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Improving cytidine and adenine base editors by expression optimization and ancestral reconstruction

Nature Biotechnology volume 36, pages 843–846 (2018)Cite this article

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Abstract

Base editors enable targeted single-nucleotide conversions in genomic DNA. Here we show that expression levels are a bottleneck in base-editing efficiency. We optimize cytidine (BE4) and adenine (ABE7.10) base editors by modification of nuclear localization signals (NLS) and codon usage, and ancestral reconstruction of the deaminase component. The resulting BE4max, AncBE4max, and ABEmax editors correct pathogenic SNPs with substantially increased efficiency in a variety of mammalian cell types.

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Figure 1: Identifying and addressing factors that limit base-editing efficiency in mammalian cells.
Figure 2: Properties of optimized AncBE4max, BE4max, and ABEmax compared to those of BE4 and ABE7.10.

References

  1. Landrum, M.J. et al. Nucleic Acids Res. 44, D862–D868 (2016).

    Article  CAS  Google Scholar 

  2. Komor, A.C., Kim, Y.B., Packer, M.S., Zuris, J.A. & Liu, D.R. Nature 533, 420–424 (2016).

    Article  CAS  Google Scholar 

  3. Gaudelli, N.M. et al. Nature 551, 464–471 (2017).

    Article  CAS  Google Scholar 

  4. Komor, A.C. et al. Sci. Adv. 3, eaao4774 (2017).

    Article  Google Scholar 

  5. Li, G. et al. Protein Cell 8, 776–779 (2017).

    Article  CAS  Google Scholar 

  6. Liang, P. et al. Protein Cell 8, 811–822 (2017).

    Article  CAS  Google Scholar 

  7. Ryu, S.-M. et al. Nat. Biotechnol. https://doi.org/dx.doi.org/10.1038/nbt.4148 (2018).

    Article  CAS  Google Scholar 

  8. Hess, G.T., Tycko, J., Yao, D. & Bassik, M.C. Mol. Cell 68, 26–43 (2017).

    Article  CAS  Google Scholar 

  9. Kim, J.H. et al. PLoS One 6, e18556 (2011).

    Article  CAS  Google Scholar 

  10. Suzuki, K. et al. Nature 540, 144–149 (2016).

    Article  CAS  Google Scholar 

  11. Hanson, G. & Coller, J. Nat. Rev. Mol. Cell Biol. 19, 20–30 (2018).

    Article  CAS  Google Scholar 

  12. Harms, M.J. & Thornton, J.W. Nat. Rev. Genet. 14, 559–571 (2013).

    Article  CAS  Google Scholar 

  13. Wheeler, L.C., Lim, S.A., Marqusee, S. & Harms, M.J. Curr. Opin. Struct. Biol. 38, 37–43 (2016).

    Article  CAS  Google Scholar 

  14. Risso, V.A., Gavira, J.A., Mejia-Carmona, D.F., Gaucher, E.A. & Sanchez-Ruiz, J.M. J. Am. Chem. Soc. 135, 2899–2902 (2013).

    Article  CAS  Google Scholar 

  15. Krokan, H.E., Drabløs, F. & Slupphaug, G. Oncogene 21, 8935–8948 (2002).

    Article  CAS  Google Scholar 

  16. Schenk, B. et al. J. Clin. Invest. 108, 1687–1695 (2001).

    Article  CAS  Google Scholar 

  17. Bennett, D.L. & Woods, C.G. Lancet Neurol. 13, 587–599 (2014).

    Article  CAS  Google Scholar 

  18. Liu, N. et al. Cell 173, 430–442 e417 (2018).

    Article  CAS  Google Scholar 

  19. Amato, A. et al. Int. J. Lab. Hematol. 36, 13–19 (2014).

    Article  CAS  Google Scholar 

  20. Badran, A.H. et al. Nature 533, 58–63 (2016).

    Article  CAS  Google Scholar 

  21. Gibson, D.G. et al. Nat. Methods 6, 343–345 (2009).

    Article  CAS  Google Scholar 

  22. Kim, Y.B. et al. Nat. Biotechnol. 35, 371–376 (2017).

    Article  CAS  Google Scholar 

  23. Li, H. & Durbin, R. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  Google Scholar 

  24. Hu, J.H. et al. Nature 556, 57–63 (2018).

    Article  CAS  Google Scholar 

  25. UniProt Consortium Nucleic Acids Res. 46, 2699 (2018).

  26. Altschul, S.F., Gish, W., Miller, W., Myers, E.W. & Lipman, D.J. J. Mol. Biol. 215, 403–410 (1990).

    Article  CAS  Google Scholar 

  27. Katoh, K. & Standley, D.M. Mol. Biol. Evol. 30, 772–780 (2013).

    Article  CAS  Google Scholar 

  28. Kalyaanamoorthy, S., Minh, B.Q., Wong, T.K.F., von Haeseler, A. & Jermiin, L.S. Nat. Methods 14, 587–589 (2017).

    Article  CAS  Google Scholar 

  29. Nguyen, L.T., Schmidt, H.A., von Haeseler, A. & Minh, B.Q. Mol. Biol. Evol. 32, 268–274 (2015).

    Article  CAS  Google Scholar 

  30. Hoang, D.T., Chernomor, O., von Haeseler, A., Minh, B.Q. & Vinh, L.S. Mol. Biol. Evol. 35, 518–522 (2018).

    Article  CAS  Google Scholar 

  31. Yang, Z. Mol. Biol. Evol. 24, 1586–1591 (2007).

    Article  CAS  Google Scholar 

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Acknowledgements

This work was supported by the Ono Pharma Foundation, DARPA HR0011-17-2-0049, US NIH RM1 HG009490, R01 EB022376, and R35 GM118062, and HHMI. Flow cytometry was supported by NCI P30CCA14051. L.W.K. is an NSF Graduate Research Fellow and was supported by NIH Training Grant T32 GM095450. J.L.D. gratefully acknowledges graduate fellowship support from the NSF and Hertz Foundation. We thank J. Coller, G. Hansen, M. Weiss, and A. Sharma for helpful discussions.

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Authors and Affiliations

  1. Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA

    Luke W Koblan, Jordan L Doman, Christopher Wilson, Jonathan M Levy, Tristan Tay, Gregory A Newby, Juan Pablo Maianti, Aditya Raguram & David R Liu

  2. Howard Hughes Medical Institute, Harvard University, Cambridge, Massachusetts, USA

    Luke W Koblan, Jordan L Doman, Christopher Wilson, Jonathan M Levy, Tristan Tay, Gregory A Newby, Juan Pablo Maianti, Aditya Raguram & David R Liu

  3. Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, USA

    Luke W Koblan, Jordan L Doman, Christopher Wilson, Jonathan M Levy, Tristan Tay, Gregory A Newby, Juan Pablo Maianti, Aditya Raguram & David R Liu

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  1. Luke W Koblan
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Contributions

L.W.K., J.L.D., C.W., J.M.L., T.T., G.A.N., and J.P.M. generated reagents and conducted experiments. C.W. and A.R. performed computational analyses. D.R.L. supervised the research. All authors contributed to writing the manuscript.

Corresponding author

Correspondence to David R Liu.

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Competing interests

D.R.L. is a consultant and co-founder of Editas Medicine, Pairwise Plants, and Beam Therapeutics, companies that use genome editing. L.W.K., J.L.D., C.W., and D.R.L. have filed patent applications on aspects on this work. The authors declare no competing non-financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–17, Supplementary Note 1, Supplementary Sequences 1–5 (PDF 2354 kb)

Life Sciences Reporting Summary (PDF 163 kb)

Supplementary Data 1

MAFFT alignment APOBEC homologs in FASTA format. (TXT 112 kb)

Supplementary Data 2

Flow cytometry gating examples for all cell types used (PDF 3674 kb)

Supplementary Data 3

APOBEC tree in Newick format (TXT 32 kb)

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Koblan, L., Doman, J., Wilson, C. et al. Improving cytidine and adenine base editors by expression optimization and ancestral reconstruction. Nat Biotechnol 36, 843–846 (2018). https://doi.org/10.1038/nbt.4172

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