Hypoxia-inducible transcription factors (HIFs) are fundamental to cellular adaptation to low oxygen levels, but it is unclear how they interact with chromatin and activate their target genes. Here, we use genome-wide mutagenesis to identify genes involved in HIF transcriptional activity, and define a requirement for the histone H3 lysine 4 (H3K4) methyltransferase SET1B. SET1B loss leads to a selective reduction in transcriptional activation of HIF target genes, resulting in impaired cell growth, angiogenesis and tumor establishment in SET1B-deficient xenografts. Mechanistically, we show that SET1B accumulates on chromatin in hypoxia, and is recruited to HIF target genes by the HIF complex. The selective induction of H3K4 trimethylation at HIF target loci is both HIF- and SET1B-dependent and, when impaired, correlates with decreased promoter acetylation and gene expression. Together, these findings show SET1B as a determinant of site-specific histone methylation and provide insight into how HIF target genes are differentially regulated.
Subscribe to Journal
Get full journal access for 1 year
only $4.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
SgRNA read count tables from the CRISPR–Cas9 genetic screen are shown in Supplementary Data 1. RNA-seq (GSE169087) and ChIP–seq (GSE159128, GSE169040 and together under SuperSeries GSE169041) data deposited at Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/). Source data are provided with this paper.
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).
Semenza, G. L. Hypoxia-inducible factor 1 (HIF-1) pathway. Sci. STKE 2007, cm8 (2007).
Epstein, A. C. et al. C. elegans EGL-9 and mammalian homologs define a family of dioxygenases that regulate HIF by prolyl hydroxylation. Cell 107, 43–54 (2001).
Maxwell, P. H. et al. The tumour suppressor protein VHL targets hypoxia-inducible factors for oxygen-dependent proteolysis. Nature 399, 271–275 (1999).
Ivan, M. et al. HIFalpha targeted for VHL-mediated destruction by proline hydroxylation: implications for O2 sensing. Science 292, 464–468 (2001).
Bruick, R. K. & McKnight, S. L. A conserved family of prolyl-4-hydroxylases that modify HIF. Science 294, 1337–1340 (2001).
Jaakkola, P. et al. Targeting of HIF-alpha to the von Hippel-Lindau ubiquitylation complex by O2-regulated prolyl hydroxylation. Science 292, 468–472 (2001).
Arany, Z. et al. An essential role for p300/CBP in the cellular response to hypoxia. Proc. Natl Acad. Sci. USA 93, 12969–12973 (1996).
Perez-Perri, J. I. et al. The TIP60 complex is a conserved coactivator of HIF1A. Cell Rep. 16, 37–47 (2016).
Dekanty, A. et al. Drosophila genome-wide RNAi screen identifies multiple regulators of HIF-dependent transcription in hypoxia. PLoS Genet. 6, e1000994 (2010).
Galbraith, M. D. et al. HIF1A employs CDK8-mediator to stimulate RNAPII elongation in response to hypoxia. Cell 153, 1327–1339 (2013).
Batie, M. et al. Hypoxia induces rapid changes to histone methylation and reprograms chromatin. Science 363, 1222–1226 (2019).
Chakraborty, A. A. et al. Histone demethylase KDM6A directly senses oxygen to control chromatin and cell fate. Science 363, 1217–1222 (2019).
Lee, J. H., Tate, C. M., You, J. S. & Skalnik, D. G. Identification and characterization of the human Set1B histone H3-Lys4 methyltransferase complex. J. Biol. Chem. 282, 13419–13428 (2007).
Shilatifard, A. The COMPASS family of histone H3K4 methylases: mechanisms of regulation in development and disease pathogenesis. Annu. Rev. Biochem. 81, 65–95 (2012).
Burr, S. P. et al. Mitochondrial protein lipoylation and the 2-oxoglutarate dehydrogenase complex controls HIF1alpha stability in aerobic conditions. Cell Metab. 24, 740–752 (2016).
Miles, A. L., Burr, S. P., Grice, G. L. & Nathan, J. A. The vacuolar-ATPase complex and assembly factors, TMEM199 and CCDC115, control HIF1alpha prolyl hydroxylation by regulating cellular iron levels. eLife 6, e22693 (2017).
Hart, T. et al. High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities. Cell 163, 1515–1526 (2015).
Kim, J., Hake, S. B. & Roeder, R. G. The human homolog of yeast BRE1 functions as a transcriptional coactivator through direct activator interactions. Mol. Cell 20, 759–770 (2005).
Prenzel, T. et al. Estrogen-dependent gene transcription in human breast cancer cells relies upon proteasome-dependent monoubiquitination of histone H2B. Cancer Res. 71, 5739–5753 (2011).
Zhang, X. et al. Histone deacetylase 3 (HDAC3) activity is regulated by interaction with protein serine/threonine phosphatase 4. Genes Dev. 19, 827–839 (2005).
Maxwell, P. H. et al. Hypoxia-inducible factor-1 modulates gene expression in solid tumors and influences both angiogenesis and tumor growth. Proc. Natl Acad. Sci. USA 94, 8104–8109 (1997).
Ryan, H. E. et al. Hypoxia-inducible factor-1alpha is a positive factor in solid tumor growth. Cancer Res. 60, 4010–4015 (2000).
Bertout, J. A., Patel, S. A. & Simon, M. C. The impact of O2 availability on human cancer. Nat. Rev. Cancer 8, 967–975 (2008).
Kung, A. L., Wang, S., Klco, J. M., Kaelin, W. G. & Livingston, D. M. Suppression of tumor growth through disruption of hypoxia-inducible transcription. Nat. Med. 6, 1335–1340 (2000).
Wang, L. et al. A cytoplasmic COMPASS is necessary for cell survival and triple-negative breast cancer pathogenesis by regulating metabolism. Genes Dev. 31, 2056–2066 (2017).
Tang, Z. et al. SET1 and p300 act synergistically, through coupled histone modifications, in transcriptional activation by p53. Cell 154, 297–310 (2013).
Wang, Z. et al. Genome-wide mapping of HATs and HDACs reveals distinct functions in active and inactive genes. Cell 138, 1019–1031 (2009).
Crump, N. T. et al. Dynamic acetylation of all lysine-4 trimethylated histone H3 is evolutionarily conserved and mediated by p300/CBP. Proc. Natl Acad. Sci. USA 108, 7814–7819 (2011).
Zhang, T., Cooper, S. & Brockdorff, N. The interplay of histone modifications – writers that read. EMBO Rep. 16, 1467–1481 (2015).
Sun, Z. W. & Allis, C. D. Ubiquitination of histone H2B regulates H3 methylation and gene silencing in yeast. Nature 418, 104–108 (2002).
Pavri, R. et al. Histone H2B monoubiquitination functions cooperatively with FACT to regulate elongation by RNA polymerase II. Cell 125, 703–717 (2006).
Zhu, B. et al. Monoubiquitination of human histone H2B: the factors involved and their roles in HOX gene regulation. Mol. Cell 20, 601–611 (2005).
Chen, Y. et al. ZMYND8 acetylation mediates HIF-dependent breast cancer progression and metastasis. J. Clin. Invest. 128, 1937–1955 (2018).
Brici, D. et al. Setd1b, encoding a histone 3 lysine 4 methyltransferase, is a maternal effect gene required for the oogenic gene expression program. Development 144, 2606–2617 (2017).
Schmidt, K. et al. The H3K4 methyltransferase Setd1b is essential for hematopoietic stem and progenitor cell homeostasis in mice. eLife 7, e27157 (2018).
Schodel, J. et al. High-resolution genome-wide mapping of HIF-binding sites by ChIP-seq. Blood 117, e207–e217 (2011).
Smythies, J. A. et al. Inherent DNA-binding specificities of the HIF-1α and HIF-2α transcription factors in chromatin. EMBO Rep. 20, e46401 (2019).
Qiu, B. et al. HIF2α-dependent lipid storage promotes endoplasmic reticulum homeostasis in clear-cell renal cell carcinoma. Cancer Disco. 5, 652–667 (2015).
Semenza, G. L. Physiology meets biophysics: visualizing the interaction of hypoxia-inducible factor 1 alpha with p300 and CBP. Proc. Natl Acad. Sci. USA 99, 11570–11572 (2002).
Douillet, D. et al. Uncoupling histone H3K4 trimethylation from developmental gene expression via an equilibrium of COMPASS, Polycomb and DNA methylation. Nat. Genet. 52, 615–625 (2020).
Fang, L. et al. SET1A-mediated mono-methylation at K342 regulates YAP activation by blocking its nuclear export and promotes tumorigenesis. Cancer Cell 34, 103–118.e9 (2018).
Luo, W., Chang, R., Zhong, J., Pandey, A. & Semenza, G. L. Histone demethylase JMJD2C is a coactivator for hypoxia-inducible factor 1 that is required for breast cancer progression. Proc. Natl Acad. Sci. 109, E3367–E3376 (2012).
Chen, W. et al. Targeting renal cell carcinoma with a HIF-2 antagonist. Nature 539, 112–117 (2016).
Scheuermann, T. H. et al. Allosteric inhibition of hypoxia inducible factor-2 with small molecules. Nat. Chem. Biol. 9, 271–276 (2013).
Cho, H. et al. On-target efficacy of a HIF-2α antagonist in preclinical kidney cancer models. Nature 539, 107–111 (2016).
Courtney, K. D. et al. HIF-2 complex dissociation, target inhibition, and acquired resistance with PT2385, a first-in-class HIF-2 inhibitor, in patients with clear cell renal cell carcinoma. Clin. Cancer Res. 26, 793–803 (2020).
Wykoff, C. C., Pugh, C. W., Maxwell, P. H., Harris, A. L. & Ratcliffe, P. J. Identification of novel hypoxia dependent and independent target genes of the von Hippel-Lindau (VHL) tumour suppressor by mRNA differential expression profiling. Oncogene 19, 6297–6305 (2000).
Demaison, C. et al. High-level transduction and gene expression in hematopoietic repopulating cells using a human immunodeficiency [correction of imunodeficiency] virus type 1-based lentiviral vector containing an internal spleen focus forming virus promoter. Hum. Gene Ther. 13, 803–813 (2002).
Bailey, P. S. J. et al. ABHD11 maintains 2-oxoglutarate metabolism by preserving functional lipoylation of the 2-oxoglutarate dehydrogenase complex. Nat. Commun. 11, 4046 (2020).
Schödel, J. et al. High-resolution genome-wide mapping of HIF-binding sites by ChIP–seq. Blood 117, e207–e217 (2011).
Salama, R. et al. Heterogeneous effects of direct hypoxia pathway activation in kidney cancer. PLoS ONE 10, e0134645 (2015).
Shen, L., Shao, N., Liu, X. & Nestler, E. ngs.plot: quick mining and visualization of next-generation sequencing data by integrating genomic databases. BMC Genomics 15, 284 (2014).
Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011).
Hower, V., Evans, S. N. & Pachter, L. Shape-based peak identification for ChIP–Seq. BMC Bioinf. 12, 15 (2011).
Stark, R. & Brown, G. DiffBind: Differential binding analysis of ChIP–Seq peak data (2011); http://bioconductor.org/packages/release/bioc/html/DiffBind.html
Dale, R. K., Pedersen, B. S. & Quinlan, A. R. Pybedtools: a flexible Python library for manipulating genomic datasets and annotations. Bioinformatics 27, 3423–3424 (2011).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
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).
Mootha, V. K. et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003).
Leek, R., Grimes, D. R., Harris, A. L. & McIntyre, A. Methods: using three-dimensional culture (spheroids) as an in vitro model of tumour hypoxia. Adv. Exp. Med. Biol. 899, 167–196 (2016).
Väyrynen, S. A. et al. Clinical impact and network of determinants of tumour necrosis in colorectal cancer. Br. J. Cancer 114, 1334–1342 (2016).
Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017).
Niemistö, A., Dunmire, V., Yli-Harja, O., Zhang, W. & Shmulevich, I. Robust quantification of in vitro angiogenesis through image analysis. IEEE Trans. Med. Imaging 24, 549–553 (2005).
Li, W. et al. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. 15, 554 (2014).
We thank the Nathan laboratory for their helpful discussions. We also thank B. Huntly and E. Meduri for initial help with the RNA-seq analysis. This work was supported by a Wellcome Senior Clinical Research Fellowship to J.A.N. (no. 215477/Z/19/Z) and a Lister Institute Research Fellowship to J.A.N. This work was also supported by the NIHR Cambridge Biomedical Research Centre and the Addenbrooke’s Charitable Trust. P.H.M. is supported by a Wellcome Senior Investigator Award (no. 096956/Z/11/Z). D.R.M. is supported by the National Institute for Health Research (grant no. NIHR-RP-2016-06-004) and the Deanship of Scientific Research, King Abdulaziz University, Ministry of High Education for Saudi Arabia. P.J.R. is supported by the Ludwig Institute for Cancer Research, the Wellcome Trust (grant no. 106241/Z/14/Z) and the Francis Crick Institute, which receives its core funding from Cancer Research UK (grant no. FC001501), the Medical Research Council (grant no. FC001501) and the Wellcome Trust (grant no. FC001501). We thank the Oxford Genomics Centre at the Wellcome Centre for Human Genetics (funded by Wellcome Trust grant no. 203141/Z/16/Z) for the generation and initial processing of sequencing data. Computational facilities included the Oxford Biomedical Research Computing (BMRC) facility, a joint development between the Wellcome Centre for Human Genetics and the Big Data Institute supported by Health Data Research UK and the NIHR Oxford Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.
P.J.R. is a scientific cofounder of ReOx Ltd., a company that is developing inhibitors of the HIF hydroxylase enzymes. P.H.M. is a scientific cofounder and equity holder of ReOx and has received speaker honoraria from Fibrogen, both of which aim to develop PHD inhibitors as therapies. The other authors declare no competing interests.
Peer review information Nature Genetics thanks Sara Sdelci and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended Data Fig. 1 Genome-wide mutagenesis screen identifies genes required to activate a HIF response.
a-g, Screen validation using CRISPR-Cas9 targeted depletion in mixed KO populations. HeLa HRE-ODDGFP reporter cells were transduced with sgRNAs against a, HIF1β or UBE2A; b–d, β2m; e, RNF20 or RNF40; f, HIF1β or PPP4C; g, SET1B. Cells were treated with 21% or 1% O2 for 24 h or DMOG 1 mM for 24 h, and GFP levels measured by flow cytometry. h, i, KO clones of HIF1β and SET1B were generated in HeLa HRE-GFPODD reporter cells by serial dilution and validated by immunoblot (representative of 3 experiments) (i). h, Control or KO clones were incubated at 21% or 1% O2 for 24 h and the GFP levels were measured by flow cytometry (n=3 biologically independent samples). j, Loss of SET1B or HIF1β does not alter basal reporter activity. HIF1β and SET1B mixed KO populations of HeLa HRE-ODDGFP reporter cells were generated using CRISPR-Cas9 targeted depletion, and GFP levels were measured with flow cytometry.
a, Wildtype HeLa or SET1B clonal KO cells were incubated at 1% O2, and analyzed by immunoblot (representative of 3 biological replicates). b, HIF-1α mRNA expression in different cell types following HIF1β or SET1B loss after 24 h hypoxia. n= 5 (HeLa), 6 (A549), 3 (MCF7), 4 (skin fibroblasts) biological replicates. c-e, Immunoblots for the HIF complex in HeLa (c), MCF7 (d) and skin fibroblast cells (e) depleted of HIF1β or SET1B (n= 3 biological replicates). f, Reconstitution of mixed KO populations of SET1B in A549 cells transfected with control or SET1B siRNA, with and without SET1B overexpression (representative of 3 biological replicates). g, HeLa cells were transfected with a control or SET1B siRNA for 48 h, mRNA extracted and SET1A or SET1B expression measured by qPCR. Control versus SET1B siRNA, P ≤ 0.0001; two-way ANOVA. n=6 biological replicates. h, Reconstitution of SET1B depleted HeLa HRE-GFPODD reporter cells by transfecting control or SET1B siRNA, with or without SET1B overexpression for 48 h. Cells were treated with 1 mM DMOG for 24 h and GFP levels measured by flow cytometry. i, j, SET1B loss decreases HIF activity in response to hypoxia mimetics. HIF1β and SET1B depleted HeLa HRE-GFPODD reporter cells were analyzed by flow cytometry (i) or immunoblotting (j) following 24 h with and without 1 mM DMOG (representative of 3 biological replicates). k, Proteasome inhibition does not increase CA9 levels following SET1B or HIF1β depletion. HeLa cells stably expressing Cas9 were transduced with sgRNAs against either HIF1β or SET1B. Following 7–10 days cells were treated at 21% or 1% O2 for 24 h and where indicated were treated with 20 µM MG132 for 3 h (representative of 3 biological replicates). wt=wildtype. Graphs show mean ± SD. **** P < 0.0001.
a-c, Volcano plots of fold change in mRNA expression in wildtype b, HIF1β depleted c, or SET1B depleted d, HeLa cells incubated in 21% or 1% O2 for 12 h. Significantly up (red) or downregulated (blue) genes shown (log2(fold change) of> 0.5 or < −0.5). d-f, Expression of HIF target genes in mixed KO populations of HIF1β or SET1B cells in A549 (d), MCF7 (e) or skin fibroblasts (f), following incubation in 21% O2 or 1% O2for 24 h. d, CA9, control versus HIF1β or SET1B sgRNA P ≤ 0.0001 (n=3 biological replicates); PHD3, control versus HIF1β or SET1B sgRNA P ≤ 0.0001 (n=3 biological replicates); VEGF, control versus HIF1β or SET1B sgRNA P ≤ 0.0001 (n=5 biological replicates); GLUT1, control versus HIF1β, P ≤ 0.0001 (n=3 biological replicates); BAP1 (n=6 biological replicates); two-way ANOVA. e, CA9, control versus HIF1β or SET1B sgRNA P ≤ 0.0001 (n=3 biological replicates); PHD3, control versus HIF1β or SET1B sgRNA P ≤ 0.0001 (n=3 biological replicates); two-way ANOVA. f, CA9, control versus HIF1β or SET1B sgRNA P ≤ 0.0001 (n=3 biological replicates); PHD3, control versus HIF1β or SET1B sgRNA P ≤ 0.0001 (n=3 biological replicates); two-way ANOVA. (g, h) mRNA expression of HIF target genes following reconstitution of SET1B depletion and incubation in 21% or 1% O2 for 24 h. g, HeLa cells: CA9, control versus siSET1B P = 0.0006 (n=3 biological replicates); VEGF, control versus siSET1B P = 0.0005; VEGF, control versus siSET1B+SET1B P = 0.029 (n=3 biological replicates); SET1B, control vs siSET1B+SET1B P = 0.0064; SET1B, control versus siSET1B+SET1B P ≤ 0.0001; BAP1 (n=3 biological replicates); two-way ANOVA. h, A549 cells: CA9, control versus siSET1B P = 0.002 (n=3 biological replicates); SET1B, control vs siSET1B+SET1B P = 0.014; SET1B, control versus siSET1B+SET1B P = 0.0002; BAP1 (n=3 biological replicates); two-way ANOVA. Ct=control. Graphs show mean ± SD, * P < 0.05, ** P < 0.01, *** P < 0.001.
Extended Data Fig. 4 Gene expression changes following SET1B loss are not dependent on HIF-α isoforms.
a–d, Venn diagrams of identified HIF and SET1B dependent genes in the HeLa RNA-seq analysis with previously reported datasets of identified HIF-1 and HIF-2 binding sites63 in MCF7 (a, b) or HepG2 (c, d) cells. e-g, Characterisation of HeLa HRE-HIFODDGFP HIF KO cell lines. e, HeLa wildtype (Wt), HIF-1α KO, HIF-2α or HIF-1/2α KO HeLa clones were generated by sgRNA depletion and dilution cloning. Representative immunoblot of 3 independent experiments confirming HIF-1α or HIF-2α depletion is shown following incubation at 21% or 1% O2 for 24 h (e). f, Combined HIF-1α and HIF-2α loss prevents activation of the GFP reporter (representative null clone). g, HeLa wildtype, HIF-1α KO and HIF-2α KO clones stably expressing Cas9 were transduced with a sgRNA against HIF1β or SET1B. Cells were treated with and without 1 mM DMOG and GFP reporter levels were measured using flow cytometry. (h, i) qPCR of selected HIF target genes following SET1B depletion in 786-0 cells. h, Representative immunoblot of SET1B and HIF1β depletion in 786-0 cells (3 biological replicates). (i) mRNA expression of selected HIF-2α target genes. CA9, control versus HIF1β sgRNA P ≤ 0.0001; CA9, control vs SET1B sgRNA P = 0.0006; PAI, control versus HIF1β P ≤ 0.0001; PAI, control vs SET1B sgRNA P = 0.001; ANGPTL4, control versus HIF1β or SET1B sgRNA P ≤ 0.0001; GLUT1, control vs HIF1β sgRNA P ≤ 0.0001; two-way ANOVA. Ct=control. Graphs show mean ± SD, ** P < 0.01, *** P < 0.001.
a, Pathway analysis of enriched genes dependent on HIF1β or SET1B in hypoxia. HIF1β and SET1B dependent genes identified from the RNA Seq were run through Metascape analysis to identify the pathways which were enriched. b, Heat map comparing the log2(fold change) between control, HIF1β and SET1B depleted cells focussing on genes involved in angiogenesis and glycolysis. c, qPCR of selected angiogenesis (left) or glycolysis genes (right) in mixed KO populations of HIF1β or SET1B HeLa cells. VEGF, control versus HIF1β or SET1B sgRNA P ≤ 0.0001; PAI, control versus HIF1β or SET1B sgRNA P ≤ 0.0001; ANGPTL4, control versus HIF1β or SET1B sgRNA P ≤ 0.0001; LDHA, control versus HIF1β sgRNA P ≤ 0.0001; GLUT1, control vs HIF1β sgRNA P ≤ 0.0001; two-way ANOVA, n= 3 biological replicates. d, VEGF ELISA in control, HIF1β or SET1B depleted HeLa cells. Cells were grown in 21% or 1% O2 before supernatants were collected. VEGF control versus HIF1β or SET1B sgRNA P ≤ 0.0001; two-way ANOVA, n= 3 biological replicates. Ct=control. Graphs show mean ± SD, *** P < 0.001.
a, b, Wildtype, HIF1β depleted or SET1B depleted HeLa cells were generated as described, and incubated with or without DMOG (1 mM), Roxadustat (0.1 mM), or Daprodustat (0.1 mM) for 24 h. Cells were analyzed using a Seahorse XF analyzer by performing a Glycolytic Rate Assay. Oxygen consumption rates (OCR), extracellular acidification rates (ECAR) and the protein efflux rate derived from glycolysis (glycoPER) as a percentage of total were calculated using WAVE version 2.6.1. c, d, Bioenergetic analyses for wildtype, HIF1β depleted or SET1B depleted A549 cells, as described for HeLa cells. In A549 cells DMOG altered OCR in a HIF1β independent manner. n=3 for HeLa, n=2 for A549 for biological replicates. Representative data from one experiment shown with mean ± SD of 6 technical replicates.
a-c) Proliferation assays of control, HIF1β or SET1B depleted A549 (a) or HeLa cells (b, c) in 21% O2 (n=3 biological replicates). d, Time course of SET1B and HIF-1α protein levels in 1% O2 (representative of 3 biological replicates). (e) qPCR analysis of SET1B and CA9 expression following 21% or 1% O2 for 24 h. HeLa SET1B 21% versus 1% O2 P ≤ 0.0001, HeLa CA9 21% versus 1% O2 P = 0.0028, A549 SET1B 21% versus 1% O2 P = 0.0006, HeLa CA9 21% versus 1% O2 P = 0.022; 3 biological replicates, unpaired two-tail t test. f, Proliferation assay of SET1B or SET1A deficient A549 cells in 21% O2. Control versus SET1A depletion at 3 days, P ≤ 0.0001; 3 biological replicates, two-way ANOVA. (g) qPCR analysis for GLUT1, HIF-1α and BAP1 in SET1A or SET1B depleted A549 cells. GLUT1 levels SET1B versus SET1A depletion P = 0.0006, HIF-1α levels SET1B versus SET1A depletion P = 0.0003, BAP1 levels SET1B versus SET1A depletion P = 0.004; 6 biological replicates, two-way ANOVA. h, CFSE assay in HIF1β or SET1B depleted HeLa cells. i, Caspase activity assay in control, HIF1β and SET1B sgRNA depleted cells (21% or 1% O2 for 24 h). j, PARP1 cleavage in control, HIF1β and SET1B depleted cells (21% or 1% for 24 h). Representative of 3 biological replicates. k, HeLa spheroid volume in control, HIF or SET1B depleted HeLa cells. n = 54 (control), 39 (SET1B KO), 51 (HIF1β KO), 28 (HIF-1/2α KO) spheroids. l, m, SET1B or HIF1β deficient HeLa (l) or A549 (m) tumor xenograft growth (n=7 mice each condition). n, Total vessel circumference (μm) in A549 tumor xenografts. Control versus HIF1β or SET1B depletion P ≤ 0.0001; n= 6 (control and HIF1β), 7 SET1B, one-way ANOVA). o, Cell growth in control, HIF1β or SET1B depleted 786-0 cells grown in 21% O2 (n=3 biological replicates). Ct=control. Graphs show mean ± SD, * P < 0.05, ** P < 0.01, *** P < 0.0001.
a, Endogenous SET1B was immunoprecipitated from HeLa cells incubated at 21% or 1% O2 for 6 h, and immunoblotted for SET1B, HIF-1α or HIF-2α. b, Endogenous HIF-1α was immunoprecipitated from HeLa cells incubated at 21% or 1% O2 for 6 h, and samples immunoblotted for HIF-1α, HIF1β or SET1B. c, Immunoprecipitated SET1B from wildtype and HIF-1α clonal knockout HeLa cells incubated at 21% or 1% O2 for 6 h, and immunoblotted for HIF-1α or SET1B. d, SET1B does not associate with the SUV12 component of PRC2. Immunoprecipitated SET1B from HeLa cells incubated at 21% or 1% O2 for 6 h, and immunoblotted for SET1B, CFP1 and SUV12. *background band. e, Endogenous CFP1 was immunoprecipitated from HeLa cells treated with 21% or 1% O2 for 6 h, and immunoblotted for HIF-1α, HIF1β, SET1B and CFP1. Representative immunoblots from 3 independent biological replicates shown in a-g. f-j, Cell fractionation studies in A549 and 786-0 cells (representative of 3 biological replicates). f, g, Cell fractionation in A549 cells treated at 21% or 1% O2 for 6 h and immublotted for HIF-α or SET1B. Tubulin used as cytoplasmic marker; PARP1 used as a nuclear marker) f, Quantified using ImageJ (n=3 biological replicates) g. h, Cell fractionation for SET1B and SET1A in A549 cells treated with 1% O2 for 6 h. i, j, Cell fractionation in A549 cells treated with Roxadustat 100 μM for 24 hr quantified using ImageJ (n = 3 biological replicates) j, k, Cell fractionation in 786-0 cells treated at 21% or 1% O2 for indicated times (representative of 3 biological replicates). l-n, SET1B overexpression in control or HIF1β null cells does not restore HIF-1α signaling. HIF-1α, SET1B and CA9 levels were measured by immunoblot (l) or qPCR (m, n) (n=3 biological replicates).
a-c, H3K4me3 increases at HIF target gene promoters in hypoxia. a, H3K4me3 ChIP-PCR in HeLa cells incubated at 21% or 1% O2 for 6 h. CA9, PHD3, or VEGF 21% versus 1% hypoxia P ≤ 0.0001 (4 biological replicates), BAP1 (4 biological replicates); two-way ANOVA (mean ± SD, *** P < 0.0001). b, c, Total H3K4me3 levels in HeLa and A549 cells (1% O2 for indicated times). Representative of 3 biolgical replicates. d, e, H3K4me3 ChIP-seq analysis. d, Heatmaps of H3K4me3 ChIP-seq signal ± 5-kb from TSS. Genes ranked according to expression in the RNA-seq analysis (highest at top). Representative data shown for one replicate in control, HIF1β and SET1B depleted cells incubated at 21% and 1% O2 for 6 h. e, H3K4me3 signal at gene promoters. Plots showing mean H3K4me3 ChIPseq signal (counts per million – cpm) ± 5-kb from TSS of all gene promoters in control, HIF1β and SET1B depleted cells incubated at 21% and 1% O2for 6 h. f, H3K4me3 at the promoters of the top hypoxia-inducible, HIF-bound genes, defined in the RNA-seq and HIF-1α, HIF-2α, and HIF1β ChIP-seq analyses. g, Venn Diagram of high stringency canonical HIF-1 and HIF-2 binding sites (present in both HIF-1α and HIF1β replicates, and/or both HIF-2α and HIF1β replicates). 600 separate binding sites were identified, at 550 gene loci. h, Combined analysis of the 550 HIF-bound gene set and RNA-seq datasets using Gene Set Enrichment Analysis. Comparisons of HIF bound genes in 21% vs 1% O2, control HeLa vs HIF1β KO, and control HeLa vs SET1B KO shown. ES = enrichment score, NES = normalized enrichment score (P value threshold define by 1/number of permutations). i, Metagene plot of H3K4me3 ChIP-seq at the TSS and gene body of HIF1β dependent or independent genes identified in the RNA-seq analyses (Fig. 3d,e).
a-c, HIF-1α, HIF1β, and H3K4me3 ChIP-sequencing in wildtype, HIF1β and SET1B mixed population knockout HeLa cells treated with 21% or 1% O2 for 6 h. Analysis of H3K4me3 peaks using the IGV genome browser for normalized reads at specific genomic regions. Selected HIF target angiogenesis genes (a), HIF target glycolysis genes (b), and the non-HIF target BAP1 (c) are shown. Arrows indicate the directionality of the gene of interest, black box highlights region where H3K4me3 varies.
About this article
Cite this article
Ortmann, B.M., Burrows, N., Lobb, I.T. et al. The HIF complex recruits the histone methyltransferase SET1B to activate specific hypoxia-inducible genes. Nat Genet 53, 1022–1035 (2021). https://doi.org/10.1038/s41588-021-00887-y