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MLL4 prepares the enhancer landscape for Foxp3 induction via chromatin looping

Nature Immunology volume 18, pages 1035–1045 (2017)Cite this article

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Abstract

MLL4 is an essential subunit of the histone H3 Lys4 (H3K4)-methylation complexes. We found that MLL4 deficiency compromised the development of regulatory T cells (Treg cells) and resulted in a substantial decrease in monomethylated H3K4 (H3K4me1) and chromatin interaction at putative gene enhancers, a considerable portion of which were not direct targets of MLL4 but were enhancers that interacted with MLL4-bound sites. The decrease in H3K4me1 and chromatin interaction at the enhancers not bound by MLL4 correlated with MLL4 binding at distant interacting regions. Deletion of an upstream MLL4-binding site diminished the abundance of H3K4me1 at the regulatory elements of the gene encoding the transcription factor Foxp3 that were looped to the MLL4-binding site and compromised both the thymic differentiation and the inducible differentiation of Treg cells. We found that MLL4 catalyzed methylation of H3K4 at distant unbound enhancers via chromatin looping, which identifies a previously unknown mechanism for regulating the T cell enhancer landscape and affecting Treg cell differentiation.

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Figure 1: Mll4 deficiency reduces the number of Treg cells in the thymus and the number of T cells in the periphery.
Figure 2: Deletion of Mll4 results in impaired Treg cell development in the gut and in in vitro culture.
Figure 3: Mll4 expression patterns in various types of CD4+ T cells.
Figure 4: Deletion of Mll4 diminishes the abundance of H3K4me1 at MLL4-unbound regions via chromatin lopping.
Figure 5: MLL4 facilitates long-range chromatin interactions.
Figure 6: MLL4 facilitates enhancer–promoter interactions.
Figure 7: Deletion of the MLL4-binding −8.5 kb region decreases the abundance of H3K4me1 at Foxp3 regulatory elements and compromises Treg cell differentiation.
Figure 8: Deletion of the MLL4-binding −8.5 kb region increases the frequency of Treg precursor cells in the thymus.

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References

  1. Sakaguchi, S., Yamaguchi, T., Nomura, T. & Ono, M. Regulatory T cells and immune tolerance. Cell 133, 775–787 (2008).

    CAS  PubMed  Google Scholar 

  2. Klein, L. & Jovanovic, K. Regulatory T cell lineage commitment in the thymus. Semin. Immunol. 23, 401–409 (2011).

    Article  CAS  PubMed  Google Scholar 

  3. Chen, W. et al. Conversion of peripheral CD4+CD25− naive T cells to CD4+CD25+ regulatory T cells by TGF-® induction of transcription factor Foxp3. J. Exp. Med. 198, 1875–1886 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Fontenot, J.D., Gavin, M.A. & Rudensky, A.Y. Foxp3 programs the development and function of CD4+CD25+ regulatory T cells. Nat. Immunol. 4, 330–336 (2003).

    Article  CAS  PubMed  Google Scholar 

  5. Khattri, R., Cox, T., Yasayko, S.A. & Ramsdell, F. An essential role for Scurfin in CD4+CD25+ T regulatory cells. Nat. Immunol. 4, 337–342 (2003).

    Article  CAS  PubMed  Google Scholar 

  6. Bennett, C.L. et al. The immune dysregulation, polyendocrinopathy, enteropathy, X-linked syndrome (IPEX) is caused by mutations of FOXP3. Nat. Genet. 27, 20–21 (2001).

    Article  CAS  PubMed  Google Scholar 

  7. Kim, J.M., Rasmussen, J.P. & Rudensky, A.Y. Regulatory T cells prevent catastrophic autoimmunity throughout the lifespan of mice. Nat. Immunol. 8, 191–197 (2007).

    Article  CAS  PubMed  Google Scholar 

  8. Lahl, K. et al. Selective depletion of Foxp3+ regulatory T cells induces a scurfy-like disease. J. Exp. Med. 204, 57–63 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Grindebacke, H. et al. Defective suppression of Th2 cytokines by CD4CD25 regulatory T cells in birch allergics during birch pollen season. Clin. Exp. Allergy 34, 1364–1372 (2004).

    Article  CAS  PubMed  Google Scholar 

  10. Suvas, S. & Rouse, B.T. Treg control of antimicrobial T cell responses. Curr. Opin. Immunol. 18, 344–348 (2006).

    Article  CAS  PubMed  Google Scholar 

  11. Roychoudhuri, R., Eil, R.L. & Restifo, N.P. The interplay of effector and regulatory T cells in cancer. Curr. Opin. Immunol. 33, 101–111 (2015).

    Article  CAS  PubMed  Google Scholar 

  12. Kasagi, S. et al. In vivo-generated antigen-specific regulatory T cells treat autoimmunity without compromising antibacterial immune response. Sci. Transl. Med. 6, 241ra78 (2014).

    Article  CAS  PubMed  Google Scholar 

  13. Taylor, P.A., Lees, C.J. & Blazar, B.R. The infusion of ex vivo activated and expanded CD4+CD25+ immune regulatory cells inhibits graft-versus-host disease lethality. Blood 99, 3493–3499 (2002).

    Article  CAS  PubMed  Google Scholar 

  14. Zheng, Y. et al. Role of conserved non-coding DNA elements in the Foxp3 gene in regulatory T-cell fate. Nature 463, 808–812 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Schmidl, C. et al. The enhancer and promoter landscape of human regulatory and conventional T-cell subpopulations. Blood 123, e68–e78 (2014).

    Article  CAS  PubMed  Google Scholar 

  16. Samstein, R.M. et al. Foxp3 exploits a pre-existent enhancer landscape for regulatory T cell lineage specification. Cell 151, 153–166 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Bannister, A.J. & Kouzarides, T. Regulation of chromatin by histone modifications. Cell Res. 21, 381–395 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Hess, J.L. Mechanisms of transformation by MLL. Crit. Rev. Eukaryot. Gene Expr. 14, 235–254 (2004).

    Article  CAS  PubMed  Google Scholar 

  19. Ang, S.Y. et al. KMT2D regulates specific programs in heart development via histone H3 lysine 4 di-methylation. Development 143, 810–821 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Lee, J.E. et al. H3K4 mono- and di-methyltransferase MLL4 is required for enhancer activation during cell differentiation. eLife 2, e01503 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Akimova, T., Beier, U.H., Wang, L., Levine, M.H. & Hancock, W.W. Helios expression is a marker of T cell activation and proliferation. PLoS One 6, e24226 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Rubtsov, Y.P. et al. Regulatory T cell-derived interleukin-10 limits inflammation at environmental interfaces. Immunity 28, 546–558 (2008).

    Article  CAS  PubMed  Google Scholar 

  23. Hu, D. et al. The MLL3/MLL4 branches of the COMPASS family function as major histone H3K4 monomethylases at enhancers. Mol. Cell. Biol. 33, 4745–4754 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Lee, J. et al. A tumor suppressive coactivator complex of p53 containing ASC-2 and histone H3-lysine-4 methyltransferase MLL3 or its paralogue MLL4. Proc. Natl. Acad. Sci. USA 106, 8513–8518 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Lee, S., Lee, J., Lee, S.K. & Lee, J.W. Activating signal cointegrator-2 is an essential adaptor to recruit histone H3 lysine 4 methyltransferases MLL3 and MLL4 to the liver X receptors. Mol. Endocrinol. 22, 1312–1319 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Zang, C. et al. A clustering approach for identification of enriched domains from histone modification ChIP-Seq data. Bioinformatics 25, 1952–1958 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. McLean, C.Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Jin, W. et al. Genome-wide detection of DNase I hypersensitive sites in single cells and FFPE tissue samples. Nature 528, 142–146 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Rao, S.S. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Hnisz, D. et al. Super-enhancers in the control of cell identity and disease. Cell 155, 934–947 (2013).

    Article  CAS  PubMed  Google Scholar 

  31. Feng, Y. et al. Control of the inheritance of regulatory T cell identity by a cis element in the Foxp3 locus. Cell 158, 749–763 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ogawa, C. et al. TGF-®-mediated Foxp3 gene expression is cooperatively regulated by Stat5, Creb, and AP-1 through CNS2. J. Immunol. 192, 475–483 (2014).

    Article  CAS  PubMed  Google Scholar 

  33. Wang, H. et al. One-step generation of mice carrying mutations in multiple genes by CRISPR/Cas-mediated genome engineering. Cell 153, 910–918 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Burchill, M.A. et al. Linked T cell receptor and cytokine signaling govern the development of the regulatory T cell repertoire. Immunity 28, 112–121 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Kitagawa, Y. et al. Guidance of regulatory T cell development by Satb1-dependent super-enhancer establishment. Nat. Immunol. 18, 173–183 (2017).

    Article  CAS  PubMed  Google Scholar 

  36. Lio, C.W. & Hsieh, C.S. A two-step process for thymic regulatory T cell development. Immunity 28, 100–111 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Wei, G. et al. Global mapping of H3K4me3 and H3K27me3 reveals specificity and plasticity in lineage fate determination of differentiating CD4+ T cells. Immunity 30, 155–167 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Yamashita, M. et al. Crucial role of MLL for the maintenance of memory T helper type 2 cell responses. Immunity 24, 611–622 (2006).

    Article  CAS  PubMed  Google Scholar 

  39. Tumes, D.J. et al. The polycomb protein Ezh2 regulates differentiation and plasticity of CD4+ T helper type 1 and type 2 cells. Immunity 39, 819–832 (2013).

    Article  CAS  PubMed  Google Scholar 

  40. Li, X., Liang, Y., LeBlanc, M., Benner, C. & Zheng, Y. Function of a Foxp3 cis-element in protecting regulatory T cell identity. Cell 158, 734–748 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Lee, P.P. et al. A critical role for Dnmt1 and DNA methylation in T cell development, function, and survival. Immunity 15, 763–774 (2001).

    Article  CAS  PubMed  Google Scholar 

  42. Zhang, P. et al. PARP-1 controls immunosuppressive function of regulatory T cells by destabilizing Foxp3. PLoS One 8, e71590 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Zanvit, P. et al. Antibiotics in neonatal life increase murine susceptibility to experimental psoriasis. Nat. Commun. 6, 8424 (2015).

    Article  CAS  PubMed  Google Scholar 

  44. Barski, A. et al. High-resolution profiling of histone methylations in the human genome. Cell 129, 823–837 (2007).

    Article  CAS  PubMed  Google Scholar 

  45. Wei, G. et al. Genome-wide analyses of transcription factor GATA3-mediated gene regulation in distinct T cell types. Immunity 35, 299–311 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Chepelev, I., Wei, G., Tang, Q. & Zhao, K. Detection of single nucleotide variations in expressed exons of the human genome using RNA-Seq. Nucleic Acids Res. 37, e106 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Langmead, B. & Salzberg, S.L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Robinson, M.D., McCarthy, D.J. & Smyth, G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  49. Whyte, W.A. et al. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell 153, 307–319 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Mortazavi, A., Williams, B.A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621–628 (2008).

    Article  CAS  PubMed  Google Scholar 

  51. Jiao, X. et al. DAVID-WS: a stateful web service to facilitate gene/protein list analysis. Bioinformatics 28, 1805–1806 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Supek, F., Bošnjak, M., Škunca, N. & Šmuc, T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS One 6, e21800 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Hollander, M. & Wolfe, D.A. Nonparametric Statistical Methods (Wiley, 1973).

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Acknowledgements

We thank the NHLBI DNA Sequencing Core facility for sequencing the libraries; the NHLBI Systems Biology Core and the NIH Biowulf High Performance Computing Systems for computing services; the NHLBI Flow Cytometry Core facility for cell sorting; the NHLBI and NIAID Animal Facilities for animal care; and X. Wang and S Gao (NHLBI Systems Biology Core) and X. Zheng (Carnegie Institution for Science) for discussions on Hi-C data analysis. Supported by the Division of Intramural Research of NHLBI (K.Z.), NIDCR (W.C.) and NIDDK (K.G.) of the US National Institutes of Health.

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Author notes

  1. Katarzyna Placek, Gangqing Hu and Kairong Cui: These authors contributed equally to this work.

Authors and Affiliations

  1. Division of Intramural Research, Systems Biology Center, NHLBI, NIH, Bethesda, Maryland, USA

    Katarzyna Placek, Gangqing Hu, Kairong Cui, Yi Ding, Chaochen Wang & Keji Zhao

  2. Division of Intramural Research, Mucosal Immunology Section, NIDCR, NIH, Bethesda, Maryland, USA

    Dunfang Zhang, Joanne Elizabeth Konkel & Wanjun Chen

  3. Department of Animal and Avian Sciences, University of Maryland, College Park, Maryland, USA

    Yi Ding & Jiuzhou Song

  4. Adipocyte Biology and Gene Regulation Section, Laboratory of Endocrinology and Receptor Biology, NIDDK, NIH, Bethesda, Maryland, USA

    Ji-Eun Lee, Younghoon Jang, Chaochen Wang & Kai Ge

  5. Division of Intramural Research, Transgenic Core, NHLBI, NIH, Bethesda, Maryland, USA

    Chengyu Liu

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Contributions

K.P., G.H., K.C., C.L., W.C. and K.Z. designed experiments; K.P., G.H., K.C., D.Z., Y.D., J.-E.L., Y.J., C.W., C.L. and K.Z. conducted experiments; K.P., G.H., K.C., D.Z., Y.D., J.-E.L., Y.J., C.W., J.E.K., J.S., C.L., K.G., W.C. and K.Z. analyzed the data; and K.P., G.H., K.C., W.C. and K.Z. wrote the paper.

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Correspondence to Wanjun Chen or Keji Zhao.

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Integrated supplementary information

Supplementary Figure 1 Efficiency of Mll4 deletion and its effect on T cell populations in the periphery.

(a) Expression of Mll4 floxed alleles (16-19) in naive CD4+ T cells isolated from lymph nodes and spleens of Mll4fl/flCd4-Cre−, Mll4fl/flCd4-Cre+, Mll4fl/+Cd4-Cre+ and Mll4+/+Cd4-Cre+ mice. n = 3 independent experiments. Error bars: standard deviations.

(b) Distribution of RNA-Seq read density across Mll4 for sorted naive CD4+CD62L+ T cells from lymph nodes and spleen of Mll4fl/fl Cd4-Cre− (WT) and Mll4fl/fl Cd4-Cre+ (Mll4KO) mice. Red rectangle: the deleted exons 16-19 of the Mll4 gene.

(c) Western Blot for Mll4, Rbbp5 and actin beta protein amount in naïve CD4+ T cells of Mll4+/+ Cd4-Cre+ (WT) and Mll4fl/fl Cd4-Cre+ (Mll4KO). n = 2 independent experiments.

(d) Two gating strategies for CD4+ T cells. L/D: dead cell stain.

(e) Representative flow cytometry plots of CD4+ and CD8+ T cell populations in lymph nodes isolated from Mll4fl/fl Cd4-Cre, Mll4fl/fl Cd4-Cre+, Mll4fl/+ Cd4-Cre+ and Mll4+/+ Cd4-Cre+ mice. Shown is one representative experiment (n=5 independent experiments).

(f) Representative flow cytometry plots of CD4+Foxp3+ cells isolated from lymph nodes as in (d). Gated CD4+ T cells from (d). Shown is one representative experiment (n=5 independent experiments).

(g) Quantification of total CD4+, CD8+ and CD4+Foxp3+ cell numbers shown in (d) and (e). ****P ≤ 0.0001 (Kruskal-Wallis test), error bars: standard deviations. Center line: mean.

Supplementary Figure 2 Cytokine expression by CD4+ T cells and characterization of Treg cells in the periphery of Mll4-KO mice.

(a) Representative flow cytometry plots of IFN-γ+ and IL-17a+ CD4+ T cells in the spleen of Mll4fl/+Cd4-Cre+ and Mll4fl/fl Cd4-Cre+ mice. Shown is one representative experiment (n=3 independent experiments).

(b) Percentages of IFN-γ- and IL-17a-producing CD4+ T cells in the spleen of Mll4fl/+ Cd4-Cre+ and Mll4fl/fl Cd4-Cre+ mice. P value n.s. (Mann-Whitney test). Error bars: standard deviations. Center line: mean.

(c) Representative flow cytometry plots of IFN-γ+ and IL-17a+ CD4+ T cells in mesenteric lymph nodes of Mll4fl/+ Cd4-Cre+ and Mll4fl/fl Cd4-Cre+ mice. Shown is one representative experiment (n=3 independent experiments).

(d) Percentages of IFN-γ- and IL-17a-producing CD4+ T cells in mesenteric lymph nodes of Mll4fl/+ Cd4-Cre+ and Mll4fl/fl Cd4-Cre+ mice. P value n.s. (Mann-Whitney test). Error bars: standard deviations. Center line: mean.

(e) Flow cytometry analysis of Ki67 in lymph nodes (right column), spleen (middle column) and thymus (left column) of Mll4fl/+ Cd4-Cre+ and Mll4fl/fl Cd4-Cre+ mice. Shown is one representative experiment (n=3 independent experiments).

(f) Flow cytometry analysis of Helios in lymph nodes (right column), spleen (middle column) and thymus (left column) of Mll4fl/+ Cd4-Cre+ and Mll4fl/fl Cd4-Cre+ mice. Shown is one representative experiment (n=3 independent experiments).

(g) In vitro suppression assay of WT and Mll4KO Treg (CD4+CD25+) cells isolated from lymph nodes and spleen of Mll4fl/fl Cd4-Cre— (WT) and Mll4fl/fl Cd4-Cre+ (Mll4KO) mice, incubated at indicated ratios in the presence of APC and anti-CD3 antibodies and assessed by CFSE dilutions at day 3 of culture. n = 3 independent experiments. Error bars: standard deviations.

Supplementary Figure 3 Treg cell–specific deletion of Mll4 does not affect Treg cell function.

(a) mRNA levels of Mll4 exons 16-19 in CD4+Foxp3-CreYFP— (effector T cells, Teff) and CD4+Foxp3-CreYFP+ (Treg) cells sorted from lymph nodes and spleen of Mll4+/+ Foxp3-CreYFP+ (WT) and Mll4fl/fl Foxp3-CreYFP+ (Mll4KO) mice. Error bars: standard deviations.

(b) Representative flow cytometry plots of CD4+Foxp3+ cells in the thymus of WT (n=2) and Mll4KO (n=2) mice.

(c) Percentages of CD4+Foxp3+ in the thymus of WT (n=2) and Mll4KO (n=2) mice. P value n.s. (Mann-Whitney test). Error bars: standard deviations.

(d) Representative flow cytometry plots of CD4+Foxp3+ cells in spleens of WT (n=2) and Mll4KO (n=2) mice.

(e) Percentages of CD4+Foxp3+ in spleens of WT (n=2) and Mll4KO (n=2) mice. P value n.s. (Mann-Whitney test). Error bars: standard deviations.

(f) Representative flow cytometry plots of IFN-γ+ and IL-17a+ CD4+ T cells in the spleen of Mll4+/+ Foxp3-CreYFP+ (n=2) and Mll4fl/fl Foxp3-CreYFP+ (n=2) mice.

(g) Representative flow cytometry plots of CD4+Foxp3+ cells in peripheral lymph nodes of WT (n=2) and Mll4KO (n=2) mice.

(h) Percentages of CD4+Foxp3+ in peripheral lymph nodes of WT (n=2) and Mll4KO (n=2) mice. P value n.s. (Mann-Whitney test). Error bars: standard deviations.

(i) Representative flow cytometry plots of IFN-γ+ and IL-17a+ CD4+ T cells in peripheral lymph nodes of Mll4+/+ Foxp3-CreYFP+ (n=2) and Mll4fl/fl Foxp3-CreYFP+ (n=2) mice.

(j) In vitro suppression assay of WT and Mll4KO Treg (CD4+Foxp3YFP+CD25+) cells isolated from lymph nodes and spleen of Mll4+/+ Foxp3-CreYFP+ (WT) and Mll4fl/fl Foxp3-CreYFP+ (Mll4KO) mice, incubated at indicated ratios in the presence of APC and anti-CD3 antibodies and assessed by CFSE dilutions at day 3 of culture. n = 3 independent experiments. Error bars: standard deviations.

Supplementary Figure 4 Effect of Mll4 deletion on in vitro CD4+ T cell cultures.

(a) Flow cytometry analysis of intracellular staining for FoxP3 and CFSE during in vitro differentiation of Mll4fl/fl Cd4-Cre— (WT) and Mll4fl/fl Cd4-Cre+ (Mll4KO) naive CD4+ T cells in Treg inducing conditions, with Foxp3 expression and CFSE dilutions measured at indicated time points of differentiation. Representative of n=2 independent experiments.

(b) IFN-γ (left column) and TBET (right column) expression in Th1 cells generated in vitro for 3 days from Mll4fl/fl Cd4-Cre— (WT) and Mll4fl/fl Cd4-Cre+ (Mll4KO) naive CD4+ T cells. n=3 independent experiments. Error bars: standard deviations.

(c) Representative flow cytometry plots of TBET and IFN-γ performed on TH1 cells generated like in (b). Shown is one representative experiment (n=2 independent experiments).

(d) IL-17a (left column) and RORC (right column) expression in TH17 cells generated in vitro for 3 days from Mll4fl/fl Cd4-Cre— (WT) and Mll4fl/fl Cd4-Cre+ (Mll4KO) naive CD4+ T cells. n=3 independent experiments. Error bars: standard deviations.

(e) Representative flow cytometry plots of CD4 and IL-17a performed on TH17 cells generated like in (d). Shown is one representative experiment (n=2 independent experiments).

(f) IL-4 (left column) and GATA3 (right column) expression in TH2 cells generated in vitro for 3 days from Mll4fl/fl Cd4-Cre— (WT) and Mll4fl/fl Cd4-Cre+ (Mll4KO) naive CD4+ T cells. n=3 independent experiments. Error bars: standard deviations.

(g) Representative flow cytometry histogram of GATA3 performed on TH2 cells generated like in (f). Shown is one representative experiment (n=3 independent experiments).

Supplementary Figure 5 MLL4 binds a small portion of non-promoter H3K4me1 regions in naïve CD4+ T cells.

(a) Western blot for H3K4me1, H3K4me2, H3K4me3 and histone H3 in Naïve CD4+ T cells of WT (Mll4fl/flCd4-Cre—) and Mll4KO (Mll4fl/flCd4-Cre+) mice. Representative of n = 4 independent experiments for H3K4me1 and n = 2 for H3K4me2 and H3K4me3 with similar results. Numbers below Western blot image: ratio of H3K4me to H3 intensity bands normalized to WT cells by ImageJ.

(b) Mll3 expression in naïve CD4+ T cells and in vitro generated TH1, TH2, TH17 and Treg cells for 3 days. n = 3 independent experiments. Error bars: standard deviations.

(c) Pie chart of genomic distribution of MLL4 binding sites under stringent settings in naïve CD4+ T cells.

(d) Comparison of the distributions of ChIP-Seq read density of MLL4 and input for MLL4 peaks called by using SICER (applied to the MLL4 ChIP-Seq data) under different E-values from 1,000, 10,000, 100,000 to 1,000,000. For E-value = 1,000, the distributions are for all predicted peaks, and for other E-values the distributions are for the additional peaks called with the current E-value over the previous lower E-value.

(e) Percentages of H3K4me1-enriched genomic regions not bound by MLL4, predicted from ChIP-Seq data with the least stringent settings, controlled by input control.

(f) GREAT gene ontology enrichment analysis on biologic processes for non-promoter H3K4me1 enriched regions not bound by MLL4. Shown are the top five enriched ontologies.

(g) Average of 30-way phastcon conservation score on genomic sequences for non-promoter H3K4me1-enriched regions not bound by MLL4.

(h) UCSC genome browser images showing the ChIP-Seq read density distribution of MLL4, H3K4me1, and DNase-Seq read density across a genomic region around Foxp3. Rectangles: H3K4me1 enriched regions that were not bound by MLL4. Representatives of n = 2 for MLL4 and H3K4me1 and n = 1 for DNase-Seq and H3K27ac.

Supplementary Figure 6 Mll4 deletion induces a reduction in H3K4me1 and chromatin interaction.

(a) A UCSC genome browser image showing the distributions of ChIP-Seq reads for MLL4 and H3K4me1 in a genomic region containing the gene Dynlt1f in WT and Mll4KO naïve CD4+ T cells. Blue rectangle: promoter region of Dynlt1f with the presence of MLL4 binding and H3K4me1 signals in the WT cells and increased H3K4me1 in the Mll4KO cells; Solid green rectangles: putative enhancers upstream of Dynlt1f with the presence of MLL4 binding and H3K4me1 peaks in the WT cells and decreased H3K4me1 in the Mll4KO cells; Red rectangles: putative enhancer regions upstream of Dynlt1f with no or background level of MLL4 binding but with H3K4me1 peaks in the WT cells and decreased H3K4me1 peaks in the Mll4KO cells. Representatives of n = 2 independent experiments. WT: Mll4fl/flCd4-Cre— and Mll4KO: Mll4fl/flCd4-Cre+ (throughout the figure).

(b) Cumulative distribution of normalized ChIP-Seq read densities for H3K4me1-enriched regions at promoter (upper panel) and non-promoter (lower panel) regions in WT and Mll4KO naïve CD4+ T cells. P-value < 2.2E-16 for both panels by Kolmogorov-Smirnov test.

(c) Volcano plot showing both fold change (FC, x-axis) and FDR (y-axis) that quantify the significance of change in H3K4me1 levels for each H3K4me1 peak upon Mll4 deletion in naïve CD4+ T cells. Black dots: MLL4-unbound H3K4me1 peaks; Red dots: MLL4-bound H3K4me1 peaks; %s: the fraction of H3K4me1 peaks with significant changes upon Mll4 deletion (FDR < 1E-6 & FC > 2).

(d) Accumulative distribution of the fold change of H3K4me1 read density of non-promoter H3K4me1 peaks, sorted based on the number of distant interacting MLL4 sites (from 0 to >=3) based on interactions predicted from Hi-C data. Group “0’”: a subset of MLL4-unbound sites that showed no PETs linking to other regions. P-value by Kolmogorov-Smirnov test: 6.1E-8 for “>=3” vs “0’”, 1.2E-4 for “2” vs “0’” and 3.8E-6 for “1” vs “0’”.

(e) UCSC genome browser images showing the distribution of read densities across genomic regions harboring Thy1 (left panel) and Foxp3 (right panel) for MLL4 ChIP-Seq in the control cells, and Hi-C interaction intensity, H3K27ac ChIP-Seq, and H3K27me3 ChIP-Seq in the control cells and in the Mll4KO cells. Representative of n = 3 for track Hi-C (WT), n = 2 for MLL4 (KO) track and H3K27me3 tracks and n = 1 for H3K27ac tracks.

(f) Accumulative distribution of the fold-change of H3K27ac ChIP-Seq read density at 2-kb bins overlapping with H3K27ac peaks that were sorted based on the decrease, increase or no response in interaction intensity upon Mll4 deletion, confined to genomic bins enriched with H3K27ac in either the KO or the control cells. P-value less than 2.2e-16 by Kolmogorov-Smirnov test for “Decrease” vs “No change”.

(g) Similar to (b), but the analysis was made for H3K27me3 on 2-kb bins overlapping with H3K27me3 peaks. P-value less than 2.2e-16 by Kolmogorov-Smirnov test for “Decrease” vs “No change”.

Supplementary Figure 7 MLL4 promotes the expression of super-enhancer targets through enhancer–promoter interaction.

(a) Accumulative distribution of the fold-change of interaction at promoters of super-enhancer targets and at promoters of other genes upon Mll4 deletion. P-value is less than 2.2e-16 by Kolmogorov-Smirnov test. WT: Mll4fl/flCd4-Cre− and Mll4KO: Mll4fl/flCd4-Cre+ (throughout the figure).

(b) UCSC genome browser images showing the distribution of read densities across genomic regions harboring Cxcr4, Rac2 and Stk4 for MLL4 ChIP-Seq and H3K27ac ChIP-Seq in the control cells, Hi-C interaction density and RNA-Seq in the control cells and in the Mll4KO cells. Representatives of n = 3 for track Hi-C (WT), n = 2 for track Hi-C (KO), track MLL4, tracks RNA-Seq and n = 1 for track H3K27ac.

(c) Accumulative distribution of the fold-change of expression of genes sorted into four equal size groups based on the fold change of interaction intensity at promoters by Mll4 deletion in Naïve CD4+ T cells. A line shifting to the left means a more decrease in expression. P-value is less than 2.2e-16 by Kolmogorov-Smirnov test for “Lowest” vs “Highest”.

(d) Accumulative distribution of the fold-change of expression of super-enhancer targets and of other genes upon Mll4 deletion. P-value is less than 2.2e-16 by Kolmogorov-Smirnov test.

Supplementary Figure 8 Deletion of the MLL4-binding –8.5 region has no effect on Treg cell function or activity.

(a) Flow cytometry analysis of Helios in Treg cells from thymus of Crispr WT control and Crispr KO mice. One representative plot of n=4.

(b) Flow cytometry analysis of Helios in Treg cells from spleen of Crispr WT control and Crispr KO mice. One representative plot of n=4.

(c) Flow cytometry analysis of Helios in Treg cells from lymph nodes of Crispr WT control and Crispr KO mice. One representative plot of n=4.

(d) Representative flow cytometry plots of IFN-γ+ and IL-17a+ CD4+ T cells in the spleen of Crispr WT (n=3) and Crispr KO (n=3) mice.

(e) Percentages of IFN-γ- and IL-17a-producing CD4+ T cells in the spleen of Crispr WT (n=3) and Crispr KO (n=3) mice. Center line: mean. P value n.s. (Mann-Whitney test). Error bars: standard deviations. Center line: mean.

(f) Representative flow cytometry plots of IFN-γ+ and IL-17a+ CD4+ T cells in peripheral lymph nodes of Crispr WT control (n=3) and Crispr KO (n=3) mice.

(g) Percentages of IFN-γ- and IL-17a-producing CD4+ T cells in peripheral lymph nodes of Crispr WT control (n=3) and Crispr KO (n=3) mice. P value n.s. (Mann-Whitney test). Center line: mean. Error bars: standard deviations.

(h) In vitro suppression assay of WT and Crispr KO Treg (CD4+CD25+) cells isolated from lymph nodes and spleen of Crispr KO and Crispr WT mice. Effector CD4+CD25- T cells (Teff) were sorted form Crispr WT mice expressing CD45.1 and stained with CFSE. Suppression assay was set up for 3 days then proliferation of CD45.1+ cells was assessed by FACS. Experiment was done in triplicates. Error bars: standard deviations. Center line: mean.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8 (PDF 3369 kb)

Life Sciences Reporting Summary (PDF 349 kb)

Supplementary Table 1

Expression change of histone modification enzymes associated H3K4me in naive CD4+ T cells upon Mll4 deletion (XLSX 10 kb)

Supplementary Table 2

Gene ontology enrichment analysis (biological process) on genes down-regulated in expression by Mll4KO in Naïve CD4+ T cells (XLSX 12 kb)

Supplementary Table 3

Statistics of Hi-C libraries for WT (Mll4 fl/flCd 4Cre-) and KO (Mll4 fl/flCd 4Cre+) cells (XLSX 10 kb)

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Placek, K., Hu, G., Cui, K. et al. MLL4 prepares the enhancer landscape for Foxp3 induction via chromatin looping. Nat Immunol 18, 1035–1045 (2017). https://doi.org/10.1038/ni.3812

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