The transcription factor c-Maf induces the anti-inflammatory cytokine IL-10 in CD4+ T cells in vitro. However, the global effects of c-Maf on diverse immune responses in vivo are unknown. Here we found that c-Maf regulated IL-10 production in CD4+ T cells in disease models involving the TH1 subset of helper T cells (malaria), TH2 cells (allergy) and TH17 cells (autoimmunity) in vivo. Although mice with c-Maf deficiency targeted to T cells showed greater pathology in TH1 and TH2 responses, TH17 cell–mediated pathology was reduced in this context, with an accompanying decrease in TH17 cells and increase in Foxp3+ regulatory T cells. Bivariate genomic footprinting elucidated the c-Maf transcription-factor network, including enhanced activity of NFAT; this led to the identification and validation of c-Maf as a negative regulator of IL-2. The decreased expression of the gene encoding the transcription factor RORγt (Rorc) that resulted from c-Maf deficiency was dependent on IL-2, which explained the in vivo observations. Thus, c-Maf is a positive and negative regulator of the expression of cytokine-encoding genes, with context-specific effects that allow each immune response to occur in a controlled yet effective manner.

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We thank R.A. Flavell (Yale University) for Foxp3RFP IL-10GFP mice; M. Sieweke and C. Birchmeier (Max Delbrück Centre for Molecular Medicine) for Maffl/fl mice; G. Trinchieri (Wistar Institute) for anti-IL-12p40 (C17.8.20); The Francis Crick Institute, Biological Services for breeding and maintenance of the mice; the Advanced Sequencing Platform and A. Sesay for help with sequence sample processing; the Flow Cytometry Platform; Bioinformatics Platform and G. Kelly for help with statistics; Photographics and M. Butt for help with figures; V. Stavropoulos for help with in vivo experiments; and A. Singhania and L. Moreira-Teixeira from the AOG laboratory for review and discussion of the manuscript. Supported by the Francis Crick Institute (Crick Core), which since 1 April 2015 has received its core funding from Cancer Research UK (FC001126 and FC010110), the UK Medical Research Council (FC001126 and FC010110) and the Wellcome Trust (FC001126 and FC010110), and before that from the UK Medical Research Council (MRC U117565642) and the European Research Council (294682-TB-PATH (Crick 10127)) (all for A.O.G., L.G., K.P., M.A.-M., L.S.C. and C.W.), the UK Medical Research Council (MRC Centenary Award for L.G.; and MRC eMedLab Medical Bioinformatics Infrastructure Award MR/L016311/1 for N.M.L.), Crick Core projects (10101 for J.L. and FC001051 for J.B. and V.M.), the Wellcome Trust (WT098326MA for J.B. and V.M.; and Joint Investigator Award 103760/Z/14/Z for N.M.L.), Inserm/CNRS and Agence Nationale de la Recherche (ANR-11-BSV3-0026), Fondation pour la Recherche Médicale (DEQ. 20110421320 ) and the European Research Council (695093 for M.H.S.).

Author information


  1. The Francis Crick Institute, Laboratory of Immunoregulation and Infection, London, UK

    • Leona Gabryšová
    • , Marisol Alvarez-Martinez
    • , Luke S. Cox
    • , Charlotte Whicher
    • , Krzysztof Potempa
    • , Xuemei Wu
    •  & Anne O’Garra
  2. The Francis Crick Institute, Computational Biology Laboratory, London, UK

    • Raphaëlle Luisier
    •  & Nicholas M. Luscombe
  3. The Francis Crick Institute, Malaria Laboratory, London, UK

    • Jan Sodenkamp
    • , Caroline Hosking
    • , Damián Pérez-Mazliah
    •  & Jean Langhorne
  4. The Francis Crick Institute, Helminth Immunology Laboratory, London, UK

    • Yashaswini Kannan
    •  & Mark Wilson
  5. The Francis Crick Institute, Advanced Sequencing Facility Laboratory, London, UK

    • Leena Bhaw
    •  & Greg Elgar
  6. Heidelberg University, Institute of Pharmacology, Heidelberg, Germany

    • Hagen Wende
  7. Aix Marseille University, CNRS, INSERM, CIML, Marseille, France

    • Michael H. Sieweke
  8. Max-Delbrück-Centrum für Molekulare Medizin in der Helmholtzgemeinschaft (MDC), Berlin, Germany

    • Michael H. Sieweke
  9. The Francis Crick Institute, Developmental Dynamics Laboratory, London, UK

    • James Briscoe
    •  & Vicki Metzis
  10. UCL Genetics Institute, Department of Genetics, Evolution and Environment, University College London, London, UK

    • Nicholas M. Luscombe
  11. National Heart and Lung Institute, Imperial College London, London, UK

    • Anne O’Garra


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L.G. co-designed the study with A.O.G., executed the experiments, interpreted and analyzed the data, and co-wrote the paper with A.O.G.; M.A.-M. analyzed the ATAC-seq, ChIP-seq and RNA-seq data and contributed to the writing of the paper; R.L. interpreted and analyzed the RNA-seq data and contributed to the writing of the paper; L.S.C. executed and helped design the in vitro experiments with c-Maf-deficient and control CD4+ T cells and analyzed the data; J.S. and C.H. helped execute and interpret malaria experiments; D.P.-M. contributed data for Supplementary Fig. 3; C.W. helped execute EAE experiments; Y.K. and M.W. helped execute and interpret allergy experiments; K.P. performed early RNA-seq analysis; X.W. executed the genetics for obtaining Cd4-cre × Maffl/fl mice and designed and performed all screening and quality control; L.B. performed processing and troubleshooting for RNA-seq analysis; H.W. constructed Maffl/fl mice and provided feedback on the study; M.H.S. provided feedback and suggestions for the study; G.E. supervised analysis of early RNA-seq data; J.B. and V.M. provided advice and input on the ATAC-seq analysis; J.L. provided expertise for the malaria model and feedback on the study; N.M.L. provided advice and input on the RNA-seq analysis and directed the integrated analysis of ATAC-seq, ChIP-seq and RNA-seq; and A.O.G. co-designed the study with L.G., interpreted and analyzed the data, and co-wrote the paper with L.G.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Anne O’Garra.

Integrated supplementary information

  1. Supplementary Figure 1 The induction and effect of c-Maf on CD4+ T cell differentiation in vitro

    a, Naive CD4+ T cells from Maf fl/fl and Maf fl/flCd4-cre were sorted and stimulated in vitro with anti-CD3 and anti-CD28 in the presence of medium alone, IL-12, IL-27, IL-12 + IL-27, IL-4, TGF-β + IL-6 or TGF-β and assessed for the mRNA expression of Maf, Il10 and master regulator transcription factors Tbx21, Gata3, Rorc and Foxp3 and hallmark cytokines Ifng, Il4 and Il17a as well as Il2ra relative to Hprt as follows. Medium, IL-12, IL-27, IL-12 + IL-27: Maf, Il10, Tbx21, Ifng (day 3); IL-4: Maf (day 5), Il10 (day 5), Gata3 (day 4), Il4 (day 5); TGF-β + IL-6: Maf (day 1), Il10 (day 2), Il17a (day 5); TGF-β: Maf, Il10, Foxp3, Il2ra (day 3) (n = 3 culture wells per condition, mean ± SD; * P< 0.05, ** P< 0.01, *** P< 0.001, **** P< 0.0001, unpaired t-test, two-tailed). Representative data from three biological experiments are shown. b, Naive CD4+ T cells from wild-type mice were sorted, stimulated as in (a) and assessed for intracellular c-Maf on day 3. Depicted are dot plots of c-Maf versus isotype control gated on live CD4+ T cells. Representative data from two independent experiments are shown.

  2. Supplementary Figure 2 Supporting information for differential gene expression analyses

    CD4+ T cells from malaria, HDM and EAE challenged Maffl/flCd4-cre vs Maffl/fl mice were profiled by RNA-seq. a, Volcano plots of differentially expressed genes, with previously associated regulators of IL-10 depicted (blue, significantly down-regulated; red, significantly up-regulated; grey, non-differentially expressed) (n = 3 independent animals (malaria) or biologically independent samples (HDM and EAE) per genotype; P< 0.05, absolute FC ≥ 1.5, moderated t-test, two-tailed). b, Manually curated list of top biological pathways as determined by GO enrichment analysis of each differentially up- and down-regulated genes in Maffl/flCd4-cre vs Maffl/fl mice (n = 3 independent animals (malaria) or biologically independent samples (HDM and EAE) per genotype).

  3. Supplementary Figure 3 Effect on pathology and phenotype of TFH cells in acute phase of malaria

    a, Schematic of P. chabaudi infection in Bcl6fl/fl and Bcl6fl/flCd4-cre mice, percentage weight loss (n = 5, mean ± SD) and temperature changes (n = 8, mean ± SD) on day 9 post P. chabaudi infection. Representative data from two biological experiments are shown. b, Representative cytokine staining of CD4+ T cells on day 14 post P. chabaudi infection in C57BL/6/J mice, plots are gated on live CD3+CD4+CD44+ T cells. Pooled data from two biological experiments are shown (n = 5, mean ± SD).

  4. Supplementary Figure 4 Changes in chromatin accessibility do not account for transriptional disregulation in the absence of c-Maf

    Volcano plots of accessibility changes in ATAC-Seq consensus peak sets in CD4+ T cells from malaria, HDM allergy and EAE challenged Maffl/flCd4-cre vs Maffl/fl mice (n = 3 independent animals (malaria) or biologically independent samples (HDM and EAE) per genotype, statistical significance called using DiffBind 2.02 with FDR < 0.05, absolute fold change ≥ 1.5) assigned to genes (see Supplementary Information for computational methods) and mapped to RNA-seq fold-change values. The top ten peaks ranked by fold-change were labeled with their assigned gene, as well as any remodeled peak assigned to Il10.

  5. Supplementary Figure 5 Framework schematic for the identification of putative direct targets of c-Maf

    For each disease model, the c-Maf ChIP-seq (GSE40918) and motif datasets were filtered according to the accessibility as determined by ATAC-seq, allowing the identification of putative c-Maf binding sites and estimation of its relevance in explaining RNA-seq-defined transcriptional changes observed upon c-Maf deletion (see Supplementary Information for computational methods).

  6. Supplementary Figure 6 Genome browser tracks of other key immune genes

    Genome browser tracks of read coverage of RNA-seq and ATAC-seq in CD4+ T cells from the malaria, HDM allergy and EAE challenged Maffl/flCd4-cre vs Maffl/fl mice (shown as an overlay of n = 3 independent animals (malaria) or biologically independent samples (HDM and EAE) per genotype), as compared to untreated control and matched to c-Maf ChIP-seq (GSE40918) and motif sites.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1-6

  2. Reporting Summary

  3. Supplementary Table 1

    TF correlation lists

  4. Supplementary Table 2

    SVD components

  5. Supplementary Table 3

    Differentially expressed genes

  6. Supplementary Table 4

    Network analysis

  7. Supplementary Table 5

    Direct and indirect c-Maf targets

  8. Supplementary Table 6

    Combined c-Maf binding evidence for direct effects on differentially expressed genes

  9. Supplementary Table 7

    BaGFoot TF statistics

  10. Supplementary Table 8

    BaGFoot TFs with altered activity are statistically enriched in differentially expressed genes

  11. Supplementary Data

    Supplementary Data for Computational Methods

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