Resource | Published:

Identification of transcriptional regulators in the mouse immune system

Nature Immunology volume 14, pages 633643 (2013) | Download Citation

Abstract

The differentiation of hematopoietic stem cells into cells of the immune system has been studied extensively in mammals, but the transcriptional circuitry that controls it is still only partially understood. Here, the Immunological Genome Project gene-expression profiles across mouse immune lineages allowed us to systematically analyze these circuits. To analyze this data set we developed Ontogenet, an algorithm for reconstructing lineage-specific regulation from gene-expression profiles across lineages. Using Ontogenet, we found differentiation stage–specific regulators of mouse hematopoiesis and identified many known hematopoietic regulators and 175 previously unknown candidate regulators, as well as their target genes and the cell types in which they act. Among the previously unknown regulators, we emphasize the role of ETV5 in the differentiation of γδ T cells. As the transcriptional programs of human and mouse cells are highly conserved, it is likely that many lessons learned from the mouse model apply to humans.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Accessions

Primary accessions

Gene Expression Omnibus

References

  1. 1.

    et al. The Immunological Genome Project: networks of gene expression in immune cells. Nat. Immunol. 9, 1091–1094 (2008).

  2. 2.

    & Myeloid lineage commitment from the hematopoietic stem cell. Immunity 26, 726–740 (2007).

  3. 3.

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

  4. 4.

    et al. Conservation and divergence in the transcriptional programs of the human and mouse immune systems. Proc. Natl. Acad. Sci. USA 110, 2946–2951 (2013).

  5. 5.

    , , & Transcriptional regulatory circuits: predicting numbers from alphabets. Science 325, 429–432 (2009).

  6. 6.

    et al. Learning a prior on regulatory potential from eQTL data. PLoS Genet. 5, e1000358 (2009).

  7. 7.

    et al. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat. Genet. 34, 166–176 (2003).

  8. 8.

    et al. Structure and function of a transcriptional network activated by the MAPK Hog1. Nat. Genet. 40, 1300–1306 (2008).

  9. 9.

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

  10. 10.

    et al. Deciphering the transcriptional network of the dendritic cell lineage. Nat. Immunol. 13, 888–899 (2012).

  11. 11.

    & Regularization and variable selection via the elastic net. J. R. Stat. Soc. Series B Stat. Methodol. 67, 301–320 (2005).

  12. 12.

    et al. IFN regulatory factor-4 and -8 govern dendritic cell subset development and their functional diversity. J. Immunol. 174, 2573–2581 (2005).

  13. 13.

    & SnapShot: hematopoiesis. Cell 132, 712.e711–712.e712 (2008).

  14. 14.

    , , , & VEGF and IHH rescue definitive hematopoiesis in Gata-4 and Gata-6–deficient murine embryoid bodies. Exp. Hematol. 37, 1038–1053 (2009).

  15. 15.

    et al. Lineage-specific regulation of the murine RAG-2 promoter: GATA-3 in T cells and Pax-5 in B cells. Blood 95, 3845–3852 (2000).

  16. 16.

    , , & ICER/CREM-mediated transcriptional attenuation of IL-2 and its role in suppression by regulatory T cells. Eur. J. Immunol. 37, 884–895 (2007).

  17. 17.

    et al. Comprehensive methylome map of lineage commitment from haematopoietic progenitors. Nature 467, 338–342 (2010).

  18. 18.

    et al. DACH1 regulates cell cycle progression of myeloid cells through the control of cyclin D, Cdk 4/6 and p21Cip1. Biochem. Biophys. Res. Commun. 420, 91–95 (2012).

  19. 19.

    , , , & Virus-induced differential expression of nuclear receptors and coregulators in dendritic cells: Implication to interferon production. FEBS Lett. 585, 1331–1337 (2011).

  20. 20.

    et al. Inhibition of activation-induced death of dendritic cells and enhancement of vaccine efficacy via blockade of MINOR. Blood 113, 2906–2913 (2009).

  21. 21.

    et al. Differential expression of Rel/NF-κB and octamer factors is a hallmark of the generation and maturation of dendritic cells. Blood 95, 277–285 (2000).

  22. 22.

    et al. KLF13 influences multiple stages of both B and T cell development. Cell Cycle 7, 2047–2055 (2008).

  23. 23.

    , , & Transcription factor ELF4 controls the proliferation and homing of CD8+ T cells via the Kruppel-like factors KLF4 and KLF2. Nat. Immunol. 10, 618–626 (2009).

  24. 24.

    et al. Intrathymic programming of effector fates in three molecularly distinct γδ T cell subtypes. Nat. Immunol. 13, 511–518 (2012).

  25. 25.

    & Impulse control: temporal dynamics in gene transcription. Cell 144, 886–896 (2011).

  26. 26.

    et al. Single-cell Mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–696 (2011).

  27. 27.

    , & Superparamagnetic clustering of data. Phys. Rev. Lett. 76, 3251–3254 (1996).

  28. 28.

    & Clustering by passing messages between data points. Science 315, 972–976 (2007).

  29. 29.

    et al. TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes. Nucleic Acids Res. 34, D108–D110 (2006).

  30. 30.

    , , , & JASPAR: an open-access database for eukaryotic transcription factor binding profiles. Nucleic Acids Res. 32 (suppl. 1), D91–D94 (2004).

  31. 31.

    et al. Diversity and complexity in DNA recognition by transcription factors. Science 324, 1720–1723 (2009).

  32. 32.

    et al. Variation in homeodomain DNA binding revealed by high-resolution analysis of sequence preferences. Cell 133, 1266–1276 (2008).

  33. 33.

    , , , & Sparsity and smoothness via the fused lasso. J. R. Stat. Soc. Series B Stat. Methodol. 67, 91–108 (2005).

  34. 34.

    & in Convex Optimization, Ch 11.7 (Cambridge University, Cambridge, UK, 2004).

  35. 35.

    et al. EnsemblCompara GeneTrees: complete, duplication-aware phylogenetic trees in vertebrates. Genome Res. 19, 327–335 (2009).

  36. 36.

    et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).

  37. 37.

    , , & FGF-regulated Etv genes are essential for repressing Shh expression in mouse limb buds. Dev. Cell 16, 607–613 (2009).

Download references

Acknowledgements

We thank the ImmGen core team (including M. Painter and S. Davis) for help with data generation and processing; eBioscience, Affymetrix and Expression Analysis for support of ImmGen; L. Gaffney for help with figure preparation and layout of the lineage tree; S. Hart for initial layout of the lineage tree; and A. Liberzon (Molecular Signatures Database) for the positional gene sets for mouse. Supported by National Institute of Allergy and Infectious Diseases (R24 AI072073 to the ImmGen Consortium), the US National Institutes of Health (A.R.; and U54-CA149145 and 149644.0103 to V.J. and D.K.), the Burroughs Wellcome Fund (A.R.), the Klarman Cell Observatory (A.R.), the Howard Hughes Medical Institute (A.R.), the Merkin Foundation for Stem Cell Research at the Broad Institute (A.R.) and the National Science Foundation (DBI-0345474 to V.J. and D.K.).

Author information

Author notes

    • Vladimir Jojic
    •  & Tal Shay

    These authors contributed equally to this work.

    • Aviv Regev
    •  & Daphne Koller

    These authors jointly directed this work.

Affiliations

  1. Computer Science Department, Stanford University, Stanford, California, USA.

    • Vladimir Jojic
    •  & Daphne Koller
  2. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.

    • Tal Shay
    • , Or Zuk
    •  & Aviv Regev
  3. Department of Pathology, University of Massachusetts Medical School, Worcester, Massachusetts USA.

    • Katelyn Sylvia
    • , Joonsoo Kang
    •  & Nidhi Malhotra
  4. Laboratory of Genetics, University of Wisconsin-Madison, Madison, Wisconsin, USA.

    • Xin Sun
  5. Howard Hughes Medical Institute, Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

    • Aviv Regev
  6. Division of Biological Sciences, University of California San Diego, La Jolla, California, USA.

    • Adam J Best
    • , Jamie Knell
    •  & Ananda Goldrath
  7. Broad Institute and Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

  8. Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital, Boston, Massachusetts, USA.

    • Nadia Cohen
    • , Patrick Brennan
    •  & Michael Brenner
  9. Joslin Diabetes Center, Boston, Massachusetts, USA.

    • Francis Kim
    • , Tata Nageswara Rao
    •  & Amy Wagers
  10. Division of Immunology, Department of Microbiology & Immunobiology, Harvard Medical School, Boston, Massachusetts, USA.

    • Tracy Heng
    • , Jeffrey Ericson
    • , Katherine Rothamel
    • , Adriana Ortiz-Lopez
    • , Diane Mathis
    •  & Christophe Benoist
  11. Department of Microbiology & Immunology, University of California San Francisco, San Francisco, California, USA.

    • Natalie A Bezman
    • , Joseph C Sun
    • , Gundula Min-Oo
    • , Charlie C Kim
    •  & Lewis L Lanier
  12. Icahn Medical Institute, Mount Sinai Hospital, New York, New York, USA.

    • Jennifer Miller
    • , Brian Brown
    • , Miriam Merad
    • , Emmanuel L Gautier
    • , Claudia Jakubzick
    •  & Gwendalyn J Randolph
  13. Department of Pathology & Immunology, Washington University, St. Louis, Missouri, USA.

    • Emmanuel L Gautier
    •  & Gwendalyn J Randolph
  14. Department of Medicine, Boston University, Boston, Massachusetts, USA.

    • Paul Monach
  15. Skirball Institute of Biomolecular Medicine, New York University School of Medicine, New York, New York, USA.

    • David A Blair
    •  & Michael L Dustin
  16. Fox Chase Cancer Center, Philadelphia, Pennsylvania, USA.

    • Susan A Shinton
    •  & Richard R Hardy
  17. Computer Science Department, Brown University, Providence, Rhode Island, USA.

    • David Laidlaw
  18. Department of Biomedical Engineering, Howard Hughes Medical Institute, Boston University, Boston, Massachusetts, USA.

    • Jim Collins
  19. Program in Molecular Medicine, Children's Hospital, Boston, Massachusetts, USA.

    • Roi Gazit
    •  & Derrick J Rossi
  20. Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts, USA.

    • Taras Kreslavsky
    • , Anne Fletcher
    • , Kutlu Elpek
    • , Angelique Bellemare-Pelletier
    • , Deepali Malhotra
    •  & Shannon Turley

Consortia

  1. the Immunological Genome Project Consortium

    A full list of members and affiliations appears at the end of the paper.

Authors

  1. Search for Vladimir Jojic in:

  2. Search for Tal Shay in:

  3. Search for Katelyn Sylvia in:

  4. Search for Or Zuk in:

  5. Search for Xin Sun in:

  6. Search for Joonsoo Kang in:

  7. Search for Aviv Regev in:

  8. Search for Daphne Koller in:

Contributions

V.J., T.S., A.R. and D.K. designed the study; V.J. developed the algorithm, with input from T.S., A.R. and D.K.; T.S. analyzed the data; K.S. did experiments; O.Z. provided the motif-related code and participated in writing; X.S. generated a mouse model; J.K. designed the experiments and participated in writing the manuscript; and V.J., T.S., A.R. and D.K. wrote the manuscript with input from all authors.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Aviv Regev or Daphne Koller.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–11 and Supplementary Notes 1–3

Excel files

  1. 1.

    Supplementary Table 1

    Supplementary Table 1

  2. 2.

    Supplementary Table 2

    Supplementary Table 2

  3. 3.

    Supplementary Table 3

    Supplementary Table 3

  4. 4.

    Supplementary Table 4

    Supplementary Table 4

  5. 5.

    Supplementary Table 5

    Supplementary Table 5

  6. 6.

    Supplementary Table 6

    Supplementary Table 6

  7. 7.

    Supplementary Table 7

    Supplementary Table 7

  8. 8.

    Supplementary Table 8

    Supplementary Table 8

  9. 9.

    Supplementary Table 9

    Supplementary Table 9

  10. 10.

    Supplementary Table 10

    Supplementary Table 10

  11. 11.

    Supplementary Table 11

    Supplementary Table 11

  12. 12.

    Supplementary Table 12

    Supplementary Table 12

  13. 13.

    Supplementary Table 13

    Supplementary Table 13

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/ni.2587

Further reading