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Epigenetic landscapes reveal transcription factors that regulate CD8+ T cell differentiation

An Erratum to this article was published on 18 May 2017

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Abstract

Dynamic changes in the expression of transcription factors (TFs) can influence the specification of distinct CD8+ T cell fates, but the observation of equivalent expression of TFs among differentially fated precursor cells suggests additional underlying mechanisms. Here we profiled the genome-wide histone modifications, open chromatin and gene expression of naive, terminal-effector, memory-precursor and memory CD8+ T cell populations induced during the in vivo response to bacterial infection. Integration of these data suggested that the expression and binding of TFs contributed to the establishment of subset-specific enhancers during differentiation. We developed a new bioinformatics method using the PageRank algorithm to reveal key TFs that influence the generation of effector and memory populations. The TFs YY1 and Nr3c1, both constitutively expressed during CD8+ T cell differentiation, regulated the formation of terminal-effector cell fates and memory-precursor cell fates, respectively. Our data define the epigenetic landscape of differentiation intermediates and facilitate the identification of TFs with previously unappreciated roles in CD8+ T cell differentiation.

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Figure 1: Epigenetic landscapes of CD8+ T cells in response to bacterial infection.
Figure 2: Dynamic use of enhancers is associated with differentially expressed genes during CD8+ T cell differentiation.
Figure 3: Accessible regulatory regions allow prediction of TF regulators.
Figure 4: Network analysis reveals subset-specific T-bet regulatory circuits.
Figure 5: PageRank-based TF ranking highlights key TF candidates.
Figure 6: YY1 is a transcriptional regulator of the differentiation of TE CD8+ T cells.
Figure 7: Nr3c1 is essential for the formation of MP CD8+ T cells.

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Change history

  • 27 March 2017

    In the version of this article initially published online, some labels in Figure 2 were illegible or incorrect. Those should read "Enhancers (× 103)" along the top and "TE, MP and M" (top to bottom) along the left margin of Figure 2a; "N, TE, MP and M" (left to right) above the plot in Figure 2b; and "GO" below the plot in Figure 2c. Also, in the third sentence of the final paragraph of the final subsection of Results (Validation of PageRank-predicted TFs), the description of the control cells ("shCon-transfected") was incorrect. The correct text is "...lower among shNr3c1-transduced cells than among shCon-transduced cells...". The errors have been corrected in the print, PDF and HTML versions of this article.

References

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

    CAS  PubMed  Google Scholar 

  2. Joshi, N.S. et al. Inflammation directs memory precursor and short-lived effector CD8+ T cell fates via the graded expression of T-bet transcription factor. Immunity 27, 281–295 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Kaech, S.M. et al. Selective expression of the interleukin 7 receptor identifies effector CD8 T cells that give rise to long-lived memory cells. Nat. Immunol. 4, 1191–1198 (2003).

    CAS  PubMed  Google Scholar 

  4. Zhou, X. et al. Differentiation and persistence of memory CD8+ T cells depend on T cell factor 1. Immunity 33, 229–240 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Chang, J.T., Wherry, E.J. & Goldrath, A.W. Molecular regulation of effector and memory T cell differentiation. Nat. Rev. Immunol. 15, 1104–1115 (2014).

    CAS  Google Scholar 

  6. 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  Google Scholar 

  7. Winter, D. et al. Making the case for chromatin profiling: a new tool to investigate the immune-regulatory landscape. Nat. Rev. Immunol. 15, 585–594 (2015).

    CAS  PubMed  Google Scholar 

  8. Neph, S. et al. Circuitry and dynamics of human transcription factor regulatory networks. Cell 150, 1274–1286 (2010).

    Google Scholar 

  9. Shen, Y. et al. A map of the cis-regulatory sequences in the mouse genome. Nature 488, 116–120 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Spitz, F. & Furlong, E.E.M. Transcription factors: from enhancer binding to developmental control. Nat. Rev. Genet. 13, 613–626 (2012).

    CAS  PubMed  Google Scholar 

  11. Calo, E. & Wysocka, J. Modification of enhancer chromatin: what, how, and why? Mol. Cell 49, 825–837 (2013).

    CAS  PubMed  Google Scholar 

  12. Lara-Astiaso, D. et al. Chromatin state dynamics during blood formation. Science 345, 943–949 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Lavin, Y. et al. Tissue-resident macrophage enhancer landscapes are shaped by the local microenvironment. Cell 159, 1312–1326 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Araki, Y. et al. Genome-wide analysis of histone methylation reveals chromatin state-based regulation of gene transcription and function of memory CD8+ T cells. Immunity 30, 912–925 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Russ, B.E. et al. Distinct epigenetic signatures delineate transcriptional programs during virus-specific CD8+ T cell differentiation. Immunity 41, 853–865 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Heintzman, N.D. et al. Histone modifications at human enhancers reflect global cell-type-specific gene expression. Nature 459, 108–112 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Best, J.A. et al. Transcriptional insights into the CD8+ T cell response to infection and memory T cell formation. Nat. Immunol. 29, 997–1003 (2013).

    Google Scholar 

  18. Rubinstein, M. et al. IL-7 and IL-15 differentially regulate CD8+ T-cell subsets during contraction of the immune response. Blood 112, 3704–3712 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Yang, C.Y. et al. The transcriptional regulators Id2 and Id3 control the formation of distinct memory CD8+ T cell subsets. Nat. Immunol. 12, 1221–1229 (2011).

    CAS  PubMed  Google Scholar 

  20. Miyazaki, M. et al. The opposing roles of E2A and Id3 that orchestrate and enforce the naïve T cell fate. Nat. Immunol. 12, 992–1001 (2012).

    Google Scholar 

  21. Rajagopal, N. et al. RFECS: A random-forest based algorithm for enhancer identification from chromatin state. PLoS Comput. Biol. 9, e1002968 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Chaix, J. et al. Cutting edge: CXCR4 is critical for CD8+ memory T cell homeostatic self-renewal but not rechallenge self-renewal. J. Immunol. 193, 1013–1016 (2014).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Ma, C. & Zhang, N. Transforming growth factor-β signaling is constantly shaping memory T-cell population. Proc. Natl. Acad. Sci. USA 112, 11013–11017 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Omilusik, K.D. et al. Transcriptional repressor ZEB2 promotes terminal differentiation of CD8+ effector and memory T cell populations during infection. J. Exp. Med. 212, 2027–2039 (2015).

    PubMed  PubMed Central  Google Scholar 

  26. Dominguez, C.X. et al. The transcription factors ZEB2 and T-bet cooperate to program cytotoxic T cell terminal differentiation in response to LCMV viral infection. J. Exp. Med. 212, 2041–2056 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Intlekofer, A.M. et al. Effector and memory CD8+ T cell fate coupled by T-bet and eomesodermin. Nat. Immunol. 6, 1236–1244 (2005).

    CAS  PubMed  Google Scholar 

  28. Kidani, Y. et al. Sterol regulatory element-binding proteins are essential for the metabolic programming of effector T cells and adaptive immunity. Nat. Immunol. 14, 489–499 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Kurachi, M. et al. The transcription factor BATF operates as an essential differentiation checkpoint in early effector CD8+ T cells. Nat. Immunol. 15, 373–383 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Zhou, X. & Xue, H. Generation of memory precursors and functional memory CD8+ T cells depends on TCF-1 and LEF-1. J. Immunol. 189, 2722–2726 (2012).

    CAS  PubMed  Google Scholar 

  31. D'Cruz, L.M., Lind, K.C., Wu, B.B., Fujimoto, J.K. & Goldrath, A.W. Loss of E protein transcription factors E2A and HEB delays memory-precursor formation during the CD8+ T-cell immune response. Eur. J. Immunol. 42, 2031–2041 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Rincón, M. & Flavell, R.a. AP-1 transcriptional activity requires both T-cell receptor-mediated and co-stimulatory signals in primary T lymphocytes. EMBO J. 13, 4370–4381 (1994).

    PubMed  PubMed Central  Google Scholar 

  33. Doering, T. et al. Network analysis reveals centrally connected genes and pathways involved in CD8+ T Cell exhaustion versus memory. Immunity 37, 1130–1144 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Hu, G. & Chen, J. A genome-wide regulatory network identifies key transcription factors for memory CD8+ T-cell development. Nat. Commun. 4, 2830 (2013).

    PubMed  Google Scholar 

  35. Szabo, S.J. et al. Distinct effects of T-bet in TH1 lineage commitment and IFN-γ production in CD4 and CD8 T cells. Science 295, 338–342 (2002).

    CAS  PubMed  Google Scholar 

  36. Lord, G.M. et al. T-bet is required for optimal proinflammatory CD4+ T-cell trafficking. Blood 106, 3432–3439 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Page, L., Brin, S., Motwani, R. & Winograd, T. The PageRank citation ranking: bringing order to the web. World Wide Web (Bussum) 54, 1–17 (1998).

    Google Scholar 

  38. Cui, W., Liu, Y., Weinstein, J.S., Craft, J. & Kaech, S.M. An interleukin-21- interleukin-10-STAT3 pathway is critical for functional maturation of memory CD8+ T cells. Immunity 35, 792–805 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Arrieta-Ortiz, M.L. et al. An experimentally supported model of the Bacillus subtilis global transcriptional regulatory network. Mol. Syst. Biol. 11, 839 (2015).

    PubMed  PubMed Central  Google Scholar 

  40. Hwang, S.S. et al. YY1 inhibits differentiation and function of regulatory T cells by blocking Foxp3 expression and activity. Nat. Commun. 7, 10789 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Hwang, S.S. et al. Transcription factor YY1 is essential for regulation of the Th2 cytokine locus and for Th2 cell differentiation. Proc. Natl. Acad. Sci. USA 110, 276–281 (2013).

    CAS  PubMed  Google Scholar 

  42. Liu, H. et al. Yin Yang 1 is a critical regulator of B-cell development. Genes Dev. 21, 1179–1189 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Herold, M.J., McPherson, K.G. & Reichardt, H.M. Glucocorticoids in T cell apoptosis and function. Cell. Mol. Life Sci. 63, 60–72 (2006).

    CAS  PubMed  Google Scholar 

  44. Franchimont, D. et al. Positive effects of glucocorticoids on T cell function by up-regulation of IL-7 receptor alpha. J. Immunol. 168, 2212–2218 (2002).

    CAS  PubMed  Google Scholar 

  45. Smoak, K.A. & Cidlowski, J.A. Mechanisms of glucocorticoid receptor signaling during inflammation. Mech. Ageing Dev. 125, 697–706 (2004).

    CAS  PubMed  Google Scholar 

  46. Wang, Q. et al. Equilibrium interactions of corepressors and coactivators with agonist and antagonist complexes of glucocorticoid receptors. Mol. Endocrinol. 18, 1376–1395 (2004).

    CAS  PubMed  Google Scholar 

  47. Tsankov, A.M. et al. Transcription factor binding dynamics during human ES cell differentiation. Nature 518, 344–349 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Zhang, K., Li, N., Ainsworth, R. & Wang, W. Systematic identification of protein combinations mediating chromatin looping. Nat. Commun. 7, 1–11 (2016).

    Google Scholar 

  49. Dixon, J.R. et al. Chromatin architecture reorganization during stem cell differentiation. Nature 518, 331–336 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Zhu, Y. et al. Constructing 3D interaction maps from 1D epigenomes. Nat. Commun. 7, 10812 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Chen, R. et al. In vivo RNA interference screens identify regulators of antiviral CD4+ and CD8+ T cell differentiation. Immunity 41, 325–338 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Zhang, Y. et al. Model-based Analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    PubMed  PubMed Central  Google Scholar 

  54. Mathelier, A. et al. JASPAR 2016: A major expansion and update of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 44, D110–D115 (2016).

    CAS  PubMed  Google Scholar 

  55. Newburger, D.E. & Bulyk, M.L. UniPROBE: An online database of protein binding microarray data on protein-DNA interactions. Nucleic Acids Res. 37, 77–82 (2009).

    Google Scholar 

  56. Jolma, A. et al. DNA-binding specificities of human transcription factors. Cell 152, 327–339 (2013).

    CAS  PubMed  Google Scholar 

  57. Grant, C.E., Bailey, T.L. & Noble, W.S. FIMO: Scanning for occurrences of a given motif. Bioinformatics 27, 1017–1018 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Jeh, G. & Widom, J. Scaling personalized web search. Proc. 2003 Int. World Wide Web Conf. (WWW'03) 12, 271–279 (2003).

    Google Scholar 

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Acknowledgements

We thank the Immgen core team for help in gene-expression data processing; and C. Murre, A. Phan, K. Omilusik, L.A. Shaw, K. Brennan and members of the Goldrath laboratory for critical discussions and review of the manuscript. Supported by the University of California, San Diego (Dr. Huang Memorial Scholarship to B.Y.), the US National Institutes of Health (AI067545 and A1072117 to A.W.G.; U19AI109976 to A.W.G, S.C. and M.E.P.; U54HG006997 and AR070310 to W.W.; and R01 AI109842 and AI40127 to A.R. for research by R.M.P. and J.P.S.-B.), the Leukemia and Lymphoma Society (A.W.G.), the Pew Scholars Fund (A.W.G.), the Pew Latin American Fellows Program in the Biomedical Sciences (R.M.P.) and the Fraternal Order of Eagles Fellow of the Damon Runyon Cancer Research Foundation (J.P.S.-B.).

Author information

Authors and Affiliations

Authors

Contributions

B.Y. designed and performed experiments, analyzed the data and wrote the paper; K.Z. performed computational analysis and wrote the paper; B.Y., J.J.M., C.T. and R.C. performed shRNA-mediated knockdown; J.P.S.-B. and R.M.P. provided ATAC-seq data sets for polyclonal CD8+ T cell populations; S.C. and M.E.P. provided reagents, advice for the design of experiments and analysis of experiments and assisted in writing the paper; J.T.C. provided advice and assisted in writing the paper; W.W. supervised the computational analysis and wrote the paper; and A.W.G. supervised the project, designed the experiments, analyzed the data and wrote the paper.

Corresponding authors

Correspondence to Wei Wang or Ananda W Goldrath.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Transcriptional program of the TE and MP CD8+ T cell subsets.

(a) Comparison of gene expression of TE and MP CD8+ T cell subsets by microarray. Genes that are 1.5-fold upregulated in TE or MP CD8+ T cell subsets are highlighted as blue or orange, respectively. Transcripts did not differ significantly in expression following correction with FDR therefore a fold-change cut-off of 1.5-fold was used for comparisons.(b) Volcano plots of the comparison of total effector and memory CD8+ T cells highlighting TE- or MP-enriched genes. Numbers in bottom corners indicate the number of highlighted genes in that region. (c) The ratio of gene expression of known TFs in TE versus MP subset from microarray. (d) Histograms of protein abundance of key TFs in TE versus MP subset. (e) Comparison of gene expression of TE and MP CD8+ T cell subsets by RNA-seq. Genes that are 2-fold upregulated in TE or MP CD8+ T cell subsets are highlighted as blue or orange, respectively. (f) GSEA plot of total effector versus memory CD8+ T cells with TE- or MP-enriched gene signature generated from (e). (g) The ratio of gene expression of known TFs in TE versus MP subset from RNA-seq. Data are the mean value of gene expression from three independent experiments with pooled spleens from three mice except the TE subset in (a) which is from two independent experiments. Data in (d) are representative of two independent experiments with three mice per group.

Supplementary Figure 2 Experimental design for the characterization of chromatin states and accessibility.

(a) Schematic view of sorting strategy for naive (N), terminal-effector (TE), memory-precursor (MP), and memory (M) CD8+ T cell subsets. (b) Schematic view of experimental design for characterization of the global epigenetic landscape and gene expression using ChIP-seq, ATAC-seq and microarray analyses.

Supplementary Figure 3 Dynamic enhancer establishment is associated with gene expression during CD8+ T cell differentiation.

(a) Violin plots showing the expression of genes associated with different enhancer clusters generated from Figure 2a in naive (N), total effector (EFF) and memory (M) CD8+ T cells generated from microarray data in Best et al. study17. (b) Volcano plots of the comparison of TE and MP CD8+ T cells showing expression of enhancer cluster associated genes. Data are representative of three independent experiments with three mice per group (median value). The statistical analysis was performed by a nonparametric Wilcoxon rank-sum test. n.s. *: p value <0.0001.

Supplementary Figure 4 Full list of TF motifs enriched in subset-specific regulatory elements.

(a) Schematic view of identification of candidate TFs enriched in subset-specific regulatory elements from ATAC-seq and ChIP-seq. (b) Venn diagram showing the overlap of enhancers between CD8+ T cell subsets. (c) Heatmap showing the p-value of transcription factor motif enrichment at subset-specific promoters (left) or enhancers (right) calculated by binomial test using randomly-picked open chromatin regions as background. Motif enrichment or depletion are indicated as red or blue, respectively.

Supplementary Figure 5 Network construction.

Construction of TF regulatory network in CD8+ T cell subsets using ChIP-seq and ATAC-seq as input.

Supplementary Figure 6 Full list of TFs identified by PageRank and comparison of PageRank with TFA.

(a) Heatmap showing PageRank fold enrichment of TFs across CD8+ T cell subsets. (b) A list of TFs revealed by PageRank analysis and motif enrichment in Figure 3. Known TFs important for CD8+ T cell differentiation are highlighted in red. (c) Heatmap showing Z score of PageRank score and TFA score of TFs across CD8+ T cell subsets generated by PageRank and TFA analysis, respectively. (d) Bar graph showing the percentage of known TFs with consistent roles in previous reports for each analysis. (e) Bar graphs showing the fold change of YY1 and Nr3c1 gene expression generated from microarray. Data in (e) are the mean value of gene expression from three independent experiments with pooled spleens from three mice.

Supplementary Figure 7 Ablation of Nr3c1 cofactor Ncor1 and treatment with dexamethasone affect the differentiation of MP CD8+ T cells.

(a) Schematic view of experimental design. OT-I CD8+ T cells were activated in vitro and transduced with control shRNA or shYy1 retroviral vectors and subsequently co-transferred into recipient mice followed by i.v. infection with Lm-OVA. Splenocytes were isolated and analyzed on day 7 of infection. (b) Flow cytometric analysis of KLRG1 and IL-7R expression for cells transduced with shCd4 and shNcor1 in PBL on day 8 of infection. (c) The percentage of TE and MP CD8+ T cells gated on transduced cells on day 8 of infection after knockdown of Ncor1. (d) Flow cytometric analysis of KLRG1 and IL-7R expression for donor cells in mice treated with either vehicle or dexamethasone for 7 days. (e) The percentage of TE and MP CD8+ T cells gated on donor cells on day 8 of Lm-OVA infection after drug treatment. Data are representative from two independent experiments with 5 mice per group. The statistical analysis was performed by two-tailed paired t-test in (c) and two-tailed unpaired t-test in (e). *: p<0.001

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7 (PDF 1346 kb)

Supplementary Table 1

Processed RNA-seq gene count dataset of the TE and MP subsets (XLSX 920 kb)

Supplementary Table 2

List of T-bet regulated targets in the TE and MP subsets predictedby TF regulatory network (XLSX 14 kb)

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Yu, B., Zhang, K., Milner, J. et al. Epigenetic landscapes reveal transcription factors that regulate CD8+ T cell differentiation. Nat Immunol 18, 573–582 (2017). https://doi.org/10.1038/ni.3706

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