A compact and inducible dCas12f-based CRISPRa platform for programmable in vivo gene activation

Domain: biology
Published: 2026-01-08
Editorial outcome: minor_revision

Abstract
Precise activation of endogenous genes is a powerful strategy for functional genomics and therapeutic development, but current CRISPR-based transcriptional activation (CRISPRa) systems are limited by the large size of Cas proteins for adeno-associated virus (AAV) delivery. Here, we present a high-efficiency dCas12f-based transcriptional activation system (HEAL), which recruits transactivators through MS2 coat protein binding to MS2 aptamers embedded within the sgRNA scaffold. Engineered to enhance DNA binding, nuclear localization, and transactivator recruitment, HEAL induces over 100,000-fold activation of endogenous genes and outperforms existing CRISPRa systems in vitro and in vivo. We further develop red-light-inducible OptoHEAL and small-molecule-inducible ChemHEAL for remote and precise transcriptional control. AAV-delivered HEAL targeting interleukin 10 alleviates acute kidney injury in mice, while ChemHEAL-mediated activation of thymic stromal lymphopoietin reduces body weight in obese mice. HEAL provides a modular, compact, and controllable platform for endogenous gene activation with strong potential for fundamental research and gene therapy.

Introduction
Transcriptional regulation orchestrates complex gene expression programs that govern development, maintain cellular homeostasis, and mediate adaptive responses to environmental cues1,2. Dysregulation of these networks drives a causal factor in a multitude of pathological states, including neurodevelopmental disorders3,4, immune dysregulation5, and metabolic syndromes6. CRISPR-based transcriptional activation (CRISPRa) systems, which repurpose catalytically inactive Cas (dCas) proteins to recruit transactivators to specific genomic loci, have been widely developed to enable targeted modulation of endogenous gene expression for functional studies and therapeutic exploration7–12.

While dCas9- and dCas12a-based CRISPRa systems have successfully demonstrated gene activation, their large coding size (~4.0–5.5 kb) poses a substantial obstacle for in vivo delivery, particularly via adeno-associated virus (AAV) vectors with a ~4.7 kb packaging limit13,14. Efforts to address this limitation have driven the development of compact CRISPR systems based on miniature Cas proteins such as Cas12f15–20. The dCasMINI system, based on Cas12f, has demonstrated in vivo gene activation via AAV delivery21,22. Yet current Cas12f-based tools remain suboptimal in activation strength and lack dynamic control or modular programmability, limiting their use in contexts requiring precise, tunable gene regulation. A compact and programmable CRISPRa platform with efficient and controllable activation remains a critical need for both basic and therapeutic applications.

The safety and effectiveness of therapeutic applications of CRISPRa systems demand precise regulation of gene expression dynamics, particularly in complex physiological and pathological contexts23,24. Sustained or uncontrolled in vivo gene activation can lead to adverse effects, exemplified by the tight physiological regulation of genes such as OPA1 (optic atrophy 1), RAI1 (retinoic acid-induced 1), MeCP2 (methyl-CpG-binding protein 2), and TSLP (thymic stromal lymphopoietin), the dysregulation of which has been linked to toxicity or developmental abnormalities25–29. Accordingly, inducible CRISPRa systems based on dCas9 or dCas12a responsive to exogenous cues-including small molecules30,31, light32–36, or ultrasound37-have been developed for dynamic and reversible control of gene expression. Such strategies enable quantitative modulation of target gene expression at predefined levels, offering promise for dosage-sensitive therapeutic applications. However, their reliance on large Cas proteins poses a challenge for AAV packaging, consequently limiting their efficiency and applicability in vivo38.

In this study, we develop a compact and precisely controllable platform for endogenous gene activation. We first engineer a high-efficiency CRISPRa system comprising an engineered ΔdCas12f protein guided by a restructured sgRNA for targeting endogenous loci and transactivators fused to an MS2 coat protein (MCP) that are recruited by MS2 aptamers embedded within the sgRNA to activate target gene transcription. The resulting high-efficiency dCas12f-based transcriptional activation (HEAL) system is compatible with single-AAV delivery and demonstrated greater efficiency in activating several endogenous genes than other CRISPRa systems. Further, we develop the red light-responsive OptoHEAL and chemically-inducible ChemHEAL systems, which enable user-defined, finely tunable spatiotemporal regulation of gene activation in vivo. The present study demonstrates the application of these systems in disease-relevant contexts by targeting Il10 with an AAV-delivered HEAL to effectively alleviate acute kidney injury in mice and activating Tslp with an abscisic acid-inducible ChemHEAL to reduce body weight in obese mice. Overall, our findings demonstrate the broad potential of HEAL as a compact, modular, and controllable CRISPRa platform for basic biological studies and therapeutic applications.

Results
Design and construction of a high-efficiency dCas12f-based transcriptional activation system
To establish a potent and compact CRISPRa platform compatible with single AAV delivery, we developed a high-efficiency dCas12f-based transcriptional activation system (HEAL) by redesigning components of the Un1Cas12f1_ge4.1 framework39. HEAL incorporates three strategically engineered features: (1) a restructured sgRNA scaffold with improved architecture to promote efficient recruitment of transactivators, (2) a modified dCas12f variant with enhanced DNA binding affinity and nuclear localization, and (3) a modular assembly of potent transactivation domains to amplify endogenous gene activation (Fig. 1a).

Building upon previous findings that RNA aptamer-mediated recruitment of transactivator can outperform direct fusion to dCas proteins10,40, we redesigned the sgRNA scaffold by incorporating MS2 aptamer structures to enable modular effector recruitment. Through structural analysis, we identified five potential engineering sites (ES1–ES5) within the scaffold (Fig. 1b) and introduced MS2 insertions and base substitutions to enhance transactivator recruitment and scaffold stability. Initial screening in HEK-293T cells revealed that MS2 aptamers inserted at ES1 and ES2 conferred the strongest activation, achieving up to a 1453-fold increase in transcription of the endogenous hemoglobin subunit gamma (HBG) gene (Fig. 1c). Further improvement was achieved by a single-base substitution (A→C) at ES5, which increased scaffold stability and enhanced HBG activation (Fig. 1d and Supplementary Fig. 1), demonstrating the additive effects of scaffold architecture refinement.

In parallel, we engineered the dCas12f protein to improve both DNA binding affinity and nuclear localization efficiency. Building on structural modeling analyses and insights from the engineering of CasMINI15, we introduced targeted amino acid substitutions into dCas12f to enhance its DNA-binding capacity. A triple mutant variant (D143R, T147R, and K330R) exhibited the greatest enhancement in transcriptional activation (Fig. 1e). To improve nuclear localization, we evaluated multiple nuclear localization signal (NLS) configurations fused to the mutated dCas12f (Supplementary Fig. 2a). The configuration containing two c-MYC NLSs at the N-terminus and one at the C-terminus—designated ΔdCas12f—resulted in the highest activation efficiency, leading to a 34,661-fold upregulation of HBG expression (Fig. 1f). In addition, we screened a panel of transcriptional activation domains (TADs) fused to MCP, including VP16 and its tetrameric variant VP64 from herpes simplex virus type 1 (HSV-1)41, as well as chimeric activators derived from human mechanosensitive transcription factors, such as eNRF2 and MSN42 (Supplementary Fig. 2b). Among the candidates, the p65-HSF1 fusion consistently produced the strongest gene activation (Fig. 1g), and was selected for incorporation into the final HEAL system. Collectively, these design efforts led to the establishment of a compact, modular, and highly potent CRISPRa system composed of an engineered dCas12f variant and a restructured sgRNA scaffold. Leveraging HEAL as a platform, we further developed inducible transcriptional activation systems applicable to both in vitro and in vivo settings.

HEAL-mediated endogenous gene activation in mammalian cells and mice
Given the above results showing that the HEAL system could upregulate the endogenous gene, HBG, by over 30,000-fold in HEK-293T cells, we next investigated the ability of this system to activate additional endogenous genes across different species and cell lines. We tested six genes in human HEK-293T cells, including HBG, achaete-scute homolog 1 (ASCL1), interferon gamma (IFNγ), rhox homeobox family member 2 (RHOXF2), uncoupling protein 1 (UCP1), and hemoglobin subunit beta (HBB), as well as five genes in mouse B16F10 cells, including Ucp1, octamer-binding transcription factor 4 (Oct4), dickkopf-related protein 1 (Dkk1), Tslp, and interleukin 10 (Il10), and one gene, follistatin (Fst), in mouse N2a cells. For each gene, we designed ten sgRNAs targeting regions upstream and downstream of the transcription start site (TSS), all using the TTTR (TTTA or TTTG) PAM (protospacer adjacent motif) sequence. The degree of activation varied markedly across targets, ranging from >10-fold to nearly 100,000-fold, with HBG exhibiting the highest induction (Supplementary Fig. 3a–f and Supplementary Fig. 4a–f). Notably, activation of gene expression was highly dependent on the sgRNA target site (Supplementary Fig. 3a–f and Supplementary Fig. 4a–f). The observed differences may be related to the intrinsic epigenetic state of the gene, chromatin accessibility, and baseline expression levels within the cells43,44. We further evaluated the activation potential of HEAL in additional human cell lines, and qPCR analysis showed significant upregulation of HBG and IFNγ across diverse cellular contexts (Supplementary Fig. 3g, h). We then assessed the ability of the HEAL system to induce the simultaneous activation of multiple endogenous genes. To this end, we constructed an sgRNA array targeting the promoters of Ucp1, Oct4, and Dkk1, and delivered it into B16F10 cells. QPCR analysis showed that the HEAL system enabled activation of multiplexed endogenous genes (Supplementary Fig. 4g). HEAL-mediated gene activation was also validated at the protein level for selected targets, including human IFNγ and murine IL-10 and TSLP (Supplementary Fig. 4h–j). Together, these findings demonstrate the potential of HEAL for user-defined, multiplexed activation of endogenous genes of interest.

To test whether the HEAL system could activate endogenous genes in vivo, we delivered HEAL-encoding plasmids targeting each gene individually by hydrodynamic tail vein injection in C57BL6 mice (Fig. 1h). At 48 h after injection, the mice were euthanized. Quantification of Ucp1, Oct4, and Dkk1 expression in liver tissues by qPCR showed that the HEAL system significantly upregulated transcription of three respective genes (Fig. 1i). Notably, delivery of HEAL-encoding plasmids with the sgRNA array resulted in successfully activating the expression of all three genes simultaneously in mouse livers (Fig. 1j), highlighting the effectiveness of the HEAL system for endogenous gene activation in both mammalian cells and mice.

Comparisons of the HEAL system with other transcriptional activation systems
We next compared the efficiency of transcriptional activation using the HEAL system with that of other current gene activation systems. To facilitate side-by-side comparison with the dCasMINI and dCpf1 activation systems, we used the same sgRNA seed sequence across all three systems, as they all recognize the TTTR PAM (Fig. 2a). In five gene activation tests (two in B16F10 cells and three in HEK-293T cells), HEAL consistently outperformed both dCasMINI and dCpf1, achieving higher maximal activation with fold increases ranging from 3.7- to 537-fold (Fig. 2b–f). We then compared HEAL to a Streptococcus pyogenes-derived dCas9-SAM system, which is considered one of the most potent dCas9-based transcriptional activation systems40 (Fig. 2g). Since dCas9 recognizes the NGGPAM, we designed five sgRNAs for the dCas9-SAM system, targeting different genomic sites for each gene, and two sgRNAs for HEAL. QPCR analysis revealed that HEAL drove markedly stronger gene activation—by 40-fold for UCP1 and 221-fold for HBG. For other targets such as Ucp1 and Tslp, the enhancement was more modest but still evident (2.8- and 7.6-fold, respectively) (Fig. 2h–k).

To assess transcriptional activation performance in vivo, we constructed a reporter that could be targeted by all tested CRISPRa systems at the same locus (Supplementary Fig. 5a), effectively minimizing differences in activation efficiency caused by variations among different target sites. Mice were then hydrodynamically injected with plasmids encoding HEAL, dCasMINI, dCpf1, or dCas9-SAM, along with the luciferase reporter. Bioluminescence imaging at 24 h post-injection revealed that the HEAL system induced significantly stronger reporter expression compared to the other groups, with signal intensities 5.6-, 4.1-, and 2.1-fold higher than those achieved by dCpf1, dCasMINI, and dCas9-SAM, respectively. In contrast, all NT control groups exhibited minimal background activity (Supplementary Fig. 5b–d). In addition, we assessed the in vivo transcriptional activation of the endogenous Ucp1 gene. In mouse livers harvested 48 h after injection (Fig. 2l), qPCR analysis showed that the HEAL system induced a significantly higher upregulation of Ucp1 compared to others systems, while maintaining high activation specificity (Fig. 2m). Collectively, these findings highlight the improved efficiency and preserved specificity of HEAL in transcriptional activation over that of existing CRISPRa systems.

HEAL-mediated Il10 activation for amelioration of acute kidney injury in mice
To investigate the therapeutic potential of the HEAL system, we evaluated its ability to mitigate disease progression in a mouse model of acute kidney injury (AKI) by specifically targeting interleukin 10 (Il10), an anti-inflammatory cytokine known to alleviate cisplatin-induced renal injury45,46. As our above results showed that the HEAL system could efficiently activate Il10 expression in murine cell lines using sgRNA-4 (Supplementary Fig. 4e), we constructed a single AAV vector encoding the HEAL system with sgRNA-4 (AAVIl10) and administered it to C57BL6 mice via tail vein injection (Fig. 3a). Fourteen days after AAV injection, AKI was induced through cisplatin treatment and mice were subsequently euthanized four days later. Compared to the untreated AKI group, Il10 expression in the liver was upregulated by 17.5-fold in the AAVIl10 treatment group, accompanied by a 3.5-fold increase in serum IL-10 levels (Fig. 3b, c). Further, Il10 activation in the AAVIl10-treated AKI mice alleviated cisplatin-induced renal injury, as evidenced by significant reductions in blood urea nitrogen (BUN) levels (from 270.2±46.4 mg/dL to 162.6±32.9 mg/dL) and serum creatinine (S-Cre) levels (from 113.9±25.1 μmol/L to 65.0±18.2 μmol/L), compared to the untreated AKI group (Fig. 3d, e). Immunohistochemical staining revealed that renal injury marker, neutrophil gelatinase-associated lipocalin (NGAL), was significantly reduced in the AAVIl10-treated mice relative to the control groups (Fig. 3f). Moreover, TUNEL staining of kidney sections revealed a lower proportion of apoptotic cells in the AAVIl10-treated group, indicating reduced cellular damage (Fig. 3g, h). Collectively, these findings suggested that HEAL-mediated Il10 activation could effectively alleviate pathological symptoms in AKI mice, underscoring its potential as a therapeutic strategy for ameliorating kidney damage.

Red light-inducible endogenous gene activation in mice
Optogenetic technologies enable remote, traceless, and reversible control of endogenous gene expression47, we next sought to apply the HEAL system in a light-inducible, customizable endogenous gene activation system by integrating our previously designed REDLIP system48. The resulting red light-inducible HEAL system, OptoHEAL, employs a dimerization-based assembly mechanism for the MCP and transactivator p65-HSF1, wherein exposure to red light (660 nm) induces DrBphP and LDB3 dimerization, facilitating p65-HSF1 recruitment to the target site to initiate gene transcription. Conversely, far-red light (780 nm) disrupts DrBphP-LDB3 interactions, leading to dissociation of p65-HSF1 and termination of transcription (Fig. 4a).

Testing eight different combinations for fusion of the transcriptional activation modules (MCP and p65-HSF1) with light-responsive dimerization modules (DrBphP and LDB3) indicated that the MCP-DrBphP and LDB3-p65-HSF1 combination resulted in the greatest increase (12-fold) in endogenous HBG expression between light and dark conditions (Fig. 4b, c). To further improve induction efficiency, we introduced nuclear localization signals (NLS) derived from the SV40 large T antigen. Increasing NLS copy number at the N-terminus of LDB3-p65-HSF1 significantly enhanced light-induced HBG mRNA expression, which reached 50-fold higher expression compared to the dark state (Supplementary Fig. 6a). In contrast, the addition of NLS at the C-terminus of MCP-DrBphP severely impaired its functionality, possibly due to interference with DrBphP-LDB3 binding (Supplementary Fig. 6b). Moreover, targeting HBG with two sgRNAs and adjusting the expression levels of each module further increased HBG activation to 1577-fold under red light conditions (Supplementary Fig. 6c, d).

To evaluate OptoHEAL performance, we measured HBG mRNA expression in transfected HEK-293T cells under varying intensities (0-2 mW/cm2) and duration (0-24 h) of red light (660 nm) illumination. QPCR-based analysis indicated that OptoHEAL exhibited both illumination time- and intensity-dependent activation of HBG expression (Fig. 4d, e). Notably, the OptoHEAL system showed negligible background leakage under dark conditions, while brief illumination (as short as 0.01 h) was sufficient to trigger significant gene activation (Fig. 4d). To test whether the OptoHEAL system could be switched on and off by alternating red/far-red light illumination, transfected HEK-293T cells were exposed to red light alone or red light followed by far-red light (Fig. 4f). We found that transcriptional activation was indeed reversible with this alternating red/far-red exposure regimen (Fig. 4g), thus providing a switchable ON-OFF control for manipulating gene expression. Moreover, OptoHEAL could activate other endogenous genes (i.e., UCP1, ASCL1, and IFNγ) in HEK-293T cells (Fig. 4h). To explore its versatility for application in varying contexts, we transfected the OptoHEAL system into four additional mammalian cell lines and found that it could effectively activate HBG in all tested cell lines (Fig. 4i). These findings thus demonstrated OptoHEAL functions as a red-light-inducible transcriptional activation tool with broad cellular compatibility.

To assess whether OptoHEAL could be applied for red-light-inducible transcriptional activation in vivo, we evaluated its ability to target our exogenous reporter system following delivery by hydrodynamic tail vein injection in C57BL6 mice. At ten hours post-injection, mice were illuminated with red light (660 nm, 20 mW/cm2) for durations ranging from 0 to 10 h, then incubated in darkness for the remaining experimental period (Supplementary Fig. 7a). Bioluminescence imaging demonstrated light-dependent reporter activation, with luciferase expression increasing by up to 127-fold after 10 h of illumination (Supplementary Fig. 7b, c). Moreover, we used OptoHEAL to target the endogenous mouse gene, Oct4. Mouse livers were harvested at 24 h post-injection. Subsequent qPCR assays revealed that Oct4 expression was indeed significantly upregulated (by 11.1-fold) following 10 h under red light illumination (Fig. 4j, k). Collectively, these results demonstrated that OptoHEAL functions as designed, enabling red-light inducible, adjustable, and reversible activation of endogenous genes both in vitro and in vivo.

Chemically inducible activation of endogenous genes
While the above data showed that the OptoHEAL system enables precise and reversible control of gene activation through light stimulation, its application may be constrained in physiological or clinical contexts where light delivery is infeasible or limited by poor tissue penetration. To address these challenges and expand its in vivo applicability, we developed a chemically inducible HEAL system (ChemHEAL) that allows controlled activation of endogenous genes through exposure to small molecules. Similar to the design principle for OptoHEAL, ChemHEAL was engineered by fusing MCP and p65-HSF1 modules with small molecule-responsive dimerization proteins. That is, in the presence of a specific small molecule, the activator, p65-HSF1, is recruited to the target promoter to initiate transcription of endogenous genes (Fig. 5a–e).

To demonstrate the adaptability of the ChemHEAL platform to various chemical agents and biological contexts, we constructed multiple ChemHEAL variants that could respond to different small molecules, including the plant hormone abscisic acid (ABA) and FDA-approved drugs such as grazoprevir (GZV), danoprevir (DNV), and rapamycin (Rapa). By integrating the transcriptional activation modules (MCP and p65-HSF1) with small molecule-responsive protein dimerization modules (ABI/PYL1 for ABA, GNCR1/NS3a for GZV, DNCR2/NS3a for DNV, and FRB/FKBP for Rapa), we identified the configurations that could induce the highest activation of HBG in HEK-293T cells upon stimulation with the respective small molecules, with fold increases of 5501 (ABA), 4610 (GZV), 3104 (DNV), and 4354 (Rapa), respectively (Supplementary Fig. 8).

We then assessed ChemHEAL performance in targeting HBG following transfection of the respective ChemHEAL constructs into HEK-293T cells. At 24 h post-transfection, cells were incubated with different concentrations of ABA (Fig. 5f), GZV (Fig. 5g), DNV (Fig. 5h), or Rapa (Fig. 5i) for 24 h. QPCR analysis demonstrated dose-dependent activation of HBG transcription upon exposure to each small molecule. We also monitored HBG mRNA levels at different time points during small molecule treatment. Across all four ChemHEAL systems, mRNA levels of HBG progressively increased over 48 h (Fig. 5j–m). Notably, mRNA expression significantly increased within 2 h of induction, highlighting the rapid responsiveness of the ChemHEAL platform. Tests of ChemHEAL in various mammalian cell lines demonstrated that it could efficiently activate HBG in all examined cell types, with induction levels ranging from 60- to 3584-fold (Supplementary Fig. 9). By delivering sgRNAs targeting UCP1, ASCL1, or IFNγ, we observed that all four ChemHEAL systems could activate each respective gene in HEK-293T cells, thus demonstrating its broad applicability across different gene targets (Fig. 5n–q). We next benchmarked the inducible HEAL variants against the constitutive system. Both OptoHEAL and ChemHEAL mediated stimulus-dependent transcriptional activation in response to light or small molecules, with moderately reduced maximal efficiencies compared to the constitutively active HEAL (Supplementary Fig. 10). This attenuated yet tightly regulated activity represents a deliberate design optimization to enhance safety and controllability.

To evaluate the chemical inducibility of transcriptional activation by ChemHEAL in vivo, we used the ABA inducible ChemHEAL system targeting our luciferase reporter as a representative model. Plasmids encoding ChemHEAL with the luciferase reporter were delivered to mouse livers by hydrodynamic injection via the tail vein. At 10 h post-injection, mice received intraperitoneal ABA (0-100 mg/kg) injection (Supplementary Fig. 11a). Bioluminescence imaging at 14 h post-induction revealed a dose-dependent increase in luciferase signal, reaching up to a 40-fold enhancement at 100 mg/kg compared to the 0 mg/kg control (Supplementary Fig. 11b, c). Taken together, these results showed that ChemHEAL could be applied in vitro or in vivo for rapid, sensitive, small molecule-inducible gene activation.

ChemHEAL-mediated Tslp activation can reduce body weight in HFD-induced obese mice
To explore the therapeutic potential of ChemHEAL, we employed the ABA-inducible ChemHEAL system to regulate Tslp expression, a cytokine well-known to alleviate obesity and related complications49. However, excessive Tslp expression can result in airway inflammation and hyperreactivity50,51. To achieve precise and controllable activation of Tslp, the Tslp-targeting ChemHEAL system was packaged into two hepatotropic AAV-2/8 vectors (AAVTslp) and delivered to the livers of high-fat diet (HFD)-induced obese mice via tail vein injection. Control mice were administered AAV targeting an exogenous luciferase reporter instead of Tslp. At two weeks post-injection, AAV-treated obese mice received intraperitoneal injections of ABA (100 mg/kg) every two days for eight weeks (G3: AAVNT+ABA, G5: AAVTslp+ABA), or PBS (G4: AAVTslp-ABA), while non-obese mice (G1) and untreated obese mice (G2) served as additional control groups (Fig. 6a).

At eight weeks post-ABA injection, ΔdCas12f mRNA could be detected in liver tissues of all AAV-injected groups, thus validating the success of viral transduction (Fig. 6b). In contrast, assessment of Tslp/TSLP at the mRNA and protein levels demonstrated that only ABA-treated AAVTslp mice showed significant increases in Tslp/TSLP expression (Fig. 6c, d), indicating that ChemHEAL effectively activated Tslp in a small molecule-specific manner. Within two weeks of ABA treatment, mice in the AAVTslp+ABA group exhibited a significant reduction in body weight compared to the AAVNT+ABA, AAVTslp-ABA, and untreated obese groups. By eight weeks post-administration, the body weight of mice in the AAVTslp+ABA group had decreased to an average of 36.1 g, whereas untreated obese mice exhibited a continued increase in body weight, reaching an average of 51.9 g (Fig. 6e, f). In addition, mice in the AAVTslp+ABA group had significantly reduced adipose tissue mass compared to untreated obese controls, with epididymal white adipose tissue (eWAT) decreasing from 2.86±0.30 g to 0.92±0.33 g, subcutaneous inguinal white adipose tissue (iWAT) from 2.72±0.15 g to 0.73±0.47 g, and brown adipose tissue (BAT) from 0.70±0.12 g to 0.08±0.02 g (Fig. 6g). Moreover, the fat mass-to-body weight ratio was significantly lower in the AAVTslp+ABA group, while lean mass was increased (Fig. 6h, i).

Metabolic parameters, including intraperitoneal glucose tolerance test (IPGTT), insulin tolerance test (ITT), and measurements of serum total cholesterol (TC), and triglycerides (TG), demonstrated that glucose metabolism and lipid profiles in the AAVTslp+ABA group improved significantly, closely resembling those of non-obese mice by eight weeks after treatment (Fig. 6j–m and Supplementary Fig. 12a, b). Histological analysis by hematoxylin and eosin (H&E) staining revealed that adipocyte sizes in adipose tissues (eWAT, iWAT, and BAT) were significantly reduced in the AAVTslp+ABA group. Moreover, ABA-treated AAVTslp mice had notably lower severity of hepatic steatosis compared with control groups, suggesting improved liver health (Fig. 6n). Long-term treatment with AAV and ABA did not result in detectable adverse effects, as evidenced by normal lung histology, stable levels of Th2-type pro-inflammatory cytokines (IL-4, IL-5, and IL-13) in bronchoalveolar lavage fluid, and unchanged systemic inflammatory markers (IL-6, TNF-α, and IgG) (Supplementary Fig. 13). Collectively, these results demonstrated that the AAV-mediated ChemHEAL system could be used for precise control of endogenous gene activation in vivo, and thus provided a promising therapeutic strategy for obesity and metabolic disorders.

Methods
Plasmid construction
Comprehensive design and construction details for all expression plasmids are provided in Supplementary Data 1. The detailed DNA sequence information for HEAL components is provided in Supplementary Data 2. All plasmids were constructed using a MultiS One Step Cloning kit (Vazyme, C113-01). All constructs were verified by Sanger sequencing (Shanghai Saiheng Biotechnology).

Cell culture and transfection
Human embryonic kidney cells (HEK-293T; ATCC, CRL-11268), HEK-293 derived Hana3A cells engineered for constitutive expression of RTP1, RTP2, REEP1, and Gαoλϕ 56, telomerase-immortalized human mesenchymal stem cells (hMSC-TERT; ATCC, SCRC-4000), human cervical adenocarcinoma cells (HeLa; ATCC, CCL-2), mouse neuroblastoma neuro-2a cells (N2a; ATCC, CCL131) cells, and adult retinal pigment epithelial cell line-19 (ARPE-19; ATCC, CRL-2302) were cultured in Dulbecco’s modified Eagle medium (DMEM; Gibco, C11995500BT) supplemented with 10% (vol/vol) fetal bovine serum (FBS; AusGeneX Industries, FBSSA500-S) and 1% (vol/vol) penicillin/streptomycin solution (Beyotime, ST488-1/ST488-2). Mouse B16F10 melanoma cancer cell line (B16F10; ATCC, CRL-6475) were cultured in Roswell Park Memorial Institute 1640 Medium (RPMI1640; Gibco, 11875093) with 10% (v/v) FBS and 1% (v/v) penicillin/streptomycin. All cell lines were cultured at 37 °C in a humidified atmosphere containing 5% CO2 and were regularly tested for the absence of Mycoplasma and bacterial contamination.

Cells were transfected using an optimized polyethyleneimine (PEI)-based protocol. Briefly, 8×104 cells were seeded per well in a 24-well plate and cultured overnight to achieve 60–70% confluency. Subsequently, cells were incubated for 6 h with 50 μl of PEI (Polysciences, 4765; molecular weight 40,000, stock solution 1 mg/ml in ddH2O) and corresponding plasmid mixture at a mass ratio of 3:1. For ARPE-19 and HEK-293T cells, transfections were performed at 70–80% confluency using LipofectamineTM 8000 Transfection Reagent (Beyotime, C0533FT) according to the manufacturer’s protocol. N2a cells were transfected at 70–80% confluency using LipofectamineTM 3000 Transfection Reagent (Thermo Fisher Scientific, L3000075) according to the manufacturer’s protocol. Detailed descriptions of transfection mixtures are provided in Supplementary Data 3 and Supplementary Data 4.

Quantitative PCR (qPCR) analysis
Cells and tissue samples were harvested for total RNA isolation using an RNAiso Plus Kit (Takara, 9108) according to the manufacturer’s instructions. A total of 1 μg RNA was reverse-transcribed into complementary DNA using a HiScript® III RT SuperMix with the genomic DNA Eraser (Vazyme, R323). QPCR was performed using the ChamQ Universal SYBR qPCR Master Mix (Vazyme, Q711-02) on the LightCycler 96 Real-Time PCR Instrument (Roche). Primer sequences are listed in Supplementary Table 1, and the target sequences of sgRNAs used in this study are listed in Supplementary Data 5. Gene expression levels were normalized to human/mouse glyceraldehyde 3-phosphate dehydrogenase coding gene (GAPDH), and relative mRNA abundance was calculated using the 2-ΔΔCt method.

Enzyme-linked immunosorbent assay (ELISA)
IL-10, TSLP, and IFNγ levels in cell culture supernatants were quantified using the mouse IL-10 ELISA kit (Invitrogen, 88-7105-88), the mouse TSLP ELISA kit (R&D, MTLP00), and the human IFNγ ELISA kit (MultiSciences Biotech, EK180-96), respectively. IL-4, IL-5, and IL-13 levels in bronchoalveolar lavage fluid were quantified using the mouse IL-4 ELISA kit (Absin, abs520003), the mouse IL-5 high sensitivity ELISA kit (MultiSciences Biotech, EK205HS), and the mouse IL-13 ELISA kit (Invitrogen, KMC2221), respectively. Serum concentrations of IL-10, IL-6, and TNF-α were quantified using the mouse IL-10 ELISA kit (Invitrogen, 88-7105-88), the mouse IL-6 ELISA kit (MultiSciences Biotech, EK206), and the mouse TNF-α ELISA kit (MultiSciences Biotech, EK282), respectively. Serum IgG levels were quantified using the mouse IgG ELISA kit (MultiSciences Biotech, EK271). All ELISA assays were conducted according to the manufacturer’s instructions.

OptoHEAL-mediated activation of endogenous gene transcription in mammalian cells
To evaluate light-controlled endogenous gene activation, 8×104 cells were plated in a 24-well plate and co-transfected with plasmids encoding the OptoHEAL system targeting the corresponding gene [pQL236 (PhCMV-3×SV40-NLS-LDB3-p65-HSF1-pA), pDQ400 (PhCMV-MCP-DrBphP-pA), pWH299 (PhCMV-2×c-MYC NLS-dUn1Cas12f (D143R/T147R/K330R)−1×c-MYC NLS-pA), and sgRNAs targeting the corresponding gene]. Twenty-four hours after transfection, the cell culture plate was placed below a custom-designed 4×6 red light LED array (660 nm; Shenzhen Bested Opto-electronic; each LED centered above a single well), and the cells were exposed to red light (660 nm, 2 mW/cm2, 24 h, unless indicated specifically). The light intensity was measured at a wavelength of 660 nm using an optical power meter (Advantest, Q8230). Relative mRNA expression levels were quantified using qPCR analysis 24 h after illumination.

To evaluate the tunability of the OptoHEAL system, HEK-293T cells were co-transfected with plasmids encoding the OptoHEAL system targeting the HBG locus [pQL236 (PhCMV-3×SV40 NLS-LDB3-p65-HSF1-pA), pDQ400 (PhCMV-MCP-DrBphP-pA), pWH299 (PhCMV-2×c-MYC NLS-dUn1Cas12f (D143R/T147R/K330R)−1×c-MYC NLS-pA), pWH187 (PU6-sgRNA HBG (ES1+2+5)-pA), pWH190 (PU6-sgRNA HBG-2-pA)]. Twenty-four hours after transfection, cells were divided into three groups. Group A was maintained in darkness throughout the experiment (OFF), group B was exposed to red light (660 nm, 2 mW/cm²) for 0.01 h (ON), and group C was illuminated with red light (660 nm, 2 mW/cm²) for 0.01 h (ON), followed by far-red light (780 nm, 2 mW/cm²) for 0.1 h (OFF). Relative mRNA expression levels were quantified using qPCR analysis 24 h after initial light exposure.

ChemHEAL-mediated activation of endogenous gene transcription in mammalian cells
To evaluate chemically controlled endogenous gene activation, 8×104 cells were plated in a 24-well plate and co-transfected with plasmids encoding the ChemHEAL system targeting the corresponding gene [pWH299 (PhCMV-2×c-MYC NLS-dUn1Cas12f (D143R/T147R/K330R)−1×c-MYC NLS-pA), pWH253 (PhCMV-PYL1-MCP-pA), pWH255 (PhCMV-ABI-p65-HSF1-pA), pWH260 (PhCMV-GNCR1-MCP-pA), pWH261 (PhCMV-NS3a-MCP-pA), pWH265 (PhCMV-NS3a-p65-HSF1-pA), pDQ601 (PhCMV-MCP-FKBP-pA), pDQ603 (PhCMV-p65-HSF1-FRB-pA), pDQ610 (PhCMV-DNCR2-p65-HSF1-pA), and sgRNAs targeting the corresponding gene]. Twenty-four hours after transfection, the culture medium was refreshed, and the cells were incubated with ABA (100 μM; Sigma-Aldrich, 90769), GZV (10 μM; Adooq Bioscience, A173303), DNV (10 μM; Adooq Bioscience, RG7227), or Rapa (10 nM; MCE, HY-10219). Relative mRNA expression levels were quantified using qPCR analysis after 24 h of induction.

Animals
All animals were approved by the Institutional Animal Care and Use Committee of Shanghai and conducted in accordance with the National Research Council Guide for the Care and Use of Laboratory Animals. Male C57BL/6 mice (4–6 weeks old) were obtained from the East China Normal University (ECNU) Laboratory Animal Center. Sex was not considered as a biological variable in this study; all experiments were conducted using age-matched male mice only to minimize hormonal variability and ensure consistency in obesity-related phenotypes. Mice were kept under a standard alternating 12 h light/12 h dark cycles at 23 °C ± 1 °C with 60% ± 5% relative humidity, with free access to water and food. Obese mice were fed a high-fat diet containing 60 kcal% fat (Research Diets, D12492), while non-obese mice received a standard chow diet with a composition of 15 kcal% fat (Xietong Biology, XTI01CR-010).

In vivo bioluminescence imaging
Each mouse was intraperitoneally injected with D-Luciferin solution (150 mg/kg; Shanghai Sciencelight Biology Science & Technology, luc001). Five minutes after Luciferin injection, bioluminescence images of the mice were captured using the IVIS Lumina II in vivo imaging system (PerkinElmer). Regions of interest were quantified using the Living Image software (version 4.3.1).

Comparison of the HEAL system with other transcriptional activation systems in mice
For exogenous gene activation experiments, 6-week-old male C57BL/6 mice were hydrodynamically injected with 2 mL of Ringer’s solution (0.147 M NaCl, 1.13 M CaCl2, 4×10−3 M KCl) containing plasmids encoding the corresponding transcriptional activation systems (HEAL, dCasMINI, dCpf1, or dCas9-SAM) targeting either a luciferase reporter or a non-targeting control site. Twenty-four hours after plasmid injection, bioluminescence imaging was performed.

For the endogenous gene activation experiment, 6-week-old male C57BL/6 mice were hydrodynamically injected with 2 mL of Ringer’s solution containing plasmids encoding the corresponding transcriptional activation systems targeting either the Ucp1 locus or a non-targeting control site. Forty-eight hours after plasmid injection, mice were euthanized, and liver tissues were collected for qPCR analysis of Ucp1 expression.

OptoHEAL/ChemHEAL-mediated activation of gene transcription in mice
For exogenous gene activation experiments, 6-week-old male C57BL/6 mice were hydrodynamically injected with 2 ml of Ringer’s solution containing plasmids encoding either the OptoHEAL or ChemHEAL system targeting an exogenous luciferase reporter. Ten hours after plasmid injection, mice receiving OptoHEAL were illuminated with red light (660 nm LED, 20 mW/cm2) for different time periods (0 to 10 h), while mice receiving ChemHEAL were intraperitoneally injected with ABA at different doses (0 to 100 mg/kg). Twenty-four hours after plasmid injection, bioluminescence imaging was performed.

For the endogenous gene activation experiment, 6-week-old male C57BL/6 mice were hydrodynamically injected with 2 ml of Ringer’s solution containing plasmids encoding the OptoHEAL system targeting the Oct4 locus. Ten hours after plasmid injection, mice were illuminated with red light (660 nm LED, 20 mW/cm2) for 10 h. Twenty-four hours after plasmid injection, mice were euthanized, and liver tissues were collected for qPCR analysis of Oct4 expression.

AAV-delivered HEAL-mediated activation of Il10 transcription in AKI mice
Recombinant AAV (serotype 2/8) was produced as previously described57. For in vivo delivery of the HEAL system, 6-week-old male C57BL/6 mice were intravenously injected with 3×1011 vg of either AAV-PU6-sgRNA Il10-PhCMV-MCP-p65-HSF1-P2A-ΔdCas12f-pA (3×1011 vg) or a non-targeting control AAV-PU6-sgRNA NT-PhCMV-MCP-p65-HSF1-P2A-ΔdCas12f-pA via the tail vein. Two weeks after AAV injection, mice were intraperitoneally injected with cisplatin to induce AKI. Briefly, mice were subjected to an 18 h period of fasting and water dehydration prior to induction and then intraperitoneally injected with cisplatin (MCE, HY-17394) at a dose of 25 mg/kg, after which food and water were restored. Healthy control mice underwent the same fasting and dehydration procedure but did not receive cisplatin injection. Four days post-cisplatin injection, mice were euthanized and, liver, kidney, and serum samples were collected. Serum samples were analyzed for IL-10 levels using the mouse IL-10 ELISA Kit (Invitrogen, 88-7105-88), and for blood urea nitrogen (BUN) and serum creatinine (S-Cre) levels using commercial assay kits (Changchun Huili Biotech, S03036 and S03076). Liver tissues were used to evaluate Il10 mRNA expression by qPCR. Kidney tissues were used for immunohistochemical staining of NGAL and for terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay to assess apoptosis.

Immunohistochemical staining
Kidney tissues were fixed in 4% paraformaldehyde (Servicebio, G1101), embedded in paraffin, and cut into 5 μm thick slices. The sections were dewaxed and subjected to antigen retrieval under appropriate conditions. Endogenous peroxidase was blocked using 3% hydrogen peroxide for 25 min at room temperature, followed by washes with PBS. Non-specific binding was blocked with 3% bovine serum albumin (BSA) for 30 min at room temperature. Sections were incubated with rabbit polyclonal anti-NGAL primary antibody (1:2000; Abcam, EPR21092) diluted in PBS overnight at 4 °C in a humidified chamber. After PBS washes, sections were incubated with Horseradish Peroxidase (HRP)-labeled secondary antibody (1:200; Servicebio, GB23303) for 50 min at room temperature. 3,3’-Diaminobenzidine (DAB) was used for color development, and the reaction was stopped by rinsing with tap water. Sections were then counterstained with hematoxylin.

TUNEL staining
Paraffin-embedded kidney tissue sections were treated with freshly prepared permeabilization solution (0.3% Triton X-100 in PBS) for 5 min at room temperature, and then incubated with fluorescein-labeled deoxyuridine triphosphate (green) for 60 min at 37 °C in a dark, humidified chamber. Fluorescence images were captured using a BX53 upright fluorescence microscope (DMI8; Leica). Apoptotic nuclei were visualized in green, while all nuclei were counterstained with DAPI and appeared blue.

AAV-delivered ChemHEAL-mediated activation of Tslp transcription in HFD-induced obese mice
Four-week-old male C57BL/6 mice were fed high-fat diets consisting of 60 kcal% fat (Research Diets, D12492) for 10 weeks until the body weight exceeded 40 g. Subsequently, HFD-induced obese mice were intravenously injected with a mixture of recombinant AAV (serotype 2/8) vectors containing AAV-PhCMV-ABI-p65-HSF1-P2A-PLY1-MCP-pA (4×1011 vg were used for each virus) and either AAV-PU6-sgRNA Tslp-PhCMV-ΔdCas12f-pA or AAV-PU6-sgRNA NT-PhCMV-ΔdCas12f-pA (4×1011 vg) via the tail vein. Two weeks after AAV injection, mice were intraperitoneally injected with ABA (100 mg/kg) or PBS every two days for 8 weeks. Body weight was monitored weekly. At week 10 post-AAV administration, metabolic parameters, including intraperitoneal glucose tolerance test (IPGTT), insulin tolerance test (ITT), serum total cholesterol (TC) and triglycerides (TG), and body composition (ratio of fat and lean weights to body weight) were assessed. Body composition was measured using the AccuFat MRI system (MAG-MED, AccuFat-1050). Bronchoalveolar lavage fluid and serum were collected for analysis of Th2-type pro-inflammatory cytokines (IL-4, IL-5, and IL-13) and systemic inflammatory markers (IL-6, TNF-α, and IgG), respectively. Liver tissues were collected for analysis of Tslp mRNA expression (by qPCR) and TSLP protein levels (by immunoblotting). Hematoxylin and eosin (H&E) staining was performed on liver, lung, and adipose tissues to assess histopathological changes.

Bronchoalveolar lavage procedure
At week 8 of ABA treatment, mice were euthanized by carbon dioxide asphyxiation, and a small incision of the trachea was made to insert a 24-G lavage catheter. The catheter and trachea were secured with a cotton thread, and 0.5 mL of sterile PBS was instilled into the lungs three times using a 1 mL syringe. Bronchoalveolar lavage fluid was pooled and centrifuged at 300×g for 5 min at 4 °C to pellet cells. The supernatant was collected and stored at −80 °C for subsequent cytokine analysis.

Intraperitoneal glucose tolerance test (IPGTT) in mice
Mice were fasted overnight for 16 h, and intraperitoneally injected with aqueous 1.5 g/kg D-glucose. Blood glucose levels were measured from tail vein blood samples at 0, 15, 30, 60, 90, and 120 min after injection using a handheld glucometer (Exactive Easy III, MicroTech Medical). The 0 min sample was used to determine fasting plasma glucose levels. The trapezoidal rule determined the area under the curve (AUC) for IPGTT.

Insulin tolerance test (ITT) in mice
Mice were fasted for 4 h and intraperitoneally injected with insulin solution (Sigma, 11061-68-0) at a dose of 0.75 U/kg body weight. Blood samples were obtained via the tail vein at 0, 15, 30, 60, 90, and 120 min after insulin injection.

Total cholesterol and triglycerides measurement
To measure total cholesterol (TC) and triglycerides (TG) in serum, mouse blood was obtained from the orbital venous plexus and centrifuged at 1000×g for 10 min at 4 °C to isolate serum. The serum levels of TC and TG were then determined using a total cholesterol assay kit (Nanjing Jian Cheng Bioengineering Institute, A111-1-1) and a triglyceride assay kit (Nanjing Jian Cheng Bioengineering Institute, A110-1-1), respectively, according to the manufacturer’s instructions.

Immunoblotting analysis
Mice were euthanized, and liver tissues were collected and homogenized using magnetic beads in RIPA lysis buffer (Yeasen, 20101ES60) supplemented with protease inhibitor cocktail (Beyotime, P1005) and phosphatase inhibitor mixture (Beyotime, P1045). Homogenates were centrifuged to collect the supernatants containing total protein. Proteins were separated by SDS-PAGE and then electro-transferred onto polyvinylidene difluoride (PVDF; Millipore, ISEQ00010) membranes. Membranes were blocked with 5% non-fat milk and incubated overnight at 4 °C with primary antibodies: rabbit polyclonal anti-TSLP (1:2000; Proteintech, 13778-1-AP) and rabbit monoclonal anti-GAPDH (1:3000; Yeasen, 30202ES40). After three washed with Tris-buffered saline with 0.05% tween-20 (Sangon, 9005-64-5), membranes were incubated with Alexa fluor-based goat anti-rabbit IgG (H+L) secondary antibody (1:25,000; Yeasen, 33119ES60) at room temperature for 1 h. Images were scanned using the Odyssey CLX imaging system (Licor).

Hematoxylin and eosin (H&E) staining of liver, lung, and adipose tissues
Liver, lung, and adipose tissues were fixed in 4% paraformaldehyde (Servicebio, G1101) overnight at room temperature. The fixed samples were sequentially dehydrated through graded ethanol solutions, cleaned in xylene, embedded in paraffin, and sliced into 4 μm-thick sections with a rotary microtome (Leica RM2235, Manual Rotary Microtome). H&E staining was performed using an H&E Staining Kit (Servicebio, G1005) according to the manufacturer’s instructions. Stained sections were imaged using an upright microscope (BX53, Olympus).

Statistics & reproducibility
All in vitro data are presented as the mean ± s.d. of three independent biological replicates unless otherwise stated. For animal experiments, each treatment group consisted of randomly selected mice (n=5-10), and the results are expressed as mean ± s.e.m. Sample sizes were not predetermined by statistical methods but were based on prior studies reporting similar experimental designs. Investigators were blinded to allocation during experiments and outcome assessment. No data were excluded from the analyses. Comparisons between groups two were performed using two-tailed unpaired t tests. One-way or two-way ANOVA was used to evaluate differences among multiple groups. Statistical analyses were performed using GraphPad (Prism v.8), and statistical significance was defined as *P<0.05, **P<0.01, ***P<0.001, and ****P<0.0001. n and P values are described in the figures or figure legends.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this manuscript, Wan et al. introduce HEAL, a controllable dCas12f-based CRISPRa system, along with its inducible variants, OptoHEAL and ChemHEAL. The HEAL platform achieves robust endogenous gene activation by integrating engineered sgRNA scaffolds, an optimized dCas12f with improved DNA binding and nuclear localization, and modular transactivator recruitment. The inducible variants further allow precise spatiotemporal control of gene expression through red light or small molecule responsiveness. Moreover, the authors demonstrate in vivo relevance by showing that HEAL-mediated Il10 activation mitigates acute kidney injury and that ChemHEAL-driven Tslp activation alleviates obesity in mouse models. Overall, the study presents a compact, programmable, and highly efficient CRISPRa platform with broad potential for research and therapeutic applications. The manuscript is well-supported by experimental evidence and clearly written. However, several points should be addressed to further strengthen the clarity and completeness of the work before publication.\nSpecific Comments:\n1. The authors present HEAL-mediated activation of various endogenous genes in HEK293T cells. To broaden the translational relevance, additional data demonstrating efficacy in other cell types would be valuable.\n2. In Figure 1g, please clarify the origins and characteristics of the activation domains tested (e.g., eNRF2). This will aid readers in interpreting the screening data.\n3. The Discussion would benefit from further elaboration on the research and therapeutic contexts in which light- or small molecule-inducible CRISPRa systems offer key advantages, highlighting examples where spatiotemporal control is particularly impactful.\n4. The statistical annotations in Figure 2e (e.g., “ns” versus “*”) appear inconsistent with the reported fold changes. Please review these annotations carefully, and ensure consistency across all figures in the manuscript.\n5. Please standardize the use of nomenclature throughout the text and figures (e.g., “ns” should be used uniformly instead of alternating with “NS”).\n6. There appears to be inconsistency in the usage of “hMSC-TERT” and “hMSC-TRET” in the manuscript. Please verify and correct these references to ensure accuracy.\n7. On Line 51, it would be helpful to include additional references that reflect the recent development of Cas12f systems to provide appropriate context.", "reviewer_2": "I. Manuscript background information\nWan et al. present a compact and inducible CRISPR activation (CRISPRa) system, termed HEAL, based on an engineered dCas12f scaffold optimized for efficient gene activation in vitro and in vivo. The authors combine several synthetic biology tools, including an MS2 aptamer-recruiting sgRNA scaffold and transcriptional activator modules (such as p65-HSF1), to enhance transcriptional activation. Moreover, they extend this system into light-inducible (OptoHEAL) and chemically inducible (ChemHEAL) versions, achieving precise and programmable gene expression control. The authors demonstrate the platform’s utility by activating endogenous targets such as Il10 and Tslp, providing in vivo data showing therapeutic relevance in mouse models of acute kidney injury and diet-induced obesity. Overall, this work presents a technically innovative platform with potential translational applications, but some important issues remain to be addressed to fully support the claims and broaden the impact of the study.\nII. Comments for transmission to the authors\nMajor comments\n1. The authors convincingly demonstrate the transcriptional activation of endogenous genes by HEAL in vitro using qPCR, and validate functional protein expression in vivo (e.g., IL-10, TSLP in Fig. 6c,d). However, for in vitro gene activation data in Figure 1 and Supplementary Figures 3–4, it would strengthen the manuscript to also show whether the increase in mRNA levels leads to corresponding increases in protein expression. This could be done by including western blot or ELISA data for key targets. As the manuscript uses the term “gene activation” in the title, establishing the translation of mRNA to functional protein would reinforce the biological relevance of the HEAL system.\n2. While the Il10 activation experiments include both untreated and non-targeting (NT) control sgRNAs as proper negative controls, the in vivo comparison between HEAL and other CRISPRa systems (Fig. 2l–m, Fig. S5) appears to lack an NT sgRNA/crRNA control. Including this group would provide a baseline for background expression levels and help confirm the specificity of activation. If such data exist, even in supplementary figures, it should be clearly presented.\n3. The authors highlight that Tslp overexpression can cause airway inflammation and hyperreactivity. Given that ChemHEAL enables strong and controllable induction of Tslp (Fig. 6c,d), it is important to confirm whether adverse effects were avoided in the ABA-treated AAVTslp group (G5) after 8 weeks. Including histological or inflammatory marker analysis (e.g., lung tissue pathology or cytokine profiling) would support the claim that the ChemHEAL system allows safe, tunable activation in therapeutic settings.\n4. The in vivo experiments employ differing post-injection time points depending on the assay (e.g., qPCR at 48 h vs. bioluminescence imaging at 24 h). Since qPCR reflects transcription and bioluminescence reports translation, it would be helpful for the authors to clarify why different time points were chosen, especially in cases where transcriptional outcomes are assessed later than translational ones. If there is no particular rationale, aligning time points or justifying the current setup would improve clarity and consistency.\nMinor comments\n1. In Figure 1, it would be interesting to see whether combining multiple MS2 insertions in the sgRNA scaffold further enhances transcriptional activation. Has this been tested?\n2. The mutations introduced into dCas12f in Fig. 1e appear to derive from previously published work on CasMINI. It would be appropriate to more clearly acknowledge this and cite the original source explicitly in the main text.\n3. Figure 1d shows the combined effect of MS2 insertion and ES5 A→C substitution in the sgRNA. To understand the contribution of each component, the authors should present the data from ES5 substitution alone.\n4. For each inducible variant (OptoHEAL and ChemHEAL), the authors are encouraged to include side-by-side comparison with the intact HEAL system to highlight the trade-offs in activation strength vs. control.\n5. In Figure 1f, the optimized sgRNA is said to be used with the best dCas12f variant. Could the authors clarify if this refers to the ES1+2+5 variant from Fig. 1d? Additionally, please clarify which control variant was used in each panel (e.g., was Fig. 1f’s control the sgRNA-1 from 1d?). A summary table listing which sgRNA, dCas12 variant, and TADs were used for each major experiment would be very helpful.\n6. In Supplementary Figure S6c, the “No.” labels for sgHBG variants are missing from the x-axis. This should be added for clarity, following the format in Fig. 2.\n7. In Figure 2b–f and 2h–k, although the schematics in Fig. 2a and 2g explain which color corresponds to each system, the legends for those panels should also describe the color-code explicitly to aid reader comprehension.\n8. In Figure 6h,i, the authors should include statistical comparison between the non-obese control group (G1) and the ABA-treated obese group (G5) to assess how fully the ChemHEAL system restores physiological levels.\n9. While statistical significance is provided in Fig. S5d, the variance in the bioluminescence signal is notably high for HEAL compared to other systems. Including more technical replicates or additional normalization could help support the conclusion that HEAL outperforms dCas9-SAM and others."}, "author_response": "1 Manuscript number: NCOMMS-25-55314-T\n4 have conducted additional experiments and addressed all the points raised. We trust you’ll agree that\n5 the careful revision process we have undertaken has substantially improved both the scientific rigor\n6 and implications of our study. We present point-by-point responses to each of the reviewer comments\n7 (below). We start by listing the major experiments undertaken during our revision process.\n8 1) We conducted additional experiments to evaluate HEAL-mediated activation of endogenous genes\n9 across four human cell types (hMSC-TERT, ARPE-19, Hana3A, and HeLa), demonstrating its\n10 broad applicability (new Supplementary Fig. 3g-h).\n11 2) We performed ELISA analyses to validate that HEAL-mediated transcriptional activation leads to\n12 increased production of functional proteins (new Supplementary Fig. 4h-j), demonstrating\n13 effective coupling between transcriptional activation and protein output and underscoring the\n14 biological relevance of the HEAL system.\n15 3) To evaluate target specificity, we performed new mouse experiments incorporating non-targeting\n16 (NT) sgRNA/crRNA controls for direct comparison among different CRISPRa systems (new Fig\n17 2l-m and Supplementary Fig. 5). All systems exhibited target-specific activation, whereas HEAL\n18 consistently induced significantly higher levels of gene expression than the others.\n19 4) We performed new mouse experiments to evaluate the safety of ChemHEAL-mediated Tslp\n20 activation, focusing on potential airway inflammation (new Supplementary Fig. 13). After eight\n21 weeks of ABA administration, no histological or immunological abnormalities were detected,\n22 supporting the safety of ChemHEAL-mediated Tslp activation during therapeutic intervention.\n23 5) We performed side-by-side comparisons of HEAL, OptoHEAL, and ChemHEAL, which revealed\n24 that inducible variants mediated controllable, stimulus-dependent activation with moderately\n25 reduced maximal efficiencies (new Supplementary Fig. 10). Given the risks associated with\n26 sustained activation of tightly regulated genes, such inducible control represents a critical design\n27 feature that enhances therapeutic safety and translational potential.\n28\n1 / 20\n31 with its inducible variants, OptoHEAL and ChemHEAL. The HEAL platform achieves robust\n32 endogenous gene activation by integrating engineered sgRNA scaffolds, an optimized dCas12f with\n33 improved DNA binding and nuclear localization, and modular transactivator recruitment. The\n34 inducible variants further allow precise spatiotemporal control of gene expression through red light or\n35 small molecule responsiveness. Moreover, the authors demonstrate in vivo relevance by showing that\n37 activation alleviates obesity in mouse models. Overall, the study presents a compact, programmable,\n38 and highly efficient CRISPRa platform with broad potential for research and therapeutic applications.\n40 points should be addressed to further strengthen the clarity and completeness of the work before\n41 publication.\n43 1. The authors present HEAL-mediated activation of various endogenous genes in HEK293T cells. To\n44 broaden the translational relevance, additional data demonstrating efficacy in other cell types would\n45 be valuable.\n48 mediated activation of endogenous genes in other human cell lines. Specifically, plasmids encoding\n49 the HEAL system targeting endogenous HBG or IFNγ were transfected into four different human cell\n50 lines, including hMSC-TERT, ARPE-19, Hana3A, and HeLa. Relative mRNA levels were measured\n51 by qPCR at 48 h post-transfection. The results demonstrated robust HEAL-mediated endogenous gene\n52 activation across all tested cell types, with fold induction ranging from 1,751- to 35,847-fold. These\n53 new data have been incorporated into the revised manuscript (Supplementary Fig. 3g-h). Please see\n54 page 5, lines 143-145 and below:\n55 “We further evaluated the activation potential of HEAL in additional human cell lines, and\n56 qPCR analysis showed significant upregulation of HBG and IFNγ across diverse cellular\n57 contexts (Fig. S3g-h).”\n2 / 20\n58\n60 and IFNγ by the HEAL system in human cell lines. The indicated human cells transfected with plasmids\n61 encoding HEAL system targeting HBG (g) or IFNγ (h), and the relative mRNA levels were measured using\n62 qPCR at 48 h post-transfection. An empty sgRNA vector was used as a non-targeted control (sgNT). Data are\n63 means ± s.d.; n = 3 independent experiments, with 3 technical replicates for each. Statistical significance was\n64 determined by two-way ANOVA (g-h). ***P < 0.001, ****P < 0.0001.\n65 2. In Figure 1g, please clarify the origins and characteristics of the activation domains tested (e.g.,\n66 eNRF2). This will aid readers in interpreting the screening data.\n69 characteristics of the activation domains tested and have added the appropriate references. Please see\n70 page 4, lines 115-119 in the revised manuscript and below:\n71 “In addition, we screened a panel of transcriptional activation domains (TADs) fused to MCP,\n72 including VP16 and its tetrameric variant VP64 from herpes simplex virus type 1 (HSV-1)41,\n73 as well as chimeric activators derived from human mechanosensitive transcription factors,\n74 such as eNRF2 and MSN42.”\n75\n76 3. The Discussion would benefit from further elaboration on the research and therapeutic contexts in\n77 which light- or small molecule-inducible CRISPRa systems offer key advantages, highlighting\n78 examples where spatiotemporal control is particularly impactful.\n81 potential applications of inducible CRISPRa systems, emphasizing contexts in which spatiotemporal\n82 control is particularly advantageous. Please see page 14, lines 383-399 in the revised manuscript and\n83 below:\n84 “For genes with stringent expression thresholds or dosage-sensitive functions, excessive or\n3 / 20\n85 sustained activation can result in cytotoxicity or developmental abnormalities. Therefore, the\n86 ability of OptoHEAL and ChemHEAL to provide precise, reversible, and tunable control over\n87 gene activation offers a significant advantage for therapeutic applications. This highlights the\n88 importance of regulatory precision in clinically oriented gene modulation systems. By\n89 extending the CRISPRa toolkit, OptoHEAL and ChemHEAL enable temporal and dose-\n90 dependent control of endogenous gene activation, potentially enhancing safety by preventing\n91 sustained or irreversible expression.\n93 restricted activation of neural transcription factors like NEUROG2 in induced pluripotent\n94 stem cells (iPSCs)34, facilitating patterned activation within organoids and supporting\n95 temporally staged differentiation while minimizing off-target effects or premature lineage\n96 commitment. For cancer immunotherapy, ChemHEAL has the potential to enable\n97 systemically tunable activation of tumor-associated antigens or immunomodulatory factors\n98 using clinically approved small molecules52. These systems allow flexible dosing to balance\n99 therapeutic efficacy with toxicity, providing a means to transiently and on-demand boost\n100 antitumor immunity without the risk of chronic overstimulation.”\n101\n102 4. The statistical annotations in Figure 2e (e.g., “ns” versus “*”) appear inconsistent with the reported\n103 fold changes. Please review these annotations carefully, and ensure consistency across all figures in\n104 the manuscript.\n107 cross-checked them against the corresponding fold changes and P values. Statistical analyses were\n108 performed using two-way ANOVA followed by multiple comparisons. In row NO. 6, the comparison\n109 between HEAL and dCpf1 yielded a P value of 0.0279, whereas the comparison between HEAL and\n110 dCasMINI yielded a P value of 0.0556. The annotations have been corrected to accurately reflect the\n111 significance levels, and we have also verified consistency of statistical annotations across all figures\n112 in the manuscript.\n113\n114 5. Please standardize the use of nomenclature throughout the text and figures (e.g., “ns” should be used\n115 uniformly instead of alternating with “NS”).\n118 of statistical notation throughout. Specifically, all instances of “NS” have been replaced with the\n119 uniform notation “ns” (not significant) across the text and figures. Please see new Fig. 5j–m in the\n4 / 20\n120 revised manuscript and below:\n121\n123 (ChemHEAL) for regulation of endogenous gene transcription. j-m, Time-dependent activation of HBG\n124 mediated by ChemHEAL system. HEK-293T cells transfected with plasmids encoding different ChemHEAL\n125 systems targeting HBG were treated with 100 µM ABA (j), 10 µM GZV (k), 10 µM DNV (l), or 10 nM Rapa\n126 (m), and the relative mRNA levels of HBG were quantified by qPCR at different time points (0-48 h).\n127\n5 / 20\n128 6. There appears to be inconsistency in the usage of “hMSC-TERT” and “hMSC-TRET” in the\n129 manuscript. Please verify and correct these references to ensure accuracy.\n132 “hMSC-TRET” to the accurate term “hMSC-TERT”. Please see new Fig. 4i and Supplementary Fig.\n133 9 in the revised manuscript and below:\n134\n136 in diverse mammalian cell lines. The indicated mammalian cells transfected with plasmids encoding\n138 0.01 h. Relative mRNA levels of HBG were quantified using qPCR at 24 h following the onset of red-light\n139 exposure.\n140\n6 / 20\n142 mediated endogenous gene activation in diverse mammalian cell lines. The indicated mammalian cell lines\n143 were transfected with plasmids encoding the respective ChemHEAL constructs and treated with 100 µM ABA\n144 (a), 10 µM GZV (b), 10 µM DNV (c), or 10 nM Rapa (d) for 24 h. Relative mRNA levels of HBG were\n145 quantified using qPCR 24 h after induction.\n146\n147 7. On Line 51, it would be helpful to include additional references that reflect the recent development\n148 of Cas12f systems to provide appropriate context.\n151 recent advances in Cas12f system development and have included the appropriate references to provide\n152 additional context. Please see page 2, lines 50-51 in the revised manuscript and below.\n153 “Efforts to address this limitation have driven the development of compact CRISPR systems\n154 based on miniature Cas proteins such as Cas12f15-20.”\n156 [15] Xu, X. et al. Engineered miniature CRISPR-Cas system for mammalian genome regulation and editing.\n158 [16] Wang, X. et al. Robust miniature Cas-based transcriptional modulation by engineering Un1Cas12f1\n159 and tethering Sso7d. Mol Ther 32, 910-919, doi:10.1016/j.ymthe.2024.02.013 (2024).\n160 [17] Zhang, X. et al. Engineered circular guide RNAs enhance miniature CRISPR/Cas12f-based gene\n161 activation and adenine base editing. Nat Commun 16, 3016, doi:10.1038/s41467-025-58367-4 (2025).\n162 [18] Wu, Z. et al. Programmed genome editing by a miniature CRISPR-Cas12f nuclease. Nature Chemical\n164 [19] Su, M. et al. Molecular basis and engineering of miniature Cas12f with C-rich PAM specificity. Nature\n166 [20] Wu, Z. et al. Structure and engineering of miniature Acidibacillus sulfuroxidans Cas12f1. Nature\n168\n170 improving our study.\n171\n7 / 20\n173\n176 based on an engineered dCas12f scaffold optimized for efficient gene activation in vitro and in vivo.\n178 scaffold and transcriptional activator modules (such as p65-HSF1), to enhance transcriptional\n179 activation. Moreover, they extend this system into light-inducible (OptoHEAL) and chemically\n180 inducible (ChemHEAL) versions, achieving precise and programmable gene expression control. The\n181 authors demonstrate the platform’s utility by activating endogenous targets such as Il10 and Tslp,\n182 providing in vivo data showing therapeutic relevance in mouse models of acute kidney injury and diet-\n183 induced obesity. Overall, this work presents a technically innovative platform with potential\n184 translational applications, but some important issues remain to be addressed to fully support the claims\n185 and broaden the impact of the study.\n189 1. The authors convincingly demonstrate the transcriptional activation of endogenous genes by HEAL\n190 in vitro using qPCR, and validate functional protein expression in vivo (e.g., IL-10, TSLP in Fig. 6c,\n191 d). However, for in vitro gene activation data in Figure 1 and Supplementary Figures 3-4, it would\n192 strengthen the manuscript to also show whether the increase in mRNA levels leads to corresponding\n193 increases in protein expression. This could be done by including western blot or ELISA data for key\n194 targets. As the manuscript uses the term “gene activation” in the title, establishing the translation of\n195 mRNA to functional protein would reinforce the biological relevance of the HEAL system.\n198 activation of endogenous genes results in corresponding increases in protein expression, we performed\n199 additional in vitro ELISA experiments. Specifically, we selected several therapeutic cytokines as\n200 representative targets, including IL-10, TSLP, and IFNγ. B16F10 cells were transfected with the HEAL\n201 system targeting Il10 or Tslp, and HEK-293T cells were transfected with the HEAL system targeting\n205 see page 6, lines 149-151 and below:\n8 / 20\n206 “HEAL-mediated gene activation was also validated at the protein level for selected targets,\n207 including human IFNγ and murine IL-10 and TSLP (Fig. S4h-i).”\n208\n210 activation of human IFNγ, mouse IL-10, and mouse TSLP in mammalian cells. HEK-293T cells were\n211 transfected with plasmids encoding the HEAL system targeting IFNγ (h), and B16F10 cells were transfected\n212 with plasmids encoding the HEAL system targeting Il10 (i) or Tslp (j). Cytokine levels in cell culture\n213 supernatants were measured by ELISA at 48 h post-transfection. An empty sgRNA vector was used as a non-\n214 targeted control (sgNT). Data are means ± s.d.; n = 3 independent experiments, with 3 technical replicates for\n215 each. Statistical significance was determined by student’s t test. ****P < 0.0001.\n216\n217 2. While the Il10 activation experiments include both untreated and non-targeting (NT) control\n218 sgRNAs as proper negative controls, the in vivo comparison between HEAL and other CRISPRa\n219 systems (Fig. 2l–m, Fig. S5) appears to lack an NT sgRNA/crRNA control. Including this group would\n220 provide a baseline for background expression levels and help confirm the specificity of activation. If\n221 such data exist, even in supplementary figures, it should be clearly presented.\n224 non-targeting (NT) sgRNA/crRNA controls in the comparison between HEAL and other CRISPRa\n225 systems. Specifically, mice were hydrodynamically injected with different CRISPRa systems carrying\n227 at 24 h post-injection. To enhance reproducibility, we also included additional biological replicates for\n228 the HEAL-luciferase group. The imaging results demonstrated that HEAL achieved markedly stronger\n229 luciferase activation, while all NT control groups exhibited consistently low background signals. In\n230 parallel, mice injected with CRISPRa systems carrying NT sgRNA/crRNA were analyzed for Ucp1\n231 expression by qPCR at 48 h post-injection. QPCR analysis of liver tissues confirmed minimal basal\n232 expression across all NT groups, while the HEAL group exhibited significantly stronger Ucp1\n233 activation compared to other CRISPRa systems. Collectively, these findings demonstrate that HEAL\n234 achieves potent and target-specific gene activation in vivo, supported by its enhanced performance and\n235 minimal background activity in NT controls. These new data have been incorporated into the revised\n236 manuscript (Supplementary Fig. 5c-d and Fig. 2l-m). Please see page 7, lines 182-190 and below:\n9 / 20\n237 “Bioluminescence imaging at 24 h post-injection revealed that the HEAL system induced\n238 significantly stronger reporter expression compared to the other groups, with signal intensities\n239 5.6-, 4.1-, and 2.1-fold higher than those achieved by dCpf1, dCasMINI, and dCas9-SAM,\n240 respectively. In contrast, all NT control groups exhibited minimal background activity (Fig.\n243 that the HEAL system induced a significantly higher upregulation of Ucp1 compared to other\n244 systems, while maintaining high activation specificity (Fig. 2m).”\n245\n247 activation efficiency among the dCpf1, dCasMINI, HEAL, and dCas9-SAM systems for exogenous gene\n248 activation in mice. Plasmids encoding the dCpf1, dCasMINI, HEAL, or dCas9-SAM system, each carrying\n249 either sgRNA /crRNA or sgRNA /crRNA , were delivered to C57BL6 mice via hydrodynamic tail vein\nLuci Luci NT NT\n250 injection. Bioluminescence signals were quantified at 24 h post-injection using an in vivo imaging system. Data\n251 are as means ± s.e.m.; n = 5-10 mice. Statistical significance was determined by two-way ANOVA; **P < 0.01,\n252 ***P < 0.001.\n253\n255 among the dCpf1, dCasMINI, HEAL, and dCas9-SAM systems for endogenous Ucp1 in mice. Plasmids\n256 encoding the dCpf1, dCasMINI, HEAL, or dCas9-SAM system, each carrying either sgRNA /crRNA or\nUcp1 Ucp1\n257 sgRNA /crRNA , were delivered to mouse livers via hydrodynamic tail vein injection. Relative Ucp1 mRNA\nNT NT\n258 expression levels were quantified using qPCR at 48 h post-injection. Data are as means ± s.e.m.; n = 6 mice.\n10 / 20\n260\n261 3. The authors highlight that Tslp overexpression can cause airway inflammation and hyperreactivity.\n263 to confirm whether adverse effects were avoided in the ABA-treated AAV Tslp group (G5) after 8 weeks.\n265 profiling) would support the claim that the ChemHEAL system allows safe, tunable activation in\n266 therapeutic settings.\n270 following long-term ABA treatment. Specifically, mice in the AAV Tslp +ABA group were treated with\n272 inflammatory cytokines were measured in both bronchoalveolar lavage fluid (BALF) and serum. No\n273 significant differences were observed in lung histopathology or cytokine levels between the ABA-\n274 treated AAV Tslp group and non-obese controls, indicating that ChemHEAL enables safe and tunable\n276 further support the safety and therapeutic potential of the ChemHEAL system. These new data have\n277 been incorporated into the revised manuscript (Supplementary Fig. 13). Please see page 13, lines\n278 357-360 and below:\n279 “Long-term treatment with AAVTslp and ABA did not result in detectable adverse effects, as\n280 evidenced by normal lung histology, stable levels of Th2-type pro-inflammatory cytokines\n281 (IL-4, IL-5, and IL-13) in bronchoalveolar lavage fluid, and unchanged systemic\n282 inflammatory markers (IL-6, TNF-α, and IgG) (Fig. S13).”\n11 / 20\n283\n285 stained lung sections and inflammatory cytokine profiling. Obese mice were intravenously injected via the\n286 tail vein with a mixture of dual AAV vectors encoding the ABA-inducible ChemHEAL system (4×1011 vg per\n287 virus). Two weeks after AAV transduction, mice received intraperitoneal injections of ABA (100 mg/kg) every\n288 other day for 8 weeks. Non-obese mice served as controls. At the end of the treatment period, lung tissues were\n289 collected for histological assessment, bronchoalveolar lavage fluid (BALF) was harvested to evaluate local\n290 cytokine levels, and blood was collected to measure systemic inflammatory markers. a. Representative H&E-\n291 stained lung sections. b-d. Levels of Th2-type pro-inflammatory cytokines in BALF, including IL-4 (b), IL-5\n292 (c), and IL-13 (d). e-g. Serum levels of IL-6 (e), TNF-α (f), and IgG (g). Data are means ± s.e.m.; n = 5 mice.\n294\n295 4. The in vivo experiments employ differing post-injection time points depending on the assay (e.g.,\n296 qPCR at 48 h vs. bioluminescence imaging at 24 h). Since qPCR reflects transcription and\n297 bioluminescence reports translation, it would be helpful for the authors to clarify why different time\n298 points were chosen, especially in cases where transcriptional outcomes are assessed later than\n299 translational ones. If there is no particular rationale, aligning time points or justifying the current setup\n300 would improve clarity and consistency.\n12 / 20\n303 imaging reflects the distinct kinetics of endogenous versus exogenous gene activation under CRISPRa\n304 regulation.\n306 regulation, which can slow CRISPRa-mediated induction (Black et al., Cell Stem Cell, 2016; Clark et\n307 al., Trends in Biotechnology, 2024; Wu et al., Molecular Cell, 2023). Detectable mRNA accumulation\n308 typically requires additional time to overcome these barriers and reach stable expression levels.\n311 promoter with a well-defined transcriptional start site, independent of chromatin context. This system\n312 is rapidly transcribed and translated upon CRISPRa activation, producing a detectable bioluminescent\n313 signal within 24 h (Fu et al., Nature Communications, 2025; Wu et al., Stem Cell Reports, 2025). These\n314 time points were therefore selected to optimally capture the respective transcriptional and translational\n315 responses, and they align well with the experimental designs used in previous CRISPRa studies.\n322 navigating technologies and applications. Trends In Biotechnology, 42(8), 1017-1034.\n323 https://doi.org/10.1016/j.tibtech.2024.02.007\n326 condensates modulate CRISPRa-mediated gene activation. Nature Communications, 16(1), 1640.\n327 https://doi.org/10.1038/s41467-025-56735-8\n329 & Bassett, A. (2023). Massively parallel characterization of CRISPR activator efficacy in human\n330 induced pluripotent stem cells and neurons. Molecular Cell, 83(7).\n331 https://doi.org/10.1016/j.molcel.2023.02.011\n333 activation for rapid assessment of gene editing products in human pluripotent stem cells. Stem Cell\n335\n336\n338 1. In Figure 1, it would be interesting to see whether combining multiple MS2 insertions in the sgRNA\n339 scaffold further enhances transcriptional activation. Has this been tested?\n342 of combining multiple MS2 insertions on transcriptional activation. We first compared different MS2\n343 insertion sites and found ES1 to be the optimal position (Response Document Figure 10a).\n345 (Response Document Figure 10b). We then investigated whether increasing the copy number\n346 specifically at ES1 (the 5’ free end) could further enhance activation. However, qPCR analysis revealed\n347 no additional improvement (Response Document Figure 10c). Finally, combining ES1 + ES2\n348 insertions with the ES5 point mutation (ES 1+2+5) yielded the strongest activation of endogenous gene\n349 expression (Response Document Figure 10d).\n350\n352 the MS2 aptamer insertion and single base mutation sites. The MS2 aptamer is either inserted at engineering\n353 site (ES) 1 or replaces the original sequence at ES 2-4. An A-to-C mutation is introduced at ES 5, with a single-\n354 base mutation site highlighted in blue. b-d. Screening of sgRNA constructs targeting endogenous HBG. HEK-\n355 293T cells were co-transfected with plasmids encoding dCas12f, MCP-p65-HSF1, and sgRNA carrying various\n356 combinations of MS2 insertions at ES 1-4 (b), multiple copies of MS2 at ES 1 (c), or combined with the ES 5\n357 mutation (d). Transcript levels were quantified by qPCR at 48 h post-injection. Data are means ± s.d.; n = 3\n358 independent experiments, with 3 technical replicates for each. Statistical significance was determined by one-\n359 way ANOVA (b-d); ***P < 0.001, ****P < 0.0001.\n360\n361 2. The mutations introduced into dCas12f in Fig. 1e appear to derive from previously published work\n362 on CasMINI. It would be appropriate to more clearly acknowledge this and cite the original source\n363 explicitly in the main text.\n366 from previously published work on CasMINI. We have now explicitly acknowledged this and cited\n367 the original source in the revised manuscript. Please see page 4, lines 108-111 in the revised manuscript\n368 and below:\n369 “Building on structural modeling analyses and insights from the engineering of CasMINI15,\n370 we introduced targeted amino acid substitutions into dCas12f to enhance its DNA-binding\n371 capacity. A triple mutant variant (D143R, T147R, and K330R) exhibited the greatest\n372 enhancement in transcriptional activation (Fig. 1e).”\n373\n374 3. Figure 1d shows the combined effect of MS2 insertion and ES5 A→C substitution in the sgRNA.\n376 substitution alone.\n379 we performed additional in vitro experiments using a sgRNA construct carrying ES5 alone. HEK-293T\n380 cells were co-transfected with plasmids encoding dCas12f, MCP-p65-HSF1, and the ES5-only sgRNA.\n382 induce detectable transcriptional activation, likely because the sgRNA scaffold lacks the MS2 aptamer\n383 required to recruit MCP-p65-HSF1 and initiate transcription. The new data have been incorporated\n384 into the revised manuscript (new Fig. 1d).\n385\n15 / 20\n387 293T cells were transfected with plasmids encoding dCas12f, MCP-p65-HSF1, and sgRNA constructs harboring\n389 h post-transfection. Data are means ± s.d.; n = 3 independent experiments, with 3 technical replicates for each.\n391\n392 4. For each inducible variant (OptoHEAL and ChemHEAL), the authors are encouraged to include\n393 side-by-side comparison with the intact HEAL system to highlight the trade-offs in activation strength\n394 vs. control.\n397 between the intact HEAL system and its inducible variants, OptoHEAL and ChemHEAL. The results\n398 demonstrate a clear trade-off between maximal activation strength and regulatory precision. While\n400 reduced peak activities but provided precise and stimulus-dependent control over gene activation. We\n401 emphasize that such attenuated yet controllable activity represents a purposeful optimization rather\n402 than a limitation. For tightly regulated therapeutic genes such as OPA1, RAI1, MeCP2, and TSLP,\n403 continuous or excessive activation can trigger toxicity or developmental abnormalities. Hence, the\n404 ability to modulate activation dynamics with spatial and temporal precision is indispensable for\n405 therapeutic safety and efficacy. The inducible OptoHEAL and ChemHEAL systems therefore\n406 transform HEAL from a constitutively strong activator into a controllable and clinically adaptable\n407 platform, where precision regulation becomes an essential advantage rather than a trade-off. These\n408 new data have been incorporated into the revised manuscript (Supplementary Fig. 10). Please see\n409 page 11, lines 304-309 and below:\n410 “We next benchmarked the inducible HEAL variants against the constitutive system. Both\n412 response to light or small molecules, with moderately reduced maximal efficiencies compared\n413 to the constitutively active HEAL (Fig. S10). This attenuated yet tightly regulated activity\n414 represents a deliberate design optimization to enhance safety and controllability.”\n16 / 20\n415\n417 activation efficiencies of the HEAL, OptoHEAL, and ChemHEAL systems in mammalian cells. HEK-\n418 293T cells were co-transfected with plasmids encoding HEAL, OptoHEAL, or ChemHEAL targeting different\n419 endogenous genes. OptoHEAL-transfected cells were either kept in the dark or illuminated with red light (660\n420 nm, 2 mW/cm2) for 24 h, whereas ChemHEAL-transfected cells were treated with 0 or 100 µM ABA for 24 h.\n422 independent experiments, with 3 technical replicates for each. Statistical significance was determined by two-\n423 way ANOVA. ****P < 0.0001.\n424\n425 5. In Figure 1f, the optimized sgRNA is said to be used with the best dCas12f variant. Could the authors\n426 clarify if this refers to the ES 1+2+5 variant from Fig. 1d? Additionally, please clarify which control\n427 variant was used in each panel (e.g., was Fig. 1f’s control the sgRNA-1 from 1d?). A summary table\n428 listing which sgRNA, dCas12f variant, and TADs were used for each major experiment would be very\n429 helpful.\n432 sgRNA” indeed refers to the ES 1+2+5 variant described in Fig. 1d. Since Fig. 1f focuses on\n433 optimizing the nuclear localization signals of dCas12f, all sgRNA used in this panel were the ES 1+2+5\n434 configuration. The control in Fig. 1f corresponds to the construct labeled “1” on the x-axis, which\n435 represents the dCas12f (D143R/T147R/K330R) variant fused to an SV40 NLS at the N terminus and\n436 a nucleoplasmin NLS at the C terminus. To enhance clarity, we have revised the legend of Fig. 1f\n437 accordingly and added a summary table specifying the sgRNA scaffolds, dCas12f variants, and\n438 transcriptional activator domains (TADs) used in each experiment (Supplementary Table 5). Please\n439 see page 30, lines 841-843 and below:\n440 “HEK-293T cells were co-transfected with plasmids encoding MCP-p65-HSF1,\n441 optimized sgRNA (ES 1+2+5), and the mutated dCas12f containing various NLS types and\n442 copy numbers.”\n17 / 20\n444 components used in Figure 1c-g.\nComponents sgRNA dCas12f TAD\nHBG\nFig. 1c Different combinations of ES 1-4 WT p65-HSF1\nES 5, ES 1+2,\nFig. 1d WT p65-HSF1\nand ES 1+2+5\nFig. 1e ES 1+2+5 Different variants p65-HSF1\nD143R/T147R/K330R and\nFig. 1f ES 1+2+5 p65-HSF1\ndifferent NLSs\nD143R/T147R/K330R, 2×c-Myc\nFig. 1g ES 1+2+5 NLS at the N terminus, and 1×c- Different TADs\nMyc NLS at the C terminus\n445\n446 6. In Supplementary Figure S6c, the “No.” labels for sgHBG variants are missing from the x-axis. This\n447 should be added for clarity, following the format in Fig. 2.\n450 the sgHBG on the x-axis in the revised manuscript. Please see Supplementary Fig. 6c and below:\n451\n453 versus dual sgRNA strategies for OptoHEAL-mediated activation of HBG. HEK-293T cells were\n454 transfected with plasmids encoding the OptoHEAL system using either a single sgRNA or dual sgRNAs\n455 targeting HBG.\n456\n457 7. In Figure 2b–f and 2h–k, although the schematics in Fig. 2a and 2g explain which color corresponds\n458 to each system, the legends for those panels should also describe the color-code explicitly to aid reader\n18 / 20\n459 comprehension.\n462 the corresponding color annotations for each system. Please see page 32, lines 868-869, lines 875 in\n463 the revised manuscript and below:\n464 “In panels b-f, colors indicate different systems: light blue, HEAL; orange-brown, dCasMINI;\n465 charcoal brown, dCpf1.”\n466 “In panels h-k, colors indicate different systems: light blue, HEAL; beige, dCas9-SAM.”\n467\n468 8. In Figure 6h, i, the authors should include statistical comparison between the non-obese control\n469 group (G1) and the ABA-treated obese group (G5) to assess how fully the ChemHEAL system restores\n470 physiological levels.\n473 non-obese control group (G1) and the ABA-treated obese group (G5). The results showed no\n474 significant difference between G1 and G5, indicating that the ChemHEAL system largely restored the\n475 physiological levels (Response Document Figure 14 and 15). Please see new Fig. 6h-i in the revised\n476 manuscript and below:\n477\n479 weight.\n480\n481 9. While statistical significance is provided in Fig. S5d, the variance in the bioluminescence signal is\n482 notably high for HEAL compared to other systems. Including more technical replicates or additional\n483 normalization could help support the conclusion that HEAL outperforms dCas9-SAM and others.\n19 / 20\n486 biological replicates for HEAL. The additional data consistently support our conclusion that HEAL\n487 outperforms dCas9-SAM and the other tested systems. These new data have been incorporated into\n488 the revised manuscript (Supplementary Fig. 5c-d). Notably, the increased replicates also led to a clear\n489 reduction in data variance, further strengthening the robustness of our results. These updated data have\n490 been incorporated into the revised manuscript (Supplementary Fig. 5c-d).\n491\n493 activation efficiency among the dCpf1, dCasMINI, HEAL, and dCas9-SAM systems for exogenous gene\n494 activation in mice. c. Detailed bioluminescence images of mice. d. Quantification of bioluminescence signals.\n495\n497 improving our study.\n498\n20 / 20"}, {"round": 2, "reviewer_comments": {"reviewer_1": "My previous concerns have been fully addressed.", "reviewer_2": "The authors have responded comprehensively to all of the points raised in my prior review. The additional experiments, including protein-level validation, expanded in vivo controls, and long-term safety assessments, meaningfully reinforce the scientific rigor and translational relevance of the work. The clarifications and revisions throughout the manuscript greatly improve clarity and coherence. Overall, I find that the authors have fully addressed my concerns, and the manuscript is now suitable for publication pending editorial review."}, "author_response": "Manuscript number: NCOMMS-25-55314-A\nPoint-by-point responses to referees’ comments:\nREVIEWERS' COMMENTS\nReviewer #1 (Remarks to the Author):\nMy previous concerns have been fully addressed.\nReply:\nWe are pleased to hear that we have satisfactorily addressed your concerns. Thank you.\nReviewer #2 (Remarks to the Author):\nThe authors have responded comprehensively to all of the points raised in my prior review. The\nadditional experiments, including protein-level validation, expanded in vivo controls, and long-term\nsafety assessments, meaningfully reinforce the scientific rigor and translational relevance of the work.\nThe clarifications and revisions throughout the manuscript greatly improve clarity and coherence.\nOverall, I find that the authors have fully addressed my concerns, and the manuscript is now suitable\nfor publication pending editorial review.\nReply:\nThanks for your positive feedback on our revised manuscript. We are grateful for your encouraging\ncomments and constructive feedback."}]

Decision letter