Target-stabilized base editors enable robust high-fidelity RNA editing

Domain: chemistry
Published: 2026-02-25
Editorial outcome: major_revision

Abstract
RNA base editing using engineered deaminases represents a powerful tool to correct mutations at the RNA level. However, widespread off-target effects, primarily arising from dissociated free deaminases, remain a significant challenge. Here, we devise the RECODE (RNA editing with conditionally stable and enhanced ADAR1 deaminase variants) system, which employs designer degron-tagged ADAR1 deaminase (ADAR1d) with guide RNA (gRNA)-regulated stability. By promoting degradation of gRNA-unbound ADAR1d, RECODE markedly reduces transcriptome-wide edits while maintaining high on-target efficacy. Engineering gRNA for target RNA-induced conformational switching confines ADAR1d stabilization to intended editing sites, further enhancing editing precision. With structure-guided rational engineering of ADAR1d, RECODE efficiently corrects an Amyotrophic Lateral Sclerosis-relevant FUS mutation and installs a therapeutic mutation to Angptl3 in vivo, which mitigate FUS mislocalization to neuronal axons and lower plasma lipids, respectively. These findings establish RECODE as a highly stringent and efficient RNA editing technology and underscore a general principle for enhancing the specificity of RNA-guided protein effectors.

Introduction
Biological organisms rely not only on the faithful transmission of genetic information from DNA to RNA and subsequently to proteins, as described by the central dogma, but also engage in a variety of post-transcriptional processes that modify RNA molecules, including RNA sequences1–3. These include alternative RNA splicing4, 3′-end processing5, and RNA editing6,7, which collectively enhance molecular diversity and adaptation to external stimuli. Among metazoans, adenosine-to-inosine (A-to-I) conversion—catalyzed by double-stranded RNA-specific adenosine deaminases (ADARs)—represents the most prevalent form of RNA editing7. This modification is crucial for functions such as immune regulation8–10, and temperature adaptation11,12. Since inosine is read as guanosine by the cellular machinery13, and G-to-A mutations are the second most common type of pathogenic single-nucleotide variants (SNVs)14, ADAR-mediated RNA editing holds considerable promise for correcting disease-causing mutations.

Compared to CRISPR-based DNA editing techniques, RNA editing offers distinct advantages in clinical applications, such as reversibility and tunability, derived from the transient nature of RNA. These characteristics make RNA correction potentially safer and particularly beneficial in contexts requiring temporal pharmacodynamic control15. However, in contrast to the rapid development of DNA editing tools, considerably less effort has been devoted to creating programmable RNA base editing platforms.

In recent years, several strategies have emerged to direct ADAR deaminases to specific RNA sequences for site-directed RNA editing (SIDE). There are two main categories that these approaches fall into based on their reliance upon either endogenous ADARs or exogenous deaminases. In the first category, native ADAR1 and/or ADAR2 are recruited to target sites by a long double-stranded RNA (dsRNA) segment formed by target-guide hybrids16–18 or by structured ADAR-recruitment motifs19–22. While effective in several in vivo studies using animal models17,22–24, these strategies are constrained by the poorly understood principles of ADAR substrate selectivity25,26, in addition to the variable expression levels of ADARs across different tissues and cellular states27,28. The use of exogenous deaminases offers a solution to these constraints, with additional benefits such as an expandable base and context compatibility29,30. However, the widespread off-target effects from overexpressed deaminases18,22,31,32 and delivery challenges, particularly for CRISPR/Cas-based effectors33–37, limit their potential clinical applicability. Moreover, deaminase mutagenesis for enhanced precision often results in a trade-off with overall activity33,37.

Here, we present a strategy termed RECODE (RNA editing with conditionally stable and enhanced ADAR1 deaminase variants), which enables selective elimination of guide RNA (gRNA)-unbound ADAR1 deaminases (ADAR1d) that cause promiscuous edits, whilst maintaining robust on-target activity. We further enhanced editing precision by engineering gRNA for target RNA-induced conformational switching. In addition, we report that RECODE demonstrated significant therapeutic potential by effectively editing an Amyotrophic Lateral Sclerosis (ALS)-relevant mutation in the FUS gene and by installing a protective mutation in Angptl3 to lower plasma lipids in vivo.

Results
An engineered degradation tag, UDeg3a, has an increased dynamic range of responsiveness to Pepper

To eliminate dissociated free deaminase responsible for global off-target edits, we aimed to generate a conditional deaminase that becomes unstable when it escapes from the editing complex. We employed the recently developed Pepper-tDeg system, an RNA aptamer-regulated protein degradation tag38. In this system, tDeg serves as a C-terminal degron that renders the protein unstable unless bound to the RNA aptamer Pepper. We envisioned that fusion with tDeg might promote degradation of free deaminases, whilst the Pepper-bearing gRNA would stabilize these deaminases at editing sites.

Before implementing this design, we speculated that tDeg would need to be sufficiently potent to drive rapid degradation of its fusion partner. Thus, we began by optimizing tDeg using a fluorescent protein-based reporter assay. We fused modified tDegs to the C-terminus of mNeonGreen39, using mScarlet340, encoded by the same mRNA, as an internal control (Fig. 1a). A degradation/stabilization index was determined by the ratio of mNeonGreen+ cells to mScarlet3+ cells determined following a flow cytometry assay (Supplementary Fig. 1a–d).

According to the tripartite degron model, protein turnover is regulated by three key components: the E3 ligase recognition motif, an accessible acceptor lysine for ubiquitination, and a disordered degradation initiation site41. Beyond the E3 ligase binding motif, the accessibility of the acceptor lysine is crucial for degradation efficiency41,42. Therefore, we hypothesized that adding a ubiquitinatable lysine adjacent to tDeg would enhance proteolysis. Previous studies have shown that residues surrounding ubiquitinated lysines are enriched with aromatic and hydrophobic amino acids41, especially leucine43, and that these ubiquitination sites are typically close to the degron41. To enhance degradation, we introduced a stretch of seven leucines centered around a lysine residue immediately upstream of tDeg. We also substituted leucine at the +3 position relative to lysine with either glycine or asparagine (Fig. 1b), both of which were found to be overrepresented in ubiquitination peptides41. As expected, all three preferred ubiquitination sites (PUS) significantly promoted mNeonGreen degradation, generating a group of degrons we termed UDeg. For example, the proportion of mNeonGreen+ in mScarlet3+ cells decreased from 9.97% to 4.52% for tDeg and UDeg1c, respectively. Importantly, UDeg1a-1c were stabilized by Pepper to comparable expression levels (Fig. 1b, and Supplementary Fig. 1b). These findings suggest that the PUS facilitated but did not initiate protein degradation, prompting us to select UDeg1c for the next round of optimization.

Recent studies have identified numerous C-terminal degrons via high-throughput screening of the peptidome, revealing that proteins ending in -GG or -RxxxKG motifs were more depleted than other types in the library44,45. We mutated the terminal two-RG residues in UDeg1c to either -GG or -KG to generate UDeg2a and UDeg2b, respectively. Correspondingly, both UDeg2a and UDeg2b exhibited a lower basal expression level of mNeonGreen than UDeg1c, with UDeg2b being most effective (Fig. 1c and Supplementary Fig. 1c). Moreover, the mutation in UDeg2b did not affect the stabilizing effect of Pepper. Hence, we selected UDeg2b for subsequent experiments.

In addition to degron optimization, it would also be beneficial to enhance the stabilizing effect of Pepper. We investigated the use of a tat peptide from the Jembrana disease virus (JDV)46, previously shown to have a high binding affinity for the bovine immunodeficiency virus (BIV) TAR and its derivatives, such as Pepper47. Specifically, the terminal four residues (KIHY) of the tat peptide are responsible for stronger binding to TAR46. Based on UDeg2b, we designed two degrons, UDeg3a and UDeg3b, ending in -RIHYKG and -KIHYKG, respectively. Both degrons led to a significantly higher percentage of mNeonGreen+ cells in the presence of Pepper compared to the previous degron, up to 90.3%, indicating much stronger protection by Pepper. Ultimately, we selected UDeg3a over UDeg3b due to its relatively lower basal expression level (Fig. 1d and Supplementary Fig. 1d).

Collectively, we calculated the responsiveness of tDeg, UDeg1c, UDeg2b, and UDeg3a to Pepper in each step, indexed by the fold change in the percentage of mNeonGreen+ cells, which were 7.27±0.19, 16.88±1.18, 25.64±1.66, and 24.39±0.62, respectively. Both UDeg2b and UDeg3a exhibited approximately a 3.5-fold increase compared to tDeg. When based on fluorescent intensity, the responsiveness of these degrons was 8.24±0.1, 11.03±0.37, 14.09±0.33, and 31.76±0.89, respectively (Fig. 1e). UDeg3a had the greatest (approx. 3.9 times) increase in fluorescent intensity compared to that of tDeg.

In addition, UDeg3a exhibited a markedly enhanced ability to induce protein destabilization. Both fluorescence imaging and western blot analyses revealed that the mNeonGreen protein level was strikingly lower when fused to UDeg3a compared with tDeg, particularly within the nucleus (Fig. 1f and Supplementary Fig. 1e). These findings underscore the significant improvement of UDeg3a in both responsiveness and destabilization efficiency, attributable to its composite design consisting of a PUS, a chimeric Tat peptide, and an optimized C-terminal degron (Fig. 1g).

To investigate the enhanced responsiveness of UDeg3a to Pepper, we constructed a structural model of the UDeg3a-Pepper complex using AlphaFold 348. The PUS was excluded as it is not involved in Pepper binding. To directly compare interaction patterns, tDeg was superimposed onto UDeg3a. Structural analysis revealed that both UDeg3a and tDeg adopt similar backbone conformations when binding to Pepper, forming a β-hairpin structure that fits into the major groove of Pepper. These results align with the BIV and JDV tat structures previously determined by nuclear magnetic resonance (NMR) spectroscopy49, supporting the reliability of the AlphaFold 3 model. Notably, the primary structural differences were observed in the peptide termini, where Y17 (numbered from the start of the tat peptide) in UDeg3a formed hydrogen bonds with R6. This intramolecular interaction might help to constrain the conformation of UDeg3a, reducing the free energy of binding to Pepper (Fig. 1h). Although potential contact between the aromatic ring on Y17 and the RNA base was not explicitly observed in this model, such an interaction could further strengthen UDeg3a-Pepper binding49. In addition, electrostatic potential mapping of the peptide surfaces indicated that mutations R16H, R17Y, and R18K reduced the positive charge on tDeg, potentially mitigating electrostatic repulsion between peptide termini during β-hairpin formation (Supplementary Fig. 1f).

We also compared UDeg3a to two commonly used protein degradation domains, PEST50 and ecDHFR51. We found that UDeg3a was more efficient in promoting proteolysis in all cell types tested including HEK293T, Huh7, HepG2, HeLa, SH-SY5Y, Hepa1-6, and C2C12 (Fig. 1i). The superior destabilization activity and smaller size highlight the usefulness of UDeg3a as a protein degradation tag (Supplementary Fig. 1g). Furthermore, this degradation process appears to be mediated by the ubiquitin-proteasome system (UPS), as UPS inhibition by MG13252 resulted in dose-dependent protein stabilization (Supplementary Fig. 1h). Taken together, we have engineered a Pepper-regulated protein degradation tag, UDeg3a, enabling both accelerated degradation and enhanced responsiveness to Pepper.

Development of a conditionally stable ADAR1d for programmable RNA editing

We next sought to utilize Pepper-UDeg3a for generating an RNA base editor with gRNA-regulated stability. As mentioned above, UDeg3a was fused to the C-terminus of the ADAR1 deaminase domain carrying a hyperactive mutation (ADAR1dE1008Q, referred to as ADAR1d*)53 to induce destabilization. Compared with tDeg, UDeg3a dramatically reduced ADAR1d* expression level in both cell cytoplasm and nucleus (Supplementary Fig. 2a). To complement this system, we designed a gRNA incorporating the Pepper domain and a targeting sequence, analogous to the direct repeat and spacer elements of CRISPR gRNAs (Fig. 2a). Crucially, we speculated that Pepper not only recruited but also stabilized ADAR1d*-UDeg3a. As anticipated, we observed Pepper-dependent stabilization of ADAR1d*-UDeg3a. Remarkably, circular gRNA generated via the Tornado system54 exhibited stronger stabilizing effects than its linear counterpart (Fig. 2b and Supplementary Fig. 2b), consistent with the increased stability of circular RNAs. As a result, we successfully developed a conditional deaminase system with circular gRNA-regulated stability.

To evaluate RNA editing activity, we used this system to correct nonsense mutations (TGG to TAG or TGA codons) in luciferase genes via A-to-I editing. The mutation sites were specified in each experiment, with another intact luciferase gene serving as the internal control (Supplementary Fig. 2c). Our initial design featured a 30-nt spacer with a central cytidine mismatched to the target adenosine. Following co-transfection into HEK293T cells, we observed gRNA- and ADAR1d*-UDeg3a-dependent restoration of firefly luciferase (Fluc) activity (Fig. 2c and Supplementary Fig. 2d). Notably, circular gRNA was significantly more efficient than its linear counterpart, likely due to the increased abundance of both gRNA and ADAR1d*-UDeg3a (Fig. 2b). Thus, circular gRNA was employed in subsequent experiments.

In accordance with the enhanced binding between UDeg3a and Pepper, ADAR1d*-UDeg3a exhibited greater activity than ADAR1d*-UDeg2b in restoring the mutated Fluc gene. Moreover, ADAR1d* outperformed ADAR2d* (ADAR2 deaminase domain with an E488Q mutation)55, implying that ADAR2d* may be less compatible with the UDeg-Pepper system in this context (Supplementary Fig. 2d). We next explored the influence of subcellular localization of deaminases on editing activity. Luciferase assays revealed that ADAR1d*-UDeg3a lacking localization signals performed better than variants with a nuclear localization signal (NLS) or a nuclear export signal (NES) (Supplementary Fig. 2d). Similar trends were observed during the editing of two endogenous genes, ALDOA and DAXX (Supplementary Fig. 2e). We then checked the subcellular localization of flag-tagged ADAR1d*-UDeg3a and found ADAR1d*-UDeg3a predominantly localized to perinuclear regions. Upon co-expression with gRNA, ADAR1d*-UDeg3a became more evenly distributed throughout the cytoplasm (Supplementary Fig. 2f, g), consistent with the localization pattern of the circular RNA54. These observations suggest that ADAR1d*-UDeg3a primarily edits mature mRNA in the cytoplasm, while a smaller nuclear fraction may act on precursor transcripts, collectively maximizing mRNA editing efficiency.

After establishing the protein component of the RNA editor, we turned to optimizing the gRNA design. We investigated the positional effects of Pepper within gRNAs targeting different reporters (Fluc or Renilla luciferase [Rluc] genes). We found that placing Pepper at both ends of the spacer led to greater editing efficiency than positioning it solely at the 5′ or 3′ end. This aligns with previous reports that increasing recruitment elements in gRNA often improves editing efficiency32,56,57, potentially amplified in our system by the resulting stabilization of deaminases. Among RECODE gRNAs with a single Pepper, a 5′ terminal position was preferred, at least in targeting Fluc (Fig. 2d).

To further refine the system, we examined the optimal spacer length using bi-Pepper gRNAs with spacers ranging from 20 to 50 nt, in 5-nt increments. Luciferase assays indicated that spacer lengths between 25 and 45 nt offered reliable editing, with the averaged activity of three different test gRNAs peaking at 40 nt (Fig. 2e). Based on these findings, we adopted a gRNA design featuring a 40-nt spacer flanked by Pepper domains at both ends. This optimized gRNA architecture, combined with ADAR1d*-UDeg3a, established a platform: RECODE version 1 (RECODEv1).

Characterization of RECODEv1

To further evaluate RECODEv1, we examined the impact of A-C mismatch position on editing efficiency. The mismatch position was defined as the distance from the 5′ end of the spacer. We systematically tiled gRNAs across target adenosines in both endogenous (KRAS, PPIB, RAB7A, SOD1) and transfected (Angptl3, Idua) transcripts, with a 5-nt step, in HEK293T cells. All adenosines within the gRNA footprint were analyzed to comprehensively map the editing preferences of RECODEv1. We found that target adenosines were reliably edited by all gRNAs, regardless of mismatch position, though the efficiency varied (Fig. 2f–i and Supplementary Fig. 3a–c).

For most sites (PPIB, RAB7A, SOD1, and Angptl3 site 1), the mismatch position at 35 nt yielded the highest editing efficiencies, ranging from 49% to 62%. However, this trend was not universal. For Angptl3 site 2 and Idua, the mismatch position of 20 nt appeared to be optimal (Supplementary Fig. 3d). Based on these observations, we recommend starting mismatch positions of 20 or 35 nt for new applications, with the caveat that site-specific optimization may be necessary.

In addition, the editing profiles revealed that bystander editing at the target loci was confined to regions covered by the gRNA spacers (Fig. 2f–i and Supplementary Fig. 3a–c). This corresponds with the established preference of ADAR deaminases for adenosines within double-stranded RNA (dsRNA) regions58. Therefore, the shorter spacers required by RECODEv1 inherently promote more precise editing compared with editing systems needing much longer gRNAs. Moreover, bystander editing can be further mitigated by introducing A•G mismatches17, single-adenosine bulges18, or G•U wobble base pairs22 in the guide-target duplex, with target A editing largely unaffected (Fig. 2j).

Development of RECODEv2 for enhanced specificity

The development of RECODEv1 established a system where the stability of ADAR1 deaminase could be regulated by gRNAs. To further improve the performance of this approach, we aimed to design a gRNA with target RNA-activated Pepper. Ideally, deaminases would be stabilized only at editing sites. Drawing inspiration from molecular beacons59, which are RNA imaging tools that transition from a “closed” to an “open” state upon hybridization with their targets, we introduced a sequence complementary to the 3′ end of the spacer downstream of Pepper, forming a stem-loop structure. Extending the stem to Pepper may interfere with its folding60. However, the presence of target RNA would open the inhibitory stem by forming a spacer-target duplex, thereby restoring Pepper’s active conformation (Fig. 3a).

We positioned the switchable Pepper at the 5′ end of the spacer, as it demonstrated higher activity in this configuration (Fig. 2d). The inhibitory stem was designed to be 10-12 bp long, with a 6-bp upper segment directly competing with Pepper folding. We reasoned that the strength of this inhibitory stem, or “Pepper lock”, would be critical for efficient transitions between the active and inactive states. Thus, we generated several gRNAs with varying stem strength via the introduction of a G•U wobble base pair or G•A mismatch (Fig. 3a). We evaluated the impact of target RNA on ADAR1d*-UDeg3a stability using these gRNAs. The results indicated that the presence of Fluc or mTagBFP2 mRNAs can significantly up-regulate ADAR1d*-UDeg3a in the corresponding gRNA groups. In contrast, control groups employing non-targeting gRNAs with or without the Pepper element showed no significant change in ADAR1d*-UDeg3a abundance, regardless of the presence of target RNA (Fig. 3b, c and Supplementary Fig. 4a, b). These results suggest our design enables effective transitions of Pepper from the locked to the active state upon hybridization of the guide with its target RNA.

In addition, we observed that both ADAR1d*-UDeg3a protein level and the editing efficiency of Fluc mRNA increased as the locking stem weakened (Fig. 3b–d). To obtain a deeper understanding of their relationship, we sought to identify correlative thermodynamic parameters (Supplementary Fig. 5a, b). We excluded the minimum free energy (MFE) of the locking stem, as it did not account for loop structures. Instead, we predicted the folding of the entire gRNA sequence (excluding circularization elements) and calculated the partition function using the RNAfold program61. Our analysis revealed a strong correlation between the probability of Pepper folding—defined by the pairing likelihood of the terminal base pair in Pepper—and both ADAR1d*-UDeg3a expression level (R2=0.86) and editing efficiency (R2=0.79) (Fig. 3e). Taken together, these results underscore the feasibility of modulating ADAR1d*-UDeg3a expression and editing output through rational gRNA engineering. Moreover, the differing slopes of the two regression lines suggest that editing efficiency increases less proportionally than ADAR1d*-UDeg3a protein abundance (Fig. 3e). These results led us to conclude that gRNAs containing a G•A mismatch emerged as the prime candidate due to the highest editing activity. However, if specificity is a priority, the other two gRNAs harboring a tight Pepper lock are viable alternatives.

Next, we optimized spacer length using locked Pepper with a G•A mismatch. Luciferase assays revealed that a 40-nt spacer was optimal for both Fluc and Rluc gRNAs, a result corroborated by editing endogenous transcripts such as ALDOA, and DAXX (Fig. 3f, g). Peak editing efficiency generally occurred with spacers between approximately 30-40 nt, though an exception was observed where a 20-nt spacer maximized editing for GUSB (Fig. 3g).

Previous studies show that the addition of a flexible RNA linker into circular gRNA contributes to release of conformational constraints and boosts editing efficiency18,24. Hence, the gRNA used above carried a flexible AC linker downstream of the spacer. Next, we tried to append AC linkers to both the 5′ end of Pepper and the 3′ end of the spacer and found this modification further enhanced editing activity (Supplementary Fig. 6a and 6b). Collectively, the gRNA architecture—comprising locked Pepper with a G•A mismatch, a 40-nt spacer, and flanking AC linkers—was designated RECODEv2.

Comparison between RECODEv1 and RECODEv2

We compared RECODEv2 to RECODEv1 in terms of editing efficiency, bystander editing, and global off-target effects using spacer-matched gRNAs. The luciferase assays indicated a significant reduction of activity in RECODEv2 compared to RECODEv1 (Fig. 3h). Analysis of editing rates also indicated lower activity of RECODEv2 in targeting Angptl3, KRAS, PPIB, and RAB7A transcripts (Fig. 3i), including bystander editing, which was almost eliminated in the cases of KRAS and PPIB. This corresponds with our results in which Peppers flanking the spacer at both ends induced more edits than a single Pepper (Fig. 2d). Transcriptome analysis further revealed fewer off-target edits (Fig. 3j), which may result from the attenuated effect of RECODEv2 gRNA in stabilizing ADAR1d*-UDeg3a (Figs. 2b, 3b).

Engineering ADAR1d for enhanced activity

Developing highly active deaminases is crucial for targeting editing-resistant sites. Alongside efforts to confer conditional stability on ADAR1d*, we sought to further enhance its catalytic activity through protein engineering. Given the absence of structural information for ADAR1d, we utilized AlphaFold 348 to model the ADAR1d*-RNA duplex, identifying several regions that interact directly with the RNA substrate. One region of particular interest was a loop spanning residues 972 to 999, homologous to the 5′ binding loop of ADAR2 deaminase25,62. This loop has been proposed as the structural basis for the differing substrate specificities between ADAR1 and ADAR2. Moreover, its primary sequence is highly variable among ADAR1 orthologs across diverse species, including fish, reptiles, and birds (Fig. 4a). Considering the broad impact of temperature on both enzymatic catalysis and dsRNA formation, we hypothesized that the ADAR1 deaminase domain may have evolved distinct functional characteristics in poikilotherms and birds, whose body temperatures differ significantly from those of mammals. This prompted us to substitute the loop in ADAR1d* with orthologous counterparts from these species to identify hyperactive variants.

To streamline the screening process, we generated consensus sequences for the ADAR1 5′ binding loop from 209 poikilotherms and 103 bird species, in which the residue frequencies at each position might reflect evolutionary fitness (Supplementary Fig. 7). These sequences were classified into eight clusters based on amino acid sequence similarity. From these clusters, we derived eight consensus motifs: P-C1 to P-C5 from poikilotherms and B-C1 to B-C3 from birds. We then selected 1–3 representative sequences from each consensus motif and replaced the native loop in ADAR1d* with these sequences (Supplementary Fig. 8a). The activity of the resulting variants was evaluated by testing their ability to restore two termination codons (UAG and UGA) in Fluc. Most variants exhibited activity comparable to the original ADAR1d*-UDeg3a, and only one substitution slightly impaired UAG editing, indicating the overall functional fitness of nature-derived sequences. Notably, two variants, P-C2-3 and P-C4, demonstrated significantly enhanced activity in editing both UAG and UGA and were selected for further analysis (Fig. 4b).

Inspired by prior high-throughput mutagenesis studies25,63, we introduced three additional mutations into P-C2-3 and P-C4. Among these, the F992W mutation notably increased the activity of P-C4 in UAG editing. However, this enhancement was not observed for UGA, likely due to a smaller dynamic range for this codon (Fig. 4c). We then compared the performance of P-C4 (F992W) with ADAR1d* in editing endogenous transcripts, with gRNAs of RECODEv2. The P-C4 (F992W) variant consistently outperformed ADAR1d*, achieving up to a three-fold increase in editing rates across three tested sites (Fig. 4d).

To gain structural insights into these hyperactive mutants, we aligned the mutated sequence with the wild type and generated structural models using AlphaFold 348. The analysis revealed that the central segment of the mutated loop was enriched in residues with high turning propensity (Fig. 4e). Specifically, four amino acids (K982, E983, G984, D985) formed a classical β-turn at the loop apex, facilitated by electrostatic interactions between K982 and D985 (Supplementary Fig. 8b). This structural modification reduced the loop angle, positioning K996 closer to the phosphate backbone of the substrate RNA. In addition, a cation-π interaction was observed between F992 and R1086 in ADAR1d*. The F992W mutation appeared to strengthen this interaction due to the larger π-electron cloud of tryptophan, thereby further stabilizing the loop structure (Fig. 4f).

Finally, we optimized the linker between P-C4 (F992W) and UDeg3a, identifying the xten linker as the most effective in promoting RNA editing (Fig. 4g and Supplementary Fig. 8c). Consequently, P-C4 (F992W) and the xten linker were integrated into the RECODE platform for subsequent applications.

RECODE compares favorably to other established technologies for RNA editing

We then benchmarked RECODE against other well-established RNA editing platforms, including REPAIRv2 (CRISPR/Cas13-based RNA editing)33, MCP-ADAR1d*32, LEAPER2.018, or cadRNAs17 (Circular gRNAs recruiting endogenous ADARs). To facilitate a fair comparison, each method was implemented using its optimal gRNA architecture. For REPAIRv2, the gRNA was designed with a 50-nt spacer and a 35-nt interval between mismatched cytosine and direct repeats. In the case of MCP-ADAR1d*, the gRNA consisted of a 22-nt spacer flanked by two MS2 structures, with an opposite cytosine positioned 7 nt from the 5’ MS2 element. LEAPER2.0 utilized a 151-nucleotide target sequence containing a centrally positioned opposite cytosine flanked by AC linkers. With regard to RECODEv2, a 40-nt spacer with a central opposite cytosine was used.

To assess both on-target and bystander editing, we quantified the editing rates of the target adenosine in addition to surrounding adenosines within a 151-nucleotide window centered on the target, across a range of transfected and endogenous transcripts. We found consistently high editing efficiencies across these sites with MCP-ADAR1d* and RECODEv2. In contrast, there was a marked dependence on the specific editing sites in the editing activity of REPAIRv2 and LEAPER2.0 (Fig. 5a–f). Notably, RECODEv2 achieved robust editing at DAXX and Rluc (W104X), with editing rates of 71% and 82.3%, respectively, whereas editing at these loci was undetectable for REPAIRv2 and LEAPER2.0 (Fig. 5a, e). Furthermore, there were markedly fewer bystander effects during RECODEv2 editing compared to MCP-ADAR1d* (Fig. 5a–f), which is also shown by the ratio of on-target to cumulative bystander (i.e., the sum of all bystander editing events within the window) editing rates (Fig. 5g). In addition, we plotted on-target editing activity against the cumulative bystander editing rates. This analysis revealed that the data points for RECODEv2 predominantly clustered in the upper-right quadrant, again indicating a superior ratio of on-target to bystander editing over the other systems (Supplementary Fig. 9a).

We then conducted transcriptome-wide sequencing to assess the global off-target effects. A relatively higher dosage (200 ng) of plasmids was used here to emphasize the off-target effect. Our analysis revealed that REPAIRv2 and RECODEv2 induced comparable off-target editing events, slightly more than LEAPER2.0 and SYFP2 control. MCP-ADAR1d* produced the most transcriptome-wide edits. These results are in agreement with previous studies demonstrating that ADAR1d generally produces more extensive off-target edits compared to ADAR2d32, with this difference being further magnified by the high-fidelity variant of ADAR2d used here in REPAIRv233. Notably, RECODEv2 strikingly mitigated the off-target effects associated with the hyperactive ADAR1d variant, underscoring the efficacy of the conditional stabilization strategy (Fig. 5h). In addition, motif enrichment analysis of the off-target editing sites revealed a consistent sequence preference of the engineered deaminases, with 5′W and 3′S motifs being enriched (Supplementary Fig. 9b).

Beyond the favorable on-target to off-target editing ratio, RECODE possesses two other advantageous features. First, the size of its protein component, ADAR1d*-UDeg3a, is smaller (486 aa) than that of MCP-ADAR1d* (587 aa) and dPspCas13b-ADAR2E488Q/T375G (1490 aa), making it suitable for viral delivery with limited payload capacity. Second, the relatively low immunogenicity, as assessed by the immunogenicity score64, suggests a reduced risk of eliciting an immune response (Supplementary Fig. 9c), further enhancing its therapeutic potential.

Efficient editing of an ALS-relevant FUS mutation in vitro and in vivo

To showcase its therapeutic potential, we used RECODE to target an ALS-relevant mutation in the FUS gene. In ALS, a cluster of non-synonymous mutations disrupts the C-terminal NLS of FUS, resulting in cytoplasmic mislocalization65,66. This mislocalization impairs intra-axonal protein synthesis, leading to synaptic dysfunction in neurons67. For instance, the c.1555C>T (p.Q519X) mutation truncates the NLS (residues 510–526)66, potentially contributing to cytoplasmic toxicity. Thus, we sought to leverage RECODEv2 to restore this truncated NLS and correct cytoplasmic mislocalization by converting premature termination codons (TAG) to tryptophan codons (TGG).

We first generated SYFP2-tagged FUS (Q519X) to visualize FUS subcellular localization (Fig. 6a). Co-delivery of FUS (Q519X) and RECODEv2 to HEK293T cells led to a conversion rate of 90.3% at the target site (Fig. 6b). In accordance with this strikingly high editing yield, we observed a robust rescue of mislocalized FUS. The nuclear localization index of FUS, measured by Pearson’s correlation between SYFP2-FUS (Q519X) and DAPI, increased from –0.02 to 0.34, approaching the wild-type (WT) FUS level of 0.36 (Fig. 6c, d). To rule out the influence of SYFP2 on FUS (Q519X) transport, we replaced SYFP2 with a small HA tag, which enabled discrimination from endogenous FUS. After co-transfection of HEK293T cells, nuclear and cytoplasmic proteins were prepared separately. Western blot analysis revealed FUS was redistributed to cell nuclei with targeting gRNA (Fig. 6e, f). Collectively, these cell model assays all accord with the robust activity of RECODEv2 in restoring the mislocalization of FUS (Q519X).

Next, we evaluated whether RECODEv2 can facilitate effective rescue in vivo. RECODEv2 was packaged into recombinant adeno-associated viruses (rAAVs) that are widely used in gene therapy. The total size of RECODEv2 components, including cis-regulatory elements, was 2.9 kb—well below the 5 kb packaging limit of rAAVs (Fig. 6g)68. We separately packaged RECODEv2 and HA-FUS (Q519X) into AAV9 vectors and co-injected them into the primary motor cortex (M1) of mice (Fig. 6h), a region frequently affected in ALS69. As a control, RECODEv2 with a non-targeting gRNA was used. Four weeks post-injection, mouse brains were harvested for RNA extraction and tissue sectioning. RECODEv2 with targeting gRNA achieved an editing rate of 58.8% (Fig. 6i). Immunostaining of brain slices revealed significantly less HA-FUS signals in neuronal axons projecting to the contralateral cortex in the RECODEv2 group, as indicated by lower axon-to-cell-body fluorescence intensity ratios (Fig. 6j, k). These findings underscore the efficacy of RECODEv2 in vivo.

RECODE is broadly applicable to disease-relevant mutations

To demonstrate the broad applicability of RECODE to clinically relevant loci, we applied it to correct multiple mutations in SOD1, another gene implicated in ALS70. Seven G-to-A mutations (c.112, c.124, c.280, c.301, c.304, c.335, and c.404) were selected from the ClinVar database71 due to their high risk of causing ALS (Supplementary Fig. 10a). After co-delivering constructs encoding SOD1 mutants and RECODEv2 into HEK293T cells, we profiled the conversion rates of all adenosines within SOD1 coding sequence (CDS). Efficient editing was observed across the target sites, ranging from 45.7% to 91.7%. Notably, six of the seven sites exhibited editing rates exceeding 70% (Supplementary Fig. 10b), highlighting the robustness of RECODEv2 in targeting diverse loci. Moreover, minor bystander edits were detected, all of which were confined to the gRNA-binding regions (Supplementary Fig. 10c, d).

Installation of a protective mutation in Angptl3 in cultured cells

Beyond correcting deleterious mutations, we aimed to use RECODE to install protective mutations in wild-type transcripts. For this, we used ANGPTL3, a hepatically secreted protein that inhibits the clearance of triglyceride-rich lipoproteins and cholesterol in the circulatory system72,73. Previous studies have shown that loss-of-function mutations, such as c.1250A>G (p.Y417C) in ANGPTL3, are associated with reduced plasma triglyceride levels and a lower risk of coronary artery disease73. We used RECODEv1 to introduce this mutation into mouse Angptl3 mRNA. After co-delivering RECODEv1 and HA-tagged Angptl3 into HEK293T cells, we quantified the editing efficiency and analyzed extracellular secretion (Fig. 7a). RECODEv1 achieved a high editing efficiency of 64.3% and dramatically reduced the extracellular secretion of Angptl3 (an 81.4% reduction) (Figs. 7b–d).

Efficient editing of Angptl3 lowers plasma lipids in mice

We next performed in vivo editing of Angptl3. RECODEv1 was packaged into AAV8 for hepatocyte transduction, with a non-targeting gRNA serving as the control. In parallel, the UDeg3b-tagged ADAR1d variant, which exhibits enhanced binding affinity to Pepper (Fig. 1d), was included for comparison. Recombinant AAVs were administered to adult mice via retro-orbital injection for efficient liver transduction74. Serum samples were collected at 14 and 28 days post-injection for analysis of plasma lipids, Angptl3, and aminotransferases. Liver tissues were collected at 28 days post-injection to assess RNA editing rates and other hepatic parameters (Fig. 7e). Sequencing of Angptl3 transcripts revealed editing efficiencies of 34.7% and 50.4% for RECODEv1 harboring UDeg3a and UDeg3b, respectively, with minimal bystander editing observed (Fig. 7f, g). Consistent with the in vitro results that the Y417C mutation inhibits secretion, Angptl3 protein levels in circulation were reduced in proportion to the observed editing efficiencies (Fig. 7h).

Importantly, the reduction of circulating Angptl3 led to decreased serum lipids at 28 days post-injection, including triglyceride (20.9±1.5% with UDeg3a and 28.8±2.3% with UDeg3b) and cholesterol (23.2±1.7% with UDeg3a and 33.4±3.4% with UDeg3b), recapitulating clinical observations73,75. No significant changes were detected in body weight, hepatic Angptl3 or triglyceride content (Fig. 7i–l), nor in serum alanine aminotransferase (ALT) or aspartate aminotransferase (AST) levels (Fig. 7h), suggesting low risk of liver damage.

Translational interference via start codon targeting

To expand RECODE’s utility, we explored its potential to edit adenosines within start codons (typically AUG), enabling translational interference. A challenge lies in the frequent presence of cytosines upstream of AUG76, which are suboptimal substrates for ADAR deaminases. To test this, we designed a reporter mRNA encoding SYFP2 with a start codon and an upstream cytosine (Supplementary Fig. 11a). Remarkably, RECODEv2 induced a conversion rate of 53.3% (Supplementary Fig. 11b), demonstrating broad compatibility with previously unfavorable sites. To confirm the role of RNA editing on translational interference, we deleted Pepper from targeting gRNA to avoid ADAR1d recruitment. While minor editing (16.3%) occurred in the absence of Pepper, the stronger SYFP2 signal and similar RNA expression levels suggested that RNA editing played a substantial role in such process (Supplementary Fig. 11c, d).

Finally, we applied RECODE to target the start codon of endogenous SOD1 (Supplementary Fig. 11e). Silencing toxic SOD1 mutants has been shown to mitigate ALS-related pathology77. As expected, RECODEv1 achieved efficient editing (48.3%) and translational interference (51.4% reduction in protein and insignificant change in mRNA levels compared to NT controls) (Supplementary Fig. 11f–h), highlighting its therapeutic promise as a translational interference tool.

Methods
Plasmid construction

Fluorescent protein reporter plasmids were generated by inserting mNeonGreen-IRES2-mScarlet3, synthesized by GENEWIZ, into pCDNA3.1(+) (V79020, ThermoFisher). tDeg variants were then fused to the C terminus of mNeonGreen.

For the deaminase vector, the ADAR1 deaminase E1008Q gene was cloned from MS2_adRNA-MCP_ADAR1 DD (E1008Q)_NES (124626, Addgene) and inserted into pCI (E1731, Promega) for mammalian cell expression. Oligonucleotides encoding the consensus 5′ binding loop were synthesized by GENEWIZ and used to generate ADAR1 deaminase mutants via seamless cloning. For the guide vector, gRNA backbones carrying Pepper, preceded by the U6 promoter, were synthesized by GENEWIZ and cloned into the pUC19 (50005, Addgene) vector. Then the guide vector was modified accordingly to target distinct sites. The gRNA sequences are provided in Supplementary Data File 1. The luciferase genes were cloned from pMirGlo (E1330, Promega) and subject to site-directed mutagenesis to induce nonsense mutations, followed by insertion into pCI (E1731, Promega).

Human FUS and SOD1 genes were amplified from cDNA of HeLa cells, and mouse Idua and Angptl3 genes were amplified from cDNA from mouse livers, followed by the introduction of the mutations via site-directed mutagenesis. Then they were cloned into pCI for mammalian cell expression. FUS was further fused to SYFP2 or an HA tag at the N terminus.

The ADAR1 deaminase gene and U6-driven gRNA were assembled into pAAV-hSyn-EGFP (50465, Addgene) to generate AAV vectors.

AAV production and titration

For AAV production, HEK293T cells were co-transfected with a mixture of plasmids: an AAV vector plasmid, a packaging plasmid encoding the AAV2 Rep protein and capsid proteins for AAV9 or AAV8, and the pHGT1-Adeno1 helper plasmid carrying adenoviral genes. Transfections were conducted using calcium phosphate when HEK293T cells reached approximately 80% confluence in two 15 cm culture dishes. After 48–72 h, the cells were harvested and resuspended in a buffer containing 150 mM NaCl and 100 mM Tris-HCl (pH 8.0). The cells were lysed through repeated freeze-thaw cycles, followed by the addition of MgCl2 to achieve a final concentration of 1 mM. AAV particles were then purified and concentrated using Millipore Amicon 100 kDa NWCO columns (UFC910008; Merck Millipore). Encapsidated viral DNA was quantified using qPCR (ThermoFisher, Waltham, USA) with primers targeting the ITR sequences, following denaturation of the AAV particles with Proteinase K. Titers are expressed as viral genomes per milliliter (VG/mL).

Cell culture and transfection

HEK293T (CRL-3216, ATCC), Huh7 (STCC10102P-1, Servicebio), HepG2 (HB-8065, ATCC), HeLa (STCC10603P-1, Servicebio), SH-SY5Y (STCC11004P-1, Servicebio), Hepa1-6 (CRL-1830, ATCC), and C2C12 (CRL-1772, ATCC) cells were cultured in Dulbecco’s Modified Eagle’s Medium (11965092, Gibco) supplemented with 10% fetal bovine serum (10091148, Gibco) and 1% penicillin-streptomycin (15140122, Gibco). The cells were maintained at 37°C in a 5% CO2 atmosphere and regularly passaged using 0.05% Trypsin-EDTA (40127ES60, Yeasen). Transfections were performed when the cells reached 60–80% confluence in a 24-well plate (3524, Corning) or 96-well plate (655098, Greiner). All plasmids were delivered by jetOPTIMUS transfection reagent (101000006, Polyplus), according to the manufacturer’s protocol. The mass of plasmids used is specified for each experiment.

Assessment of protein degradation domains

For UDeg engineering, HEK293T cells were seeded in 24-well plates, 1 day before transfection. Reporter plasmids (200 ng) and Pepper-encoded plasmids (300 ng) were co-delivered into each well. For comparison between degrons, 500 ng was used for HEK293T, 1 μg was used for Huh7, C2C12, and 1.5 μg of reporter plasmids was used for SH-SY5Y, and Hep G2 cells. At 48 h post-transfection, cells were rinsed once with 1× PBS and dissociated with Trypsin (40127ES60, Yeasen). Cells were then collected and resuspended in 1× PBS, before flow cytometry analysis was performed.

Flow cytometry analysis

Cells were resuspended in 1× PBS on ice. Live cells were gated by side scatter area versus forward scatter area (SSC-A vs. FSC-A), singlets were selected by forward scatter height versus forward scatter area (FSC-H vs. FSC-A), and the fluorescence-positive population was gated against the mock-transfected control.

Luciferase assays

HEK293T cells were seeded in 96-well polystyrene cell culture microplates (No. 655098, Greiner). Cells were transfected with a mixture of four plasmids (100 ng of deaminase vector, 25 ng of guide vector, 25 ng of dead luciferase vector, and 10 ng of another intact luciferase vector as an internal control). Each experimental group was set up with four replicate wells. After 48 h, Firefly and Renilla luciferase fluorescence values were measured separately using Dual Glo Luciferase Reporter Gene Assay Kit (11405ES80, Yeasen), according to the manufacturer’s protocol. Bioluminescence was calculated as the ratio of Firefly to Renilla luciferase activity, or vice versa.

RNA editing rate assessment and qPCR

For endogenous transcript editing, a total of 1.2 μg plasmids were used for each method, with a ratio of 3:1 for deaminase vectors to guide vectors. The ratio was adjusted to 6:2:1 (Deaminase: Guide: Target) in the cases of editing transfected transcripts. At 48 hours post-transfection, cells were collected. Total RNA was extracted using RNAiso Plus (9109, Takara) according to the manufacturer’s protocol. RNA was quantified using a NanoDrop spectrophotometer, and reverse transcription was performed using the RevertAid First Strand cDNA Synthesis Kit (K16225, Thermo Scientific) according to the kit instructions. qPCR was carried out with Hieff Unicon™ Universal Blue qPCR Master Mix (11184ES03, Yeasen). The primer sequences are shown in Supplementary Table 1.

To evaluate the editing efficiency, PCR amplicons spanning editing sites were sent to Tsingke Biotechnology for Sanger sequencing. A-to-I conversion rates were quantified using the online tool editR (https://moriaritylab.shinyapps.io/editr_v10/).

Western blot

Cells were harvested 72 h after transfection and lysed in RIPA Lysis Buffer (P0013C, Beyotime) supplemented with Protease Inhibitor Cocktail (11836153001, Roche). Following centrifugation, the supernatant was collected as the protein sample. Protein concentration was measured using the Pierce BCA Protein Assay Kit (23225, Thermo Scientific), after which the samples were mixed with 5× SDS-PAGE Protein Loading Buffer (G2075, Servicebio) and denatured by heating.

Protein separation was performed via SDS-PAGE electrophoresis according to standard procedures, followed by transfer to 0.45 µm polyvinylidene fluoride (PVDF) membranes (Merck Millipore). Membranes were incubated with the appropriate primary antibody, followed by HRP-conjugated secondary antibody (APExBIO). Protein signals were visualized using the ECL Chemiluminescent Substrate Detection Kit (K1231, APExBIO) on a Tanon 5200 Chemiluminescent Imaging System.

The HA (14031, CST) or Flag (66008-4-Ig, Proteintech) antibody was used for the detection of fusion proteins of ADAR1 deaminase or Angptl3. Human SOD1, mouse Angptl3, and mNeonGreen were detected by antibody (10269-1-AP, Proteintech), antibody (86193-1-RR, Proteintech), and antibody (29523-1-AP, Proteintech), respectively. Nuclear and cytosolic fractions of cell lysate were prepared separately using a Nuclear and Cytoplasmic Protein Extraction Kit (P0027, Beyotime). Then, the proteins of interest were detected as described above.

Protein bands were quantified using ImageJ software to assess protein expression levels. The intensity of the band of interest was normalized by dividing it by the corresponding α-tubulin (HRP-66031, Proteintech), GAPDH (HRP-60004, Proteintech) or Lamin B1 (12586, CST) bands on the same membrane after stripping.

AAV delivery in adult mice

For unilateral AAV delivery to the M1 brain area of adult mice, stereotaxic injections were performed. The coordinates used for the M1 were: X (ML) +2.0 mm, Y (AP) +2.1 mm, and Z (DV) –1.7 mm. Mice were anesthetized with isoflurane and placed in a stereotaxic frame (Reward Life Science). The surgical area was cleaned using 2% iodine and 70% alcohol. The scalp overlying the dorsal skull was incised, and a small hole was drilled over the injection site. AAV vectors were injected into the target brain regions using a 10 µl Hamilton syringe, connected to a pump. After injection, the needle was left in place for 5 min before being slowly withdrawn. Once the needle was removed and the scalp sutured, the mouse was removed from the stereotaxic apparatus and kept on a heating pad until after movement recovery, at which point it was brought back to its home cage.

For systemic delivery, adult mice were anesthetized using isoflurane (3% induction and 1.4% maintenance, mixed with 100% O2). Retro-orbital injections of rAAVs were performed with a 27.5-gauge insulin needle (BD). Once the injection was complete, the needle was slowly withdrawn. Mice were brought back to their home cages after recovery.

Only male mice were used to exclude the variability from hormonal cycles in females.

Immunostaining

Brain tissues were collected and fixed in 4% paraformaldehyde at 4°C overnight, followed by cryoprotection in 30% sucrose until the tissues sank. The tissues were then embedded in OCT and cryosectioned at a thickness of 20 µm. Sections were permeabilized with 0.3% Triton X-100 in PBS for 30 min, followed by blocking with 5% goat serum in PBS for 1 h at room temperature. Primary antibodies were applied and incubated overnight at 4°C. After washing with PBS, sections were incubated with secondary antibodies conjugated to fluorescent dyes for 1 h at room temperature. Nuclei were stained with DAPI, and sections were mounted with antifade mounting medium. Images were captured using a fluorescence or confocal microscope.

For the imaging of mNeonGreen, mScarlet3, and SYFP2-FUS, cells were grown on glass slides. At 48 h post-transfection, cells were fixed in 4% paraformaldehyde for 10 min at room temperature. Then, the cells were mounted with mounting medium and DAPI for confocal imaging. ADAR1d was visualized by HA or Flag antibodies.

Serum ELISA assays

Blood samples were collected at the time points indicated in the figure legends. For sample collection, mouse tails were nicked using sterile scissors to obtain blood. The samples were allowed to clot for 2 h at room temperature and then centrifuged at 10,000 rpm for 10 min to separate the serum. Serum levels of Angptl3 (MANL30, R&D Systems), total cholesterol (A111-1, Nanjing Jiancheng), and triglycerides (A110-1, Nanjing Jiancheng) were quantified using ELISA kits according to the manufacturers’ protocols. Absorbance was measured with a TECAN microplate reader. Alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels were determined by biochemical analysis performed by Servicebio.

Multiple sequence alignment

Orthologous ADAR1 sequences of fish, reptiles, and birds were retrieved from NCBI and were aligned using Clustal Omega. Poorly aligned regions were manually adjusted if required. Phylogenetic trees were generated with FastTree (Arkin Laboratory). Sequences of ADAR1 5’ binding loop were grouped by the Cluster tool in the Immune Epitope Database, followed by a sequence consensus for each group by WebLogo.

Structure prediction by AlphaFold 3

Sequences of tDeg and UDeg3a excluding PUS, as well as that of Pepper, were input into AlphaFold 3 to analyze their interactions, with default parameters set. Similar procedures were also applied to ADAR1 deaminase. Then, the structures were visualized and annotated using PyMOL (Schrödinger).

RNA folding analysis

The secondary structures of gRNA were predicted using ViennaRNA Web Services (http://rna.tbi.univie.ac.at/). The minimum free energy and partition function of gRNAs were analyzed using RNAFold. The predicted secondary structures were visualized using VARNA software.

Immunogenicity prediction

The protein sequences of dPspCas13b-ADAR2d, MCP-ADAR1d*, and P-C4 (F992W)-UDeg3a were uploaded to Immune Epitope Database (IEDB) for prediction of immunogenicity using T cell class I tool. NetMHCpan EL 4.1 (IEDB recommended epitope predictor 2023.09) was used to predict the strength of the peptide: MHC interaction. The top 200 predicted MHC class I restricted 9-mer candidates were subjected to analysis for each protein.

Transcriptome sequencing

HEK293T cells were transfected with 200 ng of plasmid DNA (Deaminase: Guide = 3:1) for each well in 24-well plates. Forty-eight hours post-transfection, total RNA was extracted using RNAiso Plus (Takara). The poly-A RNAs were enriched from total RNA using Dynabeads mRNA Purification Kit (61006, Invitrogen) and fragmented into small pieces using fragmentation reagent in MGIEasy RNA Library Prep Kit V3.1 (1000005276, MGI). The first strand of cDNA was synthesized using random primers and reverse transcriptase, followed by second-strand cDNA synthesis. The synthesized cDNA was end-repaired, A-tailing was added and then ligated to the sequencing adapters according to the library construction protocol. The cDNA fragments were amplified by PCR and purified with MGIEasy DNA Clean beads (1000005279, MGI). The library was analyzed using an Agilent Technologies 2100 bioanalyzer. The double-stranded PCR products were heat denatured and circularized by the splint oligo sequence in the MGIEasy Circularization Module (1000005260, MGI). The single-strand circular DNA (ssCir DNA) was formatted as the final library. The qualified libraries were sequenced using the DNBSEQ-T7RS platform.

RNA-seq data analysis

To obtain clean data, the raw sequencing data were filtered using fastp (v0.23.4) with the following parameters: -q 15 -u 40 --detect_adapter_for_pe. The filtered, clean data were then aligned to the GRCh38 reference genome using STAR (v2.7.11b). The alignment results were sorted with samtools. Next, PCR duplicates were removed using the gatk (v4.5.0.0) MarkDuplicates module (parameters: --CREATE_INDEX true --VALIDATION_STRINGENCY SILENT). The reads were split into exon segments, removing Ns while retaining grouping information, and sequences overhanging into intronic regions were removed using the gatk SplitNCigarReads module with default parameters.

Variant calling was performed with the gatk HaplotypeCaller module (parameters: -dont-use-soft-clipped-bases --standard-min-confidence-threshold-for-calling 20). The generated GVCF files were merged using the gatk CombineGVCFs module with default settings, followed by variant genotyping with the gatk GenotypeGVCFs module. SNP variants were selected using the gatk SelectVariant module (parameters: -select-type SNP). Filtering was applied using the gatk VariantFiltration module with the following criteria: --filter-expression “QD < 2.0 || MQ < 40.0 || FS > 60.0 || SOR > 3.0 || MQRankSum < −12.5 || ReadPosRankSum < −8.0”. For analyzing A-to-G variants, the proportion of G depth relative to the total depth in edited samples was calculated when the control sample was AA.

Statistics & reproducibility

All statistics were performed using GraphPad Prism 9.0. The following tests were used as appropriate: paired t test, unpaired t test, one-way ANOVA, Tukey’s test, and Dunnett’s test. All t tests were performed as two-tailed. In all statistical tests, a p-value < 0.05 was considered statistically significant. Sample sizes were chosen based on previous publications or previous experience to yield sufficient power to detect specific effects. All statistical tests used are indicated in the figure legends. The animals are randomly allocated into each group.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Ethics statement

All animal-related procedures were performed according to protocols approved by the Institutional Animal Care and Use Committee (IACUC) at the Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences. Mice were housed at 18–23°C with 40–60% humidity under a normal 12 h light-dark cycle with food and water available ad libitum under SPF (specific pathogen-free) conditions.

Data availability

The data supporting the results in this study are available within the paper and its supplementary information. Raw RNA-seq data for Fig. 3j and Fig. 5h can be accessed at the National Center for Biotechnology Information (NCBI) BioProject under the accession code: PRJNA1393754 or The Sequence Read Archive (SRA) under the accession code: SRP658796. Source data are provided in this paper.

Code availability

No original code was generated in this study.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In Fig. 6, the authors examined the efficiency of RECODEv2-mediated editing of an ALS-related FUS mutation both in culture and in vivo. In Fig. 6c, they reported a dramatic cytoplasmic mislocalization of mutant FUS. However, this observation appears inconsistent with the western blot analysis of cytoplasmic and nuclear fractions presented in Fig. 6e. Specifically, while Fig. 6c suggests that the majority of mutant FUS localizes to the cytoplasm, Fig. 6e indicates the opposite—that most mutant FUS is nuclear. Moreover, following editing, the nuclear HA-FUS levels do not appear to increase, in contrast to the result from immunostaining. The authors should clarify this discrepancy.\nThen, the authors showed that approximately 60% editing deficiency in vivo could slightly reduce the level of mutant FUS in axons. Although this result is consistent with their findings in cell cultures, it remains preliminary. Therefore, the authors should limit their conclusion to the observation that editing can occur in vivo and revise the strong claim that “These findings underscore the therapeutic potential of RECODEv2,” as no actual therapeutic effects were demonstrated.\nOther minor comments:\n1. Please ensure that the formatting of all references is consistent. For example, in some references, the first letter of each word in the article title is capitalized, while in others it is not.\n2. Line 476: \"Figure 6a\" should be written as \"Fig. 6a.\"\n3. Line 481, “Figures 6e and 6j” should be written as “Fig. 6e and 6j”.", "reviewer_2": "ADAR based adenosine-to-inosine (A-to-I) provides a potentially powerful tool to correct mutations at the RNA level. However, widespread off-target effects of exogenously delivered ADARs remain a significant drawback of this approach. In this paper, Liu devised the RECODE (RNA editing with conditionally stable and enhanced ADAR1 deaminase variants) system, which employs designer degron-tagged ADAR1d with guide RNA (gRNA)-regulated stability. By promoting degradation of gRNA-unbound ADAR1d, RECODE significantly reduced transcriptome-wide edits while maintaining high on-target efficacy. Engineering gRNA for target RNA-induced conformational switching restricts ADAR1d stabilization, further enhancing editing precision. While the idea is clever and elegant, the major drawback is the complicated design and requirement involving 1) delivery and expression of an engineered exogenous ADAR variant, 2) complicated design of a gRNA that needs to bind the degron-tagged ADAR1d on the one hand and the target RNA on the other, 3) delicate balance of the degradability of degron-tagged ADAR1d to reduce off-target effects and its activity in intended A-to-I editing. While much work in cell culture has been done to show proof of principle in vitro, there is hardly any data to demonstrate in vivo functionality. In fact, the delivery of such exogenous ADAR and gRNA to the intended target tissues and cells are by no means trivial, but are not demonstrated or even discussed. As it stands, the paper does not give any confidence that this method will be useful for in vivo applications.\nMajor points\n1. The data for the in vivo functionality of RECODE in Figure 6g-k is very thin, weak, and unconvincing. The authors need to provide much more compelling data for in vivo experiments.\n2. in vivo application is the goal of this molecular genetic technology for gene therapy etc, but is much more demanding than cell culture proof-of-principle. As a first step, the delivery of the different components of RECODE need to be solved and demonstrated. This is not only not shown, but not even discussed.\nOther points\n1. The first sentence of the Abstract is confusing: what does “expands target compatibility” mean?\n2. In the first paragraph of Introduction, line 50: “This modification is crucial for functions such as immune regulation, cancer progression, and temperature adaptation”. “Cancer progression” is not a “crucial function”.", "reviewer_3": "Overall Assessment:\nThis manuscript utilizes degron-fused ADAR proteins to achieve RNA editing. The authors optimized the tDeg protein and guide RNA to enhance the editing accuracy of overexpressed ADARs and propose that the pepper lock system enables specific activation at target sites. While the work presents a notable advancement, significant concerns regarding off-target effects, mechanistic validation, and experimental design require revision.\nMajor Concerns:\n1.Overstated Claims of Specificity:\nThe assertion that \"RECODE demonstrates superior specificity and efficiency compared to existing approaches\" is unsupported. Although RECODE degrades off-target ADAR1d while preserving on-target activity (Figure 1d), residual ADAR1d (~5%) persists post-degradation (Figures 1d, 2b, 3b). This likely contributes to the observed global off-target edits (Figure 5h), which remain comparable to REPAIRv2 despite improvement over MCP-ADAR. Critically, the manuscript fails to address bystander off-target editing.\n2.Unvalidated Pepper Lock Mechanism:\nClaims of \"enhanced editing precision via gRNA engineering for target RNA-induced conformational switching\" lack robust experimental support. The pepper lock exhibits low efficiency compared to RECODE v1 (Figure 3h).\nFigure 3b lacks clarity on whether target RNA was present during the assay. Assuming ADAR1d*-UDeg3a was used (as in Fig 3c), the minimal ADAR1d release suggests inefficient conformational switching, particularly with G-C designs. Crucially, data showing ADAR1d*-UDeg3a expression without target RNA (for G-A designs) is absent, precluding assessment of true off-target potential.\nThe conclusion of efficient Pepper transition (Line 314+) relies solely on RNA secondary structure predictions. Experimental validation (e.g., quantifying ADAR1d*-UDeg3a levels ± target RNA) is essential.\n3.Inadequate Control Experiments:\nCircular gRNA inherently recruits endogenous ADARs. Controls for this effect are absent except for pepper-deletion constructs (Extended Data Fig 11b,c).\nGlobal off-target editing assays (e.g., Fig 3j) lack essential controls (e.g., GFP transfection, gRNA-only expression).\nComparative analyses (e.g., Fig 5h) omit benchmarking against endogenous ADAR systems like LEAPER.\n4.Unresolved Concerns with Circular RNA:\nWhile circular RNA boosts expression, it likely increases levels of free (non-target-bound) ADAR1d*-UDeg. The pepper lock may only partially mitigate this, as:\nCircular gRNA levels vastly exceed endogenous targets. Conformational changes in unbound gRNA could still stabilize ADAR1d*-UDeg.\nPost-editing ADAR1d*-UDeg complexes may retain off-target editing capacity. This risk requires discussion.\n5.Compartment-Specific Degradation Limitations:\nThe ubiquitin-proteasome system functions differentially between the nucleus and cytoplasm. Our data suggest that nuclear-localized EGFP-tDeg resists degradation. The authors must:\nAddress compartmental differences in ADAR1d*-UDeg degradation. Perform localization studies (e.g., immunofluorescence).\nDiscuss implications for editing nuclear-retained RNAs and preventing nuclear off-target edits. Provide results for editing nuclear-retained RNAs and analysis of off-target editing.\n6.Promoter Choice Masks Degradation Capacity:\nIn the tDeg paper, using the weaker miniCMV promoter (vs. CMV) artificially reduces background fluorescent protein expression, potentially masking cellular degradation capacity limits. This strategic choice is not fully discussed in this manuscript. This requires explicit justification and discussion.\n7.Unverified Degradation Dynamics:\nThe universality of UDeg-mediated degradation across cell types/species remains unconfirmed and warrants further investigation.\nSpecific Comments:\nLine 218: Clarify why ADAR2d* exhibits poor compatibility (~1% efficiency) with UDeg-Pepper compared to ADAR1d* (~10%). Is nuclear localization a factor?\nLine 264: How does RECODE v1 reduce off-targets? While a 40-nt spacer enhances efficiency, it increases bystander edits. Shortening to 20-nt reduces bystanders but compromises efficiency. Integrating methods from citations 18, 19, 23 could mitigate bystander-induced codon changes—a major safety concern currently unaddressed.\nFigure 5: Provide bystander editing analysis for RECODE (analogous to data shown for other tools). Include this in Supplementary Figure 9.\nLine 345 / Figure 3h: The Fluc/Rluc ratio should approach zero in the NT group. Plotting fold-change relative to NT is misleading. Include a control group without ADAR1d*-UDeg3a (also needed in Figure 2c).\nLine 124: \"UDeg1s\" should be \"UDeg1c\".\nFigures 2f & 2g: The color scales appear blank/white. Ensure scales are visible and gradients darken logically with increasing values."}, "author_response": "RESPONSES TO REVIEWERS’ COMMENTS\nManuscript number: NCOMMS-25-45486A\nLiu et al.\n“Target-Stabilized Base Editors Enable Robust High-Fidelity RNA Editing”\nWe would like to express our gratitude to the reviewers for providing their constructive\nand valuable comments. We have revised the manuscript according to their critiques\nand suggestions. To facilitate review, all new edits in the revised manuscript are marked\nin blue.\nReviewer #1 (Remarks to the Author):\nIn Fig. 6, the authors examined the efficiency of RECODEv2-mediated editing of an\nALS-related FUS mutation both in culture and in vivo. In Fig. 6c, they reported a\ndramatic cytoplasmic mislocalization of mutant FUS. However, this observation\nappears inconsistent with the western blot analysis of cytoplasmic and nuclear\nfractions presented in Fig. 6e. Specifically, while Fig. 6c suggests that the majority of\nmutant FUS localizes to the cytoplasm, Fig. 6e indicates the opposite—that most mutant\nFUS is nuclear. Moreover, following editing, the nuclear HA-FUS levels do not appear\nto increase, in contrast to the result from immunostaining. The authors should clarify\nthis discrepancy.\nWe thank the reviewer for the valuable comment. We apologize for the confusion\nregarding Fig. 6e. After reviewing the protocols, we found that the nuclear protein\nsamples were highly concentrated during the preparation of nuclear fractions, leading\nto sample overloading in the western blot analysis. This overloading likely obscured\nthe differences between the control and treated groups. We have now corrected this\nissue by reducing the loading volume of nuclear proteins and repeating the western blot\nanalysis. The revised results clearly demonstrate an increased level of FUS protein in\nthe nucleus following editing. These updated findings are presented in the revised Fig.\n6e and shown below.\nThen, the authors showed that approximately 60% editing deficiency in vivo could\nslightly reduce the level of mutant FUS in axons. Although this result is consistent with\ntheir findings in cell cultures, it remains preliminary. Therefore, the authors should\nlimit their conclusion to the observation that editing can occur in vivo and revise the\nstrong claim that “These findings underscore the therapeutic potential of\nRECODEv2,” as no actual therapeutic effects were demonstrated.\nFollowing the reviewer’s suggestion, we have revised this conclusion to focus on in\nvivo editing efficacy as follows:\n“These findings underscore the efficacy of RECODEv2 in vivo.”\nOther minor comments:\n1. Please ensure that the formatting of all references is consistent. For example, in\nsome references, the first letter of each word in the article title is capitalized, while\nin others it is not.\n2. Line 476: \"Figure 6a\" should be written as \"Fig. 6a.\"\n3. Line 481, “Figures 6e and 6j” should be written as “Fig. 6e and 6j”.\nWe appreciate the reviewer’s careful and thorough reading of our manuscript. We\napologize for these errors and have corrected them in the revised version accordingly.\nReviewer #2 (Remarks to the Author):\nADAR based adenosine-to-inosine (A-to-I) provides a potentially powerful tool to\ncorrect mutations at the RNA level. However, widespread off-target effects of\nexogenously delivered ADARs remain a significant drawback of this approach. In this\npaper, Liu devised the RECODE (RNA editing with conditionally stable and enhanced\nADAR1 deaminase variants) system, which employs designer degron-tagged ADAR1d\nwith guide RNA (gRNA)-regulated stability. By promoting degradation of gRNA-\nunbound ADAR1d, RECODE significantly reduced transcriptome-wide edits while\nmaintaining high on-target efficacy. Engineering gRNA for target RNA-induced\nconformational switching restricts ADAR1d stabilization, further enhancing editing\nprecision. While the idea is clever and elegant, the major drawback is the complicated\ndesign and requirement involving 1) delivery and expression of an engineered\nexogenous ADAR variant, 2) complicated design of a gRNA that needs to bind the\ndegron-tagged ADAR1d on the one hand and the target RNA on the other, 3) delicate\nbalance of the degradability of degron-tagged ADAR1d to reduce off-target effects and\nits activity in intended A-to-I editing. While much work in cell culture has been done to\nshow proof of principle in vitro, there is hardly any data to demonstrate in vivo\nfunctionality. In fact, the delivery of such exogenous ADAR and gRNA to the intended\ntarget tissues and cells are by no means trivial, but are not demonstrated or even\ndiscussed. As it stands, the paper does not give any confidence that this method will be\nuseful for in vivo applications.\nMajor points\n1. The data for the in vivo functionality of RECODE in Figure 6g-k is very thin, weak,\nand unconvincing. The authors need to provide much more compelling data for in vivo\nexperiments.\nWe fully agree with the reviewer that the in vivo therapeutic potential of RECODE\nshould be more comprehensively demonstrated. To address this, we employed\nRECODE to introduce a protective mutation into the Angptl3 gene in mice as a potential\ntreatment for hyperlipidemia.\nPrevious genetic studies have revealed that several loss-of-function mutations in\nANGPTL3 are associated with reduced plasma lipid levels and a lower risk of coronary\nartery disease (https://doi.org/10.1172/JCI37118;\nhttp://dx.doi.org/10.1016/j.jacc.2017.02.030). In the original manuscript, we showed\nthat installation of Y417C mutation to mouse Angptl3 via RECODE suppresses its\nextracellular secretion. Building upon these findings, we extended our investigation in\nvivo. RECODE using either UDeg3a- or UDeg3b-tagged deaminase achieved editing\nefficiencies of 34.7% and 50.4%, respectively, at the Y417 site of Angptl3 in vivo.\nCorrespondingly, plasma levels of Angptl3 and lipids were effectively reduced.\nEditorial Note: Figure 7e in this Peer Review File is reproduced with permission from\nBioRender, Created in BioRender. Liu, T. (2026) https://BioRender.com/2pfrc45\nMoreover, no significant changes were observed in hepatic Angptl3 expression, liver\nlipid content, or circulating transaminase levels, suggesting a markedly lower risk of\nhepatotoxicity compared with Angptl3 inhibition via ASOs or siRNAs\n(https://doi.org/10.1161/CIRCULATIONAHA.122.05926). These results demonstrate\nd the potential of RECODE in treating hyperlipidemia and atherosclerotic\ncardiovascular disease by targeting ANGPTL3. The relevant data have been\nincorporated into the revised manuscript (Fig. 7) and are also shown below.\n2. in vivo application is the goal of this molecular genetic technology for gene therapy\netc, but is much more demanding than cell culture proof-of-principle. As a first step,\nthe delivery of the different components of RECODE need to be solved and\ndemonstrated. This is not only not shown, but not even discussed.\nWe thank the reviewer for the suggestion. We have now included additional\nexperiments demonstrating the successful delivery of distinct RECODE components to\nthe mouse liver for lowering plasma lipids with AAV vectors (Fig. 7). See also the above\nresponse.\nOther points\n1. The first sentence of the Abstract is confusing: what does “expands target\ncompatibility” mean?\nNative ADAR enzymes catalyze the deamination of adenosine (A) and exhibit a strong\npreference for A in a TA sequence context. Recent studies have engineered ADAR\ndeaminases with broadened substrate scopes, enabling deamination of cytidine (C) or\nadenosine (A) in previously disfavored sequence contexts\n(https://doi.org/10.1126/science.aax7063; https://doi.org/10.7554/eLife.75555).\nGiven the reviewer’s comments, we have revised the text to improve its readability as\nfollows:\n“RNA base editing using engineered deaminases represents a powerful tool to correct\nmutations at the RNA level.”\n2. In the first paragraph of Introduction, line 50: “This modification is crucial for\nfunctions such as immune regulation, cancer progression, and temperature\nadaptation”. “Cancer progression” is not a “crucial function”.\nWe thank the reviewer for the suggestion. We have removed “Cancer progression”.\nReviewer #3 (Remarks to the Author):\nOverall Assessment:\nThis manuscript utilizes degron-fused ADAR proteins to achieve RNA editing. The\nauthors optimized the tDeg protein and guide RNA to enhance the editing accuracy of\noverexpressed ADARs and propose that the pepper lock system enables specific\nactivation at target sites. While the work presents a notable advancement, significant\nconcerns regarding off-target effects, mechanistic validation, and experimental design\nrequire revision.\nMajor Concerns:\n1.Overstated Claims of Specificity:\nThe assertion that \"RECODE demonstrates superior specificity and efficiency\ncompared to existing approaches\" is unsupported. Although RECODE degrades off-\ntarget ADAR1d while preserving on-target activity (Figure 1d), residual ADAR1d\n(~5%) persists post-degradation (Figures 1d, 2b, 3b). This likely contributes to the\nobserved global off-target edits (Figure 5h), which remain comparable to REPAIRv2\ndespite improvement over MCP-ADAR. Critically, the manuscript fails to address\nbystander off-target editing.\nWe thank the reviewer for the comment. We apologize for the overstatement in our\noriginal wording. RECODE does not outperform existing technologies in every\naspect—for example, its specificity and activity are comparable to those of REPAIRv2\nand MCP-ADAR1d, respectively, in cultured cells. The primary advantage of RECODE\nlies in its exceptional balance between specificity and efficiency. We have revised the\ncorresponding statement to read:\n“RECODE demonstrates an exceptional balance between specificity and efficiency\ncompared to existing RNA editing approaches.”\nAdditionally, we have added the experiments to further reduce the bystander editing\nthrough introducing A•G mismatches, single-adenosine bulges, or G•U wobble base\npairs in guide-target duplex. The relevant data have been incorporated into the revised\nmanuscript (Fig. 2j) and are also shown below.\n2.Unvalidated Pepper Lock Mechanism:\nClaims of \"enhanced editing precision via gRNA engineering for target RNA-induced\nconformational switching\" lack robust experimental support. The pepper lock exhibits\nlow efficiency compared to RECODE v1 (Figure 3h).\nFigure 3b lacks clarity on whether target RNA was present during the assay. Assuming\nADAR1d*-UDeg3a was used (as in Fig 3c), the minimal ADAR1d release suggests\ninefficient conformational switching, particularly with G-C designs. Crucially, data\nshowing ADAR1d*-UDeg3a expression without target RNA (for G-A designs) is absent,\nprecluding assessment of true off-target potential.\nThe conclusion of efficient Pepper transition (Line 314+) relies solely on RNA\nsecondary structure predictions. Experimental validation (e.g., quantifying ADAR1d*-\nUDeg3a levels ± target RNA) is essential.\nWe thank the reviewer for raising this critical point. The target RNA was absent in\nFigure 3b of the original manuscript. Following the Reviewer’s suggestion, we\nperformed additional experiments to examine the effect of target RNA on ADAR1d*-\nUDeg3a expression levels. As expected, ADAR1d*-UDeg3a accumulated markedly in\nthe presence of Fluc mRNA across all locked gRNA groups (G•C, G•U, and G•A),\nwhile remaining constant in control groups with or without Pepper (Fig.3b, and 3c).\nMoreover, our data consistently show that ADAR1d*-UDeg3a levels increase as the\nPepper lock weakens. Similar trends were also observed for mTagBFP2-targeting\ngRNAs (Extended Data Fig.4). These results strongly support the efficient\nconformational transition of gRNAs to unlock the Pepper structure upon binding their\ntarget RNAs.\n3.Inadequate Control Experiments:\nCircular gRNA inherently recruits endogenous ADARs. Controls for this effect are\nabsent except for pepper-deletion constructs (Extended Data Fig 11b,c).\nWe appreciate the reviewer’s comment. We have now added gRNA-only groups to\nexclude the effect of native ADARs. It was observed editing using endogenous ADARs\nwas far less efficient than using ADAR1d-UDeg3a for both linear and circular gRNAs\n(Fig.2c).\nGlobal off-target editing assays (e.g., Fig 3j) lack essential controls (e.g., GFP\ntransfection, gRNA-only expression).\nWe agree with the reviewer that these controls are essential. We have now added SYFP2\ntransfection group in global off-target analysis (Fig.3j).\nComparative analyses (e.g., Fig 5h) omit benchmarking against endogenous ADAR\nsystems like LEAPER.\nWe thank the reviewer for the suggestion. We initially only included methods using\nexogenous ADAR deaminases for comparison in global off-target editing analysis. As\nsuggested, we have now added LEAPER2.0, which induced less transcriptome-wide\nedits than those using exogenous deaminases (Fig.5h).\n4.Unresolved Concerns with Circular RNA:\nWhile circular RNA boosts expression, it likely increases levels of free (non-target-\nbound) ADAR1d*-UDeg. The pepper lock may only partially mitigate this, as:\nCircular gRNA levels vastly exceed endogenous targets. Conformational changes in\nunbound gRNA could still stabilize ADAR1d*-UDeg.\nPost-editing ADAR1d*-UDeg complexes may retain off-target editing capacity.\nThis risk requires discussion.\nWe concur with the reviewer’s comment that the high abundance of circular gRNAs\nincreases on-target edits, as well as the risk of off-target editing. Actually, a trade-off\nbetween specificity and efficiency is often observed in both DNA and RNA editing\napplications. In this study, we not only propose a new strategy to mitigate off-target\nediting but also establish a series of RNA base editors with tunable specificity and\nactivity by fine-tuning the locking strength of Pepper. This approach enables the\nselection of appropriately balanced base editors for specific applications. We have\ndiscussed this issue in the revised manuscript as follows.\n“While increased levels of circular gRNA enhance on-target editing, they may also\nexacerbate off-target effects by excessively stabilizing ADAR1d-UDeg3a. This\nlimitation can be mitigated by the RECODEv2 mechanism, which allows precise\nregulation of ADAR1d protein level through rational gRNA engineering. The\nidentification of thermodynamic parameters of gRNAs that correlate with ADAR1d\nexpression levels and editing efficiency further streamlines the optimization of this\nsystem. Moreover, such mechanism is broadly applicable to other RNA-guided protein\neffectors to improve specificity.”\n5.Compartment-Specific Degradation Limitations:\nThe ubiquitin-proteasome system functions differentially between the nucleus and\ncytoplasm. Our data suggest that nuclear-localized EGFP-tDeg resists degradation.\nThe authors must:\nAddress compartmental differences in ADAR1d*-UDeg degradation. Perform\nlocalization studies (e.g., immunofluorescence).\nDiscuss implications for editing nuclear-retained RNAs and preventing nuclear off-target edits. Provide results for editing nuclear-retained RNAs and analysis of off-target editing.\nWe sincerely thank the reviewer for this insightful comment. We fully agree that the\ndestabilization efficiency of UDeg3a may differ across subcellular compartments. It is\nplausible that inefficient degradation of nuclear ADAR1d-UDeg3a could lead to its\naccumulation and potentially cause off-target effects within the nucleus. To address this\nconcern, we performed the following experiments:\n1. Comparison of mNeonGreen-tDeg and mNeonGreen-UDeg3a localization:\nConsistent with the Reviewer’s observation, we detected mNeonGreen condensates\nwithin the nuclei of cells expressing mNeonGreen-tDeg. However, both fluorescence\nimaging and western blot analysis of nuclear and cytosolic fractions of cell lysates\ndemonstrated that mNeonGreen was substantially destabilized when fused to UDeg3a,\nin both the cytoplasm and nucleus (Fig. 1f, and Extended Data Fig.1e).\n2. Destabilization of ADAR1d*-UDeg3a in both the cytoplasm and nucleus\nIn addition to mNeonGreen, we directly compared the destabilization efficiency\nbetween ADAR1d*-tDeg and ADAR1d*-UDeg3a. It leads to the similar conclusion\nthat UDeg3a promoted degradation of ADAR1d* in both the cytoplasm and nucleus\n(Extended Data Fig.2a).\n3. Subcellular localization of ADAR1d*-UDeg3a:\nImmunofluorescence analysis revealed that residual ADAR1d*-UDeg3a\npredominantly localized to perinuclear regions, but not the nucleus. Upon coexpression\nwith circular RNA containing the Pepper aptamer, ADAR1d*-UDeg3a redistributed\nmore evenly throughout the cytoplasm (Extended Data Fig.2f and 2g), consistent with\nthe localization pattern of the circular RNA (https://doi.org/10.1038/s41587-019-0090-\n6).\n4. Transcriptome-wide off-target analysis:\nMapping of RECODE off-target sites to the human genome showed that none were\nlocated within intronic regions (data not shown).\nTaken together, these results suggest that nuclear accumulation of ADAR1d*-UDeg3a\nis unlikely to pose a significant concern in our system.\n6.Promoter Choice Masks Degradation Capacity:\nIn the tDeg paper, using the weaker miniCMV promoter (vs. CMV) artificially reduces\nbackground fluorescent protein expression, potentially masking cellular degradation\ncapacity limits. This strategic choice is not fully discussed in this manuscript. This\nrequires explicit justification and discussion.\nWe appreciated the insightful comment. We have discussed this issue in the updated\nmanuscript as follows.\n“During the evaluation of protein destabilization domains, it should be noted that their\nperformance is strongly influenced by expression levels that are regulated by promoters.\nInsufficient promoter activity can conceal the domains’ destabilizing effects, whereas\nexcessively strong promoters may saturate the cellular degradation machinery by\ngenerating fusion proteins beyond its processing capacity.”\n7.Unverified Degradation Dynamics:\nThe universality of UDeg-mediated degradation across cell types/species remains\nunconfirmed and warrants further investigation.\nTo further demonstrate the generality of the UDeg3a-mediated destabilization effect,\nwe compared UDeg3a with two well-established protein destabilization domains, PEST\nand ecDHFR, across seven mammalian cell lines derived from distinct organs,\nincluding kidney, liver, brain, and muscle. The results revealed that UDeg3a\nconsistently outperformed both PEST and ecDHFR across all tested cell lines (Fig.1i).\nSpecific Comments:\nLine 218: Clarify why ADAR2d* exhibits poor compatibility (~1% efficiency) with\nUDeg-Pepper compared to ADAR1d* (~10%). Is nuclear localization a factor?\nIn Extended Data Fig.2c, both ADAR1 and ADAR2 deaminases fused with UDeg3a\nhave a nuclear export signal. We speculated that ADAR2 deaminase is more sensitive\nto C-terminal fusion.\nLine 264: How does RECODE v1 reduce off-targets? While a 40-nt spacer enhances\nefficiency, it increases bystander edits. Shortening to 20-nt reduces bystanders but\ncompromises efficiency. Integrating methods from citations 18, 19, 23 could mitigate\nbystander-induced codon changes—a major safety concern currently unaddressed.\nWe have conducted additional experiments to minimize bystander editing in\nRECODEv1 by incorporating strategies described in references 17, 18, and 22 of the\nupdated manuscript. As anticipated, these modifications substantially reduced\nbystander editing without a noticeable reduction in on-target A-to-I editing efficiency\n(Fig.2j).\nFigure 5: Provide bystander editing analysis for RECODE (analogous to data shown\nfor other tools). Include this in Supplementary Figure 9.\nWe comprehensively profiled the editing rates of all adenosines in SOD1 CDS in\nExtended Data Fig.9. Now, it has been added to the revised manuscript and was also\nshown below.\nLine 345 / Figure 3h: The Fluc/Rluc ratio should approach zero in the NT group.\nPlotting fold-change relative to NT is misleading. Include a control group without\nADAR1d*-UDeg3a (also needed in Figure 2c).\nFollowing the reviewer’s suggestion, we added the gRNA-only groups and found that\nRECODE gRNAs indeed recruited endogenous ADARs to edit target RNA. However,\ntheir activity is much lower than exogenous ADAR1d*-UDeg3a (Fig.3h).\nLine 124: \"UDeg1s\" should be \"UDeg1c\".\nWe thank the reviewer for spotting this. UDeg1s refer to UDeg1a-c. We have corrected\nthe text accordingly.\nFigures 2f & 2g: The color scales appear blank/white. Ensure scales are visible and\ngradients darken logically with increasing values.\nWe apologize for these errors and have corrected them in the revised manuscript.\nculR/culFdezilamroN"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed all my concerns. Thanks.", "reviewer_3": "The authors have skillfully addressed my concerns with their revisions. I am pleased to recommend the manuscript for publication in Nature Communications, particularly with the inclusion of all new data acquired during the peer review process.", "reviewer_4": "Comments to the Authors\nOn behalf of Reviewer #2, we have reviewed the authors’ manuscript at this revision stage. Below, we provide our evaluation of the authors’ responses to the concerns and comments raised by the original reviewer. Overall, the authors have addressed each point concisely and have included more robust in vivo datasets. We suggest a few minor points that may further improve the discussion and the transparency of the methodology applied.\n1. The therapeutically relevant range or threshold of Angptl3, TG, and T-CHO levels should be clearly indicated in the fig.7h or in the main text. Without this information, readers may find it difficult to judge whether these observed effects are clinically meaningful.\n2. It remains unclear why Angptl3 and TG levels were affected only in the serum and not in the liver (Fig. 7i-7k), especially considering that an AAV8 has strong liver tropism. Did the authors also measure RNA editing levels in the liver? At least, the tissue specificity of the delivery system should be discussed.\n3. Did the authors assess other basic physiological parameters in the AAV-treated mice, such as body weight, overall survival, or additional phenotypic changes? Inclusion of such information would help clarify the safety profile, overall relevance of the system, and potential risks.\n4. Fig.7f and 7g: At which time point was the editing efficiency measured, Day 14 or 28? Please specify.\n5. Could the authors specify the rationale for choosing retro-orbital injection, rather than, for instance, tail-vein injection?\n6. In the method section (line 1128), where the authors describe the AAV production, information regarding AAV8 production appears to be missing.\nwe found a clear (and easily addressed) mistake on the extended fig.2d. The \"middle\" and \"right\" indicated on the legend should be vice versa.", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "RESPONSES TO REVIEWERS’ COMMENTS\nManuscript number: NCOMMS-25-45486B\nLiu et al.\n“Target-Stabilized Base Editors Enable Robust High-Fidelity RNA Editing”\nWe would like to express our gratitude to the reviewers for providing their constructive\nand valuable comments. We have revised the manuscript according to their critiques\nand suggestions. To facilitate review, all new edits in the revised manuscript are marked\nin purple.\nReviewer #1 (Remarks to the Author):\nThe authors have addressed all my concerns. Thanks.\nWe appreciate the reviewer's positive feedback on our study.\nReviewer #3 (Remarks to the Author):\nThe authors have skillfully addressed my concerns with their revisions. I am pleased to\nrecommend the manuscript for publication in Nature Communications, particularly\nwith the inclusion of all new data acquired during the peer review process.\nWe thank the reviewer for the positive comments on our study.\nReviewer #4 (Remarks to the Author):\nOn behalf of Reviewer #2, we have reviewed the authors’ manuscript at this revision\nstage. Below, we provide our evaluation of the authors’ responses to the concerns and\ncomments raised by the original reviewer. Overall, the authors have addressed each\npoint concisely and have included more robust in vivo datasets. We suggest a few minor\npoints that may further improve the discussion and the transparency of the methodology\napplied.\nWe sincerely appreciate the time and effort invested by the reviewer in evaluating our\nmanuscript.\n1. The therapeutically relevant range or threshold of Angptl3, TG, and T-CHO levels\nshould be clearly indicated in the fig.7h or in the main text. Without this information,\nreaders may find it difficult to judge whether these observed effects are clinically\nmeaningful.\nWe thank the reviewer for this insightful question regarding the potential clinical\nsignificance of the lipid reductions observed in our study. To provide greater clarity,\nwe have added the specific reduction percentages of circulating triglycerides (TG) and\ntotal cholesterol (T-CHO) following RECODE treatment in the revised manuscript\n(Lines 546-548). We would also like to clarify the following points:\n1. In human patients with severe hypertriglyceridemia (≥500 mg/dL), lowering TG\nto below approximately 400 mg/dL is associated with meaningful clinical benefits,\nincluding reduced risk of acute pancreatitis and cardiovascular events\n(https://doi.org/10.1016/j.amjmed.2013.09.018). For LDL-cholesterol, large meta-\nanalyses demonstrate a continuous and linear relationship between LDL-C\nreduction and decreased cardiovascular risk. Any reduction in LDL-cholesterol is\nbeneficial, with greater absolute reductions conferring greater risk reduction\n(https://doi.org/10.1016/S0140-6736(10)61350-5). Thus, clinical meaningfulness\ndepends on baseline levels, disease context, and the specific patient population.\n2. Our experiments were performed in normolipidemic wild-type mice rather than a\nhyperlipidemic or disease model. However, the observed 30% reduction of TG\nand T-CHO reflect a potent pharmacological effect, which we anticipate may\n∼\ntranslate into therapeutically meaningful lipid lowering in relevant disease settings.\n2. It remains unclear why Angptl3 and TG levels were affected only in the serum and\nnot in the liver (Fig. 7i-7k), especially considering that an AAV8 has strong liver\ntropism. Did the authors also measure RNA editing levels in the liver? At least, the\ntissue specificity of the delivery system should be discussed.\nWe apologize for the previous ambiguity in describing Fig. 7. ANGPTL3 is a\nhepatically secreted protein that inhibits lipid clearance in the circulation (Lines 520-\n524). Thus, we employed AAV8 to specifically target the liver. Accordingly, RNA\nediting rates were assessed in liver tissues. Introducing the Y417C mutation into\nAngptl3 via RECODE effectively reduced its secretion from the liver into the\nbloodstream, which can also be inferred from Fig. 7c and 7d. Consequently, circulating\nAngptl3 and TG levels were lowered, while hepatic Angptl3 and TG remained largely\nunchanged. This selective reduction of circulating—but not hepatic—lipids represents\nan important advantage of our intervention strategy.\nTo improve the clarity of experimental procedure, we have revised the manuscript\n(Lines 537-539) as follows:\n“Serum samples were collected at 14 and 28 days post-injection for analysis of plasma\nlipids, Angptl3, and aminotransferases. Liver tissues were collected at 28 days post-\ninjection to assess RNA editing rates and other hepatic parameters (Fig. 7e).”\n3. Did the authors assess other basic physiological parameters in the AAV-treated mice,\nsuch as body weight, overall survival, or additional phenotypic changes? Inclusion of\nsuch information would help clarify the safety profile, overall relevance of the system,\nand potential risks.\nWe thank the reviewer for this helpful comment. We did monitor body weight\nthroughout the study and observed no significant differences between treatment groups.\nMoreover, no animal deaths occurred in any of the AAV-treated mice. As suggested,\nwe have now included the body weight data in the revised manuscript (Fig. 7i).\n4. Fig.7f and 7g: At which time point was the editing efficiency measured, Day 14 or\n28? Please specify.\nWe apologize for not including this information in the text. The editing efficiency was\nevaluated at 28 days post-injection, which corresponded to the endpoint of the animal\nexperiments. We have clarified this point in the revised manuscript (Lines 537-539) as\nfollows:\n“Serum samples were collected at 14 and 28 days post-injection for analysis of plasma\nlipids, Angptl3, and aminotransferases. Liver tissues were collected at 28 days post-\ninjection to assess RNA editing rates and other hepatic parameters (Fig. 7e).”\n5. Could the authors specify the rationale for choosing retro-orbital injection, rather\nthan, for instance, tail-vein injection?\nWe thank the reviewer for raising the point. For systemic delivery of AAV in mice,\nretro-orbital injection is usually preferred in our lab because it’s technically easier and\ngives consistent intravenous access. Importantly, a number of studies using systemic\nAAV8 report robust liver transduction after retro-orbital injections\n(https://doi.org/10.1038/s41551-022-00911-4; https://doi.org/10.1038/laban0511-155;\nhttps://doi.org/10.1016/j.atherosclerosis.2018.09.005). We have specified this point in\nthe updated manuscript (Lines 535, 536) as follows:\n“Recombinant AAVs were administrated to adult mice via retro-orbital injection for\nefficient liver transduction74.”\n6. In the method section (line 1128), where the authors describe the AAV production,\ninformation regarding AAV8 production appears to be missing.\nWe are sorry for this negligence. We have added AAV8 in the method section (Line\n1137) as follows:\n“For AAV production, HEK293T cells were co-transfected with a mixture of plasmids:\nan AAV vector plasmid, a packaging plasmid encoding the AAV2 Rep protein and\ncapsid proteins for AAV9 or AAV8, and the pHGT1-Adeno1 helper plasmid carrying\nadenoviral genes.”\nwe found a clear (and easily addressed) mistake on the extended fig.2d. The \"middle\"\nand \"right\" indicated on the legend should be vice versa.\nWe apologize for this mistake. We have corrected it in the revised manuscript\n(Extended Data Fig. 2d) as follows\nd, Deaminase-dependent RNA editing. Left, increased binding between ADAR1d* and\nPepper enhanced RNA editing activity. Middle, when coupled with the UDeg3a-Pepper\nsystem, ADAR1d* is much more efficient than ADAR2d* in inducing RNA edits. Right,\nADAR1d*-UDeg3a is most efficient without specific localization signals. Data are\npresented as mean ± SEM from 4 biological replicates. One-way ANOVA: ***p<0.001,\n****p<0.0001.\nReviewer #5 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review\nand to provide appropriate recognition for Early Career Researchers who co-review\nmanuscripts.\nWe sincerely appreciate the time and effort invested by the reviewer in evaluating our\nmanuscript."}, {"round": 3, "reviewer_comments": {"reviewer_4": "The authors appropriately addressed all the comments; therefore, I recommend the manuscript for publication in Nature Communications. Thank you for the opportunity to review the manuscript.", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "RESPONSES TO REVIEWERS’ COMMENTS\nManuscript number: NCOMMS-25-45486C\nLiu et al.\n“Target-Stabilized Base Editors Enable Robust High-Fidelity RNA Editing”\nREVIEWERS' COMMENTS\nReviewer #4 (Remarks to the Author):\nThe authors appropriately addressed all the comments; therefore, I recommend the\nmanuscript for publication in Nature Communications. Thank you for the opportunity\nto review the manuscript.\nWe thank the reviewer for the positive comments on the revised manuscript.\nReviewer #5 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review\nand to provide appropriate recognition for Early Career Researchers who co-review\nmanuscripts.\nWe sincerely appreciate the time and effort invested by the reviewer in evaluating our\nmanuscript."}]

Decision letter