubMed  Google Scholar 

  37. Rakyan, V.K., Down, T.A., Balding, D.J. & Beck, S. Epigenome-wide association studies for common human diseases. Nat. Rev. Genet. 12, 529–541 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Jin, F. et al. A high-resolution map of the three-dimensional chromatin interactome in human cells. Nature 503, 290–294 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Zhang, Y. et al. Chromatin connectivity maps reveal dynamic promoter-enhancer long-range associations. Nature 504, 306–310 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Bousquet, J. Global initiative for asthma (GINA) and its objectives. Clin. Exp. Allergy 30 (suppl. 1), 2–5 (2000).

    PubMed  Google Scholar 

  41. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S.L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    PubMed  PubMed Central  Google Scholar 

  42. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    PubMed  PubMed Central  Google Scholar 

  43. Trapnell, C., Pachter, L. & Salzberg, S.L. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Wang, H. et al. Widespread plasticity in CTCF occupancy linked to DNA methylation. Genome Res. 22, 1680–1688 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Huang da, W., Sherman, B.T. & Lempicki, R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).

    PubMed  Google Scholar 

  47. Huang da, W., Sherman, B.T. & Lempicki, R.A. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37, 1–13 (2009).

    PubMed  Google Scholar 

  48. Kamburov, A., Stelzl, U., Lehrach, H. & Herwig, R. The ConsensusPathDB interaction database: 2013 update. Nucleic Acids Res. 41, D793–D800 (2013).

    CAS  PubMed  Google Scholar 

  49. Kamburov, A., Wierling, C., Lehrach, H. & Herwig, R. ConsensusPathDB–a database for integrating human functional interaction networks. Nucleic Acids Res. 37, D623–D628 (2009).

    CAS  PubMed  Google Scholar 

  50. Ward, L.D. & Kellis, M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 40, D930–D934 (2012).

    CAS  PubMed  Google Scholar 

  51. Yu, W. et al. GWAS Integrator: a bioinformatics tool to explore human genetic associations reported in published genome-wide association studies. Eur. J. Hum. Genet. 19, 1095–1099 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Abecasis, G.R. et al. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).

    PubMed  Google Scholar 

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Acknowledgements

We thank the staff at the Wellcome Trust Clinical Research Facility (University of Southampton) where samples were acquired from volunteers; M. North for assisting in patient recruitment, assessment and sample collection; R. Jewel and C. McGuire for providing assistance in the flow cytometry facility (University of Southampton; J. Day for assistance with high-throughput sequencing at the La Jolla Institute for Allergy and Immunology sequencing facility, and A. Moghaddas Gholami at the La Jolla Institute for Allergy and Immunology bioinformatics core for help with the SNP enrichment analysis. L.C. is funded by a Feodor Lynen Research Fellowship from the Alexander von Humboldt Foundation. This work was supported by the Dana Foundation (K.M.A.), GlaxoSmithKline National Clinician Scientist Fellowship Award and Peel Travel Fellowship Award (P.V.), R01 HL114093 (to B.P., A.R. and P.V.) and U19 AI100275 (to B.P., A.R. and P.V.).

Author information

Authors and Affiliations

Authors

Contributions

G.S., K.M.A., B.P., A.R. and P.V. conceived the work, designed, performed and analyzed experiments, and wrote the paper; N.O., L.K., M.V. and A.P.V.G. assisted in the performing some of the experiments under the supervision of G.S. and P.V.; R.D. provided support and direction for obtaining and processing clinical specimens; L.C. identified DERs; and A.G., M.L. and A.C. performed the bioinformatic analysis under the supervision of L.C. and B.P.

Corresponding authors

Correspondence to Bjoern Peters, Anjana Rao or Pandurangan Vijayanand.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Schematic model summarizing the study design and cell sorting strategy.

Schematic diagram depicts the cell types isolated from peripheral blood of healthy and asthmatic subjects. Sorting strategy for isolating naïve and CCR4 (TH1), CCR4+ (TH2) memory T cells from peripheral blood mononuclear cells (PBMC), and FACS plots pre and post-sorting are shown. The number of samples processed (passing quality control checks) for H3K4me2 ChIP-Seq assay is shown below.

Supplementary Figure 2 Reproducibility of standard H3K4me2 ChIP-seq assay.

Density plots show pair-wise comparison of sequencing coverage (number of reads) at genome-wide 500 bp windows (MEDIPS v.1.10.0 software, Methods and Supplementary Notes) obtained from 6 independent standard H3K4me2 ChIP-Seq assays performed with 2 × 106 D10 cells. The amount of DNA (post H3K4me2 ChIP; 15ng or 30ng) used for whole genome amplification (see Methods and Supplementary Notes) is shown. Pairwise Spearman correlation values (numbers inside boxes) for genome-wide comparison of H3K4me2 enrichment patterns between replicate samples (labeled as Rep 1-6) are illustrated.

Supplementary Figure 3 Workflow of microscaled ChIP-seq assay from sample isolation to sequencing.

Supplementary Figure 4 Reproducibility of the micro-scaled H3K4me2 ChIP-seq assay.

(a) Density plots show pair-wise comparison of sequencing coverage at genome-wide 500bp windows (MEDIPS v.1.10.0 software, Methods and Supplementary Notes) obtained from standard ChIP-Seq (2 × 106 cells) and micro-scaled ChIP-Seq (105, 104 and 103 cell samples) performed with D10 cells. Pairwise Spearman correlation values (numbers inside boxes) for genome-wide comparison of H3K4me2 enrichment patterns between assays performed with different cell numbers are illustrated. (b) Shows density plots and pairwise Spearman correlation between multiple (n = 11) replicate micro-scaled ChIP-Seq assays (105 cell samples) performed in two separate sequencing runs (Run1 and Run2). (c) ROC analysis (detailed methodology is described in Methods and Supplementary Notes) shows the percentage of true and false positives identified by micro-scaled ChIP-Seq assay (105 cell sample) when tested for the top 1% of enriched windows (true positives) identified in the standard ChIP-Seq assay.

Supplementary Figure 5 Reproducibility of the microscaled H3K4me2 ChIP-seq assay.

ChIP-Seq analysis showing H3K4me2 enrichment patterns from each assay (rows), for the following gene loci: control regions: STIM 1, NUP98, SELP and SELL gene loci; non-expressed gene: SELE locus; TH2 cell-type specific regions: CCR4 and CCR6 locus; TH1 cell-type specific regions: TBX21 (encoding T-BET), performed in peripheral blood naïve, TH1 and TH2 memory T cells from all study subjects. The significant cell-specific H3K4me2 enrichment across (enhancers and promoters) in these loci is highlighted in the red dashed line boxes.

Supplementary Figure 6 Sensitivity of the H3K4me2 ChIP-seq assay.

ChIP-Seq analysis showing cell-specific H3K4me2 enrichment patterns, for the following gene loci: HNRPLL, ADAM19, miR155 and miR221-222, in naïve, TH1 and TH2 cells. For each specific cell-type, data was merged from all donors including assay duplicates. H3K4me2 enrichment values for specific 500 bp windows (highlighted in red boxes) are shown in the graphs below. Each dot represents data from a single assay-donor; error bars indicate mean ± (s.e.m.).

Supplementary Figure 7 Bioinformatic analysis of H3K4me2 ChIP-seq data set.

Diagnostic plots examining different characteristics of the ChIP-Seq data based on raw counts (left) and quantile normalized counts (right) resulting from the three pairwise cell type comparisons. The MA plots (top) contrast log2 fold changes (y-axis) against mean sequencing coverage (x-axis) for all genome wide 500 bp windows. Genomic windows with a Bonferroni adjusted P-value <0.05 are indicated in red. The density plots (middle) show the relative distribution of read counts (x-axis) at genome wide 500 bp windows for windows with read counts ≤10. The boxplots (bottom) show read counts at genome wide 500 bp windows for individual assays.

Supplementary Figure 8 Bioinformatic analysis of H3K4me2 ChIP-seq data set.

(a) As examples, cell-type specific enhancer DER tracks (pre- and post-normalization (norm)) along with UCSC tracks are shown for the following gene loci: TBX21, IFNG, CXCR3, CXCR6, STAT1 and STAT4, where additional DERs were detected following quantile normalization of the ChIP-Seq data (Supplementary Fig. 7). (b) The number and size distribution of DERs after merging consecutive DERs (see Methods and Supplementary Notes).

Supplementary Figure 9 Genomic distribution of differentially enriched cis-regulatory regions (DERs).

Pie chart (left) shows the distribution of DERs in different genomic regions, and compared to the reference annotation (right)

Supplementary Figure 10 Concordant changes in gene expression and H3K4me2 enrichment patterns at promoter and distal cis-regulatory elements (enhancers).

(a) Heat map shows the comparison of gene expression (RNA-Seq) and H3K4me2 enrichment patterns (across an extended genomic region) for transcripts having a DER in their promoter region when comparing naïve to TH2 memory CD4+ T cells (each row represents one transcript). Upstream = -20 kb from transcription start site (TSS); Promoter = +/-1 kb around TSS; Transcript body = region between TSS and transcript end site (TES); Downstream = +20 kb from TES; RNA = RNA-Seq data. The heat map indicates concordant gain (red) or loss (blue) of H3K4me2 enrichment in TH2 memory compared to naïve CD4+ T cells, at the promoter and at enhancers located in or close to that transcript (see Methods and Supplementary Notes). Similarly, the last column indicates concordant up- (red) or down- (blue) regulation of gene expression in TH2 memory cells compared to naïve CD4+ T cells for the corresponding transcripts. (b) As examples, H3K4me2 enrichment and RNA-Seq tracks for naïve and TH2 memory T cells (data merged from all donors including duplicate assays) are shown for the following gene loci: CCL20 and CCR8 (TH2 gain); S100B and LY86 (TH1 gain), along with UCSC gene tracks (top row) and cell-type specific enhancer DERs.

Supplementary Figure 11 Concordant changes in gene expression and H3K4me2 enrichment patterns at promoter and distal cis-regulatory elements.

(a) Heat map shows the comparison of gene expression (RNA-Seq) and H3K4me2 enrichment patterns (across an extended genomic region) for genes that are differentially expressed in any of the three pairwise comparisons of naïve, TH2 and TH1 cells (each row represents one gene). First column of the heat map (labeled RNA-Seq) displays log2 fold change in gene expression values. Next to the RNA-Seq data, fold change in H3K4me2 enrichment pattern across each extended gene locus (transcription start site (TSS) +/-50 kb) is displayed (see Methods and Supplementary Notes). The heat map indicates concordant changes in (up-red or down-blue) gene expression and H3K4me2 enrichment at the promoter and distal enhancer regions. Bottom panel shows genes with no significant changes in expression levels, but display a promoter-localized differentially enriched H3K4me2 region (DER) in any of the three pairwise comparisons of naïve, TH2 and TH1 cells. (b) As examples, RNA-Seq and H3K4me2 enrichment tracks for naïve and memory T cells (data merged from all donors including duplicate assays) are shown for the following gene loci: CCR6, CCR8 and FAM129a (TH2 active genes); CCL5 and CX3CR1 (TH1 active genes); IL4 and IL21 (TH2 poised genes), along with UCSC gene tracks (top row), cell-type specific DERs and differentially expressed genes (DEX). Red dashed line boxes highlight cell-specific DERs. (c) Shows MA plots (vertically displayed) for the pairwise comparisons of naïve, TH2 and TH1 cells, and red dots indicate differentially expressed genes (false discovery rate of 1%). (d-e Shows principal component analysis (PCA) for RNA-Seq data from each sample, and the genes contributing to the PCA.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–11, Supplementary Note (PDF 3029 kb)

Supplementary Table 1

The detailed description of 120 ChIP-seq assays. (XLSX 47 kb)

Supplementary Table 2

List of differentially enriched cis-regulatory regions (DERs) for cell types comparison. (XLSX 21830 kb)

Supplementary Table 3

Classification of the DERs into subgroups. (XLSX 10 kb)

Supplementary Table 4

List of all RefSeq promoters covered by DERs. (XLSX 135 kb)

Supplementary Table 5

Biological process-enrichment analysis. (XLSX 251 kb)

Supplementary Table 6

Genomic coordinates of enhancer DERs and linked genes. (XLSX 2697 kb)

Supplementary Table 7

Biological process and pathway-enrichment analysis of target genes linked to enhancer DERs. (XLSX 6051 kb)

Supplementary Table 8

Differential gene expression. (XLSX 3779 kb)

Supplementary Table 9

Transcription factors motif enrichment in DERs enhancers. (XLSX 15 kb)

Supplementary Table 10

Transcription factor binding site enrichment analysis. (XLSX 55 kb)

Supplementary Table 11

GWAS SNPs enrichment analysis. (XLSX 5147 kb)

Supplementary Table 12

Genomic coordinates of disease-specific enhancer DERs and linked genes. (XLSX 68 kb)

Supplementary Table 13

Transcription factor binding site enrichment analysis for disease-specific DERs. (XLSX 20 kb)

Supplementary Table 14

Biological process and pathway-enrichment analysis. (XLSX 12 kb)

Supplementary Table 15

Details of study subjects. (XLSX 9 kb)

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Seumois, G., Chavez, L., Gerasimova, A. et al. Epigenomic analysis of primary human T cells reveals enhancers associated with TH2 memory cell differentiation and asthma susceptibility. Nat Immunol 15, 777–788 (2014). https://doi.org/10.1038/ni.2937

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