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Histone H3 proline 16 hydroxylation regulates mammalian gene expression

Abstract

Histone post-translational modifications (PTMs) are important for regulating various DNA-templated processes. Here, we report the existence of a histone PTM in mammalian cells, namely histone H3 with hydroxylation of proline at residue 16 (H3P16oh), which is catalyzed by the proline hydroxylase EGLN2. We show that H3P16oh enhances direct binding of KDM5A to its substrate, histone H3 with trimethylation at the fourth lysine residue (H3K4me3), resulting in enhanced chromatin recruitment of KDM5A and a corresponding decrease of H3K4me3 at target genes. Genome- and transcriptome-wide analyses show that the EGLN2–KDM5A axis regulates target gene expression in mammalian cells. Specifically, our data demonstrate repression of the WNT pathway negative regulator DKK1 through the EGLN2-H3P16oh-KDM5A pathway to promote WNT/β-catenin signaling in triple-negative breast cancer (TNBC). This study characterizes a regulatory mark in the histone code and reveals a role for H3P16oh in regulating mammalian gene expression.

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Fig. 1: EGLN2 hydroxylates histone H3 on proline 16.
Fig. 2: Prolyl hydroxylation of histone H3 at proline 16 regulates H3K4me3.
Fig. 3: H3P16oh recruits KDM5A and leads to decreased H3K4me3.
Fig. 4: P16oh modification on histone H3 enhances its affinity to KDM5APHD3.
Fig. 5: EGLN2-catalyzed H3P16oh enhances the recruitment of KDM5A and affects H3K4me3 genome wide in breast cancer cells.
Fig. 6: H3P16oh catalyzed by EGLN2 regulates the KDM5A–H3K4me3–DKK1–WNT signaling axis in breast cancer cells.
Fig. 7: Genome- and transcriptome-wide analyses show that the EGLN2–H3P16oh–KDM5A axis regulates target gene expression in 293T cells.
Fig. 8: Hypoxia regulates target gene expression through the EGLN2–H3P16oh–KDM5A–H3K4me3 signaling axis.

Data availability

All of the data generated or analyzed during this study are included in Figs. 18 and Extended Data Figs. 110. CUT&RUN and RNA-seq data have been deposited in the Gene Expression Omnibus under accession code GSE179113. Publicly available National Center for Biotechnology Information datasets were used for Extended Data Fig. 6d–f,i and can be found under accession code GSE108833. Publicly available datasets used for Extended Data Fig. 6g can be found under accession code GSE136414. Source data are provided with this paper.

Code availability

All of the data, code and materials used in the analysis are available from the corresponding author upon reasonable request.

References

  1. Allfrey, V. G., Faulkner, R. & Mirsky, A. E. Acetylation and methylation of histones and their possible role in the regulation of RNA synthesis. Proc. Natl Acad. Sci. USA 51, 786–794 (1964).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Strahl, B. D. & Allis, C. D. The language of covalent histone modifications. Nature 403, 41–45 (2000).

    Article  CAS  PubMed  Google Scholar 

  3. Chi, P., Allis, C. D. & Wang, G. G. Covalent histone modifications—miswritten, misinterpreted and mis-erased in human cancers. Nat. Rev. Cancer 10, 457–469 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Husmann, D. & Gozani, O. Histone lysine methyltransferases in biology and disease. Nat. Struct. Mol. Biol. 26, 880–889 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Dunn, R. K. & Kingston, R. E. Gene regulation in the postgenomic era: technology takes the wheel. Mol. Cell 28, 708–714 (2007).

    Article  CAS  PubMed  Google Scholar 

  6. Soliman, M. A. & Riabowol, K. After a decade of study-ING, a PHD for a versatile family of proteins. Trends Biochem. Sci. 32, 509–519 (2007).

    Article  CAS  PubMed  Google Scholar 

  7. Wang, G. G. et al. Haematopoietic malignancies caused by dysregulation of a chromatin-binding PHD finger. Nature 459, 847–851 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Gu, B. et al. Pygo2 expands mammary progenitor cells by facilitating histone H3 K4 methylation. J. Cell Biol. 185, 811–826 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Jain, K. et al. Characterization of the plant homeodomain (PHD) reader family for their histone tail interactions. Epigenetics Chromatin 13, 3 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Klose, R. J. et al. The retinoblastoma binding protein RBP2 is an H3K4 demethylase. Cell 128, 889–900 (2007).

    Article  CAS  PubMed  Google Scholar 

  11. Christensen, J. et al. RBP2 belongs to a family of demethylases, specific for tri- and dimethylated lysine 4 on histone 3. Cell 128, 1063–1076 (2007).

    Article  CAS  PubMed  Google Scholar 

  12. Kaelin, W. G. Jr & Ratcliffe, P. J. Oxygen sensing by metazoans: the central role of the HIF hydroxylase pathway. Mol. Cell 30, 393–402 (2008).

    Article  CAS  PubMed  Google Scholar 

  13. Semenza, G. L. Hypoxia-inducible factors: mediators of cancer progression and targets for cancer therapy. Trends Pharmacol. Sci. 33, 207–214 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Khoury, G. A., Baliban, R. C. & Floudas, C. A. Proteome-wide post-translational modification statistics: frequency analysis and curation of the Swiss-Prot database. Sci. Rep. 1, 90 (2011).

    Article  CAS  PubMed Central  Google Scholar 

  15. Nelson, C. J., Santos-Rosa, H. & Kouzarides, T. Proline isomerization of histone H3 regulates lysine methylation and gene expression. Cell 126, 905–916 (2006).

    Article  CAS  PubMed  Google Scholar 

  16. Pientka, F. K. et al. Oxygen sensing by the prolyl-4-hydroxylase PHD2 within the nuclear compartment and the influence of compartmentalisation on HIF-1 signalling. J. Cell Sci. 125, 5168–5176 (2012).

    CAS  PubMed  Google Scholar 

  17. Morales, V. & Richard-Foy, H. Role of histone N-terminal tails and their acetylation in nucleosome dynamics. Mol. Cell. Biol. 20, 7230–7237 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Petronikolou, N., Longbotham, J. E. & Fujimori, D. G. Extended recognition of the histone H3 tail by histone demethylase KDM5A. Biochemistry 59, 647–651 (2020).

    Article  CAS  PubMed  Google Scholar 

  19. Zhang, J. et al. EglN2 associates with the NRF1–PGC1 alpha complex and controls mitochondrial function in breast cancer. EMBO J. 34, 2953–2970 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Aragones, J. et al. Deficiency or inhibition of oxygen sensor Phd1 induces hypoxia tolerance by reprogramming basal metabolism. Nat. Genet. 40, 170–180 (2008).

    Article  CAS  PubMed  Google Scholar 

  21. Zurlo, G. et al. Prolyl hydroxylase substrate adenylosuccinate lyase is an oncogenic driver in triple negative breast cancer. Nat. Commun. 10, 5177 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Galluzzi, L., Spranger, S., Fuchs, E. & Lopez-Soto, A. WNT signaling in cancer immunosurveillance. Trends Cell Biol. 29, 44–65 (2019).

    Article  CAS  PubMed  Google Scholar 

  23. Cleary, A. S., Leonard, T. L., Gestl, S. A. & Gunther, E. J. Tumour cell heterogeneity maintained by cooperating subclones in Wnt-driven mammary cancers. Nature 508, 113–117 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Van Schie, E. H. & van Amerongen, R. Aberrant WNT/CTNNB1 signaling as a therapeutic target in human breast cancer: weighing the evidence. Front. Cell Dev. Biol. 8, 25 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Koni, M., Pinnaro, V. & Brizzi, M. F. The Wnt signalling pathway: a tailored target in cancer. Int. J. Mol. Sci. 21, 7697 (2020).

    Article  CAS  PubMed Central  Google Scholar 

  26. Xu, Y. et al. Hypoxia-induced CREB cooperates MMSET to modify chromatin and promote DKK1 expression in multiple myeloma. Oncogene 40, 1231–1241 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Zhang, Q. et al. Control of cyclin D1 and breast tumorigenesis by the EglN2 prolyl hydroxylase. Cancer Cell 16, 413–424 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Brown, J. M. & Wilson, W. R. Exploiting tumour hypoxia in cancer treatment. Nat. Rev. Cancer 4, 437–447 (2004).

    Article  CAS  PubMed  Google Scholar 

  29. Batie, M. et al. Hypoxia induces rapid changes to histone methylation and reprograms chromatin. Science 363, 1222–1226 (2019).

    Article  CAS  PubMed  Google Scholar 

  30. Takada, M. et al. EglN2 contributes to triple negative breast tumorigenesis by functioning as a substrate for the FBW7 tumor suppressor. Oncotarget 8, 6787–6795 (2017).

    Article  PubMed  Google Scholar 

  31. Zheng, X. et al. Prolyl hydroxylation by EglN2 destabilizes FOXO3a by blocking its interaction with the USP9x deubiquitinase. Genes Dev. 28, 1429–1444 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Bai, X., Ni, J., Beretov, J., Graham, P. & Li, Y. Cancer stem cell in breast cancer therapeutic resistance. Cancer Treat. Rev. 69, 152–163 (2018).

    Article  CAS  PubMed  Google Scholar 

  33. Lee, P., Chandel, N. S. & Simon, M. C. Cellular adaptation to hypoxia through hypoxia inducible factors and beyond. Nat. Rev. Mol. Cell Biol. 21, 268–283 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Batie, M. & Rocha, S. Gene transcription and chromatin regulation in hypoxia. Biochem. Soc. Trans. 48, 1121–1128 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Wielockx, B., Grinenko, T., Mirtschink, P. & Chavakis, T. Hypoxia pathway proteins in normal and malignant hematopoiesis. Cells 8, 155 (2019).

    Article  CAS  PubMed Central  Google Scholar 

  36. Tsai, A. G., Johnson, P. C. & Intaglietta, M. Oxygen gradients in the microcirculation. Physiol. Rev. 83, 933–963 (2003).

    Article  CAS  PubMed  Google Scholar 

  37. Schodel, J. & Ratcliffe, P. J. Mechanisms of hypoxia signalling: new implications for nephrology. Nat. Rev. Nephrol. 15, 641–659 (2019).

    Article  PubMed  Google Scholar 

  38. Chen, P. S. et al. Pathophysiological implications of hypoxia in human diseases. J. Biomed. Sci. 27, 63 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Chakraborty, A. A. et al. Histone demethylase KDM6A directly senses oxygen to control chromatin and cell fate. Science 363, 1217–1222 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Pieters, B. J. G. E. et al. Mechanism of biomolecular recognition of trimethyllysine by the fluorinated aromatic cage of KDM5A PHD3 finger. Commun. Chem. 3, 69 (2020).

    Article  CAS  Google Scholar 

  41. Liu, X. J. et al. Genome-wide screening identifies SFMBT1 as an oncogenic driver in cancer with VHL loss. Mol. Cell 77, 1294–1306.e5 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Rothbart, S. B. et al. Association of UHRF1 with methylated H3K9 directs the maintenance of DNA methylation. Nat. Struct. Mol. Biol. 19, 1155–1160 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Liu, X. J. et al. Human cytomegalovirus IE1 downregulates Hes1 in neural progenitor cells as a potential E3 ubiquitin ligase. PLoS Pathog. 13, e1006542 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Zhao, S. et al. Histone H3Q5 serotonylation stabilizes H3K4 methylation and potentiates its readout. Proc. Natl Acad. Sci. USA 118, e2016742118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Karch, K. R., Sidoli, S. & Garcia, B. A.Identification and quantification of histone PTMs using high-resolution mass spectrometry. Methods Enzymol. 574, 3–29 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Turriziani, B. et al. On-beads digestion in conjunction with data-dependent mass spectrometry: a shortcut to quantitative and dynamic interaction proteomics. Biology 3, 320–332 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  48. Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  50. 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  PubMed  PubMed Central  Google Scholar 

  51. Hanzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics 14, 7 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Skene, P. J., Henikoff, J. G. & Henikoff, S.Targeted in situ genome-wide profiling with high efficiency for low cell numbers. Nat. Protoc. 13, 1006–1019 (2018).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  55. 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  PubMed  PubMed Central  Google Scholar 

  56. Ramirez, F. et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44, W160–W165 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Delaglio, F. et al. NMRPipe: a multidimensional spectral processing system based on UNIX pipes. J. Biomol. NMR 6, 277–293 (1995).

    Article  CAS  PubMed  Google Scholar 

  58. Waudby, C. A., Ramos, A., Cabrita, L. D. & Christodoulou, J. Two-dimensional NMR lineshape analysis. Sci. Rep. 6, 24826 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Maciejewski, M. W. et al. NMRbox: a resource for biomolecular NMR computation. Biophys. J. 112, 1529–1534 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank all members of the Qing Zhang, A.S.B. and G.G.W. laboratories for helpful discussions and suggestions. This work was supported in part by the National Cancer Institute (R01CA211732 and R01CA256833 to Qing Zhang), Cancer Prevention and Research Institute of Texas (RR190058 to Qing Zhang), American Cancer Society (RSG-18-059-01-TBE to Qing Zhang) and US Department of Defense (W81XWH1910813 to Qing Zhang.). A.S.B. is supported by NCI 1R35CA197684, B.D.S. is supported by 1R35GM126900 and G.G.W. is supported by R01CA215284 and R01CA211336.

Author information

Authors and Affiliations

Authors

Contributions

Qing Zhang conceived the project. X.L., J.W., G.G.W., A.S.B. and Qing Zhang designed, performed and interpreted the experiments and co-wrote the paper. J.W., Y.G., H.U. and G.G.W. contributed to the CUT&RUN bioinformatics analyses. G.G.W., X.S., B.D.S. and Q.Y. helped with the paper editing. W.G. and Y.-H.T. contributed to the RNA-seq bioinformatics analyses. S.Z. and G.G.W. carried out the ITC assay. C.Z. and M.L. contributed to the protein binding assay. Q.W., Qi Zhang and J.A.B. contributed and analyzed NMR data. J.R. and A.v.K. helped with the mass spectrometry analysis of histones. W.L. helped to check publicly available datasets from the National Center for Biotechnology Information (GSE108833) and provided HIF1/2α DKO MDA-MB-231 cells. L.X. and X.C. helped to analyzed the quantitative mass spectrometry data in this paper. C.L., L.H. and J.Z. provided siRNA smart pools and patient samples. K.J., B.D.S. and X.S. provided H3 peptides. K.J., B.D.S. and X.S. also helped with guidance for critical experiments and paper writing.

Corresponding author

Correspondence to Qing Zhang.

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Competing interests

Qing Zhang received a consultation fee from Bristol Myers Squibb, which is irrelevant to this study. The remaining authors declare no competing interests.

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Extended data

Extended Data Fig. 1 EGLN2 binds to and promotes H3 hydroxylation on P16.

a, Protein sequence of H3. b, Immunoprecipitation and Immunoblots of GST-pull down assay between purified GST-EGLN1/2/3 and cell lysates from MDA-MB-231 cells. c, Immunoprecipitation and immunoblots of lysates from MDA-MB-231. d, Immunoblots of immunoprecipitated lysates for indicated proteins from MDA-MB-231 cells. e, Biotin-pull down assay between biotin beads, indicated biotinylated H3 peptide and IVT Flag-EGLN2 or catalytic dead mutant Flag-EGLN2(H358A). f, Immunoprecipitation with pan hydroxyproline antibody of cell lysates from MDA-MB-231 cells with (12 h) or without DMOG treatment. g, Immunoprecipitation with pan hydroxyproline antibody of cell lysates from 293 T cells transfected with indicated plasmids. h, Immunoprecipitation with pan hydroxyproline antibody of in vitro hydroxylation reaction mixture with indicated GST-tagged purified protein as substrates.

Source data

Extended Data Fig. 2 EGLN2 catalyzes H3 hydroxylation on proline 16 residue.

a, b, MS spectrum for identified non-hydroxylated (a) or hydroxylated (b) H3 peptide at P16 derived from transfected myc-H3 in 293 T cells. c, MS intensity of hydroxylated H3 at P16 derived from transfected myc-H3 with (12 h) or without DMOG treatment in 293 T cells. d, e, The retention time (d) and MS spectrum (red arrow) (e) for non-hydroxylated and hydroxylated H3 at P16 derived from transfected myc-H3 in 293 T cells. f-g, MS spectrum for identified non-hydroxylated (f) or hydroxylated (g) H3 peptide at P16 derived from GST-H3(1-21) of in vitro hydroxylation assay. h-i, MS intensity (h) and intensity value (i) of H3 with P16 hydroxylation derived from GST-H3(1-21) of in vitro hydroxylation assay. n = 2; mean ± s.d.; unpaired two-tailed Student’s t-test; *P < 0.05, **P < 0.01, ***P < 0.001. j-k, The retention time (j) and MS spectrum (red arrow) (k) for non-hydroxylated and hydroxylated H3 at P16 derived from GST-H3(1-21) of in vitro hydroxylation assay.

Extended Data Fig. 3 EGLN2 promotes H3P16oh in vitro and in vivo.

a–c, MS spectrum for synthetic hydroxylated peptide(a), identified hydroxylated (b) or non-hydroxylated (c) H3 peptide at P16 derived from biotin-H3(12-20) of in vitro hydroxylation assay. d, MS intensity of H3P16oh derived from biotin-H3(12-20) peptide of in vitro hydroxylation assay. e, Retention time of HPLC/MS analysis and the MS signal of biotin-H3(12-20) peptide with P16oh derived from in vitro hydroxylation assay and the synthetic peptide. f, Intensity value of biotin-H3(12-20) peptide with P16oh derived from in vitro hydroxylation assay. n = 2; mean ± s.d.; unpaired two-tailed Student’s t-test; *P < 0.05, **P < 0.01, ***P < 0.001. g, Immunoblots for lysates from MDA-MB-231 cells transduced with CRISPR-V2 sgctrl and sg13. h, Intensity value of endogenous histone peptide (KSTGGKAPR) with P16oh derived from MDA-MB-231 cells transduced with CRISPR-V2 sgctrl and sg13. i, The relative intensity of the hydroxylated peptide compared to the non-hydroxylated peptide with or without EGLN2 knockout. j, Dot blot analysis with indicated biotinylated peptides diluted with different concentrations and detected with either anti-H3P16oh or anti-Biotin antibody. k, Immunoblots of lysates from 293 T cells with indicated treatment condition for 12 h.

Source data

Extended Data Fig. 4 H3P16oh regulates H3K4me3 and displays better affinity to KDM5APHD3.

a, Immunoblots for lysates from indicated cells with DMOG or hypoxia(0.5% O2) treatment for 12 h. b, Immunoblots for 293 T cell lysates transfected with indicated plasmids followed with DMOG or hypoxia (1% O2) treatment for 12 h. c, Immunoprecipitation and immunoblots of lysates from MDA-MB-231 cells transduced with EGLN2 shCtrl or sh325 and sh327. d, Biotin-pull down assay between biotin beads or indicated biotinylated peptide with GST-KDM5APHD1. e, Titration curves and fitting curves of indicated H3 peptides titrated into His-KDM5APHD3. f, Left panel, NMR 1H-15N heteronuclear single quantum correlation (HSQC) spectrum of apo 15N-labeled PHD3 domain. Right panel, overlay of NMR 1H-15N HSQC spectra of 0.125 mM 15N-labeled KDM5APHD3 in the presence of 1.80 mM H3K4me3 (cyan) and 1.80 mM H3K4me3 P16oh (red). g, MS identification of peptides: HCD mass spectrum of 16 Da H3K4me3 P16oh peptide (Bottom panel) and H3K4me3 peptide (top panel). h, Enhanced binding of H3K4me3 P16oh peptides with KDM5APHD3 compared to H3K4me3 peptides. The MS of [M + 4H]5+ and [M + 4H]6+ ions were shown. Left, peptide mixture used as input for KDM5APHD3-binding experiment; right, KDM5APHD3-bound products. i, NMR 1H-15N heteronuclear multiple quantum coherence (HMQC) spectra overlays between apo KDM5APHD3 (black) and 0.1 mM KDM5APHD3 in the presence of 0.45 mM H3K4me3 (cyan) and H3K4me3 P16oh (red) peptides, pH=6. j, Zoomed-in region of the two peptide-bound KDM5APHD3 that highlights residues K53 and K54 with the most significant chemical shift differences between the two states.

Source data

Extended Data Fig. 5 Correlation and overlap of CUT&RUN data in breast cancer cells.

a, Scatter plot showing the correlation of CUT&RUN replication samples (Rabbit1/2/3). b, Immunoblots for lysates from MDA-MB-231 cells with EGLN2 depletion by different systems. c, Heatmap and averaged plots showing the H3P16oh CUT&RUN signals genome-wide (over ± 5 kb) in MDA-MB-231 cells with EGLN2 sgCtrl or sg13 and sg14 with inducible CRISPR system. df, Scatter plot showing the correlation of indicated CUT&RUN replication samples. g, Heatmap showing CUT&RUN peaks for EGLN2, KDM5A and H3K4me3 that overlapped with of H316Poh peaks on genome wide scale (over ± 5 kb). h, Hierarchical clustering of the indicated CUT&RUN datasets based on similarity, with the pairwise Pearson correlation coefficients labeled in the table and depicted by varying color intensities. i, Principal component analysis (PCA) of indicated CUT&RUN datasets. j, Venn diagram showing H3K4me3 solo peaks (n = 8392) and common peaks with H3P16oh (n = 6651). k, l, GREAT (Genomic Regions Enrichment of Annotations Tool) analysis of function of H3K4me3 solo peaks (k) and common peaks with H3P16oh (l). m, Venn diagram showing overlapping of H3P16oh, EGLN2, KDM5A and H3K4me3 peaks (n = 5053). n, Genomic distribution of 5053 common binding peaks from panel m. o, p, Heat map and averaged CUT&RUN signals showing CUT&RUN peaks of KDM5A (o) and H3K4me3 (p) on a genome-wide scale (over ± 5 kb) in MDA-MB-231 with EGLN2 depletion.

Source data

Extended Data Fig. 6 Identification and characterization of critical target genes of H3P16oh in breast cancer cells.

a, Venn diagram showing genes that were negatively regulated by EGLN2 and KDM5A from RNA-seq. b, Venn diagram showing genes carrying the common CUT&RUN peaks at promoter regions that are negatively regulated by EGLN2 and KDM5A based on RNA-seq from panel a. c, Heat map of 48 directly target genes from panel b. d, Venn diagram showing overlapping of HIF1α bound peaks (n = 2755), H3P16oh/EGLN2, KDM5A and H3K4me3 overlapped peaks (n = 5053) and direct targets of EGLN2-H3P16oh-KDM5A-H3K4me3 signaling axis (n = 48) from MDA-MB-231 cells. e, Venn diagram showing overlap between HIF1/2α dependent genes(n = 368), EGLN2-KDM5A axis negatively regulation genes(n = 472) from RNA-Seq and direct targets of EGLN2-H3P16oh-KDM5A-H3K4me3 signaling axis(n = 48) in MDA-MB-231 cells. f, Venn diagram showing overlap between HIF1α direct genes(n = 150), EGLN2-KDM5A axis negatively regulated genes(n = 472) from RNA-Seq and direct targets of EGLN2-H3P16oh-KDM5A-H3K4me3 signaling axis(n = 48) in MDA-MB-231 cells. g, Venn diagram showing overlap between ADSL negative regulated genes (n = 1360) from RNA-Seq, EGLN2-KDM5A axis negatively regulated genes (n = 472) from RNA-Seq and targets of EGLN2-H3P16oh-KDM5A-H3K4me3 signaling axis(n = 48). h, qRT-PCR quantification of H3P16oh top ten target genes from MDA-MB-231 cells with EGLN2 or KDM5A depletion. The y axis shows averaged signals after normalization to Actin and then to siCtrl or shCtrl (n ≥ 3; mean ± s.d.; unpaired two-tailed Student’s t-test). *P < 0.05, **P < 0.01, ***P < 0.001. i, Screenshot of binding peak of HIF1α at DKK1 promoter in MDA-MB-231 cells. j, ARNT ChIP-qPCR to examine ARNT binding to promoter of DKK1, lgG and H3P16oh was set as negative and positive control. The y axis shows averaged signals after normalization to lgG (n = 3; mean ± s.d.; unpaired two-tailed Student’s t-test). *P < 0.05, **P < 0.01, ***P < 0.001.

Source data

Extended Data Fig. 7 EGLN2 regulates MDA-MD-231 cell proliferation but not 293T cells.

a, Display of Venn diagram showing overlapping of genes being affected between the MDA-MB-231 breast cancer cells and 293 T cells. b-e, Immunoblots for lysates (b), cell proliferation assays (c), representative images of soft agar assays(d) and quantification of soft agar assays(e) in MDA-MB-231 cells transduced with lentivirus expressing either sgCtrl or EGLN2 sg13/14. f-i, Immunoblots for lysates (f), cell proliferation assays (g), representative images of soft agar assays(h) and quantification of soft agar assays(i) in 293 T cells transduced with lentivirus expressing either sgCtrl or EGLN2 sg13/14. For panel c, e, g and i, the y axis shows averaged values after normalization to sgCtrl (n = 3; mean ± s.d.; unpaired two-tailed Student’s t-test). #/*P < 0.05, ##/**P < 0.01, ###/***P < 0.001.

Source data

Extended Data Fig. 8 Correlation and overlap of CUT&RUN data with or without hypoxia treatment in breast cancer cells.

a-b, Scatter plot displaying the correlation of CUT&RUN replicate (R1/2) signals. c-d, Venn diagram showing overlapping of KDM5A and EGLN2 CUT&RUN peaks detected in MDA-MB-231 cells without (20% O2) (c) or with hypoxia treatment (1% O2) for 12 h (d).

Extended Data Fig. 9 The effect of hypoxia on EGLN2-H3P16oh-KDM5A-H3K4me3 signaling axis depends largely on the inhibition of EGLN2 and H3P16oh.

a, Scatter plot showing the correlation of replicated (R1/2) CUT&RUN signals. b, Immunoblots of H3P16oh and HIF1α level in MDA-MB-231 cells with hypoxia treatment (1% O2) with indicated time. The values listed below the blots indicate the relative H3P16oh and H3K4me3 protein levels with H3 normalization, the samples derive from the same experiment and that blots were processed in parallel. c, qRT-PCR quantification of top 10 genes from MBA-MD-231 cells with hypoxia (1% O2) treatment for 30 min. d, qRT-PCR quantification of H3P16oh top 10 genes from MBA-MD-231 cells with siEGLN2 and siEGLN2 plus hypoxia(1% O2) treatment for 30 min. e, ChIP-qPCR to examine H3K4me3 binding to the promoter regions of the indicated genes with siEGLN2 and siEGLN2 plus hypoxia (1% O2) treatment for 30 min. f, qRT-PCR quantification for top 10 genes from MBA-MD-231 cells with siEGLN2 and siEGLN2 plus hypoxia (1% O2) treatment for 8 h. g, ChIP-qPCR to examine H3K4me3 binding to the promoter regions of the indicated genes with siEGLN2 siEGLN2 plus hypoxia (1% O2) treatment for 8 h. For panel c-g, the y axis shows averaged signals after normalization to indicated control (n = 3; mean ± s.d.; unpaired two-tailed Student’s t-test). *P < 0.05, **P < 0.01, ***P < 0.001.

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Extended Data Fig. 10 EGLN2-catalyzed H3P16oh leads to decreased H3K4me3 in physiologic and physiological context.

a, Presentation of box plots of EGLN2 expression between normal (N) and breast cancer tumors (T, BRCA) in TCGA data analyzed by GEPIA2. Box and whisker plots show all points, minimum to maximum, with 25th to 75th interquartile range box, the center line indicates the median, and the whiskers indicate 1.5 × the interquartile range. Normal (n = 291) versus Tumor (n = 1085). b, Dot plots of EGLN2 expression across all tumor samples(T, red) and paired normal tissues(N, green) in TCGA data analyzed by GEPIA2. For panel a-b, Log2FC Cutoff=1, p-value Cutoff=0.01, ManneWhitney U-tests. c, Immunoblots of expression level of EGLN2 and H3P16oh in indicated breast cell lines. d, Immunoblots of lysates from paired patient-derived normal (N) and tumor (T) breast tissues. e, Immunoblots of expression level of H3P16oh and indicated histone makers in different EGLN2+/+ and EGLN2−/− mice tissues. The values listed below the blots indicate the relative protein levels with H3 normalization, the protein expression level from EGLN2+/+ tissues were set as 1.0. Values in red denotes ‘upregulation’, values in green denotes ‘downregulation’ and values in bold denotes ‘no obvious change’ (EGLN2−/− vs EGLN2+/+). ND, not detected. f, Table of the summary for protein quantification. g, Immunoblots of expression level of H3P16oh and indicated histone makers in EGLN2+/+ and EGLN2−/− female mice mammary gland tissues. The values listed below the blots indicate the relative protein levels with H3 normalization. For panel e and g, we do quantitative comparisons between samples on different blots, the samples derive from the same experiment and that blots were processed in parallel. h, Dot plots of the values of relative protein expression level with H3 normalization in EGLN2+/+ and EGLN2−/− female mice breast tissues from panel g. n = 10, mean ± s.d.; unpaired two-tailed Student’s t-test; *P < 0.05, **P < 0.01, ***P < 0.001.

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Supplementary information

Supplementary Information

Supplementary Notes 1–9, Supplementary Figs. 1–5, uncropped scans for Supplementary Fig. 1 and statistical source data for Supplementary Figs. 4 and 5.

Reporting Summary.

Peer Review File.

Supplementary Tables

Supplementary Table 1. Antibodies used in this study. Supplementary Table 2. H3 peptide, siRNA, shRNA and sgRNA sequences. Supplementary Table 3. Mass spectrometry data. Supplementary Table 4. Quantitative mass spectrometry results. Supplementary Table 5. Quantitative mass spectrometry results for the control peptide H3K4me3. Supplementary Table 6. Quantitative mass spectrometry results for the peptide H3K4me4 P16oh. Supplementary Table 7. Real-time PCR and ChIP-qPCR primers. Supplementary Table 8. Ct values of EGLN2 in paired tissues of EGLN2+/+ and EGLN2−/− mice. Supplementary Table 9. Ct values of EGLN2 in mammary gland tissues of EGLN2+/+ and EGLN2−/− mice. Supplementary Table 10. NMR chemical shift perturbation data for Supplementary Fig. 3.

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Liu, X., Wang, J., Boyer, J.A. et al. Histone H3 proline 16 hydroxylation regulates mammalian gene expression. Nat Genet 54, 1721–1735 (2022). https://doi.org/10.1038/s41588-022-01212-x

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