Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

DNA damage detection in nucleosomes involves DNA register shifting

Nature volume 571, pages 79–84 (2019)Cite this article

Subjects

A Publisher Correction to this article was published on 26 June 2019

This article has been updated

Abstract

Access to DNA packaged in nucleosomes is critical for gene regulation, DNA replication and DNA repair. In humans, the UV-damaged DNA-binding protein (UV-DDB) complex detects UV-light-induced pyrimidine dimers throughout the genome; however, it remains unknown how these lesions are recognized in chromatin, in which nucleosomes restrict access to DNA. Here we report cryo-electron microscopy structures of UV-DDB bound to nucleosomes bearing a 6–4 pyrimidine–pyrimidone dimer or a DNA-damage mimic in various positions. We find that UV-DDB binds UV-damaged nucleosomes at lesions located in the solvent-facing minor groove without affecting the overall nucleosome architecture. In the case of buried lesions that face the histone core, UV-DDB changes the predominant translational register of the nucleosome and selectively binds the lesion in an accessible, exposed position. Our findings explain how UV-DDB detects occluded lesions in strongly positioned nucleosomes, and identify slide-assisted site exposure as a mechanism by which high-affinity DNA-binding proteins can access otherwise occluded sites in nucleosomal DNA.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Cryo-EM structure of the NCP6–4PP–UV-DDB complex.
Fig. 2: Mechanism of damage recognition.
Fig. 3: DNA register of the −3 nucleosome shifts in the presence of UV-DDB.
Fig. 4: Subpopulations of translational settings revealed by UV-DDB binding.
Fig. 5: Principles that govern DNA damage recognition in chromatin.

Similar content being viewed by others

Data availability

Atomic coordinates and cryo-EM maps are deposited in the Protein Data Bank (PDB) and Electron Microscopy Data Bank (EMDB) under accession codes 6R8Y and EMD-4762 (NCP6–4PP–UV-DDB); 6R8Z and EMD-4763 (NCPTHF2(−1)–UV-DDB); 6R91 and EMD-4765 (NCPTHF2(−3)–UV-DDB); 6R90 and EMD-4764 (NCPTHF2(+1)–UV-DDB class A); 6R92 and EMD-4766 (NCPTHF2(+1)–UV-DDB class B); 6R93 and EMD-4767 (NCP6–4PP); 6R94 and EMD-4768 (NCPTHF2(−3)).

Change history

  • 26 June 2019

    In this Article, in Fig. 1a, the 5′ and 3′ labels were reversed in the DNA sequence, and Fig. 4 was missing panel labels a–e. These errors have been corrected online.

References

  1. Thoma, F. Repair of UV lesions in nucleosomes – intrinsic properties and remodeling. DNA Repair (Amst.) 4, 855–869 (2005).

    Article  CAS  Google Scholar 

  2. Rodriguez, Y., Hinz, J. M. & Smerdon, M. J. Accessing DNA damage in chromatin: Preparing the chromatin landscape for base excision repair. DNA Repair (Amst.) 32, 113–119 (2015).

    Article  CAS  Google Scholar 

  3. Hanawalt, P. C. & Spivak, G. Transcription-coupled DNA repair: two decades of progress and surprises. Nat. Rev. Mol. Cell Biol. 9, 958–970 (2008).

    Article  CAS  Google Scholar 

  4. Luger, K., Mäder, A. W., Richmond, R. K., Sargent, D. F. & Richmond, T. J. Crystal structure of the nucleosome core particle at 2.8 Å resolution. Nature 389, 251–260 (1997).

    Article  ADS  CAS  Google Scholar 

  5. McGinty, R. K. & Tan, S. Nucleosome structure and function. Chem. Rev. 115, 2255–2273 (2015).

    Article  CAS  Google Scholar 

  6. Sugasawa, K. et al. UV-induced ubiquitylation of XPC protein mediated by UV-DDB-ubiquitin ligase complex. Cell 121, 387–400 (2005).

    Article  CAS  Google Scholar 

  7. Groisman, R. et al. The ubiquitin ligase activity in the DDB2 and CSA complexes is differentially regulated by the COP9 signalosome in response to DNA damage. Cell 113, 357–367 (2003).

    Article  CAS  Google Scholar 

  8. Cavadini, S. et al. Cullin–RING ubiquitin E3 ligase regulation by the COP9 signalosome. Nature 531, 598–603 (2016).

    Article  ADS  CAS  Google Scholar 

  9. Wang, H. et al. Histone H3 and H4 ubiquitylation by the CUL4-DDB-ROC1 ubiquitin ligase facilitates cellular response to DNA damage. Mol. Cell 22, 383–394 (2006).

    Article  Google Scholar 

  10. Lehmann, A. R. DNA repair-deficient diseases, xeroderma pigmentosum, Cockayne syndrome and trichothiodystrophy. Biochimie 85, 1101–1111 (2003).

    Article  CAS  Google Scholar 

  11. Luijsterburg, M. S. et al. Dynamic in vivo interaction of DDB2 E3 ubiquitin ligase with UV-damaged DNA is independent of damage-recognition protein XPC. J. Cell Sci. 120, 2706–2716 (2007).

    Article  CAS  Google Scholar 

  12. Puumalainen, M. R. et al. Chromatin retention of DNA damage sensors DDB2 and XPC through loss of p97 segregase causes genotoxicity. Nat. Commun. 5, 3695 (2014).

    Article  ADS  Google Scholar 

  13. Fei, J. et al. Regulation of nucleotide excision repair by UV-DDB: prioritization of damage recognition to internucleosomal DNA. PLoS Biol. 9, e1001183 (2011).

    Article  CAS  Google Scholar 

  14. Fischer, E. S. et al. The molecular basis of CRL4DDB2/CSA ubiquitin ligase architecture, targeting, and activation. Cell 147, 1024–1039 (2011).

    Article  CAS  Google Scholar 

  15. Chu, G. & Chang, E. Xeroderma pigmentosum group E cells lack a nuclear factor that binds to damaged DNA. Science 242, 564–567 (1988).

    Article  ADS  CAS  Google Scholar 

  16. Lehmann, A. R., McGibbon, D. & Stefanini, M. Xeroderma pigmentosum. Orphanet J. Rare Dis. 6, 70 (2011).

    Article  Google Scholar 

  17. Scrima, A. et al. Structural basis of UV DNA-damage recognition by the DDB1–DDB2 complex. Cell 135, 1213–1223 (2008).

    Article  CAS  Google Scholar 

  18. Yeh, J. I. et al. Damaged DNA induced UV-damaged DNA-binding protein (UV-DDB) dimerization and its roles in chromatinized DNA repair. Proc. Natl Acad. Sci. USA 109, E2737–E2746 (2012).

    Article  CAS  Google Scholar 

  19. Li, G., Levitus, M., Bustamante, C. & Widom, J. Rapid spontaneous accessibility of nucleosomal DNA. Nat. Struct. Mol. Biol. 12, 46–53 (2005).

    Article  CAS  Google Scholar 

  20. Zhu, F. et al. The interaction landscape between transcription factors and the nucleosome. Nature 562, 76–81 (2018).

    Article  ADS  CAS  Google Scholar 

  21. Wittschieben, B. O., Iwai, S. & Wood, R. D. DDB1–DDB2 (xeroderma pigmentosum group E) protein complex recognizes a cyclobutane pyrimidine dimer, mismatches, apurinic/apyrimidinic sites, and compound lesions in DNA. J. Biol. Chem. 280, 39982–39989 (2005).

    Article  CAS  Google Scholar 

  22. Osakabe, A. et al. Structural basis of pyrimidine–pyrimidone (6–4) photoproduct recognition by UV-DDB in the nucleosome. Sci. Rep. 5, 16330 (2015).

    Article  ADS  CAS  Google Scholar 

  23. Lan, L. et al. Monoubiquitinated histone H2A destabilizes photolesion-containing nucleosomes with concomitant release of UV-damaged DNA-binding protein E3 ligase. J. Biol. Chem. 287, 12036–12049 (2012).

    Article  CAS  Google Scholar 

  24. Kapetanaki, M. G. et al. The DDB1-CUL4ADDB2 ubiquitin ligase is deficient in xeroderma pigmentosum group E and targets histone H2A at UV-damaged DNA sites. Proc. Natl Acad. Sci. USA 103, 2588–2593 (2006).

    Article  ADS  CAS  Google Scholar 

  25. Vasudevan, D., Chua, E. Y. D. & Davey, C. A. Crystal structures of nucleosome core particles containing the ‘601’ strong positioning sequence. J. Mol. Biol. 403, 1–10 (2010).

    Article  CAS  Google Scholar 

  26. Pich, O. et al. Somatic and germline mutation periodicity follow the orientation of the DNA minor groove around nucleosomes. Cell 175, 1074–1087.e18 (2018).

    Article  CAS  Google Scholar 

  27. Brown, A. J., Mao, P., Smerdon, M. J., Wyrick, J. J. & Roberts, S. A. Nucleosome positions establish an extended mutation signature in melanoma. PLoS Genet. 14, e1007823 (2018).

    Article  Google Scholar 

  28. Mao, P., Smerdon, M. J., Roberts, S. A. & Wyrick, J. J. Chromosomal landscape of UV damage formation and repair at single-nucleotide resolution. Proc. Natl Acad. Sci. USA 113, 9057–9062 (2016).

    Article  CAS  Google Scholar 

  29. Bilokapic, S., Strauss, M. & Halic, M. Structural rearrangements of the histone octamer translocate DNA. Nat. Commun. 9, 1330 (2018).

    Article  ADS  Google Scholar 

  30. Kitevski-LeBlanc, J. L. et al. Investigating the dynamics of destabilized nucleosomes using methyl-TROSY NMR. J. Am. Chem. Soc. 140, 4774–4777 (2018).

    Article  CAS  Google Scholar 

  31. Iwai, S., Shimizu, M., Kamiya, H. & Ohtsuka, E. Synthesis of a phosphoramidite coupling unit of the pyrimidine (6−4) pyrimidone photoproduct and its incorporation into oligodeoxynucleotides. J. Am. Chem. Soc. 118, 7642–7643 (1996).

    Article  CAS  Google Scholar 

  32. Abdulrahman, W. et al. A set of baculovirus transfer vectors for screening of affinity tags and parallel expression strategies. Anal. Biochem. 385, 383–385 (2009).

    Article  CAS  Google Scholar 

  33. Marks, B. D. et al. Multiparameter analysis of a screen for progesterone receptor ligands: comparing fluorescence lifetime and fluorescence polarization measurements. Assay Drug Dev. Technol. 3, 613–622 (2005).

    Article  CAS  Google Scholar 

  34. Kuzmič, P. DynaFit—a software package for enzymology. Methods Enzymol. 467, 247–280 (2009).

    Article  Google Scholar 

  35. Thomä, N. & Goody, R. S. in Kinetic Analysis of Macromolecules: A practical approach (ed. Johnson, K. A.) 153–170 (Oxford Univ. Press, 2003).

  36. Reardon, J. T. et al. Comparative analysis of binding of human damaged DNA-binding protein (XPE) and Escherichia coli damage recognition protein (UvrA) to the major ultraviolet photoproducts: T[c,s]T, T[t,s]T, T[6–4]T, and T[Dewar]T. J. Biol. Chem. 268, 21301–21308 (1993).

    CAS  PubMed  Google Scholar 

  37. Gaidatzis, D., Lerch, A., Hahne, F. & Stadler, M. B. QuasR: quantification and annotation of short reads in R. Bioinformatics 31, 1130–1132 (2015).

    Article  CAS  Google Scholar 

  38. Tang, G. et al. EMAN2: an extensible image processing suite for electron microscopy. J. Struct. Biol. 157, 38–46 (2007).

    Article  CAS  Google Scholar 

  39. Hohn, M. et al. SPARX, a new environment for cryo-EM image processing. J. Struct. Biol. 157, 47–55 (2007).

    Article  CAS  Google Scholar 

  40. Grant, T. & Grigorieff, N. Measuring the optimal exposure for single particle cryo-EM using a 2.6 Å reconstruction of rotavirus VP6. eLife 4, e06980 (2015).

    Article  Google Scholar 

  41. Li, X. et al. Electron counting and beam-induced motion correction enable near-atomic-resolution single-particle cryo-EM. Nat. Methods 10, 584–590 (2013).

    Article  CAS  Google Scholar 

  42. Zhang, K. Gctf: Real-time CTF determination and correction. J. Struct. Biol. 193, 1–12 (2016).

    Article  ADS  CAS  Google Scholar 

  43. Scheres, S. H. RELION: implementation of a Bayesian approach to cryo-EM structure determination. J. Struct. Biol. 180, 519–530 (2012).

    Article  CAS  Google Scholar 

  44. Rosenthal, P. B. & Henderson, R. Optimal determination of particle orientation, absolute hand, and contrast loss in single-particle electron cryomicroscopy. J. Mol. Biol. 333, 721–745 (2003).

    Article  CAS  Google Scholar 

  45. Chen, S. et al. High-resolution noise substitution to measure overfitting and validate resolution in 3D structure determination by single particle electron cryomicroscopy. Ultramicroscopy 135, 24–35 (2013).

    Article  CAS  Google Scholar 

  46. Adams, P. D. et al. PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr. D 66, 213–221 (2010).

    Article  CAS  Google Scholar 

  47. de la Rosa-Trevín, J. M. et al. Xmipp 3.0: an improved software suite for image processing in electron microscopy. J. Struct. Biol. 184, 321–328 (2013).

    Article  Google Scholar 

  48. Grant, T., Rohou, A. & Grigorieff, N. cisTEM, user-friendly software for single-particle image processing. eLife 7, e35383 (2018).

    Article  Google Scholar 

  49. Morgan, M. T. et al. Structural basis for histone H2B deubiquitination by the SAGA DUB module. Science 351, 725–728 (2016).

    Article  ADS  CAS  Google Scholar 

  50. Ong, M. S., Richmond, T. J. & Davey, C. A. DNA stretching and extreme kinking in the nucleosome core. J. Mol. Biol. 368, 1067–1074 (2007).

    Article  CAS  Google Scholar 

  51. Emsley, P. & Cowtan, K. Coot: model-building tools for molecular graphics. Acta Crystallogr. D 60, 2126–2132 (2004).

    Article  Google Scholar 

  52. Nicholls, R. A., Long, F. & Murshudov, G. N. Low-resolution refinement tools in REFMAC5. Acta Crystallogr. D 68, 404–417 (2012).

    Article  CAS  Google Scholar 

  53. Chen, V. B. et al. MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr. D 66, 12–21 (2010).

    Article  CAS  Google Scholar 

  54. Winn, M. D. et al. Overview of the CCP4 suite and current developments. Acta Crystallogr. D 67, 235–242 (2011).

    Article  CAS  Google Scholar 

  55. Zheng, S. Q. et al. MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Nat. Methods 14, 331–332 (2017).

    Article  CAS  Google Scholar 

  56. Ekundayo, B., Richmond, T. J. & Schalch, T. Capturing structural heterogeneity in chromatin fibers. J. Mol. Biol. 429, 3031–3042 (2017).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank A. Osakabe and Y. Arimura for discussion and histone preparation. This work was supported by the Novartis Research Foundation and received funding from the Swiss National Science Foundation through Sinergia grant number CRSII3_160734/1 to N.H.T.; from the European Research Council under the European Union’s Horizon 2020 Research and Innovation programme grant agreement, grant numbers 666068 to N.H.T. and 667951 to D.S., and the Innovation programme under Marie Sklodowska-Curie grant number 705354 and an EMBO Long-Term fellowship to R.S.G.; in part from the Uehara Memorial Foundation to S.M.; from JSPS KAKENHI grant number JP18H05534 to H.K. and JP16H06307 to K.S.; and from the Platform Project for Supporting Drug Discovery and Life Science Research (BINDS) from AMED under grant number JP18am0101076 to H.K.

Reviewer information

Nature thanks Orlando Schärer, Bennett Van Houten and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Author notes

  1. These authors contributed equally: Syota Matsumoto, Simone Cavadini, Richard D. Bunker.

Authors and Affiliations

  1. Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland

    Syota Matsumoto, Simone Cavadini, Richard D. Bunker, Ralph S. Grand, Alessandro Potenza, Julius Rabl, Andreas D. Schenk, Dirk Schübeler & Nicolas H. Thomä

  2. University of Basel, Basel, Switzerland

    Syota Matsumoto, Simone Cavadini, Richard D. Bunker, Ralph S. Grand, Alessandro Potenza, Julius Rabl, Andreas D. Schenk, Dirk Schübeler & Nicolas H. Thomä

  3. Division of Chemistry, Graduate School of Engineering Science, Osaka University, Toyonaka, Japan

    Junpei Yamamoto & Shigenori Iwai

  4. Biosignal Research Center, and Graduate School of Science, Kobe University, Kobe, Japan

    Kaoru Sugasawa

  5. Laboratory of Chromatin Structure and Function, Institute for Quantitative Biosciences, The University of Tokyo, Tokyo, Japan

    Hitoshi Kurumizaka

  6. Graduate School of Advanced Science and Engineering, Waseda University, Tokyo, Japan

    Hitoshi Kurumizaka

Authors
  1. Syota Matsumoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Simone Cavadini
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Richard D. Bunker
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ralph S. Grand
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alessandro Potenza
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Julius Rabl
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Junpei Yamamoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Andreas D. Schenk
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Dirk Schübeler
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Shigenori Iwai
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Kaoru Sugasawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Hitoshi Kurumizaka
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Nicolas H. Thomä
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.M., S.C. and N.H.T. conceived and planned the experiments. S.M. prepared samples for biochemical and structural studies and performed electrophoretic mobility shift assays and fluorescence polarization assays. S.C. performed cryo-EM and analysis. R.D.B. interpreted the cryo-EM structures and prepared the atomic models, with S.C. providing input. R.D.B., S.C. and N.H.T. analysed the structures. A.P. and J.R. helped to develop the fluorescence polarization assays, S.M. carried out the functional experiments, and J.R., S.M. and N.H.T. analysed the results. A.D.S. contributed to cryo-EM processing and developed CryoFLARE. S.I. and J.Y. synthesized 6–4PP-containing oligonucleotides. S.M. performed biochemical analysis with guidance from K.S. and H.K. R.S.G. developed and analysed the DNase I footprinting assay with guidance from D.S. The research was directed by N.H.T., and all authors contributed to writing the manuscript.

Corresponding author

Correspondence to Nicolas H. Thomä.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Extended data figures and tables

Extended Data Fig. 1 Classification and refinement procedures for NCP6–4PP–UV-DDB.

a, Representative negative-stain micrograph (left) and reference-free 2D class averages (right) obtained with sxisac.py (SPARX) for the NCPTHF2(−1)–UV-DDB complex. b, Ab initio model generated with sxviper.py in SPARX for the complex shown in a. c, Representative Volta phase plate (VPP) cryo-EM micrographs (left) and reference-free 2D class averages (right) for NCP6–4PP–UV-DDB. d, Classification and refinement procedures for the cryo-EM reconstruction of NCP6–4PP–UV-DDB. Three different microscope sessions (4,530 micrographs) were collected under identical imaging conditions and processed independently before merging the best particles to obtain the final high-resolution reconstruction. For each session, a small dataset was manually selected to obtain 2D class averages that were used for particle autopicking with RELION. Several rounds of 2D and 3D classification were necessary to obtain homogeneous datasets. The model shown in b was low-pass-filtered to 60 Å resolution and used as an initial model for the first round of 3D classification of each session. Given the total dose of 40 e− Å−2 over 40 frames, only frames 1 to 28 were included for movie refinement and particle polishing in RELION. To improve the resolution, the best particles from the three sessions were pooled and subjected to 3D classification into four classes. Refinement of the particles included in class II using a soft mask around the entire complex produced a 4.3 Å resolution map. Refinement of the same set of particles with a soft mask that excluded DDB1 produced a 4.2 Å resolution map. e, Gold-standard FSC curves for NCP6–4PP–UV-DDB (red) and for the same complex after masking out DDB1 (blue). f, Angular distribution of the particles included in the final models. g, Local-resolution-filtered map for NCP6–4PP–UV-DDB coloured by resolution (MonoRes47). h, The 6–4PP lesion is located next to H3 α−helix α1. i, Orphaned bases are stabilized by β-hairpin loop insertion. j, Fluorescence polarization dose–response curves using 10 nM Cy5-labelled 15-bp oligonucleotide with a single THF damage site (Cy5–15-bp-DNATHF) mixed with UV-DDB (0.3–200 nM). The interaction is measured and plotted as described in Methods. All data are shown as mean ± s.d. of three technical replicates. k, A 10 nM solution of a Cy5–15-bp-DNATHF was mixed with 10 nM wild-type UV-DDB or the UV-DDB(ΔN) variant lacking residues 1–40 of DDB2, and counter-titrated with NCPTHF2(−1). Although the DDB2 N-terminal region (residues 1–40) contributes to nucleosome binding in biochemical assays, we did not find interpretable density for this segment; we also found no evidence of UV-DDB dimerization in our cryo-EM structures18.

Source data

Extended Data Fig. 2 Classification and refinement procedures for NCP6–4PP.

a, Representative VPP cryo-EM micrograph (left) and reference-free 2D class averages (right) for NCP6–4PP. b, Four microscope sessions with a total of 5,187 micrographs were collected under identical imaging conditions. All dose-fractionated micrograph stacks were subjected to beam-induced motion correction with MotionCor255. Initial processing was carried out with unweighted MotionCor2-corrected sums (including all frames). A small dataset was manually selected to obtain initial 2D class averages used for autopicking in RELION. A few rounds of 2D classification led to a dataset with 209,324 particles. A nucleosome map cut out from the 4.2 Å resolution map shown in Extended Data Fig. 1d was low-pass-filtered to 60 Å and used as an initial model. Three-dimensional classification into four classes allowed us to discard 54% of the particles (class I and class III). The remaining 106,417 particles were refined to 4.7 Å resolution. Re-extraction of the particles from dose-weighted micrographs and re-refinement with a mask led to a 4.1 Å resolution map. Particles were subjected to a final round of 2D classification before the last refinement to obtain a map at 4 Å resolution. c, Gold-standard FSC curves. d, Angular distribution of the particles included in the final model. e, Local-resolution-filtered map for NCP6–4PP. f, Middle and right, the 4 Å resolution cryo-EM map of NCP6–4PP shown in two different views, with the 6–4PP (orange) tentatively assigned. Left, cryo-EM density (grey surface) of NCP6–4PP fitted with the cryo-EM structure of NCP6–4PP–UV-DDB, illustrating the undistorted nature of the nucleosome. At this resolution, the α-satellite nucleosome carrying the damage is pseudo-symmetrical. Both SHL+2 and SHL−2 loci show continuous density around the expected site of the lesion. Whereas both half-sites are probably averaged in the process of classification, the continuous density in both loci suggests that the 6–4PP lesion is present predominantly in an apparent helical, non-extruded conformation.

Extended Data Fig. 3 Classification and refinement procedures for NCPTHF2(−1)–UV-DDB.

a, Representative cryo-EM micrograph and reference-free 2D class averages for the NCPTHF2(−1)–UV-DDB complex. b, A total of 2,811 micrographs were collected over three sessions using identical imaging conditions. For each session, the model shown in Extended Data Fig. 1b was low-pass-filtered to 60 Å and used as initial model for the first round of 3D classification. For each session, a small dataset was manually picked to obtain initial 2D class averages used for autopicking in RELION. Several rounds of 2D and 3D classification were necessary to obtain homogeneous datasets. Given the accumulated dose of 60 e− Å−2 equally distributed over 40 frames, frames 1 to 18 were retained during movie refinement and particle polishing in RELION. The best particles from each session were pooled and subjected to 3D classification into four classes. Refinement of classes I, II and IV led to a 4.5 Å resolution map. Further 3D classification into four models was performed by masking out DDB1. Next the particles included in class I and class IV were pulled together and subjected to refinement with a mask around the entire complex leading to a 3.9 Å resolution map. Refinement with a mask that excluded DDB1 led to an improved 3.8 Å resolution map. c, Gold-standard FSC curves for the 3.9 Å (blue) and 3.8 Å (purple) resolution map, respectively. d, Angular distribution of the particles included in the final models. e, Local-resolution-filtered maps for NCP6–4PP–UV-DDB and NCPTHF2(−1)–UV-DDB coloured by resolution (MonoRes47). f, g, The NCP6–4PP–UV-DDB model fitted into the NCPTHF2(−1)–UV-DDB cryo-EM map, illustrating that the two models are structurally identical given the resolution. h, Damage location and sequence of NCPTHF2(−1)–UV-DDB and NCPCPD(−1)–UV-DDB are indicated. i, As in Extended Data Fig. 1k with increasing amounts of competing nucleosomes containing 6–4PP, THF2, or CPD. All data are shown as mean ± s.d. of three technical replicates. j, Gel electrophoretic mobility shift assays were carried out with 40 nM Alexa Fluor 488-labelled undamaged nucleosome or nucleosomes containing 6–4PP, THF2 or CPD lesions. Gels were imaged using the Alexa Fluor 488 signal with a Typhoon Image Analyzer (GE Healthcare).

Source data

Extended Data Fig. 4 Representative NCP–UV-DDB cryo-EM map segments.

a, Different views for the unsharpened local-resolution-filtered map of the NCP6–4PP(−1)–UV-DDB complex. Together with the class averages (Extended Data Fig. 1a), this argues that the density observed is fully accounted for by a single UV-DDB complex engaged with a single damaged nucleosome. b, Representative, sharpened local-resolution-filtered maps of the NCP6–4PP(−1)–UV-DDB complex for which the central DDB1 B-domain features were masked out, showing map segments for the damaged DNA duplex, the DDB2–DNA–histone H3 interface, and H3/H4 position surrounding the damage, and the DDB2 β-hairpin loop involved in damage recognition. c, As in b but with map segments from NCPTHF2(−1)–UV-DDB. d, Sharpened local-resolution-filtered map obtained for NCP6–4PP(−1)–UV-DDB with DDB1 masked out. e, Segments of H2A, H2A, H3, H4, DDB2 β-sheet and DDB2-6–4PP interface excised from the 4.2 Å resolution NCP6–4PP(−1)–DDB2 map shown in d.

Extended Data Fig. 5 Biochemical and biophysical characterization of NCP–UV-DDB, and classification and refinement procedures for NCPTHF2(−3) and NCPTHF2(−3)–UV-DDB.

a, b, A 10 nM solution of Cy5–15-bp-DNATHF was mixed with 10 nM UV-DDB and counter-titrated with nucleosome or 145-bp DNA containing 6–4PP (a) or THF2 (b) at the −1 (−22/−23) position. Undamaged 145-bp DNA was used as a negative control. All data are shown as mean ± s.d. of three technical replicates. c, Gel electrophoretic mobility shift assays were carried out by mixing 40 nM of nucleosomes containing THF2 at different positions, −5∗ (−16/−17) to +3 (−18/−19), with increasing amounts of UV-DDB (0 to 320 nM). Gels were imaged by Alexa Fluor 488 with Typhoon Image Analyzer. d, As in b, but counter-titrated with different 145-bp DNA containing THF2 at the −5∗ (−16/−17) to +3 (−18/−19) positions. e, Estimation of EC50 from the counter-titration experiments (a, b and Fig. 2e). Data are shown as mean ± s.d. of three technical replicates. Lesions placed at position zero show the highest binding affinity, with an EC50 around 1.1-fold higher than those at the −1 (−22/−23) position used for structure determination in Fig. 1. However, the EC50 decreased approximately 2.5-fold for nucleosomes with THF2 lesions at the +1 (−20/−21) site, around eightfold at the +2 (−19−20) site and around 15-fold at the +3 (−18/−19) site. A similar drop in affinity is found for lesions placed successively in the other direction from the −2 (−23/−24) site through to the −3 (−24/−25) and −4 (−25/−26) sites (Fig. 2e). f, Representative VPP cryo-EM micrographs (left) and reference-free 2D class averages (right) for the NCPTHF2(−3)–UV-DDB complex. g, Ab initio model generated with RELION for the complex shown in f. h, Two different microscope datasets were collected under identical imaging conditions leading to 3,890 micrographs. All dose-fractionated micrograph stacks were subjected to beam-induced motion correction with MotionCor255. All frames (1–40) were included during this step. Further processing was carried out using MotionCor2-corrected sums that were filtered according to exposure dose (1 e− Å−2 per frame). A small dataset was manually picked to obtain 2D class averages used for autopicking within RELION. The model shown in g was low-pass-filtered to 60 Å and used as initial model for the first round of 3D classification. Several rounds of 2D and 3D classification were necessary to obtain homogeneous datasets. The last 3D classification divided the dataset into six models. Refinement of the best particles with a soft mask around the entire complex led to a 4.1 Å resolution map. i, Representative conventional (no VPP) cryo-EM micrograph (left) and reference-free 2D class averages (right) for the isolated NCPTHF2(−3). j, A total of 2,433 micrographs were collected and a small dataset was manually picked to obtain initial 2D class averages followed by autopicking in RELION. Four rounds of 2D classification led to a homogeneous dataset. The density for UV-DDB was removed from the model shown in g. The resulting map was low-pass-filtered to 60 Å and used as initial model for the first round of 3D refinement leading to a map at 4.1 Å resolution after polishing. Given the accumulated dose of 40 e− Å−2 spanning 40 frames, frames 1–28 were included during movie refinement and particle polishing in RELION. To improve the resolution, we performed 3D classification into three classes. Refinement with a mask of class II led to a 3.6 Å resolution map. Per particle CTF refinement improved the map to 3.5 Å resolution. k, l, Local-resolution-filtered map for NCPTHF2(−3)–UV-DDB (k) and NCPTHF2(−3) (l). m, Gold-standard FSC curves for NCPTHF2(−3)–UV-DDB (blue) and NCPTHF2(−3) (orange). n, o, Angular distribution for NCPTHF2(−3)–UV-DDB (n) and NCPTHF2(−3) (o). p, Overlay of the predicted NCPTHF2(−3)–UV-DDB model (red) with its cryo-EM structure (yellow), the difference between the two is reconciled by nucleosomal register shifting.

Source data

Extended Data Fig. 6 Thermodynamic dissection of UV-DDB binding with slide-assisted site-exposure.

a, DNase I digestion of undamaged (UD) nucleosomes with a range of enzyme concentrations (0.12 U to 0.015 U) show identical sensitive sites ((i), data shown are the average of two technical replicates per enzyme concentration) and are highly reproducible across replicates (ii). Note that an example of the data reproducibility is shown for the undamaged construct, but the data for all constructs were highly reproducible (Pearson correlations of R > 0.95). b, Ratio of counts at position n versus n − 3 for undamaged NCP, NCPTHF2(−1), NCPTHF2(−3) in the absence and presence of UV-DDB. c, (i) Peaks for UD NCP, NCPTHF2(−1), NCPTHF2(−3) and NCPTHF2(+1) in the presence of UV-DDB used to quantify the register-shift ratios (n = 2). Note that in the case of NCPTHF2(+1) we expect only 30–40% of molecules to shift by a maximum of 1 bp. Our data are consistent with an increased population of α-satellite DNA in the +1 nucleosome shifted by 1 bp towards the dyad axis compared to the −1 nucleosome ((ii), n = 4, mean ± s.d.); however, the width and the overlap of the shifted or unshifted peak prevented further detailed analysis. d, Titration of a 10 nM solution of a Cy5–15-bp-DNATHF with increasing concentrations of UV-DDB. The resulting curve was fitted with DynaFit resulting in a Kd of around 0.15 nM. Given the tight binding indicated by a low Kd relative to the high concentration of the Cy5–15-bp-DNATHF label, the value should be viewed as an estimate. These are, however, in line with the results of previous experiments34. All data are shown as mean ± s.d. of three technical replicates. e, The complex of 10 nM Cy5–15-bp-DNATHF and UV-DDB was back-titrated with NCPTHF2 (−3) and the data fitted numerically in DynaFit. All data are shown as mean ± s.d. of three technical replicates. f, Thermodynamic binding scheme invoking induced-fit binding, in which the register shift is induced on UV-DDB binding, and a conformational preselection branch (selected-fit), in which a pre-equilibrium exists that is shifted by UV-DDB binding. g, Equations describing the thermodynamic binding process, and approximations used to derive K1, K2, K3 and K4.

Source data

Extended Data Fig. 7 Classification and refinement procedures for NCPTHF2(+1)–UV-DDB.

a, Representative VPP cryo-EM micrograph (left) and reference-free 2D class averages (right) obtained with cisTEM48 for NCPTHF2(+1)–UV-DDB. b, Six ab initio models were generated with cisTEM. c, Five datasets were collected with VPP under identical imaging conditions obtaining a total of 11,675 micrographs. All dose-fractionated micrograph stacks were subjected to beam-induced motion correction with MotionCor255. Further processing was carried out using dose-weighted MotionCor2-corrected sums (including all frames, 1 e− Å−2 per frame). A small dataset was manually selected to obtain 2D class averages and used for autopicking in RELION. Class III shown in b was low-pass-filtered to 60 Å and used as an initial model for the first round of 3D classification for all five sessions independently. Several rounds of 2D and 3D classification were necessary to obtain homogeneous datasets. The best particles from each session were pooled and subjected to 3D classification into four models, revealing two different conformations for the complex. Thus, additional rounds of 3D classification were necessary to obtain a homogeneous subset of particles. Particles included in class I, class II and class III were merged and divided into four 3D classes. Owing to their structural similarity, particles in class V, class VI and class VII were pooled and divided into 3D classes. A homogeneous dataset was obtained after merging particles in class IX and class X, leading to a 4.2 Å resolution map (class A) with a mask that excluded DDB1. Particles in class IV and class VIII were pooled and subjected to 3D classification. The best particles led to a 4.6 Å resolution map after masking out DDB1 (class B). d, Gold-standard FSC curves for class A (blue) and class B (red) respectively. e, f, Angular distribution for the particles included in class A (e) and class B (f). g, h, Local-resolution-filtered maps for class A (g) and class B (h) (MonoRes47). i, Magnified view showing the interactions between DDB2 (green) and different cryo-EM models, demonstrating that essentially identical interactions between the protein and the DNA are maintained despite different type of lesions and rotational settings. j, Superimposition of atomic models for the repositioned NCPTHF2(+1)–UV-DDB (class B) and the repositioned NCPTHF2(−3)–UV-DDB (light grey, Extended Data Fig. 5h, k), illustrating repositioning to a common position.

Extended Data Fig. 8 Placing the damage on the opposite strand exposes UV-DDB to a different binding environment.

a, DNA sequences and position of all complex structures with different lesions as shown in Fig. 4a. The relative translational register shift is indicated. b, DNA sequence for NCPTHF2(−1)–UV-DDB with THF2 placed at position −22/−23 bp from the dyad axis on the reverse strand (3′ to 5′). c, Comparison of clash scores between SHL−2.5 (Watson, grey) and SHL+3.5 (Crick, orange) shown in Fig. 5e. d, Estimation of EC50 from the counter-titration experiments shown in Fig. 5a, c, d. All data are shown as mean ± s.d. of three technical replicates. e, Accessible contact surface area for nucleosomal DNA calculated as indicated across a range of probe radii. Most of the DNA surface (more than 93%) is inaccessible to probes with radii exceeding 4 Å. f, A mechanism invoking UV-DDB-induced nucleosome register-shifting is also conceivable for damage recognition of pyrimidine dimers in multiple nucleosomes. Modelling of available di- and tetranucleosome structures suggests that UV-DDB can access a substantial fraction of the outward-facing minor grooves in these more heterochromatic model substrates56, with similar predicted accessibility to corresponding positions in mononucleosomes (Fig. 5e).

Extended Data Table 1 NCP–UV-DDB complex cryo-EM data collection, refinement and validation statistics
Full size table
Extended Data Table 2 NCP cryo-EM data collection, refinement and validation statistics
Full size table

Supplementary information

Supplementary Figure 1

Uncropped gel scans of electrophoretic mobility shift assays.

Reporting Summary

Source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Matsumoto, S., Cavadini, S., Bunker, R.D. et al. DNA damage detection in nucleosomes involves DNA register shifting. Nature 571, 79–84 (2019). https://doi.org/10.1038/s41586-019-1259-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-019-1259-3

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing