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Foxp3 occupancy and regulation of key target genes during T-cell stimulation

Nature volume 445, pages 931–935 (2007)Cite this article

Abstract

Foxp3+CD4+CD25+ regulatory T (Treg) cells are essential for the prevention of autoimmunity1,2. Treg cells have an attenuated cytokine response to T-cell receptor stimulation, and can suppress the proliferation and effector function of neighbouring T cells3,4. The forkhead transcription factor Foxp3 (forkhead box P3) is selectively expressed in Treg cells, is required for Treg development and function, and is sufficient to induce a Treg phenotype in conventional CD4+CD25- T cells5,6,7,8. Mutations in Foxp3 cause severe, multi-organ autoimmunity in both human and mouse9,10,11. FOXP3 can cooperate in a DNA-binding complex with NFAT (nuclear factor of activated T cells) to regulate the transcription of several known target genes12. However, the global set of genes regulated directly by Foxp3 is not known and consequently, how this transcription factor controls the gene expression programme for Treg function is not understood. Here we identify Foxp3 target genes and report that many of these are key modulators of T-cell activation and function. Remarkably, the predominant, although not exclusive, effect of Foxp3 occupancy is to suppress the activation of target genes on T-cell stimulation. Foxp3 suppression of its targets appears to be crucial for the normal function of Treg cells, because overactive variants of some target genes are known to be associated with autoimmune disease.

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Figure 1: Strategy to identify direct Foxp3 transcriptional effects.
Figure 2: Direct Foxp3 targets include key modulators of T-cell function.
Figure 3: Foxp3 directly suppresses the activation of target genes.
Figure 4: Core direct regulatory effects of Foxp3.

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Acknowledgements

We thank members of the Young, von Boehmer and Fraenkel laboratories, as well as R. Jaenisch and D. K. Gifford, for discussions and critical review of the manuscript, especially T. I. Lee, J. Zeitlinger and D. T. Odom. We also thank Biology and Research Computing (BaRC), especially T. Dicesare for graphic assistance, as well as E. Herbolsheimer for computational and technical support. K.K. was supported in part by a fellowship grant from the German Research Foundation. This work was supported in part by a donation from E. Radutzky, and by the Whitaker Foundation (E.F.) and the NIH (H.v.B. and R.A.Y.). All microarray data from this study are available from ArrayExpress at the EBI under the accession code E-TABM-154

Author Contributions R.A.Y. and H.v.B. contributed both as senior and corresponding authors.

Author information

Author notes

  1. Alexander Marson and Karsten Kretschmer: These authors contributed equally to this work

Authors and Affiliations

  1. Whitehead Institute for Biomedical Research, 9 Cambridge Center, Cambridge, Massachusetts 02142, USA,

    Alexander Marson, Garrett M. Frampton, Elizabeth S. Jacobsen, Stuart S. Levine & Richard A. Young

  2. Department of Biology,,

    Alexander Marson, Garrett M. Frampton & Richard A. Young

  3. Department of Electrical Engineering and Computer Science,,

    Kenzie D. MacIsaac

  4. Biological Engineering Division,,

    Ernest Fraenkel

  5. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA,

    Ernest Fraenkel

  6. Department of Cancer Immunology and AIDS, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA,

    Karsten Kretschmer, Julia K. Polansky & Harald von Boehmer

  7. Department of Pathology, Harvard Medical School, Boston, Massachusetts 02115, USA,

    Karsten Kretschmer & Harald von Boehmer

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Correspondence to Harald von Boehmer or Richard A. Young.

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Competing interests: R.A.Y. is a consultant to Agilent Technologies.

Supplementary information

TITLE

This file contains the Supplementary Methods, Supplementary Discussion, Supplementary Notes, Supplementary Figures S1–S6 with legends and additional references. (PDF 2123 kb)

Supplementary Table 1

This file contains Supplementary Table S1 which shows Foxp3 bound regions in stimulated and unstimulated hybridoma cells. The genomic regions that are bound by Foxp3, which are identified at false discovery rates of 5% and 10% in stimulated and unstimulated hybridoma cells, are listed. (XLS 182 kb)

Supplementary Table 2

This file contains Supplementary Table S2 which shows Foxp3 bound genes in stimulated and unstimulated hybridoma cells. Genes with Foxp3 bound regions within a 10kb window (8kb upstream to 2kb downstream) from a well-annotated TSS in stimulated and unstimulated hybridoma cells are listed. (XLS 273 kb)

Supplementary Table 3

This file contains Supplementary Table S3 which shows selected Foxp3 target genes with roles in T cell biology. Many Foxp3 target genes known to play roles in T cell biology are listed and categorized, including miRNAs that are differentially expressed between Treg cells and conventional T cells. (DOC 67 kb)

Supplementary Table 4

This file contains Supplementary Table S4 which shows E2F4 bound regions and genes in unstimulated hybridoma cells. The genomic regions that are bound by E2F4, which are identified at a false discovery rate of 5% in unstimulated hybridoma cells, are listed. The genes with E2F4 bound regions within a 1kb window (800bp upstream to 200bp downstream) around a well-annotated TSS in unstimulated hybridoma cells are listed. This data was generated on the single slide promoter proximal arrays. (XLS 70 kb)

Supplementary Table 5

This file contains Supplementary Table S5 which shows DNA motif results. The log2-odds ratio PSSM of the DNA motif that was discovered by the THEME algorithm within Foxp3 bound regions is provided. The counts of total and conserved Foxp3 and Nfat DNA motifs in Foxp3 bound regions compared to all arrayed regions are provided, as is information about the enrichment of neighbouring forkhead and Nfat motifs in Foxp3 bound regions. (XLS 10 kb)

Supplementary Table 6

This file contains Supplementary Table S6 which shows hybridoma expression dataset. The complete expression dataset from the hybridoma cells is provided in quantile normalized and unnormalized forms. (XLS 10961 kb)

Supplementary Table 7

This file contains Supplementary Table S7 which shows Foxp3 dependant differentially expressed genes in stimulated hybridoma cells. In part (a) expression data for the genes that were determined to be differentially expressed between stimulated Foxp3- and Foxp3+ hybridoma cells are provided. These genes were selected according to methods described in the supplementary section, Identification of Differentially Expressed Genes. This data is displayed in Figure 3a. In part (b) expression data for the probesets that were determined to be differentially expressed between stimulated Foxp3- and Foxp3+ hybridoma cells are provided. (XLS 101 kb)

Supplementary Table 8

This file contains Supplementary Table S8 which shows Foxp3 dependant differentially expressed genes in unstimulated hybridoma cells. In part (a) Expression data for the genes that were determined to be differentially expressed between unstimulated Foxp3- and Foxp3+ hybridoma cells are provided. These genes were selected according to methods described in the supplementary section, Identification of Differentially Expressed Genes. In part (b) expression data for the probesets that were determined to be differentially expressed between unstimulated Foxp3- and Foxp3+ hybridoma cells are provided. (XLS 41 kb)

Supplementary Table 9

This file contains Supplementary Table S9 which shows Ex vivo expression dataset. The complete expression dataset from the ex vivo cells is provided in quantile normalized and unnormalized forms. (XLS 10949 kb)

Supplementary Table 10

This file contains Supplementary Table S10 which shows genes with consistent Foxp3 dependant differential expression in ex vivo and hybridoma cells. Expression data for the genes with the greatest Foxp3 dependent expression in both ex vivo and hybridoma cells are provided. These genes were selected according to methods described in the supplementary section, Identification of Differentially Expressed Genes. (XLS 67 kb)

Supplementary Table 11

This file contains SupplementaryTable S11 which shows eal-time RT-PCR dataset. The complete dataset from quantitative PCR experiments is provided. (XLS 31 kb)

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Marson, A., Kretschmer, K., Frampton, G. et al. Foxp3 occupancy and regulation of key target genes during T-cell stimulation. Nature 445, 931–935 (2007). https://doi.org/10.1038/nature05478

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