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Off-target RNA mutation induced by DNA base editing and its elimination by mutagenesis

Nature volume 571, pages 275–278 (2019)Cite this article

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Abstract

Recently developed DNA base editing methods enable the direct generation of desired point mutations in genomic DNA without generating any double-strand breaks1,2,3, but the issue of off-target edits has limited the application of these methods. Although several previous studies have evaluated off-target mutations in genomic DNA4,5,6,7,8, it is now clear that the deaminases that are integral to commonly used DNA base editors often bind to RNA9,10,11,12,13. For example, the cytosine deaminase APOBEC1—which is used in cytosine base editors (CBEs)—targets both DNA and RNA12, and the adenine deaminase TadA—which is used in adenine base editors (ABEs)—induces site-specific inosine formation on RNA9,11. However, any potential RNA mutations caused by DNA base editors have not been evaluated. Adeno-associated viruses are the most common delivery system for gene therapies that involve DNA editing; these viruses can sustain long-term gene expression in vivo, so the extent of potential RNA mutations induced by DNA base editors is of great concern14,15,16. Here we quantitatively evaluated RNA single nucleotide variations (SNVs) that were induced by CBEs or ABEs. Both the cytosine base editor BE3 and the adenine base editor ABE7.10 generated tens of thousands of off-target RNA SNVs. Subsequently, by engineering deaminases, we found that three CBE variants and one ABE variant showed a reduction in off-target RNA SNVs to the baseline while maintaining efficient DNA on-target activity. This study reveals a previously overlooked aspect of off-target effects in DNA editing and also demonstrates that such effects can be eliminated by engineering deaminases.

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Fig. 1: Base editors induce numerous off-target RNA SNVs.
Fig. 2: Characterization of off-target RNA SNVs.
Fig. 3: Single-cell RNA SNV analysis of cells transfected with base editors.
Fig. 4: Elimination of off-target RNA SNVs by engineering of deaminases.

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Data availability

All the sequencing data have been deposited in the NCBI SRA under project accession numbers PRJNA528149 and PRJNA528561 or at http://www.biosino.org/node/project/detail/OEP000277. All materials are available upon reasonable request.

Code availability

The authors declare that all code used in this study are available within the article and its Extended Data or from the corresponding author upon reasonable request.

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Acknowledgements

We thank M. Poo for discussions and comments on this manuscript; D. Li for discussions; and FACS facility H. Wu and L. Quan in the Institute of Neuroscience (ION) and M. Zhang in the Institute Pasteur of Shanghai (IPS), L. Yuan in Big Data Platform, Shanghai Institutes for Biological Sciences (SIBS) and Chinese Academy of Sciences (CAS). This work was supported by R&D Program of China (2017YFC1001302, 2018YFC2000100, 2018YFA0107701, and 2018YFC1003401), CAS Strategic Priority Research Program (XDB32060000), National Natural Science Foundation of China (31871502, 31522037, 31822035, 31822035, 31771590), Shanghai Municipal Science and Technology Major Project (2018SHZDZX05), Shanghai City Committee of Science and Technology Project (18411953700, 18JC1410100), National Science and Technology Major Project (2015ZX10004801-005) and National Key Research and Development Program of China (2017YFA0505500, 2016YFC0901704).

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Author notes

  1. These authors contributed equally: Changyang Zhou, Yidi Sun, Rui Yan, Yajing Liu, Erwei Zuo

Authors and Affiliations

  1. Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China

    Changyang Zhou, Erwei Zuo, Linxiao Han, Yu Wei, Xinde Hu, Haibo Zhou & Hui Yang

  2. College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China

    Changyang Zhou, Yidi Sun, Yajing Liu & Xinde Hu

  3. CAS Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China

    Yidi Sun & Rong Zeng

  4. Bio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China

    Yidi Sun

  5. Center for Translational Medicine, Ministry of Education Key Laboratory of Birth Defects and Related Diseases of Women and Children, Department of Obstetrics and Gynecology, West China Second University Hospital, College of Life Sciences, Sichuan University, Chengdu, China

    Rui Yan, Chan Gu, Yixue Li & Fan Guo

  6. School of Life Science and Technology, Shanghai Tech University, Shanghai, China

    Yajing Liu, Rong Zeng & Yixue Li

  7. Center for Animal Genomics, Agricultural Genome Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China

    Erwei Zuo

  8. Shanghai Jiao Tong University, Fudan University, Shanghai Academy of Science & Technology, Shanghai, China

    Yixue Li

Authors
  1. Changyang Zhou
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Contributions

C.Z. and H.Y. conceived the project. C.Z., Y. Liu, E.Z., L.H., Y.W., X.H. and H.Z. designed experiments, constructed plasmids and collected cells. Y.S., R.Z. and Y. Li performed bulk RNA-seq analysis. R.Y., C.G. and F.G. performed single-cell RNA-seq and analysis. H.Y. designed experiments and supervised the whole project. H.Z., Y.S. and H.Y. wrote the paper.

Corresponding authors

Correspondence to Yixue Li, Haibo Zhou, Fan Guo or Hui Yang.

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Extended data figures and tables

Extended Data Fig. 1 Schematics of plasmids.

The schematics show the plasmids used in this study.

Extended Data Fig. 2 Increased expression of deaminases induces an increase in off-target RNA SNVs.

a, Representative distributions of off-target RNA SNVs on human chromosomes for APOBEC1, BE3–site 3, BE3–RNF2, TadA–TadA*, ABE7.10–site 1 and ABE7.10–site 2. b, Schematics of BE3(FNLS) and ABEmax. Note that BE3(FNLS)31 and ABEmax32 have previously been reported to greatly increase the expression of base editors. c, Expression of APOBEC1 in cells transfected with BE3–site 3 for 36 or 72 h, or with BE3(FNLS)–site 3 for 72 h. d, Expression level of in cells transfected with ABE7.10–site 1 for 36 and 72 h, or with ABEmax–site 1 for 72 h. e, The number of off-target RNA SNVs in cells transfected with BE3–site 3 for 36 or 72 h, or with BE3(FNLS)–site 3 for 72 h. f, The number of off-target RNA SNVs in cells transfected with ABE7.10–site 1 for 36 or 72 h, or with ABEmax–site 1 for 72 h. Transfections: GFP for 36 h; BE3–site 3 for 36 h; GFP for 72 h; BE3–site 3 for 72 h; BE3(FNLS)–site 3 for 72 h; ABE7.10–site 1 for 36 h; ABE7.10–site 1 for 72 h; ABEmax–site 1 for 72 h. RSEM, RNA-seq by expectation maximization. All values are presented as mean ± s.e.m. Number above the bar indicates the number of biologically independent samples. *P < 0.05, **P < 0.01, ***P < 0.001, two-sided unpaired t-test. Exact P values are provided in Supplementary Table 15.

Extended Data Fig. 3 Mutation types and gene expression of off-target RNA SNVs.

a, Distribution of mutation types in each repeat of all groups. The number in each cell indicates the percentage of a certain type of mutation among all mutations. b, Expression of genes containing overlapping off-target RNA SNVs and random simulated genes in all groups transfected with BE3 (n = 8 biologically independent samples) or ABE7.10 (n = 9 biologically independent samples). Two-sided unpaired t-test. Box-and-whisker plots: centre line indicates median, box represents first and third quantiles, and whiskers indicate maximum and minimum values.

Extended Data Fig. 4 Characteristics of off-target RNA SNVs.

a, Similarity between adjacent sequences of off-target RNA SNVs and on-target sequences. n = 2 biologically independent samples for BE3–site 2, n = 3 for BE3–RNF2, n = 3 for ABE7.10–site 1, n = 3 for ABE7.10–site 2. b, Sanger sequencing chromatograms show that C to U mutation was observed only in RNA but not DNA for two BE3 off-target sites. OF, off-target. Gene names, amino acid mutations and single nucleotide conversions are indicated by blue, red and green, respectively. c, Sanger sequencing chromatograms show that U to C mutation was observed only in the RNA of three ABE7.10 off-target sites.

Extended Data Fig. 5 Biotypes and tumour-associated genes of off-target RNA SNVs.

a, Percentages of different locations of SNVs for GFP, BE3 (BE3, BE3–site 3 and BE3–RNF2) and ABE7.10 (ABE7.10, ABE7.10–site 1 and ABE7.10–site 2) groups. All values are presented as mean ± s.e.m. n denotes biologically independent samples. *P < 0.05, **P < 0.01, ***P < 0.001, two-sided unpaired t-test. Exact P values are provided in Supplementary Table 16. b, Editing rate of BE3-induced non-synonymous mutations located on oncogenes and tumour suppressor genes. c, Editing rate of ABE7.10-induced non-synonymous mutations located on oncogenes and tumour suppressor genes. Gene names, amino acid mutations and single nucleotide conversions are indicated by blue, red and green, respectively.

Extended Data Fig. 6 Expression of transfected vectors and mutation types of off-target RNA SNVs in single cells.

a, Expression of GFP, APOBEC1 and TadA–TadA* was quantified in all sequenced single cells. Thresholds are indicated by blue dashed lines. Thresholds of log2 (FPKM + 1) for GFP, BE3 and ABE7.10 are 0.3, 1 and 0.3, respectively. Cells with expression levels higher than the threshold were included for further analysis. b, c, Cells with high expression of TadA–TadA* or APOBEC1 showed greater numbers of RNA SNVs than those with low expressions in the ABE7.10 (n = 9 cells) or BE3 group (n = 4 cells), respectively. Box-and-whisker plots: centre line indicates median value, box represents first and third quantile, whisker indicates maximum and minimum values. d, Distribution of mutation types for GFP-transfected single cells (n = 16 cells). e, Distribution of mutation types for BE3–site 3-transfected single cells (n = 31 cells). Cells with expression of APOBEC1 higher than the threshold are included in the red square. f, Distribution of mutation types for ABE7.10–site 1-transfected single cells (n = 28 cells). Cells with expression of TadA–TadA* higher than the threshold are included in the red square. The number indicates the percentage of a certain type of mutation among all mutations. SC, single cell.

Extended Data Fig. 7 Distribution of off-target RNA SNVs on human chromosomes for all single cells with expression above thresholds.

a, Distribution of off-target RNA SNVs on human chromosomes for GFP-transfected single cells (n = 15 cells). b, Distribution of off-target RNA SNVs on human chromosomes for BE3–site 3-transfected single cells (n = 4 cells). c, Distribution of off-target RNA SNVs on human chromosomes for ABE7.10–site 1-transfected single cells (n = 9 cells).

Extended Data Fig. 8 Characteristics of off-target RNA SNVs in single cells.

a, b, The ratio of shared SNVs between any two samples in the same group or with predicted off-target sites by Cas-OFFinder. The proportion in each cell is calculated by the number of overlapping SNVs between two samples divided by the sample in the row. c, Editing rate of BE3-induced non-synonymous mutations on oncogenes and tumour suppressor genes in single cells. d, Editing rate of ABE7.10-induced non-synonymous mutations on oncogenes and tumour suppressor genes in single cells. Gene names, amino acid mutations and single nucleotide conversions are indicated by blue, red and green, respectively.

Extended Data Fig. 9 Characteristics of off-target RNA SNVs for engineered BE3 and ABE7.10 variants.

a, Schematic of BE3 and ABE7.10 variants. Point mutations are indicated by red lines. b, Representative distributions of off-target RNA SNVs on human chromosomes. c, Distribution of mutation types for each sample of the engineered variants of BE3 and ABE7.10. d, Ratio of shared RNA SNVs between any two samples in the engineered variants of BE3 and ABE7.10 or with off-target sites predicted by Cas-OFFinder. The proportion in each cell was calculated by the number of overlapping RNA SNVs between two samples divided by the number of RNA SNVs in the row.

Extended Data Fig. 10 DNA on-target and RNA off-target activities of different engineered variants.

a, Comparison of the width of editing windows between ABE7.10 and ABE7.10F148A. n = 3 biologically independent samples for each group. b, DNA on-target efficiency on site 1 of TadA–TadA*–Cas9n (wild-type TadA–evolved TadA heterodimer), TadAF148A–TadA*–Cas9n and TadAF148A–TadA*F148A–Cas9n. n = 3 biologically independent samples for each group. c, The number of RNA SNVs in the GFP, TadA–TadA*–Cas9n, TadAF148A–TadA*–Cas9n and TadAF148A–TadA*F148A–Cas9n groups. n = 3 biologically independent samples for each group. All values are presented as mean ± s.e.m. *P < 0.05, **P < 0.01, ***P < 0.001. Two-sided unpaired t-test. Exact P values are provided in Supplementary Table 17.

Supplementary information

Supplementary Information

This file contains Supplementary Tables 1-3 and Supplementary Tables 8-17.

Reporting Summary

Supplementary Table 4

SNVs located on oncogenes or tumour suppressor genes of BE3-transfected samples.

Supplementary Table 5

SNVs located on oncogenes or tumour suppressor genes of ABE7.10-transfected samples.

Supplementary Table 6

SNVs located on oncogenes or tumour suppressor genes of BE3-transfected single cells.

Supplementary Table 7

SNVs located on oncogenes or tumour suppressor genes of ABE7.10-transfected single cells.

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Zhou, C., Sun, Y., Yan, R. et al. Off-target RNA mutation induced by DNA base editing and its elimination by mutagenesis. Nature 571, 275–278 (2019). https://doi.org/10.1038/s41586-019-1314-0

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