Miniature and versatile genome regulation TnpB-ωRNA toolkits facilitate cancer immunotherapy

Domain: biology
Published: 2026-04-01
Editorial outcome: minor_revision

Abstract
CRISPR‒Cas systems represent powerful tools for genome regulation. However, the large size of Cas proteins limits their efficient delivery via an adeno-associated virus (AAV), thereby restricting their clinical translation. Here, we engineer the IS200/IS605 transposon-encoded nuclease TnpB, along with its ωRNA scaffold, to create an enhanced TnpB system, which serves as a compact toolkit for gene activation, genome editing, and base editing. The gene activator enTnpBa increases expression by 2889-fold with a minimized 93 nt ωRNA and robustly activates endogenous genes in mammalian cells. We develop a single-AASV-based regimen for immune activation (AAV-ImmunAct) that delivers enTnpBa to activate CXCL9, IL-15, and IFN-γ. AAV-ImmunAct effectively enhances T cell migration and activation, increases killing of cancer cell lines and patient-derived organoids, and synergize s with anti-PD-1 therapy in humanized mice. Here, we establish enTnpB as a compact and versatile platform for genome regulation and a promising tool for cancer immunotherapEy.

Introduction
The prokaryote-derived clustered regularly interspaced short palindromic repeats and CRISPR-associated proteins (CRISPR‒Cas) system has been repurposed as a versatile tool for genome manipulation in mammalian cells1. By fusing transcriptional or epigenetic effectors with nuclease-inactive dCas proteins, it is possible to achieve precise regulation of endogenous genes through programmable DNA binding2. These technologies provide promising gene therapies for targeted cancer therapy3,4, tissue regeneration5, and metabolic improvement6. For in vivo delivery via CRISPR tools, adeno-associated virus (AAV) is approveSd by the US Food and Drug Administration because of its efficiency, persistence of expression, and safety7. However, AAVs have limited payload packaging capacity (less than 4.7 kb). Most dCas fusion proteins exceed this limit, which restricts their clinical application8. Therefore, engineering highly efficient and compact CRISPR tools is urgently needed to promote in vivo genome manipulation.

The extensively studied type II SpCEas9 [1368 amino acids (aa)] and type V Cas12a (1100–1300 aa) proteins present high avCerage editing efficiency but are defective in packaging into a single AAV9. A series of miniature Cas systems were discovered for genome manipulation in human cells, including enCjeCas910, SpCas12f11, AsCas12f12, and CasΦ13. Some of these methods have been applied successfully in vivo. Recent studies highlighted the IS200/IS605 transposon-encoded nuclease superfamily members IscB (the ancestor of Cas9) and TnpB (the ancestor of Cas12)14. ISDra2 TnpB (408 aa) is typically compact in size and much smaller than currently well-known Cas proteins. Wild-type TnpB functions as an RNA-guided dsDNA nuclease with its RuvC domain. Like other Cas proteins, a cognate RNA (named ωRNA) is needed, guiding the TnpB protein to the targeted loci adjacent to the upstream target-adjacent motif (TAM)15. For gene regulation purposes, the compact TnpB-ωRNA system is an ideal single-AAV delivery tool because of the large size of the fused effector domain. However, how effectively the TnpB-ωRNA system performs in transcriptional regulation remains unclear.

In this work, we use a stepwise approach to engineer a catalytically inactive TnpB (dTnpB) and ωRNA (Fig. 1). Through multiple rounds of optimization, we develop a truncated ωRNA and improved TnpB variants, collectively named enhanced TnpB (enTnpB). Based on enTnpB, we construct a set of gene regulation tools, including an endogenous gene activator (enTnpBa), a gene editor (enTnpB-GE), and an adenine base editor (enTnpB-ABE). To further evaluate the therapeutic potential of enTnpB in tumor treatment, we construct a single-AAV-based regimen for immune activation (AAV-ImmunAct) by employing enTnpBa. AAV-ImmunAct promotes the upregulation of CXCL9, IL-15, and IFN-γ in the tumor microenvSironment, exerting a synergistic therapeutic effect when combined with immune checkpoint inhibitors. Overall, the enTnpB-based gene regulation toolkits represent a promising platform for gene therapy.

Results
We summarized the validateCd CRISPR proteins, among which TnpB is the most compact (Supplementary Fig. 1a). To obtain mammalian cell-compatible toolkits, human codon optimization was performed for TnpB from Deinococcus radiodurans ISDra214. TnpB, guided by the 5'TTGAT TAM, functions with a 231-nt-long ωRNA. The TnpB-ωRNA system has weak dsDNA cleavage activity in human cells14. We then introduced the D191A mutation at the RuvC-like active site to generate catalytically inactive dTnpB. (Supplementary Fig. 1b–c).

To enable gene activation by dTnpB, we fused it with the tripartite transcriptional activator VP64-p65AD-Rta (VPR) 16 and the SV40 nuclear localization signal (SV40-NLS) at the C-terminus, naming the fusion protein dTnpB-VPR. We used three plasmids for testing, including the dTnpB-VPR plasmid, ωRNA plasmid, and mOrange2-expressing reporter plasmid (Supplementary Fig. 1d). The gene activation function of dTnpB-VPR was quantified by flow cytometry (Supplementary Fig. 1e). Despite its larger size, the dTnpB-VPR fusion exhibited higher activation efficiency than the recently reported compact activator NFZ (Supplementary Fig. 1f–g)17. A library of 11 dTnpB-VPR effectors was generated with different VPR fusion sites and diverse types and fusion sites of NLSs (Supplementary Fig. 1h). dTnpB-VPR#3 showed the highest efficacy of 305-fold activation and was selected for subsequent experiments (Supplementary Fig. 1i–j).

The structure of ωRNA comprises one pseudoknot (PK) upstream of the spacer and four stems. A specific triple helix is formed by stem 1, which plays a cruciaSl role in stabilizing the scaffold (Fig. 2a) 18. To optimize the cognate ωRNA, we proposed five modification strategies on the basis of previous knowledge: (M1) truncation of the 5′ disordered region; (M2) truncation of the distal stem 3 region, with or without correction of mism atched base pairs; (M3) replacement of the disordered tetraloop; (M4) addition of a uridine-rich 3′ overhang; and (M5) replacement of mismatched base pairs in stem 1 anEd the PK with canonical Watson–Crick pairs. These modifications were designed to imCprove RNA structural stability, folding efficiency, and binding affinity to TnpB, thereby increasing the activity of the TnpB–ωRNA complex in mammalian systems.

M1: The 5' end of the ωRNA was resolved as a disordered region from nucleotides −231G to −117T, indicating that it contributes little to the interactions with TnpB. To test this hypothesis, we performed serial trims in this region. ωRNAs 116 nt in length (Μ1−117) showed comparable efficacy but were shorter. To determine the role of the triple helix, we further mutated or trimmed stem 1 near the 5' end. A single-base deletion at the −116G (M1−116) site led to a 561-fold increase in reporter activation compared with that in the nontargeted control group (NT). However, the efficacy was diminished after deletion of −115G or −114U, possibly due to disruption of the triple helix structure (Fig. 2b, Supplementary Fig. 2a and Supplementary Fig. 3a).

M2: Next, we independently sought to trim the stem 3B region that is spatially located at the distal end of the triple helix and then linked by a GUGA tetraloop. The deletion of stem 3b and mutations at −73C and −75A dramatically enhanced the activation efficacy, reaching 956-fold (M2.2). We then deleted stem 3A by single paired base resolution based on M2.2 to enhance compactness. Surprisingly, deletion of (−71U)−(−48A) and (−72A)−(−47U) (M2.4) led to a 1115-fold increase in activation (Fig. 2c, Supplementary Fig. 2b and Supplementary Fig. 3b). However, this effect was dampened with additional deletions.

M3: We further tested the substitution of the disordered UUUA tetraloop with a GNRA motif, which has been reported to stabilize RNA structures 19. GAGA tetraloop (M3.9) slightly increased the activation efficacy (Fig. 2d, Supplementary Fig. 2c and SupplSementary Fig. 3c).

M4: A previous study showed that a uridine-rich 3’-overhang can increase Cas12a-mediated activity. Given the homology between Cas12 and TnpB, we tested 13 ωRNAs with different overhang sequences. Compared with the other termination sequences, the wild-type 5’-T6 sequence (WT) produced greater activation (Fig. 2e, Supplementary Fig. 2d and Supplementary Fig. 3d).

M5: We also evaluated the efCfect of substituting mismatch base pairs at −96A to −96C and −100U to −100G in stem 1 and the triple helix region (M5.1) and at −102U to −102G and −106G to −106U in the PK region (M5.2). Both of them failed to amplify activation (Supplementary Fig. 3e–f).

To obtain the optimal ωRNA, we combined the above enhancement designs to assess potential synergistic effects. Since M2.4 showed the highest activation efficacy, we tested its combination with M1-116 and M3.9. The final version, named enhanced ωRNA (enωRNA), included M1−116, M2.4, and M3.9. It achieved an 1890-fold increase in activation (Fig. 2f and Supplementary Fig. 2f) and was shortened to 93 bp in length (Fig. 2g).

We hypothesized that the DNA binding activity of the Cas protein was affected by the interactions between amino acid residues and nucleic acids. As previous studies have demonstrated, basic amino acids such as arginine (R) enhance binding to negatively charged DNA and/or RNA20. Inspired by the cryo-electron microscopy (cryo-EM) structure of the dTnpB-ωRNA-target DNA complex, we selected 22 candidate amino acids for the first-round screening of dTnpB variants. Nevertheless, 7 variants showed increased activation: S117R, Q227R, A237R, N255R, P282R, G301R, and E302R (Fig. 3a and Supplementary Fig. 4a). Specifically, S117 is located in the wedge (WED) domain, whereas the others are located in the RuvC domain (Fig. 3b). The two domains reportedly interact with the sugar‒phosphate backbone and recognize ωRNA 15. This can explain the location of these functional mutations. S

On the basis of the mutant (N255R) with the highest activation efficiency, we conducted the second round of iteration. Compared with the N255R variant, the double-site mutant (N255R/P282R) showed improvement (Fig. 3c an d Supplementary Fig. 4b). However, the third round of screening failed to achieve further activation (Supplementary Fig. 4c–d). Therefore, the double-site mutant (N255R/P282R) was selected as the final modified version of dTnpB, named enhanced-dTnpB (endTnpB). TogCether with enωRNA, it forms a compact and highly effective gene activator, referred to as enTnpBa (endTnpB and enωRNA). The overall development process of the enhanced variants leading to enTnpBa is summarized in Fig. 3d, resulting in a 2889-fold increase in activation.

The dCasMINI-VPR (CasMINIa) was previously reported as a compact gene activator 20. We next compared the activation capacity between enTnpBa and CasMINIa. To mimic the in vivo single AAV delivery process, we engineered an all-in-one plasmid including all the elements for enTnpBa, as well as CasMINIa with its optimal protein version. For the reporter plasmid, the most efficient TTTG PAM was determined for CasMINIa. An overlapping protospacer for enTnpBa and CasMINIa was also tailored (Fig. 4a). These designs enable unbiased comparisons. Fluorescence imaging (Fig. 4b) and flow cytometry analysis (Fig. 4c and Supplementary Fig. 5) demonstrated that, compared with CasMINIa, enTnpBa achieved greater gene activation efficiency (P<0.0001) while maintaining a more compact size (3640 bp vs. 4064 bp).

To assess the specificity of enTnpBa-mediated activation in mammalian cells, we performed whole-transcriptome RNA sequencing (RNA-seq) using CasMINIa as a reference. The gene expression profiles of both enTnpBa and dCasMINI-VPR were highly correlated between the targeted and nontargeting guides across three biological replicates, indicating a specific and consistent transcriptional response (Fig. 4d). Moreover, comparable levels of variation in gene expression were observed between the two tools on the basis of sStandard deviation analysis (Fig. 4e), suggesting similar specificity.

Mammalian gene regulation represents the dominant application of CRISPR tools in the clinical and basic medical sciences. We next e valuated the ability of enTnpBa to activate endogenous genes. A 2000 bp region upstream and a 500 bp region downstream of the transcription start site (TSS) were analyzed, and five sEpacer sequences were selected within the 2500 bp regions of IFN-γ, CXCL9, CD48, and IL-15C. Among these targets, two out of five spacer sequences showed effective gene activation, with IFN-γ-seq5 resulting in the most significant fold change of 1308 in mRNA expression (Fig. 4f–i). To further compare the endogenous gene activation efficiency of enTnpBa with that of CasMINIa, we used the most efficient spacer for IFN-γ reported previously for CasMINIa (seq3, seq5, seq9)20, as well as an additional spacer (seq10) overlapping with IFN-γ-seq5. Compared with CasMINIa, enTnpBa induced higher mRNA expression levels, with both optimal and overlapping spacer sequences (Fig. 4j). Together, these results demonstrate that enTnpBa achieves both high activation efficiency and specificity in mammalian cells.

EnTnpBa has demonstrated a potent gene activation capacity, suggesting its superior DNA-targeting efficiency. We accordingly hypothesized that an active version of enTnpB might enable efficient gene editing. Considering the potential effects of RuvC mutations (N255R/P282R), we constructed two versions of enTnpB-based gene editors (GE): GE#1, consisting of enωRNA and wild-type catalytically active TnpB (WT TnpB), and GE#2, consisting of enωRNA and catalytically active enTnpB (Fig. 5a). We evaluated their editing performance across three endogenously activated sites tested previously. GE#1 exhibited significantly greater indel efficiency than GE#2 did, and both outperformed GE#WT (Fig. 5b). The distribution of deletion events revealed that indels predominantly occurred at the 3′ end of the spacer. Compared with GE#2 and the WT, GE#1 induced a broader range of deletions (Fig. 5c). Specifically, GE#1 produced more long-range deletions exceeding 60 bp, whereas tShe length of the insertions was almost unchanged (Supplementary Fig. 6a). We further validated the editing efficiency of GE#1 across thirteen additional sites. GE#1 outperformed GE#WT at every locus examined, with indel fold changes ranging from 1.33 to 109.13 (Fig. 5d and Supplementary Fig. 6b). The indel efficiency was validated via a T7 endonuclease I (T7EI) assay (Supplementary Fig. 6c). To evaluate the off-target effects of GE#1,E we performed GUIDE-seq analysis at three target sites with varying degrees of editing efCficiency. No detectable off-target events were observed at any of these sites (Fig. 5e). In addition, for VEGFA, the top ten predicted off-target sites identified by CAS-OFFinder also showed minimal off-target editing (Supplementary Fig. 6d). To enable a direct comparison with previously reported TnpB variants, we synthesized the published constructs using the same vector backbone and evaluated them in the same system as GE#1 to minimize potential bias21-23. Under these conditions, GE#1 exhibited comparable or greater editing efficiency at four tested target sites (Supplementary Fig. 6e). Together, these findings indicate that GE#1 has enhanced editing capacity across multiple target sites while maintaining minimal off-target activity.

We also evaluated the potential of endTnpB as a base editor. EndTnpB was fused with the deoxyadenosine deaminase TadA and its engineered variant TadA-8e21 in different configurations (V1–V4) (Supplementary Fig. 6f). Analysis of A-to-G conversion rates revealed that V4 (NLS–enTnpB–TadA-8e–NLS) achieved the highest editing efficiency at the two tested sites (Supplementary Fig. 6g). Using the V4 configuration, we generated three endTnpB-based adenine base editors (ABEs): ABE#WT, ABE#1, and ABE#2 (Fig. 5f). At three tested target sites, ABE#2, which integrates enωRNA and endTnpB, consistently showed higher base-editing efficiency than ABE#WT and ABE#1 did (Fig. 5g). To further assess the performance and editing pattern of ABE#2, we expanded the analysis to ten additional loci derived from IFN-γ and IL-15. ABE#2 induced efficient base conversion in a sequence-dependent manner, with the highest average A-to-G conversion at the IFN-γ#1 locus reaching 16.2% (Fig. 5h). Analysis of the editing window revealed that most A-to-G conversions occurred between the A3 and A10 positions of the protospacer (Fig. 5i). On the basis of the IFN-γ#1 locus, we applieSd CAS-OFFinder to predict off-target sites. However, amplicon sequencing of the top ten predicted sites failed to detect significant off-target A-to-G conversions (Supplementary Fig. 6h). Collectively, these results indicate that ABE#2 achieves efficient A-to-G conversion in a sequence-dependent manner without inducing significant off-target effects.

Given its compact size and strong gene regulatory activity, enTnpBa is well suited for single AAV-delivery. AAV-mediated intratumor cytokine release represents a potent method for cancer treatment, which can remodel the tumor microenvironment and synergize with immune checkpoint inhibitor (ICI) therapy24,25. We then constructed an enTnpBa-based single-AAV that can simultaneously target CXCL9, IFN-γ, and IL-15 for multilayer antitumor immunity activation. Fig. 6a illustrates the design and validation workflow for immune activation enTnpBa (ImmunAct). The expression of the ImmunAct plasmid was first verified by transient transfection of HEK293 cells. Protein expression measured by western blot and enzyme-linked immunosorbent assay (ELISA) confirmed simultaneously elevated CXCL9, IFN-γ, and IL-15 in both the supernatant (SN) and the cell pellet (CP) (Fig. 6b–d).

To evaluate the immune-stimulating function of ImmunAct, we evaluated the effects of SN (SN-NT or SN-ImmunAct) on CD8+ T cells derived from peripheral blood mononuclear cells (PBMCs). The SN-ImmunAct can recruit more T cells to the lower chamber (Fig. 6e) and induce activation by increasing granzyme B (GZMB) expression (Fig. 6f).

The therapeutic AAV was subsequently packaged with an ImmunAct plasmid (AAV-ImmunAct) to assess its antitumor function in vitro. We treated two human bladder cancer cell lines, UM-UC-3mCherry and T24mCherry, with either AAV-NT or AAV-ImmunAct. Then, the cancer cells were cocultured with magnetic bead-activated CD8⁺ T cells to detect their killing efficacy. The results showed that AAV-ImmunAct-treated cancer cells were more susceptible to CD8⁺ T-cell-mediated cytotoxicity (25.1% vs. 15.3% in UM-UC-3mCherrSy cells and 22.2% vs. 6.3% in T24mCherry, Fig. 6g–h).

To test the translational potential, we employed bladder cancer patient-derived organoid models to evaluate AAV-ImmunAct. Organoids were established from six individual bladder cancer patients and treated with either AAV-NT or AAV-ImmunAct. They were cocultured with PBMC-derived T cells from the same paEtient. Fluorescence imaging revealed the cell death status and enhanced the killing efficacyC of the organoids after AAV-ImmunAct treatment (Fig. 6i–j). These results indicate that AAV-ImmunAct can effectively induce cytokine expression and potentiate CD8⁺ T-cell-mediated tumor killing in both cancer cell lines and patient-derived organoids.

To investigate whether AAV-ImmunAct can enhance immunotherapy in vivo, we established a UM-UC-3 xenograft model with PBMC reconstitution (Fig. 7a). AAV-ImmunAct alone showed an antitumor effect similar to that of anti-PD-1 therapy. Notably, the combination of AAV-ImmunAct with anti-PD-1 therapy led to prominent tumor suppression (Fig. 7b–d). qPCR and immunohistochemistry (IHC) analyses revealed that CXCL9, IFN-γ, and IL-15 were successfully activated and upregulated in tumors by ImmunAct (Supplementary Fig. 7a–b). Ki-67 staining demonstrated reduced cancer cell proliferation following AAV-ImmunAct combined with anti-PD-1 treatment. TUNEL assays indicated that the combination therapy induced increased tumor cell apoptosis, which may have resulted from T-cell-mediated killing efficacy (Fig. 7e). IF analysis revealed that AAV-ImmunAct, particularly in combination with anti-PD-1, promoted the highest CD8⁺ T-cell infiltration and GZMB expression (Fig. 7f). Collectively, these findings suggest that AAV-ImmunAct synergizes with anti-PD-1 therapy to enhance CD8⁺ T-cell infiltration and cytotoxic function, thereby promoting tumor cell apoptosis and suppressing proliferation.

Methods
All experiments involving human samples and animal models were conducted in accordance with relevant ethical regulations.

Human bladder cancer tissues and peripheral blood samples were obtained from patients at Sun Yat-sen Memorial Hospital, Sun Yat-sen University, in accordance with the principles of the Declaration of Helsinki. The study protocol was approved by the Medical Ethics Committee of Sun Yat-sen Memorial Hospital, Sun Yat-sen University (SYSKY-2023-396-01).

All animal experiments were approved by the Institutional Animal Care and Use Committee of Sun Yat-sen University (SYSU-IACUC-2025-001980) and were performed in compliance with institutional guidelines for animal welfare. The maximal tumour burden permitted by the Institutional Animal Care and Use Committee was 1,500 mm³ (or when tumour diameter reached 1.5–2.0 cm). Tumour size was monitored regularly, and the maximal permitted tumour burden was not exceeded in any experiment.

The coding DNA sequences of TnpB, mOrange2, VPR, NFZ, SV40-NLS, c-Myc-NLS, dCasMINI, TadA, TadA-8e, and the TnpB protein and ωRNA engineering constructs from previous studies were optimized for human codons and synthesized by SYNBIO Technology (https://synbio-tech.com/). DNA fragments were amplified via PCSR via Phanta Flash Master Mix (Vazyme, P510). All the fragments were verified via agarose gel electrophoresis and purified via the FastPure Gel DNA Extraction Mini Kit (Vazyme, DC301). The backbone vectors pCDH and AAV2 were obtained from SYNBIO Technology. The pCDH vector was digested with SpeI-HF (New England Biolabs, R3133S) and SnaBI (New England Biolabs, R0130L), whereas the AAV2 vector was digested with NotI (New EnEgland Biolabs, R3189L) and BamHI-HF (New England Biolabs, R3136S).

The expression plasmids were assembled via seamless cloning with the ClonExpress Ultra One Step Cloning Kit (Vazyme, C115). The ωRNA and sgRNA series plasmids were generated by ligating annealed oligos into PaqCI (New England Biolabs, R0745L)-digested backbone plasmids via T4 ligase (New England Biolabs, M0202L). All the cloning products were transformed into DH5α competent cells, and positive clones were selected on ampicillin agar plates. Sanger sequencing (RuiBiotech, http://www.ruibiotech.com/) was performed to verify the sequence of the plasmid. The plasmid information, plasmid element sequences, and spacer sequences used in this study are summarized in Supplementary Data 1, 2, and 3, respectively.

The HEK293T (CRL-3216), UM-UC-3 (CRL-1749), and T24 (HTB-4) cell lines were obtained from the American Type Culture Collection (ATCC). The mCherry-overexpressing cell lines UM-UC-3mCherry and T24mCherry were constructed via lentivirus. HEK293T cells and UM-UC-3mCherry were cultured in Dulbecco’s Modified Eagle Medium (DMEM) (Thermo Fisher, 11965092), while T24mCherry, peripheral blood mononuclear cells (PBMCs), and human CD8+ T cells were cultured in Roswell Park Memorial Institute 1640 medium (Thermo Fisher, 11875093). All the above culture media were supplemented with 10% fetal bovine serum (Thermo Fisher, A5256701) and 1% penicillin‒streptomycin (Thermo Fisher, 15140122). The cells were grown in a humidified incubator at 37°C with 5% CO₂.

For transient transfection in 12-well plates, HEK293T cells Swere transfected with plasmids at approximately 70% confluence with the assistance of Opti-MEM (Thermo Fisher, 11058021) and linear polyethylenimine (PEI) (40 kDa, Yeasen, 40816ES02). The total plasmid was mixed with Opti-MEM-diluted PEI at a ratio of 2:1 (w :w) and incubated for 15 minutes at room temperature before transfection. For the reporter activation assay, 800 ng of mOrange2 reporter plasmid, 800 ng of TnpB variant plasmiEd, and 800 ng of ωRNA variant plasmid were mixed. To compare the enTnpBa and CasMICNIa reporter activation capacities, 1000 ng of reporter plasmid and 1000 ng of all-in-one plasmid were used. For endogenous gene activation, base editing, and gene editing assays, 1000 ng of all-in-one plasmid loaded with spacers targeting different loci was used. The cells were harvested for further detection at 48 h after transfection. For the negative control group in the aforementioned plasmid-based experiments, the corresponding systems were loaded with a non-targeting (NT) spacer sequence, consisting of a 20-bp sequence for the TnpB system or a 23-bp sequence for the CasMINI system, both derived from the prokaryotic protein LacI. Nucleotide BLAST (https://blast.ncbi.nlm.nih.gov/) analysis confirmed that these spacer sequences have no identical matches within the human genome.

To assess mOrange2 expression, the transfected cells were dissociated with 0.05% trypsin-EDTA (Thermo Fisher, 25200072) and analyzed via CytoFLEX (Beckman) to determine the mean fluorescence intensity (MFI) and the proportion of mOrange2⁺ cells. For surface protein staining, the cells were incubated with an anti-human CD8 antibody (BioLegend, 980910) in PBS containing 5% FBS at room temperature for 30 minutes. For the intracellular staining of Granzyme B, the cells were fixed and permeabilized via the True-Nuclear Transcription Factor Buffer Set (BioLegend, 424401) and stained with an anti-human Granzyme B antibody (BioLegend, 515403) at room temperature for 30 minutes. The samples were subsequently analyzed via CytoFLEX to quantify the proportion of CD8⁺ Granzyme B⁺ double-positive cells. The flow cytometry gating strategies used are shown in Supplementary Fig. 8.

Total RNA was isolated from cells or tumor t issues, the latter of which were snap-frozen in liquid nitrogen and mechanically homogenized via TRIzol reagent (TaKaRa, 9109). Up to 1 μg of RNA was reverse transcribed via a HiSEcript II 1st Strand cDNA Synthesis Kit (Vazyme, R212-01). Quantitative RT‒PCR (qRCRC) was performed with ChamQ SYBR Color qPCR Master Mix (Vazyme, Q411-02) in 384-well plates (Monad, MQ50701S) on a LightCycler 480 system (Roche). The housekeeping gene β-actin was used as an internal control. The relative mRNA expression levels were calculated via the 2−ΔΔCt method. Three biological replicates were performed. A quantification cycle over 35 was considered 35 because of fluctuations in the weak transcript value. The qRCR primers used are listed in Supplementary Data 4.

To evaluate the specificity of enTnpBa and CasMINIa, HEK293T cells were transfected with a reporter plasmid containing both PAM and TAM sequences along with a matched spacer, together with either pCDH-U6-enωRNA-CMV-endTnpB-VPR or pCDH-U6-sgRNA-CMV-dCasMINI-VPR. Total RNA was isolated three days post-transfection as described above. RNA library preparation and sequencing were conducted by Novogene Biotechnology (Guangzhou, China). Raw reads in fastq format were trimmed, and low-quality reads were removed via Trimmomatic v.0.6.10. Clean sequencing reads were aligned to the human genome (hg38) with an additional mOrange2 sequence via HISAT2 v.2.2.1. Gene-level counts were quantified with featureCount from the subread toolkit v.2.1.1. Normalized gene expression was measured via TPM (transcripts per million reads). Pearson correlation was used to estimate the transcriptomic consistency and validate the off-target effects in each system. The standard deviation of gene expression across samples within each system was calculated to assess the stability of endogenous activation.

To evaluate the specificity of enTnpB GE#1 and ABE#2, potential off-target sites were predicted via Cas-OFFinder (http://www.rgenome.net/cas-offinder/). The TAM sequence was set to “TTGAT”, and up to five mismatches were allow ed. Targeted deep sequencing was performed to quantify off-target editing efficiency via amplicon sequencing, with the detailed procedures described below in the “Deep sequencEing sample preparation and data analysis” section. The primers used for amplicon amplifiCcation are listed in Supplementary Data 5.

GUIDE-seq was performed by GeneRulor (Zhuhai, China) to identify off-target cleavage sites of enTnpB GE#1 at the FANCA, SERPINA1, and VEGFA loci37. The cells were electroporated with the enTnpB GE#1 plasmid together with a blunt double-stranded oligodeoxynucleotide (dsODN), which was integrated at genomic double-strand break sites as a molecular tag. Genomic DNA was extracted, randomly fragmented via sonication, end-repaired, and ligated to sequencing adapters. Strand-specific libraries flanking the dsODN were generated via bidirectional PCR, with unique molecular identifiers (UMIs) used to remove PCR duplicates. Libraries were sequenced on the MGI2000 platform via paired-end 150 bp reads and aligned to the hg38 reference genome. After sample demultiplexing and UMI collapse, candidate cleavage sites were identified on the basis of the combined read coverage from both strands. Flanking sequences were compared with the sgRNA, with perfect matches defined as on-target sites and sites with fewer than six mismatches defined as off-target sites after background filtering using control samples.

For quantification of cytokine levels in the supernatants, the cell supernatants were collected 3 days after plasmid transfection. The samples were stored at -80°C before detection. To quantify secreted protein expression, a Human CXCL9 ELISA Kit (Proteintech, # KE00165), a Human IFN-g ELISA Kit (BioLegend, #430116), and a Human IL-15 ELISA Kit (Proteintech, # KE00102) were used to quantify CXCL9, IFN-γ, and IL-15 expression, respectively, in accordance with the manufacturer’s protocol. The absorbances at 450 nm were measurSed on a Synergy H1 plate reader (BioTek), and the protein concentrations were adjusted according to the standard curve.

The cell supernatants were collected following the ELISA protocol. Cellular proteins were extracted via NP-40 buffer (Beyotime, PE0013F). The protein concentration was measured with the BCA assay kit described above. EqCual amounts of protein were loaded for each sample, separated by 10% SDS‒PAGE, and transferred onto polyvinylidene fluoride membranes (Millipore, ISEQ00010). The membranes were blocked with 5% skim milk and then incubated with primary antibodies against human CXCL9 (Proteintech, 22355-1-AP), IFN-γ (Proteintech, 15365-1-AP), IL-15 (Abcam, ab40668) and α-Tubulin (Abcam, ab7291) at 4 °C overnight. After being washed with Tris-buffered saline containing 0.1% Tween 20 (TBST), the membranes were incubated with HRP-conjugated secondary antibodies (Abcam, ab205719, ab205719) for 1 hour at room temperature. Protein signals were detected via an UltraSignal ECL kit (4Abio, 4AW011-100) and visualized via a chemiluminescence imaging system.

In accordance with the ethical standards of the Helsinki Declaration, bladder cancer tissues and peripheral blood samples were obtained from patients at Sun Yat-sen Memorial Hospital, Sun Yat-sen University. Consent was obtained from all participants prior to sample collection. Human PBMCs were isolated from peripheral blood, and CD8⁺ T cells were subsequently purified. The detailed procedures for PBMC and CD8⁺ T-cell isolation and activation were described in our previous publication38. The sex and age of the six bladder cancer patients from whom organoids and PBMC were derived have been provided in the source data file. Sex was recorded based on patient self-report in accordance with institutional documentation procedures. Sex was not considered as a variable in the study design, as it was not expected to influence the experimental outcomes. No sex based analysis was performed due to the limiteSd sample size.

The bladder cancer organoid construction proc edure was described in our previous research39. Bladder cancer tissue samples were kept on ice in PBS containing penicillin‒streptomycin. The samples were washed and cut into small Epieces. The tissues were digested at 37 °C for 30 minutes in Advanced DMEM/F-12 suppClemented with collagenase type II (Worthington), DNase I (Worthington), and the ROCK inhibitor Y-27632 (MCE, HY-10071). The digested suspensions were filtered through cell strainers, centrifuged, and washed with cold PBS. The cell pellets were resuspended in chilled Matrigel and seeded into prewarmed culture plates. After solidification at 37 °C, the organoid culture medium was added to Advanced DMEM/F-12 supplemented with HEPES (10 mM, Gibco, 15630106), GlutaMAX supplement (1×, Gibco, 35050061), B27 supplement (1×, Thermo Fisher, 17504044), Y-27632 (10 μM, MCE, HY-10071), nicotinamide (10 mM, MCE, HY-B0150), N-acetylcysteine (1.25 mM, MCE, HY-B0215), SB202190 (10 μM, MCE, HY-10295), A83-01 (500 nM, MCE, HY-10432), R-Spondin 1 (500 ng/ml, SinoBiological, 11083-HNAS), HGF (50 ng/ml, SinoBiological, 10463-HNAS), FGF4 (50 ng/ml, Novoprotein, CR08), and FGF10 (10 ng/ml, SinoBiological, 10573-HNAE). Organoids were cultured in a humidified incubator at 37°C with 5% CO₂.

AAV production and transduction were performed according to the protocol described in our previous study38. For AAV-NT or AAV-ImmunAct production, the transfer plasmids AAV2-ITR-U6-NT-endTnpB-VPR-ITR and AAV2-ITR-U6-CXCL9-U6-IFN-γ-U6-IL-15-endTnpB-VPR-ITR, together with rep/cap and helper plasmids, were transfected into HEK293T cells. After 72 hours of transfection, both the culture medium and the cells were collected. The cells were lysed by three cycles of freeze‒thaw cycles to release viral particles. The lysates and media were combined and purified via step-gradient ultracentrifugation wSith iodixanol (Sigma‒Aldrich, 92339-11-2). The titre of the AAV was determined via qPCR.

For the coculture assay with the bladder cancer cell lines UM-UC-3mCherry and T24mCherry, the cells were seeded in 24-well plates at a dEensity of 2,000 cells per well. The cells were treated with AAV-NT or AAV-ImmunAct at a Ctitre of 10⁸ vg per well for 3 days. After the culture medium was replaced, CD8⁺ T cells isolated from healthy donors (1 × 10⁵ cells per well) were added. Following 24 hours of coculture, the supernatants were collected, and the remaining adherent cells were detached via trypsin. The detached cells and cells from the collected supernatants were combined and stained with the SYTOX Green Kit (KeyGEN, KGE2504-500) according to the manufacturer’s instructions. The proportion of mCherry⁺SYTOX⁺ double-positive cells was quantified via flow cytometry to evaluate the CD8⁺ T-cell–mediated killing capacity.

For the coculture assay with bladder cancer organoids, bladder cancer organoids derived from three individual patients were seeded in 24-well plates and treated with AAV-NT or AAV-ImmunAct for 5 days. Treated organoids were collected in ice-cold PBS and dissociated mechanically. Dissociated organoids were stained with Cell Proliferation Dye eFluor™ 450 (Thermo Fisher, 65-0842-85) and cocultured with autologous CD8⁺ T cells from the corresponding patient (1 × 105 cells per well). After 18 hours of coculture, cell death was assessed by staining with NucRed Dead 647 ReadyProbe Reagent (Thermo Fisher, R37113). Representative images were acquired via an inverted fluorescence microscope (Olympus IX83) and analyzed with ImageJ (V1.54). Organoid cell death was assessed by measuring the MFI of NucRed Dead 647 (red fluorescence) within the outlined organoid region.

The humanized subcutaneous mouse models were established according to a previous study40. Specifically, a suspension of UM-UC-3 cells at a concentration oSf 5 × 10⁶ cells/mL in 100 μL of PBS was inoculated into the right dorsal subcutaneous tissue of 5-week-old male NOD/ShiLtJGpt-Prkdcem26Cd52Il2rgem26Cd22/Gpt (NCG) mice (GemPharmatech). Only male mice were used in this study, as sex was not considered a variable expe cted to influence the experimental outcomes. Subcutaneous tumors were measured every 3 days via a Vernier calliper to record the long (L) and short (W) diameters. The tumor volume was calculated via the following formula: ½ × L × W². When the average tumor volumeC reached approximately 100 mm³, the mice were randomly assigned to four groups, with seven mice per group. For humanized immunity reconstruction, PBMCs were isolated from healthy human donors and activated with human CD3/CD28 T-cell activation beads (BioLegend, 422603) for 72 hours. A total of 1 × 10⁷ activated PBMCs were intravenously injected into each mouse 6 days after tumor inoculation. Intertumoral injections of 1 × 10¹⁰ viral genomes (vg) of AAV2-NT or AAV2-ImmunAct in 25 μL of PBS were administered on days 12 and 18 postinoculation, respectively. The anti-human PD-1 antibody tislelizumab (BeiGene) was administered intraperitoneally at a dose of 10 mg/kg per mouse every 3 days, starting from day 12 postinoculation. The mice were sacrificed on day 33 postinoculation, and the subcutaneous tumors were harvested for the following assays.

HEK293T cells were transfected with an all-in-one plasmid carrying different versions of enTnpB-GE or enTnpB-ABE, together with spacers targeting various genes (spacer sequences listed in Supplementary Data 2). Three days post-transfection, the cells were harvested, and genomic DNA (gDNA) was extracted via the FastPure Cell/Tissue DNA Isolation Mini Kit (Vazyme, DC102-01). The gDNA was quantified, and 400 µg was used as a template for PCR. The target loci were amplified via the use of locus-specific primers containing sequencing adapters (the primer sequences are listed in Supplementary Data 5). Amplicons of 150–300 bp were confirmed by agarose gel electrophoresis and subjected tSo deep sequencing (Tsingke Biotechnology, China). The amplicon sequences are listed in Supplementary Data 6.

The deep sequencing data were analyzed via CRISP R Resso241. The quality control parameters were set as follows: minimum average read quality (phred33) >10; minimum single-base quality (phred33) >10; bases with quality <10 were replaced with N; and 15 bp were trimmed from both ends of the amplicon sequence befEore mutation quantification. For indel analysis, the quantification window was centeCred at position 0 relative to the 3′ end of the ωRNA, with a window size of 30 bp and a plot window size of 30 bp. Indels within this window were counted as the final results. For base-editing analysis, the quantification window center was set to –13 relative to the 3′ end of the sgRNA, with a window size of 13 bp and a plot window size of 26 bp. All A-to-G conversions within this window were counted as final results.

To assess the effect of ImmunAct on T-cell migration, activated T cells were labelled with CFSE (Thermo Fisher, 65-0850-84) according to the manufacturer’s instructions. HEK293 cells were transfected with either the enTnpB-NT or enTnpB-ImmunAct plasmid for 48 h and then cultured in fresh medium for an additional 24 h. The corresponding supernatants were collected as SN-NT or SN-ImmunAct, respectively. CFSE-labelled T cells were resuspended in SN-NT at 5 × 10⁵ cells per 200 µL and seeded into the upper chamber of a 3 µm pore-size transwell insert (Falcon, 353096-8EA). The lower chamber of a 24-well plate was filled with 800 µL of either SN-NT or SN-ImmunAct. After 8 h of incubation, the migrated cells in the lower chamber were collected, and the CFSE⁺ T cells were quantified via flow cytometry to minimize interference from cell debris.

To evaluate the effect of the ImmunAct system on the T-cell cytotoxic phenotype, activated human CD8⁺ T cells were incubated with SN-NT or SN-ImmunAct for 48 h. The proportion of CD8⁺GZMB⁺ cells was then determined via flow cytometry.

HEK293T cells were transfected with a plasmid carrying enTnpB-GE#1 with a spacer targeting VEGFA. Three days post-transfection, the cells were harvested, and gDNA was extracted as described above. The target loci were PCR- amplified via locus-specific primers, which generated ~1000 bp amplicons (listed in Supplementary Data 7). Within these amplicons, the expected indels were located approximaEtely 800 bp from the 5′ end and 200 bp from the 3′ end. The PCR products were purified fCollowing the same protocol described above, denatured at 95°C for 5 min and gradually reannealed to form heteroduplex DNA. The reannealed DNA was treated with T7 endonuclease I (New England Biolabs, M0302S) according to the manufacturer’s instructions. The cleavage products were resolved and visualized via agarose gel electrophoresis.

The tumor tissues were immediately fixed in freshly prepared 4% paraformaldehyde, paraffin-embedded, and sectioned. The sections were deparaffinized and subjected to antigen retrieval in citrate buffer via a microwave42. After three washes with PBST, the sections were blocked with 3% BSA for 30 min at room temperature. For Ki67, CXCL9, IFN-γ and IL-15 staining, the sections were incubated with anti-human Ki67 (Cell Signaling Technology, #9449), CXCL9 (Cell Signaling Technology, #9449), IFN-γ (Proteintech, 15365-1-AP), and IL-15 (Abcam, ab55276) antibodies overnight at 4°C, followed by incubation with a species-specific HRP-conjugated secondary antibody for 1 h at room temperature. The sections were then treated with 3,3’-diaminobenzidine (DAB) for the same duration across all the samples and counterstained with hematoxylin. TUNEL staining was performed via the SA-HRP TUNEL Cell Apoptosis Detection Kit (Servicebio, G1507-50T) according to the manufacturer’s instructions.

For IF, tumor tissue sections prepared as described above were stained for CD8α and Granzyme B. The sections were incubated with an anti-human CD8α primary antibody (Cell Signaling Technology, #70306) or anti-human Granzyme B primary antibody (Cell Signaling Technology, #17215) overnight at 4°C, followed by the corresSponding fluorescent-conjugated secondary antibodies (Proteintech, SA00009-1; Proteintech, SA00003-2) according to the manufacturer’s instructions, and the nuclei were counterstained with DAPI (Solarbio, C0065-50).

The prepared IHC and IF sections were image d via an upright microscope (Olympus BX53) or an inverted fluorescence microscope (Olympus IX83), respectively. For each tumor, three nonoverlapping fields were randomly selEected and independently verified by two pathologists. The exposure parameters were set to be consistent across all the samples. Ki-67 expression was evaluated via an H-score strategy. Sections were scored for the proportion of positively stained tumor cells (0–4: 0, none; 1, ≤10%; 2, 11–30%; 3, 31–70%; 4, >70%) and staining intensity (1–4: 1, none; 2, weak/light yellow; 3, moderate/brown; 4, strong/brown‒red). The H score was calculated by multiplying the proportion and intensity scores. IHC staining of CXCL9, IFN-γ, and IL-15 was quantified via a pixel-based H-score approach. DAB-positive pixels within tumor ROIs were automatically classified into three intensity categories (weak, moderate, strong) on the basis of fixed thresholds, and the H score was calculated as a weighted sum of the corresponding pixel area fractions. The percentages of TUNEL-, CD8-, or GZMB-positive cells were quantified via QuPath (v0.6.0) with standardized threshold settings applied to all the images.

All the statistical analyses were performed via GraphPad Prism (version 10.0) and R (version 4.4.1). The data are shown as the mean ± standard deviation (SD) or standard error of the mean (SEM), as indicated. Differences between two groups were analyzed via unpaired Student’s t test. Comparisons among multiple groups were performed via one-way analysis of variance (ANOVA). Tumor growth curves were analyzed via two-way ANOVA with Tukey’s post hoc adjustment. All experiments were performed independently in triplicate unless otherwise stated. A two-tailed P value < 0.05 was considered statistically significant. The experiments were randomized, and no data were excluded from the analyses.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Lu et al. report enhancing the gene-editing activity of the ISDra2 TnpB-ωRNA system through ωRNA and protein engineering. Furthermore, they demonstrate that an engineered, catalytically inactive TnpB (dTnpB) can be delivered via a single AAV vector to stimulate immune responses for cancer immunotherapy. This study is well-designed and successfully expands the application of miniature TnpB systems in cancer treatment. However, the manuscript could be strengthened by more extensive characterization of endogenous gene editing, better comparison with established Cas12f1 or TnpB variants, a thorough off-target analysis, and several minor revisions.\nSpecific points for improvement include:\n1.Several prior studies (PMIDs: 39313558, 38280857, 37386294, 37971304) have extensively engineered the cognate ωRNA for ISDra2 TnpB. The manuscript should clarify how the ωRNA variants in this study differ in sequence and structure from previous designs. Relevant prior work should be directly compared and cited rather than overlooked.\n2.The protein mutagenesis screen identified TnpB variants with enhanced activity for gene knockout and base editing. However, the on- and off-target efficacy of both the wild-type and engineered TnpB is assessed at an insufficient number of genomic loci, limiting the generalizability of the findings.\n3.The study lacks a clear performance benchmark against previously reported miniature systems. For instance:\n(1)The ωRNA variants from Figure 2 should be compared with the highly efficient 99-nt ωRNA* reported by Marquart et al. or Li et al. (PMIDs: 39313558, 38280857).\n(2)The engineered TnpB variants in Figure 5 should be compared with the advanced TnpB-AI-5.6 variant (S72R-K84R-E168R-K251R-V374R) described by Cheng et al. (Cell Res, 2024).\n(3)The rationale for selecting the specific combination of enTnpB+enωRNA for the final base editor (enTnpB-ABE) needs clarification. More comprehensive testing across multiple loci is required to validate the enhanced activity, specificity, and overall advantage of the system presented here.\n4.In lines 101-103, the authors state that the D191A mutation generates a catalytically inactive dTnpB. However, Supplementary Figure 1C shows this dTnpB variant still exhibits measurable editing activity (>5%), which contradicts the \"inactive\" claim and warrants explanation.\n5.In Figures 3, 4, and 5, the \"fold change\" values indicated in the plots are not clearly defined. The text or figure legends should specify which groups are being compared for each calculation.\n6.On line 188, the text incorrectly references \"Fig. 2D\" for enTnpBa; this should be corrected to \"Fig. 3D.\"\n7. The in vivo activation of the target gene in the mouse model should be evaluated and validated using a quantitative method like qPCR or ELISA.", "reviewer_2": "In this study, Lu et al develops a compact CRISPR toolkit named enTnpB through rational ωRNA and protein optimization. By using its compact size, the authors created an endogenous activation system called ImmunAct. It can activate three antitumor cytokines in tumors through a single AAV vector and shows a clear synergistic effect with ICI therapy. The experimental design is well-conceived, offering a new approach to enhance tumor immunotherapy. The authors obtained a large amount of high quality data, and the conclusions are supported by the results.\nThe manuscript could be further strengthened by obtaining additional evidence to validate its immune-sensitizing effect and expanding the comparison scope to include other TnpB variants. Specifically:\n1. Figure 6 employs an autologous T cell–organoid co-culture model to evaluate ImmunAct, which mimics clinical conditions. Notably, the organoids derived from different patients show significant heterogeneity. Results from three cases appear insufficient to fully demonstrate its broad applicability. Increasing the number of cases would improve the robustness of conclusion.\n2. The authors have utilized WB and ELISA to demonstrate that ImmunAct activates the expression of three cytokines in vitro, which provides a solid foundation for understanding its mechanism. To further support this finding, it would be valuable to characterize the expression levels of these cytokines in animal models as well. Evaluating cytokine induction in vivo is important to verify the endogenous gene activation effect of ImmunAct more comprehensively\n3. This study provides a nice comparison with the similarly compact CasMINI system. However, several engineered versions of TnpB have already been reported (e.g., DOI:10.1038/s41467-024-45197-z; DOI:10.1038/s41592-024-02418-z). It will be be helpful to present direct comparative data on the indel editing capability between enTnpB and the aforementioned variants. Also, more detailed discussion of these TnpB modifications would enhance the work’s depth.\n4. Several previous studies using different strategies have reported that local delivery of immune-stimulatory cytokines can enhance tumor immunotherapy (e.g., DOI: 10.1038/s41590-024-01925-7 etc.). I suggest the authors to elaborate in the Discussion section on the advantages and limitations of this endogenous cytokine activation approach compared with other cytokine delivery or immunomodulatory strategies.\n5. The gating strategy used for flow cytometry in Figure 6 should be provided in the supplementary materials to help readers better understand how the analysis was performed.\n6. In Figure 1, within the “TnpB & ωRNA engineering” box, the axis of flow cytometry seems to be shifted.\n7. In Figure 6A, the promoters used in the ImmuneAct plasmid construct should be indicated."}, "author_response": "Response letter\nManuscript # NCOMMS-25-72501\nManuscript Title: Miniature and versatile genome regulation TnpB-ωRNA toolkits\nfacilitate cancer immunotherapy\nReviewer 1\nLu et al. report enhancing the gene-editing activity of the ISDra2 TnpB-ωRNA system through\nωRNA and protein engineering. Furthermore, they demonstrate that an engineered, catalytically\ninactive TnpB (dTnpB) can be delivered via a single AAV vector to stimulate immune responses\nfor cancer immunotherapy. This study is well-designed and successfully expands the\napplication of miniature TnpB systems in cancer treatment. However, the manuscript could be\nstrengthened by more extensive characterization of endogenous gene editing, better comparison\nwith established Cas12f1 or TnpB variants, a thorough off-target analysis, and several minor\nrevisions.\nWe sincerely appreciate the reviewer for the time invested and constructive\nrecommendations. We are grateful for all the suggested revisions, each of which is\naddressed below.\nSpecific points for improvement include:\nComment 1. Several prior studies (PMIDs: 39313558, 38280857, 37386294, 37971304) have\nextensively engineered the cognate ωRNA for ISDra2 TnpB. The manuscript should clarify\nhow the ωRNA variants in this study differ in sequence and structure from previous designs.\nRelevant prior work should be directly compared and cited rather than overlooked.\nResponse 1: We thank the reviewer for raising this important question regarding structural\ncomparisons with previously reported ωRNA and TnpB systems. In prior studies, Yoon et al.\n(PMID: 37971304) established a framework to annotate and identify additional TnpB systems,\nfurther evaluating their editing potential in human cells. Xiang et al. (PMID: 37386294)\nproposed a sequential evolutionary trajectory from TnpBs to eukaryotic Fanzors. Both studies\nare now discussed and cited in the revised manuscript.\nThree studies, Xiang et al. (PMID: 37386294), Marquart et al. (PMID: 39313558) and Li et al.\n(PMID: 38280857), conducted editing efficiency-oriented truncation of ωRNA scaffolds,\nwhich corresponded to M1–114, M1–117, and M2.1, respectively, in our study. On the basis of\nour systematic ωRNA design, we identified three sites of engineering, including M1–116, M2.4\nin stem 3, and M3.9 in tetraloop, that synergistically enhance the activation and editing efficacy\nwith a minimized length (93 nt). Moreover, the comparative results revealed that, compared\nwith other TnpB engineered designs, GE#1 yielded similar or greater editing efficiency\n(Supplementary Fig. 6e; please see Response 3 for details).\nSupplementary Fig. 6e. Indel efficiencies at four loci (ABGL1, DYNC1H1, FANCA, and\nVEGFA) for GE#1 and three TnpB variants reported by Li et al., Marquart et al., and Cheng et\nal., respectively.\nThe revised content is provided below:\nPage 14, Line 354 (discussion): “Guide RNA modification is one of the most effective\nstrategies to improve the efficiency of CRISPR‒Cas tools. For TnpB, previous studies\nindependently performed editing efficiency-oriented optimization of progressively 5′-end–\ntruncated ωRNA scaffolds1-3. Scaffolds truncated to position 113 (identical to M1–114) showed\nlittle to no activity1. In contrast, truncation at position 1173 (equivalent to M1–116) and ωRNA-\nv12 (equivalent to M1–117) resulted in increased activity. Li et al. further optimized the scaffold\nto ωRNA-v22 (corresponding to M2.1), achieving greater enhancement, which is consistent\nwith our observations. Compared with these previously reported versions, we screened and\ndesigned a minimal ωRNA scaffold (93 nt) with multiple site modifications that also displays\ngreater efficiency.”\nPage 15, Line 394 (discussion): “Compared with the TnpB variants reported in previous\nstudies, these variants were initially engineered with the aim of improving indel efficiency2-4.\nFor an unbiased comparison, we synthesized the reported constructs strictly according to the\npublished sequences and evaluated them under identical experimental conditions. Under these\nconditions, GE#1 showed greater editing efficiency at all four tested loci than the variants from\nLi et al. and Marquart et al., which was attributed primarily to our ωRNA engineering strategy.\nEnωRNA was rationally truncated on the basis of functional domains suggested by cryo-EM\nstructures, whereas Li et al. mainly guided truncation via RNAfold prediction, and Marquart et\nal. truncated only the disordered region at the 5′ end. In contrast, Cheng et al. employed a deep\nlearning model (Protein Mutational Effect Predictor) to identify five beneficial mutations\n(S72R/K84R/E168R/K251R/V374R). When combined with wild-type RNA, their variant\nexhibited indel efficiency similar to that of GE#1. These findings underscore the power of\nartificial intelligence in guiding comprehensive protein engineering. Given the complementary\nstrengths of AI-optimized proteins and our engineered ωRNA, we anticipate that their\ncombination holds great promise for developing next-generation gene editing tools that are both\nhighly efficient and compact.”\nReference:\n1. Xiang, G. et al. Evolutionary mining and functional characterization of TnpB nucleases\nidentify efficient miniature genome editors. Nat Biotechnol 42, 745-757,\ndoi:10.1038/s41587-023-01857-x (2024).\n2. Li, Z. et al. Engineering a transposon-associated TnpB-ωRNA system for efficient gene\nediting and phenotypic correction of a tyrosinaemia mouse model. Nat Commun 15, 831,\ndoi:10.1038/s41467-024-45197-z (2024).\n3. Marquart, K. F. et al. Effective genome editing with an enhanced ISDra2 TnpB system\nand deep learning-predicted ωRNAs. Nat Methods 21, 2084-2093,\ndoi:10.1038/s41592-024-02418-z (2024).\n4. Cheng, P. et al. Zero-shot prediction of mutation effects with multimodal deep\nrepresentation learning guides protein engineering. Cell Res 34, 630-647,\ndoi:10.1038/s41422-024-00989-2 (2024).\nComment 2: The protein mutagenesis screen identified TnpB variants with enhanced activity\nfor gene knockout and base editing. However, the on- and off-target efficacy of both the wild-\ntype and engineered TnpB is assessed at an insufficient number of genomic loci, limiting the\ngeneralizability of the findings.\nResponse 2: We expanded our evaluation of both gene knockout and base editing to include\nmultiple genomic loci and performed corresponding off-target analyses.\nTo evaluate the gene knockout performance of GE#1, we tested an expanded panel of\nendogenous target sites and compared its on-target efficiency with that of GE#WT. All thirteen\nsites showed improved editing efficacy, with indel fold changes ranging from 1.33 to 109.13\n(Figure 5d).\nFigure 5d. Indel efficiencies of GE#WT and GE#1 across thirteen additional loci.\nTo evaluate the off-target effects of GE#1, we performed GUIDE-seq at three targeted sites\nand used CAS-OFFinder to verify the top 10 predicted off-target sites. Neither high-throughput\nscreening nor targeted analysis of specific sites revealed any off-target effects for GE#1 (Figure\n5e & Supplementary Fig. 6d). The above results indicate that the engineering TnpB in this\nstudy functions as a promising gene editing tool with minimal off-target effects.\nFigure 5e. Off-target analysis of GE#1 by GUIDE-seq at three target sites.\nSupplementary Fig. 6d. Off-target analysis of GE#1, which targets VEGFA at the top ten\npredicted off-target sites identified by CAS-OFFinder.\nTo characterize the base-editing activity of ABE#2, we extended our analysis to additional\ngenomic targets and assessed off-target effects at CAS-OFFinder–predicted sites. The results\nshowed that ABE#2 significantly improved the A-to-G conversion rate by 1.5- to 4.3-fold\n(Figure 5g). Furthermore, amplicon sequencing at the predicted off-target sites revealed a\nrather low A-to-G conversion rate (Supplementary Fig. 6h). Collectively, these results\nindicate that our engineered TnpB system represents a promising platform for base editing\napplications.\nFigure 5g. A-to-G conversion efficiencies at three target sites for ABE#WT, ABE#1, and\nABE#2.\nSupplementary Fig. 6h. Off-target analysis of ABE#2, which targets IFN-γ#1 at the top ten\npredicted off-target sites identified by CAS-OFFinder.\nThe revised content is provided below:\nPage 9, Line 231 (result): “We further validated the editing efficiency of GE#1 across thirteen\nadditional sites. GE#1 outperformed GE#WT at every locus examined, with indel fold changes\nranging from 1.33 to 109.13 (Fig. 5d and Supplementary Fig. 6b). The indel efficiency was\nvalidated via a T7 endonuclease I (T7EI) assay (Supplementary Fig. 6c). To evaluate the off-\ntarget effects of GE#1, we performed GUIDE-seq analysis at three target sites with varying\ndegrees of editing efficiency. No detectable off-target events were observed at any of these sites\n(Fig. 5e). In addition, for VEGFA, the top ten predicted off-target sites identified by CAS-\nOFFinder also showed undetectable off-target editing (Supplementary Fig. 6d).”\nPage 10, Line 246 (result): “We also evaluated the potential of endTnpB as a base editor.\nEndTnpB was fused with the deoxyadenosine deaminase TadA and its engineered variant\nTadA-8e (21) in different configurations (V1–V4) (Supplementary Fig. 6f). Analysis of A-to-\nG conversion rates revealed that V4 (NLS–enTnpB–TadA-8e–NLS) achieved the highest\nediting efficiency at the two tested sites (Supplementary Fig. 6g). Using the V4 configuration,\nwe generated three endTnpB-based adenine base editors (ABEs): ABE#WT, ABE#1, and\nABE#2 (Fig. 5f). At three tested target sites, ABE#2, which integrates enωRNA and endTnpB,\nconsistently showed higher base-editing efficiency than ABE#WT and ABE#1 did (Fig. 5g).”\nPage 10, Line 260 (result):“On the basis of the IFN-γ#1 locus, we applied CAS-OFFinder to\npredict off-target sites. However, amplicon sequencing of the top ten predicted sites failed to\ndetect significant off-target A-to-G conversions (Supplementary Fig. 6h). Collectively, these\nresults indicate that ABE#2 achieves efficient A-to-G conversion in a sequence-dependent\nmanner without inducing detectable off-target effects.”\nComment 3: The study lacks a clear performance benchmark against previously reported\nminiature systems. For instance:\n(1) The ωRNA variants from Figure 2 should be compared with the highly efficient 99-nt\nωRNA* reported by Marquart et al. or Li et al. (PMIDs: 39313558, 38280857).\n(2) The engineered TnpB variants in Figure 5 should be compared with the advanced TnpB-\nAI-5.6 variant (S72R-K84R-E168R-K251R-V374R) described by Cheng et al. (Cell Res, 2024).\nResponse 3: We agree that comparison with previously reported high-performance ωRNA and\nTnpB variants would strengthen the study and provide a more comprehensive assessment of\nthe performance of GE#1. To address this point, we conducted direct head-to-head comparisons\nby synthesizing representative published ωRNA–TnpB constructs and evaluating them under\nidentical experimental conditions, using the same vector backbone and experimental system.\nThe results showed that GE#1 yielded greater editing efficiency than did the TnpB systems\nreported by Li et al. and Marquart et al. (Supplementary Fig. 6e). These two systems focused\nmainly on the engineering of ωRNA and the signal domain of TnpB (e.g., the nuclear\nlocalization signal). In comparison, our systematic redesign of the ωRNA scaffold\ndemonstrated distinct advantages.\nIn another comparison with the TnpB-AI-5.6 variant by Cheng et al., which contained five\namino acid mutations in TnpB with wild-type ωRNA, the GE#1 variant in this study yielded\nsimilar editing efficacy (Supplementary Fig. 6e). Since our study focused primarily on gene\nactivation, less engineering has been performed on the TnpB protein itself. Although the double\nmutations in TnpB did not significantly improve editing efficiency, the combination of\nengineered ωRNA and wild-type TnpB was sufficient to achieve editing efficiencies\ncomparable to or better than those of other reported systems. Conversely, Cheng et al. focused\non gene editing and systematically mutated TnpB, achieving comparable improvements. We\npropose that combining our engineered enωRNA with TnpB-AI-5.6 (Cheng et al.) holds\npromise for further enhancing the performance of this miniaturized tool.\nSupplementary Fig. 6e. Indel efficiencies at four loci (ABGL1, DYNC1H1, FANCA, and\nVEGFA) for GE#1 and three TnpB variants reported by Li et al., Marquart et al., and Cheng et\nal., respectively.\nThe revised content is provided below:\nPage 9, Line 239 (result): “To enable a direct comparison with previously reported TnpB\nvariants, we synthesized the published constructs using the same vector backbone and evaluated\nthem in the same system as GE#1 to minimize potential bias. Under these conditions, GE#1\nexhibited comparable or greater editing efficiency at the four tested target sites (Supplementary\nFig. 6e).\nPage 15, Line 394 (discussion): “Compared with the TnpB variants reported by previous\nstudies, these variants were initially engineered with the aim of improving indel efficiency21-23.\nFor an unbiased comparison, we synthesized the reported constructs strictly according to the\npublished sequences and evaluated them under identical experimental conditions. Under these\nconditions, GE#1 showed greater editing efficiency at all four tested loci than the variants from\nLi et al. and Marquart et al., which was attributed primarily to our ωRNA engineering\nstrategy21,22. EnωRNA was rationally truncated on the basis of functional domains suggested\nby cryo-EM structures, whereas Li et al. mainly guided truncation via RNAfold prediction, and\nMarquart et al. truncated only the disordered region at the 5′ end. In contrast, Cheng et al.\nemployed a deep learning model (Protein Mutational Effect Predictor) to identify five beneficial\nmutations (S72R/K84R/E168R/K251R/V374R)3. When combined with wild-type RNA, their\nvariant exhibited indel efficiency similar to that of GE#1. These findings underscore the power\nof artificial intelligence in guiding comprehensive protein engineering. Given the\ncomplementary strengths of deep learning-optimized proteins and our engineered ωRNA, we\nanticipate that their combination holds great promise for developing next-generation gene\nediting tools that are both highly efficient and compact.”\n(3) The rationale for selecting the specific combination of enTnpB+enωRNA for the final base\neditor (enTnpB-ABE) needs clarification. More comprehensive testing across multiple loci is\nrequired to validate the enhanced activity, specificity, and overall advantage of the system\npresented here.\nResponse 3: To clarify the rationale for using the enTnpB+enωRNA combination, we adopted\na two-step screening strategy. First, we screened the position and number of the TadA and\nTadA-8e domains in the context of their fusion to TnpB. The results showed that designs\nincorporating NLS-TnpB-TAD8A-NLS (V4) achieved significantly improved base-editing\nefficiency at the IFN-γ#5 and VEGFA loci (Supplementary Fig. 6f&6g). We subsequently\nused V4 to test the improvement of enTnpB and enωRNA. Unexpectedly, compared with GE#1,\nwhich does not rely on enTnpB, ABE#2, which is composed of enTnpB and enωRNA, achieved\nsignificantly improved base-editing efficiency at multiple sites (Figure 5f&5g). Taken together,\nthese results suggest that the principles underlying the construction and optimization of\nbase‑editing tools are similar to those of gene activation, which relies more heavily on nucleic\nacid affinity. In contrast, gene‑editing tools may involve multiple factors and thus require more\ntailored designs to achieve significant improvements in efficiency.\nEditorial Note: Supplementary Fig. 6f and Figure 5f in this Peer Review File are reproduced with\npermission from BioRender, Created in BioRender. Lu, J. (2026) https://BioRender.com/yb8pbyx,\nhttps://BioRender.com/vecr6to\nSupplementary Fig. 6f. Schematic of four adenine base editors (ABE V1–V4) generated by\nfusing nuclease-inactive enTnpB (endTnpB) with TadA or TadA-8e in different configurations.\nSupplementary Fig. 6g. A-to-G conversion efficiencies for IFN-γ#5 and VEGFA via ABE V1–\nV4.\nFigure 5f. Schematic of enTnpB-based adenine base editors (ABEs): ABE#WT (WT ωRNA\n+ WT TnpB), ABE#1 (enωRNA + WT TnpB), and ABE#2 (enωRNA + enTnpB). WT, wild\ntype.\nFigure 5g. A-to-G conversion efficiencies at three target sites for ABE#WT, ABE#1, and\nABE#2.\nThe revised content is provided below:\nPage 10, Line 246 (result): “We also evaluated the potential of endTnpB as a base editor.\nEndTnpB was fused with the deoxyadenosine deaminase TadA and its engineered variant\nTadA-8e (21) in different configurations (V1–V4) (Supplementary Fig. 6f). Analysis of A-to-\nG conversion rates revealed that V4 (NLS–enTnpB–TadA-8e–NLS) achieved the highest\nediting efficiency at the two tested sites (Supplementary Fig. 6g). Using the V4 configuration,\nwe generated three endTnpB-based adenine base editors (ABEs): ABE#WT, ABE#1, and\nABE#2 (Fig. 5f). At three tested target sites, ABE#2, which integrates enωRNA and endTnpB,\nconsistently showed higher base-editing efficiency than ABE#WT and ABE#1 did (Fig. 5g).”\nReference:\n1. Li, Z. et al. Engineering a transposon-associated TnpB-ωRNA system for efficient gene\nediting and phenotypic correction of a tyrosinaemia mouse model. Nat Commun 15, 831,\ndoi:10.1038/s41467-024-45197-z (2024).\n2. Marquart, K. F. et al. Effective genome editing with an enhanced ISDra2 TnpB system\nand deep learning-predicted ωRNAs. Nat Methods 21, 2084-2093,\ndoi:10.1038/s41592-024-02418-z (2024).\n3. Cheng, P. et al. Zero-shot prediction of mutation effects with multimodal deep\nrepresentation learning guides protein engineering. Cell Res 34, 630-647,\ndoi:10.1038/s41422-024-00989-2 (2024).\nComment 4：In lines 101-103, the authors state that the D191A mutation generates a\ncatalytically inactive dTnpB. However, Supplementary Figure 1C shows this dTnpB variant\nstill exhibits measurable editing activity (>5%), which contradicts the \"inactive\" claim and\nwarrants explanation.\nResponse 4: We apologize for this mistake. The discrepancy arose from a mislabelled x-axis\nin Supplementary Fig. 1c, where dTnpB and TnpB were labelled incorrectly. This was a\nlabelling error only and did not affect any subsequent results or conclusions.\nSupplementary Fig. 1c. Insertion and deletion (indel) activity of TnpB and dTnpB with ωRNA\ntargeting AGBL1 in HEK293T cells.\nComment 5: In Figures 3, 4, and 5, the \"fold change\" values indicated in the plots are not\nclearly defined. The text or figure legends should specify which groups are being compared for\neach calculation.\nResponse 5: The definition of fold change has been clarified in the figure legends.\nThe revised content is provided below:\nLegends of Figures 2 and 3: The fold change was calculated by dividing the average MFI of\nthe modified variants by the average MFI of the NT group.\nLegend of Figure 4: The fold change was calculated on the basis of the relative mRNA\nexpression normalized to that in the NT group.\nComment 6: On line 188, the text incorrectly references \"Fig. 2D\" for enTnpBa; this should\nbe corrected to \"Fig. 3D.\"\nResponse 6: The figure citation on line 181 has been corrected to “Fig. 3D” in the revised\nmanuscript.\nComment 7: The in vivo activation of the target gene in the mouse model should be evaluated\nand validated using a quantitative method like qPCR or ELISA.\nResponse 7: To evaluate the in vivo activation of target genes in the mouse model, we\nperformed qPCR analysis using frozen tumor tissues collected from the animal experiments. In\naddition, IHC staining was conducted to further validate cytokine expression at the protein level\n(Supplementary Fig. 7). Both results confirmed significant endogenous gene activation in vivo\nby AAV-ImmunAct, which is based on the enTnpB system\nSupplementary Figure 7.ImmunAct induces in vivo upregulation of CXCL9, IFN-γ, and\nIL-15 expression in tumors.\na. Relative mRNA expression levels of CXCL9, IFN-γ, and IL-15 in tumors measured by\nqPCR following the indicated treatments.\nb. Representative images of immunohistochemical staining for CXCL9, IFN-γ, and IL-15 in\ntumor sections, with the corresponding H-scores quantified via QuPath shown on the right.\nThe revised content is provided below:\nPage 12, Line 304 (Results):“qPCR and immunohistochemistry(IHC) analyses revealed that\nCXCL9, IFN-γ, and IL-15 were successfully activated and upregulated in tumors by ImmunAct\n(Supplementary Fig. 7a–b)”.\nReviewer 2\nIn this study, Lu et al develops a compact CRISPR toolkit named enTnpB through rational\nωRNA and protein optimization. By using its compact size, the authors created an endogenous\nactivation system called ImmunAct. It can activate three antitumor cytokines in tumors through\na single AAV vector and shows a clear synergistic effect with ICI therapy. The experimental\ndesign is well-conceived, offering a new approach to enhance tumor immunotherapy. The\nauthors obtained a large amount of high-quality data, and the conclusions are supported by the\nresults.\nThe manuscript could be further strengthened by obtaining additional evidence to validate its\nimmune-sensitizing effect and expanding the comparison scope to include other TnpB variants.\nWe would like to thank the reviewer for acknowledging the importance of our words and\nthe high standard of our analyses and presentation. We are grateful for all the suggested\nrevisions, each of which is addressed in detail below.\nComment 1: Figure 6 employs an autologous T cell–organoid co-culture model to evaluate\nImmunAct, which mimics clinical conditions. Notably, the organoids derived from different\npatients show significant heterogeneity. Results from three cases appear insufficient to fully\ndemonstrate its broad applicability. Increasing the number of cases would improve the\nrobustness of conclusion.\nResponse 1: In response to this suggestion, we expanded the autologous T cell–organoid\ncoculture experiments by including three additional patient-derived organoid cases. Consistent\nwith our initial observations, ImmunAct treatment induced robust tumor cell killing across all\nnewly added cases.\nFigure 6j. Representative fluorescence images and quantification of cell death in patient-\nderived bladder cancer organoids treated with AAV-NT or AAV-ImmunAct, followed by\ncoculture with autologous PBMC-derived T cells from the same patient. Organoid cell death\nwas assessed by measuring the MFI of NucRed Dead 647 (red fluorescence) within the outlined\norganoid region.\nThe revised content is provided below:\nPage 11, Line 291 (Results):“Organoids were established from six individual bladder cancer\npatients and treated with either AAV-NT or AAV-ImmunAct.”\nComment 2: The authors have utilized WB and ELISA to demonstrate that ImmunAct activates\nthe expression of three cytokines in vitro, which provides a solid foundation for understanding\nits mechanism. To further support this finding, it would be valuable to characterize the\nexpression levels of these cytokines in animal models as well. Evaluating cytokine induction\nin vivo is important to verify the endogenous gene activation effect of ImmunAct more\ncomprehensively\nResponse 2: To assess the in vivo activation of target genes by ImmunAct, we analysed frozen\ntumor tissues from the animal experiments via qPCR. In parallel, immunohistochemistry (IHC)\nwas performed to further validate cytokine expression at the protein level. Together, these\nfindings validated the in vivo efficacy of TnpB cells delivered via a single AAV.\nSupplementary Figure 7. ImmunAct induces in vivo upregulation of CXCL9, IFN-γ, and IL-\n15 expression in tumors.\na. Relative mRNA expression levels of CXCL9, IFN-γ, and IL-15 in tumors measured by qPCR\nfollowing the indicated treatments.\nb. Representative images of immunohistochemical staining for CXCL9, IFN-γ, and IL-15 in\ntumor sections, with the corresponding H-scores quantified via QuPath shown on the right.\nThe revised content is provided below:\nPage 12, Line 304 (Results):“qPCR and immunohistochemistry(IHC) analyses revealed that\nCXCL9, IFN-γ, and IL-15 were successfully activated and upregulated in tumors by ImmunAct\n(Supplementary Fig. 7a, b)”.\nComment 3: This study provides a nice comparison with the similarly compact CasMINI\nsystem. However, several engineered versions of TnpB have already been reported (e.g.,\nDOI:10.1038/s41467-024-45197-z; DOI:10.1038/s41592-024-02418-z). It will be helpful to\npresent direct comparative data on the indel editing capability between enTnpB and the\naforementioned variants. Also, more detailed discussion of these TnpB modifications would\nenhance the work’s depth.\nResponse 3: We appreciate the reviewer's constructive suggestions for improving the\nmanuscript. To facilitate a more comprehensive comparison between enTnpB and other\nengineered TnpB systems, we have carefully read the two references mentioned by the reviewer.\nThe primary focus of both studies was ωRNA optimization. Li et al. (DOI:10.1038/s41467-\n024-45197-z) used RNAfold to guide ωRNA optimization and identified ωRNA-v2 (99 nt) as\nan enhanced variant, which is identical to M2.1 reported in our study. Marquart et al. (PMID:\n10.1038/s41467-024-45197-z) constructed TnpBmax by truncating the 5' sequence of ωRNA\n(identical to M-117 reported in our study) and combining it with a TnpB protein optimized for\nthe NLS sequence. In our present work, we identified three engineering sites on the basis of the\ncryo-EM structure of the TnpB–ωRNA system and demonstrated the synergistic effects of M1–\n116, M2.4 in stem 3, and M3.9 in the tetraloop region. With respect to gene editing efficacy,\ncomparative assays demonstrated that GE#1 produced greater editing efficiency compared to\nTnpB systems reported by Li et al. and Marquart et al. (Supplementary Fig. 6e). Overall, we\nconstructed a novel enTnpB system with increased activation efficacy and minimized size (93\nnt).\nSupplementary Fig. 6e. Indel efficiencies at four loci (ABGL1, DYNC1H1, FANCA, and\nVEGFA) for GE#1 and three TnpB variants reported by Li et al., Marquart et al., and Cheng et\nal., respectively.\nThe revised content is provided below:\nPage 9, Line 239 (result): “To enable a direct comparison with previously reported TnpB\nvariants, we synthesized the published constructs using the same vector backbone and evaluated\nthem in the same system as GE#1 to minimize potential bias1-3. Under these conditions, GE#1\nexhibited comparable or greater editing efficiency at four tested target sites (Supplementary\nFig. 6E).”\nPage 15 Line 394 (discussion): “Compared with the TnpB variants reported in previous studies,\nthese variants were initially engineered with the aim of improving indel efficiency1-3. For an\nunbiased comparison, we synthesized the reported constructs strictly according to the published\nsequences and evaluated them under identical experimental conditions. Under these conditions,\nGE#1 showed greater editing efficiency at all four tested loci than the variants from Li et al.\nand Marquart et al., which was attributed primarily to our ωRNA engineering strategy1,2.\nEnωRNA was rationally truncated on the basis of functional domains suggested by cryo-EM\nstructures, whereas Li et al. mainly guided truncation via RNAfold prediction, and Marquart et\nal. truncated only the disordered region at the 5′ end. In contrast, Cheng et al. employed a deep\nlearning model (Protein Mutational Effect Predictor) to identify five beneficial mutations\n(S72R/K84R/E168R/K251R/V374R)3. When combined with wild-type RNA, their variant\nexhibited indel efficiency similar to that of GE#1. These findings underscore the power of\nartificial intelligence in guiding comprehensive protein engineering. Given the complementary\nstrengths of deep learning-optimized proteins and our engineered ωRNA, we anticipate that\ntheir combination holds great promise for developing next-generation gene editing tools that\nare both highly efficient and compact.”\nReference:\n1. Li, Z. et al. Engineering a transposon-associated TnpB-ωRNA system for efficient gene\nediting and phenotypic correction of a tyrosinaemia mouse model. Nat Commun 15, 831,\ndoi:10.1038/s41467-024-45197-z (2024).\n2. Marquart, K. F. et al. Effective genome editing with an enhanced ISDra2 TnpB system\nand deep learning-predicted ωRNAs. Nat Methods 21, 2084-2093,\ndoi:10.1038/s41592-024-02418-z (2024).\n3. Cheng, P. et al. Zero-shot prediction of mutation effects with multimodal deep\nrepresentation learning guides protein engineering. Cell Res 34, 630-647,\ndoi:10.1038/s41422-024-00989-2 (2024).\nComment 4: Several previous studies using different strategies have reported that local\ndelivery of immune-stimulatory cytokines can enhance tumours immunotherapy (e.g., DOI:\n10.1038/s41590-024-01925-7 etc.). I suggest the authors to elaborate in the Discussion section\non the advantages and limitations of this endogenous cytokine activation approach compared\nwith other cytokine delivery or immunomodulatory strategies.\nResponse 4: We have expanded the Discussion to compare endogenous cytokine activation\nwith other cytokine delivery or immunomodulatory strategies. These revisions have been\nincorporated into the updated Discussion section.\nThe revised content is provided below:\nPage 16, Line 415 (Discussion): “The endogenous cytokine activation strategy enables in situ\nproduction of multiple cytokines within the tumour microenvironment, thereby improving local\ncytokine concentration while limiting diffusion to non-target tissues and reducing systemic\nexposure. Moreover, coordinated endogenous activation of multiple cytokines reduces the\nreliance on repeated cytokine administration and may support more effective and durable\nantitumor immune responses.”\nComment 5: The gating strategy used for flow cytometry in Figure 6 should be provided in the\nsupplementary materials to help readers better understand how the analysis was performed.\nResponse 5: Detailed flow cytometry gating strategies are now provided in Supplementary\nFigure 8, including schemes for CD8⁺ T cell activation analysis and killing assays with UM-\nUC-3 and T24 cells.\nSupplementary Figure 8. Flow cytometry gating schemes for CD8⁺ T cell activation\nanalysis (A) and killing assays with mCherry⁺ tumor cells (B).\nComment 6: In Figure 1, within the “TnpB & ωRNA engineering” box, the axis of flow\ncytometry seems to be shifted.\nResponse 6: The flow cytometry axis shift in the “TnpB & ωRNA engineering” box of Figure\n1 has been corrected in the revised manuscript.\nEditorial Note: Figures 1 and 6a in this Peer Review File is reproduced with permission from\nBioRender, Created in BioRender. Lu, J. (2026) https://BioRender.com/4yvrpxm,\nhttps://BioRender.com/p92mphp\nFigure 1. Workflow of the study.\nComment 7: In Figure 6A, the promoters used in the ImmunAct plasmid construct should be\nindicated.\nResponse 7: The promoters used in the ImmunAct plasmid construct are now clearly indicated\nin Figure 6A in the revised manuscript.\nFigure 6a. Schematic workflow for in vitro validation of immune activation by enTnpBa."}, {"round": 2, "reviewer_comments": {"reviewer_1": "All my concerns have been addressed.", "reviewer_2": "The revised manuscript addresses the previous concerns adequately and the manuscript is acceptable for publication"}, "author_response": ""}]

Decision letter