DNA-PKcs has KU-dependent function in rRNA processing and haematopoiesis

Abstract

The DNA-dependent protein kinase (DNA-PK), which comprises the KU heterodimer and a catalytic subunit (DNA-PKcs), is a classical non-homologous end-joining (cNHEJ) factor1. KU binds to DNA ends, initiates cNHEJ, and recruits and activates DNA-PKcs. KU also binds to RNA, but the relevance of this interaction in mammals is unclear. Here we use mouse models to show that DNA-PK has an unexpected role in the biogenesis of ribosomal RNA (rRNA) and in haematopoiesis. The expression of kinase-dead DNA-PKcs abrogates cNHEJ2. However, most mice that both expressed kinase-dead DNA-PKcs and lacked the tumour suppressor TP53 developed myeloid disease, whereas all other previously characterized mice deficient in both cNHEJ and TP53 expression succumbed to pro-B cell lymphoma3. DNA-PK autophosphorylates DNA-PKcs, which is its best characterized substrate. Blocking the phosphorylation of DNA-PKcs at the T2609 cluster, but not the S2056 cluster, led to KU-dependent defects in 18S rRNA processing, compromised global protein synthesis in haematopoietic cells and caused bone marrow failure in mice. KU drives the assembly of DNA-PKcs on a wide range of cellular RNAs, including the U3 small nucleolar RNA, which is essential for processing of 18S rRNA4. U3 activates purified DNA-PK and triggers phosphorylation of DNA-PKcs at T2609. DNA-PK, but not other cNHEJ factors, resides in nucleoli in an rRNA-dependent manner and is co-purified with the small subunit processome. Together our data show that DNA-PK has RNA-dependent, cNHEJ-independent functions during ribosome biogenesis that require the kinase activity of DNA-PKcs and its phosphorylation at the T2609 cluster.

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Fig. 1: Kinase dead or phosphorylation deficient DNA-PKcs causes haematopoietic defects independent of cNHEJ.
Fig. 2: Mutations in DNA-PKcs cause KU-dependent haematopoietic failure and translation defects.
Fig. 3: DNA-PK, but not other cNHEJ factors, co-purifies with the U3 snoRNA and regulates rRNA processing.
Fig. 4: U3 snoRNA drives assembly, activation, and auto-phosphorylation of DNA-PK at the T2609 cluster.

Data availability

irCLIP data are available via the Gene Expression Omnibus (GEO) under the accession number GSE109026. All uncropped blots are provided in Supplementary Fig. 1. Data underlying the graphical representations used in the figures, including all experiments presenting data from animal models, are provided as Source Data. Exact P values and defined sample sizes (n) are provided in Supplementary Data 1.

Code availability

The FAST-iCLIP software is freely available at https://github.com/ChangLab/FAST-iCLIP/tree/lite.

References

  1. 1.

    Lieber, M. R. The mechanism of double-strand DNA break repair by the nonhomologous DNA end-joining pathway. Annu. Rev. Biochem. 79, 181–211 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  2. 2.

    Jiang, W. et al. Differential phosphorylation of DNA-PKcs regulates the interplay between end-processing and end-ligation during nonhomologous end-joining. Mol. Cell 58, 172–185 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. 3.

    Zhu, C. et al. Unrepaired DNA breaks in p53-deficient cells lead to oncogenic gene amplification subsequent to translocations. Cell 109, 811–821 (2002).

    CAS  PubMed  Article  Google Scholar 

  4. 4.

    Dragon, F. et al. A large nucleolar U3 ribonucleoprotein required for 18S ribosomal RNA biogenesis. Nature 417, 967–970 (2002).

    ADS  CAS  PubMed  Article  Google Scholar 

  5. 5.

    Alt, F. W., Zhang, Y., Meng, F. L., Guo, C. & Schwer, B. Mechanisms of programmed DNA lesions and genomic instability in the immune system. Cell 152, 417–429 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  6. 6.

    Gao, Y. et al. A targeted DNA-PKcs-null mutation reveals DNA-PK-independent functions for KU in V(D)J recombination. Immunity 9, 367–376 (1998).

    CAS  PubMed  Article  Google Scholar 

  7. 7.

    Kirchgessner, C. U. et al. DNA-dependent kinase (p350) as a candidate gene for the murine SCID defect. Science 267, 1178–1183 (1995).

    ADS  CAS  PubMed  Article  Google Scholar 

  8. 8.

    Taccioli, G. E. et al. Targeted disruption of the catalytic subunit of the DNA-PK gene in mice confers severe combined immunodeficiency and radiosensitivity. Immunity 9, 355–366 (1998).

    CAS  PubMed  Article  Google Scholar 

  9. 9.

    Frank, K. M. et al. DNA ligase IV deficiency in mice leads to defective neurogenesis and embryonic lethality via the p53 pathway. Mol. Cell 5, 993–1002 (2000).

    MathSciNet  CAS  PubMed  Article  Google Scholar 

  10. 10.

    Gao, Y. et al. Interplay of p53 and DNA-repair protein XRCC4 in tumorigenesis, genomic stability and development. Nature 404, 897–900 (2000).

    ADS  CAS  PubMed  Article  Google Scholar 

  11. 11.

    Nacht, M. et al. Mutations in the p53 and SCID genes cooperate in tumorigenesis. Genes Dev. 10, 2055–2066 (1996).

    CAS  PubMed  Article  Google Scholar 

  12. 12.

    Crowe, J. L. et al. Kinase-dependent structural role of DNA-PKcs during immunoglobulin class switch recombination. Proc. Natl Acad. Sci. USA 115, 8615–8620 (2018).

    CAS  PubMed  Article  Google Scholar 

  13. 13.

    Jacks, T. et al. Tumor spectrum analysis in p53-mutant mice. Curr. Biol. 4, 1–7 (1994).

    CAS  PubMed  Article  Google Scholar 

  14. 14.

    Meek, K., Dang, V. & Lees-Miller, S. P. DNA-PK: the means to justify the ends? Adv. Immunol. 99, 33–58 (2008).

    CAS  PubMed  Article  Google Scholar 

  15. 15.

    Davis, A. J., Chen, B. P. & Chen, D. J. DNA-PK: a dynamic enzyme in a versatile DSB repair pathway. DNA Repair (Amst.) 17, 21–29 (2014).

    CAS  Article  Google Scholar 

  16. 16.

    Jiang, W. et al. Phosphorylation at S2053 in murine (S2056 in human) DNA-PKcs is dispensable for lymphocyte development and class switch recombination. J. Immunol. 203, 178–187 (2019).

    CAS  PubMed  Article  Google Scholar 

  17. 17.

    Zhang, S. et al. Congenital bone marrow failure in DNA-PKcs mutant mice associated with deficiencies in DNA repair. J. Cell Biol. 193, 295–305 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Lee, B. S. et al. Functional intersection of ATM and DNA-PKcs in coding end joining during V(D)J recombination. Mol. Cell Biol. 33, 3568–3579 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. 19.

    Houghtaling, S. et al. Epithelial cancer in Fanconi anemia complementation group D2 (Fancd2) knockout mice. Genes Dev. 17, 2021–2035 (2003).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. 20.

    Williams, E. S. et al. Telomere dysfunction and DNA-PKcs deficiency: characterization and consequence. Cancer Res. 69, 2100–2107 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. 21.

    Zhang, S. et al. Spontaneous tumor development in bone marrow-rescued DNA-PKcs(3A/3A) mice due to dysfunction of telomere leading strand deprotection. Oncogene 35, 3909–3918 (2016).

    CAS  PubMed  Article  Google Scholar 

  22. 22.

    Yoo, S. & Dynan, W. S. Characterization of the RNA binding properties of Ku protein. Biochemistry 37, 1336–1343 (1998).

    CAS  PubMed  Article  Google Scholar 

  23. 23.

    Chen, H. et al. Structural insights into yeast telomerase recruitment to telomeres. Cell 172, 331–343.e313 (2018).

    CAS  PubMed  Article  Google Scholar 

  24. 24.

    Pfingsten, J. S. et al. Mutually exclusive binding of telomerase RNA and DNA by Ku alters telomerase recruitment model. Cell 148, 922–932 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. 25.

    Britton, S., Coates, J. & Jackson, S. P. A new method for high-resolution imaging of Ku foci to decipher mechanisms of DNA double-strand break repair. J. Cell Biol. 202, 579–595 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. 26.

    Narla, A. & Ebert, B. L. Ribosomopathies: human disorders of ribosome dysfunction. Blood 115, 3196–3205 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    Signer, R. A., Magee, J. A., Salic, A. & Morrison, S. J. Haematopoietic stem cells require a highly regulated protein synthesis rate. Nature 509, 49–54 (2014).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  28. 28.

    Khajuria, R. K. et al. Ribosome levels selectively regulate translation and lineage commitment in human hematopoiesis. Cell 173, 90–103.e119 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Unnikrishnan, I., Radfar, A., Jenab-Wolcott, J. & Rosenberg, N. p53 mediates apoptotic crisis in primary Abelson virus-transformed pre-B cells. Mol. Cell. Biol. 19, 4825–4831 (1999).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. 30.

    Strauss, M. E. & Brandt, J. Attempt at preclinical identification of Huntington’s disease using the WAIS. J. Clin. Exp. Neuropsychol. 8, 210–218 (1986).

    CAS  PubMed  Article  Google Scholar 

  31. 31.

    Chu, C. et al. Systematic discovery of Xist RNA binding proteins. Cell 161, 404–416 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. 32.

    Barandun, J., Hunziker, M. & Klinge, S. Assembly and structure of the SSU processome—a nucleolar precursor of the small ribosomal subunit. Curr. Opin. Struct. Biol. 49, 85–93 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. 33.

    Zhou, F. et al. Online nanoflow RP-RP-MS reveals dynamics of multicomponent Ku complex in response to DNA damage. J. Proteome Res. 9, 6242–6255 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Flygare, J. et al. Human RPS19, the gene mutated in Diamond-Blackfan anemia, encodes a ribosomal protein required for the maturation of 40S ribosomal subunits. Blood 109, 980–986 (2007).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. 35.

    Zarnegar, B. J. et al. irCLIP platform for efficient characterization of protein-RNA interactions. Nat. Methods 13, 489–492 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. 36.

    Morchikh, M. et al. HEXIM1 and NEAT1 long non-coding RNA form a multi-subunit complex that regulates DNA-mediated innate immune response. Mol. Cell 67, 387–399.e385 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Calo, E. et al. RNA helicase DDX21 coordinates transcription and ribosomal RNA processing. Nature 518, 249–253 (2015).

    ADS  CAS  PubMed  Article  Google Scholar 

  38. 38.

    Parker, K. A. & Steitz, J. A. Structural analysis of the human U3 ribonucleoprotein particle reveal a conserved sequence available for base pairing with pre-rRNA. Mol. Cell. Biol. 7, 2899–2913 (1987).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. 39.

    Weterings, E., Verkaik, N. S., Brüggenwirth, H. T., Hoeijmakers, J. H. & van Gent, D. C. The role of DNA dependent protein kinase in synapsis of DNA ends. Nucleic Acids Res. 31, 7238–7246 (2003).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. 40.

    van Sluis, M. & McStay, B. A localized nucleolar DNA damage response facilitates recruitment of the homology-directed repair machinery independent of cell cycle stage. Genes Dev. 29, 1151–1163 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  41. 41.

    Gao, Y. et al. A critical role for DNA end-joining proteins in both lymphogenesis and neurogenesis. Cell 95, 891–902 (1998).

    CAS  PubMed  Article  Google Scholar 

  42. 42.

    Gu, Y., Jin, S., Gao, Y., Weaver, D. T. & Alt, F. W. Ku70-deficient embryonic stem cells have increased ionizing radiosensitivity, defective DNA end-binding activity, and inability to support V(D)J recombination. Proc. Natl Acad. Sci. USA 94, 8076–8081 (1997).

    ADS  CAS  PubMed  Article  Google Scholar 

  43. 43.

    Symonds, H. et al. p53-dependent apoptosis suppresses tumor growth and progression in vivo. Cell 78, 703–711 (1994).

    CAS  PubMed  Article  Google Scholar 

  44. 44.

    Avagyan, S. et al. Hematopoietic stem cell dysfunction underlies the progressive lymphocytopenia in XLF/Cernunnos deficiency. Blood 124, 1622–1625 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. 45.

    Liu, X., Shao, Z., Jiang, W., Lee, B. J. & Zha, S. PAXX promotes KU accumulation at DNA breaks and is essential for end-joining in XLF-deficient mice. Nat. Commun. 8, 13816 (2017).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. 46.

    Li, G. et al. Lymphocyte-specific compensation for XLF/cernunnos end-joining functions in V(D)J recombination. Mol. Cell 31, 631–640 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

    Bredemeyer, A. L. et al. ATM stabilizes DNA double-strand-break complexes during V(D)J recombination. Nature 442, 466–470 (2006).

    ADS  CAS  PubMed  Article  Google Scholar 

  48. 48.

    Wang, M., Parshin, A. V., Shcherbik, N. & Pestov, D. G. Reduced expression of the mouse ribosomal protein Rpl17 alters the diversity of mature ribosomes by enhancing production of shortened 5.8S rRNA. RNA 21, 1240–1248 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. 49.

    Wang, M. & Pestov, D. G. 5′-end surveillance by Xrn2 acts as a shared mechanism for mammalian pre-rRNA maturation and decay. Nucleic Acids Res. 39, 1811–1822 (2011).

    PubMed  Article  CAS  Google Scholar 

  50. 50.

    Huang, W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protocols 4, 44–57 (2009).

    CAS  Article  Google Scholar 

  51. 51.

    Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. 52.

    Zuker, M. Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res. 31, 3406–3415 (2003).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  53. 53.

    Darty, K., Denise, A. & Ponty, Y. VARNA: interactive drawing and editing of the RNA secondary structure. Bioinformatics 25, 1974–1975 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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Acknowledgements

We thank B. Sleckman and A. Smogorzewska for providing the control cell lines; W. Gu and T. Li for helping with Tp53 analyses in DNA-PKcs mouse models; R. Baer for commenting on the manuscript; L. Ding for discussion on hematopoietic stem cell analyses; T. Xiang and L. Berchowitz for assistance with the ribosomal and nucleoli analyses; C. S. Lin for germline injection and ES cell derivation; E. Zhang and S. Kaplan for help with telomere analyses; L. Zhang and the Elias laboratory for assistance running samples for MS analysis; J. Coller, D. Wagh, and the Stanford Functional Genomics Facility for assistance running samples for deep sequencing; and the Swanson Biotechnology Center at the Koch Institute for Integrative Cancer Research, especially the MIT BioMicro Center. Owing to space constraints, we often cited reviews rather than original publications. We apologize to the colleagues whose original works were not cited here. R.A.F is supported by the Damon Runyon Cancer Research Foundation. C.R.B is supported by the Howard Hughes Medical Institute. E.C. is supported by the Pew Charitable Trusts, Charles H. Hood and March of Dime Foundations, Charles E. Reed Faculty Initiative and Irwin and Helen Sizer Career Development Professorship. The project is in part supported by R01CA184187, R01CA158073 and CA215067 to S.Z. S.Z. was a Leukemia Lymphomas Society Scholar. J.L.C. was supported by 1F31CA183504. This research was funded in part through the NIH/NCI Cancer Center Support Grant P30CA013696 to Herbert Irving Comprehensive Cancer Center (HICCC) of Columbia University.

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Authors

Contributions

The Calo and Zha groups independently uncovered an RNA-dependent role for DNA-PK in ribosome biogenesis and protein synthesis. Z.S., J.L.C., Y.Z. and S.Z. conceived the mouse genetics, hematopoietic stem and progenitor cell-related experiments. Y.Z. measured protein translation and nucleoli localization. J.L.C and W.J. generated the DNA-PKcs mutant mouse models. V.M.E. and B.J.L. contributed to the generation and characterization of the TP53- and KU70-deficienct DNA-PKcs mutant mice. G.B. contributed to pathology analyses of the mouse models. R.A.F and E.C. conceived the RNA, proteomics and in vitro reconstitution experiments of this project with the help of J.L., F.A., P.A. and C.R.B. All authors interpreted results and wrote the manuscript.

Corresponding authors

Correspondence to Shan Zha or Eliezer Calo.

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The authors declare no competing interests.

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Peer review information Nature thanks Alan Warren and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Myeloid proliferative disease with MDS features in DNA-PKcsKD/KDTp53−/− mice.

a, Representative fluorescence-activated cell sorting (FACS) analyses of pro-B cell lymphomas from DNA-PKcsKD/KDTp53−/− mice. b, The frequency of megakaryocytes with hyperchromatic or pyknotic nuclei and total megakaryocytes per 400× field in the spleen. Eight fields from three independent DNA-PKcsKD/KDTp53−/− mice were quantified and are presented here. Mean ± s.e.m.; two-sided unpaired student’s t-test, **P < 0.01, ***P < 0.001. c, Representative FACS analyses of myeloid proliferative disease in DNA-PKcsKD/KDTp53−/− mice. The CD19-negative bone marrow from DNA-PKcsKD/KDTp53−/− mice shows a higher percentage of myeloid cells (CD11b+) at the expense of RBCs (TER119+). CD19+ B cells were excluded from the wild-type bone marrow, as DNA-PKcsKD/KDTp53−/− mice have no pre-B and mature B cells (CD19+) owing to cNHEJ defects. There is also an increase in the percentage of c-KIT+ cells among the myeloid cells (CD11b+GR1+) from DNA-PKcsKD/KDTp53−/− mice. Mid-sized reticulocytes and RBC progenitors are reduced in the DNA-PKcsKD/KDTp53−/− bone marrow. Exact P values and defined sample sizes (n) are provided in Supplementary Data 1.

Extended Data Fig. 2 Generation and characterization of the DNA-PKcsPQR/PQR and DNA-PKcs5A/5A mouse models.

a, Diagram of the protein sequence changes in the DNA-PKcsPQR allele16. b, c, Diagram of the protein sequence changes in the DNA-PKcs5A allele and the targeting scheme. The TQ sites are shown in upper case for ease of identification. Asterisks denote sites mutated in the previously published DNA-PKcs3A allele17. The schematic diagram (not to scale) represents the mouse Prkdc (DNA-PKcs) locus (top), targeting vector (second row), targeted allele (DNA-PKcs5AN, third row), and the neo-deleted mutant allele (DNA-PKcs5A, bottom). The red exon contains the mutations. Boxes, exons; triangles, FRT sites. d, Southern blot analyses of SpeI-digested DNA from DNA-PKcs+/+ and DNA-PKcs+/5AN (with NeoR) ES cells. e, f, The concentration of RBCs and platelets (PLT) in the peripheral blood of 5-month-old DNA-PKcsPQR/PQR mice and DNA-PKcs+/+ controls. g, The platelet concentration in the peripheral blood of 2-week-old DNA-PKcs+/+, DNA-PKcs5A/5A and DNA-PKcs−/− mice. h, i, Representative images show pale kidney (h) and liver (i) from 2-week-old DNA-PKcs5A/5A mice, indicating extreme anaemia. eg, Mean ± s.e.m.; two-sided unpaired Student’s t-test, ***P < 0.001; n.s., not significant, P > 0.05). Exact P values and defined sample sizes (n) are provided in Supplementary Data 1.

Extended Data Fig. 3 Haematopoiesis and translation defects in DNA-PKcs5A/5A mice.

a, b, Flow cytometry analyses (a) and quantification (b) of 2-week bone marrow progenitor cell populations. The same gating strategy was used throughout to define progenitor populations. LinSCA1+c-KIT+ haematopoietic stem and progenitor cells (LSK, HSPC) and LK cells (LinSCA1c-KIT+) were further divided into CMPs, granulocyte-monocyte progenitors (GMPs) and MEPs27. c, d, Flow cytometry analyses (c) and quantification (d) of E14.5 fetal liver HSPC (LinSCA1+c-KIT+) frequency from DNA-PKcsKD/KD and DNA-PKcs5A/5A embryos. e, Flow cytometry gating strategy for analysing protein translation in Lin+ cells. The gating strategy for progenitors is shown in a. This strategy (a, e) was used to determine global translation for each population in Extended Data Fig. 7d. Mean ± s.e.m.; two-sided unpaired Student’s t-test; ***P < 0.001, *P < 0.05, n.s. P > 0.05. Exact P values and defined sample sizes (n) are provided in Supplementary Data 1.

Extended Data Fig. 4 Normal V(D)J recombination in DNA-PKcs5A/5A mice and cells.

a, Flow cytometry analyses of lymphocyte development in DNA-PKcs5A/5ATp53−/− and control mice. b, Frequency of B220+IgM+ naive mature B cells from spleen. c, Quantification of peripheral RBCs in DNA-PKcs5A/5A mice with and without TP53 deficiency. Note that wild-type, DNA-PKcs5A/5A and DNA-PKcs−/− data from Fig. 2b are included here for comparison. d, e, Absolute counts of peripheral lymphocytes (d) and neutrophils (e) in DNA-PKcs5A/5A mice with or without Tp53 or Ku70 deficiency. TP53-deficiency rescued both the lymphocytopaenia and the neutropaenia in DNA-PKcs5A/5A mice. Notably, KU70 deficiency caused severe lymphocytopaenia by itself or with the DNA-PKcs5A/5A mutation, but rescued the neutropaenia in DNA-PKcs5A/5A mice. fh, Chromosomal V(D)J recombination measured in v-ABL kinase-transformed B cells. f, Diagram of pMX-INV chromosomal V(D)J recombination substrate2. Empty and filled triangles represent recombination signal sequences. RV, EcoRV, N, NcoI. Dashed line marks the C4 probe used for Southern blotting. g, Southern blotting reveals the accumulation of normal coding joins (CJ) and signal joins (SJ) and suggests successful V(D)J recombination in DNA-PKcs5A/5A B cells. h, Successful V(D)J recombination places Gfp in the same orientation as the promoter and leads to GFP expression2. Plots show the frequency of GFP+ cells after 4 days of STI571 (3 μM) treatment. i, Quantification of DN2 and DN3 cells out of all double-negative cells (left, reflecting TCRβ V(D)J recombination) and percentage of CD4+ or CD8+ single-positive (SP) cells (right, reflecting TCRα V(D)J recombination). be, i, Mean ± s.e.m.; two-sided unpaired Student’s t-test. ***P < 0.001, **P < 0.01, *P < 0.05, n.s. P > 0.05. Exact P values and defined sample sizes (n) are provided in Supplementary Data 1.

Extended Data Fig. 5 DNA-PKcs5A/5A MEFs and ES cells are not hypersensitive to DNA-damaging agents.

a, b, DNA-PKcs5A/5A MEFs are not hypersensitive to ionizing radiation (IR) or mitomycin C (MMC). Fancd2−/− cells19 are included as positive controls. ce, Representative colony formation assays (c) and quantification of sensitivity to MMC (d) and IR (e) in DNA-PKcs5A/5A and control ES cells. Mean ± s.e.m. of a representative experiment in triplicate. Two-sided unpaired Student’s t-test; P > 0.05 (n.s). f, Normal proliferation curve of DNA-PKcs5A/5A MEFs. g, DNA-PKcs5A/5A MEFs have normal S phase frequency. The accumulation of aneuploid cells is unique to Tp53−/− cells. a, df, Mean ± s.e.m. Exact P values and defined sample sizes (n) are provided in Supplementary Data 1.

Extended Data Fig. 6 DNA-PKcs5A/5A ES cells have KU-dependent proliferation defects that cannot be fully explained by cNHEJ or telomere defects.

a, Representative colony formation assay for DNA-PKcs5A/5A ES cells. Note the frequent accumulation of small colonies in DNA-PKcs5A/5A cells, which is rescued by deletion of KU80. b, Quantification of colony size (A.U., arbitrary units) from DNA-PKcs5A/5A and control ES cells. Two independently derived ES cells were assayed. c, Cell cycle analyses of two independently derived DNA-PKcs5A/5A and control ES cell lines. The percentage of BrdU+ cells is shown. d, Frequency of metaphase with telomere abnormalities (see below for definition). e, Representative telomere fluorescence in situ hybridization (FISH) images of a normal mouse chromosome (top, with four telomere dots), a chromosome with a chromatid break (middle, showing loss of one telomere signal among the two sister chromatids), or a chromatid fusion without telomere signal (bottom). f, Quantitative analyses of telomere instability and chromosomal breaks in metaphase. Telomere FISH analyses of MEFs were performed with the telomere-specific PNA probe as previously described2. Normal mouse chromosomes have four discrete telomere signals (e, top). Telomere instability or breaks considered include: i) telomere instability (indicated by more than one telomere signal per chromatid), ii) telomere/chromosome fusion (e, bottom; with telomere at the fusion junction (telomere fusion) or without telomere signal at the fusion junction (non-telomere fusion)), iii) chromosome breaks (S.B.; loss of both telomere signals on the paired sister chromatids) and iv) chromatid breaks (T.B.; loss of one of the two chromatids (e, middle)). The number of metaphases with at least one telomere instability, break or fusion is shown in d as a percentage of metaphases with abnormalities. Data derived from four independent MEF lines of each genotype. g, Representative flow cytometry analyses of erythroblasts from age-matched (2 weeks) DNA-PKcs+/+ and DNA-PKcs−/− mice. h, Representative protein translation analyses of S1 (CD71+Ter119), S2 (CD71+Ter119mid) and S3 erythroblasts (CD71+Ter119high) from 2-week-old DNA-PKcs+/+, DNA-PKcs−/− and Ku70−/− mice. Quantification is shown in Fig. 2i. b, d, Mean ± s.e.m.; two-sided unpaired Student’s t t-test, **P < 0.01, *P < 0.05, n.s. P > 0.05. Exact P values and defined sample sizes (n) are provided in Supplementary Data 1.

Extended Data Fig. 7 Global translation defects in DNA-PKcs5A/5A haematopoietic cells.

a, MCV of RBCs from 2-week-old DNA-PKcs5A/5A mice with or without TP53 deficiency. Wild-type and 5A/5A data from Fig. 2e included for comparison. b, Relative percentage of S1, S2 and S3 cells among all erythroblasts in fetal liver from DNA-PKcs5A/5A or DNA-PKcs+/+ E14.5 embryos. S4 and S5 populations have not yet evolved significantly in the E14.5 fetal liver. c, The relative frequencies of S5, the most mature erythroblast, in P14 bone marrow from DNA-PKcs5A/5A or DNA-PKcs+/+ mice. d, Relative OP-puro levels in P14 wild-type bone marrow haematopoietic cells. The average of total bone marrow was set to 1. The erythroblasts (S1, S2 and S3high) clearly have the highest OP-Puro levels. As shown in Fig. 2h, S3 erythroblasts have an OP-purohigh and an OP-purolow population. The mean OP-puro levels of both populations are included. e, Representative global protein translation in S1, S2 and S3 erythroblasts from E14.5 DNA-PKcs+/+ and DNA-PKcs5A/5A fetal liver. f, Frequency of OP-Purolow among S1, S2, and S3 erythroblasts in E14.5 fetal liver. OP-Puro labelling of fetal liver cells was performed for 30 min. g, Quantification of the frequency of OP-Purolow among S1and S2 erythroblasts from 2-week-old DNA-PKcs5A/5ATp53+/− mice. h, Relative OP-puro levels (normalized to the levels in untreated DNA-PKcs+/+ B cells) of DNA-PKcsKD/KD and DNA-PKcs3A/3A B cells. Unpaired Student’s t-test, ***P < 0.001, *P < 0.05. i, Relative OP-puro levels of DNA-PKcs+/+ B cells treated with ATM kinase inhibitor (KU55933, 15 μM for 17 h). Data represent three independent biological experiments (P = 0.38 for unpaired Student’s t-test). ad, fi, Two-sided unpaired Student’s t-test, ***P < 0.001, **P < 0.01, *P < 0.05, n.s. P > 0.05. All graphs show mean ± s.e.m. Exact P values and defined sample sizes (n) are provided in Supplementary Data 1.

Extended Data Fig. 8 rRNA synthesis-dependent localization of DNA-PK in nucleoli of human and mouse cells, and U3 ChIRP-MS of SSU processome.

a, b, Immunofluorescence staining of endogenous KU86 (a) and DNA-PKcs (b) in U2OS cells. DDX21 RNA helicase is used as a positive control for nucleoli. The CSK buffer contains Triton X-100 for pre-extraction before fixation (see Methods). When indicated, the cells were treated with 50 nM ActD for 1 h before pre-extraction, fixation and staining. c, Localization of ectopically expressed GFP-tagged KU70 in mouse ES cells. ac, n = 3 biologically independent experiments. d, U3 ChIRP-qRT–PCR analysis from HeLa cells. Enrichment levels, relative to input samples, of the U3, 7SK, 18S, and RMRP RNAs were assessed from experimental (−RNase A) or control (+RNase A) ChIRP samples. Data are from two independent biological replicates. e, DNA-PK was also recovered from U3 ChIRP-MS in IMR90 cells. Peptide spectral match (PSM) counts for control (RNase A) and experimental (U3) samples are shown. n = 2 biological replicates.

Extended Data Fig. 9 KU associates with SSU processome components.

a, KU86 IP-MS and U3 ChIRP-MS overlap. Zhou et al.33 (n = 2 independent MS assays; for each assay, n = 2 technical replicates) identified 292 proteins enriched with KU86 protein. U3 ChIRP-MS identified 483 proteins enriched more than twofold with the U3 snoRNA (Supplementary Table 1). These identified factors were intersected, resulting in 153 proteins in common between the two affinity purification strategies, which is highly significant (hypergeometric P < 6.650 × 10−166). To understand what types of protein were enriched only with KU86, only with U3, or together with both factors, we isolated the enrichment values (−log10(Benjamini)) for GO biological process terms for each of these sets and compared them. Factors commonly bound were biased for rRNA processing, ribosomal terms and SSU biogenesis. U3-specific factors had additional enrichment in these categories and KU86 had a set of unique terms that were not well represented in U3. b, Independent repeat of northern blot analyses of 18S rRNA maturation in v-ABL kinase-transformed B cells from noted genotypes. The probe covers the sequence just after the 18S rRNA (red line). This experiment was repeated independently four times. Another repeat is shown in Fig. 3c. c, d, MS characterization of commercial DNA-PK holoenzyme (Promega) used in EMSA and kinase reactions. A detailed description of proteins and their quality in this mixture has not been published. We subjected the DNA-PK enzyme mix as provided to SDS–PAGE separation followed by LC–MS identification of proteins from mass ranges between 65 and 600 kDa. KU70 and KU86 were clearly present in the gel and via MS. For masses above 130 kDa, DNA-PKcs was the major protein identified. For each of the five slices analysed (coloured regions) we tabulated the starting positions of peptides matching the DNA-PKcs polypeptide and mapped them to the position within DNA-PKcs. As expected, in the highest-molecular-weight slices, we identified peptides across the majority of the length of DNA-PKcs. DNA-PKcs peptides were present in lower slices, but poorer overall coverage was evident, suggesting that these are degradation products. As this was a confirmatory experiment of a validated and commercially available product (see Methods), it was conducted only once.

Extended Data Fig. 10 DNA-PKcs and KU86 bind nuclear and nucleolar RNAs in living cells.

a, b, irCLIP of DNA-PKcs (a) and KU86 (b) in HeLa cells. RNA (IR800) and protein blots show specific enrichment of RNA UV-crosslinked to DNA-PKcs and KU86. The increased signal in the RNaseA-treated samples corresponds to the accumulation of RNaseA-protected fragments that are directly associated with either KU86 or DNA-PKcs. Results shown are representative of three biologically independent experiments. Vertical line marks the RNA extracted for sequencing. c, e, Fractional distribution of DNA-PKcs (c) and KU86 (e) irCLIP RT stops on major genomic features annotated with HOMER. d, f, Detailed HOMER annotation of DNA-PKcs (d) and KU86 (f) RT stops categorized as intronic and intergenic from c and e. g, h, Genome browser tracks of DNA-PKcs and KU86 irCLIP on the Neat1 (g) and Terc (h) RNA. RT stop values are normalized to 107 mapped reads. i, j, Histograms of DNA-PKcs (i) and KU86 (j) irCLIP RT stops mapping to the 5′ETS, normalized for total reads mapped per experiment. Data from DMSO- (top) or ActD-treated (bottom) HeLa cells are shown. Grey highlighted region is as in Fig. 4a.

Extended Data Fig. 11 DNA-PK interacts with structured RNAs that can activate its kinase.

a, b, Correlation analysis of total RT stops mapping to non-repeat snoRNA transcripts from DDX2135 compared to DNA-PKcs (a) or KU86 (b) irCLIP experiments from DMSO-treated HeLa cells. Correlation analysis was performed using Pearson’s correlation coefficient. n denotes number of snoRNA transcripts bound by each protein. c, EMSA of purified human DNA-PK and in vitro transcribed U3-SL1. Lane 1 contains only Cy7-labelled U3-SL1. Lanes 2–4 show that KU assembles with U3-SL1 at a 1:3 molar ratio, while DNA-PK holoenzyme assembly occurs at a 1:25 molar ratio. Lanes 5 and 6 show that unlabelled U3-SL1 RNA competes away bound labelled U3-SL1 in a dose-dependent manner. d, Supershift EMSA of DNA-PK and U3-SL1 RNA with KU86 antibody. The addition of anti-KU86 confirms the identity of the KU–U3-SL1 band and also shifts up the complex to higher molecular weights. e, A structural mutant of U3-SL1 was generated by introducing point mutations predicted to disrupt the stem-loop structure. This mutant was unable to compete away wild-type U3-SL1 for binding to the KU complex, while unlabelled wild-type U3-SL1 competed efficiently. f, DNA-PK in vitro kinase phosphorylation assay in the presence of increasing amounts of U3-SL1 or DNA. Western blot was performed with an antibody recognizing DNA-PKcs phosphorylated at the T2609 cluster. Asterisks denote cross-reactive fragments that probably include phosphorylated DNA-PKcs fragments, on the basis of MS analyses of the DNA-PK complex (Extended Data Fig. 9c, d). g, As in f, but using an antibody recognizing DNA-PKcs phosphorylated at the S2056 cluster. hj, As in f with the following changes. h, dsDNA was used to activate DNA-PK, NU7441 was included to inhibit specific DNA-PK activity, and western blot analysis monitored the total DNA-PK (total DNA-PKcs) or phosphorylated DNA-PK (DNA-PKcs phoT2609). i, U3-SL1 RNA was used to activate DNA-PK in the absence or presence of the DNA-PK inhibitor NU7441. j, U3-SL1 RNA was used to activate DNA-PK, hydrolysable (ATP) or non-hydrolysable (AppCp) ATP was provided, and western blot analysis monitored KU86 (loading control) or phosphorylated DNA-PK (phoT2609). k, Baculovirus-purified human DNA-PK in vitro kinase phosphorylation assay in the presence of increasing amounts of U3-SL1 or DNA. Western blot was performed with antibodies recognizing DNA-PKcs phosphorylated at the S2056 cluster (top), total DNA-PK (middle) and KU86 (bottom). All EMSA and western blots presented here are representative of three biologically independent experiments.

Supplementary information

Uncropped Gels

Supplementary Figure 1 . This file contains the full scanned images of the electrophoretic separation experiments, including the Northern, Southern and Western blots presented in this study.

Reporting Summary

Statistics and Reproducibility

Supplementary Data 1 . This file contains detailed information on the number of biological repeats, the counts of animal/cell lines used in each of the indicated figure panels as well as the statistical methods and the exact p value whenever possible.

U3 ChIRP-MS data

Supplementary Table 1 . This file contains the list of proteins identified in our U3 ChiRP-MS experiment displayed in Fig. 3a.

List of oligonucleotides

Supplementary Table 2 . This file contains the sequence of the oligonucleotides utilize in this study.

Source Data

This file contains the raw values for all the plots.

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Shao, Z., Flynn, R.A., Crowe, J.L. et al. DNA-PKcs has KU-dependent function in rRNA processing and haematopoiesis. Nature 579, 291–296 (2020). https://doi.org/10.1038/s41586-020-2041-2

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