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Published December 23, 2008 | Published + Supplemental Material
Journal Article Open

A gene regulatory network armature for T lymphocyte specification

Abstract

Choice of a T lymphoid fate by hematopoietic progenitor cells depends on sustained Notch–Delta signaling combined with tightly regulated activities of multiple transcription factors. To dissect the regulatory network connections that mediate this process, we have used high-resolution analysis of regulatory gene expression trajectories from the beginning to the end of specification, tests of the short-term Notch dependence of these gene expression changes, and analyses of the effects of overexpression of two essential transcription factors, namely PU.1 and GATA-3. Quantitative expression measurements of >50 transcription factor and marker genes have been used to derive the principal components of regulatory change through which T cell precursors progress from primitive multipotency to T lineage commitment. Our analyses reveal separate contributions of Notch signaling, GATA-3 activity, and down-regulation of PU.1. Using BioTapestry (www.BioTapestry.org), the results have been assembled into a draft gene regulatory network for the specification of T cell precursors and the choice of T as opposed to myeloid/dendritic or mast-cell fates. This network also accommodates effects of E proteins and mutual repression circuits of Gfi1 against Egr-2 and of TCF-1 against PU.1 as proposed elsewhere, but requires additional functions that remain unidentified. Distinctive features of this network structure include the intense dose dependence of GATA-3 effects, the gene-specific modulation of PU.1 activity based on Notch activity, the lack of direct opposition between PU.1 and GATA-3, and the need for a distinct, late-acting repressive function or functions to extinguish stem and progenitor-derived regulatory gene expression.

Additional Information

© 2008 The National Academy of Sciences of the USA. Edited by Michael S. Levine, University of California, Berkeley, CA, and approved September 8, 2008 (received for review July 6, 2008). This article is a PNAS Direct Submission. Published online before print December 22, 2008, doi: 10.1073/pnas.0806501105 We thank Dr. Howard Petrie (Scripps Florida, Jupiter) for valuable discussions and sharing unpublished data for comparison with ours and Robert Butler, Ni Feng, and Koorosh J. Elihu for excellent technical help. This work was supported by National Institutes of Health Grants R33 HL089102 (to H.B.) and R33 HL089123 (to E.V.R.), using data from work supported by National Institutes of Health Grants CA90233, CA98925, DK73658, and AI064590 (to M.A.Y.), the Albert Billings Ruddock Professorship, the Louis A. Garfinkle Memorial Laboratory Fund, the Al Sherman Fund, and the DNA Sequencer Royalty Fund. This paper results from the Arthur M. Sackler Colloquium of the National Academy of Sciences, "Gene Networks in Animal Development and Evolution," held February 15–16, 2008, at the Arnold and Mabel Beckman Center of the National Academies of Sciences and Engineering in Irvine, CA. The complete program and audio files of most presentations are available on the NAS web site at: http://www.nasonline.org/SACKLER_Gene_Networks. Author contributions: H.B. and E.V.R. designed research; D.D.S.-A., E.-S.D.-F., M.A.Y., and M.A.Z. performed research; C.G., W.J.R.L., and H.B. contributed new reagents/analytic tools; C.G., W.J.R.L., D.D.S.-A., E.-S.D.-F., M.A.Y., M.A.Z., H.B., and E.V.R. analyzed data; and C.G., H.B., and E.V.R. wrote the paper. The authors declare no conflict of interest. This article contains supporting information online at www.pnas.org/cgi/content/full/0806501105/DCSupplemental.

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