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:

Opposing macrophage polarization programs show extensive epigenomic and transcriptional cross-talk

Nature Immunology volume 18, pages 530–540 (2017)Cite this article

Subjects

Abstract

Stimulation of macrophages with interferon-γ (IFN-γ) and interleukin 4 (IL-4) triggers distinct and opposing activation programs. During mixed infections or cancer, macrophages are often exposed to both cytokines, but how these two programs influence each other remains unclear. We found that IFN-γ and IL-4 mutually inhibited the epigenomic and transcriptional changes induced by each cytokine alone. Computational and functional analyses revealed the genomic bases for gene-specific cross-repression. For instance, while binding motifs for the transcription factors STAT1 and IRF1 were associated with robust and IL-4-resistant responses to IFN-γ, their coexistence with binding sites for auxiliary transcription factors such as AP-1 generated vulnerability to IL-4-mediated inhibition. These data provide a core mechanistic framework for the integration of signals that control macrophage activation in complex environmental conditions.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Figure 1: Effects of co-stimulation on the transcriptional programs induced by IFN-γ and IL-4.
Figure 2: Cross-regulation of histone acetylation induced by IFN-γ and IL-4.
Figure 3: Binding of STAT1 and STAT6 in response to stimulation with IFN-γ or IL-4 or both.
Figure 4: Characterization of IL-4-sensitive and IL-4-resistant groups of enhancers induced by IFN-γ.
Figure 5: Analysis of the roles of JUNB and C/EBPβ in the IFN-γ response and IL-4-mediated antagonism.
Figure 6: Analysis of the cis-regulatory bases for inhibition of IL-4-mediated macrophage activation by IFN-γ.
Figure 7: Involvement of Myc in the IL-4-dependent response.

Similar content being viewed by others

Accession codes

Primary accessions

Gene Expression Omnibus

References

  1. Glass, C.K. & Natoli, G. Molecular control of activation and priming in macrophages. Nat. Immunol. 17, 26–33 (2016).

    Article  CAS  Google Scholar 

  2. Murray, P.J. et al. Macrophage activation and polarization: nomenclature and experimental guidelines. Immunity 41, 14–20 (2014).

    Article  CAS  Google Scholar 

  3. Sica, A. & Mantovani, A. Macrophage plasticity and polarization: in vivo veritas. J. Clin. Invest. 122, 787–795 (2012).

    Article  CAS  Google Scholar 

  4. Lawrence, T. & Natoli, G. Transcriptional regulation of macrophage polarization: enabling diversity with identity. Nat. Rev. Immunol. 11, 750–761 (2011).

    Article  CAS  Google Scholar 

  5. Gordon, S. & Martinez, F.O. Alternative activation of macrophages: mechanism and functions. Immunity 32, 593–604 (2010).

    Article  CAS  Google Scholar 

  6. Murray, P.J. & Wynn, T.A. Protective and pathogenic functions of macrophage subsets. Nat. Rev. Immunol. 11, 723–737 (2011).

    Article  CAS  Google Scholar 

  7. Ostuni, R. et al. Latent enhancers activated by stimulation in differentiated cells. Cell 152, 157–171 (2013).

    Article  CAS  Google Scholar 

  8. Monticelli, S. & Natoli, G. Short-term memory of danger signals and environmental stimuli in immune cells. Nat. Immunol. 14, 777–784 (2013).

    Article  CAS  Google Scholar 

  9. Netea, M.G. et al. Trained immunity: A program of innate immune memory in health and disease. Science 352, aaf1098 (2016).

    Article  Google Scholar 

  10. Lavin, Y. et al. Tissue-resident macrophage enhancer landscapes are shaped by the local microenvironment. Cell 159, 1312–1326 (2014).

    Article  CAS  Google Scholar 

  11. Xue, J. et al. Transcriptome-based network analysis reveals a spectrum model of human macrophage activation. Immunity 40, 274–288 (2014).

    Article  CAS  Google Scholar 

  12. Allen, J.E. & Maizels, R.M. Diversity and dialogue in immunity to helminths. Nat. Rev. Immunol. 11, 375–388 (2011).

    Article  CAS  Google Scholar 

  13. Elliott, D.E. & Weinstock, J.V. Helminth-host immunological interactions: prevention and control of immune-mediated diseases. Ann. NY Acad. Sci. 1247, 83–96 (2012).

    CAS  Google Scholar 

  14. Osborne, L.C. et al. Virus-helminth coinfection reveals a microbiota-independent mechanism of immunomodulation. Science 345, 578–582 (2014).

    Article  CAS  Google Scholar 

  15. Reese, T.A. et al. Helminth infection reactivates latent γ-herpesvirus via cytokine competition at a viral promoter. Science 345, 573–577 (2014).

    Article  CAS  Google Scholar 

  16. Salgame, P., Yap, G.S. & Gause, W.C. Effect of helminth-induced immunity on infections with microbial pathogens. Nat. Immunol. 14, 1118–1126 (2013).

    Article  CAS  Google Scholar 

  17. Harris, J. et al. T helper 2 cytokines inhibit autophagic control of intracellular Mycobacterium tuberculosis. Immunity 27, 505–517 (2007).

    Article  CAS  Google Scholar 

  18. Potian, J.A. et al. Preexisting helminth infection induces inhibition of innate pulmonary anti-tuberculosis defense by engaging the IL-4 receptor pathway. J. Exp. Med. 208, 1863–1874 (2011).

    Article  CAS  Google Scholar 

  19. Schleicher, U. et al. TNF-mediated restriction of arginase 1 expression in myeloid cells triggers type 2 NO synthase activity at the site of infection. Cell Rep. 15, 1062–1075 (2016).

    Article  CAS  Google Scholar 

  20. Ostuni, R., Kratochvill, F., Murray, P.J. & Natoli, G. Macrophages and cancer: from mechanisms to therapeutic implications. Trends Immunol. 36, 229–239 (2015).

    Article  CAS  Google Scholar 

  21. Creyghton, M.P. et al. Histone H3K27ac separates active from poised enhancers and predicts developmental state. Proc. Natl. Acad. Sci. USA 107, 21931–21936 (2010).

    Article  CAS  Google Scholar 

  22. Rada-Iglesias, A. et al. A unique chromatin signature uncovers early developmental enhancers in humans. Nature 470, 279–283 (2011).

    Article  CAS  Google Scholar 

  23. Kaikkonen, M.U. et al. Remodeling of the enhancer landscape during macrophage activation is coupled to enhancer transcription. Mol. Cell 51, 310–325 (2013).

    Article  CAS  Google Scholar 

  24. Ivashkiv, L.B. & Donlin, L.T. Regulation of type I interferon responses. Nat. Rev. Immunol. 14, 36–49 (2014).

    Article  CAS  Google Scholar 

  25. Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).

    Article  CAS  Google Scholar 

  26. Pello, O.M. et al. Role of c-MYC in alternative activation of human macrophages and tumor-associated macrophage biology. Blood 119, 411–421 (2012).

    Article  Google Scholar 

  27. Kress, T.R., Sabo, A. & Amati, B. MYC: connecting selective transcriptional control to global RNA production. Nat. Rev. Cancer 15, 593–607 (2015).

    Article  CAS  Google Scholar 

  28. Jenkins, S.J. et al. Local macrophage proliferation, rather than recruitment from the blood, is a signature of TH2 inflammation. Science 332, 1284–1288 (2011).

    Article  CAS  Google Scholar 

  29. Sieweke, M.H. & Allen, J.E. Beyond stem cells: self-renewal of differentiated macrophages. Science 342, 1242974 (2013).

    Article  Google Scholar 

  30. Qiao, Y. et al. Synergistic activation of inflammatory cytokine genes by interferon-γ-induced chromatin remodeling and toll-like receptor signaling. Immunity 39, 454–469 (2013).

    Article  CAS  Google Scholar 

  31. Austenaa, L.M. et al. Transcription of mammalian cis-regulatory elements is restrained by actively enforced early termination. Mol. Cell 60, 460–474 (2015).

    Article  CAS  Google Scholar 

  32. Mancino, A. et al. A dual cis-regulatory code links IRF8 to constitutive and inducible gene expression in macrophages. Genes Dev. 29, 394–408 (2015).

    Article  CAS  Google Scholar 

  33. Garber, M. et al. A high-throughput chromatin immunoprecipitation approach reveals principles of dynamic gene regulation in mammals. Mol. Cell 47, 810–822 (2012).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  35. 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  Google Scholar 

  36. Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    Article  Google Scholar 

  37. Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).

    Article  Google Scholar 

  38. 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  Google Scholar 

  39. Robinson, M.D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).

    Article  Google Scholar 

  40. Robinson, M.D. & Smyth, G.K. Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics 9, 321–332 (2008).

    Article  Google Scholar 

  41. McCarthy, D.J., Chen, Y. & Smyth, G.K. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 40, 4288–4297 (2012).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank Silvia Monticelli for critical feedback on the manuscript; L. Rotta, T. Capra and S. Bianchi for the preparation and processing of the sequencing libraries; F. Cilenti for help with immunofluorescence experiments; I. Amit (Weizmann Institute) for the shRNA vectors targeting Junb and Cebpb; and D. Voehringer (University Hospital Erlangen, Germany for the Stat6−/− bone marrow cells. Supported by the European Research Council (Advanced ERC grant to G.N.), the Italian Ministry of University and Research (FIRB RBAP11H2R9 to G.N.), the Telethon Foundation (SR-Tiget Grant Award TGT16F04 to R.O.), the Cariplo Foundation (Giovani Ricercatori, 2015-0990 to R.O.) and the Italian Association for Research on Cancer (B.A.).

Author information

Author notes

  1. Viviana Piccolo and Alessia Curina: These authors contributed equally to this work.

  2. Renato Ostuni and Gioacchino Natoli: These authors jointly directed this work.

Authors and Affiliations

  1. Department of Experimental Oncology, European Institute of Oncology, Milan, Italy

    Viviana Piccolo, Alessia Curina, Bruno Amati & Gioacchino Natoli

  2. Division of Regenerative Medicine, San Raffaele Telethon Institute for Gene Therapy, Stem Cells and Gene Therapy, IRCCS San Raffaele Scientific Institute, Milan, Italy

    Marco Genua & Renato Ostuni

  3. Humanitas Clinical and Research Center, Milan, Italy

    Serena Ghisletti & Marta Simonatto

  4. Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia, Milan, Italy

    Arianna Sabò & Bruno Amati

  5. Department of Biomedical Sciences, School of Medicine, Humanitas University, Milan, Italy

    Gioacchino Natoli

Authors
  1. Viviana Piccolo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alessia Curina
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marco Genua
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Serena Ghisletti
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Marta Simonatto
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Arianna Sabò
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Bruno Amati
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Renato Ostuni
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Gioacchino Natoli
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

V.P., R.O. and G.N., conceptualization; V.P., analysis of all data and generation of figures; A.C. and R.O., design and carrying out of most experiments; M.G., S.G., M.S., generation of a subset of the data; A.S. and B.A., provision of critical reagents; R.O. and G.N., acquisition of funding; and G.N., supervision, and writing of the manuscript with input from all authors.

Corresponding authors

Correspondence to Renato Ostuni or Gioacchino Natoli.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 General properties of IL-4-mediated inhibition of the transcriptional response to IFN-γ.

(a) Dose-response analysis of IFN-γ- or IL-4-induced gene expression (4h stimulation). (b) Principal Component Analysis (PCA) and Pearson’s correlations for triplicate RNA-seq experiments. (c) Flow cytometry analysis by intracellular staining of BMDMs stimulated with vehicle (UT), IFN-γ, or IFN-γ+IL-4 for 8 hours. Percentages of CD11B+ NOS2+ cells are shown in boxes. (d) Dose-response analysis of the inhibitory effect of co-stimulation on genes induced by IFN-γ (used at 100 ng/ml) or IL-4 (10 ng/ml). (e) Repressive effect of IL-4 administered at the indicated time points after IFN-γ stimulation. RNA was collected after 4 hours of IFN-γ stimulation.

Supplementary Figure 2 Co-activation of STAT1 and STAT6 in BMDMs co-stimulated with IFN-γ and IL-4.

(a) Intracellular flow cytometry staining of phosphorylated STAT1 and STAT6 in BMDMs stimulated with IL-4, IFN-γ, or IFN-γ+IL-4 for the indicated time points. (b) Representative fields of immunofluorescence staining of phospho-STAT6 (green), phospho-STAT1 (red) and nuclei (DAPI, blue) in BMDMs stimulated with IFN-γ, IL-4 or IFN-γ+IL-4 for 60 minutes. Merged images are shown on the right for magnified sub-fields (white frames). (c) Scatterplot analysis of fluorescence intensity values for pSTAT1 and pSTAT6 in the indicated conditions. Values on each axis represent fluorescence intensity in Region of Interests (ROI) corresponding to DAPI-positive nuclei, normalized over the ROI area. (d) Colocalization analyses of pSTAT1 and pSTAT6 immunofluorescence in BMDM stimulated with the indicated conditions. Left: Pearson’s correlation test on scatterplot analyses of colocalization events (Rcoloc). Right: number of colocalizing pixels normalized for percentage of image volume and number of cells per field analyzed (Ncoloc*%Vol/cell nr). Asterisks indicate p values (* p<0.01; *** p<0.0001).

Supplementary Figure 3 Repression of IFN-γ-activated genes by IL-4 in BMDMs requires STAT6.

Stat6-/- BMDMs and matched control were stimulated as indicated. The induction of selected IL-4-sensitive (Nos2, Irg1) or IL-4-resistant genes (Stat1, Irf1) was analyzed by qRT-PCR.

Supplementary Figure 4 Signaling-independent, cell-intrinsic and persistent inhibitory effect of IL-4 on IFN-γ responses.

(a) Western blot analyses of phosphorylation of STAT1, ERK1/2 and AKT (left panel, top), induction and nuclear translocation of IRF1 (right panel) in response to IFN-γ in the absence or presence of IL-4, administered at the indicated time points. The effect of IFN-γ co-stimulation on IL-4-induced STAT6 phosphorylation is also shown (left panel, bottom) (b) RT-qPCR analyses of selected IFN-γ-induced genes after incubation with conditioned medium from IL-4-treated BMDMs (depleted or not of IL-4 with an anti-IL-4 antibody, 1μg/ml). (c) Persistent inhibitory effects on IFN-γ-induced gene expression of transient (4 hours) conditioning with IL-4 of BMDMs, followed by cytokine washout for 24 hours.

Supplementary Figure 5 Kinetics of genomic occupancy by STAT1 in IFN-γ-stimulated BMDMs.

(a) BMDMs were stimulated with IFN-γ for 2 or 4 hours and STAT1 genomic occupancy measured by ChIP-Seq. Average enrichment plots and heatmaps were centered on the summit of STAT1 peaks. (b) A representative snapshot shows STAT1 recruitment at two IFN-γ-inducible loci (Rsad2 and Cmpk2). (c) Western blot analysis of phospho-STAT1 levels in macrophages stimulated with IFN-γ (+/- IL-4) for an extended kinetics. Phospho-STAT1 levels drop at 4 hours of IFN-γ stimulation.

Supplementary Figure 6 Genomic occupancy of IRF1 in BMDMs stimulated with IFN-γ in the presence or absence of IL-4.

(a) Scatter plot showing IRF1 genomic occupancy after IFN-γ stimulation in the presence or absence of IL-4 co-stimulation. The Y-axis reports the -log10 of the FDR obtained by SICER when comparing IRF1 binding in BMDMs stimulated with IFN-g (4h) with IRF1 in unstimulated cells. The X-axis shows the log2 of the fold change in IRF1 measured in stimulated vs. costimulated cells. (b) Box plots showing the quantification of IRF1 ChIP-seq peaks in BMDMs stimulated with IFN-γ +/- IL-4 (left) and histone acetylation changes at the corresponding genomic regions (right). (c) Left: IRF1 peaks in proximity (+/- 50 kb) of IFN-γ inducible genes that are either sensitive or resistant to IL-4 co-stimulation. Right: box-plots showing the quantification of IRF1 ChIP-seq reads associated with IFN-γ-inducible genes that are sensitive or resistant to IL-4 co-stimulation.

Supplementary Figure 7 Depletion of Junb mRNA and Cebpb mRNA by lentivirus-mediated delivery of shRNA.

mRNAs were quantified by q-RT-PCR and normalized to TBP levels. Data from two biological replicates are reported (error bars: +/- SD of technical replicates of the PCR).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7 and Supplementary Note (PDF 2404 kb)

Supplementary Table 1

List of datasets generated in this study. (XLS 71 kb)

Supplementary Table 2

RNA-seq data from BMDMs stimulated with IFN-γ in the presence or absence of IL-4. (XLS 1261 kb)

Supplementary Table 3

Genomic regions of inducible histone acetylation in IFN-γ and IL-4 stimulated BMDMs identified using SICER. (XLS 4196 kb)

Supplementary Table 4

Motif enrichment analysis in acetylated genomic regions induced by either IFN-γ or IL-4 at 2 and 4 hours. Data were generated using PSCAN and divided in transcription factor families. (XLS 87 kb)

Supplementary Table 5

STAT1 and STAT6 genomic occupancy in response to IFN-γ and IL-4, alone or in combination. (XLS 11551 kb)

Supplementary Table 6

Co-stimulation-sensitive and resistant genomic regions whose acetylation was induced by IFN-γ or IL-4. (XLS 690 kb)

Supplementary Table 7

Transcription factor motifs over-represented in the IL-4-sensitive and IL-4-resistant subsets of IFN-γ-induced genomic regions relative to the FANTOM5 dataset. Data were generated with PSCAN and divided in transcription factor families. (XLS 55 kb)

Supplementary Table 8

Statistical over-representation of transcription factor DNA binding motifs in IL-4-sensitive vs. IL-4-resistant genomic regions acetylated in response to IFN-γ. (XLS 38 kb)

Supplementary Table 9

Proximity of JUNB and C/EBPβ peaks to IFN-γ-induced genes that are IL-4-sensitive or resistant. (XLS 203 kb)

Supplementary Table 10

JUNB- and C/EBPβ-dependent, IFN-γ-inducible genes as determined in shRNA-mediated depletion experiments. (XLS 116 kb)

Supplementary Table 11

Transcription factor consensus DNA binding sites over-represented in the IFN-γ-sensitive and IFN-γ-resistant subsets of IL-4-induced genomic regions relative to the FANTOM5 dataset. (XLS 56 kb)

Supplementary Table 12

Statistical over-representation of transcription factor consensus DNA binding sites in IL-4-induced and IFN-γ-resistant vs. IL-4-induced and IFN-γ-sensitive genomic regions. (XLS 54 kb)

Supplementary Table 13

Myc ChIP-seq in IL-4-stimulated BMDMs. Myc peaks were divided into three clusters. (XLS 1566 kb)

Supplementary Table 14

Motif over-representation analysis at the three clusters of Mycbound genomic regions. The analysis was carried out using PSCAN. (XLS 76 kb)

Supplementary Table 15

Gene Ontology terms associated with the genes located in proximity of the three clusters of Myc ChIP-seq peaks were retrieved using GREAT. (XLS 506 kb)

Supplementary Table 16

Myc-dependent IL-4-inducible genes. (XLS 264 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Piccolo, V., Curina, A., Genua, M. et al. Opposing macrophage polarization programs show extensive epigenomic and transcriptional cross-talk. Nat Immunol 18, 530–540 (2017). https://doi.org/10.1038/ni.3710

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ni.3710

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