Current tools for targeted RNA editing rely on the delivery of exogenous proteins or chemically modified guide RNAs, which may lead to aberrant effector activity, delivery barrier or immunogenicity. Here, we present an approach, called leveraging endogenous ADAR for programmable editing of RNA (LEAPER), that employs short engineered ADAR-recruiting RNAs (arRNAs) to recruit native ADAR1 or ADAR2 enzymes to change a specific adenosine to inosine. We show that arRNA, delivered by a plasmid or viral vector or as a synthetic oligonucleotide, achieves editing efficiencies of up to 80%. LEAPER is highly specific, with rare global off-targets and limited editing of non-target adenosines in the target region. It is active in a broad spectrum of cell types, including multiple human primary cell types, and can restore α-l-iduronidase catalytic activity in Hurler syndrome patient-derived primary fibroblasts without evoking innate immune responses. As a single-molecule system, LEAPER enables precise, efficient RNA editing with broad applicability for therapy and basic research.
Subscribe to Journal
Get full journal access for 1 year
only $21.58 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
All data presented in this manuscript are available from the corresponding author upon reasonable request. Transcriptome-wide RNA-seq data are accessible via the NCBI Sequence Read Archive database with accession code PRJNA544353.
Porteus, M. H. & Carroll, D. Gene targeting using zinc finger nucleases. Nat. Biotechnol. 23, 967–973 (2005).
Boch, J. et al. Breaking the code of DNA binding specificity of TAL-type III effectors. Science 326, 1509–1512 (2009).
Moscou, M. J. & Bogdanove, A. J. A simple cipher governs DNA recognition by TAL effectors. Science 326, 1501 (2009).
Miller, J. C. et al. A TALE nuclease architecture for efficient genome editing. Nat. Biotechnol. 29, 143–148 (2011).
Jinek, M. et al. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337, 816–821 (2012).
Cong, L. et al. Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819–823 (2013).
Mali, P. et al. RNA-guided human genome engineering via Cas9. Science 339, 823–826 (2013).
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).
Ma, Y. et al. Targeted AID-mediated mutagenesis (TAM) enables efficient genomic diversification in mammalian cells. Nat. Methods 13, 1029–1035 (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).
Tan, M. H. et al. Dynamic landscape and regulation of RNA editing in mammals. Nature 550, 249–254 (2017).
Nishikura, K. Functions and regulation of RNA editing by ADAR deaminases. Annu. Rev. Biochem. 79, 321–349 (2010).
Bass, B. L. & Weintraub, H. An unwinding activity that covalently modifies its double-stranded RNA substrate. Cell 55, 1089–1098 (1988).
Wong, S. K., Sato, S. & Lazinski, D. W. Substrate recognition by ADAR1 and ADAR2. RNA 7, 846–858 (2001).
Montiel-Gonzalez, M. F., Vallecillo-Viejo, I., Yudowski, G. A. & Rosenthal, J. J. Correction of mutations within the cystic fibrosis transmembrane conductance regulator by site-directed RNA editing. Proc. Natl Acad. Sci. USA 110, 18285–18290 (2013).
Sinnamon, J. R. et al. Site-directed RNA repair of endogenous Mecp2 RNA in neurons. Proc. Natl Acad. Sci. USA 114, E9395–E9402 (2017).
Montiel-Gonzalez, M. F., Vallecillo-Viejo, I. C. & Rosenthal, J. J. An efficient system for selectively altering genetic information within mRNAs. Nucleic Acids Res. 44, e157 (2016).
Hanswillemenke, A., Kuzdere, T., Vogel, P., Jekely, G. & Stafforst, T. Site-directed RNA editing in vivo can be triggered by the light-driven assembly of an artificial riboprotein. J. Am. Chem. Soc. 137, 15875–15881 (2015).
Schneider, M. F., Wettengel, J., Hoffmann, P. C. & Stafforst, T. Optimal guideRNAs for re-directing deaminase activity of hADAR1 and hADAR2 in trans. Nucleic Acids Res. 42, e87 (2014).
Vogel, P., Hanswillemenke, A. & Stafforst, T. Switching protein localization by site-directed RNA editing under control of light. ACS Synth. Biol. 6, 1642–1649 (2017).
Vogel, P., Schneider, M. F., Wettengel, J. & Stafforst, T. Improving site-directed RNA editing in vitro and in cell culture by chemical modification of the guideRNA. Angew. Chemie 53, 6267–6271 (2014).
Vogel, P. et al. Efficient and precise editing of endogenous transcripts with SNAP-tagged ADARs. Nat. Methods 15, 535–538 (2018).
Cox, D. B. T. et al. RNA editing with CRISPR-Cas13. Science 358, 1019–1027 (2017).
Fukuda, M. et al. Construction of a guide-RNA for site-directed RNA mutagenesis utilising intracellular A-to-I RNA editing. Sci. Rep. 7, 41478 (2017).
Wettengel, J., Reautschnig, P., Geisler, S., Kahle, P. J. & Stafforst, T. Harnessing human ADAR2 for RNA repair—recoding a PINK1 mutation rescues mitophagy. Nucleic Acids Res. 45, 2797–2808 (2017).
Heep, M., Mach, P., Reautschnig, P., Wettengel, J. & Stafforst, T. Applying human ADAR1p110 and ADAR1p150 for site-directed RNA editing-G/C substitution stabilizes guideRNAs against editing. Genes 8, E34 (2017).
Katrekar, D. et al. In vivo RNA editing of point mutations via RNA-guided adenosine deaminases. Nat. Methods 16, 239–242 (2019).
Yin, H., Kauffman, K. J. & Anderson, D. G. Delivery technologies for genome editing. Nat. Rev. Drug Discov. 16, 387–399 (2017).
Platt, R. J. et al. CRISPR-Cas9 knockin mice for genome editing and cancer modeling. Cell 159, 440–455 (2014).
Chew, W. L. et al. A multifunctional AAV-CRISPR-Cas9 and its host response. Nat. Methods 13, 868–874 (2016).
Teoh, P. J. et al. Aberrant hyperediting of the myeloma transcriptome by ADAR1 confers oncogenicity and is a marker of poor prognosis. Blood 132, 1304–1317 (2018).
Vallecillo-Viejo, I. C., Liscovitch-Brauer, N., Montiel-Gonzalez, M. F., Eisenberg, E. & Rosenthal, J. J. C. Abundant off-target edits from site-directed RNA editing can be reduced by nuclear localization of the editing enzyme. RNA Biol. 15, 104–114 (2018).
Mays, L. E. & Wilson, J. M. The complex and evolving story of T cell activation to AAV vector-encoded transgene products. Mol. Ther. 19, 16–27 (2011).
Wagner, D. L. et al. High prevalence of Streptococcus pyogenes Cas9-reactive T cells within the adult human population. Nat. Med. 25, 242–248 (2019).
Simhadri, V. L. et al. Prevalence of pre-existing antibodies to CRISPR-associated nuclease Cas9 in the USA population. Mol. Ther. Methods Clin. Dev. 10, 105–112 (2018).
Charlesworth, C. T. et al. Identification of preexisting adaptive immunity to Cas9 proteins in humans. Nat. Med. 25, 249–254 (2019).
Haapaniemi, E., Botla, S., Persson, J., Schmierer, B. & Taipale, J. CRISPR-Cas9 genome editing induces a p53-mediated DNA damage response. Nat. Med. 24, 927–930 (2018).
Ihry, R. J. et al. p53 inhibits CRISPR-Cas9 engineering in human pluripotent stem cells. Nat. Med. 24, 939–946 (2018).
Woolf, T. M., Chase, J. M. & Stinchcomb, D. T. Toward the therapeutic editing of mutated RNA sequences. Proc. Natl Acad. Sci. USA 92, 8298–8302 (1995).
Merkle, T. et al. Precise RNA editing by recruiting endogenous ADARs with antisense oligonucleotides. Nat. Biotechnol. 37, 133–138 (2019).
Zheng, Y., Lorenzo, C. & Beal, P. A. DNA editing in DNA/RNA hybrids by adenosine deaminases that act on RNA. Nucleic Acids Res. 45, 3369–3377 (2017).
East-Seletsky, A. et al. Two distinct RNase activities of CRISPR-C2c2 enable guide-RNA processing and RNA detection. Nature 538, 270–273 (2016).
Daniel, C., Widmark, A., Rigardt, D. & Ohman, M. Editing inducer elements increases A-to-I editing efficiency in the mammalian transcriptome. Genome Biol. 18, 195 (2017).
Chen, C. X. et al. A third member of the RNA-specific adenosine deaminase gene family, ADAR3, contains both single- and double-stranded RNA binding domains. RNA 6, 755–767 (2000).
Savva, Y. A., Rieder, L. E. & Reenan, R. A. The ADAR protein family. Genome Biol. 13, 252 (2012).
Nishikura, K. A-to-I editing of coding and non-coding RNAs by ADARs. Nat. Rev. Mol. Cell Biol. 17, 83–96 (2016).
Landrum, M. J. et al. ClinVar: public archive of interpretations of clinically relevant variants. Nucleic Acids Res. 44, D862–D868 (2016).
Floquet, C., Deforges, J., Rousset, J. P. & Bidou, L. Rescue of non-sense mutated p53 tumor suppressor gene by aminoglycosides. Nucleic Acids Res. 39, 3350–3362 (2011).
Kern, S. E. et al. Identification of p53 as a sequence-specific DNA-binding protein. Science 252, 1708–1711 (1991).
Doubrovin, M. et al. Imaging transcriptional regulation of p53-dependent genes with positron emission tomography in vivo. Proc. Natl Acad. Sci. USA 98, 9300–9305 (2001).
Ou, L. et al. ZFN-mediated in vivo genome editing corrects murine Hurler syndrome. Mol. Ther. 27, 178–187 (2019).
Fire, A. et al. Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans. Nature 391, 806–811 (1998).
Zuo, E. et al. Cytosine base editor generates substantial off-target single-nucleotide variants in mouse embryos. Science 364, 289–292 (2019).
Jin, S. et al. Cytosine, but not adenine, base editors induce genome-wide off-target mutations in rice. Science 364, 292–295 (2019).
Kim, D., Kim, D. E., Lee, G., Cho, S. I. & Kim, J. S. Genome-wide target specificity of CRISPR RNA-guided adenine base editors. Nat. Biotechnol. 37, 430–435 (2019).
Grunewald, J. et al. Transcriptome-wide off-target RNA editing induced by CRISPR-guided DNA base editors. Nature 569, 433–437 (2019).
Gibson, D. G. et al. Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat. Methods 6, 343–345 (2009).
Zhou, Y., Zhang, H. & Wei, W. Simultaneous generation of multi-gene knockouts in human cells. FEBS Lett. 590, 4343–4353 (2016).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Van der Auwera, G. A. et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinformatics 43, 11.10.1–33 (2013).
Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).
Genomes Project, C. et al. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).
Pertea, M., Kim, D., Pertea, G. M., Leek, J. T. & Salzberg, S. L. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat. Protoc. 11, 1650–1667 (2016).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
We acknowledge the staff of the BIOPIC High-throughput Sequencing Center (Peking University) and Genetron Health for their assistance in NGS analysis, the National Center for Protein Sciences (Beijing) and the core facilities at the School of Life Sciences (Peking University, X. Zhang, F. Wang and L. Du) for help with Fluorescence Activated Cell Sorting. We thank the High-Performance Computing Platform at Peking University for providing platforms of NGS data analysis. We thank M. Mo for technical assistance, J. Wang for providing plasmids encoding disease-relevant genes and primary cells and we also thank Z. Jiang for providing the mouse melanoma cell line B16. This project was supported by funds from Beijing Municipal Science & Technology Commission (grant no. Z181100001318009), the National Science Foundation of China (no. 31430025), Beijing Advanced Innovation Center for Genomics at Peking University and the Peking-Tsinghua Center for Life Sciences (to W.W.); the National Science Foundation of China (no. 31870893) and the National Major Science & Technology Project for Control and Prevention of Major Infectious Diseases in China (no. 2018ZX10301401, to Z.Z.) and the Beijing Nova Program (no. Z181100006218042, to P.Y.).
A patent has been filed relating to the data presented. W.W. is a founder andscientific adviser for EdiGene.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Integrated supplementary information
(a) Schematic of dLbuCas13a-ADAR1DD (E1008Q) fusion protein and the corresponding crRNA. The catalytic inactive LbuCas13a was fused to the deaminase domain of ADAR1 (hyperactive E1008Q variant) using 3× GGGGS linker. The crRNA (crRNACas13a) consisted of Lbu-crRNA scaffold and a spacer which was complementary to the targeting RNA with an A-C mismatch as indicated. (b) Schematic of dual fluorescent reporter system and the Lbu-crRNA with various lengths of spacers as indicated. (c) Quantification of the EGFP positive (EGFP+) cells. HEK293T cells stably expressing the Repoter-1 were transfected with indicated lengths of crRNACas13a, with or without co-expression of the dLbuCas13a-ADAR1DD (E1008Q), followed by FACS analysis. (d) Representative FACS result from the experiment performed with the Ctrl crRNACas13a (70-nt random spacer) or crRNACas13a (70-nt targeting spacer). (e) Representative FACS result from the experiment performed with REPAIR. Left, the Ctrl crRNACas13b (70-nt random spacer) or crRNACas13b (70-nt targeting spacer) was co-transfected with or without dPspCas13b-ADAR2DD-E488Q into HEK293T cells, which stably express Reporter-1. Right, the Ctrl crRNACas13b (70-nt random spacer) or crRNACas13b (70-nt targeting spacer) was co-transfected with Reporter-1 into HEK293T ADAR1−/− cells, in the presence or absence of dPspCas13b-ADAR2DD-E488Q. The 70-nt random or targeting spacer was fused to the 3′-end of PspCas13b-crRNA scaffold. The RNA editing effects were quantified by the percentages of EGFP+ cells. Data are presented as the mean ± s.e.m. (n = 3, n represents the number of independent experiments performed in parallel).
(a) Quantitative PCR showing the mRNA levels of ADAR1 and ADAR2 in HEK293T cells. Data are presented as the mean ± s.e.m. (n = 3, n represents the number of independent experiments performed in parallel). (b) Representative FACS results from Fig. 1e.
Supplementary Figure 3 Quantitative PCR showing the effects of LEAPER on the expression levels of targeted Reporter-1 transcripts by 111-nt arRNA or control RNA in HEK293T cells.
Data are presented as the mean ± s.e.m. (n = 3, n represents the number of independent experiments performed in parallel); unpaired two-sided Student’s t-test, ns, not significant.
(a) Western-blot results showing the expression levels of ADAR1, ADAR2 and ADAR3 in indicated human cell lines. β-tubulin was used as a loading control. Data shown is the representative of three independent experiments. ADAR1−/−/ADAR2 represents ADAR1-knockout HEK293T cells overexpressing ADAR2. (b) Relative ADAR protein expression levels normalized by β-tubulin expression. (c) Indicated human cells were transfected with Reporter-1, along with the 71-nt control arRNA (Ctrl RNA71) or with the 71-nt targeting arRNA (arRNA71) followed by FACS analysis. (d) Indicated mouse cell lines were analyzed as described in (c). EGFP+ percentages were normalized by transfection efficiency, which was determined by mCherry+. Error bars in (b, c, d) all indicate the mean ± s.e.m. (n = 3, n represents the number of independent experiments performed in parallel); unpaired two-sided Student’s t-test, *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001; ns, not significant.
Schematic of Reporter-1 (a), -2 (b), and -3 (c), as well as their corresponding arRNAs.
Supplementary Figure 6 Effects of LEAPER on the expression levels of targeted transcripts and protein products.
(a) Quantitative PCR showing the expression levels of targeted transcripts from PPIB, KRAS, SMAD4 and FANCC by the corresponding 151-nt arRNA or Control RNA in HEK293T cells. Data are presented as the mean ± s.e.m. (n = 3, n represents the number of independent experiments performed in parallel); unpaired two-sided Student’s t-test, *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001; ns, not significant. (b) Western blot results showing the effects on protein products of targeted KRAS gene by 151-nt arRNA in HEK293T cells. β-tubulin was used as a loading control.
(a) Schematic of the KARS transcript sequence covered by the 151-nt arRNA. The arrow indicates the targeting adenosine. All adenosines were marked in red. (b) Heatmap of editing rate on adenosines covered by indicated arRNAs in the KARS transcript (marked in the bold frame in blue). (c) Schematic of the SMAD4 transcript covered by the 151-nt arRNA. (d) Heatmap of editing rate on adenosines covered by indicated arRNAs in the SMAD4 transcript. (e) Schematic of the FANCC transcript covered by the 151-nt arRNA. (f) Heatmap of editing rate on adenosines covered by indicated arRNAs in the FANCC transcript. For each arRNA, the region of duplex RNA is highlighted with bold frame in blue. Data (b, d, and f) are presented as the mean (n = 3, n represents the number of independent experiments performed in parallel).
(a) Top, schematic of the highly complementary region between arRNA151-PPIB and the indicated potential off-target sites, which were predicted by searching homologous sequences through NCBI-BLAST. Bottom, Deep sequencing showing the editing rate on the on-target site and all predicted off-target sites of arRNA151-PPIB. Data are presented as the mean ± s.e.m. (n = 3, n represents the number of independent experiments performed in parallel). (b) Schematic of the highly complementary region of arRNA111-FANCC and the indicated potential off-target sequence, which were predicted by searching homologous sequences through NCBI-BLAST. (c) Deep sequencing showing the editing rate on the on-target site and all predicted off-target sites of arRNA111-FANCC. All data are presented as the mean ± s.e.m. (n = 3, n represents the number of independent experiments performed in parallel).
Supplementary Figure 9 Differential gene expression analysis with RNA-seq data at the transcriptome level.
Left, differential gene expression analysis between Ctrl RNA151 and Mock. Middle, differential gene expression analysis between arRNA151-PPIB and Mock. Right, differential gene expression analysis between arRNA151-PPIB and Ctrl RNA151. The DESeq2 (version 1.18.1) tool was used to analyze the differential gene expression with the FPKM expression data. Genes with adjusted P value smaller than 0.01 and log2(fold change) larger than 2 were viewed as significantly differentially expressed genes, labelled in red. Genes that were not differentially expressed were labelled in grey. The targeted gene PPIB was labelled in blue. Four independent experiments were performed.
(a and b) Effect of arRNA transfection on innate immune response. The indicated arRNAs or the poly(I:C) were transfected into HEK293T cells. Total RNA was then analyzed using quantitative PCR to determine expression levels of IFN-β (a) and IL-6 (b). Data (a and b) are presented as the mean ±s.e.m. (n = 3, n represents the number of independent experiments performed in parallel).
(a) Schematic representation of the selected disease-relevant cDNA containing G to A mutation from ClinVar data and the corresponding 111-nt arRNA. (b) A to I correction of disease-relevant G>A mutation from ClinVar data by the corresponding 111-nt arRNA, targeting clinical-related mutations from six pathogenic genes as indicated (Supplementary Tables 2 and 4). Data are presented as the mean ± s.e.m. (n = 3, n represents the number of independent experiments performed in parallel); unpaired two-sided Student’s t-test, *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001; ns, not significant.
Top, schematic of the TP53 transcript sequence covered by the 111-nt arRNAs. The arrow indicates the targeted adenosine. All adenosines were marked in red. Bottom, a heatmap of editing rate on adenosines covered by indicated arRNAs in the TP53 transcript. Data are presented as the mean ± s.e.m. (n = 3, n represents the number of independent experiments performed in parallel).
Supplementary Figs. 1–12
Cas13 crRNA sequences
Sequences of arRNAs and control RNAs used in this study
Differential gene expression results
Disease-relevant cDNAs used in this study
Primers used in this study
About this article
Cite this article
Qu, L., Yi, Z., Zhu, S. et al. Programmable RNA editing by recruiting endogenous ADAR using engineered RNAs. Nat Biotechnol 37, 1059–1069 (2019). https://doi.org/10.1038/s41587-019-0178-z
Advanced Science (2020)
Protein & Cell (2020)
Nucleic Acids Research (2020)
Nucleic Acid Therapeutics (2020)