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Haematopoietic stem cell-dependent Notch transcription is mediated by p53 through the Histone chaperone Supt16h

Nature Cell Biology volume 22, pages 1411–1422 (2020)Cite this article

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Abstract

Haematopoietic stem and progenitor cells (HSPCs) have been the focus of developmental and regenerative studies, yet our understanding of the signalling events regulating their specification remains incomplete. We demonstrate that supt16h, a component of the Facilitates chromatin transcription (FACT) complex, is required for HSPC formation. Zebrafish supt16h mutants express reduced levels of Notch-signalling components, genes essential for HSPC development, due to abrogated transcription. Whereas global chromatin accessibility in supt16h mutants is not substantially altered, we observe a specific increase in p53 accessibility, causing an accumulation of p53. We further demonstrate that p53 influences expression of the Polycomb-group protein PHC1, which functions as a transcriptional repressor of Notch genes. Suppression of phc1 or its upstream regulator, p53, rescues the loss of both Notch and HSPC phenotypes in supt16h mutants. Our results highlight a relationship between supt16h, p53 and phc1 to specify HSPCs via modulation of Notch signalling.

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Fig. 1: A forward genetic screen identifies supt16h−/− mutants that specifically lack HSPCs.
Fig. 2: The Notch pathway is downregulated in supt16h−/− mutants.
Fig. 3: Induction of p53 in supt16h−/− mutants perturbs HSPC formation.
Fig. 4: The transcription levels of the Notch genes are influenced by p53 abundance.
Fig. 5: HSPC specification is unaffected by ssrp1.
Fig. 6: p53 augments phc1 expression in supt16h mutants.
Fig. 7: Expression of phc1 influences Notch-gene transcription and HSPC specification.

Data availability

Raw and processed RNA-seq (linkage mapping), RNA-seq (differential expression), ATAC–seq and ChIP–seq data have been deposited into the public functional genomics data repository Gene Expression Omnibus. The accession numbers for these data are GSE106342, GSE127555, GSE106341 and GSE116088 for RNA-seq (linkage mapping), RNA-seq (differential expression), ATAC–seq and ChIP–seq, respectively. All other data supporting the findings of this study are available from the corresponding author on reasonable request. Source data are provided with this paper.

References

  1. Ciau-Uitz, A., Monteiro, R., Kirmizitas, A. & Patient, R. Developmental hematopoiesis: ontogeny, genetic programming and conservation. Exp. Hematol. 42, 669–683 (2014).

    Article  CAS  PubMed  Google Scholar 

  2. Bertrand, J. Y. et al. Haematopoietic stem cells derive directly from aortic endothelium during development. Nature 464, 108–111 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Espín-Palazón, R. et al. Proinflammatory signaling regulates hematopoietic stem cell emergence. Cell 159, 1070–1085 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Hondele, M. et al. Structural basis of histone H2A–H2B recognition by the essential chaperone FACT. Nature 499, 111–114 (2013).

    Article  CAS  PubMed  Google Scholar 

  5. Saunders, A. et al. Tracking FACT and the RNA polymerase II elongation complex through chromatin in vivo. Science 301, 1094–1096 (2003).

    Article  CAS  PubMed  Google Scholar 

  6. Hsieh, F.-K. et al. Histone chaperone FACT action during transcription through chromatin by RNA polymerase II. Proc. Natl Acad. Sci. USA 110, 7654–7659 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Kumano, K. et al. Notch1 but not Notch2 is essential for generating hematopoietic stem cells from endothelial cells. Immunity 18, 699–711 (2003).

    Article  CAS  PubMed  Google Scholar 

  8. Kim, A. D. et al. Discrete Notch signaling requirements in the specification of hematopoietic stem cells. EMBO J. 33, 2363–2373 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Clements, W. K. et al. A somitic Wnt16/Notch pathway specifies haematopoietic stem cells. Nature 474, 220–224 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Robert-Moreno, À. et al. Impaired embryonic haematopoiesis yet normal arterial development in the absence of the Notch ligand Jagged1. EMBO J. 27, 1886–1895 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Vousden, K. H. & Lane, D. P. p53 in health and disease. Nat. Rev. Mol. Cell Biol. 8, 275–283 (2007).

    Article  CAS  PubMed  Google Scholar 

  12. Levine, A. J. & Berger, S. L. The interplay between epigenetic changes and the p53 protein in stem cells. Genes Dev. 31, 1195–1201 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Beckerman, R. & Prives, C. Transcriptional regulation by p53. Cold Spring Harb. Perspect. Biol. 2, a000935 (2010).

  14. Miller, A. C., Obholzer, N. D., Shah, A. N., Megason, S. G. & Moens, C. B. RNA-seq-based mapping and candidate identification of mutations from forward genetic screens. Genome Res. 23, 679–686 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Thomas, P. D. Expansion of the gene ontology knowledgebase and resources: the Gene Ontology consortium. Nucleic Acids Res. 45, D331–D338 (2017).

    Article  CAS  Google Scholar 

  17. Burns, C. E., Traver, D., Mayhall, E., Shepard, J. L. & Zon, L. I. Hematopoietic stem cell fate is established by the Notch–Runx pathway. Genes Dev. 19, 2331–2342 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Grainger, S. et al. Wnt9a is required for the aortic amplification of nascent hematopoietic stem cells. Cell Rep. 17, 1595–1606 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Sturgeon, C. M., Ditadi, A., Awong, G., Kennedy, M. & Keller, G. Wnt signaling controls the specification of definitive and primitive hematopoiesis from human pluripotent stem cells. Nat. Biotechnol. 32, 554–561 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Ruiz-Herguido, C. et al. Hematopoietic stem cell development requires transient Wnt/β-catenin activity. J. Exp. Med. 209, 1457–1468 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Kobayashi, I. et al. Jam1a–Jam2a interactions regulate haematopoietic stem cell fate through Notch signalling. Nature 512, 319–323 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Hadland, B. K. et al. A requirement for Notch1 distinguishes 2 phases of definitive hematopoiesis during development. Blood https://doi.org/10.1182/blood-2004-03-1224 (2004).

  23. Andersson, E. R., Sandberg, R. & Lendahl, U. Notch signaling: simplicity in design, versatility in function. Development 138, 3593–3612 (2011).

    Article  CAS  PubMed  Google Scholar 

  24. Bai, X. et al. TIF1γ controls erythroid cell fate by regulating transcription elongation. Cell 142, 133–143 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Berghmans, S. et al. tp53 Mutant zebrafish develop malignant peripheral nerve sheath tumors. Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.0406252102 (2005).

  26. Ljungman, M., Zhang, F., Chen, F., Rainbow, A. J. & McKay, B. C. Inhibition of RNA polymerase II as a trigger for the p53 response. Oncogene 18, 583–592 (1999).

    Article  CAS  PubMed  Google Scholar 

  27. Lindahl, T. & Wood, R. D. Quality control by DNA repair. Science 286, 1897–1905 (1999).

    Article  CAS  PubMed  Google Scholar 

  28. Itoh, M. et al. Mind bomb is a ubiquitin ligase that is essential for efficient activation of Notch signaling by Delta. Dev. Cell 4, 67–82 (2003).

    Article  CAS  PubMed  Google Scholar 

  29. Orphanides, G., LeRoy, G., Chang, C. H., Luse, D. S. & Reinberg, D. FACT, a factor that facilitates transcript elongation through nucleosomes. Cell 92, 105–116 (1998).

    Article  CAS  PubMed  Google Scholar 

  30. Koltowska, K. et al. Ssrp1a controls organogenesis by promoting cell cycle progression and RNA synthesis. Development 140, 1912–1918 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Huang, H.-T. et al. A network of epigenetic regulators guides developmental haematopoiesis in vivo. Nat. Cell Biol. 15, 1516–1525 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Busch-Nentwich, E. et al. Sanger Institute Zebrafish Mutation Project mutant data submission. https://zfin.org/ZDB-ALT-160601-1382 (2013).

  33. Smeenk, L. et al. Characterization of genome-wide p53-binding sites upon stress response. Nucleic Acids Res. https://doi.org/10.1093/nar/gkn232 (2008).

  34. El-Deiry, W. S., Kern, S. E., Pietenpol, J. A., Kinzler, K. W. & Vogelstein, B. Definition of a consensus binding site for p53. Nat. Genet. https://doi.org/10.1038/ng0492-45 (1992).

  35. Le Bouteiller, M. et al. Notchless-dependent ribosome synthesis is required for the maintenance of adult hematopoietic stem cells. J. Exp. Med. 210, 2351–2369 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Martinez, A.-M. et al. Polyhomeotic has a tumor suppressor activity mediated by repression of Notch signaling. Nat. Genet. 41, 1076–1082 (2009).

    Article  CAS  PubMed  Google Scholar 

  37. Boyer, L. A. et al. Polycomb complexes repress developmental regulators in murine embryonic stem cells. Nature 441, 349–353 (2006).

    Article  CAS  PubMed  Google Scholar 

  38. Bracken, A. P., Dietrich, N., Pasini, D., Hansen, K. H. & Helin, K. Genome-wide mapping of Polycomb target genes unravels their roles in cell fate transitions. Genes Dev. 20, 1123–1136 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Lee, T. I. et al. Control of developmental regulators by Polycomb in human embryonic stem cells. Cell 125, 301–313 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Tolhuis, B. et al. Genome-wide profiling of PRC1 and PRC2 Polycomb chromatin binding in Drosophila melanogaster. Nat. Genet. https://doi.org/10.1038/ng1792 (2006).

  41. Ohta, H. et al. Polycomb group gene rae28 is required for sustaining activity of hematopoietic stem cells. J. Exp. Med. 195, 759–770 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Ji, Y. K. et al. Defective long-term repopulating ability in hematopoietic stem cells lacking the Polycomb-group gene rae28. Eur. J. Haematol. 73, 75–84 (2004).

    Article  Google Scholar 

  43. Keller, D. M. et al. A DNA damage-induced p53 serine 392 kinase complex contains CK2, hSpt16, and SSRP1. Mol. Cell 7, 283–292 (2001).

    Article  CAS  PubMed  Google Scholar 

  44. Keller, D. M. & Lu, H. p53 serine 392 phosphorylation increases after UV through induction of the assembly of the CK2·hSPT16·SSRP1 complex. J. Biol. Chem. 277, 50206–50213 (2002).

    Article  CAS  PubMed  Google Scholar 

  45. Gasparian, A. V. et al. Curaxins: anticancer compounds that simultaneously suppress NF-κB and activate p53 by targeting FACT. Sci. Transl. Med. 3, 95ra74 (2011).

  46. Kolundzic, E. et al. FACT sets a barrier for cell fate reprogramming in Caenorhabditis elegans and human cells. Dev. Cell https://doi.org/10.1016/j.devcel.2018.07.006 (2018).

  47. Rhoades, A. R. & Ruone, F. T. Structural features of nucleosomes reorganized by yeast FACT and its HMG box component, Nhp6. Mol. Cell. Biol. 24, 3907–3917 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Westerfield, M. The Zebrafish Book. A Guide for the Laboratory Use of Zebrafish (Danio rerio) 5th edn (Univ. Oregon Press, 2007).

  49. North, T. E. et al. Prostaglandin E2 regulates vertebrate haematopoietic stem cell homeostasis. Nature 447, 1007–1011 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Villefranc, J. A., Amigo, J. & Lawson, N. D. Gateway compatible vectors for analysis of gene function in the zebrafish. Dev. Dyn. https://doi.org/10.1002/dvdy.21354 (2007).

  51. Lin, H. F. et al. Analysis of thrombocyte development in CD41–GFP transgenic zebrafish. Blood 106, 3803–3810 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Parsons, M. J. et al. Notch-responsive cells initiate the secondary transition in larval zebrafish pancreas. Mech. Dev. 126, 898–912 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Bussmann, J., Wolfe, S. A. & Siekmann, A. F. Arterial-venous network formation during brain vascularization involves hemodynamic regulation of chemokine signaling. Development 138, 1717–1726 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Scheer, N. & Campos-Ortega, J. A. Use of the Gal4-UAS technique for targeted gene expression in the zebrafish. Mech. Dev. 80, 153–158 (1999).

    Article  CAS  PubMed  Google Scholar 

  55. Lawson, N. D. & Weinstein, B. M. In vivo imaging of embryonic vascular development using transgenic zebrafish. Dev. Biol. 248, 307–318 (2002).

    Article  CAS  PubMed  Google Scholar 

  56. Andrews, S. FastQC: a quality control tool for high throughput sequence data. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010).

  57. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Wang, L., Wang, S. & Li, W. RSeQC: quality control of RNA-seq experiments. Bioinformatics https://doi.org/10.1093/bioinformatics/bts356 (2012).

  59. Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics https://doi.org/10.1093/bioinformatics/btp698 (2010).

  60. Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Wang, L., Feng, Z., Wang, X., Wang, X. & Zhang, X. DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics https://doi.org/10.1093/bioinformatics/btp612 (2010).

  62. Lee, Y. et al. FGF signalling specifies haematopoietic stem cells through its regulation of somitic Notch signalling. Nat. Commun. 5, 5583 (2014).

    Article  CAS  PubMed  Google Scholar 

  63. Kim, A. D. et al. Discrete Notch signaling requirements in the specification of hematopoietic stem cells. EMBO J. https://doi.org/10.15252/embj.201488784 (2014).

  64. Robu, M. E. et al. p53 Activation by knockdown technologies. PLoS Genet. 3, 787–801 (2007).

    Article  CAS  Google Scholar 

  65. Buenrostro, J. D., Wu, B., Chang, H. Y. & Greenleaf, W. J. ATAC-seq: a method for assaying chromatin accessibility genome-wide. Curr. Protoc. Mol. Biol. 2015, 21.29.1–21.29.9 (2015).

    Google Scholar 

  66. Wu, J., Anczukow, O., Krainer, A. R., Zhang, M. Q. & Zhang, C. OLego: fast and sensitive mapping of spliced mRNA-seq reads using small seeds. Nucleic Acids Res. 41, 5149–5163 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. 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 

  68. Thorvaldsdóttir, H., Robinson, J. T. & Mesirov, J. P. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief. Bioinform. 14, 178–192 (2013).

    Article  PubMed  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Science Foundation Graduate Research Fellowship Program (grant no. DGE-1650112), UCSD Genetics Training Grant (grant no. T32GM008666-17), National Institutes of Health (grant no. R01-DK074482) and Institute for Basic Science (grant no. IBS-R022-D1). RNA-seq, ATAC–seq and ChIP–seq were conducted at the IGM Genomics Center, University of California, San Diego (MCC grant no. P30CA023100). We thank K. Ong, Y. G. Han, J. Park and S.-W. Jin for their technical assistance, J. Posakony for scientific guidance and the members of the D.T. laboratory for providing helpful comments.

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Authors and Affiliations

  1. Department of Cellular and Molecular Medicine and Section of Cell and Developmental Biology, University of California, San Diego, La Jolla, CA, USA

    Sophia G. Espanola, Hyemin Song, Jennifer E. Manegold, Cara Bickers, David Traver & Yoonsung Lee

  2. Center for Genomic Integrity, Institute for Basic Science (IBS), Ulsan, Republic of Korea

    Hyemin Song, Eun-Sun Kim, Chang-Kyu Oh, Yong Hwan Park, Mi-Sun Kang, Sukhyun Kang, Kyungjae Myung & Yoonsung Lee

  3. School of Life Sciences, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea

    Eunjin Ryu, Unbeom Shin, Kyungjae Myung & Yoonsung Lee

  4. Section of Cell and Developmental Biology, Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA

    Aditya Saxena, Stephanie Grainger, Lauren Pandolfo, Kimberly L. Cooper & Deborah Yelon

  5. Center for Computational Biology and Bioinformatics, University of California, San Diego, La Jolla, CA, USA

    Chanond A. Nasamran

  6. Department of Pediatrics and Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA

    Debashis Sahoo

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  1. Sophia G. Espanola
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Contributions

Conceptualization and methodology: Y.L, D.Y. and D.T. Validation: Y.L and S.G.E. Formal analysis and visualization: S.G.E. Investigation: Y.L., S.G.E., H.S., E.R., A.S., E.-S.K., J.E.M., C.-K.O., C.B, U.S., S.G., Y.H.P., L.P., M.-S.K., S.K. and K.M. Software: S.G.E., D.S. and C.A.N. Writing of the original draft: S.G.E., Y.L., D.T., D.Y. and K.L.C. Supervision: Y.L. and D.T.

Corresponding authors

Correspondence to David Traver or Yoonsung Lee.

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Extended data

Extended Data Fig. 1 Characterizing the causal mutation from our forward genetic screen.

a, Diagram of the forward genetic screen strategy. b,c, Mapping of RNA-seq using RNAmapper with whole genome view (b) and specifically looking at the linked interval on Chr 7 (c). d, Position and RNA-seq coverage of SNP on supt16h resulting in a premature stop codon. e,f, Expression of supt16h-/- based on RNA-seq (e) (Represented as mean ± s.d., two-tailed Student’s t-test, n = 3, P = 0.0005) and RT-qPCR (f) (Represented as mean ± s.d., two-tailed Student’s t-test, n = 3, P = 0.0024) for cmyb. For RT-qPCR, expressions are relative to WT sibling. g-j, WISH of WT embryos injected with supt16h-MO for runx1 (blue arrowheads) at 28 hpf and cmyb at 36 hpf. k,l, Representative confocal of Tg(cmyb:GFP;kdrl:mCherry) embryos injected with supt16h-MO from one independent experiment. Double positive HSPCs indicated by white arrowheads at 48 hpf. DA = dorsal aorta; V = vein. m, Quantification of double positive HSPCs from (g and h) (Represented as mean ± s.e.m., two- tailed t-test, n = 10, P < 0.0001). Bar, 100 μm. Source data provided in Supplementary Table 5.

Source data

Extended Data Fig. 2 The expression pattern of supt16h and the effect of its knockout on HSPC relevant tissues.

a-c, WISH of embryos from a supt16h+/- incross (IX) for supt16h expression at 0, 2.5, and 6 hpf. d,e, WISH of WT embryos for supt16h expression at 24 and 32 hpf. Insets zoom magnify the DA (red arrowheads). f-k, WISH of WT sibling and supt16h-/- embryos for supt16h expression at 12, 24, and 32 hpf. f-p, WISH of supt16h+/- incross (IX) using probes for posterior lateral mesoderm (PLM) makers scl, and lmo2 (f,g), somitic marker desma (h), sclerotome marker foxc1b and twist1b (i,j), endothelial markers cdh5 and kdrl (k,l), arterial marker efnb2a (m), venous marker flt4 (n), primitive erythroid marker gata1 (o), and primitive leukocyte marker l-plastin (p). q,r, Representative confocal of supt16h-/- and WT sibling embryos on Tg(fli1:GFP) background examining vasculature development. Based on one independent experiment. s,t, Magnified images of (q,r) highlight vein (V) and dorsal aorta (DA) formation. Bar, 100 μm.

Extended Data Fig. 3 Characterizing the effect of supt16h on Notch gene expression.

a,b, Gene Ontology Analysis of Biological Components (a) and Molecular Components (b) shows downregulated genes in supt16h-/- embryos based on a log2fold change >1. c, Volcano plot of the differentially expressed genes between WT sibling and supt16h-/- embryos based on RNA-seq. Data representative of 3 (a-c) biological replicates. d-g, WISH of supt16h-/- and WT sibling embryos using probes for notch3 and dla. h,i Representative confocal images along the DA of supt16h-/- and WT sibling embryos on Tg(Tp1:GFP) background at 22 hpf. Bar, 50 μm. j, Mean fluorescence level from (h,i) of Tp1:GFP along the DA calculated in ImageJ (Represented as mean ± s.e.m., two-tailed Student’s t-test, nWT= 15,nMUT= 8,P = 0.0373). k, Sorted double positive Tp1:GFP+;fli1:DsRed+ cells from supt16h morphants and uninjected controls at 22 hpf by flow cytometry (Represented as mean ± s.d, two-tailed Student’s t-test,n = 3,P = 0.0192). l, Gating strategy used to quantify TP1 (Notch- FITC) + endothelial (PE-Cy5) cells. m,n, DFISH of Notch-active (green) and etsrp (red) tissues in supt16h-/- and WT sibling embryos on a Tg(Tp1:GFP) background at 14 hpf. Based on one independent experiment. o-v, Global expression of NICD+ and NICD- embryos that are WT siblings or supt16h-/- (supt16h+/-;hsp70:gal4 x supt16h+/-;UAS-myc:NICD) analysed at 28 hpf by NICD immunohistochemistry (IHC) (l-o) and runx1 WISH (p-s). Representative images from two independent experiments. Bar, 100 μm. Blue arrowheads indicate HSPCs. Bar, 100 μm. Source data provided in Supplementary Table 5.

Source data

Extended Data Fig. 4 The effect of supt16h on transcript elongation and chromatin accessibility.

a, RT-qPCR of 5’ vs 3’ initiation/elongation of Notch genes in WT sibling and supt16h-/- embryos at 32 hpf. Expressions relative to WT sibling (horizontal dotted line) (Represented as mean ± s.d, two-tailed Student’s t-test with Holm-Sidak correction for multiple comparisons, n = 3, ***P < 0.001, **P < 0.01, *P <0.05 lined significance compares WT 5’ to MUT 3’, significance over MUT 5’ compares MUT 5’ to MUT 3’). b, ATAC-seq results plotting the total number of accessible peaks in WT and supt16h-/- embryos at 32 hpf (Represented as mean ± s.d, two- tailed t-test, n = 6, N.S. = not significant). c, ATAC-seq results plotting the number of accessible TSS peaks in WT sibling and supt16h-/- embryos (Represented as mean ± s.d, two-tailed Student’s t- test, n = 6, N.S. = not significant). d, p-values of the differential accessibility of Notch genes based on ATAC-seq log2fold change of supt16h-/- vs WT sibling. e,f, ATAC-seq peak plot of chromatin accessibility in supt16h-/- and WT sibling embryos for notch1a and notch1b. Bottom panel shows peak overlay. g, Plot of ATAC-seq log2fold change by increasing accessibility vs. corresponding RNA-seq log2fold change values. h, Rank order of top 10 differentially accessible genes based on ATAC-seq of supt16h-/- and WT sibling embryos at 32 hpf. i,j, WISH of p53 in WT sibling and supt16h-/- embryos at 36 hpf. k-r, WISH of p53 in embryos injected with supt16h-MO at 14, 18, 28, and 36 hpf. s-v, WISH of runx1 (blue arrowheads) for WT sibling and p53-/- injected with supt16h-MO. Data representative of 2 (i-r) and 3 (a,h) biological replicates. Bar, 100 μm. Source data provided in Supplementary Table 5.

Source data

Extended Data Fig. 5 Characterizing P53-mediated apoptosis in supt16h-/- embryos.

a,b, Representative confocal of WT sibling and supt16h-/-;Tg(fli1:GFP) and stained with Acridine Orange (AO) at 28 hpf. (arrowheads = TUNEL+;fli1+ cells). c, Quantification of double positive TUNEL+;fli1+ cells for (a,b) (nWT = 15, nMUT= 10, N.S. = not significant). d-g, TUNEL of WT sibling and supt16h-/- crossed onto Tg(fli1:GFP) at 14 and 18 hpf (arrowheads = TUNEL+;fli1+ cells). Confocal images of TUNEL, fli1 and double-positive TUNEL+fli1+ (yellow; indicated by white arrowheads; left), and TUNEL-only cells are shown (right). h,i, Quantification of double positive TUNEL+;fli1+ cells for 14 (h) (nWT = 12, nMUT= 8, N.S. = not significant) and 18 hpf (i) (nWT= 12, nMUT= 8, N.S. = not significant). j–o, TUNEL of WT sibling and supt16h-/- at 14 hpf, 18 hpf, and 28 hpf. Confocal images of TUNEL and DAPI (left) and TUNEL-only cells are shown (right). p–r, Quantification of apoptotic cells based on TUNEL at 14 hpf (nWT = 12, nMUT = 8, N.S. = not significant), 18 hpf (nWT = 15, nMUT = 8, **P = 0.0023), and 28 hpf (nWT = 25, nMUT = 7, ****P < 0.0001). s-v, Brightfield images of WT sibling or supt16h-/- in the context of p53 WT (+/+); HET(+/-); MUT(-/-) embryos at 48, 55, and 70 hpf. w, Kaplan-Meier survival curve for (s-v). x-c’, WISH of p53, runx1, and cmyb for WT embryos treated with 5 gy of ionizing radiation at 6 hpf. d’,e’, AO staining at 24 hpf following treatment of WT embryos with 5 gy of ionizing radiation at 6 hpf. Representative images based on one independent experiment. Dot plot graphs (c, h, i, p, q, r) represented as mean ± s.d., two-tailed Student’s t-test. Bar, 100 μm. Source data provided in Supplementary Table 5.

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Extended Data Fig. 6 Characterizing the effect of p53 on Notch gene expression and HSPC formation.

a–h, WISH of WT sibling (a,e) and supt16h-/- embryos that are p53+/+ (b,f), p53+/- (c,g), or p53-/- (d,h) for notch1b (a-d) and runx1 (e-h) at 28 hpf. i-l, WISH for runx1 of embryos injected with p53-MO, notch1b-MO, or both MOs at 28 hpf. m-t, WISH of runx1 (m-p) and cmyb (q-t) for WT sibling or mib-/- embryos injected with p53-MO. Blue arrowheads indicate HSPCs. Bar, 100 μm.

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Extended Data Fig. 7 Characterizing the expression profile and effect of ssrp1a on HSPCs.

a–f, WISH of embryos injected with ssrp1a-MO and probed with efnb2a (a,b), gata1 (c,d), and cdh5 (e,f) at 26 hpf. No effect is seen on dorsal aorta, red blood cell, or vasculature formation upon ssrp1a knockdown. g-o, WISH of supt16h, ssrp1a, and ssrp1b for WT embryos at 14-16 hpf with a dorsal view (g-i), and 24 hpf (j-l) and 32 hpf with a posterior lateral view (m-o). p-w, TUNEL of uninjected (p-s) and ssrp1a-MO (t-w) embryos at 32 hpf. Confocal images of TUNEL and DAPI (left) and TUNEL only (right) are shown. x, Quantification of the number of apoptotic cells in ssrp1a morphants from (p-w) based on TUNEL for the trunk region (Represented as mean ± s.d., one-way ANOVA with post-hoc Tukey, n = 10, P < 0.0001, N.S. = not significant). y, Mean fluorescence level of Tp1:GFP of the DA calculated in ImageJ based on Integrated Density (Represented as mean ± s.d., two- tailed t-test, nWT = 11, nMO = 15, *P = 0.0147, N.S. = not significant). z,a’, WISH of notch1b in WT sibling and ssrp1a-/- embryos at 28 hpf. Data representative of 2 (g-o) biological replicates. Bar, 100 μm. Source data provided in Supplementary Table 5.

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Extended Data Fig. 8 Characterizing the role of P53 on modulating Notch expression.

a,b, Gene Ontology analysis of the significant p53-ChIP-peaks for the Biological Processes and Pathways affected. c, Plot of p53-ChIP-seq peaks for a known p53-target gene, p21. d, Graph of P-values representing the log2 fold change of the differential binding of p53 to Notch genes between WT sibling and supt16h-/- embryos. No significant effect is observed in biding to Notch genes. e,f, p53-ChIP-seq plots for notch1a and notch1b, graphing WT sibling and supt16h-/- peaks. g, Venn diagram depicting the number of accessible (green) and inaccessible (orange) p53-bound genes based on p53-ChIP-seq and ATAC-seq of WT sibling and supt16h-/- embryos at 32 hpf. Genes possessing significant p53-bound peaks (n = 2 for ChIP- seq, n = 6 for ATAC-seq, two-tailed Student’s t-test, adjusted P > 0.05) were assessed for their accessibility based on ATAC-seq (two- tailed t-test, adjusted P > 0.05), where genes had at least >1 Log2 fold change between WT and supt16h-/- samples. h, The number of upregulated (green) and downregulated (orange) p53-bound genes based on p53-ChIP-seq and RNA-seq of WT sibling and supt16h-/- embryos at 32 hpf. Genes possessing significant p53-bound peaks (n = 2 for ChIP-seq, n = 3 for RNA- seq, two-tailed Student’s t-test, adjusted P > 0.05), where genes had at least >1 Log2 fold change between WT and supt16h-/- samples, were assessed for their expression based on RNA-seq (two- tailed t-test, adjusted P > 0.05). p53 binding stimulates increased gene expression. i, Expression of phc1 in supt16h mutants and WT siblings based on RNA-seq (Represented as mean ± s.d, two-tailed Student’s t-test, n = 3, P = 0.0009). j-q, WISH of phc1 expression in WT sibling and supt16h-/- embryos at 14 (j,k), 18 (l,m), 28 (n,o), and 36 hpf (p,q). r-w, WISH of phc1 expression in uninjected and supt16h-MO injected embryos at 14 (r,s), 18 (t,u), and 28 (v,w). Bar, 200 μm. Source data provided in Supplementary Table 5.

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Extended Data Fig. 9 Characterizing the effect of phc1 overexpression on Notch signalling.

a-l, WISH of WT sibling and supt16h-/- embryos injected with phc1-MO probed for notch1a, dla, and dlc at 28 hpf. m-o, Representative confocal images of wild-type embryos on a Tg(Tp1:GFP) background injected with 100 and 200 pg of phc1 mRNA at 28 hpf. Insets are magnified images of the DA and intersomitic vessels. Representative images from one independent experiment. p-c’, WISH of wild-type embryos injected with 100 pg of phc1 mRNA probed for at 28 hp for runx1 (p-q), phc1 (r-s), notch1b (t-u), notch3 (v-w), dla (x-y), dlc (z-a’), and dll4 (b-c’). d’, RT-qPCR of Notch genes in WT sibling and embryos injected with 100 pg (r) of phc1 mRNA collected at 28 hpf. No significant changes observed between samples. (Represented as mean ± s.d., two-tailed Student’s t-test with Holm-Sidak correction for multiple comparisons, n = 3). e'-j’, WISH at 28 hpf for phc1 (e’-f’), runx1 (g’-h’), and notch1b (i’-j’) of wild-type embryos injected with fli1ep:phc1 plasmid for transient expression of phc1 in the vasculature. Blue arrowheads indicate HSPCs. Bar, 100 μm.

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Extended Data Fig. 10 Model of how Supt16h and P53 regulate HSPC specification through notch transcription.

a,b, In a wild-type settng, p53 (a) and phc1 (b) maintain baseline levels of accessibility and transcriptional expression. c,d, This results in normal transcription of Notch genes (c) and allows for proper specification of HSPCs (d). e, In supt16h mutants, the p53 gene locus is highly accessible, resulting in increased p53 mRNA and protein levels. f, p53 then binds to phc1, a repressor of Notch gene expression, to allow for its enhanced expression. g, PHC1, as part of PRC1, inhibits Notch signalling by acting as direct or indirect a transcriptional repressor of Notch genes. h, In the absence of Notch expression, HSPCs fail to specify.

Supplementary information

Reporting Summary

41556_2020_604_MOESM2_ESM.xlsx

Supplementary Tables. Supplementary Table 1. Candidate SNPs within the linkage interval of chromosome 7. All non-synonymous, coding SNPs within the linkage interval with corresponding position, change and affected gene. The bold row indictaes the linked gene. Supplementary Table 2. RT–qPCR primers for initiation/elongation of Notch, Erythroid and HSPC genes. List of RT–qPCR primers. Supplementary Table 3. List of resources used during the study. All critical reagents and souware used in this study.

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Espanola, S.G., Song, H., Ryu, E. et al. Haematopoietic stem cell-dependent Notch transcription is mediated by p53 through the Histone chaperone Supt16h. Nat Cell Biol 22, 1411–1422 (2020). https://doi.org/10.1038/s41556-020-00604-7

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