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Enhanced proofreading governs CRISPR–Cas9 targeting accuracy

Nature volume 550, pages 407–410 (2017)Cite this article

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Abstract

The RNA-guided CRISPR–Cas9 nuclease from Streptococcus pyogenes (SpCas9) has been widely repurposed for genome editing1,2,3,4. High-fidelity (SpCas9-HF1) and enhanced specificity (eSpCas9(1.1)) variants exhibit substantially reduced off-target cleavage in human cells, but the mechanism of target discrimination and the potential to further improve fidelity are unknown5,6,7,8,9. Here, using single-molecule Förster resonance energy transfer experiments, we show that both SpCas9-HF1 and eSpCas9(1.1) are trapped in an inactive state10 when bound to mismatched targets. We find that a non-catalytic domain within Cas9, REC3, recognizes target complementarity and governs the HNH nuclease to regulate overall catalytic competence. Exploiting this observation, we design a new hyper-accurate Cas9 variant (HypaCas9) that demonstrates high genome-wide specificity without compromising on-target activity in human cells. These results offer a more comprehensive model to rationalize and modify the balance between target recognition and nuclease activation for precision genome editing.

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Figure 1: High-fidelity Cas9 variants enhance cleavage specificity through HNH conformational control.
Figure 2: The α-helical lobe regulates HNH domain activation.
Figure 3: Targeted mutagenesis within the REC3 domain reveals a SpCas9 variant with hyper-accurate behaviour in human cells.
Figure 4: Mutating residues involved in proofreading increases the threshold for conformational activation to ensure targeting accuracy.

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References

  1. Doudna, J. A. & Charpentier, E. The new frontier of genome engineering with CRISPR-Cas9. Science 346, 1258096 (2014)

    Article  Google Scholar 

  2. Hsu, P. D., Lander, E. S. & Zhang, F. Development and applications of CRISPR-Cas9 for genome engineering. Cell 157, 1262–1278 (2014)

    Article  CAS  Google Scholar 

  3. Mali, P., Esvelt, K. M. & Church, G. M. Cas9 as a versatile tool for engineering biology. Nat. Methods 10, 957–963 (2013)

    Article  CAS  Google Scholar 

  4. Barrangou, R. & Horvath, P. A decade of discovery: CRISPR functions and applications. Nat. Microbiol. 2, 17092 (2017)

    Article  CAS  Google Scholar 

  5. Fu, Y. et al. High-frequency off-target mutagenesis induced by CRISPR-Cas nucleases in human cells. Nat. Biotechnol. 31, 822–826 (2013)

    Article  CAS  Google Scholar 

  6. Tsai, S. Q. et al. GUIDE-seq enables genome-wide profiling of off-target cleavage by CRISPR-Cas nucleases. Nat. Biotechnol. 33, 187–197 (2015)

    Article  CAS  Google Scholar 

  7. Tsai, S. Q. & Joung, J. K. Defining and improving the genome-wide specificities of CRISPR-Cas9 nucleases. Nat. Rev. Genet. 17, 300–312 (2016)

    Article  CAS  Google Scholar 

  8. Slaymaker, I. M. et al. Rationally engineered Cas9 nucleases with improved specificity. Science 351, 84–88 (2016)

    Article  ADS  CAS  Google Scholar 

  9. Kleinstiver, B. P. et al. High-fidelity CRISPR–Cas9 nucleases with no detectable genome-wide off-target effects. Nature 529, 490–495 (2016)

    Article  ADS  CAS  Google Scholar 

  10. Dagdas, Y. S., Chen, J. S., Sternberg, S. H., Doudna, J. A. & Yildiz, A. A conformational checkpoint between DNA binding and cleavage by CRISPR-Cas9. Sci. Adv. 3, eaao0027 (2017)

    Article  Google Scholar 

  11. Bisaria, N., Jarmoskaite, I. & Herschlag, D. Lessons from enzyme kinetics reveal specificity principles for RNA-guided nucleases in RNA interference and CRISPR-based genome editing. Cell Syst. 4, 21–29 (2017)

    Article  CAS  Google Scholar 

  12. Sternberg, S. H., LaFrance, B., Kaplan, M. & Doudna, J. A. Conformational control of DNA target cleavage by CRISPR–Cas9. Nature 527, 110–113 (2015)

    Article  ADS  CAS  Google Scholar 

  13. Jiang, F. et al. Structures of a CRISPR-Cas9 R-loop complex primed for DNA cleavage. Science 351, 867–871 (2016)

    Article  ADS  CAS  Google Scholar 

  14. Palermo, G., Miao, Y., Walker, R. C., Jinek, M. & McCammon, J. A. Striking plasticity of CRISPR-Cas9 and key role of non-target DNA, as revealed by molecular simulations. ACS Cent. Sci. 2, 756–763 (2016)

    Article  CAS  Google Scholar 

  15. Palermo, G., Miao, Y., Walker, R. C., Jinek, M. & McCammon, J. A. CRISPR-Cas9 conformational activation as elucidated from enhanced molecular simulations. Proc. Natl Acad. Sci. USA 114, 7260–7265 (2017)

    Article  CAS  Google Scholar 

  16. Jinek, M. et al. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337, 816–821 (2012)

    Article  ADS  CAS  Google Scholar 

  17. Nishimasu, H. et al. Crystal structure of Cas9 in complex with guide RNA and target DNA. Cell 156, 935–949 (2014)

    Article  CAS  Google Scholar 

  18. Anders, C., Niewoehner, O., Duerst, A. & Jinek, M. Structural basis of PAM-dependent target DNA recognition by the Cas9 endonuclease. Nature 513, 569–573 (2014)

    Article  ADS  CAS  Google Scholar 

  19. Jiang, F., Zhou, K., Ma, L., Gressel, S. & Doudna, J. A. A. A Cas9-guide RNA complex preorganized for target DNA recognition. Science 348, 1477–1481 (2015)

    Article  ADS  CAS  Google Scholar 

  20. Majumdar, Z. K., Hickerson, R., Noller, H. F. & Clegg, R. M. Measurements of internal distance changes of the 30S ribosome using FRET with multiple donor-acceptor pairs: quantitative spectroscopic methods. J. Mol. Biol. 351, 1123–1145 (2005)

    Article  CAS  Google Scholar 

  21. Szczelkun, M. D. et al. Direct observation of R-loop formation by single RNA-guided Cas9 and Cascade effector complexes. Proc. Natl Acad. Sci. USA 111, 9798–9803 (2014)

    Article  ADS  CAS  Google Scholar 

  22. Cencic, R. et al. Protospacer adjacent motif (PAM)-distal sequences engage CRISPR Cas9 DNA target cleavage. PLoS ONE 9, e109213 (2014)

    Article  ADS  Google Scholar 

  23. Jinek, M. et al. Structures of Cas9 endonucleases reveal RNA-mediated conformational activation. Science 343, 1247997 (2014)

    Article  Google Scholar 

  24. Wright, A. V. et al. Rational design of a split-Cas9 enzyme complex. Proc. Natl Acad. Sci. USA 112, 2984–2989 (2015)

    Article  ADS  CAS  Google Scholar 

  25. Kleinstiver, B. P. et al. Engineered CRISPR-Cas9 nucleases with altered PAM specificities. Nature 523, 481–485 (2015)

    Article  ADS  Google Scholar 

  26. Reyon, D. et al. FLASH assembly of TALENs for high-throughput genome editing. Nat. Biotechnol. 30, 460–465 (2012)

    Article  CAS  Google Scholar 

  27. Tsai, S. Q., Topkar, V. V., Joung, J. K. & Aryee, M. J. Open-source guideseq software for analysis of GUIDE-seq data. Nat. Biotechnol. 34, 483 (2016)

    Article  Google Scholar 

  28. Lin, Y. et al. CRISPR/Cas9 systems have off-target activity with insertions or deletions between target DNA and guide RNA sequences. Nucleic Acids Res. 42, 7473–7485 (2014)

    Article  CAS  Google Scholar 

  29. Bae, S., Park, J. & Kim, J. S. Cas-OFFinder: a fast and versatile algorithm that searches for potential off-target sites of Cas9 RNA-guided endonucleases. Bioinformatics 30, 1473–1475 (2014)

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank A. V. Wright, S. N. Floor, J. C. Cofsky, D. Burstein, C. Fellman, B. L. Oakes and O. Mavrothalassitis for discussions and reading the manuscript, M. S. Prew for technical assistance, and J. M. Lopez for assistance with GUIDE-seq data processing. J.S.C. and L.B.H. are supported by National Science Foundation Graduate Research Fellowships, and B.P.K. from Banting (Natural Sciences and Engineering Research Council of Canada) and Charles A. King Trust Postdoctoral Fellowships. J.A.D. is an Investigator of the Howard Hughes Medical Institute. This work was supported by the National Institutes of Health (GM094522 and GM118773 (A.Y.), R35 GM118158 (J.K.J.)), National Science Foundation (MCB-1617028 (A.Y.) and MCB-1244557 (J.A.D.)), and the Desmond and Ann Heathwood MGH Research Scholar Award (J.K.J.).

Author information

Author notes

  1. Samuel H. Sternberg

    Present address: Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, 10032, USA

  2. Janice S. Chen, Yavuz S. Dagdas and Benjamin P. Kleinstiver: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Molecular and Cell Biology, University of California, Berkeley, 94720, USA, California

    Janice S. Chen, Lucas B. Harrington, Ahmet Yildiz & Jennifer A. Doudna

  2. Biophysics Graduate Group, University of California, Berkeley, 94720, California, USA

    Yavuz S. Dagdas

  3. Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Charlestown, 02129, Massachusetts, USA

    Benjamin P. Kleinstiver, Moira M. Welch, Alexander A. Sousa & J. Keith Joung

  4. Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, 02129, Massachusetts, USA

    Benjamin P. Kleinstiver, Moira M. Welch, Alexander A. Sousa & J. Keith Joung

  5. Department of Pathology, Harvard Medical School, Boston, 02115, Massachusetts, USA

    Benjamin P. Kleinstiver & J. Keith Joung

  6. Department of Chemistry, University of California, Berkeley, 94720, California, USA

    Samuel H. Sternberg & Jennifer A. Doudna

  7. Department of Physics, University of California, Berkeley, 94720, California, USA

    Ahmet Yildiz

  8. Howard Hughes Medical Institute, University of California, Berkeley, 94720, California, USA

    Jennifer A. Doudna

  9. Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, 94720, California, USA

    Jennifer A. Doudna

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  1. Janice S. Chen
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Contributions

J.S.C., Y.S.D. and B.P.K. contributed equally to the work, and conceived and designed experiments with input from L.B.H., S.H.S., J.K.J., A.Y. and J.A.D. J.S.C. performed protein expression, labelling and biochemical experiments. Y.S.D. performed single-molecule fluorescence assays and related data analysis. B.P.K. and M.M.W. performed human cell-based assays, and B.P.K. and A.A.S. performed and analysed GUIDE-seq experiments. J.S.C., Y.S.D., B.P.K., J.K.J., A.Y. and J.A.D. wrote the manuscript.

Corresponding author

Correspondence to Jennifer A. Doudna.

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Competing interests

J.K.J. has financial interests in Beacon Genomics, Beam Therapeutics, Editas Medicine, Pairwise Plants, Poseida Therapeutics, and Transposagen Biopharmaceuticals. J.K.J.’s interests were reviewed and are managed by Massachusetts General Hospital and Partners HealthCare in accordance with their conflict of interest policies. J.A.D. is a co-founder of Caribou Biosciences, Editas Medicine, and Intellia Therapeutics; a scientific adviser to Caribou, Intellia, eFFECTOR Therapeutics and Driver; and executive director of the Innovative Genomics Institute at the University of California, Berkeley and University of California, San Francisco. S.H.S. is an employee of Caribou Biosciences, Inc. S.H.S., J.S.C., and J.A.D. are inventors on a patent application entitled ‘Reporter Cas9 variants and methods of use thereof’ (PCT/US2016/036754), filed by The Regents of the University of California. B.P.K. and J.K.J. are inventors on a patent application entitled ‘Engineered CRISPR-Cas9 nucleases’ (US 15/060,424), filed by The General Hospital Corporation. J.S.C., Y.S.D., B.P.K., A.Y., J.K.J., and J.A.D. have filed a patent application related to this work through The General Hospital Corporation and The Regents of the University of California.

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Reviewer Information Nature thanks A. Ke 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 Figure 1 Dual-labelled SpCas9 variants are fully functional for DNA cleavage.

a, SDS–PAGE analysis of unlabelled Cas9 variants. b, SDS–PAGE analysis of Cy3/Cy5-labelled Cas9 variants. The gel was scanned for Cy3/Cy5 fluorescence (middle, bottom) before staining with Coomassie blue (top). c–f, DNA cleavage time courses of Cas9 FRET constructs and their dual-labelled counterparts for (c) WT SpCas9, (d) SpCas9-HF1, (e) eSpCas9(1.1) and (f) HypaCas9. For a–f, experiments were repeated three independent times with similar results.

Extended Data Figure 2 HNH domain in eSpCas9 variants still populate the docked state in the presence of PAM-distal mismatches.

a, Quantification of DNA cleavage time courses comparing WT SpCas9, SpCas9-HF and eSpCas9(1.1) variants with perfect and PAM-distal mismatched targets. b, Dissociation constants comparing WT SpCas9, SpCas9-HF and eSpCas9(1.1) variants with perfect and PAM-distal mismatched targets, as measured by electrophoretic mobility shift assays. For a, b, mean and s.d. are shown; n = 3 independent experiments (overlaid as white circles in b). c, d, smFRET histograms for (c) SpCas9-K855A and (d) SpCas9-N497A/R661A/Q695A. For c and d, black curves represent a fit to multiple Gaussian peaks. e, Schematic of SpCas9 domain structure with colour coding for separate domains. f, Vector map of global SpCas9 conformational changes from the sgRNA-bound (PDB accession number 4ZT0) to dsDNA-bound structures (PDB accession number 5F9R), domains coloured as in e.

Source data

Extended Data Figure 3 Kinetic analysis of transitions between active and inactive states of the HNH domain.

a, Representative time traces (top), transition density plots (TDPs, middle) and rates of the transitions in TDPs (bottom) for SpCas9-HF1 with on-target DNA (left), eSpCas9(1.1) with on-target DNA (middle) and eSpCas9(1.1) with 20–20 bp mm DNA (right); mean and s.e.m. are shown; n = 107, 24 and 74 individual molecules, respectively. The percentages of molecules showing at least one such transition was 36%, 7% and 29% for SpCas9-HF1 with on-target, eSpCas9(1.1) with on-target and eSpCas9(1.1) with 20–20 bp mm DNA, respectively. Kinetics analysis of other cases (SpCas9-HF1 and eSpCas9(1.1) bound to other off-target substrates, and HypaCas9 bound to on- and off-target substrates) is not shown, because the percentage of molecules showing at least one such transition was less than 3%. b, Comparison of on-target transition rates for WT SpCas9, SpCas9-HF1 and eSpCas9(1.1); mean and s.e.m. are shown; n = 51, 107 and 24 individual molecules, respectively. Transition rates for WT SpCas9 collected from ref. 10.

Source data

Extended Data Figure 4 Nucleic acid sensing requires engagement with the REC3 domain and outward rotation of the REC2 domain.

a, Schematic of SpCas9REC3 with FRET dyes at positions S701C and S960C, with HNH domain omitted for clarity. Inactive to active structures represent REC3 in the sgRNA-bound (PDB accession number 4ZT0) to dsDNA-bound (PDB accession number 5F9R) forms, respectively. b, c, smFRET histograms showing HNH conformational activation with black curves representing a fit to multiple Gaussian peaks for (b) WT SpCas9REC3 and (c) SpCas9-HF1REC3 bound to perfect and PAM-distal mismatched targets. The purple peak denotes the sgRNA-only bound state, while the red and green peaks represent two states of REC3 with conformational flexibility upon binding to DNA substrates. d, REC3 in vitro complementation assay with SpCas9∆REC3 by measuring cleavage rate constants. e, On-target DNA binding assay in the presence or absence of the REC3 domain; mean and s.d. are shown. f, REC3 in vitro complementation assay with SpCas9∆REC3 by measuring HNH activation with (ratio)A values. g, (Ratio)A data with SpCas9REC2 and SpCas9HNH showing reciprocal FRET states with the indicated substrates. For d–g, mean and s.d. are shown; n = 3 independent experiments (overlaid as white circles in d, f, and g). h, Schematic of SpCas9∆REC3REC2 with FRET dyes at positions E60C and D273C, with the REC3 domain added in trans. Inactive to active structures represent REC2 in the sgRNA-bound (PDB accession number 4ZT0) to dsDNA-bound (PDB accession number 5F9R) forms, respectively. i, smFRET histograms measuring REC2 conformational states with SpCas9∆REC3REC2 in the absence and presence of the REC3 domain when bound to an on-target substrate.

Source data

Extended Data Figure 5 Identification of cluster variants on the basis of nucleic acid proximity and multiple sequence alignment of residues within clusters 1–5.

a, Schematic depicting interactions of WT SpCas9 residues within clusters 1–5 with the RNA–DNA heteroduplex, on the basis of PDB accession 5F9R (adapted from ref. 9). b, Alignment of selected Cas9 orthologues using MAFFT and visualized in Geneious 10.0, with red boxes outlining residues mutated to alanine within each cluster variant.

Extended Data Figure 6 Mutation clusters in the REC3 domain along the RNA–DNA heteroduplex demonstrate localized sensitivity to mismatches along the target sequence.

a, b, Quantified DNA cleavage rates (dotted line indicates detection limit for kcleave set at 10 min−1) displayed as (a) a heatmap and (b) a bar graph. c, d, Target DNA binding assay (c) resolved by native PAGE mobility shift assays; repeated three independent times with similar results and (d) quantification with WT-normalized dissociation constants. For b and d, mean and s.d. are shown; n = 3 independent experiments (overlaid as white circles).

Source data

Extended Data Figure 7 On-target activities of altered specificity variants using a human cell eGFP disruption assay.

a, Summary of eGFP disruption activities for SpCas9-HF1, eSpCas9(1.1), eSpCas9(1.1)-HF1 and cluster variants ± Q926A with mean and s.e.m., where n = at least 3 biologically independent samples (overlaid as white circles). b, Summary of eGFP disruption activities for the series of cluster 1 variants with each substituted residue restored to the canonical amino acid; mean and s.e.m. are shown; n = at least 3 biologically independent samples (overlaid as white circles); WT, cluster 1 (HypaCas9), and cluster 1 + Q926A data from a are re-plotted for comparison. c, WT-normalized plot of data in b; error bars represent median and interquartile range for n = 12 biologically independent samples; the interval with >70% of WT activity is highlighted in light grey.

Source data

Extended Data Figure 8 Activities and specificities of high-fidelity SpCas9 variants targeted to endogenous human cell sites.

a, On-target activities of WT SpCas9, SpCas9-HF1, cluster 1 and cluster 2 variants across 24 endogenous human genes, assessed by T7E1 assay; mean and s.e.m. are shown; n = at least 3 biologically independent samples (overlaid as white circles). b, WT-normalized endogenous gene disruption data from a, for cluster 1 and 2 variants. Error bars represent median and interquartile ranges of 24 biologically independent samples with the >70% interval of WT activity highlighted in light grey; cluster 1 (HypaCas9) data from Fig. 3b are re-plotted for comparison. c–e, Summary of single mismatch tolerance of WT SpCas9, SpCas9-HF1, eSpCas9(1.1), and cluster 1 and cluster 2 variants on (c) FANCF site 1, (d) FANCF sites 4 and 6 and (e) FANCF site 2. Percentage modification in c–e assessed by T7E1 assay; mean and s.e.m. are shown for n = at least 3 biologically independent samples (overlaid as white circles).

Source data

Extended Data Figure 9 Genome-wide specificity profiles of high-fidelity SpCas9 variants defined using GUIDE-seq.

a, Number of in silico predicted target sites mismatched by ‘n’ positions for six sgRNAs against the reference human genome (hg38) via Cas-OFFinder29. b, Assessment of GUIDE-seq dsODN tag integration at the on-target site for each nuclease and guide combination, detected by RFLP assay. c, On-target editing, determined by T7E1 assay; mean and s.e.m. are shown; n = 3 biologically independent samples (overlaid as white circles) for b and c. d, dsODN tag-integration efficiency ratios (integration:mutagenesis, from b and c) for each nuclease and guide combination, with means and 95% confidence intervals shown for n = 6 biologically independent samples. e, GUIDE-seq genome-wide specificity profiles for WT SpCas9, SpCas9-HF1, eSpCas9(1.1) and HypaCas9 each paired with six different sgRNAs. Mismatched positions in off-target sites are highlighted in colour; GUIDE-seq read counts shown to the right of the sequences, which correlate with approximate cleavage efficiency at a given site; blue circles indicate sites with potential alternative alignments due to RNA or DNA bulges28 (see Supplementary Table 1); yellow circles indicate off-target sites that are only supported by asymmetric GUIDE-seq reads.

Source data

Extended Data Figure 10 Conformational gating drives targeting accuracy for SpCas9 variants.

a–c, Steady-state smFRET histograms measuring (a) HNH, (b) REC2 and (c) REC3 conformational states for HypaCas9 bound to on-target and PAM-distal mismatched substrates. Black curves represent a fit to multiple Gaussian peaks. d, e, Steady-state smFRET histograms of Cas9 variants bound to PAM-distal mismatched substrates were normalized to and subtracted from that of on-target smFRET histograms. This analysis reveals transitions from one FRET population (negative peak, shaded region) to another population (positive peak, unshaded regions) for (d) REC3 and (e) REC2. f, Measured distances between residues labelled with Cy3/Cy5 FRET dyes for different substrate-bound Cas9 structures. Residue pairs were designed to report conformational changes of the specified domain (HNH, REC2 or REC3). The distances were measured between Cα atoms of the indicated residues for the associated PDB structures.

Source data

Supplementary information

Supplementary Figure

This file contains the uncropped gel images from polyacrylamide gel electrophoresis experiments presented in the manuscript. (PDF 548 kb)

Reporting Summary (PDF 68 kb)

Supplementary Table 1

This file contains GUIDE-seq data. (XLSX 69 kb)

Supplementary Table 2

This file contains DNA plasmids and proteins used in this study. All enhanced specificity, high-fidelity, cluster and hyper-accurate SpCas9 variants tested in this study, with Addgene ID numbers for deposited plasmids. The HNH, REC2 or REC3 subscript designation with an enhanced specificity, high-fidelity or cluster SpCas9 variant denotes combination of residue substitutions with indicated FRET construct. (XLSX 43 kb)

Supplementary Table 3

This file contains a list of nucleic acids used in the study. (XLSX 18 kb)

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Chen, J., Dagdas, Y., Kleinstiver, B. et al. Enhanced proofreading governs CRISPR–Cas9 targeting accuracy. Nature 550, 407–410 (2017). https://doi.org/10.1038/nature24268

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