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Safeguard function of PU.1 shapes the inflammatory epigenome of neutrophils

Nature Immunology volume 20, pages 546–558 (2019)Cite this article

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Abstract

Neutrophils are essential first-line defense cells against invading pathogens, yet when inappropriately activated, their strong immune response can cause collateral tissue damage and contributes to immunological diseases. However, whether neutrophils can intrinsically titrate their immune response remains unknown. Here we conditionally deleted the Spi1 gene, which encodes the myeloid transcription factor PU.1, from neutrophils of mice undergoing fungal infection and then performed comprehensive epigenomic profiling. We found that as well as providing the transcriptional prerequisite for eradicating pathogens, the predominant function of PU.1 was to restrain the neutrophil defense by broadly inhibiting the accessibility of enhancers via the recruitment of histone deacetylase 1. Such epigenetic modifications impeded the immunostimulatory AP-1 transcription factor JUNB from entering chromatin and activating its targets. Thus, neutrophils rely on a PU.1-installed inhibitor program to safeguard their epigenome from undergoing uncontrolled activation, protecting the host against an exorbitant innate immune response.

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Fig. 1: Neutrophil homeostasis in PU.1∆Neu mice.
Fig. 2: PU.1ΔNeu mice are deficient in clearing C. albicans infections.
Fig. 3: Functional hyperactivity of PU.1∆Neu neutrophils.
Fig. 4: PU.1 simultaneously activates or inhibits gene expression in neutrophils.
Fig. 5: Chromatin structure alterations following C. albicans stimulation and PU.1 deletion.
Fig. 6: PU.1 limits JUNB access to enhancers.
Fig. 7: PU.1 recruits HDAC1 to inhibit accessibility of immune-gene enhancers.

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Data availability

The data that support the findings of this study are available from the corresponding author on request. All sequencing data have been deposited in the Gene Expression Omnibus (GEO) under accession number GSE110865. Publicity available Hi-C data referenced in this study were extracted from GEO with the accession number GSE35156.

References

  1. Basu, S., Hodgson, G., Katz, M. & Dunn, A. R. Evaluation of role of G-CSF in the production, survival, and release of neutrophils from bone marrow into circulation. Blood 100, 854–861 (2002).

    Article  CAS  Google Scholar 

  2. Nathan, C. Neutrophils and immunity: challenges and opportunities. Nature Rev. Immunol. 6, 173–182 (2006).

    Article  CAS  Google Scholar 

  3. Mantovani, A., Cassatella, M. A., Costantini, C. & Jaillon, S. Neutrophils in the activation and regulation of innate and adaptive immunity. Nature Rev. Immunol. 11, 519–531 (2011).

    Article  CAS  Google Scholar 

  4. Tecchio, C. & Cassatella, M. A. Neutrophil-derived chemokines on the road to immunity. Semin. Immunol. 28, 119–128 (2016).

    Article  CAS  Google Scholar 

  5. Wright, H. L., Moots, R. J. & Edwards, S. W. The multifactorial role of neutrophils in rheumatoid arthritis. Nature Rev. Rheumatol. 10, 593–601 (2014).

    Article  CAS  Google Scholar 

  6. Egesten, A. et al. The proinflammatory CXC-chemokines GRO-α/CXCL1 and MIG/CXCL9 are concomitantly expressed in ulcerative colitis and decrease during treatment with topical corticosteroids. Int. J. Colorectal Dis. 22, 1421–1427 (2007).

    Article  Google Scholar 

  7. Pelletier, M. et al. Evidence for a cross-talk between human neutrophils and Th17 cells. Blood 115, 335–343 (2010).

    Article  CAS  Google Scholar 

  8. Wipke, B. T. & Allen, P. M. Essential role of neutrophils in the initiation and progression of a murine model of rheumatoid arthritis. J. Immunol. 167, 1601–1608 (2001).

    Article  CAS  Google Scholar 

  9. Ostuni, R., Natoli, G., Cassatella, M. A. & Tamassia, N. Epigenetic regulation of neutrophil development and function. Semin. Immunol. 28, 83–93 (2016).

    Article  CAS  Google Scholar 

  10. Iwasaki, H. et al. Distinctive and indispensable roles of PU.1 in maintenance of hematopoietic stem cells and their differentiation. Blood 106, 1590–1600 (2005).

    Article  CAS  Google Scholar 

  11. Passegué, E., Wagner, E. F. & Weissman, I. L. JunB deficiency leads to a myeloproliferative disorder arising from hematopoietic stem cells. Cell 119, 431–443 (2004).

    Article  Google Scholar 

  12. Srinivas, S. et al. Cre reporter strains produced by targeted insertion of EYFP and ECFP into the ROSA26 locus. BMC Dev. Biol. 1, 4 (2001).

    Article  CAS  Google Scholar 

  13. Tuite, A., Mullick, A. & Gros, P. Genetic analysis of innate immunity in resistance to Candida albicans. Genes Immun. 5, 576–587 (2004).

    Article  CAS  Google Scholar 

  14. Ferwerda, B. et al. Human dectin-1 deficiency and mucocutaneous fungal infections. N. Engl. J. Med. 361, 1760–1767 (2009).

    Article  CAS  Google Scholar 

  15. Brieland, J. et al. Comparison of pathogenesis and host immune responses to Candida glabrata and Candida albicans in systemically infected immunocompetent mice. Infect. Immun. 69, 5046–5055 (2001).

    Article  CAS  Google Scholar 

  16. Netea, M. G. et al. Immune defence against Candida fungal infections. Nature Rev. Immunol. 15, 630–642 (2015).

    Article  CAS  Google Scholar 

  17. Tak, T., Hilvering, B., Tesselaar, K. & Koenderman, L. Similar activation state of neutrophils in sputum of asthma patients irrespective of sputum eosinophilia. Clin. Exp. Immunol. 182, 204–212 (2015).

    Article  CAS  Google Scholar 

  18. Detmers, P. A. et al. Neutrophil-activating protein 1/interleukin 8 stimulates the binding activity of the leukocyte adhesion receptor CD11b/CD18 on human neutrophils. J. Exp. Med. 171, 1155–1162 (1990).

    Article  CAS  Google Scholar 

  19. Fortunati, E., Kazemier, K. M., Grutters, J. C., Koenderman, L. & van Bosch, J. M. Human neutrophils switch to an activated phenotype after homing to the lung irrespective of inflammatory disease. Clin. Exp. Immunol. 155, 559–566 (2009).

    Article  CAS  Google Scholar 

  20. Anderson, K. L., Smith, K. A., Pio, F., Torbett, B. E. & Maki, R. A. Neutrophils deficient in PU.1 do not terminally differentiate or become functionally competent. Blood 92, 1576–1585 (1998).

    CAS  PubMed  Google Scholar 

  21. Saijo, S. & Iwakura, Y. Dectin-1 and dectin-2 in innate immunity against fungi. Int. Immunol. 23, 467–472 (2011).

    Article  CAS  Google Scholar 

  22. Elmore, S. A. et al. Recommendations from the INHAND Apoptosis/Necrosis Working Group. Toxicol. Pathol. 44, 173–188 (2016).

    Article  CAS  Google Scholar 

  23. Bréchot, N. et al. Modulation of macrophage activation state protects tissue from necrosis during critical limb ischemia in thrombospondin-1-deficient mice. PLoS One 3, e3950 (2008).

    Article  Google Scholar 

  24. Cham, B. P., Gerrard, J. M. & Bainton, D. F. Granulophysin is located in the membrane of azurophilic granules in human neutrophils and mobilizes to the plasma membrane following cell stimulation. Am. J. Pathol. 144, 1369–1380 (1994).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Sahoo, M., Del Barrio, L., Miller, M. A. & Re, F. Neutrophil elastase causes tissue damage that decreases host tolerance to lung infection with Burkholderia species. PLoS Pathog. 10, e1004327 (2014).

    Article  Google Scholar 

  26. Persy, V. P., Verhulst, A., Ysebaert, D. K., Greef, K. E. De & Broe, M. E. De Reduced postischemic macrophage infiltration and interstitial fibrosis in osteopontin knockout mice. Kidney Int. 63, 543–553 (2003).

    Article  CAS  Google Scholar 

  27. Pixley, F. J. & Stanley, E. R. CSF-1 regulation of the wandering macrophage: complexity in action. Trends Cell Biol. 14, 628–638 (2004).

    Article  CAS  Google Scholar 

  28. Salazar-Mather, T. P., Orange, J. S. & Biron, C. A. Early murine cytomegalovirus (MCMV) infection induces liver natural killer (NK) cell inflammation and protection through macrophage inflammatory protein 1ɑ (MIP-1ɑ)-dependent pathways. J. Exp. Med. 187, 1–14 (1998).

    Article  CAS  Google Scholar 

  29. Pollock, J. D. et al. Mouse model of X-linked chronic granulomatous disease, an inherited defect in phagocyte superoxide production. Nat. Genet. 9, 202–209 (1995).

    Article  CAS  Google Scholar 

  30. Vignais, P. V. The superoxide-generating NADPH oxidase: structural aspects and activation mechanism. Cell. Mol. Life Sci. 59, 1428–1459 (2002).

    Article  CAS  Google Scholar 

  31. Skrzypek, M. S. et al. The Genome Database (CGD): incorporation of Assembly 22, systematic identifiers and visualization of high throughput sequencing data. Nucleic Acids Res. 45, D592–D596 (2017).

    Article  CAS  Google Scholar 

  32. Saville, S. P., Lazzell, A. L., Monteagudo, C. & Lopez-Ribot, J. L. Engineered control of cell morphology in vivo reveals distinct roles for yeast and filamentous forms of Candida albicans during infection. Eukaryot. Cell 2, 1053–1060 (2003).

    Article  CAS  Google Scholar 

  33. Wang, G. G. et al. Quantitative production of macrophages or neutrophils ex vivo using conditional Hoxb8. Nat. Methods 3, 287–293 (2006).

    Article  CAS  Google Scholar 

  34. Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

    Article  CAS  Google Scholar 

  35. Heintzman, N. D. et al. Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome. Nat. Genet. 39, 311–318 (2007).

    Article  CAS  Google Scholar 

  36. 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 

  37. Theilgaard-Mönch, K., Knudsen, S., Follin, P. & Borregaard, N. The transcriptional activation program of human neutrophils in skin lesions supports their important role in wound healing. J. Immunol. 172, 7684–7693 (2004).

    Article  Google Scholar 

  38. Fradin, C. et al. The early transcriptional response of human granulocytes to infection with Candida albicans is not essential for killing but reflects cellular communications. Infect. Immun. 75, 1493–1501 (2007).

    Article  CAS  Google Scholar 

  39. Kruger, P. et al. Neutrophils: between host defence, immune modulation, and tissue injury. PLoS Pathog. 11, e1004651 (2015).

    Article  Google Scholar 

  40. McDonald, B. & Kubes, P. Neutrophils and intravascular immunity in the liver during infection and sterile inflammation. Toxicol. Pathol. 40, 157–165 (2012).

    Article  CAS  Google Scholar 

  41. Dakic, A. et al. PU.1 regulates the commitment of adult hematopoietic progenitors and restricts granulopoiesis. J. Exp. Med. 201, 1487–1502 (2005).

    Article  CAS  Google Scholar 

  42. van Riel, B. & Rosenbauer, F. Epigenetic control of hematopoiesis: the PU.1 chromatin connection. Biol. Chem. 395, 1265–1274 (2014).

    PubMed  Google Scholar 

  43. Hagemeier, C., Bannister, A. J., Cook, A. & Kouzarides, T. The activation domain of transcription factor PU.1 binds the retinoblastoma (RB) protein and the transcription factor TFIID in vitro: RB shows sequence similarity to TFIID and TFIIB. Proc. Natl Acad. Sci. USA 90, 1580–1584 (1993).

    Article  CAS  Google Scholar 

  44. Yamamoto, H., Kihara-Negishi, F., Yamada, T., Hashimoto, Y. & Oikawa, T. Physical and functional interactions between the transcription factor PU.1 and the coactivator CBP. Oncogene 18, 1495–1501 (1999).

    Article  CAS  Google Scholar 

  45. Bai, Y., Srinivasan, L., Perkins, L. & Atchison, M. L. Protein acetylation regulates both PU.1 transactivation and Igκ 3′ enhancer activity. J. Immunol. 175, 5160–5169 (2005).

    Article  CAS  Google Scholar 

  46. 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  Google Scholar 

  47. 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 

  48. Kihara-Negishi, F. et al. In vivo complex formation of PU.1 with HDAC1 associated with PU.1-mediated transcriptional repression. Oncogene 20, 6039 (2001).

    Article  CAS  Google Scholar 

  49. Suzuki, M., Yamada, T., Kihara-Negishi, F., Sakurai, T. & Oikawa, T. Direct association between PU.1 and MeCP2 that recruits mSin3A–HDAC complex for PU.1-mediated transcriptional repression. Oncogene 22, 8688 (2003).

    Article  CAS  Google Scholar 

  50. Fontana, M. F. et al. JUNB is a key transcriptional modulator of macrophage activation. J. Immunol. 194, 177–186 (2014).

    Article  Google Scholar 

  51. Jacobsen, I. D., Lüttich, A., Kurzai, O., Hube, B. & Brock, M. In vivo imaging of disseminated murine Candida albicans infection reveals unexpected host sites of fungal persistence during antifungal therapy. J. Antimicrob. Chemother. 69, 2785–2796 (2014).

    Article  CAS  Google Scholar 

  52. Gautier, L., Cope, L., Bolstad, B. N. & Irrizarry, R. A. affy - Analysis of Affymetrix GeneChip Data at the Probe Level (Oxford Univ. Press, 2004).

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

    Article  Google Scholar 

  54. Ashburner, M. et al. Gene ontology: tool for the unification of biology.Nature Genet. 25, 25–29 (2000).

    Article  CAS  Google Scholar 

  55. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  Google Scholar 

  56. Trapnell, C. et al. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat. Biotechnol. 31, 46–53 (2013).

    Article  CAS  Google Scholar 

  57. McQuitty, L.L. Similarity analysis by reciprocal pairs for discrete and continuous data. Educ. Psychol. Meas. 26, 825–831 (1966).

    Article  Google Scholar 

  58. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  Google Scholar 

  59. Kolodziej, K. E. et al. Optimal use of tandem biotin and V5 tags in ChIP assays. BMC Mol. Biol. 10, 6 (2009).

    Article  Google Scholar 

  60. Blecher-Gonen, R. et al. High-throughput chromatin immunoprecipitation for genome-wide mapping of in vivo protein-DNA interactions and epigenomic states. Nat. Protoc. 8, 539–554 (2013).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  62. Ross-Innes, C. S. et al. Differential oestrogen receptor binding is associated with clinical outcome in breast cancer. Nature 481, 389–393 (2012).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  64. Durinck, S. et al. BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics 21, 3439–3440 (2005).

    Article  CAS  Google Scholar 

  65. Ye, T. et al. seqMINER: an integrated ChIP-seq data interpretation platform. Nucleic Acids Res. 39, e35 (2011).

    Article  CAS  Google Scholar 

  66. Taslim, C. et al. Comparative study on ChIP-seq data: normalization and binding pattern characterization. Bioinformatics 25, 2334–2340 (2009).

    Article  CAS  Google Scholar 

  67. Dixon, J. R. et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485, 376–380 (2012).

    Article  CAS  Google Scholar 

  68. Jiang, H., Lei, R., Ding, S.-W. & Zhu, S. Skewer: a fast and accurate adapter trimmer for next-generation sequencing paired-end reads. BMC Bioinformatics 15, 182 (2014).

    Article  Google Scholar 

  69. Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  Google Scholar 

  70. Schep, A. N., Wu, B., Buenrostro, J. D. & Greenleaf, W. J. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nat. Methods 14, 975–978 (2017).

    Article  CAS  Google Scholar 

  71. Wenzel, S.-S. et al. MCL1 is deregulated in subgroups of diffuse large B-cell lymphoma. Leukemia 27, 1381–1390 (2013).

    Article  CAS  Google Scholar 

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Acknowledgements

We thank J. Rettkowski and J. Kamping for assistance with experiments, T. König for FACS sorting, J. Brands for help with figure preparations and C. Brennecka for linguistic support. This work was supported by grants from the Deutsche Forschungsgemeinschaft (DFG; RO2295/6-1) and the University of Muenster Medical Faculty (Ros2/007/15) to F.R. M.P. is supported by the DFG (SFB992, SFB1160, SFB/TRR167, Reinhart-Koselleck-Grant).

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  1. Mathias Leddin

    Present address: Roche Pharmaceuticals Research and Early Development Informatics, Roche Innovation Center Basel, Basel, Switzerland

Authors and Affiliations

  1. Institute of Molecular Tumor Biology, University of Muenster, Muenster, Germany

    Josephine Fischer, Alexander Tönges, Hanna Aleth, Verena Gröning & Frank Rosenbauer

  2. Institute of Medical Informatics, University of Muenster, Muenster, Germany

    Carolin Walter & Martin Dugas

  3. Institute of Neuropathology, Faculty of Medicine, University of Freiburg, Freiburg, Germany

    Marta Joana Costa Jordão & Marco Prinz

  4. Faculty of Biology, University of Freiburg, Freiburg, Germany

    Marta Joana Costa Jordão

  5. Max-Delbrück-Center for Molecular Medicine, Berlin, Germany

    Mathias Leddin

  6. Department of Medicine A, University Hospital Muenster, Muenster, Germany

    Tabea Erdmann & Georg Lenz

  7. Institute of Immunology, University of Muenster, Muenster, Germany

    Johannes Roth & Thomas Vogl

  8. Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Freiburg, Germany

    Marco Prinz

  9. Center for NeuroModulation, Faculty of Medicine, University of Freiburg, Freiburg, Germany

    Marco Prinz

  10. Research Group Microbial Immunology, Leibniz Institute for Natural Product Research and Infection Biology-Hans Knoell Institute, Jena, Germany

    Ilse D. Jacobsen

  11. Institute for Microbiology, Friedrich-Schiller-University, Jena, Germany

    Ilse D. Jacobsen

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Contributions

J.F., A.T., H.A., M.L., V.G., T.E. and I.D.J. conducted experiments and analyzed data, C.W., A.T. and M.D. performed computational analyses, T.V. and J.R. helped with the cell immortalization, G.L. provided important tools, and M.J.C.J. and M.P. conducted pathological analyses. J.F., I.D.J. and F.R. designed the study and wrote the manuscript. F.R. supervised the entire project and provided financial support.

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Correspondence to Frank Rosenbauer.

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Supplementary Figure 1 Validation of and gene expression in neutrophils of PU.1∆Neu mice.

(a) Scheme of the crossbreeding to generate a neutrophil-specific PU.1-knockout mouse strain. (b) Flow cytometry analysis of GFP expression in BM neutrophils (CD11b+Ly-6G+), splenic B cells (CD11b-CD19+), splenic Ly-6C- (CD11b+CD115+Ly-6C-) and Ly-6C+ (CD11b+CD115+Ly-6C+) monocytes as well as in the BM progenitor populations LSK (lin-c-kit+Sca-1+), common myeloid progenitors (CMP; lin-c-kit+Sca-1-CD34+FcγR-), granulocyte-monocyte progenitors (GMP; lin-c-kit+Sca-1-CD34+FcγR+) and megakaryocyte-erythrocyte progenitors (MEP, lin-c-kit+Sca-1-CD34-FcγR-) of wildtype (GFP-, black) and PU.1∆Neu mice (GFP+, blue). Data are representative of at least three independent experiments with similar results. (c) Flow cytometry analysis of YFP expression of neutrophils (Ly-6C+CD115-) and Ly-6C + monocytes (Ly-6C+CD115+) from the BM of PU.1∆Neu mice crossbred with loxSTOPlox-YFP reporter mice. Numbers in outlined areas indicate the percentages of cells within the gates. Data are representative of two independent experiments with similar results. (d) qPCR analysis of Spi1 expression in neutrophils (CD11b+Ly-6G+GFP+) isolated from the BM of PU.1WT (n = 5) and PU.1ΔNeu (n = 5) mice. Results are shown as fold over actin transcripts (mean±s.e.m.) and are from five independent experiments. ***P ≤ 0.0001 (unpaired t-test, two-tailed). (e) Immunoblot analysis to determine PU.1 protein levels in flow-sorted BM neutrophils (CD11b+Ly-6G+GFP+) from PU.1WT and PU.1∆Neu mice. Detection of valosin-containing protein (VCP) ensured comparable loading. Shown is one of three independent experiments with similar results. (f) qPCR analysis of Spi1 expression in monocytes (CD11b+CD115+) flow-sorted from PU.1WT and PU.1∆Neu mice (n = 2 each). Results are shown as mean fold over actin transcripts. (g,h) Flow cytometry analysis of peripheral blood B cells (CD11b-CD19+) and T cells (CD11b-CD3+) (g) and monocytes (CD11b+CD150+Ly-6C+ and CD11b+CD150+Ly-6C-) (h) in PU.1WT and PU.1ΔNeu mice (n = 8 each). Numbers in outlined areas indicate mean percent ± s.e.m. (i) White blood cell counts (WBC) of PU.1WT and PU.1∆Neu mice (n = 9 each). Each symbol represents an individual mouse; small horizontal lines indicate the mean (± s.e.m.). *P = 0.0138 (unpaired t-test, two-tailed). (j,k) Gene set enrichment analysis differentially expressed between neutrophils (Gr-1+) isolated from the BM of PU.1WT (n = 3) and PU.1ΔNeu mice (n = 2). Enrichment score calculations are based on weighted Kolmogorov-Smirnov-like statistics, adjusted for multiple testing after Benjamini-Hochberg. Compared gene set sizes were n = 12 (granulocyte differentiation), n = 231 (inflammatory response) and n = 21 (Nf-kB). (l) Expression of downregulated genes in neutrophils from PU.1ΔNeu (n = 3) compared to PU.1WT (n = 2) mice. Results were obtained from microarrays and are given in mean intensity values.

Supplementary Figure 2 The role of PU.1 during distinct neutrophil defense steps.

(a,b) Experimental setups of systemic Candida albicans (C. albicans) infections of PU.1WT and PU.1ΔNeu mice with a lethal (5x105 colony forming units, cfu) (a) or sublethal (2x105 cfu) (b) fungal dose to obtain survival curves (a) or perform time point analyses (b). (c,d) Fungal burden of brains from PU.1WT (c: n = 5, d: n = 6) and PU.1ΔNeu (c,d: n = 6) mice one (c) and three (d) days after C. albicans-infection (2x105 cfu). Data are from two independent experiments. Small horizontal lines indicate the mean ( + s.e.m.) *P = 0.0173 (unpaired t-test, two-tailed). (e) Flow cytometry analysis of CD62L expression on the surface of peripheral blood neutrophils of PU.1WT and PU.1ΔNeu mice one (d1) and three (d3) days post C. albicans-infection (2x105 cfu). (f) Frequency of CD62Llo expressing neutrophils in the PB of unstimulated PU.1WT (n = 3) and PU.1∆Neu (n = 4) mice. Each symbol represents an individual mouse; small horizontal lines indicate the mean ( ± s.e.m.). The experiment was independently performed twice with similar results. (g) Mean fluorescence intensity (MFI) of CD11b on PB neutrophils in C. albicans-challenged (2x105 cfu) PU.1WT (n = 3) and PU.1ΔNeu (n = 3) mice. Each symbol represents an individual mouse; small horizontal lines indicate the mean ( ± s.e.m.). (h) Ratio of neutrophil numbers between kidneys and the peripheral blood (number of kidney neutrophils divided by the number of blood neutrophils) in PU.1WT and PU.1ΔNeu mice (n = 3 each) three days post C. albicans-infection (2x105 cfu). Data are the mean ± s.e.m. **P = 0.0036 (unpaired t-test, two-tailed). (i,j) Expression of NADPH oxidase subunit genes Cybb, Cyba, Ncf, Ncf2 and Ncf4 (i) and genes coding for immune response receptors important for C. albicans sensing: Clec7a, Clec4e and Clec4n (j) in flow-sorted neutrophils (Gr-1+) isolated from the BM of PU.1WT (n = 2) and PU.1ΔNeu mice (n = 3). Results were obtained from microarrays and are given in normalized intensity values. Small horizontal lines indicate the mean. All expression differences of the indicated genes were independently reproduced by qRT-PCR (data not shown).

Supplementary Figure 3 PU.1 inhibits neutrophil activation.

(a) Mixed BM chimeras were generated by intravenous injection of 7.5x105 BM cells from SJL.Bl6 mice (CD45.1+) mixed with 7.5x105 BM cells from either PU.1WT or PU.1ΔNeu (both CD45.2+) mice into lethally irradiated (9 Gy) SJLxBl6 (CD45.1+CD45.2+) or SJL.Bl6 mice (CD45.1+). After 2 to 3 month of engraftment, chimera were intravenously infected with a sublethal (2x105 cfu) dose of C. albicans and time point studies were performed. (b) Weight loss of C. albicans-infected (2x105 cfu) mixed BM chimeras (n = 5) three days post infection. Each symbol represents an individual mouse; small horizontal lines indicate the mean ( ± s.e.m.).(c) Kidney weights of C. albicans-infected (2x105 cfu) mixed BM chimeras (n = 5) three days post infection. Each symbol represents an individual mouse; small horizontal lines indicate the mean ( ± s.e.m.). (d) Fungal burden of kidneys from mixed BM-reconstituted chimeras (n = 4) one and three days post C. albicans-infection (2x105 cfu). Each symbol represents an individual mouse; small horizontal lines indicate the mean ( ± s.e.m.). (e) Frequency of CD62Llo expressing neutrophils in the peripheral blood (PB) of C. albicans-infected (2x105 cfu) mixed BM chimeras (n = 5) three days post infection. Each symbol represents an individual mouse; small horizontal lines indicate the mean ( ± s.e.m.). *P = 0.0191. (f) Mean fluorescence intensity (MFI) of the degranulation marker CD63 on the surface of neutrophils (CD11b+Ly-6G+) from the BM of unstimulated PU.1WT (n = 5) and PU.1ΔNeu (n = 4) mice as determined by flow cytometry. Each symbol represents an individual mouse; small horizontal lines indicate the mean ( ± s.e.m.). ***P ≤ 0.0001. (g) qPCR analysis of the proinflammatory cytokine-encoding genes Il1b and Tnfa in BM derived macrophages (BMM) coincubated with in vitro derived PU.1WT and PU.1ΔNeu neutrophils (n = 3 each). Results are shown as fold difference relative to untreated BMM control (mean + s.e.m). Left to right: *P = 0.0443, *P = 0.0154. (h) Weight loss of C. albicans NRG1 mutant-infected (1x106 cfu) PU.1WT (n = 6), PU.1ΔNeu (n = 4) and ΔCYBB (n = 4) mice one and three days post infection. Each symbol represents an individual mouse; small horizontal lines indicate the mean ( ± s.e.m.). Left to right: ***P = 0.0008, *P = 0.0494. (i) Kidney weights of C. albicans NRG1 mutant-infected (1x106 cfu) PU.1WT (n = 6), PU.1ΔNeu (n = 4) and ΔCYBB (n = 4) mice one to three days post infection. Each symbol represents an individual mouse; small horizontal lines indicate the mean (± s.e.m.). *P = 0.0302. (j) Fungal burden of kidneys from C. albicans NRG1 mutant-infected (1x106 cfu) PU.1WT (n = 6), PU.1ΔNeu (n = 4) and ΔCYBB (n = 2) mice two and three days post infection. Each symbol represents an individual mouse; small horizontal lines indicate the mean ( ± s.e.m.). Left to right: ***P ≤ 0.0001, **P = 0.0012. (k) Absolute number (by flow cytometry) of total macrophages (CD11b+F4/80+) in the kidneys of PU.1WT (n = 6), PU.1ΔNeu (n = 4) and ΔCYBB (n = 4) mice that were challenged with the NRG1 overexpression mutant of C. albicans (1x106 cfu) for one to three days. Each symbol represents an individual mouse; small horizontal lines indicate the mean ( ± s.e.m.). Left to right: **P = 0.0085, **P = 0.0032 (l) Percentage of CD62Llo expressing neutrophils in the BM of C. albicans NRG1 mutant-challenged (1x106 cfu) PU.1WT (n = 6), PU.1ΔNeu (n = 4) and ΔCYBB (n = 4) mice one to three days post infection. Each symbol represents an individual mouse; small horizontal lines indicate the mean ( ± s.e.m.). Left to right: ***P ≤ 0.0001, ***P ≤ 0.0001. All P-values in this figure: unpaired t-test, two-tailed.

Supplementary Figure 4 Generation and validation of immortalized PU.1∆Neu neutrophil progenitor lines.

(a) Experimental procedure of the generation of immortalized neutrophil progenitor cell lines from PU.1WT and PU.1ΔNeu mice. BM cells were isolated and enriched for progenitor cells by gradient centrifugation. Progenitor cells were transfected with the ER-Hoxb8 (HoxER) retrovirus and were cultured with supplementation of β-estradiol and stem cell factor (SCF). For neutrophil differentiation, progenitor cells were cultured without β-estradiol with supplementation of granulocyte colony-stimulating factor (G-CSF) for three to four days. To stimulate neutrophils, they were exposed to heat inactivated C. albicans cells for two hours. (b) PCR analysis of the PU.1 excision in progenitors (d0) and neutrophils (d4) of HoxER-PU.1WT (WT) and HoxER-PU.1ΔNeu (ΔNeu) lines. The experiment was independently performed more than five times with similar results. (c) qPCR analysis of Spi1 expression in progenitors (d0, n = 3 both genotypes) and neutrophils (d4, n = 4 both genotypes) of HoxER-PU.1WT (WT) and HoxER-PU.1ΔNeu (ΔNeu) lines. Results are shown as fold over actin transcripts (mean±s.e.m.) of three (d0) and four (d4) independent experiments. ***P = 0.0003 (unpaired t-test, two-tailed). (d) Immunoblot analysis of PU.1 expression in HoxER-PU.1WT and HoxER-PU.1ΔNeu progenitors (d0) and neutrophils (d4). Detection of valosin-containing protein (VCP) served as loading control. The experiment was independently performed twice with similar results. (e,f) Flow cytometry analysis of HoxER-PU.1WT and HoxER-PU.1ΔNeu progenitors (e) and neutrophils (f). The experiment was independently performed more than five times with similar results. (g) Morphological analysis of cytospins stained with Giemsa of HoxER-PU.1WT and HoxER-PU.1ΔNeu neutrophils. Scale bar resembles 10 µm. The experiment was independently performed twice with similar results. (h,i) Gene set enrichment analysis of gene signatures generated from the top 500 upregulated genes in HoxER-PU.1WT (h; Top 500 HoxER-PU.1WT) or HoxER-PU.1ΔNeu (i; Top 500 HoxER-PU.1ΔNeu) neutrophils obtained from mRNA-seq in flow-sorted (Gr-1+) neutrophils from the BM of PU.1WT (n = 2) and PU.1ΔNeu (n = 3) mice. Enrichment score calculations (for n = 500 genes each) are based on weighted Kolmogorov-Smirnov-like statistics, adjusted for multiple testing after Benjamini-Hochberg. (j) ROS production in HoxER-PU.1WT (PU.1WT; n = 3) and HoxER-PU.1ΔNeu (PU.1ΔNeu; n = 3) neutrophils stimulated with heat inactivated C. albicans cells. Data were obtained from three independent experiments. Each symbol represents and individual sample, small horizontal lines indicate the mean ( + s.e.m.). ***P = 0.0003, (unpaired t-test, two-tailed). (k) Distribution of expression of cluster genes (I-IV) visualized as the log2 of the reads per kilobase per million (RPKM) values.

Supplementary Figure 5 Cluster enhancer determination and allocation.

(a) Spearman correlations for individual ATAC-seq samples (n = 2 each) based on the combined peaks of unstimulated (WT u) and 1-hour C. albicans-stimulated (WT Ca) HoxER-PU.1WT neutrophils. (b) Expression of AP-1 transcription factors in the indicated neutrophils. All samples were n = 3 except for HoxER-PU.1ΔNeu which was n = 2. Expression is shown as in reads per kilobase per million (RPKM) obtained from mRNA-seq. Bars represent mean ± s.e.m. expect for HoxER-PU.1ΔNeu which is only shown as mean. (c) Scheme of the used enhancer determination strategy and their allocation to genes. Enhancers were determined as promoter distal regions marked with H3K4me1. Enhancers with differential H3K27ac occupancy in one condition were allocated to the next differential expressed gene within one TAD. Box indicates the active enhancer, which was allocated to the differentially expressed gene. (d) PU.1 occupancy on gene regulatory elements (promoters and enhancers) of the cluster genes. (e) Number of genes dedicated to clusters I-IV, number of promoters of cluster genes with PU.1 occupancy, number of enhancers which could be allocated to the cluster genes, the number of these enhancers with PU.1 occupancy and the number of expressed genes that could be allocated to the PU.1 bound enhancers. (f) Reporter activities of putative enhancers measured by luciferase assay in transiently transfected THP-1 cells. Data represent the mean of two (E3–5, negative regions), three (E2) or four (E1) independent experiments. (g,h) Integrative genomics viewer images showing tracks of normalized tag counts of H3K4me1 (K4me1), H3K27ac (K27ac) and PU.1 ChIP-seq as well as ATAC-seq in HoxER-PU.1WT and HoxER-PU.1ΔNeu neutrophils. Shown are the PU.1 activated gene loci of the cluster I gene Cd34 and the cluster II gene Il1b (g), and PU.1 inhibited loci of the cluster III gene Spp1 and the cluster IV gene Cd63 (h). Grey boxes indicate relevant enhancer positions. Sown are representative tracks of two independent samples each.

Supplementary Figure 6 JUNB in PU.1-deleted neutrophils.

(a) Homer motif analysis in PU.1-bound cluster I and II or III and IV enhancers. Data were derived from two independent samples each. Motif enrichment was calculated with HOMER software, applying cumulative hypergeometric distribution adjusted for multiple testing with the Benjamini-Hochberg method. (b) Average and normalized (events per million) cutting events based on the ATAC-seq data over cluster enhancers containing a CEBP motif. (c) Genomic distribution of JUNB and PU.1 cobound regions in HoxER-PU.1WT neutrophils. (d) qPCR analysis of Junb expression in C. albicans stimulated HoxER-PU.1ΔNeu neutrophils that were infected with retroviruses carrying a scramble control or one of three independent shRNAs against Junb. Expression is shown as percent of the control value. Dashed line represents the mean expression value of the controls.

Supplementary Figure 7 PU.1-inhibits immune gene expression by HDAC1.

(a) Table summarizing expression (with a minimal RPKM value of 1) of HDACs in HoxER neutrophils. Expression was obtained from mRNA-seq. (b) qPCR analysis of expression of the indicated cluster III genes in HoxER-PU.1WT and HoxER-PU.1ΔNeu neutrophils (n = 3 each) treated for 6 h with DMSO, 100 nM TSA, 5 µM Entinostat, 5 µM Mocetinostat or 1 µM TMP195. Relative expression values of three independent experiments are shown as mean ± s.e.m. and were calculated as fold difference over the expression values of DMSO treated HoxER neutrophils. (c) qPCR analysis of expression of the indicated cluster III genes in HoxER-PU.1WT and HoxER-PU.1ΔNeu neutrophils (n = 3 each) treated for 6 h with DMSO or 1 µM TMP269. Relative expression values of two independent experiments are shown as mean ± s.e.m. and were calculated as fold difference over the expression values of DMSO treated HoxER neutrophils. (d) qPCR analysis of expression of the indicated cluster II genes in HoxER-PU.1WT neutrophils (n = 3) treated for 6 h with DMSO or 5 µM Entinostat. Relative expression values of three independent experiments are shown as mean ± s.e.m. and were calculated as fold difference over the expression values of DMSO treated HoxER-PU.1WT neutrophils. b-d: ns (not significant) P > 0.05, *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001 (unpaired t-test, two-tailed). (unpaired t-test, two-tailed). (e) Immunoblot analysis (IB) of the immunoprecipitation (IP) of HDAC1, HDAC2 and HDAC3 from nuclear extracts of HoxER-PU.1WT neutrophils. This ensured that HDAC1–3 were precipitated from the nuclear extracts. IgG precipitated nuclear extract from the same cells was used as control. Data are presentative of three independent experiments for HDAC1 and HDAC2, and two for HDAC3. (f) qPCR analysis of HDAC1 ChIP at cluster III/IV gene enhancers (indicated below) of chromatin extracted from HoxER-PU.1WT and HoxER-PU.1ΔNeu neutrophils. As negative control (NC), a region 7 kb downstream of the enhancer allocated to Gm14005 was used. Results are shown as mean ± s.e.m. and were calculated as percent of input divided through the IgG value. Each symbol represents a biological replicate (n = 3) from two independent experiments. (g) Immunoblot analysis of HDAC1 protein expression in tetracycline induced (72 h) shRNA HDAC1 (shHDAC1) and scramble (SC4) control THP-1 cells. Two different shRNAs against HDAC1 were used. Detection of valosin-containing protein (VCP) ensured comparable loading. The blot is representative of four experiments with similar results. (h) qPCR analysis of expression of the indicated cluster III genes in doxycycline induced (72 h) shRNA HDAC1 (bp642 or pb1719) and scramble (SC4) control THP-1 cells. Expressions of two independent experiments are shown and were calculated as fold difference over the expression values of SC4 control. Doted lines represent the expression values of the controls.

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Fischer, J., Walter, C., Tönges, A. et al. Safeguard function of PU.1 shapes the inflammatory epigenome of neutrophils. Nat Immunol 20, 546–558 (2019). https://doi.org/10.1038/s41590-019-0343-z

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