Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Quality of TCR signaling determined by differential affinities of enhancers for the composite BATF–IRF4 transcription factor complex

Nature Immunology volume 18, pages 563–572 (2017)Cite this article

Subjects

Abstract

Variable strengths of signaling via the T cell antigen receptor (TCR) can produce divergent outcomes, but the mechanism of this remains obscure. The abundance of the transcription factor IRF4 increases with TCR signal strength, but how this would induce distinct types of responses is unclear. We compared the expression of genes in the TH2 subset of helper T cells to enhancer occupancy by the BATF–IRF4 transcription factor complex at varying strengths of TCR stimulation. Genes dependent on BATF–IRF4 clustered into groups with distinct TCR sensitivities. Enhancers exhibited a spectrum of occupancy by the BATF–IRF4 ternary complex that correlated with the sensitivity of gene expression to TCR signal strength. DNA sequences immediately flanking the previously defined AICE motif controlled the affinity of BATF–IRF4 for direct binding to DNA. Analysis by the chromatin immunoprecipitation–exonuclease (ChIP-exo) method allowed the identification of a previously unknown high-affinity AICE2 motif at a human single-nucleotide polymorphism (SNP) of the gene encoding the immunomodulatory receptor CTLA-4 that was associated with resistance to autoimmunity. Thus, the affinity of different enhancers for the BATF–IRF4 complex might underlie divergent signaling outcomes in response to various strengths of TCR signaling.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: GATA-3 and CTLA-4 are differentially sensitive to graded expression of BATF and IRF4 in TH2 cells following increasing strength of TCR stimulation.
Figure 2: GATA-3, IL-10 and CTLA-4 are differentially sensitive to the level of total BATF.
Figure 3: BATF-dependent genes display a spectrum of sensitivity to the strength of TCR signaling.
Figure 4: ChIP-seq analysis of the binding of BATF and IRF4 correlates with the sensitivity of target genes to TCR signaling.
Figure 5: ChIP-exo reveals precise binding sites for BATF and IRF4 in the peaks with AICE motifs.
Figure 6: ChIP-exo analysis reveals a previously unknown AICE2 motif.
Figure 7: DNA sequences flanking identical AICE motifs regulate the DNA-binding affinity of BATF–IRF4 and enhancer activity.
Figure 8: A SNP in human CTLA4 affects the DNA-binding affinity of BATF–IRF4 and enhancer activity.

Similar content being viewed by others

Accession codes

Primary accessions

Gene Expression Omnibus

References

  1. Li, M.O. & Rudensky, A.Y. T cell receptor signalling in the control of regulatory T cell differentiation and function. Nat. Rev. Immunol. 16, 220–233 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Tubo, N.J. & Jenkins, M.K. TCR signal quantity and quality in CD4+ T cell differentiation. Trends Immunol. 35, 591–596 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Mayya, V. & Dustin, M.L. What scales the T cell response? Trends Immunol. 37, 513–522 (2016).

    Article  CAS  PubMed  Google Scholar 

  4. Constant, S. et al. Extent of T cell receptor ligation can determine the functional differentiation of naive CD4+ T cells. J. Exp. Med. 182, 1591–1596 (1995).

    Article  CAS  PubMed  Google Scholar 

  5. Hosken, N.A. et al. The effect of antigen dose on CD4+ T helper cell phenotype development in a T cell receptor-αβ-transgenic model. J. Exp. Med. 182, 1579–1584 (1995).

    Article  CAS  PubMed  Google Scholar 

  6. Tubo, N.J. et al. Single naive CD4+ T cells from a diverse repertoire produce different effector cell types during infection. Cell 153, 785–796 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Saraiva, M. et al. Interleukin-10 production by Th1 cells requires interleukin-12-induced STAT4 transcription factor and ERK MAP kinase activation by high antigen dose. Immunity 31, 209–219 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Man, K. et al. The transcription factor IRF4 is essential for TCR affinity-mediated metabolic programming and clonal expansion of T cells. Nat. Immunol. 14, 1155–1165 (2013).

    Article  CAS  PubMed  Google Scholar 

  9. Nayar, R. et al. Graded levels of IRF4 regulate CD8+ T cell differentiation and expansion, but not attrition, in response to acute virus infection. J. Immunol. 192, 5881–5893 (2014).

    Article  CAS  PubMed  Google Scholar 

  10. Yao, S. et al. Interferon regulatory factor 4 sustains CD8+ T cell expansion and effector differentiation. Immunity 39, 833–845 (2013).

    Article  CAS  PubMed  Google Scholar 

  11. Huber, M. & Lohoff, M. IRF4 at the crossroads of effector T-cell fate decision. Eur. J. Immunol. 44, 1886–1895 (2014).

    Article  CAS  PubMed  Google Scholar 

  12. De Silva, N.S. et al. The diverse roles of IRF4 in late germinal center B-cell differentiation. Immunol. Rev. 247, 73–92 (2012).

    Article  CAS  PubMed  Google Scholar 

  13. Tussiwand, R. et al. Compensatory dendritic cell development mediated by BATF-IRF interactions. Nature 490, 502–507 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Glasmacher, E. et al. A genomic regulatory element that directs assembly and function of immune-specific AP-1-IRF complexes. Science 338, 975–980 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Li, P. et al. BATF-JUN is critical for IRF4-mediated transcription in T cells. Nature 490, 543–546 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Ciofani, M. et al. A validated regulatory network for th17 cell specification. Cell 151, 289–303 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Murphy, T.L., Tussiwand, R. & Murphy, K.M. Specificity through cooperation: BATF-IRF interactions control immune-regulatory networks. Nat. Rev. Immunol. 13, 499–509 (2013).

    CAS  PubMed  Google Scholar 

  18. Ochiai, K. et al. Transcriptional regulation of germinal center B and plasma cell fates by dynamical control of IRF4. Immunity 38, 918–929 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Jabeen, R. et al. Th9 cell development requires a BATF-regulated transcriptional network. J. Clin. Invest. 123, 4641–4653 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Kurachi, M. et al. The transcription factor BATF operates as an essential differentiation checkpoint in early effector CD8+ T cells. Nat. Immunol. 15, 373–383 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Ise, W. et al. The transcription factor BATF controls the global regulators of class-switch recombination in both B cells and T cells. Nat. Immunol. 12, 536–543 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Hildner, K. et al. Batf3 deficiency reveals a critical role for CD8α+ dendritic cells in cytotoxic T cell immunity. Science 322, 1097–1100 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Yosef, N. et al. Dynamic regulatory network controlling TH17 cell differentiation. Nature 496, 461–468 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Betz, B.C. et al. Batf coordinates multiple aspects of B and T cell function required for normal antibody responses. J. Exp. Med. 207, 933–942 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Schraml, B.U. et al. The AP-1 transcription factor Batf controls TH17 differentiation. Nature 460, 405–409 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Gregersen, P.K. et al. REL, encoding a member of the NF-κB family of transcription factors, is a newly defined risk locus for rheumatoid arthritis. Nat. Genet. 41, 820–823 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Chung, S.A. et al. Meta-analysis of genetic polymorphisms in granulomatosis with polyangiitis (Wegener's) reveals shared susceptibility loci with rheumatoid arthritis. Arthritis Rheum. 64, 3463–3471 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Lohoff, M. et al. Dysregulated T helper cell differentiation in the absence of interferon regulatory factor 4. Proc. Natl. Acad. Sci. USA 99, 11808–11812 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Rhee, H.S. & Pugh, B.F. Comprehensive genome-wide protein-DNA interactions detected at single-nucleotide resolution. Cell 147, 1408–1419 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. McInnes, I.B. & Schett, G. The pathogenesis of rheumatoid arthritis. N. Engl. J. Med. 365, 2205–2219 (2011).

    Article  CAS  PubMed  Google Scholar 

  31. Chen, M. & Kallenberg, C.G. ANCA-associated vasculitides–advances in pathogenesis and treatment. Nat. Rev. Rheumatol. 6, 653–664 (2010).

    Article  CAS  PubMed  Google Scholar 

  32. Grajales-Reyes, G.E. et al. Batf3 maintains autoactivation of Irf8 for commitment of a CD8α+ conventional DC clonogenic progenitor. Nat. Immunol. 16, 708–717 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Nayar, R. et al. IRF4 regulates the ratio of T-Bet to Eomesodermin in CD8+ T cells responding to persistent LCMV infection. PLoS One 10, e0144826–(2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Allison, K.A. et al. Affinity and dose of TCR engagement yield proportional enhancer and gene activity in CD4+ T cells. eLife. 5, e10134 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Papatsenko, D. & Levine, M. Quantitative analysis of binding motifs mediating diverse spatial readouts of the Dorsal gradient in the Drosophila embryo. Proc. Natl. Acad. Sci. USA 102, 4966–4971 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Ashe, H.L. & Briscoe, J. The interpretation of morphogen gradients. Development 133, 385–394 (2006).

    Article  CAS  PubMed  Google Scholar 

  37. Levo, M. & Segal, E. In pursuit of design principles of regulatory sequences. Nat. Rev. Genet. 15, 453–468 (2014).

    Article  CAS  PubMed  Google Scholar 

  38. Kuehn, H.S. et al. Immune dysregulation in human subjects with heterozygous germline mutations in CTLA4. Science 345, 1623–1627 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Romo-Tena, J., Gomez-Martin, D. & Alcocer-Varela, J. CTLA-4 and autoimmunity: new insights into the dual regulator of tolerance. Autoimmun. Rev. 12, 1171–1176 (2013).

    Article  CAS  PubMed  Google Scholar 

  40. Lenardo, M., Lo, B. & Lucas, C.L. Genomics of immune diseases and new therapies. Annu. Rev. Immunol. 34, 121–149 (2016).

    Article  CAS  PubMed  Google Scholar 

  41. Dignam, J.D., Lebovitz, R.M. & Roeder, R.G. Accurate transcription initiation by RNA polymerase II in a soluble extract from isolated mammalian nuclei. Nucleic Acids Res. 11, 1475–1489 (1983).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Szabo, S.J. et al. Identification of cis-acting regulatory elements controlling interleukin-4 gene expression in T cells: roles for NF-Y and NF-ATc. Mol. Cell. Biol. 13, 4793–4805 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Darrah, P.A. et al. Multifunctional TH1 cells define a correlate of vaccine-mediated protection against Leishmania major. Nat. Med. 13, 843–850 (2007).

    Article  CAS  PubMed  Google Scholar 

  44. Shooshtari, P. et al. Correlation analysis of intracellular and secreted cytokines via the generalized integrated mean fluorescence intensity. Cytometry A 77, 873–880 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Ritchie, M.E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Langmead, B. et al. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    PubMed  PubMed Central  Google Scholar 

  47. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    PubMed  PubMed Central  Google Scholar 

  48. ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

  49. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Quinlan, A.R. & Hall, I.M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Serandour, A.A. et al. Development of an Illumina-based ChIP-exonuclease method provides insight into FoxA1-DNA binding properties. Genome Biol. 14, R147 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Storey, J.D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. USA 100, 9440–9445 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank the Alvin J. Siteman Cancer Center at Washington University School of Medicine for use of the Center for Biomedical Informatics and Multiplex Gene Analysis Genechip Core Facility. Supported by the Howard Hughes Medical Institute (K.M.M.) and the US National Institutes of Health (RO1 AI097244-01A1 to T.E., F30DK108498 to V.D., 1F31CA189491-01 to G.E.G.-R.).

Author information

Authors and Affiliations

  1. Department of Pathology and Immunology, Washington University in St. Louis, School of Medicine, St. Louis, Missouri, USA

    Arifumi Iwata, Vivek Durai, Carlos G Briseño, Xiaodi Wu, Gary E Grajales-Reyes, Takeshi Egawa, Theresa L Murphy & Kenneth M Murphy

  2. Department of Biomedicine, University of Basel, Basel, Switzerland

    Roxane Tussiwand

  3. Howard Hughes Medical Institute, Washington University in St. Louis, School of Medicine, St. Louis, Missouri, USA

    Kenneth M Murphy

Authors
  1. Arifumi Iwata
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vivek Durai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Roxane Tussiwand
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Carlos G Briseño
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaodi Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gary E Grajales-Reyes
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Takeshi Egawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Theresa L Murphy
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Kenneth M Murphy
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A.I., T.L.M. and K.M.M. designed the study, and wrote the manuscript with contributions from all authors; A.I. and C.G.B. performed experiments related to cell sorting, culture and flow cytometry with advice from V.D., R.T., G.E.G.-R. and T.E.; A.I. and C.G.B. performed microarray experiments with advice from X.W. and T.L.M.; and A.I. performed and analyzed ChIP-Seq experiments with advice from V.D. and T.E.

Corresponding author

Correspondence to Kenneth M Murphy.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 GATA3 and CTLA-4 are differentially sensitive to graded expression of BATF and IRF4 in TH2 cells.

(a) Flow cytometry analyzing IRF4 and BATF expression on day 4 of secondary activation in WT CD4+ T cells cultured under TH2 conditions (anti-IFN-γ, anti-IL-12 and IL-4) with anti-CD28 and the indicated concentration of anti-CD3ε crosslinked by plate-bound anti-hamster IgG. Numbers indicate the percentage of live CD4+ cells in each quadrant. (b) Flow cytometry analyzing GATA3 and CTLA-4 expression on day 4 in WT CD4+ T cells cultured as in (a). (c) Overlays of flow cytometry data from (a) and (b) showing expression of the indicated proteins at various doses of plate bound anti-CD3e. Data are representative of two biological replicates (a-c). (d,e) Flow cytometry analyzing GATA3, CTLA4 and BATF on day 4 of primary activation of naïve KJ126+ CD4 T cells from DO11.10 mice activated under TH2 conditions with the indicated concentration of ovalbumin (323-339) peptide and dendritic cells from Balb/c mice. Data are representative of two biological replicates. (f) Histograms and MFI for BATF, GATA3 and CTLA4 expression from (d,e).

Supplementary Figure 2 Kinetics of the expression of BATF, IRF4, GATA3 and CTLA4 in TH2 cells.

(a) Flow cytometry analyzing IRF4 and BATF expression at the indicated time after primary activation of WT cells cultured under TH2 condition (anti-IFN-γ, anti-IL-12 and IL-4) using 10 ng/ml of anti-CD3ε and 4 μg/ml of anti-CD28 crosslinked with plate-bound anti-hamster IgG. (b) Flow cytometry analyzing CTLA-4 and GATA3 expression in TH2 cells cultured as in (a). (c) Flow cytometry analyzing BATF and GATA3 expression in TH2 cells as in (a). (d) Flow cytometry analyzing BATF and CTLA-4 expression in TH2 cells as in (a). Data are representative of three biological replicates. (e, f) Histograms and MFI for factor expression from (a-d).

Supplementary Figure 3 Analysis of Batf mutations and phenotype of Batf1-Batf3 DKO cells.

(a) Batf1/3 DKO CD4 T cells were cultured under TH17 conditions (anti-IFN-, anti-IL-12, anti-IL-4, IL-6, TGF- and IL-1 ) on plate-bound anti-CD3 and anti-CD28 and were infected on day 1 of primary stimulation with empty retrovirus or retrovirus containing WT Batf or the indicated Batf mutant (Batf-H: Batf H55Q, Batf-HK: Batf H55Q K63D, Batf-HKE: Batf H55Q K63D E77K, Batf-HKLE: Batf H55Q K63D L56A E77K.) On day 5 after secondary, cells were stimulated with PMA/ionomycin and analyzed for IL-17 expression. Shown is the activity of each mutant for reconstitution of IL-17 positive cells relative to WT Batf in the same experiment. Bars showed the mean of two experiments. (b) Batf1/3 DKO B cells were cultured with LPS and IL-4, and infected on day 1 with empty retrovirus or retrovirus containing the indicated Batf mutant described in (a). Class switch recombination to IgG1 was analyzed on day 4. Shown is the activity of each mutant for reconstitution of IgG1 positive cells relative to WT Batf in the same experiment. Bars showed the mean of two experiments. (c) Batf1/3 DKO bone marrow cells were cultured with Flt3 ligand and infected on day 1 with empty retrovirus or retrovirus containing WT Batf or the indicated Batf mutant described in (a). CD24+ CD172a− CD11c+ MHCII+ B220− SiglecH− cells were analyzed on day 9. Shown is the activity of each mutant for reconstitution of CD24+ DCs relative to WT Batf in the same experiment. Bars show the mean of three experiments. (d) Microarray expression of cytokines and transcription factors in WT and TH2 cells on day 0 or 2 days after primary activation with 10ng/ml anti-CD3 (day2) or 4 days after primary activation with 2ng/ml or 10ng/ml anti-CD3 as indicated. Shown is the mean and SEM for three biological replicates.

Supplementary Figure 4 TH2 cell–related genes have low sensitivity to the binding of BATF and IRF4.

(a) ChIP-seq for BATF in WT TH2 cells and IRF4 in WT and Batf1/3 DKO TH2 cells (DKO) and in Batf1/3 DKO TH2 cells reconstituted with retroviral Batf-HKE (Batf-HKE). Cells were prepared for ChIP after PMA/ionomycin stimulation on day 4 of secondary stimulation. (b) IRF4 ChIP-seq in WT TH2 cells cultured with 10 ng/ml of anti-CD3ε for 4 days, 2.2 ng/ml of anti-CD3ε for 4 days, or 10 ng/ml of anti-CD3ε for 2 days. (c) ChIP-seq for BATF3 in Batf−/− TH2 cells and IRF4 in WT and Batf−/− TH2 cells within genes from Cluster I/II and Cluster III. ChIP-seq was performed after PMA/ionomycin activation on day 4 of secondary stimulation and is compared with data from Figure 4a. (d) ChIP-seq for BATF3 in Batf−/− TH2 cells and IRF4 in WT and Batf−/− TH2 cells within TH2 related genes. ChIP-seq was performed after PMA/ionomycin activation on day 4 of secondary stimulation and is compared with data from Supplementary Figure 4a. *Peaks that do not bind BATF3 in Batf−/− TH2 cells.

Supplementary Figure 5 Affinity of IRF4 ChIP-seq peaks correlates with BATF-dependent gene expression, but peak affinity cannot be distinguished by de novo motif analysis.

(a) Merged IRF4 ChIP-seq peaks from three conditions of primary stimulation were categorized as high-affinity (present in three conditions), intermediate-affinity (two conditions) and low-affinity (one condition). Color map shows the intensity of IRF4 binding centered on the peak ±1 kb. (b) The number of high-affinity peaks, intermediate-affinity peaks and low-affinity peaks within ±50 kb from TSS of Cluster I and II genes or Cluster III genes. Chi-square test, p = 3.417e-08, standardized residuals, high affinity peaks: 5.74, low sensitivity peaks: -5.61. (c) De novo motif analysis for the top 3000 IRF4 peaks in each category ranked by sum of tag counts of three conditions. P, final enrichment p-value; T, number of target sequences with motif as percent of total targets; B, number of background sequences with motif as percent of total background.

Supplementary Figure 6 The flanking region regulates the affinity of a motif for the BATF-IRF4 complex.

(a-d) ChIP-seq for IRF4 using the indicated primary activation condition of mouse TH2 cells. Shown are pairs of AICE1-containing peaks, Enpp6 -45 kb and Ptchd3 -26 kb, or Bcor +65 kb and Mzt1 +230 kb with identical AICE1 sequences but different flanking regions (a) or pairs of AICE2-containing peaks, Prdm1 +14 kb and Ctla4 -33 kb, or Ccr4 +8 kb and Snrpe +38 kb with identical AICE2 sequences but different flanking regions (b). (c) EMSA using nuclear extracts of HEK293FT cells expressing BATF, JUNB and IRF4 with probes based on sequences in (a). Open triangle: BATF/JUNB. Solid triangle: BATF/JUNB/IRF4. (d) EMSA using nuclear extract of HEK293FT cells expressing BATF, JUNB and IRF4 with probes based on sequences in (b). Data were representative of two experiments (c,d). Full sequence of probes are shown in Supplementary Table 2 (c,d).

Supplementary Figure 7 Multiple AICE motifs within single IRF4 ChIP-seq peaks.

(a) Proportion of IRF4 peaks with multiple AICE motifs identified by de novo motif analysis. (b) ChIP-seq analysis and ChIP-exo analysis of BATF and IRF4 in WT TH2 cells. Black bars: predicted AICE sites. (c,d) Example of site from (a) with ChIP-exo binding.

Supplementary Figure 8 AICE motif sites identified by de novo motif analysis show ChIP-exo binding, but ETS motif sites do not.

(a) Mean ChIP-exo tag counts of BATF and IRF4 binding on AICE1, AICE2 and ETS motifs within IRF4 ChIP-seq peaks that were identified by de novo motif analysis. (b) Mean ChIP-exo tag counts of BATF and IRF4 on consensus ETS motifs within IRF4 ChIP-seq peaks showing ±50 bp flanking regions; red box: exo binding region of IRF4, light red box: control region for IRF4, blue box: exo binding region of BATF, light blue box: control region for BATF. (c) AICE1 motifs predicted by de novo motif analysis of IRF4 ChIP-seq. (d) AICE1 motifs identified from sites with higher ChIP-exo tag count on exo binding region of both BATF and IRF4 than control region.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8 and Supplementary Tables 1–3 (PDF 2015 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Iwata, A., Durai, V., Tussiwand, R. et al. Quality of TCR signaling determined by differential affinities of enhancers for the composite BATF–IRF4 transcription factor complex. Nat Immunol 18, 563–572 (2017). https://doi.org/10.1038/ni.3714

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ni.3714

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing