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Single-strand deaminase-assisted editing for functional RNA manipulation

Nature Biotechnology (2026)Cite this article

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Abstract

Rewriting RNA information to alter function requires controllable tools to edit RNA sequences within a user-defined region. Here we report a single-strand deaminase-assisted platform for adjustable RNA information manipulation (AIM). AIM is composed of a loop-forming guide RNA bound to an RNA-targeting Cas protein and an evolved TadA. AIM induces a loop, flanked by paired regions, in the target RNA; the loop size can be adjusted to allow conversions of single and multiple bases. We evolve TadA to achieve A-to-I, C-to-U or simultaneous A+C editing in coding and noncoding regions. We apply AIM to suppress the ochre nonsense codon (UAA) in disease-relevant cell and animal models, in which the two As must be simultaneously edited to rewrite the coding information. Moreover, we use AIM to manipulate adjacent phosphorylation sites important for protein function. Collectively, AIM is a versatile platform for manipulating RNA information within user-defined regions, opening additional avenues for functional RNA modulation.

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Fig. 1: The design and implementation of AIM.
Fig. 2: AIM suppresses the ochre (UAA) nonsense codon in disease-relevant cells and a mouse model.
Fig. 3: Expanding the capacity of AIM to C-to-U editing.
Fig. 4: Simultaneous A+C dual-base editing.
Fig. 5: Manipulating single and adjacent phosphorylation sites with the AIM toolkit.
Fig. 6: Characterizing the specificity of AIM.

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Data availability

The sequence data generated in this study were deposited to the National Center for Biotechnology Information Gene Expression Omnibus under accession code GSE230140. Source data are provided with this paper.

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Acknowledgements

We thank the Optical Imaging Core Facility, National Center for Protein Sciences at Peking University for assistance with immunofluorescence experiments. We are grateful to Y. Luo, C. Shan and W. Gao for their help with sample preparation and data collection and to Y. Zhao (Indica Labs) for support with data analysis. We thank the National Center for Protein Sciences at Peking University, particularly G. Li and X. Zhang, for technical support with MGISEQ-2000 sequencing platforms and the Agilent 4150 TapeStation system. We also thank the Center for Quantitative Biology at Peking University for assistance with the ImageXpress Micro4 high-content imaging system and X. Li for help with data collection and analysis. We thank N. Luo and Q. Huang for sharing plasmids, Z. Wei for providing mouse serum and B. Lu, H. Sun and M. Zhang for the discussion. The data analysis was performed on the High-Performance Computing Platform at the School of Life Sciences, Peking University. This work was supported by the National Natural Science Foundation of China (22425071 and 22337001 to C.Y.), the Beijing Municipal Science and Technology Commission (Z231100002723005 to C.Y.) and the Ministry of Agriculture and Rural Affairs of China (NK2022010102 to C.Y.). This work was supported by the New Cornerstone Science Foundation through the XPLORER prize.

Author information

Author notes

  1. These authors contributed equally: Yuan Zhuang, Qingguo Zhu, Hao Wu, Xiangyue Lin.

Authors and Affiliations

  1. State Key Laboratory of Gene Function and Modulation Research, School of Life Sciences, Peking University, Beijing, China

    Yuan Zhuang, Qingguo Zhu, Ruoyu Shen, Yuhao Zhang, Haowei Meng & Chengqi Yi

  2. Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China

    Hao Wu, Yongchang Yan, Puze Geng, Rong Yang, Zhixin Lei & Chengqi Yi

  3. Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China

    Hao Wu, Xiangyue Lin, Yongchang Yan, Puze Geng, Rong Yang & Zhixin Lei

  4. School of Biological Sciences, University of Manchester, Manchester, UK

    Aidan Wang

  5. Modit Therapeutics Beijing Limited, Beijing, China

    Mingyao Cui

  6. NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract, Department of Obstetrics and Gynecology, the First Affiliated Hospital of Anhui Medical University, Hefei, China

    Huifen Xiang

  7. Beijing Advanced Center of RNA Biology (BEACON), Peking University, Beijing, China

    Chengqi Yi

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Contributions

C.Y. and Y.Z. conceptualized the project. Y.Z., Q.Z. and X.L. designed and performed the experiments with the assistance of Y.Y., P.G., R.Y., R.S., Y.Z. and A.W. H.W. analyzed all the sequencing data. Z.L., H.M., M.C. and H.X. participated in the design and interpretation of key experiments. C.Y., Y.Z., Q.Z., H.W. and X.L. wrote the manuscript. All the authors commented on and approved the paper.

Corresponding author

Correspondence to Chengqi Yi.

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Competing interests

A patent application has been filed by Peking University for the technology disclosed in this publication. C.Y. and Y.Z. are coinventors on a patent application describing the AIM system. The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Identification of TadA variants for programmable RNA editing and investigation of optimal parameters for LF-gRNA design.

a, Structure superposition of tRNA anticodon loop in SaTadA-tRNA complex (PDB: 2B3J) and U-turn-shaped ssDNA substrate in ABE8e-sgRNA-DNA complex (PDB: 6VPC). b, Editing efficiencies of TadA variant fusion proteins with different LF-gRNAs. TadA variants include wild-type TadA (TadA-WT), TadA7.10, and TadA8e. c, Top: the representative on-target sequence in Reporter-UAG and the corresponding LF-gRNAs with diverse base-pairing arm lengths (ranging from 24 nt to 36 nt). Bottom: editing efficiencies of TadA7.10 and TadA8e fusion proteins with the aforementioned LF-gRNAs. n = 2 biological replicates. Data are presented as mean ± SD. d, Top: the representative on-target sequence in Reporter-UAG and the corresponding LF-gRNAs with non-symmetrical base-pairing arms. Bottom: editing efficiencies of TadA7.10 and TadA8e fusion proteins with different LF-gRNAs. n = 2 biological replicates. Data are presented as mean ± SD. e-f, Top: the representative on-target sequence in Reporter-UAG and the corresponding LF-gRNAs with different 3′ or 5′ arm length. Bottom: editing efficiencies of TadA8e fusion proteins with progressively shorten complementary 3′ arm (e) 5′ arm (f) and of the LF-gRNA.

Source data

Extended Data Fig. 2 Performance of AIM-A tools across different sequence contexts and different cell types.

a, Heatmap of 5′ and 3′ base preferences of AIM-Amax for all possible three-base motifs. b, Left: Representative on-target sequences of reporter constructs containing 6–10 consecutive adenosines. The LF-gRNA was used to induce loop sizes ranging from 6–10 nt on reporter transcripts. Right: Heatmap presenting editing efficiencies mediated by AIM-Amax. c. Schematic illustration showing the AIM editing window across LF-gRNA designs with varying loop sizes. d, EGFP + /mCherry+ ratio demonstrating the A-to-I editing activity of AIM-A and AIM-Amax across multiple cell lines. Cells were co-transfected with Reporter-UAG and AIM-A tools. e, Quantifying A-to-I editing rates on two endogenous transcripts mediated by AIM-Amax in N2A cells. f, Workflow to examine RNA editing rate, GAG content and Idua enzymatic activity in primary MEF cells from a Hurler syndrome mouse model containing homozygous Idua-W392X mutation (TGG-to-TAG). g, Quantification of A-to-I editing rates mediated by AIM-Amax in primary MEF cells. n = 3 independent biological replicates. Data are presented as mean ± SD. h, Histograms showing on-target and off-target A-to-I editing of the mouse Idua transcript using the AIM-Amax in primary MEF cells. Editing rates were quantified using NGS. i, The GAG content accumulation in WT, Idua-W392X and AIM-Amax-treated Idua-W392X primary MEF cells. Data represent mean values ± s.d. of n = 3 independently biological replicates. The P value was calculated by a two-tailed Student’s t-test. Statistical significance is denoted as follows: *P < 0.05, **P < 0.01, ***P < 0.001 and ****P < 0.0001. j, Measurement of Idua enzymatic activity in WT, Idua-W392X and AIM-Amax-treated Idua-W392X primary MEF cells. Data represent mean values ± s.d. of n = 3 independently biological replicates. The P value was calculated by a two-tailed Student’s t-test. Statistical significance is denoted as follows: *P < 0.05, **P < 0.01, ***P < 0.001 and ****P < 0.0001.

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Extended Data Fig. 3 Suppression of ochre nonsense codon in disease-relevant cells and a mouse model.

a, Normalized current-voltage relationships of HEK293T-CFTR-WT cells and HEK293T-CFTR-Y122X cells, showing total membrane currents at basal, stimulation, and inhibition states. HEK293T-CFTR-Y122X cells treated with CFTR-Y122X-targeting AIM-Amax were stimulated with 10 µM Forskolin and 10 µM IBMX, followed by perfusion with 10 µM CFTR inhibitor-172. HEK293T-CFTR-WT cells served as the positive control, while HEK293T-CFTR-Y122X cells treated with non-targeting AIM-Amax as the negative control. Data were collected from independent cells: n = 4. Data are presented as mean ± SEM. b, Schematics showing the structure of AAV constructs. c, In vivo RNA editing rates (UAA > UAG, UGA, and UGG) in AIM-Amax-treated DmdQ995X mice. Data represent mean values ± s.d. of n = 4 independent TA muscles. d, Western blot analysis of dystrophin protein expression in the TA muscles from WT, DmdQ995X, and AIM-Amax-treated DmdQ995X mice. e, H&E staining of TA muscles of WT, DmdQ995X, and AIM-Amax-treated DmdQ995X mice. Red arrows indicate inflammatory areas (shown in the wide view), and green arrows indicate normal myofibers without central nuclei (shown in the magnified view).

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Extended Data Fig. 4 Performance of AIM-C tools across different sequence contexts and different cell types.

a, Heatmap of 5′ and 3′ base preferences of AIM-Cmax for all possible three-base motifs. b, EGFP + /mCherry+ ratio demonstrating the C-to-U editing activity of AIM-C and AIM-Cmax across multiple cell lines. Cells were co-transfected with Reporter-CAC and AIM-C tools. c, Quantifying C-to-U editing rates on two endogenous transcripts mediated by AIM-Cmax in N2A cells. d, Comparison of AIM-Cmax and existing RNA C-to-U editing tools (RESCUE, RESCUE-S, and CURE-N) at three transcripts. Data are presented as mean values (n = 2) and each dot represents an individual value.

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Extended Data Fig. 5 Codon and amino acid changes enabled by AIM-A, AIM-C, and AIM-A&C.

a, Circos plots showing the specific codon changes enabled by AIM-A (left), AIM-C (middle), and AIM-A&C (right), respectively. AIM-A induces 61 different types of codon changes, 46 of which (orange, 75.4%) result in amino acid substitutions. AIM-C can induce 61 different types of codon change, with 43 (green, 70.5%) leading to amino acid changes. AIM-A&C can induce 30 different types of codon alterations, 29 of which (red, 97%) change amino acids. b, Tables summarizing the codon and amino acid changes enabled by AIM-A (left), AIM-C (middle), and AIM-A&C (right). Codon changes leading to different amino acids, induced by the AIM-A or AIM-C tools, are underlined in black. Codons uniquely altered by AIM-A&C are highlighted in green.

Extended Data Fig. 6 Manipulation of single or adjacent PTM sites.

a, Bar-plot showing the distribution of PTMs among various amino acid residues according to the dbPTM database. b, Quantification of concurrent editing rates at the second A of the Y340 codon and the second A of the Y341 codon in endogenous RAF1 transcripts using NGS. c, Quantification of concurrent editing rates at the second C of the S363 codon and the second C of the S364 codon in the endogenous BRAF transcripts using NGS. d, Quantification of concurrent editing rates at the second C of the first codon and the first A of the second codon in the endogenous CDK13 transcripts using NGS.

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Extended Data Fig. 7 Transcriptome-wide specificity of AIM.

a, Sequence context motif plots of identified A-to-I and C-to-U off-targets, showing the preferred binding sequence context for the REPAIRv1, REPAIRv2, AIM-A, AIM-Amax and AIM-A&Cmax systems. b, Jitter plot showing the transcriptome-wide A-to-I off-target events in WT, AIM-Amax-treated, and untreated Idua mutant primary MEF cells. AIM-Amax was delivered via AAV-DJ. c, Scatter plots showing the comparison of the global gene expression levels between control and AIM-treated primary MEF cells. Pearson’s correlation coefficient analysis was used to assess the differential gene expressions.

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Zhuang, Y., Zhu, Q., Wu, H. et al. Single-strand deaminase-assisted editing for functional RNA manipulation. Nat Biotechnol (2026). https://doi.org/10.1038/s41587-025-02956-7

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