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Published June 6, 2017 | Supplemental Material + Published
Journal Article Open

Bcl11b and combinatorial resolution of cell fate in the T-cell gene regulatory network

Abstract

T-cell development from hematopoietic progenitors depends on multiple transcription factors, mobilized and modulated by intrathymic Notch signaling. Key aspects of T-cell specification network architecture have been illuminated through recent reports defining roles of transcription factors PU.1, GATA-3, and E2A, their interactions with Notch signaling, and roles of Runx1, TCF-1, and Hes1, providing bases for a comprehensively updated model of the T-cell specification gene regulatory network presented herein. However, the role of lineage commitment factor Bcl11b has been unclear. We use self-organizing maps on 63 RNA-seq datasets from normal and perturbed T-cell development to identify functional targets of Bcl11b during commitment and relate them to other regulomes. We show that both activation and repression target genes can be bound by Bcl11b in vivo, and that Bcl11b effects overlap with E2A-dependent effects. The newly clarified role of Bcl11b distinguishes discrete components of commitment, resolving how innate lymphoid, myeloid, and dendritic, and B-cell fate alternatives are excluded by different mechanisms.

Additional Information

© 2017 National Academy of Sciences. Edited by Neil H. Shubin, The University of Chicago, Chicago, IL, and approved January 30, 2017 (received for review October 25, 2016). We thank Georgi Marinov and Barbara Wold for advice; Igor Antoshechkin of the Millard and Muriel Jacobs Genetics and Genomics Center for sequencing; Henry Amrhein, Diane Trout, and Sagar Damle for data curation; Diana Perez, Josh Verceles, and Rochelle Diamond for flow cytometry and cell sorting; Rochelle Diamond for laboratory management; and Lorena Sandoval, Scott Washburn, and Robert Butler III for supervision of the mice (all at the California Institute of Technology). This work was supported by NIH New Innovator Award DP2GM111100 (to A.M.); NIH Grants R01HD073113 (to W.J.R.L.), K99HL119638A (to H.Y.K.), and RC2CA148278, R01CA90233, R01AI083514, and R01AI95943 (to E.V.R.); the L. A. Garfinkle Memorial Laboratory Fund and the Al Sherman Foundation; the Caltech-City of Hope Biomedical Research Initiative; and the Albert Billings Ruddock Professorship (E.V.R.). W.J.R.L. and W.Z. contributed equally to this work. Author contributions: A.M. and E.V.R. designed research; W.Z., J.A.Z., H.H., L.L., M.R.-W., and H.Y.K. performed research; W.J.R.L., P.L., and A.M. contributed new reagents/analytic tools; W.J.R.L., W.Z., J.A.Z., H.H., C.S.J., M.R.-W., H.Y.K., A.M., and E.V.R. analyzed data; and W.J.R.L., W.Z., and E.V.R. wrote the paper. The authors declare no conflict of interest. This paper results from the Arthur M. Sackler Colloquium of the National Academy of Sciences, "Gene Regulatory Networks and Network Models in Development and Evolution," held April 12–14, 2016, at the Arnold and Mabel Beckman Center of the National Academies of Sciences and Engineering in Irvine, CA. The complete program and video recordings of most presentations are available on the NAS website at www.nasonline.org/Gene_Regulatory_Networks. This article is a PNAS Direct Submission. Data deposition: The data reported in this paper have been deposited in the Gene Expression Omnibus (GEO) database, www.ncbi.nlm.nih.gov/geo (RNA-seq data accession no. GSE89198; ChIP-seq data accession no. GSE93572). This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1610617114/-/DCSupplemental.

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Published - 5800.full.pdf

Supplemental Material - pnas.1610617114.sapp.pdf

Supplemental Material - pnas.1610617114.sd01.xlsx

Supplemental Material - pnas.1610617114.sd02.xlsx

Supplemental Material - pnas.1610617114.sd03.xlsx

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Supplemental Material - pnas.1610617114.sd05.xlsx

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August 21, 2023
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