Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Dynamic regulatory network controlling TH17 cell differentiation

Nature volume 496, pages 461–468 (2013)Cite this article

Subjects

Abstract

Despite their importance, the molecular circuits that control the differentiation of naive T cells remain largely unknown. Recent studies that reconstructed regulatory networks in mammalian cells have focused on short-term responses and relied on perturbation-based approaches that cannot be readily applied to primary T cells. Here we combine transcriptional profiling at high temporal resolution, novel computational algorithms, and innovative nanowire-based perturbation tools to systematically derive and experimentally validate a model of the dynamic regulatory network that controls the differentiation of mouse TH17 cells, a proinflammatory T-cell subset that has been implicated in the pathogenesis of multiple autoimmune diseases. The TH17 transcriptional network consists of two self-reinforcing, but mutually antagonistic, modules, with 12 novel regulators, the coupled action of which may be essential for maintaining the balance between TH17 and other CD4+ T-cell subsets. Our study identifies and validates 39 regulatory factors, embeds them within a comprehensive temporal network and reveals its organizational principles; it also highlights novel drug targets for controlling TH17 cell differentiation.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Genome-wide temporal expression profiles of T H 17 differentiation.
Figure 2: A model of the dynamic regulatory network of T H 17 differentiation.
Figure 3: Knockdown screen in T H 17 differentiation using silicon nanowires.
Figure 4: Coupled and mutually antagonistic modules in the T H 17 network.
Figure 5: Mina, Fas, Pou2af1 and Tsc22d3 are key novel regulators affecting the T H 17 differentiation programs.

Similar content being viewed by others

Accession codes

Primary accessions

Gene Expression Omnibus

Data deposits

The microarray, RNA-seq and ChIP-seq data sets have been deposited in the Gene Expression Omnibus database under accession numbers GSE43955, GSE43969, GSE43948 and GSE43949.

References

  1. Bettelli, E., Oukka, M. & Kuchroo, V. K. TH-17 cells in the circle of immunity and autoimmunity. Nature Immunol. 8, 345–350 (2007)

    CAS  Google Scholar 

  2. Zhou, L. et al. TGF-β-induced Foxp3 inhibits TH17 cell differentiation by antagonizing RORγt function. Nature 453, 236–240 (2008)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  3. O’Shea, J. et al. Signal transduction and Th17 cell differentiation. Microbes Infect. 11, 599–611 (2009)

    PubMed  PubMed Central  Google Scholar 

  4. Zhou, L. & Littman, D. Transcriptional regulatory networks in Th17 cell differentiation. Curr. Opin. Immunol. 21, 146–152 (2009)

    PubMed  PubMed Central  Google Scholar 

  5. Korn, T., Bettelli, E., Oukka, M. & Kuchroo, V. K. IL-17 and Th17 cells. Annu. Rev. Immunol. 27, 485–517 (2009)

    CAS  PubMed  Google Scholar 

  6. Amit, I. et al. Unbiased reconstruction of a mammalian transcriptional network mediating pathogen responses. Science 326, 257–263 (2009)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  7. Novershtern, N. et al. Densely interconnected transcriptional circuits control cell states in human hematopoiesis. Cell 144, 296–309 (2011)

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Litvak, V. et al. Function of C/EBPδ in a regulatory circuit that discriminates between transient and persistent TLR4-induced signals. Nature Immunol. 10, 437–443 (2009)

    CAS  Google Scholar 

  9. Suzuki, H. et al. The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line. Nature Genet. 41, 553–562 (2009)

    CAS  PubMed  Google Scholar 

  10. Shalek, A. K. et al. Vertical silicon nanowires as a universal platform for delivering biomolecules into living cells. Proc. Natl Acad. Sci. USA 107, 1870–1875 (2010)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  11. Lee, Y. et al. Induction and molecular signature of pathogenic TH17 cells. Nature Immunol. 13, 991–999 (2012)

    CAS  Google Scholar 

  12. Linhart, C., Halperin, Y. & Shamir, R. Transcription factor and microRNA motif discovery: the Amadeus platform and a compendium of metazoan target sets. Genome Res. 18, 1180–1189 (2008)

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Zheng, G. et al. ITFP: an integrated platform of mammalian transcription factors. Bioinformatics 24, 2416–2417 (2008)

    CAS  PubMed  Google Scholar 

  14. Wilson, N. K. et al. Combinatorial transcriptional control in blood stem/progenitor cells: genome-wide analysis of ten major transcriptional regulators. Cell Stem Cell 7, 532–544 (2010)

    CAS  PubMed  Google Scholar 

  15. Lachmann, A. et al. ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments. Bioinformatics 26, 2438–2444 (2010)

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011)

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Jiang, C., Xuan, Z., Zhao, F. & Zhang, M. TRED: a transcriptional regulatory element database, new entries and other development. Nucleic Acids Res. 35, D137–D140 (2007)

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Elkon, R., Linhart, C., Sharan, R., Shamir, R. & Shiloh, Y. Genome-wide in silico identification of transcriptional regulators controlling the cell cycle in human cells. Genome Res. 13, 773–780 (2003)

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Heng, T. S. & Painter, M. W. The Immunological Genome Project: networks of gene expression in immune cells. Nature Immunol. 9, 1091–1094 (2008)

    CAS  Google Scholar 

  20. Schwanhäusser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011)

    ADS  PubMed  Google Scholar 

  21. Dardalhon, V. et al. Lentivirus-mediated gene transfer in primary T cells is enhanced by a central DNA flap. Gene Ther. 8, 190–198 (2001)

    CAS  PubMed  Google Scholar 

  22. McManus, M. et al. Small interfering RNA-mediated gene silencing in T lymphocytes. J. Immunol. 169, 5754 (2002)

    CAS  PubMed  Google Scholar 

  23. Shalek, A. K. et al. Nanowire-mediated delivery enables functional interrogation of primary immune cells: application to the analysis of chronic lymphocytic leukemia. Nano Lett. 12, 6498–6504 (2012)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  24. Segrè, D., Deluna, A., Church, G. M. & Kishony, R. Modular epistasis in yeast metabolism. Nature Genet. 37, 77–83 (2005)

    PubMed  Google Scholar 

  25. Peleg, T., Yosef, N., Ruppin, E. & Sharan, R. Network-free inference of knockout effects in yeast. PLOS Comput. Biol. 6, e1000635 (2010)

    ADS  MathSciNet  PubMed  PubMed Central  Google Scholar 

  26. Marson, A. et al. Foxp3 occupancy and regulation of key target genes during T-cell stimulation. Nature 445, 931–935 (2007)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  27. Zheng, Y. et al. Genome-wide analysis of Foxp3 target genes in developing and mature regulatory T cells. Nature 445, 936–940 (2007)

    ADS  CAS  PubMed  Google Scholar 

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

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

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

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  31. Okamoto, M. et al. Mina, an Il4 repressor, controls T helper type 2 bias. Nature Immunol. 10, 872–879 (2009)

    CAS  Google Scholar 

  32. Hill, J. A. et al. Foxp3 transcription-factor-dependent and -independent regulation of the regulatory T cell transcriptional signature. Immunity 27, 786–800 (2007)

    CAS  PubMed  Google Scholar 

  33. Korn, T. et al. IL-21 initiates an alternative pathway to induce proinflammatory TH17 cells. Nature 448, 484–487 (2007)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  34. Waldner, H., Sobel, R. A., Howard, E. & Kuchroo, V. K. Fas- and FasL-deficient mice are resistant to induction of autoimmune encephalomyelitis. J. Immunol. 159, 3100–3103 (1997)

    CAS  PubMed  Google Scholar 

  35. Teitell, M. A. OCA-B regulation of B-cell development and function. Trends Immunol. 24, 546–553 (2003)

    CAS  PubMed  Google Scholar 

  36. Laurence, A. et al. Interleukin-2 signaling via STAT5 constrains T helper 17 cell generation. Immunity 26, 371–381 (2007)

    CAS  PubMed  Google Scholar 

  37. Choi, S.-J. et al. Tsc-22 enhances TGF-β signaling by associating with Smad4 and induces erythroid cell differentiation. Mol. Cell. Biochem. 271, 23–28 (2005)

    CAS  PubMed  Google Scholar 

  38. Peters, A., Lee, Y. & Kuchroo, V. K. The many faces of Th17 cells. Curr. Opin. Immunol. 23, 702–706 (2011)

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Chaudhry, A. et al. Interleukin-10 signaling in regulatory T cells is required for suppression of Th17 cell-mediated inflammation. Immunity 34, 566–578 (2011)

    CAS  PubMed  PubMed Central  Google Scholar 

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

    ADS  CAS  PubMed  Google Scholar 

  41. Jing, Y. et al. A mechanistic study on the effect of dexamethasone in moderating cell death in Chinese Hamster Ovary cell cultures. Biotechnol. Prog. 28, 490–496 (2012)

    CAS  PubMed  Google Scholar 

  42. Hu, S. M., Luo, Y. L., Lai, W. Y. & Chen, P. F. Effects of dexamethasone on intracellular expression of Th17 cytokine interleukin 17 in asthmatic mice [in Chinese]. Nan Fang Yi Ke Da Xue Xue Bao 29, 1185–1188 (2009)

    CAS  PubMed  Google Scholar 

  43. Yang, X. P. et al. Opposing regulation of the locus encoding IL-17 through direct, reciprocal actions of STAT3 and STAT5. Nature Immunol. 12, 247–254 (2011)

    CAS  Google Scholar 

  44. Jostins, L. et al. Host–microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491, 119–124 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Wu, C. et al. Induction of pathogenic TH17 cells by inducible salt-sensing kinase SGK1. Nature http://dx.doi.org/10.1038/nature11984 (2013)

  46. Kim, U. et al. The B-cell-specific transcription coactivator OCA-B/OBF-1/Bob-1 is essential for normal production of immunoglobulin isotypes. Nature 383, 542–547 (1996)

    ADS  CAS  PubMed  Google Scholar 

  47. Wang, V. E., Tantin, D., Chen, J. & Sharp, P. A. B cell development and immunoglobulin transcription in Oct-1-deficient mice. Proc. Natl Acad. Sci. USA 101, 2005–2010 (2004)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  48. Bettelli, E. et al. Reciprocal developmental pathways for the generation of pathogenic effector TH17 and regulatory T cells. Nature 441, 235–238 (2006)

    ADS  CAS  PubMed  Google Scholar 

  49. Awasthi, A. et al. A dominant function for interleukin 27 in generating interleukin 10-producing anti-inflammatory T cells. Nature Immunol. 8, 1380–1389 (2007)

    CAS  Google Scholar 

  50. Awasthi, A. et al. Cutting edge: IL-23 receptor gfp reporter mice reveal distinct populations of IL-17-producing cells. J. Immunol. 182, 5904–5908 (2009)

    CAS  PubMed  Google Scholar 

  51. 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 

  52. Reich, M. et al. GenePattern 2.0. Nature Genet. 38, 500–501 (2006)

    CAS  PubMed  Google Scholar 

  53. Storey, J., Xiao, W., Leek, J., Tompkins, R. & Davis, R. Significance analysis of time course microarray experiments. Proc. Natl Acad. Sci. USA 102, 12837–12482 (2005)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  54. Leek, J. T., Monsen, E., Dabney, A. R. & Storey, J. D. EDGE: extraction and analysis of differential gene expression. Bioinformatics 22, 507–508 (2006)

    CAS  PubMed  Google Scholar 

  55. Chechik, G. & Koller, D. Timing of gene expression responses to environmental changes. J. Comput. Biol. 16, 279–290 (2009)

    CAS  PubMed  Google Scholar 

  56. Schraml, B. U. et al. The AP-1 transcription factor Batf controls TH17 differentiation. Nature 460, 405–409 (2009)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  57. Shi, L. Z. et al. HIF1α-dependent glycolytic pathway orchestrates a metabolic checkpoint for the differentiation of TH17 and Treg cells. J. Exp. Med. 208, 1367–1376 (2011)

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Durant, L. et al. Diverse targets of the transcription factor STAT3 contribute to T cell pathogenicity and homeostasis. Immunity 32, 605–615 (2010)

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Jux, B., Kadow, S. & Esser, C. Langerhans cell maturation and contact hypersensitivity are impaired in aryl hydrocarbon receptor-null mice. J. Immunol. 182, 6709–6717 (2009)

    CAS  PubMed  Google Scholar 

  60. Xiao, S. et al. Retinoic acid increases Foxp3+ regulatory T cells and inhibits development of Th17 cells by enhancing TGF-β-driven Smad3 signaling and inhibiting IL-6 and IL-23 receptor expression. J. Immunol. 181, 2277–2284 (2008)

    CAS  PubMed  Google Scholar 

  61. Wilkins, M. R. et al. Protein identification and analysis tools in the ExPASy server. Methods Mol. Biol. 112, 531–552 (1999)

    CAS  PubMed  Google Scholar 

  62. Durant, L. et al. Diverse targets of the transcription factor STAT3 contribute to T cell pathogenicity and homeostasis. Immunity 32, 605–615 (2010)

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Huh, J. R. et al. Digoxin and its derivatives suppress TH17 cell differentiation by antagonizing RORγt activity. Nature 472, 486–490 (2011)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  64. Sundrud, M. S. et al. Halofuginone inhibits TH17 cell differentiation by activating the amino acid starvation response. Science 324, 1334–1338 (2009)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  65. Chevrier, N. et al. Systematic discovery of TLR signaling components delineates viral-sensing circuits. Cell 147, 853–867 (2011)

    CAS  PubMed  Google Scholar 

  66. Geiss, G. K. et al. Direct multiplexed measurement of gene expression with color-coded probe pairs. Nature Biotechnol. 26, 317–325 (2008)

    CAS  Google Scholar 

  67. Ram, O. et al. Combinatorial patterning of chromatin regulators uncovered by genome-wide location analysis in human cells. Cell 147, 1628–1639 (2011)

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank L. Gaffney and L. Solomon for artwork, the Broad’s Genomics Platform for sequencing, and D. Kozoriz for cell sorting. Work was supported by NHGRI (1P50HG006193-01 to H.P. and A.R.), NIH Pioneer Awards (5DP1OD003893-03 to H.P., DP1OD003958-01 to A.R.), NIH (NS 30843, NS045937, AI073748 and AI45757 to V.K.K.), National MS Society (RG2571 to V.K.K.), HHMI (A.R.), and the Klarman Cell Observatory (A.R.).

Author information

Author notes

  1. Amit Awasthi

    Present address: Present address: Translational Health Science & Technology Institute, Faridabad, Haryana 122016, India.,

  2. Nir Yosef, Alex K. Shalek and Jellert T. Gaublomme: These authors contributed equally to this work.

Authors and Affiliations

  1. Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, 02142, Massachusetts, USA

    Nir Yosef, David Gennert, Rahul Satija, Diana Y. Lu, John J. Trombetta, Hongkun Park, Vijay K. Kuchroo & Aviv Regev

  2. Center for Neurologic Diseases, Brigham & Women’s Hospital, Harvard Medical School, Boston, 02115, Massachusetts, USA

    Nir Yosef, Hulin Jin, Youjin Lee, Amit Awasthi, Chuan Wu, Katarzyna Karwacz, Sheng Xiao & Vijay K. Kuchroo

  3. Department of Chemistry and Chemical Biology and Department of Physics, Harvard University, Cambridge, 02138, Massachusetts, USA

    Alex K. Shalek, Jellert T. Gaublomme, Marsela Jorgolli & Hongkun Park

  4. Department of Pathology, University of Utah School of Medicine, Salt Lake City, 84132, Utah, USA

    Arvind Shakya & Dean Tantin

  5. Department of Immunology, St. Jude Children's Research Hospital, Memphis, 38105, Tennessee, USA

    Meenu R. Pillai & Mark Bix

  6. University of Oxford, Headington Campus, Oxford OX3 7BN, UK,

    Peter J. Ratcliffe & Mathew L. Coleman

  7. Department of Biology, Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, 02140, Massachusetts, USA

    Aviv Regev

Authors
  1. Nir Yosef
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alex K. Shalek
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jellert T. Gaublomme
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hulin Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Youjin Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Amit Awasthi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chuan Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Katarzyna Karwacz
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Sheng Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Marsela Jorgolli
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. David Gennert
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Rahul Satija
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Arvind Shakya
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Diana Y. Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. John J. Trombetta
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Meenu R. Pillai
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Peter J. Ratcliffe
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Mathew L. Coleman
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Mark Bix
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Dean Tantin
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Hongkun Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Vijay K. Kuchroo
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Aviv Regev
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

N.Y., A.K.S., J.T.G., H.P., V.K.K. and A.R. conceived the study and designed experiments. N.Y. developed computational methods. N.Y., A.K.S. and J.T.G. analysed the data. A.K.S., J.T.G., H.J., Y.L., A.A., C.W., K.K., S.X., M.J., D.G., R.S., D.Y.L. and J.J.T. conducted the experiments. A.S., M.R.P., P.J.R., M.L.C., M.B. and D.T. provided knockout mice. N.Y., A.K.S., J.T.G., V.K.K., H.P. and A.R. wrote the paper with input from all the authors.

Corresponding authors

Correspondence to Hongkun Park, Vijay K. Kuchroo or Aviv Regev.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Information

This file contains Supplementary Notes, Supplementary Methods, Supplementary Figures 1-12, legends for Supplementary Tables 1-8 (see separate excel files) and Supplementary References. (PDF 1823 kb)

Supplementary Table 1

This file contains a list of microarray probesets that were differentially expressed in the TGF-β1+Il6 microarray data - see Supplementary Information file for full legend. (XLS 323 kb)

Supplementary Table 2

This file shows the functional enrichments for expression clusters -see Supplementary Information file for full legend. (XLS 1034 kb)

Supplementary Table 3

This file shows the regulatory interactions in the three canonical temporal networks (Early, Intermediate, and Late) - see Supplementary Information file for full legend. (XLS 2258 kb)

Supplementary Table 4

This file contains a table of ranked regulators of Th17 differentiation - see Supplementary Information file for full legend. (XLS 80 kb)

Supplementary Table 5

This file contains the results of Nanostring nCounter and Fluidigm analysis - see Supplementary Information file for full legend. (XLS 191 kb)

Supplementary Table 6

This file contains the Primers for Nanostring STA and qRT-PCR/Fluidigm and siRNA sequences - see Supplementary Information file for full legend. (XLS 153 kb)

Supplementary Table 7

This file shows the RNA-seq data analysis - see Supplementary Information file for full legend. (XLS 1539 kb)

Supplementary Table 8

This file shows the Tsc22d3 ChIP-seq data analysis - see Supplementary Information file for full legend. (XLS 108 kb)

PowerPoint slides

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yosef, N., Shalek, A., Gaublomme, J. et al. Dynamic regulatory network controlling TH17 cell differentiation. Nature 496, 461–468 (2013). https://doi.org/10.1038/nature11981

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature11981

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing