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The transcription factor IRF4 is essential for TCR affinity–mediated metabolic programming and clonal expansion of T cells

Nature Immunology volume 14, pages 11551165 (2013) | Download Citation

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  • A Corrigendum to this article was published on 19 August 2014

This article has been updated

Abstract

During immune responses, T cells are subject to clonal competition, which leads to the predominant expansion of high-affinity clones; however, there is little understanding of how this process is controlled. We found here that the transcription factor IRF4 was induced in a manner dependent on affinity for the T cell antigen receptor (TCR) and acted as a dose-dependent regulator of the metabolic function of activated T cells. IRF4 regulated the expression of key molecules required for the aerobic glycolysis of effector T cells and was essential for the clonal expansion and maintenance of effector function of antigen-specific CD8+ T cells. Thus, IRF4 is an indispensable molecular 'rheostat' that 'translates' TCR affinity into the appropriate transcriptional programs that link metabolic function with the clonal selection and effector differentiation of T cells.

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Change history

  • 08 January 2014

    In the version of this article initially published, the scale of the top row (Naive) for S1pr1 in Figure 5d is incorrect. With the correct scale, S1pr1 expression in naive CD8+ T cells is accurately presented as very high. The error has been corrected in the HTML and PDF versions of the article.

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References

  1. 1.

    & Effector and memory CD8+ T cell differentiation: toward a molecular understanding of fate determination. Curr. Opin. Immunol. 22, 279–285 (2010).

  2. 2.

    & Transcriptional control of effector and memory CD8+ T cell differentiation. Nat. Rev. Immunol. 12, 749–761 (2012).

  3. 3.

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

  4. 4.

    , , & Blimp-1 transcription factor is required for the differentiation of effector CD8+ T cells and memory responses. Immunity 31, 283–295 (2009).

  5. 5.

    et al. Transcriptional repressor Blimp-1 promotes CD8+ T cell terminal differentiation and represses the acquisition of central memory T cell properties. Immunity 31, 296–308 (2009).

  6. 6.

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

  7. 7.

    et al. Transcriptional regulator Id2 mediates CD8+ T cell immunity. Nat. Immunol. 7, 1317–1325 (2006).

  8. 8.

    , , & T cell fitness determined by signal strength. Nat. Immunol. 4, 355–360 (2003).

  9. 9.

    et al. Rapid CD8+ T cell repertoire focusing and selection of high-affinity clones into memory following primary infection with a persistent human virus: human cytomegalovirus. J. Immunol. 179, 3203–3213 (2007).

  10. 10.

    et al. Avidity for antigen shapes clonal dominance in CD8+ T cell populations specific for persistent DNA viruses. J. Exp. Med. 202, 1349–1361 (2005).

  11. 11.

    , , & Clonal selection of helper T cells is determined by an affinity threshold with no further skewing of TCR binding properties. Immunity 21, 669–679 (2004).

  12. 12.

    & T cell affinity maturation by selective expansion during infection. J. Exp. Med. 189, 701–710 (1999).

  13. 13.

    , & A kinetic basis for T cell receptor repertoire selection during an immune response. Immunity 10, 485–492 (1999).

  14. 14.

    , & Selective expansion of high- or low-avidity cytotoxic T lymphocytes and efficacy for adoptive immunotherapy. Proc. Natl. Acad. Sci. USA 93, 4102–4107 (1996).

  15. 15.

    et al. Control of viremia in simian immunodeficiency virus infection by CD8+ lymphocytes. Science 283, 857–860 (1999).

  16. 16.

    et al. Memory CD8+ T cells are required for protection from persistent hepatitis C virus infection. J. Exp. Med. 197, 1645–1655 (2003).

  17. 17.

    , & Complete but curtailed T-cell response to very low-affinity antigen. Nature 458, 211–214 (2009).

  18. 18.

    et al. T cell affinity regulates asymmetric division, effector cell differentiation, and tissue pathology. Immunity 37, 709–720 (2012).

  19. 19.

    et al. Apoptosis threshold set by Noxa and Mcl-1 after T cell activation regulates competitive selection of high-affinity clones. Immunity 32, 754–765 (2010).

  20. 20.

    et al. Different T cell receptor signals determine CD8+ memory versus effector development. Science 323, 502–505 (2009).

  21. 21.

    et al. T-cell receptor signals direct the composition and function of the memory CD8+ T-cell pool. Blood 116, 5548–5559 (2010).

  22. 22.

    et al. Strength of stimulus and clonal competition impact the rate of memory CD8 T cell differentiation. J. Immunol. 179, 6704–6714 (2007).

  23. 23.

    et al. Requirement for the transcription factor LSIRF/IRF4 for mature B and T lymphocyte function. Science 275, 540–543 (1997).

  24. 24.

    et al. Transcription factor IRF4 controls plasma cell differentiation and class-switch recombination. Nat. Immunol. 7, 773–782 (2006).

  25. 25.

    et al. Graded expression of interferon regulatory factor-4 coordinates isotype switching with plasma cell differentiation. Immunity 25, 225–236 (2006).

  26. 26.

    et al. Regulatory T-cell suppressor program co-opts transcription factor IRF4 to control T(H)2 responses. Nature 458, 351–356 (2009).

  27. 27.

    et al. The transcription factors Blimp-1 and IRF4 jointly control the differentiation and function of effector regulatory T cells. Nat. Immunol. 12, 304–311 (2011).

  28. 28.

    et al. The development of inflammatory TH-17 cells requires interferon-regulatory factor 4. Nat. Immunol. 8, 958–966 (2007).

  29. 29.

    et al. Interferon-regulatory factor 4 is essential for the developmental program of T helper 9 cells. Immunity 33, 192–202 (2010).

  30. 30.

    et al. Analysis of interleukin-21-induced Prdm1 gene regulation reveals functional cooperation of STAT3 and IRF4 transcription factors. Immunity 31, 941–952 (2009).

  31. 31.

    & Modelling T-cell memory by genetic marking of memory T cells in vivo. Nature 399, 593–597 (1999).

  32. 32.

    et al. Constitutive Bcl-2 expression throughout the hematopoietic compartment affects multiple lineages and enhances progenitor cell survival. Proc. Natl. Acad. Sci. USA 96, 14943–14948 (1999).

  33. 33.

    et al. Proapoptotic Bcl-2 relative Bim required for certain apoptotic responses, leukocyte homeostasis, and to preclude autoimmunity. Science 286, 1735–1738 (1999).

  34. 34.

    et al. T cell receptor antagonist peptides induce positive selection. Cell 76, 17–27 (1994).

  35. 35.

    et al. Unlike CD4+ T-cell help, CD28 costimulation is necessary for effective primary CD8+ T-cell influenza-specific immunity. Eur. J. Immunol. 42, 1744–1754 (2012).

  36. 36.

    et al. TCR affinity and negative regulation limit autoimmunity. Nat. Med. 10, 1234–1239 (2004).

  37. 37.

    et al. Runx3 and T-box proteins cooperate to establish the transcriptional program of effector CTLs. J. Exp. Med. 206, 51–59 (2009).

  38. 38.

    et al. Foxo1 links homing and survival of naive T cells by regulating L-selectin, CCR7 and interleukin 7 receptor. Nat. Immunol. 10, 176–184 (2009).

  39. 39.

    , , & Transcription factor Foxo1 represses T-bet-mediated effector functions and promotes memory CD8+ T cell differentiation. Immunity 36, 374–387 (2012).

  40. 40.

    et al. PDK1 regulation of mTOR and hypoxia-inducible factor 1 integrate metabolism and migration of CD8+ T cells. J. Exp. Med. 209, 2441–2453 (2012).

  41. 41.

    et al. BATF-JUN is critical for IRF4-mediated transcription in T cells. Nature 490, 543–546 (2012).

  42. 42.

    et al. A validated regulatory network for Th17 cell specification. Cell 151, 289–303 (2012).

  43. 43.

    et al. A genomic regulatory element that directs assembly and function of immune-specific AP-1-IRF complexes. Science 338, 975–980 (2012).

  44. 44.

    , & Metabolic regulation of T lymphocytes. Annu. Rev. Immunol. 31, 259–283 (2013).

  45. 45.

    & Metabolism, migration and memory in cytotoxic T cells. Nat. Rev. Immunol. 11, 109–117 (2011).

  46. 46.

    & Metabolic switching and fuel choice during T-cell differentiation and memory development. Immunol. Rev. 249, 27–42 (2012).

  47. 47.

    & Metabolic checkpoints in activated T cells. Nat. Immunol. 13, 907–915 (2012).

  48. 48.

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

  49. 49.

    et al. Posttranscriptional control of T cell effector function by aerobic glycolysis. Cell 153, 1239–1251 (2013).

  50. 50.

    et al. FoxOs are critical mediators of hematopoietic stem cell resistance to physiologic oxidative stress. Cell 128, 325–339 (2007).

  51. 51.

    et al. Plasma cell ontogeny defined by quantitative changes in Blimp-1 expression. J. Exp. Med. 200, 967–977 (2004).

  52. 52.

    et al. Virus-specific CD8+ T cells in primary and secondary influenza pneumonia. Immunity 8, 683–691 (1998).

  53. 53.

    , , & A previously unrecognized H-2Db-restricted peptide prominent in the primary influenza A virus-specific CD8+ T-cell response is much less apparent following secondary challenge. J. Virol. 74, 3486–3493 (2000).

  54. 54.

    et al. Enhanced phosphoinositide 3-kinase(p110α) activity prevents diabetes-induced cardiomyopathy and superoxide generation in a mouse model of diabetes. Diabetologia 55, 3369–3381 (2012).

  55. 55.

    et al. Multiparameter metabolic analysis reveals a close link between attenuated mitochondrial bioenergetic function and enhanced glycolysis dependency in human tumor cells. Am. J. Physiol. Cell Physiol. 292, C125–C136 (2007).

  56. 56.

    , & The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res. 41, e108 (2013).

  57. 57.

    et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

  58. 58.

    et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5, R80 (2004).

  59. 59.

    in Bioinformatics and Computational Biology Solutions using R and Bioconductor (eds. Gentleman, R., Carey, V., Dudoit, S., Irizarry R. & Huber, W.) 397–420 (Springer, New York, 2005).

  60. 60.

    Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3 issue 1, article 3 (2004).

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Acknowledgements

We thank T.W. Mak (Campbell Family Cancer Research Institute) for Irf4−/− mice; U. Klein (Columbia University) for mice with loxP-flanked Irf4 alleles; S.M. Kaech (Yale University School of Medicine) for mice expressing Cre under the control of Gzmb; P. Bouillet (The Walter and Eliza Hall Institute) for Bim−/− mice; S. Cory (The Walter and Eliza Hall Institute) for mice with transgenic overexpression of Bcl-2 in all hematopoietic cells (Vav-Bcl2tg mice); D. Zehn (Swiss Vaccine Research Institute) for OVA-expressing L. monocytogenes variants; A.M. Lew (The Walter and Eliza Hall Institute) for HKx31-OVA; S. Sterle, R. Cole, N. Iannarella and L. Mackiewicz for technical support; and D. Segal, P.D. Hodgkin, L.M. Corcoran, S. Heinzel, F. Masson (all The Walter and Eliza Hall Institute) and C. Palmer (Burnet Institute) for antibodies and discussions. Supported by the National Health and Medical Research Council of Australia (M.P., G.T.B., G.K.S., S.L.N., M.A.F. and A.K.), the Sylvia and Charles Viertel Foundation (A.K. and G.T.B.), the Howard Hughes Medical Institute (G.T.B.), the Australian Research Council (S.L.N. and A.K.), the National Heart Foundation (D.C.H.) and The Walter and Eliza Hall Institute Genomics Fund (A.K., G.T.B. and W.S.), the Victorian State Government Operational Infrastructure Support and Australian Government National Health and Medical Research Council Independent Research Institute Infrastructure Support scheme.

Author information

Affiliations

  1. The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.

    • Kevin Man
    • , Maria Miasari
    • , Wei Shi
    • , Annie Xin
    • , Simon Preston
    • , Marc Pellegrini
    • , Gabrielle T Belz
    • , Gordon K Smyth
    • , Stephen L Nutt
    •  & Axel Kallies
  2. The Department of Medical Biology, University of Melbourne, Parkville, Australia.

    • Kevin Man
    • , Maria Miasari
    • , Annie Xin
    • , Simon Preston
    • , Marc Pellegrini
    • , Gabrielle T Belz
    • , Stephen L Nutt
    •  & Axel Kallies
  3. The Department of Computing and Information Systems, University of Melbourne, Parkville, Australia.

    • Wei Shi
  4. Cellular and Molecular Metabolism Laboratory, Baker IDI Heart and Diabetes Institute, Melbourne, Australia.

    • Darren C Henstridge
    •  & Mark A Febbraio
  5. The Department of Mathematics and Statistics, University of Melbourne, Parkville, Australia.

    • Gordon K Smyth

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Contributions

K.M. designed, did and analyzed most experiments; M.M., A.X. and D.C.H. did and analyzed experiments; S.P. and M.P. did and designed LCMV experiments; W.S. and G.K.S. did the bioinformatics analysis; G.T.B., M.A.F. and S.L.N. helped to design experiments and write the manuscript; and A.K. oversaw and designed the study and experiments, analyzed data and wrote the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Axel Kallies.

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DOI

https://doi.org/10.1038/ni.2710

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