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.

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Accessions

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.

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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

    • Nir Yosef
    • , Alex K. Shalek
    •  & Jellert T. Gaublomme

    These authors contributed equally to this work.

    • Amit Awasthi

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

Affiliations

  1. Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, Massachusetts 02142, 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, Massachusetts 02115, 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, Massachusetts 02138, USA

    • Alex K. Shalek
    • , Jellert T. Gaublomme
    • , Marsela Jorgolli
    •  & Hongkun Park
  4. Department of Pathology, University of Utah School of Medicine, Salt Lake City, Utah 84132, USA

    • Arvind Shakya
    •  & Dean Tantin
  5. Department of Immunology, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, USA

    • Meenu R. Pillai
    •  & Mark Bix
  6. University of Oxford, Headington Campus, Oxford OX3 7BN, UK

    • Peter J. Ratcliffe
    •  & Mathew L. Coleman
  7. Howard Hughes Medical Institute, Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts 02140, USA

    • Aviv Regev

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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.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

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

Supplementary information

PDF files

  1. 1.

    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.

Excel files

  1. 1.

    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.

  2. 2.

    Supplementary Table 2

    This file shows the functional enrichments for expression clusters -see Supplementary Information file for full legend.

  3. 3.

    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.

  4. 4.

    Supplementary Table 4

    This file contains a table of ranked regulators of Th17 differentiation - see Supplementary Information file for full legend.

  5. 5.

    Supplementary Table 5

    This file contains the results of Nanostring nCounter and Fluidigm analysis - see Supplementary Information file for full legend.

  6. 6.

    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.

  7. 7.

    Supplementary Table 7

    This file shows the RNA-seq data analysis - see Supplementary Information file for full legend.

  8. 8.

    Supplementary Table 8

    This file shows the Tsc22d3 ChIP-seq data analysis - see Supplementary Information file for full legend.

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DOI

https://doi.org/10.1038/nature11981

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