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.

The histone demethylase KDM6B fine-tunes the host response to Streptococcus pneumoniae

Abstract

Streptococcus pneumoniae is a natural colonizer of the human respiratory tract and an opportunistic pathogen. Although epithelial cells are among the first to encounter pneumococci, the cellular processes and contribution of epithelial cells to the host response are poorly understood. Here, we show that a S. pneumoniae serotype 6B ST90 strain, which does not cause disease in a murine infection model, induces a unique NF-κB signature response distinct from an invasive-disease-causing isolate of serotype 4 (TIGR4). This signature is characterized by activation of p65 and requires a histone demethylase KDM6B. We show, molecularly, that the interaction of the 6B strain with epithelial cells leads to chromatin remodelling within the IL-11 promoter in a KDM6B-dependent manner, where KDM6B specifically demethylates histone H3 lysine 27 dimethyl. Remodelling of the IL-11 locus facilitates p65 access to three NF-κB sites that are otherwise inaccessible when stimulated by IL-1β or TIGR4. Finally, we demonstrate through chemical inhibition of KDM6B with GSK-J4 inhibitor and through exogenous addition of IL-11 that the host responses to the 6B ST90 and TIGR4 strains can be interchanged both in vitro and in a murine model of infection in vivo. Our studies therefore reveal how a chromatin modifier governs cellular responses during infection.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Serotype 6B induces a unique inflammatory signature.
Fig. 2: Expression of KDM6B and IL-11 is specific to 6B and requires p65 activation.
Fig. 3: 6B induces IL-11 promoter rearrangement.
Fig. 4: GSK-J4 and IL-11 treatment alters the host response to 6B and TIGR4.
Fig. 5: KDM6B and IL-11 contribute to epithelial cell integrity in response to pneumococcus.
Fig. 6: KDM6B is required for host response to serotype 6B.

Data availability

All data in the present study is available upon request from the corresponding author. The complete microarray dataset (ArrayExpress accession number E-MTAB-9603), PCA dataset and RT–qPCR ΔCT values are contained within Supplementary Table 1 and respective source data files. Full immunoblots are in Supplementary Figs. 1–3. Source data are provided with this paper.

References

  1. 1.

    Donkor, E. S. Understanding the pneumococcus: transmission and evolution. Front. Cell. Infect. Microbiol. https://doi.org/10.3389/fcimb.2013.00007 (2013).

  2. 2.

    Shak, J. R., Vidal, J. E. & Klugman, K. P. Influence of bacterial interactions on pneumococcal colonization of the nasopharynx. Trends Microbiol. 21, 129–135 (2013).

    CAS  PubMed  Google Scholar 

  3. 3.

    Weiser, J. N., Ferreira, D. M. & Paton, J. C. Streptococcus pneumoniae: transmission, colonization and invasion. Nat. Rev. Microbiol. 16, 355–367 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    World Health Organization. Pneumococcal vaccines: WHO position paper—2012. Wkly Epidemiol. Rec. 87, 129–144 (2012).

  5. 5.

    Bogaert, D., De Groot, R. & Hermans, P. W. Streptococcus pneumoniae colonisation: the key to pneumococcal disease. Lancet Infect. Dis. 4, 144–154 (2004).

    CAS  PubMed  Google Scholar 

  6. 6.

    Henriques-Normark, B., Blomberg, C., Dagerhamn, J., Battig, P. & Normark, S. The rise and fall of bacterial clones: Streptococcus pneumoniae. Nat. Rev. Microbiol. 6, 827–837 (2008).

    CAS  PubMed  Google Scholar 

  7. 7.

    Antibiotic Resistance Threats in the United States, 2013 (Centers for Disease Control and Prevention, 2013).

  8. 8.

    Ferreira, D. M. et al. Controlled human infection and rechallenge with Streptococcus pneumoniae reveals the protective efficacy of carriage in healthy adults. Am. J. Respiratory Crit. Care Med. 187, 855–864 (2013).

    CAS  Google Scholar 

  9. 9.

    Weiser, J. N. The pneumococcus: why a commensal misbehaves. J. Mol. Med. 88, 97–102 (2010).

    PubMed  Google Scholar 

  10. 10.

    Henriques-Normark, B. & Tuomanen, E. I. The pneumococcus: epidemiology, microbiology, and pathogenesis. Cold Spring Harb. Perspect. Med. 3, a010215 (2013).

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    Jochems, S. P., Weiser, J. N., Malley, R. & Ferreira, D. M. The immunological mechanisms that control pneumococcal carriage. PLoS Pathog. 13, e1006665 (2017).

    PubMed  PubMed Central  Google Scholar 

  12. 12.

    Robson, R. L., Reed, N. A. & Horvat, R. T. Differential activation of inflammatory pathways in A549 type II pneumocytes by Streptococcus pneumoniae strains with different adherence properties. BMC Infect. Dis. 6, 71 (2006).

    PubMed  PubMed Central  Google Scholar 

  13. 13.

    Weight, C. M. et al. Microinvasion by Streptococcus pneumoniae induces epithelial innate immunity during colonisation at the human mucosal surface. Nat. Commun. 10, 3060 (2019).

    PubMed  PubMed Central  Google Scholar 

  14. 14.

    Quinton, L. J. & Mizgerd, J. P. Dynamics of lung defense in pneumonia: resistance, resilience, and remodeling. Annu. Rev. Physiol. 77, 407–430 (2015).

    CAS  PubMed  Google Scholar 

  15. 15.

    Ghosh, S. & Hayden, M. S. New regulators of NF-κB in inflammation. Nat. Rev. Immunol. 8, 837–848 (2008).

    CAS  PubMed  Google Scholar 

  16. 16.

    Bhatt, D. & Ghosh, S. Regulation of the NF-κB-mediated transcription of inflammatory genes. Front. Immunol. https://doi.org/10.3389/fimmu.2014.00071 (2014).

  17. 17.

    Dong, J., Jimi, E., Zhong, H., Hayden, M. S. & Ghosh, S. Repression of gene expression by unphosphorylated NF-κB p65 through epigenetic mechanisms. Genes Dev. 22, 1159–1173 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Fujioka, S. et al. NF-κB and AP-1 connection: mechanism of NF-κB-dependent regulation of AP-1 activity. Mol. Cell. Biol. 24, 7806–7819 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Das, N. D., Jung, K. H. & Chai, Y. G. The role of NF-κB and H3K27me3 demethylase, JMJD3, on the anthrax lethal toxin tolerance of RAW 264.7 cells. PLoS ONE 5, e9913 (2010).

    PubMed  PubMed Central  Google Scholar 

  20. 20.

    De Santa, F. et al. JMJD3 contributes to the control of gene expression in LPS-activated macrophages. EMBO J. 28, 3341–3352 (2009).

    PubMed  PubMed Central  Google Scholar 

  21. 21.

    De Santa, F. et al. The histone H3 lysine-27 demethylase JMJD3 links inflammation to inhibition of polycomb-mediated gene silencing. Cell 130, 1083–1094 (2007).

    PubMed  Google Scholar 

  22. 22.

    Na, J. et al. Histone H3K27 demethylase JMJD3 in cooperation with NF-κB regulates keratinocyte wound healing. J. Invest. Dermatol. 136, 847–858 (2016).

    CAS  PubMed  Google Scholar 

  23. 23.

    Salminen, A., Kaarniranta, K., Hiltunen, M. & Kauppinen, A. Histone demethylase Jumonji D3 (JMJD3/KDM6B) at the nexus of epigenetic regulation of inflammation and the aging process. J. Mol. Med. 92, 1035–1043 (2014).

    CAS  PubMed  Google Scholar 

  24. 24.

    Diermeier, S. et al. TNFα signalling primes chromatin for NF-κB binding and induces rapid and widespread nucleosome repositioning. Genome Biol. 15, 536 (2014).

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    Lone, I. N. et al. Binding of NF-κB to nucleosomes: effect of translational positioning, nucleosome remodeling and linker histone H1. PLoS Genet. 9, e1003830 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Wong, D. et al. Extensive characterization of NF-κB binding uncovers non-canonical motifs and advances the interpretation of genetic functional traits. Genome Biol. 12, R70 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Christian, F., Smith, E. L. & Carmody, R. J. The regulation of NF-κB subunits by phosphorylation. Cells 5, 12 (2016).

    PubMed Central  Google Scholar 

  28. 28.

    Oeckinghaus, A. & Ghosh, S. The NF-κB family of transcription factors and its regulation. Cold Spring Harb. Perspect. Biol. 1, a000034 (2009).

    PubMed  PubMed Central  Google Scholar 

  29. 29.

    Avvakumov, N., Nourani, A. & Côté, J. Histone chaperones: modulators of chromatin marks. Mol. Cell 41, 502–514 (2011).

    CAS  PubMed  Google Scholar 

  30. 30.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Black, J. C. & Whetstine, J. R. Chromatin landscape: methylation beyond transcription. Epigenetics 6, 13–19 (2011).

    PubMed Central  Google Scholar 

  32. 32.

    Hammond, C. M., Stromme, C. B., Huang, H., Patel, D. J. & Groth, A. Histone chaperone networks shaping chromatin function. Nat. Rev. Mol. Cell Biol. 18, 141–158 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Kouzarides, T. Chromatin modifications and their function. Cell 128, 693–705 (2007).

    CAS  PubMed  Google Scholar 

  34. 34.

    Lorch, Y. & Kornberg, R. D. Chromatin-remodeling and the initiation of transcription. Q. Rev. Biophys. 48, 465–470 (2015).

    CAS  PubMed  Google Scholar 

  35. 35.

    Markolovic, S. et al. Structure–function relationships of human JMJC oxygenases—demethylases versus hydroxylases. Curr. Opin. Struct. Biol. 41, 62–72 (2016).

    CAS  PubMed  Google Scholar 

  36. 36.

    Satoh, T. et al. The JMJD3-Irf4 axis regulates M2 macrophage polarization and host responses against helminth infection. Nat. Immunol. 11, 936–944 (2010).

    CAS  PubMed  Google Scholar 

  37. 37.

    Pierce, J. W. et al. Novel inhibitors of cytokine-induced IκBα phosphorylation and endothelial cell adhesion molecule expression show anti-inflammatory effects in vivo. J. Biol. Chem. 272, 21096–21103 (1997).

    CAS  PubMed  Google Scholar 

  38. 38.

    Karin, M. The beginning of the end: IκB kinase (IKK) and NF-κB activation. J. Biol. Chem. 274, 27339–27342 (1999).

    CAS  PubMed  Google Scholar 

  39. 39.

    Gilmore, T. D. The Rel/NF-kappaB signal transduction pathway: introduction. Oncogene 18, 6842–6844 (1999).

    CAS  PubMed  Google Scholar 

  40. 40.

    Kruidenier, L. et al. A selective jumonji H3K27 demethylase inhibitor modulates the proinflammatory macrophage response. Nature 488, 404–408 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Xiang, Y. et al. JMJD3 is a histone H3K27 demethylase. Cell Res. 17, 850–857 (2007).

    CAS  PubMed  Google Scholar 

  42. 42.

    Agger, K. et al. UTX and JMJD3 are histone H3K27 demethylases involved in HOX gene regulation and development. Nature 449, 731–734 (2007).

    CAS  PubMed  Google Scholar 

  43. 43.

    Hong, S. et al. Identification of JMJC domain-containing UTX and JMJD3 as histone H3 lysine 27 demethylases. Proc. Natl Acad. Sci. USA 104, 18439–18444 (2007).

    CAS  PubMed  Google Scholar 

  44. 44.

    Millet, P., McCall, C. & Yoza, B. RelB: an outlier in leukocyte biology. J. Leukoc. Biol. 94, 941–951 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Kadioglu, A., Weiser, J. N., Paton, J. C. & Andrew, P. W. The role of streptococcus pneumoniae virulence factors in host respiratory colonization and disease. Nat. Rev. Microbiol. 6, 288–301 (2008).

    CAS  PubMed  Google Scholar 

  46. 46.

    Walev, I. et al. Resealing of large transmembrane pores produced by streptolysin O in nucleated cells is accompanied by NF-κB activation and downstream events. FASEB J. 16, 237–239 (2002).

    CAS  PubMed  Google Scholar 

  47. 47.

    Tran, S.-L., Puhar, A., Ngo-Camus, M. & Ramarao, N. Trypan blue dye enters viable cells incubated with the pore-forming toxin HlyII of Bacillus cereus. PLoS ONE 6, e22876 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Elias, J. A. et al. Epithelial interleukin-11. Regulation by cytokines, respiratory syncytial virus, and retinoic acid. J. Biol. Chem. 269, 22261–22268 (1994).

    CAS  PubMed  Google Scholar 

  49. 49.

    Du, X. & Williams, D. A. Interleukin-11: review of molecular, cell biology, and clinical use. Blood 89, 3897–3908 (1997).

    CAS  PubMed  Google Scholar 

  50. 50.

    Traber, K. E. et al. Roles of interleukin-11 during acute bacterial pneumonia. PLoS ONE 14, e0221029 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Yap, J., Foo, C. F. H., Lee, M. Y., Stanton, P. G. & Dimitriadis, E. Proteomic analysis identifies interleukin 11 regulated plasma membrane proteins in human endometrial epithelial cells in vitro. Reprod. Biol. Endocrinol. 9, 73 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Han, Y. et al. Repairing effects of interleukin 11 (IL-11) towards high dose methotrexate-induced rat small intestinal mucositis and its impacts on T-lymphoblastic leukemia cell line. Iran. J. Basic Med. Sci. 19, 837–843 (2016).

    PubMed  PubMed Central  Google Scholar 

  53. 53.

    Lee, C. G. et al. Endogenous IL-11 signaling is essential in Th2- and IL-13-induced inflammation and mucus production. Am. J. Respir. Cell Mol. Biol. 39, 739–746 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Wen, C.-Y. et al. IL-11 up-regulates Tie-2 expression during the healing of gastric ulcers in rats. World J. Gastroenterol. 9, 788–790 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Orazi, A., Du, X., Yang, Z., Kashai, M. & Williams, D. A. Interleukin-11 prevents apoptosis and accelerates recovery of small intestinal mucosa in mice treated with combined chemotherapy and radiation. Lab. Invest. 75, 33–42 (1996).

    CAS  PubMed  Google Scholar 

  56. 56.

    Sandgren, A. et al. Virulence in mice of pneumococcal clonal types with known invasive disease potential in humans. J. Infect. Dis. 192, 791–800 (2005).

    PubMed  Google Scholar 

  57. 57.

    Ohguchi, H. et al. KDM6B modulates MAPK pathway mediating multiple myeloma cell growth and survival. Leukemia 31, 2661–2669 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Chen, S. et al. The histone H3 Lys 27 demethylase JMJD3 regulates gene expression by impacting transcriptional elongation. Genes Dev. 26, 1364–1375 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Young, M. D. et al. ChIP-seq analysis reveals distinct H3K27me3 profiles that correlate with transcriptional activity. Nucleic Acids Res. 39, 7415–7427 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Zafar, M. A., Wang, Y., Hamaguchi, S. & Weiser, J. N. Host-to-host transmission of streptococcus pneumoniae is driven by its inflammatory toxin, pneumolysin. Cell Host Microbe 21, 73–83 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Nishina, T. et al. Interleukin-11 links oxidative stress and compensatory proliferation. Sci. Signal 5, ra5 (2012).

    PubMed  Google Scholar 

  62. 62.

    Mahboubi, K., Biedermann, B. C., Carroll, J. M. & Pober, J. S. IL-11 activates human endothelial cells to resist immune-mediated injury. J. Immunol. 164, 3837–3846 (2000).

    CAS  PubMed  Google Scholar 

  63. 63.

    Moreno, R., Sobotzik, J.-M., Schultz, C. & Schmitz, M. L. Specification of the NF-κB transcriptional response by p65 phosphorylation and TNF-induced nuclear translocation of IKKε. Nucleic Acids Res. 38, 6029–6044 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Collins, P. E., Mitxitorena, I. & Carmody, R. J. The ubiquitination of NF-κB subunits in the control of transcription. Cells 5, 23 (2016).

    PubMed Central  Google Scholar 

  65. 65.

    Chao, Y. & Zhang, T. Optimization of fixation methods for observation of bacterial cell morphology and surface ultrastructures by atomic force microscopy. Appl. Microbiol. Biotechnol. 92, 381–392 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Kowarz, E., Loscher, D. & Marschalek, R. Optimized sleeping beauty transposons rapidly generate stable transgenic cell lines. Biotechnol. J. 10, 647–653 (2015).

    CAS  PubMed  Google Scholar 

  67. 67.

    Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    CAS  Google Scholar 

  68. 68.

    Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods 25, 402–408 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Laemmli, U. K. Cleavage of structural proteins during the assembly of the head of bacteriophage T4. Nature 227, 680–685 (1970).

    CAS  Google Scholar 

  70. 70.

    Ullman-Cullere, M. H. & Foltz, C. J. Body condition scoring: a rapid and accurate method for assessing health status in mice. Lab. Anim. Sci. 49, 319–323 (1999).

    CAS  PubMed  Google Scholar 

  71. 71.

    Connor, M. G. et al. Yersinia pestis targets the host endosome recycling pathway during the biogenesis of the Yersinia-containing vacuole to avoid killing by macrophages. mBio 9, e01800-17 (2018).

    PubMed  PubMed Central  Google Scholar 

  72. 72.

    Moffatt-Jauregui, C. E. et al. Establishment and characterization of a telomerase immortalized human gingival epithelial cell line. J. Periodontal Res. 48, 713–721 (2013).

    CAS  PubMed  Google Scholar 

  73. 73.

    Nabbi, A. & Riabowol, K. Rapid isolation of nuclei from cells in vitro. Cold Spring Harb. Protoc. 2015, 769–772 (2015).

    PubMed  Google Scholar 

  74. 74.

    Wingender, E. Compilation of transcription regulating proteins. Nucleic Acids Res. 16, 1879–1902 (1988).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Metsalu, T. & Vilo, J. ClustVis: a web tool for visualizing clustering of multivariate data using principal component analysis and heatmap. Nucleic Acids Res. 43, W566–W570 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank E. Varon, B. H. Normark, M. M. Si-Tahar and T. Kohler for their generous gifts of S. pneumoniae strains. We are thankful to G. Dore (Institut Pasteur) for processing the microarray Affymetrix GeneChips. Biostatistics and R discussions with J. Nabuco (Institut Pasteur) and J. Camp (University of Veterinary Medicine Vienna) were greatly appreciated. M.G.C. is supported by the Pasteur Foundation Fellowship. T.M.N.C. is supported by Foundation pour la Recherche Médicale, grant no. PMJ201810007628 (Prix MARIANE JOSSO), and a donation from Credit Agricole. M.A.H. is supported by the Institut Pasteur, and the Agence National de la Recherche (ANREpiBActIn), and is a member of the IBEID LabEx. C.M.W. was supported by the Medical Research Council (MR/T016329/1). We thank R. S. Heyderman (UCL) for in-depth discussion on the manuscript, and he is supported by the MRC (MR/T016329/1). J.E. and L.B., members of the Dynamics of Host–Pathogen interactions Unit (Institut Pasteur), are supported by the European Commission (ERC-CoG-Endosubvert) and the ANR-HBPsensing, and are members of the IBEID and Milieu Interieur LabExes. R.J.L. is funded by NIH/NIDCR nos. DE01111, DE012505, DE017921 and DE023193.

Author information

Affiliations

Authors

Contributions

M.G.C. and M.A.H. conceived and designed all experiments. M.G.C. performed experiments. E.P. performed KDM6B microscopy and the HeLa GFP-p65 western blot. T.C. and O.R. performed the animal studies. C.M.W. performed the IL-11 ELISA. D.P.M. and R.J.L. performed all oral commensal infections and RT–qPCR. M.G.C. analysed data. L.B. generated the stable HeLa GFP-p65 cell line under the supervision of J.E. M.G.C. wrote the original manuscript draft. M.G.C. and M.A.H. edited and reviewed the manuscript. M.A.H. supervised the research. All authors approved the final manuscript.

Corresponding author

Correspondence to Melanie A. Hamon.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Human microarray, KDM6B microscopy and demethylase RT-qPCR.

a, TIGR4 and 6B inocula for animal intranasal challenge model (n = 4 biological replicates; Geometric mean of 2 technical counts). Graphed as mean ± s.d. Analyzed by One way-ANOVA with Tukey’s multiple comparison post-hoc test, ***= pV≤0.001, ns=not significant. Exact pV are reported in in vivo source data. b, Human microarray of epithelial A549 cells 2 hrs post-challenge (MOI 20) with either 6B (blue) or TIGR4 (gray). All genes differentially regulated by ± 1.5 fold-change to uninfected condition. Circled 6B genes are NF-κB associated. c, Comparison of genes identified by microarray containing NF-κB sites between 6B and TIGR4. d, Clustering heat map, using ClustVis75, (Euclidean for genes, Correlation for cell line) of the median log2 relative expression RT-qPCR data for indicated genes from A549 (n = 5 biological replicates; 2–3 technicals per biological replicate), Detroit 562 (n = 1 biological replicate with 2 technicals per replicate) and Beas2b (n = 1 biological replicate with 2 technicals per replicate) cell lines. KDM6B and IL-11 cluster highlighted in red. e, Representative images of KDM6B (green) merged with nucleus stained with DAPI (false colored red for visual display). Scale = 10 µm. f, Quantified IL-11 ELISA (n = 5 biological replicates; 4–6 technicals per biological) 6 hrs post-infection of Detroit 562 cells. Median centered (solid red line) dot plot of all values. Dashed red line is the assay limit of detection. g, Total RNA 2 hrs post-challenge with 6B or TIGR4 from A549 cells. Histone demethylase panel RT-qPCR shown as ΔCt (n = 4 biological replicates; 2–3 technicals per biological replicate per group). Graphed as mean ± s.d. Two-tailed multiple T-test using Holm-Sidak correction. ***= pV≤0.001, ns=not significant. Exact pV are reported in Extended Data Fig. 1 source data.

Source data

Extended Data Fig. 2 NF-κB transcript profiles and inhibitor treatment cell viability.

a, RT-qPCR of NF-κB transcript profiles for IL-1β, 6B, or TIGR4 challenged A549 cells over a 2 hr time course (n = 3 biological replicates; 2 technicals per replicate). Displayed as a truncated violin plot with each data point. Graphed as the relative expression of each indicated transcript to matched uninfected/unstimulated control per time point. Gray line, when applicable, denotes uninfected/unstimulated level. b, A549 cells untreated or treated with 10 µM BAY 11-7082, 10 µM GSK-J4, or DMSO vehicle control (n = 4 biological replicates; 2-3 technicals per replicate). Cell viability determined by AlamarBlue. Expressed as % of untreated cells and graphed as mean ± s.d. One way-ANOVA with Dunnett’s multiple comparison post-hoc test to untreated, no significant difference observed (ns). Exact pV are reported in Fig. 2 and Extended Data Fig. 2 source data. c, Transcript levels for IL-11, KDM6B and PTGS2 determined by RT-qPCR. Displayed as ΔCt for comparison to untreated (n = 4 biological replicates; average of 2 ΔCt technical values per replicate). Graphed as mean ± s.d. All data analyzed by One way-ANOVA with Tukey’s multiple comparison post-hoc test, no significant difference observed (ns). Exact pV are reported in Extended Data Fig. 2 source data.

Source data

Extended Data Fig. 3 ChIP-PCR KDM6B locus and IL-11 locus for TIGR4 and IL-1β.

Chromatin obtained from untreated and 10 µM GSK-J4 treated A549 cells 2 hrs post-challenge with 6B (MOI 20), TIGR4 (MOI 20) or IL-1β. 10 µg chromatin input used for ChIP of p65, KDM6B, H3K27me3 and histone H3 (H3), followed by ChIP-qPCR at primer locations (P6, P3 & P2) spanning the NF-κB sites upstream of the transcriptional start site (TSS). a, Schematic of KDM6B promoter with ChIP-qPCR primer locations (P4 & P3) and the NF-κB sites. b, % recovery of input for p65 2 hrs post-infection with 6B (n = 3 untreated; n = 3 GSK-J4 treated). Two-tailed unpaired Student’s T-Test comparisons for untreated to GSK-J4 treated or 6B infected to uninfected, *= pV≤0.05, ***= pV≤0.001, ns=not significant. c, Schematic of IL-11 promoter with ChIP-qPCR primer locations (P6, P3 & P2) and the NF-κB sites. dg, % recovery of input for p65, KDM6B, H3K27me3 normalized to H3, or H3 bound at P6, P3 & P2 in untreated and GSK-J4 treated samples (n = 3 untreated; n = 3 GSK-J4 treated). Graphed as Tukey box and whisker plot. Box boundaries are the upper and lower interquartile range (IQR), “whiskers” are ± 1.5 times IQR and the median (solid line). Dots represent outliers. One way-ANOVA with Tukey’s multiple comparison post-hoc test, no significant difference observed in comparison to uninfected/untreated or 6B samples from Fig. 3. Cell fractions from A549 cells 2 hrs post-challenge ±10 µM GSK-J4. h, Representative example of fraction purity. Full blots provided in Supplementary Information 2. i, Representative images of nuclear fractions and coomassie stained PVDF membranes. Full blots provided in Supplementary Information 2. j, Signal intensity for H3K27me2 or H3K27me3 for IL-1β, TIGR4 or 6B normalized to total protein as a ratio to uninfected/unstimulated control (n = 4; biological replicates). Bar graph with s.d. and each individual value displayed. Significance calculated between ±GSK-J4 groups by two-tailed Student’s T-test, p-values displayed. *= pV≤0.05.

Source data

Extended Data Fig. 4 IL-1β RT-qPCR with GSK-J4 or IL-11 treatments.

a, Heat map represents fold change to either untreated or treatment matched controls per condition (n = 5 biologicals for untreated, and n = 3 biologicals per treatment; average 2 technicals per biological per group). Heat mapped difference between relative expression of treatments to untreated. Two-tailed uncorrected multiple T-Test of treatment groups to untreated with significant genes highlighted with “*”. IL-1β vs. IL-1β + GSK-J4: ICAM1 pV= 0.013, RELB pV= 0.150, TNF pV= 0.032, IL-6 pV=0.035, CXCL2 pV= 0.047. IL-1β vs. IL-1β + IL-11: CXCL2 pV=0.007, TNF pV=0.030. b & c, PCA variable correlation plots for GSJ-J4 PCA and IL-11 PCA. KDM6B and IL-11 highlighted in red. Exact pV are reported in Extended Data Fig. 4 source data.

Source data

Extended Data Fig. 5 LDH cytotoxicity, pneumolysin activity and IL-11 RT-qPCR with oral commensals.

a, % Cytotoxicity (LDH release) from A549 supernatants 2 hrs post-challenge with TIGR4 (MOI 20), 6B (MOI 20) or uninfected ± indicated treatments (n = 4 biological replicates; 2-3 technicals per replicate). UT = untreated. Tukey box and whisker plot with defined box boundaries being the upper and lower interquartile range (IQR), “whiskers” (fences) being ± 1.5 times IQR and the median depicted in the middle (solid line). All data was analyzed by One way-ANOVA with Tukey’s multiple comparison post-hoc test, **= pV≤0.01, ***= pV≤0.001, ns=not significant. Exact pV are reported in Extended Data Fig. 5 source data. b, Hemolytic activity of A549 cell culture supernatants ± 10 µM GSK-J4 2 hrs post-infection with either 6B (MOI 20) or TIGR4 (MOI 20) with uninfected control. All samples n = 4 biologicals with 2-3 technicals per biological. Graphed as mean ± s.d. Analyzed by One way-ANOVA with Tukey’s multiple comparison post-hoc test, ns=not significant. Exact pV are reported in Extended Data Fig. 5 source data. c, A549 cell protein lysates 2 hrs post-infection with TIGR4 (MOI 20; n = 7 biological replicates) or 6B (MOI 20; n = 7 biological replicates) probed for pneumolysin. Relative band intensity quantified with representative blot images. ΔPlyTIGR4 is a control for immunoblot. Scatter plot of all replicates with mean denoted in red. Data analyzed by two-tailed unpaired Student’s T-Test, ns=not significant. Full blots provided in Supplementary Information 3. d, Total RNA harvested from immortalized gingival keratinocytes 21 hrs post-challenge with Streptococcus gordonii, Streptococcus sanguinis, Streptococcus oralis, Eikenella corrodens and Fusobacterium nucleatum, at an MOI of 100. Transcript levels for IL-11 determined by RT-qPCR and represented as relative expression to uninfected (n = 1; average of two technicals).

Source data

Extended Data Fig. 6 TIGR4 and 6B intranasal animal challenge.

a, 6B and TIGR4 ± GSK-J4 with matched DMSO controls. 24 hrs post-inoculation bacterial burden from the nasal lavage (NL), bronchoalveolar lavage fluid (BALF), lungs, and spleen of infected animals by conventional CFU counts on 5 µg/mL Gentamicin Columbia Blood agar selection plates (n = 9 animals; Geometric mean of 2 technical counts per lavage or organ). DMSO TIGR4 white, GSK-J4 TIGR4 gray, DMSO 6B light blue, GSK-J4 6B dark blue. All graphed as Tukey box and whisker plot with defined box boundaries being the upper and lower interquartile range (IQR), “whiskers” (fences) being ± 1.5 times IQR and the median depicted in the middle (solid line). Dots represent outliers. One way-ANOVA non-parametric Kruskal-Wallis with Dunn’s multiple comparison post-hoc test, *= pV≤0.05, **= pV≤0.01, ***= pV≤0.001, ns=not significant. Exact pV are reported in Extended Data Fig. 6 source data. CFU = colony forming unit. Dotted lines =Limit of detection (LD). LD for each organ: NL (50 CFU); BALF, Lung and Spleen (1000 CFU). b, TIGR4 and 6B inocula for animal intranasal challenge model in DMSO (vehicle control) or 5 mM GSK-J4 (n = 3 biological replicates; Geometric mean of 2 technical counts). Conventional CFU enumeration shows no significant difference. Graphed as mean ± s.d. Analyzed by One way-ANOVA with Tukey’s multiple comparison post-hoc test, ns=not significant. c, TIGR4 ± IL-11 inocula for animal intranasal challenge model (n = 3 biological replicates; 2 technical counts). Conventional CFU enumeration shows no significant difference. Graphed as mean ± s.d. Analyzed by two-tailed Student’s T-Test, ns=not significant. d, Absorbance (OD600) growth curve for mid-log TIGR4 ± IL-11 mouse inocula over 2 hrs (n = 4 biological replicates). No significant difference in growth observed. Mean (dot) ± s.d. Analyzed by two-tailed multiple T-Test with Holm-Sidak correction at each 20 min interval, ns=not significant. Exact F are reported in Extended Data Fig. 6 source data.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–3 of uncropped immunoblots.

Reporting Summary

Supplementary Table 1

Microarray expression data, NF-kB genes and wound healing GO genes.

Supplementary Table 2

Bacteria and primer information.

Source data

Source Data Fig. 1

Raw numerical data for Fig. 1.

Source Data Fig. 2

Raw numerical data for Fig. 2.

Source Data Fig. 3

Raw numerical data for Fig. 3.

Source Data Fig. 4

Raw numerical data for Fig. 4.

Source Data Fig. 5

Raw numerical data for Fig. 5.

Source Data Fig. 6

Raw numerical data for Fig. 6.

Source Data Extended Data Fig. 1

Raw numerical data for Extended Data Fig. 1.

Source Data Extended Data Fig. 2

Raw numerical data for Extended Data Fig. 2.

Source Data Extended Data Fig. 3

Raw numerical data for Extended Data Fig. 3.

Source Data Extended Data Fig. 4

Raw numerical data for Extended Data Fig. 4.

Source Data Extended Data Fig. 5

Raw numerical data for Extended Data Fig. 5.

Source Data Extended Data Fig. 6

Raw numerical data for Extended Data Fig. 6.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Connor, M.G., Camarasa, T.M.N., Patey, E. et al. The histone demethylase KDM6B fine-tunes the host response to Streptococcus pneumoniae. Nat Microbiol 6, 257–269 (2021). https://doi.org/10.1038/s41564-020-00805-8

Download citation

Further reading

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