Article

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

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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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Change history

  • Corrected online 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.

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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

Author notes

    • Renata M Pereira

    Present address: Instituto de Microbiologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.

    • Bingfei Yu
    •  & Kai Zhang

    These authors contributed equally to this work.

Affiliations

  1. Division of Biological Sciences, University of California, San Diego, La Jolla, California, USA.

    • Bingfei Yu
    • , J Justin Milner
    • , Clara Toma
    •  & Ananda W Goldrath
  2. Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, California, USA.

    • Kai Zhang
    •  & Wei Wang
  3. Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, California, USA.

    • Runqiang Chen
    •  & Shane Crotty
  4. Department of Immunology and Microbial Science, The Scripps Research Institute, Jupiter, Florida, USA.

    • Runqiang Chen
    •  & Matthew E Pipkin
  5. Division of Signaling and Gene Expression, La Jolla Institute for Allergy and Immunology, La Jolla, California, USA.

    • James P Scott-Browne
    •  & Renata M Pereira
  6. Division of Infectious Diseases, Department of Medicine, University of California, San Diego, La Jolla, California, USA.

    • Shane Crotty
  7. Department of Medicine, University of California, San Diego, La Jolla, California, USA.

    • John T Chang
  8. Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, California, USA.

    • Wei Wang
  9. Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, California, USA.

    • Wei Wang

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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.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Wei Wang or Ananda W Goldrath.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–7

Excel files

  1. 1.

    Supplementary Table 1

    Processed RNA-seq gene count dataset of the TE and MP subsets

  2. 2.

    Supplementary Table 2

    List of T-bet regulated targets in the TE and MP subsets predictedby TF regulatory network