We describe base editors that combine both cytosine and adenine base-editing functions. A codon-optimized fusion of the cytosine deaminase PmCDA1, the adenosine deaminase TadA and a Cas9 nickase (Target-ACEmax) showed a high median simultaneous C-to-T and A-to-G editing activity at 47 genomic targets. On-target as well as DNA and RNA off-target activities of Target-ACEmax were similar to those of existing single-function base editors.
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The high-throughput sequencing data of this study are available at the Sequence Read Archive (PRJNA596330) of the NCBI. The original fluorescent microscopy image data are available at https://doi.org/10.6084/m9.figshare.12016785.v1.
The source codes for the base-editing prediction model are available at https://github.com/yachielab/base-editing-prediction. The other codes used in this study are available upon request.
Mali, P. et al. RNA-guided human genome engineering via Cas9. Science 339, 823–826 (2013).
Cong, L. et al. Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819–823 (2013).
Rees, H. A. & Liu, D. R. Base editing: precision chemistry on the genome and transcriptome of living cells. Nat. Rev. Genet. 19, 770–788 (2018).
Nishida, K. et al. Targeted nucleotide editing using hybrid prokaryotic and vertebrate adaptive immune systems. Science 353, aaf8729 (2016).
Komor, A. C., Kim, Y. B., Packer, M. S., Zuris, J. A. & Liu, D. R. Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage. Nature 533, 420–424 (2016).
Gaudelli, N. M. et al. Programmable base editing of A·T to G·C in genomic DNA without DNA cleavage. Nature 551, 464–471 (2017).
Koblan, L. W. et al. Improving cytidine and adenine base editors by expression optimization and ancestral reconstruction. Nat. Biotechnol. 36, 843–846 (2018).
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).
Kleinstiver, B. P. et al. High-fidelity CRISPR–Cas9 nucleases with no detectable genome-wide off-target effects. Nature 529, 490–495 (2016).
Grunewald, J. et al. Transcriptome-wide off-target RNA editing induced by CRISPR-guided DNA base editors. Nature 569, 433–437 (2019).
Grunewald, J. et al. CRISPR DNA base editors with reduced RNA off-target and self-editing activities. Nat. Biotechnol. 37, 1041–1048 (2019).
Zhou, C. et al. Off-target RNA mutation induced by DNA base editing and its elimination by mutagenesis. Nature 571, 275–278 (2019).
Rees, H. A., Wilson, C., Doman, J. L. & Liu, D. R. Analysis and minimization of cellular RNA editing by DNA adenine base editors. Sci. Adv. 5, eaax5717 (2019).
Shen, M. W. et al. Predictable and precise template-free CRISPR editing of pathogenic variants. Nature 563, 646–651 (2018).
Allen, F. et al. Predicting the mutations generated by repair of Cas9-induced double-strand breaks. Nat. Biotechnol. 37, 64–72 (2019).
Chen, W. et al. Massively parallel profiling and predictive modeling of the outcomes of CRISPR/Cas9-mediated double-strand break repair. Nucleic Acids Res. 47, gkz487 (2019).
Landrum, M. J. et al. ClinVar: public archive of interpretations of clinically relevant variants. Nucleic Acids Res. 44, D862–D868 (2016).
Hess, G. T. et al. Directed evolution using dCas9-targeted somatic hypermutation in mammalian cells. Nat. Methods 13, 1036–1042 (2016).
Masuyama, N., Mori, H. & Yachie, N. DNA barcodes evolve for high-resolution cell lineage tracing. Curr. Opin. Chem. Biol. 52, 63–71 (2019).
Woodworth, M. B., Girskis, K. M. & Walsh, C. A. Building a lineage from single cells: genetic techniques for cell lineage tracking. Nat. Rev. Genet. 18, 230–244 (2017).
Salvador-Martinez, I., Grillo, M., Averof, M. & Telford, M. J. Is it possible to reconstruct an accurate cell lineage using CRISPR recorders? eLife 8, e40292 (2019).
Doench, J. et al. Rational design of highly active sgRNAs for CRISPR-Cas9–mediated gene inactivation. Nat. Biotechnol. 32, 1262–1267 (2014).
Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinform. 10, 421 (2009).
Magoc, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).
Rice, P., Longden, I. & Bleasby, A. EMBOSS: the European Molecular Biology Open Software Suite. Trends Genet. 16, 276–277 (2000).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).
We thank members of the Yachie lab for useful discussions and critical assessment of this work, especially A. Adel for reviewing the manuscript. We also thank K. Shiina, Y. Takai and N. Ishii for technical supports of high-throughput sequencing. This study was mainly funded by the Uehara Memorial Foundation (to N.Y.), the NOVARTIS Foundation (Japan) for the Promotion of Science (to N.Y.), and the Japan Agency for Medical Research and Development (AMED) Platform Project for Supporting Drug Discovery and Life Science Research (to N.Y., H.N. and O.N.), and partly supported by the New Energy and Industrial Technology Development Organization (NEDO), AMED PRIME program (17gm6110007), the Japan Science and Technology Agency (JST) PRESTO program (10814), the Naito Foundation, the SECOM Science and Technology Foundation (all to N.Y.), the Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research (16J06287) (to S.I.) and research funds from the Yamagata Prefectural Government and Tsuruoka City, Japan (to K.A. and M. Tomita). S.I. was supported by a JSPS DC1 Fellowship; S.I., H.M. and N.M. were supported by TTCK Fellowships; H.M. and N.M. were supported by the Mori Memorial Foundation; and N.M. was supported by the Yamagishi Student Project Support Program of Keio University.
K.N. and A.K. are shareholders and board members of BioPalette Co., Ltd.
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Integrated supplementary information
Developmental lineages of single- and dual-function base editors used in this study are represented by arrows. Base editor mix controls for dual-function base editors are indicated by dashed lines.
a, Schematic representation of the C→T base-editing reporter. C→T base editing of the antisense strand followed by DNA replication restores the translation of EGFP by converting a mutated start codon GTG (valine) to ATG (methionine). b, Schematic representation of the A→G base-editing reporter. A→G base editing of the antisense strand followed by DNA replication converts the stop codon, TAA, to CAA (glutamine) releases the translation of its downstream EGFP. c, Microscopy images of the positive control cells for C→T and A→G base-editing reporters transiently transfected with different base editor reagents and non-targeting (NT) gRNAs. Scale bar, 40 µm. d, Frequency of start codon restoration in C→T editing reporter cells. Each bar shows the mean of three independent transfection experiments represented by dots. e, Frequency of stop codon destruction in A→G editing reporter cells. f, Frequency of amplicon sequencing reads showing C→T editing at any position of the gRNA target site of C→T editing reporter cells (from –30 to +10 bp relative to the PAM). g, Frequency of amplicon sequencing reads showing A→G editing at any position of the gRNA target site of A→G editing reporter cells (from –30 to +10 bp relative to the PAM).
Editing frequencies of EMX1 site 1 and FANCF site 1 and site 2 and their corresponding off-target sites. Amplicon sequencing experiments were performed in triplicate.
a, Schematic diagram of the model to predict the frequencies of each base-editing outcome. In brief, to train a given base editor model using a training amplicon sequencing dataset for different target sites, probabilities of single base transition events and their conditional probabilities given each of the other single events are thoroughly calculated for different positions relative to the PAM. The frequency of a given editing outcome in a new test target site is then predicted as a geometric mean of probabilities of base transitions at all edited positions, each given by the other independent base transition patterns. b, Correlation of measured and predicted relative editing outcome frequencies in the 5-fold cross-validation experiment.
Extended Data Fig. 5 Heterologous trinucleotide co-editing frequencies predicted by the computational model.
To predict the multidimensional co-editing spectra of the different base-editing methods using the base-editing prediction model, 100 synthetic target sequences consisting of only cytosine and/or adenine bases in the region from −20 to −1 bp relative to the PAM were generated in silico. For each target sequence, all possible outcomes with C→T and/or A→G edits (220 outcomes in total) were predicted using the base-editing prediction model trained from all 47 amplicon sequencing data. The average homologous trinucleotide-editing spectra shown by the bubble charts were then calculated using all predicted frequencies.
Extended Data Fig. 6 Codon convertibility matrices (CCMs) of single-function base editors without allowing bystander mutations to occur.
For each codon in the human genome (hg38), possible gRNA target sites were first screened in the area of ±25 bp. For all gRNAs, base-editing outcome probabilities of all possible C→T and/or A→G editing patterns in the ±15 bp region of the target codon were predicted using the base-editing prediction model trained by the amplicon sequencing data for all 47 genomic sites. The conversion potential of the target source codon to each destination codon without allowing bystander mutations to occur was then defined as the maximum probability of generating the target outcome among those induced by all possible gRNAs. After calculating conversion potentials to different destination codons for all genomic codons, a CCM was generated to show the genome-wide frequency of each source-destination codon conversion type with a conversion potential threshold of 5%.
Codon convertibility matrices (CCMs) of base editor mixes and dual-function base editors without allowing bystander mutations to occur.
Codon conversion matrices (CCMs) of single-function base editors with allowing bystander mutations to occur.
Codon conversion matrices (CCMs) of base editor mixes and dual-function base editors with allowing bystander mutations to occur.
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Sakata, R.C., Ishiguro, S., Mori, H. et al. Base editors for simultaneous introduction of C-to-T and A-to-G mutations. Nat Biotechnol 38, 865–869 (2020). https://doi.org/10.1038/s41587-020-0509-0
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